Detecting genotype-population interaction effects by ancestry principal components

Abstract

26Heterogeneity in the phenotypic mean and variance across populations is often observed for 27 complex traits. One way to understand heterogeneous phenotypes lies in uncovering 28 heterogeneity in genetic effects. Previous studies on genetic heterogeneity across populations 29 were typically based on discrete groups of population stratified by different countries or 30 cohorts, which ignored the difference of population characteristics for the individuals within 31 each group and resulted in loss of information. Here we introduce a novel concept of 32 genotype-by-population (G×P) interaction where population is defined by the first and second 33 ancestry principal components (PCs), which are less likely to be confounded with 34 country/cohort-specific factors. We applied a reaction norm model fitting each of 70 complex 35 traits with significant SNP-heritability and the PCs as covariates to examine G×P interactions 36 across diverse populations including white British and other white Europeans from the UK 37 Biobank (N = 22,229). Our results demonstrated a significant population genetic 38 heterogeneity for behavioural traits such as age first had sexual intercourse and qualifications. 39 Our approach may shed light on the latent genetic architecture of complex traits that underlies 40 the modulation of genetic effects across different populations. 41 42 43 44 45 46 47 48 49 50 51Most human traits are polygenic and their phenotypes are typically influenced by numerous 52 genes and environmental factors, and possibly by their interactions, e.g. genotype-53 environment (G×E) interaction 1-4 . These traits have been termed as "complex traits", which 54 are distinguished from Mendelian traits that are shaped by a single or few major genes 5 . 55 Genome-wide association studies (GWAS) have successfully discovered thousands of 56 associations between single-nucleotide polymorphisms (SNPs) and complex traits, which 57 have revolutionized our understanding of the polygenic architecture of complex traits 6-8 . 58Subsequently, in order to increase the power and precision to identify more causal variants, 59 there have been numerous follow-up studies using meta-analyses of GWAS summary 60 statistics or mega-analyses of multiple GWAS by combining diverse data sources that usually 61 span across different nations or populations 9, 10 . However, many human complex traits (e.g., 62height and body mass index (BMI)) are substantially differentiated among diverse 63 populations 11 . For instance, the mean height across European nations generally increases 64 with latitude 12 . Although across-population differences in the mean values are often 65 observed for the phenotypes of complex traits, the underlying genetic and environmental 66 bases remain largely unknown 12 . 68One way to understand such phenotypic heterogeneity lies in uncovering genetic 69 differentiation for the traits captured by common variants across populations. Some studies 12-70 15 have focused on examining population genetic differentiation f...

Detecting Genotype-Population Interaction Effects by Ancestry Principal Components

“…This research was supported by the Australian Research Council (DP190100766 and FT160100229), and the Australian National Health and Medical Research Council (1087889). This manuscript has been released as a Pre-Print at BioRxiv (Yu et al, 2019).…”