title: Causal Genetic Inference Using Haplotypes as Instrumental Variables.
abstract: In genomic studies with both genotypes and gene or protein expression profile available, causal effects of gene or protein on clinical outcomes can be inferred through using genetic variants as instrumental variables (IVs). The goal of introducing IV is to remove the effects of unobserved factors that may confound the relationship between the biomarkers and the outcome. A valid inference under the IV framework requires pairwise associations and pathway exclusivity. Among these assumptions, the IV expression association needs to be strong for the casual effect estimates to be unbiased. However, a small number of single nucleotide polymorphisms (SNPs) often provide limited explanation of the variability in the gene or protein expression and can only serve as weak IVs. In this study, we propose to replace SNPs with haplotypes as IVs to increase the variant-expression association and thus improve the casual effect inference of the expression. In the classical two-stage procedure, we developed a haplotype regression model combined with a model selection procedure to identify optimal instruments. The performance of the new method was evaluated through simulations and compared with the IV approaches using observed multiple SNPs. Our results showed the gain of power to detect a causal effect of gene or protein on the outcome using haplotypes compared with using only observed SNPs, under either complete or missing genotype scenarios. We applied our proposed method to a study of the effect of interleukin-1 beta (IL-1β) protein expression on the 90-day survival following sepsis and found that overly expressed IL-1β is likely to increase mortality.
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Fan
authors.initials: F
authors.lastname: Wang
authors.affiliation: Center for Translational Lung Biology, Pulmonary, Allergy, and Critical Care Division, Perelman School of Medicine University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Nuala J
authors.initials: NJ
authors.lastname: Meyer
authors.affiliation: Center for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Keith R
authors.initials: KR
authors.lastname: Walley
authors.affiliation: Center for Heart Lung Innovation, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: James A
authors.initials: JA
authors.lastname: Russell
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Rui
authors.initials: R
authors.lastname: Feng
mesh_terms: D003198:Computer Simulation; D006239:Haplotypes; D006801:Humans; D053583:Interleukin-1beta / Q000235:genetics; D057182:Mendelian Randomization Analysis / Q000379:methods*; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D012772:Shock, Septic / Q000235:genetics / Q000401:mortality
chemical_list: D053583:Interleukin-1beta
keywords: Mendelian randomization; causal effect estimate; haplotype; instrumental variable (IV)