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Combining SNP p -values from GWAS summary data is a promising strategy for detecting novel genetic factors. Existing statistical methods for the p -value-based SNP-set testing confront two challenges. First, the statistical power of different methods depends on unknown patterns of genetic effects that could drastically vary over different SNP sets. Second, they do not identify which SNPs primarily contribute to the global association of the whole set. We propose a new signal-adaptive analysis pipeline to address these challenges using the omnibus thresholding Fisher’s method (oTFisher). The oTFisher remains robustly powerful over various patterns of genetic effects. Its adaptive thresholding can be applied to estimate important SNPs contributing to the overall significance of the given SNP set. We develop efficient calculation algorithms to control the type I error rate, which accounts for the linkage disequilibrium among SNPs. Extensive simulations show that the oTFisher has robustly high power and provides a higher balanced accuracy in screening SNPs than the traditional Bonferroni and FDR procedures. We applied the oTFisher to study the genetic association of genes and haplotype blocks of the bone density-related traits using the summary data of the Genetic Factors for Osteoporosis Consortium. The oTFisher identified more novel and literature-reported genetic factors than existing p -value combination methods. Relevant computation has been implemented into the R package TFisher to support similar data analysis.
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CERENKOV: Computational Elucidation of the Regulatory Noncoding Variome
We describe a novel computational approach, CERENKOV (Computational Elucidation of the REgulatory NonKOd- ing Variome), for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within noncoding genetic loci. CERENKOV is specifically designed for recognizing rSNPs in the context of a post-analysis of a genome-wide association study (GWAS); it includes a novel accuracy scoring metric (which we call average rank, or AV- GRANK) and a novel cross-validation strategy (locus-based sampling) that both correctly account for the “sparse positive bag” nature of the GWAS post-analysis rSNP recognition problem. We trained and validated CERENKOV using a reference set of 15,331 SNPs (the OSU17 SNP set) whose composition is based on selection criteria (linkage disequi- librium and minor allele frequency) that we designed to ensure relevance to GWAS post-analysis. CERENKOV is based on a machine-learning algorithm (gradient boosted decision trees) incorporating 246 SNP annotation features that we extracted from genomic, epigenomic, phylogenetic, and chromatin datasets. CERENKOV includes novel features based on replication timing and DNA shape. We found that tuning a classifier for AUPVR performance does not guaran- tee optimality for AVGRANK. We compared the validation performance of CERENKOV to nine other methods for rSNP recognition (including GWAVA, RSVP, DeltaSVM, DeepSEA, Eigen, and DANQ), and found that CERENKOV’s validation performance is the strongest out of all of the classifiers that we tested, by both traditional global rank-based measures (⟨AUPVR⟩ = 0.506; ⟨AUROC⟩ = 0.855) and AVGRANK (⟨AVGRANK⟩ = 3.877). The source code for CERENKOV is available on GitHub and the SNP feature data files are available for download via the CERENKOV website.
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- PAR ID:
- 10049769
- Date Published:
- Journal Name:
- Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics
- Page Range / eLocation ID:
- 79 to 88
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3107411.3107414
