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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.
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]
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’."
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.
Genetic linkage is the tendency of DNA sequences that are close together on a chromosome to be inherited together during the meiosis phase of sexual reproduction.Two genetic markers that are physically near to each other are unlikely to be separated onto different chromatids during chromosomal crossover, and are therefore said to be more linked than markers that are far apart.
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.
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Association mapping has been most widely applied to the study of human disease, specifically in the form of a genome-wide association study (GWAS). A genome-wide association study is performed by scanning an entire genome for SNPs associated with a particular trait of interest, or in the case of human disease, with a particular disease of interest.