ABSTRACT
Population genetics has been modernized by the use of high-throughput methods. DNA variation among individuals can be quantified by examining single-nucleotide polymorphisms or microsatellite DNA. F statistics measure the degree of isolation of a population. These statistics can be estimated by method of moments analysis and merged likelihood Bayesian methods. Statistical measures have four major uses in evolutionary biology: estimating migration rates, inferring demographic history, identifying genomic regions under selection, and forensic science and association mapping. Imputation, or the determination of genotypes for untyped markers, can be accomplished by analyzing SNP linkage in microarrays. Mismatch distribution analysis uses a statistical approach to compare populations by use of haplotype data. STRUCTURE analysis examines population-level data to see if there is significant substructuring in the data set. This allows the population geneticist to better characterize the genetic cross talk of subdivided populations. Principal components analysis uses dimension reduction to render complex data sets visually interpretable.
