Abstract
A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.
Highlights
We propose conducting gene set analysis with a class of tests that possesses both rigorous statistical
A common objective in genetic association studies is to search for associations between phenotypes and genomic constructs that are larger than a Single Nucleotide Polymorphism (SNP)
One popular unit of analysis is the set of all SNPs that are located near a list of related genes; inference on these sets is generally referred to as gene set analysis (GSA) or pathway analysis [1]
Summary
A common objective in genetic association studies is to search for associations between phenotypes and genomic constructs that are larger than a Single Nucleotide Polymorphism (SNP). One popular unit of analysis is the set of all SNPs that are located near a list of related genes; inference on these sets is generally referred to as gene set analysis (GSA) or pathway analysis [1]. GSA does not yet possess the popularity of individual SNP approaches [5] such as the Genome-Wide Association Study (GWAS), but there are advantages to testing for associations at a higher level [6]. Set-based inference can increase power over individual-SNP methods by pooling many weaker pieces of evidence into a larger, more detectable signal [8]. GSA can improve power by alleviating the multiple testing burden of GWAS [9]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
