Majid Khansefid

Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle.

Methane (CH) is a product of enteric fermentation in ruminants, and it represents around 17% of global CH emissions. There has been substantial effort from the livestock scientific community toward tools that can help reduce this percentage. One approach is to select for lower emitting animals. To achieve this, accurate genetic parameters and identification of the genomic basis of CH traits are required. Therefore, the objectives of this study were 1) to perform a genomewide association study to identify SNP associated with several CH traits in Angus beef cattle (1,020 animals) and validate them in a lactating Holstein population (population 1 [POP1]; 205 animals); 2) to validate significant SNP for DMI and weight at test (WT) from a second Holstein population, from a previous study (population 2 [POP2]; 903 animals), in an Angus population; and 3) to evaluate 2 different residual CH traits and determine if the genes associated with CH also control residual CH traits. Phenotypes calculated for the genotyped Angus population included CH production (MeP), CH yield (MeY), CH intensity (MI), DMI, and WT. The Holstein population (POP1) was multiparous, with phenotypes on CH traits (MeP, MeY, and MI) plus genotypes. Additionally, 2 CH traits, residual genetic CH (RGM) and residual phenotypic CH (RPM), were calculated by adjusting MeP for DMI and WT. Estimated heritabilities in the Angus population were 0.30, 0.19, and 0.15 for MeP, RGM, and RPM, respectively, and genetic correlations of MeP with DMI and WT were 0.83 and 0.80, respectively. Estimated heritabilities in Holstein POP1 were 0.23, 0.30, and 0.42 for MeP, MeY, and MI, respectively. Strong associations with MeP were found on chromosomes 4, 12, 14, 20, and 30 at < 0.001, and those chromosomes also had significant SNP for DMI in Holstein POP1. In the Angus population, the number of significant SNP for MeP at < 0.005 was 3,304, and approximately 630 of those SNP also were important for DMI and WT. When a set (approximately 3,300) of significant SNP for DMI and WT in the Angus population was used to estimate genetic parameters for MeP and MeY in Holstein POP1, the genetic variance and, consequently, the heritability slightly increased, meaning that most of the genetic variation is largely captured by these SNP. Residual traits could be a good option to include in the breeding goal, as this would facilitate selection for lower emitting animals without compromising DMI and WT.

Gene expression analysis of blood, liver, and muscle in cattle divergently selected for high and low residual feed intake

Improving feed efficiency in cattle is important because it increases profitability by reducing costs, and it also shrinks the environmental footprint of cattle production by decreasing manure and greenhouse gas emissions. Residual feed intake (RFI) is 1 measurement of feed efficiency and is the difference between actual and predicted feed intake. Residual feed intake is a complex trait with moderate heritability, but the genes and biological processes associated with its variation still need to be found. We explored the variation in expression of genes using RNA sequencing to find genes whose expression was associated with RFI and then investigated the pathways that are enriched for these genes. In this study, we used samples from growing Angus bulls (muscle and liver tissues) and lactating Holstein cows (liver tissue and white blood cells) divergently selected for low and high RFI. Within each breed-tissue combination, the correlation between the expression of genes and RFI phenotypes, as well as GEBV, was calculated to determine the genes whose expression was correlated with RFI. There were 16,039 genes expressed in more than 25% of samples in 1 or more tissues. The expression of 6,143 genes was significantly associated with RFI phenotypes, and expression of 2,343 genes was significantly associated with GEBV for RFI ( < 0.05) in at least 1 tissue. The genes whose expression was correlated with RFI phenotype (or GEBV) within each breed-tissue combination were enriched for 158 (78) biological processes (Fisher Exact Statistics for gene-enrichment analysis, EASE score < 0.1) and associated with 13 (13) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ( < 0.05 and fold enrichment > 2). These biological processes were related to regulation of transcription, translation, energy generation, cell cycling, apoptosis, and proteolysis. However, the direction of the correlation between RFI and gene expression in some cases reversed between tissues. For instance, low levels of proteolysis in muscle were associated with high efficiency in growing bulls, but high levels of proteolysis in white blood cells were associated with efficiency of milk production in lactating cows.

RNA Sequencing Applied to Livestock Production

High-throughput sequencing technology is rapidly replacing expression arrays and becoming the standard method for global expression profiling studies. The development of low-cost, rapid sequencing technologies has enabled detailed quantification of gene expression levels, affecting almost every field in the life sciences. In this chapter, we will overview the key points for gene expression analysis using RNA-seq data. First, we will discuss the workflows of RNA-seq data analysis followed by a discussion about the currently available tools for data analysis and a comparison between these tools. The chapter concludes with a discussion about the application of RNA-seq data analysis in livestock. In the appendix, using an example from livestock RNA-seq data, we show a simple script for RNA-seq data analysis.

Genome variants altering gene splicing in bovine are extensively shared between tissues

Background Mammalian phenotypes are shaped by numerous genome variants, many of which may regulate gene transcription or RNA splicing. To identify variants with regulatory functions in cattle, an important economic and model species, we used sequence variants to map a type of expression quantitative trait loci (expression QTLs) that are associated with variations in the RNA splicing, i.e., sQTLs. To further the understanding of regulatory variants, sQTLs were compare with other two types of expression QTLs, 1) variants associated with variations in gene expression, i.e., geQTLs and 2) variants associated with variations in exon expression, i.e., eeQTLs, in different tissues. Results Using whole genome and RNA sequence data from four tissues of over 200 cattle, sQTLs identified using exon inclusion ratios were verified by matching their effects on adjacent intron excision ratios. sQTLs contained the highest percentage of variants that are within the intronic region of genes and contained the lowest percentage of variants that are within intergenic regions, compared to eeQTLs and geQTLs. Many geQTLs and sQTLs are also detected as eeQTLs. Many expression QTLs, including sQTLs, were significant in all four tissues and had a similar effect in each tissue. To verify such expression QTL sharing between tissues, variants surrounding (±1Mb) the exon or gene were used to build local genomic relationship matrices (LGRM) and estimated genetic correlations between tissues. For many exons, the splicing and expression level was determined by the same cis additive genetic variance in different tissues. Thus, an effective but simple-to-implement meta-analysis combining information from three tissues is introduced to increase power to detect and validate sQTLs. sQTLs and eeQTLs together were more enriched for variants associated with cattle complex traits, compared to geQTLs. Several putative causal mutations were identified, including an sQTL at Chr6:87392580 within the 5th exon of kappa casein (CSN3) associated with milk production traits. Conclusions Using novel analytical approaches, we report the first identification of numerous bovine sQTLs which are extensively shared between multiple tissue types. The significant overlaps between bovine sQTLs and complex traits QTL highlight the contribution of regulatory mutations to phenotypic variations.