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 & Biostatistics, University 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