title: On the optimal design of genetic variant discovery studies.
abstract: The recent emergence of massively parallel sequencing technologies has enabled an increasing number of human genome re-sequencing studies, notable among them being the 1000 Genomes Project. The main aim of these studies is to identify the yet unknown genetic variants in a genomic region, mostly low frequency variants (frequency less than 5%). We propose here a set of statistical tools that address how to optimally design such studies in order to increase the number of genetic variants we expect to discover. Within this framework, the tradeoff between lower coverage for more individuals and higher coverage for fewer individuals can be naturally solved. The methods here are also useful for estimating the number of genetic variants missed in a discovery study performed at low coverage. We show applications to simulated data based on coalescent models and to sequence data from the ENCODE project. In particular, we show the extent to which combining data from multiple populations in a discovery study may increase the number of genetic variants identified relative to studies on single populations.
authors.affiliation: Columbia University, USA. ii2135@columbia.edu
authors.forename: Iuliana
authors.initials: I
authors.lastname: Ionita-Laza
authors.forename: Nan M
authors.initials: NM
authors.lastname: Laird
mesh_terms: D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D014644:Genetic Variation; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic
references.citation: Am J Hum Genet. 2008 Sep;83(3):311-21
references.pmid: 18691683
references.citation: Nat Rev Genet. 2010 Jan;11(1):31-46
references.pmid: 19997069
references.citation: Am J Hum Genet. 2009 Aug;85(2):142-54
references.pmid: 19679224
references.citation: Genetics. 2010 Sep;186(1):207-18
references.pmid: 20457880
references.citation: Nat Biotechnol. 2008 Oct;26(10):1135-45
references.pmid: 18846087
references.citation: PLoS Genet. 2009 Feb;5(2):e1000384
references.pmid: 19214210
references.citation: Bioinformatics. 2007 Jun 15;23(12):1565-7
references.pmid: 17459963
references.citation: Ann Eugen. 1951 Mar;15(4):323-54
references.pmid: 24540312
references.citation: Proc Natl Acad Sci U S A. 2009 Mar 31;106(13):5008-13
references.pmid: 19276111
references.citation: Nature. 2009 Oct 8;461(7265):747-53
references.pmid: 19812666