ArticleBioinformatics (Oxford, England)2012
Incorporating prior information into association studies.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.Nature communications · 2022Article
- Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR.PLoS genetics · 2021Article
- Article
- MARS: leveraging allelic heterogeneity to increase power of association testing.Genome biology · 2021Article
- Leveraging existing GWAS summary data of genetically correlated and uncorrelated traits to improve power for a new GWAS.Genetic epidemiology · 2020Article
- Views on GWAS statistical analysis.Bioinformation · 2020Article
- Leveraging Polygenic Functional Enrichment to Improve GWAS Power.American journal of human genetics · 2019Article
- Weighted mining of massive collections of [Formula: see text]-values by convex optimization.Information and inference : a journal of the IMA · 2018Article
- Leveraging genome characteristics to improve gene discovery for putamen subcortical brain structure.Scientific reports · 2017Article
- Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes.Bioinformatics (Oxford, England) · 2017Article
- Comprehensive evaluation of disease- and trait-specific enrichment for eight functional elements among GWAS-identified variants.Human genetics · 2017Article
- Widespread Allelic Heterogeneity in Complex Traits.American journal of human genetics · 2017Article
- Colocalization of GWAS and eQTL Signals Detects Target Genes.American journal of human genetics · 2016Article
- Using genomic annotations increases statistical power to detect eGenes.Bioinformatics (Oxford, England) · 2016Article
- Leveraging Genomic Annotations and Pleiotropic Enrichment for Improved Replication Rates in Schizophrenia GWAS.PLoS genetics · 2016Article
- Optimal multiple testing under a Gaussian prior on the effect sizes.Biometrika · 2015Article
- Identification of causal genes for complex traits.Bioinformatics (Oxford, England) · 2015Article
- Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases.American journal of human genetics · 2014Article
- Article
- A Bayesian method to incorporate hundreds of functional characteristics with association evidence to improve variant prioritization.PloS one · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
unlabelledRecent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in individuals with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistic which takes into account prior information. Our method improves both power and resolution by 8% and 27%, respectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are interpretable as the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power. AVAILABILITY: The method presented herein is available at http://masa.cs.ucla.edu.
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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.