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A Pathway-centric Approach to Rare Variant Association Analysis

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)123-129
Number of pages7
JournalEuropean Journal of Human Genetics
Volume25
Journal issue1
Early online date31 Aug 2016
DOIs
StatePublished - Jan 2017

Abstract

Current endeavours in rare variant analysis are typically underpowered when investigating association signals from individual genes. We undertook an approach to rare variant analysis which utilises biological pathway information to analyse functionally relevant genes together. Conventional filtering approaches for rare variant analysis are based on variant consequence and are therefore confined to coding regions of the genome. Therefore, we undertook a novel
approach to this process by obtaining functional annotations from the Combined Annotation Dependent Depletion (CADD) tool, which allowed potentially deleterious variants from intronic regions of genes to be incorporated into analyses. This work was undertaken using whole genome sequencing data from the UK10K project. Rare variants from the KEGG pathway for arginine and proline metabolism were collectively associated with systolic blood pressure (P=3.32x10-5) based on analyses using the optimal sequence kernel association test (SKAT-O). Variants along this pathway also showed evidence of replication using imputed data from the ALSPAC cohort (P=0.02). Subsequent analyses
found that the strength of evidence diminished when analysing genes in this pathway individually, suggesting that they would have been overlooked in a conventional gene-based analysis. Future studies which adopt similar approaches to investigate polygenic effects should yield value in better understanding the genetic architecture of complex disease.

    Research areas

  • Rare Variant Analysis, Pathway analysis, Functional annotations, Non-Coding, UK10K, ALSPAC

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Nature at http://www.nature.com/ejhg/journal/vaop/ncurrent/full/ejhg2016113a.html. Please refer to any applicable terms of use of the publisher.

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