Preservation of Genetic Variation in a Breeding Population for Long-Term Genetic Gain

Genetic analysis of a Kaijiang duck conservation population through genome-wide scan

1. The Kaijiang duck is a native Chinese breed known for its excellent egg laying performance, killing-out percentage (88.57%), and disease resistance. The assessment of population genetic structure is the basis for understanding the genetics of indigenous breeds and for their protection and management.2. In this study, whole-genome sequencing was performed on 60 Kaijiang ducks to identify genetic variations and investigate the population structure. Homozygosity (ROH) analysis was conducted to assess inbreeding levels in the population.3. The study revealed a moderate level of inbreeding, indicated by an average inbreeding coefficient of 0.1043. This may impact the overall genetic diversity.4. Genomic Regions of Interest identified included 168 genomic regions exhibiting high levels of autozygosity. These regions were associated with processes including muscle growth, pigmentation, neuromodulation, and growth and reproduction.5. The significance of these pathways indicated their potential role in shaping the desirable traits of the Kaijiang duck. These findings provide insights into the genetic basis of the Kaijiang duck's desirable traits and can inform future breeding and conservation efforts.

Oracle selection provides insight into how far off practice is from Utopia in plant breeding

Since the introduction of genomic selection in plant breeding, high genetic gains have been realized in different plant breeding programs. Various methods based on genomic estimated breeding values (GEBVs) for selecting parental lines that maximize the genetic gain as well as methods for improving the predictive performance of genomic selection have been proposed. Unfortunately, it remains difficult to measure to what extent these methods really maximize long-term genetic values. In this study, we propose oracle selection, a hypothetical frame of mind that uses the ground truth to optimally select parents or optimize the training population in order to maximize the genetic gain in each breeding cycle. Clearly, oracle selection cannot be applied in a true breeding program, but allows for the assessment of existing parental selection and training population update methods and the evaluation of how far these methods are from the optimal utopian solution.

Assessment of long-term trends in genetic mean and variance after the introduction of genomic selection in layers: a simulation study

Nucleus-based breeding programs are characterized by intense selection that results in high genetic gain, which inevitably means reduction of genetic variation in the breeding population. Therefore, genetic variation in such breeding systems is typically managed systematically, for example, by avoiding mating the closest relatives to limit progeny inbreeding. However, intense selection requires maximum effort to make such breeding programs sustainable in the long-term. The objective of this study was to use simulation to evaluate the long-term impact of genomic selection on genetic mean and variance in an intense layer chicken breeding program. We developed a large-scale stochastic simulation of an intense layer chicken breeding program to compare conventional truncation selection to genomic truncation selection optimized with either minimization of progeny inbreeding or full-scale optimal contribution selection. We compared the programs in terms of genetic mean, genic variance, conversion efficiency, rate of inbreeding, effective population size, and accuracy of selection. Our results confirmed that genomic truncation selection has immediate benefits compared to conventional truncation selection in all specified metrics. A simple minimization of progeny inbreeding after genomic truncation selection did not provide any significant improvements. Optimal contribution selection was successful in having better conversion efficiency and effective population size compared to genomic truncation selection, but it must be fine-tuned for balance between loss of genetic variance and genetic gain. In our simulation, we measured this balance using trigonometric penalty degrees between truncation selection and a balanced solution and concluded that the best results were between 45° and 65°. This balance is specific to the breeding program and depends on how much immediate genetic gain a breeding program may risk vs. save for the future. Furthermore, our results show that the persistence of accuracy is better with optimal contribution selection compared to truncation selection. In general, our results show that optimal contribution selection can ensure long-term success in intensive breeding programs using genomic selection.

Weighted kernels improve multi-environment genomic prediction

Crucial to variety improvement programs is the reliable and accurate prediction of genotype's performance across environments. However, due to the impactful presence of genotype by environment (G×E) interaction that dictates how changes in expression and function of genes influence target traits in different environments, prediction performance of genomic selection (GS) using single-environment models often falls short. Furthermore, despite the successes of genome-wide association studies (GWAS), the genetic insights derived from genome-to-phenome mapping have not yet been incorporated in predictive analytics, making GS models that use Gaussian kernel primarily an estimator of genomic similarity, instead of the underlying genetics characteristics of the populations. Here, we developed a GS framework that, in addition to capturing the overall genomic relationship, can capitalize on the signal of genetic associations of the phenotypic variation as well as the genetic characteristics of the populations. The capacity of predicting the performance of populations across environments was demonstrated by an overall gain in predictability up to 31% for the winter wheat DH population. Compared to Gaussian kernels, we showed that our multi-environment weighted kernels could better leverage the significance of genetic associations and yielded a marked improvement of 4-33% in prediction accuracy for half-sib families. Furthermore, the flexibility incorporated in our Bayesian implementation provides the generalizable capacity required for predicting multiple highly genetic heterogeneous populations across environments, allowing reliable GS for genetic improvement programs that have no access to genetically uniform material.

Sex Differences in Response to Marek's Disease: Mapping Quantitative Trait Loci Regions (QTLRs) to the Z Chromosome

Marek's Disease (MD) has a significant impact on both the global poultry economy and animal welfare. The disease pathology can include neurological damage and tumour formation. Sexual dimorphism in immunity and known higher susceptibility of females to MD makes the chicken Z chromosome (GGZ) a particularly attractive target to study the chicken MD response. Previously, we used a Hy-Line F₆ population from a full-sib advanced intercross line to map MD QTL regions (QTLRs) on all chicken autosomes. Here, we mapped MD QTLRs on GGZ in the previously utilized F₆ population with individual genotypes and phenotypes, and in eight elite commercial egg production lines with daughter-tested sires and selective DNA pooling (SDP). Four MD QTLRs were found from each analysis. Some of these QTLRs overlap regions from previous reports. All QTLRs were tested by individuals from the same eight lines used in the SDP and genotyped with markers located within and around the QTLRs. All QTLRs were confirmed. The results exemplify the complexity of MD resistance in chickens and the complex distribution of p-values and Linkage Disequilibrium (LD) pattern and their effect on localization of the causative elements. Considering the fragments and interdigitated LD blocks while using LD to aid localization of causative elements, one must look beyond the non-significant markers, for possible distant markers and blocks in high LD with the significant block. The QTLRs found here may explain at least part of the gender differences in MD tolerance, and provide targets for mitigating the effects of MD.

Progress in Genomic Mating in Domestic Animals

Simple Summary

Since animal domestication, breeders have been selecting candidates for breeding based on phenotypic performance. Estimating breeding values through the best linear unbiased prediction method represents a revolutionary shift in animal breeding. On this basis, selection and mating are utilized to improve the production level of animals. The application of genomic selection has once again revolutionized animal breeding methods. However, although this kind of truncated selection based on breeding values can significantly improve genetic gain, the genetic relationship between individuals with a high breeding value is usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the rate of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic gain and inbreeding is required. Genomic mating is the use of candidate individuals’ genomic information to implement optimized breeding and mating, which can effectively control the rate of inbreeding in the population and achieve long-term and sustainable genetic gain. It is more suitable for modern animal breeding, especially for conservation and genetic improvement of local domestic animal breeds.

Abstract

Selection is a continuous process that can influence the distribution of target traits in a population. From the perspective of breeding, elite individuals are selected for breeding, which is called truncated selection. With the introduction and application of the best linear unbiased prediction (BLUP) method, breeders began to use pedigree-based estimated breeding values (EBV) to select candidates for the genetic improvement of complex traits. Although truncated selection based on EBV can significantly improve the genetic progress, the genetic relationships between individuals with a high breeding value are usually closed, and the probability of being co-selected is greater, which will lead to a rapid increase in the level of inbreeding in the population. Reduced genetic variation is not conducive to long-term sustainable breeding, so a trade-off between genetic progress and inbreeding is required. As livestock and poultry breeding enters the genomic era, using genomic information to obtain optimal mating plans has formally been proposed by Akdemir et al., a method called genomic mating (GM). GM is more accurate and reliable than using pedigree information. Moreover, it can effectively control the inbreeding level of the population and achieve long-term and sustainable genetic gain. Hence, GM is more suitable for modern animal breeding, especially for local livestock and poultry breed conservation and genetic improvement. This review mainly summarized the principle of genomic mating, the methodology and usage of genomic mating, and the progress of its application in livestock and poultry.

Strategies of preserving genetic diversity while maximizing genetic response from implementing genomic selection in pulse breeding programs

Genomic selection maximizes genetic gain by recycling parents to germplasm pool earlier and preserves genetic diversity by restricting the number of fixed alleles and the relationship in pulse breeding programs. Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F₁ or F₂ stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F₁ intercross (F_1i) families and keeping the total number of progeny of F_1i families constant, we observed a decrease in genetic gain and increase in genetic diversity, whereas increasing the number of progeny per F_1i family while keeping a constant number of F_1i families increased the rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F₂ family phenotypes to the training population increased the accuracy of genomic breeding values (GEBVs) and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.

Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation

The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.

Long-term impact of conventional and optimal contribution conservation methods on genetic diversity and genetic gain in local pig breeds

There are rich and vast genetic resources of indigenous pig breeds in the world. Currently, great attention is paid to either crossbreeding or conservation of these indigenous pig breeds, and insufficient attention is paid to the combination of conservation and breeding along with their long-term effects on genetic diversity. Therefore, the objective of this study is to compare the long-term effects of using conventional conservation and optimal contribution selection methods on genetic diversity and genetic gain. A total of 11 different methods including conventional conservation and optimal contribution selection methods were investigated using stochastic simulations. The long-term effects of using these methods were evaluated in terms of genetic diversity metrices such as expected heterozygosity (He) and the rate of genetic gain. The results indicated that the rates of true inbreeding in these conventional conservation methods were maintained at around 0.01. The optimal contribution selection methods based either on the pedigree (POCS) or genome (GOCS) information showed more genetic gain than conventional methods, and POCS achieved the largest genetic gain. Furthermore, the effect of using GOCS methods on most of the genetic diversity metrics was slightly better than the conventional conservation methods when the rate of true inbreeding was the same, but this also required more sires used in OCS methods. According to the rate of true inbreeding, there was no significant difference among these conventional methods. In conclusion, there is no significant difference in different ways of selecting sows on inbreeding when we use different conventional conservation methods. Compared with conventional methods, POCS method could achieve the most genetic gain. However, GOCS methods can not only achieve higher genetic gain, but also maintain a relatively high level of genetic diversity. Therefore, GOCS is a better choice if we want to combine conservation and breeding in actual production in the conservation farms.

Breeding Beyond Monoculture: Putting the "Intercrop" Into Crops

Intercropping is both a well-established and yet novel agricultural practice, depending on one's perspective. Such perspectives are principally governed by geographic location and whether monocultural practices predominate. Given the negative environmental effects of monoculture agriculture (loss of biodiversity, reliance on non-renewable inputs, soil degradation, etc.), there has been a renewed interest in cropping systems that can reduce the impact of modern agriculture while maintaining (or even increasing) yields. Intercropping is one of the most promising practices in this regard, yet faces a multitude of challenges if it is to compete with and ultimately replace the prevailing monocultural norm. These challenges include the necessity for more complex agricultural designs in space and time, bespoke machinery, and adapted crop cultivars. Plant breeding for monocultures has focused on maximizing yield in single-species stands, leading to highly productive yet specialized genotypes. However, indications suggest that these genotypes are not the best adapted to intercropping systems. Re-designing breeding programs to accommodate inter-specific interactions and compatibilities, with potentially multiple different intercropping partners, is certainly challenging, but recent technological advances offer novel solutions. We identify a number of such technology-driven directions, either ideotype-driven (i.e., "trait-based" breeding) or quantitative genetics-driven (i.e., "product-based" breeding). For ideotype breeding, plant growth modeling can help predict plant traits that affect both inter- and intraspecific interactions and their influence on crop performance. Quantitative breeding approaches, on the other hand, estimate breeding values of component crops without necessarily understanding the underlying mechanisms. We argue that a combined approach, for example, integrating plant growth modeling with genomic-assisted selection and indirect genetic effects, may offer the best chance to bridge the gap between current monoculture breeding programs and the more integrated and diverse breeding programs of the future.

Identification of superior parental lines for biparental crossing via genomic prediction

A parental selection approach based on genomic prediction has been developed to help plant breeders identify a set of superior parental lines from a candidate population before conducting field trials. A classical parental selection approach based on genomic prediction usually involves truncation selection, i.e., selecting the top fraction of accessions on the basis of their genomic estimated breeding values (GEBVs). However, truncation selection inevitably results in the loss of genomic diversity during the breeding process. To preserve genomic diversity, the selection of closely related accessions should be avoided during parental selection. We thus propose a new index to quantify the genomic diversity for a set of candidate accessions, and analyze two real rice (Oryza sativa L.) genome datasets to compare several selection strategies. Our results showed that the pure truncation selection strategy produced the best starting breeding value but the least genomic diversity in the base population, leading to less genetic gain. On the other hand, strategies that considered only genomic diversity resulted in greater genomic diversity but less favorable starting breeding values, leading to more genetic gain but unsatisfactorily performing recombination inbred lines (RILs) in progeny populations. Among all strategies investigated in this study, compromised strategies, which considered both GEBVs and genomic diversity, produced the best or second-best performing RILs mainly because these strategies balance the starting breeding value with the maintenance of genomic diversity.