A principled approach to deriving approximate conditional sampling distributions in population genetics models with recombination

Genetics
Joshua S Paul, Yun S Song

Abstract

The multilocus conditional sampling distribution (CSD) describes the probability that an additionally sampled DNA sequence is of a certain type, given that a collection of sequences has already been observed. The CSD has a wide range of applications in both computational biology and population genomics analysis, including phasing genotype data into haplotype data, imputing missing data, estimating recombination rates, inferring local ancestry in admixed populations, and importance sampling of coalescent genealogies. Unfortunately, the true CSD under the coalescent with recombination is not known, so approximations, formulated as hidden Markov models, have been proposed in the past. These approximations have led to a number of useful statistical tools, but it is important to recognize that they were not derived from, though were certainly motivated by, principles underlying the coalescent process. The goal of this article is to develop a principled approach to derive improved CSDs directly from the underlying population genetics model. Our approach is based on the diffusion process approximation and the resulting mathematical expressions admit intuitive genealogical interpretations, which we utilize to introduce further approxim...Continue Reading

References

May 30, 2012·Bioinformatics·Joshua S Paul, Yun S Song
May 17, 2014·PLoS Genetics·Matthew D RasmussenAdam Siepel
Jan 15, 2014·Theoretical Population Biology·Ethan M Jewett, Noah A Rosenberg
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Jan 31, 2016·Molecular Ecology·Nathan K SchaeferRichard E Green
Apr 1, 2018·Molecular Ecology·Matthias SteinrückenYun S Song
Jan 7, 2014·Genetics·Yongtao Guan

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Related Concepts

Likelihood Functions
Infarction, Anterior Cerebral Artery
Recombination, Genetic
Phylogeny
Genomics
Sample Size
Diffusion
Computational Biology
Probability
Alleles

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