Search Results (17)

Understanding the genetic basis of economically important traits is essential for enhancing the productivity, fertility, and health of dairy cattle. This study aimed to identify the pleiotropic genes associated with the 305-day milk yield (MY305), days open (DO), and milk fat-to-protein ratio (FPR) in Thai-Holstein crossbred dairy cattle using a genome-wide association study (GWAS) approach. The dataset included 18,843 records of MY305 and milk FPR, as well as 48,274 records of DO, collected from first-lactation Thai-Holstein crossbred dairy cattle. A total of 868 genotyped animals and 43,284 informative SNPs out of 50,905 were used for the analysis. The single-nucleotide polymorphism (SNP) effects were evaluated using a weighted single-step GWAS (wssGWAS), which estimated these effects based on genomic breeding values (GEBVs) through a multi-trait animal model with single-step genomic BLUP (ssGBLUP). Genomic regions explaining at least 5% of the total genetic variance were selected for candidate gene analysis. Single-step genomic REML (ssGREML) with a multi-trait animal model was used to estimate components of (co)variance. The heritability estimates from additive genetic variance were 0.262 for MY305, 0.029 for DO, and 0.102 for milk FPR, indicating a moderate genetic influence on milk yield and a lower genetic impact on fertility and milk FPR. The genetic correlations were 0.559 (MY305 and DO), −0.306 (MY305 and milk FPR), and −0.501 (DO and milk FPR), indicating potential compromises in genetic selection. wssGBLUP showed a higher accuracy than ssGBLUP, although the improvement was modest. A total of 24, 46, and 33 candidate genes were identified for MY305, DO, and milk FPR, respectively. Pleiotropic effects, identified by SNPs showing significant influence with more than trait, were observed in 14 genes shared among all three traits, 17 genes common between MY305 and DO, 14 genes common between MY305 and milk FPR, and 26 genes common between DO and milk FPR. Overall, wssGBLUP is a promising approach for improving the genomic prediction of economic traits in multi-trait analyses, outperforming ssGBLUP. This presents a viable alternative for genetic evaluation in dairy cattle breeding programs in Thailand. However, further studies are needed to validate these candidate genes and refine marker selection for production, fertility, and health traits in dairy cattle. Full article

Genomic selection (GS) is a genetic breeding method that uses genome-wide marker information to improve the accuracy of the prediction of complex traits. The single-step GBLUP (ssGBLUP) model, which integrates pedigree, phenotypic, and genomic data, has improved genomic prediction. However, ssGBLUP assumes that all markers contribute equally to genetic variance, which can limit its predictive accuracy, especially for traits controlled by major genes. To overcome this limitation, we integrate results from genome-wide association studies (GWAS) into an enhanced ssGBLUP framework, termed single-step genome-wide association assisted BLUP (ssGWABLUP). Our approach assigns differential weights to markers on the basis of their GWAS results, thereby increasing the contribution of effective markers while diminishing the influence of ineffective ones during the construction of the genomic relationship matrix. By incorporating pseudo quantitative trait nucleotides (pQTNs) as covariates, we aim to capture the effects of markers closely associated with major causal variants, leading to the development of the ssGWABLUP_pQTNs. Compared with weighted ssGBLUP (WssGBLUP), the ssGWABLUP model demonstrated superior accuracy and dispersion across different genetic architectures. We then compared the performance of our proposed ssGWABLUP_pQTNs model against both ssGBLUP and ssGWABLUP across various genetic scenarios. Our results demonstrate that ssGWABLUP_pQTNs outperforms other models in terms of prediction accuracy, particularly in scenarios with simpler genetic architectures. Additionally, evaluation using pig dataset confirmed the effectiveness of ssGWABLUP_pQTNs, highlighting its potential for practical breeding applications. The incorporation of pQTNs and a weighted genomic relationship matrix presents a promising and potentially scalable approach to further enhance genomic prediction, with potential implications for improving the accuracy of genomic selection in breeding programs. Full article

Taiwan Country chickens are integral to Taiwanese culture and the poultry industry. By establishing a crossbreeding system, breeders must consider the growth-related traits of the dam line to achieve acceptable traits in commercial meat-type chickens. This study compared the accuracy of genomic estimated breeding values (GEBVs) predicted using the pedigree-based best linear unbiased prediction (PBLUP) model and the single-step genomic BLUP (ssGBLUP) model. Additionally, we conducted a genome-wide association study (GWAS) to identify single-nucleotide polymorphisms (SNPs) associated with growth, shank, and body conformation traits to support marker-assisted selection (MAS). The results showed that the ssGBLUP model achieved 4.3% to 16.4% higher prediction accuracy than the PBLUP model. GWAS identified four missense SNPs and four significant SNPs associated with body weight, shank length, and shank width at 12 weeks. These findings highlight the potential of integrating the ssGBLUP model with identified SNPs to improve genetic gain and breeding efficiency and provide preliminary results to assess the feasibility of genomic prediction and MAS in Taiwan Country chicken breeding programs. Further research is necessary to validate these findings and explore their mechanisms and broader application across different breeding programs, particularly for the NCHU-G101 breed of Taiwan Country chickens. Full article

Vitiligo is a depigmentation autoimmune disorder characterized by the progressive loss of melanocytes leading to the appearance of patchy depigmentation of the skin. The presence of vitiligo in horses is greater in those with grey coats. The aim of this study was therefore to perform a genome-wide association study (GWAS) to identify genomic regions and putative candidate loci associated with vitiligo depigmentation and susceptibility in the Pura Raza Español population. For this purpose, we performed a wssGBLUP (weighted single step genomic best linear unbiased prediction) using data from a total of 2359 animals genotyped with Affymetrix Axiom™ Equine 670 K and 1346 with Equine GeneSeek Genomic Profiler™ (GGP) Array V5. A total of 60,136 SNPs (single nucleotide polymorphisms) present on the 32 chromosomes from the consensus dataset after quality control were employed for the analysis. Vitiligo-like depigmentation was phenotyped by visual inspection of the different affected areas (eyes, mouth, nostrils) and was classified into nine categories with three degrees of severity (absent, slight, and severe). We identified one significant genomic region for vitiligo around the eyes, eight significant genomic regions for vitiligo around the mouth, and seven significant genomic regions for vitiligo around the nostrils, which explained the highest percentage of variance. These significant genomic regions contained candidate genes related to melanocytes, skin, immune system, tumour suppression, metastasis, and cutaneous carcinoma. These findings enable us to implement selective breeding strategies to decrease the incidence of vitiligo and to elucidate the genetic architecture underlying vitiligo in horses as well as the molecular mechanisms involved in the disease’s development. However, further studies are needed to better understand this skin disorder in horses. Full article

Semen traits play a key role in the pig industry because boar semen is widely used in purebred and crossbred pigs. The production of high-quality semen is crucial to ensuring a good result in artificial insemination. With the wide application of artificial insemination in the pig industry, more and more attention has been paid to the improvement of semen traits by genetic selection. The purpose of this study was to identify the genetic regions and candidate genes associated with semen traits of Duroc boars. We used weighted single-step GWAS to identify candidate genes associated with sperm motility, sperm progressive motility, sperm abnormality rate and total sperm count in Duroc pigs. In Duroc pigs, the three most important windows for sperm motility—sperm progressive motility, sperm abnormality rate, and total sperm count—explained 12.45%, 9.77%, 15.80%, and 12.15% of the genetic variance, respectively. Some genes that are reported to be associated with spermatogenesis, testicular function and male fertility in mammals have been detected previously. The candidate genes CATSPER1, STRA8, ZSWIM7, TEKT3, UBB, PTBP2, EIF2B2, MLH3, and CCDC70 were associated with semen traits in Duroc pigs. We found a common candidate gene, STRA8, in sperm motility and sperm progressive motility, and common candidate genes ZSWIM7, TEKT3 and UBB in sperm motility and sperm abnormality rate, which confirms the hypothesis of gene pleiotropy. Gene network enrichment analysis showed that STRA8, UBB and CATSPER1 were enriched in the common biological process and participated in male meiosis and spermatogenesis. The SNPs of candidate genes can be given more weight in genome selection to improve the ability of genome prediction. This study provides further insight into the understanding the genetic structure of semen traits in Duroc boars. Full article

The visual appearance of the fish fillet is a significant determinant of consumers’ purchase decisions. Depending on the rainbow trout diet, a uniform bright white or reddish/pink fillet color is desirable. Factors affecting fillet color are complex, ranging from the ability of live fish to accumulate carotenoids in the muscle to preharvest environmental conditions, early postmortem muscle metabolism, and storage conditions. Identifying genetic markers of fillet color is a desirable goal but a challenging task for the aquaculture industry. This study used weighted, single-step GWAS to explore the genetic basis of fillet color variation in rainbow trout. We identified several SNP windows explaining up to 3.5%, 2.5%, and 1.6% of the additive genetic variance for fillet redness, yellowness, and whiteness, respectively. SNPs are located within genes implicated in carotenoid metabolism (β,β-carotene 15,15′-dioxygenase, retinol dehydrogenase) and myoglobin homeostasis (ATP synthase subunit β, mitochondrial (ATP5F1B)). These genes are involved in processes that influence muscle pigmentation and postmortem flesh coloration. Other identified genes are involved in the maintenance of muscle structural integrity (kelch protein 41b (klh41b), collagen α-1(XXVIII) chain (COL28A1), and cathepsin K (CTSK)) and protection against lipid oxidation (peroxiredoxin, superoxide dismutase 2 (SOD2), sestrin-1, Ubiquitin carboxyl-terminal hydrolase-10 (USP10)). A-to-G single-nucleotide polymorphism in β,β-carotene 15,15′-dioxygenase, and USP10 result in isoleucine-to-valine and proline-to-leucine non-synonymous amino acid substitutions, respectively. Our observation confirms that fillet color is a complex trait regulated by many genes involved in carotenoid metabolism, myoglobin homeostasis, protection against lipid oxidation, and maintenance of muscle structural integrity. The significant SNPs identified in this study could be prioritized via genomic selection in breeding programs to improve fillet color in rainbow trout. Full article

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. Full article

The objective of this study was to uncover genomic regions explaining a substantial proportion of the genetic variance in milk production traits and somatic cell score in a Valle del Belice dairy sheep. Weighted single-step genome-wide association studies (WssGWAS) were conducted for milk yield (MY), fat yield (FY), fat percentage (FAT%), protein yield (PY), protein percentage (PROT%), and somatic cell score (SCS). In addition, our aim was also to identify candidate genes within genomic regions that explained the highest proportions of genetic variance. Overall, the full pedigree consists of 5534 animals, of which 1813 ewes had milk data (15,008 records), and 481 ewes were genotyped with a 50 K single nucleotide polymorphism (SNP) array. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. The results showed that top ranked genomic windows (1 Mb windows) explained 3.49, 4.04, 5.37, 4.09, 3.80, and 5.24% of the genetic variances for MY, FY, FAT%, PY, PROT%, and total SCS, respectively. Among the candidate genes found, some known associations were confirmed, while several novel candidate genes were also revealed, including PPARGC1A, LYPLA1, LEP, and MYH9 for MY; CACNA1C, PTPN1, ROBO2, CHRM3, and ERCC6 for FY and FAT%; PCSK5 and ANGPT1 for PY and PROT%; and IL26, IFNG, PEX26, NEGR1, LAP3, and MED28 for SCS. These findings increase our understanding of the genetic architecture of six examined traits and provide guidance for subsequent genetic improvement through genome selection. Full article

The purpose of this study was to perform a genome-wide association study to determine the genomic regions associated with heat stress tolerance in swine. Phenotypic information on carcass weight was available for 227,043 individuals from commercial farms in North Carolina and Missouri, U.S. Individuals were from a commercial cross of a Duroc sire and a dam resulting from a Landrace and Large White cross. Genotypic information was available for 8232 animals with 33,581 SNPs. The pedigree file contained a total of 553,448 animals. A threshold of 78 on the Temperature Humidity Index (THI) was used to signify heat stress. A two-trait analysis was used with the phenotypes heat stress (Trait One) and non-heat stress (Trait Two). Variance components were calculated via AIREML and breeding values were calculated using single step GBLUP (ssGBLUP). The heritability for Traits One and Two were calculated at 0.25 and 0.20, respectively, and the genetic correlation was calculated as 0.63. Validation was calculated for 163 genotyped sires with progeny in the last generation. The benchmark was the GEBV with complete data, and the accuracy was determined as the correlation between the GEBV of the reduced and complete data for the validation sires. Weighted ssGBLUP did not increase the accuracies. Both methods showed a maximum accuracy of 0.32 for Trait One and 0.54 for Trait Two. Manhattan Plots for Trait One, Trait Two, and the difference between the two were created from the results of the two-trait analysis. Windows explaining more than 0.8% of the genetic variance were isolated. Chromosomes 1 and 14 showed peaks in the difference between the two traits. The genetic correlation suggests a different mechanism for Hot Carcass Weight under heat stress. The GWAS results show that both traits are highly polygenic, with only a few genomic regions explaining more than 1% of variance. Full article

Growth is an important heritable economic trait for dromedaries and necessary for planning a successful breeding program. Until now, genome-wide association studies (GWAS) and QTL-mapping have identified significant single nucleotide polymorphisms (SNPs) associated with growth in domestic animals, but in dromedaries, the number of studies is very low. This project aimed to find biological themes affecting growth in dromedaries. In the first step, 99 candidate SNPs were chosen from a previously established set of SNPs associated with body weight, gain, and birth weight in Iranian dromedaries. Next, 0.5 kb upstream and downstream of each candidate SNP were selected from NCBI (assembly accession: GCA_000803125.3). The annotation of fragments with candidate SNPs regarding the reference genome was retrieved using the Blast2GO tool. Candidate SNPs associated with growth were mapped to 22 genes, and 25 significant biological themes were identified to be related to growth in dromedaries. The main biological functions included calcium ion binding, protein binding, DNA-binding transcription factor activity, protein kinase activity, tropomyosin binding, myosin complex, actin-binding, ATP binding, receptor signaling pathway via JAK-STAT, and cytokine activity. EFCAB5, MTIF2, MYO3A, TBX15, IFNL3, PREX1, and TMOD3 genes are candidates for improving growth in camel breeding programs. Full article

Behavior is a complex trait and, therefore, understanding its genetic architecture is paramount for the development of effective breeding strategies. The objective of this study was to perform traditional and weighted single-step genome-wide association studies (ssGWAS and WssGWAS, respectively) for yearling temperament (YT) in North American Angus cattle using haplotypes. Approximately 266 K YT records and 70 K animals genotyped using a 50 K single nucleotide polymorphisms (SNP) panel were used. Linkage disequilibrium thresholds (LD) of 0.15, 0.50, and 0.80 were used to create the haploblocks, and the inclusion of non-LD-clustered SNPs (NCSNP) with the haplotypes in the genomic models was also evaluated. WssGWAS did not perform better than ssGWAS. Cattle YT was found to be a highly polygenic trait, with genes and quantitative trait loci (QTL) broadly distributed across the whole genome. Association studies using LD-based haplotypes should include NCSNPs and different LD thresholds to increase the likelihood of finding the relevant genomic regions affecting the trait of interest. The main candidate genes identified, i.e., ATXN10, ADAM10, VAX2, ATP6V1B1, CRISPLD1, CAPRIN1, FA2H, SPEF2, PLXNA1, and CACNA2D3, are involved in important biological processes and metabolic pathways related to behavioral traits, social interactions, and aggressiveness in cattle. Future studies should further investigate the role of these candidate genes. Full article

The breeding scheme in the Rubia Gallega cattle population is based upon traits measured in farms and slaughterhouses. In recent years, genomic evaluation has been implemented by using a ssGBLUP (single-step Genomic Best Linear Unbiased Prediction). This procedure can reparameterized to perform ssGWAS (single-step Genome Wide Association Studies) by backsolving the SNP (single nucleotide polymorphisms) effects. Therefore, the objective of this study was to identify genomic regions associated with the genetic variability in growth and carcass quality traits. We implemented a ssGBLUP by using a database that included records for Birth Weight (BW-327,350 records-), Weaning Weight (WW-83,818-), Cold Carcass Weight (CCW-91,621-), Fatness (FAT-91,475-) and Conformation (CON-91,609-). The pedigree included 464,373 individuals, 2449 of which were genotyped. After a process of filtering, we ended up using 43,211 SNP markers. We used the GBLUP and SNPBLUP model equivalences to obtain the effects of the SNPs and then calculated the percentage of variance explained by the regions of the genome between 1 Mb. We identified 7 regions of the genome for CCW; 8 regions for BW, WW, FAT and 9 regions for CON, which explained the percentage of variance above 0.5%. Furthermore, a number of the genome regions had pleiotropic effects, located at: BTA1 (131–132 Mb), BTA2 (1–11 Mb), BTA3 (32–33 Mb), BTA6 (36–38 Mb), BTA16 (24–26 Mb), and BTA 21 (56–57 Mb). These regions contain, amongst others, the following candidate genes: NCK1, MSTN, KCNA3, LCORL, NCAPG, and RIN3. Full article

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. Full article

Growth traits are important economic traits of pigs that are controlled by several major genes and multiple minor genes. To better understand the genetic architecture of growth traits, we performed a weighted single-step genome-wide association study (wssGWAS) to identify genomic regions and candidate genes that are associated with days to 100 kg (AGE), average daily gain (ADG), backfat thickness (BF) and lean meat percentage (LMP) in a Duroc pig population. In this study, 3945 individuals with phenotypic and genealogical information, of which 2084 pigs were genotyped with a 50 K single-nucleotide polymorphism (SNP) array, were used for association analyses. We found that the most significant regions explained 2.56–3.07% of genetic variance for four traits, and the detected significant regions (>1%) explained 17.07%, 18.59%, 23.87% and 21.94% for four traits. Finally, 21 genes that have been reported to be associated with metabolism, bone growth, and fat deposition were treated as candidate genes for growth traits in pigs. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses implied that the identified genes took part in bone formation, the immune system, and digestion. In conclusion, such full use of phenotypic, genotypic, and genealogical information will accelerate the genetic improvement of growth traits in pigs. Full article

In recent years, studies on the biological mechanisms underlying complex traits have been facilitated by innovations in high-throughput genotyping technology. We conducted a weighted single-step genome-wide association study (WssGWAS) to evaluate backfat thickness, carcass weight, eye muscle area, marbling score, and yearling weight in a cohort of 1540 Hanwoo beef cattle using BovineSNP50 BeadChip. The WssGWAS uncovered thirty-three genomic regions that explained more than 1% of the additive genetic variance, mostly located on chromosomes 6 and 14. Among the identified window regions, seven quantitative trait loci (QTL) had pleiotropic effects and twenty-six QTL were trait-specific. Significant pathways implicated in the measured traits through Gene Ontology (GO) term enrichment analysis included the following: lipid biosynthetic process, regulation of lipid metabolic process, transport or localization of lipid, regulation of growth, developmental growth, and multicellular organism growth. Integration of GWAS results of the studied traits with pathway and network analyses facilitated the exploration of the respective candidate genes involved in several biological functions, particularly lipid and growth metabolism. This study provides novel insight into the genetic bases underlying complex traits and could be useful in developing breeding schemes aimed at improving growth and carcass traits in Hanwoo beef cattle. Full article