Mendelian sampling covariability of marker effects and genetic values

Bonk, Sarah; Reichelt, Manuela; Teuscher, F.; Segelke, Dierck; Reinsch, Norbert

doi:10.1186/s12711-016-0214-0

Cited by 27 publications

(59 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Predictions of the genetic variance are not only of interest for selection of crosses in plant breeding but have also been studied in animal breeding for mating decisions (Segelke et al 2014;Bonk et al 2016). We concentrated here on improving a single trait in a directional selection approach.…”

Section: Discussionmentioning

confidence: 99%

Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses

Teyssèdre²,

2017

View full text Add to dashboard Cite

A crucial step in plant breeding is the selection and combination of parents to form new crosses. Genome-based prediction guides the selection of high-performing parental lines in many crop breeding programs which ensures a high mean performance of progeny. To warrant maximum selection progress, a new cross should also provide a large progeny variance. The usefulness concept as measure of the gain that can be obtained from a specific cross accounts for variation in progeny variance. Here, it is shown that genetic gain can be considerably increased when crosses are selected based on their genomic usefulness criterion compared to selection based on mean genomic estimated breeding values. An efficient and improved method to predict the genetic variance of a cross based on Markov chain Monte Carlo samples of marker effects from a whole-genome regression model is suggested. In simulations representing selection procedures in crop breeding programs, the performance of this novel approach is compared with existing methods, like selection based on mean genomic estimated breeding values and optimal haploid values. In all cases, higher genetic gain was obtained compared with previously suggested methods. When 1% of progenies per cross were selected, the genetic gain based on the estimated usefulness criterion increased by 0.14 genetic standard deviation compared to a selection based on mean genomic estimated breeding values. Analytical derivations of the progeny genotypic variance-covariance matrix based on parental genotypes and genetic map information make simulations of progeny dispensable, and allow fast implementation in large-scale breeding programs.

show abstract

Section: Discussionmentioning

confidence: 99%

Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses

Teyssèdre²,

2017

View full text Add to dashboard Cite

show abstract

“…While the CI was evaluated in simulated genomes with various degrees of linkage disequilibrium (LD) among QTL, none of these metrics explicitly use recombination or linkage phase information. Recently, Bonk et al (2016) developed breeding value models for dairy herds that use linkage map information to identify matings that will produce progeny with large variances of breeding values. The premise of this approach was that a higher Mendelian sampling variance for a given pair of parents would lead to a higher chance to generating outstanding progeny with many desirable alleles.…”

mentioning

confidence: 99%

The Predicted Cross Value for Genetic Introgression of Multiple Alleles

et al. 2017

View full text Add to dashboard Cite

We consider the plant genetic improvement challenge of introgressing multiple alleles from a homozygous donor to a recipient. First, we frame the project as an algorithmic process that can be mathematically formulated. We then introduce a novel metric for selecting breeding parents that we refer to as the predicted cross value (PCV). Unlike estimated breeding values, which represent predictions of general combining ability, the PCV predicts specific combining ability. The PCV takes estimates of recombination frequencies as an input vector and calculates the probability that a pair of parents will produce a gamete with desirable alleles at all specified loci. We compared the PCV approach with existing estimated-breeding-value approaches in two simulation experiments, in which 7 and 20 desirable alleles were to be introgressed from a donor line into a recipient line. Results suggest that the PCV is more efficient and effective for multi-allelic trait introgression. We also discuss how operations research can be used for other crop genetic improvement projects and suggest several future research directions.

show abstract

“…Instead of coding SNP genotypes for additive effects via X j,k ∈ {1, 0, −1}, genotypes can be coded as X j,k ∈ {0, 1, 0} to account for dominance effects. The covariance between dominance effects has been worked out by Bonk et al . (2016).…”

Section: Discussionmentioning

confidence: 99%

“…The family-specific, i.e. sire-specific, covariance between SNP k and l of individual j is, according to Bonk et al . (2016) and Wittenburg et al .…”

Section: Supplemental Figuresmentioning

confidence: 99%

Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations

Wittenburg

Bonk

Doschoris

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

22Single nucleotide polymorphisms (SNPs) which capture a significant impact on 23 a trait can be identified with genome-wide association studies. High linkage disequi-24 librium (LD) among SNPs makes it difficult to identify causative variants correctly. 25Thus, often target regions instead of single SNPs are reported. Sample size has not 26 only a crucial impact on the precision of parameter estimates, it also ensures that a 27 desired level of statistical power can be reached. We study the design of experiments 28 for fine-mapping of signals of a quantitative trait locus in such a target region. 29A multi-locus model allows to identify causative variants simultaneously, to state 30 their positions more precisely and to account for existing dependencies. Based on 31 the commonly applied SNP-BLUP approach, we determine the z-score statistic for 32 locally testing non-zero SNP effects and investigate its distribution under the al-33 ternative hypothesis. This quantity employs the theoretical instead of observed 34 dependence between SNPs; it can be set up as a function of paternal and maternal 35 LD for any given population structure. 36 We simulated multiple paternal half-sib families and considered a target region of 37 1 Mbp. A bimodal distribution of estimated sample size was observed, particu-38 larly if more than two causative variants were assumed. The median of estimates 39 constituted the final proposal of optimal sample size; it was consistently less than 40 sample size estimated from single-SNP investigations which was used as a baseline 41 approach. The second mode pointed to inflated sample sizes and could be explained 42 by blocks of varying linkage phases leading to negative correlations between SNPs. 43Optimal sample size increased almost linearly with number of signals to be identified 44 but depended much stronger on the assumption on heritability. For instance, three 45 times as many samples were required if heritability was 0.1 compared to 0.3. These 46 results enable the resource-saving design of future experiments for fine-mapping of 47

show abstract

Mendelian sampling covariability of marker effects and genetic values

Cited by 27 publications

References 11 publications

Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses

Genetic Gain Increases by Applying the Usefulness Criterion with Improved Variance Prediction in Selection of Crosses

The Predicted Cross Value for Genetic Introgression of Multiple Alleles

Design of Experiments for Fine-Mapping Quantitative Trait Loci in Livestock Populations

Contact Info

Product

Resources

About