However, limited information is available about the underlying genetic architecture of egg production traits in ducks. In this paper, we analyzed six egg production-related traits in 352 FP = 7.05 × 10–14; Chr3:76,991,524, P = 1.06 × 10–12; Chr6:20,356,803, P = 1.14 × 10–10) were detected. Thus, we identified 31 potential candidate genes associated with FCR, among which the strongest candidates are those that are highly expressed in tissues involved in reproduction and nervous system functions of ducks: CNTN4, CRBR, GPR63, KLHL32, FHL5, TRNT1, MANEA, NDUFAF4, and SCD.Feed conversation ratio (FCR) was positively correlated with feed intake but negatively correlated with egg-related traits, including egg weight and egg production, both phenotypically and genetically. Estimates of pedigree-based heritability were higher than 0.2 for all traits investigated, except hip-width. Based on whole-genome sequencing data, we conducted genome-wide association studies to identify genomic regions associated with these traits. In total, 11 genomic regions were associated with FCR. No genomic regions were identified as significantly associated with hip-width, total feed intake, average daily feed intake, and total egg production. Analysis of selective sweeps between mallard and Pekin ducks confirmed three of these genomic regions on chromosomes 13, 3 and 6. Within these three regions, variants in candidate genes that were in linkage disequilibrium with the GWAS leader single nucleotide polymorphisms (SNPs) (Chr13:2,196,728, For the first time, we report the identification of genomic regions that are associated with FCR in ducks and our results illustrate the genomic changes that occurred during their domestication and are involved in egg production efficiency.The online version contains supplementary material available at 10.1186/s12711-021-00684-5. Eggs are considered an excellent source of animal protein, supplying proteins, fatty acids, vitamins, minerals, etc., in the daily lives of humans . Thus, eIn recent years, with the advances in molecular genetic technologies and the availability of DNA markers, identifying QTL that control egg production traits in poultry for application in marker-assisted selection (MAS) has progressed rapidly . CombineAnas platyrynchos) are one of the most important farm animals in China, efficiently providing meat and eggs for humans. All domesticated duck breeds were derived from the mallard duck in central China within a very short period, since about 500 B.C. [Ducks . The methods and protocols were in accordance with their guidelines. All efforts were made to minimize suffering.2 ducks that were raised at the Pekin Duck Breeding Center of the Chinese Academy of Agricultural Sciences. In the orthogonal cross, 100 female mallards and 10 male Pekin ducks were selected as parents. In the reciprocal cross, four male mallards and 40 female Pekin ducks were selected as parents. All experimental ducks were raised under identical environmental and management conditions. Feed (pellet form) and water were provided ad libitum. The feed nutrient components are provided in Additional file Ducks that were previously produced by reciprocal crosses of mallards and Pekin ducks  were matWe recorded the total egg production (EP) of each female duck during their laying period that ranged from 226 to 329 days of age by using individual cages  software and the following animal model [0, F1 and F2), We estimated genetic parameters for the Fal model :\documen2 ducks and was rapidly frozen at − 20 °C. Genomic DNA was extracted using the standard phenol–chloroform protocol. The quality and quantity of DNA were assessed by using a Nanodrop spectrophotometer and agarose gel electrophoresis. For each sample, two paired-end libraries were generated using standard procedures according to the manufacturer’s protocols . The average insert size was 500 bp and the length of the reads was 150 bp. All libraries were sequenced on an Illumina®Hiseq X-Ten platform in a Bio-company  to generate a raw read sequence coverage of 5×. These sequence data are stored in the Genome Sequence Archive (GSA) under the Project name of PRJCA003535 and have been published [Blood was obtained from the wing veins of 352 Fublished .Adapter sequences and low-quality raw reads were removed by using the Trimmomatic (v0.36) software  with the2 population to detect genomic regions that affect egg production traits in ducks, using the mixed linear model program EMMAX [Bonferroni-corrected threshold (− log10 (P) ≥ 8.16) were considered significant.GWAS was performed on the Fam EMMAX . The linfication , and thehttps://www.ncbi.nlm.nih.gov)  were downloaded from NCBI (FST) and the population nucleotide diversity ratio Pi . The average FST and Pi were calculated in 20-kb sliding windows with a 10-kb step. The logarithmic function was used to transform the Pi ratio. We considered the windows with the top 5% values for the FST and log2 (θπ ratio) simultaneously as candidate outliers under strong selective sweeps. And the selective sweep regions were further genetically annotated by Bedtools v2.17.0 [To analyze the regions that have been affected by long-term selection and are associated with the domestication of the Pekin duck from the mallard, we used VCFtools v0.1.13 to calculate the fixation index . The data were from 60 samples representing five tissues involved in reproductive functions, i.e. the hypothalamus , pituitary gland , ovary , oviduct , and follicular membrane . RNA was isolated from these tissues using the TRIzol  method according to the manufacturer’s instructions. The quantity and quality of the isolated total RNA were assessed with a Nanodrop spectrophotometer and by agarose gel electrophoresis. The cDNA library construction was performed according to the manufacturer’s protocol . The libraries were sequenced on the Illumina HiSeq 2500 platform, and 150-bp pair-end reads were generated. Analysis of mRNA transcript-level gene expression was performed according to the framework established by Pertea et al. . SpecifiThe phenotypic statistics for each trait are in Table Estimates of genetic parameters for all traits are in Table Bonferroni-corrected threshold − log10(P) to identify significant marker-trait associations was set at 8.16. No SNPs were identified as significantly associated with HP, FI, DFI, and EP. Only four SNPs were significantly associated with EW, of which two were located on chromosome 3  plots are shown in Fig. Bonferroni-corrected significance threshold (− log10(P) ≥ 8.16). These SNPs contributed to the formation of at least 11 GWAS peaks. The top three significant SNPs were on chromosome 5 at 37,345,836 bp (P = 1.79 × 10–22), on chromosome 13 at 2,196,728 bp (P = 7.05 × 10–14), and on chromosome 6 at 1,017,750 bp (P = 5.48 × 10–13). The genomic regions underlying the 11 significant GWAS peaks harbored at least 126 genes , IGF1R (chromosome 11), IGF1 (chromosome 1), PLA2G4F (chromosome 5), and HSD17B7 (chromosome 8), were present in this pathway, which are also involved in reproduction functions  simultaneously as candidate outliers under strong selective sweeps. We detected 34 selective regions that harbored 28 genes.The Pekin duck was domesticated from the mallard, and they are highly differentiated in egg production efficiency. In this study, we tried to reveal the genomic changes underlying egg production efficiency that occurred during the domestication of the Pekin duck from the mallard. We downloaded the whole-genome re-sequencing data of 96 ducks  and re-analyzed them based on the latest duck reference genome  to screen for genomic regions and genes that have been under selection based on a selective sweep analysis using the Then, we combined the GWAS results with the detected signatures of selection to screen for candidate genomic regions contributing to FCR in ducks. The GWAS peaks on chromosomes 13, 3, and 6 overlapped with regions of the genome with signatures of selection , the three previously identified overlapping regions, between the GWAS and selection analysis, were confined to a 0.21-Mb region on chromosome 13 between positions 2,133,672 and 2,334,566 bp  and FST also suggested that these regions were in soft selective sweeps    , whereas TRNT1 and CRBR were expressed in all investigated tissues. Among the candidate genes on chromosome 3, MANEA was highly expressed in all investigated tissues, with FHL5 being the most highly expressed gene in the ovary and the follicular membrane, whereas GPR63, NDUFAF4, KLHL32, and MMS22L had relatively moderate expression levels in all investigated tissues. Among the candidate genes on chromosome 6, SCD a key gene that regulates lipid metabolism had a moderate expression level in all tissues, with the highest expression level in the hypothalamus. LOC101795347 was annotated as a lncRNA and had the highest expression level in the oviduct, an essential organ for egg formation, and ZNF488, ANTXRL, ZFAND4 and ALOX5 were moderately expressed in the ovary, oviduct, and the follicular membrane of ducks.For laying ducks, FCR is influenced by many direct factors, such as feed intake behavior, digestion and absorption capacity, and egg production ability . TherefoWe also checked whether there were any potential regulatory relationships among these candidate genes. Using the STRING database, analysis of protein-to-protein interactions (PPI) showed that several proteins have potential interactive relations, although there was no other strong evidence to support regulatory relationships between them  ≥ 8.16). Because of the multiple statistical tests and the large number of SNPs, it was more difficult to identify significant associations [Our results show that the analyzed egg production traits were highly correlated with each other, both phenotypically and genetically, which indicates that they may share similar genetic components or be influenced by some pleiotropic genomic regions , 38. Howciations , and in 2 population that was constructed using Pekin ducks and mallards, which suggests that the phenotypic value of FCR was different in the F2 population.FCR is a very complex trait because it is influenced by feed intake behavior, digestion and absorption capacity, and egg production ability . Pekin dThrough GWAS, we identified 18 genomic regions that were associated with FCR-related traits but these association signals were not strong, as shown by the Q–Q results . Some of these functional candidate genes were highly expressed in tissues with reproductive functions. FHL5 may be involved in the regulation of spermatogenesis [TRNT1 is expressed in all investigated tissues, and has basic functions in the mRNA translation process to produce peptides [NDUFAF4 is involved in energy metabolism in the mitochondria [SCD is a crucial gene that regulates lipid metabolism [LOC101795347, as a lncRNA, had the highest expression in the oviduct, which is an essential organ for egg formation. Some of the functional candidate genes were highly expressed in nervous tissues  and it is well known that genes associated with neural processes were initially under selection during the domestication of animals [CNTN4, which has a role in synaptogenesis [CRBN, which is related to the intelligence quotient of humans [GPR63, which has a function in the brain [An important research strategy to reveal the genetic basis of complex traits is to combine signatures of selection and GWAS results , 48, espogenesis , and amopeptides ; NDUFAF4chondria ; and SCDogenesis , CRBN, wf humans , and GPRhe brain .The integration of GWAS and analyses of signatures of selection helped us identify three genomic regions that affect FCR in the duck, with positional candidate genes that are involved in nervous activity, metabolism, and reproduction processes. However, this does not mean that the other regions identified by GWAS do not contribute to the genetic architecture underlying FCR. Detection of additional genomic regions that are involved in FCR and egg production traits requires further studies, by, e.g., increasing sample size or performing functional analyses. Nevertheless, our findings not only provide GWAS results that shed light on the genetics of duck feed efficiency but also deepens our understanding of the genomic changes underlying the domestication process. By incorporating the identified SNPs into breeding programs, selection for FCR in ducks can be implemented to save production costs.2 population that was constructed from mallard and Pekin ducks. We performed a GWAS to identify genetic loci that underlie these six egg production traits but only detected 11 genomic regions, which were all associated with FCR. Three of these regions, on chromosomes 3, 6, and 13, were confirmed by analyses of signatures of selection. These three genomic regions harbor 31 functional candidate genes, among which, CNTN4, CRBR, GPR63, KLHL32, FHL5, TRNT1 MANEA, NDUFAF4, and SCD are the main candidate genes, based on their high expression in nervous and reproductive tissues. Our study is the first to provide genomic regions associated with FCR in ducks, which will be useful for genomic selection in duck breeding. Our data also illustrate that the genes that appear to have been selected in ducks during their domestication are related to nervous, reproduction and metabolic functions.We have reported genetic parameters for six egg production traits in ducks based on an FAdditional file 1: Table S1. Composition of the diet (g/kg as fed) used to feed the ducks during the laying period.Additional file 2: Figure S1. Photo of the individual cages (1 m by 1 m) used for each duck.Additional file 3: Table S2. Clean read data for 62 mallard and 34 Pekin ducks obtained by whole-genome re-sequencing and downloaded from NCBI.Additional file 4: Table S3. Annotation of the genes detected with the top 2000 SNPs associated with FCR in the GWAS. The top 2000 SNPs were screened according to − log10 (P). The cells highlighted in red on the chromosome line represent SNPs that reached the Bonferroni threshold. The average FST and Pi of the slide windows are also provided based on the analysis of selective sweeps.Additional file 5: Figure S2. The top five GO terms by enrichment factor with a significant P value, which were enriched in the categories cellular component (CC), biology process (BP), and molecular function (MF), respectively. The genes used for GO analysis were from the annotated genes according to the top 2000 SNPs in the GWAS.Additional file 6: Table S4. GO enrichment results of the genes annotated based on the top 2000 SNPs in GWAS.Additional file 7: Figure S3. The top 15 KEGG pathways by enrichment factor with significant P values. The genes used for KEGG analysis were from the annotated genes according to the top 2000 SNPs in GWAS.Additional file 8: Table S5. The KEGG enrichment results of genes annotated by the top 2000 SNPs in GWAS.Additional file 9: Figure S4. Genome-wide association and selective sweep analyses on the candidate regions associated with FCR. After checking the GWAS association peaks on chromosomes13, 3, and 6, one by one, we observed that they overlapped with the selection target genomic supported by the sliding windows. Each blue and red dot in the image of the selection analysis represents a sliding window, while each purple dot represents an SNP site in the GWAS analysis.Additional file 10: Figure S5. Protein–protein interactions (PPI) show the potential relationships between the 31 candidate genes according to the STRING database.


DMRT3 mutant allele previously associated with the paso gait especially cherished in this island breed. We also genotyped a subset of 24 samples with the Illumina Neogen Equine Community genome-wide array . This data was further combined with the publicly available PRPF genomes from other studies. Our analysis show an undeniable genetic connection between the two varieties in Puerto Rico, consistent with the hypothesis that PRNPB horses represent the descendants of the original genetic pool, a mix of horses imported from the Iberian Peninsula and elsewhere in Europe. Some of the original founders of PRNRB population must have carried the “gait-keeper” DMRT3 allele upon arrival to the island. From this admixture, the desired traits were selected by the local people over the span of centuries. We propose that the frequency of the mutant “gait-keeper” allele originally increased in the local horses due to the selection for the smooth ride and other characters, long before the PRPF breed was established. To support this hypothesis, we demonstrate that PRNPB horses, and not the purebred PRPF, carry a signature of selection in the genomic region containing the DMRT3 locus to this day. The lack of the detectable signature of selection associated with the DMRT3 in the PRPF would be expected if this native breed was originally derived from the genetic pool of PRNPB horses established earlier and most of the founders already had the mutant allele. Consequently, selection specific to PRPF later focused on allels in other genes  that have not been previously associated with the prized paso gait phenotype in Puerto Rico or anywhere else.Since the first Spanish settlers brought horses to America centuries ago, several local varieties and breeds have been established in the New World. These were generally a consequence of the admixture of the different breeds arriving from Europe. In some instances, local horses have been selectively bred for specific traits, such as appearance, endurance, strength, and gait. We looked at the genetics of two breeds, the Puerto Rican Non-Purebred (PRNPB)  horses and the Puerto Rican Paso Fino (PRPF), from the Caribbean Island of Puerto Rico. While it is reasonable to assume that there was a historic connection between the two, the genetic link between them has never been established. In our study, we started by looking at the genetic ancestry and diversity of current Puerto Rican horse populations using a 668 bp fragment of the mitochondrial DNA D-loop (HVR1) in 200 horses from 27 locations on the island. We then genotyped all 200 horses in our sample for the “gait-keeper”  It is thought that altered gait phenotypes require a specific “gait-keeper” mutation in the DMRT3 gene that affects the configuration of the spinal circuits controlling stride in vertebrates3.Since their domestication, horses have been selected for many traits including the ability to perform additional gaits other than the common walk, trot, and gallop. Among these are the ambling gaits, the four-beat gaits are particularly comfortable for the rider. The horse breeds exhibiting them are referred to as gaited horses4. PRPF has served as an icon of local pride and tradition in Puerto Rico, and its origin is directly connected to the history of the West Indies that were the destination for immigrants and their livestock in the Caribbean since the initial arrival of the Spanish settlers until the conquest of Mexico. During this time, various Iberian horses likely related to the modern Spanish Jennet, Barb, Andalusian, Lusitano, and Sevillian Jacas, as well as northern Spanish breeds including Celtic ponies5, were introduced to Cuba, Hispaniola and Puerto Rico as well as other Caribbean islands6. Those imported horses likely represented the genetic variation that existed in their countries of origin, which, in this case, meant Spain7.The Puerto Rican Paso Fino (PRPF) is a naturally gaited light horse breed prized for its specific phenotypic characteristics, including smooth, natural, four-beat, lateral ambling gait referred to as “paso”Puerto Rico (PR) promptly became the breeding ground for horses that were later exported from the island for the Spanish conquests of Mexico, Honduras and Peru. The resulting admixture of the imported breeds on the island eventually resulted in the local mixed variety called, quite literally, “the Criollo”. These horses are small in stature but powerful horses with a variety of gaits and quite capable of carrying big cargo with little effort. Even as traditional uses for horses have drastically declined in the last century, the nonpure bred (NPB) Criollo horses are still ubiquitous on the island of Puerto Rico. While it makes sense to assume that PR NPB horses must have been the original stock from which the prized purebred PRPF ultimately originated, the genetic link between the two has never been established.8. By the middle of the nineteenth century, the “paso fino” competition events were common in Puerto Rico, suggesting that the PRPF bread was already firmly established and widespread.The PRPF that arose in Puerto Rican farms were used by the landowners, and their foremen that supervised their plantations on horses, often with their entire families. They selected horses that would walk smoothly and securely on the uneven, slippery, mountainous terrain in the interior of the island. Thus, in addition to the four-beat, rhythmic, lateral ambling gait, the Puerto Rican Paso Fino developed a quick, isometric, short step in which it barely rises its hoof, and with an extensive use of its ergots and fetlocks, lands a soft footstep without any lateral deviation. In their gait, the movement of the hocks is isogonic. Through time, other characteristics were also selected, especially a long torso for a more comfortable ride. By 1840, the term “paso fino” had already been minted for this race, and additional traits with cosmetic purposes were selected for by different breeders; most common among them a thick, abundant mane, a long, elegant tail, and bright, yellow  eyesDMRT3 gene. Finally, we focused on the genetic diversity at the chromosomal level and looked at the distribution of genetic diversity and signatures of selection in the genomic regions containing specific alleles that have been previously reported to be responsible for the prized “gaitedness” trait.In this study, we looked for genetic clues to understand the origins of the famed Puerto Rican breed. We looked for mitochondrial markers in Puerto Rico and at genetic diversity in nuclear markers compared to other horse breeds. We diversity between the purebred PRPF and Puerto Rican Criollo (nonpurebred or NRB) horses to understand the connection between them, paying specific attention to the frequencies of the alleles in the 9  frequencies of 40.7%, 21% and 10.1%, respectively. All other haplotype and haplogroup frequencies are shown for the PRPF and PRNPB samples in Table Some of the haplotypes were more common than others. For instance, 20 horses carried haplotype Hap1 Table , which ip1 Table . Hap9 wap1 Table . Among tWe further surveyed the frequencies of each haplotype in PRPF and PRNRB samples. Among these, some haplotypes belonged to the same reference haplogroups . Other haplotypes, such as Hap8 and Hap15, did not exactly match earlier described haplogroups but had defining motifs that allowed them to be assigned to haplogroups K2 and H1, respectively. Among the 20 haplotypes found, only one haplotype (Hap18) assigned to haplogroup H1 was unique to the PRPF, while 8 haplotypes were unique to the PRNPB Fig. . The maj12. Then, using an overlap between genome-wide genotyping arrays, we increased our sample set of PRPFs by adding publicly available data from a broader study based on similar technology13. The two largest components (PC1 and PC2) from the principal components analysis (PCA) were then matched to all horse samples across of the studies to separate the Puerto Rican horses from other American breeds. The resulting distribution shows genetic differences between Puerto Rican horses and all other breeds . Remarkably, the frequency of the mutant allele in the PRNPB population was very high  in Fig. 15, this finding had to be verified in the specific genomic neighborhood of the candidate mutation. Therefore, we focused our attention on the variation in SNPs located in close proximity (± 2 Mb) from the DMRT3_Ser301STOP mutation on ECA23 of the PRNPB and PRPF horses using rehh 2.016.The analysis shows a strong genome-wide selection signature in the PRNPB horses Fig. . The locDMRT3_Ser301STOP (BIEC2-620109 locus) seems to extend further along the chromosome for the haplotypes containing the gait-keeper A allele than in those containing the ancestral allele  and the nonpurebred PRNPB (Criollo).Horses have been considered one of our most prized possessions, used for travel, work, food, and pleasure for at least five and a half millenniaOver the centuries, the two breeds have gained distinctive appearance that is reflected in the genetic structure revealed by our analysis. We have shown that the PRPF and PRNPB horses are distinct from other breeds Fig.  but neve24 and developed our own tools14 to look at genome contributions in the context of population variation among the worldwide breeds13. While we cannot clearly identify distinct sources of genomic admixture in the Puerto Rican breeds, the PCA clearly supports the notion that PRPF is a distinct and native breed among the American Horses of Iberian origin, most closely related to the PRNPB (Criollo) population, but completely distinct from all other breeds , there are differences in magnitude, indicating differences in population histories Fig. . At the 17. Over the years, various studies have proposed that the variability found in the mtDNA of horses can be traced and restricted to geographic regions26. One of the first to propose such a hypothesis discovered 17 frequent haplotypes (mtDNA sequences), each creating a distinctive cluster. We used mitochondrial D-loop sequences from our samples for the preliminary assessment of the origin of the domestic horses on the island, specifically using hypervariable region 1 (HVR1) in a cost-effective approach to help understand the origins and diversification of the Puerto Rican horses. The sequences were grouped into 54 unique sequences (haplotypes) that could be cross-referenced to the nomenclature used in Cieslak et al.10.Due to the maternal inheritance of mtDNA and lack of recombination, mtDNA has been widely used for studying the history of maternal lines. Mitochondrial DNA, specifically its control region, has been used effectively to study the origin and diversification of domestic horses worldwide27. Generally, these breeds were established by the haplotypes that came from multiple sources, but the frequency of Iberian haplotypes in New World breeds is generally consistent with the historical documentation of their origins7. Specifically, haplogroup D, as defined by Jansen et al.25 and later redefined by Cieslak et al.10 as haplogroup X, is well represented in both the Southern Iberian and New World breeds, thus suggesting the importance of Iberian breeds in founding horse varieties of the New World7.Recent studies using mtDNA confirmed Iberia as the geographic and genetic source for the New World horses, as several predomestic maternal lineages unique to the peninsula still survive in modern horses of Iberian descentOur study of the mtDNA diversity in the two Puerto Rican horses also points to the mainly Iberian origins, since haplotypes D and X are the ones most represented Fig. . There s7. However, there is a high level of variability within and among horse breeds, without a clearly defined geographical pattern of distribution28, so the mtDNA evidence alone is not sufficient to fully describe the ancestry of the Puerto Rican breeds. Therefore, the identification of the population origin required a more complex genetic approach that included dense genotyping across the genome.The direct inheritance from mother to daughter without recombination can provide valuable clues in the preliminary assessment of ancestry in the maternal lineages that can be used in reconstructing the history of the breed’s origin. Since previous research has shown at least some genetic clustering of haplotypes, mtDNA analysis allows us to make a preliminary assessment of maternal lineagesThanks to the analysis of the genome-wide array data, we can see that Puerto Rican horses share genome variation components with a number of horses worldwide Fig. . In part29, which may be a consequence of natural or artificial selection on genome-wide variation, as selection for one allele would have swept variation across the linked loci30. In fact, ROH approach is commonly used to test hypotheses for artificial selection in domesticated animals32. The observed differences in ROH are indeed consistent with the hypothesis that PRPF has been under selection , contiguous uninterrupted stretches of chromosomes without any heterozygous SNPsion Fig. . HoweverDMRT3 mutant allele  shows high frequency in many gaited breeds and breeds bred for harness racing, while other horse breeds were homozygous for the wild-type allele  in earlier studies1. It has also been reported at high frequencies in Northern European breeds , the genomic neighborhood of the candidate allele must be evaluated in a formal test. Since the selection for this allele should have been pretty recent, not older than the historic horse arrival to Puerto Rico, the selection tests can be evaluated based using the extended haplotype homozygosity (EHH), population differentiation tests, or a combination of both approaches37. Somewhat surprisingly, we did not observe any signatures of selection in the PRPF with genome-wide significance , typically representing recent selectionnce Fig. , which m30. A contrast of diversity and divergence would be a better approach with the reasoning that the haplotypes containing selected loci should show more differences between diverging populations compared to the other loci genome wide . This is why we followed XP-EHH and RSB tests that combined EHH statistics with the degree of population differentiation. For the addition of a phylogenetically based outgroup reference in these comparisons, we used a combination of samples from breeds in the same lineage1 . Therefore, it is not surprising that the iHs test, a recombination-based test that uses only the variation within the specific horse breed, did not identify any selection signatures in PRPF. This would be expected when (a) there is almost no variation in the locus and (b) only a few variable markers exist on chromosome 23, undermining the performance of EHHge1 Fig. .DMRT3 locus. Neither of the tests showed any selection signatures in PRNRB compared to PRPF  were not included in our genotypes, these were located very close (within 0.5 Mb) from a peak on ECA1  with them. Local farmers must have been selectively breeding for the mutant allele, and over several centuries, it has increased in frequency in the nonpurebred population of horses on the island. Consequently, the founders of PRPF were initially picked out from the existing PRNPB pool, but since the DMRT3 mutant allele was already nearly fixed, the selection in the purebred horses was focused on other genes that may or may not be associated with the paso gait, including MYH7, PRN and others. In order to further validate our current hypothesis and to identify the specific functional mutations that have been selected by the PRPF breeders, a comprehensive phenotype-genotype analysis based on horse pedigrees and sequencing data from these candidate genes needs to be conducted.In summary, we have shown that the PRPF and PR NPB horses are related, as their mitochondrial sequences are intertwined Fig. . Then we38.We collected hair samples from each of 200 unrelated horses from 27 locations grouped in seven sampling sites across the entire island of Puerto Rico  for further analysis. Total genomic DNA was extracted from hair roots  using a QIAGEN DNAeasy Blood and Tissue kit following the user-developed protocol 2 . Extracted DNA was quantified using an IMPLEN nanophotometer, and stocks of 100 µL at 20 ng/µL for each sample were made for easy access in the future.While the pedigrees of the PRPF were all well documented, there is no reliable information on ancestry beyond the F1 generation for the PRNPB horses. In each case, the owners of the horses personally confirmed the lack of known relatedness during the collection procedure.39 and according to the equations originally developed by Wright41. No first-degree relatives were detected, but two of the three PRPF horses from our study (OS_PASO) are related at the IBD > 0.271, corresponding to the relationships between the second-degree relatives (Table 13 was 0.145 ± 0.004 indicating a moderate amount of inbreeding similar to other breeds (ex. the IBD in the Peruvian Paso in the same study is 0.14 ± 0.002). The highest inbreeding found was between 0.3 < IBD > 0.35 among samples  indicating the level of inbreeding approximately equivalent to half-sibs , 0.5 µL of each primer at 10 µM, 5 µL of genomic DNA, and 2 µL of water. The thermocycler was programmed for 5 min heating at 95 °C, 30 cycles of 40 s at 94 °C, 45 s at 55 °C, 45 s at 72 °C and 10 min of final extension at 72 °C. The resulting PCR product was stained with EtBr and visually inspected on 2% agarose gels. The resulting amplicons were purified with the High Pure PCR Cleanup Micro Kit .The target region of mtDNA containing the D-loop  was amplified with the AGC TCC ACC ATC AAC ACC CAA A (forward) and CCA TGG ACT GAA TAA CAC CTT ATG GTT G (reverse) primer pair11. The haplotypes were also assigned to traditional haplogroups according to Cieslak et al.10 and are listed in Table Sequencing reactions were performed on an ABI 3730xl DNA Analyzer at EPOCH Life Sciences (Texas USA). The resulting sequences were truncated to a total length of 248 bp  using Geneious 8.1.6 software (Biomatters Ltd.) to make it compatible with the previously published data, and pairwise alignment of forward and reverse sequences was performed for each individual. The consensus sequences were retrieved and aligned to horse mtDNA reference X79547DNAsp 5.1043, and median-joining networks were reconstructed using Network 5.0 software 9. Default settings were applied, and four mutation hot spots were excluded , as recommended in previous studies42. Each haplotype node in the network was assigned a pie chart with a size scaled to represent the number of individuals and color coded (red and blue) to represent the proportions of PRPF and PRNPB for each individual haplotype . Since the 90% of the genotypes were recovered, we did not attempt to optimize the PCR reactions.All 200 samples were genotyped for the 45 to genotype 24 samples (3 PRPF and 21 PRNPB). Genotypes were obtained from the intensity data using GenomeStudio (Illumina) software suite with the minimum score threshold of 0.15. The resulting data were converted to PLINK39 format for downstream analysis. We merged the data obtained from our samples with a publicly available dataset of a broader study13 that included 795 samples of 37 horse breeds all around the world genotyped on the similar array13. After merging, filtering by the genotyping rate and soft pruning the results for LD , a total of 819 samples and 22,347 variants were retained. Notably, the dataset contained genome-wide genotypes of 20 additional PRPF horses that were merged with our data the approximately equal number of genome-wide genotypes from each bread (23 PRPF and 21 PRNPB) included in all the downstream genome-wide analyses.In addition, we used the Illumina Neogen Equine Community Array The entire datasets of the filtered 22,347 markers filtered from the Illumina Neogen Equine Community Array and covering the entire lengths of all the chromosomes (see above) were used for the PCA, Admixture and RoH analysis, as well as in the XP-ESS and RSB selection tests. In the EHH test, the 966 markers on the ECA23 were used to illustrate the haplotype homozygosity around the putatively selected locus.Principal component analysis (PCA) was performed using EIGENSOFT, and the PCA plot was produced using custom Python scripts using libraries for data processing 46. To reduce the clutter of the visualization for PCA plots, we assigned colors to each breed according to the phylogeny calculated from SNP frequencies in 38 horse populations from Petersen et al.13  analysis for PRPF and PRNPB was performed using a sliding window approach48. Given the low density of SNP coverage, 15 loci were selected as the sliding window size, with a minimum of 5 consecutive SNPs to start the run and a 10 Mbp max distance between SNPs in each run. First, ROH numbers and sizes were calculated separately for every individual and then averaged for each Puerto Rican breed. The coefficient of inbreeding was derived from the RoH analysis49. Inbreeding estimates were determined using two methods: individual inbreeding coefficients (Fmax) measure observed versus expected genetic diversity in an individual in a population. The sum of runs of homozygosity (SROH) estimates the proportion of the genome covered by fragments lacking any variation in SNPs (heterozygosity).Signatures of recent selection in the two varieties of Puerto Rican horses were evaluated by identifying genomic regions with unusually high local haplotype homozygosity using the rehh package for R16. We integrated variation in all the SNPs from the PRNPB and PRPF horses by combining genome-wide genotypes from this study and13. We discarded all the loci that were not present in both studies and phased our genotypes by the SHAPEIT tool53 using an existing recombination map54. The within-population genome-wide analysis “integrated haplotype homozygosity score” (iHS) was calculated for PRPF and PRNRB as well as other breeds using the rehh 2.0 tool described in Tang et al.15. In addition, two pairwise population statistics have been calculated comparing the breeds in their phylogenetic context: the Rsb (ratio of EHHS between populations) and the XP-EHH (cross-population EHH) statistic37. We used combined genome-wide genotype data from the three breeds shown to be the most closely related to the Puerto Rican horses13, namely, Andalusian, Lusitano, and Mangalarga Paulista . In this approach, the haplotype frequency and decay of haplotype length were evaluated for the chromosomal neighborhood flanking the candidate marker under selection, for example, the “gait-keeper” mutation BIEC2 620109 (DMRT3Stop). The distribution of EHH was tested for other candidate alleles for PRPF and PRNPB vs. reference (i.e., Andalusian, Lusitano, Mangalarga Paulista, etc.; Fig. To visualize recent selection and contrast haplotypes associated with the selected trait in the genomic region, we used extended haplotype homozygosity (EHHSupplementary Information.


Ae. tauschii synthetic wheat donors. Across the panel, 67% of the Ae. tauschii donor genome is represented as introgressions in elite backgrounds. We show how observed genetic variation together with hyperspectral reflectance data can be used to identify candidate genes for traits relating to photosynthetic capacity using association analysis. This demonstrates the value of genomic methods in uncovering hidden variation in wheat and how that variation can assist breeding efforts and increase our understanding of complex traits.To feed an ever‐increasing population we must leverage advances in genomics and phenotyping to harness the variation in wheat breeding populations for traits like photosynthetic capacity which remains unoptimized. Here we survey a diverse set of wheat germplasm containing elite, introgression and synthetic derivative lines uncovering previously uncharacterized variation. We demonstrate how strategic integration of exotic material alleviates the D genome genetic bottleneck in wheat, increasing SNP rate by 62% largely due to  Ae. tauschii (D)  and diverse Ae tauschii (=Ae. squarrosa) D genome donors  and Thinopyrum ponticum  thermal/hydration properties measured in the infrared part of the electromagnetic spectrum, (ii) pigment related indices assessed in visible bands  is the primary pigment of photosynthesis while chlorophyll b (chlb) is an accessory pigment. In a study of Australian wheat varieties released through time, a decrease in the Chl a/b ratio was associated with a decrease in electron transport capacity per unit of chlorophyll, but because total Chl content per unit leaf area increased, electron transport capacity per unit leaf area increased , SNPs from each line were separated into 500 kbp bins for all subgenomes and the number of variants that match Rye in both position and allele were counted. SNPs between Rye and CS were generated by mapping and variant calling Lo7 Rye Illumina sequencing reads (ERS446995) against the CS reference genome. This revealed the 1B/1Rs introgressions in 6 panel members spanning the first 239 Mb in each line  was high for NLamA7, medium for SPADA7, low to medium for vegetation indices, pigment composition and senescence/degradation indices, high to medium for water indices and low for LMA and RDM  for N content, vegetation indices, pigment composition, senescence, water indices and traits estimated from wheat physiology predictor indicated significant variation among genotypes, environments, and genotype × environment interactions with few exceptions Table . Broad sd Figure .a (RARSa) and 27.7% of the variation was explained when the model also considered chlbb (RARSb) and total chlorophyll content (R750_700.) In the case of TGW and GM2, the combination of enhanced vegetation index (EVI) and Plant Senescence Reflectance Index (PSRI) explained 13.3% and 16.5% of the variation, respectively, with opposite effects. RARSb explained 7.7% of the total variation observed in RUE_E40InB. However, the combination of LMA, Green Normalized Difference Vegetation Index (GNDVI), R750_700 and PSRI explained 25.6% of the variation in RUE_E40InB. For RUE_InBA7and RUE_GF, only 8.3% and 4% of the variation was explained by the combination of chlorophyll content in the flag leaf (SPAD_A7) and water index 4 (WI_4) or WI_2, respectively. Carotenoid content (RARSc) was the first component selected in the model and explained 3% of the variation in RUET. When structural independent pigment index (SIPI), WI_2 and NLamA7 were added to the model, 13.5% of RUET was explained.Multiple regression analysis (stepwise) was conducted to determine if the combination of traits presented in Table P value of 5 (P < 0.00001) of which 10 passed FDR threshold determined in GAPIT (‐Log P 7.12) for traits including total chlorophyll content  from two repetitions for each measured trait over two years. From across 23 traits 47 MTAs were identified with a ‐Log et al. HY5 a bZIP transcription factor that binds to the promoters of light‐inducible genes in NPQI‐7A, TCP20 and HK3 cytokinin receptor in PSRA‐3B. Along with candidates that link to carotenoid biosynthesis and distribution: SYTF and TRAESCS3B02G039600 in RARSc‐3B, TRAESCS7D02G503400 in RARSc‐7D and also Small ubiquitin‐related modifier 1 (SUMO1) in SIPI‐3B and KNAT3/KNOTTED1‐like in SIPI‐7D, both of which have also been implicated in chlorophyll levels in the leaf.Candidate gene searches were carried out using Knetminer  on chromosome 2A, whose closest orthologues are ATSWEET4/5. These genes have been observed to have an effect on chlorophyll content in both et al.,et al.,et al.,et al.,de novo SNP discovery to assess the contribution of strategically integrated exotic germplasm to overall CIMMYT germplasm diversity, to identify and track wild relative introgressions and demonstrate how this novel diversity can be exploited to identify candidate genes for agriculturally important traits. We also show a clear improvement in our ability to assess diversity within the HiBAP panel when comparing de novo methods to the previously available array‐based genotyping for the panel. This included demonstrating the extent to which introgression of exotic material has increased genetic variation throughout the panel which was previously masked because the predesigned nature of array‐based methods limit discovery of truly novel variation  of the D genome was identified to be of synthetic donor origin, demonstrating the value of the HiBAP panel as a resource that could be utilized to study the effects of the presence of some of the ~22 000 genes found in these regions that are already introgressed into agronomically favourable backgrounds. We also identify that the proportion of Ae. tauschii donors in the synthetic subpopulation can vary substantially, from as little as 0.5% to as high as 43% of the D subgenome, a larger range than identified in studies using synthetic octoploids as primary Ae. tauschii donors (0.075%–13.5%)  and leaf rust (Lr19)  6% of variation was explained. Carotenoids play an important role protecting the photosynthetic machinery from excessive light  explained a significant part of HI variation while chlb (RARSb) explained variation for RUE_E40InB. In wheat canopies, the uppermost layer including the flag leaf measured in this study receives the highest irradiance. However, photosynthetic machinery in wheat is saturated at 1200 μmol/m2/s , flag leaves absorb more light than they can use and need to engage photoprotective mechanisms. Lower chlorophyll content per unit area in the upper leaves facilitates light penetration in the canopy decreasing canopy extinction coefficient and therefore mitigating efficiency losses associated with light saturation  to boost wheat genetic gains.Here we uncover the contribution of exotic material to variation in CIMMYT wheat lines, confirming the value of strategic incorporation of primary synthetics to ease genetic bottlenecks in the D genome. The HiBAP panel now represents an unprecedented resource of characterized genetic diversity, containing ~67% of the et al.,et al.,The use of hyperspectral reflectance (ASD Field Spec) measured on leaves in the field is independent of sunlight (due to the light source of the device) and one leaf can be measured in less than 30 s  consists of 149 bread wheat spring types Table  that areet al. Flag leaf tissue was obtained from plants used in field trials after anthesis. Material from 10 individuals was taken per line and pooled for DNA extraction using a standard CTAB based method. DNA purity was assessed using a NanoDrop 2000 (Thermofisher Scientific) and quantified fluorometrically using the Quant‐iTTM assay kit (Life Technologies). Dual indexed DNA libraries were constructed with a modal insert size of 450 bp using TruSeq DNA library preparation kit (Illumina). Capture enrichment was carried out using the MyBaits targeted capture kit  incorporating 100 000 custom 120‐mer RNA bait sequences with 8x pre‐capture multiplexing following standard protocols. In total, 90 000 probes were designed in an island strategy distributed to facilitate enrichment and subsequent variant calling from regions spanning the entire genome. Probe sequences were designed based on a subgenome‐collapsed reference, allowing probes to target homeologous regions, increasing the genomic design space with the fewest probes possible. 10 000 probes were designed to target selected gene sequences using an end‐to‐end tiling strategy covering the gene body and the promoter region (~2000 bp). The 2 kb distance was based on the median distance between the TSS and the first transposable element, 1.52 kb to allow a high likelihood of full promoter sequence capture  was calculated in windows of 500 kbp in pairwise comparisons of the elite subpopulation with the exotic subpopulations that included landrace, synthetic and landrace + synthetic and the introgression lines subpopulation as a whole. As a control measure, the elite population was randomly split and Fst was calculated between the pseudo‐subpopulations.Genetic inference into the population structure of the panel was made using STRUCTURE 2.3.4  and SNPs from the panel matching in location and allele we removed from further analysis. SNPs across each panel member were binned into 500 kbp bins and within each bin the number of SNPs showing identity by state to Ae. tauschii were counted. To estimate the contribution of Ae. tauschii within the entirety of the HiBAP panel the maximum number of SNPs within every 500 kbp window was assessed for both the elite background and synthetic background subpopulations. Where the value for a bin in any synthetic line was 5‐fold higher than the average value for each bin from the whole elite population this was classed as a modern Ae. tauschii region. Additionally, the theoretical contribution to pedigree from Ae. tauschii was estimated using the available pedigree and selection history information from the International Wheat Information System (IWIS), curated by CIMMYT.To identify genomic regions originating from Ae. tauschii outlined previously.To determine the presence of Rye introgressions in the panel SNPs between Rye and CS were generated by mapping and variant calling Lo7 Rye Illumina sequencing reads (ERS446995) against CS as previously described. Genetic identification of regions of Rye was carried out using the same identity by state methods for et al.,et al.,The 149 lines were grown in two consecutive growing seasons . Field experiments were carried out at the IWYP‐Hub  at CENEB in the Yaqui Valley, near Ciudad Obregón, Sonora, México , under fully irrigated conditions. The soil type was a coarse sandy clay, mixed montmorillonitic typic caliciorthid, low in organic matter, and slightly alkaline (pH 7.7) in nature in five flag leaves per plot seven days after anthesis (SPADA7).Nitrogen concentration of the leaf lamina (NlamA7) was measured using the Kjeldahl digestion method putting together all green leaf laminas from 12 random stems harvested seven days after anthesis after drying (oven dried at 70°C for 48 h), milling and digestion with concentrated sulphuric acid.et al.,Wheat Physiology Predictor [https://www.metabolome‐express.org/pheno/] based on  was estimated for each trait across both years as:r = number of repetitions, e = number of environments (years), σ2 = error variance, σg2 = genotypic variance and σge2 = G × Y variance.In order to deduce the combinatorial contribution of the leaf pigment, hydration and predicted photosynthetic capacity traits described in this study to overarching agronomically important traits, multiple linear regression analysis was applied. For this analysis we utilize data for agronomic traits including biomass, HI, TGW, GMo et al. . Agronom et al.,). DTA waH2 < 0.30, medium when 0.30 ≤ H2 < 0.60 and high when H2 ≥ 0.60  was used to analyse the relationship between the studied variables using the SPSS statistical package .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.AH, MR, JRE, RTF and GM conceptualized the project. RJ, BC and LJG carried out genotyping and genetic analyses. GM, CRA and FJPC carried out phenotypic measurements. RJ and GM wrote the manuscript. All authors edited and approved the manuscript.Figure S1 Depiction of genotyping capture probe set design/tiling strategy including (A) collapsing the reference genome (B) the ‘genotyping’ portion as probes scattered across the genome (C) with the ‘gene of interest’ portion in which probes were arranged almost end‐to‐end across the gene body and promoter region.Figure S2 A heatmap ideogram demonstrating genome‐wide SNP density across the HiBAP panel after all called SNPs were combined and subsequently filtered to remove loci with >10% missing data and a minor allele frequency of less than 5%.Figure S3 Estimation of the most likely number of true subpopulations (K_number) within the HiBAP panel estimated using the Evanno Method demonstrating the presence of 2 main subpopulations.Figure S4 Principal component analysis showing genetic variation in the HiBAP panel. A) Panel members when coloured by subpopulation membership deduced using STRUCTURE software. B) Panel members coloured by presence of landrace, synthetic or a combination in their pedigree history.Figure S5 Genome‐wide Fst calculations between (A) the Elite and Exotic subpopulations and (B) two pseudo‐subpopulations of elite background wheat created by randomly splitting the elite subpopulation into two groups.Figure S6 SNP density plot of two synthetically derived sister lines where SNPs are in 500 kbp bins. Red bars indicate number of SNPs identified to match modern Ae. Tauschii, blue bars show all other SNPs. Demonstrating the altering levels of introgressed regions, even between members of the same cross and selection history.Figure S7 SNP density plots for chromosome 1B from each member of the HiBAP panel found to contain the 1BS/1RL Rye introgression. Orange bars indicate the number of SNPs per 500 kbp bin that match the position and allele with those found in between Rye and the wheat reference genome. Blue bars show number of SNPs per bin that do not match Rye genetic variation.Figure S8 SNP density plots for chromosome 7D from each member of the HiBAP panel found to contain the 7DL/7EL Thinopyrum ponticum introgression. Blue bars show number of SNPs per 500 kbp bin.Figure S9 Manhattan plots showing GWA results for 23 traits. Blue line depicts the significance threshold of ‐Log10 P 5 and the red line depicts the FDR threshold.Click here for additional data file.Table S1 DNA sequencing and mapping statisticsTable S2 The distribution of SNPs called against the Refseq1.0 Chinese Spring wheat reference genome.Table S3 Genome‐wide distribution of SNPs used for GWA analysisTable S4 Identification of regions of Ae. Tauschii origin in HiBAP lines with Synthetic pedigree historyTable S5 A list of lines found to contain S. cereale/T. ponticum introgressionsTable S6 Pedigree history information for the HiBAP panelClick here for additional data file.Table S7 Potential candidate genes identified for each association using KnetminerClick here for additional data file.


Short tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds.CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively.We identified 5203 and 5868 significantly expression STRs  in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the This study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.The online version contains supplementary material available at 10.1186/s40104-021-00658-z. Expression quantitative trait loci (eQTL) mapping studies identify DNA variants linked to gene expression traits, is a powerful approach for identifying target genes that mediate the effects of genetic variations on complex traits . To dateFMR1 gene were found to disrupt the chromatin topology domain (TAD) structure and DNA methylation, thereby altering the expression of FMR1 and causing Fragile X Syndrome [SLC11A1 gene and resistance to brucellosis in bovines [ITPR1 gene and progressive gait abnormalities in dogs [STRs are highly polymorphic genetic markers with repeat unit lengths of 1–6 base pairs that have been widely used in population genetics, forensic medicine, and aetiology research . AbnormaSyndrome . Associa bovines ; a (GAA) in dogs  and  are important agricultural animals and a good model organism for biomedical research. STR markers have been widely used as genetic markers to survey population structure [IGF1 gene promoter affect the binding of HIF1α and the expression of IGF1 [SIX1 gene are significantly associated with carcass weight and backfat thickness [Pigs  and liver transcriptome data from 260 F6 and 296 F7 individuals from a heterogeneous pig population Fig. : Fig. S1All experiments on pigs were performed under the guidance of Jiangxi Agricultural University, Nanchang, China.® (Feed Intake Recording Equipment) System . The longissimus muscle and liver tissues were collected after slaughter at 240 ± 10 d. The collected samples were frozen in liquid nitrogen immediately after slaughter within 30 min and were stored in a refrigerator at − 80 °C.The animals investigated in this study included 260 F6 and 296 F7 individuals from a heterogeneous pig population generated by crossing eight founder breeds, including Bamaxiang (BX), Erhualian (EH), Laiwu (LA), Tibetan pig (TB), Duroc (DU), Landrace (LD), Large White (LW) and Pietrain pigs (PT). We designed a circular mating program to equally mix the ancestries of the eight founder breeds in individuals of the third and later generations . Each folongissimus muscle using the standard phenolic chloroform method. The DNA concentration and quality of all samples were assessed using a NanoDrop-1000 instrument  and agarose gel electrophoresis (0.8%), respectively. DNA library construction and sequencing were performed independently for each sample at Novogene Bioinformatics Technology Co., Ltd. . In brief, genomic DNA was broken into 300–400 bp DNA fragments and then amplified by Polymerase chain reaction (PCR) using Phusion® High-Fidelity DNA polymerase Kit  following the manufacturer’s protocol. The libraries were sequenced on an Illumina HiSeq Xten platform  by generating 2 × 150 bp paired-end reads. On average, the sequencing depth for each individual was approximately 8 ×. Low-quality DNA reads that contained more than 10% N bases or 50% low-quality bases (Q ≤ 5) were filtered out. Total RNA was extracted from liver tissue using TRIzol™ . RNA purity, integrity and concentrations were measured using an eNanoPhotometer® spectrophotometer , a Bioanalyzer 2100 system  and a Qubit3.0 Fluorometer, respectively. RNA sequencing libraries for each sample (F6 and F7) were constructed with the NEBNext® UltraTMR NA Library Prep Kit for Illumina  according to the manufacturer’s specifications. In short, mRNA was purified from 2-μg total RNA from each sample using Oligo (dT) magnetic beads  and was then fragmented with fragmentation buffer . Next, the random primers and reverse transcriptase  were used for cDNA synthesis. The cDNA was then purified, end-repaired, adaptor-ligated, and amplified following the recommendations of the manufacturer. Finally, the RNA-seq libraries of F6 and F7 individuals were sequenced on the Illumina HiSeq Xten PE150 platform  and the Novaseq-6000 PE150 platform , respectively. The raw RNA reads with the percentage of low quality (Q ≤ 5) bases greater than 50% and N base contents greater than 10% were removed, generating an average of 12.74 Gb data for each individual.DNA was extracted from the http://asia.ensembl.org/Sus_scrofa) using the parameters “TRF –80, 10, 6, 2”. We retained STRs with at least three repeating units and removed redundant and complex STRs. In addition, adjacent STRs separated by less than 10 bp were also filtered out. A total of 1.72 million STRs consisting of 962,775 single-nucleotides, 267,217 dinucleotides, 75,813 trinucleotides, 75,813 tetranucleotides, 90,161 pentanucleotides, and 92,437 hexanucleotides were identified. We used the FastQC program to evaluate the quality of the DNA-seq data, BWA-MEM to map the sequences to Susscrofa11.1, Picard software to mark duplicated PCR reads and Samtools [We used Tandem Repeat Finder (TRF v4.6 ) to scanSamtools  to identSamtools . A totalHipSTR software  was usedftp://ftp.ensembl.org/pub/release-97/gff3/sus_scrofa), the liver tissue transcripts were assembled using StringTie [We used FastQC software to evaluate the quality of the RNA-Seq data and the STAR program  to map ttringTie  and quantringTie . Genes wtringTie  was usedThe eSTR analysis was performed in the F6 and F7 populations separately. We focused on STRs within 1 Mb from the corresponding genes. We corrected the gene expression data for sex, age, breeding batch, carcass weight, RNA integrity numbers (RINs) for RNA-seq, the top 10 PCs based on genome-wide SNPs, and 20 peer factors inferred from the expression data . All genP value of each gene-STR test according to the Bonferroni method and retained the most significant STR for each gene. Then, all adjusted P values were corrected by the Benjamini-Hochberg method using the qvalue package, and the threshold of significance was set to an FDR = 1%.The multiple testing correction was performed using an approach that integrates Bonferroni and Bengamini hochberg method , 9. For all) + V(e). The heritability of an eSTR was calculated as hSTR = V(eSTR) / V(p), and the heritability of a SNP was computed as hSNP = V / V(p). In the model employed for variance component analysis, the eSTR was considered a fixed effect, the aggregate effect of the other cis-SNPs was considered a random effect, and the cis-heritability was dissected using GCTA software [We fit two models (H0: Expression = SNP + errors vs. H1: Expression = SNP + eSTR + errors), and then determined whether the two regression equations were significantly different using the Python based on a significance threshold of an FDR < 5%. The SNP data were processed by using “plink --indep 50 5 2” to delete strongly linked sites. The phenotypic variance was considered to be the sum of genetic variance attributed to an eSTR, the surrounding SNPs and the residuals: V(p) = V(eSTR) + V(SNPsoftware . Both thP < 0.001) and eSTRs (FDR < 1%) were considered as the candidate variant sets, and CAVIAR combined the correlation statistic results and LD information to model and infer the probability that a variant was causal. This study assumed that only one causal variant existed per locus according to the parameter set -f1 -c1. We ranked the variants based on their causality probability given by CAVIAR, and the variant loci with a CAVIAR score greater than 0.1 were considered as fine-mapping loci.In this study, CAVIAR (V2.2) software was used to further fine map eSTR signals. All significant eSNPs (http://www.fragencode.org). In addition, we downloaded H3K4me3 and H3K27ac data from the liver tissues of three pigs under ENA accession number PRJEB6906 [P ≤ 0.001), eSTRs  and fine-mapping eSTRs  in the above mentioned genomic feature regions was determined by using Fisher’s exact test in the R program.We extracted CDSs, introns, 5’UTRs, 3’UTRs and intergenic regions from the gene annotation file (Ensembl version 97). All functional RNAs  were categorized as funcRNA regions. We also obtained the ATAC, TAD, and HIC compartment A/B annotation intervals from pig liver tissue data from the FR-AgENCODE project  . QTL regions of less than 2 Mb were considered candidate fine-mapping QTL regions, and the merged QTL regions totalled 991 Mb after the redundant areas were deleted. Then, we compared the overlap between the pig eSTR and QTL regions. We also downloaded the information of a total of 15,736 pig GWAS SNPs from the ISwine database [http://iswine.iomics.pro). We found that the mean LD of between STR and SNP drops below 0.1 when their distances surpass 100 kb. Therefore, we focused on eSTRs with a physical distance less than 100 kb from GWAS SNPs.The Linkage disequilibrium (LD) between STRs and SNPs was estimated from the square of the correlation coefficient between the STR dosage and the SNP genotypes. Among them, the genotype of an SNP was coded as 0, 1, or 2, and the STR dosage was the sum of GB tags  of the two alleles. We downloaded the complete QTL data of pigs from pigQTLdb  (https:/database  (http://P < 0.001) in F6 and F7, respectively. After multiple test corrections , we identified 5023 significant eSTRs in F6   in F6 pigs and 1375 out of 5868 (23.43%) eSTRs in F7 pigs remained significant after adjusting for SNP effects. On the other hand, we estimated the cis-heritability of each eGene and decomposed it as the sum of eSTR heritability and SNP heritability, and obtained an average eSTR heritability of 0.0885  in both F6 and F7 individuals ) and effect sizes for corresponding genes  including 235 FMeSTRs in F6 and 298 FMeSTRs in F7, respectively Fig. : Fig. S7P = 8.6e− 08; FMeSTR FC = 4.8, P = 3.2e− 05), followed by CDS regions , 3’UTRs  and intron regions   Fig. : Fig. S8We further assembled a series of annotation data on the noncoding regions inferred from the epigenomic data of pig liver tissue , HIC compartment A and HIC compartment B) obtained from public databases. We compared the distribution and enrichment of eSTRs/FMeSTRs in these functional areas, and the results showed that most FMeSTRs and eSTRs were located in active chromatin interaction areas (TAD and HIC compartment A) Fig. : Fig. S9CLPB gene  Estimation of π1 statistics with the F6 term as the discover population and F7 as the replication population. (b) Estimation of π1 statistics with the F7 term as the discover population and F6 as the replication populationAdditional file 7 Fig. S5. Heritability estimates for eSTRs in cis-regionsAdditional file 8 Fig. S6. Two examples of eSTR-gene association analyses in the F6 population. (a) and (c) Regional association plots for eSTRs that are more significant than nearby SNPs. (b) and (d) Relationship between STR dosage and gene expressionAdditional file 9 Table S3. The 498 FMeSTRs in the F6/F7 populationAdditional file 10 Fig. S7. FMeSTRs showing significant associations with gene expressionAdditional file 11 Fig. S8. Distribution of eSTRs distances to the closest gene bodyAdditional file 12 Fig. S9. Distribution of eSTRs located in TAD or non-TAD regionsAdditional file 13 Fig. S10. Linkage disequilibrium decay analysis. The LD between SNPs and STRs was evaluated based on the square of the Pearson correlation coefficientAdditional file 14 Table S4. STRs linked with GWAS SNPs are located in epigenetic peaksAdditional file 15 Fig. S11. Co-localization of epigenetic peaks and eSTR. Red dashed lines represent eSTR. Each kind of epigenetic marker was analysed in three parallel samples. (a-c) A (CCG) n STR related to the expression of the PSMD6 gene is linked to GWAS SNPs and colocalizes with epigenetic peaks. (d-f) A (AAAC) n STR related to DHDH gene expression is linked to GWAS SNPs and colocalizes with epigenetic peaksAdditional file 16 Fig. S12. Major motif components in FMeSTRs


Xanthomonas arboricola pv. pruni (Xap), is a serious peach disease with symptoms that traverse severe defoliation and black surface pitting, cracking or blemishes on peach fruit with global economic impacts. A management option for control and meeting consumer demand for chemical-free, environmentally friendly fruit production is the development of resistant or tolerant cultivars. We developed simple, accurate, and efficient DNA assays (Ppe.XapF) based on SNP genotyping with KASP technology to quickly test for bacterial spot resistance alleles in peach fruit that allows breeders to cull seedlings at the greenhouse stage. The objective of this research was to validate newly developed DNA tests that target the two major QTLs for fruit resistance in peach with diagnostic utility in predicting fruit response to bacterial spot infection. Our study confirms that with only two Ppe.XapF DNA tests, Ppe.XapF1-1 and Ppe.XapF6-2, individuals carrying susceptible alleles can be identified. Use of these efficient and accurate Ppe.XapF KASP tests resulted in 44% reduction in seedling planting rate in the Clemson University peach breeding program.Bacterial spot, caused by  Prunus persica L. Batsch) belongs to the Prunus genus of the Rosaceae family and is one of the most economically important fruit tree crops worldwide [Peach , is a serious disease that affects nearly all cultivated Prunus species and their hybrids [P. salicina), Korean cherry (P. japonica) and plum hybrids, as well as on peach and nectarines (P. persica) and their hybrids [Bacterial spot, caused by  hybrids , 9. The  hybrids . Disease hybrids . ConventXap and most peach cultivars exhibit a high degree of variation in disease susceptibility [Xap is by incorporation of resistant alleles into the host plant through breeding. Xap-tolerance was introgressed from ‘Elberta’ into the popular commercial cultivar J.H. Hale [Xap with origins that trace back to the high-quality cultivar O’Henry, which is highly susceptible to both leaf and fruit Xap infections. Therefore, a need is still present to introgress Xap resistance into high-quality varieties that span the ripening season and are adaptable to various environmental conditions.Peach is highly susceptible to tibility . The mos.H. Hale , resulti.H. Hale . Unfortu.H. Hale . Most cuXap.Pp.OC-1.2; 12.9–14.9 Mb; 23–43.5 cM) and ch 6  [www.rosaceae.org). Using the peach 9K SNP array genotype data, obtained from peach germplasm relevant for the four public U.S. peach breeding programs [Xap.Pp.OC-1.2 and Xap.Pp.OC-6.1 haploblocks were found to include five and six haplotypes/alleles, with four and eight SNPs, respectively [www.rosbreed.org) [Xap.PpOC-1.2 and Xap.Pp.OC-6.1 alleles and only had partial accuracy and agreement with 9K SNP data.Initial success in developing bacterial spot resistant peach cultivars suggested that resistance might be conferred by only a few dominant genes . Inconsi–4.7 cM)   . HoweverXap.Pp.OC-1.2 and Xap.Pp.OC-6.1 QTLs, that can be used in peach breeding for accurate and routine prediction of bacterial spot fruit response.There is great urgency among the peach breeding communities to develop more efficient ways to incorporate disease resistance with high fruit quality and productivity into newly developed peach varieties. Despite vast genetic and genomic resources for peach , 21, mosPrunus germplasm collection maintained at the Clemson University Musser Fruit Research Center, Oconee County in Seneca, SC , under standard commercial practices for irrigation, fertilization, and pest and disease control. The trees were at least 5 years old, grafted on Guardian® rootstock, planted in duplicate at 1.5 m × 4 m spacing and trained to a perpendicular V system. The material selected for this study represents a mixture of new and old peach cultivars, including important founders, breeding parents, recently released cultivars, and advanced selections developed or used in the Clemson University peach breeding (CUPB) program. Peach material was also selected based on the availability of genotypic [The peach material  when available. Bacterial spot responses used in cultivar descriptions and described in Okie [Bacterial spot response of the peach cultivars/breeding material used for assay validation were identified from literature  or plant in Okie  were con in Okie  and used in Okie : highly et al. [Development and validation of the Ppe.XapF KASP assay used high-quality DNA extracted from young leaf tissue following a protocol modified from Edge-Garza et al. . Approxiet al. [Crude DNA extraction from seedlings either in the greenhouse or in the field followed the protocol of Noh et al.  in a 96--1 for standard extractions, or between 180 and 400 ng μL-1 for crude extractions. DNA was diluted 200× from all standard extractions and 40× from all crude extractions in nuclease-free water to achieve the recommended concentration for KASP assays of 5 ng μL-1. Crude DNA extraction, dilution and PCR plate set up for both real time and endpoint reactions for both germplasm and seedling samples was conducted with the OT-2 robot from Opentrons (opentrons.com).DNA concentration after either extraction protocol was checked on a subset of samples with a Nanodrop spectrophotometer. Observed concentrations ranged between 900 and 1000 ng μLXap fruit response and used in this study for SNP selection [Xap.Pp.OC-1.2 are required, while for the six alleles on ch 6, four of the eight SNPs in Xap.Pp.OC-6.1 are sufficient    and 11.88 μL of the appropriate primer master mix. Genotyping assays used 5 μL of reaction mixture and either 5 μL of water (no-template control) or 5 μL of genomic DNA at approximately 5 ng/μL. Three replicates of no-template controls and positive controls for AA, AB, and BB genotypes for the tested SNPs were always included, along with unreplicated samples of unknown genotype (“unknowns”). Positive controls were chosen from 18 accessions previously genotyped with the peach 9K SNP array  as part eed.org) . A totaleed.org) . As contReactions were performed in a Bio-Rad CFX Connect Real-Time PCR Detection System using the following program for all SNPs except Ppe.XapF1-3 and F6-2: 15 min at 95°C (activation), followed by 10 touchdown cycles of 94°C for 20s (denaturing), 61-55°C for 60s , followed by 40 cycles of 94°C for 20 s, 55°C for 60s, 23°C for 30s (for accurate plate reading). The Ppe.XapF1-3 and Ppe.XapF6-2 required higher temperatures for annealing/elongation  to generate clearly separated genotype clusters, and consequently used a smaller temperature decrement during the 10 touchdown PCR cycles. For each plate, the cycle used for genotype assignment was chosen to maximize separation between genotype clusters and minimize background amplification, usually between cycles 22–28 of the 40-cycle period. Endpoint PCR reactions, omitting the step of 23°C for 30s and plate reading, were performed on Bio-Rad T100 thermal cyclers to assay Ppe.XapF1-1 and Ppe.XapF6-2 using 25 cycles, as identified from real-time PCR. High-quality DNA from previously genotyped samples was used first to validate the method, followed by crude DNA extracts from seedlings. Endpoint assays were read on the Bio-Rad Real-Time machine using Bio-Rad CFX Maestro™ software.To account for differences in fluorescence values between fluorophores and assays, HEX and FAM relative fluorescence units for each sample were transformed to reflect the percentage of the maximum fluorescence for each fluorophore within a plateTo assign genotypes to unknowns, the difference between % HEX fluorescence and % FAM fluorescence (“delta”) was calculated. Heterozygotes are expected to have approximately equal HEX and FAM fluorescence, yielding delta values close to 0. As primers were designed so that all A alleles were assigned to FAM fluorescence and all B alleles to HEX fluorescence, BB genotypes  are expected to yield positive delta values and AA genotypes  are expected to yield negative delta values. The exact cutoffs for genotype assignment were determined manually for each assay. Any samples with fluorescence values < 20% for both fluorophores were considered to have failed to amplify .Genotyping just one SNP on each chromosome (F1-1 and F6-2) can identify susceptible alleles, and for both SNPs the A allele (A|T) indicates the S or S|I phenotype, respectively. For high-throughput seedling screening, endpoint assays with Ppe.XapF1-1 and F6-2 were used: seedlings with A allele frequency ≥ 3  were culled .Xap.Pp.OC-1.2, and six alleles on chromosome six, in Xap.Pp.OC-6.1, can be uniquely identified with four SNPs each  or by having an unexpected genotype , resulting in a 94% accuracy in allele assignment  is always associated with the S allele. For both assays, endpoint PCR performed similarly to real-time PCR, such that four distinct clusters were observed on fluorescence scatterplots and genotypes could be readily assigned . AmplifiXap.Pp.OC-6.1 is observed to contribute more to Xap tolerance than Xap.Pp.OC-1.2. The endpoint assay results flagged 1,588 seedlings for culling, reducing the number of planted seedlings by 46%  were screened with KASP endpoint assays  for Ppe.s by 46% . Precent1 individuals. Early screening for genetic resistance prior to field planting led to a dramatic decrease in the number of seedlings planted, as well as the number to be screened at maturity for productivity and fruit quality traits, saving a significant amount of money, labor, and time. This strategy also allows annual hybrid seedling production to go beyond the CUPB production target of ~3–4,000 hybrids, as only those hybrids with desired allele combinations at both Xap QTLs are planted. Nearly complete allele identification is possible by genotyping seven SNPs, although two alleles, R1 and “-”for the QTL on chromosome 6 are indistinguishable without genotyping an eighth SNP that does not work in KASP assays. The allele “-”(ABBB) on ch 6 was seen only in the heirloom cultivar Chinese Cling [In this research, we report new DNA tests that successfully identified fruit bacterial spot resistance alleles in breeding stock germplasm, including Fse Cling , among ase Cling  analyzedse Cling . ‘Chinesse Cling , and if Xap. One explanation may be the trueness to type of the source of the ‘Reliance’ DNA. The array genotypes obtained in the RosBREED project were based on ‘Reliance’ from the Prunus germplasm collection at the National Clonal Germplasm Repository (NCGR) [Prunus collection. Obtaining genome sequences of these accessions, including the ‘Reliance’ from NCGR, is needed to verify the genotype.In the development and validation of this DNA breeding tool, real-time and endpoint PCR results were in agreement, indicating that endpoint PCR can be adopted to increase throughput after optimizing reaction parameters with real-time PCR. Disagreement between the KASP and array  genotypey (NCGR)  in DavisXap responses reported in literature are provided as a single phenotype combining both fruit and foliar responses [Xap.Pp.OC-1.2 and Xap.Pp.OC-6.1 alleles [Xap than alleles on ch 1, however alleles from both QTLs are important for resistant response. Therefore, culling decisions in the CUPB program, while based on alleles from both ch 1 and 6, are heavily influenced by the presence of S alleles on ch 6.The fruit tolerance prediction based on haplotyping with KASP assays disagreed with reported responses in four accessions , with lower tolerance predicted for three and higher tolerance for one accession than reported in the literature . Since fesponses . Moreove alleles , determi alleles  and bree alleles , suggestXap fruit response. Information about alleles associated with fruit Xap responses for important founders, breeding parents and recently released peach cultivars relevant to the U.S. modern peach breeding was generated within the RosBREED project [www.rosbreed.org. This information is used in cross design in the CUPB program with a goal to achieve resistant Xap fruit phenotype , which was sufficient to solve the problem. Crude DNA extracted from leaf tissue collected from seedlings already planted in the field  had expected concentrations of 180–400 ng μLh et al.  for straXap resistance were developed and validated on crude DNA extractions of individuals with known and unknown genotypes. These assays were highly accurate in identifying the correct genotype for 94% of samples with known genotypes. Of the seven assays developed, just two–one for ch 1 and one for ch 6 –are enough to reveal susceptible alleles. End-users have control over genotype assignment by using the delta method, which also highlights any problematic samples or assays. These assays provide a rapid, cheap, accessible method to cull seedlings with undesirable genotypes before field planting, and to flag seedlings with desired genotypes for further genotyping and phenotyping. They can also be used to profile favorable parents in peach breeding programs and design crosses based on the Xap fruit tolerance.Ppe.XapF KASP assays for SNPs associated with peach fruit S1 TableThe final lowercase nucleotide of each forward primer sequence is the assayed SNP. “Tag” indicates which fluorophore is used: “F” = FAM, “H” = HEX, “N” = none, for the reverse primer.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableS = Susceptible, I = Intermediate, R1 = Resistant-1, R2 = Resistant-2,— = unknown Xap response allele.(XLSX)Click here for additional data file.S6 TableParental Xap genotypes on chromosomes 1 and 6 are separate with |, respectively. Ppe.Xap F1-1 and F6-2 endpoint PCR results.(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S1 File(XLSX)Click here for additional data file.


Heat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock production, reproduction, health, and well-being. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were combined with a standard 50k single nucleotide polymorphism (SNP) panel used by the dairy industry.Over 40,000 dairy cattle with genotype and phenotype data were analysed. The phenotypes used to measure an individual’s heat tolerance were defined as the rate of decline in milk production traits with rising temperature and humidity. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance. The prioritised sequence variants were the most significant SNPs passing a GWAS p-value threshold selected based on sliding 100-kb windows along each chromosome. We used a bull reference set to develop the genomic prediction equations, which were then validated in an independent set of Holstein, Jersey, and crossbred cows. Prediction analyses were performed using the BayesR, BayesRC, and GBLUP methods.The accuracy of genomic prediction for heat tolerance improved by up to 0.07, 0.05, and 0.10 units in Holstein, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holstein cows were added to the 50k SNP panel. However, in some scenarios, the prediction accuracy decreased unexpectedly with the largest drop of − 0.10 units for the heat tolerance fat yield trait observed in Jersey cows when 50k plus pre-selected SNPs from Holstein cows were used. Using pre-selected SNPs discovered on a combined set of Holstein and Jersey cows generally improved the accuracy, especially in the Jersey validation. In addition, combining Holstein and Jersey bulls in the reference set generally improved prediction accuracy in most scenarios compared to using only Holstein bulls as the reference set.Informative sequence markers can be prioritised to improve the genomic prediction of heat tolerance in different breeds. In addition to providing biological insight, these variants could also have a direct application for developing customized SNP arrays or can be used via imputation in current industry SNP panels.The online version contains supplementary material available at 10.1186/s12711-022-00708-8. Heat tolerance is the ability of an animal to maintain its production and reproduction levels under hot and humid conditions. With increasing global warming effects on animal production, there is worldwide growing desire to breed for resilience to heat, in part, to meet the demand of the increasing human population while coping with the challenges of hot and ever-changing production environments . Dairy cConsiderable research has been conducted in many countries to assess heat tolerance and performance in farm livestock, including measuring changes in core body temperatures  and thermal indices  . To studhttps://datagene.com.au/)] of the first genomic breeding values for this trait in 2017, with an average reliability of 38%. While current prediction estimates are promising, even a smaller lift in reliability is economically important to the wider industry since the genetic improvement is linearly related to the selection intensity, accuracy of estimated breeding values (EBV), genetic variation and is inversely proportional to the generation interval  used in this work were described previously  compared to those from the single-breed QTL discovery set at the same significance cut-off (described above). However, when compared to the single-breed GWAS (only Holstein cows), using a multi-breed QTL discovery set (Holsteins + Jersey cows) increased the strength of the GWAS signals in some genomic regions  14 near the DGAT1 gene)  were combined with Jersey cows (N = 5143) in the QTL discovery set  over BayesR (fitting only 50k SNPs) ranged from − 0.004 to 0.09, from − 0.06 to 0.02, and from − 0.04 to 0.009 for HTMYslope, HTFYslope, and HTPYslope, respectively , we observed that the accuracy for HTMYslope increased compared to that of BayesR (using only 50k SNPs) across all prediction scenarios, with changes ranging from 0.01 to 0.05 units. However, the accuracy of prediction decreased considerably for HTFYslope (− 0.10) and HTPYslope (− 0.09) when the ‘top SNPs’ from Holstein cows were used in Jerseys with a slightly larger decrease in accuracy of prediction when using ‘top SNPs’ from the single-trait GWAS than those from the multi-trait meta-analysis Fig. . SimilarAcross all prediction scenarios Fig. , using ‘Moreover, there was no substantial difference in accuracy from BayesRC when using the ‘top SNPs’ from single-trait GWAS versus the ‘top SNPs’ from multi-trait meta-GWAS of slope traits across different prediction scenarios Fig. . The chaThe dispersion bias across heat tolerance traits in the Holstein validation set ('Scenario 1') showed that the GBV were deflated , the change in accuracy of prediction (based on the BayesRC model) was not consistent across traits Fig. . When usIn the crossbreds (N = 790), using ‘top SNPs’ discovered in the multi-breed (Holsteins + Jersey cows) set (based on the BayesRC models) yielded a larger (0.11 units) change in accuracy of prediction than with BayesR (using only 50k SNPs) for HTMYslope compared to a drop in accuracy from BayesRC over BayesR of − 0.005, and − 0.03 units for HTFYslope and HTPYslope, respectively  reference set in which the ‘top SNPs’ were only from the Holstein cow QTL discovery set see “”, we fouTo test whether allocating selected informative markers to a separate SNP class see “” in BayeIn this paper, we present a genomic prediction analysis of heat tolerance traits using a large sample size of over 40,000 cattle, comprising Holstein, Jersey, and crossbred individuals. The primary objective was to investigate if selected sequence variants from a GWAS in Holstein cattle benefits genomic prediction of heat tolerance phenotypes in the same breed . The hypothesis is that the selected variants are linked to causal mutations that underpin the genetic basis of heat tolerance, and thus could enable more accurate and sustained genomic selection for this trait. In addition, we also tested the value of pre-selected variants from Holstein cattle for the genomic prediction of breeds with numerically smaller sample sizes, such as Jersey and crossbreds. Furthermore, we investigated the benefits of using informative markers from a multi-breed (Holstein + Jersey cows) QTL discovery set for genomic prediction of heat tolerance. Overall, our results show that we can increase the accuracy of prediction of heat tolerance by up to 0.10 unit in some scenarios when pre-selected sequence variants are added to the standard-industry 50k SNP panel. However, the change in the accuracy of prediction when using pre-selected sequence variants in BayesRC  varied considerably across traits and prediction scenarios.We used the BayesR and BayesRC methods to test different prediction scenarios. For BayesR, when only 50k SNP data were used, we found a high accuracy of prediction in Holsteins and crossbreds compared to Jerseys. We expected a lower accuracy in Jerseys because we used Holstein bulls as a reference set for genomic predictions see “”. These 1 and backcrosses to Holstein). The gain in accuracy of prediction for Holsteins and crossbreds likely benefited, in part, from a powerful GWAS QTL discovery set  and the methodology used for genomic prediction. To date, comparable GWAS have used a sample size of at most 5000 individuals e.g.,  and a more stringent p-value of [− log10 ≥ 3]. The top SNPs were selected from GWAS of Holstein cows . Vertical lines represent standard errors calculated from three (Holsteins) and two (Jersey) random validation subsets. Figure S7. Bias of genomic predictions in Holsteins , Jersey , and Holstein–Jersey crossbreds  validation cows for milk (MYint), fat (FYint), and protein (PYint) yield intercept traits from different SNP sets based on the BayesR and BayesRC methods. Figure S8. Bias of genomic predictions in Holsteins , Jersey , and Holstein-Jersey crossbreds  validation cows for heat tolerance milk (HTMYslope), fat (HTFYslope), and protein (HTPYslope) yield slope traits from different SNP sets based on the BayesR and BayesRC methods. Figure S9. QTL discovery using the single-breed  and the across-breed  discovery set.