The Mahalanobis kernel for heritability estimation in genome-wide association studies: fixed-effects and random-effects methods
作者： Lee H. Dicker , Ruijun Ma
DOI:
关键词: Kernel (statistics) 、 Generalized linear mixed model 、 Linkage disequilibrium 、 Heritability 、 Mathematics 、 Euclidean distance 、 Random effects model 、 Mahalanobis distance 、 Statistics 、 Genetic association
摘要: Linear mixed models (LMMs) are widely used for heritability estimation in genome-wide association studies (GWAS). In standard approaches to with LMMs, a genetic relationship matrix (GRM) must be specified. GWAS, the GRM is frequently correlation estimated from study population's genotypes, which corresponds normalized Euclidean distance kernel. this paper, we show that reliance on kernel contributes several unresolved modeling inconsistencies GWAS. These can cause biased estimates presence of linkage disequilibrium (LD), depending distribution causal variants. We these biases resolved (at least at level) if one adopts Mahalanobis distance-based LMM analysis. Additionally, propose new definition partitioned -- attributable subset genes or single nucleotide polymorphisms (SNPs) using GRM, and it inherits many nice consistency properties identified our original Partitioned relatively area GWAS analysis, where inconsistency issues related LD have previously been known especially pernicious.
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