Work

Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty

Year: 2018

Type: article

Abstract: With the availability of high-throughput sequencing data, identification of genetic causal variants accurately requires the efficient incorporation of function annotation data into the optimization routine. This motivates the need for development of novel methods for genome wide association studies with special focus on fine-mapping capabilities. A penalty function method that is simple to implement and capable of integrating functional annotation information into the estimation procedure, is proposed in this work. The idea is to use the prior distribution of the effect sizes explicitly as a penalty function. The estimates obtained are shown to be better correlated with the true effect sizes (in comparison with a few existing techniques). An increase in the positive and negative predictive value is demonstrated using Hapgen2 simulated data.

Source: Frontiers in Genetics

DOI: 10.3389/fgene.2018.00077

Authors: V. S. Sundar, Chun-Chieh Fan, Dominic Holland, Anders M. Dale

Institution: University of California San Diego

Language: English

Topic: Genetic Associations and Epidemiology

Subfield: Genetics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,801

Funders: National Institutes of Health, National Institute on Drug Abuse