Patrick Y P Kao1, Kim Hung Leung2, Lawrence W C Chan2, Shea Ping Yip3, Maurice K H Yap1. 1. Centre for Myopia Research, School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China. 2. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China. 3. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China. Electronic address: shea.ping.yip@polyu.edu.hk.
Abstract
BACKGROUND: Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data. SCOPE OF REVIEW: 1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data. MAJOR CONCLUSIONS: To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions. GENERAL SIGNIFICANCE: Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.
BACKGROUND: Genome-wide association studies (GWAS) is a major method for studying the genetics of complex diseases. Finding all sequence variants to explain fully the aetiology of a disease is difficult because of their small effect sizes. To better explain disease mechanisms, pathway analysis is used to consolidate the effects of multiple variants, and hence increase the power of the study. While pathway analysis has previously been performed within GWAS only, it can now be extended to examining rare variants, other "-omics" and interaction data. SCOPE OF REVIEW: 1. Factors to consider in the choice of software for GWAS pathway analysis. 2. Examples of how pathway analysis is used to analyse rare variants, other "-omics" and interaction data. MAJOR CONCLUSIONS: To choose appropriate software tools, factors for consideration include covariate compatibility, null hypothesis, one- or two-step analysis required, curation method of gene sets, size of pathways, and size of flanking regions to define gene boundaries. For rare variants, analysis performance depends on consistency between assumed and actual effect distribution of variants. Integration of other "-omics" data and interaction can better explain gene functions. GENERAL SIGNIFICANCE: Pathway analysis methods will be more readily used for integration of multiple sources of data, and enable more accurate prediction of phenotypes.
Authors: Elisa Cirillo; Martina Kutmon; Manuel Gonzalez Hernandez; Tom Hooimeijer; Michiel E Adriaens; Lars M T Eijssen; Laurence D Parnell; Susan L Coort; Chris T Evelo Journal: PLoS One Date: 2018-04-04 Impact factor: 3.240
Authors: Heung Woo Park; Sang Heon Kim; Yoon Seok Chang; Sang Hoon Kim; Young Koo Jee; Ai Young Lee; In Jin Jang; Hae Sim Park; Kyung Up Min Journal: Allergy Asthma Immunol Res Date: 2018-09 Impact factor: 5.764