ArticleGenetic epidemiology2011
The use of imputed values in the meta-analysis of genome-wide association studies.
Article in Genetic epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 5 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Meta-analysis of genome-wide association from genomic prediction models.Animal genetics · 2016Pooled it
- CYP24A1 variant modifies the association between use of oestrogen plus progestogen therapy and colorectal cancer risk.British journal of cancer · 2016Pooled it
- Identification of a common variant with potential pleiotropic effect on risk of inflammatory bowel disease and colorectal cancer.Carcinogenesis · 2015Pooled it
- Identification of Genetic Susceptibility Loci for Colorectal Tumors in a Genome-Wide Meta-analysis.Gastroenterology · 2013Pooled it
- Genome-wide association analyses in East Asians identify new susceptibility loci for colorectal cancer.Nature genetics · 2013Pooled it
- Colorectal Cancer Polygenic Risk Score Is Associated With Screening Colonoscopy Findings but Not Follow-Up Outcomes.Gastro hep advances · 2024Article
- Genome-wide interaction analysis of folate for colorectal cancer risk.The American journal of clinical nutrition · 2023Article
- Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2023Article
- Genome-wide interaction analysis of menopausal hormone therapy use and breast cancer risk among 62,370 women.Scientific reports · 2022Article
- Association of Body Mass Index With Colorectal Cancer Risk by Genome-Wide Variants.Journal of the National Cancer Institute · 2021Article
- Telomere Maintenance Variants and Survival after Colorectal Cancer: Smoking- and Sex-Specific Associations.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2020Article
- Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data.Genomics, proteomics & bioinformatics · 2016Article
- Genome-Wide Interaction Analyses between Genetic Variants and Alcohol Consumption and Smoking for Risk of Colorectal Cancer.PLoS genetics · 2016Article
- Fine-Mapping of Common Genetic Variants Associated with Colorectal Tumor Risk Identified Potential Functional Variants.PloS one · 2016Article
- Advances in the Pharmacogenomics of Adverse Drug Reactions.Drug safety · 2016Review
- Association of aspirin and NSAID use with risk of colorectal cancer according to genetic variants.JAMA · 2015Article
- Integrative analysis of sequencing and array genotype data for discovering disease associations with rare mutations.Proceedings of the National Academy of Sciences of the United States of America · 2015Article
- Whole-exome imputation of sequence variants identified two novel alleles associated with adult body height in African Americans.Human molecular genetics · 2014Article
- Genome-wide diet-gene interaction analyses for risk of colorectal cancer.PLoS genetics · 2014Article
- Association studies with imputed variants using expectation-maximization likelihood-ratio tests.PloS one · 2014Article
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Authors and funding
4 authors.
Funding
Abstract
In genome-wide association studies (GWAS), it is a common practice to impute the genotypes of untyped single nucleotide polymorphism (SNP) by exploiting the linkage disequilibrium structure among SNPs. The use of imputed genotypes improves genome coverage and makes it possible to perform meta-analysis combining results from studies genotyped on different platforms. A popular way of using imputed data is the "expectation-substitution" method, which treats the imputed dosage as if it were the true genotype. In current practice, the estimates given by the expectation-substitution method are usually combined using inverse variance weighting (IVM) scheme in meta-analysis. However, the IVM is not optimal as the estimates given by the expectation-substitution method are generally biased. The optimal weight is, in fact, proportional to the inverse variance and the expected value of the effect size estimates. We show both theoretically and numerically that the bias of the estimates is very small under practical conditions of low effect sizes in GWAS. This finding validates the use of the expectation-substitution method, and shows the inverse variance is a good approximation of the optimal weight. Through simulation, we compared the power of the IVM method with several methods including the optimal weight, the regular z-score meta-analysis and a recently proposed "imputation aware" meta-analysis method (Zaitlen and Eskin [2010] Genet Epidemiol 34:537-542). Our results show that the performance of the inverse variance weight is always indistinguishable from the optimal weight and similar to or better than the other two methods.
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