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Analysis of Nucleotide Sequence Data Using Mixed Model Methodology
Author(s) -
Rodriguez-Zas Sandra L.,
Southey Bruce R.
Publication year - 2001
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.2001.21.s1.s638
Subject(s) - spurious relationship , biology , multivariate statistics , trait , statistics , sequence (biology) , quantitative trait locus , genetics , false positive paradox , single nucleotide polymorphism , gene , mathematics , computer science , genotype , programming language
Linear, logistic, and multivariate mixed model analyses were applied to simulated data of five quantitative traits and a binary liability trait to detect associations with sequence variants in seven genes. Infrequent site variants (<1%) were eliminated and conservative step‐wise procedures were used to reduce the number of variants fitted. Random effects accounting for additive genetic relationships between individuals and for common environment effects were fitted to reduce spurious significant results. Five sites in genes 1, 2, and 6 had significant effects (p < 0.0001) on the traits and were found in both replicates studied. Survival analysis using a Weibull model identified two significant sites for disease age at onset. Other less significant sites may be false positives or due to founder effects. This approach was effective in identifying putative sites while accounting for polygenic and environmental sources of variation. © 2001 Wiley‐Liss, Inc.