title: Low-coverage sequencing: implications for design of complex trait association studies.
abstract: New sequencing technologies allow genomic variation to be surveyed in much greater detail than previously possible. While detailed analysis of a single individual typically requires deep sequencing, when many individuals are sequenced it is possible to combine shallow sequence data across individuals to generate accurate calls in shared stretches of chromosome. Here, we show that, as progressively larger numbers of individuals are sequenced, increasingly accurate genotype calls can be generated for a given sequence depth. We evaluate the implications of low-coverage sequencing for complex trait association studies. We systematically compare study designs based on genotyping of tagSNPs, sequencing of many individuals at depths ranging between 2× and 30×, and imputation of variants discovered by sequencing a subset of individuals into the remainder of the sample. We show that sequencing many individuals at low depth is an attractive strategy for studies of complex trait genetics. For example, for disease-associated variants with frequency >0.2%, sequencing 3000 individuals at 4× depth provides similar power to deep sequencing of >2000 individuals at 30× depth but requires only ~20% of the sequencing effort. We also show low-coverage sequencing can be used to build a reference panel that can drive imputation into additional samples to increase power further. We provide guidance for investigators wishing to combine results from sequenced, genotyped, and imputed samples.
authors: Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599-7264, USA.
authors: Yun
authors: Y
authors: Li
authors: Carlo
authors: C
authors: Sidore
authors: Hyun Min
authors: HM
authors: Kang
authors: Michael
authors: M
authors: Boehnke
authors: Gonçalo R
authors: GR
authors: Abecasis
mesh_terms: D019295:Computational Biology / Q000379:methods; D014644:Genetic Variation; D055106:Genome-Wide Association Study / Q000379:methods*; D016013:Likelihood Functions; D008390:Markov Chains; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D017422:Sequence Analysis, DNA / Q000379:methods*