Extremely low-coverage sequencing and imputation increases power for genome-wide association studies
Open accessAbstract
Genome-wide association studies (GWAS) have proven to be a powerful method to identify common genetic variants contributing to susceptibility to common diseases. Here, we show that extremely low-coverage sequencing (0.1-0.5×) captures almost as much of the common (>5%) and low-frequency (1-5%) variation across the genome as SNP arrays. As an empirical demonstration, we show that genome-wide SNP genotypes can be inferred at a mean r(2) of 0.71 using off-target data (0.24× average coverage) in a whole-exome study of 909 samples. Using both simulated and real exome-sequencing data sets, we show that association statistics obtained using extremely low-coverage sequencing data attain similar P values at known associated variants as data from genotyping arrays, without an excess of false positives. Within the context of reductions in sample preparation and sequencing costs, funds invested in extremely low-coverage sequencing can yield several times the effective sample size of GWAS based on SNP array data and a commensurate increase in statistical power.
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The lack of supporting resources for diverse ancestries creates financial challenges for association studies with limited resources.
“The lack of supporting resources for diverse ancestries creates financial challenges for association studies with limited resources, e.g. raising questions about whether to genotype samples on GWAS arrays that may favor European allele frequencies versus sequence samples, and how dense of an array to choose or how deeply to sequence71,72.”
From Clinical use of current polygenic risk scores may exacerbate health disparities (2019) · cites this paper as [72]
The Discussion (page not provided) compares the costs and effective sample sizes of sequencing at different depths versus SNP-array genotyping under a fixed budget, supporting the financial study-design trade-off. It also says large reference-haplotype panels are important, but does not say that a lack of resources for diverse ancestries creates these financial challenges or that arrays favor European allele frequencies. (LLM, conf=0.85)
Experimental — accuracy not yet measuredImputation of a 1× sample of NE1 at a genotype probability threshold of 0.99 retained 78% of loci, and 99.20% of imputed genotypes matched high-coverage calls.
“Considering an imputed 1 × sample of NE1 and imposing a genotype probability threshold of 0.99, 78% of these loci remained, with diminishing return from increasing coverage in the subsample (Supplementary Fig. 5). Of the imputed genotypes, 99.20% (99.18% of heterozygotes) matched the observed high-coverage calls, validating this approach for expanding our data (Supplementary Figs 5–9).”
From Genome flux and stasis in a five millennium transect of European prehistory (2014) · cites this paper as [27]
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- Authors
- Pasaniuc, B., Rohland, N., McLaren, P. J., Garimella, K., Zaitlen, N., Li, H., Gupta, N., Neale, B. M., Daly, M. J., Sklar, P., Sullivan, P. F., Bergen, S., Moran, J. L., Hultman, C. M., Lichtenstein, P., Magnusson, P., Purcell, S. M., Haas, D. W., Liang, L., Sunyaev, S., Patterson, N., de Bakker, P. I. W., Reich, D., & Price, A. L.
- Affiliations
- Broad Institute (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of North Carolina at Chapel Hill (US)
- Karolinska Institutet (SE)
- Vanderbilt University (US)
- Corresponding author
- Reich, David
- Price, Alkes L
- ORCID
- Rohland, Nadin
- McLaren, Paul J
- Garimella, Kiran
- Zaitlen, Noah
- Li, Heng
- Gupta, Namrata
- Neale, Benjamin M
- Daly, Mark J
- Sklar, Pamela
- Sullivan, Patrick F
- Bergen, Sarah
- Moran, Jennifer L
- Lichtenstein, Paul
- Magnusson, Patrik
- Purcell, Shaun M
- Haas, David W
- Liang, Liming
- Sunyaev, Shamil
- Patterson, Nick
- Reich, David
- Price, Alkes L
- Venue
- Nature Genetics
- Published
- 2012-05-20
- Volume
- 44
- Issue
- 6
- Pages
- 631-635
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- PMC
- PMC3400344
- OpenAlex
- W1979543168
- Cited by
- 299
- References
- 30
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- FWCI
- 16.00
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- en
- Open access
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- Bill and Melinda Gates Foundation
- Harvard University
- Harvard University Center for AIDS Research (P30 AI060354, AI060354)
- National Institutes of Health (5u01ai069511-02, 5u01ai069495-05, 5u01ai069419-04, 5u01ai069465-04, 5u01ai069467-02, 2um1ai069501-15, 5u01ai069502-05, AI069501, AI 068636, AI069556, AI28568, AI069465, AI069450, AI069423, AI060354, AI68634, AI069424, AI077505, AI069484, AI069511, AI069513, AI069434, AI069452, AI069432, AI069474, AI069428, AI069471, AI069419, AI069495, AI069472, AI069502, RR024975, AI069532, AI069415, AI069477)
- U.S. Public Health Service (AL-32782)
- Center for AIDS Research, University of Washington
- Stanley Center for Psychiatric Research, Broad Institute
- National Institute of Mental Health (R01MH089025, R01MH089004, RC2MH089905, R01MH089208, R01MH095034, R01MH089482, R01MH084676)
- National Human Genome Research Institute (R01HG006399, HG006399)
- National Institute of Allergy and Infectious Diseases (U01-AI069465, AI – 68634, UM1AI069474, AI069502, UM1AI069513, UM1AI069495, U01AI069474, UM1AI069532, AI 069474, AI-069511, AI 069423, AI-069415, UM1AI069556, AI-25859, U01AI069434, U01-AI-069511, AI 069432, AI-069501, U01-AI069419, AI-069428, U01 AI-069502, AI-069434, AI 46370, U01-AI-069495, AI-069495, AI 38844, AI 069556, AI-069450, AI069472, U01AI069477, AI-069532, U01AI069423, U01AI069428, U01AI027661, AI-069477, AI-27661, U01AI034835, U01AI038844, AI 069471, UM1AI069477, U01AI068636, U01AI069452, AI 069452, U01AI069424, AI069465, UM1AI069434, AI-068636, UM1-AI069501, AI-69467, U01AI025859, AI 069513, AI 069484, U01AI069471, U01AI069450, R37AI028568, U01AI069556, U01AI046370, UM1AI069415, UM1AI069502, U01AI069532, AI077505, U01-AI069467, R01AI030914, U01AI068634, U01AI069432, U01AI069501, U01AI032782, AI-34853, UM1AI069467, UM1AI069428, U01AI069472, U01AI069513, UM1AI069450, U01AI069415, UM1AI069472, AI 069424, U01AI034853, AI069419, U01AI069484, AI34835, AI 30914, R01AI028568, UM1AI069484, UM1AI069432, UM1AI069419, UM1AI068636, UM1AI069471, UM1AI068634, UM1AI069465, UM1AI069423, R01AI077505, P30AI060354, UM1AI069424, UM1AI069511, UM1AI069452, AI 28568)
- National Institute of Diabetes and Digestive and Kidney Diseases (P30DK043351)
- National Institute of Environmental Health Sciences (T32ES007142)
- National Institute of Dental and Craniofacial Research (U01DE017018)
- National Center for Research Resources (RR024975, KL2RR024977, TL1RR024978, UL1RR024975)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- imputation, genome-wide association study, statistical power, exome sequencing, 1000 Genomes Project, SNP array, common genetic variants, false positives, sequencing cost reduction, effective sample size
- MeSH terms
- Humans, Sequence Analysis, DNA, Polymorphism, Single Nucleotide, Genome-Wide Association Study, Exome
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