title: On the power to detect SNP/phenotype association in candidate quantitative trait loci genomic regions: a simulation study.
abstract: We use coalescent methods to investigate the ability of linked neutral "markers" to reveal in simulated population samples the presence of one or more single nucleotide polymorphisms that is contributing to a trait having a complex genetic basis (QTN: quantitative trait nucleotide). Realistic mutation and recombination rates in our simulations allow us to generate SNP data appropriate for analyzing human variation across short chromosomal intervals corresponding to approximately 100 kilobases. We investigate the performance of both single marker and multiple-marker (haplotype) data for several ad hoc procedures. Our results with single SNP markers indicate that (1) the density of SNP markers need not be much higher than 10% in order to achieve near-maximal detection of a QTN; (2) a higher density of markers does not improve much on the ability to localize a QTN within an interval unless the recombination rate is high. Haplotype-based tests were investigated for the case in which more than one QTN is present in the studied interval. Larger sample sizes improve both the probability of detecting the haplotype with the largest number of QTNs, as well as the ability to infer correct haplotypes from genotypic data. Testing a series of short haplotypes across a longer interval can also be beneficial. The rate of false positives (i.e., when the most significant haplotype does not contain the greatest number of QTNs in the sample) can be very high when the contribution of individual QTNs to a trait is small. The elimination of low-frequency haplotypes does not substantially reduce the probability of detecting the haplotype with the largest number of QTNs but it can reduce the rate of false positives.
authors.affiliation: Department of Ecology and Evolution, University of Chicago, 1101 East 57th Street, Chicago, IL 60637, USA.
authors.forename: Josep M
authors.initials: JM
authors.lastname: Comeron
authors.forename: Martin
authors.initials: M
authors.lastname: Kreitman
authors.forename: Francisco M
authors.initials: FM
authors.lastname: De La Vega
mesh_terms: D019295:Computational Biology; D004171:Diploidy; D014644:Genetic Variation; D005828:Genetics, Population; D006801:Humans; D008957:Models, Genetic; D009154:Mutation; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide; D011336:Probability; D040641:Quantitative Trait Loci; D011995:Recombination, Genetic