Stationary distribution of the linkage disequilibrium coefficientr2
| Autor: | Jesse Goodman, Jing Liu, Bruce S. Weir, Rachel M. Fewster, Wei Zhang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
0301 basic medicine Population Probability density function 010603 evolutionary biology 01 natural sciences Article Linkage Disequilibrium 03 medical and health sciences Quantitative Biology::Populations and Evolution Statistical physics education Alleles Ecology Evolution Behavior and Systematics Mathematics education.field_of_study Models Statistical Stationary distribution Principle of maximum entropy Sampling (statistics) Quantitative Biology::Genomics Genetics Population 030104 developmental biology Sampling distribution Genetic Loci Mutation (genetic algorithm) Probability distribution Algorithms |
| Zdroj: | Theor Popul Biol |
| ISSN: | 0040-5809 |
| Popis: | The linkage disequilibrium coefficient r(2) is a measure of statistical dependence of the alleles possessed by an individual at different genetic loci. It is widely used in association studies to search for the locations of disease-causing genes on chromosomes. Most studies to date treat r(2) as a fixed property of two loci in a finite population, and investigate the sampling distribution of estimators due to the statistical sampling of individuals from the population. Here, we instead consider the distribution of r(2) itself under a process of genetic sampling through the generations. Using a classical two-locus model for genetic drift, mutation, and recombination, we investigate the probability density function of r(2) at stationarity. This density function provides a tool for inference on evolutionary parameters such as mutation and recombination rates. We reconstruct the approximate stationary density of r(2) by calculating a finite sequence of the distribution’s moments and applying the maximum entropy principle. Our approach is based on the diffusion approximation, under which we demonstrate that for certain models in population genetics, moments of the stationary distribution can be obtained without knowing the probability distribution itself. To illustrate our approach, we show how the stationary probability density of r(2) can be used in a maximum likelihood framework to estimate mutation and recombination rates from sample data of r(2). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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