4:08 PM
Mar 12, 2018
 |  BC Extra  |  Preclinical News

Getting more out of GWAS hits

A new study led by The Institute of Cancer Research presents a high throughput method for mapping hits from genome-wide association studies (GWAS) to the genes they modulate.

GWAS can link genetic loci to disease risk, but figuring out how SNPs in the loci contribute to the disease is challenging, as many are located in non-coding regions and it's unclear if or how they alter gene expression. SNPs don't have to be in a gene to modulate its expression -- they can be in a regulatory element nearby a gene, or even megabases away. Some SNPs don't regulate genes at all (see BioCentury Innovations, July 13, 2017).

In the paper, published Monday in Nature Communications, the authors used a high throughput, high resolution method they had previously developed, dubbed Capture Hi-C, to identify regulatory elements in GWAS loci and map them to the genes they control.

In breast cancer cell lines, the authors used Capture Hi-C to analyze 63 previously identified risk loci, mapping transcriptional start sites of genes in or around the loci to putative regulatory elements within them. The method identified 110 potential target genes related to 33 of the loci. 94 of the genes were protein-coding; 16 encoded non-coding RNAs.

In 995 estrogen receptor-positive breast cancer patient tumor samples, the team found associations between survival and expression of 32 of the putative target genes.

In an expression quantitative trait loci (eQTL) analysis using previously published data on risk-conferring SNPs, the authors identified 22 SNP-gene combinations that were significant in all breast cancers, suggesting the genes play a causal role in the disease (p<0.05).

The authors suggest Capture Hi-C analysis of GWAS loci can identify target genes that merit follow-up studies to better understand breast cancer risk and prognosis.

A growing number of researchers are going beyond GWAS and using additional methods to better connect risk alleles with disease phenotypes. In 2016, two groups from the Broad Institute of MIT and Harvard published papers in Cell showing a massively parallel reporter assay (MPRA) could flag variants that are likely to affect gene expression obtained from GWAS or eQTL studies (see BioCentury Innovations, June 23, 2016).

A paper published in Science last month examining gene expression signatures in psychiatric disease patients showed RNA expression in brain tissues goes a step beyond DNA results, as it can identify networks of genes whose combined expression reflects a wide range of genetic and environmental factors (see BioCentury Innovations, Feb. 22).

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