An across breed, diet and tissue analysis reveals the transcription factor NR1H3 as a key mediator of residual feed intake in beef cattle

BACKGROUND

Provision of feed is a major determinant of overall profitability in beef production systems, accounting for up to 75% of the variable costs. Thus, improving cattle feed efficiency, by way of determining the underlying genomic control and subsequently selecting for feed efficient cattle, provides a method through which feed input costs may be reduced. The objective of this study was to undertake gene co-expression network analysis using RNA-Sequence data generated from Longissimus dorsi and liver tissue samples collected from steers of two contrasting breeds (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI), across two consecutive distinct dietary phases (zero-grazed grass and high-concentrate). Categories including differentially expressed genes (DEGs) based on the contrasts of RFI phenotype, breed and dietary source, as well as key transcription factors and proteins secreted in plasma were utilised as nodes of the gene co-expression network.

RESULTS

Of the 2,929 DEGs within the network analysis, 1,604 were reported to have statistically significant correlations (≥ 0.80), resulting in a total of 43,876 significant connections between genes. Pathway analysis of clusters of co-expressed genes revealed enrichment of processes related to lipid metabolism (fatty acid biosynthesis, fatty acid β-oxidation, cholesterol biosynthesis), immune function, (complement cascade, coagulation system, acute phase response signalling), and energy production (oxidative phosphorylation, mitochondrial L-carnitine shuttle pathway) based on genes related to RFI, breed and dietary source contrasts.

CONCLUSIONS

Although similar biological processes were evident across the three factors examined, no one gene node was evident across RFI, breed and diet contrasts in both liver and muscle tissues. However within the liver tissue, the IRX4, NR1H3, HOXA13 and ZNF648 gene nodes, which all encode transcription factors displayed significant connections across the RFI, diet and breed comparisons, indicating a role for these transcription factors towards the RFI phenotype irrespective of diet and breed. Moreover, the NR1H3 gene encodes a protein secreted into plasma from the hepatocytes of the liver, highlighting the potential for this gene to be explored as a robust biomarker for the RFI trait in beef cattle.

Life at the borderlands: microbiomes of interfaces critical to One Health

Microbiomes are foundational components of the environment that provide essential services relating to food security, carbon sequestration, human health, and the overall well-being of ecosystems. Microbiota exert their effects primarily through complex interactions at interfaces with their plant, animal, and human hosts, as well as within the soil environment. This review aims to explore the ecological, evolutionary, and molecular processes governing the establishment and function of microbiome-host relationships, specifically at interfaces critical to One Health-a transdisciplinary framework that recognizes that the health outcomes of people, animals, plants, and the environment are tightly interconnected. Within the context of One Health, the core principles underpinning microbiome assembly will be discussed in detail, including biofilm formation, microbial recruitment strategies, mechanisms of microbial attachment, community succession, and the effect these processes have on host function and health. Finally, this review will catalogue recent advances in microbiology and microbial ecology methods that can be used to profile microbial interfaces, with particular attention to multi-omic, advanced imaging, and modelling approaches. These technologies are essential for delineating the general and specific principles governing microbiome assembly and functions, mapping microbial interconnectivity across varying spatial and temporal scales, and for the establishment of predictive frameworks that will guide the development of targeted microbiome-interventions to deliver One Health outcomes.

Ancestral alleles defined for 70 million cattle variants using a population-based likelihood ratio test

BACKGROUND

The study of ancestral alleles provides insights into the evolutionary history, selection, and genetic structures of a population. In cattle, ancestral alleles are widely used in genetic analyses, including the detection of signatures of selection, determination of breed ancestry, and identification of admixture. Having a comprehensive list of ancestral alleles is expected to improve the accuracy of these genetic analyses. However, the list of ancestral alleles in cattle, especially at the whole genome sequence level, is far from complete. In fact, the current largest list of ancestral alleles (~ 42 million) represents less than 28% of the total number of detected variants in cattle. To address this issue and develop a genomic resource for evolutionary studies, we determined ancestral alleles in cattle by comparing prior derived whole-genome sequence variants to an out-species group using a population-based likelihood ratio test.

RESULTS

Our study determined and makes available the largest list of ancestral alleles in cattle to date (70.1 million) and includes 2.3 million on the X chromosome. There was high concordance (97.6%) of the determined ancestral alleles with those from previous studies when only high-probability ancestral alleles were considered (29.8 million positions) and another 23.5 million high-confidence ancestral alleles were novel, expanding the available reference list to improve the accuracies of genetic analyses involving ancestral alleles. The high concordance of the results with previous studies implies that our approach using genomic sequence variants and a likelihood ratio test to determine ancestral alleles is appropriate.

CONCLUSIONS

Considering the high concordance of ancestral alleles across studies, the ancestral alleles determined in this study including those not previously listed, particularly those with high-probability estimates, may be used for further genetic analyses with reasonable accuracy. Our approach that used predetermined variants in species and the likelihood ratio test to determine ancestral alleles is applicable to other species for which sequence level genotypes are available.

Multi-breed genomic predictions and functional variants for fertility of tropical bulls

Worldwide, most beef breeding herds are naturally mated. As such, the ability to identify and select fertile bulls is critically important for both productivity and genetic improvement. Here, we collected ten fertility-related phenotypes for 6,063 bulls from six tropically adapted breeds. Phenotypes were comprised of four bull conformation traits and six traits directly related to the quality of the bull's semen. We also generated high-density DNA genotypes for all the animals. In total, 680,758 single nucleotide polymorphism (SNP) genotypes were analyzed. The genomic correlation of the same trait observed in different breeds was positive for scrotal circumference and sheath score on most breed comparisons, but close to zero for the percentage of normal sperm, suggesting a divergent genetic background for this trait. We confirmed the importance of a breed being present in the reference population to the generation of accurate genomic estimated breeding values (GEBV) in an across-breed validation scenario. Average GEBV accuracies varied from 0.19 to 0.44 when the breed was not included in the reference population. The range improved to 0.28 to 0.59 when the breed was in the reference population. Variants associated with the gene HDAC4, six genes from the spermatogenesis-associated (SPATA) family of proteins, and 29 transcription factors were identified as candidate genes. Collectively these results enable very early in-life selection for bull fertility traits, supporting genetic improvement strategies currently taking place within tropical beef production systems. This study also improves our understanding of the molecular basis of male fertility in mammals.

Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs

Background

The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs.

Results

To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h²; proportion of phenotypic variance explained by the host genetics) and microbiability (m²; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m² (0.275) compared to h² (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively.

Conclusions

Overall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00138-9.

A gene co-association network regulating gut microbial communities in a Duroc pig population

BACKGROUND

Analyses of gut microbiome composition in livestock species have shown its potential to contribute to the regulation of complex phenotypes. However, little is known about the host genetic control over the gut microbial communities. In pigs, previous studies are based on classical "single-gene-single-trait" approaches and have evaluated the role of host genome controlling gut prokaryote and eukaryote communities separately.

RESULTS

In order to determine the ability of the host genome to control the diversity and composition of microbial communities in healthy pigs, we undertook genome-wide association studies (GWAS) for 39 microbial phenotypes that included 2 diversity indexes, and the relative abundance of 31 bacterial and six commensal protist genera in 390 pigs genotyped for 70 K SNPs. The GWAS results were processed through a 3-step analytical pipeline comprised of (1) association weight matrix; (2) regulatory impact factor; and (3) partial correlation and information theory. The inferred gene regulatory network comprised 3561 genes (within a 5 kb distance from a relevant SNP-P < 0.05) and 738,913 connections (SNP-to-SNP co-associations). Our findings highlight the complexity and polygenic nature of the pig gut microbial ecosystem. Prominent within the network were 5 regulators, PRDM15, STAT1, ssc-mir-371, SOX9 and RUNX2 which gathered 942, 607, 588, 284 and 273 connections, respectively. PRDM15 modulates the transcription of upstream regulators of WNT and MAPK-ERK signaling to safeguard naive pluripotency and regulates the production of Th1- and Th2-type immune response. The signal transducer STAT1 has long been associated with immune processes and was recently identified as a potential regulator of vaccine response to porcine reproductive and respiratory syndrome. The list of regulators was enriched for immune-related pathways, and the list of predicted targets includes candidate genes previously reported as associated with microbiota profile in pigs, mice and human, such as SLIT3, SLC39A8, NOS1, IL1R2, DAB1, TOX3, SPP1, THSD7B, ELF2, PIANP, A2ML1, and IFNAR1. Moreover, we show the existence of host-genetic variants jointly associated with the relative abundance of butyrate producer bacteria and host performance.

CONCLUSIONS

Taken together, our results identified regulators, candidate genes, and mechanisms linked with microbiome modulation by the host. They further highlight the value of the proposed analytical pipeline to exploit pleiotropy and the crosstalk between bacteria and protists as significant contributors to host-microbiome interactions and identify genetic markers and candidate genes that can be incorporated in breeding program to improve host-performance and microbial traits. Video Abstract.

A low-density SNP genotyping panel for the accurate prediction of cattle breeds

Genomic tools to better define breed composition in agriculturally important species have sparked scientific and commercial industry interest. Knowledge of breed composition can inform multiple scientifically important decisions of industry application including DNA marker-assisted selection, identification of signatures of selection, and inference of product provenance to improve supply chain integrity. Genomic tools are expensive but can be economized by deploying a relatively small number of highly informative single-nucleotide polymorphisms (SNP) scattered evenly across the genome. Using resources from the 1000 Bull Genomes Project we established calibration (more stringent quality criteria; N = 1,243 cattle) and validation (less stringent; N = 864) data sets representing 17 breeds derived from both taurine and indicine bovine subspecies. Fifteen successively smaller panels (from 500,000 to 50 SNP) were built from those SNP in the calibration data that increasingly satisfied 2 criteria, high differential allele frequencies across the breeds as measured by average Euclidean distance (AED) and high uniformity (even spacing) across the physical genome. Those SNP awarded the highest AED were in or near genes previously identified as important signatures of selection in cattle such as LCORL, NCAPG, KITLG, and PLAG1. For each panel, the genomic breed composition (GBC) of each animal in the validation dataset was estimated using a linear regression model. A systematic exploration of the predictive accuracy of the various sized panels was then undertaken on the validation population using 3 benchmarking approaches: (1) % error (expressed relative to the estimated GBC made from over 1 million SNP), (2) % breed misassignment (expressed relative to each individual's breed recorded), and (3) Shannon's entropy of estimated GBC across the 17 target breeds. Our analyses suggest that a panel of just 250 SNP represents an adequate balance between accuracy and cost-only modest gains in accuracy are made as one increases panel density beyond this point.

ImmuneDEX: updated genomic estimates of genetic parameters and breeding values for Australian Angus cattle

Context Immune competence is a proxy trait for general disease resistance and is based on combined measures of an animal’s ability to mount both a cell-mediated immune response (Cell-IR) and an antibody-mediated immune response (Ab-IR). On the basis of previously described arithmetic, we combined these measures into a single proxy trait for immune competence, named ImmuneDEX (IDEX). Aims Using a population of 3715 Australian Angus steers (n = 2395) and heifers (n = 1320) with genotypes for 45 364 single-nucleotide polymorphisms, we provide the latest genomic estimates of heritability and genetic correlations for IDEX and the components Cell-IR and Ab-IR immune competence phenotypes. Accuracy and bias of genomic predictions of breeding values are also presented and discussed. Methods Measures of Cell-IR, Ab-IR and IDEX were analysed jointly in a tri-variate genomic restricted maximum-likelihood model that contained the fixed effects of contemporary group with 80 levels, the linear covariates of age at measurement and change in skin thickness at control site, and the random polygenic (genomic estimated breeding value, GEBV) and residual effects. Following Method LR procedures, we estimate accuracy, bias and dispersion of genomic predictions using a cross-validation scheme based on five year-of-birth cohorts. Key results We report genomic restricted maximum-likelihood model estimates of heritability of 0.247 ± 0.040 for Cell-IR, 0.326 ± 0.059 for Ab-IR, 0.275 ± 0.046 for IDEX. While a small positive genetic correlation (rg) was estimated between Cell-IR and Ab-IR (rg = 0.138 ± 0.095), strongly positive estimates were obtained between IDEX and Cell-IR (rg = 0.740 ± 0.044) and between IDEX and Ab-IR (rg = 0.741 ± 0.036). Averaged across the five validation sets, the accuracy of GEBV for Cell-IR, Ab-IR and IDEX was 0.405, 0.443 and 0.411 respectively. Also, some significant bias or dispersion can be expected depending on the cohort used as the validation population. Conclusions Consistent with previous findings, immune competence phenotypes are moderately heritable and accurate GEBV can be generated to allow the selection of cattle with an improved ability to mount a general immune response. Implications Our analyses suggest that ImmuneDEX will provide a tool to underpin long-term genetic strategies aimed at improving the immune competence of Australian Angus cattle in production systems, which, in turn, is expected to reduce the incidence of disease and our reliance on antibiotics to treat disease.

Global gene expression profile in canine mammary carcinomas

Mammary gland tumors are a heterogeneous group of neoplastic diseases. Genetic studies make it possible to determine genetic profiles and identify new molecular markers. The aim of the study was to evaluate the gene expression profile of canine mammary carcinomas and identify potential prognostic markers. Twelve mammary cancer samples from bitches were collected for the evaluation of global gene expression. Microarray assays were performed using commercial kits. Statistical analysis of the microarray was done using moderate t-statistic and adjusted using the Benjamini and Hochberg procedure. Differential connectivity analysis was also performed. Enrichment analyses were conducted using WebGestalt. P-values were calculated using hypergeometric statistics and adjusted using the Benjamini and Hochberg procedure. The HYAL-1 gene was validated using quantitative PCR (qPCR). There were 878 upregulated genes and 821 downregulated genes in the neoplasms studied. Enrichment analysis (individual analysis) identified the HYAL-1 gene as a potential marker of tumorigenesis and tumor recurrence. Differential connectivity analysis demonstrated 262 differentially connected genes.

Identification of two molecular subtypes in canine mast cell tumours through gene expression profiling

Mast cell tumours (MCTs) are common neoplasms in dogs and are usually regarded as potentially malignant. Several studies have attempted to identify biomarkers to better predict biological behaviours for this tumour. The aim of this study was to identify pathways connected to clinical and histopathological malignancies, shorter survival times, and poor prognoses associated with MCTs. We performed genome-wide gene expression analyses on tissues obtained from 15 dogs with single MCTs, and identified two distinct tumour subtypes—high-risk and low-risk—associated with differences in histological grades, survival times, Ki67 indices, and occurrence of death due the disease. Comparative analyses of RNA sequence profiles revealed 71 genes that were differentially expressed between high- and low-risk MCTs. In addition to these analyses, we also examined gene co-expression networks to explore the biological functions of the identified genes. The network construction revealed 63 gene modules, of which 4 were significantly associated with the more aggressive tumour group. Two of the gene modules positively correlated with high-risk MCTs were also associated with cell proliferation and extracellular matrix-related terms. At the top of the extracellular matrix module category, genes with functions directly related to those of cancer-associated fibroblasts (CAFs) were identified. Immunohistochemical analyses also revealed a greater number of CAFs in high-risk MCTs. This study provides a method for the molecular characterisation of canine MCTs into two distinct subtypes. Our data indicate that proliferation pathways are significantly involved in malignant tumour behaviours, which are known to be relevant for the induction and maintenance of MCTs. Finally, animals presenting high-risk MCTs overexpress genes associated with the extracellular matrix that can be robustly linked to CAF functions. We suggest that CAFs in the MCT stroma contribute to cancer progression.

Abstract 819: Transcriptomic profiling of two canine mammary cancer cell lines for translational oncology studies

Dogs develop spontaneous cancers as humans and shares a variety of features including histology, genomic alterations, molecular targets, biological behavior and response to conventional therapies. Although dogs can be considered good models for translation of new cancer treatments, there is a lack of established preclinical in vitro models of canine cancers. Therefore, our aim here was to establish two canine mammary cancer cell lines and characterize their transcriptomic profile to speed the translation of new cancer treatments from dogs to humans. Mammary cancer samples were obtained from female dogs that underwent surgery. Pieces of the tumors were FFPE for histopathology and the other fragments were washed with PBS, minced and dissociated with hyaluronidase and collagenase. Cells were filtered and put in culture with DMEM-F12, 5%FBS and 1% pen-strep. After 5 passages, FBS was increased to 10%. After passage 15, cells were characterized for doubling time, karyotype, immunocytochemistry for cytokeratin, vimentin and α-smooth muscle actin, matrigel-invasion assay and tumor growth in nude mice. At last, RNA-seq was performed on total RNA extracted with TRIZOL for functional enrichment analysis of differentially expressed (DE) genes from cancer cell lines. From 49 tumor samples, we characterized 2 cell lines named M5 and M25, originally from a comedocarcinoma and a mixed carcinoma, respectively. Both cell cultures showed spindle-shape cells and were positive for cytokeratin, vimentin and α-smooth actin, suggesting myoepithelial origin. The doubling time was 26.0h for M5 and 42.8h for M25 cells. Karyotyping analysis showed moderate aneuploidy for both cell cultures. M25 cell line presented significant higher invasion potential than M5 cells and formed tumors in mice but the latter didn’t. Histopathology of these tumors resembled the original tumor in the dog. An average of 16.2 million paired-end 100 bp reads were sequenced per replicate (three for each cell culture) with an average of 81.1% alignment to the reference genome. A total of 11,260 and 11,476 genes with more than 1 count per million were detected in M5 and M25 cell lines. M5 cell line have 404 and 394 up- and downregulated genes in comparison to M25 cell line, respectively. Gene ontology of DE genes showed the more malignant cell (M25) significantly enriched for pathways related to drug metabolism, ECM-receptor interaction, cell cycle, DNA replication, focal adhesion and cell differentiation. In conclusion, M25 cell line is more malignant than the M5 which is supported by their transcriptome. Thus, it is possible to use these cells as a comparative translational model for drug development especially if one wishes to work with molecular targets as described above.

Citation Format: Heidge Fukumasu, Yonara G. Cordeiro, Pedro L. Xavier, Arina L. Rochetti, Pamela A. Alexandre, Claudia C. Mori, Silvio H. Freitas, Ricardo F. Strefezzi. Transcriptomic profiling of two canine mammary cancer cell lines for translational oncology studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 819. doi:10.1158/1538-7445.AM2017-819

Abstract P1-05-28: Melatonin treatment: A transcriptomic networks in a xenograft model of breast cancer

Melatonin is a hormone produced by the pineal gland and has been shown different antitumor effects, as immunomodulatory, antioxidant, pro-apoptotic, anti-proliferative, antimetastatic and antiangiogenic, however, the pathways by which melatonin exerts its action need to be identified. Thus, the aims of this study were to perform the transcriptome analysis to evaluate the pathways of melatonin action in triple-negative breast cancer. Triple-negative breast cancer cells (MDA-MB-231) were injected into the mammary gland of the athymic nude mice (n=10), which were treated with melatonin (40 mg/kg) or vehicle during 21 days. RNA-Seq libraries were created using Truseq RNA-Seq Library Prep Kit v2. The experiment was paired-end with 100nt read length, performed on the Illumina HiSeq2500 sequencer, producing about 30 million reads per library. To differentiate human and mouse expression, the alignment was performed to filter out mouse-like reads before mapping to the human reference and vice versa, and data were mapped against human (GRCh37/hg19) and mouse (NCBI37/mm9) genomes separately, using the TopHat software. The HTSeq was employed for analyses of read counts and DESeq2 was used to identify genes differentially expressed between melatonin treated and control tumors. Differentially expressed genes (DEGs) were identified based on a false discovery rate (FDR) q-value threshold of less than 0.05. Also, we applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Results showed that animals treated with melatonin had smaller tumors volume than controls (p&lt;0.05). RNA-Seq data showed that 57.24% of reads mapped uniquely to human, 29.66% reads in mouse and 11% reads mapping to both human and mouse genomes. In human tumor cells, there was no DEGs between melatonin treated and control group (adjP&gt;0.05). In mouse cells, which represent the tumor microenvironment, there were 34 DEGs between animals treated with melatonin and controls (adjP&lt;0.05). In tumors cells, we detected 714 differentially co-expressed genes (IKdiffI&gt;0.6), which were functionally enriched for GO terms like lipid metabolic process, response to drug, oxidoreductase activity and PPAR signaling (adjP&lt;0.1). Also, we identified 3 gene modules strongly associated with melatonin treatment, which were related with metabolic pathways (adjP&lt;0.1). In mouse cells, were detected 1345 differentially co-expressed genes, which were enriched for signaling pathways like Wnt receptor, Hedgehog and TGF-beta (adjP&lt;0.1). There were 3 gene modules strongly associated with melatonin treatment, which are enriched for regulation of translation and cell cycle, immune system process and T cell differentiation, regulation of action cytoskeleton and ErbB signaling pathway (adjP&lt;0.1). Also, potential regulator genes for melatonin treatment were detected by generating clusters of co-expressed genes and individual analysis confirms these results. Transcriptomic network analysis coupled with other results showed that melatonin treatment controls the tumor growth, acting especially by metabolic pathways in tumor cells and modulating the tumor microenvironment.

Citation Format: Jardim-Perassi BV, Sonehara NM, de Paula-Junior R, Chammas R, Coutinho LL, Reis Júnior O, Alexandre PA, Fukumasu H, Zuccari DAPC. Melatonin treatment: A transcriptomic networks in a xenograft model of breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P1-05-28.

Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle

BACKGROUND

The selection of beef cattle for feed efficiency (FE) traits is very important not only for productive and economic efficiency but also for reduced environmental impact of livestock. Considering that FE is multifactorial and expensive to measure, the aim of this study was to identify biological functions and regulatory genes associated with this phenotype.

RESULTS

Eight genes were differentially expressed between high and low feed efficient animals (HFE and LFE, respectively). Co-expression analyses identified 34 gene modules of which 4 were strongly associated with FE traits. They were mainly enriched for inflammatory response or inflammation-related terms. We also identified 463 differentially co-expressed genes which were functionally enriched for immune response and lipid metabolism. A total of 8 key regulators of gene expression profiles affecting FE were found. The LFE animals had higher feed intake and increased subcutaneous and visceral fat deposition. In addition, LFE animals showed higher levels of serum cholesterol and liver injury biomarker GGT. Histopathology of the liver showed higher percentage of periportal inflammation with mononuclear infiltrate.

CONCLUSION

Liver transcriptomic network analysis coupled with other results demonstrated that LFE animals present altered lipid metabolism and increased hepatic periportal lesions associated with an inflammatory response composed mainly by mononuclear cells. We are now focusing to identify the causes of increased liver lesions in LFE animals.

A genomewide association mapping study using ultrasound-scanned information identifies potential genomic regions and candidate genes affecting carcass traits in Nellore cattle

The aim of this study was to identify candidate genes and genomic regions associated with ultrasound-derived measurements of the rib-eye area (REA), backfat thickness (BFT) and rumpfat thickness (RFT) in Nellore cattle. Data from 640 Nellore steers and young bulls with genotypes for 290 863 single nucleotide polymorphisms (SNPs) were used for genomewide association mapping. Significant SNP associations were explored to find possible candidate genes related to physiological processes. Several of the significant markers detected were mapped onto functional candidate genes including ARFGAP3, CLSTN2 and DPYD for REA; OSBPL3 and SUDS3 for BFT; and RARRES1 and VEPH1 for RFT. The physiological pathway related to lipid metabolism (CLSTN2, OSBPL3, RARRES1 and VEPH1) was identified. The significant markers within previously reported QTLs reinforce the importance of the genomic regions, and the other loci offer candidate genes that have not been related to carcass traits in previous investigations.

Genome-wide association with residual body weight gain in Bos indicus cattle

Weight gain is a key performance trait for beef cat-tle; however, attention should be given to the production costs for better profitability. Therefore, a feed efficiency trait based on per-formance can be an interesting approach to improve performance without increasing food costs. To identify candidate genes and ge-nomic regions associated with residual body weight gain (RWG), we conducted a genome-wide association study (GWAS) with 720 Nellore cattle using the GRAMMAR-Gamma association test. We identified 30 significant single nucleotide polymorphisms (SNPs), especially on chromosomes 2, 8, 12, and 17. Several genes and quantitative train loci (QTLs) present in the regions identified were appointed; we highlight DMRT2 (doublesex and mab-3 related tran-scription factor 2), IFFO2 (intermediate filament family orphan 2), LNX2 (ligand of numb-protein X 2), MTIF3 (mitochondrial transla-tional initiation factor 3), and TRNAG-CCC (transfer RNA glycine anticodon CCC). The metabolic pathways that can explain part of the phenotypic variation in RWG are related to oxidative stress and muscle control.