Integration of ssGWAS and ROH analyses for uncovering genetic variants associated with reproduction traits in Large White pigs.

The low heritability of reproduction traits such as total number born (TNB), number born alive (NBA) and adjusted litter weight until 21 days at weaning (ALW) poses a challenge for genetic improvement. In this study, we aimed to identify genetic variants that influence these traits and evaluate the accuracy of genomic selection (GS) using these variants as genomic features. We performed single-step genome-wide association studies (ssGWAS) on 17 823 Large White (LW) pigs, of which 2770 were genotyped by 50K single nucleotide polymorphism (SNP) chips. Additionally, we analyzed runs of homozygosity (ROH) in the population and tested their effects on the traits. The genomic feature best linear unbiased prediction (GFBLUP) was then carried out in an independent population of 350 LW pigs using identified trait-related SNP subsets as genomic features. As a result, our findings identified five, one and four SNP windows that explaining more than 1% of genetic variance for ALW, TNB, and NBA, respectively and discovered 358 hotspots and nine ROH islands. The ROH SSC1:21814570-27186456 and SSC11:7220366-14276394 were found to be significantly associated with ALW and NBA, respectively. We assessed the genomic estimated breeding value accuracy through 20 replicates of five-fold cross-validation. Our findings demonstrate that GFBLUP, incorporating SNPs located in effective ROH (p-value < 0.05) as genomic features, might enhance GS accuracy for ALW compared with GBLUP. Additionally, using SNPs explaining more than 0.1% of the genetic variance in ssGWAS for NBA as genomic features might improve the GS accuracy, too. However, it is important to note that the incorporation of inappropriate genomic features can significantly reduce GS accuracy. In conclusion, our findings provide valuable insights into the genetic mechanisms of reproductive traits in pigs and suggest that the ssGWAS and ROH have the potential to enhance the accuracy of GS for reproductive traits in LW pigs.

Genome-wide association study reveals the candidate genes for reproduction traits in Yunong black pigs.

Enhancing pig reproductive efficiency has the potential to have a significant positive economic impact on the pig business. We collected four reproduction records of 734 Yunong black pigs in this study, including the total number of piglets born (TNB), the number born alive (NBA), the average birth interval of piglets (ABI) and the average birth weight (ABW). A total of 453 Yunong black pigs were genotyped with Porcine 50K SNP BeadChip. Twenty-five SNPs and 35 genomic areas were found to have a substantial impact on the reproductive performance of Yunong black pigs by single-locus GWAS and single-step GWAS (ssGWAS). For the ssGWAS, we found that the two genomic regions (12.67-13.85 and 14.26-15.01 Mb) on Sus scrofa chromosome X were associated with TNB, NBA and ABI. It is worth noting that CNC10110530 and CNC100141254 significantly affected the TNB by both GWAS methods. Finally, we further determined the gene functions by enrichment analysis and a literature search, and identified 28 of them as candidate genes affecting the reproductive performance of Yunong black pigs, including RET, EIF1AX, NELL2, CTPS2, S100G, RBBP7 and PDHA1. This study further promotes understanding of the genetic mechanism of porcine reproductive performance, and also provides more molecular markers for pig breeding.

Weighted genomic prediction for growth and carcass-related traits in Nelore cattle.

This study aimed to assess the impact of differential weighting in genomic regions harboring candidate causal loci on the genomic prediction accuracy and dispersion for growth and carcass-related traits in Nelore cattle. The dataset contained 168 793 phenotypic records for adjusted weight at 450 days of age (W450), 83 624 for rib eye area (REA), 24 480 for marbling (MAR) and 82 981 for subcutaneous backfat thickness (BFT) and rump fat thickness (RFT). The pedigree harbored information from 244 254 animals born between 1977 and 2016, including 6283 sires and 50 742 dams. Animals (n = 7769) were genotyped with the low-density panel (Clarifide® Nelore 3.0), and the genotypes were imputed to a panel containing 735 044 markers. A linear animal model was applied to estimate the genetic parameters and to perform the weighted single-step genome-wide association study (WssGWAS). A total of seven models for genomic prediction were evaluated combining the SNP weights obtained in the iterations of the WssGWAS and the candidate QTL. The heritability estimated for W450 (0.35) was moderate, and for carcass-related traits, the estimates were moderate for REA (0.27), MAR (0.28) and RFT (0.28), and low for BFT (0.18). The prediction accuracy for W450 incorporating reported QTL previously described in the literature along with different SNPs weights was like those described for the default ssGBLUP model. The use of the ssGWAS to weight the SNP effects displayed limited advantages for the REA prediction accuracy. Comparing the ssGBLUP with the BLUP model, a meaningful improvement in the prediction accuracy from 0.09 to 0.63 (700%) was observed for MAR. The highest prediction accuracy was obtained for BFT and RFT in all evaluated models. The application of information obtained from the WssGWAS is an alternative to reduce the genomic prediction dispersion for growth and carcass-related traits, except for MAR. Furthermore, the results obtained herein pointed out that is possible to improve the prediction accuracy and reduce the genomic prediction dispersion for growth and carcass-related traits in young animals.

Identification of genes related to intramuscular fat independent of backfat thickness in Duroc pigs using single-step genome-wide association.

Intramuscular fat (IMF) is an important meat-quality trait of pigs, which influences pork's shearing force, hydraulics, tenderness and juicy flavor. However, to achieve a higher percentage of lean meat, pigs with lower backfat thickness (BF) are intensively selected for, which may lead to a reduction in pork quality. Therefore, the objective of this study was to locate loci that affect IMF without changing BF. A single-step GWAS was performed on 950 Duroc pigs genotyped by a 50K SNP chip in order to detect genomic variants relevant to IMF and BF. The significant SNPs detected were afterwards divided into a BF subset (seven SNPs), an IMF subset (11 SNPs) and a subset of both traits (12 SNPs), according to their P-value and LD. After SNP and QTL annotation, our results indicated that SSC1: 167938652, 166363826, 164829874 and 167171587 might be associated with IMF without changing BF. In the subset of both traits, we found that the combined effect of ALGA0006602 (SSC1: 159538854) and 12784636 (SSC1: 160773437) might improve the IMF without changing BF. Our gene annotation result showed that TLE3, ITGA11, SMAD6, PAQR5 and [RNF152A/G × MC4RA/A ] genes might affect IMF independently of BF. We believe that the SNPs and genes identified in this study will be valuable for the future molecular breeding of IMF in Duroc pigs.

The identification of novel regions for reproduction trait in Landrace and Large White pigs using a single step genome-wide association study.

OBJECTIVE: The purpose of this study was to investigate a single step genome-wide association study (ssGWAS) for identifying genomic regions affecting reproductive traits in Landrace and Large White pigs. METHODS: The traits included the number of pigs weaned per sow per year (PWSY), the number of litters per sow per year (LSY), pigs weaned per litters (PWL), born alive per litters (BAL), non-productive day (NPD) and wean to conception interval per litters (W2CL). A total of 321 animals (140 Landrace and 181 Large White pigs) were genotyped with the Illumina Porcine SNP 60k BeadChip, containing 61,177 single nucleotide polymorphisms (SNPs), while multiple traits single-step genomic BLUP method was used to calculate variances of 5 SNP windows for 11,048 Landrace and 13,985 Large White data records. RESULTS: The outcome of ssGWAS on the reproductive traits identified twenty-five and twenty-two SNPs associated with reproductive traits in Landrace and Large White, respectively. Three known genes were identified to be candidate genes in Landrace pigs including retinol binding protein 7, and ubiquitination factor E4B genes for PWL, BAL, W2CL, and PWSY and one gene, solute carrier organic anion transporter family member 6A1, for LSY and NPD. Meanwhile, five genes were identified to be candidate genes in Large White, two of which, aldehyde dehydrogenase 1 family member A3 and leucine rich repeat kinase 1, associated with all of six reproduction traits and three genes; retrotransposon Gag like 4, transient receptor potential cation channel subfamily C member 5, and LHFPL tetraspan subfamily member 1 for five traits except W2CL. CONCLUSION: The genomic regions identified in this study provided a start-up point for marker assisted selection and estimating genomic breeding values for improving reproductive traits in commercial pig populations.

Genomic Regions Associated with Resistance to Gastrointestinal Parasites in Australian Merino Sheep.

The objective of this study was to identify genomic regions and genes associated with resistance to gastrointestinal nematodes in Australian Merino sheep in Uruguay, using the single-step GWAS methodology (ssGWAS), which is based on genomic estimated breeding values (GEBVs) obtained from a combination of pedigree, genomic, and phenotypic data. This methodology converts GEBVs into SNP effects. The analysis included 26,638 animals with fecal egg count (FEC) records obtained in two independent parasitic cycles (FEC1 and FEC2) and 1700 50K SNP genotypes. The comparison of genomic regions was based on genetic variances (gVar(%)) explained by non-overlapping regions of 20 SNPs. For FEC1 and FEC2, 18 and 22 genomic windows exceeded the significance threshold (gVar(%) ≥ 0.22%), respectively. The genomic regions with strong associations with FEC1 were located on chromosomes OAR 2, 6, 11, 21, and 25, and for FEC2 on OAR 5, 6, and 11. The proportion of genetic variance attributed to the top windows was 0.83% and 1.9% for FEC1 and FEC2, respectively. The 33 candidate genes shared between the two traits were subjected to enrichment analysis, revealing a marked enrichment in biological processes related to immune system functions. These results contribute to the understanding of the genetics underlying gastrointestinal parasite resistance and its implications for other productive and welfare traits in animal breeding programs.

Identification of Candidate Genes and Pathways Linked to the Temperament Trait in Sheep.

Temperament can be defined as the emotional variability among animals of the same species in response to the same stimulus, grouping animals by their reactivity as nervous, intermediate, or calm. Our goal was to identify genomic regions with the temperament phenotype measured by the Isolation Box Test (IBT) by single-step genome-wide association studies (ssGWAS). The database consisted of 4317 animals with temperament records, and 1697 genotyped animals with 38,268 effective Single Nucleotide Polymorphism (SNP) after quality control. We identified three genomic regions that explained the greatest percentage of the genetic variance, resulting in 25 SNP associated with candidate genes on chromosomes 6, 10, and 21. A total of nine candidate genes are reported for the temperament trait, which is: PYGM, SYVN1, CAPN1, FADS1, SYT7, GRID2, GPRIN3, EEF1A1 and FRY, linked to the energetic activity of the organism, synaptic transmission, meat tenderness, and calcium associated activities. This is the first study to identify these genetic variants associated with temperament in sheep, which could be used as molecular markers in future behavioral research.

A Single-Step Genome-Wide Association Study for Semen Traits of Egyptian Buffalo Bulls.

The present study aimed to contribute to the limited research on buffalo (Bubalus bubalis) semen traits by incorporating genomic data. A total of 8465 ejaculates were collected. The genotyping procedure was conducted using the Axiom® Buffalo Genotyping 90 K array designed by the Affymetrix Expert Design Program. After conducting a quality assessment, we utilized 67,282 SNPs genotyped in 192 animals. We identified several genomic loci explaining high genetic variance by employing single-step genomic evaluation. The aforementioned regions were located on buffalo chromosomes no. 3, 4, 6, 7, 14, 16, 20, 22, and the X-chromosome. The X-chromosome exhibited substantial influence, accounting for 4.18, 4.59, 5.16, 5.19, and 4.31% of the genomic variance for ejaculate volume, mass motility, livability, abnormality, and concentration, respectively. In the examined genomic regions, we identified five novel candidate genes linked to male fertility and spermatogenesis, four in the X-chromosome and one in chromosome no. 16. Additional extensive research with larger sample sizes and datasets is imperative to validate these findings and evaluate their applicability for genomic selection.

Single-step genome-wide association study for susceptibility to Teratosphaeria nubilosa and precocity of vegetative phase change in Eucalyptus globulus.

Introduction: Mycosphaerella leaf disease (MLD) is one of the most prevalent foliar diseases of Eucalyptus globulus plantations around the world. Since resistance management strategies have not been effective in commercial plantations, breeding to develop more resistant genotypes is the most promising strategy. Available genomic information can be used to detect genomic regions associated with resistance to MLD, which could significantly speed up the process of genetic improvement. Methods: We investigated the genetic basis of MLD resistance in a breeding population of E. globulus which was genotyped with the EUChip60K SNP array. Resistance to MLD was evaluated through resistance of the juvenile foliage, as defoliation and leaf spot severity, and through precocity of change to resistant adult foliage. Genome-wide association studies (GWAS) were carried out applying four Single-SNP models, a Genomic Best Linear Unbiased Prediction (GBLUP-GWAS) approach, and a Single-step genome-wide association study (ssGWAS). Results: The Single-SNP (model K) and GBLUP-GWAS models detected 13 and 16 SNP-trait associations in chromosomes 2, 3 y 11; whereas the ssGWAS detected 66 SNP-trait associations in the same chromosomes, and additional significant SNP-trait associations in chromosomes 5 to 9 for the precocity of phase change (proportion of adult foliage). For this trait, the two main regions in chromosomes 3 and 11 were identified for the three approaches. The SNPs identified in these regions were positioned near the key miRNA genes, miR156.5 and miR157.4, which have a main role in the regulation of the timing of vegetative change, and also in the response to environmental stresses in plants. Discussion: Our results demonstrated that ssGWAS was more powerful in detecting regions that affect resistance than conventional GWAS approaches. Additionally, the results suggest a polygenic genetic architecture for the heteroblastic transition in E. globulus and identified useful SNP markers for the development of marker-assisted selection strategies for resistance to MLD.

Genome-Wide Association Study of Parasite Resistance to Gastrointestinal Nematodes in Corriedale Sheep.

Selection of genetically resistant animals is one alternative to reduce the negative impact of gastrointestinal nematodes (GIN) on sheep production. The aim of this study was to identify genomic regions associated with GIN resistance in Corriedale sheep by single-step genome-wide association studies (ssGWAS) using 170, 507 and 50K single nucleotide polymorphisms (SNPs). Analysis included 19,547 lambs with faecal egg counts (FEC) records, a pedigree file of 40,056 animals and 454, 711 and 383 genotypes from 170, 507 and 50K SNPs, respectively. Genomic estimated breeding values (GEBV) were obtained with single-step genomic BLUP methodology (ssGBLUP), using a univariate animal model, which included contemporary group, type of birth and age of dam as class fixed effects and age at FEC recording as covariate. The SNP effects as wells as p-values were estimated with POSTGSF90 program. Significance level was defined by a chromosome-wise False Discovery Rate of 5%. Significant genomic regions were identified in chromosomes 1, 3, 12 and 19 with the 170 SNP set, in chromosomes 7, 12 and 24 using the 507 SNP chip and only in chromosome 7 with the 50K SNP chip. Candidate genes located in these regions, using Oar_v4.0 as reference genome, were TIMP3, TLR5, LEPR and TLR9 (170 SNPs), SYNDIG1L and MGRN1 (507 SNP chip) and INO80, TLN2, TSHR and EIF2AK4 (50K SNP chip). These results validate genomic regions associated with FEC previously identified in Corriedale and other breeds and report new candidate regions for further investigation.

Identification of Genomic Regions and Candidate Genes for Litter Traits in French Large White Pigs Using Genome-Wide Association Studies.

The reproductive traits of sows are one of the important economic traits in pig production, and their performance directly affects the economic benefits of the entire pig industry. In this study, a total of 895 French Large White pigs were genotyped by GeneSeek Porcine 50K SNP Beadchip and four phenotypic traits of 1407 pigs were recorded, including total number born (TNB), number born alive (NBA), number healthy piglets (NHP) and litter weight born alive (LWB). To identify genomic regions and genes for these traits, we used two approaches: a single-locus genome-wide association study (GWAS) and a single-step GWAS (ssGWAS). Overall, a total of five SNPs and 36 genomic regions were identified by single-locus GWAS and ssGWAS, respectively. Notably, fourof all five significant SNPs were located in 10.72-11.06 Mb on chromosome 7, were also identified by ssGWAS. These regions explained the highest or second highest genetic variance in the TNB, NBA and NHP traits and harbor the protein coding gene ENSSSCG00000042180. In addition, several candidate genes associated with litter traits were identified, including JARID2, PDIA6, FLRT2 and DICER1. Overall, these novel results reflect the polygenic genetic architecture of the litter traits and provide a theoretical reference for the following implementation of molecular breeding.

Single-Step Genome Wide Association Study Identifies QTL Signals for Untrimmed and Trimmed Thigh Weight in Italian Crossbred Pigs for Dry-Cured Ham Production.

Protected Designation of Origin (PDO) dry-cured ham is the most important product in the Italian pig breeding industry, mainly oriented to produce heavy pig carcasses to obtain hams of the right weight and maturity. Recently, along with the traditional traits swine breeding programs have aimed to include novel carcass traits. The identification at the genome level of quantitative trait loci (QTLs) affecting such new traits helps to reveal their genetic determinism and may provide information to be integrated in prediction models in order to improve prediction accuracy as well as to identify candidate genes underlying such traits. This study aimed to estimate genetic parameters and perform a single step genome wide association studies (ssGWAS) on novel carcass traits such as untrimmed (UTW) and trimmed thigh weight (TTW) in two pig crossbred lines approved for the ham production of the Italian PDO. With this purpose, phenotypes were collected from ~1800 animals and 240 pigs were genotyped with Illumina PorcineSNP60 Beadchip. The single-step genomic BLUP procedure was used for the heritability estimation and to implement the ssGWAS. QTL were characterized based on the variance of 10-SNP sliding window genomic estimated breeding values. Moderate heritabilities were detected and QTL signals were identified on chromosome 1, 4, 6, 7, 11 and 15 for both traits. As expected, the genetic correlation among the two traits was very high (~0.99). The QTL regions encompassed a total of 249 unique candidate genes, some of which were already reported in association with growth, carcass or ham weight traits in pigs. Although independent studies are required to further verify our findings and disentangle the possible effects of specific linkage disequilibrium in our population, our results support the potential use of such new QTL information in future breeding programs to improve the reliability of genomic prediction.

A Single-Step Genome Wide Association Study on Body Size Traits Using Imputation-Based Whole-Genome Sequence Data in Yorkshire Pigs.

The body shape of a pig is the most direct production index, which can fully reflect the pig's growth status and is closely related to important economic traits. In this study, a genome-wide association study on seven body size traits, the body length (BL), height (BH), chest circumference (CC), abdominal circumference (AC), cannon bone circumference (CBC), rump width (RW), and chest width (CW), were conducted in Yorkshire pigs. Illumina Porcine 80K SNP chips were used to genotype 589 of 5,572 Yorkshire pigs with body size records, and then the chip data was imputed to sequencing data. After quality control of imputed sequencing data, 784,267 SNPs were obtained, and the averaged linkage disequilibrium (r 2) was 0.191. We used the single-trait model and the two-trait model to conduct single-step genome wide association study (ssGWAS) on seven body size traits; a total of 198 significant SNPS were finally identified according to the P-value and the contribution to the genetic variance of individual SNP. 11 candidate genes (CDH13, SIL1, CDC14A, TMRPSS15, TRAPPC9, CTNND2, KDM6B, CHD3, MUC13, MAPK4, and HMGA1) were found to be associated with body size traits in pigs; KDM6B and CHD3 jointly affect AC and CC, and MUC13 jointly affect RW and CW. These genes are involved in the regulation of bone growth and development as well as the absorption of nutrients and are associated with obesity. HMGA1 is proposed as a strong candidate gene for body size traits because of its important function and high consistency with other studies regarding the regulation of body size traits. Our results could provide valuable information for pig breeding based on molecular breeding.

Genetic Architecture of Carcass and Meat Quality Traits in Montana Tropical® Composite Beef Cattle.

The Montana Tropical® Composite is a recently developed beef cattle population that is rapidly expanding in Brazil and other tropical countries. This is mainly due to its improved meat quality and adaptation to tropical climate conditions compared to Zebu and Taurine cattle breeds, respectively. This study aimed to investigate the genetic architecture of ultrasound-based carcass and meat quality traits in Montana Tropical® Composite beef cattle. Therefore, we estimated variance components and genetic parameters and performed genome-wide association studies using the weighted single-step Genomic Best Linear Unbiased Prediction (GBLUP) approach. A pedigree dataset containing 28,480 animals was used, in which 1,436 were genotyped using a moderate-density Single Nucleotide Polymorphism panel (30K; 30,105 SNPs). A total of 9,358, 5,768, 7,996, and 1,972 phenotypic records for the traits Longissimus muscle area (LMA), backfat thickness (BFT), rump fat thickness (RFT), and for marbling score (MARB), respectively, were used for the analyses. Moderate to high heritability estimates were obtained and ranged from 0.16 ± 0.03 (RFT) to 0.33 ± 0.05 (MARB). A high genetic correlation was observed between BFT and RFT (0.97 ± 0.02), suggesting that a similar set of genes affects both traits. The most relevant genomic regions associated with LMA, BFT, RFT, and MARB were found on BTA10 (5.4-5.8 Mb), BTA27 (25.2-25.5 Mb), BTA18 (60.6-61.0 Mb), and BTA21 (14.8-15.4 Mb). Two overlapping genomic regions were identified for RFT and MARB (BTA13:47.9-48.1 Mb) and for BFT and RFT (BTA13:61.5-62.3 Mb). Candidate genes identified in this study, including PLAG1, LYN, WWOX, and PLAGL2, were previously reported to be associated with growth, stature, skeletal muscle growth, fat thickness, and fatty acid composition. Our results indicate that ultrasound-based carcass and meat quality traits in the Montana Tropical® Composite beef cattle are heritable, and therefore, can be improved through selective breeding. In addition, various novel and already known genomic regions related to these traits were identified, which contribute to a better understanding of the underlying genetic background of LMA, BFT, RFT, and MARB in the Montana Tropical Composite population.

Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens.

The purpose of this study was to compare results obtained from various methodologies for genome-wide association studies, when applied to real data, in terms of number and commonality of regions identified and their genetic variance explained, computational speed, and possible pitfalls in interpretations of results. Methodologies include: two iteratively reweighted single-step genomic BLUP procedures (ssGWAS1 and ssGWAS2), a single-marker model (CGWAS), and BayesB. The ssGWAS methods utilize genomic breeding values (GEBVs) based on combined pedigree, genomic and phenotypic information, while CGWAS and BayesB only utilize phenotypes from genotyped animals or pseudo-phenotypes. In this study, ssGWAS was performed by converting GEBVs to SNP marker effects. Unequal variances for markers were incorporated for calculating weights into a new genomic relationship matrix. SNP weights were refined iteratively. The data was body weight at 6 weeks on 274,776 broiler chickens, of which 4553 were genotyped using a 60 k SNP chip. Comparison of genomic regions was based on genetic variances explained by local SNP regions (20 SNPs). After 3 iterations, the noise was greatly reduced for ssGWAS1 and results are similar to that of CGWAS, with 4 out of the top 10 regions in common. In contrast, for BayesB, the plot was dominated by a single region explaining 23.1% of the genetic variance. This same region was found by ssGWAS1 with the same rank, but the amount of genetic variation attributed to the region was only 3%. These findings emphasize the need for caution when comparing and interpreting results from various methods, and highlight that detected associations, and strength of association, strongly depends on methodologies and details of implementations. BayesB appears to overly shrink regions to zero, while overestimating the amount of genetic variation attributed to the remaining SNP effects. The real world is most likely a compromise between methods and remains to be determined.