Using public control genotype data to increase power and decrease cost of case-control genetic association studies.
- Authors
- Ho, Lindsey A; Lange, Ethan M
- Year
- 2010
- Journal
- Human genetics
- PMID
- 20821337
- DOI
- 10.1007/s00439-010-0880-x
- PMCID
- PMC3133924
Genome-wide association (GWA) studies are a powerful approach for identifying novel genetic risk factors associated with human disease. A GWA study typically requires the inclusion of thousands of samples to have sufficient statistical power to detect single nucleotide polymorphisms that are associated with only modest increases in risk of disease given the heavy burden of a multiple test correction that is necessary to maintain valid statistical tests. Low statistical power and the high financial cost of performing a GWA study remains prohibitive for many scientific investigators anxious to perform such a study using their own samples. A number of remedies have been suggested to increase statistical power and decrease cost, including the utilization of free publicly available genotype data and multi-stage genotyping designs. Herein, we compare the statistical power and relative costs of alternative association study designs that use cases and screened controls to study designs that are based only on, or additionally include, free public control genotype data. We describe a novel replication-based two-stage study design, which uses free public control genotype data in the first stage and follow-up genotype data on case-matched controls in the second stage that preserves many of the advantages inherent when using only an epidemiologically matched set of controls. Specifically, we show that our proposed two-stage design can substantially increase statistical power and decrease cost of performing a GWA study while controlling the type-I error rate that can be inflated when using public controls due to differences in ancestry and batch genotype effects.
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| 40 | Discussion | Finally, we have performed power calculations assuming fixed sample sizes rather than fixed costs.… |
| Name | Type |
|---|---|
| African American prostate cancer cases local | cohort |
| African American prostate cancer controls local | cohort |
| batch genotype effects local | drug |
| batch genotype effects local | phenotype |
| Batch genotype effects local | phenotype |
| bipolar disorder | phenotype |
| British subjects of European descent local | cohort |
| cases | cohort |
| Caucasian descent local | cohort |
| chi-square test statistics local | drug |
| controls | cohort |
| coronary artery disease | phenotype |
| Crohn’s disease | phenotype |
| D allele | variant |
| disease | phenotype |
| Disease misclassification local | phenotype |
| disease prevalence | phenotype |
| disease susceptibility allele local | variant |
| disease-susceptibility allele local | variant |
| Edwards et al., 2005 local | cohort |
| general population | cohort |
| genetic variants | cohort |
| genomic control method | drug |
| genotype batch effects local | drug |
| genotype DD local | variant |
| genotype relative risk local | variant |
| Genotyping cost local | phenotype |
| Illumina GoldenGate custom panel local | drug |
| joint-analysis two-stage design local | drug |
| longevity | phenotype |
| Moskvina et al., 2005 local | cohort |
| NA study cases local | cohort |
| non-susceptibility allele d local | variant |
| NPU public controls local | cohort |
| NU study controls local | cohort |
| one-stage design local | cohort |
| one-stage design local | drug |
| one-stage study design local | cohort |
| outcome | phenotype |
| POP1 local | cohort |
| POP2 local | cohort |
| population | cohort |
| Public control genotype data local | cohort |
| Public control population local | cohort |
| public controls local | cohort |
| Public controls local | cohort |
| Public Controls local | cohort |
| replication-based two-stage study designs local | cohort |
| rheumatoid arthritis | phenotype |
| risk allele | cohort |
| R software code local | drug |
| Screened Study Controls local | cohort |
| Sebastiani et al. 2010 local | cohort |
| Single-stage study design with both public and study controls local | cohort |
| Single-stage study using only public controls local | cohort |
| SNP | cohort |
| stage 1 | cohort |
| stage 2 | cohort |
| Statistical power local | phenotype |
| study controls local | cohort |
| Study controls local | cohort |
| Study Controls local | cohort |
| susceptibility allele local | variant |
| susceptibility allele D local | variant |
| Susceptibility allele (fD) local | variant |
| susceptibility allele frequency local | variant |
| Systematic ancestry differences local | phenotype |
| two-stage design local | cohort |
| two-stage study design local | cohort |
| type 1 diabetes | phenotype |
| type 2 diabetes | phenotype |
| Unscreened controls local | cohort |
| Wellcome Trust Case Control Collaboration local | cohort |
| Wellcome Trust case control consortium | cohort |
| WTCCC | cohort |
| Zheng and Tian, 2005 local | cohort |
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In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Increase in power by obtaining 10 or more controls per case when type-1 error is small in large-scale association studies. | Katki HA et al. | — | 2023 | → |
| Best practices for analyzing imputed genotypes from low-pass sequencing in dogs. | Buckley RM et al. | — | 2022 | → |
| GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing. | Mathur R et al. | — | 2022 | → |
| Idéfix: identifying accidental sample mix-ups in biobanks using polygenic scores. | Warmerdam R et al. | — | 2022 | → |
| RAVAQ: An integrative pipeline from quality control to region-based rare variant association analysis. | Marenne G et al. | — | 2022 | → |
| Best practices for analyzing imputed genotypes from low-pass sequencing in dogs | Buckley RM et al. | — | 2021 | — |
| A Genome-Wide Association Study of Idiopathic Dilated Cardiomyopathy in African Americans. | Xu H et al. | — | 2018 | → |
| KAT2B polymorphism identified for drug abuse in African Americans with regulatory links to drug abuse pathways in human prefrontal cortex. | Johnson EO et al. | — | 2016 | → |
| Genome-wide association scan for variants associated with early-onset prostate cancer. | Lange EM et al. | — | 2014 | → |
| Imputation across genotyping arrays for genome-wide association studies: assessment of bias and a correction strategy. | Johnson EO et al. | — | 2013 | → |
| Artifact due to differential error when cases and controls are imputed from different platforms. | Sinnott JA et al. | — | 2012 | → |
| A unique genome-wide association analysis in extended Utah high-risk pedigrees identifies a novel melanoma risk variant on chromosome arm 10q. | Teerlink C et al. | — | 2012 | → |
| Population-based case-control association studies. | Hancock DB et al. | — | 2012 | → |
| MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. | Westra HJ et al. | — | 2011 | → |