scholarly journals Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

2015 ◽  
Vol 25 (2) ◽  
pp. 268-279 ◽  
Author(s):  
Anand Bhaskar ◽  
Y.X. Rachel Wang ◽  
Yun S. Song
Keyword(s):  
Population Size ◽  
Genomic Variation ◽  
Mutation Rates ◽  
Specific Mutation ◽  
Large Sample ◽  
Variation Data

scholarly journals Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

2014 ◽  
Author(s):  
Anand Bhaskar ◽  
Y.X. Rachel Wang ◽  
Yun S. Song
Keyword(s):  
Population Size ◽  
Frequency Spectrum ◽  
Scale Up ◽  
Small Sample ◽  
Genomic Variation ◽  
Mutation Rates ◽  
Sample Sizes ◽  
Specific Mutation ◽  
Large Sample ◽  
Variation Data

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal which is difficult to pick up with small sample sizes. Lastly, we apply our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing dataset of tens of thousands of individuals assayed at a few hundred genic regions.

scholarly journals Likelihoods and Simulation Methods for a Class of Nonneutral Population Genetics Models

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 853-867 ◽  
Author(s):  
Peter Donnelly ◽  
Magnus Nordborg ◽  
Paul Joyce
Keyword(s):  
Population Genetics ◽  
Population Size ◽  
Mutant Allele ◽  
Mutation Rates ◽  
Simulation Methods ◽  
Large Populations ◽  
Selection Intensities ◽  
Infinite Alleles Model

Abstract Methods for simulating samples and sample statistics, under mutation-selection-drift equilibrium for a class of nonneutral population genetics models, and for evaluating the likelihood surface, in selection and mutation parameters, are developed and applied for observed data. The methods apply to large populations in settings in which selection is weak, in the sense that selection intensities, like mutation rates, are of the order of the inverse of the population size. General diploid selection is allowed, but the approach is currently restricted to models, such as the infinite alleles model and certain K-models, in which the type of a mutant allele does not depend on the type of its progenitor allele. The simulation methods have considerable advantages over available alternatives. No other methods currently seem practicable for approximating likelihood surfaces.

Sex-Specific Mutation Rates in Salmonid Fish

2003 ◽  
Vol 56 (4) ◽  
pp. 458-463 ◽  
Author(s):  
Hans Ellegren ◽  
Anna-Karin Fridolfsson
Keyword(s):  
Salmonid Fish ◽  
Mutation Rates ◽  
Specific Mutation

scholarly journals Choosing Mutation and Crossover Ratios for Genetic Algorithms—A Review with a New Dynamic Approach

Information ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 390 ◽  
Author(s):  
Ahmad Hassanat ◽  
Khalid Almohammadi ◽  
Esra’a Alkafaween ◽  
Eman Abunawas ◽  
Awni Hammouri ◽  
...  
Keyword(s):  
Population Size ◽  
Optimization Problems ◽  
Large Population ◽  
Small Population ◽  
Mutation Rates ◽  
Mutation Operators ◽  
Tuning Methods ◽  
Two Parameters ◽  
The Impact ◽  
Crossover And Mutation

Genetic algorithm (GA) is an artificial intelligence search method that uses the process of evolution and natural selection theory and is under the umbrella of evolutionary computing algorithm. It is an efficient tool for solving optimization problems. Integration among (GA) parameters is vital for successful (GA) search. Such parameters include mutation and crossover rates in addition to population that are important issues in (GA). However, each operator of GA has a special and different influence. The impact of these factors is influenced by their probabilities; it is difficult to predefine specific ratios for each parameter, particularly, mutation and crossover operators. This paper reviews various methods for choosing mutation and crossover ratios in GAs. Next, we define new deterministic control approaches for crossover and mutation rates, namely Dynamic Decreasing of high mutation ratio/dynamic increasing of low crossover ratio (DHM/ILC), and Dynamic Increasing of Low Mutation/Dynamic Decreasing of High Crossover (ILM/DHC). The dynamic nature of the proposed methods allows the ratios of both crossover and mutation operators to be changed linearly during the search progress, where (DHM/ILC) starts with 100% ratio for mutations, and 0% for crossovers. Both mutation and crossover ratios start to decrease and increase, respectively. By the end of the search process, the ratios will be 0% for mutations and 100% for crossovers. (ILM/DHC) worked the same but the other way around. The proposed approach was compared with two parameters tuning methods (predefined), namely fifty-fifty crossover/mutation ratios, and the most common approach that uses static ratios such as (0.03) mutation rates and (0.9) crossover rates. The experiments were conducted on ten Traveling Salesman Problems (TSP). The experiments showed the effectiveness of the proposed (DHM/ILC) when dealing with small population size, while the proposed (ILM/DHC) was found to be more effective when using large population size. In fact, both proposed dynamic methods outperformed the predefined methods compared in most cases tested.

scholarly journals Changes in life history and population size can explain the relative neutral diversity levels on X and autosomes in extant human populations

2020 ◽  
Vol 117 (33) ◽  
pp. 20063-20069
Author(s):  
Guy Amster ◽  
David A. Murphy ◽  
William R. Milligan ◽  
Guy Sella
Keyword(s):  
Life History ◽  
Population Size ◽  
Mutation Rates ◽  
Human Populations ◽  
Effective Population ◽  
Mutation Bias ◽  
Female Ratio ◽  
Generation Times ◽  
Population Sizes ◽  
Male Mutation Bias

In human populations, the relative levels of neutral diversity on the X and autosomes differ markedly from each other and from the naïve theoretical expectation of 3/4. Here we propose an explanation for these differences based on new theory about the effects of sex-specific life history and given pedigree-based estimates of the dependence of human mutation rates on sex and age. We demonstrate that life history effects, particularly longer generation times in males than in females, are expected to have had multiple effects on human X-to-autosome (X:A) diversity ratios, as a result of male-biased mutation rates, the equilibrium X:A ratio of effective population sizes, and the differential responses to changes in population size. We also show that the standard approach of using divergence between species to correct for male mutation bias results in biased estimates of X:A effective population size ratios. We obtain alternative estimates using pedigree-based estimates of the male mutation bias, which reveal that X:A ratios of effective population sizes are considerably greater than previously appreciated. Finally, we find that the joint effects of historical changes in life history and population size can explain the observed X:A diversity ratios in extant human populations. Our results suggest that ancestral human populations were highly polygynous, that non-African populations experienced a substantial reduction in polygyny and/or increase in the male-to-female ratio of generation times around the Out-of-Africa bottleneck, and that current diversity levels were affected by fairly recent changes in sex-specific life history.

Estimation of the species-specific mutation rates at the DRB1 locus in humans and chimpanzee

2006 ◽  
Vol 68 (5) ◽  
pp. 427-431 ◽  
Author(s):  
J. Ohashi ◽  
I. Naka ◽  
A. Toyoda ◽  
M. Takasu ◽  
K. Tokunaga ◽  
...  
Keyword(s):  
Mutation Rates ◽  
Specific Mutation ◽  
Drb1 Locus ◽  
Species Specific

scholarly journals Fido-SNP: the first webserver for scoring the impact of single nucleotide variants in the dog genome

2019 ◽  
Vol 47 (W1) ◽  
pp. W136-W141 ◽  
Author(s):  
Emidio Capriotti ◽  
Ludovica Montanucci ◽  
Giuseppe Profiti ◽  
Ivan Rossi ◽  
Diana Giannuzzi ◽  
...  
Keyword(s):  
Matthews Correlation Coefficient ◽  
Genomic Variation ◽  
Gradient Boosting ◽  
Binary Classifier ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Coding Regions ◽  
Variation Data ◽  
Boosting Algorithm ◽  
The Impact

Abstract As the amount of genomic variation data increases, tools that are able to score the functional impact of single nucleotide variants become more and more necessary. While there are several prediction servers available for interpreting the effects of variants in the human genome, only few have been developed for other species, and none were specifically designed for species of veterinary interest such as the dog. Here, we present Fido-SNP the first predictor able to discriminate between Pathogenic and Benign single-nucleotide variants in the dog genome. Fido-SNP is a binary classifier based on the Gradient Boosting algorithm. It is able to classify and score the impact of variants in both coding and non-coding regions based on sequence features within seconds. When validated on a previously unseen set of annotated variants from the OMIA database, Fido-SNP reaches 88% overall accuracy, 0.77 Matthews correlation coefficient and 0.91 Area Under the ROC Curve.

scholarly journals THE DYNAMICS OF FINITE HAPLOID POPULATIONS WITH OVERLAPPING GENERATIONS. II. THE DIFFUSION APPROXIMATION

Genetics ◽  
1979 ◽  
Vol 92 (1) ◽  
pp. 339-351
Author(s):  
Ted H Emigh
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Diffusion Approximation ◽  
Overlapping Generations ◽  
Mutation Rates ◽  
Reproductive Value ◽  
Selection Coefficient ◽  
Effective Population ◽  
Haploid Population

ABSTRACT The dynamics of a gene in a haploid population can be explained approximately by considering the average reproductive value of the gene. The dynamics of the average reproductive value are similar to those of a gene in a population with nonoverlapping generations with the following modifications: The effective population size, Ne, replaces N; the average mutation rates,μ* and v* replace μ and v; the average overall selection r*+(T-l)s** replaces s; and time is measured in terms of generations, T. The implications of the average selection coefficient to adaptive life histones are discussed.

scholarly journals Stochastic modeling uncovers a novel mechanism underlying the evolution of mutation rates in sexually reproducing populations

2021 ◽  
Author(s):  
Andrii I Rozhok ◽  
Niles Eldredge ◽  
James DeGregori
Keyword(s):  
Population Size ◽  
Genetic Linkage ◽  
Negative Impact ◽  
Monte Carlo Model ◽  
Mutation Rates ◽  
Biased Sampling ◽  
Phenotypic Traits ◽  
Effective Population ◽  
Individual Fitness ◽  
Selective Process

Natural selection is believed to universally work to lower mutation rates (MR) due to the negative impact of mutations on individual fitness. Mutator alleles have only been found to be co-selected by genetic linkage with adaptive alleles in prokaryotes. Sexual reproduction substantially reduces genetic linkage, allowing selection to effectively eradicate mutator alleles. The current understanding, therefore, is that in sexually reproducing populations selection always works to lower MR, limited by the effective population size that determines the overall selection efficiency. In the present paper, we apply a Monte Carlo model of a sexually reproducing population and demonstrate that selection acting on MR does not universally favor lower MR but depends on the mode of selection acting on adaptive phenotypic traits. We demonstrate a unique previously unreported co-selective process that can drive the evolution of higher MR in sexually reproducing populations. Our results show that MR evolution is significantly influenced by multigenic inheritance of both MR and adaptive traits that are under selection. Our results also show that, contrary to the generally accepted axiom, population size appears not to affect the strength of selection uniformly but likely forms an intra-population gradient that generates a "biased sampling" process that has an opposite effect on selection strength and thus modulates or even negates the effect of population size on MR evolution. Based on our results, we propose an expanded population genetics theory of the evolution of mutation rates in sexually reproducing organisms. Our results have potential implications for understanding processes underlying rapid adaptive change in speciation and related macroevolutionary patterns

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