Title

Building Bayesian networks from GWAS statistics based on Independence of Causal Influence

Document Type

Article

Publication Date

2016

Keywords

Bayes methods, Noise measurement, Data models, Mathematical model, Buildings, Genetics, Data mining, genomics, statistical analysis, Bayesian networks, GWAS statistics, genome-wide association studies, genotype-phenotype relationships, single-nucleotide polymorphisms, conditional probability table, multiple parent variables, independence of causal influences

Abstract

Genome-wide association studies (GWASs) have received an increasing attention to understand genotype-phenotype relationships. In this paper, we study how to build Bayesian networks from publicly released GWAS statistics to explicitly reveal the conditional dependency between single-nucleotide polymorphisms (SNPs) and traits. The key challenge in building a Bayesian network is the specification of the conditional probability table (CPT) of an variable with multiple parent variables. We employ the Independence of Causal Influences (ICI) which assumes that the causal mechanism of each parent variable is mutually independent. Specifically, we derive a formulation from the Noisy-or model, one of the ICI models, to specify the CPT using the released GWAS statistics. We prove that the specified CPT is accurate as long as the underlying individual-level genotype and phenotype profile data follows the Noisy-or model. We empirically evaluate the Noisy-or model and its derived formulation using data from openSNP. Experimental results demonstrate the effectiveness of our approach.

Comments

Principal Investigator: Xintao Wu

Acknowledgements: The work is supported in part by US National Science Foundation (DGE-1523115 and IIS-1502273 to QP and XW, and DGE-1523154 and IIS-1502172 to XS).

Share

COinS