|Title||MOST: Multivariate outcome score test (C).|
|Year of Publication||2013|
|Authors||He, Qianchuan, and Dan-Yu Lin|
Genetic association studies often collect data on multiple traits that are correlated. Discovery of genetic variants influencing multiple traits can lead to better understanding of the etiology of complex human diseases. Conventional univariate association tests may miss variants which have weak or moderate effects on individual traits. We propose several multivariate test statistics to complement univariate tests. Our framework covers both studies of unrelated individuals and family studies and allows any type/mixture of traits. We construct score-type statistics, which are computationally fast and numerically stable even in the presence of covariates and which can be combined efficiently across studies with different designs and arbitrary patterns of missing data. We also provide a strategy to determine genomewide significance that properly accounts for the linkage disequilibrium (LD) of genetic variants.
|Original Publication||MOST: Multivariate outcome score test (C).|
MOST: Multivariate outcome score test (C).