scholarly journals Joint estimation of selection intensity and mutation rate under balancing selection withapplications to HLA

2021 ◽  
Author(s):  
Montgomery Slatkin
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Balancing Selection ◽  
Composite Likelihood ◽  
Joint Estimation ◽  
Likelihood Method ◽  
Mutation Rates ◽  
Symmetric Model ◽  
Effective Population ◽  
Selection Intensities

A composite likelihood method is introduced for jointly estimating the intensity of selection and the rate of mutation, both scaled by the effective population size, when there is balancing selection at a single multi-allelic locus in an isolated population at demographic equilibrium. The performance of the method is tested using simulated data. Average estimated mutation rates and selection intensities are close to the true values but there is considerable variation about the averages. Allowing for both population growth and population subdivision do not result in qualitative differences but the estimated mutation rates and selection intensities do not in general reflect the current effective population size. The method is applied to three class I (HLA-A, HLA-B and HLA-C) and two class II loci (HLA-DRB1 and HLA-DQA1) in the 1000 Genomes populations. Allowing for asymmetric balancing selection has only a slight effect on the results from the symmetric model. Mutations that restore symmetry of the selection model are preferentially retained because of the tendency of natural selection to maximize average fitness. However, slight differences in selective effects result in much longer persistence time of some alleles. Trans-species polymorphism (TSP), which is characteristic of MHC in vertebrates, is more likely when there are small differences in allelic fitness than when complete symmetry is assumed. Therefore, variation in allelic fitness expands the range of parameter values consistent with observations of TSP.

scholarly journals Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

2018 ◽  
Author(s):  
Amy Ko ◽  
Rasmus Nielsen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Population Size ◽  
Effective Population Size ◽  
Simulated Data ◽  
Composite Likelihood ◽  
Joint Estimation ◽  
Pedigree Information ◽  
Effective Population

Pedigrees provide a fine resolution of the genealogical relationships among individuals and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of short-term effective population size (Ne), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and Ne from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals with the use of composite likelihood, which significantly increases computational efficiency. We show on simulated data that our method is able to jointly estimate relationships up to first cousins and Ne with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.

scholarly journals Joint Estimation of Pedigrees and Effective Population Size Using Markov Chain Monte Carlo

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 855-868 ◽  
Author(s):  
Amy Ko ◽  
Rasmus Nielsen
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Population Size ◽  
Effective Population Size ◽  
Simulated Data ◽  
Composite Likelihood ◽  
Joint Estimation ◽  
Pedigree Information ◽  
Effective Population

Pedigrees provide the genealogical relationships among individuals at a fine resolution and serve an important function in many areas of genetic studies. One such use of pedigree information is in the estimation of the short-term effective population size (Ne), which is of great relevance in fields such as conservation genetics. Despite the usefulness of pedigrees, however, they are often an unknown parameter and must be inferred from genetic data. In this study, we present a Bayesian method to jointly estimate pedigrees and Ne from genetic markers using Markov Chain Monte Carlo. Our method supports analysis of a large number of markers and individuals within a single generation with the use of a composite likelihood, which significantly increases computational efficiency. We show, on simulated data, that our method is able to jointly estimate relationships up to first cousins and Ne with high accuracy. We also apply the method on a real dataset of house sparrows to reconstruct their previously unreported pedigree.

Estimating Effective Population Size and Migration Rates From Genetic Samples Over Space and Time

Genetics ◽  
2003 ◽  
Vol 163 (1) ◽  
pp. 429-446 ◽  
Author(s):  
Jinliang Wang ◽  
Michael C Whitlock
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Joint Estimation ◽  
Large Sample Size ◽  
Effective Population ◽  
Likelihood Methods ◽  
Marker Allele ◽  
Migration Rates ◽  
Space And Time ◽  
And Migration

Abstract In the past, moment and likelihood methods have been developed to estimate the effective population size (Ne) on the basis of the observed changes of marker allele frequencies over time, and these have been applied to a large variety of species and populations. Such methods invariably make the critical assumption of a single isolated population receiving no immigrants over the study interval. For most populations in the real world, however, migration is not negligible and can substantially bias estimates of Ne if it is not accounted for. Here we extend previous moment and maximum-likelihood methods to allow the joint estimation of Ne and migration rate (m) using genetic samples over space and time. It is shown that, compared to genetic drift acting alone, migration results in changes in allele frequency that are greater in the short term and smaller in the long term, leading to under- and overestimation of Ne, respectively, if it is ignored. Extensive simulations are run to evaluate the newly developed moment and likelihood methods, which yield generally satisfactory estimates of both Ne and m for populations with widely different effective sizes and migration rates and patterns, given a reasonably large sample size and number of markers.

scholarly journals Effective population size and tests of neutrality at cytoplasmic genes in Arabidopsis

2008 ◽  
Vol 90 (1) ◽  
pp. 119-128 ◽  
Author(s):  
STEPHEN I. WRIGHT ◽  
NARDIN NANO ◽  
JOHN PAUL FOXE ◽  
VAQAAR-UN NISA DAR
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Likelihood Estimation ◽  
Composite Likelihood ◽  
Nuclear Genes ◽  
Nucleotide Polymorphism ◽  
Effective Population ◽  
Arabidopsis Lyrata ◽  
Tests Of Neutrality ◽  
Low Levels

SummaryCytoplasmic genomes typically lack recombination, implying that genetic hitch-hiking could be a predominant force structuring nucleotide polymorphism in the chloroplast and mitochondria. We test this hypothesis by analysing nucleotide polymorphism data at 28 loci across the chloroplast and mitochondria of the outcrossing plant Arabidopsis lyrata, and compare patterns with multiple nuclear loci, and the highly selfing Arabidopsis thaliana. The maximum likelihood estimate of the ratio of effective population size at cytoplasmic relative to nuclear genes in A. lyrata does not depart from the neutral expectation of 0·5. Similarly, the ratio of effective size in A. thaliana is close to unity, the neutral expectation for a highly selfing species. The results are thus consistent with neutral organelle polymorphism in these species or with comparable effects of hitch-hiking in both cytoplasmic and nuclear genes, in contrast to the results of recent studies on gynodioecious taxa. The four-gamete test and composite likelihood estimation provide evidence for very low levels of recombination in the organelles of A. lyrata, although permutation tests do not suggest that adjacent polymorphic sites are more closely linked than more distant sites across the two genomes, suggesting that mutation hotspots or very low rates of gene conversion could explain the data.

scholarly journals ANALYZING GENE-FREQUENCY DATA WHEN THE EFFECTIVE POPULATION SIZE IS FINITE

Genetics ◽  
1980 ◽  
Vol 95 (2) ◽  
pp. 489-502
Author(s):  
Susan R Wilson
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Genetic Model ◽  
Size Structure ◽  
Balancing Selection ◽  
General Situation ◽  
Frequency Data ◽  
Effective Population ◽  
Data Set ◽  
Experimental Procedure

ABSTRACT The statistical methods used by SCHAFFER, YARDLEY and ANDERSON (1977) and by GIBSON et al. (1979) to analyze the variation in allele frequencies in two common types of experimental procedure, where the effective population size is finite, are extended to a more general situation involving a greater range of experiments. The analysis developed is more sensitive in detecting changes in allele frequency due to both fluctuating and balancing selection, as well as to directional selection. The error involved in many studies due to ignoring the effective population size structure would appear to be large. The range of hypotheses that can be considered may be increased as well. Finally, the method of determining bounds for the effective population size, when a particular genetic model is known to hold for a data set, is also outlined.

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 Background selection theory overestimates effective population size for high mutation rates

2022 ◽  
Author(s):  
Joseph D Matheson ◽  
Joanna Masel
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Deleterious Mutation ◽  
Neutral Theory ◽  
Mutation Rates ◽  
Background Selection ◽  
Effective Population ◽  
Selection Theory ◽  
Linkage Disequilibria ◽  
Genome Wide

Simple models from the neutral theory of molecular evolution are claimed to be flexible enough to incorporate the complex effects of background selection against linked deleterious mutations. Complexities are collapsed into an "effective" population size that specifies neutral genetic diversity. To achieve this, current background selection theory assumes linkage equilibrium among deleterious variants. Data do not support this assumption, nor do theoretical considerations when the genome-wide deleterious mutation is realistically high. We simulate genomes evolving under background selection, allowing the emergence of linkage disequilibria. With realistically high deleterious mutation rates, neutral diversity is much lower than predicted from previous analytical theory.

scholarly journals Estimation of Effective Population Size and Migration Rate From One- and Two-Locus Identity Measures

Genetics ◽  
2001 ◽  
Vol 157 (2) ◽  
pp. 911-925
Author(s):  
Renaud Vitalis ◽  
Denis Couvet
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Joint Estimation ◽  
Demographic Parameters ◽  
Effective Population ◽  
Mutation Bias ◽  
Infinite Allele Model ◽  
Immigration Rate ◽  
And Migration ◽  
Allele Model

Abstract Standard methods for inferring demographic parameters from genetic data are based mainly on one-locus theory. However, the association of genes at different loci (e.g., two-locus identity disequilibrium) may also contain some information about demographic parameters of populations. In this article, we define one- and two-locus parameters of population structure as functions of one- and two-locus probabilities for the identity in state of genes. Since these parameters are known functions of demographic parameters in an infinite island model, we develop moment-based estimators of effective population size and immigration rate from one- and two-locus parameters. We evaluate this method through simulation. Although variance and bias may be quite large, increasing the number of loci on which the estimates are derived improves the method. We simulate an infinite allele model and a K allele model of mutation. Bias and variance are smaller with increasing numbers of alleles per locus. This is, to our knowledge, the first attempt of a joint estimation of local effective population size and immigration rate.

scholarly journals The genetic structure of populations of orchids and its evolutionary importance: understanding processes from patterns

Lankesteriana ◽  
2016 ◽  
Vol 3 (2) ◽  
Author(s):  
Raymond L. Tremblay
Keyword(s):  
Gene Flow ◽  
Natural Selection ◽  
Genetic Structure ◽  
Population Size ◽  
Effective Population Size ◽  
Genetic Drift ◽  
Mating Systems ◽  
Mutation Rates ◽  
Effective Population ◽  
Genetic Structure Of Populations

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Evolution through either natural selection or genetic drift is dependent on variation at the genetic and mor- phological levels. Processes that influence the genetic structure of populations include mating systems, effective population size, mutation rates and gene flow among populations. </span></p></div></div></div>

scholarly journals Inferring demographic history using two-locus statistics

2017 ◽  
Author(s):  
Aaron P. Ragsdale ◽  
Ryan N. Gutenkunst
Keyword(s):  
Population Size ◽  
Effective Population Size ◽  
Demographic History ◽  
Composite Likelihood ◽  
Effective Population ◽  
Allele Frequency Spectrum ◽  
Variation Data ◽  
Population Genetic Inference ◽  
Genetic Inference ◽  
Population Demographic

AbstractPopulation demographic history may be learned from contemporary genetic variation data. Methods based on aggregating the statistics of many single loci into an allele frequency spectrum (AFS) have proven powerful, but such methods ignore potentially informative patterns of linkage disequilibrium (LD) between neighboring loci. To leverage such patterns, we developed a composite-likelihood framework for inferring demographic history from aggregated statistics of pairs of loci. Using this framework, we show that two-locus statistics are indeed more sensitive to demographic history than single-locus statistics such as the AFS. In particular, two-locus statistics escape the notorious confounding of depth and duration of a bottleneck, and they provide a means to estimate effective population size based on the recombination rather than mutation rate. We applied our approach to a Zambian population of Drosophila melanogaster. Notably, using both single– and two-locus statistics, we found substantially lower estimates of effective population size than previous works. Together, our results demonstrate the broad potential for two-locus statistics to enable powerful population genetic inference.

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