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Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study

Identifieur interne : 000277 ( Pmc/Corpus ); précédent : 000276; suivant : 000278

Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study

Auteurs : Chenan Zhang ; Jennifer A. Doherty ; Stephen Burgess ; Rayjean J. Hung ; Sara Lindström ; Peter Kraft ; Jian Gong ; Christopher I. Amos ; Thomas A. Sellers ; Alvaro N. A. Monteiro ; Georgia Chenevix-Trench ; Heike Bickeböller ; Angela Risch ; Paul Brennan ; James D. Mckay ; Richard S. Houlston ; Maria Teresa Landi ; Maria N. Timofeeva ; Yufei Wang ; Joachim Heinrich ; Zsofia Kote-Jarai ; Rosalind A. Eeles ; Ken Muir ; Fredrik Wiklund ; Henrik Grönberg ; Sonja I. Berndt ; Stephen J. Chanock ; Fredrick Schumacher ; Christopher A. Haiman ; Brian E. Henderson ; Ali Amin Al Olama ; Irene L. Andrulis ; John L. Hopper ; Jenny Chang-Claude ; Esther M. John ; Kathleen E. Malone ; Marilie D. Gammon ; Giske Ursin ; Alice S. Whittemore ; David J. Hunter ; Stephen B. Gruber ; Julia A. Knight ; Lifang Hou ; Loic Le Marchand ; Polly A. Newcomb ; Thomas J. Hudson ; Andrew T. Chan ; Li Li ; Michael O. Woods ; Habibul Ahsan ; Brandon L. Pierce

Source :

RBID : PMC:4550826

Abstract

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10−15), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10−6). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.


Url:
DOI: 10.1093/hmg/ddv252
PubMed: 26138067
PubMed Central: 4550826

Links to Exploration step

PMC:4550826

Le document en format XML

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<addr-line>Department of Public Health Sciences</addr-line>
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<name sortKey="Hung, Rayjean J" sort="Hung, Rayjean J" uniqKey="Hung R" first="Rayjean J." last="Hung">Rayjean J. Hung</name>
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<name sortKey="Lindstrom, Sara" sort="Lindstrom, Sara" uniqKey="Lindstrom S" first="Sara" last="Lindström">Sara Lindström</name>
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<name sortKey="Monteiro, Alvaro N A" sort="Monteiro, Alvaro N A" uniqKey="Monteiro A" first="Alvaro N. A." last="Monteiro">Alvaro N. A. Monteiro</name>
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<name sortKey="Chenevix Trench, Georgia" sort="Chenevix Trench, Georgia" uniqKey="Chenevix Trench G" first="Georgia" last="Chenevix-Trench">Georgia Chenevix-Trench</name>
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<name sortKey="Bickeboller, Heike" sort="Bickeboller, Heike" uniqKey="Bickeboller H" first="Heike" last="Bickeböller">Heike Bickeböller</name>
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<addr-line>Lyon</addr-line>
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<name sortKey="Landi, Maria Teresa" sort="Landi, Maria Teresa" uniqKey="Landi M" first="Maria Teresa" last="Landi">Maria Teresa Landi</name>
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<addr-line>Bethesda, MD</addr-line>
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<addr-line>Lyon</addr-line>
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<addr-line>Sutton, Surrey</addr-line>
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<country>UK</country>
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<name sortKey="Kote Jarai, Zsofia" sort="Kote Jarai, Zsofia" uniqKey="Kote Jarai Z" first="Zsofia" last="Kote-Jarai">Zsofia Kote-Jarai</name>
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<nlm:aff id="af21">
<institution>The Institute of Cancer Research</institution>
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<addr-line>Sutton</addr-line>
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<name sortKey="Eeles, Rosalind A" sort="Eeles, Rosalind A" uniqKey="Eeles R" first="Rosalind A." last="Eeles">Rosalind A. Eeles</name>
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<nlm:aff id="af21">
<institution>The Institute of Cancer Research</institution>
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<addr-line>Sutton</addr-line>
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<country>UK</country>
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<institution>Royal Marsden National Health Service (NHS) Foundation Trust</institution>
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<addr-line>London and Sutton</addr-line>
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<name sortKey="Muir, Ken" sort="Muir, Ken" uniqKey="Muir K" first="Ken" last="Muir">Ken Muir</name>
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<addr-line>Warwick Medical School</addr-line>
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<institution>University of Warwick</institution>
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<addr-line>Coventry</addr-line>
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<country>UK</country>
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<name sortKey="Wiklund, Fredrik" sort="Wiklund, Fredrik" uniqKey="Wiklund F" first="Fredrik" last="Wiklund">Fredrik Wiklund</name>
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<addr-line>Department of Medical Epidemiology and Biostatistics</addr-line>
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<addr-line>Stockholm</addr-line>
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<country>Sweden</country>
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<name sortKey="Gronberg, Henrik" sort="Gronberg, Henrik" uniqKey="Gronberg H" first="Henrik" last="Grönberg">Henrik Grönberg</name>
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<nlm:aff id="af25">
<addr-line>Department of Medical Epidemiology and Biostatistics</addr-line>
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<institution>Karolinska Institute</institution>
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<addr-line>Stockholm</addr-line>
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<country>Sweden</country>
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<name sortKey="Berndt, Sonja I" sort="Berndt, Sonja I" uniqKey="Berndt S" first="Sonja I." last="Berndt">Sonja I. Berndt</name>
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<nlm:aff id="af19">
<addr-line>Division of Cancer Epidemiology and Genetics</addr-line>
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<institution>National Cancer Institute, National Institutes of Health, U.S. Public Health Service</institution>
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<addr-line>Bethesda, MD</addr-line>
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<name sortKey="Chanock, Stephen J" sort="Chanock, Stephen J" uniqKey="Chanock S" first="Stephen J." last="Chanock">Stephen J. Chanock</name>
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<nlm:aff id="af19">
<addr-line>Division of Cancer Epidemiology and Genetics</addr-line>
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<institution>National Cancer Institute, National Institutes of Health, U.S. Public Health Service</institution>
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<addr-line>Bethesda, MD</addr-line>
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<name sortKey="Schumacher, Fredrick" sort="Schumacher, Fredrick" uniqKey="Schumacher F" first="Fredrick" last="Schumacher">Fredrick Schumacher</name>
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<nlm:aff id="af26">
<addr-line>Department of Preventive Medicine, Keck School of Medicine</addr-line>
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<institution>University of Southern California/Norris Comprehensive Cancer Center</institution>
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<addr-line>Los Angeles, CA</addr-line>
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<country>USA</country>
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</affiliation>
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<author>
<name sortKey="Haiman, Christopher A" sort="Haiman, Christopher A" uniqKey="Haiman C" first="Christopher A." last="Haiman">Christopher A. Haiman</name>
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<nlm:aff id="af26">
<addr-line>Department of Preventive Medicine, Keck School of Medicine</addr-line>
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<institution>University of Southern California/Norris Comprehensive Cancer Center</institution>
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<addr-line>Los Angeles, CA</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Henderson, Brian E" sort="Henderson, Brian E" uniqKey="Henderson B" first="Brian E." last="Henderson">Brian E. Henderson</name>
<affiliation>
<nlm:aff id="af26">
<addr-line>Department of Preventive Medicine, Keck School of Medicine</addr-line>
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<institution>University of Southern California/Norris Comprehensive Cancer Center</institution>
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<addr-line>Los Angeles, CA</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Amin Al Olama, Ali" sort="Amin Al Olama, Ali" uniqKey="Amin Al Olama A" first="Ali" last="Amin Al Olama">Ali Amin Al Olama</name>
<affiliation>
<nlm:aff id="af8">
<addr-line>Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care</addr-line>
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<institution>University of Cambridge</institution>
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<addr-line>Cambridge</addr-line>
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<country>UK</country>
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</affiliation>
</author>
<author>
<name sortKey="Andrulis, Irene L" sort="Andrulis, Irene L" uniqKey="Andrulis I" first="Irene L." last="Andrulis">Irene L. Andrulis</name>
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<nlm:aff id="af27">
<addr-line>Molecular Genetics/Laboratory Medicine and Pathobiology</addr-line>
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<institution>Mount Sinai Hospital, University of Toronto</institution>
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<addr-line>Toronto</addr-line>
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<country>Canada</country>
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</affiliation>
</author>
<author>
<name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L." last="Hopper">John L. Hopper</name>
<affiliation>
<nlm:aff id="af28">
<addr-line>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health</addr-line>
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<institution>The University of Melbourne</institution>
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<addr-line>Parkville</addr-line>
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<country>Australia</country>
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</affiliation>
</author>
<author>
<name sortKey="Chang Claude, Jenny" sort="Chang Claude, Jenny" uniqKey="Chang Claude J" first="Jenny" last="Chang-Claude">Jenny Chang-Claude</name>
<affiliation>
<nlm:aff id="af29">
<addr-line>Division of Cancer Epidemiology</addr-line>
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<institution>German Cancer Research Center (DKFZ)</institution>
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<addr-line>Heidelberg</addr-line>
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<country>Germany</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="John, Esther M" sort="John, Esther M" uniqKey="John E" first="Esther M." last="John">Esther M. John</name>
<affiliation>
<nlm:aff id="af30">
<institution>Cancer Prevention Institute of California</institution>
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<addr-line>Fremont, CA</addr-line>
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<country>USA</country>
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</affiliation>
<affiliation>
<nlm:aff id="af33">
<institution>Stanford University School of Medicine</institution>
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<addr-line>Stanford, CA</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Malone, Kathleen E" sort="Malone, Kathleen E" uniqKey="Malone K" first="Kathleen E." last="Malone">Kathleen E. Malone</name>
<affiliation>
<nlm:aff id="af11">
<addr-line>Division of Public Health Sciences</addr-line>
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<institution>Fred Hutchinson Cancer Research Center</institution>
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<addr-line>Seattle, WA</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Gammon, Marilie D" sort="Gammon, Marilie D" uniqKey="Gammon M" first="Marilie D." last="Gammon">Marilie D. Gammon</name>
<affiliation>
<nlm:aff id="af31">
<addr-line>Department of Epidemiology</addr-line>
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<institution>University of North Carolina School of Public Health</institution>
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<addr-line>Chapel Hill, NC</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Ursin, Giske" sort="Ursin, Giske" uniqKey="Ursin G" first="Giske" last="Ursin">Giske Ursin</name>
<affiliation>
<nlm:aff id="af32">
<addr-line>Kreftregisteret</addr-line>
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<institution>Cancer Registry of Norway</institution>
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<addr-line>Oslo</addr-line>
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<country>Norway</country>
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</affiliation>
</author>
<author>
<name sortKey="Whittemore, Alice S" sort="Whittemore, Alice S" uniqKey="Whittemore A" first="Alice S." last="Whittemore">Alice S. Whittemore</name>
<affiliation>
<nlm:aff id="af33">
<institution>Stanford University School of Medicine</institution>
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<addr-line>Stanford, CA</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Hunter, David J" sort="Hunter, David J" uniqKey="Hunter D" first="David J." last="Hunter">David J. Hunter</name>
<affiliation>
<nlm:aff id="af10">
<addr-line>Department of Epidemiology</addr-line>
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<institution>Harvard School of Public Health</institution>
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<addr-line>Boston, MA</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Gruber, Stephen B" sort="Gruber, Stephen B" uniqKey="Gruber S" first="Stephen B." last="Gruber">Stephen B. Gruber</name>
<affiliation>
<nlm:aff id="af34">
<addr-line>USC Norris Comprehensive Cancer Center</addr-line>
,
<institution>University of Southern California</institution>
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<addr-line>Los Angeles, CA</addr-line>
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<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Knight, Julia A" sort="Knight, Julia A" uniqKey="Knight J" first="Julia A." last="Knight">Julia A. Knight</name>
<affiliation>
<nlm:aff id="af35">
<addr-line>Ontario Cancer Genetics Network, Fred A. Litwin Center for Cancer Genetics</addr-line>
,
<institution>Samuel Lunenfeld Research Institute, Mount Sinai Hospital</institution>
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<addr-line>Toronto, ON</addr-line>
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<country>Canada</country>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af36">
<addr-line>Division of Epidemiology, Dalla Lana School of Public Health</addr-line>
,
<institution>University of Toronto</institution>
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<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af37">
<addr-line>Samuel Lunenfeld Research Institute</addr-line>
,
<institution>Mount Sinai Hospital</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Hou, Lifang" sort="Hou, Lifang" uniqKey="Hou L" first="Lifang" last="Hou">Lifang Hou</name>
<affiliation>
<nlm:aff id="af38">
<addr-line>Department of Preventive Medicine</addr-line>
,
<institution>Northwestern University</institution>
,
<addr-line>Chicago, IL</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Le Marchand, Loic" sort="Le Marchand, Loic" uniqKey="Le Marchand L" first="Loic" last="Le Marchand">Loic Le Marchand</name>
<affiliation>
<nlm:aff id="af39">
<addr-line>Epidemiology Program</addr-line>
,
<institution>University of Hawaii Cancer Center</institution>
,
<addr-line>Honolulu, HI</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Newcomb, Polly A" sort="Newcomb, Polly A" uniqKey="Newcomb P" first="Polly A." last="Newcomb">Polly A. Newcomb</name>
<affiliation>
<nlm:aff id="af11">
<addr-line>Division of Public Health Sciences</addr-line>
,
<institution>Fred Hutchinson Cancer Research Center</institution>
,
<addr-line>Seattle, WA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af40">
<addr-line>Department of Epidemiology</addr-line>
,
<institution>University of Washington School of Public Health</institution>
,
<addr-line>Seattle, WA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Hudson, Thomas J" sort="Hudson, Thomas J" uniqKey="Hudson T" first="Thomas J." last="Hudson">Thomas J. Hudson</name>
<affiliation>
<nlm:aff id="af41">
<institution>Ontario Institute for Cancer Research</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<institution>Canada</institution>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Chan, Andrew T" sort="Chan, Andrew T" uniqKey="Chan A" first="Andrew T." last="Chan">Andrew T. Chan</name>
<affiliation>
<nlm:aff id="af42">
<addr-line>Division of Gastroenterology</addr-line>
,
<institution>Massachusetts General Hospital and Harvard Medical School</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af43">
<addr-line>Channing Division of Network Medicine</addr-line>
,
<institution>Brigham and Women's Hospital and Harvard Medical School</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Li, Li" sort="Li, Li" uniqKey="Li L" first="Li" last="Li">Li Li</name>
<affiliation>
<nlm:aff wicri:cut=" and" id="af44">
<addr-line>Department of Family Medicine and Community Health</addr-line>
,
<institution>Case Western Reserve University</institution>
,
<addr-line>Cleveland, OH</addr-line>
,
<country>USA</country>
</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Woods, Michael O" sort="Woods, Michael O" uniqKey="Woods M" first="Michael O." last="Woods">Michael O. Woods</name>
<affiliation>
<nlm:aff id="af45">
<addr-line>Discipline of Genetics, Faculty of Medicine</addr-line>
,
<institution>Memorial University</institution>
,
<addr-line>Newfoundland and Labrador</addr-line>
,
<country>Canada</country>
</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Ahsan, Habibul" sort="Ahsan, Habibul" uniqKey="Ahsan H" first="Habibul" last="Ahsan">Habibul Ahsan</name>
<affiliation>
<nlm:aff id="af1">
<addr-line>Department of Public Health Sciences</addr-line>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af2">
<addr-line>Center for Cancer Epidemiology and Prevention</addr-line>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af3">
<addr-line>Department of Medicine</addr-line>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af4">
<addr-line>Department of Human Genetics</addr-line>
,
<institution>The University of Chicago</institution>
,
<addr-line>Chicago, IL</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Pierce, Brandon L" sort="Pierce, Brandon L" uniqKey="Pierce B" first="Brandon L." last="Pierce">Brandon L. Pierce</name>
<affiliation>
<nlm:aff id="af1">
<addr-line>Department of Public Health Sciences</addr-line>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af2">
<addr-line>Center for Cancer Epidemiology and Prevention</addr-line>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af4">
<addr-line>Department of Human Genetics</addr-line>
,
<institution>The University of Chicago</institution>
,
<addr-line>Chicago, IL</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
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<idno type="RBID">PMC:4550826</idno>
<idno type="doi">10.1093/hmg/ddv252</idno>
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<author>
<name sortKey="Zhang, Chenan" sort="Zhang, Chenan" uniqKey="Zhang C" first="Chenan" last="Zhang">Chenan Zhang</name>
<affiliation>
<nlm:aff id="af1">
<addr-line>Department of Public Health Sciences</addr-line>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Doherty, Jennifer A" sort="Doherty, Jennifer A" uniqKey="Doherty J" first="Jennifer A." last="Doherty">Jennifer A. Doherty</name>
<affiliation>
<nlm:aff id="af5">
<addr-line>Section of Biostatistics and Epidemiology</addr-line>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Burgess, Stephen" sort="Burgess, Stephen" uniqKey="Burgess S" first="Stephen" last="Burgess">Stephen Burgess</name>
<affiliation>
<nlm:aff id="af7">
<addr-line>Department of Public Health and Primary Care</addr-line>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Hung, Rayjean J" sort="Hung, Rayjean J" uniqKey="Hung R" first="Rayjean J." last="Hung">Rayjean J. Hung</name>
<affiliation>
<nlm:aff id="af9">
<institution>Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital</institution>
,
<addr-line>Toronto</addr-line>
,
<country>Canada</country>
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</affiliation>
</author>
<author>
<name sortKey="Lindstrom, Sara" sort="Lindstrom, Sara" uniqKey="Lindstrom S" first="Sara" last="Lindström">Sara Lindström</name>
<affiliation>
<nlm:aff id="af10">
<addr-line>Department of Epidemiology</addr-line>
,
<institution>Harvard School of Public Health</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Kraft, Peter" sort="Kraft, Peter" uniqKey="Kraft P" first="Peter" last="Kraft">Peter Kraft</name>
<affiliation>
<nlm:aff id="af10">
<addr-line>Department of Epidemiology</addr-line>
,
<institution>Harvard School of Public Health</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Gong, Jian" sort="Gong, Jian" uniqKey="Gong J" first="Jian" last="Gong">Jian Gong</name>
<affiliation>
<nlm:aff id="af11">
<addr-line>Division of Public Health Sciences</addr-line>
,
<institution>Fred Hutchinson Cancer Research Center</institution>
,
<addr-line>Seattle, WA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Amos, Christopher I" sort="Amos, Christopher I" uniqKey="Amos C" first="Christopher I." last="Amos">Christopher I. Amos</name>
<affiliation>
<nlm:aff id="af6">
<addr-line>Center for Genomic Medicine, Department of Community and Family Medicine</addr-line>
,
<institution>Geisel School of Medicine, Dartmouth College</institution>
,
<addr-line>Lebanon, NH</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Sellers, Thomas A" sort="Sellers, Thomas A" uniqKey="Sellers T" first="Thomas A." last="Sellers">Thomas A. Sellers</name>
<affiliation>
<nlm:aff id="af12">
<addr-line>Department of Cancer Epidemiology</addr-line>
,
<institution>H. Lee Moffitt Cancer Center</institution>
,
<addr-line>Tampa, FL</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Monteiro, Alvaro N A" sort="Monteiro, Alvaro N A" uniqKey="Monteiro A" first="Alvaro N. A." last="Monteiro">Alvaro N. A. Monteiro</name>
<affiliation>
<nlm:aff id="af12">
<addr-line>Department of Cancer Epidemiology</addr-line>
,
<institution>H. Lee Moffitt Cancer Center</institution>
,
<addr-line>Tampa, FL</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Chenevix Trench, Georgia" sort="Chenevix Trench, Georgia" uniqKey="Chenevix Trench G" first="Georgia" last="Chenevix-Trench">Georgia Chenevix-Trench</name>
<affiliation>
<nlm:aff id="af13">
<addr-line>Department of Genetics</addr-line>
,
<institution>QIMR Berghofer Medical Research Institute</institution>
,
<addr-line>Brisbane</addr-line>
,
<country>Australia</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Bickeboller, Heike" sort="Bickeboller, Heike" uniqKey="Bickeboller H" first="Heike" last="Bickeböller">Heike Bickeböller</name>
<affiliation>
<nlm:aff id="af14">
<addr-line>Department of Genetic Epidemiology</addr-line>
,
<institution>University Medical Center, Georg-August-University Göttingen</institution>
,
<addr-line>Göttingen</addr-line>
,
<country>Germany</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Risch, Angela" sort="Risch, Angela" uniqKey="Risch A" first="Angela" last="Risch">Angela Risch</name>
<affiliation>
<nlm:aff id="af15">
<addr-line>Division of Epigenomics and Cancer Risk Factors, DKFZ, German Cancer Research Center</addr-line>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af16">
<addr-line>Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), Heidelberg, Germany</addr-line>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Brennan, Paul" sort="Brennan, Paul" uniqKey="Brennan P" first="Paul" last="Brennan">Paul Brennan</name>
<affiliation>
<nlm:aff id="af17">
<institution>International Agency for Research on Cancer</institution>
,
<addr-line>Lyon</addr-line>
,
<country>France</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Mckay, James D" sort="Mckay, James D" uniqKey="Mckay J" first="James D." last="Mckay">James D. Mckay</name>
<affiliation>
<nlm:aff id="af17">
<institution>International Agency for Research on Cancer</institution>
,
<addr-line>Lyon</addr-line>
,
<country>France</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Houlston, Richard S" sort="Houlston, Richard S" uniqKey="Houlston R" first="Richard S." last="Houlston">Richard S. Houlston</name>
<affiliation>
<nlm:aff id="af18">
<addr-line>Division of Genetics and Epidemiology</addr-line>
,
<institution>Institute of Cancer Research</institution>
,
<addr-line>Sutton, Surrey</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Landi, Maria Teresa" sort="Landi, Maria Teresa" uniqKey="Landi M" first="Maria Teresa" last="Landi">Maria Teresa Landi</name>
<affiliation>
<nlm:aff id="af19">
<addr-line>Division of Cancer Epidemiology and Genetics</addr-line>
,
<institution>National Cancer Institute, National Institutes of Health, U.S. Public Health Service</institution>
,
<addr-line>Bethesda, MD</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Timofeeva, Maria N" sort="Timofeeva, Maria N" uniqKey="Timofeeva M" first="Maria N." last="Timofeeva">Maria N. Timofeeva</name>
<affiliation>
<nlm:aff id="af17">
<institution>International Agency for Research on Cancer</institution>
,
<addr-line>Lyon</addr-line>
,
<country>France</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Wang, Yufei" sort="Wang, Yufei" uniqKey="Wang Y" first="Yufei" last="Wang">Yufei Wang</name>
<affiliation>
<nlm:aff id="af18">
<addr-line>Division of Genetics and Epidemiology</addr-line>
,
<institution>Institute of Cancer Research</institution>
,
<addr-line>Sutton, Surrey</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Heinrich, Joachim" sort="Heinrich, Joachim" uniqKey="Heinrich J" first="Joachim" last="Heinrich">Joachim Heinrich</name>
<affiliation>
<nlm:aff id="af20">
<addr-line>Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany</addr-line>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Kote Jarai, Zsofia" sort="Kote Jarai, Zsofia" uniqKey="Kote Jarai Z" first="Zsofia" last="Kote-Jarai">Zsofia Kote-Jarai</name>
<affiliation>
<nlm:aff id="af21">
<institution>The Institute of Cancer Research</institution>
,
<addr-line>Sutton</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Eeles, Rosalind A" sort="Eeles, Rosalind A" uniqKey="Eeles R" first="Rosalind A." last="Eeles">Rosalind A. Eeles</name>
<affiliation>
<nlm:aff id="af21">
<institution>The Institute of Cancer Research</institution>
,
<addr-line>Sutton</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af22">
<institution>Royal Marsden National Health Service (NHS) Foundation Trust</institution>
,
<addr-line>London and Sutton</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Muir, Ken" sort="Muir, Ken" uniqKey="Muir K" first="Ken" last="Muir">Ken Muir</name>
<affiliation>
<nlm:aff id="af23">
<addr-line>Warwick Medical School</addr-line>
,
<institution>University of Warwick</institution>
,
<addr-line>Coventry</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af24">
<addr-line>Institute of Population Health</addr-line>
,
<institution>University of Manchester</institution>
,
<addr-line>Manchester</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Wiklund, Fredrik" sort="Wiklund, Fredrik" uniqKey="Wiklund F" first="Fredrik" last="Wiklund">Fredrik Wiklund</name>
<affiliation>
<nlm:aff id="af25">
<addr-line>Department of Medical Epidemiology and Biostatistics</addr-line>
,
<institution>Karolinska Institute</institution>
,
<addr-line>Stockholm</addr-line>
,
<country>Sweden</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Gronberg, Henrik" sort="Gronberg, Henrik" uniqKey="Gronberg H" first="Henrik" last="Grönberg">Henrik Grönberg</name>
<affiliation>
<nlm:aff id="af25">
<addr-line>Department of Medical Epidemiology and Biostatistics</addr-line>
,
<institution>Karolinska Institute</institution>
,
<addr-line>Stockholm</addr-line>
,
<country>Sweden</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Berndt, Sonja I" sort="Berndt, Sonja I" uniqKey="Berndt S" first="Sonja I." last="Berndt">Sonja I. Berndt</name>
<affiliation>
<nlm:aff id="af19">
<addr-line>Division of Cancer Epidemiology and Genetics</addr-line>
,
<institution>National Cancer Institute, National Institutes of Health, U.S. Public Health Service</institution>
,
<addr-line>Bethesda, MD</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Chanock, Stephen J" sort="Chanock, Stephen J" uniqKey="Chanock S" first="Stephen J." last="Chanock">Stephen J. Chanock</name>
<affiliation>
<nlm:aff id="af19">
<addr-line>Division of Cancer Epidemiology and Genetics</addr-line>
,
<institution>National Cancer Institute, National Institutes of Health, U.S. Public Health Service</institution>
,
<addr-line>Bethesda, MD</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Schumacher, Fredrick" sort="Schumacher, Fredrick" uniqKey="Schumacher F" first="Fredrick" last="Schumacher">Fredrick Schumacher</name>
<affiliation>
<nlm:aff id="af26">
<addr-line>Department of Preventive Medicine, Keck School of Medicine</addr-line>
,
<institution>University of Southern California/Norris Comprehensive Cancer Center</institution>
,
<addr-line>Los Angeles, CA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Haiman, Christopher A" sort="Haiman, Christopher A" uniqKey="Haiman C" first="Christopher A." last="Haiman">Christopher A. Haiman</name>
<affiliation>
<nlm:aff id="af26">
<addr-line>Department of Preventive Medicine, Keck School of Medicine</addr-line>
,
<institution>University of Southern California/Norris Comprehensive Cancer Center</institution>
,
<addr-line>Los Angeles, CA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Henderson, Brian E" sort="Henderson, Brian E" uniqKey="Henderson B" first="Brian E." last="Henderson">Brian E. Henderson</name>
<affiliation>
<nlm:aff id="af26">
<addr-line>Department of Preventive Medicine, Keck School of Medicine</addr-line>
,
<institution>University of Southern California/Norris Comprehensive Cancer Center</institution>
,
<addr-line>Los Angeles, CA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Amin Al Olama, Ali" sort="Amin Al Olama, Ali" uniqKey="Amin Al Olama A" first="Ali" last="Amin Al Olama">Ali Amin Al Olama</name>
<affiliation>
<nlm:aff id="af8">
<addr-line>Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care</addr-line>
,
<institution>University of Cambridge</institution>
,
<addr-line>Cambridge</addr-line>
,
<country>UK</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Andrulis, Irene L" sort="Andrulis, Irene L" uniqKey="Andrulis I" first="Irene L." last="Andrulis">Irene L. Andrulis</name>
<affiliation>
<nlm:aff id="af27">
<addr-line>Molecular Genetics/Laboratory Medicine and Pathobiology</addr-line>
,
<institution>Mount Sinai Hospital, University of Toronto</institution>
,
<addr-line>Toronto</addr-line>
,
<country>Canada</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L." last="Hopper">John L. Hopper</name>
<affiliation>
<nlm:aff id="af28">
<addr-line>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health</addr-line>
,
<institution>The University of Melbourne</institution>
,
<addr-line>Parkville</addr-line>
,
<country>Australia</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Chang Claude, Jenny" sort="Chang Claude, Jenny" uniqKey="Chang Claude J" first="Jenny" last="Chang-Claude">Jenny Chang-Claude</name>
<affiliation>
<nlm:aff id="af29">
<addr-line>Division of Cancer Epidemiology</addr-line>
,
<institution>German Cancer Research Center (DKFZ)</institution>
,
<addr-line>Heidelberg</addr-line>
,
<country>Germany</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="John, Esther M" sort="John, Esther M" uniqKey="John E" first="Esther M." last="John">Esther M. John</name>
<affiliation>
<nlm:aff id="af30">
<institution>Cancer Prevention Institute of California</institution>
,
<addr-line>Fremont, CA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
<affiliation>
<nlm:aff id="af33">
<institution>Stanford University School of Medicine</institution>
,
<addr-line>Stanford, CA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Malone, Kathleen E" sort="Malone, Kathleen E" uniqKey="Malone K" first="Kathleen E." last="Malone">Kathleen E. Malone</name>
<affiliation>
<nlm:aff id="af11">
<addr-line>Division of Public Health Sciences</addr-line>
,
<institution>Fred Hutchinson Cancer Research Center</institution>
,
<addr-line>Seattle, WA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Gammon, Marilie D" sort="Gammon, Marilie D" uniqKey="Gammon M" first="Marilie D." last="Gammon">Marilie D. Gammon</name>
<affiliation>
<nlm:aff id="af31">
<addr-line>Department of Epidemiology</addr-line>
,
<institution>University of North Carolina School of Public Health</institution>
,
<addr-line>Chapel Hill, NC</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Ursin, Giske" sort="Ursin, Giske" uniqKey="Ursin G" first="Giske" last="Ursin">Giske Ursin</name>
<affiliation>
<nlm:aff id="af32">
<addr-line>Kreftregisteret</addr-line>
,
<institution>Cancer Registry of Norway</institution>
,
<addr-line>Oslo</addr-line>
,
<country>Norway</country>
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</affiliation>
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<author>
<name sortKey="Whittemore, Alice S" sort="Whittemore, Alice S" uniqKey="Whittemore A" first="Alice S." last="Whittemore">Alice S. Whittemore</name>
<affiliation>
<nlm:aff id="af33">
<institution>Stanford University School of Medicine</institution>
,
<addr-line>Stanford, CA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
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<author>
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<affiliation>
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<addr-line>Department of Epidemiology</addr-line>
,
<institution>Harvard School of Public Health</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
,</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Gruber, Stephen B" sort="Gruber, Stephen B" uniqKey="Gruber S" first="Stephen B." last="Gruber">Stephen B. Gruber</name>
<affiliation>
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,
<institution>University of Southern California</institution>
,
<addr-line>Los Angeles, CA</addr-line>
,
<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Knight, Julia A" sort="Knight, Julia A" uniqKey="Knight J" first="Julia A." last="Knight">Julia A. Knight</name>
<affiliation>
<nlm:aff id="af35">
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,
<institution>Samuel Lunenfeld Research Institute, Mount Sinai Hospital</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
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</affiliation>
<affiliation>
<nlm:aff id="af36">
<addr-line>Division of Epidemiology, Dalla Lana School of Public Health</addr-line>
,
<institution>University of Toronto</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
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</affiliation>
<affiliation>
<nlm:aff id="af37">
<addr-line>Samuel Lunenfeld Research Institute</addr-line>
,
<institution>Mount Sinai Hospital</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
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</affiliation>
</author>
<author>
<name sortKey="Hou, Lifang" sort="Hou, Lifang" uniqKey="Hou L" first="Lifang" last="Hou">Lifang Hou</name>
<affiliation>
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<addr-line>Department of Preventive Medicine</addr-line>
,
<institution>Northwestern University</institution>
,
<addr-line>Chicago, IL</addr-line>
,
<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Le Marchand, Loic" sort="Le Marchand, Loic" uniqKey="Le Marchand L" first="Loic" last="Le Marchand">Loic Le Marchand</name>
<affiliation>
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<addr-line>Honolulu, HI</addr-line>
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<country>USA</country>
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<addr-line>Seattle, WA</addr-line>
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<country>USA</country>
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<affiliation>
<nlm:aff id="af40">
<addr-line>Department of Epidemiology</addr-line>
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<institution>University of Washington School of Public Health</institution>
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<addr-line>Seattle, WA</addr-line>
,
<country>USA</country>
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,
<addr-line>Toronto, ON</addr-line>
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<institution>Canada</institution>
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,
<institution>Massachusetts General Hospital and Harvard Medical School</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
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</affiliation>
<affiliation>
<nlm:aff id="af43">
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,
<institution>Brigham and Women's Hospital and Harvard Medical School</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Li, Li" sort="Li, Li" uniqKey="Li L" first="Li" last="Li">Li Li</name>
<affiliation>
<nlm:aff wicri:cut=" and" id="af44">
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,
<institution>Case Western Reserve University</institution>
,
<addr-line>Cleveland, OH</addr-line>
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<country>USA</country>
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</affiliation>
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<author>
<name sortKey="Woods, Michael O" sort="Woods, Michael O" uniqKey="Woods M" first="Michael O." last="Woods">Michael O. Woods</name>
<affiliation>
<nlm:aff id="af45">
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,
<institution>Memorial University</institution>
,
<addr-line>Newfoundland and Labrador</addr-line>
,
<country>Canada</country>
</nlm:aff>
</affiliation>
</author>
<author>
<name sortKey="Ahsan, Habibul" sort="Ahsan, Habibul" uniqKey="Ahsan H" first="Habibul" last="Ahsan">Habibul Ahsan</name>
<affiliation>
<nlm:aff id="af1">
<addr-line>Department of Public Health Sciences</addr-line>
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</affiliation>
<affiliation>
<nlm:aff id="af2">
<addr-line>Center for Cancer Epidemiology and Prevention</addr-line>
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</affiliation>
<affiliation>
<nlm:aff id="af3">
<addr-line>Department of Medicine</addr-line>
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</affiliation>
<affiliation>
<nlm:aff id="af4">
<addr-line>Department of Human Genetics</addr-line>
,
<institution>The University of Chicago</institution>
,
<addr-line>Chicago, IL</addr-line>
,
<country>USA</country>
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</affiliation>
</author>
<author>
<name sortKey="Pierce, Brandon L" sort="Pierce, Brandon L" uniqKey="Pierce B" first="Brandon L." last="Pierce">Brandon L. Pierce</name>
<affiliation>
<nlm:aff id="af1">
<addr-line>Department of Public Health Sciences</addr-line>
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</affiliation>
<affiliation>
<nlm:aff id="af2">
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</affiliation>
<affiliation>
<nlm:aff id="af4">
<addr-line>Department of Human Genetics</addr-line>
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<institution>The University of Chicago</institution>
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<addr-line>Chicago, IL</addr-line>
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<country>USA</country>
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</analytic>
<series>
<title level="j">Human Molecular Genetics</title>
<idno type="ISSN">0964-6906</idno>
<idno type="eISSN">1460-2083</idno>
<imprint>
<date when="2015">2015</date>
</imprint>
</series>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc>
<textClass></textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">
<p>Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (
<italic>P</italic>
= 6.3 × 10
<sup>−15</sup>
), even after exclusion of a SNP residing in a known lung cancer susceptibility region (
<italic>TERT-CLPTM1L</italic>
)
<italic>P</italic>
= 6.6 × 10
<sup>−6</sup>
). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.</p>
</div>
</front>
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<pmc article-type="research-article">
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<journal-meta>
<journal-id journal-id-type="nlm-ta">Hum Mol Genet</journal-id>
<journal-id journal-id-type="iso-abbrev">Hum. Mol. Genet</journal-id>
<journal-id journal-id-type="publisher-id">hmg</journal-id>
<journal-id journal-id-type="hwp">hmg</journal-id>
<journal-title-group>
<journal-title>Human Molecular Genetics</journal-title>
</journal-title-group>
<issn pub-type="ppub">0964-6906</issn>
<issn pub-type="epub">1460-2083</issn>
<publisher>
<publisher-name>Oxford University Press</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">26138067</article-id>
<article-id pub-id-type="pmc">4550826</article-id>
<article-id pub-id-type="doi">10.1093/hmg/ddv252</article-id>
<article-id pub-id-type="publisher-id">ddv252</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Association Studies Articles</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Genetic determinants of telomere length and risk of common cancers: a Mendelian randomization study</article-title>
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<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Zhang</surname>
<given-names>Chenan</given-names>
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<given-names>Jennifer A.</given-names>
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<name>
<surname>Kraft</surname>
<given-names>Peter</given-names>
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<surname>Gong</surname>
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<given-names>Jenny</given-names>
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<given-names>Esther M.</given-names>
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<given-names>Kathleen E.</given-names>
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<given-names>Marilie D.</given-names>
</name>
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<given-names>Giske</given-names>
</name>
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<given-names>Alice S.</given-names>
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<given-names>David J.</given-names>
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<given-names>Stephen B.</given-names>
</name>
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<name>
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<given-names>Julia A.</given-names>
</name>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Hou</surname>
<given-names>Lifang</given-names>
</name>
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</contrib>
<contrib contrib-type="author">
<name>
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<given-names>Loic</given-names>
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<name>
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<given-names>Polly A.</given-names>
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<xref ref-type="aff" rid="af11">11</xref>
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<name>
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<given-names>Thomas J.</given-names>
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<name>
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<given-names>Li</given-names>
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</name>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Ahsan</surname>
<given-names>Habibul</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af3">3</xref>
<xref ref-type="aff" rid="af4">4</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Pierce</surname>
<given-names>Brandon L.</given-names>
</name>
<xref ref-type="aff" rid="af1">1</xref>
<xref ref-type="aff" rid="af2">2</xref>
<xref ref-type="aff" rid="af4">4</xref>
<xref ref-type="corresp" rid="cor1">*</xref>
</contrib>
<on-behalf-of>on behalf of GECCO and the GAME-ON Network: CORECT, DRIVE, ELLIPSE, FOCI, and TRICL</on-behalf-of>
<aff id="af1">
<label>1</label>
<addr-line>Department of Public Health Sciences</addr-line>
,</aff>
<aff id="af2">
<label>2</label>
<addr-line>Center for Cancer Epidemiology and Prevention</addr-line>
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<aff id="af3">
<label>3</label>
<addr-line>Department of Medicine</addr-line>
,</aff>
<aff id="af4">
<label>4</label>
<addr-line>Department of Human Genetics</addr-line>
,
<institution>The University of Chicago</institution>
,
<addr-line>Chicago, IL</addr-line>
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<country>USA</country>
,</aff>
<aff id="af5">
<label>5</label>
<addr-line>Section of Biostatistics and Epidemiology</addr-line>
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<aff id="af6">
<label>6</label>
<addr-line>Center for Genomic Medicine, Department of Community and Family Medicine</addr-line>
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<institution>Geisel School of Medicine, Dartmouth College</institution>
,
<addr-line>Lebanon, NH</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af7">
<label>7</label>
<addr-line>Department of Public Health and Primary Care</addr-line>
,</aff>
<aff id="af8">
<label>8</label>
<addr-line>Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care</addr-line>
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<institution>University of Cambridge</institution>
,
<addr-line>Cambridge</addr-line>
,
<country>UK</country>
,</aff>
<aff id="af9">
<label>9</label>
<institution>Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital</institution>
,
<addr-line>Toronto</addr-line>
,
<country>Canada</country>
,</aff>
<aff id="af10">
<label>10</label>
<addr-line>Department of Epidemiology</addr-line>
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<institution>Harvard School of Public Health</institution>
,
<addr-line>Boston, MA</addr-line>
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<country>USA</country>
,</aff>
<aff id="af11">
<label>11</label>
<addr-line>Division of Public Health Sciences</addr-line>
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<institution>Fred Hutchinson Cancer Research Center</institution>
,
<addr-line>Seattle, WA</addr-line>
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<country>USA</country>
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<aff id="af12">
<label>12</label>
<addr-line>Department of Cancer Epidemiology</addr-line>
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<institution>H. Lee Moffitt Cancer Center</institution>
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<addr-line>Tampa, FL</addr-line>
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<country>USA</country>
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<aff id="af13">
<label>13</label>
<addr-line>Department of Genetics</addr-line>
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<institution>QIMR Berghofer Medical Research Institute</institution>
,
<addr-line>Brisbane</addr-line>
,
<country>Australia</country>
,</aff>
<aff id="af14">
<label>14</label>
<addr-line>Department of Genetic Epidemiology</addr-line>
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<institution>University Medical Center, Georg-August-University Göttingen</institution>
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<addr-line>Göttingen</addr-line>
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<country>Germany</country>
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<aff id="af15">
<label>15</label>
<addr-line>Division of Epigenomics and Cancer Risk Factors, DKFZ, German Cancer Research Center</addr-line>
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<aff id="af16">
<label>16</label>
<addr-line>Translational Lung Research Center Heidelberg (TLRC-H), Member of the German Center for Lung Research (DZL), Heidelberg, Germany</addr-line>
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<aff id="af17">
<label>17</label>
<institution>International Agency for Research on Cancer</institution>
,
<addr-line>Lyon</addr-line>
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<country>France</country>
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<aff id="af18">
<label>18</label>
<addr-line>Division of Genetics and Epidemiology</addr-line>
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<institution>Institute of Cancer Research</institution>
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<addr-line>Sutton, Surrey</addr-line>
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<country>UK</country>
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<aff id="af19">
<label>19</label>
<addr-line>Division of Cancer Epidemiology and Genetics</addr-line>
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<institution>National Cancer Institute, National Institutes of Health, U.S. Public Health Service</institution>
,
<addr-line>Bethesda, MD</addr-line>
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<country>USA</country>
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<aff id="af20">
<label>20</label>
<addr-line>Institute of Epidemiology I, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany</addr-line>
,</aff>
<aff id="af21">
<label>21</label>
<institution>The Institute of Cancer Research</institution>
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<addr-line>Sutton</addr-line>
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<country>UK</country>
,</aff>
<aff id="af22">
<label>22</label>
<institution>Royal Marsden National Health Service (NHS) Foundation Trust</institution>
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<addr-line>London and Sutton</addr-line>
,
<country>UK</country>
,</aff>
<aff id="af23">
<label>23</label>
<addr-line>Warwick Medical School</addr-line>
,
<institution>University of Warwick</institution>
,
<addr-line>Coventry</addr-line>
,
<country>UK</country>
,</aff>
<aff id="af24">
<label>24</label>
<addr-line>Institute of Population Health</addr-line>
,
<institution>University of Manchester</institution>
,
<addr-line>Manchester</addr-line>
,
<country>UK</country>
,</aff>
<aff id="af25">
<label>25</label>
<addr-line>Department of Medical Epidemiology and Biostatistics</addr-line>
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<institution>Karolinska Institute</institution>
,
<addr-line>Stockholm</addr-line>
,
<country>Sweden</country>
,</aff>
<aff id="af26">
<label>26</label>
<addr-line>Department of Preventive Medicine, Keck School of Medicine</addr-line>
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<institution>University of Southern California/Norris Comprehensive Cancer Center</institution>
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<addr-line>Los Angeles, CA</addr-line>
,
<country>USA</country>
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<aff id="af27">
<label>27</label>
<addr-line>Molecular Genetics/Laboratory Medicine and Pathobiology</addr-line>
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<institution>Mount Sinai Hospital, University of Toronto</institution>
,
<addr-line>Toronto</addr-line>
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<country>Canada</country>
,</aff>
<aff id="af28">
<label>28</label>
<addr-line>Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health</addr-line>
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<institution>The University of Melbourne</institution>
,
<addr-line>Parkville</addr-line>
,
<country>Australia</country>
,</aff>
<aff id="af29">
<label>29</label>
<addr-line>Division of Cancer Epidemiology</addr-line>
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<institution>German Cancer Research Center (DKFZ)</institution>
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<addr-line>Heidelberg</addr-line>
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<country>Germany</country>
,</aff>
<aff id="af30">
<label>30</label>
<institution>Cancer Prevention Institute of California</institution>
,
<addr-line>Fremont, CA</addr-line>
,
<country>USA</country>
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<aff id="af31">
<label>31</label>
<addr-line>Department of Epidemiology</addr-line>
,
<institution>University of North Carolina School of Public Health</institution>
,
<addr-line>Chapel Hill, NC</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af32">
<label>32</label>
<addr-line>Kreftregisteret</addr-line>
,
<institution>Cancer Registry of Norway</institution>
,
<addr-line>Oslo</addr-line>
,
<country>Norway</country>
,</aff>
<aff id="af33">
<label>33</label>
<institution>Stanford University School of Medicine</institution>
,
<addr-line>Stanford, CA</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af34">
<label>34</label>
<addr-line>USC Norris Comprehensive Cancer Center</addr-line>
,
<institution>University of Southern California</institution>
,
<addr-line>Los Angeles, CA</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af35">
<label>35</label>
<addr-line>Ontario Cancer Genetics Network, Fred A. Litwin Center for Cancer Genetics</addr-line>
,
<institution>Samuel Lunenfeld Research Institute, Mount Sinai Hospital</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
,</aff>
<aff id="af36">
<label>36</label>
<addr-line>Division of Epidemiology, Dalla Lana School of Public Health</addr-line>
,
<institution>University of Toronto</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
,</aff>
<aff id="af37">
<label>37</label>
<addr-line>Samuel Lunenfeld Research Institute</addr-line>
,
<institution>Mount Sinai Hospital</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<country>Canada</country>
,</aff>
<aff id="af38">
<label>38</label>
<addr-line>Department of Preventive Medicine</addr-line>
,
<institution>Northwestern University</institution>
,
<addr-line>Chicago, IL</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af39">
<label>39</label>
<addr-line>Epidemiology Program</addr-line>
,
<institution>University of Hawaii Cancer Center</institution>
,
<addr-line>Honolulu, HI</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af40">
<label>40</label>
<addr-line>Department of Epidemiology</addr-line>
,
<institution>University of Washington School of Public Health</institution>
,
<addr-line>Seattle, WA</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af41">
<label>41</label>
<institution>Ontario Institute for Cancer Research</institution>
,
<addr-line>Toronto, ON</addr-line>
,
<institution>Canada</institution>
,</aff>
<aff id="af42">
<label>42</label>
<addr-line>Division of Gastroenterology</addr-line>
,
<institution>Massachusetts General Hospital and Harvard Medical School</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af43">
<label>43</label>
<addr-line>Channing Division of Network Medicine</addr-line>
,
<institution>Brigham and Women's Hospital and Harvard Medical School</institution>
,
<addr-line>Boston, MA</addr-line>
,
<country>USA</country>
,</aff>
<aff id="af44">
<label>44</label>
<addr-line>Department of Family Medicine and Community Health</addr-line>
,
<institution>Case Western Reserve University</institution>
,
<addr-line>Cleveland, OH</addr-line>
,
<country>USA</country>
and</aff>
<aff id="af45">
<label>45</label>
<addr-line>Discipline of Genetics, Faculty of Medicine</addr-line>
,
<institution>Memorial University</institution>
,
<addr-line>Newfoundland and Labrador</addr-line>
,
<country>Canada</country>
</aff>
</contrib-group>
<author-notes>
<corresp id="cor1">
<label>*</label>
To whom correspondence should be addressed at: Department of Public Health Sciences, The University of Chicago, 5841 S. Maryland Ave. MC2000, Chicago, IL 60637, USA. Tel: +1 7737021917; Fax: +1 7738340139; Email:
<email>brandonpierce@uchicago.edu</email>
</corresp>
</author-notes>
<pub-date pub-type="ppub">
<day>15</day>
<month>9</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="epub">
<day>02</day>
<month>7</month>
<year>2015</year>
</pub-date>
<pub-date pub-type="pmc-release">
<day>02</day>
<month>7</month>
<year>2015</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on the . </pmc-comment>
<volume>24</volume>
<issue>18</issue>
<fpage>5356</fpage>
<lpage>5366</lpage>
<history>
<date date-type="received">
<day>22</day>
<month>12</month>
<year>2014</year>
</date>
<date date-type="rev-recd">
<day>3</day>
<month>6</month>
<year>2015</year>
</date>
<date date-type="accepted">
<day>25</day>
<month>6</month>
<year>2015</year>
</date>
</history>
<permissions>
<copyright-statement>© The Author 2015. Published by Oxford University Press.</copyright-statement>
<copyright-year>2015</copyright-year>
<license license-type="creative-commons" xlink:href="http://creativecommons.org/licenses/by/4.0/">
<license-p>This is an Open Access article distributed under the terms of the Creative Commons Attribution License (
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.</license-p>
</license>
</permissions>
<self-uri content-type="pdf" xlink:href="ddv252.pdf"></self-uri>
<abstract>
<p>Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (
<italic>P</italic>
= 6.3 × 10
<sup>−15</sup>
), even after exclusion of a SNP residing in a known lung cancer susceptibility region (
<italic>TERT-CLPTM1L</italic>
)
<italic>P</italic>
= 6.6 × 10
<sup>−6</sup>
). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.</p>
</abstract>
<funding-group>
<award-group id="funding-1">
<funding-source>Genetic Associations and Mechanisms in Oncology Network, GAME-ON: Discovery, Biology, and Risk of Inherited Variants in Breast Cancer, DRIVE, PI: D.J.H.</funding-source>
<award-id>U19 CA148065</award-id>
</award-group>
<award-group id="funding-2">
<funding-source>Colorectal Transdisciplinary Study, CORECT, PI: S.B.G.</funding-source>
<award-id>U19 CA148107</award-id>
</award-group>
<award-group id="funding-3">
<funding-source>Transdisciplinary Research in Cancer of the Lung, TRICL, PI: C.A.</funding-source>
<award-id>U19 CA148127</award-id>
</award-group>
<award-group id="funding-4">
<funding-source>FOCI, PI: T.A.S.</funding-source>
<award-id>U19 CA148112</award-id>
</award-group>
<award-group id="funding-5">
<funding-source>Elucidating Loci Involved in Prostate Cancer Susceptibility, ELLIPSE, PI: B.E.H.</funding-source>
<award-id>U19 CA148537</award-id>
</award-group>
<award-group id="funding-6">
<funding-source>Genetics and Epidemiology of Colorectal Cancer Consortium, GECCO: National Cancer Institute</funding-source>
</award-group>
<award-group id="funding-7">
<funding-source>National Institutes of Health
<named-content content-type="funder-id">http://dx.doi.org/10.13039/100000002</named-content>
</funding-source>
<award-id>U01 CA137088, R01 CA059045</award-id>
</award-group>
<award-group id="funding-8">
<funding-source>National Institute of Health, National Institute on Aging</funding-source>
<award-id>T32AG000243; P30AG012857</award-id>
</award-group>
<award-group id="funding-9">
<funding-source>Cancer Research Foundation Young Investigator Award, R01 ES020506, and U01 HG007601 to BLP; The Wellcome Trust</funding-source>
<award-id>100114</award-id>
</award-group>
<award-group id="funding-10">
<funding-source>The NHMRC Senior Principal Research Fellow to G.C.T</funding-source>
</award-group>
</funding-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<title>Introduction</title>
<p>Telomeres are DNA–protein complexes at chromosome ends that help maintain genome stability by protecting DNA from damage and fusion. The DNA component is a six-base TTAGGG repeat sequence that shortens with each cell division. In differentiated cells, telomere shortening eventually leads to loss of telomere protection and genome instability, typically triggering cell senescence or programmed cell death (
<xref rid="DDV252C1" ref-type="bibr">1</xref>
). In stem and progenitor cells, the telomerase enzyme elongates telomeres, enabling prolonged cell survival (
<xref rid="DDV252C2" ref-type="bibr">2</xref>
). Telomerase is also activated in >90% of human tumors (
<xref rid="DDV252C3" ref-type="bibr">3</xref>
), which typically have short telomeres (a potential cause of genome instability), thus promoting proliferation and survival (
<xref rid="DDV252C4" ref-type="bibr">4</xref>
).</p>
<p>The critical role of telomeres and telomerase in carcinogenesis has led to the hypothesis that short telomere length (TL) is a risk factor for cancer (
<xref rid="DDV252C5" ref-type="bibr">5</xref>
). Indeed, short relative TL measured in surrogate tissues, such as peripheral blood cells, has been associated with increased risk for lung (
<xref rid="DDV252C6" ref-type="bibr">6</xref>
,
<xref rid="DDV252C7" ref-type="bibr">7</xref>
), ovarian (
<xref rid="DDV252C8" ref-type="bibr">8</xref>
), colorectal (
<xref rid="DDV252C9" ref-type="bibr">9</xref>
) and breast cancers (
<xref rid="DDV252C10" ref-type="bibr">10</xref>
,
<xref rid="DDV252C11" ref-type="bibr">11</xref>
) in epidemiological studies (with the interpretation that blood TL predicts cancer risk because it is a proxy for TL in cancer-prone tissues). However, such associations are not consistent across all cancers or even within cancer types, with some studies reporting null, U-shaped or positive associations (
<xref rid="DDV252C11" ref-type="bibr">11</xref>
<xref rid="DDV252C16" ref-type="bibr">16</xref>
). Furthermore, due to the retrospective nature of case–control studies from which many of these association estimates are obtained, telomere shortening that occurs after diagnosis, potentially due to treatment (
<xref rid="DDV252C17" ref-type="bibr">17</xref>
,
<xref rid="DDV252C18" ref-type="bibr">18</xref>
) or disease progression, can result in biased estimates of the association between TL and cancer risk (
<xref rid="DDV252C6" ref-type="bibr">6</xref>
,
<xref rid="DDV252C8" ref-type="bibr">8</xref>
,
<xref rid="DDV252C14" ref-type="bibr">14</xref>
).</p>
<p>Genome-wide association (GWA) studies have identified several genomic regions containing variants associated with TL in peripheral blood cells (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
<xref rid="DDV252C21" ref-type="bibr">21</xref>
), including the
<italic>TERT</italic>
(telomerase reverse transcriptase) region (5p15.33). Furthermore, GWA studies of cancer risk have observed that variants in the
<italic>TERT</italic>
region influence risk for multiple cancer types, including breast (
<xref rid="DDV252C22" ref-type="bibr">22</xref>
), colorectal (
<xref rid="DDV252C23" ref-type="bibr">23</xref>
), lung (
<xref rid="DDV252C24" ref-type="bibr">24</xref>
), prostate (
<xref rid="DDV252C24" ref-type="bibr">24</xref>
) and ovarian (
<xref rid="DDV252C22" ref-type="bibr">22</xref>
) cancer, although these associations do not appear to all be driven by the same causal variant. In light of this evidence indicating an important role for telomeres in carcinogenesis, we undertook a comprehensive examination of associations between genetic determinants of TL and cancer risk.</p>
<p>In this work, we describe the associations between nine TL-associated genetic variants and risk for five cancer types (breast, lung, colorectal, ovarian and prostate), using data from the Genetic Associations and Mechanisms in Oncology (GAME-ON) network of consortia for post-GWA research. In addition, we estimate the association between a multi-variant TL score and cancer risk, which corresponds to the effect of TL on cancer risk under Mendelian randomization assumptions (
<xref rid="DDV252C25" ref-type="bibr">25</xref>
). However, this interpretation requires caution because the validity of the Mendelian randomization assumptions (such as the absence of pleoitropy) cannot be proven.</p>
<p>Because genotype–phenotype associations are not vulnerable to biases caused by reverse causation or confounding by environment, the Mendelian randomization approach used in this study is an attractive approach for estimating relationships between TL and cancer risk.</p>
</sec>
<sec sec-type="results" id="s2">
<title>Results</title>
<p>The Genetic Associations and Mechanisms in Oncology (GAME-ON) Consortium is a network of five consortia focused on cancers of the breast, colon, lung, ovary and prostate. The GAME-ON network represents 33 GWA studies contributing data on >51 000 cancer cases and >62 000 controls (
<xref rid="DDV252C26" ref-type="bibr">26</xref>
). Samples sizes for each cancer type and subtype are listed in Table
<xref ref-type="table" rid="DDV252TB1">1</xref>
.
<table-wrap id="DDV252TB1" orientation="portrait" position="float">
<label>Table 1.</label>
<caption>
<p>Sample sizes for cancer types included in the Genetic Associations and Mechanisms in Oncology (GAME-ON) consortium. Details on the GAME-ON Network and the contributing GWA studies have been previously described (
<xref rid="DDV252C26" ref-type="bibr">26</xref>
) (
<uri xlink:href="http://epi.grants.cancer.gov/gameon/">http://epi.grants.cancer.gov/gameon/</uri>
)</p>
</caption>
<table frame="hsides" rules="groups">
<colgroup span="1">
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
</colgroup>
<thead>
<tr>
<th align="left" rowspan="1" colspan="1">Cancer type</th>
<th align="left" rowspan="1" colspan="1">Cases</th>
<th align="left" rowspan="1" colspan="1">Controls</th>
<th align="left" rowspan="1" colspan="1">GWA studies
<sup>a</sup>
</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="4" rowspan="1">Breast</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">15 748</td>
<td rowspan="1" colspan="1">18 084</td>
<td rowspan="1" colspan="1">11</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> ER-negative</td>
<td rowspan="1" colspan="1">4939</td>
<td rowspan="1" colspan="1">13 128</td>
<td rowspan="1" colspan="1">8</td>
</tr>
<tr>
<td rowspan="1" colspan="1">Colorectal</td>
<td rowspan="1" colspan="1">5100</td>
<td rowspan="1" colspan="1">4831</td>
<td rowspan="1" colspan="1">6</td>
</tr>
<tr>
<td colspan="4" rowspan="1">Lung
<sup>b</sup>
</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">12 160</td>
<td rowspan="1" colspan="1">16 838</td>
<td rowspan="1" colspan="1">9</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Adenocarcinoma</td>
<td rowspan="1" colspan="1">3718</td>
<td rowspan="1" colspan="1">15 871</td>
<td rowspan="1" colspan="1">9</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Squamous</td>
<td rowspan="1" colspan="1">3422</td>
<td rowspan="1" colspan="1">16 015</td>
<td rowspan="1" colspan="1">9</td>
</tr>
<tr>
<td colspan="4" rowspan="1">Ovarian</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">4369</td>
<td rowspan="1" colspan="1">9123</td>
<td rowspan="1" colspan="1">3</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Clear-cell</td>
<td rowspan="1" colspan="1">356</td>
<td rowspan="1" colspan="1">9123</td>
<td rowspan="1" colspan="1">3</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Endometrioid</td>
<td rowspan="1" colspan="1">715</td>
<td rowspan="1" colspan="1">9123</td>
<td rowspan="1" colspan="1">3</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Serous</td>
<td rowspan="1" colspan="1">2556</td>
<td rowspan="1" colspan="1">9123</td>
<td rowspan="1" colspan="1">3</td>
</tr>
<tr>
<td colspan="4" rowspan="1">Prostate</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">14 160</td>
<td rowspan="1" colspan="1">12 724</td>
<td rowspan="1" colspan="1">6</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Aggressive</td>
<td rowspan="1" colspan="1">4450</td>
<td rowspan="1" colspan="1">12 724</td>
<td rowspan="1" colspan="1">6</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="tblfn1">
<p>
<sup>a</sup>
Not including studies from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO).</p>
</fn>
<fn id="tblfn2">
<p>
<sup>b</sup>
Subtypes listed do not represent all subtypes within cancer type.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</p>
<sec id="s2a">
<title>Association estimates for individual SNPs</title>
<p>Based on the existing literature, we identified nine SNPs showing genome-wide significant associations (
<italic>P</italic>
< 5 × 10
<sup>−8</sup>
) with TL in GWA studies (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
<xref rid="DDV252C21" ref-type="bibr">21</xref>
). From these prior papers we obtained the identifier for the lead SNP at each reported locus as well as the ‘long TL’ allele, association estimate for the ‘long’ allele (in terms of kb of TL per allele), and the standard error and
<italic>P</italic>
-value for each SNP's association with TL (Table
<xref ref-type="table" rid="DDV252TB2">2</xref>
). We estimated associations between each of the nine TL-associated SNPs and risk for each of the five common cancer types and subtypes in the GAME-ON study, shown as forest plots in
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Figure S1</ext-link>
. Of note, for all nine SNPs, the long TL allele had an OR > 1 for lung adenocarcinoma, with four of the nine associations being statistically significant (
<italic>P</italic>
< 0.05) (Fig.
<xref ref-type="fig" rid="DDV252F1">1</xref>
, top left). In contrast, no TL-associated SNP was significantly associated with squamous cell carcinoma of the lung (Fig.
<xref ref-type="fig" rid="DDV252F1">1</xref>
, bottom left). Prostate cancer risk also showed nominally significant positive associations with the long TL alleles for three of the nine SNPs (
<italic>P</italic>
< 0.05) (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Fig. S1</ext-link>
).
<table-wrap id="DDV252TB2" orientation="portrait" position="float">
<label>Table 2.</label>
<caption>
<p>Characteristics of genetic variants associated with TL as reported in prior GWA studies</p>
</caption>
<table frame="hsides" rules="groups">
<colgroup span="1">
<col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="left" span="1"></col>
<col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="left" span="1"></col>
</colgroup>
<thead>
<tr>
<th align="left" rowspan="1" colspan="1">SNP identifier</th>
<th align="left" rowspan="1" colspan="1">Chromosome</th>
<th align="left" rowspan="1" colspan="1">Locus</th>
<th align="left" rowspan="1" colspan="1">‘Long’ allele</th>
<th align="left" rowspan="1" colspan="1">
<italic>β</italic>
estimate
<sup>a</sup>
</th>
<th align="left" rowspan="1" colspan="1">
<italic>P</italic>
-value</th>
<th align="left" rowspan="1" colspan="1">Source</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="1" colspan="1">rs10936599</td>
<td rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1">
<italic>TERC</italic>
</td>
<td rowspan="1" colspan="1">C</td>
<td rowspan="1" colspan="1">0.117</td>
<td rowspan="1" colspan="1">2.5 × 10
<sup>−31</sup>
</td>
<td rowspan="1" colspan="1">Codd
<italic>et al</italic>
. (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs2736100</td>
<td rowspan="1" colspan="1">5</td>
<td rowspan="1" colspan="1">
<italic>TERT</italic>
</td>
<td rowspan="1" colspan="1">C</td>
<td rowspan="1" colspan="1">0.094</td>
<td rowspan="1" colspan="1">4.4 × 10
<sup>−19</sup>
</td>
<td rowspan="1" colspan="1">Codd
<italic>et al</italic>
. (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs7726159
<sup>b</sup>
</td>
<td rowspan="1" colspan="1">5</td>
<td rowspan="1" colspan="1">
<italic>TERT</italic>
</td>
<td rowspan="1" colspan="1">A</td>
<td rowspan="1" colspan="1">0.073</td>
<td rowspan="1" colspan="1">4.7 × 10
<sup>−17</sup>
</td>
<td rowspan="1" colspan="1">Pooley
<italic>et al</italic>
. (
<xref rid="DDV252C21" ref-type="bibr">21</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs7675998</td>
<td rowspan="1" colspan="1">4</td>
<td rowspan="1" colspan="1">
<italic>NAF1</italic>
</td>
<td rowspan="1" colspan="1">G</td>
<td rowspan="1" colspan="1">0.090</td>
<td rowspan="1" colspan="1">4.3 × 10
<sup>−16</sup>
</td>
<td rowspan="1" colspan="1">Codd
<italic>et al</italic>
. (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs9420907</td>
<td rowspan="1" colspan="1">10</td>
<td rowspan="1" colspan="1">
<italic>OBFC1</italic>
</td>
<td rowspan="1" colspan="1">C</td>
<td rowspan="1" colspan="1">0.083</td>
<td rowspan="1" colspan="1">6.9 × 10
<sup>−11</sup>
</td>
<td rowspan="1" colspan="1">Codd
<italic>et al</italic>
. (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs6772228</td>
<td rowspan="1" colspan="1">3</td>
<td rowspan="1" colspan="1">
<italic>PXK</italic>
</td>
<td rowspan="1" colspan="1">T</td>
<td rowspan="1" colspan="1">0.120</td>
<td rowspan="1" colspan="1">3.9 × 10
<sup>−10</sup>
</td>
<td rowspan="1" colspan="1">Pooley
<italic>et al</italic>
. (
<xref rid="DDV252C21" ref-type="bibr">21</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs8105767</td>
<td rowspan="1" colspan="1">19</td>
<td rowspan="1" colspan="1">
<italic>ZNF208</italic>
</td>
<td rowspan="1" colspan="1">G</td>
<td rowspan="1" colspan="1">0.058</td>
<td rowspan="1" colspan="1">1.1 × 10
<sup>−9</sup>
</td>
<td rowspan="1" colspan="1">Codd
<italic>et al</italic>
. (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs755017</td>
<td rowspan="1" colspan="1">20</td>
<td rowspan="1" colspan="1">
<italic>RTEL1</italic>
</td>
<td rowspan="1" colspan="1">G</td>
<td rowspan="1" colspan="1">0.074</td>
<td rowspan="1" colspan="1">6.7 × 10
<sup>−9</sup>
</td>
<td rowspan="1" colspan="1">Codd
<italic>et al</italic>
. (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs412658
<sup>c</sup>
</td>
<td rowspan="1" colspan="1">19</td>
<td rowspan="1" colspan="1">
<italic>ZNF676</italic>
</td>
<td rowspan="1" colspan="1">T</td>
<td rowspan="1" colspan="1">0.050</td>
<td rowspan="1" colspan="1">9.8 × 10
<sup>−9</sup>
</td>
<td rowspan="1" colspan="1">Mangino
<italic>et al</italic>
. (
<xref rid="DDV252C20" ref-type="bibr">20</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs3027234</td>
<td rowspan="1" colspan="1">17</td>
<td rowspan="1" colspan="1">
<italic>CTC1</italic>
</td>
<td rowspan="1" colspan="1">C</td>
<td rowspan="1" colspan="1">0.057</td>
<td rowspan="1" colspan="1">2.3 × 10
<sup>−8</sup>
</td>
<td rowspan="1" colspan="1">Mangino
<italic>et al</italic>
. (
<xref rid="DDV252C20" ref-type="bibr">20</xref>
)</td>
</tr>
<tr>
<td rowspan="1" colspan="1">rs11125529</td>
<td rowspan="1" colspan="1">2</td>
<td rowspan="1" colspan="1">
<italic>ACYP2</italic>
</td>
<td rowspan="1" colspan="1">A</td>
<td rowspan="1" colspan="1">0.067</td>
<td rowspan="1" colspan="1">4.5 × 10
<sup>−8</sup>
</td>
<td rowspan="1" colspan="1">Codd
<italic>et al</italic>
. (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn id="tblfn3">
<p>
<sup>a</sup>
Reported in kb telomere per ‘long’ allele.</p>
</fn>
<fn id="tblfn4">
<p>
<sup>b</sup>
In linkage disequilibrium (
<italic>r</italic>
<sup>2</sup>
= 0.382) with rs2736100 of the
<italic>TERT</italic>
locus, excluded from all analyses.</p>
</fn>
<fn id="tblfn5">
<p>
<sup>c</sup>
In linkage disequilibrium(
<italic>r</italic>
<sup>2</sup>
= 0.704) with rs8105767 of the
<italic>ZNF208</italic>
locus, excluded from all analyses.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<fig id="DDV252F1" orientation="portrait" position="float">
<label>Figure 1.</label>
<caption>
<p>Forest plots (left) and scatter plots (right) of associations between TL-associated SNPs and risk for lung adenocarcinoma (top) and squamous cell carcinoma (bottom). Forest plots show association estimates (with horizontal bars indicating 95% CI) for the ‘long telomere’ allele of each SNP with cancer risk. SNPs are ordered by increasing magnitude of association with TL. Scatter plots show the per-allele association with cancer risk plotted against the per-allele association with kb of TL (with vertical and horizontal black lines showing 95% cCI for each SNP). The scatter plot is overlaid with the Mendelian randomization estimate (slope of red solid line with dotted lines showing 95% CI) of the effect of TL on cancer risk</p>
</caption>
<graphic xlink:href="ddv25201"></graphic>
</fig>
</p>
</sec>
<sec id="s2b">
<title>Mendelian randomization estimates based on multi-SNP scores</title>
<p>We estimated the associations between a multi-SNP TL score and risk for each cancer (Table
<xref ref-type="table" rid="DDV252TB3">3</xref>
) using a previously described Mendelian randomization approach that obtains an estimate using an inverse-variance weighted average of SNP-specific associations (
<xref rid="DDV252C27" ref-type="bibr">27</xref>
,
<xref rid="DDV252C28" ref-type="bibr">28</xref>
) (see Materials and Methods). Of note, we identified a highly statistically significant association between long TL and increased risk of lung adenocarcinoma with an odds ratio (OR) of 2.78 per 1 kb increase TL [95% confidence interval (CI) 2.16, 3.58;
<italic>P</italic>
= 6.3 × 10
<sup>−15</sup>
]. However, we observed no such association for squamous cell carcinoma of the lung. Associations for these two lung cancer subtypes are displayed in Figure
<xref ref-type="fig" rid="DDV252F1">1</xref>
(right) as solid red lines (slope = ln(OR)) overlaid on the association estimates for the nine SNPs that were used to generate the OR. A positive slope indicates that longer TL is associated with increased cancer risk, while a negative slope indicates that longer TL is associated with decreased risk. The correlation (
<italic>r</italic>
) between the magnitude of the SNPs' associations with TL and the magnitude of the SNPs' associations with adenocarcinoma risk was 0.17. Other than lung adenocarcinoma, no other cancer types showed a statistically significant association with the multi-SNP score. However, prostate cancer risk showed suggestive evidence of positive association with long TL with a Mendelian randomization OR of 1.21 per 1 kb increase in TL (95% CI 0.99, 1.46;
<italic>P</italic>
= 0.06). Scatter plots for all cancer types are displayed in
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Figure S2</ext-link>
.
<table-wrap id="DDV252TB3" orientation="portrait" position="float">
<label>Table 3.</label>
<caption>
<p>ORs of cancer risk per 1000 bp increase in TL according to a multi-SNP TL score using the inverse-variance weighted method (left) and the likelihood method (right)</p>
</caption>
<table frame="hsides" rules="groups">
<colgroup span="1">
<col align="left" span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
<col align="char" char="." span="1"></col>
</colgroup>
<thead>
<tr>
<th rowspan="1" colspan="1"></th>
<th align="center" colspan="3" rowspan="1">Inverse-variance weighted method
<hr></hr>
</th>
<th align="center" colspan="3" rowspan="1">Likelihood method
<hr></hr>
</th>
</tr>
<tr>
<th align="left" rowspan="1" colspan="1">Cancer type</th>
<th align="left" rowspan="1" colspan="1">OR</th>
<th align="left" rowspan="1" colspan="1">95% CI</th>
<th align="left" rowspan="1" colspan="1">
<italic>P</italic>
-value</th>
<th align="left" rowspan="1" colspan="1">OR</th>
<th align="left" rowspan="1" colspan="1">95% CI</th>
<th align="left" rowspan="1" colspan="1">
<italic>P</italic>
-value</th>
</tr>
</thead>
<tbody>
<tr>
<td colspan="7" rowspan="1">Breast</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">1.02</td>
<td rowspan="1" colspan="1">0.86, 1.21</td>
<td rowspan="1" colspan="1">0.82</td>
<td rowspan="1" colspan="1">1.02</td>
<td rowspan="1" colspan="1">0.86, 1.21</td>
<td rowspan="1" colspan="1">0.81</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> ER-negative</td>
<td rowspan="1" colspan="1">1.05</td>
<td rowspan="1" colspan="1">0.81, 1.38</td>
<td rowspan="1" colspan="1">0.70</td>
<td rowspan="1" colspan="1">1.05</td>
<td rowspan="1" colspan="1">0.80, 1.38</td>
<td rowspan="1" colspan="1">0.70</td>
</tr>
<tr>
<td rowspan="1" colspan="1">Colorectal</td>
<td rowspan="1" colspan="1">1.25</td>
<td rowspan="1" colspan="1">0.92, 1.69</td>
<td rowspan="1" colspan="1">0.15</td>
<td rowspan="1" colspan="1">1.26</td>
<td rowspan="1" colspan="1">0.92, 1.71</td>
<td rowspan="1" colspan="1">0.15</td>
</tr>
<tr>
<td colspan="7" rowspan="1">Lung</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">1.65</td>
<td rowspan="1" colspan="1">1.39, 1.96</td>
<td rowspan="1" colspan="1">1.3 × 10
<sup>−8</sup>
</td>
<td rowspan="1" colspan="1">1.67</td>
<td rowspan="1" colspan="1">1.40, 2.00</td>
<td rowspan="1" colspan="1">1.3 × 10
<sup>−8</sup>
</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Adenocarcinoma</td>
<td rowspan="1" colspan="1">2.87</td>
<td rowspan="1" colspan="1">2.20, 3.74</td>
<td rowspan="1" colspan="1">6.3 × 10
<sup>−15</sup>
</td>
<td rowspan="1" colspan="1">3.03</td>
<td rowspan="1" colspan="1">2.29, 4.01</td>
<td rowspan="1" colspan="1">8.2 × 10
<sup>−15</sup>
</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Squamous</td>
<td rowspan="1" colspan="1">1.04</td>
<td rowspan="1" colspan="1">0.79, 1.36</td>
<td rowspan="1" colspan="1">0.79</td>
<td rowspan="1" colspan="1">1.04</td>
<td rowspan="1" colspan="1">0.79, 1.36</td>
<td rowspan="1" colspan="1">0.79</td>
</tr>
<tr>
<td colspan="7" rowspan="1">Ovarian</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">1.13</td>
<td rowspan="1" colspan="1">0.87, 1.47</td>
<td rowspan="1" colspan="1">0.37</td>
<td rowspan="1" colspan="1">1.13</td>
<td rowspan="1" colspan="1">0.87, 1.48</td>
<td rowspan="1" colspan="1">0.36</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Clear-cell</td>
<td rowspan="1" colspan="1">1.65</td>
<td rowspan="1" colspan="1">0.78, 3.51</td>
<td rowspan="1" colspan="1">0.19</td>
<td rowspan="1" colspan="1">1.68</td>
<td rowspan="1" colspan="1">0.78, 3.61</td>
<td rowspan="1" colspan="1">0.19</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Endometrioid</td>
<td rowspan="1" colspan="1">1.30</td>
<td rowspan="1" colspan="1">0.75, 2.24</td>
<td rowspan="1" colspan="1">0.35</td>
<td rowspan="1" colspan="1">1.30</td>
<td rowspan="1" colspan="1">0.75, 2.25</td>
<td rowspan="1" colspan="1">0.35</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Serous</td>
<td rowspan="1" colspan="1">1.19</td>
<td rowspan="1" colspan="1">0.86, 1.65</td>
<td rowspan="1" colspan="1">0.30</td>
<td rowspan="1" colspan="1">1.19</td>
<td rowspan="1" colspan="1">0.86, 1.66</td>
<td rowspan="1" colspan="1">0.29</td>
</tr>
<tr>
<td colspan="7" rowspan="1">Prostate</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> All</td>
<td rowspan="1" colspan="1">1.21</td>
<td rowspan="1" colspan="1">0.99, 1.46</td>
<td rowspan="1" colspan="1">0.06</td>
<td rowspan="1" colspan="1">1.22</td>
<td rowspan="1" colspan="1">1.00, 1.48</td>
<td rowspan="1" colspan="1">0.06</td>
</tr>
<tr>
<td rowspan="1" colspan="1"> Aggressive</td>
<td rowspan="1" colspan="1">1.10</td>
<td rowspan="1" colspan="1">0.83, 1.45</td>
<td rowspan="1" colspan="1">0.52</td>
<td rowspan="1" colspan="1">1.10</td>
<td rowspan="1" colspan="1">0.83, 1.46</td>
<td rowspan="1" colspan="1">0.51</td>
</tr>
</tbody>
</table>
</table-wrap>
</p>
<p>Additional age- and sex-stratified analyses were conducted for overall lung cancer, with findings indicating similar estimates for younger subjects (≤50 years old) (OR = 1.95; 95% CI 1.19, 3.21;
<italic>P</italic>
= 0.008) and older subjects (>50 years old) (OR = 1.78; 95% CI 1.47, 2.15;
<italic>P</italic>
= 2.98 × 10
<sup>−9</sup>
) and similar estimates for men (OR = 1.72; 95% CI 1.38, 2.14;
<italic>P</italic>
= 1.06 × 10
<sup>−6</sup>
) and women (OR = 1.99; 95% CI 1.42, 2.77;
<italic>P</italic>
= 5.3 × 10
<sup>−5</sup>
).</p>
<p>In addition to the inverse-variance weighted approach for obtaining Mendelian randomization estimates, we also used a likelihood-based Mendelian randomization method (
<xref rid="DDV252C28" ref-type="bibr">28</xref>
). Both methods produced very similar estimates for all cancer types, although the lung adenocarcinoma estimates varied more between the two methods compared with the other cancer types (Table
<xref ref-type="table" rid="DDV252TB3">3</xref>
).</p>
</sec>
<sec id="s2c">
<title>Sensitivity analyses</title>
<p>The estimates reported above can only be interpreted as the causal effect of average TL on cancer risk when the Mendelian randomization assumptions are valid—namely, when (1) the SNPs from the literature are truly predictive of TL in the cancer-prone tissue, (2) the SNPs are not associated with other factors (confounders) that influence both TL and cancer risk and (3) the SNPs only affect cancer risk through their effects on TL, i.e. there are no alternative causal pathways by which the SNPs influence cancer risk. Violation of any of the assumptions can result in a biased causal estimate for the effect of TL on cancer risk. We performed sensitivity analyses in which SNPs were excluded from the multi-SNP score based on potential violation of these assumptions.</p>
<p>To assess a potential violation of the first assumption, an additional analysis was performed after excluding the SNP near the
<italic>PXK</italic>
region (rs6772228), which may be a false-positive association evidenced by its lack of plausible biological explanation, and the lack of consistency in its association with TL across several study sites (
<xref rid="DDV252C21" ref-type="bibr">21</xref>
). This ‘strict’ analysis resulted in a notable difference only for prostate cancer risk, which now showed a statistically significant estimate (OR 1.27; 95% CI 1.03, 1.54;
<italic>P</italic>
= 0.02) (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S1</ext-link>
).</p>
<p>To assess potential violation of the second and third assumptions, an additional analysis was performed including only SNPs for which the magnitude of association with cancer risk appeared to be proportional to the magnitude of association with TL. Under Mendelian Randomization assumptions two and three, the ratio of the SNP-cancer association to the SNP–TL association for each SNP should be similar for all SNPs used in the SNP score. An inflation of the ratio for any single SNP may be an indication that the SNP exerts a pleiotropic effect on cancer that is unrelated to its effect on TL. Deviation from this expectation is tested using a goodness-of-fit test (
<xref rid="DDV252C29" ref-type="bibr">29</xref>
,
<xref rid="DDV252C30" ref-type="bibr">30</xref>
), in which SNPs that exhibit evidence of pleiotropy due to an inflated SNP–cancer to SNP–TL ratio can be detected and excluded from Mendelian randomization analyses (see Materials and Methods).</p>
<p>The exclusion of SNPs that failed the goodness-of-fit test (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S2</ext-link>
) resulted in two notable findings (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S1</ext-link>
). First, exclusion of the SNPs in the
<italic>TERT</italic>
and
<italic>CTC1</italic>
regions from the SNP set used for overall prostate cancer resulted in a stronger, statistically significant association (OR = 1.45; 95% CI 1.18, 1.82;
<italic>P</italic>
= 7.9 × 10
<sup>−4</sup>
). Second, the exclusion of the SNP in the
<italic>TERT</italic>
region from the SNP set used for lung adenocarcinoma resulted in a somewhat attenuated association, but the association remained statistically significant: (OR = 2.00, 95% CI 1.48, 2.70,
<italic>P</italic>
= 6.6 × 10
<sup>−6</sup>
). Lung adenocarcinoma had a significant heterogeneity test statistic (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S2</ext-link>
), which likely explains why there was a difference in the estimates obtained using the inverse-variance weighted method and the likelihood method noted earlier; the optimization algorithm of the likelihood-based method can have poor convergence when the heterogeneity statistic is strongly significant (
<xref rid="DDV252C28" ref-type="bibr">28</xref>
). This difference in estimates between methods is eliminated after excluding the
<italic>TERT</italic>
SNP (rs2736100) that drives the heterogeneity in association estimates for lung adenocarcinoma.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s3">
<title>Discussion</title>
<p>In this analysis of cancer risk across five cancer-prone organs, we observed that a multi-SNP score for long telomeres was significantly associated with increased risk of lung adenocarcinoma (but not squamous cell carcinoma) and suggestively associated with increased risk of prostate cancer. We did not observe an association between the multi-SNP score and risk of breast, colorectal or ovarian cancer (including subtypes). Under Mendelian randomization assumptions, the associations reported here can be interpreted as effects of TL on cancer risk, although caution regarding such an interpretation is warranted because the validity of these assumptions (such as the absence of pleiotropy) cannot be proven. Our results were consistent using two different analytic approaches. After performing sensitivity analyses in which SNPs were excluded from the multi-SNP score based on potential violation of Mendelian randomization assumptions, the association with prostate cancer risk became statistically significant. In addition, the exclusion of the SNP in the
<italic>TERT</italic>
region (a known susceptibility locus for lung cancer) from the lung adenocarcinoma analysis resulted in an attenuated but still highly statistically significant association, indicating that the observed association is not solely driven by the SNP in the
<italic>TERT</italic>
region. Even after dropping the three SNPs showing nominally significant (
<italic>P</italic>
< 0.05) association with lung adenocarcinoma (in
<italic>TERT</italic>
,
<italic>OBFC1</italic>
and
<italic>NAF1</italic>
) the association is still significant (OR = 1.54,
<italic>P</italic>
= 0.018).</p>
<p>Several epidemiologic studies have examined the association between leukocyte TL and lung cancer risk. Three retrospective case–control studies reported an association between long TL and decreased lung cancer risk in US and Korean subjects (
<xref rid="DDV252C7" ref-type="bibr">7</xref>
,
<xref rid="DDV252C31" ref-type="bibr">31</xref>
,
<xref rid="DDV252C32" ref-type="bibr">32</xref>
). However, a fourth stratified retrospective case–control study showed a positive association between TL and adenocarcinoma risk but an inverse association for squamous cell carcinoma (
<xref rid="DDV252C33" ref-type="bibr">33</xref>
). In two studies with prospective TL measurement, long TL was found to be associated with increased overall lung cancer risk among Caucasian male smokers (
<xref rid="DDV252C34" ref-type="bibr">34</xref>
) and East Asian female never-smokers (
<xref rid="DDV252C13" ref-type="bibr">13</xref>
), while a large Danish general population study found no association (
<xref rid="DDV252C12" ref-type="bibr">12</xref>
). In a pooled analysis of three prospective cohort studies including the two aforementioned studies and a third study conducted in the USA, long telomeres were associated with increased lung cancer risk, and the association was present in adenocarcinoma while absent in squamous cell carcinoma (
<xref rid="DDV252C35" ref-type="bibr">35</xref>
). Consistent with findings from the prospective studies and the stratified case–control study, we observed a positive association between the long TL SNP score and lung cancer risk corresponding to an OR of 1.65 per 1000 bp TL for overall lung cancer (
<italic>P</italic>
= 1.3 × 10
<sup>−8</sup>
) and an OR of 2.87 per 1000 bp TL for lung adenocarcinoma (
<italic>P</italic>
= 6.3 × 10
<sup>−15</sup>
) (assuming valid Mendelian randomization assumptions). Seow
<italic>et al</italic>
. (
<xref rid="DDV252C35" ref-type="bibr">35</xref>
) reported the risk of overall lung cancer between the lowest quartile and highest quartile as OR = 1.86 (95% CI 1.33–2.62), and the risk of adenocarcinoma cancer as OR = 2.52 (95% CI 1.38–4.60). While our estimates are not directly comparable to these prior estimates due to differences in the scale of the TL variable (kb vs. quartiles), we used a simple simulation to show that our estimates are similar to these prior estimates. We simulated normally distributed TL variables with a mean of 6000 bp and standard deviations ranging from 400 to 700 bp based on values observed from the prior literature (
<xref rid="DDV252C36" ref-type="bibr">36</xref>
<xref rid="DDV252C38" ref-type="bibr">38</xref>
). The difference between the mean values for quartiles one and four ranged between 1018 and 1781 bp. We then rescaled our ORs to correspond to a difference in 1018–1781 bp rather than 1000 bp. The resulting estimates are OR = 1.68–2.94 for overall lung cancer risk between the lowest and highest quartiles, and OR = 2.92–5.11 for lung adenocarcinoma risk between the lowest and highest quartiles. These are comparable with the estimates reported by Seow
<italic>et al</italic>
. The observed heterogeneity in our association estimates is likely due to the two subtypes being biologically distinct, having previously been characterized as having different genetic susceptibilities (
<xref rid="DDV252C39" ref-type="bibr">39</xref>
), unique gene expression profiles (
<xref rid="DDV252C40" ref-type="bibr">40</xref>
), distinct molecular features (
<xref rid="DDV252C41" ref-type="bibr">41</xref>
) and different patterns of chromosomal imbalance (
<xref rid="DDV252C42" ref-type="bibr">42</xref>
).</p>
<p>The Asian female non-smokers among whom a TL-lung cancer association was observed (
<xref rid="DDV252C13" ref-type="bibr">13</xref>
) were also recently studied to evaluate seven TL-associated SNPs in relation to lung cancer risk (
<xref rid="DDV252C43" ref-type="bibr">43</xref>
). Consistent with our findings, the risk score for long TL was associated with an increase in lung cancer. Furthermore, their stratified analyses suggested a stronger association among younger individuals ( <60 years old) and significant associations for both adenocarcinoma and squamous cell subtypes. In contrast, our stratified analyses produced similar estimates by age and different estimates by subtype. Potential factors driving these differences in findings may be differences in ancestry or differences in the etiology of subtypes between the two study populations.</p>
<p>A protective effect of short TL on lung cancer risk has a biologically plausible explanation, as short telomeres could protect against cancer by triggering cell senescence or programmed cell death in the presence of functional cell cycle checkpoints and intact apoptotic pathways (
<xref rid="DDV252C44" ref-type="bibr">44</xref>
). Conversely, long telomeres may enable additional rounds of cell division, allowing more opportunities for the accumulation of somatic mutations that promote carcinogenesis, resulting in greater susceptibility to malignant transformation (
<xref rid="DDV252C45" ref-type="bibr">45</xref>
,
<xref rid="DDV252C46" ref-type="bibr">46</xref>
). The association between long TL SNPs and increased risk has also been previously observed for melanoma (
<xref rid="DDV252C47" ref-type="bibr">47</xref>
), with a proposed mechanism being that long telomeres increase the proliferative duration of cells, thus delaying senescence and allowing further mutations to occur (
<xref rid="DDV252C48" ref-type="bibr">48</xref>
).</p>
<p>Cigarette smoking is a potential confounder in many epidemiologic studies of TL and cancer due to its correlation with short TL (
<xref rid="DDV252C49" ref-type="bibr">49</xref>
). However, our study utilizes genetic variants associated with TL as proxy for actual measured TL, and is therefore not subject to the potential confounding effects of smoking and other exposures. TL-outcome confounders such as smoking would only introduce bias if the genetic variants used in the score are also associated with smoking behavior, as it would be a violation of the second Mendelian randomization assumption. Smoking-related violation of Mendelian randomization assumptions (e.g., SNPs that influence TL through smoking behavior) is unlikely due to the lack of an association between the TL SNP score and squamous cell carcinoma of the lung, which would be expected to have a stronger association with smoking-related SNPs than adenocarcinoma (
<xref rid="DDV252C50" ref-type="bibr">50</xref>
), and due to the lack of evidence that these SNPs influence smoking behavior based on prior GWA studies.</p>
<p>There are few published studies on TL and prostate cancer risk. In a small retrospective case–control study (
<xref rid="DDV252C51" ref-type="bibr">51</xref>
) and two prospective studies (
<xref rid="DDV252C12" ref-type="bibr">12</xref>
,
<xref rid="DDV252C52" ref-type="bibr">52</xref>
), no statistically significant association was observed. In contrast, we observe a suggestive association between long TL and increased risk for prostate cancer, an association that increases in significance in the context of the ‘goodness-of-fit’-based sensitivity analyses. This finding warrants further investigation.</p>
<p>Results from prior studies of TL and colorectal cancer risk are also inconsistent. An inverse association between TL and colorectal cancer risk was described in two case–control studies (
<xref rid="DDV252C9" ref-type="bibr">9</xref>
,
<xref rid="DDV252C14" ref-type="bibr">14</xref>
), while a U-shaped association (
<xref rid="DDV252C16" ref-type="bibr">16</xref>
) and three null-associations (
<xref rid="DDV252C12" ref-type="bibr">12</xref>
,
<xref rid="DDV252C14" ref-type="bibr">14</xref>
,
<xref rid="DDV252C15" ref-type="bibr">15</xref>
) were observed in prospective studies. Consistent with the previous null-findings, our results show no significant association between TL and colorectal cancer risk, despite the inclusion of the
<italic>TERC</italic>
rs10936599 SNP, which was previously reported to be associated with both increased TL and increased risk of colorectal cancer (
<xref rid="DDV252C53" ref-type="bibr">53</xref>
). The null findings were also consistent in an analysis of data from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) (
<xref rid="DDV252C54" ref-type="bibr">54</xref>
) (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Fig. S3</ext-link>
).</p>
<p>Although one prospective study showed evidence for association between long TL and increased breast cancer risk (
<xref rid="DDV252C12" ref-type="bibr">12</xref>
), two different meta-analyses of TL and breast cancer risk based on multiple retrospective and prospective studies concluded there was no overall evidence of association (
<xref rid="DDV252C6" ref-type="bibr">6</xref>
,
<xref rid="DDV252C8" ref-type="bibr">8</xref>
). Our findings for overall breast cancer are in agreement with these prior null studies. In addition, we observe no association between the TL SNP score and ER-negative breast cancer risk. For ovarian cancer, two prior case–control studies observed an association between longer telomeres and decreased risk (
<xref rid="DDV252C55" ref-type="bibr">55</xref>
,
<xref rid="DDV252C56" ref-type="bibr">56</xref>
), one case–control study reported no association (
<xref rid="DDV252C57" ref-type="bibr">57</xref>
), while a prospective study also reported no association (
<xref rid="DDV252C12" ref-type="bibr">12</xref>
). Our results for overall ovarian cancer, as well as three subtypes, provide no evidence of association with TL-associated SNPs. This lack of association is observed despite the inclusion of SNP rs2736100 located in the
<italic>TERT</italic>
region, which showed a nominally significant association with the serous subtype of ovarian cancer (
<italic>P</italic>
= 0.023) and is in high LD (
<italic>r</italic>
<sup>2</sup>
= 0.8) with a SNP previously observed to be associated with ovarian cancer (
<xref rid="DDV252C22" ref-type="bibr">22</xref>
)
<italic>.</italic>
Estrogen has been demonstrated in experimental studies to have positive effects on telomerase activity (
<xref rid="DDV252C58" ref-type="bibr">58</xref>
), and in epidemiologic studies estrogen has been shown to have a positive association with TL (
<xref rid="DDV252C59" ref-type="bibr">59</xref>
). With estrogen as a potential confounder of the association between TL and ER-positive breast and ovarian cancers, it will likely be difficult to parse out the specific role of TL in estrogen-related cancer risk in epidemiological studies. However, Mendelian randomization estimates such as those reported here will not be biased due to confounding by estrogen level. These multi-SNP null findings are similar to what Pooley et al. (
<xref rid="DDV252C21" ref-type="bibr">21</xref>
) also observed while investigating individual TL SNP associations with breast, ovarian and prostate cancer risks.</p>
<p>Although it is not possible to prove the validity of the Mendelian randomization assumptions, it is possible to conduct sensitivity analyses to protect against some potential violations of these assumptions. To address a potential violation of the first assumption—that the SNPs are associated with TL in our study population—we conducted analyses excluding
<italic>PXK</italic>
SNP rs6772228, whose association with TL has been questioned due to the lack of consistency in its association across several study sites (
<xref rid="DDV252C21" ref-type="bibr">21</xref>
). After exclusion, the results were essentially unchanged. For overall prostate cancer, however, the association became statistically significant, lending support to the hypothesis that long TL is associated with an increased risk of prostate cancer.</p>
<p>To address potential violations of the assumption that the SNPs do not have effects on cancer risk independent of their effects on TL, we re-estimated the associations between the TL SNP score and cancer after stepwise removal of potentially problematic SNPs from the SNP set using a goodness-of-fit test of the proportionality of the SNPs' associations with TL and cancer risk. These exclusions resulted in some attenuation of the association with lung adenocarcinoma, but did not substantially alter our conclusions. For prostate cancer, the association with the multi-SNP score became statistically significant after excluding
<italic>TERT</italic>
SNP rs2736100 and
<italic>CTC1 SNP</italic>
rs3027234. The heterogeneity in association of SNPs in
<italic>TERT</italic>
and
<italic>CTC1</italic>
identified by the goodness-of-fit test suggests potential pleiotropic effects of these genetic variants through mechanisms other than TL. The association between
<italic>TERT</italic>
SNPs and breast and ovarian cancer risks via pathways other than TL has been previously observed (
<xref rid="DDV252C22" ref-type="bibr">22</xref>
), and potential extra-telomeric roles have previously been suggested for both telomerase (
<xref rid="DDV252C60" ref-type="bibr">60</xref>
) and
<italic>CTC1</italic>
(
<xref rid="DDV252C61" ref-type="bibr">61</xref>
), providing a plausible biological basis for excluding the
<italic>TERT</italic>
and
<italic>CTC1</italic>
SNPs from our analysis. It is important to note however, that these secondary sensitivity analyses are data-driven, and are presented as a supplement to the primary analyses that include all nine SNPs.</p>
<p>There are several limitations of this work. The summary-level data did not allow for analyses stratified by covariates of interest such as sex and age (with the exception of lung cancer, for which TRICL conducted stratified analyses). Additionally, our analysis assumed a log-linear association between TL and cancer risk, and the existence of a non-linear (e.g. U-shaped) association may limit our ability to detect an association. Our estimates generated using Mendelian randomization are unbiased only if the SNPs analyzed do not affect cancer risk through causal pathways other than those involving TL. This assumption cannot be proven; however, our confidence in the validity of this assumption is strengthened by the fact that our primary finding is robust to the exclusion of SNPs with potential pleiotropic actions based on prior evidence (
<italic>TERT</italic>
) and a goodness-of-fit test (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Tables S1, S2</ext-link>
) (although it is possible that the goodness-of-fit test is underpowered to identify pleiotropic effects). Our power to detect associations is limited by the small variance in measured TL explained by SNPs used in this analysis (1–2%) (
<xref rid="DDV252C62" ref-type="bibr">62</xref>
), although the GAME-ON Network provides very large sample sizes that enable the detection of strong-to-moderate associations (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S3</ext-link>
). Finally, we cannot confirm that genetic determinants of leukocyte TL also predict tissue-specific TL due to the lack of tissue-based TL measures in GWA studies. A potential consequence of selecting SNPs lacking tissue-specific association with TL would be a bias toward the null, reducing our power to detect associations using TL SNP scores. However, correlations between TL measured in blood and TL measured in lung (
<xref rid="DDV252C63" ref-type="bibr">63</xref>
,
<xref rid="DDV252C64" ref-type="bibr">64</xref>
) and other tissues (
<xref rid="DDV252C65" ref-type="bibr">65</xref>
) have been reported (
<italic>r</italic>
= 0.35–0.84), consistent with the assumption that SNPs predict TL across multiple tissues. Systematic studies on other tissue types are needed to further address this uncertainty.</p>
<p>In conclusion, in this comprehensive Mendelian randomization study of TL and risk for common cancers, we observed a highly significant association between genetic determinants of long TL and increased risk for lung adenocarcinoma. The estimates reported here are not vulnerable to biases caused by reverse causation or confounding by unmeasured environmental factors, strengthening the evidence for a causal role for TL in lung adenocarcinoma. However, the validity of Mendelian randomization estimates is dependent upon several assumptions, namely no pleiotropic effects (independent of TL) of SNPs on the cancer risk or confounders of the TL-cancer relationship. The multi-SNP score for TL should be further investigated as a predictor of adenocarcinoma of the lung, a common lung cancer subtype in both smokers and non-smokers. Future research efforts need to be undertaken to determine the value of telomeres as a novel risk measure or a modifiable pharmacological target, with the long-term goal of improving cancer prediction and prevention.</p>
</sec>
<sec sec-type="materials|methods" id="s4">
<title>Materials and Methods</title>
<sec id="s4a">
<title>The GAME-ON network of consortia for post-GWA research</title>
<p>The goals of the GAME-ON consortium were to pool data from GWA studies to identify new loci, conduct functional studies to identify causal SNPs and biological mechanisms, and to investigate gene–gene and gene–environment interactions as a part of efforts to develop risk prediction models. A secondary goal was to test hypotheses across the centers that might illuminate common mechanisms of susceptibility. Details of GAME-ON and the participating studies are available at
<uri xlink:href="http://epi.grants.cancer.gov/gameon/">http://epi.grants.cancer.gov/gameon/</uri>
, and described previously (
<xref rid="DDV252C26" ref-type="bibr">26</xref>
).</p>
</sec>
<sec id="s4b">
<title>Identification of SNPs associated with TL</title>
<p>We identified nine SNPs showing independent genome-wide significant associations (
<italic>P</italic>
< 5 × 10
<sup>−8</sup>
) with TL in previously published GWA studies among individuals of European ancestry (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
<xref rid="DDV252C21" ref-type="bibr">21</xref>
). Although there are specific cancer susceptibility regions of interest such as the previously described
<italic>TERT</italic>
locus, our selection of SNPs is based entirely on the SNPs' ability to predict TL based on prior literature, because predictive accuracy is directly related to statistical power for Mendelian randomization (
<xref rid="DDV252C62" ref-type="bibr">62</xref>
). The proportion of variance in measured average TL that is explained by individual SNPs ranges from 0.06 to 0.2% (
<xref rid="DDV252C19" ref-type="bibr">19</xref>
), and is up to 1.6% for a combined subset of four SNPs (
<xref rid="DDV252C20" ref-type="bibr">20</xref>
) (no estimate is currently available for all nine SNPs). Based on the existing literature, we obtained the identifier for the lead SNP at each reported locus as well as the ‘long TL’ allele, association estimate for the ‘long’ allele (in terms of kb increase in TL per allele), standard error and
<italic>P</italic>
-value for each SNP's association with TL (Table
<xref ref-type="table" rid="DDV252TB2">2</xref>
). Only the lead SNP from each region was included in the analysis. Although the estimates for these nine SNPs were obtained from three different studies using two different methods (quantitative PCR and Southern blot of the terminal restriction fragment), we scaled the estimates to the same units (kb of TL per risk allele). Comparability between studies is supported by previous studies showing that T/S ratio from qPCR using the Cawthon method is strongly correlated with mean terminal restriction fragment obtained from Southern blot for non-extreme TL values (
<xref rid="DDV252C66" ref-type="bibr">66</xref>
). Data on these nine SNPs were available as summary statistics for all cancer types analyzed in the GAME-ON consortium, with the exception of colorectal cancer, for which we obtained proxy SNPs based on strong linkage disequilibrium using the Broad Institute's SNP Annotation and Proxy Search tool (
<xref rid="DDV252C67" ref-type="bibr">67</xref>
) (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S4</ext-link>
).</p>
</sec>
<sec id="s4c">
<title>Statistical analysis</title>
<p>For each cancer type, standard fixed-effects meta-analysis methods were used to combine results from individual GWA studies. For each cancer type, genotyping was performed using Illumina or Affymetrix arrays of varying densities described elsewhere (
<xref rid="DDV252C26" ref-type="bibr">26</xref>
). Quality control steps taken include gender identity and chromosomal anomaly check, exclusion of related individuals, principal component-based exclusion of individuals of non-European ancestry, exclusion of SNPs and individuals with substantial missingness, exclusion of SNPs in Hardy–Weinberg disequilibrium, and other sample and SNP quality measures. For each study, imputation was performed using the 1000 Genomes Project Phase 1 version 3 reference haplotypes, resulting in up to ∼10 million SNPs being available for the analysis for each cancer type.</p>
<p>Associations between SNPs and cancer risk were estimated using unconditional logistic regression adjusted for age, sex (when applicable) and top principal components (ranging from 2 to 6 across 48 contributing GWA studies). For the lung cancer study, the association was also adjusted for smoking pack-years. We performed the analyses separately for cancer subtypes, including breast (estrogen receptor negative), lung (adenocarcinoma and squamous cell), ovarian (clear cell, endometrioid and serous) and prostate [aggressive and non-aggressive as previously defined (
<xref rid="DDV252C68" ref-type="bibr">68</xref>
)]. We performed age- and sex-stratified analyses for overall lung cancer, for which only SNPs imputed to the Illumina 500 K array using the HapMap2 reference panel were available across all sites. For the TL SNPs not available on the 500 K array, we were able to identify tag SNPs (
<italic>r</italic>
<sup>2</sup>
> 0.8) for all SNPs except PXK SNP rs6772228 (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S5</ext-link>
).</p>
<p>We estimated the association between a multi-SNP TL score and risk for each cancer using two different Mendelian randomization methods that require only summarized association estimates for each SNP (hence, no actual score is created for each individual, but we estimated the association that would be observed for such as score if individual-level data were used). This approach is appropriate given that the consortium provides only summary estimates rather than individual-level data for SNP associations. The first method is an inverse-variance weighted average of SNP-specific associations that has been described previously (
<xref rid="DDV252C27" ref-type="bibr">27</xref>
,
<xref rid="DDV252C28" ref-type="bibr">28</xref>
). The Mendelian randomization estimate
<inline-formula>
<mml:math id="ImEquation1">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mover>
<mml:mi>β</mml:mi>
<mml:mo stretchy="false">ˆ</mml:mo>
</mml:mover>
</mml:mrow>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">IVW</mml:mi>
</mml:mrow>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:math>
</inline-formula>
, and its standard error
<inline-formula>
<mml:math id="ImEquation2">
<mml:mtext>SE</mml:mtext>
<mml:mo stretchy="false">(</mml:mo>
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mover>
<mml:mi>β</mml:mi>
<mml:mo stretchy="false">ˆ</mml:mo>
</mml:mover>
</mml:mrow>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">IVW</mml:mi>
</mml:mrow>
</mml:mrow>
</mml:msub>
</mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:math>
</inline-formula>
were calculated using the following equations:
<disp-formula id="DDV252M1">
<label>(1)</label>
<mml:math id="DmEquation1">
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mover>
<mml:mi>β</mml:mi>
<mml:mo stretchy="false">ˆ</mml:mo>
</mml:mover>
</mml:mrow>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">IVW</mml:mi>
</mml:mrow>
</mml:mrow>
</mml:msub>
</mml:mrow>
<mml:mo>=</mml:mo>
<mml:mstyle>
<mml:mrow>
<mml:mfrac>
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mo></mml:mo>
</mml:mrow>
<mml:mi>k</mml:mi>
</mml:msub>
<mml:mrow>
<mml:msub>
<mml:mi>X</mml:mi>
<mml:mi>k</mml:mi>
</mml:msub>
</mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mi>Y</mml:mi>
<mml:mi>k</mml:mi>
</mml:msub>
</mml:mrow>
<mml:msubsup>
<mml:mi>σ</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mi>Y</mml:mi>
<mml:mi>k</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mrow>
<mml:mrow>
<mml:mo></mml:mo>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
</mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mo></mml:mo>
</mml:mrow>
<mml:mi>k</mml:mi>
</mml:msub>
<mml:mo></mml:mo>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mi>k</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:msubsup>
<mml:mi>σ</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mi>Y</mml:mi>
<mml:mi>k</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mrow>
<mml:mrow>
<mml:mo></mml:mo>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
<mml:mo>,</mml:mo>
</mml:mstyle>
</mml:math>
</disp-formula>
<disp-formula id="DDV252M2">
<label>(2)</label>
<mml:math id="DmEquation2">
<mml:mtext>SE(</mml:mtext>
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mover>
<mml:mi>β</mml:mi>
<mml:mo stretchy="false">ˆ</mml:mo>
</mml:mover>
</mml:mrow>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">IVW</mml:mi>
</mml:mrow>
</mml:mrow>
</mml:msub>
</mml:mrow>
<mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mo>=</mml:mo>
<mml:msqrt>
<mml:mstyle>
<mml:mrow>
<mml:mfrac>
<mml:mn>1</mml:mn>
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mo></mml:mo>
</mml:mrow>
<mml:mi>k</mml:mi>
</mml:msub>
<mml:mo></mml:mo>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mi>k</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:msubsup>
<mml:mi>σ</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mi>Y</mml:mi>
<mml:mi>k</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mrow>
<mml:mrow>
<mml:mo></mml:mo>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:mstyle>
</mml:msqrt>
<mml:mo>,</mml:mo>
</mml:math>
</disp-formula>
where
<italic>X
<sub>k</sub>
</italic>
is the per-allele estimate of the
<italic>k</italic>
th SNP on TL,
<italic>Y
<sub>k</sub>
</italic>
is the per-allele estimate of the SNP on the log-odds of cancer and
<italic>σ
<sub>Yk</sub>
</italic>
is the corresponding standard error. A schematic summarizing the aforementioned steps is shown in
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Figure S4</ext-link>
.</p>
<p>The second method is a likelihood-based method that has been described previously (
<xref rid="DDV252C28" ref-type="bibr">28</xref>
). In brief, TL and cancer risk were jointly modeled using a bivariate normal distribution for each genetic variant. The model parameters, including a joint linear effect between TL and cancer log-odds, were estimated using maximum likelihood on the observed data. The likelihood-based analyses were performed using web-based software (
<uri xlink:href="http://spark.rstudio.com/sb452/summarized/">http://spark.rstudio.com/sb452/summarized/</uri>
) (
<xref rid="DDV252C28" ref-type="bibr">28</xref>
).</p>
<p>The estimates obtained from the methods described above can be interpreted as the effect of average TL on cancer risk under the following assumptions as previously described for causal inference based on Mendelian randomization (
<xref rid="DDV252C25" ref-type="bibr">25</xref>
): (1) the SNPs from the literature are truly predictive of TL in the cancer-prone tissue, (2) the SNPs are not associated with other factors (confounders) that influence both TL and cancer risk and (3) the SNPs only affect cancer risk through their effects on TL, i.e. there are no alternative causal pathways by which the SNPs influence cancer risk.</p>
<p>To visualize the association results for the SNP score, we plotted the association between each SNP and cancer risk against associations with TL (based on the prior literature). Under the assumption that a SNP's association with TL is proportional to its association with cancer risk, one would expect the plotted points to fall along a line that passes through the origin and has a slope equal to the Mendelian randomization estimate. Thus, a steeper slope indicates a stronger magnitude of association between TL and cancer risk. Conversely assuming no causal effect of TL on cancer risk, the Mendelian randomization estimate would correspond to a slope of zero.</p>
<p>To assess a potential violation of the first assumption (i.e., a true association between each of the variant and TL), an additional analysis was performed after excluding the SNP near the
<italic>PXK</italic>
region (rs6772228), which may be a false-positive association evidenced by its lack of plausible biological explanation, and the lack of consistency in its association with TL across several study sites (
<xref rid="DDV252C21" ref-type="bibr">21</xref>
). This analysis is referred to as the ‘strict’ analysis. To assess potential violation of the second and third assumptions (i.e., no confounding or pleiotropy), a goodness-of-fit test was performed for each SNP set under the null hypothesis that each SNP included in the SNP score has an association with cancer risk that is proportional to its association with TL. The rejection of the null hypothesis indicated heterogeneity of the associations between SNPs and cancer risk relative to the associations between the SNPs and TL. In instances where the null hypothesis was rejected, stepwise removal of SNPs from the SNP set was performed until there was no significant heterogeneity, based on a method previously described (
<uri xlink:href="http://cran.r-project.org/web/packages/gtx/">http://cran.r-project.org/web/packages/gtx/</uri>
) (
<xref rid="DDV252C29" ref-type="bibr">29</xref>
). Specifically, For
<italic>K</italic>
uncorrelated SNPs,
<disp-formula id="DDV252M3">
<label>(3)</label>
<mml:math id="DmEquation3">
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mi>k</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>=</mml:mo>
<mml:munder>
<mml:mrow>
<mml:mo></mml:mo>
</mml:mrow>
<mml:mi>k</mml:mi>
</mml:munder>
<mml:mo></mml:mo>
<mml:msubsup>
<mml:mi>Y</mml:mi>
<mml:mi>k</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:msubsup>
<mml:mi>σ</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mi>Y</mml:mi>
<mml:mi>k</mml:mi>
</mml:msub>
</mml:mrow>
</mml:mrow>
<mml:mrow>
<mml:mo></mml:mo>
<mml:mn>2</mml:mn>
</mml:mrow>
</mml:msubsup>
<mml:mo>,</mml:mo>
</mml:math>
</disp-formula>
<disp-formula id="DDV252M4">
<label>(4)</label>
<mml:math id="DmEquation4">
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">rs</mml:mi>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>=</mml:mo>
<mml:mrow>
<mml:msup>
<mml:mfenced open="(" close=")">
<mml:mrow>
<mml:mstyle>
<mml:mrow>
<mml:mfrac>
<mml:mrow>
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mrow>
<mml:mover>
<mml:mi>β</mml:mi>
<mml:mo stretchy="false">ˆ</mml:mo>
</mml:mover>
</mml:mrow>
</mml:mrow>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">IVW</mml:mi>
</mml:mrow>
</mml:mrow>
</mml:msub>
</mml:mrow>
</mml:mrow>
<mml:mrow>
<mml:mtext>SE(</mml:mtext>
<mml:mrow>
<mml:msub>
<mml:mrow>
<mml:mrow>
<mml:mover>
<mml:mi>β</mml:mi>
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<mml:mrow>
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</mml:mrow>
</mml:mrow>
</mml:msub>
</mml:mrow>
<mml:mrow>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
</mml:mrow>
</mml:mfrac>
</mml:mrow>
</mml:mstyle>
</mml:mrow>
</mml:mfenced>
<mml:mn>2</mml:mn>
</mml:msup>
</mml:mrow>
<mml:mo>,</mml:mo>
</mml:math>
</disp-formula>
in which
<inline-formula>
<mml:math id="ImEquation3">
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mi>k</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:math>
</inline-formula>
and
<inline-formula>
<mml:math id="ImEquation4">
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">rs</mml:mi>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:math>
</inline-formula>
are
<inline-formula>
<mml:math id="ImEquation5">
<mml:msubsup>
<mml:mi>χ</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>k</mml:mi>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:math>
</inline-formula>
and
<inline-formula>
<mml:math id="ImEquation6">
<mml:msubsup>
<mml:mi>χ</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:math>
</inline-formula>
distributed test statistics, respectively, for the association between each cancer type and all
<italic>K</italic>
SNPs under an unconstrained
<italic>K</italic>
degree-of-freedom model, and for the nested 1 degree-of-freedom risk score model, respectively. Then the goodness-of-fit test statistic is
<disp-formula id="DDV252M5">
<label>(5)</label>
<mml:math id="DmEquation5">
<mml:mrow>
<mml:msub>
<mml:mi>Q</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">rs</mml:mi>
</mml:mrow>
</mml:mrow>
</mml:msub>
</mml:mrow>
<mml:mo>=</mml:mo>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mi>k</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo></mml:mo>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mrow>
<mml:mrow>
<mml:mi mathvariant="normal">rs</mml:mi>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>,</mml:mo>
</mml:math>
</disp-formula>
in which
<italic>Q</italic>
<sub>rs</sub>
is
<inline-formula>
<mml:math id="ImEquation7">
<mml:msubsup>
<mml:mi>χ</mml:mi>
<mml:mrow>
<mml:mo stretchy="false">(</mml:mo>
<mml:mi>k</mml:mi>
<mml:mo></mml:mo>
<mml:mn>1</mml:mn>
<mml:mo stretchy="false">)</mml:mo>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:math>
</inline-formula>
distributed under the null hypothesis that all
<italic>K</italic>
SNPs are associated with cancer risk with true association sizes proportional to the associations with TL. For each analysis in which the goodness-of-fit test null hypothesis was rejected (
<italic>P</italic>
< 0.05), we removed the SNP that resulted in the greatest reduction of the
<italic>Q</italic>
<sub>rs</sub>
test statistic, and repeated the goodness-of-fit test. If still
<italic>P</italic>
< 0.05, we repeated the exclusion procedure until
<italic>P</italic>
> 0.05. The association estimates obtained from the subsequent analysis using the remaining SNPs that pass the goodness-of-fit test is referred to as the ‘goodness-of-fit based’ estimates and displayed in
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S1</ext-link>
.
<italic>P</italic>
-values for the goodness-of-fit test before and after exclusion of SNPs, and which SNPs were excluded for each cancer analysis for the ‘goodness-of-fit based’ analysis are shown in
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S2</ext-link>
. An example for this procedure is as follows: for lung adenocarcinoma, the inclusion of all nine SNPs results in an OR of 2.87 (
<italic>P</italic>
= 6.3 × 10
<sup>−15</sup>
), but the goodness-of-fit test
<italic>P</italic>
-value of 9.0 × 10
<sup>−6</sup>
indicates the presence of heterogeneity with at least one of the SNPs used in the SNP score. The
<italic>TERT</italic>
SNP was identified to be the SNP contributing the most to this heterogeneity (by stepwise exclusion), and once removed, resulted in a SNP score with a goodness-of-fit
<italic>P</italic>
-value of 0.09, indicating a lack of substantial heterogeneity in ratio of SNP–cancer to SNP–telomere associations across SNPs. After exclusion of the
<italic>TERT</italic>
SNP, we still observe a significant association in the same direction, albeit attenuated (OR = 2.00,
<italic>P</italic>
= 6.6 × 10
<sup>−6</sup>
).</p>
<p>We performed power analyses to estimate the minimum detectable magnitude of association for each cancer type given the sample sizes available in the GAME-ON study, in terms of OR per 1 kb increase in TL. This was done using a web-based application (
<uri xlink:href="http://glimmer.rstudio.com/kn3in/mRnd/">http://glimmer.rstudio.com/kn3in/mRnd/</uri>
) (
<xref rid="DDV252C69" ref-type="bibr">69</xref>
), specifying 80% power, 0.05 type I error rate, and assuming the variance in TL explained by the nine SNPs is
<italic>R</italic>
<sup>2</sup>
= 0.01 or
<italic>R</italic>
<sup>2</sup>
= 0.02, respectively. Because the web application calculates the detectable OR of cancer risk per one standard deviation of TL, which is roughly equivalent to 500 bp (
<xref rid="DDV252C36" ref-type="bibr">36</xref>
,
<xref rid="DDV252C37" ref-type="bibr">37</xref>
), we exponentiated this OR to the power of 0.5 in order to obtain the detectable OR per 1000 bp increase TL. (
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material, Table S3</ext-link>
).</p>
</sec>
</sec>
<sec id="s5">
<title>Supplementary Material</title>
<p>
<ext-link ext-link-type="uri" xlink:href="http://hmg.oxfordjournals.org/lookup/suppl/doi:10.1093/hmg/ddv252/-/DC1">Supplementary Material is available at
<italic>HMG</italic>
online</ext-link>
.</p>
</sec>
<sec id="s6">
<title>Funding</title>
<p>This work was supported by the
<funding-source>Genetic Associations and Mechanisms in Oncology Network, GAME-ON: Discovery, Biology, and Risk of Inherited Variants in Breast Cancer, DRIVE, PI: D.J.H.</funding-source>
(
<award-id>U19 CA148065</award-id>
);
<funding-source>Colorectal Transdisciplinary Study, CORECT, PI: S.B.G.</funding-source>
(
<award-id>U19 CA148107</award-id>
);
<funding-source>Transdisciplinary Research in Cancer of the Lung, TRICL, PI: C.A.</funding-source>
(
<award-id>U19 CA148127</award-id>
); Follow-up of ovarian cancer genetic association and interaction studies,
<funding-source>FOCI, PI: T.A.S.</funding-source>
(
<award-id>U19 CA148112</award-id>
);
<funding-source>Elucidating Loci Involved in Prostate Cancer Susceptibility, ELLIPSE, PI: B.E.H.</funding-source>
(
<award-id>U19 CA148537</award-id>
);
<funding-source>Genetics and Epidemiology of Colorectal Cancer Consortium, GECCO: National Cancer Institute</funding-source>
,
<funding-source>National Institutes of Health</funding-source>
, US Department of Health and Human Services (
<award-id>U01 CA137088, R01 CA059045</award-id>
);
<funding-source>National Institute of Health, National Institute on Aging</funding-source>
(
<award-id>T32AG000243; P30AG012857</award-id>
) to C.Z.;
<funding-source>Cancer Research Foundation Young Investigator Award, R01 ES020506, and U01 HG007601 to BLP; The Wellcome Trust</funding-source>
(
<award-id>100114</award-id>
) to S.B.;
<funding-source>The NHMRC Senior Principal Research Fellow to G.C.T</funding-source>
. Funding to pay the Open Access publication charges for this article was provided by the University of Cambridge (COAF Open Access block grant).</p>
<p>
<italic>Conflict of Interest statement</italic>
. None declared</p>
</sec>
<sec sec-type="supplementary-material">
<title>Supplementary Material</title>
<supplementary-material id="PMC_1" content-type="local-data">
<caption>
<title>Supplementary Data</title>
</caption>
<media mimetype="text" mime-subtype="html" xlink:href="supp_24_18_5356__index.html"></media>
<media xlink:role="associated-file" mimetype="application" mime-subtype="msword" xlink:href="supp_ddv252_ddv252supp.docx"></media>
</supplementary-material>
</sec>
</body>
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