Genomic Prediction in Hereford Cattle

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1 World Hereford Conference Calgary 2012 Genomic Prediction in Hereford Cattle Dorian Garrick Lush Endowed Chair in Animal Breeding & Genetics Genomics Genomic Prediction Ranking candidates for selection using knowledge of the complete set of genes along with conventional pedigree and performance information Using everything we ve got to obtain the most accurate EPD/EBV (at as young an age as possible)

2 Performance of the Progeny Sire Offspring of one sire exhibit more than ¾ diversity of the entire population Progeny +30 kg +15 kg -10 kg + 5 kg +10 kg +10 kg We Learn about Parents from Progeny Sire (EBV is shrunk ) (<2x progeny) Sire EBV kg Progeny How much we shrink depends upon the number of progeny +30 kg +15 kg -10 kg + 5 kg +10 kg +10 kg EBVs on widely-used old sires are accurate Sire Sire EBV kg With enough progeny, this is usually close to the bulls true EBV/EPD (not surprisingly!)

3 Chromosomes are a sequence of base pairs Part of 1 pair of chromosomes Cattle usually have 30 pairs of chromosomes One member of each pair inherited from the sire, one from the dam Each chromosome has about 100 million base pairs (A, G, T or C) About 3 billion describe the animal Blue base pairs represent genes Yellow represents the strand inherited from the sire Orange represents the strand inherited from the dam EBV is sum of the Gene Effects Blue base pairs represent genes Sum=+2 Sum=+8 EBV=10 EPD=5 EPD is HALF the sum of the gene effects Consider 3 Bulls EBV= EBV= EBV= Below-average bulls will have some above-average alleles and vice versa!

4 SNP Genotyping the Bulls of 50,000 loci (50k chip) AB BB AA EBV=10-5 EBV= -6-5 EBV = 2-5 Regress performance on SNP genotype Estimated Breeding Value Variation due to other genes Slope = advantage of substituting an A allele with a B allele AA AB BB Linkage Disequilibrium (LD) AB BB AA LD occurs when genotypes at one locus are predictive of genotypes at another

5 Research finds Genomic Locations This kind of approach shows Gene(s) that control horned/polled in Bos taurus& Bos indicusare at the end of chromosome 1 Horned animals have 2 copies of the horned allele Gene(s) that control scurs are on chromosome 19 They behave differently in males and females Polled and scurred genes interact (are epistatic) The actual genes & causal mutations are unknown S Schmutz, University of Saskatoon Horns and Scurs Polled (P) is dominant to horned (p) Scurs (S) is dominant in males to smooth (s) Chrom 1 Chrom 19 Gender Polled/Horned Scurs Male Female PP, Pp SS Scurs Scurs PP, Pp Ss Scurs Polled PP, Pp ss Polled Polled pp SS, Ss, ss Horns Horns Scurs cannot be seen when horns are present! Scurs are horn tissue NOT attached to the skull but can be hard to classify Can be very small and hard to detect especially at a young age Can become attached in older animals and mistaken for horns Long & Gregory 1978 J Heredity Apply same Technique to Growth Use a historical population of bulls and cows with EPD/EBV information that have been genotyped with 50k panels Derive an EPD/EBV for every chromosome fragment (we call this training), and find the regions with biggest effects

6 Cut genome into 2,700 1Mb windows #SNPs %Var Cum%Var map_pos 7_93 20_4 13_58 26_34 6_29 4_75 4_114 2_121 18_55 8_88 Regions with biggest effects Angus Birth Weight Major Regions for Birth Weight Genetic Variance % Chr_mb Angus Hereford Limousin Simmental Gelbvieh 7_ _ _ _ Some effects appear to be missing in some breeds Are Herefords nearly all homozygous for the large or small variant on chromosome 14? Some of these same regions have big effects on one or more of weaning weight, yearling weight, marbling, ribeye area, calving ease Iowa State University (ISU) A land-grant institution with responsibilities for research, teaching and extension Such activities have been applied to genetic improvement of animals since 1930 s when Iowa State Professor, Dr JL Lush, wrote the first textbook on animal breeding That tradition continues just as strongly today as we research the role of genomics for improvement

7 National Beef Cattle Evaluation Consortium (NBCEC) Iowa State University leads a consortium of researchers and extension personnel to: Develop and implement improved predictions so selection can enhance economic viability Streamline adoption of new genetic evaluation methodologies Identify new traits & technologies Not a service consortium, that is a role for business & industry Genomics provides new evaluation options, especially for hard-to-measure traits Pfizer and Igenity ISU/NBCEC had previously worked with Pfizer Animal Genetics to develop multi-breed genomic prediction tools and identified from their data that predictions developed in one breed did not perform well in other breeds Merial Igenity to develop genomic prediction tools for Angus cattle that could be marketed as a cheaper 384 SNP panel to be more cost-effective than using 50k Validation is Critical The NBCEC had been promoting independent validation of any genomic tests before their widespread adoption by industry This work showed that some of the tests Pfizer Animal Genetics and Merial Igenity had wanted to market did not perform to specifications We promoted the same validation be adopted by any Breed Association considering the use of genomics to enhance their EBV calculation

8 Good Predictive Power in Angus Trait Angus (3,500) Igenity Pfizer BirthWt WeanWt YearlingWt Milk Fat REA Marbling CalvEase (D) 0.69 CalvEase (M) 0.73 Scrotal Circ 0.71 Genetic Correlations from k-fold or independent validation Business Model (US Angus) Breeder sends DNA American Angus Association recodes sample and ships DNA Blending molecular & conventional Genotyping Prediction equation $$$$$$ Pfizer or Merial Investment in training to develop a proprietary prediction system Each test does nothing to improve the prediction Angus Predictions No Good in Red Angus Trait Validating in American Angus Validating in Red Angus BirthWt WeanWt YearlingWt Fat REA Marbling CalvEase (D) CalvEase (M)

9 American Hereford Association Vision To be the preferred beef breed Mission To provide leadership, to record, promote & facilitate production & consumption of Hereford beef 7 Core Strategies Increase the quality, consistency & reliability of Hereford genetics A number of initiatives including Validate Genomic Predictions Angus Predictions No Good in Hereford Genetic Correlations Trait Validation in Hereford Birthweight 0.18 Weaning wt 0.14 Yearling wt 0.17 Milk 0.02 Calving Ease D 0.10 Calving Ease M 0.19 Fat 0.07 Marbling 0.16 Ribeye Area 0.06 Scrotal Circum 0.03 Using 50k genotypes from USDA-funded project to characterize 2,000 bulls Raw Correlations standardized for EPD accuracy Cannot predict across-breed using the 50k procedures that are adequate within breed Hereford Predictions Genetic Correlations Trait ASREML 4-fold x validn Birthweight 0.52 Weaning wt 0.38 Yearling wt 0.44 Milk 0.26 Calving Ease D 0.42 Calving Ease M 0.20 Fat 0.43 Marbling 0.27 Ribeye Area 0.44 Scrotal Circum 0.27 Accuracy from 1,050 Herefords Poorer than accuracy from 3,500 Angus Size of the training population is important

10 International Hereford Comparisons Genetic Correlations Trait ASREML 4-fold x validn 400 URG bulls Mean= CDN bulls Mean= 0.26 Non US 59 ARG bulls Mean= US-like 41 ARG bulls Mean= 0.14 Birthweight Weaning wt Yearling wt Milk Calving Ease D Calving Ease M Fat Marbling Ribeye Area Scrotal Circum PanAmerican International Evaluation Within- and Across-breed Prediction Within-breed prediction is reliable in close relativesif you have enoughhistorical genotypic and phenotypic data for training Predictive ability erodesif the test gets applied to animals only distantly related to those historical animals used for training Poorest predictions will be across-breed Different Business Model Every sample and $$$$ contributes to improved prediction equation Breeder sends DNA gets back more accurate EPD American Hereford Association arranges DNA shipment $$$$$$ Investment in training to develop prediction system Blending molecular & conventional GeneSeek Genotyping Prediction equation Public R&D

11 Blending Genomic information should be combined with pedigree and performance information Via a selection index (after EBV) eg AHA Breedplan As a genetically correlated trait (with rg) eg US Angus By including like an external EBV eg US Simmental By using the genotypes directly in evaluation to modify the pedigree relationship matrix eg US Dairy Impact on Accuracy--%GV=10% Genetic correlation=0.3 Blended Accuracy BIF Accuracy Impact on Accuracy--%GV=40% Genetic correlation=0.64 Blended Accuracy BIF Accuracy

12 Improved Accuracy Genomic testing will not improve the accuracy of an already accurate EBV Investing in a test for a low accuracy bull guarantees accuracy will increase EBV is equally likely to increase or decrease Amount of accuracy increase depends upon reliability of the EBV/EPD of chromosome fragments Predictions in US Breeds Trait Angus (3,500) Hereford (1,050) Simmental (2,800) Limousin (2,400) Gelbvieh (1,181) BirthWt WeanWt YlgWt Milk Fat REA Marbling CED CEM SC Genetic correlations from k-fold validation Summary More data for estimating effects of the genomic regions will improve the accuracy of prediction The best way to get more data is to make genomic predictions available in National Cattle Evaluation to breeders prepared to pay for 50k genotyping their animals (USD$80 from hair) Working on implementation of a smaller (10k) panel that would be a little cheaper but requires an extra imputation step to predict the 50k genotypes

13 Major Regions for Birth Weight Genetic Variance % Chr_mb Angus Hereford Limousin Simmental Gelbvieh 7_ _ _ _ Can we use findings in other breeds to help find the actual mutations in Herefords? This requires sequencing individual bulls Plans to Improve Predictive Accuracy Sequencing To find variants that might be responsible Currently doing 10 Hereford bulls Imputation So we don t need to re-genotype animals with 50k Identify better markers To identify the most predictive variants Add improved markers to SNP panel To reduce the need for imputation in future Summary Genomics will increase accuracy of evaluation The technology is starting to mature but works better in some traits and breeds than in others It works better with greateramounts of data Genomic prediction will get more accurate than it is today if we continue to undertake research

14 Acknowledgements Dr Mahdi Saatchi, NBCEC postdoc Did most of the Breed Association analyses AHA breeders and board members that saw strategic advantage of genomic technology Canadian, Uruguayan and Argentine cattleman and researchers that collaborated with data, funding and expertise ISU colleagues contributing to development or methodologies NBCEC collaborators involved in validation and extension of research results