Statistical genetics

Genetic association assists in finding out if a trait associates with a genetic variation. Statistical exploration of the responsible factors helps to accomplish the objectives.
Ideally, genetic linkage is a characteristic of proximal location of genes on a chromosome to cause group inheritance. There is a less likelihood of separation of genes located together. Scientists use linkage maps to determine the position of genetic markers or genes relative to one another. Newton Morton developed a statistical test, LOD score useful in linkage analysis. LOD scores relate the likelihood of finding test data in case there is a linkage in the loci (Posthuma 175). Linkage analysis falls into two groups namely parametric and non-parametric. Parametric analysis adopts that models unfolding the maker and trait loci are well known without any error while the nonparametric analysis makes diminutive axioms regarding the trait model or simply put, it ponders all pedigree information. Linkage disequilibrium is used to describe DNA recombination. Generally, linkage concerns the physical segments of the genome that gives characteristics to a given trait.
Various statistical methodologies have been useful in detecting genetic variation and analyzing genotype data. In the recent years, scientists have come up with high quantity genotyping technologies that are cost-effective and assist in understanding the genetic basis of phenotypes of interest. The presence of many SNPs has facilitated the success of statistical genetic studies. The first step in gene mapping used to be linkage analysis (Lin &amp. Hongyu 103). SNPs that have close relationship as far as proximity is concerned can easily co-segregate as a result of linkage disequilibrium. Association mapping is based on theoretical allelic association, which has been more apparent in recent years.
The two main approaches that scientists use to map genetic loci are association and linkage analyses. Factors that are