Designing genome-wide association studies: sample size, power, imputation, and the choice of genotyping chip.
- Authors
- Spencer, Chris C A; Su, Zhan; Donnelly, Peter; Marchini, Jonathan
- Year
- 2009
- Journal
- PLoS genetics
- PMID
- 19492015
- DOI
- 10.1371/journal.pgen.1000477
- PMCID
- PMC2688469
Genome-wide association studies are revolutionizing the search for the genes underlying human complex diseases. The main decisions to be made at the design stage of these studies are the choice of the commercial genotyping chip to be used and the numbers of case and control samples to be genotyped. The most common method of comparing different chips is using a measure of coverage, but this fails to properly account for the effects of sample size, the genetic model of the disease, and linkage disequilibrium between SNPs. In this paper, we argue that the statistical power to detect a causative variant should be the major criterion in study design. Because of the complicated pattern of linkage disequilibrium (LD) in the human genome, power cannot be calculated analytically and must instead be assessed by simulation. We describe in detail a method of simulating case-control samples at a set of linked SNPs that replicates the patterns of LD in human populations, and we used it to assess power for a comprehensive set of available genotyping chips. Our results allow us to compare the performance of the chips to detect variants with different effect sizes and allele frequencies, look at how power changes with sample size in different populations or when using multi-marker tags and genotype imputation approaches, and how performance compares to a hypothetical chip that contains every SNP in HapMap. A main conclusion of this study is that marked differences in genome coverage may not translate into appreciable differences in power and that, when taking budgetary considerations into account, the most powerful design may not always correspond to the chip with the highest coverage. We also show that genotype imputation can be used to boost the power of many chips up to the level obtained from a hypothetical "complete" chip containing all the SNPs in HapMap. Our results have been encapsulated into an R software package that allows users to design future association studies and our methods provide a framework with which new chip sets can be evaluated.
Schematic of how power is estimated.At the top of the figure is the recombination map and haplotypes estimated from the HapMap project [1]. Using this population genetic information we simulate a case-control sample (grey lines) where the red dots indicate the disease locus and blue dots indicate linked genetic variation. By performing a test of association at each SNP on the genotyping chip we can estimate power by counting the number of simulation for which a test statistic exceed a significance threshold (dotted line). We compare genotyping chips by changing the set of SNP at which we carry out a test. See Methods.
Plots of power (solid lines) and coverage (dotted line) for increasing sample sizes of cases and controls (x-axis).From left to right plots are given for increasing effect sizes (relative risk per allele). Both power and coverage range from 0 to 1 and are given on the y-axis. Results are for single-marker test of association and for simulations where the risk allele frequency of the causal allele is >0.05. The top row shows power for case-control studies simulated in a Caucasian population based on the CEU HapMap panel. The bottom row relates to case-control studies simulated from the YRI HapMap panel.
Power for Common versus Rare alleles.Plots of power (solid lines) and coverage (dotted line) for increasing sample sizes of cases and controls (x-axis). From left to right plots are given for increasing effect sizes (relative risk per allele). Both power and coverage range from 0 to 1 and are given on the y-axis. Results are for single-marker test of association. The top two rows show the power for rare risk alleles (RAF<0.1) and the bottom two rows show the power for common risk alleles (RAF>0.1). Rows 1 and 3 show power for case-control studies simulated in a Caucasian population based on the CEU HapMap panel. Rows 2 and 4 relate to case-control studies simulated from the YRI HapMap panel.
Histograms of the proportion of SNPs in the 22 1Mb regions (see Methods) in HapMap Phase II for which the maximum r2 with a SNP on the genotyping chip in in one of eleven bins (increasing in correlation (LD) from left to right).The same histograms are coloured in two ways. The top row shows in red the percentage of the SNPs in each bin detected (See Methods and text) when selected to be the causal SNP in our simulations (the proportion of the total volume of the bars coloured red is therefore an estimate of power). In the bottom row all r2 bins above 0.8 are coloured red (the proportion of the total volume of all the bars is therefore an estimate of coverage). Note that the use of HapMap data in choosing SNPs for the Illumina chip leads to a higher proportion of SNPs in high r2 bins.
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| Two-stage comprehensive evaluation of genetic susceptibility of common variants in FBXO38, AP3B2 and WHAMM to severe chronic periodontitis. | Shang D et al. | β | 2015 | β |
| Unravelling genes and pathways implicated in working memory of schizophrenia in Han Chinese. | Ren H et al. | β | 2015 | β |
| Admixture mapping and subsequent fine-mapping suggests a biologically relevant and novel association on chromosome 11 for type 2 diabetes in African Americans. | Jeff JM et al. | β | 2014 | β |
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| Association of 8q24.21 rs10505477-rs6983267 haplotype and age at diagnosis of colorectal cancer. | Haerian MS et al. | β | 2014 | β |
| Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology. | Read TD et al. | β | 2014 | β |
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| Fine mapping of a quantitative trait locus for bovine milk fat composition on Bos taurus autosome 19. | Bouwman AC et al. | β | 2014 | β |
| GACT: a Genome build and Allele definition Conversion Tool for SNP imputation and meta-analysis in genetic association studies. | Sulovari A et al. | β | 2014 | β |
| Genome-wide association analysis of radiation resistance in Drosophila melanogaster. | Vaisnav M et al. | β | 2014 | β |
| Genome-wide association study of handedness excludes simple genetic models. | Armour JA et al. | β | 2014 | β |
| Genome-wide SNP analysis reveals a genetic basis for sea-age variation in a wild population of Atlantic salmon (Salmo salar). | Johnston SE et al. | β | 2014 | β |
| Genomewide study and validation of markers associated with production traits in German Landrace boars. | Strucken EM et al. | β | 2014 | β |
| Identification of genetic loci associated with primary angle-closure glaucoma in the basset hound. | Ahram DF et al. | β | 2014 | β |
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| Impact of family structure and common environment on heritability estimation for neuroimaging genetics studies using Sequential Oligogenic Linkage Analysis Routines. | Koran ME et al. | β | 2014 | β |
| Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds. | Saatchi M et al. | β | 2014 | β |
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| Pathway analysis of genetic factors associated with spontaneous preterm birth and pre-labor preterm rupture of membranes. | Capece A et al. | β | 2014 | β |
| Probing genetic overlap in the regulation of systolic and diastolic blood pressure in Danish and Chinese twins. | Li S et al. | β | 2014 | β |
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| Resequencing studies of nonmodel organisms using closely related reference genomes: optimal experimental designs and bioinformatics approaches for population genomics. | Nevado B et al. | β | 2014 | β |
| Seeking genetic signature of radiosensitivity--a novel method for data analysis in case of small sample sizes. | Zyla J et al. | β | 2014 | β |
| The CF-modifying gene EHF promotes p.Phe508del-CFTR residual function by altering protein glycosylation and trafficking in epithelial cells. | Stanke F et al. | β | 2014 | β |
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| The genetic epidemiology of prostate cancer and its clinical implications. | Eeles R et al. | β | 2014 | β |
| The genetics of major depression. | Flint J et al. | β | 2014 | β |
| The impact of large-scale genomic methods in orthopaedic disorders: insights from genome-wide association studies. | Paria N et al. | β | 2014 | β |
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| Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. | Li YR et al. | β | 2014 | β |
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| Variant calling in low-coverage whole genome sequencing of a Native American population sample. | Bizon C et al. | β | 2014 | β |
| A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibility. | Meredith BK et al. | β | 2013 | β |
| An analysis of measures of effect size by age of onset in cancer genomewide association studies. | Raynor LA et al. | β | 2013 | β |
| Association between variants of EXT2 and type 2 diabetes: a replication and meta-analysis. | Liu L et al. | β | 2013 | β |
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| GABRG2 rs211037 polymorphism and epilepsy: a systematic review and meta-analysis. | Haerian BS et al. | β | 2013 | β |
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| Genetic association studies between SNPs and suicidal behavior: a meta-analytical field synopsis. | Schild AH et al. | β | 2013 | β |
| Genetic Simulation Resources: a website for the registration and discovery of genetic data simulators. | Peng B et al. | β | 2013 | β |
| Genome wide association studies for diabetes: perspective on results and challenges. | Torres JM et al. | β | 2013 | β |
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| Imputation-based meta-analysis of severe malaria in three African populations. | Band G et al. | β | 2013 | β |
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| MetaSeq: privacy preserving meta-analysis of sequencing-based association studies. | Singh AP et al. | β | 2013 | β |
| Multilocus genetic models of handedness closely resemble single-locus models in explaining family data and are compatible with genome-wide association studies. | McManus IC et al. | β | 2013 | β |
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| OPRM1 rs1799971 polymorphism and opioid dependence: evidence from a meta-analysis. | Haerian BS et al. | β | 2013 | β |
| Properties and modeling of GWAS when complex disease risk is due to non-complementing, deleterious mutations in genes of large effect. | Thornton KR et al. | β | 2013 | β |
| PUMA: a unified framework for penalized multiple regression analysis of GWAS data. | Hoffman GE et al. | β | 2013 | β |
| Reply to Just the facts, please. | Dorfman R | β | 2013 | β |
| Resequencing and fine-mapping of the chromosome 12q13-14 locus associated with multiple sclerosis refines the number of implicated genes. | Cortes A et al. | β | 2013 | β |
| Role of interactions in pharmacogenetic studies: leukotrienes in asthma. | Via M et al. | β | 2013 | β |
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| The Brisbane Longitudinal Twin Study: Pathways to Cannabis Use, Abuse, and Dependence project-current status, preliminary results, and future directions. | Gillespie NA et al. | β | 2013 | β |
| The Missing Heritability in T1D and Potential New Targets for Prevention. | Pierce BG et al. | β | 2013 | β |
| The thirteenth international childhood acute lymphoblastic leukemia workshop report: La Jolla, CA, USA, December 7-9, 2011. | Hunger SP et al. | β | 2013 | β |
| Using genetic prediction from known complex disease Loci to guide the design of next-generation sequencing experiments. | Jostins L et al. | β | 2013 | β |
| Weighted SNP set analysis in genome-wide association study. | Dai H et al. | β | 2013 | β |
| Windfalls and pitfalls: Applications of population genetics to the search for disease genes. | Edge MD et al. | β | 2013 | β |
| A coalescent model for genotype imputation. | Jewett EM et al. | β | 2012 | β |
| A comparison of gene region simulation methods. | Hendricks AE et al. | β | 2012 | β |
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| Anonymization of longitudinal electronic medical records. | Tamersoy A et al. | β | 2012 | β |
| Association test based on SNP set: logistic kernel machine based test vs. principal component analysis. | Zhao Y et al. | β | 2012 | β |
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| Common genetic variation contributes significantly to the risk of childhood B-cell precursor acute lymphoblastic leukemia. | Enciso-Mora V et al. | β | 2012 | β |
| Computer simulations: tools for population and evolutionary genetics. | Hoban S et al. | β | 2012 | β |
| Cumulative meta-analysis for genetic association: when is a new study worthwhile? | Rotondi MA et al. | β | 2012 | β |
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| Detection of parent-of-origin specific expression quantitative trait loci by cis-association analysis of gene expression in trios. | Garg P et al. | β | 2012 | β |
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| Efficiency and power as a function of sequence coverage, SNP array density, and imputation. | Flannick J et al. | β | 2012 | β |
| Efficiency of trans-ethnic genome-wide meta-analysis and fine-mapping. | Ong RT et al. | β | 2012 | β |
| Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs. | Krithika S et al. | β | 2012 | β |
| Exact coalescent simulation of new haplotype data from existing reference haplotypes. | Kang CJ et al. | β | 2012 | β |
| Genetic association analysis of complex diseases incorporating intermediate phenotype information. | Li Y et al. | β | 2012 | β |
| Genome-wide association study to identify the genetic determinants of otitis media susceptibility in childhood. | Rye MS et al. | β | 2012 | β |
| Genome-wide pathway association studies of multiple correlated quantitative phenotypes using principle component analyses. | Zhang F et al. | β | 2012 | β |
| HLA class II locus and susceptibility to podoconiosis. | Tekola Ayele F et al. | β | 2012 | β |
| Including known covariates can reduce power to detect genetic effects in case-control studies. | Pirinen M et al. | β | 2012 | β |
| Insights from genomics into bacterial pathogen populations. | Wilson DJ | β | 2012 | β |
| Insights into the regulation of human CNV-miRNAs from the view of their target genes. | Wu X et al. | β | 2012 | β |
| Linkage-disequilibrium-based binning affects the interpretation of GWASs. | Christoforou A et al. | β | 2012 | β |
| Method for evaluating multiple mediators: mediating effects of smoking and COPD on the association between the CHRNA5-A3 variant and lung cancer risk. | Wang J et al. | β | 2012 | β |
| Molecular genetic studies of complex phenotypes. | Marian AJ | β | 2012 | β |
| MyelomA Genetics International Consortium. | Morgan G et al. | β | 2012 | β |
| Performance of genotype imputations using data from the 1000 Genomes Project. | Sung YJ et al. | β | 2012 | β |
| Power and sample size calculations for SNP association studies with censored time-to-event outcomes. | Owzar K et al. | β | 2012 | β |
| Predicting signatures of "synthetic associations" and "natural associations" from empirical patterns of human genetic variation. | Chang D et al. | β | 2012 | β |
| Sample size and statistical power calculation in genetic association studies. | Hong EP et al. | β | 2012 | β |
| SNP set association analysis for familial data. | Schifano ED et al. | β | 2012 | β |
| The impact of pharmacogenetics on radiation therapy outcome in cancer patients. A focus on DNA damage response genes. | Borchiellini D et al. | β | 2012 | β |
| Toward a roadmap in global biobanking for health. | Harris JR et al. | β | 2012 | β |
| Using prior information from the medical literature in GWAS of oral cancer identifies novel susceptibility variant on chromosome 4--the AdAPT method. | Johansson M et al. | β | 2012 | β |
| Accurate estimation of heritability in genome wide studies using random effects models. | Golan D et al. | β | 2011 | β |
| A method for identifying haplotypes carrying the causative allele in positive natural selection and genome-wide association studies. | Ong RT et al. | β | 2011 | β |
| Combining genome-wide association mapping and transcriptional networks to identify novel genes controlling glucosinolates in Arabidopsis thaliana. | Chan EK et al. | β | 2011 | β |
| Controlling Population Structure in Human Genetic Association Studies with Samples of Unrelated Individuals. | Liu N et al. | β | 2011 | β |
| Cosmopolitan and ethnic-specific replication of genetic risk factors for asthma in 2 Latino populations. | Galanter JM et al. | β | 2011 | β |
| Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm. | Hoffmann TJ et al. | β | 2011 | β |
| Disease model distortion in association studies. | Vukcevic D et al. | β | 2011 | β |
| GCTA: a tool for genome-wide complex trait analysis. | Yang J et al. | β | 2011 | β |
| Genetics of lung-cancer susceptibility. | Brennan P et al. | β | 2011 | β |
| Genetics of rheumatoid arthritis: GWAS and beyond. | McAllister K et al. | β | 2011 | β |
| Genome-wide association studies of chronic kidney disease: what have we learned? | O'Seaghdha CM et al. | β | 2011 | β |
| Genomic inflation factors under polygenic inheritance. | Yang J et al. | β | 2011 | β |
| Glucocorticoid receptor gene haplotype predicts increased risk of hospital admission for depressive disorders in the Helsinki birth cohort study. | Lahti J et al. | β | 2011 | β |
| HAPGEN2: simulation of multiple disease SNPs. | Su Z et al. | β | 2011 | β |
| Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies. | Fortier I et al. | β | 2011 | β |
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| Multilocus association testing of quantitative traits based on partial least-squares analysis. | Zhang F et al. | β | 2011 | β |
| Nominal association between a polymorphism in DGKH and bipolar disorder detected in a meta-analysis of East Asian case-control samples. | Takata A et al. | β | 2011 | β |
| Practical and theoretical considerations in study design for detecting gene-gene interactions using MDR and GMDR approaches. | Chen GB et al. | β | 2011 | β |
| Practical Consideration of Genotype Imputation: Sample Size, Window Size, Reference Choice, and Untyped Rate. | Zhang B et al. | β | 2011 | β |
| Progress and promise of genome-wide association studies for human complex trait genetics. | Stranger BE et al. | β | 2011 | β |
| Rationale for an international consortium to study inherited genetic susceptibility to childhood acute lymphoblastic leukemia. | Sherborne AL et al. | β | 2011 | β |
| Realizing the promise of population biobanks: a new model for translation. | Murtagh MJ et al. | β | 2011 | β |
| Single and combined IL28B, ITPA and SLC28A3 host genetic markers modulating response to anti-hepatitis C therapy. | LΓΆtsch J et al. | β | 2011 | β |
| Technological issues and experimental design of gene association studies. | Distefano JK et al. | β | 2011 | β |
| The meta-analysis of genome-wide association studies. | Thompson JR et al. | β | 2011 | β |
| Accounting for multiple comparisons in a genome-wide association study (GWAS). | Johnson RC et al. | β | 2010 | β |
| Advances in genomic analysis of stroke: what have we learned and where are we headed? | Lanktree MB et al. | β | 2010 | β |
| A weighted false discovery rate control procedure reveals alleles at FOXA2 that influence fasting glucose levels. | Xing C et al. | β | 2010 | β |
| Bioinformatics challenges for genome-wide association studies. | Moore JH et al. | β | 2010 | β |
| DataSHIELD: resolving a conflict in contemporary bioscience--performing a pooled analysis of individual-level data without sharing the data. | Wolfson M et al. | β | 2010 | β |
| Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. | Park JH et al. | β | 2010 | β |
| Evaluation of BLID and LOC399959 as candidate genes for high myopia in the Chinese Han population. | Zhao F et al. | β | 2010 | β |
| Forward-time simulation of realistic samples for genome-wide association studies. | Peng B et al. | β | 2010 | β |
| Genomewide association studies and assessment of the risk of disease. | Manolio TA | β | 2010 | β |
| Genome-wide association studies for the identification of biomarkers in metabolic diseases. | Pattin KA et al. | β | 2010 | β |
| Genome wide screen identifies microsatellite markers associated with acute adverse effects following radiotherapy in cancer patients. | Michikawa Y et al. | β | 2010 | β |
| Genotype imputation for genome-wide association studies. | Marchini J et al. | β | 2010 | β |
| Identifying candidate causal variants via trans-population fine-mapping. | Teo YY et al. | β | 2010 | β |
| Integrative systems biology for data-driven knowledge discovery. | Greene CS et al. | β | 2010 | β |
| Leveraging genetic variability across populations for the identification of causal variants. | Zaitlen N et al. | β | 2010 | β |
| Methods: genetic epidemiology. | Benke KS et al. | β | 2010 | β |
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| Population neuroscience: why and how. | Paus T | β | 2010 | β |
| Powerful SNP-set analysis for case-control genome-wide association studies. | Wu MC et al. | β | 2010 | β |
| Search for cancer risk factors with microarray-based genome-wide association studies. | Zhang L et al. | β | 2010 | β |
| The complex genetic architecture of the metabolome. | Chan EK et al. | β | 2010 | β |
| The role of genetics in the etiology of schizophrenia. | Gejman PV et al. | β | 2010 | β |
| Understanding the evolution of defense metabolites in Arabidopsis thaliana using genome-wide association mapping. | Chan EK et al. | β | 2010 | β |
| Genome-wide association studies--a summary for the clinical gastroenterologist. | Melum E et al. | β | 2009 | β |
| Genomic dissection of population substructure of Han Chinese and its implication in association studies. | Xu S et al. | β | 2009 | β |
| Haplotype-based analysis: a summary of GAW16 Group 4 analysis. | Hauser E et al. | β | 2009 | β |
| Host genetic determinants of spontaneous hepatitis C clearance. | Rauch A et al. | β | 2009 | β |
| Role for protein-protein interaction databases in human genetics. | Pattin KA et al. | β | 2009 | β |