Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes

Sungyoung Lee,Yongkang Kim,Heungsun Hwang,Taesung Park,Sungkyoung Choi

doi:10.1186/s12859-018-2066-9

Sungyoung Lee, Yongkang Kim + Show 3 more

Open Access

https://doi.org/10.1186/s12859-018-2066-9

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Journal: BMC bioinformatics	Publication Date: May 1, 2018
Citations: 6	License type: open-access

Affiliation: Seoul National University, McGill University

Abstract

BackgroundAs one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways.ResultsSimulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset.ConclusionsHerein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously.

Highlights

As one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by generation sequencing technology
Large-scale genetic analyses continue to suffer from incomplete association, of single nucleotide variants
We introduce a new method, “PHARAOHmulti” (Pathway-based approach using HierArchical component of collapsed RAre variants Of High-throughput sequencing data), for analyzing multiple phenotypes

Summary

Introduction

As one possible solution to the “missing heritability” problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by generation sequencing technology. While a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways. Large-scale genetic analyses continue to suffer from incomplete association, of single nucleotide variants (SNVs), with distinct phenotypes (“missing heritability”), and difficulties of biological interpretation [4]. As the number of publicly available biological resources is increasing, recent methods for analyzing rare variants utilize pathway knowledge as a priori information. Since most biological behaviors manifest from a complex interaction of biological pathways [10, 11], analyzing pathway information for identifying rare variants has several advantages.

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