Use of inbred sires to exploit epistatic variance (short communication)

The difficulty in designing an optimal breeding programme arises from a conflict between improvement in genetic gain and increase in inbreeding since selection procedures which increase genetic progress are usually associated with increased rates of inbreeding. Best linear unbiased prediction (BLUP) has optimal properties regarding the expected genetic gain after one generation of selection. However, since full genetic relationships are accounted for, selected animals are likely to be more related, leading to a higher rate of inbreeding and a larger decrease in genetic variance than less accurate methods.

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Improved genomic prediction of clonal performance in sugarcane by exploiting non-additive genetic effects

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03822-1 ◽

2021 ◽

Author(s):

Seema Yadav ◽

Xianming Wei ◽

Priya Joyce ◽

Felicity Atkin ◽

Emily Deomano ◽

...

Keyword(s):

Genomic Prediction ◽

Complex Traits ◽

Genetic Variance ◽

Prediction Models ◽

Additive Genetic Variance ◽

Genetic Effects ◽

Additive Effects ◽

Genetic Progress ◽

Accurate Identification ◽

Additive Genetic Effects

AbstractKey messageNon-additive genetic effects seem to play a substantial role in the expression of complex traits in sugarcane. Including non-additive effects in genomic prediction models significantly improves the prediction accuracy of clonal performance.AbstractIn the recent decade, genetic progress has been slow in sugarcane. One reason might be that non-additive genetic effects contribute substantially to complex traits. Dense marker information provides the opportunity to exploit non-additive effects in genomic prediction. In this study, a series of genomic best linear unbiased prediction (GBLUP) models that account for additive and non-additive effects were assessed to improve the accuracy of clonal prediction. The reproducible kernel Hilbert space model, which captures non-additive genetic effects, was also tested. The models were compared using 3,006 genotyped elite clones measured for cane per hectare (TCH), commercial cane sugar (CCS), and Fibre content. Three forward prediction scenarios were considered to investigate the robustness of genomic prediction. By using a pseudo-diploid parameterization, we found significant non-additive effects that accounted for almost two-thirds of the total genetic variance for TCH. Average heterozygosity also had a major impact on TCH, indicating that directional dominance may be an important source of phenotypic variation for this trait. The extended-GBLUP model improved the prediction accuracies by at least 17% for TCH, but no improvement was observed for CCS and Fibre. Our results imply that non-additive genetic variance is important for complex traits in sugarcane, although further work is required to better understand the variance component partitioning in a highly polyploid context. Genomics-based breeding will likely benefit from exploiting non-additive genetic effects, especially in designing crossing schemes. These findings can help to improve clonal prediction, enabling a more accurate identification of variety candidates for the sugarcane industry.

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The use of Mendelian indices for balancing genetic response and inbreeding

Proceedings of the British Society of Animal Science ◽

10.1017/s0308229600030828 ◽

1996 ◽

Vol 1996 ◽

pp. 114-114

Author(s):

B Grundy ◽

Z W Luo ◽

B Villanueva ◽

J A Woolliams

Keyword(s):

Genetic Gain ◽

Genetic Variance ◽

Genetic Relationships ◽

Best Linear Unbiased Prediction ◽

Breeding Programme ◽

Genetic Response ◽

Genetic Progress ◽

Linear Unbiased Prediction ◽

Selection Procedures ◽

Best Linear Unbiased

The difficulty in designing an optimal breeding programme arises from a conflict between improvement in genetic gain and increase in inbreeding since selection procedures which increase genetic progress are usually associated with increased rates of inbreeding. Best linear unbiased prediction (BLUP) has optimal properties regarding the expected genetic gain after one generation of selection. However, since full genetic relationships are accounted for, selected animals are likely to be more related, leading to a higher rate of inbreeding and a larger decrease in genetic variance than less accurate methods.

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Characterization of Deleterious Mutations in Outcrossing Populations

Genetics ◽

10.1093/genetics/150.2.945 ◽

1998 ◽

Vol 150 (2) ◽

pp. 945-956 ◽

Cited By ~ 4

Author(s):

Hong-Wen Deng

Keyword(s):

Genetic Variation ◽

Genetic Variance ◽

Estimation Bias ◽

Deleterious Mutations ◽

Environmental Variance ◽

Higher Power ◽

Sib Mating ◽

The Mean ◽

Simulation Results

Abstract Deng and Lynch recently proposed estimating the rate and effects of deleterious genomic mutations from changes in the mean and genetic variance of fitness upon selfing/outcrossing in outcrossing/highly selfing populations. The utility of our original estimation approach is limited in outcrossing populations, since selfing may not always be feasible. Here we extend the approach to any form of inbreeding in outcrossing populations. By simulations, the statistical properties of the estimation under a common form of inbreeding (sib mating) are investigated under a range of biologically plausible situations. The efficiencies of different degrees of inbreeding and two different experimental designs of estimation are also investigated. We found that estimation using the total genetic variation in the inbred generation is generally more efficient than employing the genetic variation among the mean of inbred families, and that higher degree of inbreeding employed in experiments yields higher power for estimation. The simulation results of the magnitude and direction of estimation bias under variable or epistatic mutation effects may provide a basis for accurate inferences of deleterious mutations. Simulations accounting for environmental variance of fitness suggest that, under full-sib mating, our extension can achieve reasonably well an estimation with sample sizes of only ∼2000-3000.

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A NOTE ON KIDWELL'S CHROMOSOMAL ANALYSIS OF EGG PRODUCTION AND CHAETA NUMBER IN DROSOPHILA MELANOGASTER

Canadian Journal of Genetics and Cytology ◽

10.1139/g70-052 ◽

1970 ◽

Vol 12 (2) ◽

pp. 356-358 ◽

Cited By ~ 1

Author(s):

P. Glaser ◽

J. F. Kldwell

Keyword(s):

Drosophila Melanogaster ◽

Egg Production ◽

Genetic Variance ◽

Chromosomal Analysis ◽

Epistatic Variance

An earlier paper (Kidwell, J.F., 1969, Can. J. Genet. Cytol 11: 547-557) has described partitioning of the genetic variance of egg production and chaeta number in Drosophila melanogaster, assuming equal frequencies of all chromosomes. Kidwell's data were analyzed again, and the new analyses were based on several panmictic populations with varying frequencies for each genotype. The importances of the several portions of the genetic variance were estimated for each population; several cases are presented. In most cases the ranges were substantial, especially those of the dominance and four-factor epistatic variances. The results of the present study generally support Kidwell's previous conclusions and suggest that epistatic variance should not routinely be assumed negligible.

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Genetic progress and potential of common bean families obtained by recurrent selection

Cropp Breeding and Applied Biotechnology ◽

10.1590/1984-70332015v15n4a38 ◽

2015 ◽

Vol 15 (4) ◽

pp. 218-226 ◽

Cited By ~ 2

Author(s):

Anatércia Ferreira Alves ◽

José Ângelo Nogueira de Menezes Júnior ◽

Vanessa Maria Pereira Silva Menezes ◽

José Eustáquio de Souza Carneiro ◽

Pedro Crescêncio Souza Carneiro ◽

...

Keyword(s):

Common Bean ◽

Genetic Gain ◽

Recurrent Selection ◽

Population Cycle ◽

Base Population ◽

Genetic Progress ◽

Simultaneous Evaluation ◽

Agronomically Important Traits

Abstract The objective of this study was to estimate the genetic gain of two recurrent selection cycles in common bean breeding and identify families with the potential to generate superior lines. The base population, cycle zero (C0), was obtained by combining 20 carioca bean parents, populations with favorable phenotypes for several agronomically important traits. The parents were recombined in a circulant diallel scheme, in which each parent participated in two crosses, generating 20 populations. From these populations, families were derived and evaluated for three seasons in the generations F2:3, F2:4 and F2:5. The same procedures of recombination and evaluation in C0 were performed in cycle one (CI). The genetic gain for yield, estimated from the simultaneous evaluation of the 40 best families of each cycle, was 8.6%. Families with potential to generate superior lines to cultivar Pérola were identified, especially among the CI families.

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The incorporation of fertility indices in genetic improvement programmes

BSAP Occasional Publication ◽

10.1017/s0263967x00033711 ◽

2001 ◽

Vol 26 (1) ◽

pp. 237-249 ◽

Cited By ~ 28

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.

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Short communication: QTL mapping for ear tip-barrenness in maize

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-9325 ◽

2016 ◽

Vol 14 (3) ◽

pp. e07SC01 ◽

Cited By ~ 1

Author(s):

Junqiang Ding ◽

Jinliang Ma ◽

Jiafa Chen ◽

Tangshun Ai ◽

Zhimin Li ◽

...

Keyword(s):

Quantitative Trait ◽

Genetic Basis ◽

Mixed Model ◽

Short Communication ◽

Genotypic Variation ◽

Interval Mapping ◽

Mapping Method ◽

Additive Effects ◽

Breeding Programs ◽

Model Composite

Barren tip on corn ear is an important agronomic trait in maize, which is highly associated with grain yield. Understanding the genetic basis of tip-barrenness may help to reduce the ear tip-barrenness in breeding programs. In this study, ear tip-barrenness was evaluated in two environments in a F2:3 population, and it showed significant genotypic variation for ear tip-barrenness in both environments. Using mixed-model composite interval mapping method, three additive effects quantitative trait loci (QTL) for ear tip-barrenness were mapped on chromosomes 2, 3 and 6, respectively. They explained 16.6% of the phenotypic variation, and no significant QTL × Environment interactions and digenic interactions were detected. The results indicated that additive effect was the main genetic basis for ear tip-barrenness in maize. This is the first report of QTL mapped for ear tip-barrenness in maize.

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Beef cattle breeding in Australia with genomics: opportunities and needs

Animal Production Science ◽

10.1071/an11116 ◽

2012 ◽

Vol 52 (3) ◽

pp. 100 ◽

Cited By ~ 14

Author(s):

D. J. Johnston ◽

B. Tier ◽

H.-U. Graser

Keyword(s):

Beef Cattle ◽

Genomic Selection ◽

Genetic Gain ◽

Information Needs ◽

Genomic Information ◽

Female Reproduction ◽

Genetic Progress ◽

Cattle Breeding ◽

Phenotypic Data ◽

Genomic Predictions

Opportunities exist in beef cattle breeding to significantly increase the rates of genetic gain by increasing the accuracy of selection at earlier ages. Currently, selection of young beef bulls incorporates several economically important traits but estimated breeding values for these traits have a large range in accuracies. While there is potential to increase accuracy through increased levels of performance recording, several traits cannot be recorded on the young bull. Increasing the accuracy of these traits is where genomic selection can offer substantial improvements in current rates of genetic gain for beef. The immediate challenge for beef is to increase the genetic variation explained by the genomic predictions for those traits of high economic value that have low accuracies at the time of selection. Currently, the accuracies of genomic predictions are low in beef, compared with those in dairy cattle. This is likely to be due to the relatively low number of animals with genotypes and phenotypes that have been used in developing genomic prediction equations. Improving the accuracy of genomic predictions will require the collection of genotypes and phenotypes on many more animals, with even greater numbers needed for lowly heritable traits, such as female reproduction and other fitness traits. Further challenges exist in beef to have genomic predictions for the large number of important breeds and also for multi-breed populations. Results suggest that single-nucleotide polymorphism (SNP) chips that are denser than 50 000 SNPs in the current use will be required to achieve this goal. For genomic selection to contribute to genetic progress, the information needs to be correctly combined with traditional pedigree and performance data. Several methods have emerged for combining the two sources of data into current genetic evaluation systems; however, challenges exist for the beef industry to implement these effectively. Changes will also be needed to the structure of the breeding sector to allow optimal use of genomic information for the benefit of the industry. Genomic information will need to be cost effective and a major driver of this will be increasing the accuracy of the predictions, which requires the collection of much more phenotypic data than are currently available.

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Relative efficiency of individual selection and reference sire progeny test schemes for beef production

Australian Journal of Agricultural Research ◽

10.1071/ar9800601 ◽

1980 ◽

Vol 31 (3) ◽

pp. 601 ◽

Cited By ~ 2

Author(s):

CA Morris ◽

LP Jones ◽

IR Hopkins

Keyword(s):

Genetic Gain ◽

Progeny Test ◽

Progeny Testing ◽

Selection Intensity ◽

Individual Selection ◽

Test Results ◽

Genetic Progress ◽

Test Selection ◽

Artificial Breeding ◽

Natural Service

Individual selection on the basis of adjusted yearling weight records (policy 1) was compared with selection of proven sires based on progeny test results ('progeny test selection'). The major assumptions in the comparisons were that herd sizes were 100 recorded cows, and that each herd used four joining groups. It was assumed that 25 herds cooperated in using two reference sires in artificial breeding to link progeny test data from young bulls in natural service, thereby increasing selection intensity without the loss in accuracy normally suffered in a single multi-sired herd. In the progeny test comparisons, preselection of young bulls for progeny testing (policy 2) was also compared with random selection among young bulls for progeny testing (policy 3). This paper contains a limited number of comparisons only, in order to indicate the possible extent of selection pressure with different policies. Comparisons in terms of annual genetic progress ranked the policies in the order 2 (greatest), 1, 3, with policy 2 being better than 3 by 90-110%. The advantage of policy 2 over policy 1 was 26-38%. In all cases, using bulls first as yearlings was preferable to 2 1/4 years in terms of annual genetic gain. With individual selection, keeping bulls for 1 year compared with 2 or 3 years had little effect on annual gain, as the rise in selection intensity balanced the rise in generation interval. Inbreeding change per year was more affected, lower rates resulting from bulls being used for 1 year only. Inbreeding rates were small with progeny test selection as described here, as long as proven sons came from young bulls as well as proven sires. The effect of selection intensity under progeny test selection with preselection becomes diluted to 25% in its contribution to annual genetic change. Thus some degree of assortative mating may be useful, or wider use of proven sires relative to young sires. With preselection the break-even number of cooperating progeny test herds was low (three herds), compared with equal rates of genetic gain from individual selection.

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