Genomics and epigenomics analysis of animal production and welfare in Denmark

Abstract

Denmark is one of the largest producers and exporters of dairy milk and pig meat in the world. However, dairy and pig farming is undergoing transformation to be even more cost-efficient and welfare-friendly, partly due to international competitions and European Union regulations. In this context, improving animal efficient production and addressing the animal welfare (e.g., avoiding surgical castration for boar taint problem) is one of the major goals in Danish farms. This project is based on genomics in simulated data and epigenetics in pigs aimed at delivering genomic selection and epigenetic markers to improve production and welfare. The summary in details are listed below: 1. Genotyping by sequencing (GBS) is a robust method to genotype markers. Many factors can influence the genotyping quality. One is that heterozygous genotypes could be wrongly genotyped as homozygotes, dependent on the genotyping depths. In this study, a method correcting this type of genotyping error was demonstrated. The efficiency of this correction method and its effect on genomic prediction were assessed using simulated data of livestock populations. Chip array (Chip) and four depths of GBS data was simulated. After quality control (call rate ≥ 0.8 and minor allele frequency (MAF) ≥ 0.01), the remaining number of Chip and GBS SNPs were both approximately 7,000, averaged over 10 replicates. GBS genotypes were corrected with the proposed method. The reliability of genomic prediction was calculated using GBS, corrected GBS (GBSc), true genotypes for the GBS loci and Chip data. The results showed that GBSc had higher rates of correct genotype calls and higher correlations with true genotypes than GBS. For genomic prediction, using Chip data resulted in the highest reliability. As the depth increased to 10, the prediction reliabilities using GBS and GBSc data approached those using true GBS data. The reliabilities of genomic prediction using GBSc data were 0.604, 0.672, 0.684 and 0.704 after genomic correction, with the improved values of 0.013, 0.009, 0.006 and 0.001 at depth = 2, 4, 5 and 10, respectively. The current study showed that a correction method for GBS data increased the genotype accuracies and, consequently, improved genomic predictions. These results suggest that a correction of GBS genotype is necessary, especially for the GBS data with low depths. 2. GBS is a robust genotyping method but also has problems with missing genotypes. Imputation is important for using GBS for genomic predictions, especially for lower depths, due to the large number of missing genotypes. MAF is widely used as a genotype editing criteria for genomic predictions. Therefore, three imputation methods (Beagle, IMPUTE2 and FImpute software) based on four MAF editing criteria were investigated to increase the accuracy of missing genotypes and, consequently, improve genomic predictions. Four MAFs (no MAF limit, MAF ≥ 0.001, MAF ≥ 0.01 and MAF ≥ 0.03) were used for editing genotype data before imputation. Beagle, IMPUTE2 and FImpute software were applied to impute the original GBS. Additionally, IMPUTE2 also imputed the expected genotype dosage after genotype correction (GcIM). The results showed that imputation accuracies were the same for the three imputation methods, except for the data of depth = 2, where FImpute had a slightly lower imputation accuracy than Beagle and IMPUTE2. GcIM was observed to be the best for all of the imputations at depth = 4, 5 and 10, but the worst for depth = 2. For genomic prediction, retaining more SNPs with no MAF limit resulted in higher reliability. Beagle and IMPUTE2 had the largest increases in prediction reliability of 5 percentage points, and FImpute gained 3 percentage points at depth = 2. The best prediction was observed at depth = 4, 5 and 10 using GcIM, but the worst prediction was also observed using GcIM at depth = 2. The current study showed that imputation accuracies were relatively low for GBS with low depths and high for GBS with high depths. Imputation resulted in larger gains in the reliability of genomic predictions for GBS with lower depths. These results suggest that the application of IMPUTE2, based on a corrected GBS to improve genomic predictions for higher depths, and FImpute software could be a good alternative for routine imputation. 3. Epigenetic changes are important for understanding complex trait variation and inheritance in pigs that are also a valuable biomedical model for human health research. Testis is the main organ for reproduction and boar taint (BT) in pigs; however, there have been no studies to-date on adult pig testis epigenome. The main objective of this study was to establish a genome-wide DNA methylation map of pig testis. Reduced Representation Bisulfite Sequencing (RRBS) was used to study methylation levels of cytosine in nine pig testis samples. The results showed that genomewide methylation status of nine samples overlapped greatly and their variation among pigs were low. The methylation levels of promoter, exon, intron, cytosine and guanine dinucleotide (CpG) islands and CpG island shores regions were 0.15, 0.47, 0.55, 0.39, and 0.53, respectively. Cytosines binding to CpG islands showed different methylation levels between exon and intron regions. All methylation levels of CpG islands were lower than CpG island shores in different genic features. Our analysis revealed the methylation patterns in different genic features and CpG island regions of testis in pigs. These findings are helpful to understand the relationship between DNA methylation and genic CpG islands, and to provide epigenetic information for translational epigenomic studies that use pigs as an animal model for human research. 4. BT is an offensive flavor observed in non‐castrated male pigs that reduces the carcass price. Surgical castration effectively avoids the taint but is associated with animal welfare concerns. The functional annotation of farm animal genomes for understanding the biology of complex traits can be used in the selection of breeding animals to achieve favorable phenotypic outcomes. The characterization of pig epigenomes/methylation changes between animals with high and low BT and genome‐wide epigenetic markers that can predict BT are lacking. RRBS of DNA methylation patterns based on next generation sequencing (NGS) is an efficient technology to identify candidate epigenetic biomarkers associated with BT. Three different BT levels were analyzed using RRBS data to calculate the methylation levels of CpG sites. The co‐analysis of differentially methylated CpG sites identified by this study and differentially expressed genes identified by a previous study found 32 significant co‐located genes. The joint analysis of GO terms and pathways revealed that methylation and gene expression of seven candidate genes were associated with BT; in particular, FASN plays a key role in fatty acid biosynthesis, and PEMT might be involved in estrogen regulation and the development of BT. This study is the first to report the genome‐wide DNA methylation profiles of BT in pigs using NGS and summarize candidate genes associated with epigenetic markers of BT, which could contribute to the understanding of the functional biology of BT traits and selective breeding of pigs against BT based on epigenetic biomarkers. 5. DNA methylation in gene or promoter or gene body could restrict/promote the gene transcription. Moreover, methylation in the gene regions along with CpG island regions could modulate the transcription to undetectable gene expression levels. Therefore, it is necessary to investigate the methylation levels within the gene, gene body, CpG island regions and their overlapped regions and then identify the gene-based differentially methylated regions (GeneDMRs). Here, R package GeneDMRs aims to facilitate computing gene based methylation rate using NGS-based methylome data. A user-friendly R package GeneDMRs is presented to analyze the methylation levels in each gene/promoter/exon/intron/CpG island/CpG island shore or each overlapped region (e.g., geneCpG island/promoter-CpG island/exon-CpG island/intron-CpG island/gene-CpG island shore/promoter-CpG island shore/exon-CpG island shore/intron-CpG island shore). Here, we used the public RRBS data of mouse (GSE62392) for evaluating software and found novel biologically significant results to supplement the previous research. GeneDMRs is freely available at https://github.com/xiaowangCN/GeneDMRs.