scholarly journals A Fast Estimate for the Population Recombination Rate Based on Regression

Genetics ◽  
2013 ◽  
Vol 194 (2) ◽  
pp. 473-484 ◽  
Author(s):  
Kao Lin ◽  
Andreas Futschik ◽  
Haipeng Li
Keyword(s):  
Recombination Rate ◽  
Population Recombination

scholarly journals A glance at recombination hotspots in the domestic cat

2015 ◽  
Author(s):  
Hasan Alhaddad ◽  
Chi Zhang ◽  
Bruce Rannala ◽  
Leslie A Lyons
Keyword(s):  
Recombination Rate ◽  
Gc Content ◽  
Domestic Cat ◽  
Recombination Rates ◽  
Recombination Hotspots ◽  
Regional Population ◽  
Variable Population ◽  
Line Elements ◽  
Population Recombination ◽  
Region Size

Recombination has essential roles in increasing genetic variability within a population and in ensuring successful meiotic events. The objective of this study is to (i) infer the population scaled recombination rate (ρ), and (ii) identify and characterize localities of increased recombination rate for the domestic cat, Felis silvestris catus. SNPs (n = 701) were genotyped in twenty-two cats of Eastern random bred origin. The SNPs covered ten different chromosomal regions (A1, A2, B3, C2, D1, D2, D4, E2, F2, X) with an average region size of 850 Kb and an average SNP density of 70 SNPs/region. The Bayesian method in the program inferRho was used to infer regional population recombination rates and hotspots localities. The regions exhibited variable population recombination rates and four decisive recombination hotspots were identified on cat chromosome A2, D1, and E2 regions. No correlation was detected between the GC content and the locality of recombination spots. The hotspots enclosed L2 LINE elements and MIR and tRNA-Lys SINE elements in agreement with hotspots found in other mammals.

A Coalescent-Based Method for Detecting and Estimating Recombination From Gene Sequences

Genetics ◽  
2002 ◽  
Vol 160 (3) ◽  
pp. 1231-1241 ◽  
Author(s):  
Gil McVean ◽  
Philip Awadalla ◽  
Paul Fearnhead
Keyword(s):  
Recombination Rate ◽  
Evolutionary Biology ◽  
Sequence Data ◽  
Recurrent Mutation ◽  
Likelihood Method ◽  
Viral Population ◽  
Sequence Evolution ◽  
Infinite Sites Model ◽  
High Level ◽  
Population Recombination

Abstract Determining the amount of recombination in the genealogical history of a sample of genes is important to both evolutionary biology and medical population genetics. However, recurrent mutation can produce patterns of genetic diversity similar to those generated by recombination and can bias estimates of the population recombination rate. Hudson (2001) has suggested an approximate-likelihood method based on coalescent theory to estimate the population recombination rate, 4Ner, under an infinite-sites model of sequence evolution. Here we extend the method to the estimation of the recombination rate in genomes, such as those of many viruses and bacteria, where the rate of recurrent mutation is high. In addition, we develop a powerful permutation-based method for detecting recombination that is both more powerful than other permutation-based methods and robust to misspecification of the model of sequence evolution. We apply the method to sequence data from viruses, bacteria, and human mitochondrial DNA. The extremely high level of recombination detected in both HIV1 and HIV2 sequences demonstrates that recombination cannot be ignored in the analysis of viral population genetic data.

A Coalescent Estimator of the Population Recombination Rate

Genetics ◽  
1997 ◽  
Vol 145 (3) ◽  
pp. 833-846 ◽  
Author(s):  
Jody Hey ◽  
John Wakeley
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Mutation Rate ◽  
Dna Sequences ◽  
Recombination Rate ◽  
Population Sample ◽  
Population Level ◽  
Effective Population ◽  
Genome Level ◽  
Population Recombination

Population genetic models often use a population recombination parameter 4Nc, where N is the effective population size and c is the recombination rate per generation. In many ways 4Nc is comparable to 4Nu, the population mutation rate. Both combine genome level and population level processes, and together they describe the rate of production of genetic variation in a population. However, 4Nc is more difficult to estimate. For a population sample of DNA sequences, historical recombination can only be detected if polymorphisms exist, and even then most recombination events are not detectable. This paper describes an estimator of 4Nc, hereafter designated γ (gamma), that was developed using a coalescent model for a sample of four DNA sequences with recombination. The reliability of γ was assessed using multiple coalescent simulations. In general γ has low to moderate bias, and the reliability of γ is comparable, though less, than that for a widely used estimator of 4Nu. If there exists an independent estimate of the recombination rate (per generation, per base pair), γ can be used to estimate the effective population size or the neutral mutation rate.

scholarly journals Calculation of the ion–ion recombination rate coefficient via a hybrid continuum-molecular dynamics approach

2020 ◽  
Vol 152 (9) ◽  
pp. 094306 ◽  
Author(s):  
Tomoya Tamadate ◽  
Hidenori Higashi ◽  
Takafumi Seto ◽  
Christopher J. Hogan
Keyword(s):  
Molecular Dynamics ◽  
Recombination Rate ◽  
Rate Coefficient ◽  
Ion Recombination ◽  
Recombination Rate Coefficient
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