Exploring Causal Links Between Gut Microbiota and Geriatric Syndromes: A Two-Sample Mendelian Randomization Analysis.

Background: Both observational studies and clinical trials have demonstrated a link between the gut microbiota and the geriatric syndrome. Nevertheless, the exact nature of this relationship, particularly concerning causality, remains elusive. Mendelian randomization (MR) is a method of inference based on genetic variation to assess the causal relationship between an exposure and an outcome. In this study, we conducted a two-sample Mendelian randomization (TSMR) study to fully reveal the potential genetic causal effects of gut microbiota on geriatric syndromes. Methods: This study used data from genome wide association studies (GWAS) to investigate causal relationships between the gut microbiota and geriatric syndromes, including frailty, Parkinson's disease (PD), delirium, insomnia, and depression. The primary causal relationships were evaluated using the inverse-variance weighted method, MR Egger, simple mode, weighted mode and weighted median. To assess the robustness of the results, horizontal pleiotropy was examined through MR-Egger intercept and MR-presso methods. Heterogeneity was assessed using Cochran's Q test, and sensitivity was evaluated via the leave-one-out method. Results: We identified 41 probable causal relationships between gut microbiota and five geriatric syndrome-associated illnesses using the inverse-variance weighted method. Frailty showed five positive and two negative causal relationships, while PD revealed three positive and four negative causal connections. Delirium showed three positive and two negative causal relationships. Similarly, insomnia demonstrated nine positive and two negative causal connections, while depression presented nine positive and two negative causal relationships. Conclusions: Using the TSMR method and data from the public GWAS database and, we observed associations between specific microbiota groups and geriatric syndromes. These findings suggest a potential role of gut microbiota in the development of geriatric syndromes, providing valuable insights for further research into the causal relationship between gut microbiota and these syndromes.

Sequence-based GWAS meta-analyses for beef production traits.

BACKGROUND: Combining the results of within-population genome-wide association studies (GWAS) based on whole-genome sequences into a single meta-analysis (MA) is an accurate and powerful method for identifying variants associated with complex traits. As part of the H2020 BovReg project, we performed sequence-level MA for beef production traits. Five partners from France, Switzerland, Germany, and Canada contributed summary statistics from sequence-based GWAS conducted with 54,782 animals from 15 purebred or crossbred populations. We combined the summary statistics for four growth, nine morphology, and 15 carcass traits into 16 MA, using both fixed effects and z-score methods. RESULTS: The fixed-effects method was generally more informative to provide indication on potentially causal variants, although we combined substantially different traits in each MA. In comparison with within-population GWAS, this approach highlighted (i) a larger number of quantitative trait loci (QTL), (ii) QTL more frequently located in genomic regions known for their effects on growth and meat/carcass traits, (iii) a smaller number of genomic variants within the QTL, and (iv) candidate variants that were more frequently located in genes. MA pinpointed variants in genes, including MSTN, LCORL, and PLAG1 that have been previously associated with morphology and carcass traits. We also identified dozens of other variants located in genes associated with growth and carcass traits, or with a function that may be related to meat production (e.g., HS6ST1, HERC2, WDR75, COL3A1, SLIT2, MED28, and ANKAR). Some of these variants overlapped with expression or splicing QTL reported in the cattle Genotype-Tissue Expression atlas (CattleGTEx) and could therefore regulate gene expression. CONCLUSIONS: By identifying candidate genes and potential causal variants associated with beef production traits in cattle, MA demonstrates great potential for investigating the biological mechanisms underlying these traits. As a complement to within-population GWAS, this approach can provide deeper insights into the genetic architecture of complex traits in beef cattle.

Identification of candidate genes and enriched biological functions for feed efficiency traits by integrating plasma metabolites and imputed whole genome sequence variants in beef cattle.

BACKGROUND: Feed efficiency is one of the key determinants of beef industry profitability and sustainability. However, the cellular and molecular background behind feed efficiency is largely unknown. This study combines imputed whole genome DNA variants and 31 plasma metabolites to dissect genes and biological functions/processes that are associated with residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) in beef cattle. RESULTS: Regression analyses between feed efficiency traits and plasma metabolites in a population of 493 crossbred beef cattle identified 5 (L-valine, lysine, L-tyrosine, L-isoleucine, and L-leucine), 4 (lysine, L-lactic acid, L-tyrosine, and choline), 1 (citric acid), and 4 (L-glutamine, glycine, citric acid, and dimethyl sulfone) plasma metabolites associated with RFI, DMI, ADG, and MWT (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using 10,488,742 imputed SNPs, 40, 66, 15, and 40 unique candidate genes were identified as associated with RFI, DMI, ADG, and MWT (P-value < 1 × 10-5), respectively. These candidate genes were found to be involved in some key metabolic processes including metabolism of lipids, molecular transportation, cellular function and maintenance, cell morphology and biochemistry of small molecules. CONCLUSIONS: This study identified metabolites, candidate genes and enriched biological functions/processes associated with RFI and its component traits through the integrative analyses of metabolites with phenotypic traits and DNA variants. Our findings could enhance the understanding of biochemical mechanisms of feed efficiency traits and could lead to improvement of genomic prediction accuracy via incorporating metabolite data.

Investigating the genetic architecture of disease resilience in pigs by genome-wide association studies of complete blood count traits collected from a natural disease challenge model.

BACKGROUND: Genetic improvement for disease resilience is anticipated to be a practical method to improve efficiency and profitability of the pig industry, as resilient pigs maintain a relatively undepressed level of performance in the face of infection. However, multiple biological functions are known to be involved in disease resilience and this complexity means that the genetic architecture of disease resilience remains largely unknown. Here, we conducted genome-wide association studies (GWAS) of 465,910 autosomal SNPs for complete blood count (CBC) traits that are important in an animal's disease response. The aim was to identify the genetic control of disease resilience. RESULTS: Univariate and multivariate single-step GWAS were performed on 15 CBC traits measured from the blood samples of 2743 crossbred (Landrace × Yorkshire) barrows drawn at 2-weeks before, and at 2 and 6-weeks after exposure to a polymicrobial infectious challenge. Overall, at a genome-wise false discovery rate of 0.05, five genomic regions located on Sus scrofa chromosome (SSC) 2, SSC4, SSC9, SSC10, and SSC12, were significantly associated with white blood cell traits in response to the polymicrobial challenge, and nine genomic regions on multiple chromosomes (SSC1, SSC4, SSC5, SSC6, SSC8, SSC9, SSC11, SSC12, SSC17) were significantly associated with red blood cell and platelet traits collected before and after exposure to the challenge. By functional enrichment analyses using Ingenuity Pathway Analysis (IPA) and literature review of previous CBC studies, candidate genes located nearby significant single-nucleotide polymorphisms were found to be involved in immune response, hematopoiesis, red blood cell morphology, and platelet aggregation. CONCLUSIONS: This study helps to improve our understanding of the genetic basis of CBC traits collected before and after exposure to a polymicrobial infectious challenge and provides a step forward to improve disease resilience.

Phenotypic and genetic correlations of beef replacement heifer feeding behaviour, feed intake and feed efficiency with cow performance and lifetime productivity.

Objectives were to quantify the phenotypic (rp ) and genetic (rg ) correlations between early-life feeding behaviours, dry matter intake, and feed efficiency and measures of cow performance and lifetime productivity traits. Traits were measured on 1,145 crossbred replacement beef heifers and then on cows over parities one to four. Feeding event duration (FD) was phenotypically correlated with cow prebreeding body weight (PBWT; rp 0.29-0.45), cow prebreeding back fat thickness (PBBF; rp 0.35-0.49), progeny weaning weight (WW; rp 0.09-0.31) and progeny birthweight (BW; rp -0.06 to 0.17). Feeding event frequency (FF) was phenotypically correlated with PBBF (rp 0.16-0.30). Dry matter intake (DMI) was phenotypically correlated with PBWT (rp 0.16-0.20) and PBBF (rp -0.22 to -0.05). Feeding event duration was genetically correlated with PBWT (rg 0.38-0.41). Feeding event frequency was genetically correlated with PBWT (rg -0.43 to -0.39). Dry matter intake was genetically correlated with PBWT (rg -0.27 to 0.14). Days in herd (DIH) was phenotypically correlated with FD and DMI (rp  = 0.12, 0.20, respectively). Lifetime productivity was phenotypically correlated with FD and FF (rg  = 0.25, 0.22, respectively). Calving interval was phenotypically correlated with FD and FF (rp  = -0.12, -0.14, respectively) and genetically correlated with FF (rg  = -0.41). Due to moderate positive correlations with cow weight, caution would be required in selection to prevent an increase in mature cow size. Use of FF, FD, DMI and a measure of feed efficiency such as residual feed intake adjusted for back fat (RFIFAT ) in a balanced selection index is recommended.

Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: II: carcass merit traits.

BACKGROUND: Genome wide association studies (GWAS) were conducted on 7,853,211 imputed whole genome sequence variants in a population of 3354 to 3984 animals from multiple beef cattle breeds for five carcass merit traits including hot carcass weight (HCW), average backfat thickness (AFAT), rib eye area (REA), lean meat yield (LMY) and carcass marbling score (CMAR). Based on the GWAS results, genetic architectures of the carcass merit traits in beef cattle were elucidated. RESULTS: The distributions of DNA variant allele substitution effects approximated a bell-shaped distribution for all the traits while the distribution of additive genetic variances explained by single DNA variants conformed to a scaled inverse chi-squared distribution to a greater extent. At a threshold of P-value < 10-5, 51, 33, 46, 40, and 38 lead DNA variants on multiple chromosomes were significantly associated with HCW, AFAT, REA, LMY, and CMAR, respectively. In addition, lead DNA variants with potentially large pleiotropic effects on HCW, AFAT, REA, and LMY were found on chromosome 6. On average, missense variants, 3'UTR variants, 5'UTR variants, and other regulatory region variants exhibited larger allele substitution effects on the traits in comparison to other functional classes. The amounts of additive genetic variance explained per DNA variant were smaller for intergenic and intron variants on all the traits whereas synonymous variants, missense variants, 3'UTR variants, 5'UTR variants, downstream and upstream gene variants, and other regulatory region variants captured a greater amount of additive genetic variance per sequence variant for one or more carcass merit traits investigated. In total, 26 enriched cellular and molecular functions were identified with lipid metabolisms, small molecular biochemistry, and carbohydrate metabolism being the most significant for the carcass merit traits. CONCLUSIONS: The GWAS results have shown that the carcass merit traits are controlled by a few DNA variants with large effects and many DNA variants with small effects. Nucleotide polymorphisms in regulatory, synonymous, and missense functional classes have relatively larger impacts per sequence variant on the variation of carcass merit traits. The genetic architecture as revealed by the GWAS will improve our understanding on genetic controls of carcass merit traits in beef cattle.

Genetic architecture of quantitative traits in beef cattle revealed by genome wide association studies of imputed whole genome sequence variants: I: feed efficiency and component traits.

BACKGROUND: Genome wide association studies (GWAS) on residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) were conducted in a population of 7573 animals from multiple beef cattle breeds based on 7,853,211 imputed whole genome sequence variants. The GWAS results were used to elucidate genetic architectures of the feed efficiency related traits in beef cattle. RESULTS: The DNA variant allele substitution effects approximated a bell-shaped distribution for all the traits while the distribution of additive genetic variances explained by single DNA variants followed a scaled inverse chi-squared distribution to a greater extent. With a threshold of P-value < 1.00E-05, 16, 72, 88, and 116 lead DNA variants on multiple chromosomes were significantly associated with RFI, DMI, ADG, and MWT, respectively. In addition, lead DNA variants with potentially large pleiotropic effects on DMI, ADG, and MWT were found on chromosomes 6, 14 and 20. On average, missense, 3'UTR, 5'UTR, and other regulatory region variants exhibited larger allele substitution effects in comparison to other functional classes. Intergenic and intron variants captured smaller proportions of additive genetic variance per DNA variant. Instead 3'UTR and synonymous variants explained a greater amount of genetic variance per DNA variant for all the traits examined while missense, 5'UTR and other regulatory region variants accounted for relatively more additive genetic variance per sequence variant for RFI and ADG, respectively. In total, 25 to 27 enriched cellular and molecular functions were identified with lipid metabolism and carbohydrate metabolism being the most significant for the feed efficiency traits. CONCLUSIONS: RFI is controlled by many DNA variants with relatively small effects whereas DMI, ADG, and MWT are influenced by a few DNA variants with large effects and many DNA variants with small effects. Nucleotide polymorphisms in regulatory region and synonymous functional classes play a more important role per sequence variant in determining variation of the feed efficiency traits. The genetic architecture as revealed by the GWAS of the imputed 7,853,211 DNA variants will improve our understanding on the genetic control of feed efficiency traits in beef cattle.

Genome-wide association scan for heterotic quantitative trait loci in multi-breed and crossbred beef cattle.

BACKGROUND: Heterosis has been suggested to be caused by dominance effects. We performed a joint genome-wide association analysis (GWAS) using data from multi-breed and crossbred beef cattle to identify single nucleotide polymorphisms (SNPs) with significant dominance effects associated with variation in growth and carcass traits and to understand the mode of action of these associations. METHODS: Illumina BovineSNP50 genotypes and phenotypes for 11 growth and carcass traits were available for 6796 multi-breed and crossbred beef cattle. After performing quality control, 42,610 SNPs and 6794 animals were used for further analyses. A single-SNP GWAS for the joint association of additive and dominance effects was conducted in purebred, crossbred, and combined datasets using the ASReml software. Genomic breed composition predicted from admixture analyses was included in the mixed effect model to account for possible population stratification and breed effects. A threshold of 10% genome-wide false discovery rate was applied to declare associations as significant. The significant SNPs with dominance association were mapped to their corresponding genes at 100 kb. RESULTS: Seven SNPs with significant dominance associations were detected for birth weight, weaning weight, pre-weaning daily gain, yearling weight and marbling score across the three datasets at a false discovery rate of 10%. These SNPs were located on bovine chromosomes 1, 3, 4, 6 and 21 and mapped to six putative candidate genes: U6atac, AGBL4, bta-mir-2888-1, REPIN1, ICA1 and NXPH1. These genes have interesting biological functions related to the regulation of gene expression, glucose and lipid metabolism and body fat mass. For most of the identified loci, we observed over-dominance association with the studied traits, such that the heterozygous individuals at any of these loci had greater genotypic values for the trait than either of the homozygous individuals. CONCLUSIONS: Our results revealed very few regions with significant dominance genetic effects across all the traits studied in the three datasets used. Regarding the SNPs that were detected with dominance associations, further investigation is needed to determine their relevance in crossbreeding programs assuming that dominance effects are the main cause of (or contribute usefully to) heterosis.

Blockage of Glyoxalase I Inhibits Colorectal Tumorigenesis and Tumor Growth via Upregulation of STAT1, p53, and Bax and Downregulation of c-Myc and Bcl-2.

GlyoxalaseI (GLOI) is an enzyme that catalyzes methylglyoxal metabolism. Overexpression of GLOI has been documented in numerous tumor tissues, including colorectal cancer (CRC). The antitumor effects of GLOI depletion have been demonstrated in some types of cancer, but its role in CRC and the mechanisms underlying this activity remain largely unknown. Our purpose was to investigate the antitumor effects of depleted GLOI on CRC in vitro and in vivo. RNA interference was used to deplete GLOI activity in four CRC cell lines. The cells' proliferation, apoptosis, migration, and invasion were assessed by using the Cell Counting Kit-8, plate colony formation assay, flow cytometry, and transwell assays. Protein and mRNA levels were analyzed by western blot and quantitative real-time PCR (qRT-PCR), respectively. The antitumor effect of GLOI depletion in vivo was investigated in a SW620 xenograft tumor model in BALB/c nude mice. Our results show that GLOI is over-expressed in the CRC cell lines. GLOI depletion inhibited the proliferation, colony formation, migration, and invasion and induced apoptosis of all CRC cells compared with the controls. The levels of signal transducer and activator of transcription 1 (STAT1), p53, and Bcl-2 assaciated X protein (Bax) were upregulated by GLOI depletion, while cellular homologue of avian myelocytomatosis virus oncogene (c-Myc) and B cell lymphoma/lewkmia-2 (Bcl-2) were downregulated. Moreover, the growth of SW620-induced CRC tumors in BALB/c nude mice was significantly attenuated by GLOI depletion. The expression levels of STAT1, p53, and Bax were increased and those of c-Myc and Bcl-2 were decreased in the GLOI-depleted tumors. Our findings demonstrate that GLOI depletion has an antitumor effect through the STAT1 or p53 signaling pathways in CRC, suggesting that GLOI is a potential therapeutic target.

Genome-wide association and genomic prediction of breeding values for fatty acid composition in subcutaneous adipose and longissimus lumborum muscle of beef cattle.

BACKGROUND: Identification of genetic variants that are associated with fatty acid composition in beef will enhance our understanding of host genetic influence on the trait and also allow for more effective improvement of beef fatty acid profiles through genomic selection and marker-assisted diet management. In this study, 81 and 83 fatty acid traits were measured in subcutaneous adipose (SQ) and longissimus lumborum muscle (LL), respectively, from 1366 purebred and crossbred beef steers and heifers that were genotyped on the Illumina BovineSNP50 Beadchip. The objective was to conduct genome-wide association studies (GWAS) for the fatty acid traits and to evaluate the accuracy of genomic prediction for fatty acid composition using genomic best linear unbiased prediction (GBLUP) and Bayesian methods. RESULTS: In total, 302 and 360 significant SNPs spanning all autosomal chromosomes were identified to be associated with fatty acid composition in SQ and LL tissues, respectively. Proportions of total genetic variance explained by individual significant SNPs ranged from 0.03 to 11.06% in SQ, and from 0.005 to 24.28% in the LL muscle. Markers with relatively large effects were located near fatty acid synthase (FASN), stearoyl-CoA desaturase (SCD), and thyroid hormone responsive (THRSP) genes. For the majority of the fatty acid traits studied, the accuracy of genomic prediction was relatively low (<0.40). Relatively high accuracies (> = 0.50) were achieved for 10:0, 12:0, 14:0, 15:0, 16:0, 9c-14:1, 12c-16:1, 13c-18:1, and health index (HI) in LL, and for 12:0, 14:0, 15:0, 10 t,12c-18:2, and 11 t,13c + 11c,13 t-18:2 in SQ. The Bayesian method performed similarly as GBLUP for most of the traits but substantially better for traits that were affected by SNPs of large effects as identified by GWAS. CONCLUSIONS: Fatty acid composition in beef is influenced by a few host genes with major effects and many genes of smaller effects. With the current training population size and marker density, genomic prediction has the potential to predict the breeding values of fatty acid composition in beef cattle at a moderate to relatively high accuracy for fatty acids that have moderate to high heritability.

Multi-population genomic prediction using a multi-task Bayesian learning model.

BACKGROUND: Genomic prediction in multiple populations can be viewed as a multi-task learning problem where tasks are to derive prediction equations for each population and multi-task learning property can be improved by sharing information across populations. The goal of this study was to develop a multi-task Bayesian learning model for multi-population genomic prediction with a strategy to effectively share information across populations. Simulation studies and real data from Holstein and Ayrshire dairy breeds with phenotypes on five milk production traits were used to evaluate the proposed multi-task Bayesian learning model and compare with a single-task model and a simple data pooling method. RESULTS: A multi-task Bayesian learning model was proposed for multi-population genomic prediction. Information was shared across populations through a common set of latent indicator variables while SNP effects were allowed to vary in different populations. Both simulation studies and real data analysis showed the effectiveness of the multi-task model in improving genomic prediction accuracy for the smaller Ayshire breed. Simulation studies suggested that the multi-task model was most effective when the number of QTL was small (n = 20), with an increase of accuracy by up to 0.09 when QTL effects were lowly correlated between two populations (ρ = 0.2), and up to 0.16 when QTL effects were highly correlated (ρ = 0.8). When QTL genotypes were included for training and validation, the improvements were 0.16 and 0.22, respectively, for scenarios of the low and high correlation of QTL effects between two populations. When the number of QTL was large (n = 200), improvement was small with a maximum of 0.02 when QTL genotypes were not included for genomic prediction. Reduction in accuracy was observed for the simple pooling method when the number of QTL was small and correlation of QTL effects between the two populations was low. For the real data, the multi-task model achieved an increase of accuracy between 0 and 0.07 in the Ayrshire validation set when 28,206 SNPs were used, while the simple data pooling method resulted in a reduction of accuracy for all traits except for protein percentage. When 246,668 SNPs were used, the accuracy achieved from the multi-task model increased by 0 to 0.03, while using the pooling method resulted in a reduction of accuracy by 0.01 to 0.09. In the Holstein population, the three methods had similar performance. CONCLUSIONS: Results in this study suggest that the proposed multi-task Bayesian learning model for multi-population genomic prediction is effective and has the potential to improve the accuracy of genomic prediction.

Analyses of plasma metabolites using a high performance four-channel CIL LC-MS method and identification of metabolites associated with enteric methane emissions in beef cattle.

Reducing enteric methane (one greenhouse gas) emissions from beef cattle not only can be beneficial in reducing global warming, but also improve efficiency of nutrient utilization in the production system. However, direct measurement of enteric methane emissions on individual cattle is difficult and expensive. The objective of this study was to detect plasma metabolites that are associated with enteric methane emissions in beef cattle. Average enteric methane emissions (CH4) per day (AVG_DAILYCH4) for each individual cattle were measured using the GreenFeed emission monitoring (GEM) unit system, and beef cattle with divergent AVG_DAILYCH4 from Angus (n = 10 for the low CH4 group and 9 for the high CH4 group), Charolais (n = 10 for low and 10 for = high), and Kinsella Composite (n = 10 for low and 10 for high) populations were used for plasma metabolite quantification and metabolite-CH4 association analyses. Blood samples of these cattle were collected near the end of the GEM system tests and a high performance four-channel chemical isotope labeling (CIL) liquid chromatography (LC) mass spectrometer (MS) method was applied to identify and quantify concentrations of metabolites. The four-channel CIL LC-MS method detected 4235 metabolites, of which 1105 were found to be significantly associated with AVG_DAILYCH4 by a t-test, while 1305 were significantly associated with AVG_DAILYCH4 by a regression analysis at p<0.05. Both the results of the t-test and regression analysis revealed that metabolites that were associated with enteric methane emissions in beef cattle were largely breed-specific whereas 4.29% to 6.39% CH4 associated metabolites were common across the three breed populations and 11.07% to 19.08% were common between two breed populations. Pathway analyses of the CH4 associated metabolites identified top enriched molecular processes for each breed population, including arginine and proline metabolism, arginine biosynthesis, butanoate metabolism, and glutathione metabolism for Angus; beta-alanine metabolism, pyruvate metabolism, glycolysis / gluconeogenesis, and citrate cycle (TCA cycle) for Charolais; phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism, arginine biosynthesis, and arginine and proline metabolism for Kinsella Composite. The detected CH4 associated metabolites and enriched molecular processes will help understand biological mechanisms of enteric methane emissions in beef cattle. The detected CH4 associated plasma metabolites will also provide valuable resources to further characterize the metabolites and verify their utility as biomarkers for selection of cattle with reduced methane emissions.

Association analysis for feed efficiency traits in beef cattle using preserved haplotypes.

This study reports a genome wide scan for chromosome regions and their haplotypes that significantly associated with average daily gain (ADG), dry matter intake (DMI), and residual feed intake (RFI) in beef cattle. The study used data from 597 Angus, 450 Charolais, and 616 crossbred beef cattle, and the Illumina Bovine SNP50 beadchip. Extended haplotype homozygosity was used to identify chromosome regions that had been recently selected for in the three groups of animals. Such regions in the crossbreds were tested for association with ADG, DMI, and RFI. At false discovery rates of 5% and 10%, there were six and eight chromosome regions showing significant associations with the traits, respectively. At nominal significance levels (at least P < 0.05), 23 regions with a total number of 31 haplotypes were found significantly associated with at least one of the three traits. The proportion of phenotypic variance explained by these 23 regions varied depending on the trait; the highest proportion for ADG, DMI, and RFI was 13.50%, 9.92%, and 2.64%, respectively. Most of the haplotypes affected single traits, except for GAA (BTA4), GCG (BTA7), and TAGT (BTA12) that affected multiple traits. Thirty-six quantitative trait loci for 16 production traits, from the current literature, covered fully or in part the 23 chromosome regions. The findings from this study might be an important contribution to the current knowledge of the beef cattle genome and to the effective identification of causative genes associated with important traits in cattle.

Estimation of genetic parameters for primal tissue component traits in commercial crossbred beef cattle.

Knowledge of genetic parameters is required to select for optimal yield of primal cuts that may be used as the selection criteria for designing future breeding programs. This study aimed to estimate the heritability, as well as genetic and phenotypic correlations of primal cut lean and fat tissue components, and carcass traits in Canadian crossbred beef cattle. All tissue component traits presented a medium to high heritability (lean 0.41 to 0.61; fat 0.46 to 0.62; bone 0.22 to 0.48), which indicates a probable increase in their response to genetic selection. In addition, high genetic correlations were found among the primal cut lean trait group (0.63 to 0.94) and fat trait group (0.63 to 0.94), as well as strong negative correlations between lean and fat component traits (-0.63 to -1). Therefore, results suggested inclusion of primal cut tissue composition traits as a selection objective in breeding programs with consideration of correlations among the traits could help in optimizing lean yield for the highest carcass value.

Genome-wide association study for primal cut lean traits in Canadian beef cattle.

This study identified genomic variants and underlying candidate genes related to the whole carcass and individual primal cut lean content in Canadian commercial crossbred beef cattle. Genotyping information of 1035 crossbred beef cattle were available alongside estimated and actual carcass lean meat yield and individual primal cut lean content in all carcasses. Significant fixed effects and covariates were identified and included in the animal model. Genome-wide association analysis were implemented using the weighted single-step genomic best linear unbiased prediction (WssGBLUP). A number of candidate genes identified linked to lean tissue production were unrelated to estimated lean meat yield and were specific to the actual lean traits. Among these, 41 genes were common for actual lean traits, on specific regions of BTA4, BTA13 and BTA25 indicating potential involvement in lean mass synthesis. Therefore, the results suggested the inclusion of primal cut lean traits as a selection objective in breeding programs with consideration of further functional studies of the identified genes could help in optimizing lean yield for maximal carcass value.

Construction of genetic linkage map and mapping of QTL for seed color in Brassica rapa.

A genetic linkage map of Brassica rapa L. was constructed using recombinant inbred lines (RILs) derived from a cross between yellow-seeded cultivar Sampad and a yellowish brown seeded inbred line 3-0026.027. The RILs were evaluated for seed color under three conditions: field plot, greenhouse, and controlled growth chambers. Variation for seed color in the RILs ranged from yellow, like yellow sarson, to dark brown/black even though neither parent had shown brown/black colored seeds. One major QTL (SCA9-2) and one minor QTL (SCA9-1) on linkage group (LG) A9 and two minor QTL (SCA3-1, SCA5-1) on LG A3 and LG A5, respectively, were detected. These collectively explained about 67% of the total phenotypic variance. SCA9-2 mapped in the middle of LG A9, explained about 55% phenotypic variance, and consistently expressed in all environments. The second QTL on LG A9 was ~70 cM away from SCA9-2, suggesting that independent assortment of these QTLs is possible. A digenic epistatic interaction was found between the two main effect QTL on LG A9; and the epistasis × environment interaction was nonsignificant, suggesting stability of the interaction across the environments. The QTL effect on LG A9 was validated using simple sequence repeat (SSR) markers from the two QTL regions of this LG on a B(1)S(1) population (F(1) backcrossed to Sampad followed by self-pollination) segregating for brown and yellow seed color, and on their self-pollinated progenies (B(1)S(2)). The SSR markers from the QTL region SCA9-2 showed a stronger linkage association with seed color as compared with the marker from SCA9-1. This suggests that the QTL SCA9-2 is the major determinant of seed color in the A genome of B. rapa.

Outcomes of epidural steroids following percutaneous transforaminal endoscopic discectomy: a meta-analysis and systematic review.

BACKGROUND: Percutaneous transforaminal endoscopic discectomy (PTED) has been widely used in the treatment of lumbar degenerative diseases. Epidural injection of steroids can reduce the incidence and duration of postoperative pain in a short period of time. Although steroids are widely believed to reduce the effect of surgical trauma, the observation indicators are not uniform, especially the long-term effects, so the problem remains controversial. Therefore, the purpose of this paper was to evaluate the efficacy of epidural steroids following PTED. METHODS: We searched PubMed, Embase, and the Cochrane Database from 1980 to June 2021 to identify randomized and non-randomized controlled trials comparing epidural steroids and saline alone following PTED. The primary outcomes included postoperative pain at least 6 months as assessed using a visual analogue scale (VAS) and the Oswestry Disability Index (ODI). The secondary outcomes included length of hospital stay and the time of return to work. RESULTS: A total of 451 patients were included in three randomized and two nonrandomized controlled trials. The primary outcomes, including VAS and ODI scores, did not differ significantly between epidural steroids following PTED and saline alone. There were no significant intergroup differences in length of hospital stay. Epidural steroids were shown to be superior in terms of the time to return to work (P < 0.001). CONCLUSIONS: Intraoperative epidural steroids did not provide significant benefits, leg pain control, improvement in ODI scores, and length of stay in the hospital, but it can enable the patient to return to work faster.

Genome-Wide Association Study of Nucleotide Variants Associated with Resistance to Nine Antimicrobials in Mycoplasma bovis.

Antimicrobial resistance (AMR) studies of Mycoplasma bovis have generally focused on specific loci versus using a genome-wide association study (GWAS) approach. A GWAS approach, using two different models, was applied to 194 Mycoplasma bovis genomes. Both a fixed effects linear model (FEM) and a linear mixed model (LMM) identified associations between nucleotide variants (NVs) and antimicrobial susceptibility testing (AST) phenotypes. The AMR phenotypes represented fluoroquinolones, tetracyclines, phenicols, and macrolides. Both models identified known and novel NVs associated (Bonferroni adjusted p < 0.05) with AMR. Fluoroquinolone resistance was associated with multiple NVs, including previously identified mutations in gyrA and parC. NVs in the 30S ribosomal protein 16S were associated with tetracycline resistance, whereas NVs in 5S rRNA, 23S rRNA, and 50S ribosomal proteins were associated with phenicol and macrolide resistance. For all antimicrobial classes, resistance was associated with NVs in genes coding for ABC transporters and other membrane proteins, tRNA-ligases, peptidases, and transposases, suggesting a NV-based multifactorial model of AMR in M. bovis. This study was the largest collection of North American M. bovis isolates used with a GWAS for the sole purpose of identifying novel and non-antimicrobial-target NVs associated with AMR.

Integrative analyses of genomic and metabolomic data reveal genetic mechanisms associated with carcass merit traits in beef cattle.

Improvement of carcass merit traits is a priority for the beef industry. Discovering DNA variants and genes associated with variation in these traits and understanding biological functions/processes underlying their associations are of paramount importance for more effective genetic improvement of carcass merit traits in beef cattle. This study integrates 10,488,742 imputed whole genome DNA variants, 31 plasma metabolites, and animal phenotypes to identify genes and biological functions/processes that are associated with carcass merit traits including hot carcass weight (HCW), rib eye area (REA), average backfat thickness (AFAT), lean meat yield (LMY), and carcass marbling score (CMAR) in a population of 493 crossbred beef cattle. Regression analyses were performed to identify plasma metabolites associated with the carcass merit traits, and the results showed that 4 (3-hydroxybutyric acid, acetic acid, citric acid, and choline), 6 (creatinine, L-glutamine, succinic acid, pyruvic acid, L-lactic acid, and 3-hydroxybutyric acid), 4 (fumaric acid, methanol, D-glucose, and glycerol), 2 (L-lactic acid and creatinine), and 5 (succinic acid, fumaric acid, lysine, glycine, and choline) plasma metabolites were significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using the 10,488,742 imputed SNPs, 103, 160, 83, 43, and 109 candidate genes were identified as significantly associated with HCW, REA, AFAT, LMY, and CMAR (P-value < 1 × 10-5), respectively. By applying functional enrichment analyses for candidate genes of each trait, 26, 24, 26, 24, and 28 significant cellular and molecular functions were predicted for HCW, REA, AFAT, LMY, and CMAR, respectively. Among the five topmost significantly enriched biological functions for carcass merit traits, molecular transport and small molecule biochemistry were two top biological functions associated with all carcass merit traits. Lipid metabolism was the most significant biological function for LMY and CMAR and it was also the second and fourth highest biological function for REA and HCW, respectively. Candidate genes and enriched biological functions identified by the integrative analyses of metabolites with phenotypic traits and DNA variants could help interpret the results of previous genome-wide association studies for carcass merit traits. Our integrative study also revealed additional potential novel genes associated with these economically important traits. Therefore, our study improves understanding of the molecular and biological functions/processes that influence carcass merit traits, which could help develop strategies to enhance genomic prediction of carcass merit traits with incorporation of metabolomic data. Similarly, this information could guide management practices, such as nutritional interventions, with the purpose of boosting specific carcass merit traits.