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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
Evaluating methods for combining rare variant data in pathway-based tests of genetic association
Ashley Petersen1, Alexandra Sitarik2, Alexander Luedtke3
1Departments of Mathematics, Computer Science, and Statistics, St. Olaf College, 1520 St. Olaf Avenue, Northfield, MN 55057, USA.
BMC Proceedings
|March 1, 2012
Summary
Pathway analysis for rare variants shows two main methods. Fisher's combined probability and direct weighted-sum (WS) methods offer the best power for analyzing gene sets with rare variants.
Area of Science:
- Genetics
- Bioinformatics
- Statistical Genetics
Background:
- Pathway analysis leverages biological pathway knowledge for gene set analysis in genome-wide association studies.
- This approach has not been widely applied to the analysis of rare variants.
- Analyzing rare variants presents unique challenges and opportunities for pathway analysis.
Purpose of the Study:
- To compare two competing pathway analysis approaches for rare variants.
- To evaluate the performance of different methods using simulated and real next-generation sequencing data.
- To identify optimal strategies for rare variant set analysis.
Main Methods:
- Two approaches were compared: 1) generating gene-level p-values using rare variant statistics (e.g., combined multivariate collapsing [CMC], weighted-sum [WS]) and combining them with standard pathway analysis methods (e.g., gene set enrichment analysis, Fisher's combined probability method), and 2) applying rare variant methods directly to sets of single-nucleotide polymorphisms (SNPs) within pathway genes.
- Utilized simulated phenotype and real next-generation sequencing data from Genetic Analysis Workshop 17.
- Assessed statistical power and method performance under various scenarios.
Main Results:
- Substantial differences were observed between the two competing pathway analysis approaches.
- Fisher's combined probability method and the direct application of the weighted-sum (WS) method demonstrated the highest statistical power.
- The WS method generally performed well, while Fisher's method was particularly effective when causal SNPs were few but had high risk.
Conclusions:
- The choice of pathway analysis method significantly impacts the analysis of rare variants.
- Fisher's combined probability method and direct WS application are promising strategies for rare variant set analysis.
- Further investigation is warranted to optimize rare variant pathway analysis for different genetic architectures.
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