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Gene‐Trait Similarity Regression for Multimarker‐Based Association Analysis
Author(s) -
Tzeng JungYing,
Zhang Daowen,
Chang ShengMao,
Thomas Duncan C.,
Davidian Marie
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01176.x
Subject(s) - regression , similarity (geometry) , trait , regression analysis , association (psychology) , gene , computational biology , genetic association , biology , genetics , statistics , computer science , mathematics , artificial intelligence , genotype , psychology , single nucleotide polymorphism , image (mathematics) , psychotherapist , programming language
Summary We propose a similarity‐based regression method to detect associations between traits and multimarker genotypes. The model regresses similarity in traits for pairs of “unrelated” individuals on their haplotype similarities, and detects the significance by a score test for which the limiting distribution is derived. The proposed method allows for covariates, uses phase‐independent similarity measures to bypass the needs to impute phase information, and is applicable to traits of general types (e.g., quantitative and qualitative traits). We also show that the gene‐trait similarity regression is closely connected with random effects haplotype analysis, although commonly they are considered as separate modeling tools. This connection unites the classic haplotype sharing methods with the variance‐component approaches, which enables direct derivation of analytical properties of the sharing statistics even when the similarity regression model becomes analytically challenging.