Search Results (34)

Lipopolysaccharide (LPS) endotoxin is a well-characterized microbe-associated molecular pattern (MAMP) that forms the outer membrane of both pathogenic and commensal Gram-negative bacteria. It plays a crucial role in triggering inflammatory disorders such as mastitis, acidosis, and septicemia. In heifers, an LPS challenge induces a dynamic stress response, marked by elevated cortisol levels, increased body temperature, and altered immune function. Research indicates that LPS administration leads to a significant rise in cortisol post-challenge. Building on this understanding, the present study aimed to estimate genetic parameters for serum cortisol response to LPS challenge in Holstein heifers and its linear associations with production, health, reproduction, and conformation traits. Additionally, a genome-wide association study (GWAS) was conducted to identify genetic regions associated with cortisol response. A total of 252 animals were evaluated for cortisol response, with correlations estimated between cortisol levels and 55 genomic breeding values for key traits. Genetic parameters and heritability for cortisol response were estimated using Residual Maximum Likelihood (REML) in the Blupf90+ v 2.57 software. Single-Step GWAS (ssGWAS) employing a 10-SNP window approach and 42,123 SNP markers was performed to identify genomic regions that explained at least 0.5% of additive genetic variance. Finally, candidate genes and QTLs located 50 kb up and downstream of those windows were identified. The cortisol response showed significant but weak linear associations with cystic ovaries, body maintenance requirements, lactation persistency, milk yield, and protein yield (p-value ≤ 0.05) and showed suggestive weak linear associations with udder texture, clinical ketosis, heel horn erosion, and milking speed (p-value ≤ 0.15). Cortisol response showed significant additive genetic variance, along with moderate heritability of 0.26 (±0.19). A total of 34 windows explained at least 0.5% of additive genetic variance, and 75 QTLs and 11 candidate genes, comprising the genes CCL20, DAW1, CSMD2, HMGB4, B3GAT2, PARD3, bta-mir-2285aw, CFH, CDH2, ENSBTAG00000052242, and ENSBTAG00000050498, were identified. The functional enrichment analysis allowed us to infer two instances where these gene products could interfere with cortisol production: the first instance is related to the complement system, and the second one is related to the EMT (Epithelium–Mesenchymal Transition) and pituitary gland formation. Among the QTLs, 13 were enriched in the dataset, corresponding to traits related to milk (potassium content), the exterior (udder traits, teat placement, foot angle, rear leg placement, and feet and leg conformation), production (length of productive life, net merit, and type), and reproduction (stillbirth and calving ease). In summary, the cortisol response to LPS challenge in Holstein heifers seems to be moderately heritable and has weak but significant linear associations with important production and health traits. Several candidate genes identified could perform important roles, in at least two ways, for cortisol production, and QTLs were identified close to regions of the genome that explained a significant amount of additive genetic variance for cortisol response. Therefore, further investigations are warranted to validate these findings with a larger dataset. Full article

(1) Background: Litter size traits are critical for pig breeding efficiency but pose challenges due to low heritability and sex-limited influences. This study aimed to elucidate the genetic architecture and identify candidate genes for these traits in Large White pigs using genomic selection (GS) and genome-wide association analyses (GWAS). (2) Methods: This study utilized phenotypic data from nine litter size traits in Large White sows. Genotyping-by-sequencing (GBS) was performed to obtain genotype data, retaining 153,782 high-quality SNPs after quality control. Genetic evaluation was conducted using single-step genomic best linear unbiased prediction (ssGBLUP), with genetic parameters (heritability and genetic correlations) estimated via an animal model (repeatability model). To assess prediction accuracy, 10-fold cross-validation was employed to compare traditional BLUP with ssGBLUP. Furthermore, a single-step genome-wide association study (ssGWAS) integrated genomic information and pedigree-based relationship matrices to screen for significant SNPs associated with litter size traits across the genome. Functional analysis of key candidate genes was subsequently conducted based on ssGWAS results. (3) Results: Heritabilities for litter traits ranged from 0.01 to 0.06. ssGBLUP improved genomic prediction accuracy by 6.38–13.33% over BLUP. Six genomic windows explaining 1.07–1.77% of genetic variance were identified via ssGWAS, highlighting GPR12 on SSC11 as a key candidate gene linked to oocyte development. (4) Conclusions: This study demonstrates the efficacy of ssGBLUP for low-heritability traits and identifies GPR12 as a pivotal gene for litter size. Prioritizing NHB and LBWT in breeding programs could enhance genetic gains while mitigating adverse effects on piglet health. These findings advance genomic strategies for improving reproductive efficiency in swine. Full article

Soybean (Glycine max) seeds are rich in amino acids, offering key nutritional and physiological benefits. In this study, 290 soybean accessions from the USDA Germplasm Collection based in Urbana, IL Information Network (GRIN) were analyzed. Four Genome-Wide Association Study (GWAS) models—Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Multi-Locus Mixed Model (MLMM)—identified two significant Single Nucleotide Polymorphisms (SNPs) associated with arginine content: Gm06_19014194_ss715593808 (LOD = 9.91, 3.91% variation) at 19,014,194 bp on chromosome 6 and Gm11_2054710_ss715609614 (LOD = 9.05, 19% variation) at 2,054,710 bp on chromosome 11. Two candidate genes, Glyma.06g203200 and Glyma.11G028600, were found in the two SNP marker regions, respectively. Genomic Prediction (GP) was performed for arginine content using several models: Bayes A (BA), Bayes B (BB), Bayesian LASSO (BL), Bayesian Ridge Regression (BRR), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Random Forest (RF), and Support Vector Machine (SVM). A high GP accuracy was observed in both across- and cross-populations, supporting Genomic Selection (GS) for breeding high-arginine soybean cultivars. This study holds significant commercial potential by providing valuable genetic resources and molecular tools for improving the nutritional quality and market value of soybean cultivars. Through the identification of SNP markers associated with high arginine content and the demonstration of high prediction accuracy using genomic selection, this research supports the development of soybean accessions with enhanced protein profiles. These advancements can better meet the demands of health-conscious consumers and serve high-value food and feed markets. Full article

This study aims to identify genetically influenced metabolites (GIMs) associated with SSBP and elucidate their regulatory pathways through metabolome genome-wide association studies (mGWASs). Untargeted metabolomics and genome-wide genotyping were performed on 54 participants from the Systematic Epidemiological Study of Salt Sensitivity (EpiSS). The mGWAS was conducted on 970 plasma metabolites, and their potential biological mechanisms were explored. The multivariable logistic regression model and mendelian randomization (MR) were employed to investigate the association and causal relationship between GIMs and SSBP. Metabolomic analysis was performed on 100 subjects in the replication analysis to validate the GIMs identified in the discovery set and their causal association with SSBP. The mGWAS revealed associations between 1485 loci and 18 metabolites. After performing linkage disequilibrium analysis, 368 independent mQTLs were identified and annotated to 141 genes. These functional genes were primarily implicated in the signal transduction of sinoatrial node and atrial cardiac muscle cells. Five key genes were identified using CytoHubba, including CAMK2A, TIAM1, RYR2, RBFOX1, and NRXN3. One-sample MR analysis revealed 14 GIMs with causal associations to SSBP, with LysoPC (0:0/22:5n-3) positively associated with SSBP (p < 0.05). The causal relationship between Phe-lle and SSBP was validated in the replication analysis. This study elucidates the genetic regulatory mechanisms underlying metabolites and identifies GIMs that are causally associated with SSBP. These findings provide insights into identifying metabolic biomarkers of SSBP and characterizing its genetic and metabolic regulation mechanisms. Full article

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

Soybean mosaic virus (SMV), a pathogen responsible for inducing leaf mosaic or necrosis symptoms, significantly compromises soybean seed yield and quality. According to the classification system in the United States, SMV is categorized into seven distinct strains (G1 to G7). In this study, we performed a genome-wide association study (GWAS) in GAPIT3 using four analytical models (MLM, MLMM, FarmCPU, and BLINK) on 218 soybean accessions. We identified 22 SNPs significantly associated with G1 resistance across chromosomes 1, 2, 3, 12, 13, 17, and 18. Notably, a major quantitative trait locus (QTL) spanning 873 kb (29.85–30.73 Mb) on chromosome 13 exhibited strong association with SMV G1 resistance, including the four key SNP markers: Gm13_29459954_ss715614803, Gm13_29751552_ss715614847, Gm13_30293949_ss715614951, and Gm13_30724301_ss715615024. Within this QTL, four candidate genes were identified: Glyma.13G194100, Glyma.13G184800, Glyma.13G184900, and Glyma.13G190800 (3Gg2). The genomic prediction (GP) accuracies ranged from 0.60 to 0.83 across three GWAS-derived SNP sets using five models, demonstrating the feasibility of GP for SMV-G1 resistance. These findings could provide a useful reference in soybean breeding targeting SMV-G1 resistance. 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

Abalone is an economically important mollusk, whose slow growth has impeded the recovery of its wild populations and development of aquaculture. The three-way cross hybrid abalone ((Haliotis discus hannai♀ × H. fulgens♂)♀ × H. gigantea♂, DF × SS) demonstrated notable diversity in growth traits across the population with genetic differentiation, offering a model for exploring the molecular mechanisms of abalone growth. In this study, a total of 89 SNPs and 97 candidate genes were identified to be associated with growth-related traits of abalone using whole-genome resequencing and a genome-wide association study (GWAS) analysis. Then, ten overlap genes were found among these candidate genes by combining the results of GWAS and comparative transcriptomic analyses between the large individuals (L group) and small individuals (S group) of DF × SS. These overlap genes include up-regulated genes (fabG) and down-regulated genes (HMCN1, TLR3, ITIH3) between the L and the S groups, which are thought to function in growth in other organisms. The biological functions of these candidate genes in abalone still have to be confirmed, but they have improved our understanding of the molecular mechanisms behind abalone growth traits and provided molecular markers for abalone breeding programs. Full article

Days open (DO) is a critical economic and reproductive trait that is commonly employed in genetic selection. Making improvements using conventional genetic techniques is exceedingly challenging. Therefore, new techniques are required to improve the accuracy of genetic selection using genomic data. This study examined the genetic approaches of traditional AIREML and single-step genomic AIREML (ssGAIREML) to assess genetic parameters and the accuracy of estimated breeding values while also investigating SNP regions associated with DO and identifying candidate genes through a genome-wide association study (GWAS). The dataset included 59415 DO records from 36368 Thai–Holstein crossbred cows and 882 genotyped animals. The cows were classified according to their Holstein genetic proportion (breed group, BG) as follows: BG1 (>93.7% Holstein genetics), BG2 (87.5% to 93.6% Holstein genetics), and BG3 (<87.5% Holstein genetics). AIREML was utilized to estimate genetic parameters and variance components. The results of this study reveal that the average DO values for BG1, BG2, and BG3 were 97.64, 97.25, and 96.23 days, respectively. The heritability values were estimated to be 0.02 and 0.03 for the traditional AIREML and ssGAIREML approaches, respectively. Depending on the dataset, the ssGAIREML method produced more accurate estimated breeding values than the traditional AIREML method, ranging from 40.5 to 45.6%. The highest values were found in the top 20% of the dam dataset. For the GWAS, we found 12 potential candidate genes (DYRK1A, CALCR, MIR489, MIR653, SLC36A1, GNA14, GNAQ, TRNAC-GCA, XYLB, ACVR2B, SLC22A14, and EXOC2) that are believed to have a significant influence on days open. In summary, the ssGAIREML method has the potential to enhance the accuracy and heritability of reproductive values compared to those obtained using conventional AIREML. Consequently, it is a viable alternative for transitioning from conventional methodologies to the ssGAIREML method in the breeding program for dairy cattle in Thailand. Moreover, the 12 identified potential candidate genes can be utilized in future studies to select markers for days open in regard to dairy cattle. Full article

The Chlamydiaceae are a family of obligate intracellular bacteria known for their unique biphasic developmental cycle. Chlamydial are associated with various host organisms, including humans, and have been proposed as emerging pathogens. Genomic studies have significantly enhanced our understanding of chlamydial biology, host adaptation, and evolutionary processes. In this study, we conducted a complete pangenome association analysis (pan-GWAS) using 101 genomes from the Chlamydiaceae family to identify differentially represented genes in Chlamydia and Chlamydophila, revealing their distinct evolutionary strategies for interacting with eukaryotic hosts. Our analysis identified 289 genes with differential abundance between the two clades: 129 showed a strong association with Chlamydia and 160 with Chlamydophila. Most genes in Chlamydia were related to the type III secretion system, while Chlamydophila genes corresponded to various functional categories, including translation, replication, transport, and metabolism. These findings suggest that Chlamydia has developed a high dependence on mammalian cells for replication, facilitated by a complex T3SS for intracellular manipulation. In contrast, the metabolic and functional diversity in Chlamydophila allows it to colonize a broad range of hosts, such as birds, reptiles, amphibians, and mammals, making it a less specialized clade. Full article

Sensitive skin (SS) is associated with discomfort, including burning, stinging, and itching. These symptoms are often exacerbated by environmental factors and personal care products. In this genome-wide association study (GWAS), we aimed to identify the genetic variants associated with SS in 1690 Korean female participants; 389 and 1301 participants exhibited sensitive and non-sensitive skin, respectively. Using a combination of self-reported questionnaires, patch tests, and sting tests, we selected 115 sensitive and 181 non-sensitive participants for genetic analysis. A GWAS was performed to identify the loci associated with SS. Although none of the single-nucleotide polymorphisms (SNPs) met the genome-wide significance threshold, we identified several SNPs with suggestive associations. SNP rs11689992 in the 2q11.3 region increased SS risk by approximately 3.67 times. SNP rs7614738 in the USP4 locus elevated SS risk by 2.34 times and was found to be an expression quantitative trait locus for GPX1, a gene involved in oxidative stress and inflammation. Additionally, SNPs rs12306124 in the RASSF8 locus and rs10483893 in the NRXN3 region were identified. These results suggest that the genetic variations affecting oxidative stress, cell growth regulation, and neurobiology potentially influence skin sensitivity, providing a basis for further investigation and the development of personalized approaches to manage sensitive skin. Full article

Background: Gut microbiota have been previously reported to be related to a variety of immune diseases. However, the causal connection between Sjögren’s syndrome (SS) and gut microbiota has yet to be clarified. Methods: We employed a two-sample Mendelian randomization (MR) analysis to evaluate the causal connection between gut microbiota and SS, utilizing summary statistics from genome-wide association studies (GWASs) obtained from the MiBioGen and FinnGen consortia. The inverse variance weighted (IVW) approach represents the primary method of Mendelian randomization (MR) analysis. Sensitivity analysis was used to eliminate instrumental variables heterogeneity and horizontal pleiotropy. In addition, we performed an analysis using independent GWAS summary statistics for SS from the European Bioinformatics Institute (EBI) dataset for further verify our results. Results: IVW results demonstrated that the phylum Lentisphaerae (OR = 0.79, 95% CI: 0.63–0.99, p = 0.037), class Deltaproteobacteria (OR = 0.67, 95% CI: 0.47–0.96, p = 0.030), family Porphyromonadaceae (OR = 0.60, 95% CI: 0.38–0.94, p = 0.026), genus Eubacterium coprostanoligenes group (OR = 0.61, 95% CI: 0.4–0.93, p = 0.021), genus Blautia (OR = 0.62, 95% CI: 0.43–0.90, p = 0.012), genus Butyricicoccus (OR = 0.61, 95% CI: 0.42–0.90, p = 0.012), genus Escherichia.Shigella (OR = 0.7, 95% CI: 0.49–0.99, p = 0.045) and genus Subdoligranulum (OR = 0.61, 95% CI: 0.44–0.86, p = 0.005) exhibited protective effects on SS. Relevant heterogeneity of horizontal pleiotropy or instrumental variables was not detected. Furthermore, repeating our results with an independent cohort provided by the EBI dataset, only the genus Eubacterium coprostanoligenes group remained significantly associated with the protective effect on SS (OR = 0.41, 95% CI: 0.18–0.91, p = 0.029). Two-step MR analysis further revealed that genus Eubacterium coprostanoligenes group exerts its protective effect by reducing CXCL6 levels in SS (OR, 0.87; 95% CI = 0.76–0.99, p = 0.033). Conclusions: Our study using two-sample MR analysis identified a causal association between multiple genera and SS. A two-step MR result calculated that genus Eubacterium coprostanoligenes group mediated its protective effect by reducing CXCL6 levels in SS. However, the datasets available from the MiBioGen and FinnGen consortia do not provide sufficient information or comprehensive demographic data for subgroup analyses. Additional validation using various omics technologies is necessary to comprehend the development of SS in the intricate interplay between genes and the environment over a period of time. Full article

The linear conformation of animals exerts an influence on health, reproduction, production, and welfare, in addition to longevity, which directly affects the profitability of milk-producing farms. The objectives of this study were (1) to perform genome-wide association studies (GWASs) of conformation traits, namely the Rump, Feet and Legs, Mammary System, Dairy Strength, and Final Classification traits, and (2) to identify genes and related pathways involved in physiological processes associated with conformation traits in Brazilian Holstein cattle. Phenotypic and genotypic data from 2339 Holstein animals distributed across the states of Rio Grande do Sul, Paraná, São Paulo, and Minas Gerais were used. The genotypic data were obtained with a 100 K SNP marker panel. The single-step genome-wide association study (ssGWAS) method was employed in the analyses. Genes close to a significant SNP were identified in an interval of 100 kb up- and downstream using the Ensembl database available in the BioMart tool. The DAVID database was used to identify the main metabolic pathways and the STRING program was employed to create the gene regulatory network. In total, 36 significant SNPs were found on 15 chromosomes; 27 of these SNPs were linked to genes that may influence the traits studied. Fourteen genes most closely related to the studied traits were identified, as well as four genes that showed interactions in important metabolic pathways such as myogenesis, adipogenesis, and angiogenesis. Among the total genes, four were associated with myogenesis (TMOD2, TMOD3, CCND2, and CTBP2), three with angiogenesis (FGF23, FGF1, and SCG3), and four with adipogenesis and body size and development (C5H12orf4, CCND2, EMILIN1, and FGF6). These results contribute to a better understanding of the biological mechanisms underlying phenotypic variability in conformation traits in Brazilian Holstein cattle. Full article

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. Full article