scholarly journals Linkage disequilibrium and genetic variances under mutation-selection balance.

Genetics ◽  
1989 ◽  
Vol 121 (4) ◽  
pp. 857-860 ◽  
Author(s):  
A Hastings
Keyword(s):  
Linkage Disequilibrium ◽  
Mutation Rate ◽  
Genetic Variance ◽  
Harmonic Mean ◽  
Recombination Rates ◽  
Genetic Variances ◽  
Locus Selection

Abstract I determine the contribution of linkage disequilibrium to genetic variances using results for two loci and for induced or marginal systems. The analysis allows epistasis and dominance, but assumes that mutation is weak relative to selection. The linkage disequilibrium component of genetic variance is shown to be unimportant for unlinked loci if the gametic mutation rate divided by the harmonic mean of the pairwise recombination rates is much less than one. For tightly linked loci, linkage disequilibrium is unimportant if the gametic mutation rate divided by the (induced) per locus selection is much less than one.

scholarly journals Linkage disequilibrium and genetic variability

1974 ◽  
Vol 23 (3) ◽  
pp. 281-289 ◽  
Author(s):  
M. G. Bulmer
Keyword(s):  
Linkage Disequilibrium ◽  
Simple Model ◽  
Genetic Variability ◽  
Computer Simulations ◽  
Genetic Variance ◽  
Recombination Fraction ◽  
Harmonic Mean ◽  
Exact Results ◽  
Appreciable Change ◽  
Chromosome Maps

SUMMARYIt has been shown previously that, even in the absence of linkage, selection can cause an appreciable change in the genetic variance of a metric character due to disequilibrium; this change is temporary and is rapidly reversed when selection ceases. This result is here extended to allow for the effect of linkage, and it is shown that the change in the variance is effectively determined by the harmonic mean of the recombination fractions. The validity of the approximate general formula derived here has been checked by comparison with exact results obtained from models with five or six loci. In order to determine the likely value of the harmonic mean recombination fraction, a simple model was constructed in which it was assumed that loci are distributed at random along the chromosome maps. Results of computer simulations of this model are reported for different chromosome numbers and numbers of loci.

scholarly journals Variance of neutral genetic variances within and between populations for a quantitative character.

Genetics ◽  
1991 ◽  
Vol 129 (2) ◽  
pp. 535-553
Author(s):  
Z B Zeng ◽  
C C Cockerham
Keyword(s):  
Linkage Disequilibrium ◽  
Coefficient Of Variation ◽  
Genetic Variance ◽  
Quantitative Character ◽  
Sampling Variance ◽  
Effective Population ◽  
Identity By Descent ◽  
Multiple Allele ◽  
Genetic Variances ◽  
Parameter Variance

Abstract The variances of genetic variances within and between finite populations were systematically studied using a general multiple allele model with mutation in terms of identity by descent measures. We partitioned the genetic variances into components corresponding to genetic variances and covariances within and between loci. We also analyzed the sampling variance. Both transient and equilibrium results were derived exactly and the results can be used in diverse applications. For the genetic variance within populations, sigma 2 omega, the coefficient of variation can be very well approximated as [formula: see text] for a normal distribution of allelic effects, ignoring recurrent mutation in the absence of linkage, where m is the number of loci, N is the effective population size, theta 1(0) is the initial identity by descent measure of two genes within populations and t is the generation number. The first term is due to genic variance, the second due to linkage disequilibrium, and third due to sampling. In the short term, the variation is predominantly due to linkage disequilibrium and sampling; but in the long term it can be largely due to genic variance. At equilibrium with mutation [formula: see text] where u is the mutation rate. The genetic variance between populations is a parameter. Variance arises only among sample estimates due to finite sampling of populations and individuals. The coefficient of variation for sample gentic variance between populations, sigma 2b, can be generally approximated as [formula: see text] when the number of loci is large where S is the number of sampling populations.

scholarly journals 303 ASAS-EAAP Talk: On the estimation of genetic parameters from molecular data

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 33-33
Author(s):  
Miguel Toro Ibáñez
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Correlation ◽  
Genomic Selection ◽  
Genetic Parameters ◽  
Genetic Variance ◽  
Genetic Correlations ◽  
Molecular Data ◽  
Snp Markers ◽  
Directional Selection ◽  
Recombination Rates

Abstract We deal with several problems that arise when inferring genetic parameters at the level of quantitative trait loci (QTL) from molecular data such as SNP markers. Linkage Disequilibrium (LD) is recognized as a factor creating ambiguity in the partition of genetic variance. Here, using a simple model with three loci (one QTL and two markers), it is shown that the markers generate apparent AxA and DxD epistasis, even if only third order disequilibrium exists and the QTL is dominant. The problem of “phantom epistasis” is not alleviated by larger sample sizes (de los Campos et al., 2019). We also show that markers can give a distorted picture of the genetic correlation between traits: The genomic correlation could be greater, lower or even of opposite sign than the true genetic correlation. Therefore, speculating about genetic correlations and even about their causes (e.g., pleiotropy) using genomic data is often conjectural. Thirdly, we examine the problem of directional selection generating negative linkage disequilibrium (“Bulmer effect”) in the short term from a genomic selection perspective. It seems that the reduction in response due to the Bulmer effect is the same for genomic selection as for selection based on traditional BLUP. However, the reduction in response with genomic selection is greater than when selection is based directly on phenotypes only (Van Grevenhof et al. 2012). It is also expected that directional selection for a polygenic trait should increase recombination rate, provided there is genetic variance for recombination. It is then of relevance to ask whether recombination rates could be manipulated in order to increase selection response (Battagin et al., 2016). Finally, we consider that recombination and epistasis are closely intertwined: Epistasis generate LD and recombination break them up. Then, we should expect genomes to be modular: regions with low recombination containing functionally related genes loosely linked to other regions.

scholarly journals Selection Response in Finite Populations

Genetics ◽  
1996 ◽  
Vol 144 (4) ◽  
pp. 1961-1974 ◽  
Author(s):  
Ming Wei ◽  
Armando Caballero ◽  
William G Hill
Keyword(s):  
Linkage Disequilibrium ◽  
Inbreeding Depression ◽  
Genetic Variance ◽  
Relative Rate ◽  
Selection Response ◽  
Rate Of Change ◽  
Effective Population ◽  
Short Term ◽  
Model Account

Formulae were derived to predict genetic response under various selection schemes assuming an infinitesimal model. Account was taken of genetic drift, gametic (linkage) disequilibrium (Bulmer effect), inbreeding depression, common environmental variance, and both initial segregating variance within families (σAW02) and mutational (σM2) variance. The cumulative response to selection until generation t(CRt) can be approximated asCRt≈R0[t−β(1−σAW∞2σAW02)t24Ne]−Dt2Ne,where Ne is the effective population size, σAW∞2=NeσM2 is the genetic variance within families at the steady state (or one-half the genic variance, which is unaffected by selection), and D is the inbreeding depression per unit of inbreeding. R  0 is the selection response at generation 0 assuming preselection so that the linkage disequilibrium effect has stabilized. β is the derivative of the logarithm of the asymptotic response with respect to the logarithm of the within-family genetic variance, i.e., their relative rate of change. R  0 is the major determinant of the short term selection response, but σM2, Ne and β are also important for the long term. A selection method of high accuracy using family information gives a small Ne and will lead to a larger response in the short term and a smaller response in the long term, utilizing mutation less efficiently.

scholarly journals Modeling Linkage Disequilibrium and Identifying Recombination Hotspots Using Single-Nucleotide Polymorphism Data

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 2213-2233 ◽  
Author(s):  
Na Li ◽  
Matthew Stephens
Keyword(s):  
Linkage Disequilibrium ◽  
Recombination Rate ◽  
Population Sample ◽  
Simulated Data ◽  
Region Of Interest ◽  
Population Data ◽  
Recombination Rates ◽  
Single Nucleotide ◽  
Recombination Hotspots ◽  
Genomic Regions

AbstractWe introduce a new statistical model for patterns of linkage disequilibrium (LD) among multiple SNPs in a population sample. The model overcomes limitations of existing approaches to understanding, summarizing, and interpreting LD by (i) relating patterns of LD directly to the underlying recombination process; (ii) considering all loci simultaneously, rather than pairwise; (iii) avoiding the assumption that LD necessarily has a “block-like” structure; and (iv) being computationally tractable for huge genomic regions (up to complete chromosomes). We examine in detail one natural application of the model: estimation of underlying recombination rates from population data. Using simulation, we show that in the case where recombination is assumed constant across the region of interest, recombination rate estimates based on our model are competitive with the very best of current available methods. More importantly, we demonstrate, on real and simulated data, the potential of the model to help identify and quantify fine-scale variation in recombination rate from population data. We also outline how the model could be useful in other contexts, such as in the development of more efficient haplotype-based methods for LD mapping.

scholarly journals Mutation rate analysis via parent–progeny sequencing of the perennial peach. II. No evidence for recombination-associated mutation

2016 ◽  
Vol 283 (1841) ◽  
pp. 20161785 ◽  
Author(s):  
Long Wang ◽  
Yanchun Zhang ◽  
Chao Qin ◽  
Dacheng Tian ◽  
Sihai Yang ◽  
...  
Keyword(s):  
Mutation Rate ◽  
Recombination Rate ◽  
Mutation Rates ◽  
Recombination Rates ◽  
Rate Analysis ◽  
A Genome ◽  
Break Points ◽  
New Mutations

Mutation rates and recombination rates vary between species and between regions within a genome. What are the determinants of these forms of variation? Prior evidence has suggested that the recombination might be mutagenic with an excess of new mutations in the vicinity of recombination break points. As it is conjectured that domesticated taxa have higher recombination rates than wild ones, we expect domesticated taxa to have raised mutation rates. Here, we use parent–offspring sequencing in domesticated and wild peach to ask (i) whether recombination is mutagenic, and (ii) whether domesticated peach has a higher recombination rate than wild peach. We find no evidence that domesticated peach has an increased recombination rate, nor an increased mutation rate near recombination events. If recombination is mutagenic in this taxa, the effect is too weak to be detected by our analysis. While an absence of recombination-associated mutation might explain an absence of a recombination–heterozygozity correlation in peach, we caution against such an interpretation.

scholarly journals Inferring the Genomic Landscape of Recombination Rate Variation in European Aspen (Populus tremula)

2019 ◽  
Vol 10 (1) ◽  
pp. 299-309 ◽  
Author(s):  
Rami-Petteri Apuli ◽  
Carolina Bernhardsson ◽  
Bastian Schiffthaler ◽  
Kathryn M. Robinson ◽  
Stefan Jansson ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
Linkage Map ◽  
Recombination Rate ◽  
Gene Density ◽  
Populus Tremula ◽  
Rate Variation ◽  
Recombination Rates ◽  
Genomic Features ◽  
European Aspen ◽  
Recombination Rate Variation

The rate of meiotic recombination is one of the central factors determining genome-wide levels of linkage disequilibrium which has important consequences for the efficiency of natural selection and for the dissection of quantitative traits. Here we present a new, high-resolution linkage map for Populus tremula that we use to anchor approximately two thirds of the P. tremula draft genome assembly on to the expected 19 chromosomes, providing us with the first chromosome-scale assembly for P. tremula (Table 2). We then use this resource to estimate variation in recombination rates across the P. tremula genome and compare these results to recombination rates based on linkage disequilibrium in a large number of unrelated individuals. We also assess how variation in recombination rates is associated with a number of genomic features, such as gene density, repeat density and methylation levels. We find that recombination rates obtained from the two methods largely agree, although the LD-based method identifies a number of genomic regions with very high recombination rates that the map-based method fails to detect. Linkage map and LD-based estimates of recombination rates are positively correlated and show similar correlations with other genomic features, showing that both methods can accurately infer recombination rate variation across the genome. Recombination rates are positively correlated with gene density and negatively correlated with repeat density and methylation levels, suggesting that recombination is largely directed toward gene regions in P. tremula.

scholarly journals GCTA-GREML accounts for linkage disequilibrium when estimating genetic variance from genome-wide SNPs

2016 ◽  
Vol 113 (32) ◽  
pp. E4579-E4580 ◽  
Author(s):  
Jian Yang ◽  
S. Hong Lee ◽  
Naomi R. Wray ◽  
Michael E. Goddard ◽  
Peter M. Visscher
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Variance ◽  
Genome Wide
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