Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Feb 18;14:108.
doi: 10.1186/1471-2164-14-108.

The Genomic Signature of Trait-Associated Variants

Affiliations
Free PMC article

The Genomic Signature of Trait-Associated Variants

Alida S D Kindt et al. BMC Genomics. .
Free PMC article

Abstract

Background: Genome-wide association studies have identified thousands of SNP variants associated with hundreds of phenotypes. For most associations the causal variants and the molecular mechanisms underlying pathogenesis remain unknown. Exploration of the underlying functional annotations of trait-associated loci has thrown some light on their potential roles in pathogenesis. However, there are some shortcomings of the methods used to date, which may undermine efforts to prioritize variants for further analyses. Here, we introduce and apply novel methods to rigorously identify annotation classes showing enrichment or depletion of trait-associated variants taking into account the underlying associations due to co-location of different functional annotations and linkage disequilibrium.

Results: We assessed enrichment and depletion of variants in publicly available annotation classes such as genic regions, regulatory features, measures of conservation, and patterns of histone modifications. We used logistic regression to build a multivariate model that identified the most influential functional annotations for trait-association status of genome-wide significant variants. SNPs associated with all of the enriched annotations were 8 times more likely to be trait-associated variants than SNPs annotated with none of them. Annotations associated with chromatin state together with prior knowledge of the existence of a local expression QTL (eQTL) were the most important factors in the final logistic regression model. Surprisingly, despite the widespread use of evolutionary conservation to prioritize variants for study we find only modest enrichment of trait-associated SNPs in conserved regions.

Conclusion: We established odds ratios of functional annotations that are more likely to contain significantly trait-associated SNPs, for the purpose of prioritizing GWAS hits for further studies. Additionally, we estimated the relative and combined influence of the different genomic annotations, which may facilitate future prioritization methods by adding substantial information.

Figures

Figure 1
Figure 1
Genic and regulatory features. Enrichment of trait-associated SNPs in selected genic features. Sampling and permutation based results are compared (□,◊) for a variety of genic features (see Methods for full details); solid symbols indicate odds ratios significantly different from 1 (p ≤ 0.05). Odds ratios below or above one show depletions or enrichments respectively.
Figure 2
Figure 2
Regions with conserved and evolutionary signatures. Enrichment of trait-associated SNPs in selected evolutionary signatures. Sampling and permutation results (□,◊) are compared for regions identified as unusually conserved or divergent by a variety of measures (see Methods for full details); solid symbols indicate odds ratios significantly different from 1 (p ≤ 0.05). Odds ratios below or above one show depletions or enrichments respectively.
Figure 3
Figure 3
Chromatin states. Enrichment of trait-associated SNPs in selected chromatin features. Sampling and permutation based results are compared (□,◊) for regions associated with chromatin states with varying functions (see Methods for full details); solid symbols indicate odds ratios significantly different from 1 (p ≤ 0.05). Odds ratios below or above one show depletions or enrichments respectively.
Figure 4
Figure 4
Suggestive SNPs show modest enrichment/depletion. Enrichment of significant and suggestive trait-associated SNPs (○,Δ) in annotations significant for suggestive trait-associated SNPs. See Additional file 2 and Table 2 for numeric values of all annotations. Solid symbols indicate odds ratios significantly different from 1 (p ≤ 0.05). Odds ratios below or above one show depletions or enrichments respectively.
Figure 5
Figure 5
Logistic regression identifies most influential annotations. Significant ratio between estimated effect and standard error of annotations in the logistic regression models for the significantly and suggestively trait-associated SNPs (○,Δ) sorted after decreasing significance in the logistic regression model for significantly trait-associated SNPs. The final models for the significant and suggestive trait-associated SNPs included 27 and 12 annotations of which 17 and 6 were significant in the models, respectively. Corresponding values can be found in Table 4.

Similar articles

See all similar articles

Cited by 24 articles

See all "Cited by" articles

References

    1. Hakonarson H, Grant SF. Planning a genome-wide association study: Points to consider. Ann Med. 2011;43(6):451–460. - PubMed
    1. Moore JH, Asselbergs FW, Williams SM. Bioinformatics challenges for genome-wide association studies. Bioinformatics. 2010;26(4):445–455. - PMC - PubMed
    1. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;447(7145):661–678. - PMC - PubMed
    1. Montgomery SB, Sammeth M, Gutierrez-Arcelus M, Lach RP, Ingle C, Nisbett J, Guigo R, Dermitzakis ET. Transcriptome genetics using second generation sequencing in a caucasian population. Nature. 2010;464(7289):773–777. - PMC - PubMed
    1. A Catalog of Published Genome-Wide Association Studies. http://www.genome.gov/gwastudies/

Publication types

LinkOut - more resources

Feedback