In the simplest case, coalescent theory assumes no recombination, no natural selection, and no gene flow or population structure, meaning that each variant is equally likely to have been passed from one generation to the next.
The model looks backward in time, merging alleles into a single ancestral copy according to a random process in coalescence events.
[5] Advances in coalescent theory include recombination, selection, overlapping generations and virtually any arbitrarily complex evolutionary or demographic model in population genetic analysis.
The model can be used to produce many theoretical genealogies, and then compare observed data to these simulations to test assumptions about the demographic history of a population.
The ancestry of this sample is traced backwards in time to the point where these two lineages coalesce in their most recent common ancestor (MRCA).
[1] Coalescent theory can also be used to model the amount of variation in DNA sequences expected from genetic drift and mutation.
It is immensely useful in understanding these diseases and their processes to know where they are located on chromosomes, and how they have been inherited through generations of a family, as can be accomplished through coalescent analysis.
[2] Polygenic diseases have a genetic basis even though they don't follow Mendelian inheritance models, and these may have relatively high occurrence in populations, and have severe health effects.
Complications in these approaches include epistatic effects, the polygenic nature of the mutations, and environmental factors.
[3] The human single-nucleotide polymorphism (SNP) map has revealed large regional variations in heterozygosity, more so than can be explained on the basis of (Poisson-distributed) random chance.
[10] In part, these variations could be explained on the basis of assessment methods, the availability of genomic sequences, and possibly the standard coalescent population genetic model.
The local density of SNPs along chromosomes appears to cluster in accordance with a variance to mean power law and to obey the Tweedie compound Poisson distribution.