MASTOR: association testing of a quantitative trait in samples with related individuals, allowing for covariates and missing data. MASTOR is a C program that performs mixed-model association testing of a quantitative trait in samples with known relatedness, allowing for covariates and missing data. The main reference for this program is Jakobsdottir J., McPeek M. S. 2013 ”Mixed-Model Association Mapping of Quantitative Traits in Samples with Related Individuals” American Journal of Human Genetics 92(5):625-666: Genetic association studies often sample individuals with known familial relationships in addition to unrelated individuals, and it is common for some individuals to have missing data (phenotypes, genotypes, or covariates). When some individuals in a sample are related, power can be gained by incorporating all individuals in the analysis, including individuals with partially missing data, while properly accounting for the dependence among them. We propose MASTOR, a mixed-model, retrospective score test for genetic association with a quantitative trait. MASTOR achieves high power in samples that contain related individuals by making full use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Individuals with available phenotype and covariate information who are not genotyped but have genotyped relatives in the sample can still contribute to the association analysis because of the dependence among genotypes. Similarly, individuals who are genotyped but are missing covariate or phenotype information can contribute to the analysis. MASTOR is valid even when the phenotype model is misspecified and with either random or phenotype-based ascertainment. In simulations, we demonstrate the correct type 1 error of MASTOR, the increase in power that comes from making full use of the relationship information, the robustness to misspecification of the phenotype model, and the improvement in power that comes from modeling the heritability. We show that MASTOR is computationally feasible and practical in genome-wide association studies. We apply MASTOR to data on high-density lipoprotein cholesterol from the Framingham Heart study.
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References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Wu, Xiaowei; Guan, Ting; Liu, Dajiang J.; Novelo, Luis G. León; Bandyopadhyay, Dipankar: Adaptive-weight burden test for associations between quantitative traits and genotype data with complex correlations (2018)
- Keith, Jonathan M. (ed.): Bioinformatics. Volume II: structure, function, and applications (2017)