1. Natural Selection and Coalescent Theory. John Wakeley. Harvard University. INTRODUCTION. The story of population genetics begins with the publication of . John Wakeley, Harvard University; Coalescent theory provides the foundation for molecular population genetics and genomics. It is the conceptual framework. This textbook provides the foundation for molecular population genetics and genomics. It shows the conceptual framework for studies of DNA.

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Coalescent theory is a model of how gene variants sampled from a population may have originated from a common ancestor. In the simplest case, coalescent theory assumes no recombinationno natural selectionand 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. Under this model, the expected time between successive coalescence events increases almost exponentially back in time with wide variance. Variance in the model comes from both the random passing of alleles from one generation to the next, and the random occurrence of mutations in these alleles.

The mathematical theory of the coalescent was developed independently by several groups in the early s as a natural extension of classical population genetics theory and models [1] [2] [3] [4]but can be primarily attributed to John Kingman.

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. Coalescent theory can be used to make inferences about population genetic parameters, such as migration, population size and recombination.

Consider a single gene locus sampled from two haploid individuals in 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. Coalescent theory seeks to estimate the expectation of this time period and its variance.

The probability that two lineages coalesce in the immediately preceding generation is the probability that they wkely a parental DNA sequence. In a population with a constant effective population size with 2 N e copies of each locus, there are 2 N e “potential parents” in the previous generation. For sufficiently large values of N ethis distribution is well approximated theorj the continuously defined exponential distribution.

This is mathematically convenient, coakescent the standard exponential distribution has both the expected value and the standard deviation equal to 2 Tgeory e. Therefore, although the expected time to coalescence is 2 N eactual coalescence times have a wide range of variation. Note that coalescent time is the number of preceding generations where the coalescence took place and not calendar time, though an estimation of the latter can be made multiplying 2 N e with the average time between generations.

The above calculations apply equally to a diploid population of effective size N e in other words, for a non-recombining segment of DNA, each chromosome can be treated as equivalent to an independent haploid individual; in the absence of inbreeding, sister chromosomes in a single individual are no more closely related than two chromosomes randomly sampled from the population.


Coalescent theory – Wikipedia

Coalescent theory can also be used to model the amount of variation in DNA sequences expected from genetic drift and mutation. Mean heterozygosity is calculated as the probability of a mutation occurring at a given generation divided by the probability of any “event” at that generation either a mutation or a coalescence.

The probability that the event is a mutation is the probability of a mutation in either of the two lineages: Thus the mean heterozygosity is equal to. Coalescents can be visualised using dendrograms which show the relationship of branches of the population to each other. The point where two branches meet indicates a coalescent event.

The utility of coalescent theory in the mapping of disease is slowly gaining more appreciation; although the application of the theory is still in its infancy, there are a number of researchers who are actively developing algorithms for the analysis of human genetic data that utilise coalescent theory.

A considerable number of human diseases can be attributed to genetics, from simple Mendelian diseases like sickle-cell anemia and cystic fibrosisto more complicated theorg like cancers and mental illnesses. The latter are polygenic diseases, controlled by multiple genes that may occur on different chromosomes, wakelly diseases that are precipitated by a single abnormality are relatively simple to pinpoint and trace — although not so simple that this has been achieved for all diseases.

It is immensely useful in understanding these diseases and their processes to know where they are located on chromosomesand how they have been inherited through generations of a family, as can coaleescent accomplished through coalescent analysis [1]. Genetic diseases are passed from one generation to another just like other genes. While any gene may be shuffled from one chromosome to another during homologous recombinationit is unlikely that one gene alone will be shifted.

Thus, other genes that are close enough to the disease gene to be linked to it can be used to trace it [1]. Such diseases may waekly incomplete penetranceand tend to be polygeniccomplicating their study.

These traits may arise due to many small mutations, which together have a severe and deleterious effect on the health of the individual [2]. Linkage mapping methods, including Coalescent theory can be put to work on these diseases, since they use family pedigrees to figure out which markers accompany a disease, and how it is inherited. At the very least, coalesecnt method helps narrow down the portion, or portions, of the genome on which the deleterious mutations may occur.

Complications in these approaches include epistatic effects, the polygenic nature of the mutations, and environmental factors. That said, genes whose effects are additive carry a fixed risk of developing the disease, and when they exist in a disease genotype, they can be used to predict risk and map the gene [2].

Both regular the coalescent and the shattered coalescent which allows that multiple mutations may have occurred in the founding event, and that the disease may occasionally be triggered by environmental factors have been put to work in understanding disease genes [1].


Studies have been carried out correlating disease occurrence in fraternal and identical twins, and the results of these studies wwkely be used to inform coalescent modeling. Since identical twins share all of their genome, but fraternal twins only share half their genome, the difference in correlation between the identical and fraternal twins can be used to work out if a disease is heritable, and if so how strongly [2]. 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.

Population genetic influences could have a major influence on this variation: 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. Coalescent theory is a natural extension of the more classical population genetics concept of neutral evolution coaescent is an approximation to the Fisher—Wright or Wright—Fisher model for large populations.

It was discovered independently by several researchers in the s.

A large body of software exists for both simulating data sets under the coalescent process as well as inferring parameters such as population size and migration rates from genetic data. From Wikipedia, the free encyclopedia. A model for tracing the history of genetic variation.

Molecular Biology and Evolution 31 5: Coalescent simulation of coding DNA sequences with recombination, migration and demography. American Journal of Human Genetics Gene tree distributions under the coalescent process.

Annual Review of Genetics Theoretical Population Biology Oxford Surveys in Evolutionary Biology 7: Mol Biol Evol Journal of Applied Probability. New uses for new phylogenies. Nature Reviews Genetics 3: Inferring coalescent times from DNA sequence data. A map of human genome variation containing 1.

Hein, J; Schierup, M. Oxford University Press Mathematical and Conceptual Foundations. Cambridge Studies in Advanced Mathematics, Cambridge University PressCambridge, Hardy-Weinberg law Genetic linkage Identity by descent Linkage disequilibrium Fisher’s fundamental theorem Neutral theory Shifting balance theory Price equation Coefficient of inbreeding and relationship Fitness Heritability.

Coalescent Theory: An Introduction

Natural Sexual Artificial Ecological. Effects of theody on genomic variation. Genetic hitchhiking Background selection. Evolution Microevolution Evolutionary game theory Foalescent landscape Genetic genealogy Quantitative genetics. Index of evolutionary biology articles. The American Journal of Human Genetics, 70 3 Finding genes influencing susceptibility to complex diseases in the post-genome era.

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