title: Design of association studies with pooled or un-pooled next-generation sequencing data.
abstract: Most common hereditary diseases in humans are complex and multifactorial. Large-scale genome-wide association studies based on SNP genotyping have only identified a small fraction of the heritable variation of these diseases. One explanation may be that many rare variants (a minor allele frequency, MAF <5%), which are not included in the common genotyping platforms, may contribute substantially to the genetic variation of these diseases. Next-generation sequencing, which would allow the analysis of rare variants, is now becoming so cheap that it provides a viable alternative to SNP genotyping. In this paper, we present cost-effective protocols for using next-generation sequencing in association mapping studies based on pooled and un-pooled samples, and identify optimal designs with respect to total number of individuals, number of individuals per pool, and the sequencing coverage. We perform a small empirical study to evaluate the pooling variance in a realistic setting where pooling is combined with exon-capturing. To test for associations, we develop a likelihood ratio statistic that accounts for the high error rate of next-generation sequencing data. We also perform extensive simulations to determine the power and accuracy of this method. Overall, our findings suggest that with a fixed cost, sequencing many individuals at a more shallow depth with larger pool size achieves higher power than sequencing a small number of individuals in higher depth with smaller pool size, even in the presence of high error rates. Our results provide guidelines for researchers who are developing association mapping studies based on next-generation sequencing.
authors.affiliation: Department of Integrative Biology, UC Berkeley, Berkeley, California 94720, USA. suyeonkim@berkeley.edu
authors.forename: Su Yeon
authors.initials: SY
authors.lastname: Kim
authors.forename: Yingrui
authors.initials: Y
authors.lastname: Li
authors.forename: Yiran
authors.initials: Y
authors.lastname: Guo
authors.forename: Ruiqiang
authors.initials: R
authors.lastname: Li
authors.forename: Johan
authors.initials: J
authors.lastname: Holmkvist
authors.forename: Torben
authors.initials: T
authors.lastname: Hansen
authors.forename: Oluf
authors.initials: O
authors.lastname: Pedersen
authors.forename: Jun
authors.initials: J
authors.lastname: Wang
authors.forename: Rasmus
authors.initials: R
authors.lastname: Nielsen
mesh_terms: D000483:Alleles; D002874:Chromosome Mapping / Q000191:economics / Q000379:methods*; D003198:Computer Simulation; D003718:Denmark; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D005828:Genetics, Population / Q000191:economics / Q000379:methods*; D055106:Genome-Wide Association Study / Q000191:economics / Q000379:methods*; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012107:Research Design; D017422:Sequence Analysis, DNA / Q000191:economics / Q000379:methods