Genetical systems biology in livestock: application to gonadotrophin releasing hormone and reproduction

Genetic variations (eQTLs) in muscle transcriptome and mitochondrial genes, and trans-eQTL molecular pathways in feed efficiency from Danish breeding pigs

Feed efficiency (FE) is a key trait in pig production, as improvement in FE has positive economic and environmental impact. FE is a complex phenotype and testing animals for FE is costly. Therefore, there has been a desire to find functionally relevant single nucleotide polymorphisms (SNPs) as biomarkers, to improve our biological understanding of FE as well as accuracy of genomic prediction for FE. We have performed a cis- and trans- eQTL (expression quantitative trait loci) analysis, in a population of Danbred Durocs (N = 11) and Danbred Landrace (N = 27) using both a linear and ANOVA model based on muscle tissue RNA-seq. We analyzed a total of 1425x19179 or 2.7x10⁷ Gene-SNP combinations in eQTL detection models for FE. The 1425 genes were from RNA-Seq based differential gene expression analyses using 25880 genes related to FE and additionally combined with mitochondrial genes. The 19179 SNPs were from applying stringent quality control and linkage disequilibrium filtering on genotype data using a GGP Porcine HD 70k SNP array. We applied 1000 fold bootstrapping and enrichment analysis to further validate and analyze our detected eQTLs. We identified 13 eQTLs with FDR < 0.1, affecting several genes found in previous studies of commercial pig breeds. Examples include MYO19, CPT1B, ACSL1, IER5L, CPT1A, SUCLA2, CSRNP1, PARK7 and MFF. The bootstrapping results showed statistically significant enrichment (p-value<2.2x10^-16) of eQTLs with p-value < 0.01 in both cis and trans-eQTLs. Enrichment analysis of top trans-eQTLs revealed high enrichment for gene categories and gene ontologies associated with genomic context and expression regulation. This included transcription factors (p-value = 1.0x10^-13), DNA-binding (GO:0003677, p-value = 8.9x10^-14), DNA-binding transcription factor activity (GO:0003700,) nucleus gene (GO:0005634, p-value<2.2x10^-16), negative regulation of expression (GO:0010629, p-value<2.2x10^-16). These results would be useful for future genome assisted breeding of pigs to improve FE, and in the improved understanding of the functional mechanism of trans eQTLs.

The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype

With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal's transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal's genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals' microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.

Genome-wide association and pathway analysis of feed efficiency in pigs reveal candidate genes and pathways for residual feed intake

Residual feed intake (RFI) is a complex trait that is economically important for livestock production; however, the genetic and biological mechanisms regulating RFI are largely unknown in pigs. Therefore, the study aimed to identify single nucleotide polymorphisms (SNPs), candidate genes and biological pathways involved in regulating RFI using Genome-wide association (GWA) and pathway analyses. A total of 596 Yorkshire boars with phenotypes for two different measures of RFI (RFI1 and 2) and 60k genotypic data was used. GWA analysis was performed using a univariate mixed model and 12 and 7 SNPs were found to be significantly associated with RFI1 and RFI2, respectively. Several genes such as xin actin-binding repeat-containing protein 2 (XIRP2),tetratricopeptide repeat domain 29 (TTC29),suppressor of glucose, autophagy associated 1 (SOGA1),MAS1,G-protein-coupled receptor (GPCR) kinase 5 (GRK5),prospero-homeobox protein 1 (PROX1),GPCR 155 (GPR155), and FYVE domain containing the 26 (ZFYVE26) were identified as putative candidates for RFI based on their genomic location in the vicinity of these SNPs. Genes located within 50 kbp of SNPs significantly associated with RFI and RFI2 (q-value ≤ 0.2) were subsequently used for pathway analyses. These analyses were performed by assigning genes to biological pathways and then testing the association of individual pathways with RFI using a Fisher's exact test. Metabolic pathway was significantly associated with both RFIs. Other biological pathways regulating phagosome, tight junctions, olfactory transduction, and insulin secretion were significantly associated with both RFI traits when relaxed threshold for cut-off p-value was used (p ≤ 0.05). These results implied porcine RFI is regulated by multiple biological mechanisms, although the metabolic processes might be the most important. Olfactory transduction pathway controlling the perception of feed via smell, insulin pathway controlling food intake might be important pathways for RFI. Furthermore, our study revealed key genes and genetic variants that control feed efficiency that could potentially be useful for genetic selection of more feed efficient pigs.

Genome-wide association and systems genetic analyses of residual feed intake, daily feed consumption, backfat and weight gain in pigs

Background

Feed efficiency is one of the major components determining costs of animal production. Residual feed intake (RFI) is defined as the difference between the observed and the expected feed intake given a certain production. Residual feed intake 1 (RFI1) was calculated based on regression of individual daily feed intake (DFI) on initial test weight and average daily gain. Residual feed intake 2 (RFI2) was as RFI1 except it was also regressed with respect to backfat (BF). It has been shown to be a sensitive and accurate measure for feed efficiency in livestock but knowledge of the genomic regions and mechanisms affecting RFI in pigs is lacking. The study aimed to identify genetic markers and candidate genes for RFI and its component traits as well as pathways associated with RFI in Danish Duroc boars by genome-wide associations and systems genetic analyses.

Results

Phenotypic and genotypic records (using the Illumina Porcine SNP60 BeadChip) were available on 1,272 boars. Fifteen and 12 loci were significantly associated (p < 1.52 × 10^-6) with RFI1 and RFI2, respectively. Among them, 10 SNPs were significantly associated with both RFI1 and RFI2 implying the existence of common mechanisms controlling the two RFI measures. Significant QTL regions for component traits of RFI (DFI and BF) were detected on pig chromosome (SSC) 1 (for DFI) and 2 for (BF). The SNPs within MAP3K5 and PEX7 on SSC 1, ENSSSCG00000022338 on SSC 9, and DSCAM on SSC 13 might be interesting markers for both RFI measures. Functional annotation of genes in 0.5 Mb size flanking significant SNPs indicated regulation of protein and lipid metabolic process, gap junction, inositol phosphate metabolism and insulin signaling pathway are significant biological processes and pathways for RFI, respectively.

Conclusions

The study detected novel genetic variants and QTLs on SSC 1, 8, 9, 13 and 18 for RFI and indicated significant biological processes and metabolic pathways involved in RFI. The study also detected novel QTLs for component traits of RFI. These results improve our knowledge of the genetic architecture and potential biological pathways underlying RFI; which would be useful for further investigations of key candidate genes for RFI and for development of biomarkers.

Pre- and early-postnatal nutrition modify gene and protein expressions of muscle energy metabolism markers and phospholipid Fatty Acid composition in a muscle type specific manner in sheep

We previously reported that undernutrition in late fetal life reduced whole-body insulin sensitivity in adult sheep, irrespective of dietary exposure in early postnatal life. Skeletal muscle may play an important role in control of insulin action. We therefore studied a range of putative key muscle determinants of insulin signalling in two types of skeletal muscles (longissimus dorsi (LD) and biceps femoris (BF)) and in the cardiac muscle (ventriculus sinister cordis (VSC)) of sheep from the same experiment. Twin-bearing ewes were fed either 100% (NORM) or 50% (LOW) of their energy and protein requirements during the last trimester of gestation. From day-3 postpartum to 6-months of age (around puberty), twin offspring received a high-carbohydrate-high-fat (HCHF) or a moderate-conventional (CONV) diet, whereafter all males were slaughtered. Females were subsequently raised on a moderate diet and slaughtered at 2-years of age (young adults). The only long-term consequences of fetal undernutrition observed in adult offspring were lower expressions of the insulin responsive glucose transporter 4 (GLUT4) protein and peroxisome proliferator-activated receptor gamma, coactivator 1α (PGC1α) mRNA in BF, but increased PGC1α expression in VSC. Interestingly, the HCHF diet in early postnatal life was associated with somewhat paradoxically increased expressions in LD of a range of genes (but not proteins) related to glucose uptake, insulin signalling and fatty acid oxidation. Except for fatty acid oxidation genes, these changes persisted into adulthood. No persistent expression changes were observed in BF and VSC. The HCHF diet increased phospholipid ratios of n-6/n-3 polyunsaturated fatty acids in all muscles, even in adults fed identical diets for 1½ years. In conclusion, early postnatal, but not late gestation, nutrition had long-term consequences for a number of determinants of insulin action and metabolism in LD. Tissues other than muscle may account for reduced whole body insulin sensitivity in adult LOW sheep.

The integration of 'omic' disciplines and systems biology in cattle breeding

Enormous progress has been made in the selection of animals, including cattle, for specific traits using traditional quantitative genetics approaches. Nevertheless, considerable variation in phenotypes remains unexplained, and therefore represents potential additional gain for animal production. In addition, the paradigm shift in new disciplines now being applied to animal breeding represents a powerful opportunity to prise open the 'black box' underlying the response to selection and fully understand the genetic architecture controlling the traits of interest. A move away from traditional approaches of animal breeding toward systems approaches using integrative analysis of data from the 'omic' disciplines represents a multitude of exciting opportunities for animal breeding going forward as well as providing alternatives for overcoming some of the limitations of traditional approaches such as the expressed phenotype being an imperfect predictor of the individual's true genetic merit, or the phenotype being only expressed in one gender or late in the lifetime of an animal. This review aims to discuss these opportunities from the perspective of their potential application and contribution to cattle breeding. Harnessing the potential of this paradigm shift also poses some new challenges for animal scientists - and they will also be discussed.

Building gene co-expression networks using transcriptomics data for systems biology investigations: Comparison of methods using microarray data

Gene co-expression networks (GCN), built using high-throughput gene expression data are fundamental aspects of systems biology. The main aims of this study were to compare two popular approaches to building and analysing GCN. We use real ovine microarray transcriptomics datasets representing four different treatments with Metyrapone, an inhibitor of cortisol biosynthesis. We conducted several microarray quality control checks before applying GCN methods to filtered datasets. Then we compared the outputs of two methods using connectivity as a criterion, as it measures how well a node (gene) is connected within a network. The two GCN construction methods used were, Weighted Gene Co-expression Network Analysis (WGCNA) and Partial Correlation and Information Theory (PCIT) methods. Nodes were ranked based on their connectivity measures in each of the four different networks created by WGCNA and PCIT and node ranks in two methods were compared to identify those nodes which are highly differentially ranked (HDR). A total of 1,017 HDR nodes were identified across one or more of four networks. We investigated HDR nodes by gene enrichment analyses in relation to their biological relevance to phenotypes. We observed that, in contrast to WGCNA method, PCIT algorithm removes many of the edges of the most highly interconnected nodes. Removal of edges of most highly connected nodes or hub genes will have consequences for downstream analyses and biological interpretations. In general, for large GCN construction (with > 20000 genes) access to large computer clusters, particularly those with larger amounts of shared memory is recommended.

Genetic architecture of gene expression in ovine skeletal muscle

Background

In livestock populations the genetic contribution to muscling is intensively monitored in the progeny of industry sires and used as a tool in selective breeding programs. The genes and pathways conferring this genetic merit are largely undefined. Genetic variation within a population has potential, amongst other mechanisms, to alter gene expression via cis- or trans-acting mechanisms in a manner that impacts the functional activities of specific pathways that contribute to muscling traits. By integrating sire-based genetic merit information for a muscling trait with progeny-based gene expression data we directly tested the hypothesis that there is genetic structure in the gene expression program in ovine skeletal muscle.

Results

The genetic performance of six sires for a well defined muscling trait, longissimus lumborum muscle depth, was measured using extensive progeny testing and expressed as an Estimated Breeding Value by comparison with contemporary sires. Microarray gene expression data were obtained for longissimus lumborum samples taken from forty progeny of the six sires (4-8 progeny/sire). Initial unsupervised hierarchical clustering analysis revealed strong genetic architecture to the gene expression data, which also discriminated the sire-based Estimated Breeding Value for the trait. An integrated systems biology approach was then used to identify the major functional pathways contributing to the genetics of enhanced muscling by using both Estimated Breeding Value weighted gene co-expression network analysis and a differential gene co-expression network analysis. The modules of genes revealed by these analyses were enriched for a number of functional terms summarised as muscle sarcomere organisation and development, protein catabolism (proteosome), RNA processing, mitochondrial function and transcriptional regulation.

Conclusions

This study has revealed strong genetic structure in the gene expression program within ovine longissimus lumborum muscle. The balance between muscle protein synthesis, at the levels of both transcription and translation control, and protein catabolism mediated by regulated proteolysis is likely to be the primary determinant of the genetic merit for the muscling trait in this sheep population. There is also evidence that high genetic merit for muscling is associated with a fibre type shift toward fast glycolytic fibres. This study provides insight into mechanisms, presumably subject to strong artificial selection, that underpin enhanced muscling in sheep populations.

Genetic effects of polymorphisms in candidate genes and the QTL region on chicken age at first egg

Background

The age at first egg (AFE), an important indicator for sexual maturation in female chickens, is controlled by polygenes. Based on our knowledge of reproductive physiology, 6 genes including gonadotrophin releasing hormone-I (GnRH-I), neuropeptide Y (NPY), dopamine D2 receptor (DRD2), vasoactive intestinal polypeptide (VIP), VIP receptor-1 (VIPR-1), and prolactin (PRL), were selected as candidates for influencing AFE. Additionally, the region between ADL0201 and MCW0241 of chromosome Z was chosen as the candidate QTL region according to some QTL databases. The objective of the present study was to investigate the effects of mutations in candidate genes and the QTL region on chicken AFE.

Results

Marker-trait association analysis of 8 mutations in those 6 genes in a Chinese native population found a highly significant association (P < 0.01) between G840327C of the GnRH-I gene with AFE, and it remained significant even with Bonferroni correction. Based on the results of the 2-tailed χ² test, mutations T32742394C, T32742468C, G32742603A, and C33379782T in the candidate QTL region of chromosome Z were selected for marker-trait association analysis. The haplotypes of T32742394C and T32742468C were significantly associated (P < 0.05) with AFE. Bioinformatics analysis indicated that T32742394C and T32742468C were located in the intron region of the SH3-domain GRB2-like 2 (SH3GL2) gene, which appeared to be associated in the endocytosis and development of the oocyte.

Conclusion

This study found that G840327C of the GnRH-I gene and the haplotypes of T32742394C-T32742468C of the SH3GL2 gene were associated with the chicken AFE.

Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers

This study reports on the molecular systems biology of gastrointestinal nematode (GIN) infection and potential biomarkers for GIN resistance in sheep. Microarray gene expression data were obtained for 3 different tissues at 4 time points from sheep artificially challenged with two types of nematodes, Haemonchus contortus (HC) and Trichostrongylus colubriformis (TC). We employed an integrated systems biology approach, integrating 3 main methods: standard differential gene expression analyses, weighted gene co-expression network analyses (WGCNA) and quantitative genetic analyses of gene expression traits of key biomarkers. Using standard differential gene expression analyses we identified differentially expressed genes (DE) which responded differently in sheep challenged with HC compared to those challenged with TC. These interaction genes (e.g. MRPL51, SMEK2, CAT, MAPK1IP1 and SLC25A20A) were enriched in Wnt receptor signalling pathway (p = 0.0132) and positive regulation of NFκβ transcription factor activity (p = 0.00208). We report FCER1A, a gene encoding a high-affinity receptor for the Fc region of immunoglobulin E, which is linked to innate immunity to GIN in sheep. Using weighted gene co-expression network analysis (WGCNA) methods, we identified gene modules that were correlated with the length of infection (disease modules). Hub genes (with high intramodular connectivity) were filtered further to identify biomarkers that are related to the length of infection (e.g. CAT, FBX033, COL15A1, IGFBP7, FBLN1 and IgCgamma). The biomarkers we found in HC networks were significantly associated with functions such as T-cell and B-cell regulations, TNF-alpha, interleukin and cytokine production. In TC networks, biomarkers were significantly associated with functions such as protein catabolic process, heat shock protein binding, protein targeting and localization, cytokine receptor binding, TNF receptor binding, apoptosis and IGF binding. These results provide specific gene targets for therapeutic interventions and provide insights into GIN infections in sheep which may be used to infer the same in related host species. This is also the first study to apply the concept of estimating breeding values of animals to expression traits and reveals 11 heritable candidate biomarkers (0.05 to 0.92) that could be used in selection of animals for GIN resistance.

FunctSNP: an R package to link SNPs to functional knowledge and dbAutoMaker: a suite of Perl scripts to build SNP databases

Background

Whole genome association studies using highly dense single nucleotide polymorphisms (SNPs) are a set of methods to identify DNA markers associated with variation in a particular complex trait of interest. One of the main outcomes from these studies is a subset of statistically significant SNPs. Finding the potential biological functions of such SNPs can be an important step towards further use in human and agricultural populations (e.g., for identifying genes related to susceptibility to complex diseases or genes playing key roles in development or performance). The current challenge is that the information holding the clues to SNP functions is distributed across many different databases. Efficient bioinformatics tools are therefore needed to seamlessly integrate up-to-date functional information on SNPs. Many web services have arisen to meet the challenge but most work only within the framework of human medical research. Although we acknowledge the importance of human research, we identify there is a need for SNP annotation tools for other organisms.

Description

We introduce an R package called FunctSNP, which is the user interface to custom built species-specific databases. The local relational databases contain SNP data together with functional annotations extracted from online resources. FunctSNP provides a unified bioinformatics resource to link SNPs with functional knowledge (e.g., genes, pathways, ontologies). We also introduce dbAutoMaker, a suite of Perl scripts, which can be scheduled to run periodically to automatically create/update the customised SNP databases. We illustrate the use of FunctSNP with a livestock example, but the approach and software tools presented here can be applied also to human and other organisms.

Conclusions

Finding the potential functional significance of SNPs is important when further using the outcomes from whole genome association studies. FunctSNP is unique in that it is the only R package that links SNPs to functional annotation. FunctSNP interfaces to local SNP customised databases which can be built for any species contained in the National Center for Biotechnology Information dbSNP database.