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Exploiting gene‐environment independence in haplotype‐based inferences for population‐based case‐control studies with complex sampling
Author(s) -
Wang Lingxiao,
Lin Daoying,
Li Yan
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8395
Subject(s) - sampling (signal processing) , haplotype , sampling design , population , computer science , population stratification , statistics , independence (probability theory) , econometrics , single nucleotide polymorphism , biology , genetics , mathematics , gene , medicine , genotype , environmental health , filter (signal processing) , computer vision
The use of complex sampling in population‐based case‐control studies is becoming more common. Although most single nucleotide polymorphism‐based association studies with complex sampling account for the design complications, many of haplotype‐based genetic association studies with complex sampling tend to ignore them when estimating haplotype frequencies, regression coefficients, or both. In this article, we develop innovative one‐step and two‐step statistical methods that account for the design complications in haplotype‐based association studies when cases and/or controls are sampled with complex sampling. Attracted by the efficiency advantage of the retrospective method, we explore the assumptions of Hardy‐Weinberg equilibrium and gene‐environment independence in the underlying population. Results of our simulation studies demonstrate superior performance of the proposed methods over selected existing methods under various complex sampling designs. An application of the proposed methods is illustrated using a population‐based case‐control study of kidney cancer.