Increasing long-term response to selection

Selection on estimated breeding value (EBV) alone maximises response to selection observed in the next generation, but repeated use of this selection criterion does not necessarily result in a maximum response over a longer time horizon. Selection decisions made in the current generation have at least 2 consequences. Firstly, they influence the immediate genetic response to selection and, secondly, they influence the inbreeding of the next and subsequent generations. Accumulation of inbreeding has a negative impact on future genetic response through reduction in future genetic variance and a negative impact on future performance if inbreeding depression affects the selected trait. Optimum selection decisions depend on the time horizon of interest. If this is known, then a breeding objective can be defined. A selection criterion is proposed in which the positive contributions of a selected group of parents to immediate genetic response (determined by their average EBV) is balanced against their negative contribution to future genetic response (determined by their contribution to inbreeding). The value assigned to the contribution to inbreeding is derived from the breeding objective. Selection of related individuals will be restricted if the detrimental value associated with inbreeding is high; restrictions on the selection of sibs, however, is flexible from family to family depending on their genetic merit. A selection algorithm is proposed which uses the selection criterion to select sires on 3 selection strategies, to select on i) a fixed number of sires; ii) a variable number of sires each allocated an equal number of matings; or iii) a variable number of sires allocated an optimal proportion of matings. Using stochastic simulation, these selection strategies for sires are compared with selection on EBV alone. When compared at the time horizon specified by the selection goal, the proposed selection criterion is successful in ensuring a higher response to selection at a lower level of inbreeding despite the selection of fewer sires. The selection strategy iii) exploits random year-to-year variations in the availability of individuals for selection and is successful in maximising response to the selection goal. The derivation of the value assigned to inbreeding is not exact and cannot guarantee that the overall maximum response is found. However, simulation results suggest that the response is robust to the detrimental value assigned to inbreeding

The use of marker haplotypes in animal breeding schemes

International audienc

A Sustainability Framework for Engineering Carbon Capture Soil In Transport Infrastructure

Recent research has demonstrated considerable potential for artificial soils to be designed for carbon capture. The incorporation of quarry fines enables the accumulation of atmospheric CO2 in newly formed carbonate minerals. However, the rate and trajectory of carbon accumulation has been little studied. The relative contribution of biotic (e.g. vegetation, micro-organisms) and abiotic (water, light, temperature) factors to the carbonation process is also unknown. This article presents a sustainability framework which aims to determine the multi-functionality of soils to which fines have been added not only in their role as carbon sinks but also in their role of providing additional opportunities for improvement to ecosystem services. Such frameworks are required specifically where land designed for CO2 capture must also provide other ecosystem services, such as flood mitigation and biodiversity conservation. land within linear transport infrastructure provides a case study, focusing on 238,000 ha of vegetated land associated with roadside verges in the UK. Hypothetically this area could remove 2.5 t CO2 per year from the atmosphere, equivalent to 1% 2011 total UK emissions or 2% of current transport emissions and saving an equivalent of £1.1 billion in non-traded mitigation values. roadside verges should be designed to minimize flooding onto the highway and perform other important functions such as removal of dust and suspended solids from surface waters. Vegetation on 30,000 ha of railway land also provides opportunities for carbon sequestration, but management of this vegetation is subject to similar constraints to protect the rail tracks from debris extending from autumn leaves to fallen trees

Accuracy of breeding values of 'unrelated' individuals predicted by dense SNP genotyping

<p>Abstract</p> <p>Background</p> <p>Recent developments in SNP discovery and high throughput genotyping technology have made the use of high-density SNP markers to predict breeding values feasible. This involves estimation of the SNP effects in a training data set, and use of these estimates to evaluate the breeding values of other 'evaluation' individuals. Simulation studies have shown that these predictions of breeding values can be accurate, when training and evaluation individuals are (closely) related. However, many general applications of genomic selection require the prediction of breeding values of 'unrelated' individuals, i.e. individuals from the same population, but not particularly closely related to the training individuals.</p> <p>Methods</p> <p>Accuracy of selection was investigated by computer simulation of small populations. Using scaling arguments, the results were extended to different populations, training data sets and genome sizes, and different trait heritabilities.</p> <p>Results</p> <p>Prediction of breeding values of unrelated individuals required a substantially higher marker density and number of training records than when prediction individuals were offspring of training individuals. However, when the number of records was 2*N_e*L and the number of markers was 10*N_e*L, the breeding values of unrelated individuals could be predicted with accuracies of 0.88 – 0.93, where N_eis the effective population size and L the genome size in Morgan. Reducing this requirement to 1*N_e*L individuals, reduced prediction accuracies to 0.73–0.83.</p> <p>Conclusion</p> <p>For livestock populations, 1N_eL requires about ~30,000 training records, but this may be reduced if training and evaluation animals are related. A prediction equation is presented, that predicts accuracy when training and evaluation individuals are related. For humans, 1N_eL requires ~350,000 individuals, which means that human disease risk prediction is possible only for diseases that are determined by a limited number of genes. Otherwise, genotyping and phenotypic recording need to become very common in the future.</p

Genetic prediction of complex traits: integrating infinitesimal and marked genetic effects

Genetic prediction for complex traits is usually based on models including individual (infinitesimal) or marker effects. Here, we concentrate on models including both the individual and the marker effects. In particular, we develop a ''Mendelian segregation'' model combining infinitesimal effects for base individuals and realized Mendelian sampling in descendants described by the available DNA data. The model is illustrated with an example and the analyses of a public simulated data file. Further, the potential contribution of such models is assessed by simulation. Accuracy, measured as the correlation between true (simulated) and predicted genetic values, was similar for all models compared under different genetic backgrounds. As expected, the segregation model is worthwhile when markers capture a low fraction of total genetic variance. (Résumé d'auteur

The complete linkage disequilibrium test: a test that points to causative mutations underlying quantitative traits

<p>Abstract</p> <p>Background</p> <p>Genetically, SNP that are in complete linkage disequilibrium with the causative SNP cannot be distinguished from the causative SNP. The Complete Linkage Disequilibrium (CLD) test presented here tests whether a SNP is in complete LD with the causative mutation or not. The performance of the CLD test is evaluated in 1000 simulated datasets.</p> <p>Methods</p> <p>The CLD test consists of two steps i.e. analysis I and analysis II. Analysis I consists of an association analysis of the investigated region. The log-likelihood values from analysis I are next ranked in descending order and in analysis II the CLD test evaluates differences in log-likelihood ratios between the best and second best markers. Under the null-hypothesis distribution, the best SNP is in greater LD with the QTL than the second best, while under the alternative-CLD-hypothesis, the best SNP is alike-in-state with the QTL. To find a significance threshold, the test was also performed on data excluding the causative SNP. The 5^th, 10^thand 50^thhighest T_CLDvalue from 1000 replicated analyses were used to control the type-I-error rate of the test at p = 0.005, p = 0.01 and p = 0.05, respectively.</p> <p>Results</p> <p>In a situation where the QTL explained 48% of the phenotypic variance analysis I detected a QTL in 994 replicates (p = 0.001), where 972 were positioned in the correct QTL position. When the causative SNP was excluded from the analysis, 714 replicates detected evidence of a QTL (p = 0.001). In analysis II, the CLD test confirmed 280 causative SNP from 1000 simulations (p = 0.05), i.e. power was 28%. When the effect of the QTL was reduced by doubling the error variance, the power of the test reduced relatively little to 23%. When sequence data were used, the power of the test reduced to 16%. All SNP that were confirmed by the CLD test were positioned in the correct QTL position.</p> <p>Conclusions</p> <p>The CLD test can provide evidence for a causative SNP, but its power may be low in situations with closely linked markers. In such situations, also functional evidence will be needed to definitely conclude whether the SNP is causative or not.</p

Best Linear Unbiased Prediction of Genomic Breeding Values Using a Trait-Specific Marker-Derived Relationship Matrix

With the availability of high density whole-genome single nucleotide polymorphism chips, genomic selection has become a promising method to estimate genetic merit with potentially high accuracy for animal, plant and aquaculture species of economic importance. With markers covering the entire genome, genetic merit of genotyped individuals can be predicted directly within the framework of mixed model equations, by using a matrix of relationships among individuals that is derived from the markers. Here we extend that approach by deriving a marker-based relationship matrix specifically for the trait of interest

The importance of identity-by-state information for the accuracy of genomic selection

<p>Abstract</p> <p>Background</p> <p>It is commonly assumed that prediction of genome-wide breeding values in genomic selection is achieved by capitalizing on linkage disequilibrium between markers and QTL but also on genetic relationships. Here, we investigated the reliability of predicting genome-wide breeding values based on population-wide linkage disequilibrium information, based on identity-by-descent relationships within the known pedigree, and to what extent linkage disequilibrium information improves predictions based on identity-by-descent genomic relationship information.</p> <p>Methods</p> <p>The study was performed on milk, fat, and protein yield, using genotype data on 35 706 SNP and deregressed proofs of 1086 Italian Brown Swiss bulls. Genome-wide breeding values were predicted using a genomic identity-by-state relationship matrix and a genomic identity-by-descent relationship matrix (averaged over all marker loci). The identity-by-descent matrix was calculated by linkage analysis using one to five generations of pedigree data.</p> <p>Results</p> <p>We showed that genome-wide breeding values prediction based only on identity-by-descent genomic relationships within the known pedigree was as or more reliable than that based on identity-by-state, which implicitly also accounts for genomic relationships that occurred before the known pedigree. Furthermore, combining the two matrices did not improve the prediction compared to using identity-by-descent alone. Including different numbers of generations in the pedigree showed that most of the information in genome-wide breeding values prediction comes from animals with known common ancestors less than four generations back in the pedigree.</p> <p>Conclusions</p> <p>Our results show that, in pedigreed breeding populations, the accuracy of genome-wide breeding values obtained by identity-by-descent relationships was not improved by identity-by-state information. Although, in principle, genomic selection based on identity-by-state does not require pedigree data, it does use the available pedigree structure. Our findings may explain why the prediction equations derived for one breed may not predict accurate genome-wide breeding values when applied to other breeds, since family structures differ among breeds.</p