Work

MODE for detecting and estimating genetic causal variants

Year: 2018

Type: preprint

Abstract: Determining the genetic causal variants and estimating their effect sizes are considered to be correlated but independent problems. Fine-mapping studies often rely on the ability to integrate useful functional annotation information into genome wide association univariate/multivariate analysis. In the present study, by modeling the probability of a SNP being causal and its effect size as a set of correlated Gaussian/non-Gaussian random variables, we design an optimization routine for simultaneous fine-mapping and effect size estimation. The algorithm is released as an open source C package MODE. Availability and Implementation: http://sites.google.com/site/sundarvelkur/mode Contact: [email protected] , [email protected]

Source: bioRxiv (Cold Spring Harbor Laboratory)

DOI: 10.1101/357228

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

SDG: Good health and well-being

Open Access status: green

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