A weighted empirical Bayes risk prediction model using multiple traits
Li Gengxin (),
Liu Xiaoyu and
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Li Gengxin: Department of Mathematics and Statistics, University of Michigan Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Hou Lin: Center for Statistical Science, Tsinghua University, 30 Shuangqing Rd, Haidian District, Beijing 100084, China
Liu Xiaoyu: Department of Mathematics and Statistics, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USA
Wu Cen: Department of Statistics, Kansas State University, 1116 Mid-Campus Drive N., Manhattan, KS 66506, USA
Statistical Applications in Genetics and Molecular Biology, 2020, vol. 19, issue 3, 14
With rapid advances in high-throughput sequencing technology, millions of single-nucleotide variants (SNVs) can be simultaneously genotyped in a sequencing study. These SNVs residing in functional genomic regions such as exons may play a crucial role in biological process of the body. In particular, non-synonymous SNVs are closely related to the protein sequence and its function, which are important in understanding the biological mechanism of sequence evolution. Although statistically challenging, models incorporating such SNV annotation information can improve the estimation of genetic effects, and multiple responses may further strengthen the signals of these variants on the assessment of disease risk. In this work, we develop a new weighted empirical Bayes method to integrate SNV annotation information in a multi-trait design. The performance of this proposed model is evaluated in simulation as well as a real sequencing data; thus, the proposed method shows improved prediction accuracy compared to other approaches.
Keywords: empirical Bayes; next-generation sequencing; rare variants; risk prediction (search for similar items in EconPapers)
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