Genetic association testing using the GENESIS R/Bioconductor package-Reference-Cited by-同舟云学术

Genetic association testing using the GENESIS R/Bioconductor package

Published:2019-07-22 Issue:24 Volume:35 Page:5346-5348
ISSN:1367-4803
Container-title:Bioinformatics
language:en
Short-container-title:

Author:

Gogarten Stephanie M¹^ORCID,Sofer Tamar²³^ORCID,Chen Han⁴⁵^ORCID,Yu Chaoyu¹,Brody Jennifer A⁶,Thornton Timothy A¹,Rice Kenneth M¹,Conomos Matthew P¹^ORCID

Affiliation:

1. Department of Biostatistics, University of Washington, Seattle, WA, USA

2. Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA

3. Department of Biostatistics, Harvard T. H. Chan School of Public Health, 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

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

6. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA

Abstract

Abstract Summary 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. Availability and implementation https://bioconductor.org/packages/GENESIS; vignettes included. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

NHLBI

TOPMed Data Coordinating Center

NIH

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Link

http://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz567/29097073/btz567.pdf

Reference21 articles.

1. Approximate inference in generalized linear mixed models;Breslow;J. Am. Stat. Assoc,1993

2. Sequence kernel association test for quantitative traits in family samples;Chen;Genet. Epidemiol,2013

3. Control for population structure and relatedness for binary traits in genetic association studies via logistic mixed models;Chen;Am. J. Hum. Genet,2016

4. Efficient variant set mixed model association tests for continuous and binary traits in large-scale whole genome sequencing studies;Chen;Am. J. Hum. Genet,2019

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