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The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees.


ABSTRACT: The generalized linear mixed models (GLMMs) methodology is the standard framework for genome-wide association studies (GWAS) of complex diseases in family-based cohorts. Fitting GLMMs in very large cohorts, however, can be computationally demanding. Also, the modified versions of GLMM using faster algorithms may underperform, for instance when a single nucleotide polymorphism (SNP) is correlated with fixed-effects covariates. We investigated the extent to which disregarding family structure may compromise GWAS in cohorts with simple pedigrees by contrasting logistic regression models (i.e., with no family structure) to three LMMs-based ones. Our analyses showed that the logistic regression models in general resulted in smaller P values compared with the LMMs-based models; however, the differences in P values were mostly minor. Disregarding family structure had little impact on determining disease-associated SNPs at genome-wide level of significance (i.e., P?

SUBMITTER: Nazarian A 

PROVIDER: S-EPMC6980752 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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The impact of disregarding family structure on genome-wide association analysis of complex diseases in cohorts with simple pedigrees.

Nazarian Alireza A   Arbeev Konstantin G KG   Kulminski Alexander M AM  

Journal of applied genetics 20191121 1


The generalized linear mixed models (GLMMs) methodology is the standard framework for genome-wide association studies (GWAS) of complex diseases in family-based cohorts. Fitting GLMMs in very large cohorts, however, can be computationally demanding. Also, the modified versions of GLMM using faster algorithms may underperform, for instance when a single nucleotide polymorphism (SNP) is correlated with fixed-effects covariates. We investigated the extent to which disregarding family structure may  ...[more]

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