title: Extremely low-coverage sequencing and imputation increases power for genome-wide association studies.
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.
authors.affiliation: Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA. bpasaniu@hsph.harvard.edu
authors.forename: Bogdan
authors.initials: B
authors.lastname: Pasaniuc
authors.forename: Nadin
authors.initials: N
authors.lastname: Rohland
authors.forename: Paul J
authors.initials: PJ
authors.lastname: McLaren
authors.forename: Kiran
authors.initials: K
authors.lastname: Garimella
authors.forename: Noah
authors.initials: N
authors.lastname: Zaitlen
authors.forename: Heng
authors.initials: H
authors.lastname: Li
authors.forename: Namrata
authors.initials: N
authors.lastname: Gupta
authors.forename: Benjamin M
authors.initials: BM
authors.lastname: Neale
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Daly
authors.forename: Pamela
authors.initials: P
authors.lastname: Sklar
authors.forename: Patrick F
authors.initials: PF
authors.lastname: Sullivan
authors.forename: Sarah
authors.initials: S
authors.lastname: Bergen
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Moran
authors.forename: Christina M
authors.initials: CM
authors.lastname: Hultman
authors.forename: Paul
authors.initials: P
authors.lastname: Lichtenstein
authors.forename: Patrik
authors.initials: P
authors.lastname: Magnusson
authors.forename: Shaun M
authors.initials: SM
authors.lastname: Purcell
authors.forename: David W
authors.initials: DW
authors.lastname: Haas
authors.forename: Liming
authors.initials: L
authors.lastname: Liang
authors.forename: Shamil
authors.initials: S
authors.lastname: Sunyaev
authors.forename: Nick
authors.initials: N
authors.lastname: Patterson
authors.forename: Paul I W
authors.initials: PI
authors.lastname: de Bakker
authors.forename: David
authors.initials: D
authors.lastname: Reich
authors.forename: Alkes L
authors.initials: AL
authors.lastname: Price
mesh_terms: D059472:Exome; D055106:Genome-Wide Association Study / Q000191:economics* / Q000379:methods; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000191:economics* / Q000379:methods