Genetic diversity is essential for the sustainability and adaptability of populations, and is thus a central pillar of the agro-ecological transition. However, within a population, it is inevitable that some amount of genetic variability is lost, and efforts must be made to limit this as much as possible. A valuable tool in this endeavour could be the use of cryopreserved genetic resources in cryobanks, which could assist in the management of various animal populations in the contexts of both selection and conservation. We performed simulations that revealed that the most appropriate use of ex situ genetic resources depends on characteristics of the target population and its management objectives. For populations under conservation, the aim is to maintain genetic diversity, which was best achieved by the regular use of cryopreserved genetic resources at each generation. For populations under selection, instead, the concern is the addition of additive genetic variability, which benefited from the use of cryopreserved collections over only a few generations based primarily on the genetic values of donors. The use of cryopreserved semen had a beneficial effect when breeding objectives were changed. In both cases, the use of cryopreserved individuals in animal populations requires a large amount of reproductive material: for breeds under selection because the number of offspring is high, and for breeds under conservation because the frozen semen is used repeatedly over a long period. The use of cryopreserved material appears to be an effective means of managing the genetic variability of an animal population, either by slowing down the erosion of variability or by helping to redirect a selection objective. However, care must be taken with populations under selection to limit the disadvantages associated with the reintroduction of old genetic material, in particular the gap in breeding values for traits of interest. Finally, our study highlights the need for a sufficiently large stock of cryopreserved material in collections (e.g., number of doses, straws) to ensure the most efficient use.
The breeding goal of the Swedish Warmblood horse (SWB) is to produce internationally competitive horses in dressage and show jumping. In the current genetic evaluation, breeding values are estimated in multiple-trait animal models where competition performance is the target trait and results from two different young horse tests serve as indicator traits. However, preselection of horses, both for young horse tests and for competitions, is not considered in the current evaluation. The overall aim of this study was to analyse the all-or-none trait start status, in competition and in young horse tests, for possible use in the genetic evaluation for SWB. All starts in young horse tests have been recorded since long (1973), whereas start status in competition is known from the year 2007 and onwards. Therefore, the studied population was restricted to SWB horses born between 2003 and 2018 that had the possibility to compete during the period from 2007 until 2022. Horses were categorised into four disciplines according to their sire's and grandsire's discipline categories, and only horses in the two major categories, dressage and jumping, were included in this study. In total, 23,125 jumping horses and 14,470 dressage horses were studied separately. Information on discipline-specific start status in show jumping or dressage competitions, young horse test (YHT) and riding horse test (RHT) was available as well as lifetime accumulated competition points, assessed gaits and jumping traits from YHT and RHT. Out of the jumping horses, 31% had participated in YHT, 10% in RHT and 56% in show jumping competition. For dressage horses, the participation rates were 35% for YHT, 11% for RHT and 34% for dressage competition. The genetic analyses were performed with threshold and linear animal models. Horses that had participated in YHT or RHT had competed to a larger extent and had a higher mean of competition points than horses that had not participated in YHT or RHT. The heritability for start status in competition was estimated using a threshold model at 0.48 for show jumping and 0.39 for dressage. Using linear models, the heritability for start status in show jumping was estimated to be 0.30 on the observable 0/1-scale and 0.47 when transformed to the underlying continuous scale. For start status in dressage, the corresponding heritability estimates were 0.20 and 0.34. Genetic correlations, estimated with linear models, were strong between start status in show jumping and jumping traits at YHT and RHT (0.78-0.93) and moderate to strong between start status in dressage competition and gait traits at YHT and RHT (0.46-0.88). The genetic correlations between start status and accumulated lifetime points in competition were strong, 0.93 for show jumping and 0.86 for dressage. Using linear models, heritability estimates for start status in young horse tests ranged from 0.07 to 0.42 on the observable scale and from 0.11 to 0.71 after transformation to the underlying continuous scale. Inclusion of start status in the breeding value estimation of competition performance affected stallion ranking somewhat and increased the accuracies of the stallions' breeding values. We conclude that start status is a heritable trait that would be possible to include in the genetic evaluation of SWB horses.
Foot conformation is one of the main breeding goals in recent beef cattle breeding programs because it directly affects productivity, economic losses, animal welfare and longevity. Genetic heterogeneity of residual variance can be used to improve production uniformity in animal breeding programs because recent studies have shown that residual variance is partially under genetic control, allowing reduction of variability through selection. Despite being an important breeding goal, research on genetic heterogeneity of residual variance for conformation traits, such as foot angle and foot claw, is still scarce in livestock species. The objectives of our study were (1) to investigate the extent of genetic heterogeneity of residual variances on two conformation traits: foot angle (FA) and claw set (CS) in Angus cattle using genetic homogeneity (M1) and two genetic heterogeneity of residual variance models, including a double hierarchical generalised linear model (DHGLM, M2) and a genetically structured environmental variance model (M3). Genetic parameters for means and residual variances were estimated using M2 and M3. The dataset included 45,667 phenotypic records for FA and CS (scores from 1 to 9 with 5 being ideal) of American Angus cattle recorded from 2009 to 2021. M1 and M2 were fitted using average information restricted maximum likelihood, and M3 was fitted using Markov chain Monte Carlo. Heritability estimates for the means of FA (0.19 ± 0.007 for M1, 0.11 ± 0.005 for M2 and 0.09 ± 0.003) and CS (0.16 ± 0.005 for M1, 0.10 ± 0.004 for M2 and 0.08 ± 0.03) were within the range reported in the literature, but M2 and M3 estimates were lower than M1. Genetic heterogeneity of residual variance was assessed using three parameters: heritability for residual variance, genetic coefficient of variation, and correlation between mean and residual variance. Although heritability estimates for residual variance in M2 were low (0.08 for FA and 0.001 for CS), our results suggest that residual variance is partially under genetic control. The genetic coefficients of variation estimates were 0.08 (M2) and 0.06 (M3) for FA, and 0.06 (M2) and 0.02 (M3) for CS, indicating that selection on the trait mean would also change the residual variance. Our results for FA and CS showed moderate positive genetic correlations in M2 (0.52 for FA and 0.41 for CS) and M3 (0.35 for FA and 0.33 for CS) between mean and residual variance. Positive correlations may limit the response to selection unless other breeding strategies, such as selection indices, are used. FA and CS are promising traits for uniformity or resilience indicators because they are phenotypes that can be collected throughout the production cycle using traditional or digital data recording systems. Our results demonstrate the potential to modulate variability through breeding strategies and present an opportunity to evaluate the uniformity of foot score traits in beef cattle.
Genetic progress of breeding programs is highly dependent on the size of reference populations and the relatedness between reference populations and selection candidates. Many reasons can lead a population to split into several subpopulations (sanitary, physiological, political reasons, etc.). More specifically, alternative (e.g., organic) farming may lead to farms breaking away from the conventional scheme to form a distinct breeding scheme, especially in organic sheep farming where the ban on hormones makes the use of artificial insemination (AI) difficult. However, these potential splits of the population into several smaller subpopulations could decrease genetic progress. The aim of our study was to investigate, using stochastic simulations, the impact of separation of the population into two subpopulations while still applying the same breeding objective and methods. We simulated a breeding program inspired by a dairy program but applicable to different species. We simulated two different initial population sizes with 5400 (10,800) females mated to 90 (180) males and a trait of heritability 0.30. This population was under selection for several discrete generations (G-9 to G-1) as a single population. Then, for the last 11 cycles of selection, the population was either maintained as a unique population (scenario "NoSep", which was the reference scenario) or split into two subpopulations with different ratios: 50/50, 60/40, 70/30, 80/20, and 90/10. We studied three scenarios in which the population was split: CE (separation and Common Evaluation), in which the evaluation remained common between both subpopulations; SE (separation and separate evaluation), in which the subpopulations were evaluated individually; and NoSel (Separation and No Selection), in which the breeding males were randomly selected, as opposed to the two previous scenarios in which we selected the males based on their GEBVs. We studied the evolution of differentiation of populations (Fst), accuracy of predictions, genetic progress, and rate of inbreeding over generations. We observed a faster genetic divergence in the case of an unbalanced split and separate evaluation (Fst in G11 equal to 0.134 for the ratio 90/10 scenario SE). The separate evaluation had a significant, negative effect on both the accuracy and genetic gain of the smallest population (minimal accuracy of 0.53 and maximal loss of 16.6% for ratio 90/10 with 5400 females), whereas the accuracy and genetic gain of the largest population were not impacted. Combining the evaluations led to smaller but still significant deterioration of the genetic gain of the smallest population when the ratio was very unbalanced (loss of genetic gain of 14.3% for a ratio of 90/10 with 5400 females). In conclusion, population separation has a negative impact on genetic gain, particularly for small populations. Although it does help in alleviating divergence and loss of genetic gain, joint evaluation can not fully compensate for the split of the populations.
Reducing the number of days from birth to slaughter is one strategy to improve animal feed and environmental efficiency. The export market use yearling sheep weighing 22-30 kg and usually purchases from large and small-scale traders at an early age (lambs weighing ≥ 15 kg) for mutton production. Hence, this study aimed to derive a new trait phenotype, which helps to reduce sheep market age without adverse effects on the market weight of Menz sheep and to evaluate the extent of exploitable genetic variation in this new trait. To this end, 11,258 lambs weighing ≥ 15 kg between 66 and 395 days of age were considered in this study. Co (variance) components and heritability estimates for novel traits were estimated using the average information restricted maximum likelihood method in WOMBAT fitting the animal model. The best-fitted model was selected from six models based on likelihood ratio test and Akaike's information criterion. The days-to-market weight of 16.3% of the animals was shorter by 48.2 days (with an estimated breeding value of -26.2 days) compared to the mean of the contemporary groups. The market weight of 15.5% of the animals was higher by 1.73 kg compared to the mean of the contemporary groups. There was a phenotypic variability of deviation in age at market weight (DAMW) and deviation in weight at market age (DWMA) for the sheep population in Molalie village compared to other villages. Likewise, the genetic standard deviation for DAMW and DWMA was 25 days and 0.79 kg, respectively. Based on the best-fitted model, the direct heritability estimate for DAMW and DWMA was 0.65 and 0.57, respectively. In addition, the maternal genetic effect explains 28% of the phenotypic variation in DAMW and 26% of the phenotypic variation in DWMA. The DAMW of Menz sheep in Dargegn and Molalie villages decreased significantly by 2.113 and 1.192 days year-1, respectively. The observed additive genetic variance for DAMW suggests further scope for genetic improvement in the flock to reduce the days-to-market weight of Menz sheep. Including this novel trait in a breeding objective could shorten days to market weight without necessarily reducing the genetic merit of the live weight included in the breeding objective.
Heat stress is an increasingly important challenge for the performance and welfare of equine athletes, particularly in competitions conducted across diverse climatic conditions. Understanding the genetic basis of performance responses to increasing thermal load is therefore essential to support robust genetic evaluation and sustainable selection strategies. The objective of this study was to evaluate barrel racing performance of Brazilian Quarter Horses across thermal environments defined by two widely used indicators, the temperature-humidity index (THI) and the wet bulb globe temperature (WBGT), and to estimate genetic parameters associated with baseline performance and environmental sensitivity. A total of 351,993 barrel racing time (BRT) records from 13,960 Quarter Horses were analyzed. Segmented regression models were applied to least squares means to identify heat stress thresholds, while random regression models with polynomial functions were used to estimate covariance components, genetic parameters, and correlations along the thermal gradients. Distinct thermal thresholds were identified for both indices, at THI = 74.7 and WBGT = 23.6, beyond which performance deteriorated more rapidly. Heat stress conditions were observed in 31.15% and 20.71% of barrel racing events according to the THI and WBGT thresholds, respectively, emphasizing the practical relevance of the evaluated thermal gradients. Random regression models assuming continuous thermal variation provided the best overall fit to the data. Additive genetic variance remained relatively stable across thermal environments, whereas permanent environmental and residual variances increased under higher thermal load. Mean heritability under thermoneutral conditions was approximately 0.21 for THI and WBGT, declining modestly to 0.17-0.18 under extreme heat stress. Genetic correlations between thermoneutral and extreme environments remained high for both indices (at approximately 0.97), indicating strong genetic continuity of performance and no substantial reranking of estimated breeding values across the thermal gradient. Overall, THI and WBGT yielded highly consistent results in terms of thresholds, genetic parameters, and selection outcomes. Although WBGT showed slightly greater sensitivity under extreme conditions, THI offered smoother response patterns and clear operational advantages due to its simplicity and ease of calculation. No evidence of substantial genotype-by-environment interaction was detected for BRT. However, the results suggest that incorporating genetic tolerance to heat stress as a complementary selection criterion may help sustain barrel racing performance under increasingly challenging climatic conditions.
Recent theoretical work shows that the potential of genetic selection to reduce the prevalence of infectious diseases is much larger than expected from classical quantitative genetic theory, due to indirect genetic effects that arise in the transmission process. However, to fully benefit from these indirect effects, we need to estimate genetic parameters and breeding values, which requires statistical methods tailored to the transmission process. Here, we evaluate Generalized-Linear-Mixed Models (GLMMs) implemented using software commonly used in animal breeding to estimate genetic parameters and breeding values for susceptibility of hosts to infection, using simulated data of epidemics. Longitudinal records of individuals' infection state provide information on the order of infection, as well as on the exposure dose of non-infected animals. Such information can be harnessed to estimate genetic parameters for susceptibility, and can be included in a GLMM as a so-called offset. Therefore, we used longitudinal records of individual infection state to assess the impact of sampling interval, population structure, infection characteristics, and model formulation on the estimated genetic variance and breeding values for susceptibility. The results show that a GLMM fitted to longitudinal records of individual binary infection state can produce accurate and unbiased estimates of genetic variance, as well as good prediction accuracies of breeding values for susceptibility to an infectious disease. Of the data requirements, the time interval between consecutive observations on individual infection state was the main factor affecting estimation, while group size had a limited effect. The required observation interval depends on the infection and recovery rates of individuals. The GLMM thus seems an accurate and easily implementable model to estimate genetic parameters and breeding values for susceptibility when dense longitudinal records on individual infection status are available.
Sheep production contributes to a secure and diverse food and fibre supply in the United States, with growing ethnic diversity strengthening demand. Katahdin is a composite hair-type sheep breed developed in the United States that has become the most popular breed in many regions of the country and the first one to have genomic selection implemented in its breeding program. Therefore, the main objectives of this study were to estimate variance components of reproductive traits, including number of lambs born (NLB), number of lambs weaned (NLW), age at first lambing (AFL), and interval from first to second lambing (LI), in Katahdin sheep using the AIREML method and the single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) approach, and to identify genomic regions and candidate genes associated with these traits. The datasets used consisted of 127,536 animals in the pedigree, phenotypic records of 56,128 parities from 24,067 ewes, and genomic data from 10,032 animals with 30,308 single-nucleotide polymorphisms (SNP) after quality control. Analyses were performed using the BLUPF90 family of programs. We observed low heritability estimates for all studied traits (0.09 ± 0.00 for NLB, 0.08 ± 0.00 for NLW, 0.09 ± 0.01 for AFL, and 0.08 ± 0.01 for LI). The genetic correlations between the traits ranged from 0.17 ± 0.02 (AFL and LI) to 0.79 ± 0.02 (NLB and NLW). All traits were found to be highly polygenic with all 14 significant SNP on eight (OAR) chromosomes (3, 6, 7, 8, 9, 12, 13, and 15) having small effects on the total variability on the traits. These SNP were located near or within 18 candidate genes: four genes associated with NLB (AAK1, GFPT1, SLC23A2, and GDAP1), four with NLW (ARHGAP18, TTLL2, UNC93A, and GPR31), six with AFL (NAP1L5, FAM13A, HS3ST1, CCDC181, NME7, and BLZF1), and four with LI (TAF4, CDH4, CADM1, and SEL1L). These candidate genes have been previously associated with fertility, embryonic development, growth, disease resistance, and climatic adaptation traits. Our findings indicate that fertility and reproduction traits in Katahdin sheep can be improved through direct genetic selection. Genetic improvement for these traits will benefit from genomic selection as more accurate estimates of breeding values for selection candidates can be obtained at a younger age. Although the studied traits are influenced by a complex interplay of genetic and environmental factors, the candidate genes identified enabled a better understanding of the biological mechanisms underlying reproductive performance in Katahdin sheep.
The evaluation of breeding schemes against established objectives and selection traits is essential for assessing the performance, outputs, and overall impacts of breeding programmes. In Ethiopia, most Community-Based Breeding Programmes (CBBPs) have prioritised growth traits, particularly live weight, as the main selection criteria. However, since productivity relies on both reproductive and growth traits, it is critical to evaluate how these traits are evolving to make necessary adjustments in management practices and breeding schemes. This study considered five indigenous sheep breeds (Menz, Semein, Horro, Bonga and Doyogena), managed under CBBPs since 2009. Fixed effects for reproductive traits were estimated using the GLM procedures of SAS 9.4. Genetic parameters were estimated for all traits using the restricted maximum likelihood (REML) method with WOMBAT software, employing a multivariate repeated model, except for age at first lambing (AFL), which was analysed using a non-repeated multivariate model. Significant effects (p < 0.001) were observed for year of birth, breed of ewe, parity and birth season across all traits in the breeds studied. A general trend of improvement in litter size at birth (LSB), total litter weight at birth (TLWB), litter size at weaning (LSW), total litter weight at weaning (TLWW) and annual reproductive rate (ARR) was noted with increasing ewe parity until the seventh parity, followed by a decline thereafter. Direct heritability estimates for the traits according to the ewe breeds ranged from 0.03 to 0.25 for LSB, 0.02 to 0.16 for LSW, 0.08 to 0.21 for TLWB, 0.07 to 0.22 for TLWW, 0.03 to 0.19 for LI, 0.08 to 0.32 for ARR and 0.15 to 0.36 for AFL. Estimates of direct heritability and repeatability varied by breed and location, generally falling within small to medium ranges. Moderate to high genetic correlations were found between TLWW and other traits suggesting that selection for TLWW may significantly influence reproductive performances across most sheep breeds, with the exception of Menz sheep. The variations in genetic estimates across different breeds and locations indicate that genetic influences may vary depending on the specific context. Moderate to high genetic correlations between TLWW and other reproductive traits suggest that prioritising selection for TLWW could have a significant positive impact on reproductive performance across most sheep breeds, though the Menz breed may not exhibit the same expected benefits. These findings emphasise the need to integrate genetic selection with effective management practices tailored to each breed's specific needs, recommending the culling of unproductive ewes after the seventh parity to enhance the sustainability and productivity of CBBPs in Ethiopia.
Reproductive traits related to litter size are the main indicators of reproductive efficiency in pig production and are continuously evaluated for the selection of maternal lines. Several environmental and genetic factors are involved with the development of these traits. Genome-wide association studies (GWAS) allow a better understanding of the genetic control of complex traits, especially those with low heritability (h2). Therefore, this study aims to estimate the genetic parameters and to identify genomic regions and candidate genes associated with total number born (TNB), number born alive (NBA), and viable piglets at Day 5 (PV5) in a Large White female line. For this, 17,011 phenotypic records, 190,000 pedigree records, and 4366 animals genotyped with the Illumina 50 K and 80 K panels were used. Estimates of h2, genetic (rg) and phenotypic (rp) correlations, and GWAS were performed with the BLUPF90 family programs. Positional candidate genes, their main biological processes, and networks were investigated using the Ensembl database and the BioMart, PANTHERdb, and STRING tools. The studied traits presented low h2 estimates, but with high and positive rg and rp. In the GWAS, 14 significant genomic windows were identified for TNB, 10 for NBA, and 15 for PV5. These regions include 157 genes for TNB, 101 for NBA, and 140 for PV5, mapped across 10 different chromosomes. Among the genes located in those regions, the ESR1, THRB, SLIT2, and ZBTB2 were common to the three traits and are involved in processes of hormonal regulation, embryogenesis, immunity, and homeostasis. Moreover, 12 of those genes were new positional candidates for TNB, NBA, or PV5. Among them, we highlight the FSTL4, PAPPA, and TCF7 genes associated with PV5, which are involved with hormonal regulation, growth factors, and immunity, respectively. The SLIT2, MTHFD1L, OVOL2, SHB, and EXOSC3 genes, involved with embryogenesis and neurogenesis, were associated with TNB and NBA. Furthermore, uncharacterized genes, such as ENSSSCG00000058091, related to mitochondrial homeostasis, were associated with TNB, while ENSSSCG00000040472, related to protein synthesis, was identified for PV5. These new findings reveal common and exclusive genetic mechanisms that may influence important litter traits in pigs, helping the development of breeding strategies to optimise reproductive efficiency.
High litter sizes in pigs are associated with lower birth weights and increased within-litter variation, which poses challenges to pig farming. Improving piglet uniformity and survival via genetic selection of terminal boars could be a strategy to enhance these traits. This study evaluated how well estimated breeding values for uniformity and survival of terminal Piétrain sires predict within-litter uniformity and mortality rates in their crossbred offspring from birth until slaughter. We selected six Piétrain boars with contrasting estimated breeding values for uniformity and survival (3 high vs. 3 low) and mated these with 93 hybrid sows, which produced 1421 liveborn piglets. Moderate correlations were observed between crossbred piglets' within-litter uniformity and the paternal estimated breeding values (r = 0.17 to 0.28), indicating that the used estimated breeding values for uniformity and survival of terminal sires have a low predictive value for within-litter uniformity. Nonetheless, significant associations were found between these estimated breeding values and specific survival- and uniformity-related traits, such as pre-weaning survival and uniformity of growth, supporting their potential for selection. When analysing piglets' weights over time, low correlations were observed between the coefficient of variation at birth and later in life (r = 0.05 to 0.24). On the other hand, moderate to high correlations were found between coefficients of variation at weaning and later in life (r = 0.34 to 0.88), indicating stability of the within-litter body weight uniformity after weaning. These findings suggest that pre- and post-weaning within-litter body weight uniformity should be considered as two distinct traits.
During the last decade, there has been a growing interest for local and endangered breeds as they are often seen as more resilient and healthier than mainstream breeds. They can also provide high added-value products like meat or dairy products. To better preserve these breeds, it is of main importance to characterize their genetic diversity and have an insight of historical gene flow with more mainstream breeds. In this study, we focused on the genetic relationships of five red-pied cattle breeds: the east Belgian red and white (EBRW); the red-pied of the Ösling (RPO), from Luxembourg; the deep red (DR) and the Meuse-Rhine-Yssel (MRY), both from the Netherlands; and the German red and white dual purpose (RDN). The EBRW, RPO and DR breeds have an official European endangered status. We first investigated the pedigree completeness of available genotyped animals as well as their complex historical relationships through the analysis of common ancestors. We also compared pedigree and SNP-based inbreeding coefficients, defined as the sum of homozygosity-by-descent segments (HBD). We then dived into genomic relationships through a classical multi-dimensional scaling (MDS) of genotyped animals and their admixture. The pedigree analyses showed the complex gene flow between all breeds and that the RPO breed was the most connected to other breeds. Results also showed that the level of inbreeding was so far not an issue in all five breeds even if some animals, for example, in EBRW and MRY breeds, showed higher inbreeding levels than the average of their breeds. Finally, the MDS and admixture analysis also highlighted complex gene flow between the studied breeds and that they may be considered as a genetic continuum. This genomic proximity has the potential to improve genetic evaluations of local breeds by the inclusion of information from more mainstream breeds like MRY and RDN.
Detecting selection footprints offers valuable insight into evolutionary processes and the mechanisms underlying phenotypic diversity in selected traits. Domestication, natural and artificial selection, and breeding have produced indigenous goats well-adapted to their local environments, making them crucial genetic resources. Understanding the genetic foundation of these adaptations can guide the development of effective breeding strategies to preserve and improve local goat breeds. This study investigated selection signatures in Lao native goats using Illumina's Goat SNP50 BeadChip, analysing 420 Lao native goats, 87 goats from three Chinese breeds, and 51 Teddi goats from Pakistan as test populations. We applied the de-correlation composite multiple signals (DCMS) method, incorporating p values from nine statistical tests, including runs of homozygosity in the Lao goat population, and fixation index and cross-population extended haplotype homozygosity between Lao goats and test populations. Significant genomic regions were identified using a 0.05 threshold adjusted for multiple testing. Our results uncovered 24 genomic regions harbouring 68 unique-coding genes. Analysis revealed both annotated and novel candidate genes linked to a variety of characteristics, including adaptation to the tropical monsoon climate (e.g., ABHD6, GATA4 and MSRA) and economic traits like growth and status (e.g., CNTNAP5, FAM135B and GATA4), reproduction (e.g., NPHP3, ARSJ and GATA4), milk production (e.g., MRPL32, PRSS51 and EPHA7), and carcass characteristics (e.g., GNAI1, SOX7 and FAM135B). These results offered insightful information about genetic mechanisms driving economic traits and tropical climate adaptation of Lao native goats. Combining p values from various statistical tests into a single DCMS framework effectively assists in selecting and prioritising candidate genes for further analysis.
The objective was to evaluate the genetic relationship between the surface temperature of regions of interest, measured using infrared images of young horses and functional longevity in jumping. This relationship was assessed by comparing the temperatures measured in the offspring of two groups of sires, one favourable and one unfavourable, to longevity. The study used a specific data collection protocol on a sample of 921 young progeny, before they began competing, of 141 extreme stallions, comprising 61 favourable and 80 unfavourable sires. These stallions had been selected based on estimated breeding values for functional longevity derived from official competition data of 202,320 horses. Infrared imaging provided 49 temperature variables, including average and maximum values for regions of interest such as temperature differences from the body for eyes, hocks, fetlocks, feet, carpi and back. It also included differentials between these regions, asymmetry between right and left sides and variability within each area. Heritability was estimated using a mixed model with fixed effects, of age, sex, coat colour, weight and visit, along with random genetic effects (considering a pedigree of 8002 horses). The effect of temperature on the group of sires was assessed using multivariate partial least squares logistic regression, adjusting temperature for fixed effects. Results indicated high heritability for the temperature of regions of interest: body (0.53 ± 0.14), carpi (0.55 ± 0.19), fetlocks (0.47 ± 0.12), feet (0.46 ± 0.12 and 0.38 ± 0.12). Lower heritability was observed for differences between regions (around 0.20) and even lower for asymmetry and variability. Lower average and maximum eye temperatures, lateral asymmetry in hind feet temperature and temperature variability in the back were associated with a higher probability of belonging to the favourable group of sires for functional longevity. Infrared imaging may be a tool for identifying easily measurable selection criteria associated with longevity. Given the limited number of horses, the limited number of significant variables associated with the group of sires and the specificity of the protocol, verification and validation studies are necessary before its use.
Microbiota composition represents a promising tool in precision farming, simultaneously serving as a benchmark of environmental challenge, a predictor of animal physiological status, and a direct target for host selection. In this paper, we compared the ability of microbiota composition and genomic information to predict swine performance in two production settings, namely a purebred nucleus (NU) and a terminal cross commercial population (TE). Microbiota consistently predicted all traits in both scenarios (NU-TE: training on NU to predict TE; TE-NU: training on TE to predict NU) and at two time points: mid-test and off-test. The highest correlation (i.e., prediction accuracy) was achieved for back fat, with values of 0.08 and 0.04, and 0.30 and 0.23 for mid and off-tests, predicting from nucleus to terminal, and vice versa. Similarly, daily gains correlations were 0.05 and 0.04, and 0.18 and 0.15 for the same time points and scenario combinations. Including genomic information yielded correlations ranging from low for loin area to moderate for back fat (0.19 nucleus to terminal, 0.16 for the opposite). Microbiota had higher prediction accuracies than genomic for back fat both from nucleus to terminal and vice versa (+0.11, +0.07) and daily gain (+0.08, +0.02) at off-test. Lower accuracies were obtained for the IMF. Including genomic and microbial information produced higher accuracies than microbiota or genomic alone for back fat (0.37 and 0.29 for nucleus to terminal and opposite) and daily gain (0.19 and 0.21 for nucleus to terminal and opposite). Results for other traits differed for different scenarios. Results show that microbiota composition effectively predicted most growth and carcass traits, particularly growth and fat deposition, across production systems, prediction scenarios (NU-TE and TE-NU), and time points (mid-test and off-test). These findings highlight the potential of microbiota profiles to predict phenotypes across production systems and support their use as a tool for selecting animals in environments they have not been exposed to.
The aim of the present study was to infer genetic (co) variance components and to estimate parity-specific breeding values for the female fertility traits non-return rate after 56 days, the interval from calving to first service and days open by applying random regression models on a time-dependent parity scale. In this regard, we considered a female fertility dataset comprising 592,829 records on 190,269 German Holstein cows and heifers kept in 45 large-scale dairy contract herds. From a subset of 21,316 cattle with phenotypic records, (imputed) 50 K genotypes were available. The applied genomic random regression model considered Legendre polynomials of order 2 for the additive-genetic effects along the parity scale, and combined pedigree and genomic relationships through the H-matrix. Results were compared with genetic parameter estimates from a multiple-trait model, considering the same fertility trait in different parities as different traits. From both modelling approaches, we observed the trend of increasing genetic variances and heritabilities with increasing parity. Especially for the non-return rate, the genetic variance in heifers was substantially smaller than in all parities of cows. With regard to the random regression model, genetic correlations between the same fertility traits from adjacent parities were close to 1, but gradually declined with increasing parity distances. Small genetic correlations were also estimated between non-return rates in heifers with non-return rates in all cow parities, i.e., 0.50 with parity 1, 0.44 with parity 2, 0.41 with parity 3, 0.35 with parity 4, 0.33 with parity 5, and 0.25 with parity 6. A similar pattern for genetic correlations in the same traits across parities was confirmed from the multiple-trait model application. Estimated breeding values for all fertility traits in different parities of sires with at least 10 phenotyped daughters per trait (estimates from the random regression model) were correlated with their official breeding indexes from the national genetic evaluation. In this regard, moderate differences were observed when comparing breeding value correlations for non-return rates in heifers with respective correlations in all cow parities. From a practical breeding perspective, the most important results were the rather small genetic correlations for the same traits in different parities (e.g., 0.24 between calving to first service in parities 1 and 6), and differing breeding value correlations with other breeding indexes in different parities. These findings suggest the implementation of specific genetic evaluations for specific cow parities, as an extension to the existing separation between heifer and cow fertility traits. Parity-specific breeding value correlations from the random regression and the multiple-trait model considering the sires with at least 10 daughters were larger than 0.85, suggesting only minor re-rankings of sires from the two different modeling approaches.
Including fat thickness as a covariate in the regression model used to calculate residual feed intake (RFI) could help preserve carcass quality traits, such as marbling, flavour and juiciness, by accounting for variation in fat deposition. This study aimed to: (1) investigate the benefits of adjusting RFI for rump fat thickness (RFT); (2) estimate variance components and genetic correlations between RFI-calculated with (RFIF) and without (RFIW) adjustment for RFT-and growth, reproduction and carcass traits using genomic information in beef cattle; and (3) compute accuracy, bias and dispersion of RFIF and RFIW genomic breeding values predicted using single-step GBLUP (ssGBLUP). We hypothesised that adjusting for RFT would account for a small proportion of RFI variability, and that genetic parameter estimates would support more balanced selection decisions. Phenotypic records were collected from 9094 Nellore animals (3253 females and 5952 males) over 14 feed efficiency tests conducted from 2011 to 2024. The pedigree included 17,407 animals, of which 5812 were genotyped. Linear and threshold animal models were applied for continuous and categorical traits, respectively. Heritability estimates were low for RFIW (0.17) and RFIF (0.16), with a strong genetic correlation between them (0.98), and a weak genetic correlation between RFIW and RFT (0.15). Spearman correlations between RFIF and RFIW breeding values were high: 0.98 in females and 0.95 in males. Genetic correlations of RFIW and RFIF with growth, reproduction and carcass traits ranged from -0.33 to 0.35. Prediction accuracy was similar for RFIF (0.43) and RFIW (0.44), whereas bias (0.00 for RFIw and 0.00 for RFIF) and dispersion (0.05 for RFIw and 0.03 for RFIF) showed minor differences. Although RFIF captured slightly more genetic variability, the impact was minimal and no differences were observed between RFIF and RFIW. The genetic correlations between RFI and traits related to growth, reproduction and carcass were close to zero to moderate, indicating that selection for RFI is unlikely to negatively impact these other traits. However, it is essential to consider the full set of traits in the selection process to avoid potential drawbacks to the overall genetic progress of the herd.
Successful reproduction is a key factor for efficient breeding schemes and sustainable animal farming. Aquaculture breeding programs rely heavily on small fractions of selected breeders to yield large production stocks, given the high fecundity typically observed in these species. In Sweden, Arctic charr (Salvelinus alpinus) is a salmonid with notable commercial potential, with a selective breeding program operating for 10 generations under a growth-rate focused breeding goal. Despite significant gains, the nucleus faces challenges with low and fluctuating fertility impeding expansion efforts. In this study, we estimate genetic parameters for charr milt quality phenotypes measured with specialised cytometry and Computer-Assisted Sperm Analysis (CASA). At the same time, we assess the sex-specific architecture governing egg count and sperm concentration along body size. Finally, we propose a novel analytical framework for the analysis of realised fertilisation success rates by considering a multiplicative system of latent maternal and paternal contributions. Low to moderate heritability estimates and genetic correlations were obtained from multi-trait modelling for traits reflecting sperm quality along with high estimates for fork length. Genetic correlations among sperm kinematic parameters appeared strong, while the same traits showed weak positive and weak negative correlations with sperm concentration and fork length, respectively. Furthermore, a negative genetic correlation between sperm concentration and both male body size and egg count suggests a complex interplay of a possible trade-off and sexual antagonism. Our latent fertility analytical approach returned low to moderate heritability estimates depending on the modelling configuration. Overall, our study demonstrated the complexity characterising the heritable portions of reproductive traits in Arctic charr and tested alternative tools that have the potential for integration into selective breeding programs.
In tropical extensive beef cattle systems, heifers raised on pasture are exposed to various environmental challenges that affect their growth and reproductive performance during the first breeding. Resilience indicators derived from deviations in longitudinal traits can quantify the magnitude of these challenges and the ability of an animal to recover after disturbances. Hence, this study aimed to estimate genetic parameters for resilience indicators derived from weight deviations across growth in Nellore heifers, and their genetic correlations with yearling weight (YW), reproductive traits, calf performance and pre-weaning survival (PWSc). Phenotypic records were available for 3072 heifers, while 3226 animals were genotyped with 383,856 SNP markers (after quality control). A total of 30,720 weight records were used for growth curve modelling across three developmental phases: yearling, first breeding and first calf weaning. The resilience indicators derived and analysed were as follows: (i) natural logarithm of residual variance (LnVar); (ii) lag-1 autocorrelation of residuals (rauto); and (iii) skewness of residuals. The weight, reproductive, calf performance and survival traits analysed were as follows: YW, weight at the beginning of the breeding season (WBS), heifer pregnancy (HP), calves birth weight (BWc), calves weaning weight (WWc), calves average daily gain from birth to weaning (ADGc) and PWSc. Genetic parameters were estimated using the ssGBLUP method under a Bayesian framework. Heritability estimates (h2) were highest for LnVar, ranging from 0.32 ± 0.03 (calf weaning) to 0.42 ± 0.03 (breeding). Moderate h2 values were observed for rauto (0.22 ± 0.03 to 0.29 ± 0.03), whereas skewness had low heritability (0.08 ± 0.02 to 0.13 ± 0.02). Genetic correlations (rg) between LnVar and weight traits were unfavourable. In contrast, rauto exhibited favourable correlations with YW (-0.29 ± 0.08 to -0.50 ± 0.08). LnVar at breeding showed favourable and moderate rg with HP (-0.37 ± 0.10). All resilience indicators were favourably correlated with PWSc, with the strongest estimate observed for LnVar at calf weaning (-0.28 ± 0.15). These findings provide novel insights into the genetic basis of resilience in growing beef heifers. LnVar and rauto, in particular, emerge as promising traits for selecting animals better adapted to environmental variability. Additionally, favourable genetic correlations with fertility and survival traits suggest that more resilient heifers are more likely to become pregnant during their first breeding season and raise calves with higher survival rates until weaning.
Genetic and phenotypic parameters of traits related to growth, production and reproduction are crucial in the formulation of successful breeding programmes. Given the diversity in the cattle population of India, meta-analysis offers a comprehensive insight into the overall performance of the crossbred population for the efficient implementation of animal improvement programmes. The present study aimed to undertake meta-analysis and estimate the genetic and phenotypic parameters of performance traits in Indian crossbred cattle. A total of 130 articles were included in this study after data editing and quality control. The heterogeneity index reached 91.2% for phenotypic parameters, 99.4% for heritability estimates, and 99.4% for phenotypic correlations. Pooled least squares mean ranged from 20.54 to 25.84 kg for body weight at birth, 1542.05 to 2691.44 kg for first lactation milk yield, 996.05 to 1259.38 days for age at first calving in crossbred cattle. Pooled heritability estimates ranged from 0.13 to 0.69 for growth traits, 0.09 to 0.76 for production traits, and 0.05 to 0.25 for reproduction traits. The pooled repeatability estimates were moderate and ranged from 0.22 to 0.42. Pooled genetic correlations were positive and ranged from 0.09 to 0.97. This is the first attempt to estimate the genetic and phenotypic parameters of performance traits in Indian crossbred cattle using a meta-analytical approach. Meta-analysis revealed that the heritability estimate of production and reproduction performance was low to moderate in all the crossbred populations. This suggested the influence of environment and other non-additive genetic effects, wherein improvement is possible through the integration of effective selection and optimum management strategies. Pooled estimates of genetic and phenotypic parameters from this investigation may be used for the effective implementation of breeding programmes in the herds/populations, where these parameters are not estimated/available for any reason.