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    How much biological insight do genome-wide disease studies offer?

    Jonathan Pritchard (center) and his colleagues, Yang Li (left) and Evan Boyle, discuss their recent work positing that almost any gene can influence disease

    A core assumption in the study of disease-causing genes has been that they are clustered in molecular pathways directly connected to the disease. Such genome-wide association studies (GWAS) have been used for more than a decade to find genetic ties to diseases such as schizophrenia and rheumatoid arthritis. But a provocative analysis now calls the future of that strategy into question — and raises doubts about whether funders should pour more money into these experiments.

    In a paper published in Cell on 15 June, Pritchard and two other geneticists suggest that many GWAS hits have no specific biological relevance to disease and wouldn’t serve as good drug targets. Rather, these 'peripheral' variants probably act through complex biochemical regulatory networks to influence the activity of a few ‘core’ genes that are more directly connected to an illness.

    The Cell paper re-analysed the data from a 2014 study of 250,000 people, which identified nearly 700 DNA variants linked to height. Yet these variants altogether explained about merely 16% of differences in height across a population. Pritchard’s team estimate that as many as 100,000 single-letter DNA variants can influence a person’s height, but each one has a minuscule impact; on average just about one-tenth of a millimetre. These variants tend to lie in regions that do not themselves encode genes but which influence the activity of regions that do.

    The researchers also re-analysed data from GWAS of schizophrenia, rheumatoid arthritis and Crohn’s disease. They found GWAS hits in DNA regions that are expressed in the particular cells relevant to the disease: neurons for schizophrenia, and immune cells for the two autoimmune diseases. But regions of DNA active in many types of body tissue were just as likely to be hits as those that were active only in neurons or immune cells, the team found. That lends credence to the idea that large GWAS are simply picking up most of the DNA variants that have an influence on gene regulation, and that happen to be active in broad functions of disease-relevant cells, rather than in particular activities linked to illness.

    Pritchard's team argues against the traditional approach to gene discovery via larger and larger genomewide association studies, because the sample sizes are expensive and the thousands of peripheral genes uncovered are likely to have tiny, indirect effects. "After you get the first 100 hits," said Pritchard, "you've probably found most of the core genes you're going to get through genomewide association studies."

    Instead, he recommends switching to deep sequencing the core genes to hunt down rare variants that might have bigger effects. For clinical use, Pritchard said, there's still a rationale for genomewide association studies: to predict the peripheral gene-based risk factors in individual patients in order to personalize medicine.

    Read more:

    Evan A. Boyle, Yang I. Li, Jonathan K. Pritchard. An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell, 2017; 169 (7): 1177 DOI: 10.1016/j.cell.2017.05.038