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Quantitative trait loci identification by estimating the genetic model based on the extremal samples

Author(s):

Zining Yang , Yaning Yang , Xu Steven Xu and Min Yuan*  

Abstract:


Background: In genetic association studies with quantitative trait loci (QTL), the association between a candidate genetic marker and the trait of interest is commonly examined by the omnibus F test or by the t-test corresponding to a given genetic model or mode of inheritance. It is known that the t-test with a correct model specification is more powerful than the F test. However, since the underlying genetic model is rarely known in practice, the use of a model-specific t-test may incur substantial power loss. Robust-efficient tests, such as the Maximin Efficiency Robust Test (MERT) and MAX3 have been proposed in the literature.

Methods: In this paper, we propose a novel two-step robust-efficient approach, namely, the genetic model selection (GMS) method for quantitative trait analysis. GMS selects a genetic model by testing Hardy-Weinberg disequilibrium (HWD) with extremal samples of the population in the first step and then applies the corresponding genetic model-specific t-test in the second step.

Results: Simulations show that GMS is not only more efficient than MERT and MAX3, but also has comparable power to the optimal t-test when the genetic model is known.

Conclusion: Application to the data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort demonstrate that the proposed approach can identify meaningful biological SNPs on chromosome 19.

Keywords:

Genetic association studies, Quantitative trait loci, Extreme samples, Genetic model selection, Hardy-Weinberg disequilibrium.

Affiliation:

Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, Genmab US, Inc, Princeton, NJ 08540, Center for Data Science in Health, School of Public Health Administration, Anhui Medical University, Hefei 230032



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