Abstract
Linear mixed models are increasingly used for the analysis of genome-wide association studies (GWAS) of binary phenotypes because they can efficiently and robustly account for population stratification and relatedness through inclusion of random effects for a genetic relationship matrix. However, the utility of linear (mixed) models in the context of meta-analysis of GWAS of binary phenotypes has not been previously explored. In this investigation, we present simulations to compare the performance of linear and logistic regression models under alternative weighting schemes in a fixed-effects meta-analysis framework, considering designs that incorporate variable case–control imbalance, confounding factors and population stratification. Our results demonstrate that linear models can be used for meta-analysis of GWAS of binary phenotypes, without loss of power, even in the presence of extreme case–control imbalance, provided that one of the following schemes is used: (i) effective sample size weighting of Z-scores or (ii) inverse-variance weighting of allelic effect sizes after conversion onto the log-odds scale. Our conclusions thus provide essential recommendations for the development of robust protocols for meta-analysis of binary phenotypes with linear models.
Highlights
Linear mixed models (LMMs) have received increasing prominence in the analysis of genome-wide association studies (GWAS) of complex human traits because they account for genetic structure, across participants, which arises from population stratification, cryptic relatedness or close familial relationships.[1,2,3,4,5,6,7] In this framework, structure is modelled by means of a genetic relationship matrix (GRM), constructed from genome-wide SNP genotype data across study participants
No population stratification or confounders We first considered the properties of fixed-effects meta-analysis of association summary statistics obtained from linear and logistic regression models without random effects for the GRM and for simulations generated in the absence of structure or confounders
Impact of a confounding variable in the absence of population stratification We considered the properties of fixed-effects meta-analysis of association summary statistics obtained from linear and logistic regression models without random effects for the GRM and for simulations generated in the absence of structure, but where the binary phenotype was correlated with a confounding variable
Summary
Linear mixed models (LMMs) have received increasing prominence in the analysis of genome-wide association studies (GWAS) of complex human traits because they account for genetic structure, across participants, which arises from population stratification, cryptic relatedness or close familial relationships.[1,2,3,4,5,6,7] In this framework, structure is modelled by means of a genetic relationship matrix (GRM), constructed from genome-wide SNP genotype data across study participants (or from known familial relationships). A random-effects model is used to evaluate the evidence of association for an SNP by accounting for the contribution of the GRM to the overall variance of the trait. This flexible modelling framework can incorporate fixed effects to account for covariates, and can be used to estimate components of heritability that are explained by (subsets of) genotyped SNPs.[8,9]. It has become increasingly common to use LMM approaches in population- and family-based GWAS of binary phenotypes because of their flexibility in accounting for structure, and their computational tractability in comparison with logistic mixed models. Recent studies have demonstrated that LMMs have less power than traditional logistic regression modelling techniques in GWAS of case–control phenotypes unless ascertainment is adequately accounted for.[11,12]
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