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

By Bogdan Pasaniuc, N. Rohland, P. McLaren, K. Garimella, Noah A. Zaitlen, Heng Li, N. Gupta, Benjamin M. Neale, Mark J. Daly, P. Sklar and 14 more. 2012

257 citations recorded by the source corpus.

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 r2 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.

Subfields: Cancer Research, Genetics, Molecular Biology

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Citations in the corpus

Fast and accurate long-range phasing in a UK Biobank cohort (2015, unknown): “As noted above, some caution is warranted in interpreting these results, as in-sample imputation of missing data distributed across SNPs generally does not arise in GWAS (except in the context of low-coverage sequencing [37–39]).”
Genome flux and stasis in a five millennium transect of European prehistory (2014, unknown): “%) for the two high-coverage genomes (Supplementary Methods).”
Estimating Individual Admixture Proportions from Next Generation Sequencing Data (2013, background): “Low depth sequencing is a much cheaper alternative which still retains most of the information in the genome (Pasaniuc et al., 2012).”
Natural variations and genome-wide association studies in crop plants. (2014, result): “The use and simulations of sequencing data on human population showed that, after effective imputations, even extremely low-coverage sequencing could increase the power of GWAS when compared with the microarray method (82).”
Rare-variant association analysis: study designs and statistical tests. (2014, background): “20,21 When sample-preparation costs are low, sequencing costs dominate.”
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data (2014, unknown): “However, new technologies using low coverage sequencing with imputation may in some cases yield several times the effective sample size of GWAS based on SNP array data, and a commensurate increase in statistical power as described in Pasaniuc et al. (2012).”