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.

Combining genomics and semen microbiome increases the accuracy of predicting bull prolificacy.

Commercial livestock producers need to prioritize genetic progress for health and efficiency traits to address productivity, welfare, and environmental concerns but face challenges due to limited pedigree information in extensive multi-sire breeding scenarios. Utilizing pooled DNA for genotyping and integrating seminal microbiome information into genomic models could enhance predictions of male fertility traits, thus addressing complexities in reproductive performance and inbreeding effects. Using the Angus Australia database comprising genotypes and pedigree data for 78,555 animals, we simulated percentage of normal sperm (PNS) and prolificacy of sires, resulting in 713 sires and 27,557 progeny in the final dataset. Publicly available microbiome data from 45 bulls was used to simulate data for the 713 sires. By incorporating both genomic and microbiome information our models were able to explain a larger proportion of phenotypic variation in both PNS (0.94) and prolificacy (0.56) compared to models using a single data source (e.g., 0.36 and 0.41, respectively, using only genomic information). Additionally, models containing both genomic and microbiome data revealed larger phenotypic differences between animals in the top and bottom quartile of predictions, indicating potential for improved productivity and sustainability in livestock farming systems. Inbreeding depression was observed to affect fertility traits, which makes the incorporation of microbiome information on the prediction of fertility traits even more actionable. Crucially, our inferences demonstrate the potential of the semen microbiome to contribute to the improvement of fertility traits in cattle and pave the way for the development of targeted microbiome interventions to improve reproductive performance in livestock.

Detection of potential functional variants based on systems-biology: the case of feed efficiency in beef cattle.

BACKGROUND: Potential functional variants (PFVs) can be defined as genetic variants responsible for a given phenotype. Ultimately, these are the best DNA markers for animal breeding and selection, especially for polygenic and complex phenotypes. Herein, we described the identification of PFVs for complex phenotypes (in this case, Feed Efficiency in beef cattle) using a systems-biology driven approach based on RNA-seq data from physiologically relevant organs. RESULTS: The systems-biology coupled with deep molecular phenotyping by RNA-seq of liver, muscle, hypothalamus, pituitary, and adrenal glands of animals with high and low feed efficiency (FE) measured by residual feed intake (RFI) identified 2,000,936 uniquely variants. Among them, 9986 variants were significantly associated with FE and only 78 had a high impact on protein expression and were considered as PFVs. A set of 169 significant uniquely variants were expressed in all five organs, however, only 27 variants had a moderate impact and none of them a had high impact on protein expression. These results provide evidence of tissue-specific effects of high-impact PFVs. The PFVs were enriched (FDR < 0.05) for processing and presentation of MHC Class I and II mediated antigens, which are an important part of the adaptive immune response. The experimental validation of these PFVs was demonstrated by the increased prediction accuracy for RFI using the weighted G matrix (ssGBLUP+wG; Acc = 0.10 and b = 0.48) obtained in the ssGWAS in comparison to the unweighted G matrix (ssGBLUP; Acc = 0.29 and b = 1.10). CONCLUSION: Here we identified PFVs for FE in beef cattle using a strategy based on systems-biology and deep molecular phenotyping. This approach has great potential to be used in genetic prediction programs, especially for polygenic phenotypes.

Bias, dispersion, and accuracy of genomic predictions for feedlot and carcase traits in Australian Angus steers.

BACKGROUND: Improving feedlot performance, carcase weight and quality is a primary goal of the beef industry worldwide. Here, we used data from 3408 Australian Angus steers from seven years of birth (YOB) cohorts (2011-2017) with a minimal level of sire linkage and that were genotyped for 45,152 SNPs. Phenotypic records included two feedlot and five carcase traits, namely average daily gain (ADG), average daily dry matter intake (DMI), carcase weight (CWT), carcase eye muscle area (EMA), carcase Meat Standard Australia marbling score (MBL), carcase ossification score (OSS) and carcase subcutaneous rib fat depth (RIB). Using a 7-way cross-validation based on YOB cohorts, we tested the quality of genomic predictions using the linear regression (LR) method compared to the traditional method (Pearson's correlation between the genomic estimated breeding value (GEBV) and its associated adjusted phenotype divided by the square root of heritability); explored the factors, such as heritability, validation cohort, and phenotype that affect estimates of accuracy, bias, and dispersion calculated with the LR method; and suggested a novel interpretation for translating differences in accuracy into phenotypic differences, based on GEBV quartiles (Q1Q4). RESULTS: Heritability (h2) estimates were generally moderate to high (from 0.29 for ADG to 0.53 for CWT). We found a strong correlation (0.73, P-value < 0.001) between accuracies using the traditional method and those using the LR method, although the LR method was less affected by random variation within and across years and showed a better ability to discriminate between extreme GEBV quartiles. We confirmed that bias of GEBV was not significantly affected by h2, validation cohort or trait. Similarly, validation cohort was not a significant source of variation for any of the GEBV quality metrics. Finally, we observed that the phenotypic differences were larger for higher accuracies. CONCLUSIONS: Our estimates of h2 and GEBV quality metrics suggest a potential for accurate genomic selection of Australian Angus for feedlot performance and carcase traits. In addition, the Q1Q4 measure presented here easily translates into possible gains of genomic selection in terms of phenotypic differences and thus provides a more tangible output for commercial beef cattle producers.

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.

Genome-wide association analysis of feed intake and residual feed intake in Nellore cattle.

BACKGROUND: Feed intake plays an important economic role in beef cattle, and is related with feed efficiency, weight gain and carcass traits. However, the phenotypes collected for dry matter intake and feed efficiency are scarce when compared with other measures such as weight gain and carcass traits. The use of genomic information can improve the power of inference of studies on these measures, identifying genomic regions that affect these phenotypes. This work performed the genome-wide association study (GWAS) for dry matter intake (DMI) and residual feed intake (RFI) of 720 Nellore cattle (Bos taurus indicus). RESULTS: In general, no genomic region extremely associated with both phenotypic traits was observed, as expected for the variables that have their regulation controlled by many genes. Three SNPs surpassed the threshold for the Bonferroni multiple test for DMI and two SNPs for RFI. These markers are located on chromosomes 4, 8, 14 and 21 in regions near genes regulating appetite and ion transport and close to important QTL as previously reported to RFI and DMI, thus corroborating the literature that points these two processes as important in the physiological regulation of intake and feed efficiency. CONCLUSIONS: This study showed the first GWAS of DMI to identify genomic regions associated with feed intake and efficiency in Nellore cattle. Some genes and QTLs previously described for DMI and RFI, in other subspecies (Bos taurus taurus), that influences these phenotypes are confirmed in this study.

Relationship between the rumen microbiome and liver transcriptome in beef cattle divergent for feed efficiency.

BACKGROUND: Feed costs account for a high proportion of the variable cost of beef production, ultimately impacting overall profitability. Thus, improving feed efficiency of beef cattle, by way of determining the underlying genomic control and selecting for feed efficient cattle provides a method through which feed input costs may be reduced whilst also contributing to the environmental sustainability of beef production. The rumen microbiome dictates the feed degradation capacity and consequent nutrient supply in ruminants, thus potentially impacted by feed efficiency phenotype. Equally, liver tissue has been shown to be responsive to feed efficiency phenotype as well as dietary intake. However, although both the rumen microbiome and liver transcriptome have been shown to be impacted by host feed efficiency phenotype, knowledge of the interaction between the rumen microbiome and other peripheral tissues within the body, including the liver is lacking. Thus, the objective of this study was to compare two contrasting breed types (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI) over contrasting dietary phases (zero-grazed grass and high-concentrate), based on gene co-expression network analysis of liver transcriptome data and microbe co-abundance network of rumen microbiome data. Traits including RFI, dry matter intake (DMI) and growth rate (ADG), as well as rumen concentrations of volatile fatty acids were also included within the network analysis. RESULTS: Overall, DMI had the greatest number of connections followed by RFI, with ADG displaying the fewest number of significant connections. Hepatic genes related to lipid metabolism were correlated to both RFI and DMI phenotypes, whilst genes related to immune response were correlated to DMI. Despite the known relationship between RFI and DMI, the same microbes were not directly connected to these phenotypes, the Succiniclasticum genus was however, negatively connected to both RFI and ADG. Additionally, a stepwise regression analysis revealed significant roles for both Succiniclasticum genus and Roseburia.faecis sp. in predicting RFI, DMI and ADG. CONCLUSIONS: Results from this study highlight the interactive relationships between rumen microbiome and hepatic transcriptome data of cattle divergent for RFI, whilst also increasing our understanding of the underlying biology of both DMI and ADG in beef cattle.

Stress-induced epigenetic effects driven by maternal lactation in dairy cattle: a comethylation network approach.

Epigenetic marks do not follow the Mendelian laws of inheritance. The environment can alter the epigenotype of an individual when exposed to different external stressors. In lactating cows, the first stages of gestation overlap with the lactation peak, creating a negative energy balance that is difficult to overcome with diet. This negative energy balance could affect early embryo development that must compete with the mammary tissue for nutrients. We hypothesize that the methylation profiles of calves born to nonlactating heifers are different from those of calves born to lactating cows. We found 50,277 differentially methylated cytosines and 2,281 differentially methylated regions between these two groups of animals. A comethylation network was constructed to study the correlation between the phenotypes of the mothers and the epigenome of the calves, revealing 265 regions associated with the phenotypes. Our study revealed the presence of DMCs and DMRs in calves gestated by heifers and lactating cows, which were linked to the dam's lactation and the calves' ICAP and milk EBV. Gene-specific analysis highlighted associations with vasculature and organ morphogenesis and cell communication and signalling. These finding support the hypothesis that calves gestated by nonlactating mothers have a different methylation profile than those gestated by lactating cows.

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.

Exploration of mobile genetic elements in the ruminal microbiome of Nellore cattle.

Metagenomics has made it feasible to elucidate the intricacies of the ruminal microbiome and its role in the differentiation of animal production phenotypes of significance. The search for mobile genetic elements (MGEs) has taken on great importance, as they play a critical role in the transfer of genetic material between organisms. Furthermore, these elements serve a dual purpose by controlling populations through lytic bacteriophages, thereby maintaining ecological equilibrium and driving the evolutionary progress of host microorganisms. In this study, we aimed to identify the association between ruminal bacteria and their MGEs in Nellore cattle using physical chromosomal links through the Hi-C method. Shotgun metagenomic sequencing and the proximity ligation method ProxiMeta were used to analyze DNA, getting 1,713,111,307 bp, which gave rise to 107 metagenome-assembled genomes from rumen samples of four Nellore cows maintained on pasture. Taxonomic analysis revealed that most of the bacterial genomes belonged to the families Lachnospiraceae, Bacteroidaceae, Ruminococcaceae, Saccharofermentanaceae, and Treponemataceae and mostly encoded pathways for central carbon and other carbohydrate metabolisms. A total of 31 associations between host bacteria and MGE were identified, including 17 links to viruses and 14 links to plasmids. Additionally, we found 12 antibiotic resistance genes. To our knowledge, this is the first study in Brazilian cattle that connect MGEs with their microbial hosts. It identifies MGEs present in the rumen of pasture-raised Nellore cattle, offering insights that could advance biotechnology for food digestion and improve ruminant performance in production systems.

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.

Competing endogenous RNA-networks reveal key regulatory microRNAs involved in the response of Atlantic salmon to a novel orthomyxovirus.

One of the most intriguing discoveries of the genomic era is that only a small fraction of the genome is dedicated to protein coding. The remaining fraction of the genome contains, amongst other elements, a number of non-coding transcripts that regulate the transcription of protein coding genes. Here we used transcriptome sequencing data to explore these gene regulatory networks using RNA derived from gill tissue of Atlantic salmon (Salmo salar) infected with Pilchard orthomyxovirus (POMV), but showing no clinical signs of disease. We examined fish sampled early during the challenge trial (8-12 days after infection) to uncover potential biomarkers of early infection and innate immunity, and fish sampled late during the challenge trial (19 dpi) to elucidate potential markers of resistance to POMV. We analysed total RNA-sequencing data to find differentially expressed messenger RNAs (mRNA) and identify new long-noncoding RNAs (lncRNAs). We also evaluated small RNA sequencing data to find differentially transcribed microRNAs (miRNAs) and explore their role in gene regulatory networks. Whole-genome expression data (both coding and non-coding transcripts) were used to explore the crosstalk between RNA molecules by constructing competing endogenous RNA networks (ceRNA). The teleost specific miR-462/miR-731 cluster was strongly induced in POMV infected fish and deemed a potential biomarker of early infection. Gene networks also identified a selenoprotein (selja), downregulated in fish sampled late during the challenge, which may be associated to viral clearance and the return to homeostasis after infection. This study provides the basis for further investigations using molecular tools to overexpress or inhibit miRNAs to confirm the functional impact of the interactions presented here on gene expression and their potential application at commercial level.

Identification of Predictor Genes for Feed Efficiency in Beef Cattle by Applying Machine Learning Methods to Multi-Tissue Transcriptome Data.

Machine learning (ML) methods have shown promising results in identifying genes when applied to large transcriptome datasets. However, no attempt has been made to compare the performance of combining different ML methods together in the prediction of high feed efficiency (HFE) and low feed efficiency (LFE) animals. In this study, using RNA sequencing data of five tissues (adrenal gland, hypothalamus, liver, skeletal muscle, and pituitary) from nine HFE and nine LFE Nellore bulls, we evaluated the prediction accuracies of five analytical methods in classifying FE animals. These included two conventional methods for differential gene expression (DGE) analysis (t-test and edgeR) as benchmarks, and three ML methods: Random Forests (RFs), Extreme Gradient Boosting (XGBoost), and combination of both RF and XGBoost (RX). Utility of a subset of candidate genes selected from each method for classification of FE animals was assessed by support vector machine (SVM). Among all methods, the smallest subsets of genes (117) identified by RX outperformed those chosen by t-test, edgeR, RF, or XGBoost in classification accuracy of animals. Gene co-expression network analysis confirmed the interactivity existing among these genes and their relevance within the network related to their prediction ranking based on ML. The results demonstrate a great potential for applying a combination of ML methods to large transcriptome datasets to identify biologically important genes for accurately classifying FE animals.

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.

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.

Chromatin accessibility and regulatory vocabulary across indicine cattle tissues.

BACKGROUND: Spatiotemporal changes in the chromatin accessibility landscape are essential to cell differentiation, development, health, and disease. The quest of identifying regulatory elements in open chromatin regions across different tissues and developmental stages is led by large international collaborative efforts mostly focusing on model organisms, such as ENCODE. Recently, the Functional Annotation of Animal Genomes (FAANG) has been established to unravel the regulatory elements in non-model organisms, including cattle. Now, we can transition from prediction to validation by experimentally identifying the regulatory elements in tropical indicine cattle. The identification of regulatory elements, their annotation and comparison with the taurine counterpart, holds high promise to link regulatory regions to adaptability traits and improve animal productivity and welfare. RESULTS: We generate open chromatin profiles for liver, muscle, and hypothalamus of indicine cattle through ATAC-seq. Using robust methods for motif discovery, motif enrichment and transcription factor binding sites, we identify potential master regulators of the epigenomic profile in these three tissues, namely HNF4, MEF2, and SOX factors, respectively. Integration with transcriptomic data allows us to confirm some of their target genes. Finally, by comparing our results with Bos taurus data we identify potential indicine-specific open chromatin regions and overlaps with indicine selective sweeps. CONCLUSIONS: Our findings provide insights into the identification and analysis of regulatory elements in non-model organisms, the evolution of regulatory elements within two cattle subspecies as well as having an immediate impact on the animal genetics community in particular for a relevant productive species such as tropical cattle.

In silico validation of pooled genotyping strategies for genomic evaluation in Angus cattle.

In this study, we aimed to assess the value of genotyping DNA pools as a strategy to generate accurate and cost-effective genomic estimated breeding values (GEBV) of sires in multi-sire mating systems. In order to do that, we used phenotypic records of 2,436 Australian Angus cattle from 174 sires, including yearling weight (YWT; N = 1,589 records), coat score (COAT; N = 2,026 records), and Meat Standards Australia marbling score (MARB; N = 1,304 records). Phenotypes were adjusted for fixed effects and age at measurement and pools of 2, 5, 10, 15, 20, and 25 animals were explored. Pools were created either by phenotype or at random. When pools were created at random, 10 replicates were examined to provide a measure of sampling variation. The relative accuracy of each pooling strategy was measured by the Pearson correlation coefficient between the sire's GEBV with pooled progeny and the GEBV using individually genotyped progeny. Random pools allow the computation of sire GEBV that are, on average, moderately correlated (i.e., r > 0.5 at pool sizes [PS] ≤ 10) with those obtained without pooling. However, for pools assigned at random, the difference between the best and the worst relative accuracy obtained out of the 10 replicates was as high as 0.41 for YWT, 0.36 for COAT, and 0.61 for MARB. This uncertainty associated with the relative accuracy of GEBV makes randomly assigning animals to pools an unreliable approach. In contrast, pooling by phenotype allowed the estimation of sires' GEBV with a relative accuracy ≥ 0.9 at PS < 10 for all three phenotypes. Moreover, even with larger PS, the lowest relative accuracy obtained was 0.88 (YWT, PS = 20). In agreement with results using simulated data, we conclude that pooling by phenotype is a robust approach to implementing genomic evaluation using commercial herd data, and PS larger than 10 individuals can be considered.

Dynamics of Gene Co-expression Networks in Time-Series Data: A Case Study in Drosophila melanogaster Embryogenesis.

Co-expression networks tightly coordinate the spatiotemporal patterns of gene expression unfolding during development. Due to the dynamic nature of developmental processes simply overlaying gene expression patterns onto static representations of co-expression networks may be misleading. Here, we aim to formally quantitate topological changes of co-expression networks during embryonic development using a publicly available Drosophila melanogaster transcriptome data set comprising 14 time points. We deployed a network approach which inferred 10 discrete co-expression networks by smoothly sliding along from early to late development using 5 consecutive time points per window. Such an approach allows changing network structure, including the presence of hubs, modules and other topological parameters to be quantitated. To explore the dynamic aspects of gene expression captured by our approach, we focused on regulator genes with apparent influence over particular aspects of development. Those key regulators were selected using a differential network algorithm to contrast the first 7 (early) with the last 7 (late) developmental time points. This assigns high scores to genes whose connectivity to abundant differentially expressed target genes has changed dramatically between states. We have produced a list of key regulators - some increasing (e.g., Tusp, slbo, Sidpn, DCAF12, and chinmo) and some decreasing (Rfx, bap, Hmx, Awh, and mld) connectivity during development - which reflects their role in different stages of embryogenesis. The networks we have constructed can be explored and interpreted within Cytoscape software and provide a new systems biology approach for the Drosophila research community to better visualize and interpret developmental regulation of gene expression.