Academic journal article Genetics

Identifying the Environmental Factors That Determine the Genetic Structure of Populations

Academic journal article Genetics

Identifying the Environmental Factors That Determine the Genetic Structure of Populations

Article excerpt

ABSTRACT

The study of population genetic structure is a fundamental problem in population biology because it helps us obtain a deeper understanding of the evolutionary process. One of the issues most assiduously studied in this context is the assessment of the relative importance of environmental factors (geographic distance, language, temperature, altitude, etc.) on the genetic structure of populations. The most widely used method to address this question is the multivariate Mantel test, a nonparametric method that calculates a correlation coefficient between a dependent matrix of pairwise population genetic distances and one or more independent matrices of environmental differences. Here we present a hierarchical Bayesian method that estimates F^sub ST^ values for each local population and relates them to environmental factors using a generalized linear model. The method is demonstrated by applying it to two data sets, a data set for a population of the argan tree and a human data set comprising 51 populations distributed worldwide. We also carry out a simulation study to investigate the performance of the method and find that it can correctly identify the factors that play a role in the structuring of genetic diversity under a wide range of scenarios.

(ProQuest-CSA LLC: ... denotes formulae omitted.)

ONE of the fundamental problems in population genetics is the study of the nature of genetic differentiation that is found in real populations and, if possible, to identify the factors that are responsible for the observed spatial structuring of genetic diversity. A clear understanding of these issues is of fundamental importance for a wide range of applications that include, among others, the inference of population histories, biodiversity conservation, and the identification of disease genes and/or disease-resistant genes in humans and economically important species. There are many methods that estimate different measures of genetic differentiation among populations (EXCOFFIER 2001; ROUSSET 2001) but there is a paucity of methods that allow us to identify factors that influence genetic structuring. The most commonly used method, the multivariate Mantel test (SMOUSE et al. 1986), is based on the calculation of genetic and environmental distance measures between every pair of populations.

Genetic structuring of neutral markers is a consequence of the amount of genetic drift to which each local population has been subjected, due to its local effective size and/or due to its overall degree of geo-graphic/ ecologic isolation. Thus, it seems appropriate to base the estimation on parameters and variables that are specific to each local population. However, the study of population genetic structuring is traditionally done using global measures such as F^sub ST^ or GST, which ignore differences in the strength of genetic drift across populations. Over a decade ago, BALDING and NICHOLS (1995) proposed the use of population-specific F^sub ST^ 's in the context of a migration-drift equilibrium model. They considered biallelic markers and modeled allele frequencies using a beta distribution with expectation p and variance p(1 - p)/(1 + θ) so that F^sub ST^ = 1/(1 + θ). Such a formulation enabled them to use a likelihood-based approach to estimate population-specific F^sub ST^'s. More recently, NICHOLSON et al. (2002) used a truncated normal distribution with mean p and variance cp(1 - p) instead of a beta because they were interested in a nonequilibrium fission model where subpopulations evolve in isolation after splitting from an ancestral population in which the allele frequency is p. F^sub ST^ and c are the same in the limit as c [arrow down] 0 but differ when p is close to 0 or 1 or c is large (BALDING 2003). MARCHINI and CARDON (2002) compared the two formulations by fitting them to two human data sets and concluded that although NICHOLSON et al. parameterization fitted better the European data set, both formulations performed equally well with the global data set. …

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