Modeling Heterogeneity in the Genetic Architecture of Ethnically Diverse Groups Using Random Effect Interaction Models

Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation

article

Yogasudha Veturi¹  Gustavo de los Campos³  Nengjun Yi²  Wen Huang⁶  Ana I. Vazquez²  Brigitte Kühnel⁷ 

[1] Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania 19104;Department of Biostatistics, University of Alabama at Birmingham, Alabama 35205;Department of Epidemiology and Biostatistics,Michigan State University, East Lansing, Michigan 48824;Institute for Quantitative Health Science and Engineering,Michigan State University, East Lansing, Michigan 48824;Department of Statistics and Probability,Michigan State University, East Lansing, Michigan 48824;Department of Animal Science, Michigan State University, East Lansing, Michigan 48824;Department of Molecular Epidemiology, Helmholtz Zentrum München, Germany 85764

关键词: population structure;    GWAS;    random effect interactions;    Bayesian spike slab;    effect heterogeneity;   

DOI  :  10.1534/genetics.119.301909

学科分类：医学（综合）

来源: Genetics Society of America

【 摘 要 】

In humans, most genome-wide association studies have been conducted using data from Caucasians and many of the reported findings have not replicated in other populations. This lack of replication may be due to statistical issues (small sample sizes or confounding) or perhaps more fundamentally to differences in the genetic architecture of traits between ethnically diverse subpopulations. What aspects of the genetic architecture of traits vary between subpopulations and how can this be quantified? We consider studying effect heterogeneity using Bayesian random effect interaction models. The proposed methodology can be applied using shrinkage and variable selection methods, and produces useful information about effect heterogeneity in the form of whole-genome summaries ( e.g. , the proportions of variance of a complex trait explained by a set of SNPs and the average correlation of effects) as well as SNP-specific attributes. Using simulations, we show that the proposed methodology yields (nearly) unbiased estimates when the sample size is not too small relative to the number of SNPs used. Subsequently, we used the methodology for the analyses of four complex human traits (standing height, high-density lipoprotein, low-density lipoprotein, and serum urate levels) in European-Americans (EAs) and African-Americans (AAs). The estimated correlations of effects between the two subpopulations were well below unity for all the traits, ranging from 0.73 to 0.50. The extent of effect heterogeneity varied between traits and SNP sets. Height showed less differences in SNP effects between AAs and EAs whereas HDL, a trait highly influenced by lifestyle, exhibited a greater extent of effect heterogeneity. For all the traits, we observed substantial variability in effect heterogeneity across SNPs, suggesting that effect heterogeneity varies between regions of the genome.

【 授权许可】

CC BY