Analysis of nucleotide sequence data using mixed model methodology

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.