Challenges in conducting genome-wide association studies in highly admixed multi-ethnic populations: the Generation R Study

Abstract

Genome-wide association studies (GWAS) have been successful in identifying loci associated with a wide range of complex human traits and diseases. Up to now, the majority of GWAS have focused on European populations. However, the inclusion of other ethnic groups as well as admixed populations in GWAS studies is rapidly rising following the pressing need to extrapolate findings to non-European populations and to increase statistical power. In this paper, we describe the methodological steps surrounding genetic data generation, quality control, study design and analytical procedures needed to run GWAS in the multiethnic and highly admixed Generation R Study, a large prospective birth cohort in Rotterdam, the Netherlands. Furthermore, we highlight a number of practical considerations and alternatives pertinent to the quality control and analysis of admixed GWAS data.Electronic supplementary materialThe online version of this article (doi:10.1007/s10654-015-9998-4) contains supplementary material, which is available to authorized users.

The Generation R Study: design and cohort update 2017

“…For genotyping, we used the infrastructure of the Human Genomics Facility (HuGe-F) of the Genetic Laboratory of the Department of Internal Medicine (www.glimdna.org). The GWAS dataset underwent a stringent QC process, which has been described in detail previously [5, 7, 84]. Most GWAS analyses are strongly embedded in the Early Growth Genetics (EGG) (http://egg-consortium.org/) and Early Genetics and Longitudinal Epidemiology (EAGLE) Consortia, in which several birth cohort studies combine their GWAS efforts focused on multiple outcomes in fetal life, childhood and adolescence.…”

Associations of genetic variants for adult lipid levels with lipid levels in children. The Generation R Study

“…The Generation R Study is a multiethnic cohort. The largest subgroup is of Dutch/European origin (57%), with other ethnic groups including Turkish (7.4%), Surinamese (7.3%), and Moroccan (6.4%) (15). Our analyses were restricted to singleton live born children who had genomewide association data and at least one of the lipid measurements available.…”

“…SNPs were excluded if they had a minor allele frequency 0.001, a call rate <0.98, or if they were out of Hardy-Weinberg Equilibrium (P < 1E 6 ) (15). The genotyped dataset was then imputed to the 2.5 million SNPs of the HapMap II (release 22) cosmopolitan panel with MACH (version 1.0.15) software (14,15,17,18).…”

“…Participants were defined as being of non-Northwestern European ancestry when deviating more than four SDs from the CEU panel mean value in any of the first four genomic components. Further details of these analyses are described in Medina-Gomez et al (15). We applied Bonferroni correction for four phenotypes to adjust for multiple testing, giving a significance cutoff level of 0.0125 for the GRSs, and for the number of SNPs per phenotype for the analyses of individual SNPs.…”

Genetic discovery in multi-ethnic populations

“…Studies assessing the contribution of variants to disease have shown that, in general, associated variants transcend ethnic groups, and play a role in disease susceptibility worldwide. There are analytical challenges to performing genome-wide association studies in the mixed ancestry and admixed populations, with care needed in both quality control and association testing, 9 but the benefits to genetic discovery from recruiting ancestrally heterogeneous populations are considerable. 10 The larger sample sizes attainable lead to novel loci being identified, 11 and differences in linkage disequilibrium patterns across populations open up the potential to identify causal variants at associated loci through using fine-mapping approaches.…”