Genome-wide association study for growth traits in Blanco Orejinegro and Romosinuano cattle

Growth traits are economically important characteristics for the genetic improvement of local cattle breeds. Genome-wide association studies (GWAS) provide valuable information to enhance the understanding on the genetics of complex traits. The aim of this study was to perform a GWAS to identify genomic regions and genes associated to birth weight, weaning weight adjusted for 240 days, 16 months, and 24 months weight in Romosinuano (ROMO) and Blanco Orejinegro (BON) cattle. A single-step genomic-BLUP was implemented using 596 BON and 569 ROMO individuals that were genotyped with an Illumina BovineSNP50 BeadChip. There were 25 regions of interest identified on different chromosomes, with few of them simultaneously associated with two or more growth traits and some were common to both breeds. The gene mapping allowed to find 173 annotations on these regions, from which 49 represent potential candidate genes with known growth-related functions in cattle and other species. Among the regions that were associated with several growth traits, that at 24 - 27 MB of BTA14, has important candidate genes such as LYPLA1, XKR4, TMEM68 and PLAG1. Another region of interest at 0.40-0.77 Mb of BTA23 was identified in both breeds, containing KHDRBS2 as a potential candidate gene influencing body weight. Future studies targeting these regions could provide more knowledge to uncover the genetic architecture underlying growth traits in BON and ROMO cattle. The genomic regions and genes identified in this study could be used to improve the prediction of genetic merit for growth traits in these creole cattle breeds.

Genes Involved in Feed Efficiency Identified in a Meta-Analysis of Rumen Tissue from Two Populations of Beef Steers

In cattle, the rumen is an important site for the absorption of feed by-products released by bacterial fermentation, and variation in ruminal function plays a role in cattle feed efficiency. Studies evaluating gene expression in the rumen tissue have been performed prior to this. However, validating the expression of genes identified in additional cattle populations has been challenging. The purpose of this study was to perform a meta-analysis of the ruminal transcriptome of two unrelated populations of animals to identify genes that are involved in feed efficiency across populations. RNA-seq data from animals with high and low residual feed intake (RFI) from a United States population of cattle (eight high and eight low RFI) and a Canadian population of cattle (nine high and nine low RFI) were analyzed for differences in gene expression. A total of 83 differentially expressed genes were identified. Some of these genes have been previously identified in other feed efficiency studies. These genes included ATP6AP1, BAG6, RHOG, and YPEL3. Differentially expressed genes involved in the Notch signaling pathway and in protein turnover were also identified. This study, combining two unrelated populations of cattle in a meta-analysis, produced several candidate genes for feed efficiency that may be more robust indicators of feed efficiency than those identified from single populations of animals.

Expression of candidate genes for residual feed intake in tropically adapted Bos taurus and Bos indicus bulls under thermoneutral and heat stress environmental conditions

The objectives of this study were to measure the relative expression of the ATP1A1, NR3C1, POMC, NPY, and LEP genes in Caracu (Bos taurus) and Nelore (Bos indicus) bulls submitted to feed efficiency tests at high environmental temperatures, and to evaluate differences in adaptability to tropical conditions between breeds. Thirty-five Caracu and 30 Nelore bulls were submitted to a feed efficiency test using automated feeding stations. At the end of the test, the animals were subjected to thermoneutral (TN) and heat stress (HS) conditions. Blood samples were collected after the exposure to the TN and HS conditions and the relative expression of genes was measured by qPCR. The bulls exhibited lower expression of ATP1A1 in the HS condition than in the TN condition (1.98 ± 0.27 and 2.86 ± 0.26, P = 0.02), while the relative expression of NR3C1, POMC, and LEP did not differ (P > 0.05) between climatic conditions. The breed and feed intake influenced NPY and LEP expression levels (P < 0.05). Different climate conditions associated with residual feed intake can modify the gene expression patterns of ATP1A1 and NPY. The association observed among all genes studied shows that they are involved in appetite control. Bos taurus and Bos indicus bulls exhibited similar adaptability to tropical climate conditions.

Systems genetic analysis of binge-like eating in a C57BL/6J x DBA/2J-F2 cross

Binge eating is a heritable trait associated with eating disorders and refers to the rapid consumption of a large quantity of energy-dense food that is, associated with loss of control and negative affect. Binge eating disorder is the most common eating disorder in the United States; however, the genetic basis is unknown. We previously identified robust mouse inbred strain differences between C57BL/6J and DBA/2J in binge-like eating of sweetened palatable food in an intermittent access, conditioned place preference paradigm. To map the genetic basis of changes in body weight and binge-like eating (BLE) and to identify candidate genes, we conducted quantitative trait locus (QTL) analysis in 128 C57BL/6J x DBA/2J-F2 mice combined with PheQTL and trait covariance analysis in GeneNetwork2 using legacy BXD-RI trait datasets. We identified a QTL on Chromosome 18 influencing changes in body weight across days in females (log of the odds [LOD] = 6.3; 1.5-LOD: 3-12 cM) that contains the candidate gene Zeb1. We also identified a sex-combined QTL influencing initial palatable food intake on Chromosome 5 (LOD = 5.8; 1.5-LOD: 21-28 cM) that contains the candidate gene Lcorl and a second QTL influencing escalated palatable food intake on Chromosome 6 in males (LOD = 5.4; 1.5-LOD: 50-59 cM) that contains the candidate genes Adipor2 and Plxnd1. Finally, we identified a suggestive QTL in females for slope of BLE on distal Chromosome 18 (LOD = 4.1; p = 0.055; 1.5-LOD: 23-35 cM). Future studies will use BXD-RI strains to fine map loci and support candidate gene nomination for gene editing.

Assessment of Imputation from Low-Pass Sequencing to Predict Merit of Beef Steers

Decreasing costs are making low coverage sequencing with imputation to a comprehensive reference panel an attractive alternative to obtain functional variant genotypes that can increase the accuracy of genomic prediction. To assess the potential of low-pass sequencing, genomic sequence of 77 steers sequenced to >10X coverage was downsampled to 1X and imputed to a reference of 946 cattle representing multiple Bos taurus and Bos indicus-influenced breeds. Genotypes for nearly 60 million variants detected in the reference were imputed from the downsampled sequence. The imputed genotypes strongly agreed with the SNP array genotypes (r¯=0.99) and the genotypes called from the transcript sequence (r¯=0.97). Effects of BovineSNP50 and GGP-F250 variants on birth weight, postweaning gain, and marbling were solved without the steers' phenotypes and genotypes, then applied to their genotypes, to predict the molecular breeding values (MBV). The steers' MBV were similar when using imputed and array genotypes. Replacing array variants with functional sequence variants might allow more robust MBV. Imputation from low coverage sequence offers a viable, low-cost approach to obtain functional variant genotypes that could improve genomic prediction.

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 and pathway analysis of carcass and meat quality traits in Piemontese young bulls

A key concern in beef production is how to improve carcass and meat quality traits. Identifying the genomic regions and biological pathways that contribute to explaining variability in these traits is of great importance for selection purposes. In this study, genome wide-association (GWAS) and pathway-based analyses of carcass traits (age at slaughter (AS), carcass weight (CW), carcass daily gain (CDG), conformation score and rib-eye muscle area) and meat quality traits (pH, Warner-Bratzler shear force, purge loss, cooking loss and colour parameters (lightness, redness, yellowness, chroma, hue)) were conducted using genotype data from the 'GeneSeek Genomic Profiler Bovine LD' array in a cohort of 1166 double-muscled Piemontese beef cattle. The genome wide-association analysis was based on the GRAMMAR-GC approach and identified 37 significant single nucleotide polymorphisms (SNPs), which were associated with 12 traits (P<5 × 10-5). In particular, 14 SNPs associated with CW, CDG and AS were located at 38.57 to 38.94 Mb on Bos taurus autosome 6 and mapped within four genes, that is, Leucine Aminopeptidase 3, Family with Sequence Similarity 184 Member B, Non-SMC Condensin I Complex Subunit G and Ligand-Dependent Nuclear Receptor Corepressor-Like. Strong pairwise linkage disequilibrium was found in this region. For meat quality traits, most associations were 1 SNP per trait, except for a signal on BTA25 (at ~11.96 Mb), which was significant for four of the five meat colour parameters assessed. Gene-set enrichment analyses yielded significant results for six traits (right-sided hypergeometric test, false discovery rate <0.05). In particular, several pathways related to transmembrane transport (i.e., oxygen, calcium, ion and cation) were overrepresented for meat colour parameters. The results obtained provide useful information for genomic selection for beef production and quality in the Piemontese breed.

Liver proteomics unravel the metabolic pathways related to Feed Efficiency in beef cattle

Improving nutrient utilization efficiency is essential for livestock, given the current scenario of increasing demand for animal protein and sustainable resource use. In this context, understanding the biology of feed efficiency (FE) in beef cattle allows the development of markers for identification and selection of best animals for animal production. Thus, 98 young Nellore bulls were evaluated for FE and at the end of the experiment liver samples from six High Feed Efficient (HFE) and six Low Feed Efficient (LFE) animals were collected for protein extraction, digestion and analysis by HPLC-MS/MS. Data were analyzed for differential abundant proteins (DAPs), protein networks, and functional enrichment. Serum endotoxin was also quantified. We found 42 DAPs and 3 protein networks significantly related to FE. The main pathways associated with FE were: microbial metabolism; biosynthesis of fatty acids, amino acids and vitamins; glycolysis/gluconeogenesis; xenobiotic metabolism and; antigen processing and presentation. Serum endotoxins were significantly higher in LFE animals supporting the results. Therefore, the findings presented here confirmed the altered hepatic metabolism and pronounced hepatic inflammation in LFE animals supporting that the increased bacterial load is at least in part responsible for the hepatic lesions and inflammation in LFE animals.

Evaluation of Linkage Disequilibrium, Effective Population Size and Haplotype Block Structure in Chinese Cattle

Simple Summary

Evaluation of the population structure and linkage disequilibrium can offer important insights to fully understand the genetic diversity and population history of cattle, which can enable us to appropriately design and implement GWAS and GS in cattle. In this study, we characterized the extent of genome-wide LD and the haplotype block structure, and estimated the persistence of phase of Chinese indigenous cattle with Illumina BovineHD BeadChip. According to our study, 58K, 87K, 95K, 52K, and 52K markers would be necessary for SCHC, NCC, SWC, SIM, and WAG, respectively, in the implementation of GWAS and GS and combining a multipopulation with high persistence of phase is feasible for the implication of genomic selection for Chinese beef cattle.

Abstract

Understanding the linkage disequilibrium (LD) across the genome, haplotype structure, and persistence of phase between breeds can enable us to appropriately design and implement the genome-wide association (GWAS) and genomic selection (GS) in beef cattle. We estimated the extent of genome-wide LD, haplotype block structure, and the persistence of phase in 10 Chinese cattle population using high density BovinHD BeadChip. The overall LD measured by r² between adjacent SNPs were 0.60, 0.67, 0.58, 0.73, and 0.71 for South Chinese cattle (SCHC), North Chinese cattle (NCC), Southwest Chinese cattle (SWC), Simmental (SIM), and Wagyu (WAG). The highest correlation (0.53) for persistence of phase across groups was observed for SCHC vs. SWC at distances of 0–50 kb, while the lowest correlation was 0.13 for SIM vs. SCHC at the same distances. In addition, the estimated current effective population sizes were 27, 14, 31, 34, and 43 for SCHC, NCC, SWC, SIM, and WAG, respectively. Our result showed that 58K, 87K, 95K, 52K, and 52K markers were required for implementation of GWAS and GS in SCHC, NCC, SWC, SIM, and WAG, respectively. Also, our findings suggested that the implication of genomic selection for multipopulation with high persistence of phase is feasible for Chinese cattle.

Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters

Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.

Genome-wide definition of selective sweeps reveals molecular evidence of trait-driven domestication among elite goat (Capra species) breeds for the production of dairy, cashmere, and meat

Background

The domestication of wild goats and subsequent intensive trait-driven crossing, inbreeding, and selection have led to dramatic phenotypic purification and intermediate breeds for the high-quality production of dairy, cashmere wool, and meat. Genomic resequencing provides a powerful means for the direct identification of trait-associated sequence variations that underlie molecular mechanisms of domestication.

Results

Here, we report our effort to define such variations based on data from domestic goat breeds (Capra aegagrus hircus; five each) selected for dairy, cashmere, and meat production in reference to their wild ancestors, the Sindh ibex (Capra aegagrus blythi; two) and the Markhor (Capra falconeri; two). Using ∼24 million high-quality single nucleotide polymorphisms (SNPs), ∼1.9 million insertions/deletions, and 2,317 copy number variations, we define SNP-desert-associated genes (SAGs), domestic-associated genes (DAGs), and trait-associated genes (TAGs) and attempt to associate them with quantitative trait loci (QTL), domestication, and agronomic traits. A greater majority of SAGs shared by all domestic breeds are classified into Gene Ontology categories of metabolism and cell cycle. DAGs, together with some SAGs, are most relevant to behavior, immunity, and trait specificity. Whereas, TAGs such as growth differentiation factor 5 and fibroblast growth factor 5 for bone and hair growth, respectively, appear to be directly involved in growth regulation.

Conclusions

When investigating the divergence of Capra populations, the sequence variations and candidate function-associated genes we have identified provide valuable molecular markers for trait-driven genetic mapping and breeding.

Genome-wide association study for feed efficiency traits using SNP and haplotype models

Feed costs comprise the majority of variable expenses in beef cattle systems making feed efficiency an important economic consideration within the beef industry. Due to the expense of recording individual feed-intake phenotypes, a genomic-enabled approach could be advantageous toward improving this economically relevant trait complex. A genome-wide association study (GWAS) was performed using 748 crossbred steers and heifers representing seven sire breeds with phenotypes for ADG and ADFI. Animals were genotyped with the BovineSNP50v2 BeadChip containing approximately 54,000 SNP. Both traits were analyzed using univariate SNP-based (BayesC) and haplotype-based (BayesIM) models and jointly using BayesIM to perform a bivariate GWAS. For BayesIM, a hidden Markov model (HMM) of haplotype segments of variable length was built where haplotypes were mapped to clusters based on local similarity. The estimated HMM was then used to assign haplotype cluster genotypes, instead of SNP genotypes, as latent covariates in a Bayesian mixture model. The number of haplotype clusters at each location was assumed to be either 8 (BayesIM8) or 16 (BayesIM16). A total of three univariate analyses for each trait and two bivariate analyses were performed. Posterior SD (PSD) for ADG were 0.28 (0.08), 0.37 (0.11), 0.37 (0.11), 0.35 (0.11), and 0.35 (0.12) for BayesC, BayesIM8, BayesIM16, BayesIM8 bivariate, and BayesIM16 bivariate, respectively. ADFI PSD were 0.30 (0.07), 0.44 (0.13), 0.42 (0.12), 0.38 (0.10), and 0.38 (0.10) for the same models. The top 1% of 1-Mb windows that explained the largest fraction of genetic variation in common between univariate SNP and haplotype models ranged from 24% to 40% and from 20% to 32% for ADG and ADFI, respectively. Spearmen rank correlations between molecular breeding values from SNP and haplotype-based models in the training data were similar for both traits (>0.96) suggesting that either model would lead to similar rankings of animals, although resolution of potential QTL appeared to be greater for BayesIM.

Rumen bacterial community structure impacts feed efficiency in beef cattle

The importance of the rumen microbiota on nutrient cycling to the animal is well recognized; however, our understanding of the influence of the rumen microbiome composition on feed efficiency is limited. The rumen microbiomes of two large animal cohorts (125 heifers and 122 steers) were characterized to identify specific bacterial members (operational taxonomic units [OTUs]) associated with feed efficiency traits (ADFI, ADG, and G:F) in beef cattle. The heifer and steer cohorts were fed a forage-based diet and a concentrate-based diet, respectively. A rumen sample was obtained from each animal via esophageal tubing and bacterial community composition was determined through 16S rRNA gene sequencing of the V4 region. Based on a regression approach that used individual performance measures, animals were classified into divergent feed efficiency groups. Within cohort, an extreme set of 16 animals from these divergent groups was selected as a discovery population to identify differentially abundant OTUs across the rumen bacterial communities. The remaining samples from each cohort were selected to perform forward stepwise regressions using the differentially abundant OTUs as explanatory variables to distinguish predictive OTUs for the feed efficiency traits and to quantify the OTUs collective impact on feed efficiency phenotypes. OTUs belonging to the families Prevotellaceae and Victivallaceae were present across models for heifers, whereas OTUs belonging to the families Prevotellaceae and Lachnospiraceae were present across models for steers. Within the heifer cohort, models explained 19.3%, 25.3%, and 19.8% of the variation for ADFI, ADG, and G:F, respectively. Within the steer cohort, models explained 27.7%, 32.5%, and 26.9% of the variation for ADFI, ADG, and G:F, respectively. Overall, this study suggests a substantial role of the rumen microbiome on feed efficiency responses.

Genome-wide mapping of quantitative trait loci in admixed populations using mixed linear model and Bayesian multiple regression analysis

Background

Population stratification and cryptic relationships have been the main sources of excessive false-positives and false-negatives in population-based association studies. Many methods have been developed to model these confounding factors and minimize their impact on the results of genome-wide association studies. In most of these methods, a two-stage approach is applied where: (1) methods are used to determine if there is a population structure in the sample dataset and (2) the effects of population structure are corrected either by modeling it or by running a separate analysis within each sub-population. The objective of this study was to evaluate the impact of population structure on the accuracy and power of genome-wide association studies using a Bayesian multiple regression method.

Methods

We conducted a genome-wide association study in a stochastically simulated admixed population. The genome was composed of six chromosomes, each with 1000 markers. Fifteen segregating quantitative trait loci contributed to the genetic variation of a quantitative trait with heritability of 0.30. The impact of genetic relationships and breed composition (BC) on three analysis methods were evaluated: single marker simple regression (SMR), single marker mixed linear model (MLM) and Bayesian multiple-regression analysis (BMR). Each method was fitted with and without BC. Accuracy, power, false-positive rate and the positive predictive value of each method were calculated and used for comparison.

Results

SMR and BMR, both without BC, were ranked as the worst and the best performing approaches, respectively. Our results showed that, while explicit modeling of genetic relationships and BC is essential for models SMR and MLM, BMR can disregard them and yet result in a higher power without compromising its false-positive rate.

Conclusions

This study showed that the Bayesian multiple-regression analysis is robust to population structure and to relationships among study subjects and performs better than a single marker mixed linear model approach.

Searching for new loci and candidate genes for economically important traits through gene-based association analysis of Simmental cattle

Single-marker genome-wide association study (GWAS) is a convenient strategy of genetic analysis that has been successful in detecting the association of a number of single-nucleotide polymorphisms (SNPs) with quantitative traits. However, analysis of individual SNPs can only account for a small proportion of genetic variation and offers only limited knowledge of complex traits. This inadequacy may be overcome by employing a gene-based GWAS analytic approach, which can be considered complementary to the single-SNP association analysis. Here we performed an initial single-SNP GWAS for bone weight (BW) and meat pH value with a total of 770,000 SNPs in 1141 Simmental cattle. Additionally, 21836 cattle genes collected from the Ensembl Genes 83 database were analyzed to find supplementary evidence to support the importance of gene-based association study. Results of the single SNP-based association study showed that there were 11 SNPs significantly associated with bone weight (BW) and two SNPs associated with meat pH value. Interestingly, all of these SNPs were located in genes detected by the gene-based association study.

A Review of Selected Genes with Known Effects on Performance and Health of Cattle

There are genetic conditions that influence production in dairy and beef cattle. The objective of this review was to describe relevant genetic conditions that have been associated with productivity and health in cattle. Genes or genomic regions that have been identified as a candidate for the condition will be included, and the genetic basis of the condition will be defined. Genes and genetic conditions included in this review are bovine leukocyte adhesion deficiency, deficiency of the uridine monophosphate synthase, bovine chronic interstitial nephritis, horn development, myostatin, complex vertebral malformation, leptin, osteopetrosis, apoptosis peptide activating factor 1, chondrodysplastic dwarfism, caseins, calpastatin, umbilical hernia, lactoglobulin, citrullinemia, cholesterol deficiency, prions, thyroglobulin, diacylglycerol acyltransferase, syndactyly, maple syrup urine disease, slick hair, Factor XI deficiency, and μ-Calpain. This review is not meant to be comprehensive, and relevant information is provided to ascertain genetic markers associated with the conditions.

Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS

Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u²) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u² plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u²) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation. The ability to detect the simulated QTL was also investigated. For most of the QTL scenarios, the accuracies obtained with all WssGBLUP procedures were higher compared to those from BayesB and BayesC, partly due to automatic inclusion of parent average in the former. Manhattan plots had higher resolution with 5 and 100 QTL. Using a common weight for a window of 20 SNP that sums or averages the SNP variance enhances accuracy of predicting GEBV and provides accurate estimation of marker effects.

Differential gene expression in the duodenum, jejunum and ileum among crossbred beef steers with divergent gain and feed intake phenotypes

Small intestine mass and cellularity were previously associated with cattle feed efficiency. The small intestine is responsible for the digestion of nutrients and absorption of fatty acids, amino acids and carbohydrates, and it contributes to the overall feed efficiency of cattle. The objective of this study was to evaluate transcriptome differences among the small intestine from cattle with divergent gain and feed intake. Animals most divergent from the bivariate mean in each of the four phenotypic Cartesian quadrants for gain × intake were selected, and the transcriptomes of duodenum, jejunum and ileum were evaluated. Gene expression analyses were performed comparing high gain vs. low gain animals, high intake vs. low intake animals and each of the phenotypic quadrants to all other groups. Genes differentially expressed within the high gain-low intake and low gain-high intake groups of animals included those involved in immune function and inflammation in all small intestine sections. The high gain-high intake group differed from the high gain-low intake group by immune response genes in all sections of the small intestine. In all sections of small intestine, animals with low gain-low intake displayed greater abundance of heat-shock genes compared to other groups. Several over-represented pathways were identified. These include the antigen-processing/presentation pathway in high gain animals and PPAR signaling, starch/sucrose metabolism, retinol metabolism and melatonin degradation pathways in the high intake animals. Genes with functions in immune response, inflammation, stress response, influenza pathogenesis and melatonin degradation pathways may have a relationship with gain and intake in beef steers.

Identification of Gene Networks for Residual Feed Intake in Angus Cattle Using Genomic Prediction and RNA-seq

Improvement in feed conversion efficiency can improve the sustainability of beef cattle production, but genomic selection for feed efficiency affects many underlying molecular networks and physiological traits. This study describes the differences between steer progeny of two influential Angus bulls with divergent genomic predictions for residual feed intake (RFI). Eight steer progeny of each sire were phenotyped for growth and feed intake from 8 mo. of age (average BW 254 kg, with a mean difference between sire groups of 4.8 kg) until slaughter at 14-16 mo. of age (average BW 534 kg, sire group difference of 28.8 kg). Terminal samples from pituitary gland, skeletal muscle, liver, adipose, and duodenum were collected from each steer for transcriptome sequencing. Gene expression networks were derived using partial correlation and information theory (PCIT), including differentially expressed (DE) genes, tissue specific (TS) genes, transcription factors (TF), and genes associated with RFI from a genome-wide association study (GWAS). Relative to progeny of the high RFI sire, progeny of the low RFI sire had -0.56 kg/d finishing period RFI (P = 0.05), -1.08 finishing period feed conversion ratio (P = 0.01), +3.3 kg^0.75 finishing period metabolic mid-weight (MMW; P = 0.04), +28.8 kg final body weight (P = 0.01), -12.9 feed bunk visits per day (P = 0.02) with +0.60 min/visit duration (P = 0.01), and +0.0045 carcass specific gravity (weight in air/weight in air-weight in water, a predictor of carcass fat content; P = 0.03). RNA-seq identified 633 DE genes between sire groups among 17,016 expressed genes. PCIT analysis identified >115,000 significant co-expression correlations between genes and 25 TF hubs, i.e. controllers of clusters of DE, TS, and GWAS SNP genes. Pathway analysis suggests low RFI bull progeny possess heightened gut inflammation and reduced fat deposition. This multi-omics analysis shows how differences in RFI genomic breeding values can impact other traits and gene co-expression networks.

Copy number variations and genome-wide associations reveal putative genes and metabolic pathways involved with the feed conversion ratio in beef cattle

The use of genome-wide association results combined with other genomic approaches may uncover genes and metabolic pathways related to complex traits. In this study, the phenotypic and genotypic data of 1475 Nellore (Bos indicus) cattle and 941,033 single nucleotide polymorphisms (SNPs) were used for genome-wide association study (GWAS) and copy number variations (CNVs) analysis in order to identify candidate genes and putative pathways involved with the feed conversion ratio (FCR). The GWAS was based on the Bayes B approach analyzing genomic windows with multiple regression models to estimate the proportion of genetic variance explained by each window. The CNVs were detected with PennCNV software using the log R ratio and B allele frequency data. CNV regions (CNVRs) were identified with CNVRuler and a linear regression was used to associate CNVRs and the FCR. Functional annotation of associated genomic regions was performed with the Database for Annotation, Visualization and Integrated Discovery (DAVID) and the metabolic pathways were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG). We showed five genomic windows distributed over chromosomes 4, 6, 7, 8, and 24 that explain 12 % of the total genetic variance for FCR, and detected 12 CNVRs (chromosomes 1, 5, 7, 10, and 12) significantly associated [false discovery rate (FDR) < 0.05] with the FCR. Significant genomic regions (GWAS and CNV) harbor candidate genes involved in pathways related to energetic, lipid, and protein metabolism. The metabolic pathways found in this study are related to processes directly connected to feed efficiency in beef cattle. It was observed that, even though different genomic regions and genes were found between the two approaches (GWAS and CNV), the metabolic processes covered were related to each other. Therefore, a combination of the approaches complement each other and lead to a better understanding of the FCR.

Linkage disequilibrium, persistence of phase and effective population size estimates in Hereford and Braford cattle

Background

The existence of moderate to high levels of linkage disequilibrium (LD) between genetic markers and quantitative trait loci (QTL) affecting traits of interest is fundamental for the success of genome-wide association (GWAS) and genomic selection (GS) studies. Knowledge about the extent and the pattern of LD in livestock populations is essential to determine the density of single nucleotide polymorphisms (SNP) required for accurate GWAS and GS. Moreover, observed LD is related to historical effective population sizes (N_e), and can provide insights into the genetic diversity history of populations. Estimates of the consistency of linkage phase across breeds (R_H,B) can be used to determine if there is sufficient relationship to use pooled reference populations in multi-breed GS programs. The objective of this study was to estimate LD levels, persistence of phase and effective population size in Hereford and Braford cattle populations sampled in Brazil.

Results

Mean LD estimates, measured using the squared correlation of alleles at two loci (r²), obtained between adjacent SNP across all chromosomes were 0.21 ± 0.27 for Herefords (391 samples with 41,241 SNP) and 0.16 ± 0.22 for Brafords (2044 samples and 41,207 SNP). Estimated r² was > 0.2 and 0.3, respectively, for 34 and 25 % of adjacent markers in Herefords, and 26 and 17 % in Brafords. Estimated N_e for Brafords and Herefords at the current generation was 220 and 153 individuals, respectively. The two breeds demonstrated moderate to strong persistence of phase at all distances (R_H,B = 0.53 to 0.97). The largest phase correlations were found in the 0 to 50 Kb bins (R_H,B = 0.92 to 0.97). Estimated LD decreased rapidly with increasing distance between SNP, however, useful linkage for GWAS and GS (r² > 0.2) was found spanning to ~50 Kb.

Conclusions

Panels containing about 50,000 and 150,000 SNP markers are necessary to detect minimal levels of LD between adjacent markers that would be useful for GWAS and GS studies to Hereford and Braford breeds, respectively. Markers are expected to be linked to the same QTL alleles in distances < 50 Kb in both populations due to observed high persistence of phase levels.

Differences in transcript abundance of genes on BTA15 located within a region associated with gain in beef steers

Using results from a previous GWAS, we chose to evaluate seven genes located within a 229Kb region on BTA15 for variation in RNA transcript abundance in a library of tissue samples that included adipose, liver, rumen papillae, spleen, muscle, and small intestine epithelial layers from the duodenum, ileum and jejunum collected from steers (n = 14) with positive and negative residual GN near mean dry matter intake (DMI). The genes evaluated were two olfactory receptor-like genes (LOC525033 and LOC618173), RRM1, STIM1, RHOG, PGAP2, and NUP98. The rumen papillae transcript abundance of RHOG was positively correlated with residual GN (P = 0.02) and ruminal STIM1 exhibited a trend towards an association with residual GN (P = 0.08). The transcript abundance of one olfactory receptor (LOC618173) in the ileum was also positively associated with residual GN (P = 0.02) and PGAP2 and LOC525033 in the ileum displayed trends for association with GN (P ≤ 0.1). To further evaluate the differential expression detected in the ileum and rumen of these animals, the transcript abundance of STIM1 and RHOG in the rumen and of PGAP2 and the olfactory receptors in the ileum were assessed in an additional group of 32 animals with divergent average daily gain (ADG) and average daily feed intake (ADFI) collected over two groups. The olfactory receptor, LOC525033, was not expressed in the ileum for the majority of these animals. Only RHOG showed a slight, but non-significant trend towards greater expression in animals with greater gain. We have detected differences in the transcript abundance of genes within this region in the rumen and ileum of animals selected for greater and less residual gain; however, we were unable to validate the expression of these genes in the larger group of cattle possibly due to the differences in phenotype or contemporary group.

QTLs associated with dry matter intake, metabolic mid-test weight, growth and feed efficiency have little overlap across 4 beef cattle studies

Background

The identification of genetic markers associated with complex traits that are expensive to record such as feed intake or feed efficiency would allow these traits to be included in selection programs. To identify large-effect QTL, we performed a series of genome-wide association studies and functional analyses using 50 K and 770 K SNP genotypes scored in 5,133 animals from 4 independent beef cattle populations (Cycle VII, Angus, Hereford and Simmental × Angus) with phenotypes for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake.

Results

A total of 5, 6, 11 and 10 significant QTL (defined as 1-Mb genome windows with Bonferroni-corrected P-value <0.05) were identified for average daily gain, dry matter intake, metabolic mid-test body weight and residual feed intake, respectively. The identified QTL were population-specific and had little overlap across the 4 populations. The pleiotropic or closely linked QTL on BTA 7 at 23 Mb identified in the Angus population harbours a promising candidate gene ACSL6 (acyl-CoA synthetase long-chain family member 6), and was the largest effect QTL associated with dry matter intake and mid-test body weight explaining 10.39% and 14.25% of the additive genetic variance, respectively. Pleiotropic or closely linked QTL associated with average daily gain and mid-test body weight were detected on BTA 6 at 38 Mb and BTA 7 at 93 Mb confirming previous reports. No QTL for residual feed intake explained more than 2.5% of the additive genetic variance in any population. Marker-based estimates of heritability ranged from 0.21 to 0.49 for residual feed intake across the 4 populations.

Conclusions

This GWAS study, which is the largest performed for feed efficiency and its component traits in beef cattle to date, identified several large-effect QTL that cumulatively explained a significant percentage of additive genetic variance within each population. Differences in the QTL identified among the different populations may be due to differences in power to detect QTL, environmental variation, or differences in the genetic architecture of trait variation among breeds. These results enhance our understanding of the biology of growth, feed intake and utilisation in beef cattle.

Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens

Background

Growth and carcass traits are very important traits for broiler chickens. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool for such traits because of its accurate prediction of breeding values of animals without own phenotypic information. This study investigated the efficiency of genomic prediction in Chinese triple-yellow chickens. As a new line, Chinese triple-yellow chicken was developed by cross-breeding and had a small effective population. Two growth traits and three carcass traits were analyzed: body weight at 6 weeks, body weight at 12 weeks, eviscerating percentage, breast muscle percentage and leg muscle percentage.

Results

Genomic prediction was assessed using a 4-fold cross-validation procedure for two validation scenarios. In the first scenario, each test data set comprised two half-sib families (family sample) and the rest represented the reference data. In the second scenario, the whole data were randomly divided into four subsets (random sample). In each fold of validation, one subset was used as the test data and the others as the reference data in each single validation. Genomic breeding values were predicted using a genomic best linear unbiased prediction model, a Bayesian least absolute shrinkage and selection operator model, and a Bayesian mixture model with four distributions. The accuracy of genomic estimated breeding value (GEBV) was measured as the correlation between GEBV and the corrected phenotypic value. Using the three models, the correlations ranged from 0.448 to 0.468 for the two growth traits and from 0.176 to 0.255 for the three carcass traits in the family sample scenario, and were between 0.487 and 0.536 for growth traits and between 0.312 and 0.430 for carcass traits in the random sample scenario. The differences in the prediction accuracies between the three models were very small; the Bayesian mixture model was slightly more accurate. According to the results from the random sample scenario, the accuracy of GEBV was 0.197 higher than the conventional pedigree index, averaged over the five traits.

Conclusions

The results indicated that genomic selection could greatly improve the accuracy of selection in chickens, compared with conventional selection. Genomic selection for growth and carcass traits in broiler chickens is promising.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-014-0110-y) contains supplementary material, which is available to authorized users.

Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds

Background

The availability of high-density SNP assays including the BovineSNP50 (50 K) enables the identification of novel quantitative trait loci (QTL) and improvement of the resolution of the locations of previously mapped QTL. We performed a series of genome-wide association studies (GWAS) using 50 K genotypes scored in 18,274 animals from 10 US beef cattle breeds with observations for twelve body weights, calving ease and carcass traits.

Results

A total of 159 large-effects QTL (defined as 1-Mb genome windows explaining more than 1% of additive genetic variance) were identified. In general, more QTL were identified in analyses with bigger sample sizes. Four large-effect pleiotropic or closely linked QTLs located on BTA6 at 37–42 Mb (primarily at 38 Mb), on BTA7 at 93 Mb, on BTA14 at 23–26 Mb (primarily at 25 Mb) and on BTA20 at 4 Mb were identified in more than one breed. Several breed-specific large-effect pleiotropic or closely linked QTL were also identified. Some identified QTL regions harbor genes known to have large effects on a variety of traits in cattle such as PLAG1 and MSTN and others harbor promising candidate genes including NCAPG, ARRDC3, ERGIC1, SH3PXD2B, HMGA2, MSRB3, LEMD3, TIGAR, SEPT7, and KIRREL3. Gene ontology analysis revealed that genes involved in ossification and in adipose tissue development were over-represented in the identified pleiotropic QTL. Also, the MAPK signaling pathway was identified as a common pathway affected by the genes located near the pleiotropic QTL.

Conclusions

This largest GWAS ever performed in beef cattle, led us to discover several novel across-breed and breed-specific large-effect pleiotropic QTL that cumulatively account for a significant percentage of additive genetic variance (e.g. more than a third of additive genetic variance of birth and mature weights; and calving ease direct in Hereford). These results will improve our understanding of the biology of growth and body composition in cattle.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-442) contains supplementary material, which is available to authorized users.

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.

Single nucleotide polymorphisms for feed efficiency and performance in crossbred beef cattle

Background

This study was conducted to: (1) identify new SNPs for residual feed intake (RFI) and performance traits within candidate genes identified in a genome wide association study (GWAS); (2) estimate the proportion of variation in RFI explained by the detected SNPs; (3) estimate the effects of detected SNPs on carcass traits to avoid undesirable correlated effects on these economically important traits when selecting for feed efficiency; and (4) map the genes to biological mechanisms and pathways. A total number of 339 SNPs corresponding to 180 genes were tested for association with phenotypes using a single locus regression (SLRM) and genotypic model on 726 and 990 crossbred animals for feed efficiency and carcass traits, respectively.

Results

Strong evidence of associations for RFI were located on chromosomes 8, 15, 16, 18, 19, 21, and 28. The strongest association with RFI (P = 0.0017) was found with a newly discovered SNP located on BTA 8 within the ELP3 gene. SNPs rs41820824 and rs41821600 on BTA 16 within the gene HMCN1 were strongly associated with RFI (P = 0.0064 and P = 0.0033, respectively). A SNP located on BTA 18 within the ZNF423 gene provided strong evidence for association with RFI (P = 0.0028). Genomic estimated breeding values (GEBV) from 98 significant SNPs were moderately correlated (0.47) to the estimated breeding values (EBVs) from a mixed animal model. The significant (P < 0.05) SNPs (98) explained 26% of the genetic variance for RFI. In silico functional analysis for the genes suggested 35 and 39 biological processes and pathways, respectively for feed efficiency traits.

Conclusions

This study identified several positional and functional candidate genes involved in important biological mechanisms associated with feed efficiency and performance. Significant SNPs should be validated in other populations to establish their potential utilization in genetic improvement programs.

Adipose and muscle tissue gene expression of two genes (NCAPG and LCORL) located in a chromosomal region associated with cattle feed intake and gain

A region on bovine chromosome 6 has been implicated in cattle birth weight, growth, and length. Non-SMC conodensin I complex subunit G (NCAPG) and ligand dependent nuclear receptor corepressor-like protein (LCORL) are positional candidate genes within this region. Previously identified genetic markers in both genes were associated with average daily gain (ADG) and average daily feed intake (ADFI) in a crossbred population of beef steers. These markers were also associated with hot carcass weight, ribeye area and adjusted fat thickness suggesting that they may have a role in lean muscle growth and/or fat deposition. The purpose of this study was to determine whether the transcript abundance of either of these genes in cattle adipose and muscle tissue was associated with variation in feed intake and average daily gain phenotypes. Transcript abundance for NCAPG and LCORL in adipose and muscle tissue was measured in heifers (adipose only), cows and steers using real-time polymerase chain reaction. In the adipose tissue from cows and heifers, a negative correlation between LCORL transcript abundance and ADFI were detected (P = 0.05). In the muscle tissue from cows, transcript abundance of NCAPG was associated with ADG (r = 0.26; P = 0.009). A positive correlation between LCORL transcript abundance from muscle tissue of steers and ADFI was detected (P = 0.04). LCORL protein levels in the muscle of steers were investigated and were associated with ADFI (P = 0.01). These data support our earlier genetic associations with ADFI and ADG within this region and represent the potential for biological activity of these genes in the muscle and adipose tissues of beef cattle; however, they also suggest that sex, age and/or nutrition-specific interactions may affect the expression of NCAPG and LCORL in these tissues.

Bivariate genome-wide association analysis of the growth and intake components of feed efficiency

Single nucleotide polymorphisms (SNPs) associated with average daily gain (ADG) and dry matter intake (DMI), two major components of feed efficiency in cattle, were identified in a genome-wide association study (GWAS). Uni- and multi-SNP models were used to describe feed efficiency in a training data set and the results were confirmed in a validation data set. Results from the univariate and bivariate analyses of ADG and DMI, adjusted by the feedlot beef steer maintenance requirements, were compared. The bivariate uni-SNP analysis identified (P-value <0.0001) 11 SNPs, meanwhile the univariate analyses of ADG and DMI identified 8 and 9 SNPs, respectively. Among the six SNPs confirmed in the validation data set, five SNPs were mapped to KDELC2, PHOX2A, and TMEM40. Findings from the uni-SNP models were used to develop highly accurate predictive multi-SNP models in the training data set. Despite the substantially smaller size of the validation data set, the training multi-SNP models had slightly lower predictive ability when applied to the validation data set. Six Gene Ontology molecular functions related to ion transport activity were enriched (P-value <0.001) among the genes associated with the detected SNPs. The findings from this study demonstrate the complementary value of the uni- and multi-SNP models, and univariate and bivariate GWAS analyses. The identified SNPs can be used for genome-enabled improvement of feed efficiency in feedlot beef cattle, and can aid in the design of empirical studies to further confirm the associations.

Single nucleotide polymorphisms and haplotypes associated with feed efficiency in beef cattle

BACKGROUND

General, breed- and diet-dependent associations between feed efficiency in beef cattle and single nucleotide polymorphisms (SNPs) or haplotypes were identified on a population of 1321 steers using a 50 K SNP panel. Genomic associations with traditional two-step indicators of feed efficiency - residual feed intake (RFI), residual average daily gain (RADG), and residual intake gain (RIG) - were compared to associations with two complementary one-step indicators of feed efficiency: efficiency of intake (EI) and efficiency of gain (EG). Associations uncovered in a training data set were evaluated on independent validation data set. A multi-SNP model was developed to predict feed efficiency. Functional analysis of genes harboring SNPs significantly associated with feed efficiency and network visualization aided in the interpretation of the results.

RESULTS

For the five feed efficiency indicators, the numbers of general, breed-dependent, and diet-dependent associations with SNPs (P-value < 0.0001) were 31, 40, and 25, and with haplotypes were six, ten, and nine, respectively. Of these, 20 SNP and six haplotype associations overlapped between RFI and EI, and five SNP and one haplotype associations overlapped between RADG and EG. This result confirms the complementary value of the one and two-step indicators. The multi-SNP models included 89 SNPs and offered a precise prediction of the five feed efficiency indicators. The associations of 17 SNPs and 7 haplotypes with feed efficiency were confirmed on the validation data set. Nine clusters of Gene Ontology and KEGG pathway categories (mean P-value < 0.001) including, 9nucleotide binding; ion transport, phosphorous metabolic process, and the MAPK signaling pathway were overrepresented among the genes harboring the SNPs associated with feed efficiency.

CONCLUSIONS

The general SNP associations suggest that a single panel of genomic variants can be used regardless of breed and diet. The breed- and diet-dependent associations between SNPs and feed efficiency suggest that further refinement of variant panels require the consideration of the breed and management practices. The unique genomic variants associated with the one- and two-step indicators suggest that both types of indicators offer complementary description of feed efficiency that can be exploited for genome-enabled selection purposes.

Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models

Different methods for genomic evaluation were compared for accuracy and feasibility of evaluation using phenotypic, pedigree, and genomic information for a trait influenced by a maternal effect. A simulated population was constructed that included 15,800 animals in 5 generations. Genotypes from 45,000 SNP were available for 1,500 animals in the last 3 generations. Genotyped animals in the last generation had no phenotypes. Weaning weight data were simulated using an animal model with direct and maternal effects. Additive direct and maternal effects were considered either noncorrelated (formula in text) or negatively correlated (formula in text). Methods of analysis were traditional BLUP, BayesC using phenotypes and ignoring maternal effects (BayesCPR), BayesC using deregressed EBV (BayesCDEBV), and single-step genomic BLUP (ssGBLUP). Whereas BayesCPR can be used when phenotypes of only genotyped animals are available, BayesCDEBV can be used when BLUP EBV of genotyped animals are available, and ssGBLUP is suitable when genotypes, phenotypes, and pedigrees are jointly available. For all genotyped and young genotyped animals, mean accuracies from BayesCPR and BayesCDEBV were lower than accuracies from BLUP for direct and maternal effects. The differences in mean accuracy were greater when genetic correlation was negative. Gains in accuracy were observed when ssGBLUP was compared with BLUP; for the direct (maternal) effect the average gain was 0.01 (0.02) for all genotyped animals and 0.03 (0.02) for young genotyped animals without phenotypes. Similar gains were observed for 0 and negative genetic correlation. Accuracy with BayesCPR was affected by ignoring phenotypes of nongenotyped animals and maternal effect and by not accounting for parent average. Accuracy with BayesCDEBV was affected by approximations needed for deregression, not accounting for parent average, and sequential rather than simultaneous fitting of genomic and nongenomic information. Whereas BayesCDEBV presented a considerable bias, especially for maternal effect, ssGBLUP was unbiased for both effects. The computing time was 1 s for BLUP, 44 s for ssGBLUP, and over 2,000 s for BayesC. Greatest computational efficiency and accuracy of genomic prediction for a maternally affected trait was obtained when information from all nongenotyped but related individuals was included and phenotypes, pedigree, and genotypes were available and considered jointly. Increasing the gain in accuracy of genomic predictions obtained by ssGBLUP over BLUP may require an increase in the number of genotyped animals.

Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations

The reliability of genomic evaluations depends on the proportion of genetic variation explained by the DNA markers. In this study, we have estimated the proportion of variance in daughter trait deviations (DTDs) of dairy bulls explained by 45 993 genome wide single-nucleotide polymorphism (SNP) markers for 29 traits in Australian Holstein-Friesian dairy cattle. We compare these proportions to the proportion of variance in DTDs explained by the additive relationship matrix derived from the pedigree, as well as the sum of variance explained by both pedigree and marker information when these were fitted simultaneously. The proportion of genetic variance in DTDs relative to the total genetic variance (the total genetic variance explained by the genomic relationships and pedigree relationships when both were fitted simultaneously) varied from 32% for fertility to approximately 80% for milk yield traits. When fitting genomic and pedigree relationships simultaneously, the variance unexplained (i.e. the residual variance) in DTDs of the total variance for most traits was reduced compared to fitting either individually, suggesting that there is not complete overlap between the effects. The proportion of genetic variance accounted by the genomic relationships can be used to modify the blending equations used to calculate genomic estimated breeding value (GEBV) from direct genomic breeding value (DGV) and parent average. Our results, from a validation population of young dairy bulls with DTD, suggest that this modification can improve the reliability of GEBV by up to 5%.

Candidate genes and single nucleotide polymorphisms associated with variation in residual feed intake in beef cattle

The candidate gene approach was used to identify genes associated with residual feed intake (RFI) in beef steers. The approach uses prior knowledge of gene functions to predict their biological role in the variation observed in a trait. It is suited to identify genes associated with complex traits where each gene has a relatively small effect. First, positional candidate genes were identified within the genomic positions of previously reported QTL associated with component traits related to RFI such as dry matter intake (DMI), growth, feed conversion ratio (FCR), average daily gain (ADG), and energy balance. Secondly, the positional candidate genes were prioritized into functional candidate genes according to their biological functions and their relationship with the biological processes associated with RFI including carbohydrate, fat and protein metabolism, thermoregulation, immunity and muscle activity. Single nucleotide polymorphisms (SNPs) located within the functional candidate genes were identified using mRNA sequences and prioritized into functional classes such as non-synonymous (nsSNP), synonymous (sSNP) or intronic SNP. A total of 117 nsSNP were considered as functional SNP and genotyped in steers at the University of Alberta ranch in Kinsella. Multiple marker association analysis in ASReml was performed using RFI data obtained from 531 beef steers. Twenty-five SNP were significantly associated with RFI (P < 0.05) accounting for 19.7% of the phenotypic variation. Using SIFT program to predict the effect of the SNP on the function of the corresponding protein, 3 of the 25 SNP were predicted to cause a significant effect on protein function (P < 0.05). One of the 3 SNP was located in the GHR gene and was also associated with a significant effect on the tertiary structure of the GHR protein (P < 0.05) as modeled using SWISSModel software. Least square means for each genotype were estimated and an over-dominance effect was observed for the SNP located in the GHR, CAST, ACAD11 and UGT3A1 genes. In addition, 2 other SNP showed a dominance effect and 3 genes had an additive effect. Gene network analysis performed in Ingenuity pathway analysis (IPA) software (Ingenuity Systems, www.ingenuity.com) indicated that the significant genes were involved in biological pathways such as lipid, protein and energy metabolism, electron transport and membrane signaling. The genes in this study, if validated in other beef cattle populations, may be useful for marker assisted selection for feed efficiency.

Accuracies of direct genomic breeding values in Hereford beef cattle using national or international training populations

The objective of this study was to estimate accuracies of direct genomic breeding values (DGV) for nationally evaluated traits of 1,081 American (AMH), 100 Argentine (ARH), 75 Canadian (CAH), and 395 Uruguayan (URH) Hereford animals genotyped using the Illumina BovineSNP50 BeadChip. Deregressed EBV (DEBV) were used as observations in a weighted analysis to derive DGV using BayesB and BayesC methods. The AMH animals were clustered into 4 groups, using either K-means or random clustering. Cross validation was performed with the group not used in training providing validation of the accuracies of estimated DGV. Genomic predictions were also evaluated for AMH animals by training on older animals and validating on younger animals. Bivariate animal models were used for each trait to estimate genetic correlations between DEBV and DGV. Genomic predictions were separately evaluated for foreign animals from each country using marker estimates from training on AMH or pooled international data. Pedigree estimated breeding values were developed for AMH animals, using traditional, pedigree-based BLUP (PBLUP) for comparison purposes. Using BayesB (BayesC) method, the average simple correlations between DGV and DEBV in AMH animals was 0.24 (0.21), 0.39 (0.36), and 0.32 (0.30) when training and validation sets were formed by K-means clustering, random allocation or year of birth of the animals, respectively. Genetic correlations between DEBV and DGV ranged from 0.20 (0.18) to 0.52 (0.45) in AMH animals. The DGV from BayesB were more accurate than from BayesC for most traits in AMH animals. Genomic predictions for foreign animals were less accurate than those obtained in AMH animals. Among foreign animals, genomic predictions were more accurate for CAH animals, which reflect the greater use of AMH sires in CAH in comparison with ARH and URH populations. Small changes in accuracies of DGV were observed for foreign animals by using admixed training populations. On average, genomic predictions across countries were more accurate for CAH and URH animals using BayesB. On average, accuracies of genomic predictions using BayesB (BayesC) method were 66% (55%) greater than those obtained from PBLUP. These results demonstrate the feasibility of developing DGV for American Hereford beef cattle. However, foreign breeders, especially South American Hereford breeders, need to genotype more animals to obtain more accurate genomic predictions.

Identification of a short region on chromosome 6 affecting direct calving ease in Piedmontese cattle breed

Calving in cattle is affected by calf morphology and by dam characteristics. It is described by two different traits: maternal calving ease, which is the ability to generate dams with good physiological predisposition to calving, and direct calving ease, which is the ability to generate calves that are easily born. The aim of this study was to identify regions of cattle genome harboring genes possibly affecting direct calving ease in the Piedmontese cattle breed. A population of 323 bulls scored for direct calving ease (EBV) was analyzed by a medium-density SNP marker panel (54,001 SNPs) to perform a genome-wide scan. The strongest signal was detected on chromosome 6 between 37.8 and 38.7 Mb where 13 SNPs associated to direct calving ease were found. Three genes are located in this region: LAP3, encoding for a leucine aminopeptidase involved in the oxytocin hydrolysis; NCAPG, encoding for a non-SMC condensin I complex, which has been associated in cattle with fetal growth and carcass size; and LCORL, which has been associated to height in humans and cattle. To further confirm the results of the genome-wide scan we genotyped additional SNPs within these genes and analyzed their association with direct calving ease. The results of this additional analysis fully confirmed the findings of the GWAS and particularly indicated LAP3 as the most probable gene involved. Linkage Disequilibrium (LD) analysis showed high correlation between SNPs located within LAP3 and LCORL indicating a possible selection signature due either to increased fitness or breeders' selection for the trait.