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Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty.

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.

SUBMITTER: Sundar VS

PROVIDER: S-EPMC5844985 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Determining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty.

Sundar V S VS Fan Chun-Chieh CC Holland Dominic D Dale Anders M AM

Frontiers in genetics 20180305

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 pr ...[more]

PMID: 29556250

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