ArticleBioinformatics (Oxford, England)2016
GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ANNORE: genetic fine-mapping with functional annotation.Human molecular genetics · 2021Pooled it
- Evaluating 17 methods incorporating biological function with GWAS summary statistics to accelerate discovery demonstrates a tradeoff between high sensitivity and high positive predictive value.Communications biology · 2023Article
- graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data.Frontiers in genetics · 2023Article
- Liver-Specific Polygenic Risk Score Is Associated with Alzheimer's Disease Diagnosis.Journal of Alzheimer's disease : JAD · 2023Article
- Leveraging omics data to boost the power of genome-wide association studies.HGG advances · 2022Article
- fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS.BMC bioinformatics · 2022Article
- GPA-Tree: statistical approach for functional-annotation-tree-guided prioritization of GWAS results.Bioinformatics (Oxford, England) · 2022Article
- M-DATA: A statistical approach to jointly analyzing de novo mutations for multiple traits.PLoS genetics · 2021Article
- Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.PLoS genetics · 2021Article
- Advances and challenges in quantitative delineation of the genetic architecture of complex traits.Quantitative biology (Beijing, China) · 2021Article
- Leveraging existing GWAS summary data of genetically correlated and uncorrelated traits to improve power for a new GWAS.Genetic epidemiology · 2020Article
- Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.Frontiers in genetics · 2020Review
- TAGOOS: genome-wide supervised learning of non-coding loci associated to complex phenotypes.Nucleic acids research · 2019Article
- An evaluation of noncoding genome annotation tools through enrichment analysis of 15 genome-wide association studies.Briefings in bioinformatics · 2019Article
- The contributions of genetics to premature birth.Pediatric research · 2019Article
- Biological relevance of computationally predicted pathogenicity of noncoding variants.Nature communications · 2019Article
- Leveraging Polygenic Functional Enrichment to Improve GWAS Power.American journal of human genetics · 2019Article
- Computational Molecular Phenotypic Analysis of PTPN22 (W620R), IL6R (D358A), and TYK2 (P1104A) Gene Mutations of Rheumatoid Arthritis.Frontiers in genetics · 2019Article
- Beyond heritability: improving discoverability in imaging genetics.Human molecular genetics · 2018Review
- Statistical methods to detect pleiotropy in human complex traits.Open biology · 2017Review
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4 authors.
Funding
Abstract
motivationGenome-wide association study (GWAS) has been a great success in the past decade. However, significant challenges still remain in both identifying new risk loci and interpreting results. Bonferroni-corrected significance level is known to be conservative, leading to insufficient statistical power when the effect size is moderate at risk locus. Complex structure of linkage disequilibrium also makes it challenging to separate causal variants from nonfunctional ones in large haplotype blocks. Under such circumstances, a computational approach that may increase signal replication rate and identify potential functional sites among correlated markers is urgently needed.
resultsWe describe GenoWAP, a GWAS signal prioritization method that integrates genomic functional annotation and GWAS test statistics. The effectiveness of GenoWAP is demonstrated through its applications to Crohn's disease and schizophrenia using the largest studies available, where highly ranked loci show substantially stronger signals in the whole dataset after prioritization based on a subset of samples. At the single nucleotide polymorphism (SNP) level, top ranked SNPs after prioritization have both higher replication rates and consistently stronger enrichment of eQTLs. Within each risk locus, GenoWAP may be able to distinguish functional sites from groups of correlated SNPs. AVAILABILITY AND IMPLEMENTATION: GenoWAP is freely available on the web at http://genocanyon.med.yale.edu/GenoWAP.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.