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In genetics, the coefficient of coincidence (c.o.c.) is a measure of interference in the formation of chromosomal crossovers during meiosis. It is generally the case that, if there is a crossover at one spot on a chromosome, this decreases the likelihood of a crossover in a nearby spot. [1] This is called interference.
Crossover interference is the term used to refer to the non-random placement of crossovers with respect to each other during meiosis.The term is attributed to Hermann Joseph Muller, who observed that one crossover "interferes with the coincident occurrence of another crossing over in the same pair of chromosomes, and I have accordingly termed this phenomenon ‘interference’."
The Kosambi mapping function was introduced to account for the impact played by crossover interference on recombination frequency. It introduces a parameter C, representing the coefficient of coincidence, and sets it equal to 2r. For loci which are strongly linked, interference is strong; otherwise, interference decreases towards zero. [5]
There are two distinctive mapping approaches used in the field of genome mapping: genetic maps (also known as linkage maps) [7] and physical maps. [3] While both maps are a collection of genetic markers and gene loci, [8] genetic maps' distances are based on the genetic linkage information, while physical maps use actual physical distances usually measured in number of base pairs.
Forward genetics (or a forward genetic screen) starts with a phenotype and then attempts to identify the causative mutation and thus gene(s) responsible for the phenotype. For instance, the famous screen by Christiane Nüsslein-Volhard and Eric Wieschaus mutagenized fruit flies and then set out to find the genes causing the observed mutant ...
In population genetics, the Hill–Robertson effect, or Hill–Robertson interference, is a phenomenon first identified by Bill Hill and Alan Robertson in 1966. [1] It provides an explanation as to why there may be an evolutionary advantage to genetic recombination .
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Genetic correlations have applications in validation of genome-wide association study (GWAS) results, breeding, prediction of traits, and discovering the etiology of traits & diseases. They can be estimated using individual-level data from twin studies and molecular genetics, or even with GWAS summary statistics.