Biology subjects

Lobach, S.

Publications and source records attributed to Lobach, S..

5 recordsLinked to original sources

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↗

bioRxiv2018

A SIMPLE APPROXIMATION TO THE BIAS IN THE GENETIC EFFECT ESTIMATES WHEN MULTIPLE DISEASE STATES SHARE A CLINICAL DIAGNOSIS

Case-control genome-wide association (CC-GWAS) studies might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease, cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished in a biomarker evaluation that might not be feasible on the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or non-genetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a logistic regression model with the clinical diagnosis as an outcome variable to the estimates in the relationship to the true disease state of interest. We performed simulation studies to assess accuracy of the approximation that weve derived. We next applied the derived approximation to the analysis of the genetic basis of innate immune system of Alzheimers disease.

genetics↗

bioRxiv2018

A Simple Approximation To The Bias Of Gene-Environment Interactions In Case/Control Studies With Silent Disease

One of the most important research areas in case-control Genome-Wide Association Studies is to determine how the effect of a genotype varies across the environment or to measure the gene-environment interaction (GxE). We consider the scenario when some of the \"healthy\" controls actually have the disease and when the frequency of these latent cases varies by the environmental variable of interest. In this scenario, performing logistic regression of clinically defined case status on the genetic variant, environmental variable, and their interaction will result in biased estimates of GxE interaction. Here, we derive a general theoretical approximation to the bias in the estimates of the GxE interaction and show, through extensive simulation, that this approximation is accurate in finite samples. Moreover, we apply this approximation to evaluate the bias in the effect estimates of the genetic variants related to mitochondrial proteins a large-scale Prostate Cancer study.

genetics↗

bioRxiv2018

A simple approximation to bias in gene-environment interaction estimates when a case might not be the case

Case-control genetic association studies are often used to examine the role of the genetic basis in complex diseases, such as cancer and neurodegenerative diseases. The role of the genetic basis might vary by non-genetic (environmental) measures, what is traditionally defined as gene-environment interactions (GxE). A commonly overlooked complication is that the set of clinically diagnosed cases might be contaminated by a subset with a nuisance pathologic state that presents with the same symptoms as the pathologic state of interest. The genetic basis of the pathologic state of interest might differ from that of the nuisance pathologic state. Often frequencies of the pathologically defined states within the clinically diagnosed set of cases vary by the environment. We derive a simple and general approximation to bias in GxE parameter estimates when presence of the nuisance pathologic state is ignored. We then perform extensive simulation studies to show that ignoring presence of the nuisance pathologic state can result in substantial bias in GxE estimates and that the approximation we derived is reasonably accurate in finite samples. We demonstrate the applicability of the proposed approximation in a study of Alzheimers disease.

genetics↗