Oryza glaberrima Steud), a short-day cereal crop closely related to Asian rice (Oryza sativa L.), has been cultivated in Sub-Saharan Africa for ∼ 3,000 years. Although less cultivated globally, it is a valuable genetic resource in creating high-yielding cultivars that are better adapted to diverse biotic and abiotic stresses. While inflorescence architecture, a key trait for rice grain yield improvement, has been extensively studied in Asian rice, the morphological and genetic determinants of this complex trait are less understood in African rice. In this study, using a previously developed association panel of 162 O. glaberrima accessions and new SNP variants characterized through mapping to a new version of the O. glaberrima reference genome, we conducted a genome-wide association study of four major morphological panicle traits. We have found a total of 41 stable genomic regions that are significantly associated with these traits, of which 13 co-localized with previously identified QTLs in O. sativa populations and 28 were unique for this association panel. Additionally, we found a genomic region of interest on chromosome 3 that was associated with the number of spikelets and primary and secondary branches. Within this region was localized the O. sativa ortholog of the PHYTOCHROME B gene (Oglab_006903/OgPHYB). Haplotype analysis revealed the occurrence of natural sequence variants at the OgPHYB locus associated with panicle architecture variation through modulation of the flowering time phenotype, whereas no equivalent alleles were found in O. sativa. The identification in this study of genomic regions specific to O. glaberrima indicates panicle-related intra-specific genetic variation in this species, increasing our understanding of the underlying molecular processes governing panicle architecture. Identified candidate genes and major haplotypes may facilitate the breeding of new African rice cultivars with preferred panicle traits.African rice ( Gn1a  (LAX PANICLE1 (LAX1)    genes  rice genes were identified to regulate heading date phenotype in rice under short-day and long-day conditions. The Hd1 gene activates rice flowering under short-day conditions by up-regulating the Hd3a gene , with each gene playing a distinct, but partially redundant, role in light-mediated developmental processes, including floral induction  or heading date is another important character known to influence rice yield-related traits in relation to a wide range of environmental cues (8) genes . Two inOryza glaberrima Steud.) is a short-day cereal crop closely related to Asian rice (Oryza sativa L.) and has been cultivated in west Sub-Saharan Africa for ∼ 3,000 years , and panicle morphological traits, providing valuable resources for genetic analyses of agronomic traits in this species. Although several QTLs related to panicle architecture phenotype have been identified, the key genes and major effect alleles underlying panicle-related traits in African rice remain less well documented, and a global view of how genes related to flowering time interact with panicle traits in African rice is still lacking.Given that advances in sequencing technologies have provided useful genomic resources for several rice species, genome-wide association studies (GWAS) have become popular in rice, especially O. glaberrima accessions obtained from a previously genotyped association panel representing a wide geographical range from West Africa to East Africa  from central plants to measure panicle traits, from the early sowing only, over the 2 years. These panicles were photographed and analyzed using the P-TRAP software  of each trait, the model was modified by treating all the variables as random effects. The variance component estimates obtained from each fitted model were utilized to estimate the broad-sense heritability following the methodologies described by O. glaberrima accessions under study, principal component analysis (PCA) was performed on the yearly phenotypic means and the BLUEs using the dudi.pca function from ade4 R package . Filtered reads were mapped to the CG14 reference genome  genome annotation (https://ftp.gramene.org/oryza/release-6/gff3/oryza_glaberrima/) using SnpEff software v5.1 (−6) (We used a set of 162 2r) using the PopLDdecay software (K = 1–10) and 10 replications were used for each K. To further assess the population genetic structure of our panel, PCA was carried out using the -pca command in PLINK version 1.9 (Linkage disequilibrium (LD) between SNPs in the 162 accessions was estimated using the squared correlation coefficient  plots were drawn using the qmplot Python package (2r > 0.6 (O. glaberrima acc. CG14 reference genome (version OglaRS2).We conducted GWAS to identify genomic regions associated with panicle traits. SNP-trait association analyses were performed using 687,436 high-quality SNP markers while correcting for population structure. The association analyses focused on four panicle traits  with high phenotypic variation and known to contribute to panicle branching biological processes, specifically the number and order of branching. To capture the overall trait structure and variability, GWAS was also conducted using the first three principal components (PC1 to PC3) derived from the BLUEs of all eight panicle traits analyzed in this study. The GWAS was performed for each of these traits as described by s method . Furthe package . Candir2 > 0.6 . AssociP < 10−4 threshold in the GWAS analysis, focusing on regions exhibiting multiple candidate SNP-trait associations in the LD block. Only haplotypes represented by at least five accessions (∼ 3% of the entire population) were considered for further analyses. Two main haplotypes  were identified for each genomic region based on significantly differential phenotypic values. The polymorphic sites including both SNPs and InDels shared by two major haplotypes across all the accessions were assessed. The phenotypic values of different haplotypes were compared using a Welsh t-test (P ≤ 0.05), allowing unequal variances between haplotypes. The haplotypes of identified candidate genes were defined using all the SNPs and InDels present within the gene after removing the variants with MAF < 0.05 and missing data > 0.2.Haplotype analysis was performed for genomic regions and candidate genes using the Pegas R package . HaplotO. glaberrima accessions across 2 years. All the traits, except for RL, PBL, and PBintL, showed a CV higher than 20%. The highest phenotypic variation was observed for SBN (CV = 48.41%) followed by SpN (CV = 27.07%), SBintL (CV = 25.45%), and PBN (CV = 20.97). The panicle traits, namely SpN, PBN, PBintL, RL, and SBL, demonstrated moderate to relatively high broad-sense heritability scores ranging from 0.53 to 0.66  of the traits measured can be found in  to 0.66 , highligR = 0.84, R = 0.65). Overall, traits related to length, such as SBL, PBintL, and SBintL, showed negative relationships with traits related to number, such as SpN, PBN , which employs a Bayesian model-based method for clustering. Ancestral populations were identified by varying levels of K means from 2 to 10 groups . With K is point . Hence, ectively . Taken t2r = 0.2 at ∼ 350 kb (O. glaberrima (2r = 0.2 at a distance between SNP markers varying from ∼ 92 kb (Chr2) to ∼ 600 kb (Chr3). Strong patterns of LD were detected for chromosomes 6 and 10, with 2r = 0.2 for up to 1,000 kb. The very high extent of LD observed for some chromosomes could result in the inclusion of a large number of candidate genes within LD blocks which can complicate the search of candidate genes from significant peaks. Thus, we employed local LD decay (2r = 0.6) around significant peaks to define genomic regions for candidate gene disclosure.Within our association panel, the average genome-wide LD decays to ∼ 350 kb , which iaberrima . The LD O. glaberrima accessions, we employed the set of 687,436 high-quality SNPs (MAF > 0.05 with no missing data) uniformly distributed across the 12 O. glaberrima chromosomes. The phenotypic values of the evaluated traits were previously checked for normality and were transformed  between 1,302 SNPs and 1,140 associations  between 1,132 SNP markers across all 12 chromosomes for LFMM  for LFMM and FarmCPU, respectively, with 210 associations overlapping between the two methods  and consists of three genomic regions . Our study identified several candidate genes that mapped within or flanking GWAS regions displaying a relationship to panicle development and architecture. Moreover, by using significant markers, we identified haplotypes for the 28 genomic regions that encompass at least two significant SNP markers. The two main haplotypes were classified into high-value and low-value haplotypes based on their effects on panicle traits tested here. These analyses were focused on genomic regions that co-localized candidate genes related to panicle architecture, although consistent results were obtained for other regions  (O. sativa PHYTOCHROME B gene (Oglab_006903/OgPHYB). The rice PHYB gene was shown to be involved in day-length-dependent flowering time regulation  gene , the region in question co-localized with three candidate genes associated with the development of the panicle . A clust(OsSET1) . This gein shape , with foin shape . When loin shape . We evalin shape . Of thes regions .2r > 0.6), and contained seven LD blocks (10(P) = 4.84) was mapped in the coding region of the Oglab_006320/OgBRR1 gene orthologous to BLAST RESISTANT RELATED 1 (OsBRR1), which encodes a leucine-rich repeat receptor-like kinase  that play an important role in meristem activities and in the development of male organs (Oglab_006323/OgGS3.1 gene orthologous to O. sativa GRAIN SIZE3.1 (GS3.1) encoding a MATE (multidrug and toxic compounds extrusion) transporter, regulating grain size and flavonoid and lignin biosynthesis (n > 5 accessions), designated as H1 and H2 (P ≤ 0.01) than those carrying the H2 haplotype  encompassing 40 significant SNPs associated with the RL trait. The candidate region was mapped from 6.40 Mb to 6.50 Mb based on pairwise LD correlations  gene. The O. sativa OsLAC19 regulates lignin biosynthesis involved in plant development and stress responses in rice  was mapped from 15.072 Mb to 15.418 Mb (346.49 kb) with a cluster of signals with strong LD values . This re in rice , of whic in rice . AccessiC19 gene .10(P) = 5.74), which was mapped in the coding region of Oglab_018111 orthologous to the O. sativa LOC_Os06g03390 gene. The LOC_Os06g03390 gene was annotated as a homolog to a NUCLEOTIDE BINDING SITE–LEUCINE-RICH REPEAT (NBS-LRR) gene (10(P) = 4.82) on chromosome 5 mapped closely to various hypothetical genes, none of which is known to be related to panicle architecture.In addition to these candidate genes, we noted that some strong GWAS signals detected in this study were found within or in close proximity to genes whose functions in panicle development are not yet known. This is the case for the most significant SNP for the PBN trait localized in the qPBN6_SpN6 region on chromosome 6  these genes. Regarding OgNRL2, no H1/H3 polymorphic sites were identified in the vicinity of this gene, suggesting that it is not of significance within this genomic region in relation to panicle architecture. The OgSET1 gene was characterized by two non-synonymous SNPs  shared by haplotypes H1 and H3. Concerning the OgPHYB gene, only a C/A mutation located in exon-2  of this gene was detected in the two haplotypes, with H3 accessions carrying the reference allele C (i.e. that of the CG14 accession) and H1 accessions carrying the alternate A allele , as for O. glaberrima haplotype H1  leading to high impact at protein level for two annotated genes of unknown function . In addiA allele . Sequencn domain  known tootype H1 .OgPHYB and OgSET1 gene polymorphic sites suggested functionally distinct alleles in this association panel, we investigated in more detail the haplotype structure of these genes based on SNPs and InDels detected for the whole O. glaberrima population. A total of 11 SNPs/InDels were detected for the OgSET1 gene in the full O. glaberrima panel, with three variants leading to non-synonymous changes at protein level. Haplotype analysis identified eight haplotypes, with three main haplotypes (i.e. representing more than five accessions) covering 97% of the full population , namely cytOgPHYB for haplotypes with a C allele (e.g. Hb and Hc) and adeOgPHYB for haplotypes with A allele (e.g. Ha). These haplogroups correspond to the two aforementioned haplotypes H1 and H3 from the overlapping candidate regions qPBN3, qSpN3, and qSBN3.2, with H1 accessions carrying adeOgPHYB allele and H3 accessions carrying adeOgPHYB allele. The haplotypes Ha and Hc were distinguished by the C/A mutation at position 10,465,781. The haplotypes Hb and Hc carry both the A allele at position 10,465,781 but possess different alleles in the other five polymorphic sites of the coding sequence. Analyses indicated that 54% of the accessions carry the cytOgPHYB allele, while 46% of the accessions carry the adeOgPHYB allele, indicating that this mutation is present at a high frequency in this O. glaberrima panel.A total of 25 SNPs/InDels were detected for the in level . Ten hapin level . The hapPHYB gene and the co-location of the GWAS site for flowering time, we tested the association of OgPHYB haplotypes with flowering time and observed that the cytOgPHYB alleles were associated with earlier flowering compared to the adeOgPHYB alleles in both early and late sowing conditions  revealed six main haplotypes, of which none showed evidence for polymorphism at the site corresponding to the cytOgPHYB and adeOgPHYB alleles (O. glaberrima (or African rice species) or was filtered out in the 3 K Rice Genome datasets.An examination of  alleles . This woO. glaberrima) over a two-year period. High variability and heritability were observed for four major traits , with strong correlations detected among them, except for the SBN and RL traits, for which the pairwise relationship was not significant. Compared to the other traits measured here, the PBN and SpN traits contributed most to panicle diversity in this panel. Variation of the spikelet number per panicle in O. glaberrima relies most on variation of primary branch number per panicle. As indicated in this study, these traits are more under genetic than environmental influence in this panel. Similar phenotypic relationships were reported in various diversity panels for the Asian rice O. sativa  compared to that of O. sativa , and to a lesser extent by the different SNP datasets and methodology used to delineate genomic regions. The present study benefits from SNP/InDel datasets obtained through mapping to the high-quality O. glaberrima acc. CG14 (version OglaRS2) reference genome  this gene was mapped in an LD block with several association peaks for the PBN, SBN, and SpN traits; (ii) several significant SNPs of this region were found in close proximity to significant SNPs associated with flowering time; and (iii) when comparing the polymorphisms shared by the two major haplotypes (H1 and H3) of this genomic region, one mutation located in exon-2 of OgPHYB  was found to alter the protein sequence in the HisKA domain involved in signal transduction. GWAS peaks associated with heading date phenotype were previously detected in several O. sativa populations. OsPHYB was found to be localized near a GWAS signal for photoperiod sensitivity in an O. sativa population  through the Hd1 flowering gene  phytochromes, mainly PHYA and PHYB, compete with the OsGI-Ghd7 complex to stabilize the Ghd7 protein, thus delaying flowering  of phytochrome. The OPM mediates dimerization and signal transmission to the downstream effectors, through differential interactions with other proteins necessary for nuclear localization and interactions with several nuclear proteins, including transcription factors . The adeOgPHYB allele is equivalent to the OsPHYB gene in O. sativa cv. Nipponbare, while cyOgPHYBt allele showed no equivalent in the 3 K O. sativa genomes panel. cytOgPHYB-induced early flowering could mirror the loss of function mutant of PHYB in O. sativa -like genes by suppressing auxin signaling  are plastic traits prone to mutual compensation under competition for pre-floral assimilate resources in O. glaberrima revealed that variation in primary branch number per panicle is the main contributor to spikelet number variation. Several GWAS loci related to panicle architecture diversity were identified in O. glaberrima, notably a genomic region of interest on chromosome 3 with three overlapping regions affecting numbers of spikelets and primary and secondary branches. We hypothesize that OgPHYB is the strongest candidate gene for this genomic region, supporting the concept of a functional relationship between flowering time and panicle architecture, which could reflect adaptation to local ecosystems. Nevertheless, single gene variations do not in general explain fully the genetic basis of rice cultivar adaptation to different ecological regions. Thus, it will be of interest to analyze the geographical distribution of haplotype combinations of panicle and flowering time-related genes. Moreover, most of the genomic regions identified were specific to O. glaberrima, rather than being co-localized with the QTLs reported in the Asian rice O. sativa, suggesting that intra-specific variation in the African rice species for panicle architecture might rely on specific factors and that O. glaberrima may harbor a unique diversity. To apply the findings of this study for rice crop improvement, it is crucial to conduct further characterization and validation of functionally significant polymorphisms. This will not only deepen our understanding of the genetic elements that underlie the diversity and local adaptation of panicle architecture, but also contribute to enhancing yield potential. By harnessing this knowledge, future rice breeding programs can strategically target and improve specific panicle traits, leading to overall improvements in crop productivity in this species. Additionally, the observed phenotypic relationships between panicle traits and flowering time highlight the potential for further investigations using a multi-trait GWAS model. Such an approach would provide a comprehensive analysis of the genetic associations between these traits and flowering time, revealing the intricate relationships between them.In this study, a detailed phenotypic analysis of panicle architecture in a diversity panel of jkad174_Supplementary_DataClick here for additional data file.


Canadienne cattle are the oldest breed of dairy cattle in North America. The Canadienne breed originates from cattle that were brought to America by the mid-seventeenth century French settlers. The herd book was established in 1886 and the current breed characteristics include dark coat color, small size compared to the modern Holstein breed, and overall rusticity shaped by the harsh environmental conditions that were prevalent during the settlement of North America. The Canadienne breed is an invaluable genetic resource due to its high resilience, longevity and fertility. However, it is heavily threatened with a current herd limited to an estimated 1200 registered animals, of which less than 300 are fullblood. To date, no effort has been made to document the genetic pool of this heritage breed in order to preserve it.In this project, we used genomic data, which allow a precise description of the genetic makeup of a population, to provide valuable information on the genetic diversity of this heritage breed and suggest management options for its long-term viability. Using a panel that includes 640,000 single nucleotide polymorphisms (SNPs), we genotyped 190 animals grouped into six purity ranges. Unsupervised clustering analyses revealed three genetically distinct groups among those with the higher levels of purity. The observed heterozygosity was higher than expected even in the 100% purebreds. Comparison with Holstein genotypes showed significantly shorter runs of homozygosity for the Canadienne breed, which was unexpected due to the high inbreeding value calculated from pedigree data.Overall, our data indicate that the fullblood gene pool of the Canadienne breed is more diversified than expected and that bloodline management could promote breed sustainability. In its current state, the Canadienne is not a dead-end breed but remains highly vulnerable due to its small population size. In livestock management, genetic diversity is an important element to consider. To provide options for long-term breed improvement or the selection of animals for specific immediate purposes, diversity must be maintained by careful planning. Traditionally driven by profit margins, today dairy cattle breeding takes consumer expectations regarding product quality or safety and animal wellbeing increasingly into account. This often introduces new selection goals that challenge current genetic lineages, for example, adaptability to climate change, lower dependency on antibiotic usage, lower feed consumption  and decreasing the overall environmental footprint of animal husbandry. Achieving such goals involves major changes to practices and to the environmental conditions to which the animals are subjected. Tolerating wider ranges of temperature, more variable diets, emerging pathogens and different husbandry methods will all require genetic adaptation. Heritage dairy breeds that have fallen into disuse due to their lower milk yield or productivity may be pressed back into service, since they often exhibit higher resilience and adaptive capacity. The Canadienne is one of these heritage cattle breeds. It is known for its high fertility and its remarkable calving ability . CompareThe origins of this breed date back to the first French settlements in North America. The first cattle were brought from the Bretagne and Normandy regions of France in the mid-seventeenth century. They were multi-purpose, providing milk and meat and pulling carts and ploughs. In the seventeenth and eighteenth century Canada, cattle had to be robust, autonomous and capable of surviving neglect . The Canadienne cattle developed high strength, strong resistance to the cold and to disease, and thrived on whatever it was given or could find in the fields. Up to the end of the nineteenth century, the Canadienne was most representative of the dairy cattle in New France and later in the Canadian province of Quebec.The breed was officially recognized in 1886 and then followed a development through selective breeding mainly to improve milk production. However, European breeds soon began to gradually replace Canadienne cattle, and by the 1970s, only about 5000 registered Canadienne individuals remained. In an attempt to revitalize the breed, it was crossed with the Brown Swiss to improve milk production, stature and frame. In more recent years, Canadienne individuals have been mostly crossbred with Jersey to preserve a smaller stature and frame compared to other dairy breeds as well as to maintain their uniform coat color. Current herdbook registration lists animals that were never crossed and identifies them as “originals” and about 300 such full-bloods are alive today. Based on pedigree data, the estimated effective size of the Canadienne population dropped down to 43 in 2013 [The hypothesis of our research project stipulates that the use of genomics will enable to properly describe the Canadienne genetic pool to offer long-term management options. The aims of the project are to describe the genetic pool of the Canadienne cow using high-density genotyping and to use the information to determine if the low effective size has pushed the breed to a genetic dead-end. Current genotyping platforms targeting large panels of known single nucleotide polymorphisms (SNPs) allow an efficient description and comparison of mammalian genomes. In this work, we used genotypes to generate diversity metrics to compare groups of animals with different percentages of Canadienne blood. The analyses included evaluation of its population structure and genomic inbreeding and comparisons with other bovine breeds and populations.Pedigrees and the Canadienne Cattle Association and Society were consulted to obtain a representative sample of the population and different levels of genomic diversity and purity. The Canadienne population was sampled in 2016 from 10 herds located across the Province of Quebec. A small herd maintained in France was also sampled and a bull living in the USA was included. The founders of these foreign animals were imported from Quebec in the 1990s. DNA was extracted from tail hair follicles. Cryopreserved semen from ancestral bulls that lived from 1950 to 1980 were obtained from an insemination center  and were also genotyped. Based on breed purity, six genomic groups were formed  were obtained from previous studies , 7. Bos us study  were inc6 cells) were mixed gently with one mL of phosphate-buffered saline (pH 7.4) in a 1.5-mL microcentrifuge tube, and then centrifuged at 15,000g for five min. The supernatant was discarded, and the sperm pellet (100 µL) was mixed with 100 µL of X2 buffer  and 12.5 µL of the kit proteinase K solution. The suspension was held overnight (12–16 h) at 56 °C in a hybrization incubator  with rotation set at 12 rpm, then mixed with 200 µL of buffer AL (QIAgen) and 200 µL of ethanol (96–100%), loaded onto the spin column (QIAgen) and centrifuged for one min at > 6000 g. Column-bound DNA was washed and eluted according to the manufacturer’s instructions.Among the 190 Canadienne DNA samples, 62 were extracted from cryopreserved semen and 128 from tail hair. All the samples were stored at − 20 °C until analysis. Two samples were analyzed in duplicate for SNP genotyping quality control. Genomic DNA was extracted from bovine spermatozoa using the DNeasy blood and tissue kit  and following the user-developed protocol 2 for purification of total DNA from sperm. Briefly, the entire contents of one semen straw or vial . Briefly, 20 to 30 tail hair follicles with 0.5–1.0 cm of bristle were placed in a 1.5-mL microtube. A lysis buffer containing 300 µL of ATL buffer, 20 µL of proteinase K and 20 µL of 1 M dithiothreitol (QIAgen) was added and the contents were mixed by pulse-vortexing, held overnight at 56 °C in the hybridization incubator set at 20 rpm, treated with RNase A  for 2 min at room temperature then mixed with 300 µL of AL buffer (QIAgen) and 300 µL of ethanol (96–100%), loaded onto a spin column (QIAgen) and centrifuged for 1 min at > 6000Genomic DNA quality was assessed by electrophoresis on 1% agarose gels and staining with ethidium bromide (1 µg/mL). DNA was quantitated by measuring the absorbance at 260 nm using a NanoDrop1000 .In total, 192 Canadienne samples  were genotyped using the Affymetrix/ThermoFisher Axiom BOS1 platform targeting 648,874 SNPs. Canadian Holstein genotypes were acquired within the context of a different project on the Illumina Bovine SNP50 v3 platform targeting 53,218 SNPs. All sample hybridizations were performed at the Centre d’expertise et de services Génome Québec .Quality control and SNP filtering were carried out using the Axiom Analysis Suite. SNPs with a call rate lower than 97% across all Canadienne samples were excluded. Monomorphic SNPs and those with a minor allele frequency lower than 0.05 were also excluded because due to their very low statistical power, they are not useful for analyses comprising only the Canadienne breed. Significant departure from the Hardy–Weinberg equilibrium (HWE) was also considered since it can indicate potential genotyping errors. Thus, SNPs with a HWE FDR/Bonferroni corrected p-value lower than 1e-3 were filtered out. Only autosomal SNPs with a DishQC score ≥ 0.82 and a call rate ≥ 97% were retained for further analysis.Bos taurus). The same procedure, i.e. positioning all SNPs across all datasets on the same reference genome (UMD 3.1 Bos taurus) was performed to identify the SNPs that were common to all datasets for the across-breed and population comparisons. All common SNPs were kept and were not filtered for allelic frequencies. Allele flipping between both genotyping platforms was checked and for none of the SNPs was the reference allele considered as the alternate allele in the other platform. About half of the SNPs were found to be detected on opposite strands  but this does not impact the analyses since genotypes do not report the nucleotides but the status of both alleles, i.e. AA, AB or BB.Since the Thermo Fisher-Affymetrix/and Illumina platforms were both developed using the SNP catalog generated from the bovine genome project, there is great commonality between the two panels. For the initial comparison of the Canadienne with the Canadian Holstein breed, a common set of SNPs shared by both genotyping platforms was identified by positioning targeted SNPs from both platforms on the same reference genome (UMD 3.1 O) was evaluated using the—het option in VCFtools v0.1.11 and genome-wide SNP data. Genomic inbreeding (FROH) was evaluated using runs of homozygosity (ROH) with the—lroh option and expressed as the FROH statistic, that is, the ratio of the total ROH length (LROH) to the total autosomal genome length covered by the SNPs (LGENOME) [Observed heterozygosity (HLGENOME) :\documene) was estimated using the SNP excess heterozygosity method implemented by the NeEstimator program [O was calculated to match the number of “original” Canadienne individuals.The genomic effective population size (N program . The perST statistic) using the—weir-fst-pop option. Individuals were grouped according to breed purity for comparison. To assess the genomic variation among the Canadienne populations, an unsupervised clustering using a discriminant analysis of principal components (DAPC) [The VCFtools v0.1.11 program was used to compute the fixation index (Fs (DAPC) .To assess genomic variation among the 12 breeds/populations considered, principal component analysis was performed using the R function snpgdsPCA in the SNPRelate package . Sample O, FROH and ROH length classes using Duncan’s test in the agricolae R package [Significant differences between the purity group means were estimated for H package  with theFor the Canadienne samples genotyped on the high-density panel (640K), 634,116 high-quality SNPs for 190 animals remained after filtering. For the 192 Holsteins genotyped on the medium-density panel (50K), 48,725 acceptable SNPs were obtained and 47,573 remained after selecting the common target loci between both SNP panels. When public data from the nine other breeds/populations were considered, 47,175 high-quality SNPs were found across all datasets.O whereas moderate-purity individuals had intermediate values  estimated for each animal ranged from 0 to 0.197. As expected, crossbred animals had very low inbreeding values , from 0 to 0.620 for the Canadian Holstein, and from 0 to 1 for the “original” Canadienne individuals  than for the Canadian Holstein individuals.The per site HBos taurus breeds at the opposite end of Bos indicus cattle breeds. On the PC2 axis, Canadienne cluster away from the other breeds. On PC2, both Canadian and French Holsteins are further away from the other assessed breeds. The closest cluster is Jersey, a breed that has recently been crossed with Canadienne. The Canadienne clustering is not as tightly centered as many other purebred gene pools indicative of more diversity.Unsupervised clustering using genome-wide SNP data showed inter-population diversity and the genetic relationships across cattle breeds. The first and second principal components (PC1 and PC2) are plotted in Fig. The goal of this project was to describe the genetic makeup of the Canadienne bovine breed based on genomics. This breed has declined from 300,000 individuals in the early 1900s to about 10,000 in 1983 and then to 1000 in 2012 with less than 300 full blooded animals , 16. TheOur objective was to use genomics to estimate genetic diversity and to determine if the inbreeding coefficients based on pedigree data would increase when the population was segmented according to purity class. To maintain the “original '' status, an animal must be the offspring of a dam and sire that are also “original”. The breed currently relies on artificial insemination limited to about 15 sires of which only two are purebred “originals”. To maintain this status, producers can raise their own bulls or use cryopreserved semen collected prior to 1980. With such constraints on sires, it was expected that the inbreeding coefficient would be higher for “originals'' than for the breed. However, the genetic diversity of this purity class was comparable to that of classes down to 7/8 Canadienne. This was inferred from the extent of heterozygous genotypes and the lengths of homozygous genomic stretches. At 6.8%, the calculated genomic inbreeding coefficient is lower than the reported 9.65% . By compAlthough both pedigree- and genomic-based values are used to estimate the probability of receiving identical alleles from common ancestors, SNP analysis and pedigrees differ considerably. Pedigrees are subject to human error such as misidentification of the sire  and therRuns of homozygosity are caused either by a high degree of relatedness or intensive selection for specific traits. Studies have shown that the proportion of ROH in the genome is a good proxy for autozygosity, i.e. superior to pedigree data , 21, andFor all dairy breeds, inbreeding coefficients tend to increase due to limited numbers of sires and shorter generation intervals . Genealoe) is considered critical when it drops below 50 individuals [e when the number of progenitors is small. Intense selective pressure can lead to the use of a small number of elite bull progenitors, especially when artificial insemination is highly accessible, and the current objectives to respond to consumer preferences and market trends can limit the number of sires that actually contribute to the next generation. Thus, generations of top-rated bulls may end up sharing ancestry [e rose to 114 through diversification of sire provenance and usage.For breeding purposes, the effective population size (Nividuals . Even laancestry . The effancestry . During e based on pedigree data were equal to 54, 47, 46, 66 and 40, for Ayrshire, Brown Swiss, Guernsey, Milking Shorthorn and Canadienne, respectively [e averaging 51 and 75, respectively, across estimation methods [e of the Canadienne was below 26.3 for purity classes > 3/4 and 12.8 for originals. This is alarming considering that the Canadienne gene pool does not exist in any other bovine population. One potential solution would be to increase the number of bulls available for artificial insemination, since currently there are only two purebred bulls at a local insemination center. Cryopreserved semen and embryos from genetic lineages less represented in the current herd could be used to generate new sire lines.Estimates of Nectively . Based o methods . In our e.Analysis of the population structure showed that there are three Canadienne lineages enriched with high-purity animals. Genetic clusters are expected when different breeds are compared, but such genetic groups can also occur within breeds when high-density genomic markers are used, as seen in parentage assignment and pedigree reconstruction . Pedigree is a considerable challenge. The solution may reside in the uniqueness of the gene pool of the Canadienne breed, since comparative genomic analysis has shown that it clusters independently of the other breeds. This could justify not only its conservation but also further analyses that may reveal unique and important traits for producing milk in different settings.Regardless of genetic management, the long-term survival of any gene pool depends on a sustainable and increased use of the breed. Yearly milk production is currently lower for Canadienne cows than for any other dairy breed in Canada  and imprIn our study, the Holstein individuals were also genetically isolated, which is probably due to the extensive selection programs for dairy production traits and conformity. Holstein cows are known to produce large volumes of milk but also to suffer from fragile health and lower fertility , which cSince the Canadienne breed was developed mainly in Quebec, it is regarded as cultural patrimony, and in 1999, the provincial government claimed the designation “Québec heritage breed” . Given tBased on the use of genomic data, we demonstrate that the current effective population size of the Canadienne breed is alarming due to the decline in its popularity during the last century. The need for a wider selection of sires is urgent. In spite of the limited population and the constraining registration criteria for “original” status, its genetic diversity remains acceptable, and signs of recent inbreeding events are fewer than those observed in large population breeds under intense commercial selection pressure. To date, genomic-based selection programs have not been attempted to improve heritage breeds, and are not likely to be used because of the difficulties to establish a reference population necessary for genomic prediction and the small numbers of individuals in such populations . However


Bacterial Healthcare-Associated Infections (HAIs) are a major threat worldwide, which can be counteracted by establishing effective infection control measures, guided by constant surveillance and timely epidemiological investigations. Genomics is crucial in modern epidemiology but lacks standard methods and user-friendly software, accessible to users without a strong bioinformatics proficiency. To overcome these issues we developed P-DOR, a novel tool for rapid bacterial outbreak characterization. P-DOR accepts genome assemblies as input, it automatically selects a background of publicly available genomes using k-mer distances and adds it to the analysis dataset before inferring a Single-Nucleotide Polymorphism (SNP)-based phylogeny. Epidemiological clusters are identified considering the phylogenetic tree topology and SNP distances. By analyzing the SNP-distance distribution, the user can gauge the correct threshold. Patient metadata can be inputted as well, to provide a spatio-temporal representation of the outbreak. The entire pipeline is fast and scalable and can be also run on low-end computers.https://github.com/SteMIDIfactory/P-DOR under the GPL-3.0 license.P-DOR is implemented in Python3 and R and can be installed using conda environments. It is available from GitHub  Bacterial infections are a constant threat to public health worldwide. When dealing with Healthcare-Associated Infections (HAIs) and outbreaks, timely epidemiological investigation is pivotal to establish effective infection control measures  or k-mers. When reconstructing outbreaks, strains isolated from different sources  and having SNP-distances below specific thresholds can be considered part of the same transmission cluster. The network of these genetically correlated strains can be used to reconstruct the pathogen transmission route. Although threshold-based methods are largely applied in genomic epidemiology  a folder containing the query genome assemblies; (ii) a reference genome for SNP extraction; (iii) a sketch database file in the Mash format  are available in the P-DOR repository. Personalized SD sketches can alternatively be generated by the user using the “makepdordb.py” script. This script can automatically download the high-quality genomes of a species from the BV-BRC collection (n most similar SD genomes are selected and joined in a Background Dataset (BD). Lastly, the query genomes are joined with the BD to obtain the Analysis Dataset (AD). Optionally, the entire AD can be scanned for the presence of antimicrobial resistance and virulence genes using AMRFinderPlus (Sketch files contain the genomic information of the strains from a Source Dataset (SD) chosen by the user. A sketch file is a vastly reduced representation of the genomes, which is produced via the After that, each genome of the resulting AD is aligned to the reference genome using Nucmer, the alignment obtained is polished using “Delta-Filter” and SNPs are called using “show-snps”, all the commands being part of the Mummer4 package  a SNP-based phylogenetic tree; (ii) a heatmap reporting the phylogenetic tree and presence/absence of resistance and virulence factors; (iii) a heatmap showing the coreSNP distance matrix; (iv) the histogram of the distribution of the SNP distances; and (v) a graph visualization of the epidemiological clusters, in which each pair of strains (nodes) is connected if the coreSNP distance between them is below the threshold. The epidemiological clusters are also highlighted on the phylogenetic tree. In addition, if patients metadata are provided, P-DOR creates a spatio-temporal representation of the outbreak and outputs a patients timeline plot where strains are placed based on the date of isolation and are connected on the basis of epidemic clusters.K.pneumoniae isolates, involved in a complex epidemic event, with 2 distinct bacterial strains (both belonging to Sequence Type 258) circulating in a hospital in the same period. In detail, we obtained six simulated sequences starting from genome NJST258_1 and 5 sequences from genome NJST258_2 using the software simuG .We separately tested the performance and accuracy of our SNP-calling approach, which is based on the Mummer4 package . PhylogeTo test the pipeline performance on a large-scale dataset, the P-DOR analysis on the simulated dataset was repeated using 1000 nearest genomes for each genome in the input folder. The AD was composed of 1388 total genomes and the analysis took 7 h and 50 min to complete, using a peak of 4.49 Gb of RAM on 20 threads . These results show that P-DOR is a fast tool, which can be used in clinical contexts even when high informatic skills or resources are not available. Future efforts will be focused on developing a web-based interface, to further improve the ease of use and removing the need for computational resources.btad571_Supplementary_DataClick here for additional data file.


Flavonoids, secondary plant metabolites with many health-promoting properties, including antioxidant, are a valuable component of food products, especially functional foods. In the latter, plant extracts are commonly used, the properties of which are attributed to the characteristic main ingredients. However, in a mixture the antioxidant properties of the individual ingredients do not always show an additive effect. This paper presents and discusses the antioxidant properties of naturally occurring flavonoid aglycones and their binary mixtures. In the experiments, model systems were used that differed in the volume of the alcoholic antioxidant solution in the measuring system and its concentration in the range in which it occurs in nature. Antioxidant properties were determined by ABTS and DPPH methods. The presented data proved that the dominant resultant effect in the mixtures is antioxidant antagonism. The magnitude of the observed antagonism depends on the mutual relations of individual components, their concentrations and the method used to assess antioxidant properties. It was shown that the observed non-additive antioxidant effect of the mixture results from the formation of intramolecular hydrogen bonds between phenolic groups of the antioxidant molecule. The presented results may be useful in the context of proper design of functional foods. The recently observed increase in awareness of food’s impact on human health means that more and more importance is attached to a well-considered choice of consumed food products. This is the result of the warnings of doctors who, in their daily practice, see the relationship between an improper diet and the occurrence of civilization diseases and aging at the molecular level, as well as deliberate education of the society in recognizing the nutritional value of individual food products ,2. With When designing functional food, special attention is paid to the use of natural ingredients with antioxidant properties . These cPlant extracts, especially those rich in polyphenolic compounds, are the source of natural antioxidants commonly used in the creation of functional foods. Myricetin, quercetin and kaempferol are representatives of one of their subgroups, i.e., flavonols, which, due to their high prevalence in the plant kingdom, are an important component of a healthy diet ,9,10,11.Phenolic compounds exhibit various antioxidant properties including preventing the formation of reactive species, neutralizing (scavenging) free radicals, forming chelate complexes with pro-oxidizing metals and removing or repairing damage caused by reactive compounds . In mixtThere are various reasons for the antagonistic (synergistic) effects of antioxidants in mixtures. The most commonly reported is the regeneration of a less effective (more effective) antioxidant by a more (less) effective antioxidant ,16,17; tIn the literature, one can find many articles on the study of the antioxidant properties of phenolic compounds. There are also papers on synergistic and antagonistic effects of antioxidant activity in mixtures of compounds, including flavonoids . Neverth●+ and DPPH●) were used. These methods seem to be the most popular and willingly used due to the simplicity of measurement, short time of conducting research and the use of an inexpensive and widely available spectrophotometer. It should be added that despite the identical methodology for measuring antioxidant properties, these tests are not the same. The model DPPH and ABTS radicals differ structurally, which may differentiate the structure–activity relationship of the antioxidant in both methods [The literature emphasizes that the observed resultant antioxidant effect of the mixture depends on the type of antioxidant or the research method used. Antioxidant activity is typically determined for a particular concentration ratio of selected antioxidants. The impact of changing the concentration ratio on the observed effect of the resultant action of the mixture is rarely determined. Therefore, the research presented in this paper fills this gap by discussing the concentration-dependent formation of intramolecular hydrogen bonds and their effect on the resultant antioxidant activity of the mixture. Myricetin, quercetin and kaempferol, i.e., “healthy flavonoid compounds” that are aglycone forms of flavonols ubiquitous in nature and assimilable by the human body, were selected for the study. The experiments took into account the effect of both different concentrations of antioxidants in the range in which these compounds are typically found in plants and different volume ratios of components in the measurement system. Two spectrophotometric methods for measuring color radicals (ABTS methods .Point “a” refers to the inhibition percent in the measuring system containing 20 μL of myricetin methanolic solution and 80 μL of MeOH in a 100 μL sample (system no 1 in Point “b” refers to the inhibition percent in the system containing 80 μL of kaempferol methanolic solution and 20 μL of MeOH in a 100 μL sample (system no 6 in Point “c” refers to the inhibition percent in the system containing 20 μL of myricetin and 80 μL of kaempferol (both dissolved in MeOH) (system no 10);Point “d” is the so-called “theoretical” point representing the inhibition percent expected for the mixture containing 20 μL of myricetin and 80 μL of kaempferol, assuming the additive antioxidant effects. Hence, the value at point “d” was calculated by summing up the inhibition percent obtained for the sample contained 20 μL of myricetin with 80 μL of MeOH  and 80 μL of kaempferol with 20 μL of MeOH .For a better understanding of the presented data, for the pair of antioxidants myricetin–kaempferol with a concentration of 0.1 mmol/L in the ABTS method see  and a coe” is the inhibition percent of the binary mixture determined experimentally, and “Ic” is the calculated inhibition percent.The results of the statistical analysis of differences between the experimentally determined value of the antioxidant activity of a given pair of flavonoids and the value calculated as a result of summing up the activity of individual compounds forming a given pair for different volume ratios of methanolic antioxidant solutions at the three concentration levels are presented in As can be seen from the plots presented in As is known from the literature , the greIn the case of polyphenolic compounds, not only the number of hydroxyl groups differentiates the antioxidant properties of individual compounds. Their location should also be taken into account. Flavonoids show better radical scavenging and/or antioxidant activity when their molecules contain hydroxyl substituents in the ortho position of the B ring . Hence, As shown in The participation of intramolecular hydrogen bonds in the stabilization of the phenoxyl radical is equally likely for myricetin in the myricetin/kaempferol system and for quercetin in the kaempferol/quercetin system. Nevertheless, these compounds differ in their ability to form intramolecular hydrogen bonds. In addition, the differences in the antioxidant activity of the compounds in the indicated pairs may be significant enough to mask the stabilizing effect of the emerging intramolecular hydrogen interactions. The validity of this conclusion seems to be confirmed by the data obtained by the DPPH method for the pair myricetin/quercetin at the lowest concentration levels. According to these, as a reminder, myricetin has a lower antioxidant effect than quercetin. This observation may also be explained by the lower accuracy of the DPPH method. In addition, and more importantly, the unpaired electron on the DPPH radical is less available to myricetin (due to its structure), resulting in less reactivity of this compound, similar to the formation of intramolecular hydrogen bonds, and finally worsening the antioxidant effect ,24.As regards the comparison of the experimentally estimated antioxidant properties of binary mixtures of antioxidants with their properties calculated from the data obtained for individual components (these data in the plots are shown respectively by a solid line with "x" and a dashed line with triangles), which is important for this study, at first glance it seems that the courses of the experimental and calculated curves are similar. This fact suggests the additive antioxidant effect. However, the statistical analysis of the obtained results revealing the significance of the difference , which leads to a weakening of its antioxidant effect.The non-additive antioxidant effect of ingredients in mixtures of various antioxidants is the subject of many papers ,26,27. T•+/DPPH• radical neutralization process is based on the single electron transfer (SET) mechanism accompanied by the proton detachment from the phenolic -OH group, in general, the stronger the hydrogen bond, the weaker the antioxidant activity of the hydrogen atom/proton-donating group in the antioxidant molecule and the lower the resultant effect of the mixture.The results presented in this paper, showing comparable or lower effects of the myricetin solution (a stronger antioxidant) compared to the quercetin solution (a weaker antioxidant) and antagonism in their mixture, proved the participation of intramolecular hydrogen bonds formed between the hydroxyl groups of the antioxidant molecule in reducing the mixture’s antioxidant activity, as suggested in the literature ,33. In oKaempferol, myricetin, quercetin, 2,2′-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) and 2,2′-diphenylpicrylhydrazyl (DPPH) were purchased from Sigma Aldrich . Methanol (MeOH) and potassium persulfate (di-potassium peroxdisulfate) were purchased from the Polish Chemical Plant POCh . Water was purified on a Milli-Q system from Millipore .•+ and DPPH•, were monitored. All measurements were performed using a UV Probe-1800 Spectrophotometer  and an optical glass cuvette (1 cm × 1 cm × 3.5 cm). The studies used methanol solutions of kaempferol, myricetin and quercetin prepared at three concentration levels, i.e., 0.03, 0.1 and 0.2 mmol/L, within the range in which these compounds occur in plants. The volume ratios of antioxidant standard solutions and antioxidant standard solutions in binary mixtures are presented in Measurements of antioxidant properties of the examined antioxidants and their binary mixtures were assessed using two spectrophotometric methods presented below in which changes of the colored radical cations, i.e., ABTS●+) was formed by the reaction of 2,’-azinobis (3-ethylbenzenothiazoline-6-sulfonate) (ABTS) with potassium persulfate after mixing 5 mL of 7 mmol/L ABTS solution with 88 µL of 140 mmol/L potassium persulfate. Then, the mixture was incubated in the dark for 16 h and diluted with methanol until the absorbance value equaled 0.7 ± 0.05 [●+ was mixed in a 4 mL test tube with 100 µL of antioxidant solution or antioxidant binary mixture. The exact compositions of the examined measurement systems are shown in ABTS cation radical absorbance changes were monitored at 744 nm. The ABTS cation radical , which is a measure of the antioxidant properties of the tested antioxidants and their two-component mixtures. The % I values were calculated according to the following equation:• methanolic solution of initial absorbance 0.7 ± 0.05 at 516 nm (2900 μL) was mixed with a methanolic solution (100 µL) of the examined substance or antioxidant binary mixture in a 4 mL test tube  only with the difference that AF) value were used to assess the influence of experimental factors on the activity. If the calculated value of F  exceeds the tabular value F (tabF), this indicates a statistically significant influence of the given parameter. To determine the significance of each Fisher coefficient, the p-values were used. The values were considered to be significantly different when the results of the compared parameters differed at the p = 0.05 significance level. The statistical analysis was performed using Excel (Microsoft Excel 2010).All experimental data are presented as mean values of five independent measurements ± standard deviation (SD). The one-way analysis of variance (ANOVA) and Fisher coefficient  and their binary mixtures, proving the following:The latter conclusion complements and extends the knowledge about the non-additive antioxidant properties of compounds in mixtures. In order to correctly predict the antioxidant behavior of a mixture of natural polyphenols, it is necessary to take into account the impact of their interactions. The more so, as it has been shown, as confirmed by literature data , that onDetailed knowledge of the antagonistic, synergistic and additive antioxidant effects of various antioxidant blends can be helpful in the proper design of functional foods and supplements. Nevertheless, given the complexity of the issue, further experiments are needed to expand our understanding of the antioxidant activity of mixtures.


Genomic inbreeding coefficients of each SNP panel were compared with genomic inbreeding coefficients derived from the 84,445 imputation SNP. Coefficients of the HD SNP panels were consistent between genotyped-imputed SNP (Pearson correlations ~99%), while variability across SNP panels and estimators was observed in the MD SNP panels, with Labogena MD providing, on average, more consistent estimates. The robustness of Labogena MD, can be partly explained by the fact that 97.85% of the SNP of this panel is included in the 84,445 SNP selected by ANAFIBJ for routine genomic imputations, while this percentage for the other MD SNP panels varied between 55 and 60%. Runs of homozygosity was the most robust estimator. Genomic inbreeding estimates using imputation SNP are influenced by the SNP number of the SNP panel that are included in the imputed SNP, and performance of genomic inbreeding estimators depends on the imputation.The objective of this study was to evaluate the effect of imputation of single nucleotide polymorphisms (SNP) on the estimation of genomic inbreeding coefficients. Imputed genotypes of 68,127 Italian Holstein dairy cows were analyzed. Cows were initially genotyped with two high density (HD) SNP panels, namely the Illumina Infinium BovineHD BeadChip  and the Genomic Profiler HD-150K , and four medium density (MD): GeneSeek Genomic Profiler 3 , GeneSeek Genomic Profiler 4 , GeneSeek MD  and the Labogena MD . After imputation, all cows had genomic information on 84,445 SNP. Seven genomic inbreeding estimators were tested: (i) four PLINK v1.9 estimators (F, F The evolution in recent years of genotyping technologies enabled a continuous drop in costs and increased availability in the market of various single nucleotide polymorphism (SNP) microarrays (hereafter denoted as SNP panels) for livestock species diverse in quantity (number of SNP) and quality . This promoted advanced genomic tools in animal breeding, but also led many breeding companies to genotype, in time, different groups of animals with diverse SNP panels. Moreover, the combination of overlapping SNP among panels and imputation pipelines allows to further reduce costs. Nowadays, it is a common practice to genotype few core animals with high density (HD) SNP panels (or whole genome sequencing), genotype a high number of animals with low density or medium density  SNP panels and to impute the LD/MD to HD genotypes, hence ending up with a common number of imputed genotypes for all genotyped animals , 2. VeryThe imputation procedures in livestock breeding programs increased the interest to assess the effect of SNP panels in estimating inbreeding coefficients. The objective of this study was to evaluate the effect of imputation of SNP on the estimation of genomic inbreeding coefficients. Thus, we extended a previous work on genomic inbreeding with imputed SNP , aiming P < 0.005).The available dataset contained 95,540 Italian Holstein dairy cows, all registered to the official herd book of the Italian National Association of Holstein, Brown and Jersey Breeders (ANAFIBJ). Cows were born between 1998 and 2020 and genotyped with 30 different SNP panels of varying densities  implemented in PLINK v1.9 [(http://pngu.mgh.harvard.edu/purcell/plink/]. F (flag –het in PLINK v1.9) was proposed by Li and Horvitz (1953) and counts the proportion of homozygous SNP. Fhat1 − 3 were primarily implemented in the GCTA software . The first method (Fgrm) (Z = X – 2(qm – 0.5); where Fgrm = diag(grm) – 1. To alleviate the problem of using observed allele frequencies in Fgrm, a simplified version of VanRaden's 3rd proposed method was used Fgrm2, where we regressed the diagonal of XX′ on pedigree inbreeding coefficients (Fped) to get the mean and the slope and then to obtain XX′, as, for e.g., reported by (Two genomic relationship matrix (grm) – based inbreeding estimators (based on VanRaden's 1d (Fgrm) – 16 was orted by ,roh), where Froh expresses the sum of rohs identified in an individual to the total genome length. We used the consecutive runs method in the R software (v. 3.6.3) package detectRUNS v. 0.9.5  described in (A roh-based estimator (Fv. 0.9.5 –20. To dare v. 3..3 packagare v. 3..3 packagare v. 3..3 packagped) were also estimated in the pedigree R package . This means that the genotyped SNP per panel were 79,900 (Illumina Infinium BovineHD BeadChip), 77,085 (Genomic Profiler HD-150K), 13,870 (GeneSeek Genomic Profiler 3), 16,862 (GeneSeek Genomic Profiler 4), 27,331 (GeneSeek MD) and 40,218 (Labogena MD) (Pairwise comparisons were made between genotyped-imputation gena MD) . AverageThe average SNP distance over the 29 autosomes  was 29,1ped was 0.05 for the cows genotyped with Illumina Infinium BovineHD BeadChip, 0.07 for GeneSeek Genomic Profiler 3, 4 and Labogena MD and 0.08 for Genomic Profiler HD-150K and GeneSeek MD. Average fSNP was close to 0 (for both genotyped and imputed SNP) for the genomic estimators, except for Fgrm2 and Froh. Specifically, average fSNP across all SNP panels for Fgrm2 varied between−0.92 and−0.95 with inbreeding coefficients being always negative. The highest mean fSNP across all SNP panels was observed for Froh (0.11 to 0.16). Moreover, although the mean fSNP was, in general, equal between genotyped – imputed SNP for all estimators, the space of the inbreeding coefficients differed when estimated using genotyped vs. imputation SNP. This was observed for all estimators and SNP panels. The most consistent results were found for the two HD SNP panels when fSNP was estimated with Froh. For e.g., F in the group of cows genotyped with the GeneSeek Genomic Profiler 3 varied between −0.35 to 0.26 for the genotyped and−0.16 to 0.82 for the imputation SNP data. Similarly, for the same group of cows, Froh ranged between 0.00–0.37 and 0.02–0.60 for the genotyped and imputation SNP, respectively.Descriptive statistics of the pedigree and SNP inbreeding coefficients are reported in fSNP were found for all estimators, except Froh. Although with pedigree this cannot happen, with SNP data this is possible. Theoretically, inbreeding coefficients below zero reflect potential gain of genetic variability, given an unselected base population consisted of unrelated individuals. The interpretation and the theoretical background of inbreeding coefficients has been elaborated in previous studies  for all genomic inbreeding estimators  between genotyped-imputation fSNP. Moreover, for GeneSeek Genomic Profiler 3 the estimators F, Fhat3 and Fgrm2 had correlations ~0.60, Fhat1 and Fgrm values ranged between 0.68-0.75 with Froh being more consistent (~0.79) compared to the rest of the estimators. For GeneSeek Genomic Profiler 4, the lowest correlation (0.55) was observed for Fhat1, followed by Fhat3 and Fgrm (~0.62 for both), F (0.68), Froh (0.89) and Fgrm2 (0.98). For GeneSeek MD . In this case, however, it should be noted that an extreme and influential inbreeding coefficient was observed for Fhat1 and Fhat3 that impacts the values of Pearson correlations. In the case of Labogena MD (hat3 had correlations of between 0.82-0.89 with the highest (0.98) observed for Froh; correlations between genotyped – imputed fSNP for Fhat3 were at 0.77. Moreover, for Labogena MD the inbreeding coefficients of the imputed SNP had always greater variability and higher values than the genotyped SNP, across all estimators . For the four MD SNP panels, higher correlations were found for the Labogena MD , followed by the GeneSeek MD, GeneSeek Genomic Profiler 4 and GeneSeek Genomic Profiler 3  and population levels –26. It iper se, rather on the way they are applied in breeding programs. The rapid increase in number and quality of SNP panels in the market, the drastic drop for genotyping and novel imputation methods resulted in genotyping subgroups within breeding populations. For instance, in the ANAFIBJ genomic breeding program to date, 43 SNP panels have been utilized to genotype different groups of cattle. This situation is representative of other genomic breeding programs mainly in cattle , and 33,394 cows with GeneSeek Genomic Profiler 4 . However, for those cows only 13,870 and 16,862 SNP were used in the imputation data . Less SNPs of these chips were selected because they have a lower overlap with other DNA chips. This means, that (i) ~50% of the SNP of those panels are omitted and (ii) cows genotyped with those SNP panels have ~80–85% of their genotypes imputed. For some of those cows discrepancies were found between observed vs. imputed SNP genomic inbreeding coefficients, with the question being which estimates represent the real state. Moreover, results varied among estimators.ped, assuming that a higher correlation with Fped is favorable. Our results showed that, in general, the genotyped fSNP were strongly correlated to the Fped for all estimators . Furthermore, there where cows with inbreeding coefficients close to 0 with the genotyped SNP, and high inbreeding coefficients with the imputed SNP. This was observed even with Froh that was the most robust estimator. For instance, there was a group of cows genotyped with the GeneSeek Genomic Profiler 3 with inbreeding coefficients (based on genotyped SNP) ranging between ~0-0.15 while with imputation SNP the inbreeding coefficients were estimated between ~0.4 and 0.6 (fSNP ranged between 0 and 0.15 while the imputation fSNP for those cows was >0.3. We could hypothesize that fSNP estimated from ~15k SNP (as was the case of GeneSeek Genomic Profilers 3 and 4 in our study) might be biased. However, it must be also very unlikely that cows could have 40–60% of their genome in homozygous state, as was found with fSNP estimated with imputation SNP. It is known that for a successful imputation three important components are (i) the distribution along the genome and the number of SNP in the LD/MD panels, (ii) the linkage disequilibrium between SNP in the MD and SNP in the HD . This degrade in accuracy varies across the SNP panels and has been observed even with cows genotyped in HD, albeit to a much lower degree compared to cows genotyped in MD. This needs further investigation and quantification.In the present study, we emphasized on the effect that imputation might have on the estimation of genomic inbreeding coefficients, relative to the density of SNP panels. Another important aspect of imputation relates to the relationship between animals genotyped with LD and MD SNP panels (to be imputed) and the animals that consist of the reference panel, and which were genotyped in HD. In a preliminary analysis, we have evidenced that indeed the correlation between genotyped – imputation We investigated the effect of imputation, regarding the density of SNP panels used to genotype cows, in a routine dairy cattle genomic breeding program on SNP inbreeding coefficients. Correlations between genotyped vs. imputation SNP inbreeding coefficients were high and consistent for the HD SNP panels. Accuracies were degraded for the four MD density SNP panels. This drop in accuracy was linked to the number of SNP of the SNP panel included in the imputation SNP set and the average distance of SNP on the genome. Assuming that high correlation with pedigree inbreeding coefficients reflects more realistic values, genomic inbreeding coefficients estimated from imputation SNP were biased for the cows genotyped with MD SNP panels, because they were less correlated to pedigree inbreeding coefficients compared to inbreeding coefficients estimated only from genotyped SNP.per se, but rather it highlights the effect that the imputation pipeline and the overall genotyping management might have on the genomic inbreeding coefficients. Our results indicate that SNP panels that contain more informative SNP for the population under study can have more genotyped SNP that remain in the imputation data and thereby provide with more robust results on the genomic inbreeding coefficients of the cows. Cows that were genotyped with MD SNP panels that had few SNP included in the final imputed SNP data were more likely to have biased genomic inbreeding estimates for some groups of cows. In such a concept, Froh can be considered as a more robust estimator, reflecting identity-by-descent, compared to estimators summing homozygosity over individual SNP, measuring identity-by-state.We wish to state that our analysis is not critical on the quality of commercial SNP panels jtkaam@anafi.it.The data analyzed in this study is subject to the following licenses/restrictions: Data supporting this paper were obtained from ANAFIBJ. The genotype data are available only upon agreement with ANAFIBJ. Requests to access these datasets should be directed to J-TK, Ethical review and approval was not required for the animal study because no animals were used in this study, and ethical approval for the use of animals was thus deemed unnecessary.CD, J-TK, CC-G, and MA conceived the idea and formulated the objectives of this study. J-TK helped in data preparation. CD conducted the analysis and wrote the first draft of the paper. ASu and ASa supervised the project. ASu, CC-G, ASa, MM, RF, and MC critically reviewed the text. All authors read and approved the final manuscript.


The recently identified swine inflammation and necrosis syndrome (SINS) occurs in high prevalence from newborn piglets to fattening pigs and resembles an important concern for animal welfare. The primary endogenous syndrome affects the tail, ears, teats, coronary bands, claws and heels. The basis of clinical inflammation and necrosis has been substantiated by histopathology, metabolomic and liver transcriptomic. Considerable variation in SINS scores is evident in offspring of different boars under the same husbandry conditions. The high complexity of metabolic alterations and the influence of the boar led to the hypothesis of a polygenic architecture of SINS. This should be investigated by a genome-wide association study. For this purpose, 27 sows were simultaneously inseminated with mixed semen from two extreme boars. The mixed semen always contained ejaculate from a Pietrain boar classified as extremely SINS susceptible and additionally either the ejaculate from a Pietrain boar classified as SINS stable or from a Duroc boar classified as SINS stable. The 234 piglets were phenotyped on day 3 of life, sampled and genetically assigned to the respective boar. The piglets showed the expected genetic differentiation with respect to SINS susceptibility. The suspected genetic complexity was confirmed both in the number and genome-wide distribution of 221 significantly associated SNPs, and led to 49 candidate genes. As the SNPs were almost exclusively located in noncoding regions, functional nucleotides have not yet been identified. The results suggest that the susceptibility of piglets to SINS depends not only on environmental conditions but also on genomic variation.The online version contains supplementary material available at 10.1007/s00335-023-10011-6. Swine inflammation and necrosis syndrome (SINS) is a newly identified, specific syndrome, resulting from the combined presence and signs of inflammation and necroses in acral body parts. It particularly affects the tail base, tail tip, ears, coronary bands, heels, soles, claw walls, teats, navel, and face and can be observed in suckling piglets, weaners, and finishing pigs  The simultaneous occurrence in such disparate body parts as the tail, teats and claws  mated to 39 sows  approach. To examine whether evidence of associations with SINS phenotypes can be detected in the porcine genome. In addition, their distribution and magnitude should be evaluated, and candidate genes should be identified, particularly in the areas of inflammation, vasculitis, and necrosis.The animal experiment was carried out in the conventional pig breeding stable of the Oberer Hardthof teaching and research station at Justus-Liebig University Giessen under the approval of the authorities in Giessen, Germany with file number 708_M.The sow herd used (Topigs x German landrace) was a rotational cross of Topigs with German Landrace. The boars came from completely different breeds and had no breeding relation between them. The environment was the same for all animals.The herd had a performance of 15 live born and 1.4 dead born piglets per sow. The sows were artificially inseminated with three extreme boars based on the above mentioned study by Kuehling et al. (2021b). Boars were used in pairs as mixed semen, where the semen of two boars was mixed within one dose. This means that piglets from two different boars were present in each litter at the same time. The design was applied to (i) limit the number of litters and experimental animals, (ii) to minimise environmental effects, (iii) to increase genetic variability within the piglets, (iv) to increase the sow-boar combinations and (v) to nevertheless achieve a manageable number of piglets.The three boars were a Duroc boar whose progeny had the lowest levels of SINS in the preliminary study (4.3) and the two Pietrain boars whose progeny had the lowest (7.26) and highest (12.17) levels of inflammation and necrosis, respectively, within the Pietrain cohort. These boars were selected to achieve segregation of favourable and unfavourable gene variants in the progeny. All sows were inseminated only once, with mixed semen from two boars, so that piglets of the Pietrain boar classified as unfavourable (PI−) occurred in one litter together with piglets of the Duroc boar (DU) (in 13 litters) or together with piglets of the Pietrain boar classified as favourable (PI +) (in 14 litters). Taken together, a total of 27 sows produced 27 litters. Each sow had only one litter, but with piglets from two different boars. 234 piglets were used, if they were anatomically normal developed and their SINS phenotype was recorded at their 3rd day of life. The piglets’ father was detected by paternity testing after phenotyping. The results of paternity testing revealed 14 mixed litters (from 14 sows) with 77 piglets from the favourable Pietrain boar and 39 piglets from the unfavourable Pietrain boar, as well as 13 mixed litters from 13 sows with 48 piglets from the Duroc boar and 70 piglets from the unfavourable Pietrain boar. On average, 8.4 healthy piglets per litter with at least one piglet per inseminated boar were randomly selected in a blinded manner and used.Genetic matches between offspring and boars were used in paternity testing. DNA was extracted from the piglet’s docked tails. Tail docking was done at day 4 of life, one day after clinical scoring  kit  and quantified by Qubit Flex Fluorometer  using the Qubit dsDNA broad range assay kit . The DNA was diluted to a uniform concentration of 50 ng/μl. During library preparation, the samples were prepared to be compatible for sequencer processing. The paired-end library generated in this process was amplified.Complete genome sequencing was performed using the Illumina NextSeq500/550 v2 and Illumina NovaSeq 6000 . In this process, 150 bp paired-end reads were generated with a coverage of 15x.The files received from the sequencing company were decoded and converted from.bz2 to gzipped fastq files.The available raw data were transferred to the open source workflow OVarFlow for further bioinformatic analysis. This workflow is used for variant discovery of single nucleotide polymorphisms (SNPs) and indels, insertions and deletions in model and non-model organisms  format. This file contains the reference genome and the annotation , the min sequence length  and sample information on the Illumina short read sequencing data used in the analysis.https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Mapping to the reference genome was done using the mem algorithm introduced in the Burrows Wheeler Alignment Software . These files were passed to gatk SortSam (https://gatk.broadinstitute.org/hc/en-us/articles/360051307011-SortSam-Picard-) and sorted in the next step. MarkDuplicates (https://gatk.broadinstitute.org/hc/en-us/articles/360051306171-MarkDuplicates-Picard-) was used to locate and mark multiple reads.The reads were undergone quality control using FastQC version 0.11.9 . First, active regions were identified in which a significant number of reads showed variations beyond the expected background noise. De Bruijn-like graphs were created for these regions which were used to recreate a possible sequence and develop haplotype candidates. To narrow down potential sites of variation, each possible haplotype was aligned with the reference sequence using the Smith-Waterman algorithm. The existing reads were then aligned with each of the possible haplotypes and a matrix was created with the expected likelihoods of occurrence. Therefore, the PairHMM algorithm was used. The assignment of the most likely genotypes for the available samples was done according to Bayes’ theorem.OVarflow performs variant detection at several intervals per individual to enable the highest possible degree of parallelisation. The actual variant calling was done using gatk Haplotype Caller (https://gatk.broadinstitute.org/hc/en-us/articles/360050814232-GatherVcfs-Picard-) and gatk CombineGVCFs (https://gatk.broadinstitute.org/hc/en-us/articles/360050815372-CombineGVCFs) were used for this purpose.The individual Genome Variant Call Format (GVCF) files of the analysed intervals, which were output after variant calling, were recombined for each individual and then merged into an aggregated file containing the variant information for all analysed individuals. Embedded in the workflow, gatk GatherVcfs (https://gatk.broadinstitute.org/hc/en-us/articles/360050816072-GenotypeGVCFs), the data preprocessed with HaplotypeCaller were subjected to an additional joint genotyping across several individuals. The genotype information lost during the combination of the GVCF files was restored and the genotyping accuracy was improved.With gatk GenotypeGVCFs (https://gatk.broadinstitute.org/hc/en-us/articles/360051305531-SelectVariants).The separation of the detected SNPs and indels into separate files was performed with gatk SelectVariants (https://gatk.broadinstitute.org/hc/en-us/articles/360050815032-VariantFiltration) were chosen. The following filters were passed through :I.‘QD2’: The quality by depth , Assessment of the quality score  in relation to the present sequencing depth (Depth); SNPs: QD < 2.0, Indels: QD < 2.0.II.‘QUAL30’: general quality score ; SNPs: Qual < 30, Indels: QUAL < 30.0III.‘SOR3’: Determination whether there is allele-specific strand bias, estimated by the symmetrical odds ratio test ; SNPs: SOR > 3.0IV.P-value ; SNPs: FS > 60.0, Indels: FS > 200.0‘FS60’/‘FS200’: detection of strand bias by Fisher exact tests and output as phred-scaled V.‘MQ40’: the root mean square (RMS) mapping quality of reads across samples (MQ); SNPs: MQ < 40.0VI.‘MQRankSum-12.5’: the Mann–Whitney–Wilcoxon rank sum test for the mapping quality of reference and alternative reads ; SNPs: MQRankSum < -12.5VII.‘ReadPosRankSum-8’/‘ReadPosRankSum-20’: Allele-specific rank sum test according to Mann, Whitney and Wilcoxon for the relative positioning of the reference allele versus the alternative allele within the reads ; SNPs: ReadPosRankSum < − 8.0, Indels: ‘ReadPosRankSum < − 20.0In order to detect false positive variants of the variant call set and remove them, a hard filtering according to GATK was performed. For SNPs and indels, different thresholds of the filters in gatk VariantFiltration (The variants that did not pass the filter criteria were marked.https://gatk.broadinstitute.org/hc/en-us/articles/360050815732-SortVcf-Picard-) again. The tool gatk SelectVariants removed the previously marked variants.Following the quality control, the split SNP and indel files were combined with gatk SortVcf  > 0,05Hardy–Weinberg equilibrium (HWE) p > 0.000001The annotated VCF file output at the end of the OVarFlow workflow was converted to binary PLINK format using the PLINK package  was performed using TASSEL 5.0 , contemporary group, sex, and PCA were used as fixed effects and birth weight was used as covariate. For PCA, 4 principal components were considered. Because each sow was used only once in the study, effects of litter and sow were identical.Manhattan plots and quantile–quantile plots were generated using the R package qqman, version 0.1.8 Turner .After GWAS, associations were excluded from further analysis, if one of the expressions of the characteristics (0 vor 1) was represented by less than 5% of the cases .The effects of the genotypes of the significant SNPs from GWAS on the SINS characteristics were tested with Anova (metric data) and with a General Linear Model (bivariate data) in IBM-SPSS, version 27 . SNPs were only included if they had at least three genotypes, if the effect was additive, i.e., if the heterozygote value did not exceed the value of the highest homozygous or fall below the value of the lowest genotype by more than 20%, and if the negative decadic logarithm of significance exceeded 8.4. We tested not only the effects of SNPs on the phenotypes with significant association in GWAS, but on all SINS genotypes. The rationale for this approach was the assumption that due to the syndrome character of SINS, involved gene loci should affect different SINS characteristics at the same time.https://www.ncbi.nlm.nih.gov/genome/gdv/?org=sus-scrofa) based on release Sscrofa11.1 (GCF_000003025.6) was used for this purpose. Positional candidate genes were selected as such if they were located no further than 1 Mbp from the SNP in either direction.Positional candidate genes were identified based on their distance from SNPs. The Genome data viewer . The primers were designed according to the rs numbers and sequences covering the range of 50 bases around the polymorphism that we had provided for the different SNPs.In the initial PCR cycle, the matching allele-specific forward primer binds to the target region together with the common reverse primer. During amplification, the tail sequence located at the 5′-end of the primer is added to the newly synthesised strand. In the following cycles, further amplification of these takes place.The KASP master mix used for the assay contains universal FRET (fluorescence resonance energy transfer) cassettes with Fam- or Hex-labelled regions. These regions correspond to one of the allele-specific tail sequences and enable their binding. In this case, the FRET cassette is no longer quenched and emits the corresponding fluorescence signal. If the genotype of the examined animal is homozygous at the SNP, only one out of two possible fluorescence signals is generated. In case of heterozygosity, a mixed fluorescence signal can be detected. The effects of the genotypes predicted by KASP were analysed by onefactorial variance analysis (Anova) in metric and with a generalised linear model in bivariat data.More than 86% of the three-day-old suckling piglets already showed swelling and bleeding of the heels and haemorrhages in the claw wall . The piglets were affected by eyelid oedema at the same level. The ears showed vein congestion in 86% and a shiny surface and bristle loss in over 65% of the animals. About 40% to 60% of the piglets showed swelling and redness of the tail base, venous congestion at the teats and signs of inflammation at the coronary bands of the front and hind limbs. Over 20% of the piglets had no bristles at the tail base, scab formation at the tail tip and swelling at the teats. As expected, the more severe symptoms, such as bleeding and necroses, were only detectable in 4 to 8% of the piglets at this age .P = 2.9 × 10–8)  and 165 were genome-wide significant (P ≤ 8.4) . With few exceptions, the SNPs were already known and listed under their rs-ID. These SNPs were associated with 25 different phenotypic signs. Seventy SNPs were closer to each other than 106 basepairs. They were condensed into 71 chromosomal regions, e.g., 1, 1 to 1, 9 on SSC1 . Twentynine of the SNPs were associated with more than one phenotype .Associations were excluded from further analysis, if one of the expressions of the characteristics (0 or 1) was represented by less than 5% of the cases . GWAS identified 221 significant SNPs, from which 56 were chromosome-wide (λ = 1.09) did not indicate population stratification and/or cryptic relatedness between animals  and SINS score (n = 25) were also associated with numerous SNPs. Fewer associations were detected for tail tip, teats, face and heels.Signs at the ears and claw wall were most often associated with SNPs . Tail base  . Of the genes located within a range of at most 100 kbp from a significant SNPs, 15 with direct involvement in inflammation, vasculitis, and necrosis were defined as candidate genes .However, SNPs were preferentially found in non-coding gene regions, so their function could not be easily inferred. Thus, they could be non-functional markers in linkage disequilibrium with the as yet unidentified functional gene variant, because of the relatively low coverage of the sequence data it is not to be expected that all existing gene variants were already detected by the present study. On the other hand, other high-throughput genomic studies during the last years show that around 90% of more than a hundred of gene variants associated with immune-mediated diseases that have been identified, are located in non-coding regions, making it difficult to assign them to molecular functions  involved in inflammatory responses to trauma and acute ischemia in humans  is involved in endoplasmatic reticulum stress, apoptosis and inflammation  is involved in the regulation of monocytes and the regulation of TNF-alpha-induced NF-kappa-B activation and apoptosis  supports lymphocyte migration in response to chemokines by cytoskeletal rearrangement  in the region of SSC12, associated with SINS. As a member of the chemokine superfamily of secreted proteins involved in immunoregulatory and inflammatory processes, CCL2 displays chemotactic activity for monocytes and basophils in humans Below is the link to the electronic supplementary material.


Bos taurus indicus (Bos indicus) and Bos taurus taurus (Bos taurus). Methods to derive the underlying genetic architecture for these two subspecies are essential to develop accurate genomic predictions in these hybrid populations. We propose a novel method to achieve this. First, we use haplotypes to assign SNP alleles to ancestral subspecies of origin in a multi-breed and multi-subspecies population. Then we use a BayesR framework to allow SNP alleles originating from the different subspecies differing effects. Applying this method in a composite population of B. indicus and B. taurus hybrids, our results show that there are underlying genomic differences between the two subspecies, and these effects are not identified in multi-breed genomic evaluations that do not account for subspecies of origin effects. The method slightly improved the accuracy of genomic prediction. More significantly, by allocating SNP alleles to ancestral subspecies of origin, we were able to identify four SNP with high posterior probabilities of inclusion that have not been previously associated with cattle fertility and were close to genes associated with fertility in other species. These results show that haplotypes can be used to trace subspecies of origin through the genome of this hybrid population and, in conjunction with our novel Bayesian analysis, subspecies SNP allele allocation can be used to increase the accuracy of QTL association mapping in genetically diverse populations.Many of the world’s agriculturally important plant and animal populations consist of hybrids of subspecies. Cattle in tropical and sub-tropical regions for example, originate from two subspecies,  Bos indicus and Bos taurus , Hereford (n = 43), Limousin (n = 62), Shorthorn (n = 95) and Charolais (n = 13). This dataset was used to calculate the haplotype frequency of each haplotype in purebred animals from both subspecies. These haplotype frequencies were consequently used to allocate validation animal haplotypes to a given subspecies.A reference dataset of 1181 purebred animals, genotyped with the Bovine HD array , and mapped to reference genome ARS-UCD 1.2 were used for haplotype assignment. This dataset consisted of 868 n = 979), Santa Gertrudis (n = 1802) and Droughtmaster (n = 914). Santa Gertrudis and Droughtmaster heifers are stabilised B. indicus × B. taurus composites, and the Brahman heifers are ‘graded up’ B. indicus, consisting of approximately 90% B. indicus and 10% B. taurus origins  using FImpute software , and the sum of the B. taurus Hamming distances, sum(Bt), respectively. The probability of the undefined haplotype belonging to either subspecies was calculated using Eqs. (B. indicus (Prob(Bi)) or B. taurus (Prob(Bt)).Subspecies of origin was calculated using a method derived from Bolormaa et al. . In thisentclass1pt{minimaing Eqs. . The undb-values were used to estimate if an animal was homozygous B. indicus (Bi), homozygous B. taurus (Bt) or B. indicus × B. taurus (Bx) hybrid for each haplotype window. Each of the m SNP within the haplotype window was assigned this subspecies of origin . We then used this classification of subspecies of origin when building the X-matrix for Bayesian analysis.As each animal has two haplotypes per window, b-values to each haplotype for each of the validation heifers, we calculated the average B. indicus percentage of each of the validation heifers, based upon haplotype subspecies of origin. This data was then used to calculate the average B. indicus percentage of each of the three validation breeds, Brahman, Santa Gertrudis and Droughtmaster. These averages were compared to the original theoretical breed compositions from these hybrid breeds to determine if haplotypes were able to track B. indicus content through the genome of these populations.After assigning B. indicus, homozygous B. taurus or the effect of being a composite B. indicus × B. taurus (capturing heterosis) at each SNP, simultaneously. The X-matrix has the dimensions of nanim × 3nsnp where nanim is the number of animals in the validation population, and nsnp is the number of SNP on the marker panel that were used to construct haplotypes. Within the X-matrix, each SNP is represented by three columns, one for homozygous B. indicus (Bi), one for homozygous B. taurus (Bt) and one for B. indicus × B. taurus (Bx)  added to the Bi column in the X-matrix, and both the Bt column and the Bx column will be empty (0). The resulting X-matrix fits all SNP from the BovineHD array, assigned to an estimated subspecies of origin, that was calculated from the fixed window haplotype, using the reference population haplotype frequencies.To account for genomic architecture in genetically divergent populations, a customised X-matrix was designed to allow simultaneous estimation of multiple subspecies-specific effects in a BayesR analyses. This X-matrix can be used to estimate the effect of an animal being homozygous Bx) Fig. . Phased Bx) Fig. . For exaRMS is a vector of phenotypes, 1n is a vector of ones, µ is the overall mean, cg is contemporary group defined as year, herd and season and age is age at measurement fitted as a covariate. There were no mixed breed contemporary groups in this dataset therefore, breed was not fitted as a covariate in the model as contemporary group described all of the variation due to breed in this analysis. X is the customised X-matrix described above with dimension nanim x 3nsnp, g is a vector of SNP effects with a distribution gBayesR analyses used the model:Pr where the prior of The proportion of SNP that fall into each of the four distributions are defined by the parameter nanim × nsnp.BayesR analyses were conducted with Gibbs sampling using 50,000 iterations, discarding the first 20,000 as burn-in  as training and the remaining 20% (n = 739) as validation, each time. This strategy of allocating validation group was designed to reflect the mixed breed, mixed subspecies populations in the north Australian beef industry and was used to test the efficacy of our model at accurately predicting GEBV in these mixed breed cohorts. Each animal only occurred in a single validation group and the validation groups remained the same for each of our analyses.Genomic selection was performed in the validation population animals using both control and subspecies-specific BayesR analyses in a fivefold cross-validation method. All the validation population heifers were randomly split into five, even sized, mixed breed groups and the analysis was repeated five times using 80% , B. taurus (Bt) and B. indicus × B. taurus (Bx). Similar to the genomic selection analysis, a multi-breed control analysis was also performed where no subspecies-specific effects were assumed. In addition, for comparison purposes, we also performed a genome wide association study (GWAS) analysis in GCTA .After performing Bayesian analyses, estimated SNP effects and posterior probabilities of inclusion were plotted for the control analysis and each of the three SNP origins, simultaneously, using the R package CMplot . As the size of the haplotype windows increases, the number of haplotype segments across the chromosome decreases, n = 2735, 1368 and 548 for the 50 kb, 100 kb and 250 kb haplotype windows, respectively. However, there is a more consistent distribution of b-values across the continuum for the 50 and 100 kb than the 250 kb haplotype windows. The 250 kb haplotype windows show more clustering of b-values around the 0.5 threshold and towards the B. taurus axis.To further investigate the effect of haplotype length on subspecies of origin assignment, we plotted the Accuracy of genomic prediction was not significantly improved when using a subspecies-specific X-matrix, however, the 100 kb haplotype window defined X-matrix resulted in the highest prediction accuracy for RMS in this population of heifers Table . FurtherB. indicus content through the genome and the improvements in both prediction accuracy and bias in genomic predictions, the 100 kb haplotype window appears to be the optimal haplotype length in this population. As such, we used the X-matrix created using the 100 kb haplotype windows for QTL association mapping to further define subspecies differences in genetic architecture in this population of heifers.One of the aims of this study was to determine the optimal haplotype window length to estimate subspecies-specific effects in this hybrid population. Based upon the ability to track Similar to the genomic selection analysis, QTL association mapping was performed using both the subspecies-specific X-matrices defined by different haplotype windows, and a multi-breed control analysis where the X-matrix does not contain subspecies of origin effects. This multi-breed control analysis was unable to identify some subspecies-specific SNP of moderate effect and frequency that have an effect on the RMS trait in this population of heifers Fig. . The maxB. taurus (Bt) and B. indicus × B. taurus (Bx) specific SNP that appear to have a moderate effect upon RMS in this population of heifers, and these SNP do not appear to have the same magnitude of effect in the other subspecies.These results show that there are subspecies-specific effects for RMS, with some SNP having a moderate effect upon the trait Fig. . In partB. indicus (Bi) origins, BOVINEHD0400006614 and BOVINEHD1600012877, two were identified from B. taurus (Bt) origins, BOVINEHD0200001401 and BOVINEHD030022359, and one was identified from B. indicus × B. taurus (Bx), BOVINEHD1200027774. Of these five SNP, four were situated in protein-coding regions of genes . Our results show that, despite the small improvements in prediction accuracy, there were marked improvements in the bias estimates of genomic selection when subspecies of origin effects of alleles were accounted for. This finding is in accordance with a recent study in broiler chickens that showed that the incorporation of breed of origin alleles in crossbred genomic evaluations reduced bias in breeding value estimation . This gene has been associated with folliculogenesis in rats  which has been shown in human studies to be upregulated in the placental tissues of pre-eclampsia patients . In mice, a study has shown that WLS knockout animals had a significant decrease in fertility due to reduced ovary sizes, decreased number of follicles and lower numbers of corpus luteum, which are all essential for female fertility . In the pituitary of rats, DGKβ is expressed in the dopamine receptors which is involved in the phosphoinositide cycle within the pituitary, which is involved with downstream signalling cascades involved in hormone action  it uses a Bayesian approach, with a prior assumption of zero, very small or moderate effect of QTL, and (ii) no correlation between allele effects between subspecies is assumed. The latter seems appropriate for subspecies which diverged so long ago , Hereford (n = 43), Limousin (n = 62), Shorthorn (n = 95) and Charolais (n = 13) whereas the B. indicus reference population consisted of 868 animals from a single breed, Brahman. Within breeds it is expected that animals will share more haplotypes as there is increased probability that they have recent ancestors in common  (Bolormaa et al. B. indicus breed, there may be error in the subspecies of origin assignment of some B. indicus haplotypes in this study. The impact of this incorrect assignment is likely to be minimal, as our results have shown that the subspecies of origin haplotype assignment was able to assign 10% of the Brahman genome to B. taurus origins. However, future studies using a purebred B. indicus breed in the reference population will be required to test the efficacy of this method.There were three main limitations to our study, the absence of mixed breed contemporary groups, the lack of purebred In this paper, we have developed a method to simultaneously map subspecies-specific effects in hybrid populations, in order to better understand the underlying genomic architecture of genetically diverse populations. It is essential to identify the appropriate haplotype window size to use in each population to optimise accuracy of both QTL mapping and genomic evaluations. However, our method demonstrated that, in the absence of pedigree information, marker haplotypes can be used to accurately assign ancestral subspecies of origin to genomes. When used in conjunction with our BayesR analysis, we were able to identify novel QTL that have not previously been identified in cattle, but were closely linked to genes biologically involved with fertility in other species.Supplementary Figure 1Supplementary Table 1