A multistep approach to single nucleotide polymorphism–set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Valcarcel, Alessandra; Grinde, Kelsey; Cook, Kaitlyn; Green, Alden; Tintle, Nathan
• Is Part Of: BMC proceedings. Volume 10:Issue 7(2016)
• Page Start: 349
• Page End: 355
• Publication Date: 2016-10
• Subjects: Medicine -- Congresses; Biology -- Congresses; 610.5
• DOI: 10.1186/s12919-016-0055-4
• Languages: English
• ISSNs: 1753-6561

Abstract

Abstract The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a singlep value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within a pathway). Testing sets of variants such as pathways for association with a disease phenotype reduces multiple testing penalties, may increase power, and allows for straightforward biological interpretation. However, a significant variant-set association test does not indicate precisely which variants contained within that set are causal. Because pathways often contain many variants, it may be helpful to follow-up significant pathway tests by conducting gene-based tests on each gene in that pathway to narrow in on the region of causal variants. In this paper, we propose such a multistep approach for variant-set analysis that can also account for covariates and complex pedigree structure. We demonstrate this approach on simulated phenotypes from Genetic Analysis Workshop 19. We find generally better power for the multistep approach when compared to a more conventional, single-step approach that simply runs gene-based tests of association on each gene across the genome. Further work is necessary to evaluate the multistep approach on different data sets with different characteristics.