Sponsor Day on animal feeding: The role of genetics in Product quality and feed efficiency in swine

THE ROLE OF GENETICS IN PRODUCT QUALITY AND FEED EFFICIENCY IN SWINE

Raquel Quintanilla

IRTA - Animal Breeding and Genetics ProgramTorre Marimon, 08140 Caldes de Montbui, Barcelona, Spain.

[email protected]

IRTA Sponsor Day, Mas de Bover, May 15th-16th 2014

• Polyunsaturated FA (PUFA)• Monounsaturated FA (MUFA)• Saturated FA (SFA)• cholesterol content

Sensory qualityHedonic evaluation

Technological Quality

Nutritional QualityConsumers 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

Principal components analysis of 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 CHARACTERISATIONDRY-CURED HAMS:

6 expert panelists • Appearence• Taste-flavour • Texture

2 muscles• biceps femoris• semimembranosus

A

B SM

BF

Sección B

Sección A

SM

BF

-1 -0.5 0 0.5 1-1

-0.5

0

0.5

1

colour

marbling

overall likingadhesiveness

bitternessaged

mould

matured

sweetness

hardnesscrumbliness

fibrousness

fat meltingmetallic

pastiness

piquantness

saltiness

umami

colourmarbling

overall liking

adhesiveness

bitterness

aged

mould

matured

sweetness

hardness

crumbliness

fibrousness

fat melting

metallicpastiness

piquantness

saltiness

umami

gluteus medius biceps femoris semimembranosus

Component 1 (19.25%)

Co

mp

on

en

t 2

(1

8.3

4%

)

GENETICS OF LIPID METABOLISM & 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 1996-2011IRTA-INIA-UAB

LIPGEN projects 2003-presentIRTA-CRAG

IBERCAL project2013-presentIRTA - UEX

THE ‘OMES AND ‘OMICS PUZZLE

MULTIDISCIPLINARY APPROACHES ARE REQUIRED !!!!

Genome Proteome

mRNA

DNA

Transcriptome

Microbiome

Metabolome

PHENOME

ENVIRONMENT(NUTRITION)

PIGGENETICS

THE BASIC MODEL FOR A GENETIST

= Genetics + Environment (Nutrition) + G * E + e

Genetic gain

GenerationN+1 Genetic variability within population

basis for SELECTION

Individual phenotype

Selection

EpigeneticsNutrigenomics

Recording PHENOME – IRTA EXPERIMENTAL FACILITIES

ControllingGrowing periodIRTA-CAP- Weight, Fat- Behavoir- Individual Feed intake control

ExperimentalSlaughterhouseCarcass traitsMeat colorPhysical-chemical measures

Meat AnalysisIRTA-CTA- Intramuscular fat- Cholesterol in muscle-Fatty acids profile- Color

Sensory characterisation- Visual, texture and

flavour attributes (carried out by expert panelists)

INFORMATION used in our 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)

PHEN

OM

ICSG

ENO

MICS &

TRAN

SCRIPTOM

ICSLIPGEN (350 Du)IBERCAL (200 Ib)

CAN WE IMPROVE IMF 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. 19970.38 (LW) 0.67 (LD) 0.32 (Pi)Larzul et al. 19970.44 (LW) Hermesch et al. 20000.35 (LW & LD) Fernández et al. 20030.25 (IB)Suzuki et al. 20050.39 (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

GenerationN+1

Selection

Varona et al. Genetical Research 2002Ovilo et al. J. Anim. Sci. 2002Perez-Enciso et al. Genetics. 2002Clop et al. Mamm. Genome. 2003Peréz-Enciso et al. J. Anim. Sci. 2005Mercadé et al. Mamm. Genome 2005Gallardo et al. Physiol. Genom. 2008Quintanilla et al. J. Anim. Sci. 2011Gallardo et al. Anim. Genet. 2011Pena 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 QTLQ – Iberian alleleq – 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

GENE EXPRESSION

• Differential gene 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

Fo

ld-c

han

ge

rati

o

Microarray ratio

qPCR ratio

TRANSCRIPTOMICS

ACACA Fatty acids profile Gallardo et al. 2009. Anim. Genet.

LDLR, APOB Serum lipidsPena et al. 2009. Anim. Biotech.

HDLBP Intramuscular fatCánovas 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

• 104 genes • 139 interactions

GENE NETWORKSLIVER - 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

17 m

od

ule

s o

f co

-exp

ress

ed

ge

nes

TRANSCRIPTOMICS

Use of genomic information in a selection scheme

Genomic Selection: using massive genotypes (60K SNPs) information in genetic evaluation (GEBV)

Current studies regarding implementation of GSmodels and methods (genomic BLUP, Bayes A, B and C, Lasso)increase of selection accuracy in several scenariosintegration of crossbreed information in purebreed selectionreduced 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

• NutrigenomicsExperiment in progress: 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 STUDIEStowards more holistic and multidisciplinary approaches

CHALLENGE: Integral analyses of all available information

Investigating possibilities and benefits 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 STUDIESFeed 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

Collaborators

Armand SánchezMarcel AmillsMiguel Pérez-EncisoJosep M. FolchQuim CasellasAngela CánovasDavid Gallardo

Luis SilióM. Carmen RodríguezCristina ÓviloAna FernándezEstefania Alvés

Romi PenaJoan EstanyLuis Varona

ANIMAL BREEDING & GENETICSJoan TibauJosé L. NogueraNoelia IbáñezJuan P. SánchezMiriam PilesQuim SolerMateu Tulsà

PRODUCT QUALITYM.Angels OliverMarina GispertFOOD TECHNOLOGYDolors GuàrdiaJacint ArnauFUNCTIONALITY & NUTRITIONIsabel Díaz

Josep Reixach

Emilio Magallón

NUTRITION & WELFAREJ. Brufau TorrallardonaE. EsteveR. LizardoE. FàbregaA. DalmauA. Velarde

GIROF. PrenafetaM. VinyesA. Bonmatí

THANK YOU

LleidaPIGs Torre Marimon

PIGS & RABBITS

MonellsPIGS

Mas de BoverPOULTRY