Wolbachia have evolved across the phyla nematoda and arthropoda. In Wolbachia phylogeny, supergroup F is the only clade known so far with members from both arthropod and filarial nematode hosts and therefore can provide unique insights into their evolution and biology. In this study, four new supergroup F Wolbachia genomes have been assembled using a metagenomic assembly and binning approach, wMoz and wMpe from the human filarial parasites Mansonella ozzardi and M. perstans, and wOcae and wMoviF from the blue mason bee Osmia caerulescens and the sheep ked Melophagus ovinus, respectively. A comprehensive phylogenomic analysis revealed two distinct lineages of filarial Wolbachia in supergroup F, indicating multiple horizontal transfer events between arthropod and nematode hosts. The analysis also reveals that the evolution of Wolbachia-filaria symbioses is accompanied by a convergent pseudogenization and loss of the bacterioferritin gene, a phenomenon found to be shared by all filarial Wolbachia, even those outside supergroup F. These observations indicate that differences in heme metabolism might be a key feature distinguishing filarial and arthropod Wolbachia. The new genomes provide a valuable resource for further studies on symbiosis, evolution, and the discovery of new antibiotics to treat mansonellosis.The intracellular endosymbiotic proteobacteria  Wolbachia are bacterial endosymbionts of medically important parasitic filarial nematodes and arthropods. The evolutionary history and biological roles of Wolbachia in these different hosts are not well understood. The supergroup F in Wolbachia phylogeny harbors members from both filarial and arthropod hosts, providing an unparalleled opportunity for genomic comparisons to uncover distinguishing characteristics. This study provides new genomes from filarial and arthropod Wolbachia from this unique supergroup. Their phylogenomic analysis reveals multiple, independent transfers of Wolbachia between arthropod and filarial hosts. Remarkably, filaria–Wolbachia associations are associated with a convergent loss of the bacterioferritin gene, a key regulator of heme metabolism. Heme supplementation is considered a critical component of Wolbachia–filaria symbiosis. We demonstrate bacterioferritin loss is a novel feature exclusive to filarial Wolbachia.Wolbachia are Gram-negative α-proteobacteria of the order Rickettsiales, present as intracellular symbionts in many species of parasitic filarial nematodes and arthropods , assigns Wolbachia to various monophyletic supergroups  corresponding to the Wolbachia genome, distinguishable from blobs corresponding to the genomes of the worm hosts and associated microbiota.A metagenomic assembly and binning pipeline suitable for complex clinical samples were utilized to analyze sequencing data from four M. ozzardi isolate Moz1 from Brazil, only fragments of Wolbachia genome at low coverage were obtained and were not analyzed further  was performed on these proteins to check for presence of the 219 genes conserved across most proteobacteria (“proteobacteria_odb10” database in BUSCO). The BUSCO scores of wMoz and wMpe were 77.6% and 74.4%, respectively , the median coverage of nematode scaffolds and Wolbachia scaffolds were 1,032 × and 30 × respectively, yielding an estimated titer of 30 Wolbachia per microfilaria. In the other M. perstans isolate Mpe2, even though the median coverage for nematode scaffolds is relatively high at 633X, only a very small portion of the Wolbachia genome, 15 kb in 12 scaffolds (less than 2% of 1.073 Mb wMpe1 genome) with a median coverage of 7 × could be obtained during the metagenomic assembly, indicating a very low titer of Wolbachia in this isolate. The low titer was also confirmed using a complementary analysis, namely mapping the Mpe2 reads against the wMpe1 assembly. In this analysis, the median depth of coverage obtained was only 9 × , whereas the breadth of coverage (number of bases covered at 1 × depth of coverage) was observed to be 83 kb only, just 8% of the wMpe1 genome. Because both isolates were collected in Cameroon, these observations point to variation in Wolbachia titers within the same species in a geographical area and could explain the conflicting reports of PCR-based detection of Wolbachia in M. perstans  and was not analyzed further. Recently, a metagenomic assembly of Wolbachia (GenBank accession WOLB1015), based on the same raw reads, became available  conserved across Wolbachia, an unusually high rate in contrast to 0% duplications that are observed in almost all other Wolbachia  element transposons in the wCle genome , a plant parasitic nematode Pratylenchus penetrans (Family Pratylenchidae), and arthropod hosts  could be found in wMoz, wMpe, wMoviF, or wOcae proteomes. Additionally, when the sequencing reads from these four supergroup F Wolbachia were mapped against the wCle genome, no alignments could be found within the region harboring the biotin operon, even though alignments could be obtained in the neighboring regions flanking the biotin operon  followed by “ankyrin domain protein” (n = 12). The filarial Wolbachia wMhie had 32 phage-associated orthogroups and shared 22 of these with wMoz and wMpe. Neither wMoz, wMpe, nor wMhie was found to possess any core components of phage structural modules, like the filarial Wolbachia from other supergroups. Among arthropod Wolbachia of supergroup F, wOcae had 29 phage structural genes, wMoviF had only three such genes, whereas wCle had only one, suggesting different dynamics of phage evolution in these Wolbachia.Many rophages . The phaymbiosis . Phage-dhogroups  online aa phages . In the Wolbachia genomes: for example, 53 copies are found in the wAlbB genome . The filarial Wolbachia wMoz and wMpe were most closely related to the arthropod Wolbachia wMoviF and wOcae, whereas the filarial Wolbachia wMhie was more closely related to the arthropod Wolbachia wCle. The topology of the Wolbachia phylogeny was also compared with the phylogeny of multiple Wolbachia hosts (A), and limited congruence was found between the two phylogenies (A). Particularly, within supergroup F, the clear separation of the filarial Wolbachia into two separate, well-supported clades, and the discordant phylogenetic topology of their corresponding hosts suggests at least two horizontal transfers of Wolbachia leading to these two clades.Remarkably, the filarial pergroup A. The fiia hosts A, and lilogenies A. ParticWolbachia beyond those with whole genome sequences, a phylogenetic tree based on MLST loci  and groEL, dnaA, and 16S rRNA genes was constructed based on data available in GenBank  and four from different termite hosts. The corresponding sequences from wCfeJ, the Wolbachia found to be closest to supergroup F (A), were used as an outgroup. A more distant Wolbachia, wChemPL13 from supergroup T, which is a sister clade to supergroups F, C, J, S, and D was also included. This expanded tree (B) also robustly supports the separation of the Mansonella Wolbachia clade from the wMhie clade. Interestingly, the only other known filarial Wolbachia in supergroup F, from the deer tick nematode C. japonica, is placed in a clade with wMhie, with the arthropod Wolbachia wCle sharing a common ancestor with both these filarial Wolbachia. Together these observations demonstrate at least two distinct filarial Wolbachia lineages in supergroup F.To expand the analysis of supergroup F  GenBank  online. rgroup F A, were uded tree B also roWolbachia are often found to be even more reduced as compared to their arthropod counterparts. A study of the genes lost during this evolutionary transition can therefore reveal genes that are either potentially dispensable or have been selected against during the evolution of filaria-Wolbachia symbiosis. The orthology relationships across 81 Wolbachia genomes . These pseudogenes had missing canonical start codons and premature stop codons resulting in truncated proteins about 75% of the length of the intact protein . The bacterioferritin proteins functions as a complex of 24 subunits forming a spherical cage to trap heme molecules  were shared between different Wolbachia, consistent with the genetic drift model. An exception to this trend was the high number (n = 15) of genes that were found to be pseudogenized in each of the three filarial Wolbachia wMoz, wMpe, and wMhie, whereas their closest sister arthropod Wolbachia had intact versions of the corresponding genes . The corresponding pseudogenes might have arisen either in the last common ancestor of wMoz and wMpe or through convergent evolution specific to Wolbachia within Mansonella or a combination of both factors.Intracellular bacterial symbionts, such as mescales . Howevereudogene  online. olbachia . For eacng genes . The bacWolbachia endosymbionts play critical roles in the biology of their arthropod and filarial hosts, and there is great interest in understanding their diverse functions through genomic approaches. The enigmatic supergroup F in Wolbachia phylogeny is the only supergroup comprising symbionts of arthropod as well as filarial nematode hosts. In this report, four new Wolbachia genomes from supergroup F are described, expanding the number of genomes from two to six in this underrepresented supergroup. Two genomes are from wMoz and wMpe, endosymbionts of Mansonella spp., important filarial parasites of humans, whereas the other two genomes are from wOcae and wMoviF, endosymbionts of the mason bee O. caerulescens and the sheep ked M. ovinus respectively.Wolbachia are intracellular symbionts that cannot be cultured independently of their hosts, it is not possible to obtain their DNA independent of the host and the associated microbiome, presenting a challenge for accurate genome assembly. In the case of wMoz and wMpe, complex human clinical samples were used as the source material, containing a mixture of DNA from human blood, the filarial parasite host, its intracellular symbiont Wolbachia, as well as other constituents of the human microbiome. Similarly, for wOcae and wMoviF genomes, the publicly available sequencing data were derived from field-collected, wild isolates of their arthropod hosts, and comprised DNA from the arthropod host, Wolbachia, as well as the arthropod microbiome. Therefore, a metagenomic assembly and binning pipeline, customized to each of the datasets, were necessary and played a critical role in obtaining high quality Wolbachia genomes.Because Wolbachia titers via the measurement of relative copy numbers of genomes of Wolbachia versus its host. This principle has been used in a metagenomic study for Wolbachia titer calculations across multiple hosts  in supergroup F and identifies wCfeJ as the most closely related and robust outgroup to this supergroup. The phylogenomic analyses revealed two distinct clades of filarial Wolbachia within supergroup F. The wMoz and wMpe pair shared a common ancestor with the arthropod Wolbachia wOcae and wMoviF, whereas wMhie shared a different common ancestor with the bed bug Wolbachia wCle, indicating multiple events of horizontal transfer of Wolbachia between arthropod and filarial hosts. While this scenario has previously been proposed based on a few gene loci , but their hosts are from different clades, ONC5 and ONC4, respectively , and administrative clearance from the Delegation of Public Health, South-West region of Cameroon (Re: R11/MINSANTE/SWR/RDPH/PS/259/382). Approval for the study was granted by the “National Ethics Committee of Research for Human Health” in Cameroon. Special consideration was taken to minimize the health risks to which any participant in this study was exposed. The objectives of the study were explained to the consenting donor after which they signed an informed consent form. The participant's documents were assigned a code to protect the privacy of the study subject. At the end of the study, the donor received a cure of mebendazole (100 mg twice daily for 30 days).For M. ozzardi, study protocols were approved by the Institutional Review Board of the Institute of Biomedical Sciences, University of São Paulo, Brazil . Written informed consent was obtained from all patients or their parents or guardians if participants were minors aged <18 years. Diagnosed infections were treated with a single dose of 0.2 mg/kg of ivermectin after blood sampling  and then stored at –20 °C. Two independent isolates of M. perstans microfilariae, denoted as Mpe1 and Mpe2 in this study, were collected on nylon filters from blood samples from Cameroon  was used to enrich parasite DNA and reduce human DNA contamination (except for the Moz2 sample). The enrichment process is based on selective binding and removal of CpG-methylated DNA islands present in human genome but absent from nematode and us study , and a shttps://sourceforge.net/projects/bbmap/). Duplicate raw reads and bad tiles were removed using the clumpify.sh and filterbytile.sh utilities. Adapter trimming as well as removal of phiX reads and reads with 13-nt or longer stretches of the same nucleotide  were performed using bbduk.sh. Human host reads were removed by mapping against the human genome (grch38) using the bbmap.sh utility. The quality metrics of the processed reads at each step were assessed using FastqC v0.11.9 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).Raw reads were processed using the BBTools package v38.51 . The bait database was updated to include the newly assembled sequences, and a new subset of reads was obtained by mapping to this updated database. These reads were then used as input for another round of metagenomic assembly. Candidate Wolbachia scaffolds were selected using BlobTools. Whole genome alignments of the assembled scaffolds to three representative and complete reference genomes, namely wCle (supergroup F), wMel (supergroup A), and wAlbB (supergroup B) were performed using nucmer v4.0.0beta2 , which uses minimap2 (v 2.17-r941) for whole-genome alignments  scores were calculated using the OrthoANIu tool v1.2 . For a more sensitive measure of sequence similarity and divergence between closely related Wolbachia, dDDH scores (https://ggdc.dsmz.de/ggdc.php). The orthologs of protein coding genes across multiple Wolbachia genomes were inferred using OrthoFinder v2.4 (For both pipeline . The compipeline . Synteniignments . The paros plots  drawn usos plots . For gloool v1.2 . The paiH scores  were comder v2.4 .http://eggnogdb.embl.de/#/app/emapper). The analysis of different metabolic pathways was based on the annotations of wCle and wBm genomes available in the KEGG database  uon files  with 1,0on files  and furtWolbachia based on a combined supermatrix of multiple genes, the sequences for their MLST loci ftsZ, fbpA, coxA, gatB, hcpA, and other loci  were obtained from their genome assemblies and GenBank. The corresponding sequences from wCfeJ was used as outgroup. Only the Wolbachia with sequences available for at least three of these eight loci were included in this analysis. The multiple sequence alignment of the combined supermatrix of sequences was performed using MUSCLE . Gene predictions were carried out on WebAUGUSTUS online server  with the soft-masked genome as input. The soft-masked genomes obtained were used as input to gene prediction using online AUGUSTS web server (M. ovinus and C. hemipterus were assembled de novo from SRA datasets SRR17267914 (https://github.com/TransDecoder/TransDecoder), and arthropod sequences were selected using kraken version 2.0.9-beta (The protein coding genes of different escribed . Curatedrecently . The genon 2.0.3  and Dfamon 2.0.3 . These rb server . The tra17267914  and SRR117267914  and asse17267914 . The lon0.9-beta  and krak0.9-beta . Conserv0.9-beta .Wolbachia, by cataloging orthogroups which had gene members present in all arthropod Wolbachia but absent in filarial Wolbachia.OrthoFinder results, from the set of 81 genomes described above, were analyzed to identify gene loss events across different Wolbachia were identified as follows. A catalog of pseudogenes for each Wolbachia was first obtained from its NCBI-PGAP annotation. Next, for each pseudogene, the GenBank accession for its most likely intact ancestral protein was also obtained from the NCBI-PGAP annotations. For a phylogenetically consistent and robust analysis, instead of comparing these protein accessions directly, the corresponding ancestral orthogroup was retrieved from the results of OrthoFinder, and these “pseudogene-orthogroups” were compared across different Wolbachia. If the same orthogroup was found to be pseudogenized in more than one Wolbachia, the corresponding pseudogenes were designated as shared or common pseudogenes. The overlap between the ancestral pseudogene-orthogroups across different Wolbachia was visualized using the R package UpsetR (Pseudogenization of protein-coding genes is a major factor contributing to gene loss. Pseudogenes common across different supergroup F e UpsetR . PseudogwBpa-bfr and wMhie-bfr loci were calculated from pairwise alignments with intact genes from closely related Wolbachia, using PAL2NAL utility (bfr locus for this analysis was obtained via PhyML (wBpa and wBma against the wChemPL13 reference genome were obtained using Pseudofinder (The dN/dS values for  utility . The sub utility  using DA utility . The proia PhyML  analysisdofinder .Wolbachia phages (Wolbachia phage genes and pseudogenes were obtained based on the Phage-WO maps recently described (Wolbachia genomes were first identified using blastp and blastx searches. The orthogroups corresponding to these best hits were considered as phage-derived orthogroups, and the corresponding orthologs in supergroup F Wolbachia were annotated as phage-derived genes. Genes encoding IS-elements were excluded from this analysis to avoid mismatches and overcounting arising from the repeated and fragmented nature of genes in these families. Pseudogenes were annotated as phage-derived if their predicted ancestral proteins had an ortholog in the phage WO database. For cifA and cifB gene annotations, reference gene sequences were compiled from published data (Wolbachia genomes used in the phylogenomic analysis above, and the corresponding orthogroups and orthologs within supergroup F Wolbachia were annotated as cifA and cifB.Phage-derived genes and pseudogenes were annotated following the latest taxonomic classification scheme of a phages . The seqescribed . Their bhed data  and usedevad073_Supplementary_DataClick here for additional data file.


Mycobacterium avium subsp. paratuberculosis (MAP) infection are largely unknown. In the current study, we hypothesize that cows with an ability to produce higher levels of interferon-gamma (IFNɣ) might control MAP infection more successfully. To test this hypothesis, IFNɣ production was measured using a specific IFNɣ ELISA kit in avian purified protein derivative (aPPD)-stimulated blood samples collected from 152 Holstein cattle. DNA isolated from peripheral blood samples of the animals included in the study was genotyped with the EuroG Medium-Density Bead Chip, and the genotypes were imputed to whole-genome sequencing. A genome-wide association analysis (GWAS) revealed that high levels of IFNɣ in response to the aPPD were associated with a specific genetic profile (heritability = 0.64) and allowed the identification of 71 SNPs, 40 quantitative trait loci (QTL), and 104 candidate genes. A functional analysis using the 104 candidate genes revealed a significant enrichment of genes involved in the innate immune response and, more specifically, in necroptosis. Taken together, our results define a heritable and distinct immunogenetic profile associated with the production of high IFNɣ levels and with the capacity of the host to lyse MAP-infected macrophages by necroptosis.The mechanisms underlying host resistance to  Mycobacterium avium subsp. paratuberculosis (MAP), an intracellular, Gram-positive pathogen. The economic impact of PTB on the dairy industry has been estimated between USD 250 million and USD 1.5 billion annually in the US, and at USD 364.31 million per year in Europe [Paratuberculosis (PTB) is a chronic granulomatous enteritis that affects domestic and wild ruminants worldwide. It is caused by n Europe . Moreoven Europe . Currentn Europe . Currentn Europe . One comOnce ingested, MAP reaches the jejunum and ileum and crosses the intestinal mucosa by binding to fibronectin β1 receptors present on M cells located in Peyer’s patches and is then engulfed by subepithelial macrophages . MAP’s iPTB is a multifactorial disease that is the result of the interaction of genetic, environmental, and microbial factors. A possible explanation of the differences between cows in the disease outcome could be the presence of certain genetic profiles that could modify the immune response against MAP, thus making some cows more resistant than others. Host genetics play an important role in the susceptibility/resistance to MAP infection; therefore, a breeding strategy focused on increasing resistance to MAP infection and on reducing susceptibility is feasible and currently implemented in some countries ,20,21,22Defining the adequate phenotype is the main challenge in identifying the genetic profile of resistance against MAP infection. Reductionist approaches investigate a host biological subsystem, such as a key cellular function, whose performance is the criterion for the classification of the population . RecentlFriesian cattle included in this study were not submitted to any in vivo experimentation; therefore, no specific ethics committee authorization was needed. The cows were slaughtered in the Bilbao and Donostia municipal slaughterhouses  under the pertinent Basque (Basque Government Decree 454/1994), Spanish , and European (Council Regulation No1099/2009) legislation on animal welfare. The animals selected for the study consisted of 152 culled Holstein cattle from several herds located in seven Spanish regions: Basque Country (N = 45), Catalonia (N = 67), Navarre (N = 1), Cantabria (N = 30), Aragon (N = 1), Castile and Leon (N = 5), and Asturias (N = 1), along with two slaughtered cows of unknown origin. All cows were older than 2 years. These animals belonged to a reference population of 986 culled Holstein cattle that were slaughtered from March 2007 to May 2010. In each weekly visit to the abattoirs, 2–6 Holstein cows older than 2 years were sampled. The infection status of the 986 animals was determined by ELISA for the detection of MAP antibodies, PCR for the detection of MAP DNA in gut tissues, and histopathological analysis of gut tissues as previously described . For the® SA, Porriño, Spain), 100 µL of bovine purified protein derivative (bPPD) (0.3 µg/µL) , and 100 µL of lectin (1 μg/mL) as a positive control. Avian tuberculin PPD stimulating antigen contains a purified protein derivative prepared from the filtrate of a heat-killed Mycobacterium avium (strain D4ER) grown on a synthetic medium. Bovine tuberculin PPD is derived from Mycobacterium bovis, strain AN-5. After incubation for 16–24 h at 37 °C in a 5% CO2 incubator, the plasmas were separated by centrifugation at 500× g for 10 min at room temperature (RT) and then frozen at −20 °C until testing. Subsequently, IFNɣ levels were measured in triplicate in the plasma samples using a specific IFNɣ ELISA test according to the manufacturer’s instructions . Briefly, 50 µL of plasma diluent and 50 µL of each stimulated sample were pipetted into a 96-well plate, and the plate was incubated at RT for 60 min. The plate was then washed six times, 100 µL of the conjugate antibody was added, and the plate was incubated at RT for 60 min. After six more washes, 100 µL of the chromogen solution was added, and the plate was incubated for 30 min at RT. Finally, 50 µL of STOP solution was added, and the plate was read at 650 and 450 nm . IFNɣ levels were expressed as the OD of the aPPD or bPPD-stimulated plasmas minus the OD of the PBS-stimulated samples. The 152 cows from the 343 animals with an IGRA record were selected because they had an OD (aPPD − PBS) higher than the OD (bPPD − PBS), and the OD (aPPD) was also higher than the OD (PBS). This selection avoids false-positive reactions with Mycobacterium bovis infection and negative OD values, respectively. Three of the 152 animals with IGRA results did not have histopathology results. According to the interpretation criteria of the kit, 106 of the 152 cows included in the study were PTB-positive because the OD of the aPPD − PBS was higher than 0.05, and the OD (aPPD − PBS) was higher than the OD (bPPD − PBS). None of the 152 cows were tuberculosis-positive according to the IGRA kit interpretation criteria.IGRA was performed in blood samples from only 343 of the 986 culled cows. Blood stimulation was performed within the first 8 h after blood collection. Briefly, four 1.4 mL aliquots of lithium heparinized whole blood samples from each animal were added to four wells of a 24-well plate . The blood samples were then stimulated with 100 µL of phosphate-buffered saline (PBS), 100 µL of avian purified protein derivative (aPPD) (0.3 µg/µL) . The individual genotypes were imputed to WGS as previously described [Eagle2.-4. [minimac4 1.0.2 [Bos taurus from Run7.0 of the 1000 Bull Genomes project [2) > 0.7. The final number of SNPs per animal was 12,856,161.Peripheral blood (PB) samples were collected into 10 mL Vacutainer EDTA tubes  from the 152 cows of the study population at the time of slaughter. Total DNA was extracted from the PB samples using the QIAmp DNA Blood Mini Kit according to the manufacturer’s instructions . Purified DNA was then quantified by spectrophotometry and genotyped using the EuroG Medium-Density Bead Chip  at the molecular genetic laboratory service of the Spanish Federation of Holstein Cattle (CONAFE) using the escribed . Brieflygle2.-4.  and impuc4 1.0.2  to the B project . Finallyh2) explained by all the SNPs were calculated using the genome-wide complex trait analysis  and quantile–quantile plots were used to compare the observed distributions of the −log  to the expected distribution under the no-association model. A λ value close to 1 suggests appropriate adjustment for potential substructure, and λ > 1.2 suggests population stratification. The SNP effects  were also calculated using the GCTA1.93.2 software. If the sign of the b-value is positive, it implies that there is a positive relationship between the variables, i.e., SNP and IFNɣ levels.The IFNɣ levels in response to the aPPD, OD (aPPD − PBS), represented the quantitative phenotype in the GWAS analysis. The variance components and heritability  of 0.9.Results from the GWAS analysis were filtered by clumping. Briefly, clumping is a process that first selects the most significant SNP and clumps (removes) other SNPs in a particular window that are in linkage disequilibrium with the selected SNP. The clumping was performed using the software PLINK1.9  with a wp-value ≤ 5 × 10−7), QTLs, and candidate genes was performed using the ARS-UCD1.2 reference genome as previously described [After the clumping, the localization of the significant SNPs  pathways using the ClusterProfiler Bioconductor library in R ,43. The GCTA 1.93.2. [EBVs for IFNγ production and the effect of the of the 310 SNPs with evidence of association with the IFNγ production (FDR ≤ 0.05) were calculated for each animal in the study population using the genomic best linear unbiased prediction (gBLUP) model of  1.93.2. . Subsequt-test with the Welch–Satterthwaite correction was used to compare the IFNγ levels between animals with distinct PTB-associated lesions versus control cows without lesions . Associations between the number of animals with the highest IFNγ levels and positive ELISA and PCR results in gut tissues were analyzed with a Fisher exact test using jamovi2.3 [p-values were less than 0.05. Correlations between the EBVs for IFNγ levels and 65 phenotypes and traits included in the evaluations of Spanish Holstein cattle were calculated in a larger population (N = 1739) with the Spearman’s rank correlation (ρ) implemented in R4.1.2.An unpaired Student amovi2.3 . DiffereIFNɣ levels after blood stimulation with aPPD (OD (aPPD − PBS)) took values between 0.001 and 1.693, with most of the animals showing OD values between 0.001 and 0.750 . Figure p-value = 0.023). No statistically significant differences between the means of the other groups were found. The highest IFNγ levels corresponded to 12 cows with IFNγ levels that were two standard deviations above the mean and with diffuse (2/12), multifocal (1/12), and focal (8/12) lesions.. Interestingly, 50% (6/12) of the animals with the highest IFNγ levels had a positive PCR result for the detection of MAP DNA in gut tissues while only 16% (23/140) of the remaining animals had a PCR positive result . This result suggests that the highest IFNγ levels are significantly associated with the presence of MAP in gut tissues. No statistical differences were observed between high IFNγ levels and ELISA-positive results.The mean OD (aPPD − PBS) of the population was 0.217, and the highest value was 1.693. According to the interpretation criteria of the kit, 106 of the 152 cows included in the study had an IGRA-positive result. From the 56 animals without lesions, 35 showed a positive IGRA result. Those animals could be infected with MAP but did not have visible histopathological lesions at the time of slaughter. Significant differences between the cows without lesions (mean = 0.14) and with diffuse lesions (mean = 0.45) were observed . The Manhattan plot showing −log10  of the association test between IFNɣ levels and each SNP is represented in The associations between genome-wide imputed SNPs and IFNɣ levels after blood stimulation with aPPD (OD (aPPD – PBS)) (N = 152) were analyzed using p-value ≤ 5 × 10−7) located on 19 different Bos taurus chromosomes remained. This suggests that IFNɣ production in response to aPPD is a polygenic trait depending on many SNPs located in different chromosomes that altogether can explain approximately 64% of the variance of the phenotype. As seen in p-values) to the expected p-values under the null hypothesis is shown in The total number of SNPs that surpassed the significance criteria (FDR ≤ 0.05) was 310. After clumping, 71 SNPs . The QTL that harbored the most significantly associated SNP was in chromosome 26 , while the SNPs with the stronger effects were in chromosomes 10, 15, 17, and 21 . The b-values of all the identified SNPs were positive, which suggests that all variants were associated with high IFNɣ levels.The identified QTLs were distributed along the By looking in the cattle genome database, we found that some of the identified QTLs overlapped with QTLs associated with health , immunity (IgG levels), length of production life, milk content (milk linoleic acid content), fertility traits , and heat tolerance . The recSome of the identified candidate genes were previously associated with several bovine traits, highlighting their importance not only in health but also in fertility and length of productive life. For instance, the protein kinase cGMP-dependent type I (PRKG1) was associated with 39 bovine traits including PTB susceptibility (QTL: 169927) , bovine STRING11.5, a significant enrichment (FDR ≤ 0.05) of the necroptosis pathway (bta04217) was observed with five candidate genes belonging to this route .In addition, the identified candidate genes were also compared with human candidate genes previously identified for CD, UC, IBD, colorectal cancer, and tuberculosis. Interestingly, some candidate genes identified in our study, interleukin 18 receptor 1 (IL18R1) and interleukin 1 receptor like-1 (IL1RL1), were also found associated with CD, UC, and IBD. The interleukin 7 receptor (IL7R) was identified as a candidate gene associated with high IFNɣ production, as well as with UC and IBD. NEDD4, CNOT6L, and DNA repair protein RAD51 homolog 2 (RAD51B) were previously associated with IBD, tuberculosis, and colorectal cancer, respectively. From the 83 candidate genes with a recognized gene symbol, 57 (69%) were included in the InnateDB database and had a role in signaling pathways involved in the innate immune response of bovines to microbial infections including IL18R1, IL1RL1, IL7R, Ankyrin repeat domain-containing protein 1 acetylserotonin O-methyltransferase-like (ANKRD1), CD44 antigen precursor (CD44), two integrins (insulin-like growth factor 2 mRNA-binding protein 3 and integrin beta-like protein 1 precursor), an activator of the nuclear factor kappa B (NFKB1) signaling pathway , the mitogen-activated protein kinase 9 (MAPK9), the TNF receptor superfamily member 25 (TNFRSF25), and the TNF receptor-associated factor 5 (TRAF5). When the 57 candidate genes with roles in the innate immune response were analyzed with the p ≤ 0.05). As seen in To identify common biological functions in the 104 candidate genes, an enrichment analysis of metabolic pathways was performed using ClusterProfiler. Our results revealed a total of 15 enriched pathways , foot health index (ρ = 0.084), combined genetic index (ICO) (ρ = 0.082), longevity (ρ = 0.075), open days (ρ = 0.065), and all the economical indices included in the Spanish evaluations of Friesian cattle. All of these correlations were lower than ρ = 0.2.Understanding the host–pathogen interaction is key to advancing the development of effective control strategies against MAP infection. There is still a significant gap in our understanding of the protective mechanisms triggered after MAP exposure which might be variable depending on the specific host genetics. Even though the cellular immune response is crucial in the control of intracellular infections, the use of cellular immunity traits in genetic linkage studies in Holstein cattle is scarce. Recently, a strong effect of host genetics on the control of MAP load within infected monocyte-derived macrophages (MDMs) infected with MAP ex vivo was described . Similarp-value = 0.023), which suggests that the production of IFNɣ is not sufficient to protect against the most severe PTB forms. Even so, the highest IFNγ levels were significantly associated with the presence of focal lesions and MAP bacilli in gut tissues, which suggests a significant role of IFNγ in cows with focal lesions and with a specific genetic profile.In the current study, we measured the IFNγ production in response to the aPPD in 152 Holstein cows in two steps consisting of (i) incubating whole blood samples from the selected animals with aPPD, and (ii) detecting the presence of IFNγ released by sensitized lymphocytes in the whole blood sample to indicate a cell-mediated immune response to the specific antigen. The number of samples used in the current study could be considered small in comparison with traditional GWAS analysis using a larger population. However, the assessment of immunocompetence by measuring IFNɣ levels in stimulated blood is a functional and more controlled trait. Similar reductionist phenotypes such as the assessment of macrophage performance by measuring MAP load within MDMs used only 60 samples . In our h2 = 0.64) and identified 71 SNPs, 40 QTLs, 103 candidate genes, and one microRNA associated with high IFNɣ levels. Some of the QTLs identified in the current study overlapped with QTLs associated with health , immunity (IgG levels), length of production life, milk content (milk linoleic acid content), fertility traits , and heat tolerance. A QTL identified in our study on BTA1 overlapped with a QTL previously associated with PTB susceptibility (QTL:169891) [2+, and that PLA2G preferentially hydrolyzes phospholipids, resulting in the formation of arachidonic acid and lysophospholipids, the precursors of prostaglandins, leukotrienes, and platelet-activated factors [We showed that high IFNγ production is dependent on host genetics (:169891) , and two:169891) : 16:7151:169891) . In our  factors . Wang et factors , highlig2+, PLA2, and ultimately ROS [Emergent evidence strongly supports a role for necroptosis, a recently discovered proinflammatory cell programmed death pathway, in host defense against viral and bacterial pathogens including mycobacteria. Once produced by activated Th1 lymphocytes, IFNɣ is usually secreted to the extracellular environment. In macrophages, IFNɣ binds to its receptor complex composed of IFNGR1 and IFNGR2, and promotes the phosphorylation of STAT1 via JAK1 and JAK2. Phosphorylated STAT1 forms homodimers that bind gamma-activated sequences (GASs) in the nucleus resulting in the induction of proinflammatory cytokines and apoptotic factors such as TNF, IFN types I and III, and caspases 4 and 8 ,54,55. Itely ROS . To our tely ROS . Cells dMycobacterium tuberculosis-infected macrophages, but macrophage necroptosis is ultimately abrogated, thereby mitigating any pathophysiological role of necroptosis in tuberculosis [It was presumed that IFNI and III-driven necroptosis activation might exacerbate inflammation in specific circumstances. In contrast, some studies have demonstrated that necroptosis suppresses inflammation through abruptly terminating cytokine production and cell viability, and that the liberation of endogenous DAMPs is insufficient to cause aberrant inflammation . In humarculosis . Similarrculosis .h2 = 0.64) which opens the possibility of ranking Holstein cows on the basis of predicted IFNɣ production. The identified SNPs can be used to develop genetic evaluations for immunocompetence in the Spanish breeding program which would allow producers to select cattle more resistant to MAP infection, and likely for other intracellular pathogens, ultimately reducing the prevalence of diseases and the dependence on antimicrobials, preventing economic losses, increasing the length of cattle productive life, and improving food safety. In addition, our results have important implications regarding the use of the IGRA for the early diagnosis and control of MAP infection. Culling animals producing high levels of IFNɣ could result in the elimination of animals that are controlling MAP load, and that may never shed MAP or show clinical signs of PTB.We report a new phenotype, the production of high levels of IFNɣ, for evaluating resistance to MAP infection in dairy cows. The SNPs and candidate genes identified revealed an association between high IFNɣ levels after stimulation of blood samples with aPPD and genetics, which helps fill the knowledge gap linking host genetics, innate immune responses, and resistance to MAP infection in cattle. Our results define a heritable and distinct immunogenetic profile associated with the production of high IFNɣ levels and with the ability of the host to lyse infected macrophages by necroptosis. A strong effect of host genetics in producing high IFNɣ levels was observed (


In human genetics, heterozygote advantage (heterosis) has been detected in studies that focused on specific genes but not in genome-wide association studies (GWAS). For example, heterosis is believed to confer resistance to certain strains of malaria in patients heterozygous for the sickle-cell gene, haemoglobin S (HbS). Yet the power of allelic tests can be substantially diminished by heterosis. Since GWAS (and haplotype-associations) also utilize allelic tests, it is unclear to what degree GWAS could underachieve because heterosis is ignored.p-values across all the models and the proportion of tests that a given genetic model was deemed the best. An example dataset, based on 17 SNPs, from a robust genetic association study and simulated genotype datasets, were used to illustrate the method. Case–control genotype data on malaria from Kenya and Gambia were used for validation.In this study, a two-step approach to genetic association testing in malaria studies in a GWAS setting that may enhance the power of the tests was proposed, by identifying the underlying genetic model first before applying the association tests. Generalized linear models for dominant, recessive, additive, and heterotic effects were fitted and model selection was performed. This was achieved via tests of significance using the MAX and allelic tests, noting the minimum Results showed that the allelic test returned some false negatives under the heterosis model, suggesting reduced power in testing genetic association. Disparities were observed for some chromosomes in the Kenyan and Gambian datasets, including the sex chromosomes. Thus, GWAS and haplotype associations should be treated with caution, unless the underlying genetic model had been determined.The online version contains supplementary material available at 10.1186/s12936-023-04533-2. In human genetics, heterozygote advantage (heterosis) has been detected in studies that focused on specific genes , 2, but Simulation studies by Omolo and colleagues  showed tp-values for the MAX and allelic tests. The algorithm has reduced the computation burden of association tests. Zintzaras et  al. [Existing tests for association studies include Pearson’s chi-square test, the allelic test, the Cochran Armitage trend tests, and the MAX test among other tests. Pearson’s Chi-square test and the Cochran Armitage trend test (CATT) , 8 are k et  al.  studied  et  al.  derived  et  al. , the tes et  al.  used con et  al. . Similar et  al. , the mai et  al. –17 for ap value was selected for different markers. The p-values were adjusted for multiple comparisons using the Bonferroni method for SNPs with an allelic odds ratio greater than or equal to 1.5. The most significant SNPs were selected based on a threshold of p-values of the MAX4 test were obtained using the parametric bootstrap (boot), bivariate normal (bvn), and the asymptotic method (asy) [https://www.ebi.ac.uk/ega/).In this study, a two-step approach to genetic association testing in malaria studies in a GWAS setting was proposed that may enhance the power of the tests by identifying the underlying genetic model before applying the association tests. Firstly, generalized linear models for the dominant, recessive, additive, and heterotic effects were fitted using case–control genotype data. The model selection was then performed using the MAX test procedure . Here, tod (asy) . Genotypod (asy) , and malA and S with genotypes AA, AS, and SS as shown in Table A and S is found in Sasieni [A is the allele causing disease, which confers a high risk of malaria disease. The corresponding three genotypes are AA, AS and SS, denoted by r cases and s controls be represented by A allele in the heterozygous phenotype is sufficient to cause a disease similar to two copies of the A allele, i.e AA genotype. The penetrance relationship is AS has the largest effect on disease risk, i.e Consider a genetic marker with alleles  Sasieni . Assume entclass1pt{minimapq, and A was set at 0.1 and was used to determine the genotype distributions under the conditions of HWE [Genotype data sets from a case–control study design were simulated. The frequency of both cases and controls maintained the HWE under multinomial distribution. Data were also simulated to violate the HWE assumption of allele frequencies Rassoc [R was used to generate the statistics and the p-values of the tests. This R package has Monte Carlo and asymptotic algorithms of the MAX3, CATT, allelic, and other commonly used tests in case–control studies. The algorithms calculated the p-values using the parametric bootstrap method, the bivariate normal distribution, and the asymptotic null distribution method. The algorithms were improved to incorporate the heterotic effect using the overdominance-related penetrance function.The MAX4 test was applied to an example dataset . There were 3340 samples from Kenya and 2780 samples from the Gambia in the datasets. Each sample had 23 chromosomes, including the sex chromosome. There were different frequencies of markers on each chromosome. All cases were diagnosed in a hospital, where blood samples from children diagnosed with severe malaria were collected. The controls were from within the general population and from new births with unrelated individuals. The blood samples were from the same geographic area as the cases. Deoxyribonucleic acid (DNA) was extracted from blood samples and examined at SNP Illumina arrays [Malaria datasets with genotype data for cases and controls from two populations obtained from the Gambia and Kenya were used  for the Kenyan population and dataset ID EGAD00010000572 (1533 controls and 1247 cases) for the Gambian population. Different samples were picked from different geographical locations across the two countries to enhance genetic diversity in African countries. The initial study and description of the datasets are available at Band et al. [SNPTEST v2.4.1 software was used to pre-process data to obtain case–control summary statistics on genotype counts, chromosome positions, allele frequency, and odds ratios (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest_v2.4.1.html). The MAX4 and the allelic tests were performed in the presence of an overdominant model. All statistical analyses were conducted in R studio version 4.2.0 [The malaria datasets for the study were under EGA data d et al. . SNPTESTon 4.2.0 .r is the number of cases, s is the number of controls and n is the total number of cases and controls. The Cochran-Armitage trend test (CATT) and the chi-square have been well-studied for single variants . The CATe models . ConsideN and can be expressed as a joint density function m replicates was used to approximate the null distribution of the MAX4. The p-values were estimated from the empirical null distribution of the MAX4 [The MAX test statistic is defined 2|,|Z1|) . It consare used . Therefoussed in , 11. ParA simulation study to investigate the significance of the MAX4 test in comparison with the allelic test was performed. A multinomial distribution was assumed for both cases and controls in violation of HWE, with model selection performed to investigate the underlying genetic models. Additional file rs17157903, rs7696175, and rs2820037. Many SNPs returned significant results for the dominant, recessive, additive, and heterotic chi-square tests with more significance under the additive model compared with the other genetic models , 2712, 2One of the significant findings from the GWAS analyses was the uneven distribution of the disparities in the association test results between the MAX4 test and the allelic test across the chromosomes Tables . It turnFigures All SNPs were tested for compliance with the HWE before genetic association testing. It was noted that the prevalence of heterotic associations was higher in the Kenyan dataset than the Gambian dataset, further highlighting the genetic diversity between the two populations from the Eastern and Western regions of Africa, respectively. Recent GWAS have implicated chromosome 6 with the SNPs associated with drug-resistant to severe malaria . The recExisting research in single-SNP and genome-wide studies tend to overlook overdominance and underdominance, even though the circumstances reduce the power of allelic tests . The resBased on simulation studies conducted by Omolo et al. , who cauAdditional file 1: Text S1. Distribution of Z under overdominance.Additional file 2: Table S1. Genotype distributions of the 17 SNPs selected from GWAS; Age-related macular degeneration(AMD), Prostate Cancer (PC), Breast cancer(BC) and Hypertension(HP).Addition file 3: Table S2. Chi-square tests and the MAX test results of the 17 SNPs selected from the GWAS; Age-related macular degeneration (AMD), Prostate Cancer (PC), Breast cancer (BC) and Hypertension (HP). The chi-square tests were performed under dominant(DOM), recessive (REC), additive (ADD), and heterotic (HET) models. The genotype test was also performed and the results shown.Additional file 4: Table S3. The MAX4 and the ABT tests were performed on the Gambian datasets for the additive, recessive, and dominant models. Results show there was no disparity between the two tests.Additional file 5: Table S4. The MAX4 and the ABT tests were performed on the Kenyan datasets for the additive, recessive, and dominant models. Results show there was no disparity between the two tests.Additional file 6: Table S5. Simulation results of some most significant SNPs selected.Additional file 7: Text S2. R codes for MAX4 the and Allelic test for Kenyan datasets.Additional file 8: Text S3. Manhattan and quantile-quantile plots for selected chromosomes for Kenyan datasets.Additional file 9: Text S4. Simulation codes.Additional file 10: Figure S1. Manhattan plots of association findings for additive, dominant, recessive, and overdominant models for selected chromosomes. The MAX4 test performs model selection using the P-value approach for the Kenyan datasets.Additional file 11: Figure S2. Quantile-quantile plots of association findings for additive, dominant, recessive, and overdominant models for selected chromosomes. The MAX test performs model selection using the P-value approach for the Kenyan datasets.


The cuticle is an invisible barrier that protects the internal egg contents from microorganisms entering through gas exchange pores. Eggs which have a good cuticle are least likely to be penetrated by microorganisms and improved cuticle cover should reduce vertical transmission of microorganisms and improve biosecurity. The aim was to carry out a genome wide association study for cuticle deposition in 3 independent populations of laying hens using tartrazine and lissamine green staining. Eggs from ∼8,000 hens represented 2 White Leghorn and 1 Rhode Island Red breed. Estimates of heritability using pedigree or genomic relationship matrices were in the 0.2 to 0.3 range.The results were breed specific. Across the populations, genomic regions on chromosomes 1, 2, 4, 5, and 8 were identified as significantly associated with cuticle deposition. No single loci had a large effect. A comparison was made with genes differentially expressed in the shell gland when cuticle deposition was manipulated, however none were obvious candidates for cuticle deposition. The results support the polygenic nature of the trait and the information will help in the future to understand the genetic variance and what might control cuticle deposition and the microbiological safety of the egg. The cuticle on domestic hen's egg is essentially invisible. It forms a gas permeable barrier layer on the exterior of the egg . The cutWe have previously demonstrated that cuticle deposition has a strong genetic determination with moderate heritability of around 0.38 in a meta-analysis across breeds . Beyond Understanding what controls the deposition of the cuticle and thus the potential of genomic selection can help strengthen the protection the cuticle offers to the egg content from harmful micro-organisms. The transmission of bacteria from parent to offspring is an obvious aspect of biosecurity that the cuticle can improve for the poultry industry. In chickens such vertical transmission, as well as horizontal transmission from the environment, can also threaten embryo development .Although the poultry industry already has an advantage with the separation of the mother and offspring due to artificial incubation, there is still the potential for exposure of the egg to microorganisms while being laid and from the environment before the egg is collected for incubation.Thus, the aim of the current study was to discover if there were genomic loci that were responsible for a significant component of the genetic variance for cuticle deposition that might lead to understanding what controls the deposition of the cuticle. It was hoped that the genes identified from comparing hens where the cuticle deposition was manipulated hormonally might be colocated in these regions . HoweverThis study did not involve experimentation on animals only the measurement of eggs.1 was a White Leghorn breed contributing to the Hy-line W-36 commercial egg layer, and WL2 is also a White Leghorn breed contributing to the Lohmann LSL commercial egg layer. One egg was collected from each hen at 43 to 50 wk of age for RIR, 47 to 54 wk of age for WL1 and 2 eggs at 46 wk of age for WL2. Changes in cuticle deposition with age were not observed previously when individual hens were followed  or measurement was made using the Ecutimeter 2 (breed WL2) as previously described using the difference in absorbance at 640 nm (ΔAbs@640 nm)  . For bro1 only poststaining color measurement value was taken and used as an estimate of cuticle deposition and the L* value was used to estimate the intensity of staining as we had also shown that it was highly correlated  with absorbance at 640 nm , White Leghorn , and White Leghorn  hens with cuticle phenotypes were genotyped on separate breed-specific Axiom 54k SNP chips. Quality control using 5% MAF and 90% call rate performed in PLINK  followed by the RIR breed (h2 = 0.20) and finally the WL1 breed (h2 = 0.14). Variance components are given in h2 = 0.38 ±0.08 for the WL2 breed, h2 = 0.28 ± 0.04 for the RIR breed and h2 = 0.19 ± 0.03 for the WL1 breed.In all breeds estimates of genomic heritability were significantly different from zero, with the highest estimate being in WLGWAS) results can be found in 1 and WL2 breed. Of these, 7 SNPs were on chromosome 8 in the WL2 breed. Further, 7 SNPs were considered suggestive at a multiple testing adjusted alpha of 0.1. These were distributed across 4 genomic regions on 4 chromosomes  in the RIR, WL1, and WL2 breeds. Of these, 4 SNPs were associated with the chromosome 8 locus in the WL2 breed.A summary of genome wide association analysis (2 breed (The most significant SNP was on chromosome 2 (AX-76089627) in the WL2 breed . This SN2, breed contains a number of significant markers . When this was compared to a list of genes that were differentially expressed in an experiment to manipulate cuticle deposition  there we2 they were moderate and similar to that previously reported  and not informative in the RIR breed. However, that does not mean the loci do not harbor genes or control regions that influence the variation in cuticle deposition in those breeds. Comparison of the potential candidate genes from the shell gland when cuticle deposition was manipulated  of variation in cuticle deposition was explained by genetics, whether estimated by pedigree or markers as has been previously reported for a number of poultry breeds using slightly different measurement methods based on tartrazine and lissamine green staining. A few significant association signals were identified but no QTL with large effect were found, suggesting a polygenic nature for the trait. There are a number of leads to follow which have been discussed and a wider list of potential candidates generated that may help understand what is responsible for genetic variance in cuticle deposition and perhaps ultimately to what may control its deposition when put in the context of future studies.For the purpose of open access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript version arising from this submission.


Whole-genome sequence (WGS) data harbor causative variants that may not be present in standard single nucleotide polymorphism (SNP) chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k sequence imputed animals per line.Two maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1365 to 1491 for the maternal lines and 381 to 1865 for the terminal lines. Imputation to sequence occurred within each line for 66k to 76k animals for the maternal lines and 29k to 104k animals for the terminal lines. Two preselected SNP sets were generated based on a genome-wide association study (GWAS). Top40k included the SNPs with the lowest p-value in each of the 40k genomic windows, and ChipPlusSign included significant variants integrated into the porcine SNP chip used for routine genotyping. We compared the performance of single-step genomic predictions between using preselected SNP sets assuming equal or different variances and the standard porcine SNP chip.In the maternal lines, ChipPlusSign and Top40k showed an average increase in accuracy of 0.6 and 4.9%, respectively, compared to the regular porcine SNP chip. The greatest increase was obtained with Top40k, particularly for fertility traits, for which the initial accuracy based on the standard SNP chip was low. However, in the terminal lines, Top40k resulted in an average loss of accuracy of 1%. ChipPlusSign provided a positive, although small, gain in accuracy (0.9%). Assigning different variances for the SNPs slightly improved accuracies when using variances obtained from BayesR. However, increases were inconsistent across the lines and traits.The benefit of using sequence data depends on the line, the size of the genotyped population, and how the WGS variants are preselected. When WGS data are available on hundreds of thousands of animals, using sequence data presents an advantage but this remains limited in pigs.The online version contains supplementary material available at 10.1186/s12711-023-00831-0. Using single nucleotide polymorphism (SNP) chip data for genomic prediction relies on the linkage disequilibrium (LD) between SNPs and causative variants . BecauseImputation is an inevitable step when working with WGS data because sequencing many individuals is still not feasible. Currently, the most efficient approach is to sequence a subset of the animals in a population and impute the sequence data to other animals that are already genotyped with SNP arrays . Using aFragomeni et al.  used simNe = 200). In contrast, populations with a small effective size (Ne = 20) require more genotyped animals. Although Ne plays a role, discovering causative variants remains difficult if the genotyped animals have a limited number of progeny with records.Using simulated sequence data, Jang et al.  investigNe of typical commercial breeding populations ranges from 30 to 50 and the dimensionality of the genomic information or the number of independent chromosome segments (Me) that segregate in a population ranges from 4000 to 6000 [Ne of 20 allowed the detection of causative variants that explain 20% of the additive genetic variance. Moreover, larger sample sizes resulted in better prediction accuracies with variants selected from GWAS. Recently, Ros-Freixedes et al. [In pigs, the  to 6000 . Based o to 6000 , using as et al.  and Ros-s et al. , 22. Hows et al. –25 or sss et al. ) are advs et al. , 27–29. Datasets provided by the Pig Improvement Company  comprised two maternal lines ML1 and ML2) and four terminal lines  with diverse genetic backgrounds, for which the breeds of origin were Landrace and Large White for the maternal lines, and Duroc, Hampshire, and Large White for the terminal lines. For the maternal lines, we analyzed average daily feed intake (ADFI), average daily gain (ADG), backfat thickness (BF), loin depth (LDP), total number of piglets born (TNB), number of stillborn (NSB), return to oestrus seven days after weaning (RET), and litter weaning weight (WWT). For the terminal lines, we analyzed ADFI in the purebreds, and ADG, BF, and LDP in both purebreds and crossbreds . The total number of animals in the pedigree and records for each trait are in Table  and ML2 Some traits were jointly analyzed in multi-trait models for genomic prediction. For the maternal lines, two-trait models were considered for ADG and ADFI (ADFI model), ADG, and BF (GROWTH model), ADG and LDP (LOIN model), and TNB and NSB (REPROD model), but single-trait models were used for RET (RET model) and WWT (WWT model). For the terminal lines, the ADFI model used for the maternal lines was also applied, but four-trait models were used for the GROWTH  and the LOIN  models.Pigs were initially genotyped with either the GGP-Porcine LD BeadChip or the HD BeadChip  and then were imputed up to 50k. In each line, we filtered out SNPs that were monomorphic, with a call rate lower than 0.90, a minor allele frequency lower than 0.01, and a difference between observed and expected genotype frequencies greater than 0.15. We also removed the individuals with more than 10% missing genotypes. Table The WGS data used in this study were generated by Ros-Freixedes et al.  and Ros-Before the GWAS, all animals with WGS data were separated into training and test sets, as defined in Ros-Freixedes et al. . Test se–6), based on a significance threshold of 0.05 that accounts for multiple-testing using the Bonferroni correction. We assumed that the markers from the Chip (~ 40k) were independent. When multiple significant variants were within a 55-kb window, only the variant with the lowest p-value was selected. We used a univariate linear mixed model for single-SNP GWAS within each trait and line through the FastLMM software [Two different pre-selected SNP panels were created based on the WGS data for genomic prediction, as described in Ros-Freixedes et al. : 1) Topsoftware , by incls et al. . Genomicentclass1pt{minima Topsoftw0, and Single-trait, two-trait, or four-trait models were used for genomic prediction, depending on the traits. Here, only the four-trait GROWTH model  of terminal lines is described:programs , which u follows :\documenVanRaden :\documenThe algorithm for proven and young (APY) was applied to obtain cument}G  for linehip data . Based oFor WssGBLUP, we calculated SNP variances from BayesR  and assie et al. . This ap1) of the regression of dEBV on GEBV. Estimates of b1 smaller than 1 indicated inflation of GEBV and estimates greater than 1 indicated deflation. Animals without dEBV, due to the lack of phenotypes, were removed from the test sets. Therefore, each model had different numbers of test animals. The number of test animals for each model in all lines are summarized in Additional file The accuracy of genomic prediction was calculated by correlating GEBV with deregressed EBV (dEBV) for the animals in the test sets , where dEBV were derived without genomic information using the method of VanRaden et al. . InflatiFigure For the four terminal lines, the changes in prediction accuracy (%) of ChipPlusSign and Top40k compared to Chip are described in Fig. 1 for all genotyped maternal (a) and terminal lines (b) scenarios. Estimates for each genotype scenario were averaged across all traits for each line. All other estimates for b1 in the maternal and terminal lines are summarized in Additional file 1 approached 1.0 (0.68 in ML1 and 0.81 in ML2). More specifically, all genotyping panels resulted in a smaller inflation of GEBV in ML2 than in ML1. Results for Chip, ChipPlusSign, and Top40k were similar within each line.Figure Compared to the results for the maternal lines, those for the terminal lines were inconsistent among traits and lines. Overall, all terminal lines showed inflated GEBV for all traits and genotyping panels. On average, the best result was obtained in TL3 (0.75), followed by TL4, TL1, and TL2 . Chip, ChipPlusSign, and Top40k yielded similar results in TL3 and TL4, but in TL1 and TL2, Top40k showed either less inflation (+ 0.05) or more inflation (− 0.13), respectively, compared to Chip and ChipPlusSign.1 are in Table 1 estimates, except for a few scenarios  for growth-related traits  with Top40k and ChipPlusSign. The results for prediction accuracy and bThe current study investigated the impact of using large-scale WGS data for genomic prediction through ssGBLUP and WssGBLUP in maternal and terminal pig lines. This is the first study that applies ssGBLUP to large-scale WGS pig datasets, with up to 1.8k sequenced and 104k imputed sequenced animals per line. We used two sets of preselected WGS variants to compare the performance of genomic predictions to that of the regular SNP chip. Our results show that preselected variants can outperform the regular SNP chip in genomic prediction. However, this advantage is not consistent across the lines and traits examined, and the improvement achieved was relatively limited, as previously reported for the same data using a BayesR approach [–6) based on GWAS were added to the regular SNP chip with an expectation of better prediction accuracy if the significant SNPs had large effects or were causative and were not present on the regular SNP chip. Incorporating preselected, significant SNPs into the regular SNP chip has been investigated in many studies with WGS data [Theoretically, using WGS data can improve genomic predictions because they cover the entire genome, and thus it is assumed that they include the causative variants. As a result, genomic prediction using WGS data does not rely on the LD between SNPs and causative variants but can directly use the causative variants . Thus, uWGS data , 13, 39.WGS data , who useAmong the preselected genotyping sets, ChipPlusSign showed small to moderate gains in accuracy for many traits in the maternal and terminal lines. This panel also showed the most consistent results across lines and traits, with gains in accuracy observed in most cases, but within a limited range (from 0.1 to 2.6%). ChipPlusSign also showed greater robustness in the performance of genomic prediction than Top40k when the genomic prediction was performed using BayesR on the same data .Several studies have been conducted to investigate genomic prediction by adding preselected variants to the regular chip data using real or simulated datasets , 17, 39.Ne the maximum gain in accuracy ranged from 0.86 to 1.98% when either significant variants or hundreds of variants with a large effect size that were preselected from GWAS were added to a 50k SNP chip. In their scenario, they simulated a Ne of 20, which is close to the Ne in pig populations (32–48) [Ne of real pig data seemed to be greater than 20 due to the variation between the populations.In a simulation study, Jang et al.  investig (32–48)  althoughhttps://www.angus.org/AGI/global/AngusGS.pdf). In addition, heritabilities for RET were lower than for other traits. Consequently, this trait had the lowest prediction accuracies (0.14 in ML1 and 0.20 in ML2 with Chip) among the other traits in the maternal lines. Thus, genomic prediction using preselected genotype data is expected to lead to greater improvements in accuracy if the SNPs on the regular chip do not explain a large proportion of the genetic variance. Therefore, we speculated that there would be more informative SNPs in Top40k for RET, which are not included in the regular SNP chip. Likewise, the small differences in accuracy observed between Chip and Top40k were likely to result from variants on these chips capturing similar proportions of genetic variance and having similar LD patterns across the genome. Although we observed inconsistent results between the terminal lines using Top40k, ADGX showed the highest gain (16.2% in TL2), which was recorded in the crossbred animals. ADGX was investigated in the GROWTH model along with three correlated traits , and Top40k was created based on GWAS for ADG and BF, individually, after which each Top40k was combined for genomic prediction. Thus, this result shows the potential benefits for improving genomic prediction of traits recorded in crossbred animals if many phenotypes are available for both purebreds and crossbreds. However, crossbred traits were not directly used for preselecting variants because WGS information was available only on purebred animals.In our study, the results obtained with Top40k depended highly on the trait and line. Top40k showed the greatest gain in accuracy for RET across all maternal lines (22.9 to 34.8%) but relatively marginal gains or losses for the other traits. In the terminal lines, the results for Top40k fluctuated more among lines, with increases or decreases in accuracy. The large improvement observed for reproduction or fertility traits in the maternal lines might be due to the nature of these traits and the lack of informative SNPs for these traits on the regular SNP chip. For example, the lack of informative SNPs for fertility traits led to a recent change in the SNP chip for beef cattle evaluations (Ne = 200). However, only a small proportion of the causative variants were discovered for highly polygenic traits and their study showed that populations with a smaller effective size (Ne = 20) required much more data to capture the causative variants. For example, when 30k genotyped animals were used in GWAS for highly polygenic traits, only three causative variants that explained 3.9% of the genetic variance were identified. In addition, adding preselected variants to regular chip data yielded a maximum gain in accuracy of nearly 2% for the scenarios with Ne = 20. In the current study, the number of WGS animals used for GWAS ranged from 29 to 104k, which is the largest WGS data used for GWAS in pigs by far. However, fine-mapping the causative variants was still challenging and the benefits for genomic predictions were limited [Ne and most of the traits are highly polygenic, to capture the most informative variants, it is necessary to have a very large number of WGS animals  with many progeny records [The marginal gains in accuracy that were observed for most traits when using ChipPlusSign and Top40k raised a question about the amount of information that has been used for preselecting the variants and performing genomic predictions. Examining the dimensionality of the genomic information can help assess the number of genotyped animals needed to maximize the percentage of variant discoveries in GWAS and gains in prediction accuracy . Accordi limited . Since c records .Ne, Jang et al. [In an initial series of analyses (results not shown), we used only significant variants (TopSign) for genomic prediction, and found no benefit compared to Chip. In fact, we observed a loss in accuracy for most traits and lines. Depending on the line and trait, the number of variants in TopSign ranged from 6 to 1705. Fragomeni et al.  reportedNe and highly polygenic traits.With the decrease in sequencing costs, the use of WGS data for genomic prediction in livestock  is more affordable than in the past. Several studies have reported marginal or no benefits of using WGS for genomic prediction in sheep, beef, and dairy cattle , 12, 39.Ne. Therefore, increasing only the sample size might not help improve the performance of both variant selection and genomic prediction. Jang et al. [Ne implies a lower Me, which means that the number of SNPs on the 50k chip is likely sufficient to represent the chromosome segments in the populations to cover the entire genome. Theoretically, the ideal number of SNPs on chip panels can be calculated as 12 Me [Me ranges from ~ 4k to 6k [In the current study, the number of sequenced animals ranged from ~ 380 to 1.8k across lines, which represented nearly 2% of the population in each line , 7. Incrg et al.  reportedent}×Me . In comm4k to 6k , meaning4k to 6k .Another possible reason for the limited benefit of using WGS data could be the imputation accuracy , 43. TheThe current study performed genomic prediction using ssGBLUP for the following reasons: it can consider multi-trait models and virtually any mixed linear model used for genomic evaluation  . The stuFollowing the increased accuracy reported by Gualdrón-Duarte et al. , we usedment}σg2 , which wPreselection of significant variants from WGS data and their use in genomic prediction can help to improve genomic predictions in maternal and terminal pig lines with tens of thousands of sequenced/imputed animals. However, limited gains are noted even in large populations. Improvements may be observed when significant variants for some traits are not already represented by the SNPs present on the commercial SNP chips and with that, traits that have a limited accuracy may experience extra gains. Weighting SNPs using BayesR variances slightly improved prediction accuracies. The performance of genomic predictions using preselected variant sets depends highly on the population structure, number of genotyped animals, and method used to select the variants.Additional file 1: Table S1. Combination of selected variants for each model. ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; TNB: total number of piglets born; NSB: number of stillborn; RET: return to oestrus seven days after weaning; WWT: litter weaning weight; Top40k: Top40k preselected genotype panel; ChipPlusSign: ChipPlusSign preselected genotype panel. Table S2. Number of animals and SNPs for the pre-selected SNP panels in the maternal lines. ML1: maternal line 1; ML2: maternal line 2; Top40k: Top40k preselected genotype panel; *ChipPlusSign: ChipPlusSign preselected genotype panel. Table S3. Number of animals and SNPs for the pre-selected SNP panels in the terminal lines. TL1: terminal line 1; TL2: terminal line 2; TL3: terminal line 3; TL4: terminal line 4; Top40k: Top40k preselected genotype panel; ChipPlusSign: ChipPlusSign preselected genotype panel. Table S4. Number of training and test animals for each model in the two maternal and four terminal lines. ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; TNB: total number of piglets born; NSB: number of stillborn; RET: return to oestrus seven days after weaning; WWT: litter weaning weight; ADGX: ADG recorded in crossbred; BFX: BF recorded in crossbred; LDPX: LDP recorded in crossbred. Table S5. Prediction accuracy of the maternal lines using ssGBLUP. ML1: maternal line 1; ML2: maternal line 2; ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; TNB: total number of piglets born; NSB: number of stillborn; RET: return to oestrus seven days after weaning; WWT: litter weaning weight; standard errors are presented in parenthesis. Table S6. Accuracy gain and reduction (%) compared to Chip data in the maternal lines. ML1: maternal line 1; ML2: maternal line 2; ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; TNB: total number of piglets born; NSB: number of stillborn; RET: return to oestrus seven days after weaning; WWT: litter weaning weight. Table S7. Prediction accuracy of the terminal lines using ssGBLUP. TL1: terminal line 1; TL2: terminal line 2; TL3: terminal line 3; TL4: terminal line 4; ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; ADGX: ADG recorded in crossbred; BFX: BF recorded in crossbred; LDPX: LDP recorded in crossbred; standard errors are presented in parenthesis. Table S8. Accuracy gain and reduction (%) compared to Chip data in the terminal lines. TL1: terminal line 1; TL2: terminal line 2; TL3: terminal line 3; TL4: terminal line 4; ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; ADGX: ADG recorded in crossbred; BFX: BF recorded in crossbred; LDPX: LDP recorded in crossbred. Table S9. Slope (b1) of the regression of dEBV on GEBV for the maternal lines using ssGBLUP. ML1: maternal line 1; ML2: maternal line 2; ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; TNB: total number of piglets born; NSB: number of stillborn; RET: return to oestrus seven days after weaning; WWT: litter weaning weight; Standard errors are presented in parenthesis. Table S10. Slope (b1) of the regression of dEBV on GEBV for the terminal lines using ssGBLUP. TL1: terminal line 1; TL2: terminal line 2; TL3: terminal line 3; TL4: terminal line 4; ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; ADGX: ADG recorded in crossbred; BFX: BF recorded in crossbred; LDPX: LDP recorded in crossbred; Standard errors are presented in parenthesis. Table S11. Slope (b1) of the regression of dEBV on GEBV for WssGBLUP compared to ssGBLUP. ML1: maternal line 1; ML2: maternal line 2; TL1: terminal line 1; TL4: terminal line 4; ADFI: average daily feed intake; ADG: average daily gain; BF: backfat thickness; LDP: loin depth; Top40k: top40k preselected genotype panel; Top40k weighted: top40k using BayesR weighting; ChipPlusSign: ChipPlusSign preselected genotype panel; ChipPlusSign weighted: ChipPlusSign using BayesR weighting; Standard errors are presented in parenthesis


Several biological mechanisms affecting the sperm and ova fertility and viability at developmental stages of the reproductive cycle resulted in observable transmission ratio distortion . Gene-by-gene interactions (or epistasis) could also potentially cause specific transmission ratio distortion patterns at different loci as unfavorable allelic combinations are under-represented, exhibiting deviation from Mendelian proportions. Here, we aimed to detect pairs of loci with epistatic transmission ratio distortion using 283,817 parent-offspring genotyped trios (sire-dam-offspring) of Holstein cattle. Allelic and genotypic parameterization for epistatic transmission ratio distortion were developed and implemented to scan the whole genome. Different epistatic transmission ratio distortion patterns were observed. Using genotypic models, 7, 19 and 6 pairs of genomic regions were found with decisive evidence with additive-by-additive, additive-by-dominance/dominance-by-additive and dominance-by-dominance effects, respectively. Using the allelic transmission ratio distortion model, more insight was gained in understanding the penetrance of single-locus distortions, revealing 17 pairs of SNPs. Scanning for the depletion of individuals carrying pairs of homozygous genotypes for unlinked loci, revealed 56 pairs of SNPs with recessive epistatic transmission ratio distortion patterns. The maximum number of expected homozygous offspring, with none of them observed, was 23. Finally, in this study, we identified candidate genomic regions harboring epistatic interactions with potential biological implications in economically important traits, such as reproduction. Although the genetic background of TRD is mainly related to single-locus factor , interactions between different loci  could also potentially cause specific TRD patterns , defined as a departure from Mendelian expectations , is assopatterns . Within patterns , whereaspatterns . In addipatterns .There are documented cases in model organisms, plants and fish where epistatic interactions explain the variation of the observed TRD . EpistatDespite the additive paradigm being widely predominant for the technical statistical and computational limitations, the importance of interaction components of the genetic architecture of traits has recently been emphasized . It mustThe conventional method of identifying epistatic TRD relies on performing a screen for pairs of loci showing deviations from the expected assuming random segregation and the majority of methods are restricted to an F2 design. The allelic and genotypic TRD models developed by Bos taurus genome assembly. This data was previously imputed using FImpute . The number of genotyped trios (sire-dam-offspring) was 283,817 and were sampled from all available Holstein genotypes  with offspring genotyped within 90 days of the birth, thus minimizing selection artifacts . The num FImpute  to 47,91 FImpute .Epistatic transmission ratio distortion was evaluated on pairs of SNPs across the whole genome  using three different methods. In order to avoid confounding due to linkage disequilibrium (LD) within chromosomes and to reduce the computational time, epistatic TRD scan was restricted to inter-chromosomal pairs.Genotypic parameterization of epistatic transmission ratio distortion. Following off) for each combination of genotypes of a given pair of two loci (A and B) as: A and δA are additive- and dominance-TRD parameters for locus A, respectively, αB and δB are additive- and dominance-TRD parameters for locus B, respectively, ααe, αδe, δαe and δδe are additive-by-additive, additive-by-dominance, dominance-by-additive and dominance-by-dominance epistatic TRD parameters, respectively. The parametric space considered for all the parameters ranges from −1 to 1. Due to the complexity of this model and the number of parameters involved, negative probabilities must be rescaled to 0 for simplification. An additional restriction is needed to guarantee Poff (AABB) + Poff (AaBB) + Poff (aaBB) + Poff (AABb) + Poff (AaBb) + Poff (aaBb) + Poff (AAbb) + Poff (Aabb) + Poff (aabb) = 1. On the other hand, it must be emphasized that all the equations of (offspring) probabilities described here correspond to the unique case of heterozygous-by-heterozygous mating for both loci , and the probabilities needs to be adapted for each specific mating accordingly. For instance, the Poff (AABB) becomes 4 + 1 αA + 1 αB - 1 δA - 1 δB + 1 ααe + 1 αδe + 1 δαe - 1 δδe whereas Poff (AABb) is 0 for AaBB × AaBB mating. The total number of possible informative matings is 65 and which could be combined in 27 types of matings summarized in the y is the column vector of genotypes of the offspring generation.Under a Bayesian implementation, the conditional posterior probabilities of the TRD parameters are defined as:αα, BFαδ, BFδα, BFδδ)/maximum  > 1,000 was used to select the regions with more statistical evidence of epistatic effects rather direct effects. The BF was also used for the multiple test correction where the top 0.1% significant regions were selected as the most relevant epistatic TRD regions.In order to reduce the computational time, only pairs of SNPs with ≥1,000 informative offspring and ≥20 heterozygous sires or/and ≥50 heterozygous dams were used. Secondly, the observed and expected number of offspring for the 27 combined matings were determined for each pair of loci. The analyses included only pairs of SNPs with ≥1,000 under- or over-represented offspring for a specific two-locus genotype offspring and mating and this difference (between the observed and expected) being relatively 10% higher/lower than the expected number of offspring. Notice that these criteria and cut-off values were chosen based in the results observed in single-locus TRD analyses  and all Allelic parameterization of epistatic transmission ratio distortion. An allelic epistatic TRD could be targeted by tracing back the inheritance of a combination of two specific alleles of the two loci  from parents to offspring. Thus, the probability of an offspring (Poff) for each combination of genotypes of a given par of two loci (A and B) was parameterized to include both direct and epistatic allelic TRD effects as:A and βB are direct TRD parameters for locus A and B, respectively, βAB/Ab, βAB/aB, βAB/ab, βAb/aB, βAb/ab and βaB/ab are the 6 heterozygous pairwise combinations for epistatic TRD parameters. Note that AB, Ab, aB and ab are the 4 possible artificial haplotype alleles of the two implicated SNPs. For all parameters, flat priors were assumed within a parametric space ranging from −0.5 to 0.5 for direct TRD effects and from −1.0 to 1.0 for epistatic TRD effects. The parametric space of direct effect is based on the principles of Mendelian inheritance  to 1 , and where 0.5  corresponds to null TRD). In contrast, the range from −1 to 1 for epistatic parameters could be viewed as the positive/negative preferential transmission of one combination of two alleles  or its opposite heterozygous combinations, respectively, whereas 0 indicates null epistatic TRD. Also, these probabilities corresponded to the unique case of heterozygous-by-heterozygous mating , and the probabilities must be adapted for each specific mating accordingly.y is the column vector of genotypes of the offspring generation.Under a Bayesian implementation, the conditional posterior probabilities of the TRD parameters are defined as:j) for each specific j artificial haplotype (AH) allele (P (AHj) = 1—P (AH-j) = 0.5 + αj).This method is similar to the heterozygous pairwise combination procedure of haplotype analysis (single locus) described by As an exhaustive search for epistatic allelic TRD is computationally unfeasible, epistatic overall TRD were estimated only for the combination of pairs of SNPs which include one SNP with significant direct TRD effect already identified from analysis of single-locus TRD /maximum  > 1,000). Secondly, estimates with large credible intervals were discarded as artifacts of the convergence of the model following Pairs of regions with epistatic TRD estimates were found widely across the Holstein genome. Using a threshold of ≥1,000 for BF, a large number of pairs of SNPs were observed with decisive evidence for epistatic TRD. In order to discard false TRD and to identify the most relevant regions, the following steps were considered. Firstly, in order to focus on epistasis phenomenon, regions with more statistical relevance for direct effects than epistatic effects were discarded. Thus, only region with a minimal ratio of 1,000 between the maximum BF of epistatic and direct effects were considered  are presented in the After implementing a multiple test correction, which was based on selecting the top 0.1% of the regions within each category according to BF, the number of regions reduced to 169, being 60, 88 and 21 with additive-by-additive, additive-by-dominance/dominance-by-additive and dominance-by-dominance effects, respectively. This strategy was implemented with the objective to select the most significant “top hits” of epistatic TRD signals following 10(BF) = 1,964.13) in comparison to TRD estimated of allelic model (up to log10(BF) = 269.55). It is important to remember that DIC was computed based on all TRD parameters combined and the regions here were separated and selected based on their type of effects  and BF.When comparing between the genotypic and allelic models, different goodness-of-fit values were observed among the regions with additive-by-additive effects displaying reductions from 1,186.35 to 46,445.15 DIC units favoring the genotypic model over the allelic model. Notice that models with smaller DIC values indicate a better fit, and differences between models greater than 3 DIC units are considered statistically relevant . In the j × 2 × number of informative offspring) were considered to identify candidate regions with allelic epistatic TRD pattern. The total number of obtained regions detected with BF ≥ 1,000 was 4,852. For the identified artificial haplotypes, the absolute TRD magnitude ranged from |0.06| to |0.42|, and the maximum number of under- and/or over-represented offspring was 12,756 genotypes. The additional magnitude of TRD explained by artificial haplotypes reached a maximum of 0.25, with 8, 166 and 569 pairs with 0.20, 0.15 and 0.10, respectively. For example, pairs from the top regions (with highest BF) were found with individual SNP  displaying an overall TRD of −0.22 and 3,078 under-represented offspring (log10(BF) = 302.62) and exhibited a deviation of −0.37 and 3,908 under-represented offspring (log10(BF) = 692.38) when paired with a specific allele in another SNP . Thus, when the same SNP was found interacting in several pairs, we considered the pair of loci with the highest BF as the best candidate region with epistatic TRD. This reduced the number of pairs of regions to 78 (10(BF) > 9.61, being log10(BF) = 969.06 the maximum value observed for an epistatic heterozygous pairwise combination. The number of regions that showed clear epistatic effects with the full model in all heterozygous pairwise combinations including the artificial haplotype were 29 and reduced to 17 when only one single artificial haplotype displayed epistatic TRD  compared to the single SNP and an equal or greater number of under-/over-represented offspring /maximum  was used to select the regions with more statistical evidence of epistatic effects rather direct effects. Assuming, for example, that BFαα [= p (ααe = 0)/p (ααe = 0|y)] and BFαA [= p (αA = 0)/p (αA = 0|y)] are the maximum values, this ratio becomes = p (αA = 0|y)/p (ααe = 0|y). Given p (αA = 0|y) = (p (y|αA = 0)p (αA = 0))/p(y), the ratio simplifies to p (y|αA = 0)/p (y|ααe = 0), a simple and direct measure of comparison between direct and epistatic effects. Thus, by using a ratio of 1,000, we ensured that the epistatic effect is much more relevant to explain the observed data of the implicated regions than the direct effects.Given the scope of the study, which focus mainly on epistasis phenomenon, the ratio of maximum (BF10(BF) obtained after the multiple test correction was 1,223.8 and 20,223, respectively. Notice that BF is a measure of the strength of statistical evidence (for both TRD magnitude and sample size of informative offspring). The likelihood of the data assuming epistatic effects for these regions is ≥ 101223.8 times more probable that assuming null TRD effects, very strongly supporting the presence of epistatic effects.After the multiple test correction (top 0.1% of the regions within each category according to BF), the minimal and maximal loge = 0.99  = 4,880.73) displayed an over-representation of 8,054 offspring  for AABB and 14,800  for aaBB, whereas an under-representation of 5,567  for aaBB and 4,227  for AAbb  = 16,219.5) showed an under-representation of 14,338, 14,079, 25,459 and 24,814 for AaBB, AABb, aaBb and Aabb, respectively, and an over-representation of 17,864, 1, 37,346, 9 and 29,853 for AABB, aaBB, AaBb, AAbb and aabb, respectively, following the parameterization for dominance-by-dominance effects  interact with one or different physically linked SNPs, supporting the importance of the epistatic interaction found. For this reason, only the pair of SNPs with the highest BF were maintained as the best candidate regions when one single locus interacted with several physically linked SNPs. On the other hand, an individual SNP interacting with several not linked SNP were also found. This latter case might potentially suggest a multiple interaction involving different unlinked loci (of order 3 or more).The preliminary scan with the allelic model was implemented only in pairs of loci with already identified SNP with direct TRD effect in order to target possible epistatic interaction explaining the incomplete penetrance (deviation) of single-locus TRD. The criteria implemented in the simplified method were used to ensure more TRD was explained with epistatic interaction and that the observed TRD of the individual SNP was captured by an artificial haplotype. Explicitly, the restriction of an equal or greater number of under-/over-represented offspring was used to guarantee that the under-/over-represented offspring generated from a specific parent with a particular SNP allele corresponds to a single artificial haplotype (gamete) on those parents. Note that the linkage of specific alleles in a single gamete could be a result of selection or random effects, as it is known by theory that selection or genetic drift could favor combinations of alleles and consequently induce LD at physically unlinked loci . HoweverOn the other hand, it is important to mention that, as the analyses are restricted to SNPs with single-locus TRD in which the majority had low frequencies , the mul10(BF) = 769.12) that, using biallelic-haplotype method, showed a TRD of −0.78 (log10(BF) = 351.96), −0.67 (log10(BF) = 216.05) and 0.74 (log10(BF) = 192.72) for βAB/aB, βAb/aB and βaB/ab, respectively, whereas keeping βA = −0.31 (log10(BF) = 471.30) and βB = 0.29 (log10(BF) = 328.74) for direct effect. The number of regions which showed clear epistatic effects in all heterozygous pairwise combinations including the artificial haplotype, was 29. Nevertheless, in some cases, epistatic TRD was also observed in heterozygous pairwise combinations with other artificial haplotype alleles. This result adds difficulties in understanding epistatic TRD, but could be explained by the different linkage with the causal mutations among families, higher order of epistasis interactions, and does not rule out the possibility of involvement of specific interactions between the artificial haplotypes. A total of 17 regions were identified when only pairs of regions displaying epistatic TRD for a single artificial haplotype were considered  or some of the heterozygous pairwise combinations including the artificial haplotype identified. An example of this was an artificial haplotype (aB) with a TRD magnitude of 0.37 . More specifically, the epistatic parameters seems to be very importance as the likelihood ratio between the full model and an alternative model without epistatic parameters exhibited values up to 101,261.89 , supporting the importance of the epistatic TRD phenomenon in the data.Moreover, we observed that the magnitude obtained from single-locus analyses were similar  to those obtained from the epistatic allelic model (direct effects) when both direct and epistatic TRD effect were simultaneously estimated. This interesting behavior support the robustness and the accuracy of allelic model, where basically the model it tried to track the inheritance of alleles from parents to offspring independently of its source. It is also important to mention that a similar behavior was already reported in single-locus TRD analyses, thus highlighting the robustness of allelic model when comparing both allelic and genotypic model . Thus, tFinally, it is important to highlight that not always a disadvantage was observed when a specific artificial haplotype was transmitted, but also artificial haplotype alleles with positive effects were found. In this sense, one single artificial haplotype with a negative TRD was observed, associated with an under-represented offspring and potentially lethality of the offspring of carrier parent, whereas the alternative artificial haplotypes had positive effects. In contrast, when one single artificial haplotype had a positive effect , it suggests the advantage of one haplotype to be transmitted over other haplotypes. This could be due to an increase in fitness when an allele is paired with a specific allele at another locus. In addition, it could be hypothesized that these regions with positive selection are more related to biological processes acting before fertilization, where gametes with specific allele combinations have a preferential transmission to the next-generation.Absence (or depletion) of individuals with two-locus genotypes in the homozygous statue were found across the Holstein genome, suggesting multiple variants with recessive epistatic TRD pattern. The number of candidate pairs of loci were 56, with 23 of them having the maximum number of expected homozygous offspring, but none of them being observed. It is important to mention that Holstein haplotype 3 was identified initially by www.animalgenome.org/cgi-bin/QTLdb/BT/index) were found to overlap with epistatic TRD regions. Among them, the pair of SNPs BTA1:36209316 and BTA21:26405185 were reported with QTL for reproduction traits such as gestation length  from the Cattle QTL database (CattleQTLdb, n length  and firsn length . Cole etn length , intervan length  and concn length  were alsn length  and daugn length . The SNPOur results aimed to elucidate the prevalence and patterns of epistatic transmission ratio distortion across the Holstein genome. Different epistatic TRD patterns were observed. Using genotypic models, 7, 19 and 6 pairs of SNPs with additive-by-additive, additive-by-dominance/dominance-by-additive and dominance-by-dominance effects were identified with decisive evidence, respectively. The allelic TRD model revealed 17 pairs of SNPs that offered more insights into understating the penetrance of single-locus lethal alleles, providing a more exact probability of lethality when the genotype of both implicated loci is taken into consideration. Scanning for the depletion of individuals carrying double homozygous genotypes for unlinked loci compared to expected, revealed 56 pairs of SNPs with a recessive epistatic TRD pattern. Additionally, the detection of epistatic TRD on pairs of SNPs with QTL for reproductive traits supported the reliability of epistatic TRD findings. Finally, in this study, we demonstrated candidate genomic regions harboring epistatic interactions with potential biological implications in economically important traits, such as reproduction.