Zablotska, L. | Search bioRxiv

bioRxiv2019

Case-Control Vs. Case-Only Estimates Of Gene-Environment Interactions With Common And Misclassified Clinical Diagosis

Genetic studies provide valuable information to assess if the effect of genetic variants varies by the non-genetic (\"environmental\") variables, what is traditionally defined to be gene-environment interaction. A common complication is that multiple disease states present with the same set of symptoms, and hence share the clinical diagnosis. Because 1) disease states might have distinct genetic bases; and 2) frequencies of the disease states within the clinical diagnosis vary by the environmental variables, analyses of association with the clinical diagnosis as an outcome variable might result in false positive or false negative findings. We develop estimates for assessment of GxE in a case-only study and compare the case-control and case-only estimates. We report extensive simulation studies that evaluate empirical properties of the estimates and show the application to a study of Alzheimers disease.

genetics↗

bioRxiv2019

Genetic effect estimates in case-control studies when a continuous variable is omitted from the model

Large-scale genome-wide analyses scans provide massive volumes of genetic variants on large number of cases and controls that can be used to estimate the genetic effects. Yet, the sets of non-genetic variables available in publicly available databases are often brief. It is known that omitting a continuous variable from a logistic regression model can result in biased estimates of odds ratios (OR) (e.g., Gail et al (1984), Neuhaus et al (1993), Hauck et al (1991), Zeger et al (1988)). We are interested to assess what information is needed to recover the bias in the OR estimate of genotype due to omitting a continuous variable in settings when the actual values of the omitted variable are not available. We derive two estimating procedures that can recover the degree of bias based on a conditional density of the omitted variable or knowing the distribution of the omitted variable. Importantly, our derivations show that omitting a continuous variable can result in either under- or over-estimation of the genetic effects. We performed extensive simulation studies to examine bias, variability, false positive rate, and power in the model that omits a continuous variable. We show the application to two genome-wide studies of Alzheimers disease.\n\nData Availability StatementThe data that support the findings of this study are openly available in the Database of Genotypes and Phenotypes at [https://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?study_id=phs000372.v1.p1], reference number [phs000372.v1.p1] and at the Alzheimers Disease Neuroimaging Initiative http://adni.loni.usc.edu/.

genetics↗