Genomic selection involves choosing as parents those elite individuals with the higher genomic estimated breeding values (GEBV) to accelerate the speed of genetic improvement in domestic animals. But after multi-generation selection, the rate of inbreeding and the occurrence of homozygous harmful alleles might increase, which would reduce performance and genetic diversity. To mitigate the above problems, we can utilize genomic mating (GM) based upon optimal mate allocation to construct the best genotypic combinations in the next generation. In this study, we used stochastic simulation to investigate the impact of various factors on the efficiencies of GM to optimize pairing combinations after genomic selection of candidates in a pig population. These factors included: the algorithm used to derive inbreeding coefficients; the trait heritability ; the kind of GM scheme (focused average GEBV or inbreeding); the approach for computing the genomic relationship matrix (by SNP or runs of homozygosity (ROH)). The outcomes were compared to three traditional mating schemes . In addition, the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population.ROH-based GM schemes with the maximum genetic gain resulted in 0.9%–2.6% higher rates of genetic gain ΔG, and 13%–83.3% lower ΔF than positive assortative mating regardless of heritability. The rates of inbreeding were always the fastest with positive assortative mating. Results from a purebred Large White pig population, confirmed that GM with ROH-based GRM was more efficient than traditional mating schemes.Genomic mating outperforms other approaches in limiting the inbreeding accumulation for the same expected genetic gain. The use of ROH-based genealogical relatedness in GM achieved faster genetic gains than using relatedness based on individual SNPs. The GCompared with traditional mating schemes, genomic mating can not only achieve sustainable genetic progress but also effectively control the rates of inbreeding accumulation in the population. Our findings demonstrated that breeders should consider using genomic mating for genetic improvement of pigs.The online version contains supplementary material available at 10.1186/s40104-023-00872-x. Animal breeding methods have changed dramatically over the past one hundred year . One of The long-term goal of selection should be to achieve sustainable genetic gain while population genetic diversity by restricting the rate of increase of inbreeding . ReducedOCS can be implemented using a variety of algorithms or methods. Methods implementing OCS theory have been proposed based on minimum coancestry or minimizing the covariance between ancestral contributions. These approaches tend to disperse the contribution of individuals in the breeding population and tend to increase the number of ancestors represented in each offspring. This brings the ancestors closer to the exact threshold linear relationship and reduces the inbreeding rate , 16. AnoWith the widespread adoption of genotyping for genome-wide single nucleotide polymorphisms (SNP), the realized genealogical relatedness can be derived, and collected into matrix of genomic relationships (GRM) . Since GIn traditional breeding schemes, selecting individuals with genetically or phenotypically similar characteristics to mate is known as positive assortative mating. That mating scheme can achieve maximum short-term genetic gains but at the expense of increased rates of inbreeding. The opposite approach is negative assortative mating where selected animals are chosen as mating pairs when they exhibit dissimilar genetic or phenotypic characteristics. In this study, we investigate impacts of different factors on genomic mating in simulated and actual purebred pig populations. Based on the demographic history of the pig population, we simulated traits with different heritabilities. After selecting the best individuals as identified by the highest GEBV rankings, genomic mating is used to optimize mate allocation of those selected animals. Under different mating schemes, the average genetic gain and inbreeding coefficients in the offspring population were compared. There were three aims to this study: (1) evaluate whether implementing GM after GS can obtain the maximum genetic gain while effectively controlling the inbreeding accumulation compared with other traditional mating schemes, (2) explore the effects of using a GRM constructed from SNP genotypes compared to a GRM based on runs of homozygosity (ROH) of SNP genotypes, and (3) validate the application using real data from a purebred pig population.–5. The population size each generation was 2,000 individuals, consisting of 1,000 males and 1,000 females. The sire: dam mating ratio was 1:1 and the number of offspring per mating was 10 with an equal sex ratio. After 1,000 generations of random mating, the population size was gradually reduced to include only 400 individuals for the next 1,000 generations. In a second step, we selected 30 males and 900 females from the last generation of the ancestral population to represent modern founders. Each male mated 30 females randomly. The litter size was 10 with an equal sex ratio. The resultant offspring and their subsequent generation of 9,000 offspring represented the reference population of animals with pedigree, performance and genomic information to begin genomic prediction.Based on the demographic history of pigs, the QMSim  softwareThe simulated genome consisted of 18 pairs of chromosomes of 100 cM each. The number of SNP markers on each chromosome was 1,700 so the total number of SNPs was 30,600. Both SNPs and quantitative trait loci (QTL) were biallelic and evenly distributed on all the chromosomes. The detailed genome parameters simulated are listed in Table Genetic and phenotypic values were simulated for three traits for every individual. The additive genetic effect of QTLs was sampled from a gamma distribution with a shape parameter μ was generated from the uniformly distribution . If μ < 0.5, the first allele at the first locus of an individual was inherited from the paternal chromosome of the sire, if μ > 0.5, the first allele was inherited from the first locus on the sire’s maternal chromosome. (2) For the ith  locus, the recombination rate between the two adjacent loci was calculated according to the Haldane mapping function  random number μ was sampled, and if μ > r, no recombination occurred, and the first allele at the ith locus of the individual came from the ith locus on the same chromosome of the sire that contributed the allele for the (i − 1)th locus. If μ < r, recombination occurred, and the first allele at the ith locus of the individual came from the ith locus on the other chromosome of the sire. (3) The remaining markers followed this process for simulating inheritance of the paternal alleles. (4) The same process was repeated to sample the maternally inherited alleles of the individual.The simulated offspring will have inherited alleles at each locus following the principles of Mendelian inheritance. When QMSim was used to simulate the base population data (generation 0), the haplotypes of the parents were defined, each locus was represented by two alleles (1 and 2), and the first allele was from the sire whereas the second allele was from the dam. Thus, the genotype of the offspring was simulated from the genotypes of the parents. The specific process was as follows : (1) Forfunction , using tgs is the breeding value of the sire, gd is the breeding value of the dam, and wi is the Mendelian sampling term of individual i, which is the summation of Mendelian sampling errors of sire and dam using the following equation: ws is the Mendelian sampling error of the sire, and wd is the Mendelian sampling error of the dam. Hence, wi follows the N  of individual i, and ei is the random residual of individual i which follows N  A GRM based on the ROH relationship matrix which was 2 times the segment-based kinship matrix computed using the segIBD function in the R package optisel [In this study, two formulations of a GRM were constructed for the calculation of the inbreeding coefficients from genomic mating. Namely: (1) A GRM calculated using the formula in VanRaden  by A.mat optisel . The hapTo obtain the optimal mate allocation, the R package TrainSel  was execRandom mating. The 30 sires and 900 dams selected by GS were randomly mated, with no restriction on mating among siblings.th highest ranking dams, and so on, until the rank 30 sire was mated to the 871–900th ranking dams with no restriction on sibling mating.Positive assortative mating. The 30 sires and the 900 dams selected by GS were sorted in the order of GEBV, then the highest ranking sire was mated with the 30Negative assortative mating. The 30 sires selected by GS were sorted in the order of GEBV from low to high, and the 900 dams were sorted in the order of GEBV from high to low. The highest ranking sire was mated to the 30 lowest ranking of the 900 dams, and so on, until the rank 30 sire was mated to the 30 highest ranking of the 900 dams, with no restriction on siblings mating.There were three traditional mating schemes as below:u is the generation number.The rate of genetic gain was calculated using the following equation: FPED) proposed by Wright [N is the number of related pathway chains from the individual's sire to the common ancestor and FA is the inbreeding coefficient of the common ancestor A. The other was the estimated inbreeding coefficient (FGRM) based on SNPs:m is the number of SNPs, pi is the minor allele frequency, and xi is the copy number of the ith SNP. Inbreeding rate, ΔF, was calculated as 1 − eβ, where β is the slope of the linear regression of ln (1 − Fu) on u and Fu is the mean inbreeding coefficient for animals born at the uth generation, as used in Nirea et al. [Two measures of inbreeding were used. One was the pedigree-based inbreeding coefficient  There is no overlap between generations; (2) Environmental variation is homogeneous across generations; (3) Every individual has a phenotypic value for all 3 traits; (4) The litter size is 10. The specific process is shown in Fig.  There isThe performance of the genomic mating program was tested on a real dataset obtained from a herd of purebred Large White pig breeding population run by a commercial company in Shanghai city. Only the total number of piglets born was analysed and the data for that trait consisted of 16,326 records. The pedigree data contained 57,135 animals and was used in a repeatability animal model to estimate the EBV following Wang et al. . Some 6,ROH was denoted by GM_G_Gain or GM_GROH_Gain, respectively, and the genomic mating scheme focused on the minimum inbreeding via the use of G or GROH was denoted by GM_G_Inb and GM_GROH_Inb, respectively.Genetic gain and inbreeding are not independent but antagonistic since more intense selection will increase both genetic gain and inbreeding and thereby reduce effective population size. By balancing these two key performance indicators (KPI), GM will obtain a series of solutions, as shown in Fig. ROH_Gain (9.296) was the highest, which was higher than negative assortative mating (8.387). GM_G_Gain (9.042), GM_G_Inb (8.768), GM_GROH_Gain and GM_GROH_Inb achieved 0.2% to 6.2% more ΔG than random mating (8.754) and 4.5% to 10.8% than negative assortative mating. Moreover, the ΔG of the GM_GROH_Gain was 2.7% and 2.8% higher than positive assortative mating and GM_G_Gain, respectively  of different mating schemes at the heritability of 0.1. The FPED of positive assortative mating showed a rapid upward trend, especially in the third generation, which was higher than other schemes. The FPED of GM_G_Gain was lower than positive assortative mating in the second and third generations. The FPED values of GM_GROH_Gain in 2–4 generations were significantly lower than those of positive assortative mating, but in the fifth generation, it was higher than GM_G_Gain. The FPED of the GM_GROH_Inb was the lowest among all schemes. The ΔF of the four genomic mating schemes were 13%–62.5% lower than positive assortative mating, and the ΔF of GM_GROH_Inb was the same as that of negative assortative mating, which was 12.5% lower than random mating and 43.75% lower than GM_G_Gain. The ΔF of GM_G_Gain was 4.5% lower than GM_GROH_Gain  was higher than that of negative assortative mating (82.173) and random mating (83.339) in the fifth generation. The average GEBVs of GM_GROH_Gain from the second to the fifth generation had accelerated rapidly. In the fifth generation, it (90.710) was higher than random mating. The ΔG of the four genomic mating schemes was 2.7%–11.4% higher than random mating. The ΔG of GM_GROH_Gain (18.601) was the highest among all mating schemes, 1.0% higher than positive assortative mating, and 1.3% higher than GM_G_Gain  in the first generation was higher than those of the three traditional mating schemes, and the average GEBV of the two schemes increased simultaneously. In the fifth generation, the GM_GROH_Gain (151.161) had the highest average GEBV, which was higher than random mating (128.539), negative assortative mating (122.271), and GM_GROH_Inb (133.062). The ΔG of the four mating schemes in genomic mating was higher than those of random mating and negative assortative mating, 3.6%–17.8% higher than random mating, and 9%–24% higher than negative assortative mating. GM_GROH_Gain was 2.3% higher than positive assortative mating and 4.2% higher than GM_G_Gain. The ΔG of GM_G_Inb was 2.7% higher than GM_GROH_Inb  FPED value is the statistical expectation of the probable genomic proportion of identity by descent (IBD) [In this study, the genetic relationship of individuals in GM was constructed by SNP or ROH. Compared to SNPs, ROH can allow for lengths of the genomic regions shared between individuals, which can track the way that the alleles inherited from parents to offspring more accurately . So, ROHn et al.  reported studies –41. Nevent (IBD) ; 2) the α = 0.4. There are several major effects related to target traits. The SNP effects across the whole genome under different heritabilities were shown in Fig. SGM needs estimating marker effects, and combine inbreeding coefficients and the estimated marker effects to determine which genotypes should be crossed to produce progenies , 43. In There are numerous algorithms to solve the GM optimization problem . In thisIn this study, a series of optimal mating combinations can be obtained from TrainSel based on genomic information. Although the optimal set of mating combinations was not unique, all of them were better than other non-genomic mating schemes. The genetic algorithm using in TrainSel is still significantly different from others such as optimal genetic contribution selection –47. The In the empirical data analysis, the average of GEBV in the real offspring was smaller than those of all these mating data by simulation. There are two factors influencing the results: sire: dam mating ratio and the number of offspring per mate allocation. In the simulation analysis based on the real data, the average of the sire: dam mating ratio was about 8.07 and the number of offspring per mating allocate was 16, however they varied in real population. Both of them can directly affect the independence of genetic contributions of ancestors .Relative to GS, GM also uses the estimated marker effects and the genetic information to decide which genotypes should be crossed to obtain the next breeding population. In the current study, we only focused on the additive genetic effects of a single trait in GM in the purebred breed. In addition to being used to optimize the mating scheme, GM can also be used to estimate crossbred animals, predict the probability of occurrence of high-yielding or low-yielding individuals, etc. . At presIn this study, we used simulation to investigate the effects of different genomic mating schemes in pig breeding. Our simulation study shows that implementing genomic mating after genomic selection is more beneficial than genomic selection followed by traditional mating systems in pig breeding programs. The use of ROH-based genealogical relatedness in genomic mating can obtain the optimal solution with the maximum genetic gain. Genomic mating not only achieves sustainable genetic progress but also control rates of inbreeding. The real data results further validated the simulate study. Through the optimization and tradeoff of genetic gain and inbreeding, a series of optimal solutions are calculated for breeders to choose according to the real condition. Our findings contribute to understanding the effect of using genomic mating in pig genetic improvement.Additional file 1: Fig. S1. SNP effects cross the whole genome at the heritability of 0.1. Fig. S2. SNP effects cross the whole genome at the heritability of 0.3. Fig. S3. SNPeffects cross the whole genome at the heritability of 0.5.


Oryza sativa) is a major crop and a main food for a major part of the global population. Rice species have derived from divergent agro-climatic regions, and thus, the local germplasm has a large genetic diversity. This study investigated the relationship between phenotypic and genetic variabilities of yield and yield-associated traits in Aus rice to identify short-duration, high-yielding genotypes. Targeting this issue, a field experiment was carried out to evaluate the performance of 51 Aus rice genotypes, including 50 accessions in F5 generation and one short-duration check variety BINAdhan-19. The genotypes exhibited a large and significant variation in yield and its associated traits, as evidenced by a wide range of their coefficient of variance. The investigated traits, including days to maturity (DM), plant height (PH), panicle length (PL) and 1000-grain weight (TW) exhibited a greater genotypic coefficient of variation than the environmental coefficient of variation. In addition, the high broad-sense heritability of DM, PH, PL and TW traits suggests that the genetic factors significantly influence the observed variations in these traits among the F5 Aus rice accessions. This study also revealed that the grain yield per hill (GY) displayed a significant positive correlation with PL, number of filled grains per panicle (FG) and TW at both genotype and phenotype levels. According to the hierarchical and K-means cluster analyses, the accessions BU-R-ACC-02, BU-R-ACC-08 and R2-36-3-1-1 have shorter DM and relatively higher GY than other Aus rice accessions. These three accessions could be employed in the ongoing and future breeding programs for the improvement of short-duration and high-yielding rice cultivars.Rice ( Oryza sativa) is a member of the Poaceae family. A larger part of the people in the world, mainly in Asian countries, consume rice as a staple food, making it the third most popular cereal grain crop worldwide [The cereal crop rice  utilizing the mean sum of squares revealed significant differences in the 10 investigated agronomical traits across the various F5 Aus rice genotypes (0.05) test to perform pairwise comparisons between the genotypes  values exceeding the Fisher’s LSD0.05 thresholds , plant height (PH), total tillers per hill (TT), effective tillers per hill (ET), panicle length (PL), number of filled grains per panicle (FG), number of unfilled grains per panicle (UFG), 1000-grain weight (TW), grain yield per hill (GY) and straw yield per hill (SY) . This exenotypes . This reenotypes . All theresholds . On the resholds . Among aresholds . Consistresholds . In contresholds .σ2p) of the Aus rice population is a result of environmental variance (σ2e), genotypic variance (σ2g) and their possible interactions [σ2g values of DM, PH, TW and PL traits were higher than their corresponding σ2e values, implying that the discerned diversity in these traits within the Aus rice population predominantly emanated from the genetic effects rather than environmental ones (H2) captures the proportion of σ2p due to genetic effects and is expressed as the ratio of σ2g to σ2p [H2 exhibited a range of 65.37 to 96.31% in DM, PH, TW and PL traits  and DM and SY (r = 0.17), implying that the increase in DM might contribute to increasing tiller numbers and, subsequently, SY    a, implyictively) b. On thectively) b. In genHeatmap hierarchical clustering using Euclidean distance was carried out to identify the trait–genotype association . All 51 In order to further validate the genotype–trait association, we conducted a K-means clustering with a special focus on the DM and GY traits across the 51 genotypes . K-means5 Aus rice accessions and a short-duration check variety BINAdhan-19 under field conditions , medium (30 to 60%) and high (>60%), aligning with the degree of genetic contribution to the observed phenotypic traits [σ2g values for DM, PH, PL and TW were notably greater than their corresponding σ2e values, and their H2 values were >60%, implying that these traits are primarily controlled by genetic factors  low (0 to 0.30), (ii) medium (0.30 to 0.70) and (iii) high (0.70 to 1) for positive linear correlations, while (iv) low (0 to −0.30), (v) medium (−0.30 to −0.70) and (vi) high (−0.70 to −1) for negative linear correlations [The elations . Our pheelations . Li et aelations  also obselations  demonstrelations  also rev5 generation, future research should be considered to verify their similar characteristics in succeeding generations.Previous studies reported that BINAdhan-19 is a standard short-duration Aus variety with a semi-dwarf phenotype ,36. In thttps://bsmrau.edu.bd/age/weather-data/; accessed on 25 August 2021) were maintained for the monthly average temperatures, humidity levels, rainfall patterns and evaporation rates at the study location  twenty-eight. The experimental site’s soil is a type of shallow red-brown terrace soil, which is locally called the Salna series. It possesses less-than-optimal physical and chemical attributes and is characterized by a predominantly heavy clay texture, acidity and a silt loam composition in the upper 50 cm layer. This soil type falls under the category of typic Paleudults as per the United States Department of Agriculture classification, specifically belonging to the Orchrept suborder within the Inceptisol order . For thelocation .5 Aus rice accessions in conjunction with the suitable reference variety BINAdhan-19 ), triple super phosphate (as a source of phosphorus (P)) and muriate of potash (as a source of potassium (K)) were used. NPK fertilizers were applied at the rate of 175, 80 and 110 Kg, respectively, per ha. P and K fertilizers were applied during the final land preparation, whereas N fertilizer was applied in three splits. In each split, 58.34 Kg of N fertilizer was applied for each ha. The first split of N fertilizer was added to the soil during the final land preparation. The second and third splits of N fertilizer were administered at 35 and 55 days after sowing, respectively. Each plot underwent uniform intercultural activities, such as irrigation, weeding, drainage and plant protection measures to guarantee that the rice crop grew normally.n = 3; 5 plants per replicate) were used to collect data on growth, yield and yield-related traits. The studied DM trait was measured from the sowing to harvesting day. At harvest, PH was determined from the soil surface to the highest part of the plant; TT was counted as the number of tillers per hill when the tillers had at least one leaf; ET was counted as the number of panicles bearing tillers per hill; PL was determined from the bottom of the peduncle to the topmost of the panicle; FG and UFG were counted when spikelets had endosperm and without endosperm, respectively. TW, GY and SY were all measured in grams and corrected to 14% moisture content (MC) using formula 1 as follows:Plants were harvested when 85% of the grains of the panicles turned a golden yellow color. Three biological replicates  and phenotypic variance (σ2p) were estimated by formulae 2, 3 and 4, respectively, according to Faysal et al. [Environmental variance σ2e=EThe environmental coefficient of variation (ECV) percentage was calculated by formula 5 according to Azimi et al. (5)ECV%=The genotypic coefficient of variation (GCV) percentage and phenotypic coefficient of variation (PCV) percentage were estimated by formulae 6 and 7, respectively, according to Faysal et al. (6)GCV%=H2) percentage was estimated by formula 8 according to Burton [Broad-sense heritability (o Burton (8)H2%=σhttps://www.r-project.org/ accessed on 17 February 2023). The genotype-based and phenotype-based correlation analyses were carried out using the ‘variability’ and ‘ggcorrplot’ packages in R. K-means clustering and heatmap hierarchical cluster analysis were carried out using the ‘factoextra’ and ‘cluster’ packages in R 3.2.0, respectively.The one-way ANOVA, descriptive statistics and Fisher’s LSD test of the investigated parameters were conducted by the ‘doebioresearch’ package in R 3.2.0 (5 Aus rice genotypes alongside the BINAdhan-19 check variety. Among these attributes, FG, PL and TW demonstrated substantial correlations with GY as deduced from both phenotype-based and genotype-based correlation analyses. This indicates the viability of utilizing these traits as discerning markers for germplasm selection. Certain genotypes exhibited shorter DM characteristics similar to the BINAdhan-19 check variety. Notably, the Aus rice accessions BU-R-ACC-02, BU-R-ACC-08 and R2-36-3-1-1 demonstrated a remarkable combination of shorter DM and heightened GY within optimal field conditions. In the future, these rice accessions need to be examined under diverse agronomic management practices, thereby substantiating their performance and adaptability across varied environmental contexts. Hence, these identified accessions warrant consideration as prospective germplasm candidates for inclusion within rice breeding programs aimed at the development of short-growth cycle, high-yielding rice varieties endowed with favorable agronomic attributes.Significant variations were evident across all 10 investigated agronomical traits within the cohort of 50 F


Angora and meat producing goats play an important role in food security and sustainable livelihoods of livestock producers in South Africa. To maintain their role, it is important to select animals that can thrive in the harsh South African climate, especially under the envisioned climate change conditions.Several phenotypes have been identified as selection criteria for adaptation. These include litter size at birth, litter size at weaning and litter weight at weaning (reproduction), weaning weight (growth), fecal egg counts, FAMACHA score, packed cell volume, tick counts, hair length, and coat type (endo- and ecto-parasite resistance) and coat type, coat thickness, color of skin and coat, rectal temperature (heat tolerance). Many of these phenotypic indicator traits have limitations such as low heritabilities or being difficult to measure.The application of new technologies in breeding and selection is under-utilized in goats in general, and especially in South African goats. The use of validated causative mutations affecting traits relating to adaptation to harsh environments in selection programs would facilitate genetic progress in herds where phenotypic recording is challenging.In addition, increased investments by various role players in applying new technology in the systematic collection of phenotypic indicator traits , can be used to improve the rate of animal improvement in the South African goat breeds.Goats play an important role in the agricultural sector in South Africa and are raised under varying climatic conditions in most parts of the country, almost exclusively under extensive farming systems. The animals are subjected to a range of adverse conditions ranging from hot, dry summers, limited and seasonal food sources, external parasites and internal parasite challenges during the rainy seasons, to very cold winters.The most populous goat breed in South Africa is the mohair-producing Angora goat, followed by the commercially farmed, meat producing Boer goat, Kalahari Red goat, and Savannah goat breeds. These commercial breeds constitute between 30% and 40% of the South African goat population, with the exotic dairy goats (Saanen and Toggenburg) making up less than 1% of South Africa’s goat numbers. The origin and description of these commercial breeds, as well as their production and reproductive performance standards, distribution over the country and breed numbers (where available) were summarized by Almost 70% of the meat goats in South Africa are, however, indigenous goats. Even though their role and relative importance are dependent on many variables , indigenous goats generally contribute to meat, milk, fiber, and hide production in rural communities. Four indigenous goat ecotypes are recognized in South Africa, namely the Nguni type, Eastern Cape Xhosa type, Northern Cape, lob eared, speckled type and the Kunene type . These eFor an animal to optimally produce and reproduce, it should be adapted to its production environment. The animal must be able to survive, thrive, produce and reproduce without excessively high input costs or managerial demands in terms of feed and health care . The lonBreeding for efficiency, especially in a climate-change environment, has become a new challenge for most livestock species. The feasibility of incorporating new technologies in breeding plans for South African goats in harsh environments is discussed here.Breeding for increased efficiency and adaptability under harsh environments involves a wide array of breeding objectives and traits, from routinely measured traits such as weights and reproduction to less-common traits such as heat resistance and faecal egg counts. The rate of genetic progress expected can differ vastly between these traits, and will be discussed in more detail for some of the traits.Reproductive ability in the presence of endemic stressors is one of the aspects that define an animal’s ability to adapt to harsh environments . ReproduThe performance trait that is the most indicative of adaptation is growth rate, especially pre- and postweaning growth rate. Preweaning growth rate also depends on the milk production and maternal ability of the dam, while postweaning growth is mainly a function of the additive genetic component and it impacts largely on age at marketing, selection opportunities and future reproduction of the maiden doe. Weaning and postweaning weight are recorded by breeders participating in the National Small Stock Improvement Scheme.Disease resistance is often used as a generic term that refers to resistance to infection, and thus to the animal’s ability to restrain a pathogen or parasite lifecycle . It can Gastrointestinal parasites have a direct adverse effect on animal reproduction efficiency, health and performance. The generally accepted phenotypic measures indicating relative nematode resistance include fecal egg counts (FEC), FAMACHA scores, dag scores and packed cell volume (PCV), depending upon the specific nematode involved. A summary of potential phenotypes as indicators of resistance to gastrointestinal nematodes, was provided by Apart from ticks transmitting disease-causing pathogens to animals, which may cause serious losses in productivity and may lead to high mortality levels, their bites also cause damage to the skin and tissues . The couHeat stress results in a decrease in growth, milk, and meat production and immune response in goats. There exists significant variation regarding heat resistance both between breeds, but also within a breed, based on a combination of the individual’s inherent physiological, morphological, behavioral and genetic responses to heat stress. Phenotypic measurements to assess heat tolerance may include coat type, coat thickness, number and length of hairs, pigmentation (color) of the skin and coat, number of sweat glands, sweating rate, rectal temperature, skin temperature, heart rate, respiratory rate and various blood parameters . Rectal Tick and lice counts can be difficult to record under extensive farming conditions. Certain environmental factors also have an influence on the parasite load on animals. Season, rainfall and temperature directly influence parasitic load, while sex and age of animals might influence the susceptibility of cattle . Many poEven though the phenotypic indicator traits for reproduction, namely litter size at birth and litter size at weaning are direct measurements of the breeding objective, it is not always easy to accurately record under extensive farming conditions. Kids that are stillborn or kids that died soon after birth are not recorded in many instances. Recording of pedigrees under extensive farming conditions are also difficult and prone to mis-allocation of offspring to dams. Furthermore, reproductive traits have lower heritability estimates and are expressed at a later stage in life.Noncomprehensive summaries of heritability estimates for production, reproduction, and disease resistance traits related to adaptation in Angora and Boer goats are given in From The identification of causative mutations and genetic markers associated with traits of economic importance, has been a focus of research in all livestock species for the past two decades. Identification of quantitative trait loci (QTL), was performed first using microsatellite markers and then single nucleotide polymorphisms (SNPs). The use of dense SNP arrays enabled genome-wide association studies (GWAS) and currently whole-genome sequencing techniques are also employed. The literature abound with studies in which potential genes were identified as markers for various reproduction, production, adaptation and disease related traits in livestock . These rApart from the genes included in It is commonly acknowledged that genetic improvement in all livestock species depends on the accurate measurement of phenotypic data, and where possible, the added benefit of genomic data. In developing countries such as South Africa, with large numbers of noncommercial farmers, the collection of accurate phenotypes in extensive production systems is problematic. This results in the collection of some easy to measure phenotypes, such as weights. However, traits linked to reproduction, survival, and adaptation have largely been ignored, especially in communal and smallholder farming systems. Although the commercial goat breeds have Breeders’ Associations and stud breeders participate in the National Small Stock Improvement Scheme, the uptake of large-scale phenotyping remains low. Even with more cost-effective genotyping becoming more feasible for developing countries, accurate phenotypes are necessary to be able to apply genomics in animal improvement.The technology that enables the recording of large volumes of phenotypes in a cost-effective, easy manner, is defined as phenomics. It could assist in capturing some of the hard-to-measure traits associated with adaptation, such as fitness and disease resistance traits. Sensors  that can detect behavior patterns could be used for oestrus detection in livestock species. The systematic collection of phenotypes that can address adaptation and welfare will have to be prioritized to make use of the technologies that are currently available for animal improvement. Knowledge on heat tolerance traits in specifically the South African breeds is still lacking. New technologies involving recording of heat tolerance related traits could be employed to fill this gap.Due to advances in mobile technology, information and communications technology models can possibly assist even small holders to record phenotypes and give them feedback to assist with management decisions . HoweverAs far as genomics is concerned, goal-oriented research should be undertaken with the already identified genes as basis, to validate the association between the alleles/genotypes at these candidate genes and the respective traits in the South African goat populations. Validated markers could then be included in marker assisted selection programs for the respective breeds. This could be implemented where the use of genomic estimated breeding values is not feasible due to difficulty in recording the necessary phenotypic data. In addition, for nematode resistance, more in-depth research into the exact immune-regulatory mechanisms is still required, especially for the South African breeds. This might also lead to the identification of SNPs associated with candidate genes involved in immunoregulatory mechanisms affecting nematode resistance in South African goat breeds.The application of new technologies in breeding and selection is under-utilized in goats in general, and especially in South African goats. The knowledge already available on candidate genes affecting traits relating to adaptation to harsh environments should be exploited and expanded through validation of these genes in the South African goat population. Inclusion of the validated markers in selection programs would facilitate genetic progress in herds where phenotypic recording is challenging. The determining factor whether selection will be based on phenotypic indicator traits, phenotypic traits plus genomics, or genetic markers would be the cost of collecting phenotypic data or samples  and analyses of the samples, compared to the cost of genotyping. Increased investments by various role players in applying new technology in recording of phenotypic indicator traits , improved pedigree recording and large-scale genotyping would contribute to accelerated genetic progress in the South African goat breeds.Conflict of interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Fine-mapping prioritizes risk variants identified by genome-wide association studies (GWASs), serving as a critical step to uncover biological mechanisms underlying complex traits. However, several major challenges still remain for existing fine-mapping methods. First, the strong linkage disequilibrium among variants can limit the statistical power and resolution of fine-mapping. Second, it is computationally expensive to simultaneously search for multiple causal variants. Third, the confounding bias hidden in GWAS summary statistics can produce spurious signals. To address these challenges, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias. By using cross-population GWAS summary statistics from global biobanks and genomic consortia, we show that XMAP can achieve greater statistical power, better control of false positive rate, and substantially higher computational efficiency for identifying multiple causal signals, compared to existing methods. Importantly, we show that the output of XMAP can be integrated with single-cell datasets, which greatly improves the interpretation of putative causal variants in their cellular context at single-cell resolution. Fine-mapping prioritizes risk variants identified by genome-wide association studies to uncover biological mechanisms underlying complex traits. Here, the authors develop a reliable fine-mapping method (XMAP) by leveraging genetic diversity and accounting for confounding bias. As the cell type and cellular process in which the identified SNPs are active remains largely unknown, the GWAS findings remain hard to interpret. Fine-mapping seeks to prioritize the causal SNPs underlying complex traits and diseases. Recent progress shows that, by integrating fine-mapping results and single-cell data, it becomes feasible to identify disease/trait-relevant cell types and cell states6. Therefore, fine-mapping is a critical step to interpret GWAS findings by elucidating their biological mechanisms of identified risk variants, and fine-mapping results will offer an invaluable resource for precision medicine7.Genome-wide association studies (GWASs) have reported hundreds of thousands of associations between single-nucleotide polymorphisms (SNPs) and various phenotypes9. For example, an eQTL associated with ERPA2 and Crohn’s disease was found to be driven by 13 separate variants9. However, it becomes computationally expensive to simultaneously search for multiple SNPs by enumerating causal combinations. Third, the unadjusted socioeconomic status10 and geographic clustering12 in GWAS samples can induce confounding bias in GWAS estimates13. These confounding factors cannot be fully corrected through linear mixed models (LMMs)15 or principal component analysis (PCA)16. Fine-mapping without correcting the confounding bias in GWAS data can yield spurious results.Despite the great promise of fine-mapping, efforts toward reliable prioritization of causal SNPs have been hampered by three key challenges. First, when GWAS samples come from a single population, SNPs in a local genomic region can be highly correlated due to the low recombination rates in that region. It is very difficult for statistical methods to distinguish the causal variants from a set of SNPs in strong linkage disequilibrium (LD). Second, genetic signals at trait-associated regions are commonly conferred by many variants acting together. A very recent study of 744 human expression quantitative trait loci (eQTLs) reported that 17.7% of the eQTLs harbour more than one variant with major effects on gene expression levels, emphasizing the importance of identifying multiple genetic variants within an associated locus18 rely on an exhaustive search for all possible causal configurations of variants. They become computationally unaffordable when searching for more than three causal associations among thousands of variants. More efficient methods have been developed based on approximated inference, including CAVIARBF19, FINEMAP20, and DAP-G22. A very recent method, SuSiE24, introduces a novel framework by assuming the overall genetic effects can be decomposed as a sum of single effects. The model structure of SuSiE enables an efficient algorithm to detect multiple causal SNPs with minor computational overhead. Its extension, SuSiE-inf25, incorporates a polygenic component to improve reproducibility. Despite their improvement in computational efficiency, the statistical power of these methods is usually limited because it is difficult for them to distinguish the causal variants from the highly correlated variants in the single population setting. To boost the statistical power of fine-mapping, several methods were developed to leverage different LD patterns with cross-population GWASs, including trans-ethnic PAINTOR26 and MsCAVIAR27. Although these methods allow a locus to harbour multiple causal variants in principle, they require enumerating all causal combinations of variants, hence become too time-consuming to search for more than three causal variants. As a recent extension to SuSiE, SuSiEx28 improves the computational efficiency of cross-population fine-mapping by inheriting the algorithm design from SuSiE. However, existing fine-mapping methods do not account for confounding bias in GWAS summary statistics, leading to spurious results.While many efforts have been devoted to the development of fine-mapping methods, existing methods only partially addressed the above major challenges. The classical fine-mapping methods29. By jointly analyzing cross-population GWASs, XMAP can effectively improve the power and resolution of fine-mapping. Second, XMAP can identify multiple causal signals with a linear computational cost, while many existing fine-mapping methods are too time-consuming to identify multiple causal signals. Third, XMAP can correct the confounding bias in GWAS summary data to avoid false positive findings and improve reproducibility. Through comprehensive simulation studies, we show that XMAP not only improves the statistical accuracy of fine-mapping but also offers a substantial computational advantage over existing methods. The evidence from real data analysis indicates that XMAP achieves substantial power gain with high reproducibility. By combining the GWASs of low-density lipoprotein (LDL) from East Asian (EAS), African, and European, XMAP identifies three times more putative causal SNPs than SuSiE. These SNPs are strongly enriched in the eQTL of the liver, suggesting their important roles underlying the biological process of LDL. Furthermore, using the height GWAS as an example, we show that XMAP can effectively correct confounding bias and substantially improve reproducibility. Lastly but importantly, XMAP results can be integrated with single-cell data to identify trait-relevant cell populations at single-cell resolution, maximizing the utility of single-cell data for the inference of the pathological mechanisms. We apply XMAP to 12 blood traits and perform integrative analyses of the XMAP results and single-cell profiles of 23 hematopoietic cell populations. The analysis results suggest that XMAP enables the identification of the trait-relevant cell types in which putative causal SNPs are active. For example, SNPs identified by XMAP show a significant enrichment of the mean corpuscular volume in 99.3% of late-stage erythroid cells, which is very helpful to interpret GWAS results.In this paper, we develop a statistical method for cross-population fine-mapping (XMAP) by leveraging genetic diversity and accounting for confounding bias Fig. . The suchttps://github.com/YangLabHKUST/XMAP. The technical details of XMAP are described in the Methods section.XMAP is a computationally efficient and statistically accurate method for fine-mapping causal variants using GWAS summary statistics. With innovations in its model and algorithm design, XMAP has three features: (i) It can better distinguish causal variants from a set of associated variants by leveraging different LD structures of genetically diverged populations. (ii) By jointly modeling SNPs with putative causal effects and polygenic effects, XMAP allows a linear-time computational cost to identify multiple causal variants, even in the presence of an over-specified number of causal variants. (iii) It further corrects confounding bias hidden in the GWAS summary statistics to reduce false positive findings and improve replication rates. The fine-mapping results given by XMAP can be further used for downstream analysis to illuminate the causal mechanisms at different cascades of biological processes, including tissues, cell populations, and individual cells. In particular, XMAP results can be effectively integrated with single-cell datasets to identify disease/trait-relevant cells. We provide the implementation of XMAP in an efficient and freely available R package at j from different populations separately, denoted as PIPj1,..,PIPjT, where T is the number of populations. Then, for SNP j, its PIPs are compared across populations, and the largest one is selected to represent the ‘merged PIP’ of the SNP  of SNP 31. We considered a region between the base pair position 45,202,602 and 45,435,202 in chromosome 22 (GRCH37), which comprises p = 500 SNPs. To demonstrate the benefit of leveraging genetic diversity in different populations, we selected three candidate SNPs that satisfy the following properties: (i) In EUR population, they are in high LD  with at least three non-causal SNPs. (ii) In EAS population, they are weakly correlated with non-causal SNPs . The heat maps in Fig. Ktrue causal SNPs, where Ktrue ∈ {1, 2, 3}, we randomly sampled Ktrue from the three candidate SNPs as the causal ones. To mimic the unbalanced composition of GWAS samples in global populations, we considered n2 = 20,000 samples from the EUR population and explored different sample sizes n1 from the EAS population: 5000, 10,000, 15,000, and 20,000. For reference LD matrices, we used the EUR LD matrix estimated with 337,491 British UKBB samples provided in a recent study32 and estimated the EAS LD matrix with 35,989 EAS samples from the Chinese cohort30. We designed our simulations in two scenarios. First, we illustrated the benefit of cross-population fine-mapping by generating GWAS data without confounding bias. In the second scenario, we examined the effectiveness of XMAP in correcting confounding bias by simulating GWAS summary data with unadjusted sample structure.To mimic realistic LD patterns in different populations, we used genotypes of EUR samples from UKBB and genotypes of EAS samples from a Chinese cohortj = 1,..., 44,728, where 0.005 is the total heritability contributed by polygenic effects of the 500 SNPs in the target locus, with a per-SNP heritability 0.005/500 = 10−5 and a genetic correlation k = 1,..., Ktrue. This specification means that each causal SNP has a 0.25/0.005 = 50 fold per-SNP heritability enrichment compared to non-causal SNPs, and the effect sizes of SNP k are not necessarily the same across the two populations. The Ktrue causal SNPs jointly contribute 0.25/500 × Ktrue = 5 × 10−4 × Ktrue heritability. We obtained the standardized genotype matrices xk]1[ and xk]2[ are the columns of X1 and X2 corresponding to the k-th causal SNP, and fdrj = 1 − PIPj. Then we sort SNPs by local FDR in ascending order and regard the j-th re-ordered SNP as a risk SNP if fdri). We identified putative causal SNPs with a given FDR level and computed the empirical FDR as We first consider the scenario in the absence of confounding bias. First, we generated the polygenic effects for all 44,728 SNPs in chromosome 22 which include the 500-SNPs target region. Specifically, we constructed the polygenic effects with Ktrue = 3 and n1 = n2 = 20,000. As expected, XMAP had well-controlled FDR across all thresholds. By contrast, without accounting for the polygenic effects, SuSiEx and XMAP with Ω = 0 are inflated with a very similar pattern, suggesting they produced many false positives. To better understand these results, we compared the variance of causal effects  estimated by XMAP and XMAP (Ω = 0), which is equivalent to SuSiEx when there is no confounding bias. We did not include SuSiEx in this comparison because it does not output Ω = 0) to have similar parameter estimates with SuSiEx because they have the same statistical model and similar calibration performance. For ease of demonstration, we considered the case of only one causal effect, i.e., Ktrue = 1, and sorted K = 5 > Ktrue, XMAP produced unbiased estimate of Ω = 0) overestimated σk1 and σk2, making its PIP inflated. PAINTOR had a satisfactory performance with stringent FDR thresholds (FDR ≤ 0.3) but became inflated with larger FDR thresholds. The ad-hoc method by merging discoveries from a single-population method produced inflated PIP because this procedure involves a step of taking the maximum of PIP obtained from multiple populations. Indeed, this post-selection step introduces selection bias and inflates false positive findings. Among the single-population methods, FINEMAP and SuSiE had satisfactory FDR with stringent thresholds. Although SuSiE-inf can account for the polygenic effects, it was conservative because it only used GWAS from a single population. Furthermore, compared to SuSiE-inf, XMAP produced more stable estimates of polygenic parameters . When FPR = 0.1, all methods had similar pAUC. It is important to note that pAUC only represents the relative rankings of true causal SNPs under an empirical FPR threshold. A method can have a high pAUC but low power if the PIP of all SNPs are small. For example, although SuSiE-inf had the highest pAUC, its statistical power was much lower than XMAP. We used K = 5 for XMAP in the main results and investigated K = 10 in the Supplementary Figs. K. More comparisons under different settings of n1, n2 and Ktrue are provided in the Supplementary Figs. Next, we evaluated the statistical power of compared methods. We excluded DAP-G in this comparison because it had inflated FDR. As shown in Fig. K and p. As shown in Fig. K and p. When analyzing a locus with p = 100 SNPs, MsCAVIAR could only include K ≤ 4 causal signals and PAINTOR could only include K ≤ 5 causal signals. It took more than one week for them to finish the analysis when more signals were included. By contrast, the computational cost of XMAP is linear to K, which makes it highly efficient when applied to a locus with multiple causal SNPs. To identify multiple causal signals, the computational efficiency of XMAP allows us to set K to a large value  when Ktrue is unknown. While SuSiE-inf, SuSiEx, DAP-G, and FINEMAP had CPU times comparable to XMAP, they could not simultaneously leverage cross-population GWASs and account for polygenic effects to improve fine-mapping. This benchmark was evaluated using a Linux computing platform with 20 CPU cores of Intel (R) Xeon (R) Gold 6152 CPU at 2.10 GHz processor.To investigate the computational efficiency, we evaluated the CPU time of compared methods under different settings of t-distributions with degrees of freedom 16 and 4  from the two populations as representations of sample structures, denoted as PC1 to have mean zero and variance 0.05 and re-scaled PC2 to have mean zero and variance 0.2. These variance values were selected to introduce the proper level of inflation in the summary statistics. Next, we generated quantitative phenotypes with βk1 and βk2 are the same as those in the first scenario and the independent errors were generated with 10 and geographic clustering12 in GWAS samples, cannot be fully corrected by including PCs as covariates. By contrast, XMAP was the only method that could still achieve satisfactory FDR control performance by effectively correcting the confounding bias in GWAS data when the confounding effects are not adjusted in the summary data. We summarized the receiver operating characteristic (ROC) curve of compared methods in Fig. p-values were inflated in the left regions of the locus , and the EUR GWAS data from GLGC with a sample size of 664,45034. These GWAS summary statistics included 11,569,928-35,328,891 genotyped and imputed autosomal SNPs, minimizing the risk of omitting causal variants. Details of GWAS summary statistics are summarized in Supplementary Table 32 to partition all autosomal chromosomes into 2763 consecutive loci, each with a width of 1 million base pairs (Mbp). To fully account for LD when analyzing each 1 Mbp locus, we included all SNPs in an extended region that also covers 1 Mbp before the starting position and 1 Mbp beyond the ending position of the locus, leading to a 3 Mbp extended region. We excluded the MHC region (25.5 Mbp–33.5 Mbp in chromosome 6) and two other long-range LD regions (8 Mbp–12 Mbp in chromosome 8 and 46 Mbp–57 Mbp in chromosome 11) because many spurious results were reported in these regions32. We applied XMAP to all regions that have more than 100 SNPs after overlapping the reference LD matrices with GWAS data. Because SuSiE, SuSiE-inf, and SuSiEx achieved the best performance among existing methods in our simulation studies, we applied them to the GWAS of LDL, serving as a baselines for comparison. We set K = 10 in all compared methods.We first applied XMAP to analyze LDL by combining GWASs from EUR, EAS, and AFR. As discovery cohorts, we used the GWASs of AFR and EAS released by the Global Lipids Genetics Consortium (GLGC)34 from the EUR population with a median sample size of 85785  to correct the confounding bias; (ii) a special case of XMAP forced not to correct the confounding bias by setting c1 = c2 = 1, denoted as ‘XMAP (C=I)’. In this example, the SNP rs2053005 locating at the locus 66.55 Mbp–66.85 Mbp in chromosome 15 was significantly associated  in UKBB GWAS . Although SuSiE and SuSiEx can also identify multiple causal signals  for each cell via network propagation. Finally, we simulated null distributions of TRS by using random seed cells for propagation and computed a p-value of trait-enrichment for each cell. The cells with p-value < 0.05 were considered as significantly enriched for the trait.Integration of fine-mapping results with single-cell datasets is expected to offer a better interpretation of putative causal variants in their relevant cellular context at single-cell resolutionons Fig. a. Specif+ monocytes. For traits related to red cells such as red blood cell count (RBC), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), mean corpuscular volume (MCV), and hemoglobin (HB), a large amount of enriched cells were erythroid cells. These observations indicate that the biological mechanisms of putative causal SNPs identified by XMAP can be interpreted at single-cell resolution. Due to the unbalanced cell type composition in the single-cell dataset, cells from rare populations can be under-represented. To rule out the influence of cell type composition on our analysis, we further investigated the proportion of trait-relevant cells within each cell type. We observed that biologically related cell types had largest proportion of enriched cells. For example, MCV was significantly enriched in 99.3% of late stage erythroid cells  denotes the non-informative categorical distribution of class counts drawn with class probabilities given by 1/p for each SNP, 0 and covariance matrices Σ = {Σ1, . . . , ΣK} capture the genetic covariance of the two populations attributed to the K causal effects, and Ω captures the genetic covariance attributed to the polygenic effects.To leverage the cross-population GWASs for fine-mapping, we propose to specify model  by decomP1 on both sides of the first equation and P2 on both sides of the second equation in model  algorithm that only updates Σ. This algorithmic design not only improves the convergence of the VEM algorithm by reducing the number of parameters but also offers a stable estimate of Ω.We adopt a two-step procedure in the XMAP algorithm. In the first step, we apply LDSC to estimate the parameters Σ by maximizing the marginal likelihoodγ, the computational cost for Equation  is a variational approximation of the posterior  = q(γk)q and q(ϕ) are the distributions of {bk1, bk2} and ϕ under the variational approximation, respectively. Unlike previous methods61 that require bk1 and bk2 to be fully factorizable across their p elements, the variational approximation in Equation  and In the following, we describe the VEM algorithm. Traditional maximum likelihood approach estimates osterior . To achiosterior . For conE-step At the t-th iteration, the variational distributions are given assoftmax denotes the softmax function to make sure B) denotes the trace of the square matrix B, and the constant term does not involve Σ.uations , at a given locus.k-th causal signal is contributed by the j-th SNP. With the variational approximation given by Equation , is defined as the smallest α credible set  indicates a higher confidence of the identified causal variants.The decomposition of causal effects  offers aK to be the number of causal SNPs in the target locus. Instead, because the computational cost of our VEM algorithm only increases linearly with K, we can set K to a reasonably large number  with minor computational overhead. When K is larger than the ground truth, the posterior probabilities in the excessive components will be broadly distributed across all SNPs in the locus because there is high uncertainty in the assignment of these causal effects. Importantly, the polygenic component will account for the small genetic effects, forcing the variance of excessive signals toward zero. Therefore, it has very minor influence in prioritization of causal SNPs when including extra causal effects than necessary. To exclude credible sets associated with redundant signal clusters, we follow SuSiE23 to introduce the purity of credible sets. The purity of a credible set is defined as the smallest absolute correlation between pairs of SNPs within it. In XMAP, we consider the credible sets with purity less than 0.1 in all populations as redundant and discard the associated credible sets.The number of causal signals is usually unknown in practice. In XMAP, we do not require Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Software 1Reporting Summary


The development of dairy production systems has continued to progress, especially in purebred dairy cows in the United States and Europe. However, in-depth knowledge is still needed to support decision-making for crossbreed dairy cows raised in Asia to be used in breeding programs. The development of a new genetic method that is more effective than the current method is what everyone wants. Therefore, to ensure that the new method can replace the current method, it must be tested with the utmost confidence.Reproductive traits are important traits that directly affect a farmer’s income and are difficult to improve upon using traditional genetic methods. Therefore, there is a need to consider new options for increasing the accuracy of the genetic selection of dairy cows. The objective of this study was to compare the genetic methods of the traditional BLUP and ssGBLUP techniques in terms of the estimated genetic parameters and accuracy of the estimated breeding values. The data comprised 101,331 services per conception (NSPC) records from 54,027 Thai–Holstein crossbred cows, 109,233 pedigree data, and 770 genotyped animals. A Bayesian analysis via threshold Gibbs sampling was used to analyze the estimated variance components and genetic parameters. The results showed that the means of the NSPC data were 2.21, 2.31, and 2.42 for less than 87.5% for Holstein genetics , 87.5 to 93.6% for Holstein genetics (BG2), and greater than 93.7% for Holstein genetics (BG3), respectively. The estimated heritability values were 0.038 and 0.051, and the repeatability values were 0.149 and 0.157 for the traditional BLUP and ssGBLUP methods, respectively. The accuracy of the estimated breeding values from the ssGBLUP method was higher than that from the traditional BLUP method, ranging from 6.05 to 17.69%, depending on the dataset, especially in the top 20% of the bull dataset had the highest values. In conclusion, the ssGBLUP method could improve the heritability value and accuracy of the breeding values compared with the traditional BLUP method. Therefore, switching from traditional methods to the ssGBLUP method for the Thai dairy cattle breeding program is a viable option. Bos taurus (Holstein) and Bos indicus  and are selected primarily for the purpose of increasing milk yield and milk composition [Reproductive characteristics are essential to livestock and dairy production and are also related to the productivity of dairy cows with a direct correlation to the cost and profits of farmers ,2. Howevposition ,7. Howevposition . Therefoposition . In Thaiposition ,10, whilposition  and 1.20position ,13. The The heritability of fertility traits in dairy cows is generally low because the environmental factors, feed and feeding effects, and management influences are more relevant than genetic effects ,15. RahbThe Institutional Animal Care and Use Committee of Khon Kaen University reviewed and approved this research based on the Ethics of Animal Experimentation of the National Research Council of Thailand . A total of 101,331 services per conception (NSPC) records for the first fifth lactation of 54,027 Thai–Holstein crossbred cows collected between 1996 and 2017 were provided by the Bureau of Biotechnology in Livestock Production, Department of Livestock Development, Thailand. The NSPC data were defined as the outcome of all the insemination events, with “1” being a successful conception and an unsuccessful service conception event being “0”. The cows with ambiguous identification for the date of each record, identity data, and more than one successful insemination per parity or an unrealistic interval between consecutive inseminations were removed. The pedigree data included 109,233 animals born between 1994 and 2016, and 770 animals were genotyped using the Illumina BovineSNP50 Bead Chip . Quality control on the animals and markers was performed according to the following parameters: a minimum call rate equal to 90% and a minor allele frequency for each marker greater than 5%. The animals and markers that failed these quality control criteria were removed. The cows were grouped by the percentage of Holstein genetics  as follows: BG1 less than 87.5%, BG2 from 87.5 to 93.6%, and BG3 greater than 93.7%. The age at the first service was divided into seven classes at 3-month intervals, with less than 25 months comprising the first class and greater than 39 months comprising the last. The details of the descriptive statistics of the studied traits are shown in Scheffe’ (p < 0.05) post hoc test in the generalized linear model for an unbalanced analysis of variance (GLM procedure) using the SAS package. The genetic analysis used the repeatability threshold animal model to estimate the variance components, heritability, and breeding value. The outcomes of the inseminations after calving during the breeding period were considered to be repeated observations. Therefore, the animal’s additive genetic effects (The number of services per conception data (NSPC) were validated and analyzed for the least square means, and significant differences were compared by breed group and parity using the The variance components, genetic parameters , estimated breeding values (EBVs), and genomic estimated breeding values (GEBVs) were estimated using a Bayesian implementation via Gibbs sampling. The computations were carried out using the THRGIBBS1F90 program . The numThe estimated heritability variance .Genomic analyses were implemented using a single-step genomic method. The inverse of the numerator relationship matrix (A) in the mixed model equations was replaced by the inverse of the matrix H , as follVanRaden . The estVanRaden  with thep > 0.05). However, there was a significant difference from the third lactation onward (p < 0.05), and the difference was more pronounced from the fourth and fifth lactations. The mean value of the NSPC characteristics in the BG3 group (greater than 93.7% Holstein genetics) was the highest, while those in the BG1 group (less than 87.5% Holstein genetics) were the lowest.A comparison of the average number of services per conception (NSPC) separated by parity and breed group in Thai–Holstein crossbreds is presented in A comparison between the variance components and genetic parameters of the NSPC traits between the BLUP and ssGBLUP methods is presented in The accuracies of the estimated breeding values (EBVs) and genomic estimated breeding values (GEBVs) from the BLUP and ssGBLUP methods are presented in The average estimated breeding values (EBVs) from the BLUP method and genomic estimated breeding values (GEBVs) from the ssGBLUP method for the number of services per conception separated by dataset and breed group are presented in The number of services per conception (NSPC) is a fertility trait in dairy cows related to reproductive performance and the cost of management in artificial insemination service. In this study, the average values of the NSPC in the Thai–Holstein population ranged from 2.10 to 2.52 times. Compared to what was reported in the same dairy population in 2015 ,10 and 2The estimated heritability values of the NSPC from the BLUP and ssGBLUP methods were 0.038 and 0.051, respectively. These heritability values were higher than those of the same population in 2015, which were 0.02–0.03 . In addiThe estimated additive genetic variance from the ssGBLUP method is often higher than that from the traditional BLUP method for the following reasons. The ssGBLUP method incorporates genomic information, such as single-nucleotide polymorphism (SNP) markers, which directly measure an individual’s genetic makeup. By leveraging genomic data, the method can capture the genetic variations that are not accounted for by pedigree information alone. Therefore, the increased accuracy results in a better estimation of the additive genetic variance. The ssGBLUP method can handle incomplete pedigrees more effectively by integrating genomic data, which helps to compensate for the missing or uncertain relationships between the individuals. Additionally, the ssGBLUP method can account for some of these nonadditive genetic effects by using genomic data, leading to a higher estimated additive genetic variance. Finally, the ssGBLUP method can utilize linkage disequilibrium (LD) patterns between the markers and causal genes. LD refers to the nonrandom association of alleles at different loci, which can infer the effects of specific genes even if they are not directly genotyped. This extra information contributes to a more accurate estimation of the additive genetic variance. Although the ssGBLUP method increases the overall heritability value, in the expression of the NSPC in dairy cows, it was found that most of the factors that control the expression of these traits were environmental factors rather than genetic . For thiWe found that the accuracy of the GEBVs from genomic selection using the ssGBLUP method was higher than that using the traditional BLUP method . From thOur results presented in In conclusion, the animals with greater than 93.7% Holstein genetics had more services per conception than the animals with lower Holstein genetics. In addition to the factors other than genetic predisposition, it is likely this result also reflected the greater genetic adaptation of the crossbred cattle to the tropical environments in Thailand. At the same time, the single-step genomic BLUP method is a high-potential tool for improving fertility traits. Although that trait still has low heritability, it is possible to improve and increase the accuracy of the breeding value, and it can help determine an animal’s genetic progress. The accuracies in genomic selection depend on the number of reference populations, size of the genotype data, distribution, and contributions of genotypes and phenotypes to the genomic evaluation. Environmental and AI service management factors are important to the NSPC in small dairy farms in Thailand. In future research, it is recommended that the genomic estimated breeding value should be used where possible for multivariate production, reproduction, and possibly even adaptive traits such as the tolerance of the animals to high temperatures and humidity.


TaHST1, was profiled to identify different haplotypes of this locus in SHWs and their association with grain yield and related traits in SHWs. There was a 36% decrease in grain yield (GY), a 23% decrease in thousand-grain weight (TKW), and an 18% decrease in grains per spike (GpS) across three locations in the population due to the heat stress conditions. GWASs identified 143 quantitative trait nucleotides (QTNs) distributed over all 21 chromosomes in the SHWs. Out of these, 52 QTNs were associated with morphological and yield-related traits under heat stress, while 15 of them were pleiotropically associated with multiple traits. The heat shock protein (HSP) framework of the wheat genome was then aligned with the QTNs identified in this study. Seventeen QTNs were in proximity to HSPs on chr2B, chr3D, chr5A, chr5B, chr6D, and chr7D. It is likely that QTNs on the D genome and those in proximity to HSPs may carry novel alleles for heat-tolerance genes. The analysis of TaHST1 indicated that 15 haplotypes were present in the SHWs for this locus, while hap1 showed the highest frequency of 25% (33 SHWs). These haplotypes were significantly associated with yield-related traits in the SHWs. New alleles associated with yield-related traits in SHWs could be an excellent reservoir for breeding deployment.The projected rise in global ambient temperature by 3–5 °C by the end of this century, along with unpredicted heat waves during critical crop growth stages, can drastically reduce grain yield and will pose a great food security challenge. It is therefore important to identify wheat genetic resources able to withstand high temperatures, discover genes underpinning resilience to higher temperatures, and deploy such genetic resources in wheat breeding to develop heat-tolerant cultivars. In this study, 180 accessions of synthetic hexaploid wheats (SHWs) were evaluated under normal and late wheat growing seasons (to expose them to higher temperatures) at three locations , and data were collected on 11 morphological and yield-related traits. The diversity panel was genotyped with a 50 K SNP array to conduct genome-wide association studies (GWASs) for heat tolerance in SHW. A known heat-tolerance locus,  Wheat is a major staple food for almost 40% of the global population, cultivated on almost 220 million hectares of the land, with 750 million tons of production and a trade value of almost USD 50 billion per year . Wheat yAegilops tauschii, D’D’) and tetraploid durum wheat . For more than three decades, the CIMMYT  has made and used SHW to overpass gene shifts from durum wheat and Ae.tauschii to bread wheat (hexaploid). This is an exclusive example of success in exploiting wild relatives in conventional breeding at a huge, global scale [Genetic diversity in major food crops, including wheat, has been successfully manipulated via conventional breeding approaches, resulting in a 0.8–1.2% annual genetic yield gain over the last 100years. However, the abovementioned gain will not be enough to meet the increasing global demand of food by 2050 . SHW is al scale . SHWs arGenome-wide association studies (GWASs) have been shown to be an effective method for discovering genes that underpin complex phenotypes. The genetic basis of heat tolerance in wheat has also been studied using GWASs. Heat stress tolerance (HST) is a very complex process and is controlled by many genes. Bread wheat contains a large and complex genome  characterize a diversity panel consisting of SHWs against HS at the reproductive stage, (ii) identify quantitative trait nucleotides (QTNs) associated with HST at the reproductive stage in synthetic hexaploid wheat accessions, and (iii) characterize the Phenotypic data showed significant variation among all traits in both conditions (control and HS). The analysis of variance (ANOVA) for all of the morphological traits is presented in max), daily minimum temperature (Tmin), daily average temperature (Tavg) in °C, and relative humidity (%) during the field trials—are described in The ambient environmental variables—such as daily maximum temperature  to 8 t/ha . For TKW in control conditions, the mean value was 47 g, with a range from 19 g (AUS30660) to 68 g (AUS33417), while in HS conditions the mean value was 36 g, with a range from 18 g (AUS34257) to 49 g (AUS30296). Phenotypic data are presented in Pearson’s correlation coefficients are given in Similarly, most of the other traits showed significant positive correlation with one another. Some of the traits showed negative correlation with one another, i.e., SpSP showed highly negative correlation with DH (r = −0.814 ***), DM (r = −0.775 ***), TpP (r = −0.575 ***), PH(r = −0.384 ***), SL (r = −0.585 ***), GpS (r = −0.067), SW (r = −0.268 ***), GY(r = −0.611 ***), and TKW (r = −0.480 ***).n = 2484) and the minimum number of SNPs on chr4D (n = 794). The B-genome contained the greatest number of SNPs , followed by the A-genome  and the D-genome (n = 9926). In total, 66,836 markers were genotyped using the 50 K SNP chip. Thirty-eight markers were removed after quality control due to missing data. A further 27,485 markers were removed due to having MAF < 0.05. Subsequently, 39,313 markers were used for the GWASs. These SNPs were distributed on all chromosomes, with the maximum number of SNPs on chr6A (n = 24), while the fewest QTNs were identified for SpSP (n = 6). On chr2B and 5B, the highest number of QTNs (n = 12) was identified, while the lowest number of QTNs (n = 1) was found on chr5A and chr7A. In total, two QTNs were consistently identified in control and HS conditions. A QTN on chr7B at ~437 Mb was consistently associated with DM in control and HS conditions. Another QTN on chr6A at 390 Mb was consistently associated with DM both in control and HS conditions. In total, 143 QTNs were identified, out of which 48 QTNs were identified in control conditions, 52 QTNs were identified in HS, and 43 QTNs were identified for HSI. The greatest number of QTNs was identified for GpS  on chr3D at ~45 Mb associated with PH was found in proximity to HSP TaHSP40.95. A QTN (AX-109911103) on chr5A at ~611 Mb and a QTN (AX-94886530) on chr5B at ~186 Mb, associated with GWpS, were found in proximity to HSPs TaHSP40.150 and TaHSP40.155, respectively. In total, eight QTNs were found in proximity to HSPs under HS conditions . Two QTN2) indicated that effect of favorable alleles ranged from R2 = 0.77 (DH) to R2 = 0.66 (GY) (2 = 0.75 (DH) to R2 = 0.67 (GY)  , while f.67 (GY) .TaHST1 locus in synthetic wheat accessions was performed using PCR-based primers. Based on the results of these primers, 15 haplotypes were identified. Hap-1 showed the highest frequency, at 25%, and all markers showed positive amplification. Hap-2 was present in 16% of accessions, and only two markers showed positive amplification, while Hap-3 and Hap-4 were present in 15% and 10% of accessions, with one and two deleted sites, respectively. The effect of top Hap1 to Hap4 was significant on HSI for TpP  showed higher resilience to HS conditions for yield-related traits.GY is highly affected by the environment, as described by Sharma et al. . During Identification of QTNs associated with HST is crucial for breeders. Therefore, a number of gene mapping studies were conducted at the flowering and reproductive stages ,37. ReceIn our studies, a QTN was identified on chr3B for SL in HS. Three QTNs were identified for SpSP on chr1A, chr2B, and chr2D. No QTL had been identified previously for SpSP on these chromosomes. In contrast, eight QTLs were identified on chr1D, chr3A, chr4B, chr4D, chr5A, chr6A, and chr6B . Three QTaHSP100, TaHSP70, and TaHSP90) are induced underheat stress conditions [All of the HS-specific QTNs in proximity to HSPs could be ideal candidates for genes underpinning heat tolerance, and due to the use of SHWs they are likely to carry new alleles of HSPs. Several HSPs  have shown significant associations with phenotypic traits under heat stress conditions. It is likely that haplotype distribution in SHW is different from that in cultivated wheats, and that new alleles are available due to the use of durum wheat in developing SHWs. The combination of 127 bp alleles at Xhau-4 and the presence of alleles at Xhau-5 have a positive impact on yield-related traits and could be used as a positive marker for selecting heat-tolerant accessions. The s in SHW . Recentls in SHW  identifiConclusively, the data generated here on a collection of SHWs represent a promising source for selecting heat-tolerant candidates for use in breeding, and the loci identified here can enhance our knowledge of the distribution of heat-tolerant alleles in wheat. Ae. Tauschii accessions as a subset of the panels previously characterized for grain morphology [A collection of 180 SHWs was used for this experiment . These Srphology , biotic rphology ,46, and rphology .−2 of P2O5) was applied by furrow placement, and urea was applied earlier than the second irrigation (8.6 g m−2 of N). During the crop season, irrigation was carried out four times at the following growth stages: (1) from germination to the appearance of seedlings, (2) from emergence to the double-ridge stage, (3) from double-ridge to anthesis, and (4) from anthesis to maturity [−2), which was calculated as the weight of grains produced per unit area. To allow exposure of the genotypes to different heat treatments for the duration of flowering and ripening, six field tests were carried outat three different sites for 2 years (2014 and 2015), with two sowing time treatments ). The three locations were the Regional Agricultural Research Institute at Bahawalpur in Punjab Province (BWP), the National Agricultural Research Centre (NARC) at Islamabad (ISB), and the Nuclear Institute of Agriculture at Tando Jam in Sindh (SND). The dates of normal sowing and late sowing were 15 November and 20 December, respectively. The sowing of genotypes was performed in two-row plots that were 2 m long and spaced 30 cm apart, with 15cm between rows, and in a randomized complete block design with three replications. The weight of seeds sown per plot was adjusted to obtain a constant seeding rate of 30 viable seeds per row by considering measurements of TKW and germination level. A small-plot grain drill was used for sowing purposes. Before plantation, triple superphosphate (4.3 g mmaturity . The folThe heat susceptibility index (HSI) was calculated using the following formula: for example, for grain yield, HSIGY = (1 − Y/Yp)/D, where Y represents the yield of a genotype during the late planting, Yp shows the mean yield of genotypes at the time of normal planting, and stress intensity D = 1 − X/Xp, where X indicates the mean Y of all genotypes and Xp is the mean Yp of all genotypes.TaHST1 locus was identified by using five primers: xhau1, xhau2, xhau3, xhau4, and xhau5. The primers’ details are listed in 2O (3 µL), master mix (5 µL), forward and reverse primers (0.5 µLeach), and DNA (1 µL). The conditions for PCR were as follows: initial denaturation at 95 °C for 5 min (1 cycle) or 94 °C for 1 min; annealing at 56 °C, 58 °C, 60 °C, and 65 °C for 1 min; and extension at 72 °C for 1 min (35 cycles); 2% agarose gel was used to check the PCR products.The Extraction of DNA was carried out using an amended CTAB method . Synthetylijk be the observed value of the trait of interest, where i is the accession and kis the replicate under the jth environmental condition along with the years and locations in this study, while l is the treatment. The linear model applied in ANOVA is as follows:l = 1, 2, …, L , i = 1, 2, …, n (n = 203), j = 1, 2, …, e (e = 6 with 3 locations and 2 years), k = 1, 2, …, r (r = 2) is the total mean value of the entire population, Rk/j is the kth effect on the replicate by the jth environment, Gi is the effect of the genotype on the ith accession, Ej is the effect of the environment on the jth environment, GEij is the effect of the interaction between the ith accession and the jth environment, GDil is the effect of the interaction between the ith accession and the lth treatment, εlijk is the random error effect , and the variance is The phenotypic data were collected and arranged in Microsoft Excel. To test the statistical significance among different sources of variation, analysis of variance (ANOVA) was applied for all 11 traits. Phenotypic effects in the ANOVA model were divided into overall mean, genotypic effect, effect of random error, effect on replicate  in the environment (including both year and location), effect of environment, genotype with effect of treatment, genotype with environmental effect, and effect of treatment. Let yijk, where i is the accession, k is the replicate, and j is the environment, with the locations and years used in this study. The BLUP values for the mixed model were determined as follows:i = 1, 2, …, n (n = 203), j = 1, 2, …, e (e = 6 by three locations and two years), k = 1, 2, …, and r (r = 2), jE,ij,GiGE, k/jR, are as described above. Otherwise, normal distribution was followed, and all of the effects were observed as random effects The genotypic values from the best linear unbiased prediction (BLUP) under normal and heat stress treatments for all of the accessions were considered as the subsequent phenotypes for comparison. The BLUP values were deliberated as follows: the trait experimental value was defined as p-value threshold that FarmCPU used was a Bonferroni-corrected threshold of 0.01. This Bonferroni-corrected threshold was overly restrictive when the LD among genotypic markers was large, so the threshold was calculated using the formula “p = 0.05/number of markers”, with 1000 permutations. In this function, the phenotypes were permuted to break the relationship with the genotypes. A vector of minimum p-values of each experiment was outputted, and the 95% quantile value of the vector was recommended for the p threshold in the FarmCPU model. The quantile–quantile (Q–Q) plot was used for assessing the fitness of the model to the population structure.GWASs for 11 phenotypic traits were conducted using a recently developed model selection algorithm—the Fixed and Random Model Circulating Probability Unification  . FarmCPUTaHSPs.All of the SNPs associated with phenotypes under heat stress conditions were compared with the heat shock protein (HSP) framework of wheat , and tho2) was determined between the number of favorable/unfavorable alleles and the relevant phenotype as a measure of the combined allelic effects of all trait-associated SNPs on the phenotype. Favorable and unfavorable alleles were identified for all marker–trait associations (MTAs). For each MTA, the allele with an increasing effect was deemed to be a favorable allele, while the alternate allele was deemed to be an unfavorable allele . For some traits, such as DH and PH, the allele with decreasing effect was deemed to be a favorable allele and the alternate allele was deemed to be an unfavorable allele . In each accession, the frequency of favorable and unfavorable alleles was counted based on the number of trait-associated SNPs. The coefficient of determination (R