Application of Modern Genetic Technologies to Improve Efficiency of Pig Production and Pork Quality

Transcription

1 Application of Modern Genetic Technologies to Improve Efficiency of Pig Production and Pork Quality RMC, June 18, 2012 Dave McLaren, Genus & Andrzej Sosnicki, PIC BLUP and SNPs OPTIMAL CONTRIBUTION THEORY PERFORMANCE TESTING CANDIDATE GENES GENOME WIDE ASSOCIATION STUDIES GENOMIC RELATIONSHIP MATRIX AND GENOMIC IMPUTATION NEXT GENERATION SEQUENCING CONCLUSIONS

2 Genetic Selection Number of Pigs Parental Generation Mean Performance Parental Generation Worst Average Selection Intensity Mean Performance of Selected Parents Selected Dams Selected Sires Best Rate of RESPONSE Superiority of selected parents (SELECTION INTENSITY) Proportion of superiority passed on to progeny (HERITABILITY) Difference between worst & best (VARIATION) Progeny Generation Selection Response Progeny Generation Mean Performance Rate of change of parents GENERATION INTERVAL G yr = r TI x s A x (i/t) Worst Average Best

3 Genetic Evaluation Today Combines Data From Multiple Sources EBV economic weights Lean Yield Meat Quality Robustness Feed efficiency Growth rate Sow productivity Index = a 1 GEBV 1 + a 2 GEBV

4 128 FIRE Feeders ~ 15,000 crossbred pigs per year

5 Real Time Ultrasound (back fat, muscle depth, marbling)

6 26 24 EBV for Physiological Fitness Lactic ATP Acid CHO Frequency, % Lactate Values on Off-Test Boars

7 GN-Xbred systems SIRE LINE DAM LINE EBVs CBVs and selection decisions GN MULTIPLICATION GN Progeny Performance Data PICTraq Database COMMERCIAL Breeding stock Commercial Sow Performance Data COMMERCIAL CROSS BRED SLAUGHTER PIGS Commercial Progeny Performance Data

8 Genus High Performance Computing cluster

9 Number of SNPs DNA marker use in commercial pig breeding is an example of the long nose of innovation 1991 HAL 1994 ESR 1999 FUT1 & PRKAG RN & MC4R 2004 Large-scale SNP discovery 2003 MIS Large-scale SNP discovery, 2009 genome scans, sequencing Single genes, QTL Candidate genes

10 We are now living in the Genomics Era Livestock genomes sequenced Hundreds of thousands of SNPs in the public domain Marker testing costs drop dramatically 100x decrease in the last 10 years Advent of genome wide association studies (GWAS), GW-MAS (marker assisted selection), possibility of genomic selection (GS) Human genome: 2003 Chicken genome: 2004 Cattle genome: 2009 Pig genome: 2012

11 Availability of the Illumina PorcineSNP60 BeadChip in January 2009 was a key enabling tool 64,000 SNP marker tests Cost for genotyping one animal ~$150 $100 PorcineSNP60 BeadChip The beadchip holds probes that simultaneously identify the alleles present for every marker on the chip in the DNA sample applied

12 Abundant phenotypes and genotypes are essential to implementing genomic selection 0 2,000,000 4,000,000 6,000,000 8,000,000 10,000,000 12,000,000 14,000,000 16,000, Year Animals (cumulative) 0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000 3,500, Year Tissue(cumulative) 0 250, , ,000 1,000,000 1,250,000 1,500, Year Animals 0 40,000 80, , , , , , Year Tissue

13 Genomic selection accelerates genetic improvement in pigs by increasing the accuracy of selection and, potentially, selection intensity s G yr = r TI x A x (i/t) where: G yr = Annual improvement in Index breeding value r TI = Accuracy of selection = Correlation between the true commercial breeding value (T) & the Index (I) of trait EBVs s A i = Genetic standard deviation of the Index, $ = Intensity of selection t = Generation interval, years

14 The first PIC applications of genome wide association results in September 2010 were trait line specific Scrotal hernia Grow-finisher mortality Total number born

15 Blended Estimated Breeding Values Genomic Enhanced Breeding Values = Blended EBV bebv = pebv + gebv ( i ) Genomic EBV Polygenic EBV N.Deeb

16 Genomic Selection Genomic selection uses a very large number of marker genotypes (Genome Scan) ideally spaced evenly across the entire genome - to predict the total breeding value of an animal The underlying assumption of genomic selection is that a subset of the marker genotypes are in linkage disequilibrium (LD) with QTL alleles that affect the economic traits under selection Requires prior genotyping of a very large number of marker genotypes on a large population of animals (Training Set) with accurate phenotypes for the traits under selection, to predict the phenotypes of the current set of animals being genotyped

17 Genomic Selection Without genomics, all we know about a young animal s genetic potential is the average of its parents estimated genetic values. We have no way to determine whether this young animal has a better than average or a poorer than average sample of genetic material from its parents, until we have measured its phenotype.

18 Genomic Selection More Accurate Estimate of Relationships R = 0.5

19 Genomic Selection More Accurate Estimate of Relationships R = 0

20 Genomic Selection More Accurate Estimate of Relationships R = 1.0

21 Information Extracted from the DNA Can be Used Mainly in Two Ways: Two-Step Marker effects: estimate the effect of each SNP on each trait. Genomic relationships: estimate the actual fraction of genes identical by descendent Single-Step

22 To simultaneously improve dozens of traits across eight major lines required a new approach Sept Jan Custom small panel tests Single step analysis Ignacy Misztal, University of Georgia Department of Animal and Dairy Science

23 The full benefit of genomic selection requires 60k genotypes on selection candidates, which are not economically feasible* without imputation Sept Custom small panel tests Jan Summer 2012 Single stage analysis Imputation of missing genotypes John Hickey, University of New England Hickey, J.M., B.P. Kinghorn, B. Tier, J.H.J. van der Werf and M.A. Cleveland A phasing and imputation method for pedigree populations that results in a single-stage genomic evaluation. Genetics Selection Evolution, 44:9. $100 = $12M p.a.

24 Imputation*: using well-spaced LD genotypes on selection candidates to fill-in missing HD genotypes *Imputation the substitution of some value for a missing data point. Once all missing values have been imputed, the dataset can then be analyzed using standard techniques for complete data. GN parents, grandparents, All HD genotyped $150 $150 GN progeny HD genotype imputed from LD panel and pedigree HD $15 $15

25 How it works Aoccdrnig to rscheearch at Cmabrigde Uinervtisy, it deosn't mttaer in waht oredr the ltteers in a wrod are, the olny iprmoetnt tihng is taht the frist and lsat ltteer be at the rghit pclae. The rset can be a toatl mses and you can sitll raed it wouthit a porbelm. Tihs is bcuseae the huamn mnid deos not raed ervey lteter by istlef, but the wrod as a wlohe. Your brain can do this after you learn the language Imputation algorithms can do this after they learn which haplotypes are present in the population

26 Building a library Library Sire Dam Offspring 0xxxx1xxx0xxxxx0x 1xxxx1xxx0xxxxx1x

27 Summary: The evolution of genomic selection in a commercial pig breeding company Then Then Now Future Candidate gene approach Multiple markers All markers Sequence Significant markers added to EBVs; 1-7k SNP scans Blending genomic EBVs (sum SNP effects) with traditional pedigree EBVs to give EBVs with higher accuracy; 60k SNP chip Integrating Genomic relationship matrix into traditional Additive relationship matrix in a single step in multiple trait BLUP genetic evaluations; 60k SNP chip Imputation of high density genotype from low density scans; sequence data

28 Sequencing price

29 Sequencing will enable... Detection and elimination of recessive lethal alleles Identification of QTL across breeds Framework for targeted genome editing Nanopore Sequencing Towards the 15-minute Genome Nanopore DNA sequencing by Oxford Nanopore

30 Changing Attitudes to GM Livestock? Will world food needs and global agribusiness erode societal resistance to GM? Will non-gm food become a rich country niche? More than 10 years of GMO crops without any safety issues New technology Capable of modifying a genome without introducing foreign genetic material Indistinguishable from naturally occurring polymorphisms

31 New Genome Editing Tools ZFNs & TALENs* Molecular Scissors Cleave a double strand break at a single predetermined base pair site in the genome Cutting triggers DNA repair that can be used to disrupt coding or enhance insertion Not transgenic, no recombinant DNA used, no exogenous DNA inherited, no left over viral vector DNA Animals would today not fall under FDA jurisdiction *ZFNs: Zinc Finger Nucleases TALENs:Transcription Activator-Like Effector Nucleases

32 Some early conclusions from implementing genomic selection in PIC Genomics is here to stay Genetic tools have become increasingly sophisticated: Genome Wide Association Studies Marker Assisted Selection Genomic Selection using Single Step Genomic Evaluation and Imputation Large numbers of genotypes are required for implementation of GS Genus has, with UNE, developed a powerful, accurate imputation tool called AlphaImpute Used with SSGE, accuracy of EBVs for selection candidates will be significantly increased using AlphaImpute Large-scale genomic evaluation and selection has been developed to operate in a pig breeding company production environment, and further research will continue to improve the applications Genome sequencing and editing promise powerful tools for future genetic improvement

33 Thank you for your attention Questions? Pioneering animal genetic improvement to help nourish the world