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Sponsor Day on animal feeding: The role of genetics in Product quality and feed efficiency in swine
This document summarizes research conducted at IRTA on the role of genetics in pig product quality and feed efficiency. The research aims to identify genetic mechanisms underlying traits related to muscle fat deposition and meat quality. Studies examine intramuscular fat content, fatty acid profiles, cholesterol levels, and sensory attributes. Genome-wide association studies and gene expression analyses have identified genomic regions and genes associated with these traits. Research also investigates using genomic selection to improve traits like intramuscular fat and efficiency. Overall, the research takes multidisciplinary approaches including genomics, transcriptomics, and nutrition studies to better understand the genetic basis of important pig production traits.
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Sponsor Day on animal feeding: The role of genetics in Product quality and feed efficiency in swine
- 1. THE ROLE OFGENETICS IN PRODUCT QUALITY AND FEED EFFICIENCY IN SWINE Raquel Quintanilla IRTA - Animal Breeding and Genetics Program Torre Marimon, 08140 Caldes de Montbui, Barcelona, Spain. raquel.quintanilla@irta.cat IRTA Sponsor Day, Mas de Bover, May 15th-16th 2014
- 2. • Polyunsaturated FA(PUFA) • Monounsaturated FA (MUFA) • Saturated FA (SFA) • cholesterol content Sensory quality Hedonic evaluation Technological Quality Nutritional Quality Consumers health Intramuscular fat and meat quality Intramuscular fat content and composition (fatty acids profile) LIPID METABOLISM • Water loss • Rancidity • Firmless • Melting • Tenderness • Taste • Flavour • Juiceness
- 3. Principal components analysisof dry-cured ham sensory attributes and fresh meat fat Pena, Gallardo, Guàrdia, Reixach, Arnau, Amills & Quintanilla. Journal of Animal Science 2013. 350 Duroc barrows RAW MEAT Gluteus medius •Intramuscular fat •Fatty Acids •Cholesterol content ORGANOLEPTIC CHARACTERISATION DRY-CURED HAMS: 6 expert panelists •Appearence •Taste-flavour •Texture 2 muscles •biceps femoris •semimembranosus A B SM BF Sección B Sección A SM BF IMF CHOL SFA PUFA MUFA colour marbling overall liking adhesiveness bitterness aged mould matured sweetness hardness crumbliness fibrousness fat melting metallic pastiness piquantness saltiness umami colour marbling overall likingadhesiveness bitterness aged mould matured sweetness hardness crumbliness fibrousness fat melting metallic pastiness piquantness saltiness umami -1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1 Component2(18.34%) Component 1 (19.25%) gluteus medius biceps femoris semimembranosus
- 4. GENETICS OF LIPIDMETABOLISM & MEAT QUALITY A research line conducted at IRTA since 1996 in different pig populations (Du and Ib) GENERAL OBJECTIVE: Identifying genetic mechanisms underlying lipid metabolism traits in pigs, with special attention in those related with muscle fat deposition, and nutritional and sensorial properties of fresh pork and dry-cured ham, and contributing new selection strategies. • Identify genes and polymorphisms (GAS – MAS) • Disentangle genetic pathways and regulatory mechanisms • Implement Genomic Selection schemes IBMAP projects 1996-2011 IRTA-INIA-UAB LIPGEN projects 2003-present IRTA-CRAG IBERCAL project 2013-present IRTA - UEX
- 5. THE ‘OMES AND‘OMICS PUZZLE MULTIDISCIPLINARY APPROACHES ARE REQUIRED !!!! Genome Proteome mRNA DNA Transcriptome Microbiome Metabolome PHENOME ENVIRONMENT (NUTRITION) PIG GENETICS THE BASIC MODEL FOR A GENETIST = Genetics + Environment (Nutrition) + G * E + e Genetic gain Generation N+1 Genetic variability within population basis for SELECTION Individual phenotype Selection Epigenetics Nutrigenomics
- 6. Recording PHENOME –IRTA EXPERIMENTAL FACILITIES Controlling Growing period IRTA-CAP - Weight, Fat - Behavoir - Individual Feed intake control Experimental Slaughterhouse Carcass traits Meat color Physical-chemical measures Meat Analysis IRTA-CTA - Intramuscular fat - Cholesterol in muscle -Fatty acids profile - Color Sensory characterisation - Visual, texture and flavour attributes (carried out by expert panelists)
- 7. INFORMATION used inour researches 350 Duroc individuals: - Genealogy (pedigree) - more than 100 Phenotypes Fattening Control at IRTA-CAP - Weights - Backfat - Serum lipid levels - Feed control Slaughter Carcass and Meat - Weights - Conformation - Fatness - Physical-chemical measures of meat Meat (two muscles) Analyses at IRTA-CTA - Color - Intramuscular fat - Cholesterol in muscle - Fatty acids profile Organoleptic profile of dry-cured hams Analyses at IRTA-CTA - Visual, texture and flavour attributes (carried out by expert panelists) • DNA - High density genotypes (Illumina Porcine 60K+SNP iSelect Beadchip) > 60,000 SNP distributed across the porcine genome • RNA - Expression profiles (GeneChip Porcine Genome Array, Affymetrix) 20,201 GENES (23,937 PROBES) • Sequence (Next Generation Sequencing; Illumina HiSeq 2000) • DNA (genome) sequence • mRNA sequence (RNA-Seq) PHENOMICSGENOMICS& TRANSCRIPTOMICS LIPGEN (350 Du) IBERCAL (200 Ib)
- 8. CAN WE IMPROVEIMF BY SELECTION? HERITABILITIES Longissimus dorsi Gluteus medius Intramuscular fat (%) 0.65 **** 0.58 **** Cholesterol content 0.20 * 0.22 * Oleic FA (%) 0.15 * 0.21 * Palmitic FA (%) 0.30 ** 0.30 ** Stearic FA (%) 0.33 *** 0.53 *** Omega 6 FA (%) 0.14 n.s. 0.14 * Omega 3 FA (%) 0.14 n.s. 0.16 * Heritability of intramuscular fat content and FA profile in studied populations Casellas, Noguera, Reixach, Díaz, Amills & Quintanilla. Journal of Animal Science 2010. Heritability of intramuscular fat Knapp et al. 1997 0.38 (LW) 0.67 (LD) 0.32 (Pi) Larzul et al. 1997 0.44 (LW) Hermesch et al. 2000 0.35 (LW & LD) Fernández et al. 2003 0.25 (IB) Suzuki et al. 2005 0.39 (Du) YES, we could implement a selection scheme on IMF and expect a response to selection (moderate to high heritabilities) BUT Phenotyping (measurements) expensive / difficult EBV for candidates based on relatives records (lower accuracy) Correlated response in BFT Genetic correlation with fatness (BFT): 0.25-0.40 Gene Assisted Selection (GAS) Genomic Selection (GS) Genetic gain Generation N+1 Selection
- 9. Varona et al.Genetical Research 2002 Ovilo et al. J. Anim. Sci. 2002 Perez-Enciso et al. Genetics. 2002 Clop et al. Mamm. Genome. 2003 Peréz-Enciso et al. J. Anim. Sci. 2005 Mercadé et al. Mamm. Genome 2005 Gallardo et al. Physiol. Genom. 2008 Quintanilla et al. J. Anim. Sci. 2011 Gallardo et al. Anim. Genet. 2011 Pena et al. J. Anim. Sci. 2013 GENOMIC REGIONS ASSOCIATED TO PHENOTYPES QTL detected with low density panels 0 2 4 6 8 10 12 SSC6 IMF QTL Q – Iberian allele q – Landrace allele IBERIAN (QQ) Q allele high IMF GWAS (Genome-Wide Association Studies) using high density >60K SNP panels Possibility of a reciprocal introgression of the Iberian allele in other breeds Hernández, Amills, Pena, Mercadé, Manunza & Quintanilla. J. Anim. Sci. 2013 Backfat thickness and Intramuscular fat: distribution of additive genetic variance across genome GENOMICS
- 10. GENE EXPRESSION • Differentialgene expression in muscle Muscle transcriptomic profiles in pigs with divergent phenotypes for fatness. Cánovas , Quintanilla, Amills, Pena. BMC Genomics 2010 • eQTL mapping (genetical genomics) 11 trans-regulatory hotspots on chromosomes 1, 2, 3, 5, 6, 7, 12 Cánovas, Pena, Gallardo, Ramírez, Amills & Quintanilla. PLOS ONE 2011 0 1 2 3 4 5 6 Fold-changeratio Microarray ratio qPCR ratio TRANSCRIPTOMICS
- 11. ACACA Fattyacids profile Gallardo et al. 2009. Anim. Genet. LDLR, APOB Serum lipids Pena et al. 2009. Anim. Biotech. HDLBP Intramuscular fat Cánovas et al. 2009. Livest. Sci. HMGCR Cholesterol & Fatty acids Cánovas et al. 2010. Animal. FATP1, FATP2 Fatty acids profile. Melo et al. Livest. Sci. 2012 LRP12, TRIB1, APOD Serum lipids & Fatty acids profile. Melo et al. J Anim Sci. 2012 SCD Fatty acids profile. Aznárez et al. 2012. Amplification & Sequencing Identification segregating polymorphisms Genotyping animal population DNA from a sample of pigs ASSOCIATION STUDIES CANDIDATE GENE POSITIONAL & FUNCTIONAL CANDIDATE GENESGENOMICS & TRANSCRIPTOMICS
- 12. • 104 genes •139 interactions GENE NETWORKS LIVER - Weighted Gene Co-expression Networks 12 HUBS (highly connected genes): ESR1, HMGCS1, LIPIN2, LIPIN1, GPAM, PPARD, EBP, LDLR, HMGCR, IDI1, SCD, and GOT1 3 CONNECTORS: FST, ETS2, and GLUL ASSOCIATION with Phenotypes 17modulesofco-expressedgenes TRANSCRIPTOMICS
- 13. Use of genomicinformation in a selection scheme Genomic Selection: using massive genotypes (60K SNPs) information in genetic evaluation (GEBV) Current studies regarding implementation of GS models and methods (genomic BLUP, Bayes A, B and C, Lasso) increase of selection accuracy in several scenarios integration of crossbreed information in purebreed selection reduced SNP panels – Reduced cost Genomic selection of purebreeds for crossbred performance. Genetic Selection Evolution 2009. Modifying growth curve parameters by multitrait genomic selection. Journal of Animal Science 2011. Expected advantages • Increase of accuracy • Avoid phenotyping of candidates • Reduce generation interval Questions • Models and methods • Reference population size • LD must be reevaluated. Frequency? • Long-term consequences • Cost - Revenue
- 14. • Nutrigenomics Experiment inprogress: effect of different FA diet in gene expression and muscle fat deposition • Gene networks • Epistatic & co-expression networks • Gene networks across tissues • Key genes modulating topology • Analysis of DNA & RNA sequence: • New transcriptomic & genomic variants • Metabolome and Microbiome information IN PROGRESS & FUTURE STUDIES towards more holistic and multidisciplinary approaches CHALLENGE: Integral analyses of all available information
- 15. Investigating possibilities andbenefits of different methods to improve feed efficiency in sire lines of pigs and rabbits Alternative selection criteria (RFI, group measures, regression) NUTRIGENOMICS experiment: Interaction genotype*feeding regime Genetics of feeding behavioral traits Breeding value estimation models: • Including social interactions and/or individual sensitivity (robustness) to environmental factors such as sub-clinical diseases • Including genomic information (GS) IN PROGRESS & FUTURE STUDIES Feed efficiency Improving efficiency of sows taking into account the relationships between the use of food resources, mobilization of body reserves, robustness, reproductive function, litter homogeneity and survival. FEED EFFICIENCY
- 16. Collaborators Armand Sánchez Marcel Amills MiguelPérez-Enciso Josep M. Folch Quim Casellas Angela Cánovas David Gallardo Luis Silió M. Carmen Rodríguez Cristina Óvilo Ana Fernández Estefania Alvés Romi Pena Joan EstanyLuis Varona ANIMAL BREEDING & GENETICS Joan Tibau José L. Noguera Noelia Ibáñez Juan P. Sánchez Miriam Piles Quim Soler Mateu Tulsà PRODUCT QUALITY M.Angels Oliver Marina Gispert FOOD TECHNOLOGY Dolors Guàrdia Jacint Arnau FUNCTIONALITY & NUTRITION Isabel Díaz Josep Reixach Emilio Magallón NUTRITION & WELFARE J. Brufau Torrallardona E. Esteve R. Lizardo E. Fàbrega A. Dalmau A. Velarde GIRO F. Prenafeta M. Vinyes A. Bonmatí
- 17. THANK YOU Lleida PIGs TorreMarimon PIGS & RABBITS Monells PIGS Mas de Bover POULTRY