Weighting sequence variants based on their annotation increases power of whole-genome association studies

Abstract

The consensus approach to genome-wide association studies (GWAS) has been to assign equal prior probability of association to all sequence variants tested. However, some sequence variants, such as loss-of-function and missense variants, are more likely than others to affect protein function and are therefore more likely to be causative. Using data from whole-genome sequencing of 2,636 Icelanders and the association results for 96 quantitative and 123 binary phenotypes, we estimated the enrichment of association signals by sequence annotation. We propose a weighted Bonferroni adjustment that controls for the family-wise error rate (FWER), using as weights the enrichment of sequence annotations among association signals. We show that this weighted adjustment increases the power to detect association over the standard Bonferroni correction. We use the enrichment of associations by sequence annotation we have estimated in Iceland to derive significance thresholds for other populations with different numbers and combinations of sequence variants.

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

“…This locus replicated in an independent group of subjects from the European HD REGISTRY study using a parallel disease progression measure, and was genome-wide significant in a meta-analysis of the two studies. The lead SNP in TRACK-HD, rs557874766, is a coding variant in MSH3; it is classed of moderate impact, making it genome-wide significant given its annotation (21). This SNP becomes clearly genome-wide One way to increase the power of genetic studies is to obtain a more accurate measure of phenotype.…”

Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues

“…This conclusion was possible because the heritability approach employed by stratified LD-score regression produces enrichment estimates that are independent of sample size, 10 in the sense that small sample size does not bias point estimates (although small sample size could limit power to detect significant enrichments). On the other hand, methods for assessing functional enrichment by using only top eQTLs could be highly dependent on sample size because the enrichment of associated variants in regulatory annotations could vary with effect size (see Table 1 from Sveinbjornsson et al 11 ). In addition, our results on Tables S16 and S17. over restricting to top eQTLs in efforts to identify functional enrichments.…”

“…1,2 In particular, researchers have gained new insights into the functional effects of genetic variants on many complex diseases and traits. [3][4][5][6][7][8][9][10][11][12] In parallel, large-scale expression quantitative trail locus (eQTL) mapping studies in multiple human tissues have revealed a large number of genetic variants that affect gene expression [13][14][15][16][17][18][19] (reviewed by Albert and Kruglyak 20 ). Gene expression serves as an important intermediate cellular phenotype that affects complex diseases and traits, [21][22][23][24] and the functional effects of eQTLs provide another lens through which researchers can investigate molecular mechanisms.…”

Genome-wide analysis yields new loci associating with aortic valve stenosis

“…7 )). We noted that rs7543130 was recently reported to associate with aortic root size in an independent GWAS 8 and we replicate this association in our Icelandic aortic root dimension sample (P=1.30×10 -8 ) ( We replicate the reported association of the intronic LPA variant 4 rs10455872 with AS in Iceland and the follow-up sample sets (combined OR=1.46; 95% CI: 1.37-1.56, P=1.9×10 -31 ) ( Table 1).…”