High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants, Urko et a

"Describing and identifying the genetic variants that increase risk for complex diseases remains a central focus
of human genetics and is fundamental for the emergent field of personalized medicine. Over the last six years,
GWAS have revolutionized the field, discovering hundreds of disease loci. However, with only a handful of exceptions,
the causal variants that generate the associations unveiled by GWAS have not been identified, and their frequency
and degree of sharing across populations remains unknown. Here, we present a comprehensive comparison of
GWAS results designed to try to understand the nature of causal variants. By examining the results of GWAS
for 28 diseases that have been performed with peoples of European, East Asian, and African ancestries, we
conclude that a large fraction of associations are caused by common causal variants that should map relatively
close to the associated markers. Our results indicate that many of the disease risk variants discovered by GWAS
are shared across Eurasians."

The concern has been that rare variants in, say, East Asians, aren't present in Europeans, and so we won't learn about them from studies based on Europeans. The above paper seems to indicate that the concern may have been overblown.

Other papers have taken a somewhat different view:

Alicia R. Martin et al: Population genetic history and polygenic risk biases in 1000 Genomes populations

"Background: Genome-wide association studies (GWAS) have largely focused on European descent populations, and the transferability of these findings to diverse populations is dependent on many factors, including selection, genetic divergence, heritability, and phenotype complexity. As medical genomics studies become increasingly large and ethnically diverse, gaining clear insight into population history and genetic diversity from available reference panels is critically important. Results: We disentangle the population history of the widely-used 1000 Genomes Project reference panel, with an emphasis on underrepresented Hispanic/Latino and African descent populations. By leveraging haplotype sharing, linkage disequilibrium decay, and ancestry deconvolution along chromosomes in admixed populations, we gain insights into ancestral allele frequencies, the origins, rates, and timings of admixture, and sex-biased demography. We make empirical observations to evaluate the impact of population structure in association studies, with conclusions that inform rare variant association in diverse populations, how we use standard GWAS tools, and transferability of findings across populations. Finally, we show through coalescent simulations that inferred polygenic risk scores derived from European GWAS are biased when applied to diverse populations. Conclusions: Our study provides fine-scale insight into the sampling, genetic origins, divergence, and sex-biased history of admixture in the 1000 Genomes Project populations. We show that the transferability of results from GWAS are dependent on the ancestral diversity of the study cohort as well as the phenotype polygenicity, causal allele frequency divergence, and heritability. This work highlights the need for inclusion of more diverse samples in medical genomics studies to enable broadly applicable disease risk information."

Razib Khan has posted about it:

"But some of the concern about population structure has to do with the fact thatgenetic background matters, and we are unlikely to ever have total omniscience as to the nature of genetic interactions and dependencies. By this, I mean that if we have a strong causal signal which associates disease risk with a genetic variant, that risk is still conditional on dependencies of other genetic variations across the genome. Those variations are the outcome of demographic pathways, which one can “control” for to some extent by accounting for population structure. In more plain language, a signal that predicts an outcome in Norwegians may not predict the same outcome in Nigerians. The may be due to different frequencies of other variants which are not directly causal, but interact with the causal ones, which vary between populations.""More recently I’ve been a bit sanguine. I don’t follow the literature closely, but papers like High Trans-ethnic Replicability of GWAS Results Implies Common Causal Variants, make me wonder if the genetic background concerns weren’t over-wrought.A new preprint, Population genetic history and polygenic risk biases in 1000 Genomes populations, suggests we should be worried. Or, more precisely, we should be cognizant of the limitations genetic background imposes upon us for certain classes of variants and disease. In particular, rare variants are going to be less portable across populations because of shallower time depth of their emergence, after, populations have diverged. So, if you have a low frequency major effect causal variant in Europeans, there is a much lower likelihood that it is in other populations."

He has also posted some admixture charts and a PCA.

  • Urko M. Marigorta,
  • Arcadi Navarro