Extremely low-coverage sequencing and imputation increases power for genome-wide association studies

Bogdan Pasaniuc, Nadin Rohland, Paul J. McLaren, Kiran Garimella, Noah Zaitlen, Heng Li, Namrata Gupta, Benjamin M. Neale, Mark J. Daly, Pamela Sklar, Patrick F. Sullivan, Sarah Bergen, Jennifer L. Moran, Christina M. Hultman, Paul Lichtenstein, Patrik Magnusson, Shaun M. Purcell, David W. Haas, Liming Liang, Shamil SunyaevNick Patterson, Paul I.W. De Bakker, David Reich, Alkes L. Price

Research output: Contribution to journalArticlepeer-review

193 Scopus citations

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.

Original languageEnglish
Pages (from-to)631-635
Number of pages5
JournalNature Genetics
Volume44
Issue number6
DOIs
StatePublished - Jun 2012

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