ArticleAnnals of human genetics2014
Comparing the efficacy of SNP filtering methods for identifying a single causal SNP in a known association region.
Article in Annals of human genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Breast cancer risk variants at 6q25 display different phenotype associations and regulate ESR1, RMND1 and CCDC170.Nature genetics · 2016Pooled it
- Causal Characteristics of Immune Cells Associated with Aortic Dissection: A Mendelian Randomisation Analysis.European cardiology · 2025Article
- A 584 bp deletion in CTRB2 inhibits chymotrypsin B2 activity and secretion and confers risk of pancreatic cancer.American journal of human genetics · 2021Article
- Functional characterization of a chr13q22.1 pancreatic cancer risk locus reveals long-range interaction and allele-specific effects on DIS3 expression.Human molecular genetics · 2016Article
- Fine scale mapping of the 17q22 breast cancer locus using dense SNPs, genotyped within the Collaborative Oncological Gene-Environment Study (COGs).Scientific reports · 2016Article
- Incorporating Functional Genomic Information in Genetic Association Studies Using an Empirical Bayes Approach.Genetic epidemiology · 2016Article
- A genome-wide screening and SNPs-to-genes approach to identify novel genetic risk factors associated with frontotemporal dementia.Neurobiology of aging · 2015Article
- Polymorphisms in a Putative Enhancer at the 10q21.2 Breast Cancer Risk Locus Regulate NRBF2 Expression.American journal of human genetics · 2015Article
- Novel bayes factors that capture expert uncertainty in prior density specification in genetic association studies.Genetic epidemiology · 2015Article
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3 authors.
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Abstract
Genome-wide association studies have successfully identified associations between common diseases and a large number of single nucleotide polymorphisms (SNPs) across the genome. We investigate the effectiveness of several statistics, including p-values, likelihoods, genetic map distance and linkage disequilibrium between SNPs, in filtering SNPs in several disease-associated regions. We use simulated data to compare the efficacy of filters with different sample sizes and for causal SNPs with different minor allele frequencies (MAFs) and effect sizes, focusing on the small effect sizes and MAFs likely to represent the majority of unidentified causal SNPs. In our analyses, of all the methods investigated, filtering on the ranked likelihoods consistently retains the true causal SNP with the highest probability for a given false positive rate. This was the case for all the local linkage disequilibrium patterns investigated. Our results indicate that when using this method to retain only the top 5% of SNPs, even a causal SNP with an odds ratio of 1.1 and MAF of 0.08 can be retained with a probability exceeding 0.9 using an overall sample size of 50,000.
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