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Uncovering Local Trends in Genetic Effects of Multiple Phenotypes via Functional Linear Models
Author(s) -
Vsevolozhskaya Olga A.,
Zaykin Dmitri V.,
Barondess David A.,
Tong Xiaoren,
Jadhav Sneha,
Lu Qing
Publication year - 2016
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.21955
Subject(s) - statistical power , trait , covariate , flexibility (engineering) , linear model , genetic architecture , sample size determination , biology , statistical model , computational biology , population , quantitative trait locus , genotyping , computer science , evolutionary biology , econometrics , genetics , machine learning , statistics , genotype , mathematics , gene , programming language , demography , sociology
Recent technological advances equipped researchers with capabilities that go beyond traditional genotyping of loci known to be polymorphic in a general population. Genetic sequences of study participants can now be assessed directly. This capability removed technology‐driven bias toward scoring predominantly common polymorphisms and let researchers reveal a wealth of rare and sample‐specific variants. Although the relative contributions of rare and common polymorphisms to trait variation are being debated, researchers are faced with the need for new statistical tools for simultaneous evaluation of all variants within a region. Several research groups demonstrated flexibility and good statistical power of the functional linear model approach. In this work we extend previous developments to allow inclusion of multiple traits and adjustment for additional covariates. Our functional approach is unique in that it provides a nuanced depiction of effects and interactions for the variables in the model by representing them as curves varying over a genetic region. We demonstrate flexibility and competitive power of our approach by contrasting its performance with commonly used statistical tools and illustrate its potential for discovery and characterization of genetic architecture of complex traits using sequencing data from the Dallas Heart Study.