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
Environment - Landscape Processed Products
Commercial and Marketing Tourism/quality of life
Local Economy 7 Farmer
Animal Modern Integrated Chain Traditional Product Chain Environmental Chain Grassland-Pastures Milk-Meat
Local breeds – Landscape Processed Products
Commercial and Marketing Tourism/quality of life
Local Economy 8 Introduction
Chianina-Romagnola Consortium 5R Burlina Morlacco Marchigiana-Podolica Italian Meat Maremmana
Reggiana Parmigiano Pitina Veneto Sheep Reggiano 9 di razza Reggiana (37 euro/kg) Breeds Introduction
BREED-PRODUCT LINK
Cinta Senese Lardo di Colonnata Rendena Cheese Rendena
Valdostana Fontina Padovana Chicken Pro Avibus breed 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 0 months
210 eggs by first family – period 1 0,5 months 210 eggs by second family –period 2
420 eggs in 6 weeks 4 months 50% N/I
210 chicks born alive 5 months
105 males 105 females 6/7 months 5 males/family 8/9 females/family
10 young males 17 young females 8/12 months Genomic Characterization of AnGR
Breed Alleles/ HE (nb) HO Breed FIS fij breed Code breed ±SD ±SD 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
PB (36) PN (52) RL (43) RM (45) ER (45) PD (24) PC (26) PP (45)
14 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.
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15 Genomic Characterization of AnGR
16 N-J tree drawn by Dk distances among nuclei Genomic Characterization of AnGR
Alpagota Lamon
Foza Brogna
Bergamasca Fiemmese Suffolk 17 Genomic Characterization of AnGR
■ ▲ , ▲ , ● , ■ , ■ , ● Alpagota, Bergamasca Brogna 3 2 Foza Lamon Suffolk Fiemmese8 0 6
1 2 31 2 2 7 4 2 7 2
P 2 2 P P 9 P P 3 2 3 6
L L L P P 1 L L 1 5 P 2 3 6 LP 6 L L A A 3 A E A A L P 4 E 2 0 I P A 5 I UPGMA dendrogram using A “pairwise”A distances 0 P L A 2 F L P 0
F L 2 7
14
ALP1 A
L P 2 A 1 B A P 2 5 3 A L P B L 2 E P 4 0 8 B A L B E A L P P 2 0 3 E R A 4 1 B R L L P 2 E R 3 A 2 0 2 B E A A P R 2 4 L 2 2 9 E 1 L P B B R 3 4 A L P 2 0 5 R 6 A 2 2 4 B E E 3 5 A L P 3 B 2 0 L P 2 1 R R 6 A 2 4 E E 0.2 A L P B B 2 6 A L O 1 1 R R 2 R 1 2 B E E 1 5 A M P 2 3 B E R R B A 2 7 L L P 2 4 B B E R 1 9 A L 2 7 B RO E R 2 2 A P 2 0 R 2 0 L P 2 9 R A L 3 B O 2 1 5 A P P 0 B R 2 3 0 L L 4 3 B O 3 7 A P 1 R R 3 A L P 7 B O O 8 A L 1 8 R 7 2 A P 1 B 24 2 L P 8 BR R O 1 A L 3 1 O 8 A LP 1 B O 7 1 A P2 9 BR R 24 1 L P1 1 B O O 4 A L 4 R 1 59 A P 53 B O 4 AL P 0 B R 24 2 L 21 1 B RO O 2 A P 2 R 2 74 AL P2 5 B O 4 L P2 2 R 23 0 A L 20 BR O 5 A P 5 B O 76 AL LP B RO 69 A P8 B RO 6 AL 55 R 23 2 LP 9 B O2 9 A P2 RO 38 AL 30 BR 7 LP 2 B O 6 8 A P3 RO 8 AL 33 BR 67 ALP 9 B O 7 LP RO 3 A 21 BRO 58 ALP 0 BR 94 LP2 BR O 93 A LP 6 O26 A 16 BRO 2 ALP 6 BR 79 LP21 O 95 A 34 B RO 8 A LP BE 5 ALP51 R 33 P215 AL 2 A LP5 A LP10 A LP42 S UF26 AL P218 S UF30 BR O 236 S UF25 A LP47 SU F4 A LP54 S UF18 A LP56 S UF32 ALP 4 S UF29 A LP24 SU F7 ALP213 S UF31 S UF23 F28 S U 5 S UF1 UF11 S U F9 S 14 SU F 3 SUF U F1 S F8 SU 7 UF1 0 S UF1 S F27 SU 13 UF 16 S UF 2 S O 9 BR
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1 3 5 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” 200
0 € 0 -200
-400 -€ 321
-600 -€ 566 -800 Diff. in profit, €/year per cow cow €/year per profit, in Diff. -€ 766 -1.000 Regional Premium no 200 €/head 200 €/head 200 €/head
Milk valorization no no + 0,05 €/kg + 0,05 €/kg
Holstein Friesian yield 9079 kg 9079 kg 9079 kg 7706 kg
22 Genomic Characterization of AnGR
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 BURLINA BROWN HOLSTEIN Breed? SWISS 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 Genomic Traceability of AnGR
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 locus pki(j) = Allelic frequency of the allele i (j) at k 26 Genomic Traceability of AnGR
N° loci Dairy Beef All breeds breeds breeds 12 1.57E-10 1.89E-11 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 Genomic Traceability of AnGR
Breed traceability approaches
Deterministic approach: Probabilistic approach: allelic variants fixed in different allelic frequencies typical in breeds (Melanocortin receptor 1 and kit genes) 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 = Black S = not spotted E+ = Brown s = spotted e = Red
Genotypes Genotypes ED ED Black Holstein SS not spotted Brown Swiss, ED E+ Black Reggiana ED e Black Ss not spotted E+ E+ Brown BrownSwiss ss spotted Holstein E+ e Brown Simmental e e Red Simmental Reggiana
30 Breed Traceability in Cattle (15 microsatellite) Probabilistic Approach
Breed assignement CHI ROM MAR PIE HF CHI 98 % 2 % - - ROM 2 % 90 % 8 % - MAR 8 % 88 % 2 % 2 % PIE - 2 % 5 % 93 % - HF - - - - 100%
(Ciampolini e coll., 1999) 31 Genomic Traceability of AnGR
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
33