Efficiency of trans-ethnic genome-wide meta-analysis and fine-mapping

Abstract

Genome-wide association studies have seen unprecedented success in identifying genetic loci that correlate with disease susceptibility and severity. Early phases of these studies have predominantly been performed in the Caucasian populations. The next phase in medical genetics is to extend the exploration across genetically diverse populations to leverage on larger sample sizes for locating smaller effects that may be present in most human populations. However, discoveries from these studies do not actually reveal the underlying functional changes to the human genome, but only point to broad regions stipulated by the extent of linkage disequilibrium (LD). Fine-mapping the functional variants can, however, be hampered by extensive LD, which can yield multiple perfect surrogates that are not distinguishable from the underlying causal variants, although several studies have illustrated the value of relying on multiple genetically diverse populations to narrow the candidate regions where the functional variants can be found in. Here, we explore the efficiency of trans-ethnic meta-analysis in discovering genetic association and in fine-mapping the causal variants by asking: are there any population diversity metrics that will be useful for: (i) identifying the populations or genomic regions where meta-analysis are likely to be more successful for discovering associations?; (ii) identifying the populations or loci to perform deep targeted sequencing for the purpose of fine-mapping causal variants? Our results indicate that simple metrics like the F ST or the population specificity of haplotypes are useful in trans-ethnic meta-analyses, while the degree of haplotype sharing and LD variation are informative of the efficiency in trans-ethnic fine-mapping.

Comparison of the Performance of Two Commercial Genome-Wide Association Study Genotyping Platforms in Han Chinese Samples

“…Transethnic mapping, or comparing association findings in ethnically remote populations, can be highly informative about established loci in addition to identifying novel loci (Hughes et al 2011). When disease associations are shared (with the same polymorphisms) between ethnically remote groups, this implies that the same causal variant is in linkage disequilibrium with the genotyped variant in each population, indicating a common founder mutation (Franceschini et al 2012; Twee-Hee Ong et al 2012). …”

Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs)

“…In categories of genes where H|H is a weak or negative predictor of eQTLs, such as innate immune system genes, this would be consistent with balancing selection acting on such eQTLs. The evolutionary signatures underlying eQTLs in different biological processes, populations[ 44 ] and systems vary widely.…”

Dissecting the genetics of complex traits using summary association statistics

“…Intuitively, the set of tag SNPs linked to a causal variant will vary across populations, so that aggregating evidence of association across populations will dilute signals from tag SNPs and strengthen signals from causal variants. A standard approach to combining information across multiple studies is to compute posterior probabilities of causality from fixed-effects meta-analysis results 67,69,71,72 . Alternately, posterior probabilities can be computed from results of random-effects trans-ethnic meta-analysis methods 64,68 .…”