In light of previous studies that profiled breed-specific traits or used genome-wide association studies to refine loci associated with characteristic morphological features in dogs, the field has gained tremendous genetic insights for known dog traits observed among breeds. Here we aim to address the question from a reserve perspective: whether there are breed-specific genotypes that may underlie currently unknown phenotypes. This study provides a complete set of breed-specific genetic signatures (BSGS). Several novel BSGS with significant protein-altering effects were highlighted and validated.Using the next generation whole-genome sequencing technology coupled with unsupervised machine learning for pattern recognitions, we constructed and analyzed a high-resolution sequence map for 76 breeds of 412 dogs. Genomic structures including novel single nucleotide polymorphisms (SNPs), SNP clusters, insertions, deletions (INDELs) and short tandem repeats (STRs) were uncovered mutually exclusively among breeds. We also partially validated some novel nonsense variants by Sanger sequencing with additional dogs.Four novel nonsense BSGS were found in the Bernese Mountain Dog, Samoyed, Bull Terrier, and Basset Hound, respectively. Four INDELs resulting in either frame-shift or codon disruptions were found in the Norwich Terrier, Airedale Terrier, Chow Chow and Bernese Mountain Dog, respectively. A total of 15 genomic regions containing three types of BSGS  were identified in the Akita, Alaskan Malamute, Chow Chow, Field Spaniel, Keeshond, Shetland Sheepdog and Sussex Spaniel, in which Keeshond and Sussex Spaniel each carried one amino-acid changing BSGS in such regions.Given the strong relationship between human and dog breed-specific traits, this study might be of considerable interest to researchers and all. Novel genetic signatures that can differentiate dog breeds were uncovered. Several functional genetic signatures might indicate potentially breed-specific unknown phenotypic traits or disease predispositions. These results open the door for further investigations. Importantly, the computational tools we developed can be applied to any dog breeds as well as other species. This study will stimulate new thinking, as the results of breed-specific genetic signatures may offer an overarching relevance of the animal models to human health and disease.The online version contains supplementary material available at 10.1186/s12864-023-09390-6. Dogs (Canis lupus familiaris) are known as the first species that was fully domesticated by humans about 12,000 ~ 15,000 years ago . Strong Borrelia burgdorferi, the causative agent of Lyme disease and SARS-CoV-2, the virus responsible for COVID. As such, especially following the discovery of insulin to treat diabetes, dogs have been commonly used as subjects for preclinical studies for the development of vaccines and therapeutics [Understanding the different genetic structures underlying numerous dog phenotypes has long been the key for humans to improve breeding strategies as well as study the connections between dog traits and their counterparts in humans , 10. In apeutics .All documented studies have demonstrated strong relationships between human and breed-specific traits in dogs. In fact, selecting proper breeds is crucial when using dogs as effective animal models to discover genetic causes of human traits, as different dog breeds are predisposed to different diseases. Although previous dog genetic studies have greatly enhanced our understanding of genetic components associated with body size –21, skulTo do so, we collected, assembled, and analyzed the whole genome sequencing (WGS) data of dogs from 76 breeds. By applying unsupervised machine learning coupled with stepwise optimizations for large volumes of data, we developed a suite of computational algorithms for pattern recognitions at the whole-genome scale. We constructed a high-resolution genetic signature map that characterized the genetic backbones of each dog breed. We also deciphered the common genetic structures shared by multiple breeds to uncover the complex intertwined relationship between them. Besides, we examined the distinctive genetic backbones of each dog breed and established comprehensive information composed of genome-wide SNPs, INDELs and STRs that are exclusively present in specific breeds. Lastly, we selected and validated breed-specific nonsense variants from WGS analyses of additional dogs by Sanger sequencing.We assembled the raw WGS data and mapped the genome-wide single nucleotide polymorphisms as well as short insertions and deletions for each dog. After quality filtering, a total of 22,419,814 bi-allelic SNPs and 5,068,857 bi-allelic short INDELs were identified across 412 dogs from 76 breeds. We further identified STRs from the pool of 3,140,027 multi-allelic INDELs, resulting in 1,294,687 candidate STR loci.We discovered the unique SNP combinations for each breed included in the study at the whole genome scale. Specifically, the genetic signature for a breed was defined that all dogs in the corresponding breed carry the same SNP combinations. The genetic signatures shared between a pair of breeds was defined that all dogs of both breeds carry the same SNP combinations. We also reported our findings on BSGS on the basis of genome-wide genetic signatures. BSGS to a breed are a small set of genetic signatures that are present in all dogs from the corresponding breed but absent in all dogs from other breeds. In other words, BSGS can distinguish dogs of one breed from the others.We summarized our results on all three types of BSGS  in subsequent subsections followed by our findings on genome-wide genetic signatures.Sixty-eight of the 76 breeds had breed-specific SNPs, consisting of a total of 27,845 SNPs Data S with 120Besides, we identified 18 breeds each carrying at least 10 contiguous breed-specific SNPs in the short genomic regions of about 1 kb in length. They were Airedale Terrier, Akita, Alaskan Malamute, Border Terrier, Cairn Terrier, Chow Chow, Collie, Doberman Pinscher, Field Spaniel, Irish Wolfhound, Keeshond, Manchester Terrier, Miniature Schnauzer, Norwich Terrier, Rhodesian Ridgeback, Rottweiler, Sussex Spaniel, and Tibetan Terrier Table . These sEight breeds did not have any breed-specific SNP signatures. They were Australian Shepherd, Beagle, Bichon Frise, Dachshund, English Springer Spaniel, Golden Retriever, Labrador Retriever and Pointer. Noteworthily these eight breeds had few genetic signatures compared to other breeds in the collection as depicted above. This could essentially limit the size of candidate signature pool that can uniquely characterize them.Most breed-specific SNPs were in non-coding regions  as “signature hot spots” each having at least 5 closely located breed-specific SNPs in the specific breed. These hot spots harbored a total of 15,264 breed-specific SNPs (54.82%), which suggested that these breed-characterizing signatures were enriched in certain gene regions rather than sporadically distributed across the genome. We found two genes that harbored the highest numbers of breed-specific SNPs in two respective breeds. The NKAIN3 gene (sodium/potassium transporting ATPase interacting 3) with 102 breed-specific SNPs was exclusive to Bull Terriers. The STK12 gene (Syntaxin 12) with 76 signatures was exclusive to Flat-coated Retrievers. Additionally, this 31 kb-long segment was also the gene region with the densest breed-specific SNPs distribution (2.49 signatures /kb in average) found across the genome , Sussex Spaniel (NBreed-specific INDELs = 557), Chow Chow (NBreed-specific INDELs = 502) and Alaskan Malamute (NBreed-specific INDELs = 382) were found to have the highest amount of breed-specific INDELs. These four dog breeds also ranked highest with the most breed-specific SNPs, marking their the genetic uniqueness. When evaluated by variant categories, the vast majority of breed-specific INDELs were located within the non-coding region (99.91%), which is significantly higher than 98.93% of the non-coding breed-specific SNPs found (pFisher’s exact = 3.16 × 10–14). Notably, this is also significantly higher than 99.59% of overall non-coding INDELs found within the dataset (pFisher’s exact = 1.88 × 10–4). These indicated the importance of breed-specific INDELs in coding regions, as they were mostly nonsynonymous and had a huge impact on the corresponding protein sequences.A total of 4,341 breed-specific short INDELs (insertion or deletion size less than 10 nucleotides) were identified across 58 breeds Data S, which wBreed-specific STRs = 109), Alaskan Malamute (NBreed-specific STRs = 54), Chow Chow (NBreed-specific STRs = 64) and Sussex Spaniel (NBreed-specific STRs = 65) again stayed on top of the signature count list, owing to their genetic uniqueness within the 76-breed collection. Forty-two of 604 signatures were found to feature differences of at least three repetitive units between target breeds and reference breeds  identified from the genome, we found 604 breed-specific STR signatures that featured either a large or a small number of repeats in certain breeds compared to all other breeds (Data SOne of the variant loci with a large number of copy differences between the high-repeat group and low-repeat group was identified to be within one intron of the SGIP gene (Chr5: 44129368). At this locus, the Alaskan Malamute was found to carry alleles with at least 8 consecutive (TTCT) repetitive units while all other breeds carried less than 3 units at this locus. Three Akita-specific STR signatures  were identified in the upstream, intron and downstream regions of the ZCCHC10 gene. All reference breeds only had one repeat or did not have any repeats of three loci while the Akita carried a considerably higher number of repeats. Notably, a long stretch of 51 Akita-specific SNP signatures have also been identified within the gene region of ZCCHC10, which further indicates the genetic uniqueness of the Akita at this specific locus. Furthermore, we found that English Bulldog uniquely carried at least eight units of (AGAT) tetra-nucleotide repeat within LOC111095873 (Chr1: 48446198), contrasting to one or no repeat in the remaining dog breeds. Though the biological function of the corresponding protein had not been yet characterized, the long segment of repeats provided evidence of unique past mutation events that specifically took place and got selected during the breed formation process of the English bulldog.Four breeds with an East Asian origin including the Chinese Shar-Pei, Chow Chow, Alaskan Malamute and Akita were found to have the largest number of genetic signatures across the genome Fig S. The BeaWe further investigated the shared genetic signatures between different breeds. By overlapping the genetic signatures of both breeds within each breed pair, we generated a genetic-signature-based relatedness profile for all breed pairs in the 76-breed collection Fig. . Among aThis figure shows the degree of relatedness among 76 breeds. The 76 dog breeds in GS-76 are labeled in alphabetical order on the left and the top. In between, we see 2850 breed pairs. The color of each grid represents relatedness of each corresponding pair of breeds. The color gradient from black to white represents the degree of relatedness. The more related between a breed-pair, the darker the cell would appear.corrSpearman). Both scores based on INDEL and STR GS showed strong correlations with the scores based on SNP GS for all breed pairs   Table S.th similar to the French Bulldogs, respectively . Each bar was resized proportionally to reflect the relative length of chromosome. The left bar in black of each group indicates the chromosomal distribution of nonoverlapping 10 kb-long genomic blocks with at least 40 breed signatures. Each block was shown as a horizontal black line and mapped to its relative chromosomal position on the bar. The right bar in red of each group indicates the average number of breeds carrying each signature. The length of each red line  reflects such measurement within corresponding 10 kb-long blocks.As it would be valuable to identify the genomic regions enriched for BSGS containing protein-coding genes, we performed analysis of breed-specific long genomic segments, literature surveys and curated public databases for functions and biological pathways Table S. We firsTable The Akita, Alaskan Malamute and Boxer were also found to have breed-specific long genomic structure containing multiple functional BSGS. One Akita-specific genomic structure harbored a total of three missense BSGS located in the coding regions of AQP3, NFX1 and NOL6, respectively. The entire segment spanned 1.08mb in length, containing 269 BSGS of all three types including 214 SNPs, 47 INDELs and 8 STRs. This is also one of the most significant breed-specific genomic structures we found in terms of length, BSGS number per variant category and functional BSGS count. Notably, the signature density of this segment is 0.25 per kb, which is also on the upper side compared to the median value of 0.16 per kb. The Alaskan-Malamute-specific structure featured one missense variant in FBXO40 and one in GOLGB1. Although signatures in this BSGS stretch were relatively distantly spaced (average signature density 0.05 per kb), they still covered all three types of BSGS which indicated the signature diversity of this region. Lastly, two relatively adjacent genomic segments (chr9:5285601-5673185 and chr9:5976051-6889524) each with a missense BSGS duo were found in the Boxer. The first segment featured two missense BSGS in MYO15B and SAP30BP while the second featured two in CD300A and OTOP2. Both segments contained a relatively high amount of BSGS , providing extensive evidence on selective marks of Boxer over these two long genomic regions.We conducted Sanger sequencing over four nonsense BSGS loci in SLC28A1, MYH16, SLAMF8 and PIBF11 for five additional dogs Table S. The resDog breeds have long been a fascinating object for studying population differentiation as the phenotypes are highly homogeneous within each breed while drastically vary across breeds. There is a strong interest in localizing the genetic elements that differentiate dog breeds and contribute to their breed-defining traits. In this study, we assembled a WGS dataset featuring 76 different dog breeds to discover the core genetic signatures that can be stably inherited in each dog breed and comparatively investigated the common and differential genetic signatures across breeds. On the basis of genetic signatures, we constructed a comprehensive genetic variant catalog that captures significant breed-differentiation signatures at the whole genome scale. We exhaustively analyzed all common types of short genetic variants including SNPs, INDELs and STRs, which provided evidence on how dog breeds differentiated from multiple perspectives. Overall, all these signatures are significant by nature as each of them is homogeneously presented in all dogs from the target breeds but are absent in all other dog breeds. Intrinsically the breed-specific signatures of each dog breed represent the unique set of genetic variants that sets each breed apart from all others. This catalog generates abundant information on candidate gene targets behind breed-defining traits as well as uncovered complicated and intertwined evolutionary history of different dog breeds.Overall, in this study, we searched the whole genome of 412 dogs covering 76 breeds and identified a large variety of breed-specific signatures that were exclusively present in a single breed. In summary, we identified 27,845 SNP signatures in 68 breeds, 4,341 short INDEL signatures in 58 breeds and 604 STRs signatures in 54 breeds. Among them, 143 functional signatures were identified, spanning a total of 30 breeds. Long segments of breed-specific signatures as well as large breed-specific STR expansions were also found within certain genomic regions, revealing genes underwent excessive selection in certain breeds.GS = 690,487), Chow Chow , Bernese Mountain Dog  and Shin Tzu , were discovered to have a large number of breed signatures.The genetic signatures contain rich information about the breeding history of each breed. As the homozygous genetic variants within different breeds can be considered as the results of selection , the number of signatures across breeds reflect the relative standing of a breed in its formation process. We propose that, a larger number of breed signatures across the genome, which corresponds to lower within-breed genetic heterogeneity, indicates the relative maturity of a breed from the evolution perspective. On the contrary, a lower number of genetic signatures in turn indicates an early stage that a breed is currently at, showing its active evolving status. Some well-acknowledged ancient breeds with thousand-year breed histories Table S such as GS = 70,907) and German Shepherd  that were both introduced in late nineteenth century. This might reflect modern breeding strategies to produce favorable traits observed by humans during interactions with dogs. Thus, their genomes are still actively evolving in the ongoing processes of selective breeding. However, the year of traceable breed history does not have any overall correlation with the number of breed signatures as many other factors could also influence the genetic background of modern dog breeds. For instance, some ancient breeds that were rebuilt in the post-world-war era such as the Bichon Frise , Maltese  and Vizsla  resemble much younger breeds genetically in respect to the number of breed signatures. Moreover, breeds with high within-breed heterogeneity such as the Dachshund  and breeds with recent admixture history such as the Beagle  also tend to have fewer genetic signatures when compared to breeds emerged during the same era. Similarly, breeds such as the Sussex Spaniel , Boxer  and Bull Terrier  were found to have considerably higher amount of breed signatures than breeds of similar age, indicating either the underestimated breed history of them or selective pressure imposed on them during the breed formation process. In this sense, the magnitude of breed signatures can both help us recover a less biased breed history and reveal certain event that impacted the formation of modern dog breeds.Eight breeds did not have any breed-specific signatures . Lacking a breed-specific signature might be due to high similarity to a progenitor breed that was included in the analyses. It has been documented that dog breeds were typically descended from a small number of founders and created by crossing closely related individuals , 32. BreThe genetic signatures shared by different breeds provided insights on the genetic relatedness between breeds. The fraction of total shared genetic signatures between two breeds among the total genetic signatures of each of the single breeds provided a sensible metric to quantify breed similarities. High genetic-signature-sharing based similarity scores were mostly observed between breeds with high morphological similarities, indicating the unique common evolution history between them. These results were further supported by the identified stretches of breed-pair-specific signatures as showing in Table SFor BSGS, we showed that the breed distribution appeared to be uneven across breeds, regardless of variant types. Ancient breeds such as the Chow Chow, Alaskan Malamute, Akita, and Sussex Spaniel were found to have a large number of unique signatures in contrast to recently diverged breeds, such as the Golden Retriever and German Shepherd which had no specific signatures at all. Notably, these four ancient breeds ranked as the top four breeds with the highest BSGS discovered in all variant categories. This suggests that these breeds have been genetically isolated from all other breeds for more generations compared to breeds with significantly less BSGS, which reflect the genetic uniqueness of the breed.Considering the exclusivity of BSGS, they are reasonably linked to phenotypes that are in favor of either artificial or natural selection drives of corresponding breeds. With this purpose, we identified a set of functional signatures with significant biological impacts. We found that gene SLC28A1 was heavily truncated within the Bernese Mountain Dog, which were originally bred in the cold mountain region of Switzerland. Interestingly, this gene has previously been shown to be differentially expressed in Min pigs after cold treatment at the transcript level . This inWe constructed a high-resolution sequence map for 412 dogs and analyzed the breed-specific genetic signatures for 76 breeds. We identified novel functional BSGS presumably with phenotypic impacts. Four nonsense BSGS were found. SLC28A1 (Solute Carrier Family 28 Member 1) and SLAMF8 (SLAM Family Member 8) were severely truncated in the Bernese Mountain Dog and Samoyed, respectively. PIBF1 (Progesterone Immunomodulatory Binding Factor 1) and MYH16 (Myosin Heavy Chain 16) were partially truncated in the Bull Terrier and Basset Hound, respectively.Four breed-specific INDEL were found to cause either frameshift or disruptions of codons in four different breeds. The Norwich Terrier and Airedale Terrier carried a frame-shift variant in ZDHHC1 (Zinc Finger DHHC-Type Containing 1) and OR5J2 (Olfactory Receptor Family 5 Subfamily J Member 2) gene, respectively. The Chow Chow carried an INDEL that can cause disruptive in-frame deletion in SIPA1L1 . The Bernese Mountain Dog carried an INDEL leading to the loss of one codon in CENPU (Centromere Protein U).Eighteen breeds were found to carry novel breed-specific SNP-clusters in at least 10 contiguous breed-specific SNPs in the short genomic regions of about 1 kb in length. These breeds included the Airedale Terrier, Akita, Alaskan Malamute, Border Terrier, Cairn Terrier, Chow Chow, Collie, Doberman Pinscher, Field Spaniel, Irish Wolfhound, Keeshond, Manchester Terrier, Miniature Schnauzer, Norwich Terrier, Rhodesian Ridgeback, Rottweiler, Sussex Spaniel, and Tibetan Terrier.Breed-specific STR expansions were found, in which the Akita, Alaskan Malamute, Chow Chow and Sussex Spaniel carried the highest numbers of such expansions. When compared to other dog breeds, the Alaskan Malamute was found to carry significantly long STR expansions around three gene regions, SGIP (SH3 Domain GRB2 Like Endophilin Interacting Protein 1), DPP6 (Dipeptidyl Peptidase Like 6) and EAF1 (ELL Associated Factor 1). The Akita carried a long STRs expansion upstream the ABRA (Actin Binding Rho Activating Protein) gene. Besides, the Akita were also found to have three different breed-specific STRs expansions in the gene region of ZCCHC10 (Zinc Finger CCHC-Type Containing 10). Interestingly, ZCCHC10 also contained breed-specific SNP-clusters in this breed.Together, we found 15 signature genomic regions with all three types of BSGS  in seven breeds . Notably, the Keeshond and Sussex Spaniel each had a signature set covering the genes resulting in an amino acid change in PUS7 (Pseudouridine Synthase 7) and OTOGL (Otogelin-Like Protein) proteins, respectively.According to the similarity scores based on the SNP data, the most genetically similar pairs were the Collie vs. Shetland Sheepdog and Akita vs. Chow Chow. The least genetically similar breed-pairs were the Dachshund vs. Labrador Retriever and Dachshund vs. Golden Retriever. Surprisingly, the English Bulldog were genetically distant to Chinese Shar-Pei (a low similarity score between the two) despite their similarities in observable phenotypes, which, in turn, coincides our hypothesis that there might be unknown phenotypes yet to be uncovered that distinguished the two breeds.In conclusion, every dog breed is genetically related to at least one other breed at various degrees. The BSGS map is a high-resolution genetic atlas that quantitatively distinguishes the breeds of dogs and pinpoints previously unknown genetic markers that are specific to a single breed. The exclusivity of BSGS further provided valuable information on linking certain breed-defining traits to breed-specific genetic variants. Importantly, the approach we employed can be easily generalized to other species besides dogs, as selecting the right genetic backgrounds of the breeds or strains of the animal models has always been one of the most crucial yet unsolved puzzles in the research field of medical science.We collected the leftover blood samples of 28 dogs through our collaboration with local veterinary clinics. Experienced veterinarians drew blood from the front arm of participating dogs with dog owners’ consent during the medical care of dogs. Sample and breed information was collected from the dog owners during their visits S Data. AlSample quality controls (QC): Yale Center of Genome Analysis (YCGA) whole genome sequencing (WGS) pipeline starts with stringent quantification and quality control of the received samples. Samples delivered to YCGA are immediately entered into WikiLIMS with the provided sample identifiers. Entrance of the samples to the database generate and assign a second database identifier unique to each submission and sample (sample tracking number). This is followed by standard quantity, quality and purity assessments via determination of the 260/280 nm for values of 1.7–2.0, and 260/230 absorbance ratios for values ≥ and 1% agarose gel electrophoresis to ensure that the gDNA is neither degraded nor displays RNA contamination. Combination of PCR-free library preparation and sequencing on patterned flow-cells of Novaseqs, makes quantification of the starting gDNA of paramount importance. To that end, all samples undergoing PCR-free library preparation will also be quantified using a fluorometric method by Qubit (ThermoFisher Scientific Part#Q33226) for proper assessment of double stranded DNA concentration.Library Preparation: 0.5ug of well quantified gDNA is undergoing enzymatic fragmentation, end-repair and “A” base in a single reaction using Lotus DNA Library Prep kit, are then ligated to the ends of the DNA fragments for hybridization to the flow-cell for cluster generation. Size of the final library construct is determined on Caliper LabChip GXsystem and quantification is performed by qPCR SYBR Green reactions with a set of DNA standards using the Kapa Library Quantification Kit . Size and concentration values will be entered into the WikiLIMS database for the sequencing team’s use for appropriate flow-cell loading.Flow Cell Preparation and Sequencing: Sample concentrations are normalized to 2 nM and loaded onto Illumina NovaSeq 6000 flow cells at a concentration that yields at least 700Gbp of passing filter data per lane. Loading concentration for WGS libraries has been optimized to maximize both well occupancy and unique read output while limiting duplicates associated with patterned flow cell technology. Samples are sequenced using 151 bp paired-end sequencing reads according to Illumina protocols. The 10 bp indexes are read during additional sequencing reads that automatically follow the completion of read 1. Data generated during sequencing runs are simultaneously transferred to the YCGA high-performance computing cluster. A positive control (prepared bacteriophage Phi X library) provided by Illumina is spiked into every lane at a concentration of 1% to monitor sequencing quality in real time.Signal intensities are converted to individual base calls during a run using the system’s Real Time Analysis (RTA) software. Base calls are transferred from the machine’s dedicated personal computer to the Yale High Performance Computing cluster via a 1 Gigabit network mount for downstream analysis. Primary analysis—sample de-multiplexing and alignment to the human genome—is performed using Illumina’s CASAVA 1.8.2 software suite. The data is returned returned to the user if the sample error rate is less than 2%. Data is retained on the cluster for at least 6 months, after which it is transferred to a tape backup system.ROS_Cfam1.0 (https://www.ncbi.nlm.nih.gov/assembly/GCF_014441545.1/) for each sample using BWA v0.7.17 [Whole genome sequencing of 23 lab-collected gDNA samples was performed on Illumina Novaseq 6000 platform in pair-ended mode detailed as described above. Each sample was sequenced using 151 bp reads with 30 × average genome coverage. We additionally downloaded the whole genome sequencing data of 429 dogs from 95 breeds  value calculated over pruned autosomal variants. We built the phylogenetic tree using R package phytools  and remoStarting here, we developed a suite of C +  + based computational programs for the purpose of discovery and analyses of genome-wide genetic signatures. To discover differences and similarities of variants between dog breeds, we calculated the breed variant frequency (BVF) using the number of dogs carrying the variant alleles on both chromosomes divided by the total number of dogs with solid allele type calls for each breed . STR were identified from the candidate multi-allelic loci. Only loci with STR of a single type of repetitive units were involved in the discovery process. Repetitive units were identified via exhaustive comparison between all allele types at a given locus. We characterized STR alleles into either high-repeat group and low-repeat group based on the distribution of repeat counts at the locus. A standard K-mean algorithm was employed to automatically find the classification boundary using the lowest repeat count and highest repeat count at each locus as the initial centroids of two groups. We examined the allele depth of each variant call and only included samples whose allele depth was above a certain threshold at the given locus. The minimal depth threshold is 10 for autosome variants and 5 for X chromosome variants. For identified STR loci with multiple alleles, the total effective allele depth was calculated as the sum of depth of final called allele types. Dogs with unsolid allele calls at a given site were neither counted into the nominator nor the denominator of such equation during the frequency analysis at that site. Considering that the reference dog itself might carry some unique variants, we excluded variants that were presented in all the included dogs from the candidate variant pool. We defined a breed as valid breed at a given variant locus if at least three dogs from the breed are with solid allele calls. Based on previous quality metrics, we identified a variant as the breed genetic signature if such variant has a BVF ≥ 0.9 in any valid breed. In the meanwhile, we identified a variant as the breed-specific genetic signature (BSGS) to a certain breed if such variant has a BVF≥0.9 in the target breed and BVF ≤ 0.1 in all other valid breeds. We exhaustively searched for the genetic signatures and BSGS regarding both the variant allele and reference allele in separate runs and combined the scanning results together. Lastly, we examined the total effective sample size during the discovery of each BSGS and genetic signatures and removed those discovered with relatively small number of samples. For both genetic signatures and BSGS, we filtered out those discovered with less than 300 samples from all effective breeds not carrying the genetic signatures or BSGS. The original scanning results for BSGS and genetic signatures were reported in SAll identified BSGS and breed signatures were summarized by breed and by annotation flag. Nonsynonymous signatures were extracted separately and later summarized by breed. The BSGS stretches were detected using two different schemes for different target properties. We counted the number of BSGS by breed and by genomic region (separate by different genes). Genomic regions with at least 5 BSGS from the same breeds were reported as they reflected gene functional regions with a high amount of BSGS S Data. AlWe additionally scanned for genetic signatures (both BSGS and genetic signatures) shared by multiple breeds to investigate the signature similarity between them. We summarized the detailed information of genetic signatures exclusively observed in two breeds in Table SSince SNP BSGS, INDEL BSGS and STR BSGS were drawn from different pools of genomic variants which required different QC and categorization procedures , the discovery process was relatively independent to each other overall. To analyze genomic structures potentially comprised of all three types of BSGS, we first pooled them together. To do so, we prepared filtered SNP BSGS, INDEL BSGS and STR BSGS results to be sorted by chromosome coordinates while only keeping positional, annotation and breed information for each BSGS. We later put each BSGS list into an independent queue and kept track of the foremost elements within each queue. At each time, the element with smallest chromosome coordinate was removed from the original queue and put into the merged list. If multiple BSGS with the same chromosome coordinate but a different BSGS type was found at the same time, they were merged into one query. Throughout the merging process, none of such conditions was found.After obtaining the merged BSGS catalog, we first grouped all types of BSGS by breed while maintaining their chromosomal positions in sorted order. We then traversed the BSGS list by breed and chromosome to identify breed-specific genomic structures. Such structures were defined as the longest BSGS stretches that met the following conditions: 1) adjacent BSGS within the same structure located no more than 100 kb away from each other and 2) each stretch contained at least 10 BSGS. We set the maximum distance between two BSGS as 100 kb for the purpose of maintaining contiguity of each breed-specific structure while taking account of naturally long haplotype of dog breeds. During the scanning process, we kept track of pre-annotated BSGS effect flags and output genomic structures harboring BSGS with protein changing effects. Targeted functional BSGS flags included “missense_variant”, “stop_gained”, “frameshift_variant”, “disruptive_inframe_deletion”, “disruptive_inframe_insertion”, “conservative_inframe_deletion” and “conservative_inframe_insertion”. We also kept track of the composition of BSGS within each qualified breed-specific genomic structure as well as the average variant density within the structure. All these relevant statistics were reported along with each structure identified.We partitioned the reference genome into contiguous 10 kb-long blocks and investigated the chromosomal distribution of breed signatures across such block. We counted the number of identified genetic signatures, average genetic signature density and average number of signature-carrying breeds per locus within each block. We stratified the number of such 10 kb-blocks by average genetic signature density (1 genetic signature per KB) for each breed. We sequentially traversed each block to investigate the overall genetic signature distribution across the genome. We calculate the average signature density regardless of the breed for each block and identified the blocks with relatively high density (≥ 40 genetic signatures per KB). Along with it we used the average number of genetic-signature-carrying breeds to reflect the breed genetic signatures diversity of each block. We additionally plotted the high-density genetic signature blocks to highlight genomic regions with large number of genetic signatures as indicated in Fig SDuring the analysis of the dog WGS data, we designed a fast and efficient C +  + based command-line tool to conduct genome-wide screening for variants with certain population frequency constraints. The tool takes a standard Variant Call Format (VCF) variant file and a sample label file as inputs. It then outputs a list of variants that satisfy a series of highly customizable constraints.Specifically, the tool has three main modules, namely a quality filter, a variant selector, and a population frequency analyzer Fig S. The quaThe tool was made to be highly flexible as users can customize all the three important variables in the analysis, including: 1) the target variants of interest; 2) the samples taken into each comparison group; and 3) the frequency constraints used to prioritize variants. Besides, with all the three kernel modules thread up seamlessly, this tool allows users to do quality control, variant selection and population specific variant discovery in one-click starting from the widely accessible VCF files without additional data formatting steps. Although the tool was initially designed to study pedigree dogs as we showcased in previous sections, it can also be effectively applied to other study populations  or scenarios  with flexible parameter settings.We additionally implemented multi-threading methods to boost computational performance to handle terabytes of the raw WGS data. We benchmarked three main analytic modules which aimed to discover breed-enriched, breed-unique and breed-pair unique variants, respectively, on the discovery dataset of 412 dogs Fig Sa. With 1We benchmarked the tool on a server with Intel Xeon E5-2660 v3 CPUs. The dataset used for the benchmark contains a total of 29,703,668 variants . The entire dataset uses 363 GB storage space. We timed each variant discovery analysis using an internal clock implemented in the tool. The average memory usage data was obtained from the Slurm (job scheduler of the server) report. We calculated the per-core efficiency by taking the proportion of average per-core processing speed of each analysis and the highest average per-core processing speed observed during the benchmark.Additional file 1: Fig S1. The number of the nonsense genetic signatures (GS) for each breed. The y-axis represents the number of nonsense variants in GS represented by dots. The x-axis corresponds to each of the 76 breeds in the alphabetical order. Fig S2. Histogram showing the breed sample size distribution. The histogram showed the sample size within each valid breed enrolled in the GS discovery process. The minimal breed sample size was capped at three as shown in the histogram. Fig S3. Phylogenetic tree showing the breed membership of samples in the collection. Each sample was colored based on its breed label. Samples from the same breed were marked with the same color. a) Tree A includes 451 pure-bred dogs from 97 breeds in the collection. b) Tree B includes 412 pure-bred dogs from 76 valid breeds after removing potential wrongfully labeled samples. Fig S4. Pairwise genetic signature sharing between all 76 breeds. The 76 by 76 grid plot indicates the genome-wide GS sharing for all breed-pairs. The upper triangular part shows the standardized GS sharing score  Line plot showing the real-world run-time of using this tool to discover GS, BSGS and breed-pair unique shared GS in the dataset of 412 samples, when different number of CPUs were provided. b) The relative per-CPU efficiency for each of the three analyses when different number of CPUs were provided. core See . All scoAdditional file 2: Table S1. Breed-specific SNP distribution by breed and by variant category. Table S2. Genomic regions with at least 40 breed-specific SNP signature from the single breed. Table S3. Detailed information of SNPs exclusively shared by breed-pairs. Table S4. Breed-specific INDEL distribution by breed and by variant category. Table S5. Breeds with breed-specific large STR expansions. Table S6. SNP based genetic signatures distribution across 76 breeds. Table S7. Breed similarity matrix based on shared SNP signatures. Table S8. Breed-pair list sorted by SNP signature similarity score. Table S9. Number of SNP signature within X chromosome pseudo autosomal region. Table S10. Breed history information of the 76 breeds. Table S11. GS-based similarity analysis of the 23 dogs. Table S12. Information of four nonsense BSGS loci for validation. Table S13. Sanger sequencing results for five additionally collected samples. Table S14. Correlation between SNP, INDEL and STR based GS similarity scores. Table S15. Top five breeds similar to French Bulldog according to SNP based GS similarity score. Table S16. Biological pathway annotations of highlighted breed-specific functional structures.Additional file 3: Data S1. Genomic regions with at least 5 SNP BSGS from a single breed. This file contains detailed information on genomic segments with at least 5 SNP BSGS from a single breed. Segments were grouped and sorted by breed.Additional file 4: Data S2. SNP GS discovered across the genome. This file contains information on all genome-wide SNP GS discovered from 76 valid breeds.Additional file 5: Data S3. Basic sample information of the 23 dogs from the lab collection. This file contains basic sample information of newly sequenced dogs.Additional file 6: Data S4. Basic sample information of 429 dogs from the Sequence Read Archive (SRA). This file contains basic sample information of dogs whose WGS data was downloaded from SRA.Additional file 7: Data S5. All SNP BSGS discovered across the genome. This file contains information on all genome-wide SNP BSGS discovered from 76 valid breeds.Additional file 8: Data S6. GS-based breed composition of 452 dogs. This file contains GS-carrying percentages of five most and least similar breeds for each individual dog involved in this study.Additional file 9: Data S7. All INDEL BSGS discovered across the genome. This file contains information on all genome-wide INDEL BSGS discovered from 76 valid breeds.Additional file 10: Data S8. All STR BSGS discovered across the genome. This file contains information on all genome-wide STR BSGS discovered from 76 valid breeds.Additional file 11: Data S9. Detailed list of 10-kb-long genomic block with enriched SNP GS signals. This is the summarized data used for generating Fig 2. Information on genes overlapped with each highlighted 10kb block was presented in the last column.Additional file 12: Data S10. Detailed list of SNP, INDEL and STR based GS similarity scores for each breed. This zip file contains 76 text files each corresponds to one valid breed involved in this study. Each text file contains detailed pairwise similarity scores calculated using genome-wide SNP, INDEL and STR GS.


Drosophila has been instrumental in identifying ‘clock’ genes, which are well conserved in mammals, both in sequence and function. Many ‘clock’ genes have been identified using laboratory mutagenesis screens, which look for mutants that show aberrant circadian behavior. The recent accumulation of genomic sequences from wild populations of Drosophila provides an opportunity to identify natural genetic variations in ‘clock’ genes, which serve as molecular adaptations to local habitats. Here, we focused on nine polymorphic sites in genes associated with the clock and tested their impact on diurnal behavior. We found seven sites whose alleles confer different circadian behavior or seasonal response. This analysis of natural variation can provide information about fine-tuning of the clock, and it is essential for understanding the evolution of the circadian system.Circadian clocks are molecular pacemakers that drive daily rhythms in physiology, metabolism, and behavior. They are present in a diverse range of organisms, from cyanobacteria to humans. In the last few decades, enormous progress has been made in understanding the molecular details of the circadian clock. The fruit-fly Drosophila as a model system. In contrast, the analysis of natural genetic variation that enables the robust function of the clock under a broad range of environments has developed more slowly. In the current study, we analyzed comprehensive genome sequencing data from wild European populations of Drosophila, which were densely sampled through time and space. We identified hundreds of single nucleotide polymorphisms (SNPs) in nine genes associated with the clock, 276 of which exhibited a latitudinal cline in their allele frequencies. While the effect sizes of these clinal patterns were small, indicating subtle adaptations driven by natural selection, they provided important insights into the genetic dynamics of circadian rhythms in natural populations. We selected nine SNPs in different genes and assessed their impact on circadian and seasonal phenotypes by reconstructing outbred populations fixed for either of the SNP alleles, from inbred DGRP strains. The circadian free-running period of the locomotor activity rhythm was affected by an SNP in doubletime (dbt) and eyes absent (Eya). The SNPs in Clock (Clk), Shaggy (Sgg), period (per), and timeless (tim) affected the acrophase. The alleles of the SNP in Eya conferred different levels of diapause and the chill coma recovery response.Our understanding of the gene regulatory network that constitutes the circadian clock has greatly increased in recent decades, notably due to the use of  The circadian clock is an evolutionarily conserved system, which is present in a broad range of organisms, from cyanobacteria to humans. The circadian clock drives a large number of physiological and cellular diurnal rhythms. The ubiquity of the circadian clock alludes to its importance to fitness: having a functional clock allows organisms to predict rhythmic environmental changes, and consequently improve their fitness .Drosophila, the transcription of clock genes period (per) and timeless (tim), which starts at midday, is driven by two transcription factors: Clock (CLK) and Cycle (CYC). These two proteins form a dimer that binds to E-box enhancers in the per and tim promoters. Transcripts of per and tim accumulate in the cytoplasm, and their protein products form a dimer that translocates back to the nucleus, where it binds to CLK/CYC and inhibits their own transcription. This inhibition lasts until the expression of per and tim is reduced, and the whole cycle starts again [Clk transcription, while PAR-domain protein 1ε (PDP1ε) activates it [The molecular circuit underlying the circadian system has been extensively investigated, and studies on both insects and rodents revealed that it is based on a transcription-translation negative feedback loop (TTFL) . In Drosts again . The pacvates it . The outDrosophila, which has been a major model system in population genetics since the pioneering work of Dobzhanski [Fluctuations in the environment, particularly in temperature, present a significant challenge to the circadian system, which is required to generate stable 24 h rhythms. One may expect that wild populations in various geographical regions will evolve molecular variations that serve as local adaptations to different environmental conditions. bzhanski , became Drosophila [per gene alleles in D. melanogaster natural populations in Europe revealed a length polymorphism in the Thr-Gly region. Two major alleles, (Thr-Gly)17 and (Thr-Gly)20, accounted for 90% of the variation, per (Thr-Gly)23 making up 8% and per (Thr-Gly)14 at 1%. Importantly, there was a significant difference in their geographic pattern, with (Thr-Gly)20 being more abundant in Northern Europe, whereas (Thr-Gly)17 was more frequent in the south, resulting in a robust latitudinal cline. The per polymorphism was shown to be important for the thermal adaptation of flies in different environments [The study of the spatial pattern of genetic variation in wild populations is particularly informative for studying adaptation. When genetic or phenotypic variation changes gradually across a geographical gradient, an adaptive significance of that variation is generally assumed and is considered the signature of natural selection. Latitudinal clines  are a classic example of the signature of natural selection and were studied in numerous genes in osophila . Latitudosophila . For exatim [ls-tim/s-tim) that segregate in wild populations, with ls-tim being most frequent in southeastern Europe, whereas s-tim prevails in the north. This polymorphism was shown to be driven by a directional selection that favors the new allele (ls-tim), which is more cold-adaptive. The clinal pattern in this polymorphism reflects the recent spread of the new allele from the south of Italy and represents an evolutionary transient. A recent study [ls-tim allele are more rhythmic than the s-tim flies  and are able to delay their evening activity under extended photoperiods. Another recent study [ls-tim flies (but not s-tim) can synchronize their pacemaker to temperature diurnal cycles, which negates the disruptive effect of long photoperiods. The fact that both these behavioral adaptations seem to work best at high latitudes substantiates the putative role of directional selection responsible for the propagation of the ls-tim allele across Europe.A latitude cline was also found in tim . A singlnt study  revealednt study  found thcryptochrome (cry), a gene that in Drosophila encodes a blue light circadian-dedicated photoreceptor. Cry also serves as an electromagnetic field sensor and as a geotaxis behavior regulator. A recent survey of wild alleles of cry [cry polymorphisms corroborates the importance of these haplotypes for local adaptation of the clock.A functional molecular polymorphism was also found in s of cry  revealedDrosophila melanogaster. The analysis consisted of 48 Pool-seq samples from 32 population samples collected in 2014 by the European Drosophila Population Genomics Consortium (DrosEU) [Until recently, studying genetic variation at the genomic level in natural populations was challenging due to the cost of sequencing whole genomes in a large number of individuals. However, the development of new sequencing strategies, such as sequencing DNA from pools of individuals (Pool-Seq ) made geper, tim, Clk, Dbt, and Sgg and in the output genes pdf and pdfr. In addition, we analyzed a clinal SNP in the gene eyes absent (eya), which was recently shown to drive the photoperiodic diapause in Drosophila through interaction with TIM [The current study was aimed at analyzing the genetic variation that is encapsulated in the DrosEU dataset and identifying polymorphisms that serve as molecular adaptations of the circadian clock. We identified single nucleotide polymorphisms (SNPs) that show latitudinal clines and tested their functional role. We analyzed eight SNPs in the core clock genes with TIM .2 anesthesia (see details below).Flies were maintained using standard sugar food at 20 °C under a 12:12 light:dark (LD) cycle. The flies were kept in population cages, which were constructed from 1.5 litter plastic bottles. A 50 mL polypropylene tube containing 30 mL of food was attached to the opening of the bottle. The base of the bottle was made of wire mesh for ventilation of the cage. Sixty-four flies were transferred to each cage using CO2 measure was used for estimating the variance explained with the logistic regression models. We also used extended models that included ‘longitude’ and ‘altitude’ variables in addition to ‘latitude’. The simple (‘latitude’ only) and extended models were compared using a χ2 difference test.The number of reads supporting each allele at a specific SNP locus in each Pool-seq library was previously counted , and theTo test the effects of candidate alleles at a particular SNP on the phenotype, we used the Mendelian randomization (MR) approach . By crosDrosophila Genetic Reference Panel (DGRP), which is a community resource of 205 inbred lines derived from a wild population in Raleigh, USA [To establish the MR populations, we used strains from the igh, USA . The genigh, USA . For eacDrosophila Activity Monitors  as previously described [The locomotor activity behavior of male offspring from each of the MR populations was analyzed using escribed . Adherinescribed .Flies were held at 20 °C for 8 days under LD 12:12 h, followed by 8 days of continuous darkness (DD). The data were analyzed using the TriKinetics software and the BioDare2 web server . The freTo analyze ovarian diapause, females were collected within 7 h after eclosion, a period during which ovaries are previtellogenic . DiapausSurvival R library to fit Kaplan–Meier curves, and log-rank tests were used to compare the CCR of different genotypes using χ2 statistics with one degree of freedom.Emerging flies were maintained at LD 12:12 for 3–4 d and were tested for their chill coma recovery time as previously described . BrieflyPdf (0.46 SNP/bp) and cry (0.15 SNP/bp), while sgg and Pdfr showed the lowest density (both 0.02). The largest number of SNPs that were clinal were present in dbt and vri, while sgg and per harbored the smallest number of clinal SNPs.Analysis of the 11 genes associated with the circadian clock revealed 9776 SNPs, of which 276 showed a significant latitudinal cline . The mosClk and eya showed a strong correlation with bioclimatic variables in the DrosEU study [. We also tested the effect of longitude and latitude on the allele distribution using an extended model. The difference between the extended and null (latitude only) models was not significant in Dbt569TG > T (p = 0.22), Pdfr7165A > T (p = 0.30), tim4740T > A (p = 0.22), or Clk5472G > T (p = 0.95). In sgg3812A > G, sgg3833C > A, and per7600A > T, the contribution of longitude and altitude was highly significant  and dominated latitude . In the case of Pdfr7165A > T and Clk5472G > T, a significant association was observed only with PC1 . For per7600A > T, only PC2 exhibited a significant association (McFadden’s R2 = 0.34). No significant association was found for eya1849A > G or Dbt569G > T.Previous studies have shown that 19 climatic variables can be summarized using two principal components, PC1 and PC2 . PC1 str1,79 = 9.91, p = 0.002). In dbt6569G > T, the median FRP for the “G” allele was nearly 20 min longer .The selected SNPs were assessed for their impact on circadian phenotypes . For each SNP, we compared the circadian free-running period (FRP) between the MR populations that carry different alleles. Two SNPs were significantly associated with FRP variation . In eya12 = 4.10, p = 0.043), eya1849A > G , per7600A > T , sgg3812A > G , and tim4740T > A .The phase of locomotor activity in DD (acrophase) differed significantly between flies carrying different alleles for five SNPs . These wp < 0.001) and altitudinal (z = 2.8 p < 0.005) clines. The frequency of reference allele “A” was higher in the east  and in higher altitudes .Under both short and long photoperiods, developmental arrest of ovaries was significantly higher in females carrying the allele ‘G’ than in females with the ‘A’ allele .We also tested the association between the eya1849A > G polymorphism and variation in the cold response using the chill coma recovery assay b. Flies One of the main goals of analyzing genetic variation in wild populations is the potential for identifying local molecular adaptations that are driven by natural selection. Here, we identified 276 SNPs in genes associated with the circadian clock, whose spatial allelic distribution follows a latitudinal cline. We tested the functional role of nine SNPs and found phenotypic effects in seven of them.dbt, the reference allele “G” is present at a higher frequency at higher latitudes. The FRP of flies carrying this allele is longer  value associated with their effect was low, indicating that the models do not explain much of the variability in the data. This suggests that there are likely other factors at play that we did not measure, or that the spatial distribution of the alleles being studied is shaped with more complex intricate processes. Despite these limitations, it is important to consider the broader implications of our findings. The small clinal effects observed across numerous wild populations of Drosophila in Europe may appear subtle, but they contribute valuable insights into the dynamics of genetic variation and local adaptation. By conducting the first comprehensive survey of single nucleotide polymorphisms (SNPs) in circadian clock genes across multiple populations, our study reveals a nuanced landscape of genetic diversity and adaptation. Furthermore, the identification of associations between SNP alleles and climatic principal components expands our understanding of the complex interplay between genetic variation and environmental factors. Thus, despite the subtlety in the geographical clines and the low R2 values, our study significantly advances the field by providing a comprehensive dataset and shedding light on the genetic dynamics of circadian rhythms in natural populations. We believe that our work makes a valuable contribution by identifying several alleles that mediate phenotypic variation in circadian behavior and seasonal response.ls-tim polymorphism expresses two variants, S-TIM, which binds robustly to CRY, and L-TIM, which exhibits a much weaker interaction [To understand the mechanisms by which these SNPs mediate their phenotypic effects, further experiments will be required, looking at the expression of the different variants at the transcript and protein levels. The phenotypic effect of different alleles may also be manifested by affecting the interaction between their encoded proteins and other clock proteins. For example, the eraction . The diferaction .Most of the SNPs that were tested here were intronic, alluding to their potential role in gene regulation. Indeed, studies in recent years suggest that variation in non-coding sequences may have an important regulatory role . IntronsDrosophila populations have been carried out in the last decade, and notably, ‘clock’ genes are often present among loci that show substantial genetic differentiation or clinal variation. Polymorphisms in tim, timout, Clk, and cry, which were associated with bioclimatic principle components in European populations [Drosophila populations [sgg was flagged as being associated with climatic variables both in Europe [sgg as a significant candidate gene in both European and North American datasets. The fact that the same polymorphisms are clinal on different continents may further indicate their adaptive role. It is noteworthy that all the SNPs that were studied here are segregated in appreciable frequencies in both Europe and North America, as evident from the DGRP strains that were used to generate the MR population in this study. Overall, these studies underscore ‘clock’ genes as a major target for natural selection, which allows rapid adaptation to different climatic conditions.Several genomic surveys of wild ulations , were alulations . A polymn Europe  and in an Europe . ConsistDrosophila, our experience from the current work suggests that the main ‘bottleneck’ is the generation of congenic strains that are fixed for different SNP alleles. This process, either carried out using the Mendelian randomization approach or transgenics, is laborious, costly, and time-consuming. Hopefully, new CRISPR transgenesis protocols  will heWe identified a large number of SNPs in circadian clock genes that segregate in European populations and exhibit latitudinal clines. These polymorphisms may represent local molecular adaptions of the circadian system. We tested the functional role of nine SNPs and found phenotypic effects in seven of them. This study adds to previous research toward the understanding of how the clock is fine-tuned to perform in different environments and molecular evolution of the clock genes, an area in which relatively little is known about.


LOC_Os01g43700, LOC_Os09g25784, and LOC_Os04g47890, may be significantly related to panicle traits of rice. Haplotype analysis indicated that LOC_Os01g43700 and LOC_Os09g25784 are highly associated with PL and that LOC_Os04g47890 is highly associated with TGP, FGP, and GWP. Our results offer essential genetic information for the molecular improvement of panicle traits. The identified candidate genes and elite haplotypes could be used in marker-assisted selection to improve rice yield through pyramid breeding.Panicle traits are important for improving the panicle architecture and grain yield of rice. Therefore, we performed a genome-wide association study (GWAS) to analyze and determine the genetic determinants of five panicle traits. A total of 1.29 million single nucleotide polymorphism (SNP) loci were detected in 162 rice materials. We carried out a GWAS of panicle length (PL), total grain number per panicle (TGP), filled grain number per panicle (FGP), seed setting rate (SSR) and grain weight per panicle (GWP) in 2019, 2020 and 2021. Four quantitative trait loci (QTLs) for PL were detected on chromosomes 1, 6, and 9; one QTL for TGP, FGP, and GWP was detected on chromosome 4; two QTLs for FGP were detected on chromosomes 4 and 7; and one QTL for SSR was detected on chromosome 1. These QTLs were detected via a general linear model (GLM) and mixed linear model (MLM) in both years of the study period. In this study, the genomic best linear unbiased prediction (BLUP) method was used to verify the accuracy of the GWAS results. There are nine QTLs were both detected by the multi-environment GWAS method and the BLUP method. Moreover, further analysis revealed that three candidate genes,  Oryza sativa L.) is one of the most important food crop species and serves as the primary nutrition source for almost half of the world’s population  and genes associated with panicle traits have been mapped and cloned, which is inseparable from the rapid development of molecular biology techniques however, the molecular mechanisms underlying panicle traits have been insufficiently studied. To date, at a minimum 300 QTLs for panicle traits distributed across 12 chromosomes have been detected . For ins in rice . Zhang e in rice . Zhang e in rice . Using 4 in rice . Zhong e in rice . In addi have become an efficient means for mapping QTLs or genes related to target traits. GWASs based on second-generation sequencing technology enable resequencing of many genomes and provide the possibility of identifying favorable alleles and genetic variations associated with complex traits on a large scale with high accuracy . AdditioA total of 162 rice accessions were obtained from the State Key Laboratory of Crop and Genetics and Germplasm Innovation of Nanjing Agricultural University . Of the The 162 accessions were planted at the Jiangpu Experimental Station of Nanjing Agricultural University in Nanjing, China, in 2019 and at the Experimental Station of Anhui Agricultural University in Hefei, China, in 2020 and 2021. The varieties were sown in the middle of May and transplanted in the middle of June; each single plant was spaced 26 cm × 16 cm in all 3 years (2019–2021). A randomized block design with two replications was used for the 3-year experiments. Each variety was planted in 5 rows, with 8 plants in each row. These varieties were subjected to normal fertilizer and water management practices. At maturity, we harvested five panicles of the main culm of each accession and measured the PL, TGP, FGP, SSR, and GWP. The mean phenotypes of each accession were used to calculate the SSR, FGP, and TGP.Excel 2010 (Microsoft) software was used for data collation, SPSS software (version 25.0) was used to process the sorted data, and the mean and standard deviation of each agronomic trait were calculated.Generalized heritability refers to that all genetic variation accounts for the total phenotypic variation. The calculation formula of broad-sense heritability is determined by comparing the relationship between genetic differences among individuals and total variation. In a population, the genetic differences of individuals can be divided into two parts: one is caused by genetic differences, namely, genetic variance (VA), and the other is caused by environmental factors, namely, environmental variance (VE), Total variation (VP) is the sum of genetic variance (VA) and environmental variance (VE).The calculation formula of broad heritability is as follows:The value range of generalized heritability is between 0 and 1. The closer the value is to 1, the greater the influence of genetic factors on the trait is. On the contrary, if the generalized heritability is close to 0, it indicates that the trait is mainly affected by environmental factors.In this study, we performed a correlation analysis on the 3-year data of the five traits. Regarding the threshold, when | r | ≥ 0.8, it can be considered that the two variables are highly correlated; 0.5 ≤ | r | < 0.8, it can be considered that the two variables are moderately correlated; 0.3 ≤ | r | < 0.5, it can be considered that the two variables are weakly correlated; and | r | < 0.3, the two variables can be considered basically irrelevant.To sequence the 162 accessions, young leaves were collected from a single plant at the tillering stage, and genomic DNA was extracted using a standard cetyltrimethylammonium bromide protocol. A double-end sequencing library was constructed using 5 μg genomic DNA, and the inserted fragment was approximately 350 bp reads. After further processing of the original sequence, the genomic sequence data of 0.532 Tb was obtained after removing joint contamination and low-quality reads. The average genome coverage of each test material was 5.48 times.http://www.megagenomics.cn/mobile.php/, accessed on 10 April 2019).Accessions in our population were filtered using PLINK (version 1.9) command and SNPs were further filtered using plink with maf (0.05) and geno (0.2). A total of 1,209,388 SNPs were identified. Library construction, sequencing, and sequence cleaning were performed by staff at Mega Genomics Beijing .  based on SNPs and an NJ clustering diagram was constructedusing MEGA version 7. Principal component analysis was conducted using GCTA (version 1.93.2) software on Linux. PLINK (version 1.9) was used for LD analysis of the genotypic data, and the LD diagram, population structure diagram, NJ tree, and PCA (We used PLINK (version 1.9) software to filter the SNP loci of 162 rice materials, retain the unconnected SNP loci, and convert them into STRUCTURE format . To preder2022). . The dis and PCA  diagram p = 4.1 × 10−8, and the threshold of MLM was 1.0 × 10−5. A QTL covers all SNPs located in the same LD region, and the SNP with the smallest p-value is considered the leading SNP. We used the R package ‘CMplot’ to construct the Manhattan diagram.In this research, we obtained 1,209,388 SNPs (MAF >0.05) and five groups of phenotypic data. Two models, including GLM and MLM  in TASSE−5, which is the same as the threshold setting of the multi-environment GWAS method.In this study, the genomic BEST Linear Unbiased Prediction (BLUP)  method whttps://www.ricedata.cn/, accessed on 19 October 2022). Nonsynonymous SNPs lead to amino acid changes, and we focused on nonsynonymous SNPs by comparison with the Nipponbare reference genome sequence . The identification of candidate genes was mainly through nonsynonymous SNPs in exons and gene function. The nonsynonymous SNPs in exons were selected for haplotype analysis to select favorable haplotypes. The haplotypes of the candidate genes were analyzed based on geographical region and subgroup. Analysis of different phenotypes of candidate gene haplotypes was performed using ANOVA combined with Duncan’s multiple range test. Favorable haplotype analysis of candidate genes was performed using the 3KRG gcHap dataset  and the Rice Functional Genomics and Breeding Database . The “favorable” gcHap of a gene was defined as the one associated with the highest trait value. Five subpopulations of XI  and four subpopulations of GJ  were classified by Based on the LD decay distance and the results of our GWAS analysis, the candidate areas of genes on chromosomes were estimated, and the genes in the candidate regions were identified at the China Rice Data Center. (The average positive (negative) haplotype effect (AHE) at a gene locus was calculated as follows:Among them, nc represents the number of haplotypes with positive (negative) effects on gene loci. hc represents the phenotypic value of haplotypes with positive (negative) effects.Rice materials with the highest positive haplotype effect were predicted to be the most promising parents for panicle trait improvement in rice breeding at all panicle trait-related gene loci.The panicle traits of 162 rice accessions showed significant differences. Generalized heritability refers to all genetic variation accounting for the total phenotypic variation. All the traits had high generalized heritability, ranging from 73.5% to 98.4% in 2019, 2020 and 2021  FurthermIn general, the phenotypic changes during all 3 years were similar . Most ofThe correlation analysis of the 3-year data showed that SSR was negatively correlated with PL and TGP . Howeverindica group and a japonica group  as covariates within the GWAS model to control for subpopulation structure  locus on chromosome 4 was found to be associated with TGP, FGP, and GWP simultaneously in at least 2 years and via two models. qTGP4, qFGP4, and qGWP4 were the same QTL; hereafter, this QTL is referred to as qTGP4. In addition, qFGP7 was identified on chromosome 7 and was detected in both models for two consecutive years  were significantly associated with PL, TGP, FGP, SSR, and GWP . These Qve years .qPL1, qPL6, qPL9.1 and qPL9.2 were identified on chromosomes 1, 6, and 9. For SSR, qSRR1 was identified on chromosome 1. For TGP, FGP, and GWP, the Chr4_28,917,371  locus on chromosome 4 was found to be associated with TGP, FGP, and GWP simultaneously, qTGP4, qFGP4, and qGWP4 were the same QTL; hereafter, this QTL is referred to as qTGP4. In addition, qFGP7 was identified on chromosome 7. In summary, the results of single-environment GWAS are consistent with the results of multi-environment GWAS.The MLM with correction of kinship bias was mainly used in this study, We conducted a GWAS between panicle traits and SNPs across the 162 rice accessions. The GWAS results showed that a total of 9 SNPs (QTLs) were significantly associated with PL, TGP, FGP, SSR, and GWP . For PL,qPL1  in the range of 24.9–25.1 Mb  in the 15.3–15.5 Mb region, there were 20 candidate genes related to remarkable SNPs  was predicted as a candidate gene QTL. According to the SNP site in the cDNA sequence of the LOC_Os04g47890 gene, it was divided into three haplotypes (LOC_Os04g47890 was chosen for further research.81 genes . Among t81 genes . A previ81 genes . Therefoplotypes . The aveplotypes . TherefoLOC_Os01g43700 and LOC_Os09g25784 were mainly distributed in indica rice subgroups such as Southern China (SC), Southwest China (SWC) and Southeast Asia (SEA) and low latitudes. In contrast, the nonfavorable HapB allele of LOC_Os01g43700 and the nonfavorable HapA allele of LOC_Os09g25784 were mainly distributed in japonica rice subgroups and high latitudes, such as Northeast China (NEC). Similarly, the favorable haplotypes of the three candidate genes were mainly distributed in indica rice, and Hap A of LOC_Os04g47890 was mainly distributed in SC, SWC and SEA. Therefore, germplasms with better PLs, more grains and heavier panicles were mainly distributed in subpopulations of indica rice and low-latitude regions. We further analyzed the frequencies of the favorable gcHap of 3 important candidate genes affecting two panicle traits (PL and GW) in Xian/indica (XI) and Geng/japonica (GJ) modern varieties and different rice subpopulations and the results showed that the GW candidate genes were mainly distributed in japonica rice, and the favorable haplotypes of the two PL candidate genes in modern varieties were mainly distributed in indica rice. In addition, the favorable haplotypes of 3KRG candidate genes were distributed in different subgroups  among the 9 stable QTLs and one QTL (LP1) reported previously  is a powerful method to understand the genetic structure of multiple traits in rice. Generally, regression models are constructed to test whether there is a correlation between markers and phenotypes. Previous studies have shown that MLMs control false positives better than GLMs do, which was strongly reflected by experimental results. Therefore, we used the MLM and GLM for further analysis. In this study, a total of 14 QTLs related to panicle traits were detected, and 9 QTLs were detected in at least 2 years and via two models. The local LD regions of the four QTLs associated with panicle traits overlapped with the flanking regions of the two genes , which was found to be associated with PL, was detected in all 3 years in this study and this gene was selected as the focus for further analysis. LOC_Os09g25784 encodes the auxin-induced protein 5NG4, and auxin is an essential molecule that controls almost every aspect of plant development  is a candidate gene in qTGP4, qFGP4, and qGWP4. The MYB transcription factor family is present in all eukaryotes. Compared with other organisms, plants encode many MYB genes (LOC_Os04g47890 may be a candidate gene for regulating rice panicle traits.Haplotype analysis of plotypes . In the plotypes , indicataterials . In addielopment  and is ielopment . The reselopment . Analysiplotypes . SignifiYB genes . MYB proYB genes . Consequindica rice improved panicle traits better than indica rice, and these results need to be further verified in production. Overall, further functional analysis of these candidate genes will help us better understand the genetic basis for natural variation in rice panicle traits and show that these genes may be used to improve rice yield. With the development of molecular biology, our research on the mining of excellent alleles of rice panicle traits and the prediction of excellent parents will provide a theoretical basis for molecular design breeding. Therefore, these candidate genes are to be used in breeding programme not only the breeding program discussed here but also other breeding programs.The results showed that favorable haplotypes could improve the panicle traits of rice. Among the 10 predicted parents, 9 LOC_Os01g43700 and LOC_Os09g25784 were identified as candidate genes for PL, while LOC_Os04g47890 was identified as a candidate gene for TGP, FGP, and GWP. These genes are important for further research. At the same time, we infer that these 10 rice materials can be used as excellent parents with favorable alleles of panicle trait genes. Using the favorable alleles detected in this study can provide a theoretical basis for the improvement and breeding of panicle traits.In our study, we dissected the genetic basis underlying rice panicle traits. According to the results of gene function annotation and haplotype analysis, we identified three candidate genes related to panicle traits.


Potato is the fourth most important food crop in the world. Although with a long history for breeding approaches, genomic information and association between genes and agronomic traits remain largely unknown particularly in autotetraploid potato cultivars, which limit the molecular breeding progression. By resequencing the genome of 108 main cultivar potato accessions with rich genetic diversity and population structure from International Potato Center, with approximate 20-fold coverage, we revealed more than 27 million Single Nucleotide Polymorphisms and ~ 3 million Insertion and Deletions with high quality and accuracy. Domestication analysis and genome-wide association studies (GWAS) identified candidate loci related to photoperiodic flowering time and temperature sensitivity as well as disease resistance, providing informative insights into the selection and domestication of cultivar potato. In addition, GWAS with GWASploy for 25 agronomic traits identified candidate loci by association signals, especially those related to tuber size, small-sized tuber weight and tuber thickness that was also validated by transcriptome analysis. Our study provides a valuable resource that facilitates the elucidation of domestication process as well as the genetic studies and agronomic improvement of autotetraploid potato.The online version contains supplementary material available at 10.1186/s43897-022-00027-y. Seletive interval analysis combined GWAS of autotetraploid potato identified candidate loci under domestication and associated with agronomic traits, providing informative insights for elucidating  the domestication history and selective process of cultivar potato and facilitating the genetic studies and agronomic improvement of autotetraploid cultivar potato.Solanum tuberosum L.) is the predominant vegetable crop in the global food system and also a critical food crop  and Expressed sequence tag-SSR (EST-SSR) have been applied to assess the genetic diversity, germplasm identification and population structure  and 64,080 1.37%) InDels were located in the coding regions, among which 60,265 SNPs or InDels showed potentially significant effects, including 21,694 SNPs (0.058%) that may affect 2329 genes by causing start codon changes, premature stop codons or elongated transcripts, and 38,571 InDels (0.832%) that may lead to a frameshift in 11,132 annotated genes , the 170 accessions are mainly divided into five categories including Wild, Outgroup, Landrace, reported cultivar and CIP cultivar  value showed the optimal K value, a parameter in K-means clustering used to identify groups and describe the relationship between groups. When K = 2, the population were divided into two distinct clusters only, Wild species and CIP species, while landrace was differentiated when K = 3 or higher. Interestingly, from K = 4, the reported cultivars begun to differentiate from domesticated ones, but a sophisticated genetic structure was observed in CIP cultivars Fig. . The difDomestication of wild potatoes were originated from the Andes of Southern Peru approximately 8000 years ago and were mainly cultivated in altitudes 2000–4000 m regions that are characterized by short day (SD) condition, high light intensities, and cool temperatures  and θπ (the log2 ratio of nucleotide diversity) based cross approach. As a result, a total of 138 with strong selection signals were identified being involved in various physiological processes including photoperiodic flowering, temperature responses, tuber shape and disease resistance , which play essential roles in mediating stomatal closure in response to drought stress and ABA treatment. Selection of these genes provides informative hints on the environmental adaptation of potato cultivars and related selective loci can serve as the candidate genetic resources for potato improvement.Cultivated potatoes produce bigger tubers than wild potato during domestication of tuber size. Tuber growth is also crucial for yield and productivity after tuberization and genes correlated with cell division were selected. CYCLIN A3;4  is an important protein controlling formative cell divisions in International Potato Center created multiple germplasm resources for variety improvement in different countries. Although their genetic background is mixed without obvious subgrouping, the 108 CIP accessions are core cultivars with a rich diversity among agronomic traits, particularly tuber shape and color Fig. A as wellAnalysis of the analyzed 25 agronomic traits showed that most of them are normally distributed, indicating they are quantitative traits controlled by polygenes and susceptible to environmental influences. Evaluation by Pearson factor further revealed the close correlations of overall 25 traits quantitatively Fig. . PearsonSmall-sized tuber number is positively correlated to small-sized tuber weight and tuber number per plant with high Pearson factor  which closely relates to aspect ratio of tuber is dissected as well, which is consistent with that peroxidases play important roles in cell wall elongation as well as in determining shape of potato tubers.A peak strongly associated with aspect ratio on chromosome 10 was identified within the region of previously reported FST) in 20-kb sliding windows across chromosome 6  showed the insignificant population difference between Wild and CIP populations at this position, indicating the conserved nucleotides during domestication  and affects tuberization and tuber growth by altering GA content, which has been validated in transgenic potato plants  that interacts with PHYTOCHROME B (PHYB) and CONSTANS in Arabidopsis  and nucleotide polymorphisms analysis with log10 ratio of diversity (Π) showed that there is significant population difference between Wild and CIP populations in chromosome 5 associated with tuber thickness and the Wild species signals are significantly higher in this region, indicating a very strong population differentiation relating to domestication  as well as homolog of PHYB Soltu.DM.05G023390 . The identified candidate regions and genes associated with key agronomic traits may be important targets for biotechnological or breeding approaches in the future.In addition, key loci associated with other agronomic traits which are closely correlated with potato appearance, yield and quality, and crucial for its commodity value are identified Fig. S. All assVegetative propagated crops such as potato are globally important in terms of agricultural production, long-term history of early agriculture and plant domestication as well as planning for more sustainable agricultural futures Harris, . DomestiRevelation of genomic sequence at the whole-genome level and identification of functional genes is urgent and essential considering the importance of autotetraploid potato cultivars in product and breeding. Resequencing of autotetraploid CIP accessions with whole-genome sequencing (WGS) fills in blanks of genomic information for autotetraploid cultivars and referenced the newly revealed haploid genome which is a chromosome-scale long-read assembled reference genome  due to the lack of autotetraploid potato reference genome. Analysis of the diverse agronomic traits is also essential since there are significant differences in yield, resistance and quality, genetic background, ecological adaptability, outward appearance and flavor types. Additionally, 138 high-confidence selective sweeps comprising 54 predicted genes related to flowering time, temperature sensitivity and cell division were also identified by selective interval analysis, which will help to understand the variable traits and complex domestication process of potato adaptation to changed environment and are potential new molecular markers that can be applied for potato breeding. Moreover, the SNPs in candidate genes of GWAS are related to domestication, indicating the changes of key genes in potato selection. Genome variation correlated with domestication revealed how genome reshaped under selection for better phenotypic traits and provides a rich resource for potato cultivation and breeding.Complex genome with different ploidy of potato and the quantitative nature of most agronomic traits are challenging for accurately understanding the phenotype and genotype relations. GWAS might not be as promising in polyploid species as in diploids since the software tailored to polyploidy is lacking although it has been widely used in diploid species to study the complex traits in diversity and breeding populations. Appropriate GWAS software tailored to polyploidy is the main constraint and biggest challenge to autotetraploid potato species. In this study, we introduced GWASpoly in R, a software tailored for polyploidy and has been utilized in sugarcane and chrysanthemum , identification of them will provide informative clues for potato genetics and breeding practice.https://cipotato.org/catalogue) were analyzed  of each library was performed on an Illumina HiSeq X Ten system at Shanghai OEbiotech Co Ltd. .Total genomic DNA was extracted from tuber tissues using the DNeasy Plant Mini Kit (Qiagen). All DNA samples were diluted to 20 ng·μLS. tuberosum group Phureja DM reference genome  were used to remove the PCR duplicates according to the mapping coordinates.For the reported 67 accession panel, resequencing data for cultivars, landraces and wild were downloaded from the NCBI database under PRJNA378971  (version 3.8.1). In brief, the variants were called for each accession by GATK Haplotype Caller. A joint genotyping step for comprehensive variations union was performed on the gVCF, the ploidy was set 2 or 4 according to the ploidy of accessions. In the filtering step, the SNP filter expression was set as QD < 2.0 || MQ < 20.0 || FS > 60.0 || SOR > 3.0 || MQRankSum < − 12.5 || ReadPosRankSum < − 8.0, and the InDel filter expression was set as QD < 2.0 || FS > 200.0 || MQ < 40.0 || SOR > 10.0 || ReadPosRankSum < − 20.0.Three subsets of potato SNPs were defined using following filtering criteria: (1) the basic set of 27,565,997 SNPs were created excluding non bi-allelic, > 20% missing calls and MAF < 5%; (2) Phylogeny SNP set of 72,923 SNPs randomly sampled at equal counts from genome-wide 10-kb windows; (3) GWAS SNP set of 27,494,422 SNPs were created by selecting 108 phenotyped CIP individuals by filtering MQ < 40 and MAF < 0.05 form all 170 samples raw vcf.SNPs and InDels annotation were performed according to the wild castor genome using SNPeff  and colored by R package ggtree  procedure. ADMIXTURE was then run again on the whole core SNP set 10 times with varying random seeds; the Q-matrices were aligned using pong , nucleotide diversity (θπ) and XP-CLR analysis based cross approach was performed to investigate the selection signals across the whole genome. A 20-kb sliding window with 10-kb step approach was applied to quantify the FST, θπ and XP-CLR by using R package PopGenome P < 0.05/27,494,422 = 1.82 × 10− 9 (−log10P = 8.74).Since the DAPC analysis showed the best cluster of 108 CIP accessions were 2, we only considered cryptic kinship relationships (K) to minimize false positives and increase statistical power. GWASpoly  is tailored for autopolyploid based on the Q + K mixed model and could model gene actions for polyploids. Six models were used for GWAS by using GWASpoly, including general model, additive model, two simplex dominant models , diplo-general model, and diplo-additive model. We defined the whole-genome significance cutoff with the adjusted Bonferroni test threshold, which was set as http://solanaceae.plantbiology.msu.edu/pgsc_download.shtml). Genes in the upstream and downstream 500-kb region of the leading SNPs of each trait were selected and expression pattern associated with the trait was shown except genes whose FPKM equals to 0 across all libraries.FPKM values of all representative transcripts across 40 DM and 16 RH libraries were downloaded from the PGSC data  in potato tuber aspect ratio, small-sized tuber weight and tuber thickness. Fig. S5. Transcriptome analysis of the candidate genes by GWAS.Additional file 2: Supplemental Table S1. Sample information for 108 CIP accessionsAdditional file 3: Supplemental Table S2. Overview of sequencing of samples.Additional file 4: Supplemental Table S3. Statistics of SNPs and InDels for CIP 108 accessions.Additional file 5: Supplemental Table S4. Statistics of SNPs and InDels for 170 accessions.Additional file 6: Supplemental Table S5. Selected intervals and genes between Wild and CIP cultivars.Additional file 7: Supplemental Table S6. Selected intervals and genes between reported and CIP cultivars.Additional file 8: Supplemental Table S7. Phenotypic information of 108 CIP accessions.Additional file 9: Supplemental Table S8. GWAS signals and candidate genes.


The genetics of male fertility is complex and not fully understood. Male subfertility can adversely affect the economics of livestock production. For example, inadvertently mating bulls with poor fertility can result in reduced annual liveweight production and suboptimal husbandry management. Fertility traits, such as scrotal circumference and semen quality are commonly used to select bulls before mating and can be targeted in genomic studies. In this study, we conducted genome-wide association analyses using sequence-level data targeting seven bull production and fertility traits measured in a multi-breed population of 6,422 tropically adapted bulls. The beef bull production and fertility traits included body weight (Weight), body condition score (CS), scrotal circumference (SC), sheath score (Sheath), percentage of normal spermatozoa (PNS), percentage of spermatozoa with mid-piece abnormalities (MP) and percentage of spermatozoa with proximal droplets (PD).− 8 was imposed. This effort led to identifying genetic variants and candidate genes underpinning bull fertility and production traits. Genetic variants in Bos taurus autosome (BTA) 5 were associated with SC, Sheath, PNS, PD and MP. Whereas chromosome X was significant for SC, PNS, and PD. The traits we studied are highly polygenic and had significant results across the genome . We also highlighted potential high-impact variants and candidate genes associated with Scrotal Circumference (SC) and Sheath Score (Sheath), which warrants further investigation in future studies.After quality control, 13,398,171 polymorphisms were tested for their associations with each trait in a mixed-model approach, fitting a multi-breed genomic relationship matrix. A Bonferroni genome-wide significance threshold of 5 × 10The work presented here is a step closer to identifying molecular mechanisms that underpin bull fertility and production. Our work also emphasises the importance of including the X chromosome in genomic analyses. Future research aims to investigate potential causative variants and genes in downstream analyses.The online version contains supplementary material available at 10.1186/s12864-023-09475-2. Northern Australia represents a critical region for the Australian beef breeding industry, and bull fertility is an important contributor to profitability –3. HowevPrevious work used medium or high-density SNP arrays, such as the Illumina 50 K panel or the BovineHD chip. Thus, genetic variants associated with GWAS are usually not causal mutations but are single nucleotide polymorphisms (SNP) in Linkage Disequilibrium (LD) to causal variants . With adA total of 6,422 animals of six breeds with BBSE measurements of seven phenotypes were used in this study. These animals are from two research populations and four stud herds from the industry. The two-research populations consisted of animal data obtained from the Cooperative Research Centre for Beef Genetic Technologies (Beef CRC) project , which iPhenotypic measures for all six populations were collected from 2003 to 2020. Each bull was assessed once, and the year of measurement was recorded as the fixed effect - year of birth. The individuals involved in the assessment and collection of phenotypes are different for the two research populations and the four stud herds. Phenotypes for the two research populations were collected and assessed by two experienced veterinarians who worked together throughout the collection period. In the four stud herds, an experienced animal scientist and veterinarian conducted the examinations. For semen morphology traits, semen samples from the two research populations were analysed in the same laboratory. Whereas, sperm samples from the four stud herds were assessed in different accredited labs selected by the producers. The age of the bulls when the phenotypes were collected differed across the six populations. In the Beef CRC, when SC was collected, BRH and TRC bulls were around 360 days old. Sheath score and sperm morphology assessments were conducted at around 700 days old. In the four stud herds, all phenotypes for SGT and DMT bulls were obtained at around 600 days, whereas UBK and BTC bulls had their phenotypes measured around 440 days old and 390 days old, respectively. Phenotypes were pre-adjusted using a generalised linear model analysis (PROC GLM) for their fixed effects  and covariates  using SAS ® software 9.4 (SAS Inst. Inc.). A subset of this population was previously used in a multi-breed analysis .2 > 0.8, a call rate > 0.9 and a minor allele frequency > 0.01 were kept for further analysis, leaving 13,398,171 SNPs, including 92,134 SNPs mapped onto the X chromosome after quality control for all 6422 animals. The Genomic Relationship Matrices (GRM) were constructed in the software GCTA [SNP genotype imputation up to whole-genome sequence level was conducted in two rounds. In the first round, the reference population was established using Beef CRC and industry cattle. This reference population consisted of 2,452 animals made of BTC, BRH, DMT, SGT, UBK, Angus, Bonsmara, Boran, Composite, and Tuli breeds that were genotyped with the bovine high-density chip (~ 700 K) and phased using Eagle 2 (v2.4.1) which formed the imputation targets for the next step . In the are GCTA  using a are GCTA .The first two principal components calculated PLINK 1.9 , and the– represents the joined effect of all variants, excluding any variants on which the chromosome of the tested SNP is located, and e is the residual variance. A genome-wide significance threshold of 5 × 10− 8 was used, which is a conservative Bonferroni correction. After the first round of GWAS was completed, the most significant SNPs in each chromosome were refitted as a discrete covariate in the second round of GWAS for each trait in GCTA [− 8) were merged into a Quantitative Trait Loci (QTL). Using GALLO [Bos taurus ARS UCD 1.2 genome assembly obtained from Ensembl version 105 [Where y represents the phenotype in question, a represents the mean, b represents the additive genetic effect of the tested SNP, x represents the SNP genotype indicator variable which is coded as 0, 1 and 2, g  in GCTA . This wa in GCTA  using th in GCTA . Using b in GCTA , SNPs wing GALLO , genes fsion 105 . The finhttps://www.animalgenome.org/cgi-bin/QTLdb/BT/index) [2 threshold of 0.4 to be in LD. Finally, the percentage of genetic variance explained by each SNP was calculated using a formula made available in a previous report [Similarly, the same regions were used to identify any previously reported QTL in the Animal QTL database (T/index)  that oveT/index) . PairwisT/index) . We conss report :\documeni and qi are the SNP’s allele frequencies Where pIn this study, we conducted GWAS using sequence-level genotypes and targeted seven bull fertility and production traits measured in a multi-breed population of 6,422 bulls. This section discusses important regions and candidate genes identified through GWAS and QTL analysis. A summary of GWAS results for the most significant genomic region discovered for each trait is provided in Table Heritability estimates across traits range from low  to high  Table . The estp = 2.27 × 10− 10). This result is somewhat expected because previous GWAS in datasets containing Bos Taurus and Bos Indicus breeds have been reporting a QTL in BTA 14 for stature and weight traits [The number of associated SNP varied enormously, depending on the target trait Table . A totalt traits –37.p = 1.79 × 10 − 9) was located at 6.7 Mb of BTA 23. This is a new discovery as there are no CS QTL in BTA 23 currently recorded in the cattle QTL database . The strongest SNP association for SC (p = 1.15 x − 79) was located at 79 Mb on the X chromosome. This is not the first time we detect SNP associations on X for SC, and so this result confirms previous GWAS carried out with smaller datasets [p = 1.98 × 10− 288) for Sheath was located at 47.8 Mb of BTA 5. This finding is consistent with previous GWAS work in sheath score that used a subset of the data included in this study [The strongest SNP association for CS  for PNS was located at 46.4 Mb at BTA 5. Previously, we had only identified SNPs on X for PNS [− 13) for PD was located at 46 Mb of BTA 5. A total of 173 significant SNP associations were detected for MP. The strongest SNP association (p = 2.77 × 10 − 10) was located at 6.2 Mb of the X chromosome. This multibreed dataset confirmed that chromosome X harbors SNP associations for semen traits as expected [Similarly, the current dataset enhanced our ability to detect associations for the three semen traits: PNS, PD and MP. The strongest SNP association  . To mitiA total of 1,120 previously reported QTL overlapped with the regions identified in this study for weight. While most of the identified QTL were associated with weight-related traits, some of these regions were also important female traits such as milk fat yield, health-related traits, and reproductive traits.A total of 20,095 previously reported QTL overlapped with the significant regions reported for SC. Most QTL were associated with SC reported in Canchim bulls , and theIn Sheath, 2,671 previously reported QTL overlapped with the significant regions in this study. Most QTL were associated to female traits such as milk protein percentage or milk yield. However, QTL were also associated to male traits such as inhibin hormone levels and SC. Inhibin hormone levels are considered an early indicator of sexual development, and genes such as INHBE and INHBC are located in BTA 5 , 43. Of Previously reported QTL for PNS mirrored the result for SC Figs.  and 4. TSignificant QTL mapping to the X chromosome for SC, PNS and sperm abnormalities highlights its importance in male fertility and spermatogenesis. The X chromosome is a candidate region for species divergence genes which are highly expressed in the testis of mice and humans . Sexual Due to the vast number of genes detected for some traits, we have included the list of genes in each associated region for each trait in Additional file Across traits, we observed that BTA 5 is an important region for male fertility in bulls. Regions in BTA 5 that have overlapping results point to SNP and genes associated with five out of the seven studied traits: SC, Sheath, PNS, PD and MP  listed in Table The remaining candidate genes from Table Notably, three genes within BTA 5 regions  which has been previously associated with fertility. The expression of IRAK3 by Sertoli cells, which play an important role in spermatogenesis, has been discussed in detail in the previous section. The high-impact variant rs439285466 lies within the protein-coding region of a gene called RLIM. Although RLIM has not been associated to bull fertility or bull production traits, it has been previously associated with the regulation of cell proliferation which is fundamental process for spermatogenesis [We identified 17 high-impact variants, predicted with VEP, as shown in Table ogenesis . Conside2 < 0.4) with the top variants of the corresponding QTL, were significantly associated with either SC or Sheath themselves. Some of the high impact variants identified in this study, lie within known genes  previously not connected with bull traits or male fertility [The remaining 10 high-impact variants, while not in LD (Rertility –82. We pThis study highlights the importance of BTA5 for bull fertility and production traits and demonstrates the need to include the X chromosome in genomic analyses. We also highlighted candidate genes of relevance across several traits, which should be further investigated in ascertaining gene effects on spermatogenesis and fertility. Finally, we identified several high-impact variants for SC and Sheath, which required further validation in future work.Below is the link to the electronic supplementary material.Supplementary Material 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6