scholarly journals Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

2021 ◽  
Author(s):  
Marnin D. Wolfe ◽  
Ariel W. Chan ◽  
Peter Kulakow ◽  
Ismail Rabbi ◽  
Jean--Luc Jannink
Keyword(s):  
Mate Selection ◽  
Genetic Variance ◽  
Cross Validation ◽  
Selection Index ◽  
Dominance Model ◽  
Genetic Covariance ◽  
Breeding Values ◽  
Root Crop ◽  
Genetic Variances ◽  
Truncation Selection

AbstractDiverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. We were able to predict all genetic variances and most covariances with non-zero accuracy. We also tested a directional dominance model and found significant inbreeding depression for most traits and a boost in total merit accuracy for root yield. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with excellent breeding values) and varieties (progeny with top performance).Author SummaryBreeders typically use truncation selection and invest significant resources evaluating progeny to find exceptional genotypes. We extended genetic variance and trait covariance prediction to include dominance and predicting the multivariate selection index variance. We enable mate selection based on potential to produce future parents (progeny with excellent breeding values) and/or varieties (progeny with top performance). Using cross-validation, we demonstrate that genetic variances and covariances can be predicted with non-zero accuracy in cassava, a staple tropical root crop.

scholarly journals Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

Genetics ◽  
2021 ◽  
Author(s):  
Marnin D Wolfe ◽  
Ariel W Chan ◽  
Peter Kulakow ◽  
Ismail Rabbi ◽  
Jean-Luc Jannink
Keyword(s):  
Mate Selection ◽  
Selection Criteria ◽  
Genetic Variance ◽  
Selection Index ◽  
Dominance Model ◽  
Genetic Covariance ◽  
Good Correspondence ◽  
Mean And Variance ◽  
Genetic Distribution ◽  
Selection Of

Abstract Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. Most variance (89%) and covariance (70%) prediction accuracy estimates were greater than zero. The usefulness of crosses were accurately predicted with good correspondence between the predicted and the actual mean performance of family members breeders selected for advancement as new parents and candidate varieties. We also used a directional dominance model to quantify significant inbreeding depression for most traits. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with top breeding values) and varieties (progeny with top own performance).

Controlling inbreeding in dairy MOET nucleus schemes

1995 ◽  
Vol 60 (3) ◽  
pp. 379-387 ◽  
Author(s):  
Z. W. Luo ◽  
J. A. Woolliams ◽  
R. Thompson
Keyword(s):  
Genetic Variance ◽  
Selection Index ◽  
Progeny Testing ◽  
Factorial Designs ◽  
Genetic Progress ◽  
Breeding Values ◽  
Embryo Recovery ◽  
Multiple Ovulation ◽  
Significant Difference ◽  
Breeding Schemes

AbstractA nucleus dairy population using multiple ovulation and embryo transfer (MOET) was stochastically modelled with overlapping generations. The aim was to investigate the feasibility of controlling inbreeding in MOET breeding schemes using more realistic parameters for embryo recovery and best linear unbiased prediction (BLUP) for genetic evaluation. Four different cases (involving the culling of donors, more donors and the use of organized progeny testing of nucleus bulls) were studied in combination with nested and factorial designs. Further studies involved modifications of the selection index, including subtracting parental breeding values, inflating the genetic variance in the BLUP evaluation and penalizing inbred animals; these options were examined both with and without organized progeny testing. The effects of applying these schemes on both genetic response and rate of inbreeding were investigated. The results stressed the importance of incorporating progeny testing into MOET schemes for value of reducing inbreeding whilst maintaining genetic progress. There was no significant difference between nested and factorial designs. In the absence of progeny testing the inflation of genetic variance was more effective than subtracting parental breeding values at controlling inbreeding; however incorporating progeny testing made the latter strategy more potent and the superiority of inflating the genetic variance was in this case much smaller and non-significant.

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.

Multi-trait selection index and cluster analyses in Angus cattle

2021 ◽  
pp. 1-8
Author(s):  
G. M. Fernandes ◽  
R. P. Savegnago ◽  
L. A. Freitas ◽  
L. El Faro ◽  
V. M. Roso ◽  
...  
Keyword(s):  
Production System ◽  
Selection Process ◽  
Selection Index ◽  
Genetic Selection ◽  
Genetic Profile ◽  
Breeding Values ◽  
Cluster Analyses ◽  
Scrotal Circumference ◽  
Breeding Programmes ◽  
Economic Selection

Abstract In breeding programmes, the genetic selection process is based on the prediction of animal breeding values, and its results may vary according to the employed selection method. The current study developed an economic selection index for animals of the Angus breed; performed cluster analyses using the breeding values in order to evaluate the genetic profile of the animals candidates to selection, and compared the obtained results between the economic selection index and the cluster analyses. The evaluated traits included weaning weight, 18-month weight, scrotal circumference, fat thickness and ribeye area. Economic values were obtained using bioeconomic modelling, simulating a complete cycle production system of beef cattle breeds in Brazil, and the selection objective were the weaning rate and slaughter weight. The chosen selection index was composed of all of the traits used as selection criteria for the simulated production system. During the cluster analyses, the population was divided into two to four groups, in which the groupings containing potential animals were assessed. The animals of the grouping which was used for comparison with the selection index were identified, and most of the bulls that were included in the index were among the best in the analysed group. These results suggest that the cluster analyses can be used as a tool for the selection of animals to be used as parents for future generations.

The incorporation of fertility indices in genetic improvement programmes

2001 ◽  
Vol 26 (1) ◽  
pp. 237-249 ◽  
Author(s):  
J.E. Pryce ◽  
R.F. Veerkamp
Keyword(s):  
Genetic Variance ◽  
Selection Index ◽  
Genetic Correlations ◽  
Live Weight ◽  
Parameter Estimates ◽  
Phenotypic Variance ◽  
Production Traits ◽  
Genetic Progress ◽  
Detection Rates ◽  
Days Open

AbstractIn recent years there has been considerable genetic progress in milk production. Yet, increases in yield have been accompanied by an apparent lengthening of calving intervals, days open, days to first heat and a decline in conception rates, which appears to be both at the genetic and phenotypic level. Fertility has a high relative economic value compared to production traits such as protein, making it attractive to include in a breeding programme. To do this there needs to be genetic variance in fertility. Measures of fertility calculated from service dates have a small genetic compared to phenotypic variance, hence heritability estimates are small, typically less than 5%, although coefficients of genetic variance are comparable to those of production traits. Heritabilities of commencement of luteal activity determined using progesterone profiles are generally higher, and have been reported as being from 0.16 to 0.28, which could be because of a more precise quantification of genetic variance, as management influences such as delaying insemination and heat detection rates are excluded. However, it might not be the use of progesterone profiles alone, as days to first heat observed by farm staff has a heritability of 0.15. The most efficient way to breed for improved fertility is to construct a selection index using the genetic and phenotypic parameter estimates of all traits of interest in addition to their respective economic values. Index traits for fertility could include measures such as calving interval, days open, days to first service, or days to first heat but there may also be alternative measures. Examples include traits related to energy balance, such as live weight and condition score (change), both of which have higher heritabilities than fertility measures and have genetic correlations of sufficient magnitude to make genetic progress by using them feasible. To redress the balance between fertility and production, some countries already publish genetic evaluations of fertility including: Denmark, Finland, France, Germany, Israel, The Netherlands, Norway and Sweden.

scholarly journals Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods

2019 ◽  
Author(s):  
Daniel Runcie ◽  
Hao Cheng
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Prediction Models ◽  
Selection Index ◽  
Parametric Method ◽  
Multiple Traits ◽  
Gold Standard Method ◽  
Secondary Traits ◽  
Validation Strategy

ABSTRACTIncorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful.

scholarly journals Genetic variance in fitness and its cross-sex covariance predict adaptation during experimental evolution

2020 ◽  
Author(s):  
Eva L. Koch ◽  
Sonja H. Sbilordo ◽  
Frédéric Guillaume
Keyword(s):  
Experimental Evolution ◽  
Genetic Variance ◽  
Environmental Changes ◽  
Additive Genetic Variance ◽  
Relative Fitness ◽  
Male Fitness ◽  
Adaptive Potential ◽  
Genetic Covariance ◽  
Flour Beetles ◽  
Single Stress

AbstractIn presence of rapid environmental changes, it is of particular importance to assess the adaptive potential of populations, which is mostly determined by the additive genetic variation (VA) in fitness. In this study we used Tribolium castaneum (red flour beetles) to investigate its adaptive potential in three new environmental conditions (Dry, Hot, Hot-Dry). We tested for potential constraints that might limit adaptation, including negative genetic covariance between female and male fitness. Based on VA estimates for fitness, we expected the highest relative fitness increase in the most stressful condition Hot-Dry and similar increases in single stress conditions Dry and Hot. High adaptive potential in females in Hot was reduced by a negative covariance with male fitness. We tested adaptation to the three conditions after 20 generations of experimental evolution and found that observed adaptation mainly matched our predictions. Given that body size is commonly used as a proxy for fitness, we also tested how this trait and its genetic variance (including non-additive genetic variance) were impacted by environmental stress. In both traits, variances were sex and condition dependent, but they differed in their variance composition, cross-sex and cross-environment genetic covariances, as well as in the environmental impact on VA.

Genetic and statistical analyses of strong selection on polygenic traits: what, me normal?

Genetics ◽  
1994 ◽  
Vol 138 (3) ◽  
pp. 913-941 ◽  
Author(s):  
M Turelli ◽  
N H Barton
Keyword(s):  
Allele Frequency ◽  
Genetic Variance ◽  
Gaussian Approximation ◽  
Selection Response ◽  
Disruptive Selection ◽  
Breeding Values ◽  
Central Moments ◽  
Linkage Disequilibria ◽  
The Mean ◽  
Frequency Changes

Abstract We develop a general population genetic framework for analyzing selection on many loci, and apply it to strong truncation and disruptive selection on an additive polygenic trait. We first present statistical methods for analyzing the infinitesimal model, in which offspring breeding values are normally distributed around the mean of the parents, with fixed variance. These show that the usual assumption of a Gaussian distribution of breeding values in the population gives remarkably accurate predictions for the mean and the variance, even when disruptive selection generates substantial deviations from normality. We then set out a general genetic analysis of selection and recombination. The population is represented by multilocus cumulants describing the distribution of haploid genotypes, and selection is described by the relation between mean fitness and these cumulants. We provide exact recursions in terms of generating functions for the effects of selection on non-central moments. The effects of recombination are simply calculated as a weighted sum over all the permutations produced by meiosis. Finally, the new cumulants that describe the next generation are computed from the non-central moments. Although this scheme is applied here in detail only to selection on an additive trait, it is quite general. For arbitrary epistasis and linkage, we describe a consistent infinitesimal limit in which the short-term selection response is dominated by infinitesimal allele frequency changes and linkage disequilibria. Numerical multilocus results show that the standard Gaussian approximation gives accurate predictions for the dynamics of the mean and genetic variance in this limit. Even with intense truncation selection, linkage disequilibria of order three and higher never cause much deviation from normality. Thus, the empirical deviations frequently found between predicted and observed responses to artificial selection are not caused by linkage-disequilibrium-induced departures from normality. Disruptive selection can generate substantial four-way disequilibria, and hence kurtosis; but even then, the Gaussian assumption predicts the variance accurately. In contrast to the apparent simplicity of the infinitesimal limit, data suggest that changes in genetic variance after 10 or more generations of selection are likely to be dominated by allele frequency dynamics that depend on genetic details.

The evolutionary genetics of personality: Does mutation load signal relationship load?

2006 ◽  
Vol 29 (4) ◽  
pp. 409-409 ◽  
Author(s):  
David M. Buss
Keyword(s):  
Mating Success ◽  
Mate Selection ◽  
Genetic Variance ◽  
Additive Genetic Variance ◽  
Evolutionary Genetics ◽  
Resource Acquisition ◽  
Mutation Load ◽  
Normal Personality ◽  
Difference Detection ◽  
Show Evidence

The mutation-selection hypothesis may extend to understanding normal personality variation. Traits such as emotional stability, agreeableness, and conscientiousness figure strongly in mate selection and show evidence of non-additive genetic variance. They are linked with reproductively relevant outcomes, including longevity, resource acquisition, and mating success. Evolved difference-detection adaptations may function to spurn individuals whose high mutation load signals a burdensome relationship load.

scholarly journals Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype

2015 ◽  
Vol 282 (1819) ◽  
pp. 20151119 ◽  
Author(s):  
Vincent Careau ◽  
Matthew E. Wolak ◽  
Patrick A. Carter ◽  
Theodore Garland
Keyword(s):  
Covariance Matrix ◽  
Adaptive Response ◽  
Genetic Variance ◽  
Environmental Changes ◽  
Wheel Running ◽  
Additive Genetic Variance ◽  
Phenotypic Selection ◽  
Directional Selection ◽  
Genetic Covariance ◽  
Variance Covariance Matrix

Given the pace at which human-induced environmental changes occur, a pressing challenge is to determine the speed with which selection can drive evolutionary change. A key determinant of adaptive response to multivariate phenotypic selection is the additive genetic variance–covariance matrix ( G ). Yet knowledge of G in a population experiencing new or altered selection is not sufficient to predict selection response because G itself evolves in ways that are poorly understood. We experimentally evaluated changes in G when closely related behavioural traits experience continuous directional selection. We applied the genetic covariance tensor approach to a large dataset ( n = 17 328 individuals) from a replicated, 31-generation artificial selection experiment that bred mice for voluntary wheel running on days 5 and 6 of a 6-day test. Selection on this subset of G induced proportional changes across the matrix for all 6 days of running behaviour within the first four generations. The changes in G induced by selection resulted in a fourfold slower-than-predicted rate of response to selection. Thus, selection exacerbated constraints within G and limited future adaptive response, a phenomenon that could have profound consequences for populations facing rapid environmental change.

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