Martino Cassandro Italy Application of Genomic ...

Application of Genomic Analyses on Local Animal Genetic Resources

Martino Cassandro Italy

Outline

Introduction

Genomic Assisted CONSERVATION Scheme for Local AnGR

Genomic Assisted CHARACTERIZATION of local AnGR

Genomic Assisted TRACEABILITY for local Animal Products 2

Introduction

•

Why Animal Biodiversity is so important and large in Italy:

1. Area refuge after last glaciation 2. Large variety of environments (Alps, Hills, Plains, Coasts, Islands) 3. A rich history of migrations of human and animals populations 4. Animal biodiversity as tool and to valorize animals products, local/niche markets, preserve territory and culture, and population history 5. Over 57,000 animal species (and 8,000 plant species) are the numbers that make the Italy as the European nation's richest biodiversity. 3

Introduction

•

Why Genomic Approach is useful for “Local” Animal Biodiversity

1. No pedigree information are available 2. Absence of studies on performances/characterization 3. No reliable information within and among breeds/populations 4. Scarce economic interest to preserve original “genome”

4

Introduction An important strategy to INCREASE Add Value for Animal Products to PRESERVE Environment and Biodiversity to ORIENTATE Tourism & Food Consumptions might be to “PROMOTE” the LINK

LOCAL BREED

TERRITORY

PRODUCT 5

Farmer

Animal Traditional Yield Chain

Modern Integrated Chain Grassland-Pastures

Milk-Meat

Environment - Landscape

Processed Products

Commercial and Marketing

Tourism/quality of life

Economy 6

Farmer

Animal Modern Integrated Chain Traditional Yield Chain

Environmental Chain

Grassland-Pastures

Milk-Meat

Processed Products


Environment - Landscape


Local Economy 7

Farmer

Animal Modern Integrated Chain Traditional Product Chain Environmental Chain Grassland-Pastures Milk-Meat

Processed Products


Local breeds – Landscape


Local Economy 8

Introduction

Chianina-Romagnola Marchigiana-Podolica Maremmana

Reggiana

Consortium 5R Italian Meat

Parmigiano Reggiano di razza Reggiana (37 euro/kg)

Burlina

Veneto Sheep Breeds

Morlacco

Pitina 9

Introduction

BREED-PRODUCT LINK

Cinta Senese

Valdostana

Lardo di Colonnata

Fontina

Rendena

Padovana Chicken breed

Cheese Rendena

Pro Avibus Nostris 10

Conservation of Local Poultry Breeds

Genomic Conservation of AnGR

•

COVA Project, since 2000. 11 Chicken local breeds (+ other 3 avian species)

•

An In situ conservation scheme based on 3 breeding units/breed

•

Each nucleus breed, within breeding unit, is based on 20 M + 34 F

•

Males are divided in 2 families

•

Females of the same breed, within breeding unit, are reared all together

•

Individuals are genotyped extracting genomic DNA by whole blood

•

Individuals are selected in order to: – phenotypic standard breeding – family origin – individual contribution at overall genomic diversity

12

MARKER ASSISTED CONSERVATION SCHEME BIANNUAL CHANGE OF ALL ANIMALS 10 old males

17 old females 20 males

34 females

210 eggs by first family – period 1 210 eggs by second family –period 2 420 eggs in 6 weeks

0 months

0,5 months

4 months 50% N/I

210 chicks born alive

105 males 5 males/family 10 young males

5 months

105 females

6/7 months

8/9 females/family 17 young females

8/12 months

Genomic Characterization of AnGR

Breed Code

Alleles/ breed

HE (nb) ±SD

HO ±SD

FIS

fij breed

Brown Layer

BL

3.80

0.559 0.141

0.622 0.233

-0.117***

0.439

Ermellinata di Rovigo

ER

3.14

0.420±0.175

0.384±0.248

0.089***

0.573

Pèpoi

PP

2.51

0.243±0.239

0.240±0.236

0.018*

0.769

Robusta Lionata

RL

2.43

0.367±0.229

0.317±0.264

0.131***

0.657

Robusta Maculata

RM

2.17

0.293±0.225

0.292±0.226

0.003

0.721

Polverara Bianca

PB

3.01

0.436±0.190

0.366±0.201

0.161***

0.577

Polverara Nera

PN

3.45

0.463±0.177

0.413±0.170

0.109***

0.559

Padovana Camosciata

PC

2.27

0.305±0.257

0.287±0.271

0.062

0.704

Padovana Dorata

PD

2.66

0.340±0.199

0.329±0.230

0.034

0.689

Breed

PB (36)

PN (52)

RL (43) RM (45)

ER (45)

PD (24)

PC (26)

PP (45)

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Genomic Characterization of AnGR Structure analysis of six Italian local chicken breeds assuming K = 6, 7, 8, 9, 10. Only most probable solutions for each K are shown.

BL

1) 2 (

ER

5) 4 (

PP

5) 4 (

RL

3) 4 (

RM

5) 4 (

PB

6) 3 (

PN

2) 5 (

PC

6) 2 (

PD

4) 2 (

15


16 N-J tree drawn by Dk distances among nuclei


Alpagota

Lamon

Foza Brogna

Bergamasca

Fiemmese

Suffolk

17


ALP1 ALP23 ALP31 ALP28 ALP46 ALP212 ALP220 ALP2 ALP27 ALP37 FIE19 FIE2 ALP13 ALP 2 ALP 36 AL 35 AL P26 ALPP206 AL 204 AL P205 AL P214 P A 217 AL LP45 AL P20 AL P20 3 AL P2 8 A P2 43 ALLP2 01 A P2 22 B LP 09 LARO 225 AL M1 234 A P 4 A L 1 1 A LP P2 1 AL LP 22 2 A P2 224 3 LP 0 39 7

■Alpagota, ▲Bergamasca, ▲Brogna, ● Fiemmese, ■ Foza, ■ Lamon, ● Suffolk UPGMA dendrogram using “pairwise” distances 4 R3 BE R24 BE R16 BE R35 BE 30 R BE R266 BE ER 2 B R2 5 BE R BER179 BEBER12 R 20 BE R 25 BEER 10 B R 37 BEO2 233 2 8 BRRO O B BR

BRBR BR O O7 BRBR O 241 2 O 8 B BR O2 71 1 BRRO O5 44 O2 14 9 B B R 4 2 BRRO O742 2 B O2 40 BRRO735 B O 6 BR RO 69 BR O2 62 3 B O23 9 BRRO788 BR O68 O BR 67 BR O73 O5 BRO 8 94 BRO BRO 93 2 BRO 62 BRO 79 BRO 95 85 BER3 3

0 .2

40 3 LP 1 7 A LP P1 8 A L P1 8 A L P3 1 A L 21 9 A LP 1 1 A ALPP4 3 ALLP5210 A LP 221 A LP 25 A LP 202 A P 5 ALALP 8 P AL P55 ALLP290 A LP3 A P32 AL P33 ALLP9 A P21 AL P20 A L P6 AL 16 ALP216 ALPP 4 AL 351 ALP 15 ALP252 ALP ALP10 ALP42 ALP218 BRO236 ALP47 ALP54 ALP56 ALP4 ALP24 ALP213

SUF26 SUF30 SUF25 SUF4 SUF18 SUF32 SUF29 SUF7 SUF31 SUF23 SUF2185 SUF 11 SUF F9 SU 14 SUF F3 SU F1 SU F8 SUF17 SU F10 SU F27 SU F136 SUUF1 2 S O9 BR

1 R1 BE Z101 FO Z260 FO Z96 FO 113 FOZ 110 FOZ 251 FOZ 250 FOZ 107 FOZ 254 FOZ 11 FOZ1 15 FOZ1 FOZ109 FOZ120 FOZ143 FOZ108 FOZ118 FOZ259 FOZ99 FOZ103 FOZ261 FOZ105 FOZ253 FOZ98 FOZ106 FOZ257 FOZ256 FOZ117 FOZ104 FOZ25 FOZ1 2 FOZ2 00 FOZ1 58 19

18

Genomic Conservation of AnGR

A B

Family #1

C D

Family #2

19

Local Cattle Breed - BURLINA

“Morlacco cheese of Burlina cattle breed”

21

Economic Characterization of AnGR

400

“Economic comparison Burlina vs HF”

Diff. in profit, €/year per cow

200 0 €0 -200 -400

-€ 321

-600

-€ 566

-800 -€ 766 -1.000

Regional Premium

no

200 €/head

200 €/head

200 €/head

Milk valorization

no

no

+ 0,05 €/kg

+ 0,05 €/kg

9079 kg

9079 kg

9079 kg

7706 kg

Holstein Friesian yield

22


Assignment of individuals at 4 clusters breeds using software STRUCTURE 2.1, Group #1 : unknown BREED? Group #2: Burlina breed Group #3: Bruna breed Group #4: Frisona breed. Unknown Breed ?

BURLINA

BROWN SWISS

HOLSTEIN FRIESIAN

23

Outline

Introduction

Genomic Assisted CONSERVATION Scheme for Local AnGR

Genomic Assisted CHARACTERIZATION of local AnGR

Genomic Assisted TRACEABILITY for local Animal Products 24

Genomic Traceability of AnGR

Individual Genetic Fingerprint is constant 25


Probabilistc approach based on allelic frequencies Match Probability inferior to 1*E-6

(Weir, 1996)

m = number of loci n = number of alleles at locus k pki(j) = Allelic frequency of the allele i (j) at locus k 26


N° loci 12

Dairy breeds 1.57E-10

Beef breeds 1.89E-11

All breeds 1.13E-11

8

7.02E-09

2.23E-09

1.27E-09

4

1.25E-05

1.26E-05

6.76E-06

1

5.3E-02

3.64E-02

2.74E-02 27


Breed traceability approaches

Deterministic approach:

Probabilistic approach:

allelic variants fixed in different breeds (Melanocortin receptor 1 and kit genes)

allelic frequencies typical in different breeds

28

Breed Traceability for Cheese Deterministic Approach using MC1R e KIT genes Brown

Holstein

Simmental

Reggiana

(Russo e Fontanesi, 2004)

29

Model of inheritance of the Extension (E) locus and Spotted (S) locus MC1R gene

KIT gene

(Melanocortin receptor 1)

Alleles

Alleles

ED E+ e

S = not spotted s = spotted

= Black = Brown = Red

Genotypes ED ED Black Holstein ED E+ Black ED e Black E+ E+ Brown Brown Swiss E+ e Brown e e Red Simmental Reggiana

Genotypes SS not spotted Brown Swiss, Reggiana Ss not spotted ss spotted Holstein Simmental

30

Breed Traceability in Cattle (15 microsatellite) Probabilistic Approach

Breed assignement CHI CHI

98 %

ROM

2%

MAR

ROM

MAR

PIE

HF

2%

-

-

90 %

8%

-

8%

88 %

2%

2%

PIE

-

2%

5%

93 %

-

HF

-

-

-

-

100%

(Ciampolini e coll., 1999)

31


Genome wide information is necessary for several aims for AnGR:

- Conservation - Characterization - Traceability - Authenticity

…....and other traditional/already known uses as - Parentage test - Pedigree validation - Heterosis prediction - Other uses in animal breeding 32

Thank for the attention

A FUTURE PERSPECTIVE IS A MULTIFUNCTIONAL USE OF GENOME WIDE INFORMATION

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