title: The limits of fine-scale mapping.
abstract: When a novel genetic trait arises in a population, it introduces a signal in the haplotype distribution of that population. Through recombination that signal's history becomes differentiated from the DNA distant to it, but remains similar to the DNA close by. Fine-scale mapping techniques rely on this differentiation to pinpoint trait loci. In this study, we analyzed the differentiation itself to better understand how much information is available to these techniques. Simulated alleles on known recombinant coalescent trees show the upper limit for fine-scale mapping. Varying characteristics of the population being studied increase or decrease this limit. The initial uncertainty in map position has the most direct influence on the final precision of the estimate, with wider initial areas resulting in wider final estimates, though the increase is sigmoidal rather than linear. The Theta of the trait (4Nmu) is also important, with lower values for Theta resulting in greater precision of trait placement up to a point--the increase is sigmoidal as Theta decreases. Collecting data from more individuals can increase precision, though only logarithmically with the total number of individuals, so that each added individual contributes less to the final precision. However, a case/control analysis has the potential to greatly increase the effective number of individuals, as the bulk of the information lies in the differential between affected and unaffected genotypes. If haplotypes are unknown due to incomplete penetrance, much information is lost, with more information lost the less indicative phenotype is of the underlying genotype.
authors.affiliation: Department of Genome Sciences, University of Washington, Seattle, WA 98195-5065, USA. lpsmith@u.washington.edu
authors.forename: Lucian P
authors.initials: LP
authors.lastname: Smith
authors.forename: Mary K
authors.initials: MK
authors.lastname: Kuhner
mesh_terms: D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D005828:Genetics, Population; D006801:Humans; D016013:Likelihood Functions; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D009154:Mutation; D019683:Penetrance; D020641:Polymorphism, Single Nucleotide; D011156:Population Density; D040641:Quantitative Trait Loci; D011995:Recombination, Genetic; D018401:Sample Size; D012984:Software