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Publication Detail

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

Authors: Pasaniuc, Bogdan; 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; Hultman, Christina M; Lichtenstein, Paul; Magnusson, Patrik; Purcell, Shaun M; Haas, David W; Liang, Liming; Sunyaev, Shamil; Patterson, Nick; de Bakker, Paul I W; Reich, David; Price, Alkes L

Published In Nat Genet, (2012 May 20)

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.

PubMed ID: 22610117 Exiting the NIEHS site

MeSH Terms: Exome; Genome-Wide Association Study/economics*; Genome-Wide Association Study/methods; Humans; Polymorphism, Single Nucleotide; Sequence Analysis, DNA/economics*; Sequence Analysis, DNA/methods

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