ArticleNature genetics2012
Extremely low-coverage sequencing and imputation increases power for genome-wide association studies.
Article in Nature genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 169 papers, 1 of them a synthesis that pooled it.
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Who cites it
169 citing papers in PubMed, 1 synthesis or guideline pooled it.
- CMDB: the comprehensive population genome variation database of China.Nucleic acids research · 2023Pooled it
- Development and deployment of a public mid-density genotyping panel for crimson clover (Trifolium incarnatum L.) based on whole-genome re-sequencing of diverse germplasm.The plant genome · 2026Article
- Low-Coverage Whole-Genome Resequencing in Livestock and Poultry: Statistical Foundations, Applications and Future Directions.Biology · 2026Review
- Archaic ancestry inference in imputed ancient human genomes.Nature communications · 2026Article
- A blended genome and exome sequencing method captures genetic variation in an unbiased and cost-effective manner.Nature genetics · 2026Article
- Imputation in the Wild: Genome-Wide Robustness and Fine-Scale Limitations of Low-Coverage Genomes in Endangered Species.Molecular ecology resources · 2026Article
- Selphi, a tool for improving genotype imputation accuracy.Scientific reports · 2026Article
- Mining of important genetic loci and evaluation of genetic effects for growth traits in Baicheng You Chicken.Poultry science · 2026Article
- Empirical evaluation of analytic validity of polygenic scores.Genome medicine · 2026Article
- Benchmarking imputation accuracy in the presence or absence of a reference panel.Molecular biology and evolution · 2026Article
- A novel simulation tool for low-coverage whole-genome sequencing using multivariate Gaussian mixture models.BMC bioinformatics · 2026Article
- Advances in haplotype phasing and genotype imputation.Nature reviews. Genetics · 2026Review
- Machine learning improves SNP microarray performance in challenged samples.Bioinformatics advances · 2026Article
- Removing array-specific batch effects in GWAS mega-analyses by applying a two-step imputation workflow.Bioinformatics advances · 2026Article
- High-quality Population-specific Haplotype-resolved Reference Panel in the Genomic and Pangenomic Eras.Genomics, proteomics & bioinformatics · 2025Review
- Flexible read-aware genotype imputation from sequence using biobank sized reference panels.Nature communications · 2025Article
- A Multi-Approach for In Silico Detection of Chromosome Inversions in Mosquito Vectors.Microorganisms · 2025Article
- Fine mapping genetic variants affecting birth weight in sheep: a GWAS of 3007 individuals using low-coverage whole genome sequencing.Journal of animal science and biotechnology · 2025Article
- Evaluation of Low-Coverage Sequencing Strategies for Whole-Genome Imputation in Pacific AbaloneInternational journal of molecular sciences · 2025Article
- High performance imputation of structural and single nucleotide variants using low-coverage whole genome sequencing.Genetics, selection, evolution : GSE · 2025Article
109 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
24 authors.
Funding
Abstract
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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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.