San Francisco Bay Area Chapter of American Statistical Association

Monthly Seminar

 

Title: Genetic Effect Estimates When Distinct Disease States Share A Clinical Diagnosis

Iryna Lobach, Ph.D.

Assistant Professor, Department of Epidemiology & BiostatisticsUniversity of California San Francisco



When:   Thursday, Jan 24th, 2019, 4:30 pm – 6:00 pm

(4:30-5:00 social networking and light refreshment, 5:00-6:00 seminar)

 

Where:  Room 2700, Mission Hall: Global Health and Clinical Sciences Building, UCSF

550 16th Street, San Francisco, CA

 

Abstract:
Genome-wide association studies often measure how an effect of the genetic basis varies by non-genetic (environmental) variables, what is traditionally referred to as gene-environment interaction (GxE) analyses. We consider a problem of accurately estimating a GxE in a case-control study when 1) subset of the controls have silent, or undiagnosed disease; and/or 2) distinct disease states share a clinical diagnosis. We show that when 1) the frequency of the silent disease; and/or 2) frequency of the disease state of interest within the clinically diagnosed set of cases varies by the key environmental variables, using case-control status without accounting for misdiagnosis can lead to biased estimates of the GxE. We further propose a pseudolikelihood approach to remove the bias and accurately estimate how the relationship between the genetic variant and the true disease status varies by the environmental variable. We show our method in extensive simulations and apply our method to a GWAS of prostate cancer and Alzheimer’s disease.

References:
Lobach I, Sampson J, Alekseyenko A, Lobach S, Zhang L (2018) Case-control studies of gene-environment interactions. When a case might not be the case, PLOS One 
https://journals.p