Characterization of Complex Traits through Transcriptomics and Genomics in Beef Cattle

Abstract

<p>In the last decades, multiple methods using molecular genetic data have been developed and their application in breeding programs have been evaluated. Particularly, due to the development and cost of dense genotyping arrays (SNP chips) and sequencing technologies (whole genome and RNA sequencing), these techniques have become standard methods to study the association between genetic variants and phenotypic variation in important traits in livestock (‘genomics’) as well as levels of expression for contrasting groups in age of development or phenotype (‘transcriptomics’). This thesis explores the use of transcriptomics and genomics applied to better understand economically important complex traits in beef cattle (marbling and residual feed intake) as well as the utility of using information generated by these technologies in the estimation of breeding values.</p> <p>In this thesis, in chapter 3, we describe the characterization of muscle development and the progress of fat deposition at the transcriptomic level in Hanwoo cattle. This Korean breed has the genetic potential to accumulate intramuscular fat to reach very high levels of marbling and high prices in the market. To examine the key genes and pathways that regulate the differentiation process in satellite cells, we extracted these cells from <i>Longissimus dorsi</i> (LD) and <i>semimembranosus</i> (SM) of three newborn calves, promote cell differentiation in culture cells and evaluated the differences between muscles in a time-series RNA-seq experiment. The histological (differentiation index) results indicated that LD muscle differentiated faster from myoblast into multinucleated myotubes than SM. These results agreed with the gene expression of the myogenic regulatory factors (MRF) which tend to be significantly up-regulated at the end of the differentiation in LD, specifically the genes <i>MYOD</i>, MYF6 and MYOG. The number of genes differentially expressed was larger across time than across muscles. In total, thirteen genes (<i>HOXB2, HOXB4, HOXB9, HOXC8, FOXD1, IGFN1, ZIC2, ZIC4, HOXA11, HOXC11, PITX1, SIM2</i> and <i>TBX4</i>) were differentially expressed (DE) between muscles, which seem to be involved in modulating the muscle lineage development during myogenesis. In addition, our results indicated, in agreement with previous studies in other species, that some of the DE genes modulated the expression of myogenic regulatory factors (MYOD and <i>MYF5</i>) during the differentiation process. </p> <p>The use of RNA-seq on the marbling development of Hanwoo helped to better understand variation in gene expression related to high or low marbling phenotypes. In chapter 4, we describe an experiment where muscle samples from <i>Longissimus dorsi</i> were studied at the age of 18 (by biopsy) and 30 months. Twelve animals were grouped according to their marbling score in Low (average 2.4, range from 1-5), and High (average 6.28, range from 6-9). In total, 1,883 differentially expressed genes were identified from multiple contrasts, among them 782 genes were up-regulated and 1,101 were down-regulated. Differences in transcriptome were higher between ages rather than between marbling groups. The genes <i>SLC38A4, ABCA10, APOL6</i>, and two novel genes (ENSBTAG00000015330, ENSBTAG00000046041) were up-regulated in the High marbling group at 18 months of age. These genes are likely to have important roles in energy transport and utilization during growth of steers. Potential markers for marbling development (<i>LEP, MEDAG, FOXO1, ADIG, ADIPOQ, CMKLR1,</i> and <i>FABP4</i>) were identified from the functional analysis as involved in regulation of fat cell differentiation or brown fat cell differentiation. These results imply a potential use of gene expression technologies to identify younger steers that will develop high marbling. Further functional studies would need to be conducted to better understand the role of these genes on marbling.</p> <p>The combination of multiple omic technologies opens up the possibility to improve the interpretation of a trait from many approaches. The combination of expression studies with mapping quantitative trait loci was applied in chapter 5 for studying the genetic architecture of residual feed intake (RFI) in 2190 Angus steers. First, the imputation from low density to medium density and later to high-density genotyping arrays was performed for 2,190 animals using a larger population of Angus with genotypes as reference. Additionally, the RNA sequences from 126 Angus cattle divergently selected for RFI were analyzed in a multi-tissue experiment (from liver, blood and muscle). The estimated heritability for RFI was 0.3 and we identified 78 SNPs associated with RFI on six QTL located on BTA1, BTA6, BTA14, BTA17, BTA20 and BTA26. The most significant SNP was on chromosome BTA20 (rs42662073) for which <i>STC2</i> was the closest gene. The genes <i>OAS2, SHOX, XKR4, and SGMS1</i> were the closest to the significant QTL on BTA17, BTA1, BTA14, and BTA26, respectively. In the 2 Mb windows around the six significant QTL, we identified fifteen genes whose expression was significantly associated with RFI selection line: <i>NEURL1B, CPEB4, RITA1, CCDC42B, OAS2, RPL6, ERP29, ATP6V1H, MRPL15, MFSD1, RARRES1, A1CF, SGMS1, PAPSS2</i>, and <i>PTEN</i>. The results imply that the integration of GWAS and gene expression analysis may help to contribute with knowledge and help to understand better genetic variation in complex traits like RFI.</p> <p>The ultimate goal in livestock breeding programs is always to find the most accurate way to select breeding animals according to a defined breeding objective and one way to achieve this could be to apply genomic selection. The final study in this thesis (chapter 6) attempts to investigate the utility of integrating information from GWAS or gene expression studies into increasing the accuracy of genomic prediction of RFI. We used the results from chapter 5 and evaluated the accuracy of genomic prediction using data on 2,190 Angus steers with two cross-validation designs; one where the GWAS was performed on the same data used for training the genomic prediction named four cross-validation (4CV) and a design where GWAS and training sets were separate data denoted as four by four cross validation (4x4CV). The accuracy of prediction of RFI did not improve when using a 770k SNP panel compared with 50k SNP panel. There were 1.2% and 2.7% point increase in accuracy when top SNPs from GWAS were added to the 50k or 770k panels in the 4x4CV design. The 4CV design showed lower accuracy when using top SNPs and the predictions were much more biased. Genomic prediction accuracy can be slightly improved when using selected SNPs from GWAS and GSA. Further analysis would need to be conducted using a larger population to confirm these results.</p> <p>Finally, the thesis ends with some thoughts on the application of “omics” in livestock breeding programs, the impact on accuracy of prediction and the improvement of experimental design. This chapter features a final summary and concluding remarks about the research involved in producing this thesis as well as notes on the interpretation of complex trait research and reflections future research directions regarding the use of gene expression and gene association to improve livestock production systems.</p>