Epistasis Blog

From the Computational Genetics Laboratory at the University of Pennsylvania (www.epistasis.org)

Wednesday, October 25, 2017

50% of GWAS hits for breast cancer fail to replicate

A new paper in Nature reports 65 new loci identified using genome-wide association studies in a multi-site sample of more than 100,000 subjects. Some of these loci look interesting and will likely yield some new insights into breast cancer. However, there is one sentence in this paper that I think deserves more discussion:

"Of the 102 loci that have previously been associated with breast cancer in Europeans, 49 showed evidence of association with breast cancer in the OncoArray dataset at p < 5 * 10 ^-8.

Less than half of the previous hits replicated at a genome-wide significance level. I am surprised that this paper doesn't address in any detail this significant lack of replication. Dropping the significance level to 0.05 yields a much higher replication rate.

The replicability of GWAS hits in breast cancer would make a great discussion topic for students.
risk loci

Friday, October 20, 2017

Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions

A nice review on GxE interactions.

Ritchie MD, Davis JR, Aschard H, Battle A, Conti D, Du M, Eskin E, Fallin MD, Hsu L, Kraft P, Moore JH, Pierce BL, Bien SA, Thomas DC, Wei P, Montgomery SB. Incorporation of Biological Knowledge Into the Study of Gene-Environment Interactions. Am J Epidemiol. 2017 Oct 1;186(7):771-777. [PubMed


A growing knowledge base of genetic and environmental information has greatly enabled the study of disease risk factors. However, the computational complexity and statistical burden of testing all variants by all environments has required novel study designs and hypothesis-driven approaches. We discuss how incorporating biological knowledge from model organisms, functional genomics, and integrative approaches can empower the discovery of novel gene-environment interactions and discuss specific methodological considerations with each approach. We consider specific examples where the application of these approaches has uncovered effects of gene-environment interactions relevant to drug response and immunity, and we highlight how such improvements enable a greater understanding of the pathogenesis of disease and the realization of precision medicine.