SMAD4, RPS6KA2, CAMK2A, NDST1, and ADCY5). Functional enrichment analysis revealed that these genes are mainly enriched in the calcium signaling pathway, ovarian steroidogenesis, and GnRH signaling pathways. In conclusion, our results help to clarify the genetic basis of porcine reproductive traits and provide molecular markers for genomic selection in pig breeding.Reproductive traits have a key impact on production efficiency in the pig industry. It is necessary to identify the genetic structure of potential genes that influence reproductive traits. In this study, a genome-wide association study (GWAS) based on chip and imputed data of five reproductive traits, namely, total number born (TNB), number born alive (NBA), litter birth weight (LBW), gestation length (GL), and number of weaned (NW), was performed in Yorkshire pigs. In total, 272 of 2844 pigs with reproductive records were genotyped using KPS Porcine Breeding SNP Chips, and then chip data were imputed to sequencing data using two online software programs: the Pig Haplotype Reference Panel (PHARP v2) and Swine Imputation Server (SWIM 1.0). After quality control, we performed GWAS based on chip data and the two different imputation databases by using fixed and random model circulating probability unification (FarmCPU) models. We discovered 71 genome-wide significant SNPs and 25 potential candidate genes (e.g.,  The reproductive performance of pigs plays a key role in the pig industry. Improving the reproductive performance of sows can lead to higher economic benefits for pig farms. However, reproductive traits are low-heritability traits, and their genetic structure is much more complex . Therefohttps://www.animalgenome.org/cgi-bin/QTLdb/SS/summary, 25 April 2022). In pigs, GWAS has identified numerous SNPs significantly associated with growth traits  ] for TNB, LBW, NBA, and GL, respectively ] A. These nd DDPA2 . Additioectively .p < 4.91 × 10−8 ] for TNB and GL are identified, respectively ], 8, 3, 1, and 186 SNPs are found to be associated with TNB, NBA, LBW, and GL, respectively ] were found to be associated with the five reproductive traits (MYOCD, HMGN1, DACH1, GPC5, RPS6KA2, ARAP2, CAMK2A, and RGS18 (e traits . Furthernd RGS18 .PDGFRB, CAMK2A, and MMP2, are identified, mainly enriched in the calcium signaling pathway, GnRH signaling pathway, and embryonic organ development process. Finally, the functional genes related to GL are enriched in the mTOR signaling pathway, ovarian steroidogenesis, prolactin signaling pathway, embryo development, and regulation of G protein-coupled receptor signaling pathway (In this research, candidate functional genes were found by searching 0.5 Mb upstream and downstream of the suggestive SNPs using GWAS based on chip data and two imputed databases. The genes associated with TNB are found to be linked to glycolysis/gluconeogenesis, TGF-β, the oxytocin signaling pathway, and oocyte maturation processes. For NBA and LBW, the same genes,  pathway .Reproductive traits such as TNB, NBA, LBW, GL, and NW are closely related to sow fertility and are important quantitative indicators of pig production. However, most of them have low heritability due to the complexity of the genetic structure. Therefore, it is important to clarify the genetic relationships between reproductive traits and to identify key candidate genes. In this study, a repeatability model was used to estimate the heritability of reproductive traits. The heritability estimates of the TNB, NBA, LBW, GL, and NW traits were 0.0442, 0.0442, 0.0476, 0.1571, and 0.0727, respectively. This is in agreement with the results of previous studies ,36,37. AGenotype imputation has been widely used in recent years with the development of sequencing technologies, price reductions, and the demand for high-density markers. This approach allows the imputation of chip data with low-density markers to WGS data, and the imputation accuracy is affected by the density of the target SNPs, the size of the reference population, the genetic distance between the target and imputation reference population, and the imputation procedure . In our In our study, we conducted GWAS for five reproductive traits using imputation data from two different online imputation platforms. Imputation data based on the SWIM platform detected more significant or potentially significant loci compared to the PHARP platform. This may be due to the fact that the SWIM platform has a larger number of pigs in its reference panel. In addition, an imputation strategy could improve on previous SNP-based studies without the need for additional data and expense. Furthermore, a common set of SNPs can be obtained with an imputation approach, thus making a meta-analysis possible.RPS6KA2 gene plays a major role in the EGF signaling cascade at ovulation, which is also correlated with oocyte developmental quality [SMAD4 plays an important role in the porcine reproductive system. It has been shown that miR-143 [SMAD4 in porcine granulosa cells. For both the NBA and LBW traits, GWAS based on imputed data identified the CAMK2A, NDST1, and RPS14 genes. In a meta-analysis of reproductive traits in heifers, the CAMK2A gene was identified as being involved in calcium signaling mechanisms and acting on pituitary gonadotropin secretion [NDST1 has been shown to be critical for many organogenesis processes, and the targeted disruption of the NDST1 gene impaired heart development in mice [NSDT1f/f/2 null/3 null mice with defective decidualization resulted in female infertility [RSP14 is a key gene in early embryonic development [RSP14 gene showed defects in embryoid body differentiation [ADCY5 was identified as being associated with seasonal estrus in Sunite sheep [ADCY5 was found to be associated with gestational duration [ADCY5 is associated with fetal growth and birth weight [Some studies have shown that the FarmCPU model can be effective in GWASs for identifying loci with low-heritability traits . So, we  quality . As a tr miR-143 , miR-26b miR-143  can inhiecretion . This is in mice . NSDT1fertility . It has elopment . Embryonntiation . For thete sheep , egg prote sheep , and ferte sheep , while iduration . Interesh weight . HoweverSMAD4, RPS6KA2, CAMK2A, NDST1, and ADCY5 were identified as novel genes, and some of them have not been studied in livestock, so they may be key candidate genes affecting reproductive traits in pigs. The results of this study highlight some new major genes regulating reproductive traits in pigs and can benefit genome selection for pig genetic breeding.In this study, the genetic parameters of TNB, NBA, LBW, GL, and NW in Yorkshire pigs were estimated using a repeatability model. These traits are low-heritability traits. There were strong positive correlations between TNB, NBA, LBW, and NW, excluding the GL trait, which was weakly negatively correlated with them. GWASs based on chip data and imputed data were performed for five reproductive traits in Yorkshire pigs. Finally, combining the results of GWAS and bioinformatics annotation analysis,


Background: Individuals with a diagnosis of schizophrenia are known to be at high risk of premature mortality due to poor physical health, especially cardiovascular disease, diabetes, and obesity. The reasons for these physical health outcomes within this patient population are complex. Despite well-documented cardiometabolic adverse effects of certain antipsychotic drugs and lifestyle factors, schizophrenia may have an independent effect.Aims: To investigate if there is evidence that schizophrenia is causally related to cardiometabolic traits  and vice versa using bi-directional two-sample Mendelian randomization (MR) analysis.Methods: We used 185 genetic variants associated with schizophrenia from the latest Psychiatric Genomics Consortium GWAS  in the forward analysis (schizophrenia to cardiometabolic traits) and genetic variants associated with the cardiometabolic traits from various consortia in the reverse analysis (cardiometabolic traits to schizophrenia), both at genome-wide significance (5 × 10−8). The primary method was inverse-variance weighted MR, supported by supplementary methods such as MR-Egger, as well as median and mode-based methods.Results: In the forward analysis, schizophrenia was associated with slightly higher low-density lipoprotein (LDL) cholesterol levels  and total cholesterol levels . However, these associations did not survive multiple testing corrections. There was no evidence of a causal effect of cardiometabolic traits on schizophrenia in the reverse analysis.Discussion: Dyslipidemia and obesity in schizophrenia patients are unlikely to be driven primarily by schizophrenia itself. Therefore, lifestyle, diet, antipsychotic drugs side effects, as well as shared mechanisms for metabolic dysfunction and schizophrenia such as low-grade systemic inflammation could be possible reasons for the apparent increased risk of metabolic disease in people with schizophrenia. Further research is needed to examine the shared immune mechanism hypothesis. Schizophrenia is a psychiatric disorder affecting approximately 1% of the world population . CompareThe reasons for adverse cardiovascular and metabolic health conditions within this patient population are complex and multifactorial. Despite well-documented cardiometabolic side effects of certain antipsychotic drugs, schizophrenia may have an independent effect, and this has been suggested by the high prevalence of metabolic syndrome in drug-naïve patients with schizophrenia . Indeed,The direction of this relationship is yet to be established. A longitudinal study demonstrated that persistently high fasting insulin levels from 9 years of age was associated with a higher risk of developing psychosis at 24 years, indicating possible early-life origins of the observed schizophrenia-diabetes association . Individvice versa. MR uses genetic variants as instrumental variables (IVs) to examine whether an exposure is likely to be causally related to an outcome , waist-hip ratio (WHR)], glycaemic traits , blood lipids  and blood pressure (systolic and diastolic blood pressure)To derive a reliable conclusion on the causal association between schizophrenia and cardiometabolic factors, a two-sample framework was used, i.e., the exposure and the outcome were measured using two non-overlapping samples. Summary-level datasets were obtained from large consortia of genome-wide association studies as summarized data are available for larger sample sizes, improving the power to detect a causal effect . Due to https://www.med.unc.edu/pgc/). Cases were defined as individuals diagnosed with schizophrenia spectrum disorder based on DSM-IV criteria.The largest and most up-to-date GWAS was selected for schizophrenia from the PGC, including a total of 53,386 cases and 77,258 controls of European ancestry . The GWAhttps://portals.broadinstitute.org/collaboration/giant/index.php/GIANT_consortium). Summary data for blood lipids were obtained from the Million Veteran Program GWAS, including 215,551 individuals of European ancestry . Data for fasting glucose and fasting insulin were derived from a sample of 281,416 individuals, and HbA1c was derived from a sample of 46,368 individuals. Both samples included adults of European descent (https://www.ebi.ac.uk/gwas/publications/30224653).Summary-level data for BMI and WHR was selected from the Genetic Investigation of ANthropometric Traits (GIANT) consortium, including up to 322,154 and 21,244 individuals, respectively  (availabancestry . This da descent . Summary descent . Summary−8) were selected. Secondly, to determine linkage disequilibrium (LD) between SNPs, we used the clump_data function  was retained and the other SNP in the pair was excluded. Genetic variants not found in the reference panel were excluded. Genetic variants not found in the reference panel were excluded. Finally, harmonization was conducted using the harmonise_data function as the MR analysis involved the use of two independent datasets with genetic variants which may not share the same allele pair. Thus, harmonization ensured that the effect of a SNP on the exposure, and the effect of the same SNP on the outcome, corresponded to the same allele  were obtained using the ratio method, where the effect of the SNP on the outcome (βZY) was divided by the corresponding effect of the SNP from the exposure (βZX)  .βIV=βZYThe ratio estimates were subsequently pooled using inverse variance weighted (IVW) analysis to derive an IVW effect estimate . Howeverp-value of p < 0.05/11 = 5 × 10−3 as being statistically significant. A p-value <0.05 was suggestive evidence of a causal association.Altogether, we tested 11 different causal associations using univariable MR analysis. To account for multiple testing, we used a Bonferroni-corrected Sensitivity analyses was conducted using the TwoSampleMR package in R. MR-Egger regression was conducted to test for pleiotropy. If the horizontal pleiotropic effects are in a particular direction, constraining the slope to go through zero will lead to bias. Thus, MR-Egger allows the intercept to pass through a value other than zero rather than constraining the slope to go through zero. This method, therefore, returns an unbiased estimate even if the IVs are invalid . HeterogIn the forward analysis, up to 185 LD-independent SNPs significantly associated with schizophrenia were identified . Howeverp = 0.027) and total cholesterol level  using the primary IVW analysis method . Iterative removal of each individual SNP using leave-one-out analysis did not affect the IVW estimates for LDL or TC, suggesting that they were not driven by one singular SNP .We found evidence for associations between schizophrenia and LDL , WHR , HDL , triglycerides , fasting glucose , fasting insulin , HbA1c , systolic blood pressure  or diastolic blood pressure  using the primary IVW analysis method , WHR , HDL , LDL , total cholesterol , triglycerides , fasting glucose , fasting insulin , HbA1c , systolic blood pressure  or diastolic blood pressure  and schizophrenia using the primary IVW analysis method . The second assumption which states that the genetic variants must not be associated with confounders should be satisfied as the genetic variants are randomly allocated during conception. The third assumption, which requires the genetic variants do not affect the outcome unless it is through the exposure, is difficult to explicitly test but our sensitivity analyses indicated that pleiotropy was unlikely to affect the results. Furthermore, this study was restricted to individuals of European ancestry as these were the datasets with appropriate sizes to enable the MR analysis. Nevertheless, despite using the largest dataset available for schizophrenia, our study could still have lacked statistical power. Whether these results also apply to other populations will require investigating in diverse, large-scale samples which are currently being collected.In this study, we employed a bidirectional MR framework, which avoided reverse causality and minimized residual confounding. We built upon previous MR studies using an updated set of instruments of schizophrenia from the PGC, thus improving the power to detect a causal association and accurately estimate the magnitude of the effect. We also included a complete set of traits  to be comprehensive and fully representative of metabolic syndrome. However, genetic variants are subject to three assumptions which must be considered when interpreting the results. The first assumption, which indicates that the genetic variants are associated with the exposure of interest, was satisfied by excluding SNPs that did not reach genome-wide significance , antipsychotic medication, inflammation, among others. Ultimately, we need further research with larger global populations to elucidate the links between schizophrenia and metabolic syndrome.


ATG7, EXT1, ITGA1, PPARD, SCUBE3, and SHOX) that may play a role in FLM expression due to their known role in skeletal muscle development, aberrant bone growth, lipid metabolism, intramuscular fat deposition and skeletogenesis. Identifying genes linked to foot and leg malformations enables selective breeding for healthier herds by reducing the occurrence of these conditions. Genetic markers can be used to develop tests that identify carriers of these mutations, assisting breeders in making informed breeding decisions to minimize the incidence of malformations in future generations, resulting in greater productivity and animal welfare.Beef cattle affected by feet and legs malformations (FLM) cannot perform their productive and reproductive functions satisfactorily, resulting in significant economic losses. Accelerated weight gain in young animals due to increased fat deposition can lead to ligaments, tendon and joint strain and promote gene expression patterns that lead to changes in the normal architecture of the feet and legs. The possible correlated response in the FLM due to yearling weight (YW) selection suggest that this second trait could be used as an indirect selection criterion. Therefore, FLM breeding values and the genetic correlation between FLM and yearling weight (YW) were estimated for 295,031 Nellore animals by fitting a linear-threshold model in a Bayesian approach. A genome-wide association study was performed to identify genomic windows and positional candidate genes associated with FLM. The effects of single nucleotide polymorphisms (SNPs) on FLM phenotypes (affected or unaffected) were estimated using the weighted single-step genomic BLUP method, based on genotypes of 12,537 animals for 461,057 SNPs. Twelve non-overlapping windows of 20 adjacent SNPs explaining more than 1% of the additive genetic variance were selected for candidate gene annotation. Functional and gene prioritization analysis of candidate genes identified six genes ( Brazil is the largest exporter of beef in the world in millions of tons  and ranks third in value (US$ 7 billion) . The NelAnimals affected by feet and legs malformations cannot perform their productive and reproductive functions satisfactorily, resulting in significant economic losses. The term “limb deformities” usually refers to defects in the legs and feet caused by the most varied etiological agents in different species of production animals . Here weIdentification of significant regions in the bovine genome and candidate genes associated with FLM can also help to understand the biology behind the phenotype. In this way, Genome-wide Association Studies (GWAS), using phenotype, genotype, and pedigree data through the weighted single step genomic best linear unbiased predictor (WssGBLUP) method  enables This work aims to better understand the molecular mechanisms underlying FLM and provide subsidies that help to reduce the incidence of this problem in breeding programs for Nellore cattle, contributing to improve their production and reproductive efficiency. Approval from the Welfare and Animal Use Committee was not required for this study because the data was obtained from an existing database of phenotypic and genotypic records from Nellore cattle. Previous studies using these data also did not need this approval.A final dataset of 295,031 Nellore cattle from the Nellore Alliance , all witith trait, A priori, the residual covariance was considered equal to zero, and remained at zero after being estimated by the software.After checking consistency and editing data, estimates of variance components and estimated breeding values (EBV) were obtained for each animal by Bayesian inference using THRGIBBS1F90 software . To evaly models , the parwas used . A priorThe relationship between the score for the (0 or 1) . The pri(0 or 1)  and BOA (0 or 1)  in langu(0 or 1) , in addiGenotyping was completed from 6,624 animals using the Illumina BovineHD Genotyping BeadChip , which included 612,174 SNP markers, and 28,155 animals genotyped with medium density panels (20–90 k SNP). All animals belong to the Nellore Alliance population  and had p-value < 10–5 for the Hardy-Weinberg equilibrium test; minor allele frequency (MAF) < 0.02; and a call rate <90%.Quality control (QC) of the genotypic and pedigree datasets was performed using QCF90 software  and the The GWAS results were reported as the proportion of the variance explained by non-overlapping genomic windows of 20 adjacent SNP . The pheThe POSTGSF90 software was used to estimate the proportion of variance explained by SNP effects and the criterion used to identify potentially important genomic regions was the sum of the variance in non-overlapping windows of 20 adjacent SNPs, approximately 130 Kb (average density of one SNP per 6.5 kb). Windows that explained greater than 1% of the genetic variance were selected for gene annotation, conducted using R Version 4.1.2  and the 2) > 0.08 are observed up to 400 Kb of distance between marker pairs for the Nellore breed, with higher linkage disequilibrium estimates (r2 > 0.15) observed in the first 100 Kb  interaction network to identify orthologous genes between humans and cattle Then, the ToppGene Suite was used to perform a prioritization analysis comparing the functional information shared between the list of “trained” genes and the list of “test” genes (genes located within windows that explained more than 1% of the genetic variance). Gene Ontology terms , human and mouse phenotypes, metabolic pathways, PubMed publications, coexpression patterns and diseases were used to retrieve the functional information from genes in training and testing lists  (The candidate gene prioritization was conducted using GUILDify and ToppGene . GUILDifng lists . We sele ≤ 10–3) .t-test (p < 0.05) in the annual incidence averages between genders. Males (average incidence = 4.98%) were more affected than females (average incidence = 4.23%) by FLM in the period evaluated. After QC, 461,057 SNP remained. All genotyped samples had a call rate per individual greater than 0.9. Genotypes from 12,537 animals passed QC and were used in the GWAS analyses.The incidence value (approximately 5%) for FLM after consistency checks and preliminary editing described above should be highlighted . The pedThe posterior mean of the heritability estimates  for FLM was of moderate magnitude (0.1856). In single-trait analyses for comparison purposes, the mean heritability estimated for FLM  was 0.17 . The linear trait (YW) had a high heritability value (0.4718) , while tEXT1, ITGA1, ATG7, and PPARD) prioritized by GUILDify and ToppGene analysis were in four windows, which together explained 5.43% of the total genetic variance. Additionally, skeletal development-related genes SCUBE3 and SHOX that were not prioritized in the ToppGene analysis were identified by visual inspection in the Genome Data Viewer (LOC) for which no associated sign gene was present  . In the The magnitude of heritability estimated for the FLM depends on the rating assigned to the animal at the time of assessment and may be directly related to the quality of the recording set measured for the classification of the trait . FurtherThe assessment of the genetic correlation between YW and FLM (−0.2311) was considered favourable, since in FLM, the lowest taxonomic rank (score 0) indicated that animals did not have feet and legs defects, whereas in YW animals with stronger expression of the trait are required. For example, in the population studied, animals that had difficulty walking due to congenital deformities of the legs and feet (score 1) tended to gain less weight. Consistent with the results of this study, A literature search, such as that carried out by ATG7 gene (BTA22) in mice that resulted in profound muscle atrophy and age-dependent decrease in strength (ATG7 gene (BTA22) encodes an E1-like essential activating enzyme for autophagy and cytoplasmic transport to the vacuole  encodes a glycosyltransferase responsible for heparan sulfate (HS) polymerization, related to fibroblast growth factor , encodes a subunit of the cell-surface receptor integrin α1β1, which acts in the regulation of hepatic glucose and lipid metabolism under conditions of overnutrition in vivo. On a high fat diet, mice with the ITGA1 gene inactivated show severe hepatic insulin resistance and decreased hepatic fat accumulation , the most abundant form of Peroxisome Proliferator-Activated Receptor (PPAR) family in skeletal muscle  and SHOX (BTA2) genes, identified by visual inspection of significant windows of the FLM, have relevant roles in bone formation. The SCUBE3 gene acts as a BMP2/BMP4 (bone morphogenetic protein) coreceptor and positively regulates signalling possibly enhancing the specific interaction between BMP and BMP type I receptors , aberrant bone growth (EXT1), lipid metabolism , intramuscular fat deposition (ITGA1), and skeletogenesis  underscore the importance of these genes in malformation incidence. The identification of genes associated with foot and leg malformations allows selective breeding of animals that are less likely to develop these conditions, leading to healthier and more productive herds. The identified genetic markers could facilitate the development of genetic tests that can identify carriers of these mutations, allowing breeders to make more informed breeding decisions that reduce the incidence of malformations in future generations.Overall, this study provides valuable information on the genetic basis of feet and leg malformations in Nellore cattle, with important implications for both animal welfare and agricultural productivity. Estimates of additive genetic variability and heritability for this trait suggest that selection may reduce the incidence of this problem. The phenotypic incidence shows the importance of reinforcing this selection, although the estimated trend curve shows a relatively favorable genetic gain over the study period. Furthermore, the genetic association of yearling weight with feet and legs malformations may have implications for selection criteria used in breeding programs. The current GWAS was able to identify chromosomal regions associated with feet and legs malformation in Nellore cattle. The roles of prioritized candidate genes in skeletal muscle development (


DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (  Pstol1  is the spatial configuration of the root system that enables and facilitates a number of important agronomic traits, including plant soil anchorage, nutrient and water absorption, and abiotic and biotic stress signaling, and therefore is one of the major factors that can impact plant yield –5. RSA v P soils . Subsequal roots . Furtheral roots ,14.DRO-1 and VRN, which are responsible for deeper rooting in rice and steeper nodal root angle in wheat, respectively ), and total surface area of thicker roots (diameter class 1 to 1.5 mm diameter [TSA3])—were obtained from WinRhizo software. Five root system architectural traits—total root system width (TRW), total convex area (TCA), root depth index (RDI), solidity (SOL), and center of projected mass (COPM)—were calculated from root images at 5 and 8 DAT (Table ), and toem depth . SoliditThe scale for image analysis was obtained by placing a ruler in the very first image of the imaging session, and then the camera height was kept constant for that imaging session to maintain the same scale for all root systems.Whole-genome resequencing (15× to 20× coverage) was performed for 286 soybean lines, and SNPs were extracted using the soybean cultivar, William82, as a reference line ,40. Morer)), where Vg is the genetic variance, Ve is the residual variance, and r is the number of replicates  were grown in the pouch system, and root tissues from 3 biological replicates were collected for each genotype to extract RNA. RNA was extracted using the TRIzol  method from root tissue of plants harvested at 8 and 11 days after germination. Five micrograms of total RNA was reverse-transcribed using the Maxima First Strand cDNA Synthesis Kit . Synthesized cDNA was diluted 5-fold with nucleotide-free water, and 1 μl of the cDNA solution was used for quantitative real-time polymerase chain reaction (qRT-PCR) analysis with gene-specific primers. These primers for qRT-PCR expression analysis of candidate genes were designed using Primer3 software . AmplifiFor evaluation of the promotor region of candidate genes for existence of structural variants, 2-kb DNA sequences upstream of genic sequences of the lines selected based on haplotypes were extracted from Bam files after blasting the whole-genome resequencing data to the reference genome for William 82 V4, using Integrative Genomics Viewer . The extUntil now, most of the studies have employed the method of rolling the seeds in a roll of wet germination paper to germinate seeds. However, in our experience, this method of germination often alters the root initiation angles of the first lateral roots that can change the RSA as the plant develops. Therefore, to maintain root initiation angle, we modified the germination approach in which seeds were germinated in vertically stacked layers of water-moistened germination paper to minimize alterations in root architecture during transplanting to the 2D growth pouches  between manually counted root tips and computationally derived root tip number improved from 0.79 (without the background correction) to 0.95, when the background cleaning correction was employed  were also being included as pieces of roots and not background, which resulted in each of them being counted as a root tip Fig. C. TherefWe also used statistical genetics to estimate the reproducibility of our root trait measurements by calculating the broad-sense heritability of root morphological and architectural traits for multiple replicates of plants. This resulted in moderately high (0.74) to high (0.89) broad-sense heritability for root traits from plants grown for 5 DAT and also for 8 DAT (0.74 to 0.92) Table . ModeratOther pouch systems have a solid plastic background to provide the pouch rigidity and a base to place the sheets of germination paper and then the seedling on top of the final sheet of blue filter paper. When we first built our pouches with a solid backing, we noticed the roots grew less vigorously than when grown freely in hydroponics. Hence, we replaced the solid plastic backing with a plastic perforated sheet and vigorously aerated the nutrient solution in which the pouch was immersed in. We found that this significantly increased plant growth and especially root growth compared to plants grown in a pouch with a solid plastic backing (data not shown).Population structure analysis generated a Delta K plot that had 3 peaks based on the top 3 Delta K values, representing 2, 3, and 5 possible subpopulations. Although the top Delta K value was for 2 population subgroups, the second highest Delta K value suggested 3 subpopulations and the smallest Delta K of top 3 values suggested that soybean has 5 subpopulation groups Fig. . HoweverR2 > 0.5) and negatively correlated with DIM  in early maturing soybean varieties  higher values (deeper root systems) for COPM  TCA compared to those in late maturity groups  and late  maturity groups at the final growth stage, 8DAT Figs.  and S8. ies Fig. . Among rGWAS identified a total of 113 putative loci for all root morphological and architectural traits, except COPM_5DAT, TCA_5DAT, SOL_8DAT, and RDI Table  and S10.We defined a genomic region as a root trait “hotspot” if it was less than 750 kb in size and contained 5 or more putative loci related to one or more root traits. We identified a total of 5 hotspots that contained 5 to 26 loci Table . ChromosSelection of candidate genes within or close to GWAS peaks was based on their putative functional relationship with root morphological traits based on gene annotation in other crops or Arabidopsis. We identified 21 candidate genes, and 19 of them were within 10 kb of the most significant SNP associations , and the significantly associated SNP were in the same linkage block of 6 kb on chromosome 4. The expression of Glyma.04G233300 was higher in the soybean lines with larger COPM values , which carry the favorable allele, T, whereas this gene’s expression was lower in lines with the unfavorable allele, C  was an uncharacterized protein, which was in the same LD block (14 kb) and located 4 kb from the significantly associated SNP. Results showed that its level of gene expression was higher in accessions carrying the favorable G allele  and lower in genotypes with the unfavorable A allele  for the most significant SNP , although located 20 kb from the GWAS peak, was in the same LD block (22 kb) as the SNP on chromosome 14. The expression of Glyma.14G157400 was lower in accessions carrying the favorable C allele (higher TRT_5DAT) compared to those accessions carrying the unfavorable G allele (lower TRT_5DAT)  on chromosome 20, and a genic SNP detected as a pleiotropic GWAS peak, PPQ08, was evaluated for differential expression in genotypes with favorable (G) and unfavorable (A) alleles. Genotypes with accessions carrying the favorable G allele had significantly higher expression than the genotypes with the unfavorable A allele  most promising candidate genes that were associated with the root traits COPM_8DAT, TRL_8DAT and TSA1_8DAT, TRV_5DAT and TSA3_5DAT, and TRT_5DAT ] at 275 bp upstream of Apart from the significant overall genetic variation for root morphological and architectural traits, distribution of RSA traits among subpopulations  was not random for 3 of the 4 traits of interests Fig.  that harGlyma.04G233300 for this trait.To validate that the root system traits grown in the hydroponic pouch system are representative of plants grown for longer periods in soil or soil-like growth media, we grew 4 replicate plants for each of 6 soybean lines contrasting for COPM [3 lines with low COPM  and 3 lines with the highest COPM (deeper roots)]. These same lines also contrasted for TRV and TRL, which are measures of the size of the entire root system. The plants were grown until the R1 reproductive stage (approximately 45 days of growth) in the greenhouse. At the end of the growth period, the potting mix was carefully washed from the root systems of the contrasting lines and the root systems were imaged and quantified for COPM. As shown in Fig. PSTOL1, which was first discovered in rice [PSTOL function has been best shown in sorghum, where Hufnagel et al. [PTOL1 genes, validating these genes’ role in sorghum P efficiency. For fast-moving nutrients like water and nitrate, it has been shown that deeper rooting can confer drought avoidance and improve yields under drought and lower N levels, by accessing deeper groundwater and NO3−. A major gene that helps confer deeper lateral rooting, DRO-1, has been identified in rice and shown to confer yield increases under moderate and severe drought in the field [Root system traits are often complex and relatively understudied because of their growth in opaque, heterogeneous, and complex soils. But the root system’s importance to the plant is undisputed due to its essential roles in nutrient and water acquisition, abiotic and biotic stress response signaling, and anchoring and stabilizing the plant in the soil. Therefore, a more in-depth understanding of the genetic and molecular regulation of root growth, development, and functional traits is of utmost importance to improve crop adaptation to marginal soils and climate change. This is necessary to develop crop varieties with more efficient root systems that can lead to enhanced yields in both favorable and stressed environments. A major role for root systems that has become apparent over the past several decades is the importance of RSA in more efficient water and fertilizer acquisition. It has been shown that macronutrient P (as the phosphate anion), which moves slowly in the soil because it tend to be fixed to soil particles, is efficiently harvested by the shallow and more extensive root systems, with longer, thinner, and shallower lateral roots, with more root hairs, and in general with greater root surface areas in the topsoil. This trait is often termed P acquisition efficiency. Furthermore, the P acquisition efficiency gene,  in rice , confersl et al.  showed she field .Because of the importance of RSA to nutrient and water acquisition efficiency, there has been considerable interest in developing relatively sensitive root imaging methodologies that have the throughput to conduct genetic analysis of root growth and architecture traits. Certainly, one of the difficulties in studying RSA is the labor and time it takes to phenotype roots in the field. The primary approach, shovelomics, where usually primarily the upper portion of the root system is excavated from the soil, has the strength of providing and quantifying root traits in their in situ soil environment. However, the excavation of the root system is destructive and disruptive to the RSA, often with loss of deeper and finer roots. Furthermore, this approach is labor intensive and has relatively low throughput. The other type of approach is to grow the roots in artificial and transparent growth media, such as hydroponics, gels, or hydroponic-fed 2D pouches that have a transparent root viewing plate. In this study, we made some significant improvements in the often-used root growth pouch systems as described in Results to phenotype root growth and 2D RSA traits in more than thousand plants from a 286 soybean line association panel assembled from the USDA germ plasm collection by the Soybean Genetics and Genomics Lab, University of Missouri, USA. This enabled us to have the throughput to phenotype 5 replicate plants for each of the 286 lines, with the precision and accuracy to reproducibly quantify root traits for subsequent GWAS analysis, and also the identification and characterization of some root trait candidate genes. For example, image segmentation and the cleaning pipeline to count number of root tips more precisely by removing the background noise caused by the small particles might have played an important role directly in the identification of an important candidate gene for a number of lateral roots (TRT), GA20OX3.Also, a false-positive number of root tips can indirectly affect total root length and volume traits as these traits are interrelated; hence, this pipeline can lead to digital computation of root traits that are closer to realistic values and therefore help in identification of causative genetic variants for these traits with better resolution.We observed a large amount of root growth and RSA phenotypic variation among soybean genotypes, subpopulation groups, and maturity groups. Early maturity group accessions were found to exhibit greater early season root growth compared to late maturity groups. Larger root morphological and architectural trait values for early maturity group lines compared to late maturity group lines suggest that higher root growth rate in the earlier stages of plant growth is advantageous for shorter season soybean lines. These results are similar to those found on root systems of early and late maturing mung bean reported by Singh and Bell . Our finDRO-1 in rice [Pstol1 [GWAS of our set of root traits identified putative genetic variants that underlie a portion of the phenotypic variation in root system growth/morphological and architectural traits Figs.  and S10. in rice  and P ac [Pstol1 ,12. Simi [Pstol1 ,55. For Interestingly, significantly associated SNPs with lower allele frequencies seem to be relatively commonly shared among different root trait mapping studies. A root length association in this study  colocalized with root length, area, weight, and lateral root density QTL from the Liang et al.  study anGlyma.04G233300 encodes a DOF1 transcription factor, DAG1, that is very closely located to a GWAS peak for variation in COPM, which is a measure of the depth of the entire root system. Allele analysis suggests that it could be a positive regulator of COPM  developmental processes and has been suggested to negatively regulate the number of lateral roots in a root system [GhDOF1 had a greater total root system volume and surface area as well as increased tolerance to NaCl toxicity, based on root growth inhibition in hydroponic media containing 150 mM NaCl.As described above, OPM Fig. A. DAG1 ht system ,58. Furtt system . These oGlyma.14G157400, on chromosome 14, encodes the enzyme, GA 20 oxidase 3. GA oxidases have been reported to control the number of lateral roots via crosstalk with auxin [Glyma.14G157400 might be a negative regulator of lateral root number based on our observation of an inverse relationship between Glyma.14G157400 expression and lateral root number . A GA-dth auxin . Della pth auxin . Furtherth auxin . Recentlth auxin  discoverth auxin  further ber Fig. C. FinallGlyma.02G095900 is an uncharacterized protein, and its Arabidopsis ortholog, TRM18 (Tonneau/TON 1 recruiting motif 18), is a phosphatidylinositol N-acetylglucosaminyltransferase subunit P-like protein. TRMs (TON1 recruiting motif proteins) have not been reported in root development but have been reported to play a role in cortical microtubule organization. TRMs recruit TON1 to form the TTP (TON1-TRM-PP2A) complex by binding protein phosphatase 2A, and this complex has been reported to regulate arrangement of cortical microtubules arrays, and thus be involved in cell division plane formation and cell growth, and the shape of plant organs [pp2a (pp2a-3/pp2a-4) have defects in the organization of root tip cells and shorter primary roots compared to WT Arabidopsis plants [GnTII, exhibited reduced root growth compared to WT Arabidopsis plants [Glyma.02G095900 . The Arabidopsis DOT5 homolog harbors a WIP domain, which consists of 4 zinc finger motifs and is involved in the regulation of venation in leaves as well as root development [dot-5 loss-of-function mutant are consistent with DOT5’s putative positive regulation of root growth in our results  into Arabidopsis resulted in improved aluminum (Al) tolerance with greater root growth in Al-toxic media compared to WT Arabidopsis plants [elopment . Short responses ,70. ReceDrought and nutrient deficiency are the 2 major limitations of soybean yield –74. The Glyma.04G233300, Glyma.02G095900, and Glyma.20G186400, are possibly involved in positive regulation of center of mass, root length, and root volume, while Glyma.14G157400 may be a negative regulator of the number of lateral roots in soybean. Differential expression of putative candidate genes tightly linked with these significant GWAS peaks provides further support that possibly one or more of these genes are bona fide root architecture genes and will be the focus of our subsequent research. Also, the genetic markers linked to these genes can be used for the generation of soybean lines with improved and more efficient root systems by marker-assisted breeding or gene editing. Having said that, it is necessary to further validate the effect of the favorable alleles we have identified by introgressing them into elite backgrounds and testing their performance in different field environments, before they can be employed in marker-assisted introgression breeding, genomic selection, or gene editing approaches to improve the RSA of soybean.In this study, we were able to capture a significant portion of the large genetic diversity underlying the phenotypic variation for root architectural and growth/morphological traits. Our results suggest that a large number of GWAS peaks for root system architectural traits except total convex area were generally independent of root morphological traits. This strengthens the idea of incorporating and mapping of more root architectural traits that might contribute to higher yield and abiotic stress tolerance in resource-limited environments, including drought and lower levels of mineral nutrients. Nevertheless, the importance of root morphological traits has been paramount from the early years of root system research as traits such as increased root length, surface area and volume, and development of longer and thinner roots increase the absorption capacity of the root systems while reducing the carbon cost for those root systems. Given the high complexity of root architectural traits, identification of genetic variants controlling root length, volume, and surface area has been the prime focus in most prior research and enabled us to support the previously mapped QTLs and identify novel genetic variants for root morphological traits in this study. For example, a locus significantly associated with total root volume at chromosome 18 was colocalized with root volume or the closely associated trait, root biomass, in 2 previous root biparental studies and one GWAS mapping study. The putative candidate genes we identified in this study,


The Comune di Sicilia goat, originating in Sicily , is in the process of being officially acknowledged as a breed. To better characterize this population, this study examined 78 goats from two locations, recording the goats’ morphological traits and measurements. Moreover, these goats were genotyped using a medium-density SNPchip and compared with goats from 15 different Italian breeds. The study found that the Comune di Sicilia goats have unique physical and genetic characteristics that distinguish them from other breeds in the same area. However, moderate variability was observed, likely influenced by how the goats were chosen by breeders. Additionally, the genomic comparison of goats with different traits led to the identification of genes possibly linked to anotia and wattle presence in goat species. To preserve this breed and avoid problems with inbreeding, a combined approach integrating phenotypic and genomic analyses is warranted.ST and runs of homozygosity, leading to the identification of potential candidate genes associated with anotia and wattle presence in goats. The Comune di Sicilia breed exhibited distinctive genomic and phenotypic features, setting it apart from other breeds in the same region. However, moderate variability, possibly influenced by selection practices, was also observed. To ensure the breed’s preservation and prevent excessive inbreeding, a comprehensive approach considering both morphology and genomic background is recommended. This study contributes valuable insights into the genetic peculiarities of the Comune di Sicilia goat, supporting its recognition as a unique and valuable breed.The Comune di Sicilia, a local goat breed from Sicily , is currently undergoing recognition as a distinct breed. This study aims to characterize the population both phenotypically and genomically to advance its recognition process. A total of 78 subjects from two locations were enrolled, and their phenotypic data, including qualitative traits and morphometric measurements of adult animals, were recorded and statistically analyzed. The goats were genotyped using the Illumina 50 k Goat SNPchip, comparing them with 473 goats from 15 Italian breeds. Population structure, phylogenetic relationships, admixture, and genomic inbreeding were analyzed. Additionally, subjects with different morphological traits were compared using F Riproduzione, Allevamento e Miglioramento degli Animali Domestici in Sicilia” . Chicoli described these goats as long- and wire-haired, with a variety of possible coat colors , a large head, a rather large size, and very developed udders in females, with a daily production of about three litres of milk.In recent years, the environment was influenced by globalization, urbanization, population growth, global warming, and climate change. Consequently, local breeds were affected as well, and biodiversity is under threat, making it evident that ensuring the sustainability of livestock breeding necessitates the surveillance and preservation of native breeds that are well adapted to the local environment ,2,3, henHistorically, studies aimed at describing livestock populations primarily relied on phenotypic and historical data. However, recent advancements in molecular tools now offer the opportunity to augment these conventional zootechnical evaluations, providing a more comprehensive and accurate characterization of animal breeds and facilitating an improved recognition process. In this context, the BIOSAVE project, “Use of phenotypic and genomic descriptors for the recovery, definition of genetic originality, and zootechnical management of Sicilian endangered local breeds”, was approved in 2021 and financed by the PSR Sicilia 2014–2020—Sub-measure 10.2b “Support for the conservation of genetic resources in agriculture and forestry”. The overall aim of this project is to ascertain the official recognition and subsequent developmental significance of Sicilian livestock breeds in relation to land, landscape, and sustainability. It also seeks to explore the role of public policies and the multifunctional approach involving research institutions in supporting these local breeds. Hence, based on the Chicoli’s first description of the “Comune di Sicilia” goat , and witThis study was performed according to the ethical principles that have their origins in the Italian Veterinarians’ Ethical Code  and the The study was conducted in western Sicily, in two farms located in Petralia Sottana and Bolognetta, two administrative areas in the province of Palermo representing different agro-ecological areas of Sicily. Petralia Sottana is part of the Madonie Park, with a warm and temperate climate, and is located at an altitude of 1039 m above sea level, specifically between latitude 37°48′0″ N and longitude 14°5′0″ E. This mountainous area is characterized by large extents for grazing with extensive arable crops. The territory of Bolognetta is mainly hilly and characterized by a warm and temperate climate; it is located between latitude 37°57′39″ N, longitude 13°27′78″ E, and altitude 348 m above sea level. In both farms, goats were reared under a semi-extensive farming system, where feeding management is based on grazing spontaneous fodder essences during the day, and during the night, shelter is provided in the stable called “mannara” (from Arabic ‘manzrah’: closed area), which in the local dialect refers to a traditional enclosure where sheep and goats are usually penned at night [A total of 78 goats (comprising 9 bucks and 69 does) exhibiting the most typical phenotypic characteristics of the Comune di Sicilia (CCS) goat population were carefully selected from the two herds. Specifically, 25 does and 6 bucks were selected from Petralia Sottana farm, and 44 does and 3 bucks from Bolognetta farm.Morphometric linear measurements were taken only on the 41 adult subjects (4 bucks and 37 does with an age comprised between 18 months and 5 years); specifically, their age was estimated through the evaluation of the dentition ,16. DataWither height (WH) was measured as the vertical distance from the top of the withers to the ground.Croup height (CrH) was measured as the vertical distance from the top of the croup to the ground.Chest height (ChH) was measured as the vertical distance from sternum to withers.Chest length (ChL) was measured as the distance between the top behind the scapular and the costal arch bounded by the last rib.Trunk length (TL) measured as the distance from the point at the top behind the scapular to the base of the tail.Croup length (CrL) was measured as the distance between the iliac tuberosity and the ischial tuberosity.Chest width (CW) it was measured as the distance between the right retro-scapular area and the left retro-scapular area.Hip breadth (HB) was taken as the distance between the two iliac tuberosities.Coxofemoral width (CxW) was taken as the distance between the two trochanteric tuberosities.Hearth girth (HG) was measured as a circumferential measure taken around the chest just behind the front legs and withers.Shin circumference (SC) was measured from the left mid-metacarpus.2 − 0.69 × HG + 14.7. According to the latter authors, heart girth measurement was determined to be the best predictor of live BW with a regression coefficient of 0.92. Measurements were recorded in the morning before the animals were released for grazing to avoid the effect of feeding and watering on the goats’ size and conformation. All measurements were performed by the same person in order to avoid inter-individual variations as reported by Sheriff et al. (2021) [Moreover, live body weight (BW) was calculated according to Natsir et al. 2010)  with the10  with . (2021) . All meaAccordingly, the following traits were recorded using a graduated stick and a measuring tape and expressed in centimeters (cm):Regarding qualitative data, coat color pattern and type, presence or absence of horn, ears, wattles, and beard were also registered for all 78 subjects according to Sponenberg et al. (1998) and Henkel et al. (2019) ,22.n = 37 female goats) obtained: mean, first quartile, median, third quartile, standard deviation, 95% confidence interval of the mean, skewness, kurtosis, and Shapiro–Wilk test to assess if data were normally distributed.Phenothypic data were analyzed with SAS software . The univariate procedure of SAS was used to determine the following descriptive statistics for the distribution of the morphometric values , horn (yes or no), and wattles (yes or no). Individual goats were included as random effect. In addition, for the ANOVA analysis, trunk length (TL), chest width (CW), and shin circumference (SC) were log-transformed and presented as back-transformed data due to their not-normal distribution. Pair-wise comparisons were performed using the least significant difference test. Statistical significance was declared at ®, San Diego, CA, USA). Experimental protocol was authorized by the Regional Department of Agriculture, Rural Development and Mediterranean Fisheries—Sicilian Region  Italy, n. G49J21006760009, prot. 0012062, 14 July 2021.The genomic analyses were performed on 78 CCS goats, consisting of 9 bucks and 69 does. This group comprised all the animals that underwent morphological evaluation as previously described, along with additional subjects selected to ensure a representative sample of the breed. Moreover, special attention was given to limit direct relatedness among the individuals in the cohort. Blood samples were collected and about 3 mL of each sample was placed in a sterile tube containing ethylenediamine tetra-acetic acid (EDTA) and stored in the refrigerator or freezer until analysis. The DNA extraction and genotyping were outsourced and performed using the Goat 60 K SNP BeadChips on an iScan System  [PLINK software (version 1.9)  was usedPLINK 1.9 was used to perform a multidimensional scaling analysis (MDS) to visualize the genetic distances among the goat populations included in the study. In-house scripts were used for computing bootstrapped Reynolds distances among breeds  and idenIS) were calculated using PLINK 1.9. A sliding window approach was used to estimate runs of homozygosity (ROH) in all subjects using the following parameters: ROHquartile = 0.99, minNsnp = 10, maxNsnp = 30, windef = 20, interval = 5, hetallowed = 0, minKblength = 1000, density = 500, maxInternalGap = 500, and maxmiss = 2. The ROH-based inbreeding coefficient (FROH) was calculated by dividing the total length of ROH in a subject by the total length of the autosomes covered by the SNPs, as described by McQuillan et al. (2008) [To elucidate the genetic diversity of the analyzed breeds, expected heterozygosity (He), observed heterozygosity (Ho), and Wright’s fixation index (F. (2008) ,28. ThisThe genomic effective population size (Ne) trend, ranging from 13 to 983 generations ago, was estimated using the linkage disequilibrium (LD) method for all the populations using SneP software (version 1.1) .ST and ROH analyses. Specifically, the following groups were compared: (a) 39 horned vs. 39 polled goats ; (b) 74 goats with ears  vs. 4 presenting microtia ; and (c) 44 goats with wattles vs. 34 without wattles . In particular, the SNPs falling in the top 1% FST values and delta H-score  were retained and mapped on the ARS 1.2. Their associated genes were further investigated.In addition, we investigated the selection signatures associated with specific morphological features found in CCS goats, such as microtia and the presence or absence of wattles and horns using FQualitative phenotypic data were assessed in 78 goats . Most ofWattles were present in 44% of the analyzed goats. Additionally, 40% of the individuals, both male and female, were horned, in most cases presenting spiral or lyre horns. It is noteworthy that the presence of horns in goats is advantageous for self-defense and thermoregulation  and seemp > 0.05). However, the trunk length (TL), chest width (CW), and shin circumference (SC) showed a not normal distribution (p < 0.05): TL presented a left-skewed asymmetrical and slightly leptokurtic distribution; CW was right-skewed; and SC was leptokurtic.p < 0.05), and a tendency for SC , whereas there were not observed statistical differences for other morphological traits. No significant effects on the morphological measurements were instead found between animals with and without horns and with and without wattles.Comparison of quantitative and qualitative traits between the two farms are summarized in When comparing the CCS goats’ average body traits with those reported in the breed standards of the other native Sicilian goats  , some diThese results, in line with historical references of the breed , highligThe observed morphologic variability might be attributed to lack of a systematic selection program in the breed that would help for setting up specific selection criteria. Moreover, the differences between the two sampled farms might depend on the management system, genetic by environment interaction, the breed characteristic itself, or the presence of strains within the breed. The existed variation is an opportunity for sustainable improvement, conservation, and utilization work that would be designed for this breed.Following quality control and the removal of direct relatives, a total of 487 animals and 48,039 SNPs were retained and used for ROH and selection signature investigation. To ensure homogeneity across the 15 Italian breeds, a maximum of 35 animals were considered for each breed, resulting in a final dataset of 411 subjects for population structure analyses .The results of multidimensional scaling (MDS) analyses are presented in The results of the phylogenetic tree analysis based on Reynold distances  are consThe admixture analysis revealed that K = 11 was the best-fitting number of clusters, as determined by the model’s c-v value. Collectively, these findings provide strong evidence supporting the genomic originality of the CCS breed, marking a significant advancement in its recognition process. However, the evidence of the presence of subjects with a more admixed background highlights the importance of complementing phenotypical evaluations with genomic analyses to select the most suitable breeding animals and optimize their matings.o) than expected heterozygosity (He), with a FIS equal to 0.012. This is in line with what was observed for most of the other Southern Italian breeds. Genomic inbreeding (FROH) ranged from 1.5% (MES) to 15% (MAL). CCS, in particular, had a mean FROH equal to 6.0%, a value near most of the other breeds. Interestingly, more than 50% of the FROH derived from ROH > 16 MB in NIC, MON, CCS, and ASP breeds, implying recent inbreeding events [Genetic variability and ROH were investigated for all the included breeds . CCS breg events . These rThe genomic effective population size (Ne) based on LD was computed for each of the studied breeds. In ST and ROH were used to compare CCS goats with different phenotypic characteristics. For these analyses, all 78 CCSs were included. All the results related to the selection signature analyses, including the complete name of the genes, are reported in Fn = 39) and polled (n = 39), we identified 480 SNPs within the top 1% FST values (0.12–0.25), which fell within 136 genes. With regard to ROH analysis, 197 SNPs on 184 genes were in the top 1% delta H-score. Three genes—PCDHAC2, NRG2, and HBEGF—were found by both analyses. As previously mentioned, polled animals in our study exhibited normally formed genitalia, and PIS was excluded through PCR analysis. However, to the best of our knowledge, none of the genes identified by our analyses were previously associated with horn development in goats or other species. It is plausible that a breed-specific mutation is responsible for polledness in this particular breed. For example, this was the case in cattle species, where several were identified as causative factors for polledness [Comparing horned  and with anotia (n = 4), 480 SNPs and 143 genes were identified with FST analysis (top 1% = 0.44–0.87), and 189 SNPs and 178 genes with ROH analysis. Three genes, namely ATP12A, RNF17, and CENPJ, were in common. Interestingly, one of the ROH regions, located on chromosome 7, was found to be a selection signature in La Mancha goats, a breed in which the anotia trait is fixed [HSPA9, mutations of which are responsible for human Even-Plus syndrome [ST and/or ROH analyses are associated with various syndromes that frequently include microtia or similar ear malformations among their symptoms according to the Human Phenotype Ontology database [CENPJ and RNF17, both found by both the analyses, to primary autosomal recessive microcephaly 6 and Seckel syndrome; TCOF1 to Treacher Collins syndrome; EYA1 to brachio-oto-renal, branchiootic, and oto-facio-cervical syndromes; SPEN and RERE to 1p36 deletion syndrome and RERE-related neurodevelopmental syndrome; and ORC6 to Meier–Gorlin syndrome 3 and ear-patella-short stature syndrome. Moreover, GJB2 and 6 are associated to syndromic or non-syndromic deafness, whereas SUFU, which presented extremely high FST values, is considered to play a pivotal role in mammalian cochlear hair cell differentiation [In the comparison of CCS goats with ears  and without wattles (n = 34) led to the identification of 480 SNPs on 137 genes in the top 1% (0.08–0.20). Instead, 203 SNPs on 197 genes were comprised in the top 1% of delta H-score in ROH analysis. FAN1 and TRPM1 genes were retained in both methods. Among the identified genes, SLC9A9 and NEDD4 genes were previously accounted as a potential candidate gene for wattle presence in goat [NEDD4 gene  and its binding protein N4BP1 (through FST), further supporting their potential roles in the development of wattles in goats. Another study, instead, suggested possible association between the same phenotype and CSMD1 [KIF7, whose mutation causes acrocallosal syndrome in humans, often presenting preauricular skin tags [ADAMTSL3, known to influence the shape of the comb in chickens [F in goat . Interesnd CSMD1 , which ewith n =  and withchickens .Local livestock populations, such as the Comune di Sicilia goat breed, play a crucial role in the preservation of rural communities in harsh and marginal areas, ensuring income for these communities and contributing to the preservation of the territory.The investigation of the Comune di Sicilia goat revealed that, presently, this population demonstrates both genomics and phenotype distinctiveness, setting it apart from other breeds reared in the same breeding ranges, representing a step forward in its recognition process. However, it is important to acknowledge that some degree of variability exists within the population, likely influenced by breeders’ selection preferences and the relatively recent initiation of a formal selection process.To ensure the recognition and conservation of this population, a combined approach is imperative, taking into account both the morphology and appearance of the animals as well as their genomic background. This comprehensive evaluation will enable the identification and choice of the most suitable individuals, promoting the breed’s originality while preventing excessive inbreeding.Furthermore, the Comune di Sicilia breed presents intriguing phenotypic peculiarities that warrant further investigation from a genomic perspective. Such research can provide valuable insights into the development of these traits within the goat species.


The GWAS revealed 7 novel SNPs associated with the studied traits; one genome-wide and two suggestive significant SNPs for SCC , one suggestive significant SNP for BCS (Chios: rs424834097 on chromosome 4) and three suggestive significant SNPs for LP . Nineteen candidate genes were detected: two for BCS , thirteen for SCC  and four for LP . Present results show that resilience in the studied dairy sheep breeds is heritable and advance existing knowledge on the genomic background of SCC, LP, and BCS. Future research will quantify effects of different alleles of significant SNPs on the studied traits and search for possible correlations among traits to facilitate their effective incorporation in breeding programs aiming to improve resilience.Genetic selection for higher productivity increased dairy sheep susceptibility to diseases and environmental stressors, challenging their health and welfare status and production efficiency. Improving resilience to such stressors can enhance their ability to face these challenges without compromising productivity. Our objective was to estimate genomic heritability and perform genome-wide association studies (GWAS) to detect SNPs and candidate genes associated with three proxy traits for resilience  of Chios and Frizarta dairy ewes. We used genome-wide genotypes of 317 Chios and 346 Frizarta ewes. Individual records of milk yield and BCS, and milk samples were collected monthly for two consecutive milking periods; samples were analyzed to determine SCC. The LP was calculated as the regression coefficient of daily milk yield on days from lambing. Within breed, variance components analyses and GWAS were performed using genomic relatedness matrices in single-trait animal linear mixed models. Genomic-based heritability estimates were relatively high (BCS: h Dairy sheep production in the Mediterranean basin has been practiced over thousands of years and still constitutes an important part of the animal production sector with socioeconomic and cultural impact . A largeThe genetic study of sheep resilience has so far involved diverse traits regarding resistance to disease, resilience to weather conditions, longevity and resource use efficiency . In dairLactation persistency (LP) is another important dairy trait, which represents the decline rate of milk yield following the peak of lactation; a lower rate is related to extended and more productive lactations . The obsBody condition scoring is a technique for subjectively assessing body fat reserves on live sheep. Since its conception in the 1960s it has become widely used because it is easily applicable on farm without the need for special equipment or handling, and foremost it is not biased by other tissue conformation and weight . The bodIn the context of exploring the genetic potential for resilience in sheep breeds that are well-adapted to the local environment, the present study focuses on two highly productive local dairy sheep breeds, namely, Chios and Frizarta, reared under intensive and semi-intensive conditions in Greece. The two populations have never been under selection for resilience traits . In the ad libitum access to water. Furthermore, ewes in the two semi-intensively reared flocks were grazing during spring and summer months for 2–3 h daily in natural or cultivated grasslands. Details regarding the location and management practices of the farms are presented in A total of 317 Chios and 346 Frizarta ewes were randomly selected from four farms (two per breed) located in northern and western Greece, respectively. Within each breed, one of the selected farms was managed intensively and the other one semi-intensively, so that the two most common sheep farming systems in Greece are represented. Selecting an equal number of representative flocks of both systems, eliminates introduction of biases related to system-specific farming practices and allows for a broader impact and practical implementation of results. Therefore, flocks were selected on the basis of being typical of the above farming systems and having the best management practices among members of the Chios Sheep Breeders’ Cooperative “Macedonia” and the Agricultural and Livestock Union of Western Greece. Ewes were fed roughages and a concentrate mix with the quantities being appropriately adjusted to meet their nutritional requirements, while they had ®, New Zealand) attached to each milking unit, which enabled also the collection of individual milk samples (an amount of approximately 50 mL) using Falcon tubes (70 mL volume capacity). Milk samples were stored in a portable freezer at 4°C and transferred to the laboratory within 24 h to assess SCC using Fossomatic™ FC . Blood samples were collected from the jugular vein once per ewe in vacuum tubes with anticoagulant factor  and stored at −20°C, until further processing.Ewes were monitored once per month for two consecutive milking periods following the weaning of lambs at 42 days postpartum. Animal handling and recording was always performed by the same qualified veterinarian. Ewe BCS was assessed by palpation of the lumbar region using the methodology described by DMY is the daily milk yield, TY is the recorded milk yield from one milking and Δt is the time period between the recorded and the previous milking (in minutes). The latter equation assumes that milk is produced evenly throughout the day and allows for calculating daily milk yield based on recording one milking per day when more than two milkings are implemented. It is used by the Chios Sheep Breeders’ Cooperative “Macedonia” for farms with high milking frequency, as an alternative to the standard methods suggested by the International Committee of Animal Recording  on days from lambing , accordin = 3,239) were used to calculate the mean for each year of the study weighted over the respective daily milk yield, according to the following formula:DMYi is the daily milk yield in the ith milk recording occasion and SCCi is the respective SCC. The average of the weighted means of SCC for the 2 years of the study was considered for genomic analyses. In case ewes had records of SCC or LP in only 1 year of the study, the respective annual values were used. Ewes with only one SCC record per year were excluded. Due to deviations from the normal distribution, SCC data were logarithmically transformed prior to further analyses. The approach is a modified version of the methodology suggested by Individual repeated measurements of SCC  were averaged over both years of the study. Poor body conformation was considered an indication of compromised health and welfare, which could possibly impact the expression of genetic potential , as previously described in detail by p-value = 10–6) were filtered out. Sample call rate threshold was set at 90%. Chromosomal coordinates were allocated to SNPs based on the Oar_v4.0 genome assembly  of animal genotypes was performed within breed using GEMMA software version 0.98.1  to invesy is the phenotypes vector, τ is the vector of fixed effects, u is the vector of random effects, X and Z are the design matrices that associate phenotypes with fixed and random effects, respectively, whereas ε is the vector of random residual errors. Preliminary analyses were performed to identify the fixed effects for the models. The tested variables included farm (2 levels for each breed), lactation number , recording years , lambing season  and prolificacy . Principal Components (PCs) from PCA accounting for measurable proportions of variance were also tested. Only variables with a statistically significant effect (p < 0.05) on each studied trait were retained in the final models of statistical analyses. The Akaike Information Criterion, the Bayesian Information Criterion and the magnitude of the residual variance were also considered as goodness-of-fit indicators, to compare tested models. The random additive genetic effect of ewe and residual effect were included in all models. In the Chios analyses, the model for BCS included lactation number, lambing season and the first two PCs as fixed effects; the model for SCC included lactation number and the first PC as fixed effects; the model for LP included farm and lambing season as fixed effects. In the Frizarta analyses, the model for BCS included farm and lambing season as fixed effects; respectively, farm and lactation number were included as fixed effects in the SCC model; the model for LP included the fixed effects of lambing season and recording years.Variance components of LP, BCS and logarithmically transformed SCC were estimated within breed by residual maximum likelihood (REML) using a genomic relatedness matrix with the following single-trait animal linear mixed model:α = 0.05) of heritability estimates was assessed based on the magnitude of the respective standard errors, with the two-tailed Student’s t-distribution.All data analyses were performed with ASReml software version 4.2 . The cenwβ, where w is the vector of SNPs and β the associated effects. Furthermore, all GWAS models on Frizarta and Chios sheep traits included the first and first three PCs, respectively, to account for population structure. Genome-wide significance threshold was set at p < 0.05. A suggestive significance threshold was also set expecting one false positive association to occur once per GWAS  of 5.94 and 5.98. Corrected suggestive thresholds were p < 2.27E-05 and p < 2.11E-05 (corresponding to -log10 of 4.64 and 4.68) for Chios and Frizarta sheep, respectively. Analyses were performed with GEMMA software version 0.98.1  illustrating associations of SNPs with the studied traits and the respective Quantile-Quantile (Q-Q) plots for quality assessment of results were created in RStudio  2,157 SNPs were unmapped or located in non-autosomal regions, ii) 1,945 SNPs had call rate lower than 97%, iii) 236 deviated from HWE and iv) 5,905 had MAF lower than 2%; the respective numbers for Frizarta sheep were i) 2,144, ii) 1,999, iii) 244 and iv) 1,678 SNPs. The final datasets included 43,998 and 47,451 SNPs for Chios and Frizarta sheep, respectively. No samples were removed due to low call rate.Results of PCA on Chios and Frizarta genomic relatedness matrices are demonstrated in 2 = 0.54 and 0.55, for Chios and Frizarta sheep, respectively) and moderate to high for LP (h2 = 0.43 and 0.45) and SCC (h2 = 0.25 and 0.38). All estimates were statistically significant (α = 0.05), except for heritability of SCC in Chios sheep.Heritability estimates for all the studied traits are presented in p-value = 3.45E-05 and 3.54E-05, respectively). Finally, one suggestive significant SNP (rs424834097) located on chromosome 4 was associated with BCS in Chios sheep, whereas no significant associations were detected in Frizarta.Results from GWAS of BCS, SCC and LP are presented in ZHX2—zinc fingers and homeoboxes 2). Almost all suggestive significant SNPs were located within introns of protein coding genes. Specifically, GRIP1 (glutamate receptor interacting protein 1) and GRID2 (glutamate ionotropic receptor delta type subunit 2) genes included two SNPs, rs428128299 and rs412648955, associated with LP in Chios and Frizarta sheep, respectively. The rs424064526 and rs428540973, that were associated with SCC in Frizarta sheep, were located within FSTL1 (follistatin like 1) and RBP7 (retinol binding protein 7) genes, respectively. Last, rs424834097, associated with BCS in Chios sheep, was located within POT1 (Protection of telomeres protein 1) gene. The only exception was rs193632931 associated with LP in Frizarta sheep, which was located in an intergenic region approximately 600 bp downstream of the nearest gene (DZIP1L—DAZ interacting zinc finger protein 1 like). According to the AnimalQTLdb, none of the significant SNPs were in regions of previously reported QTL associated with the studied traits in sheep. Within 1 Mb upstream or downstream of significant SNPs, a total of 105 genes were identified, 86 of which refer to Frizarta and 19 to Chios sheep; most of the genes were protein coding, 9 were pseudogenes and 4 were non-coding RNA genes (The genome-wide significant SNP (rs403061409) associated with SCC in Frizarta sheep was located in an intergenic region approximately 530 kb from the nearest gene (NA genes .α = 0.05). Further investigation on a larger sample size may be necessary to allow safe conclusions regarding heritability of SCC in the studied Chios population.The present heritability estimates of SCC were higher than the ones reported in previous studies on the same or other dairy sheep breeds. Specifically, pedigree-based heritability estimates of SCC test-day records for Chios sheep were low to moderate and ranged from 0.09 to 0.18 depending on the stage of lactation at which records were collected . SlightlResearch data on heritability of BCS in dairy sheep are scarce. In the present study, heritability of LP lies close to the upper limit of previously reported estimates that ranged from 0.11 to 0.46, depending on the population and the definition of the trait which is largely inconsistent among studies . DiffereOverall, the present heritability estimates were higher or similar to the highest estimates reported in the literature. However, in the present study, heritability of the studied traits was calculated based on genomic relatedness matrices of Chios and Frizarta populations instead of pedigree data which have been used in the above-mentioned studies. The accuracy of sheep pedigree has been questioned due to the specific characteristics and limitations of sheep rearing systems and reproduction practices that often result in false or missing parentage data. On the contrary, genomic relatedness matrices accurately capture family structure and relationships between animals , resultiPopulation structure revealed by PCA was associated with the farm of origin of animals in both breeds, in accordance with the fact that targeted selection and mating practices is mainly performed on farm level. The effect was less intense in Frizarta sheep since artificial insemination is performed in some ewes (less than 30% of the flocks); in this case, the use of common sires between farms allows for more genetic admixture.p = 3.45E-05), indicating relevance to previous results of n = 609) that increased statistical power. Similarly, no common regions with previously reported associations of SCC in Frizarta sheep were identified; WDYHV1 (NTAQ1), ZHX1 and ZHX2 are involved in apoptosis and immune response pathways  is involved in synthesis of hyaluronan, which is associated with mammary growth during gestation , FAIM gene is involved in processes inhibiting cell apoptosis and its expression in sheep skin has been associated with superfine wool quality , SCC  and LP  of Chios and Frizarta ewes, on chromosomes 1, 3, 4, 6, 9 and 12. The absence of common SNPs or genes between the two breeds and discrepancies from previously reported genomic regions associated with the studied traits in other breeds, underline their polygenic inheritance and the need for breed-specific studies that will facilitate the effective incorporation of such traits in breeding programs. The present study is expected to expand existing knowledge regarding the inheritance of such traits and contribute towards breeding more resilient animals. Studies on a larger sample size would be beneficial to confirm our findings. Further investigation of the present findings will focus on the different alleles of significant SNPs and quantification of their genetic contribution and impact on the associated traits. Moreover, possible correlations among the studied and other important production traits constitute another key aspect that needs to be investigated. Such studies will facilitate identification and selection of favorable alleles, and inclusion of the studied traits as objectives in breeding programs aiming to improve resilience without compromising productivity.Overall, the results of the present study indicate that resilience traits of highly productive dairy ewes are heritable and report 7 novel SNP markers and 19 candidate genes for BCS (rs424834097 and


In this study, we aimed to estimate the genetic parameters of the reproductive traits in three popular commercial pig breeds: Duroc, Landrace, and Yorkshire. Additionally, we evaluated the factors that influence these traits.We collected data from a large number of litters, including 1,887 Duroc, 21,787 Landrace, and 74,796 Yorkshire litters. Using the ASReml-R software to analyze 11 traits, which included: total number of pigs born (TNB); number of piglets born alive (NBA); number of piglets born healthy (NBH); number of piglets born weak (NBW); number of new stillborn piglets (NS); number of old stillborn piglets (OS); number of piglets born with malformation (NBM); number of mummified piglets (NM); total litter birthweight (LBW); litter average weight (LAW); duration of gestational period (GP). We investigated the effects of 4 fixed factors on the genetic parameters of these traits.p < 0.05).Among the 11 reproductive-related traits, the gestational period belonged to the medium heritability traits (0.251–0.430), while remaining traits showed low heritability, ranging from 0.005 to 0.159. TNB, NBA, NBH, LBW had positive genetic correlation (0.737 ~ 0.981) and phenotype correlation (0.711 ~ 0.951). There was a negative genetic correlation between NBW and LAW (−0.452 ~ −0.978) and phenotypic correlation (−0.380 ~ −0.873). LBW was considered one of the most reasonable reproductive traits that could be used for breeding improvement. Repeatability of the three varieties was within the range of 0.000–0.097. In addition, the fixed effect selected in this study had a significant effect on Landrace and Yorkshire (We found a positive correlation between LBW and TNB, NBA, and NBH, suggesting the potential for multi-trait association breeding. Factors such as farm, farrowing year, breeding season, and parity should be taken into consideration in practical production, as they may impact the reproductive performance of breeding pigs. ImproveOver the years, research has focused on determining EBVs of reproductive traits, and genetic parameters for farrowing traits have been estimated , 5 over In order to enhance breeding decisions and improve population reproductive efficiency, the aim of this study was to determine 11 reproductive-related phenotypic traits: total number of pigs born (TNB); number of piglets born alive (NBA); number of piglets born healthy (NBH); number of piglets born weak (NBW); number of new stillborn piglets (NS); number of old stillborn piglets (OS); number of piglets born with malformation (NBM); number of mummified piglets (NM); total litter birthweight (LBW); litter average weight (LAW); duration of gestational period (GP), to build a variety of models for estimating genetic parameters. In addition, the different model and fixed effects were tested by statistical methods to verify the accuracy of estimated heritability in order to explore the influencing factors.2.2.1.The data utilized in this study were obtained from 11 breeding pig farms of the same company located in Southwest China, where Canadian breeding lines were raised. It is important to note that there is genetic exchange among the breeding pig populations across different farms. A total of 98,470 litters of sow farrowing records obtained within the years of 2016–2022, among which were 1,887 Duroc, 21,787 Landrace, and 74,796 Yorkshire. In addition, we collected a total of 48,434 pedigree data from 3 breeds, each of which consisted of 8 generations. Reproductive trait records were TNB, NBA, NBH, NBW, NS, OS, NBM, NM, LBW, LAW, and GP; NBA was the number of piglets born alive per litter within 24 h of birth; NS referred to piglets with no vital signs after parturition; OS referred to stillborn piglets obtained from sows was yellow, old and dark in color, but the fetus is not mummified, that is, the piglet died in the mother for a period of time; LAW was the average of total litter weight within 24 h after birth, excluding stillborn and mummified piglets.2.2.Depending on data distribution and prior knowledge, TNB or NBA data with values <3 or >30 were discarded. LAW records <0.3 kg or >3 kg were excluded from this study. Gp values were limited to 104 ~ 124 days. Duplicating pedigree records were removed, individuals with phenotype data were completed, no pedigree records through self-written python scripts were recorded, and completes the number of zero was assigned to parental individual information.The variance of components of 11 reproductive traits using the ‘ASReml-R’  R packag2.3.2.3.1.y is the vector of phenotypic records; f is the fixed effect vector of the year of gestation, parturition season, farm, and parity, including gestation year effect, gestation season effect, parity effect and farm effect; ɑ is the additive genetic effect ~variance matrices to account for random and residual effects in our analysis. Specifically, we employed Var(a) = G0 ⊗ A to represent the additive genetic (co)variance matrix between traits, where G0 captures the additive genetic relationships among individuals as indicated by Wright’s numerator relationship matrix (A). Similarly, we used Var(e) = R0 ⊗ I to represent the residual (co)variance matrix between traits, where R0 represents the residual covariance matrix and I represents the identity matrix.In the present study, single-trait and two-trait animal models were used to estimate the variance of components of 11 reproductive traits. Model1 formulas were determined as follows:h2 is heritability, σa2 is additive genetic variance, and σe2 is residual variance.Breeding seasons of the sows on each breed were defined into spring (March–May), summer (June–August), autumn (September–November), and winter (December–February). Breeding records of all three varieties were purebred. The formula for calculating heritability was:X and Y; X and Y; X and Y; X and Y; X and Y; X and Y.Genetic and phenotypic correlations were calculated using the following formula:2.3.2.f is the fixed effect; α is the additive genetic effect; X, Z, and W are the corresponding structural matrices.Previously, we calculated the proportion of additive variance in the simple animal model. In contrast, for the repeatability model, we calculated the proportions of additive variance and permanent environmental effects variance separately, taking into account the influence of permanent environmental effects. The repeatability model is formulated as follows:h2 is the heritability; rep is the repeatability;The formulas for calculating heritability and repeatability were:2.4.The model was tested using the REML likelihood ratio test (LRT) in ASReml-R, and fixed effects testing was conducted using the Wald function . In the 3.3.1.The table  presents3.2.Heritability estimated based on single-trait animal models are shown in 3.3.Large sample sizes are often required to accurately estimate genetic correlations in animal breeding, since they are often subjected to large sampling errors . In our Furthermore, the 11 reproductive traits evaluated in the present study were classified into two groups: (i) genetic correlation and phenotypic correlation of traits that are expected to be improved during production, such as TNB, NBA, NBH, LAW, and LBW and (ii) genetic correlation and phenotypic correlation of traits that are not expected to be improved during production, NBW, NS, OS, NBM, and GP. Considering the first group, a very high consistency in genetic correlation and phenotypic correlation was found in the three pig breeds ,E, which3.4.The reproductive traits selected in this study are considered repeatable measurable traits. When estimating heritability, the value of permanent environmental effects was calculated by the constructed repeatability model, thus yielding a more accurate estimate of the additive variance ratio. Repeatability results estimated with the optimal model are shown in Heritability results estimated with the repeatability model are shown in 3.5.Farm, farrowing year, breeding season, parity are fixed effect factors commonly used for estimating heritability , 14. Thep < 0.05). Farrowing year did not have a significant effect on the reproductive performance of Duroc pigs, whereas it significantly affected the reproductive performance of Yorkshire pigs as well as certain traits related to reproductive performance in Landrace pigs  (p < 0.05). The effect of breeding season on reproductive traits was evident in Yorkshire pigs, but NBM LRT results were not significant in all three pig breeds. Breeding season was not significantly affected in Duroc pigs. Moreover, parity has an important influence on reproductive performance, but it had no significant effect on reproductive traits of Duroc pigs; whereas TNB, NBA, NBH, NS, LBW, and LAW was significant affected by parity in Landrace pigs (p < 0.05); finally, NBM and GP were not significantly affected by parity in Yorkshire, whereas the remainder traits were significantly level (p < 0.05).The results are shown in Therefore, the majority of fixed effects had no significant impact on the reproductive traits of Duroc pigs, This result suggests that the factors influencing the reproductive performance of Duroc pigs were not attributed to the three fixed effects examined in this study. In addition, some traits of Landrace, such as NBW, OS, NBM, NM were not affected by season and party. In Yorkshire pigs, NBM is significantly influenced by site and year, but season and parity are not; other reproductive traits were affected by farm, breeding season and parity, and were considered in the multi-trait linear model.4.4.1.The additive effects have long been the focus point in genetic and breeding research. Estimates of heritability for a particular trait can vary across populations due to the influence of population characteristics, models used, and datasets employed , 16. MorIn previous studies, NS and OS have been rarely calculated separately, and the causes of NS and OS may have differed. NS may occur during sow parturition, while OS may be influenced by a combination of genetic factors, environmental conditions, and diseases during pregnancy. In this study, NS and OS were low heritability traits with the range of 0.018–0.084.This result also revealed the proportion of the additive effect of lower heritability of stillbirths , 21. Ye Birth weight is a highly heritable trait and is appropriately influenced by maternal and permanent litter effects . Severalh2 = 0.29–0.34). Thus, the three pig breeds evaluated herein had different gestational heritability, which reveals that different pig breeds have different proportions of gestational heritability. However, it cannot be ruled out that this difference was caused by the difference in the amount of data collected.GP can serve as a significant selection indicator for breeders aiming to reduce production cycles and increase economic gains . In the Multiple abundant and partially novel phenotypic traits were carefully measured, and their heritability was estimated for the Duroc, Landrace, and Yorkshire pig breeds. This comprehensive investigation serves as a crucial step towards establishing a genetic parameter database specifically tailored for pig breeding in Southwest China. By expanding the scope of phenotypic traits and obtaining heritability estimates, this study provides valuable insights for future breeding programs and genetic improvement efforts in the region.4.2.Genetic correlation of reproductive traits can play an important role in the selection of associated traits. The genetic correlation and phenotypic correlation between TNB and NBA were within the range of 0.839 ~ 0.969 in the three pig breeds, which indicated that TNB and NBA had high positive genetic correlation and positive phenotypic correlation; moreover, TNB, NBA, NBH, and LBW had high positive genetic correlation and phenotypic correlation. The genetic correlation map –C revealConversely, TNB, NBA, and NBH were negatively correlated with LAW, which is also consistent with the findings revealed in a previous study . Therefo4.3.Repeatability is the ratio between permanent environmental effects and phenotypic effects that can explain the proportion of phenotypic variation within a trait . The mod4.4.The reproductive traits of sows are affected by both genetic and environmental factors. Among environmental factors, it can be included farm, parity, year, breeding season, and others. In contrast, genetic factors are mainly related to the genetic effects of breeds . In the The effect of farm and farrowing year was more noticeable in each reproductive trait in Yorkshire pigs, and certain traits in Landrace pigs were significantly affected, indicating that different farming conditions have an influence on the reproductive performance of pigs . AlthougBreeding season affected certain reproductive traits of Landrace and Yorkshire pigs. In a previous study, Untaru et al.  found thThe impact of parity on the reproductive performance of sows remains a topic that requires further investigation . While f5.In the present study, multiple models were used to test whether permanent environmental effects should be considered to the models according to LRT to adjust the bias of heritability calculation of certain reproductive traits in three pig breeds: Duroc, Landrace, and Yorkshire. TNB, NBA, NBH, NBW, NS and heritability of 11 reproduction-related traits were estimated. Differences in genetic parameters of reproductive traits were found in these populations, which indicated that the use of targeted genetic parameters in joint breeding can lead to more accurate genetic selection. With the exception of GP, which exhibited moderate heritability, the majority of reproductive traits were found to have low heritability. Moreover, the strong genetic correlation between litter-related traits is likely to play a role in multi-trait breeding, and LAW and LBW could be genetically selected by TNB or NBA. Furthermore, the factors influencing these reproductive traits were analyzed, revealing the impact of farm, farrowing year, breeding season, and parity on the evaluated sow populations. The genetic parameters, including heritability, genetic correlation, and repeatability, provide valuable insights into the genetic performance of purebred populations of the three breeds in relation to reproductive traits, thus offering data support for breeding and selection programs in pig breeding. In practical pig farming conditions, it is essential to improve management practices, and these genetic parameters are crucial for implementing optimal breeding and selection strategies.The original contributions presented in the study are included in the article/This research scheme was approved by the Animal Protection and Ethics Committee of Sichuan Agricultural University .YY and MG: project design and manuscript—draft writing. LZ and LS: conceptualization. JW, LN, YZ, LC, YW, HZ, and WC: methodology. XY: software. KZ, PZ, YL, and BL: data curation. All authors contributed to the article and approved the submitted version.This research was funded by National Key Research and Development Program of China (2021YFD1200801); Sichuan Science and Technology Program ; China Agriculture Research System (CARS-pig-35); and National Center of Technology Innovation for Pigs.YL, BL, and KZ are employed by the Sichuan Dekon Livestock Foodstuff Group. WC is employed by the National Animal Husbandry Service. HZ is employed by the Sichuan Center for Animal Disease Control.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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