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

• Published in: Nature genetics Vol. 48; no. 3; pp. 314 - 317
• Main Authors: Sveinbjornsson, Gardar, Albrechtsen, Anders, Zink, Florian, Gudjonsson, Sigurjón A, Oddson, Asmundur, Másson, Gísli, Holm, Hilma, Kong, Augustine, Thorsteinsdottir, Unnur, Sulem, Patrick, Gudbjartsson, Daniel F, Stefansson, Kari
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.03.2016; Nature Publishing Group
• Subjects: 45/43; 631/208/205/2138; 631/208/514/1948; Agriculture; Animal Genetics and Genomics; Annotations; Biomedicine; Cancer Research; DNA sequencing; Gene Function; Genome-wide association studies; Genome-Wide Association Study - methods; Genome-Wide Association Study - statistics & numerical data; Genomes; Human Genetics; Humans; Iceland - epidemiology; Identification and classification; letter; Methods; Molecular Sequence Annotation; Nucleotide sequencing; Phenotype; Polymorphism, Single Nucleotide - genetics; Population; Properties; Sequence Analysis, DNA; Studies; Iceland
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng.3507

Daniel Gudbjartsson, Kari Stefansson and colleagues propose a new weighted Bonferroni approach for determining significance thresholds for human genome-wide association studies (GWAS). They demonstrate that the weighted approach, which is based on sequence annotation enrichments, improves power over standard GWAS methods. 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.