Genetic association testing using the GENESIS R/Bioconductor package

The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mix...

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Vydané v:	Bioinformatics (Oxford, England) Ročník 35; číslo 24; s. 5346 - 5348
Hlavní autori:	Gogarten, Stephanie M, Sofer, Tamar, Chen, Han, Yu, Chaoyu, Brody, Jennifer A, Thornton, Timothy A, Rice, Kenneth M, Conomos, Matthew P
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	England Oxford University Press 15.12.2019
Predmet:	Applications Notes Genetic Testing Genome Genomics Sequence Analysis Software
ISSN:	1367-4803, 1367-4811, 1367-4811
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Abstract	The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment. https://bioconductor.org/packages/GENESIS; vignettes included. Supplementary data are available at Bioinformatics online.
AbstractList	The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment. https://bioconductor.org/packages/GENESIS; vignettes included. Supplementary data are available at Bioinformatics online. The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment.SUMMARYThe Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment.https://bioconductor.org/packages/GENESIS; vignettes included.AVAILABILITY AND IMPLEMENTATIONhttps://bioconductor.org/packages/GENESIS; vignettes included.Supplementary data are available at Bioinformatics online.SUPPLEMENTARY INFORMATIONSupplementary data are available at Bioinformatics online.
Author	Sofer, Tamar Rice, Kenneth M Yu, Chaoyu Chen, Han Thornton, Timothy A Conomos, Matthew P Brody, Jennifer A Gogarten, Stephanie M
AuthorAffiliation	1 Department of Biostatistics, University of Washington , Seattle, WA, USA 5 Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA 3 Department of Biostatistics, Harvard T. H. Chan School of Public Health , Boston, MA, USA 2 Division of Sleep and Circadian Disorders, Department of Medicine , Brigham and Women's Hospital, Boston, MA, USA 4 Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences , School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA 6 Cardiovascular Health Research Unit, Department of Medicine, University of Washington , Seattle, WA, USA
AuthorAffiliation_xml	– name: 4 Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences , School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA – name: 6 Cardiovascular Health Research Unit, Department of Medicine, University of Washington , Seattle, WA, USA – name: 1 Department of Biostatistics, University of Washington , Seattle, WA, USA – name: 2 Division of Sleep and Circadian Disorders, Department of Medicine , Brigham and Women's Hospital, Boston, MA, USA – name: 5 Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, TX 77030, USA – name: 3 Department of Biostatistics, Harvard T. H. Chan School of Public Health , Boston, MA, USA
Author_xml	– sequence: 1 givenname: Stephanie M orcidid: 0000-0002-7231-9745 surname: Gogarten fullname: Gogarten, Stephanie M – sequence: 2 givenname: Tamar orcidid: 0000-0001-8520-8860 surname: Sofer fullname: Sofer, Tamar – sequence: 3 givenname: Han orcidid: 0000-0002-9510-4923 surname: Chen fullname: Chen, Han – sequence: 4 givenname: Chaoyu surname: Yu fullname: Yu, Chaoyu – sequence: 5 givenname: Jennifer A surname: Brody fullname: Brody, Jennifer A – sequence: 6 givenname: Timothy A surname: Thornton fullname: Thornton, Timothy A – sequence: 7 givenname: Kenneth M surname: Rice fullname: Rice, Kenneth M – sequence: 8 givenname: Matthew P orcidid: 0000-0001-9744-0851 surname: Conomos fullname: Conomos, Matthew P
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31329242$$D View this record in MEDLINE/PubMed
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Snippet	The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to...
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Title	Genetic association testing using the GENESIS R/Bioconductor package
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