Hidden Markov Models for Controlling False Discovery Rate in Genome-Wide Association Analysis

2011 ◽  
pp. 337-344 ◽  
Author(s):  
Zhi Wei
Keyword(s):  
False Discovery Rate ◽  
Hidden Markov Models ◽  
Association Analysis ◽  
Markov Models ◽  
Hidden Markov ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
False Discovery ◽  
Genome Wide

scholarly journals Controlling the false discovery rate in GWAS with population structure

2020 ◽  
Author(s):  
Matteo Sesia ◽  
Stephen Bates ◽  
Emmanuel Candès ◽  
Jonathan Marchini ◽  
Chiara Sabatti
Keyword(s):  
Population Structure ◽  
False Discovery Rate ◽  
Markov Models ◽  
State Of The Art ◽  
Hidden Markov ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
False Discovery ◽  
Identical By Descent ◽  
Genome Wide

AbstractThis paper proposes a novel statistical method to address population structure in genome-wide association studies while controlling the false discovery rate, which overcomes some limitations of existing approaches. Our solution accounts for linkage disequilibrium and diverse ancestries by combining conditional testing via knockoffs with hidden Markov models from state-of-the-art phasing methods. Furthermore, we account for familial relatedness by describing the joint distribution of haplotypes sharing long identical-by-descent segments with a generalized hidden Markov model. Extensive simulations affirm the validity of this method, while applications to UK Biobank phenotypes yield many more discoveries compared to BOLT-LMM, most of which are confirmed by the Japan Biobank and FinnGen data.

scholarly journals FFselect: An improved linear mixed model for genome-wide association study in populations featuring shared environments confounded by relatedness

2020 ◽  
Author(s):  
Nichol Schultz ◽  
Kent Weigel
Keyword(s):  
Association Analysis ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Genome Wide Association Study ◽  
Genome Wide Association ◽  
Genome Wide Association Analysis ◽  
False Discovery ◽  
Genome Wide ◽  
Novel Method ◽  
Cryptic Relatedness

AbstractLinear mixed models are effective tools to identify genetic loci contributing to phenotypic variation while handling confounding due to population structure and cryptic relatedness. Recent improvements of the linear mixed model for genome-wide association analysis have been directed at more accurately modeling loci of large effect. We describe FFselect (https://github.com/NicholSchultz/FFselect), a novel method that both builds upon recent advances and further extends the linear mixed model for genome-wide association analysis to allow modeling of shared environmental effects. FFselect improves power, controls false discovery rate, and simultaneously corrects for environmental confounding to improve the utility of GWAS.

A Caution in Association of ABO Blood Group with COVID-19

2020 ◽  
Keyword(s):  
Gene Cluster ◽  
Clinical Outcomes ◽  
Blood Group ◽  
Association Analysis ◽  
Blood Type ◽  
Genome Wide Association ◽  
Chromosome 3 ◽  
Abo Blood Group ◽  
Genome Wide Association Analysis ◽  
Genome Wide

A comment on Zhao J, Yang Y, Huang H, Li D, Gu D, Lu X, et al. Association of ABO blood group and Covid19 susceptability. medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.03.11.20031096. Zeng X, Fan H, Lu D, Huang F, Meng X, Li Z, et al. Association between ABO blood group and clinical outcomes of Covid19. medRxiv[PREPRINT].2020; https://doi.org/10.1101/2020.04.15.20063107. Zietz M, Tatonetti N. Testing the association between blood type and COVID-19 infection, intubation, and death medRxiv [PREPRINT]. 2020; https://doi.org/10.1101/2020.04.08.20058073. Ellinghaus D, Degenhardt F, Bujanda L, al. e. The ABO blood group and a chromosome 3 gene cluster associate with SRAS-CoV2 respitarory failure in an Italy-Spain genome-wide association analysis. medRxiv. 2020; https://doi.org/10.1101/2020.05.31.20114991.

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