ArticleGenetics2011
Increasing power of genome-wide association studies by collecting additional single-nucleotide polymorphisms.
Article in Genetics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Transcriptome-wide association analysis of Alzheimer's disease: construction and clinical validation of transcriptomic risk scores.European archives of psychiatry and clinical neuroscience · 2026Article
- A Unifying Framework for Imputing Summary Statistics in Genome-Wide Association Studies.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- A phenomics-based approach for the detection and interpretation of shared genetic influences on 29 biochemical indices in southern Chinese men.BMC genomics · 2019Article
- Improving Imputation Accuracy by Inferring Causal Variants in Genetic Studies.Journal of computational biology : a journal of computational molecular cell biology · 2019Article
- Enhanced methods to detect haplotypic effects on gene expression.Bioinformatics (Oxford, England) · 2017Article
- Widespread Allelic Heterogeneity in Complex Traits.American journal of human genetics · 2017Article
- Dissecting the genetics of complex traits using summary association statistics.Nature reviews. Genetics · 2017Review
- Colocalization of GWAS and eQTL Signals Detects Target Genes.American journal of human genetics · 2016Article
- Multiple testing correction in linear mixed models.Genome biology · 2016Article
- DISSCO: direct imputation of summary statistics allowing covariates.Bioinformatics (Oxford, England) · 2015Article
- Gene-Gene Interactions Detection Using a Two-stage Model.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Pathway-guided identification of gene-gene interactions.Annals of human genetics · 2014Article
- Efficiently identifying significant associations in genome-wide association studies.Journal of computational biology : a journal of computational molecular cell biology · 2013Article
- Regional replication of association with refractive error on 15q14 and 15q25 in the Age-Related Eye Disease Study cohort.Molecular vision · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Genome-wide association studies (GWASs) have been effectively identifying the genomic regions associated with a disease trait. In a typical GWAS, an informative subset of the single-nucleotide polymorphisms (SNPs), called tag SNPs, is genotyped in case/control individuals. Once the tag SNP statistics are computed, the genomic regions that are in linkage disequilibrium (LD) with the most significantly associated tag SNPs are believed to contain the causal polymorphisms. However, such LD regions are often large and contain many additional polymorphisms. Following up all the SNPs included in these regions is costly and infeasible for biological validation. In this article we address how to characterize these regions cost effectively with the goal of providing investigators a clear direction for biological validation. We introduce a follow-up study approach for identifying all untyped associated SNPs by selecting additional SNPs, called follow-up SNPs, from the associated regions and genotyping them in the original case/control individuals. We introduce a novel SNP selection method with the goal of maximizing the number of associated SNPs among the chosen follow-up SNPs. We show how the observed statistics of the original tag SNPs and human genetic variation reference data such as the HapMap Project can be utilized to identify the follow-up SNPs. We use simulated and real association studies based on the HapMap data and the Wellcome Trust Case Control Consortium to demonstrate that our method shows superior performance to the correlation- and distance-based traditional follow-up SNP selection approaches. Our method is publicly available at http://genetics.cs.ucla.edu/followupSNPs.
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Registered trials
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.