A computational framework to assess genome-wide distribution of polymorphic human endogenous retrovirus-K In human populations.
Identifieur interne : 000592 ( PubMed/Corpus ); précédent : 000591; suivant : 000593A computational framework to assess genome-wide distribution of polymorphic human endogenous retrovirus-K In human populations.
Auteurs : Weiling Li ; Lin Lin ; Raunaq Malhotra ; Lei Yang ; Raj Acharya ; Mary PossSource :
- PLoS computational biology [ 1553-7358 ] ; 2019.
English descriptors
- KwdEn :
- MESH :
- genetics : Continental Population Groups, Endogenous Retroviruses, Genome, Human, Genome, Viral, Proviruses.
- methods : Genetics, Population, Genomics.
- Algorithms, Humans, Molecular Epidemiology, Software.
Abstract
Human Endogenous Retrovirus type K (HERV-K) is the only HERV known to be insertionally polymorphic; not all individuals have a retrovirus at a specific genomic location. It is possible that HERV-Ks contribute to human disease because people differ in both number and genomic location of these retroviruses. Indeed viral transcripts, proteins, and antibody against HERV-K are detected in cancers, auto-immune, and neurodegenerative diseases. However, attempts to link a polymorphic HERV-K with any disease have been frustrated in part because population prevalence of HERV-K provirus at each polymorphic site is lacking and it is challenging to identify closely related elements such as HERV-K from short read sequence data. We present an integrated and computationally robust approach that uses whole genome short read data to determine the occupation status at all sites reported to contain a HERV-K provirus. Our method estimates the proportion of fixed length genomic sequence (k-mers) from whole genome sequence data matching a reference set of k-mers unique to each HERV-K locus and applies mixture model-based clustering of these values to account for low depth sequence data. Our analysis of 1000 Genomes Project Data (KGP) reveals numerous differences among the five KGP super-populations in the prevalence of individual and co-occurring HERV-K proviruses; we provide a visualization tool to easily depict the proportion of the KGP populations with any combination of polymorphic HERV-K provirus. Further, because HERV-K is insertionally polymorphic, the genome burden of known polymorphic HERV-K is variable in humans; this burden is lowest in East Asian (EAS) individuals. Our study identifies population-specific sequence variation for HERV-K proviruses at several loci. We expect these resources will advance research on HERV-K contributions to human diseases.
DOI: 10.1371/journal.pcbi.1006564
PubMed: 30921327
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Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Yang, Lei" sort="Yang, Lei" uniqKey="Yang L" first="Lei" last="Yang">Lei Yang</name><affiliation><nlm:affiliation>Department of Biology, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Acharya, Raj" sort="Acharya, Raj" uniqKey="Acharya R" first="Raj" last="Acharya">Raj Acharya</name><affiliation><nlm:affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Poss, Mary" sort="Poss, Mary" uniqKey="Poss M" first="Mary" last="Poss">Mary Poss</name><affiliation><nlm:affiliation>Department of Biology, The Pennsylvania State University, University Park, PA, United 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Lin" sort="Lin, Lin" uniqKey="Lin L" first="Lin" last="Lin">Lin Lin</name><affiliation><nlm:affiliation>Department of Statistics, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Malhotra, Raunaq" sort="Malhotra, Raunaq" uniqKey="Malhotra R" first="Raunaq" last="Malhotra">Raunaq Malhotra</name><affiliation><nlm:affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Yang, Lei" sort="Yang, Lei" uniqKey="Yang L" first="Lei" last="Yang">Lei Yang</name><affiliation><nlm:affiliation>Department of Biology, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Acharya, Raj" sort="Acharya, Raj" uniqKey="Acharya R" first="Raj" last="Acharya">Raj Acharya</name><affiliation><nlm:affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author><author><name sortKey="Poss, Mary" sort="Poss, Mary" uniqKey="Poss M" first="Mary" last="Poss">Mary Poss</name><affiliation><nlm:affiliation>Department of Biology, The Pennsylvania State University, University Park, PA, United States of America.</nlm:affiliation></affiliation></author></analytic><series><title level="j">PLoS computational biology</title><idno type="eISSN">1553-7358</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms</term><term>Continental Population Groups (genetics)</term><term>Endogenous Retroviruses (genetics)</term><term>Genetics, Population (methods)</term><term>Genome, Human (genetics)</term><term>Genome, Viral (genetics)</term><term>Genomics (methods)</term><term>Humans</term><term>Molecular Epidemiology</term><term>Proviruses (genetics)</term><term>Software</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Continental Population Groups</term><term>Endogenous Retroviruses</term><term>Genome, Human</term><term>Genome, Viral</term><term>Proviruses</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Genetics, Population</term><term>Genomics</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Humans</term><term>Molecular Epidemiology</term><term>Software</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Human Endogenous Retrovirus type K (HERV-K) is the only HERV known to be insertionally polymorphic; not all individuals have a retrovirus at a specific genomic location. It is possible that HERV-Ks contribute to human disease because people differ in both number and genomic location of these retroviruses. Indeed viral transcripts, proteins, and antibody against HERV-K are detected in cancers, auto-immune, and neurodegenerative diseases. However, attempts to link a polymorphic HERV-K with any disease have been frustrated in part because population prevalence of HERV-K provirus at each polymorphic site is lacking and it is challenging to identify closely related elements such as HERV-K from short read sequence data. We present an integrated and computationally robust approach that uses whole genome short read data to determine the occupation status at all sites reported to contain a HERV-K provirus. Our method estimates the proportion of fixed length genomic sequence (k-mers) from whole genome sequence data matching a reference set of k-mers unique to each HERV-K locus and applies mixture model-based clustering of these values to account for low depth sequence data. Our analysis of 1000 Genomes Project Data (KGP) reveals numerous differences among the five KGP super-populations in the prevalence of individual and co-occurring HERV-K proviruses; we provide a visualization tool to easily depict the proportion of the KGP populations with any combination of polymorphic HERV-K provirus. Further, because HERV-K is insertionally polymorphic, the genome burden of known polymorphic HERV-K is variable in humans; this burden is lowest in East Asian (EAS) individuals. Our study identifies population-specific sequence variation for HERV-K proviruses at several loci. We expect these resources will advance research on HERV-K contributions to human diseases.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">30921327</PMID><DateCompleted><Year>2019</Year><Month>05</Month><Day>03</Day></DateCompleted><DateRevised><Year>2020</Year><Month>02</Month><Day>25</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1553-7358</ISSN><JournalIssue CitedMedium="Internet"><Volume>15</Volume><Issue>3</Issue><PubDate><Year>2019</Year><Month>03</Month></PubDate></JournalIssue><Title>PLoS computational biology</Title><ISOAbbreviation>PLoS Comput. Biol.</ISOAbbreviation></Journal><ArticleTitle>A computational framework to assess genome-wide distribution of polymorphic human endogenous retrovirus-K In human populations.</ArticleTitle><Pagination><MedlinePgn>e1006564</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pcbi.1006564</ELocationID><Abstract><AbstractText>Human Endogenous Retrovirus type K (HERV-K) is the only HERV known to be insertionally polymorphic; not all individuals have a retrovirus at a specific genomic location. It is possible that HERV-Ks contribute to human disease because people differ in both number and genomic location of these retroviruses. Indeed viral transcripts, proteins, and antibody against HERV-K are detected in cancers, auto-immune, and neurodegenerative diseases. However, attempts to link a polymorphic HERV-K with any disease have been frustrated in part because population prevalence of HERV-K provirus at each polymorphic site is lacking and it is challenging to identify closely related elements such as HERV-K from short read sequence data. We present an integrated and computationally robust approach that uses whole genome short read data to determine the occupation status at all sites reported to contain a HERV-K provirus. Our method estimates the proportion of fixed length genomic sequence (k-mers) from whole genome sequence data matching a reference set of k-mers unique to each HERV-K locus and applies mixture model-based clustering of these values to account for low depth sequence data. Our analysis of 1000 Genomes Project Data (KGP) reveals numerous differences among the five KGP super-populations in the prevalence of individual and co-occurring HERV-K proviruses; we provide a visualization tool to easily depict the proportion of the KGP populations with any combination of polymorphic HERV-K provirus. Further, because HERV-K is insertionally polymorphic, the genome burden of known polymorphic HERV-K is variable in humans; this burden is lowest in East Asian (EAS) individuals. Our study identifies population-specific sequence variation for HERV-K proviruses at several loci. We expect these resources will advance research on HERV-K contributions to human diseases.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Li</LastName><ForeName>Weiling</ForeName><Initials>W</Initials><AffiliationInfo><Affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Lin</LastName><ForeName>Lin</ForeName><Initials>L</Initials><Identifier Source="ORCID">0000-0002-7464-1172</Identifier><AffiliationInfo><Affiliation>Department of Statistics, The Pennsylvania State University, University Park, PA, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Malhotra</LastName><ForeName>Raunaq</ForeName><Initials>R</Initials><Identifier Source="ORCID">0000-0002-7253-850X</Identifier><AffiliationInfo><Affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yang</LastName><ForeName>Lei</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Department of Biology, The Pennsylvania State University, University Park, PA, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Acharya</LastName><ForeName>Raj</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>The School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, PA, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, United States of America.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Poss</LastName><ForeName>Mary</ForeName><Initials>M</Initials><Identifier Source="ORCID">0000-0003-4147-2410</Identifier><AffiliationInfo><Affiliation>Department of Biology, The Pennsylvania State University, University Park, PA, United States of America.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Veterinary and Biomedical Sciences, The Pennsylvania State University, University Park, PA, United States of America.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2019</Year><Month>03</Month><Day>28</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS Comput 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