University of Michigan Center for Statistical 


Low coverage sequencing: Implications for the design of complex trait association studies.

Li Y, Sidore C, Kang HM, Boehnke M and Abecasis G

Genome Res (2011) 21:940-51

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 2X and 30X, 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 3,000 individuals at 4X depth provides similar power to deep sequencing of >2,000 individuals at 30X 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.


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