Obiettivi e recenti acquisizioni del progetto di ricerca SelMol Paolo Ajmone Marsan Giornata di studio AVANTEA Cremona, 5 Maggio 2009
Summary! Overview of the SelMol project! The research line on Dairy Cattle! Perspectives of Genomics in Animal Breeding
Overview of the SelMol project
Objective INTEGRATE MOLECULAR METHODS IN ANIMAL BREEDING P = G + E
Biodiversity Evolution Breeding Ecosystem Human Health Safety Traceability Conservation Management
Water buffalo Dairy cattle Beef cattle Pigs Sheep Donkeys Goats Horses
Research lines and researchers 1. Dairy cattle 2. Other dairy species 3. Pigs 38 Partners 91 Researchers Link with National Breeder Organizations 4. Beef cattle and other meat species 5. Diseases and fertility 6. Traceability 7. Animal products and human health 8. Models and applications 9. Dissemination General Coordinator: Prof. Alessandro Nardone Università della Tuscia (VT)
The research line on Dairy Cattle
Organization UNIMI UNIBO Fine QTL Mapping Genome-wide analyses UNICATT UNIMI UNIBO UNITUS UNICATT UNIMI UNIBO UNITUS UNIBS ILSI Candidate genes UNIBS ILSI Milk proteins
Traditional selection scheme (Progeny test) www.anafi.it
A step back: Traditional selection Genetic Parameters Statistical Model Avg. Relationship Phenotypes Fisher s Infinitesimal Model # g/anno EBV i " = Acc L "!g www.anafi.it
Traditional Selection: Drawbacks of progeny test Negative aspects: Long time required High costs Greater help needed on: Traits with low h 2 Traits measured on one sex Traits difficult or expensive to collect. Traits measured post-mortem www.anafi.it
Perspectives of genomics in animal breeding
Molecular information in(to to) breeding MARKER ASSISTED SELECTION (MAS) GENE ASSISTED SELECTION (GAS) GENOMIC SELECTION (GS)
MAS QTLs explain only a fraction of genetic variance Trait BTA BTA1 BTA2 BTA2 Total 6 4 0 6 Milk yield 1.2 14.7 7.0 4.1 27 Fat yield 5.4 10.3 6.0 9.0 30.7 Protein yield 3.3 9.1 4.1 7.6 24.1 Fat % 4.8 35.5 7.8 0.1 48.2 Protein % 11.4 8.7 13.5 0.0 33.6 (Druet et al., 2006)
MAS Family 1 M m Q q Family 2 m M Q q Markers in LD within family and in LE In the population have limited applications
GAS " Markers in linkage disequilibrium with causative genes " Markers in causative genes (QTG) and on causative mutations (QTN) " Selection in the population (same phase) " Knowledge on the biologic base of the trait " Possible to limit the number of interaction effects to be tested on the basis of gene function (functional genomics, gene ontology, e-qtl) " Fraction of variance explained " Only a handful of causative genes identified
New technologies " Ultra high throughput sequencing Single molecule sequencing Sequencing durang synthesis Up to 600Mbases/day
New technologies [ ] the advent of DNA chip technology may make genotyping of many animals for many of these markers feasible (and perhaps even cost effective). [...] Meuwissen et al., 2001 3) Sequencing: Aprox. 54.000 SNP cattle panels now available at low cost
Genomic selection (Meuwissen, Hayes and Goddard, 2001) " Subdivision of whole genome in short (<<1Cm) intervals " Estimate of the effects of every interval " Calculation of Genomic EBVs (G- EBVs) as sum of individual effects
Genomic Selection: WHY NOW?! 1) Meuwissen, Hayes and Goddard, 2001.: Theory & Simulated statistical models.! Dividing whole genome into small segments delimited by markers! Measuring each segments effects using specific statistical models.! Obtaining a G(enomic)-EBV by summing all single effects.! high EBV accuracies from dense marker only (without Progeny Test). Sire 1 1 1 2 1 2 1 3 1 2 4 Sire 2 2 2 2 1 2 1 2 1 1 2
Genomic Selection: WHY NOW? 2) Cattle population structure & history. Linkage disequilibrium. Reduced number of markers (30.000 if equally distributed) Hayes, B. (2008)- QTL mapping, MAS, and Genomic selection - Course notes
Estimate of haplotype value (training) " EBV of sires (DYD, phenotypes) " Biological samples " Genotyping Haplotype Int. 1 Int. 2 Int. 3 Int. 4 Int. 5 1 +0.01 +1.03 +6.35 +0.89 +1.10 2 +0.06 +0.74 +2.19 +0.67 +0.20 3-0.07-0.36 +0.05-0.58 +0.05 4 +0.00-1.41-8.59-0.98-1.25
G-EBV estimate of sire 1 Haplotype Int. 1 Int. 2 Int. 3 Int. 4 Int. 5 1 +0.01 +1.03 +6.35 +0.89 +1.10 2 +0.06 +0.74 +2.19 +0.67 +0.20 3-0.07-0.36 +0.05-0.58 +0.05 4 +0.00-1.41-8.59-0.98-1.25 Sire 1 1 1 2 1 2 1 3 1 2 4 G-EBV=0,01+0,01+1,03-0,36+2,19+6,35+0,89-0,98+0,20-1,25 G-EBV= 9,16
Main goals that can be acheived!bulls with reliable (G)-EBVs at birth age.!greater accuracies in cow s indexes.!more accurate relationships between all genotyped animals.!greater genetic progress (just reducing generation interval)!g/y = i * r(ebv-tbv) *!g L!Inbreeding reduction - More bulls genotyped & less half-sibs.!great impact on costs/bull tested for I.A. Centres Progeny Test GS Time 6 years 2 years Genetic Progress 0,215 0,628 Costs $25.000.000 $850.000 (Schaeffer, 2006)
Simulated accuracies results n_phenotypes Method 500 1000 2200 LS 0.12 0.20 0.32 BLUP 0.58 0.66 0.73 Bayes 0.71 0.79 0.85 (Shaeffer et al., 2001) (Meuwissen et al., 2001)
The new frontier Avg. Genomic Relationship Relationship Genotypes Phenotypes Genetic Parameters Statistical Statistical Model Genetic Model Parameters Genomic Fisher s Model Infinitesimal (new assumptions) Model G-EBV # g/anno = i " Acc L "!g Genetic structure Quantitative Traits
GS: present and future! First results on applied GS in:! United States! Australia! New Zealand! the Netherlands! Importance of GS in worldwide research.! Economic ( $ in research)! Scientific ( number of scientific publications)
SelMol research activities Information and samples gathering Defining sampling strategies Simulations Sampling procedure DNA estraction and genotyping Now that genotypes are available Test GS in Italy
Three main Italian dairy cattle breeds. Frisona Italiana. 1200 bulls sampled for genotyping (in progress). Bruna Italiana 761 bulls genotyped (almost all available bulls) Pezzata Rossa Italiana 488 bulls genotyped (almost all available bulls) Information available on all bulls: Pedigree and family structure Relationship EBVs and their accuracies on all traits DYD
Defining sampling criteria for Frisona Italiana bulls GOAL: To sample bulls mantaining as much variabilty as possible. A) 1200 bulls:! PFT Accuracy threshold;! Most father-son couples as possible;! Most number of families as possible PFT 250 200 FREQUENCY 150 100 50 0 <=-1800-1800>=-1600-1600>=-1400-1400>=-1200-1200>=-1000-1000>=-800-800>=-600-600>=-400-400>=-200-200>=0 0>=200 200>=400 400>=600 600>=800 800>=1000 1000>=1200 1200>=1400 1400>=1600 1600>=1800 1800>=2000 >2000 PFT - SAMPLED PFT - ALL
SNPs information is useful NOT ONLY to selection..! G. Population structure studies! Signatures of Selection! Differences between (milk & meat) breeds.! Linkage Disequilibrium in Italian breeds.! Association studies (geno-pheno).! QTLs (as by-product of GS)! QTLs -#Candidate genes.
Thank you for your attention!