Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Genomic signatures of selection,local adaptation and production type characterisation of East Adriatic sheep breeds

Background The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations.Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production(carcass,wool and milk yield).Therefore,eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation(EAS),are generally considered as multipurpose breeds(milk,meat and wool),not specialised for a particular type of production,but known for their robustness and resistance to certain environmental conditions.Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures,decipher their biological and productive functionality,and provide a"genomic"characterization of EAS adaptation and determine its production type.Results We identified positive selection signatures in EAS using several methods based on reduced local variation,linkage disequilibrium and site frequency spectrum(eROHi,iHS,nSL and CLR).Our analyses identified numerous genomic regions and genes(e.g.,desmosomal cadherin and desmoglein gene families)associated with environmental adaptation and economically important traits.Most candidate genes were related to meat/production and health/immune response traits,while some of the candidate genes discovered were important for domestication and evolutionary processes(e.g.,HOXa gene family and FSIP2).These results were also confirmed by GO and QTL enrichment analysis.Conclusions Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type,ultimately providing a new opportunity for future breeding programmes.At the same time,the numerous genes identified will improve our understanding of ruminant(sheep)robustness and resistance in the harsh and specific Mediterranean environment.

Genomic Selection for Frogeye Leaf Spot Resistance in Soybean

Soybean frogeye leaf spot(FLS) disease is a global disease affecting soybean yield, especially in the soybean growing area of Heilongjiang Province. In order to realize genomic selection breeding for FLS resistance of soybean, least absolute shrinkage and selection operator(LASSO) regression and stepwise regression were combined, and a genomic selection model was established for 40 002 SNP markers covering soybean genome and relative lesion area of soybean FLS. As a result, 68 molecular markers controlling soybean FLS were detected accurately, and the phenotypic contribution rate of these markers reached 82.45%. In this study, a model was established, which could be used directly to evaluate the resistance of soybean FLS and to select excellent offspring. This research method could also provide ideas and methods for other plants to breeding in disease resistance.

Genetic dissection of and genomic selection for seed weight, pod length,and pod width in soybean

A biparental soybean population of 364 recombinant inbred lines(RILs)derived from Zhongdou 41×ZYD02.878 was used to identify quantitative trait loci(QTL)associated with hundred-seed weight(100-SW),pod length(PL),and pod width(PW).100-SW,PL,and PW showed moderate correlations among one another,and 100-SW was correlated most strongly with PW(0.64–0.74).Respectively 74,70,75 and19 QTL accounting for 38.7%–78.8%of total phenotypic variance were identified by inclusive composite interval mapping,restricted two-stage multi-locus genome-wide association analysis,3 variancecomponent multi-locus random-SNP-effect mixed linear model analysis,and conditional genome-wide association analysis.Of these QTL,189 were novel,and 24 were detected by multiple methods.Six loci were associated with 100-SW,PL,and PW and may be pleiotropic loci.A total of 284 candidate genes were identified in colocalizing QTL regions,including the verified gene Seed thickness 1(ST1).Eleven genes with functions involved in pectin biosynthesis,phytohormone,ubiquitin-protein,and photosynthesis pathways were prioritized by examining single nucleotide polymorphism(SNP)variation,calculating genetic differentiation index,and inquiring gene expression.The prediction accuracies of genomic selection(GS)for 100-SW,PL,and PW based on single trait-associated markers reached 0.82,0.76,and 0.86 respectively,but selection index(SI)-assisted GS strategy did not increase GS efficiency and inclusion of trait-associated markers as fixed effects reduced prediction accuracy.These results shed light on the genetic basis of 100-SW,PL,and PW and provide GS models for these traits with potential application in breeding programs.

Genomic Mutations Within the Host Microbiome: Adaptive Evolution or Purifying Selection

Next-generation sequencing technology has transformed our ability to assess the taxonomic composition functions of host-associated microbiota and microbiomes. More human microbiome research projects—particularly those that explore genomic mutations within the microbiome—will be launched in the next decade. This review focuses on the coevolution of microbes within a microbiome, which shapes strain-level diversity both within and between host species. We also explore the correlation between microbial genomic mutations and common metabolic diseases, and the adaptive evolution of pathogens and probiotics during invasion and colonization. Finally, we discuss advances in methods and algorithms for annotating and analyzing microbial genomic mutations.

Performance Comparison of Two Efficient Genomic Selection Methods(gsbay & MixP ) Applied in Aquacultural Organisms

Genomic selection is more and more popular in animal and plant breeding industries all around the world, as it can be applied early in life without impacting selection candidates. The objective of this study was to bring the advantages of genomic selection to scallop breeding. Two different genomic selection tools Mix P and gsbay were applied on genomic evaluation of simulated data and Zhikong scallop(Chlamys farreri) field data. The data were compared with genomic best linear unbiased prediction(GBLUP) method which has been applied widely. Our results showed that both Mix P and gsbay could accurately estimate single-nucleotide polymorphism(SNP) marker effects, and thereby could be applied for the analysis of genomic estimated breeding values(GEBV). In simulated data from different scenarios, the accuracy of GEBV acquired was ranged from 0.20 to 0.78 by Mix P; it was ranged from 0.21 to 0.67 by gsbay; and it was ranged from 0.21 to 0.61 by GBLUP. Estimations made by Mix P and gsbay were expected to be more reliable than those estimated by GBLUP. Predictions made by gsbay were more robust, while with Mix P the computation is much faster, especially in dealing with large-scale data. These results suggested that both algorithms implemented by Mix P and gsbay are feasible to carry out genomic selection in scallop breeding, and more genotype data will be necessary to produce genomic estimated breeding values with a higher accuracy for the industry.

Prescreening of large-effect markers with multiple strategies improves the accuracy of genomic prediction

Presently,integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.Here,we set the genomic and transcriptomic data as the training population data,using BSLMM,TWAS,and eQTL mapping to prescreen features according to |β_(b)|>0,top 1%of phenotypic variation explained(PVE),expression-associated single nucleotide polymorphisms(eSNPs),and egenes(false discovery rate(FDR)<0.01),where these loci were set as extra fixed effects(named GBLUP-Fix)and random effects(GFBLUP)to improve the prediction accuracy in the validation population,respectively.The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle(LDM),water holding capacity(WHC),shear force(SF),and pH in Huaxi cattle on average from 2.14 to 8.69%,especially the improvement of GFBLUP-TWAS over GBLUP was 13.66%for SF.These methods also captured more genetic variance than GBLUP.Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracyofgenomic prediction.

Genomic selection: A breakthrough technology in rice breeding

Rice(Oryza sativa)provides a staple food source for more than half the world population.However,the current pace of rice breeding in yield growth is insufficient to meet the food demand of the everincreasing global population.Genomic selection(GS)holds a great potential to accelerate breeding progress and is cost-effective via early selection before phenotypes are measured.Previous simulation and experimental studies have demonstrated the usefulness of GS in rice breeding.However,several affecting factors and limitations require careful consideration when performing GS.In this review,we summarize the major genetics and statistical factors affecting predictive performance as well as current progress in the application of GS to rice breeding.We also highlight effective strategies to increase the predictive ability of various models,including GS models incorporating functional markers,genotype by environment interactions,multiple traits,selection index,and multiple omic data.Finally,we envision that integrating GS with other advanced breeding technologies such as unmanned aerial vehicles and open-source breeding platforms will further improve the efficiency and reduce the cost of breeding.

Genomic selection methods for crop improvement:Current status and prospects

With marker and phenotype information from observed populations, genomic selection(GS) can be used to establish associations between markers and phenotypes. It aims to use genome-wide markers to estimate the effects of all loci and thereby predict the genetic values of untested populations, so as to achieve more comprehensive and reliable selection and to accelerate genetic progress in crop breeding. GS models usually face the problem that the number of markers is much higher than the number of phenotypic observations.To overcome this issue and improve prediction accuracy, many models and algorithms,including GBLUP, Bayes, and machine learning have been employed for GS. As hot issues in GS research, the estimation of non-additive genetic effects and the combined analysis of multiple traits or multiple environments are also important for improving the accuracy of prediction. In recent years, crop breeding has taken advantage of the development of GS.The principles and characteristics of current popular GS methods and research progress in these methods for crop improvement are reviewed in this paper.

Development of a 50K SNP Array for Japanese Flounder and Its Application in Genomic Selection for Disease Resistance

Single nucleotide polymorphism(SNP)armays are a powerful genotyping tool used in genetic research and genomic breeding programs.Japanese flounder(Paralichthys olivaceus)is an economically-important aquaculture flatfish in many countries.However,the lack of high-efficient genotyping tools has impeded the genomic breeding programs for Japanese flounder.We developed a 50K Japanese flounder SNP array,"Yuxin No.1,"and report its utility in genomic selection(GS)for disease resistance to bacterial pathogens.We screened more than 42,.2 million SNPs from the whole-genome resequencing data of 1099 individuals and selected 48697 SNPs that were evenly distributed across the genome to anchor the array with Affymetrix Axiom genotyping technology.Evaluation of the array performance with 168 fishs howed that 74.7%of the loci were successfully genotyped with high call rates(>98%)and that the poly-morphic SNPs had good cluster separations.More than 85%of the SNPs were concordant with SNPs obtained from the whole-genome resequencing data.To validate"Yuxin No.1"for GS,the arrayed geno-typing data of 27 individuals from a candidate population and 931 individuals from a reference popula-tion were used to calculate the genomic estimated breeding values(GEBVs)for disease resistance toEdwardsiella tarda.There was a 21.2%relative increase in the accuracy of GEBV using the weighted geno-mic best linear unpiased prediction(wGBLUJP),compared to traditional pedigree-based best linear unbi-ased prediction(ABLUP),suggesting good performance of the'Yuxin No.1"SNP array for GS.In summary,we developed the"Yuxin No.1"50K SNP array,which provides a useful platform for high-quality geno-typing that may be beneficial to the genomic selective breeding of Japanese flounder.

Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.)

In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory,the best cross should have high progeny mean and large genetic variance, and ideally yield and quality should be less negatively or positively correlated. Usefulness is built on population mean and genetic variance, which can be used to select the best crosses or populations to achieve the breeding objective. In this study, we first compared five models(RR-BLUP, Bayes A, Bayes B, Bayes ridge regression, and Bayes LASSO) for genomic selection(GS) with respect to prediction of usefulness of a biparental cross and two criteria for parental selection, using simulation. The two parental selection criteria were usefulness and midparent genomic estimated breeding value(GEBV). Marginal differences were observed among GS models. Parental selection with usefulness resulted in higher genetic gain than midparent GEBV. In a population of 57 wheat fixed lines genotyped with 7588 selected markers, usefulness of each biparental cross was calculated to evaluate the cross performance, a key target of breeding programs aimed at developing pure lines. It was observed that progeny mean was a major determinant of usefulness, but the usefulness ratings of quality traits were more influenced by their genetic variances in the progeny population. Near-zero or positive correlations between yield and major quality traits were found in some crosses, although they were negatively correlated in the population of parents. A selection index incorporating yield, extensibility, and maximum resistance was formed as a new trait and its usefulness for selecting the crosses with the best potential to improve yield and quality simultaneously was calculated. It was shown that applying the selection index improved both yield and quality while retaining more genetic variance in the selected progenies than the individual trait selection. It was concluded that combining genomic selection with simulation allows the prediction of cross performance in simulated progenies and thereby identifies candidate parents before crosses are made in the field for pure-line breeding programs.

Predictive ability of genomic selection models for breeding value estimation on growth traits of Pacific white shrimp Litopenaeus vannamei

Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value(GEBV).This study is a fi rst attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits.The performance of GS models in L.vannamei was evaluated in a population consisting of 205 individuals,which were genotyped for 6 359 single nucleotide polymorphism(SNP)markers by specifi c length amplifi ed fragment sequencing(SLAF-seq)and phenotyped for body length and body weight.Three GS models(RR-BLUP,Bayes A,and Bayesian LASSO)were used to obtain the GEBV,and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes.The mean reliability of the GEBVs for body length and body weight predicted by the dif ferent models was 0.296 and 0.411,respectively.For each trait,the performances of the three models were very similar to each other with respect to predictability.The regression coeffi cients estimated by the three models were close to one,suggesting near to zero bias for the predictions.Therefore,when GS was applied in a L.vannamei population for the studied scenarios,all three models appeared practicable.Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.

Accuracy of genomic selection in biparental populations of flax(Linum usitatissimum L.)

Flax is an important economic crop for seed oil and stem fiber. Phenotyping of traits such as seed yield, seed quality, stem fiber yield, and quality characteristics is expensive and time consuming. Genomic selection(GS) refers to a breeding approach aimed at selecting preferred individuals based on genomic estimated breeding values predicted by a statistical model based on the relationship between phenotypes and genome-wide genetic markers. We evaluated the prediction accuracy of GS(rMP) and the efficiency of GS relative to phenotypic selection(RE) for three GS models: ridge regression best linear unbiased prediction(RR-BLUP),Bayesian LASSO(BL), and Bayesian ridge regression(BRR), for seed yield, oil content, iodine value, linoleic, and linolenic acid content with a full and a common set of genome-wide simple sequence repeat markers in each of three biparental populations. The three GS models generated similar rMPand RE, while BRR displayed a higher coefficient of determination(R^2)of the fitted models than did RR-BLUP or BL. The mean rMPand RE varied for traits with different heritabilities and was affected by the genetic variation of the traits in the populations.GS for seed yield generated a mean RE of 1.52 across populations and marker sets, a value significantly superior to that for direct phenotypic selection. Our empirical results provide the first validation of GS in flax and demonstrate that GS could increase genetic gain per unit time for linseed breeding. Further studies for selection of training populations and markers are warranted.

Genetic gains with genomic versus phenotypic selection for drought and waterlogging tolerance in tropical maize(Zea mays L.)

Erratic rainfall often results in intermittent drought and/or waterlogging and limits maize(Zea mays L.)productivity in many parts of the Asian tropics.Developing climate-resilient maize germplasm possessing tolerance to these key abiotic stresses without a yield penalty under optimal growing conditions is a challenge for breeders working in stress-vulnerable agro-ecologies in the region.Breeding stress-resilient maize for rainfed stress-prone ecologies is identified as one of the priority areas for CIMMYT-Asia maize program.We applied rapid cycle genomic selection(RCGS)on two multiparent yellow synthetic populations(MYS-1 and MYS-2)to improve grain yield simultaneously under drought and waterlogging conditions using genomic-estimated breeding values(GEBVs).Also,the populations were simultaneously advanced using recurrent phenotypic selection(PS)by exposing them to managed drought and waterlogging and intermating tolerant plants from the two selection environments.Selection cycles per se(C1,C2,and C3)of the two populations developed using RCGS and PS approach and their test-cross progenies were evaluated separately in multilocation trials under managed drought,waterlogging,and optimal moisture conditions.Significant genetic gains were observed with both GS and PS,except with PS in MYS-2 under drought and with GS in MYS-1 under waterlogging.Realized genetic gains from GS were relatively higher under drought conditions(110 and 135 kg ha^(-1) year^(-1))compared to waterlogging(38 and 113 kg ha^(-1) year^(-1))in both MYS-1 and MYS-2,respectively.However,under waterlogging stress PS showed at par or better than GS as gain per year with PS was 80 and 90 kg ha^(-1),whereas with GS it was 90 and 43 kg ha^(-1) for MYS-1 and MYS-2,respectively.Our findings suggested that careful constitution of a multiparent population by involving trait donors for targeted stresses,along with elite highyielding parents from diverse genetic background,and its improvement using RCGS is an effective breeding approach to build multiple stress tolerance without compromising yield when tested under optimal conditions.

Natural selection in vertebrate evolution under genomic and biosphere biases based on amino acid content: Primitive vertebrate hagfish (<i>Eptatretus burgeri</i>)

Cluster analyses using the amino acid content predicted from the coding regions (13 genes) of complete vertebrate mitochondrial genomes as traits grouped selected vertebrates into two clusters, terrestrial and aquatic vertebrates. Exceptions were the hagfish (Eptatretus burgeri), thought to be an early ancestor of vertebrates, and the black spotted frog (Rana nigromaculata), which is terrestrial as an adult and aquatic as a larva. These two species fall into the terrestrial and aquatic clusters, respectively. Using the nucleotide (G, C, T and A) content in the coding and non-coding regions, and in the complete genome as traits, similar results were obtained but with some additional exceptions. In addition, phylogenetic analyses of 16S rRNA sequences produced a consistent result. The results of this study indicated that vertebrate evolution is controlled by natural selection under both an internal bias as a result of nucleotide replacement genomic rules, and an external bias caused by environmental biospheric conditions.

Genetic gain and inbreeding from simulation of different genomic mating schemes for pig improvement

Background 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(0.1,0.3 or 0.5);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(random,positive assortative or negative assortative matings).In addition,the performance of the GM approach was tested on real datasets obtained from a Large White pig breeding population.Results 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 GROH-based GM schemes with the maximum genetic gain resulted in 0.9%-2.6%higher rates of genetic gainΔG,and 13%-83.3%lowerΔF 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.Conclusion Compared 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.

Comparison of sequencing-based and array-based genotyping platforms for genomic prediction of maize hybrid performance

Genomic selection(GS)is a powerful tool for improving genetic gain in maize breeding.However,its routine application in large-scale breeding pipelines is limited by the high cost of genotyping platforms.Although sequencing-based and array-based genotyping platforms have been used for GS,few studies have compared prediction performance among platforms.In this study,we evaluated the predictabilities of four agronomic traits in 305 maize hybrids derived from 149 parental lines subjected to genotyping by sequencing(GBS),a 40K SNP array,and target sequence capture(TSC)using eight GS models.The GBS marker dataset yielded the highest predictabilities for all traits,followed by TSC and SNP array datasets.We investigated the effect of marker density and statistical models on predictability among genotyping platforms and found that 1K SNPs were sufficient to achieve comparable predictabilities to 10K and all SNPs,and BayesB,GBLUP,and RKHS performed well,while XGBoost performed poorly in most cases.We also selected significant SNP subsets using genome-wide association study(GWAS)analyses in three panels to predict hybrid performance.GWAS facilitated selecting effective SNP subsets for GS and thus reduced genotyping cost,but depended heavily on the GWAS panel.We conclude that there is still room for optimization of the existing SNP array,and using genotyping by target sequencing(GBTS)techniques to integrate a few functional markers identified by GWAS into the 1K SNP array holds great promise of being an effective strategy for developing desirable GS breeding arrays.

Zea mays(L.) P1 locus for cob glume color identified as a post-domestication selection target with an effect on temperate maize genomes

Artificial selection during domestication and post-domestication improvement results in loss of genetic diversity near target loci. However, the genetic locus associated with cob glume color and the nature of the genomic pattern surrounding it was elusive and the selection effect in that region was not clear. An association mapping panel consisting of 283 diverse modern temperate maize elite lines was genotyped by a chip containing over 55,000 evenly distributed SNPs. Ten-fold resequencing at the target region on 40 of the panel lines and 47 tropical lines was also undertaken. A genome-wide association study(GWAS) for cob glume color confirmed the P1 locus, which is located on the short arm of chromosome 1, with a-log10 P value for surrounding SNPs higher than the Bonferroni threshold(α/n, α < 0.001) when a mixed linear model(MLM) was implemented. A total of 26 markers were identified in a 0.78 Mb region surrounding the P1 locus, including 0.73 Mb and 0.05 Mb upstream and downstream of the P1 gene, respectively. A clear linkage disequilibrium(LD) block was found and LD decayed very rapidly with increasing physical distance surrounding the P1 locus. The estimates of π and Tajima's D were significantly(P < 0.001) lower at both ends compared to the locus. Upon comparison of temperate and tropical lines at much finer resolution by resequencing(180-fold finer than chip SNPs), a more structured LD block pattern was found among the 40 resequenced temperate lines. All evidence indicates that the P1 locus in temperate maize has not undergone neutral evolution but has been subjected to artificial selection during post-domestication selection or improvement. The information and analytical results generated in this study provide insights as to how breeding efforts have affected genome evolution in crop plants.

A genome scan of recent positive selection signatures in three sheep populations

Domesticated sheep have been exposed to artificial selection for the production of fiber, meat, and milk as well as to natural selection. Such selections are likely to have imposed distinctive selection signatures on the sheep genome. Therefore, detecting selection signatures across the genome may help elucidate mechanisms of selection and pinpoint candidate genes of interest for further investigation. Here, detection of selection signatures was conducted in three sheep breeds, Sunite （n=66）, German Mutton （n=159）, and Dorper （n=93）, using the Illumina OvineSNP50 Genotyping BeadChip array. Each animal provided genotype information for 43 273 autosomal single nucleotide polymorphisms （SNPs）. We adopted two complementary haplotype-based statistics of relative extended haplotype homozygosity （REHH） and the cross-popu- lation extended haplotype homozygosity （XP-EHH） tests. In total, 707,755, and 438 genomic regions subjected to positive selection were identified in Sunite, German Mutton, and Dorper sheep, respectively, and 42 of these regions were detected using both REHH and XP-EHH analyses. These genomic regions harbored many important genes, which were enriched in gene ontology terms involved in muscle development, growth, and fat metabolism. Fourteen of these genomic regions overlapped with those identified in our previous genome-wide association studies, further indicating that these genes under positive selection may underlie growth developmental traits. These findings contribute to the identification of candidate genes of interest and aid in understanding the evolutionary and biological mechanisms for controlling complex traits in Chinese and western sheep.