Use of support vector machines for disease risk prediction in genome‐wide association studies: Concerns and opportunities
Identifieur interne : 001916 ( Istex/Corpus ); précédent : 001915; suivant : 001917Use of support vector machines for disease risk prediction in genome‐wide association studies: Concerns and opportunities
Auteurs : Florian Mittag ; Finja Büchel ; Mohamad Saad ; Andreas Jahn ; Claudia Schulte ; Zoltan Bochdanovits ; Javier Sim N-Sánchez ; Mike A. Nalls ; Margaux Keller ; Dena G. Hernandez ; J. Raphael Gibbs ; Suzanne Lesage ; Alexis Brice ; Peter Heutink ; Maria Martinez ; Nicholas W. Wood ; John Hardy ; Andrew B. Singleton ; Andreas Zell ; Thomas Gasser ; Manu SharmaSource :
- Human Mutation [ 1059-7794 ] ; 2012-12.
English descriptors
- KwdEn :
Abstract
The success of genome‐wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top‐validated single‐nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1–5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross‐validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ∼0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ∼0.56; heritability ∼38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni‐tuebingen.de/software/MACLEAPS/. Hum Mutat 33:1708–1718, 2012. © 2012 Wiley Periodicals, Inc.
Url:
DOI: 10.1002/humu.22161
Links to Exploration step
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<affiliation><mods:affiliation>AP‐HP, Hôpital de la Salpêtrière, Département de Génétique et Cytogénétique, Paris, France</mods:affiliation>
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<affiliation><mods:affiliation>Département des Sciences du Vivant, Paul Sabatier University, Toulouse, France</mods:affiliation>
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<author><name sortKey="Mittag, Florian" sort="Mittag, Florian" uniqKey="Mittag F" first="Florian" last="Mittag">Florian Mittag</name>
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<author><name sortKey="Buchel, Finja" sort="Buchel, Finja" uniqKey="Buchel F" first="Finja" last="Büchel">Finja Büchel</name>
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<author><name sortKey="Saad, Mohamad" sort="Saad, Mohamad" uniqKey="Saad M" first="Mohamad" last="Saad">Mohamad Saad</name>
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<affiliation><mods:affiliation>Département des Sciences du Vivant, Paul Sabatier University, Toulouse, France</mods:affiliation>
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<author><name sortKey="Jahn, Andreas" sort="Jahn, Andreas" uniqKey="Jahn A" first="Andreas" last="Jahn">Andreas Jahn</name>
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<author><name sortKey="Schulte, Claudia" sort="Schulte, Claudia" uniqKey="Schulte C" first="Claudia" last="Schulte">Claudia Schulte</name>
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<author><name sortKey="Bochdanovits, Zoltan" sort="Bochdanovits, Zoltan" uniqKey="Bochdanovits Z" first="Zoltan" last="Bochdanovits">Zoltan Bochdanovits</name>
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</affiliation>
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<author><name sortKey="Sim N Nchez, Javier" sort="Sim N Nchez, Javier" uniqKey="Sim N Nchez J" first="Javier" last="Sim N-Sánchez">Javier Sim N-Sánchez</name>
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</affiliation>
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<author><name sortKey="Keller, Margaux" sort="Keller, Margaux" uniqKey="Keller M" first="Margaux" last="Keller">Margaux Keller</name>
<affiliation><mods:affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Department of Biological Anthropology. Temple University, Philadelphia, Pennsylvania</mods:affiliation>
</affiliation>
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<author><name sortKey="Hernandez, Dena G" sort="Hernandez, Dena G" uniqKey="Hernandez D" first="Dena G." last="Hernandez">Dena G. Hernandez</name>
<affiliation><mods:affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Gibbs, J Raphael" sort="Gibbs, J Raphael" uniqKey="Gibbs J" first="J. Raphael" last="Gibbs">J. Raphael Gibbs</name>
<affiliation><mods:affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Lesage, Suzanne" sort="Lesage, Suzanne" uniqKey="Lesage S" first="Suzanne" last="Lesage">Suzanne Lesage</name>
<affiliation><mods:affiliation>Université Pierre et Marie Curie‐Paris, Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière, UMR‐S975, Paris, France</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Institut National de la Sante et de la Recherche Medicale, UMR_S975 CRicm, Paris, France</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Centre National de la Recherche Scientifique, UMR 7225, Paris, France</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Brice, Alexis" sort="Brice, Alexis" uniqKey="Brice A" first="Alexis" last="Brice">Alexis Brice</name>
<affiliation><mods:affiliation>Université Pierre et Marie Curie‐Paris, Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière, UMR‐S975, Paris, France</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>AP‐HP, Hôpital de la Salpêtrière, Département de Génétique et Cytogénétique, Paris, France</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Institut National de la Sante et de la Recherche Medicale, UMR_S975 CRicm, Paris, France</mods:affiliation>
</affiliation>
<affiliation><mods:affiliation>Centre National de la Recherche Scientifique, UMR 7225, Paris, France</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Heutink, Peter" sort="Heutink, Peter" uniqKey="Heutink P" first="Peter" last="Heutink">Peter Heutink</name>
<affiliation><mods:affiliation>Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, The Netherlands</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Martinez, Maria" sort="Martinez, Maria" uniqKey="Martinez M" first="Maria" last="Martinez">Maria Martinez</name>
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</affiliation>
<affiliation><mods:affiliation>Département des Sciences du Vivant, Paul Sabatier University, Toulouse, France</mods:affiliation>
</affiliation>
</author>
<author><name sortKey="Wood, Nicholas W" sort="Wood, Nicholas W" uniqKey="Wood N" first="Nicholas W" last="Wood">Nicholas W. Wood</name>
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</affiliation>
</author>
<author><name sortKey="Hardy, John" sort="Hardy, John" uniqKey="Hardy J" first="John" last="Hardy">John Hardy</name>
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</affiliation>
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<author><name sortKey="Singleton, Andrew B" sort="Singleton, Andrew B" uniqKey="Singleton A" first="Andrew B." last="Singleton">Andrew B. Singleton</name>
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</affiliation>
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<author><name sortKey="Zell, Andreas" sort="Zell, Andreas" uniqKey="Zell A" first="Andreas" last="Zell">Andreas Zell</name>
<affiliation><mods:affiliation>Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany</mods:affiliation>
</affiliation>
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<author><name sortKey="Gasser, Thomas" sort="Gasser, Thomas" uniqKey="Gasser T" first="Thomas" last="Gasser">Thomas Gasser</name>
<affiliation><mods:affiliation>Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, and DZNE, German Centre for Neurodegenerative Diseases, Tübingen, Germany</mods:affiliation>
</affiliation>
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<author><name sortKey="Sharma, Manu" sort="Sharma, Manu" uniqKey="Sharma M" first="Manu" last="Sharma">Manu Sharma</name>
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</affiliation>
<affiliation><mods:affiliation>Hertie‐Institute of Clinical Brain Research, Department of Neurology, University of Tuebingen, Hoppe‐Seyler‐Str. 3, 72076 Tübingen, Germany</mods:affiliation>
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<profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Parkinson disease</term>
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<front><div type="abstract" xml:lang="en">The success of genome‐wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top‐validated single‐nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1–5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross‐validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ∼0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ∼0.56; heritability ∼38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni‐tuebingen.de/software/MACLEAPS/. Hum Mutat 33:1708–1718, 2012. © 2012 Wiley Periodicals, Inc.</div>
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<json:item><name>Margaux Keller</name>
<affiliations><json:string>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</json:string>
<json:string>Department of Biological Anthropology. Temple University, Philadelphia, Pennsylvania</json:string>
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<affiliations><json:string>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</json:string>
<json:string>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</json:string>
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<affiliations><json:string>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</json:string>
<json:string>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</json:string>
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<json:item><name>Suzanne Lesage</name>
<affiliations><json:string>Université Pierre et Marie Curie‐Paris, Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière, UMR‐S975, Paris, France</json:string>
<json:string>Institut National de la Sante et de la Recherche Medicale, UMR_S975 CRicm, Paris, France</json:string>
<json:string>Centre National de la Recherche Scientifique, UMR 7225, Paris, France</json:string>
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<json:string>AP‐HP, Hôpital de la Salpêtrière, Département de Génétique et Cytogénétique, Paris, France</json:string>
<json:string>Institut National de la Sante et de la Recherche Medicale, UMR_S975 CRicm, Paris, France</json:string>
<json:string>Centre National de la Recherche Scientifique, UMR 7225, Paris, France</json:string>
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<json:item><name>Thomas Gasser</name>
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</json:item>
<json:item><name>Manu Sharma</name>
<affiliations><json:string>Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, and DZNE, German Centre for Neurodegenerative Diseases, Tübingen, Germany</json:string>
<json:string>Hertie‐Institute of Clinical Brain Research, Department of Neurology, University of Tuebingen, Hoppe‐Seyler‐Str. 3, 72076 Tübingen, Germany</json:string>
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</json:item>
</author>
<subject><json:item><lang><json:string>eng</json:string>
</lang>
<value>genome‐wide association studies</value>
</json:item>
<json:item><lang><json:string>eng</json:string>
</lang>
<value>disease risk prediction</value>
</json:item>
<json:item><lang><json:string>eng</json:string>
</lang>
<value>machine learning</value>
</json:item>
<json:item><lang><json:string>eng</json:string>
</lang>
<value>support vector machines</value>
</json:item>
<json:item><lang><json:string>eng</json:string>
</lang>
<value>Parkinson disease</value>
</json:item>
</subject>
<articleId><json:string>HUMU22161</json:string>
</articleId>
<language><json:string>eng</json:string>
</language>
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<abstract>The success of genome‐wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top‐validated single‐nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1–5%) as well as rare variants (frequency >1%) in disease etiology of complex diseases. Using a cross‐validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ∼0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ∼0.56; heritability ∼38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni‐tuebingen.de/software/MACLEAPS/. Hum Mutat 33:1708–1718, 2012. © 2012 Wiley Periodicals, Inc.</abstract>
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<title>From disease association to risk assessment: an optimistic view from genome‐wide association studies on type 1 diabetes</title>
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<notesStmt><note type="content">*Communicated by Christopher G. Mathew</note>
<note>Intramural Research Programs of the National Institute on Aging; National Institute of Neurological Disorders and Stroke; National Institute of Environmental Health Sciences; National Human Genome Research Institute of the National Institutes of Health; Department of Health and Human Services (project numbers Z01‐AG000949‐02 and Z01‐ES101986); Human subjects protocol 2003‐077; US Department of Defense (award number W81XWH‐09‐2‐0128); National Institutes of Health (grants NS057105 and RR024992); American Parkinson disease Association (APDA); Barnes Jewish Hospital Foundation; Greater St Louis Chapter of the APDA; Hersenstichting Nederland; Neuroscience Campus Amsterdam; the section of medical genomics, the Prinses Beatrix Fonds; and the Michael J. Fox foundation; the KORA (Cooperative Research in the Region of Augsburg) research platform was started and financed by the Forschungszentrum für Umwelt und Gesundheit, which is funded by the German Federal Ministry of Education, Science, Research, and Technology and by the State of Bavaria; German National Genome Network (NGFNplus number 01GS08134, German Ministry for Education and Research); German Federal Ministry of Education and Research (NGFN 01GR0468, PopGen); Initiative and Networking Fund of the Helmholtz Association (01EW0908 in the frame of ERA‐NET NEURON); French National Agency of Research (ANR‐08‐MNP‐012)</note>
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<sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Use of support vector machines for disease risk prediction in genome‐wide association studies: Concerns and opportunities</title>
<author xml:id="author-1"><persName><forename type="first">Florian</forename>
<surname>Mittag</surname>
</persName>
<affiliation>Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany</affiliation>
</author>
<author xml:id="author-2"><persName><forename type="first">Finja</forename>
<surname>Büchel</surname>
</persName>
<affiliation>Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany</affiliation>
</author>
<author xml:id="author-3"><persName><forename type="first">Mohamad</forename>
<surname>Saad</surname>
</persName>
<affiliation>Institut National de la Sante et de la Recherche Medicale, UMR 1043, Centre de Physiopathologie de Toulouse‐Purpan, Toulouse, France</affiliation>
<affiliation>Département des Sciences du Vivant, Paul Sabatier University, Toulouse, France</affiliation>
</author>
<author xml:id="author-4"><persName><forename type="first">Andreas</forename>
<surname>Jahn</surname>
</persName>
<affiliation>Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany</affiliation>
</author>
<author xml:id="author-5"><persName><forename type="first">Claudia</forename>
<surname>Schulte</surname>
</persName>
<affiliation>Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, and DZNE, German Centre for Neurodegenerative Diseases, Tübingen, Germany</affiliation>
</author>
<author xml:id="author-6"><persName><forename type="first">Zoltan</forename>
<surname>Bochdanovits</surname>
</persName>
<affiliation>Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, The Netherlands</affiliation>
</author>
<author xml:id="author-7"><persName><forename type="first">Javier</forename>
<surname>Simón‐Sánchez</surname>
</persName>
<affiliation>Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, The Netherlands</affiliation>
</author>
<author xml:id="author-8"><persName><forename type="first">Mike A.</forename>
<surname>Nalls</surname>
</persName>
<affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</affiliation>
</author>
<author xml:id="author-9"><persName><forename type="first">Margaux</forename>
<surname>Keller</surname>
</persName>
<affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</affiliation>
<affiliation>Department of Biological Anthropology. Temple University, Philadelphia, Pennsylvania</affiliation>
</author>
<author xml:id="author-10"><persName><forename type="first">Dena G.</forename>
<surname>Hernandez</surname>
</persName>
<affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</affiliation>
<affiliation>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</affiliation>
</author>
<author xml:id="author-11"><persName><forename type="first">J. Raphael</forename>
<surname>Gibbs</surname>
</persName>
<affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</affiliation>
<affiliation>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</affiliation>
</author>
<author xml:id="author-12"><persName><forename type="first">Suzanne</forename>
<surname>Lesage</surname>
</persName>
<affiliation>Université Pierre et Marie Curie‐Paris, Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière, UMR‐S975, Paris, France</affiliation>
<affiliation>Institut National de la Sante et de la Recherche Medicale, UMR_S975 CRicm, Paris, France</affiliation>
<affiliation>Centre National de la Recherche Scientifique, UMR 7225, Paris, France</affiliation>
</author>
<author xml:id="author-13"><persName><forename type="first">Alexis</forename>
<surname>Brice</surname>
</persName>
<affiliation>Université Pierre et Marie Curie‐Paris, Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière, UMR‐S975, Paris, France</affiliation>
<affiliation>AP‐HP, Hôpital de la Salpêtrière, Département de Génétique et Cytogénétique, Paris, France</affiliation>
<affiliation>Institut National de la Sante et de la Recherche Medicale, UMR_S975 CRicm, Paris, France</affiliation>
<affiliation>Centre National de la Recherche Scientifique, UMR 7225, Paris, France</affiliation>
</author>
<author xml:id="author-14"><persName><forename type="first">Peter</forename>
<surname>Heutink</surname>
</persName>
<affiliation>Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, The Netherlands</affiliation>
</author>
<author xml:id="author-15"><persName><forename type="first">Maria</forename>
<surname>Martinez</surname>
</persName>
<affiliation>Institut National de la Sante et de la Recherche Medicale, UMR 1043, Centre de Physiopathologie de Toulouse‐Purpan, Toulouse, France</affiliation>
<affiliation>Département des Sciences du Vivant, Paul Sabatier University, Toulouse, France</affiliation>
</author>
<author xml:id="author-16"><persName><forename type="first">Nicholas W</forename>
<surname>Wood</surname>
</persName>
<affiliation>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</affiliation>
</author>
<author xml:id="author-17"><persName><forename type="first">John</forename>
<surname>Hardy</surname>
</persName>
<affiliation>Department of Molecular Neuroscience, Institute of Neurology, University College London, London, UK</affiliation>
</author>
<author xml:id="author-18"><persName><forename type="first">Andrew B.</forename>
<surname>Singleton</surname>
</persName>
<affiliation>Laboratory of Neurogenetics. National Institute on Aging, National Institutes of Health, Bethesda, Maryland</affiliation>
</author>
<author xml:id="author-19"><persName><forename type="first">Andreas</forename>
<surname>Zell</surname>
</persName>
<affiliation>Center for Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany</affiliation>
</author>
<author xml:id="author-20"><persName><forename type="first">Thomas</forename>
<surname>Gasser</surname>
</persName>
<affiliation>Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, and DZNE, German Centre for Neurodegenerative Diseases, Tübingen, Germany</affiliation>
</author>
<author xml:id="author-21"><persName><forename type="first">Manu</forename>
<surname>Sharma</surname>
</persName>
<note type="biography">for the International Parkinson's Disease Genomics Consortium (IPDGC)</note>
<affiliation>for the International Parkinson's Disease Genomics Consortium (IPDGC)</affiliation>
<affiliation>Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, and DZNE, German Centre for Neurodegenerative Diseases, Tübingen, Germany</affiliation>
<affiliation>Hertie‐Institute of Clinical Brain Research, Department of Neurology, University of Tuebingen, Hoppe‐Seyler‐Str. 3, 72076 Tübingen, Germany</affiliation>
</author>
</analytic>
<monogr><title level="j">Human Mutation</title>
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<abstract xml:lang="en"><p>The success of genome‐wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top‐validated single‐nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1–5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross‐validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ∼0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ∼0.56; heritability ∼38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni‐tuebingen.de/software/MACLEAPS/. Hum Mutat 33:1708–1718, 2012. © 2012 Wiley Periodicals, Inc.</p>
</abstract>
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<item><term>disease risk prediction</term>
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<item><term>machine learning</term>
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<fundingInfo><fundingAgency>Intramural Research Programs of the National Institute on Aging; National Institute of Neurological Disorders and Stroke; National Institute of Environmental Health Sciences; National Human Genome Research Institute of the National Institutes of Health; Department of Health and Human Services (project numbers Z01‐AG000949‐02 and Z01‐ES101986); Human subjects protocol 2003‐077; US Department of Defense (award number W81XWH‐09‐2‐0128); National Institutes of Health (grants NS057105 and RR024992); American Parkinson disease Association (APDA); Barnes Jewish Hospital Foundation; Greater St Louis Chapter of the APDA; Hersenstichting Nederland; Neuroscience Campus Amsterdam; the section of medical genomics, the Prinses Beatrix Fonds; and the Michael J. Fox foundation; the KORA (Cooperative Research in the Region of Augsburg) research platform was started and financed by the Forschungszentrum für Umwelt und Gesundheit, which is funded by the German Federal Ministry of Education, Science, Research, and Technology and by the State of Bavaria; German National Genome Network (NGFNplus number 01GS08134, German Ministry for Education and Research); German Federal Ministry of Education and Research (NGFN 01GR0468, PopGen); Initiative and Networking Fund of the Helmholtz Association (01EW0908 in the frame of ERA‐NET NEURON); French National Agency of Research (ANR‐08‐MNP‐012)</fundingAgency>
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<abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title>
<p>The success of genome‐wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top‐validated single‐nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1–5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross‐validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ∼0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ∼0.56; heritability ∼38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at <url href="http://www.ra.cs.uni-tuebingen.de/software/MACLEAPS/">http://www.ra.cs.uni‐tuebingen.de/software/MACLEAPS/</url>
. Hum Mutat 33:1708–1718, 2012. © 2012 Wiley Periodicals, Inc.</p>
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<name type="personal"><namePart type="given">Maria</namePart>
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<name type="personal"><namePart type="given">Andrew B.</namePart>
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<affiliation>Hertie‐Institute of Clinical Brain Research, Department of Neurology, University of Tuebingen, Hoppe‐Seyler‐Str. 3, 72076 Tübingen, Germany</affiliation>
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<abstract lang="en">The success of genome‐wide association studies (GWAS) in deciphering the genetic architecture of complex diseases has fueled the expectations whether the individual risk can also be quantified based on the genetic architecture. So far, disease risk prediction based on top‐validated single‐nucleotide polymorphisms (SNPs) showed little predictive value. Here, we applied a support vector machine (SVM) to Parkinson disease (PD) and type 1 diabetes (T1D), to show that apart from magnitude of effect size of risk variants, heritability of the disease also plays an important role in disease risk prediction. Furthermore, we performed a simulation study to show the role of uncommon (frequency 1–5%) as well as rare variants (frequency <1%) in disease etiology of complex diseases. Using a cross‐validation model, we were able to achieve predictions with an area under the receiver operating characteristic curve (AUC) of ∼0.88 for T1D, highlighting the strong heritable component (∼90%). This is in contrast to PD, where we were unable to achieve a satisfactory prediction (AUC ∼0.56; heritability ∼38%). Our simulations showed that simultaneous inclusion of uncommon and rare variants in GWAS would eventually lead to feasible disease risk prediction for complex diseases such as PD. The used software is available at http://www.ra.cs.uni‐tuebingen.de/software/MACLEAPS/. Hum Mutat 33:1708–1718, 2012. © 2012 Wiley Periodicals, Inc.</abstract>
<note type="content">*Communicated by Christopher G. Mathew</note>
<note type="funding">Intramural Research Programs of the National Institute on Aging; National Institute of Neurological Disorders and Stroke; National Institute of Environmental Health Sciences; National Human Genome Research Institute of the National Institutes of Health; Department of Health and Human Services (project numbers Z01‐AG000949‐02 and Z01‐ES101986); Human subjects protocol 2003‐077; US Department of Defense (award number W81XWH‐09‐2‐0128); National Institutes of Health (grants NS057105 and RR024992); American Parkinson disease Association (APDA); Barnes Jewish Hospital Foundation; Greater St Louis Chapter of the APDA; Hersenstichting Nederland; Neuroscience Campus Amsterdam; the section of medical genomics, the Prinses Beatrix Fonds; and the Michael J. Fox foundation; the KORA (Cooperative Research in the Region of Augsburg) research platform was started and financed by the Forschungszentrum für Umwelt und Gesundheit, which is funded by the German Federal Ministry of Education, Science, Research, and Technology and by the State of Bavaria; German National Genome Network (NGFNplus number 01GS08134, German Ministry for Education and Research); German Federal Ministry of Education and Research (NGFN 01GR0468, PopGen); Initiative and Networking Fund of the Helmholtz Association (01EW0908 in the frame of ERA‐NET NEURON); French National Agency of Research (ANR‐08‐MNP‐012)</note>
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