Response to selection while maximizing genetic variance in small populations

Blunted Glucocorticoid Responsiveness to Stress Causes Behavioral and Biological Alterations That Lead to Posttraumatic Stress Disorder Vulnerability

BACKGROUND

Understanding why only a subset of trauma-exposed individuals develop posttraumatic stress disorder is critical for advancing clinical strategies. A few behavioral (deficits in fear extinction) and biological (blunted glucocorticoid levels, small hippocampal size, and rapid-eye-movement sleep [REMS] disturbances) traits have been identified as potential vulnerability factors. However, whether and to what extent these traits are interrelated and whether one of them could causally engender the others are not known.

METHODS

In a genetically selected rat model of reduced corticosterone responsiveness to stress, we explored posttraumatic stress disorder-related biobehavioral traits using ex vivo magnetic resonance imaging, cued fear conditioning, and polysomnographic recordings combined with in vivo photometric measurements.

RESULTS

We showed that genetic selection for blunted glucocorticoid responsiveness led to a correlated multitrait response, including impaired fear extinction (observed in males but not in females), small hippocampal volume, and REMS disturbances, supporting their interrelatedness. Fear extinction deficits and concomitant disruptions in REMS could be normalized through postextinction corticosterone administration, causally implicating glucocorticoid deficiency in two core posttraumatic stress disorder-related risk factors and manifestations. Furthermore, reduced REMS was accompanied by higher norepinephrine levels in the hippocampal dentate gyrus that were also reversed by postextinction corticosterone treatment.

CONCLUSIONS

Our results indicate a predominant role for glucocorticoid deficiency over the contribution of reduced hippocampal volume in engendering both REMS alterations and associated deficits in fear extinction consolidation, and they causally implicate blunted glucocorticoids in sustaining neurophysiological disturbances that lead to fear extinction deficits.

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.

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.

Breeding Strategies to Optimize Effective Population Size in Low Census Captive Populations: The Case of Gazella cuvieri

Simple Summary

Small-sized populations frequently undergo a significant loss of genetic variability that can lead to their extinction. Therefore, research on animal breeding has focused on mating systems for minimizing the disappearance of genetic variability. Minimizing the average coancestry of offspring has been described as the best strategy for this purpose. Traditionally, the preservation of genetic variability has been approached via breeding strategies for increasing the effective population size (Ne). The main objective of this study was to compare, via computer simulations, the performance of different breeding schemes to limit the losses of genetic diversity in small populations. This objective was achieved by monitoring the evolution of the effective size obtained by different strategies across 20 generations with a starting point of two pedigree real populations of Gazella cuvieri. The results showed that minimizing average coancestry in a cohort did not maximize the effective size as compared with new strategies that were designed for this study. Furthermore, the best strategy may vary for each population and should be studied individually.

Abstract

Small-sized animal populations can undergo significant loss of genetic variability that can lead to their extinction. Therefore, studies on animal breeding have focused on mating systems for minimizing the disappearance of genetic variability. The main objective of this study was to compare, using computer simulations, the performance of different breeding schemes to limit the loss of genetic diversity in small-sized populations. This objective was achieved by monitoring the evolution of the effective population size obtained by 23 strategies throughout 20 generations in two populations of Gazella cuvieri. The scenarios were designed with different assumptions, in both reference subpopulations, regarding: the use of parents coancestry or offspring coancestry, the use of their increases or the coefficients themselves, and the number of males and females involved. Computations were performed using an experimental module of Endog v4.9 developed for this purpose. The results of the study showed that strategies for minimizing the coancestry of the parents were better in the short term; however, these strategies were worse in the long term. Minimizing the average coancestry of the offspring was a better approach in the long term. Nevertheless, in both populations, the best results were obtained when both the coancestry of the parents and the coancestry of the offspring were weighted at 5% each and neither males nor females were assumed to contribute to the next generation. In any case, not all strategies had the same evolutionary pattern throughout generations in both populations. The current results show that neither traditional nor new strategies have any general use. Therefore, it is important to carefully test these strategies before applying them to different populations with different breeding needs under different conditions, such as different generation intervals, and different natural breeding systems such as monogamy or polygyny.

Herd clustering strategies and corresponding genetic evaluations based on social-ecological characteristics for a local endangered cattle breed

The accuracy of breeding values strongly depends on the population and herd structure, i.e., the number of animals considered in genetic evaluations and the size of contemporary groups (CGs). Local breeds are usually kept in small-sized family farms under alternative husbandry conditions. For such herd structure, consideration of classical herd or herd-test-day effects in CG modeling approaches implies only a few records per effect level. In consequence, the present study aimed on methodological evaluations of different herd clustering strategies, considering social–ecological and herd characteristics. In this regard, we considered 19 herds keeping cows from the small local population of German Black Pied cattle (Deutsches Schwarzbuntes Niederungsrind; DSN), 10 herds keeping Holstein Friesian (HF) cows and one mixed herd with HF and DSN cows. Herds were characterized for 106 variables, reflecting farm conditions, husbandry practices, feeding regime, herd management, herd fertility status, herd health status and breeding strategies as well as social–ecological descriptors. The variables were input data for different clustering approaches including agglomerative hierarchical clustering (AHC), partition around medoids (PAM), fuzzy clustering (FZC) and a clustering of variables combined with agglomerative hierarchical clustering (CoVAHC). The evaluation criterion was the average silhouette width (ASW), suggesting a CoVAHC application and consideration of four herd clusters (HCs) for herd allocation (ASW of 0.510). HC1 comprised the larger, half organic and half conventional DSN family farms, which generate their main income from milk production. HC2 consisted of small organic DSN family farms where cows are kept in tie stables. HC3 included the DSN sub-population from former East Germany, reflecting the large-scale farm types. The specialized HF herds were well separated and allocated to HC4. Generalized linear mixed models with appropriate link functions were applied to compare test-day and female fertility traits of 5538 cows (2341 DSN and 3197 HF) from the first three lactations among the four HCs. Least squares means for milk, fat and protein yield (Mkg, Fkg and Pkg) significantly differed between HC. The significant differences among the four HCs clearly indicate the influence of varying herd conditions on cow traits. The similarities of herds within HC suggested the application of HCs in statistical models for genetic evaluations for DSN. In this regard, we found an increase of accuracies of estimated breeding values of cows and sires and of heritabilities for milk yield when applying models with herd-cluster-test-day or herd-cluster-test-month effects compared to classical herd-test-day models. The identified increase for the number of cows and cow records in CG due to HC effects may be the major explanation for the identified superiority.

Genetic Diversity in the Portuguese Mertolenga Cattle Breed Assessed by Pedigree Analysis

Simple Summary

The conservation and maintenance of genetic diversity is one of the priorities of the Convention of Biological Diversity and is included in the United Nations (UN’s) Sustainable Development Goals. The evaluation of the genetic variability of a breed is fundamental for its future use in a sustainable way, being indispensable to outline a successful conservation or improvement strategy. Preserving genetic diversity in a population is one of the main objectives for a breed conservation program. Nevertheless, the correct management of genetic diversity is also essential for the adaptation of a population to a new environment, production system or genetic improvement. For the purpose of population monitoring, assessing changes in genetic variability and genetic erosion in animal populations, many methodologies based on pedigree analyses of inbreeding and relationships, and on the probability of genetic origin from different herds, founders and ancestors, have been used. This study presents several genetic diversity indicators in a Portuguese native cattle breed, Mertolenga, assessed by pedigree analysis, and demonstrates the usefulness of these indicators and how they can be used in the genetic management of a breed.

Abstract

The Mertolenga beef cattle, currently with 27,000 breeding females in Portugal, is the largest Portuguese native breed, despite some variation in the breeding stock over the last years. The purpose of this study was to estimate parameters related to the population structure and genetic diversity and to investigate the major factors affecting genetic erosion in the breed, based on the pedigree herdbook information collected since the 1950s, including records on 221,567 animals from 425 herds. The mean generation intervals were 6.4 years for sires and 7.1 years for dams, respectively. The rate of inbreeding per year was 0.183% ± 0.020% and the correspondent effective population size was 38.83. In the reference population (35,017 calves born between 2015 and 2019), the average inbreeding and relatedness were 8.82% ± 10% and 2.05% ± 1.26%, respectively. The mean relationship among animals from the same and from different herds was 29.25% ± 9.36% and 1.87% ± 1.53%, respectively. The estimates for the effective number of founders, ancestors, founding herds and herds supplying sires were 87.9, 59.4, 21.4 and 73.5, respectively. Although the situation of the Mertolenga breed is not alarming, these results indicate the need to adopt measures to maintain the genetic variability of the population.

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

Genomic selection has been successfully implemented in plant and animal breeding. The transition of parental selection based on phenotypic characteristics to genomic selection (GS) has reduced breeding time and cost while accelerating the rate of genetic progression. Although breeding methods have been adapted to include genomic selection, parental selection often involves truncation selection, selecting the individuals with the highest genomic estimated breeding values (GEBVs) in the hope that favorable properties will be passed to their offspring. This ensures genetic progression and delivers offspring with high genetic values. However, several favorable quantitative trait loci (QTL) alleles risk being eliminated from the breeding population during breeding. We show that this could reduce the mean genetic value that the breeding population could reach in the long term with up to 40%. In this paper, by means of a simulation study, we propose a new method for parental mating that is able to preserve the genetic variation in the breeding population, preventing premature convergence of the genetic values to a local optimum, thus maximizing the genetic values in the long term. We do not only prevent the fixation of several unfavorable QTL alleles, but also demonstrate that the genetic values can be increased by up to 15 percentage points compared with truncation selection.

Long-Term Evaluation of Breeding Scheme Alternatives for Endangered Honeybee Subspecies

Modern breeding structures are emerging for European honeybee populations. However, while genetic evaluations of honeybees are becoming increasingly well understood, little is known about how selection decisions shape the populations' genetic structures. We performed simulations evaluating 100 different selection schemes, defined by selection rates for dams and sires, in populations of 200, 500, or 1000 colonies per year and considering four different quantitative traits, reflecting different genetic parameters and numbers of influential loci. Focusing on sustainability, we evaluated genetic progress over 100 years and related it to inbreeding developments. While all populations allowed for sustainable breeding with generational inbreeding rates below 1% per generation, optimal selection rates differed and sustainable selection was harder to achieve in smaller populations and for stronger negative correlations of maternal and direct effects in the selection trait. In small populations, a third or a fourth of all candidate queens should be selected as dams, whereas this number declined to a sixth for larger population sizes. Furthermore, our simulations indicated that, particularly in small populations, as many sires as possible should be provided. We conclude that carefully applied breeding provides good prospects for currently endangered honeybee subspecies, since sustainable genetic progress improves their attractiveness to beekeepers.

Genetic Variability and Management in Nero di Parma Swine Breed to Preserve Local Diversity

Simple Summary

The Nero di Parma is an Italian pig breed with a peculiar breed history. It originates from a native breed called “Nera Parmigiana”, which, in the beginning of the 20th century, was crossed with highly productive breeds, causing the extinction of the original type in the 1970s. During the 1990s a growing interest for organic products and outdoor farming brought the attention back to the local type and a breed recovery project started to reestablish the original breed. The aim of the study was to investigate the genetic diversity of the Nero di Parma breed to provide further insights for breed conservation and to propose breeding strategies.

Abstract

Nero di Parma is an endangered swine breed reared in the North of Italy which nowadays counts 1603 alive pigs. The aims of this study were (i) to explore the genetic diversity of the breed at pedigree level to determine the actual genetic structure, (ii) to evaluate the effectiveness of the breeding recovery project and (iii) to potentially propose breeding strategies for the coming generations. The pedigree dataset contained 14,485 animals and was used to estimate demographic and genetic parameters. The mean equivalent complete generations was equal to 6.47 in the whole population, and it reached a mean value of 7.94 in the live animals, highlighting the quality of the available data. Average inbreeding was 0.28 in the total population, whereas it reached 0.31 in the alive animals and it decreased to 0.27 if only breeding animals were considered. The rate of inbreeding based on the individual increase in inbreeding was equal to 7%. This study showed the effectiveness of the recovery project of the breed. Nevertheless, we found that inbreeding and genetic diversity have reached alarming levels, therefore novel breeding strategies must be applied to ensure long-term survival of this breed.

Genotyping more cows increases genetic gain and reduces rate of true inbreeding in a dairy cattle breeding scheme using female reproductive technologies

Both small dairy cattle populations and dairy cattle populations with a low level of linkage disequilibrium (LD) suffer from low reliability of genomic prediction. In this study, we investigated whether adding more genotyped cows to the reference population influences the rate of genetic gain and rate of inbreeding by affecting the reliability. A standard breeding program with a large reference population and high LD, which mimicked a breeding program for Danish Holstein population, was simulated as a reference. A Danish Jersey population with a small reference population and high LD and a Red Dairy Cattle population with a large reference population and low LD were also simulated. Two additional breeding programs were simulated for Danish Jersey and Red Dairy Cattle populations, where 2,000 additional genotyped cows were included in the population for genomic selection. All 5 simulated breeding programs were initiated by a founder population to generate LD resembling the real LD pattern, followed by a 20-yr conventional progeny-testing scheme with 1,000 or 10,000 genotyped progeny-tested bulls and a 10-yr genomic selection scheme with or without 2,000 additional genotyped cows. Evaluation criteria were annual monetary genetic gain and rate of true inbreeding. Our results showed that adding more genotyped cows to the reference in dairy cattle populations has the potential to increase genetic gain and reduce the rate of inbreeding, regardless of reference population size and level of LD. However, it is still not possible to reach the same genetic gain as in the simulated Danish Holstein population with either a small reference population or low LD. Our results also showed that in a small reference population with high LD, it is difficult to manage inbreeding because of lower accuracy compared with the simulated Danish Holstein population and a smaller number of relevant families to select from. Therefore, breeding strategies need to be chosen to match population size and structure. The rate of true inbreeding is always underestimated by pedigree inbreeding and even more in genomic breeding programs, indicating that some forms of genome-wide inbreeding, instead of pedigree-based inbreeding, should be used to monitor inbreeding when genomic selection is implemented.

Key Genetic Parameters for Population Management

Population management has the primary task of maximizing the long-term competitiveness of a breed. Breeds compete with each other for being able to supply consumer demands at low costs and also for funds from conservation programs. The competition for consumer preference is won by breeds with high genetic gain for total merit who maintained a sufficiently high genetic diversity, whereas the competition for funds is won by breeds with high conservation value. The conservation value of a breed could be improved by increasing its contribution to the gene pool of the species. This may include the recovery of its original genetic background and the maintenance of a high genetic diversity at native haplotype segments. The primary objective of a breeding program depends on the genetic state of the population and its intended usage. In this paper, we review the key genetic parameters that are relevant for population management, compare the methods for estimating them, derive the formulas for predicting their value at a future time, and clarify their usage in various types of breeding programs that differ in their main objectives. These key parameters are kinships, native kinships, breeding values, Mendelian sampling variances, native contributions, and mutational effects. Population management currently experiences a transition from using pedigree-based estimates to marker-based estimates, which improves the accuracies of these estimates and thereby increases response to selection. In addition, improved measures of the factors that determine the competitiveness of a breed and utilize auxiliary parameters, such as Mendelian sampling variances, mutational effects, and native kinships, enable to improve further upon historic recommendations for genetic population management.