Beyond GWASs: illuminating the dark road from association to function

Abstract

Genome-wide association studies (GWASs) have enabled the discovery of common genetic variation contributing to normal and pathological traits and clinical drug responses, but recognizing the precise targets of these associations is now the major challenge. Here, we review recent approaches to the functional follow-up of GWAS loci, including fine mapping of GWAS signal(s), prioritization of putative functional SNPs by the integration of genetic epidemiological and bioinformatic methods, and in vitro and in vivo experimental verification of predicted molecular mechanisms for identifying the targeted genes. The majority of GWAS-identified variants fall in noncoding regions of the genome. Therefore, this review focuses on strategies for assessing likely mechanisms affected by noncoding variants; such mechanisms include transcriptional regulation, noncoding RNA function, and epigenetic regulation. These approaches have already accelerated progress from genetic studies to biological knowledge and might ultimately guide the development of prognostic, preventive, and therapeutic measures.

Figure 1

Workflow for Functionally Analyzing and Interpreting GWAS Loci

Figure 2

Integrated Genetic and Genomic Data at the 11q13 Breast Cancer Susceptibility Locus (A) Manhattan plot displaying the strength of genetic association (−log₁₀ p) versus chromosomal position (Mb). Each dot represents a genotyped or imputed SNP. Dot colors signify the degree of pairwise correlation (r²) with the top SNP, as presented in the color key. White dots depict SNPs for which r² values are unknown. The gray shaded stripe represents the iCHAV, encompassing a physical area bound by SNPs that are statistically indistinguishable by stepwise conditional analysis and including the GWAS lead SNP rs614367. The purple dotted line represents the threshold for genome-wide significance (p = 5 × 10⁻⁸). (B) Linkage disequilibrium plots depicting pairwise correlation between SNPs genotyped in the 1000 Genomes Project for European (CEU), African (YRI), and Asian (CHB) populations. The plots are in grayscale, for which white and black signify r² = 0 and 1, respectively. The pink and green bars denote haplotype blocks described in the text in relation to transethnic fine mapping. (C) Inset from panel (A). The UCSC Genome Browser was used for visualizing ENCODE data tracks, which are indicative of regulatory function. The pink stripe indicates the genomic region corresponding to the iCHAV at 11q13, and the locations of the fine-mapped SNPs are shown as red marks. Regions of open chromatin, indicative of putative regulatory signals, are detected as DNaseI hypersensitive sites (DHSs) and are marked. ChIP-seq data for histone marks associated with regulatory regions and specific TFs relevant to breast cancer are shown. The ENCODE ChromHMM track represents integrated analysis of chromatin states based upon histone ChIP-seq data from human mammary epithelial cells (HMECs). Color coding is as follows: green, weak transcription; yellow and orange, enhancer; red, promoter; blue, insulator; gray, repressed. RNA Pol II ChIA-PET data from MCF7 cells are represented as a grayscale bar; darker regions indicate more frequent interactions.