Advanced genetics

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This is the 5th and final presentation in a 5-part webinar series on Breeding Better Sheep & Goats. The presenter is Susan Schoenian, University of Maryland Extension Sheep & Goat Specialist.

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Advanced genetics

  1. 1. 2013 Winter Webinar Series: Breeding Better Sheep & GoatsAdvanced Genetic Improvement SUSAN SCHOENIAN Sheep & Goat Specialist University of Maryland Extension sschoen@umd.edu - www.sheepandgoat.com
  2. 2. Advanced genetic improvement1. Across-flock genetic evaluation National performance record keeping programs a) NSIP/LambPlan (meat and fiber animals) b) AIPL/DHI/AGDA (dairy animals)2. Genomics - marker-assisted selection (MAS) a) Single and lowly complex quantitative traits b) Complex quantitative traits
  3. 3. National Sheep Improvement Program (NSIP) • Established in 1986 to assist producers in compiling records into a usable form. • Spreadsheet data was sent to a breed coordinator who compiled data and sent it to Virginia Tech for processing. • Geneticists used complex software and mainframe computers to calculate flock and across-flock EPDs.
  4. 4. National Sheep Improvement Program (NSIP) • In 2010, a cooperative agreement was reached between NSIP and Meat and Livestock Australia (MLA). • Genetic evaluations are now performed by LambPlan, Australia’s national sheep performance program. • LambPlan calculates flock www.sheepgenetics.org.au and across-flock EBVs (NSIP calculated EPDs).
  5. 5. Terminology • EPD: Expected Progeny Difference Estimates the genetic value of an animal as a parent. Predict differences in performance between future offspring. (½ x EBV) • EBV - Estimated Breeding Value A value that expresses the difference between an individual animal and the benchmark to which the animal is being compared (2x EPD). • Accuracy Reliability of EPD / EBV. The higher the accuracy is the closer the EBV is to the animal’s true breeding value.
  6. 6. Information used to calculate EPDs/EBVs + Animal’s own performance for a particular trait • Data is adjusted for known environmental effects, e.g. type of birth and rearing, age of dam. + Animal’s own performance for genetically-related traits. + The performance of relatives for those traits.
  7. 7. Across-flock EPDs/EBVsComparing animals in different flocks regardless of location or management.  Flocks must be genetically linked/connected.  Most effective way to create linkage is to have progeny from a sire in different flocks.  Should use rams from other NSIP flocks to establish linkages.
  8. 8. EBVs for weight traits 1) Birth weight [BWT] Direct genetic effects 2) Maternal birth weight [MBWT] Ewe effects (uterine environment) 3) Weaning weight [WWT] Pre-weaning growth potential 4) Maternal weaning weight [MWWT] Maternal milk (mothering ability) 5) Total Maternal Weaning weight Milk + growth (calculated)
  9. 9. EBVs for weight traits 6) Post-weaning weight [PWWT] (120-d) 7) Yearling weight @ 12 mos. [YWT] 8) “Hogget” weight @ 18 mos. [HWT] 9) Adult body weights
  10. 10. EBV for wool traits 1) Fleece weight [GFW] % of mean grease fleece weight 2) Fiber diameter [FD] (microns) 3) Staple length [SL] (mm) 4) Fiber diameter coefficient of variation [FDCV] (%) Fleece uniformity 5) Fiber curvature [CURV] (°) Crimp frequency 6) Clean fleece weight 7) Staple strength
  11. 11. EBVs for body composition traitsdetermined via ultrasound (certified technician) 1) Fat depth [CF] (mm) Adj. to 110-lb. post-weaning weight Adj. to 187-lb. yearling weight 2) Loin eye muscle depth [EMD] (mm) Adj. to 110-lb. post-weaning weight Adj. to 187-lb. yearling weight
  12. 12. EBVs for reproductive traits 1) Number of lambs born [NLB] 2) Number of lambs weaned [NLW] 3) Scrotal circumference [SC] (cm)
  13. 13. EBVs for parasite resistance 1) Worm egg count [WEC] (%) At weaning or at early or late post-weaning ages
  14. 14. Interpreting dataTrait abbreviation BWT, kg MWWT (mm) WWT, kg PWT, kg YWT, kgEBV 0.28 1.4 6.4 9.8 6Accuracy 65 55 72 76 68Compared to a ram with an 0.14 kg Produce daughters 3.2 kg heavier at 4.9 kg 3 kg heavier atEBV of 0, this ram’s progeny heavier at birth who wean 0.7 kg weaning heavier at yearlingwill be: heavier post weaningTrait abbreviation NLB, % NLW,% PSC, cmEBV 2 3 1.2Accuracy 42 43 55Compared to a ram with an EBV of 0, Produce 1% more Wean 1.5% more Have 0.6 cm greaterthis ram’s progeny will: lambs lambs scrotal circumferenceTrait abbreviation PWEC, %EBV -20Accuracy 60Compared to a ram with an EBV of 0, this ram’s progeny will be: 10% more resistant at weaning
  15. 15. Breed sire summaries
  16. 16. ImpactsSpecies Annual Traitbreed progressHolstein Milk + 0.8 %cattle productionAngus Yearling + 0.5 %cattle weightSuffolk 120-d + 0.3%sheep weight
  17. 17. Selecting sheep (or goats) using EBVs• Never select for only one trait: there may be undesirable consequences.• Used balanced selection – Use a selection index • NSIP/LambPlan • Make your own index. • Selecting on the basis of two or more EBVs is similar to selecting on the basis of an index. I = (V1 x EBV1) + (V2 x EBV2) + (V3 x EBV3) + … My index = (0.33 x NLW EBV) + (0.33 X WWT EBV) + (0.33 WEC EBV)
  18. 18. NSIP/LambPlan Selection Indexes1) Western Range Index (PWWT EBV) + (0.26 x MWWT EBV) – (0.26 YWT EBV) + (1.92 x YFW EBV) - (0.47 x YFD EBV) + (0.36 x NLB EBV)2) Ewe productivity index 1) Katahdin (hair sheep) (0.246 x WWT EBV) + (2.226 x MWWT EBV) + (0.406 x NLW EBV ) – (0.035 x NLB EBV) 2) Polypay (maternal breeds) (0.265 x WWT EBV) + (1.200 x MWWT EBV ) + (0.406 x NLW EBV) – (0.035 x NLB EBV)3) Carcass plus (for terminal sire breeds) (5.06 x PWWT EBV) – (13.36 x CF EBV) + (7.83 x EMD EBV)4) Lamb 2020 (0.32 x WWT EBV) + (0.47 x PWWT EBV) – (0.21 x BWT EBV) – (55 x CV EBV) + (1.54 x EMD EBV) – (0.04 x PWEC EBV)
  19. 19. What about meat goats? • Previously, some Boer goat breeders participated in NSIP via B-GIN (Boer Goat Improvement Network). • NSIP/LambPlan could be used by meat goat producers who are interested in making genetic improvements in their herd and breed. – Kiko data from NSIP has already been migrated to LambPlan • Several states operate meat goat buck performance tests.
  20. 20. Participating in NSIP/LambPlan • Send enrollment form and check to NSIP. • Install Pedigree Wizard software on your computer. • Enter your data. • You will receive data back 3-4 days after the 15th or end of the month. • NSIP publishes trait leader summaries. Costs Enrollment fee ($50-$350) $25 for additional breed(s) Database fee ($2/animal lifetime)
  21. 21. Genetic evaluation of dairy goats • Genetic evaluation of dairy goats (and cows) is done by the Animal Improvement Programs Laboratory (AIPL) in partnership with industry organizations. • As of 1/1/13, enrollment in DHI was 15,357 does from 446 herds in 41 states. http://aipl.arsusda.gov/
  22. 22. Genetic evaluation of dairy goats incorporates production records fromDHI and linear appraisal and pedigree records from ADGA. Production evaluation Type evaluation DHI records Linear appraisal 1. Stature • Milk yield 2. Strength • Fat yield 3. Dairyness 4. Rear legs • Fat percentage 5. Rump angle 6. Rump width • Protein yield 7. Fore udder attachment • Protein percentage 8. Rear udder height 9. Rear udder arch 10. Udder depth 11. Medial ligament 12. Teat placement 13. Teat diameter Final score
  23. 23. Different terminology – same technology1. Predicted transmitting ability (PTA) Average genetic value for a certain trait that an animal transmits to its offspring. Performance of animal + genetic merit of parents and relatives + progeny2. Estimated transmitting ability (ETA) Artificial index based on PTAs of sire and dam. An estimate of transmission of genetic merit to offspring.3. Production type index (PTI) Artificial index calculated from production and type evaluations. – PTA of fat corrected milk [FCM] + PTA of linear appraisal final score – PTA 2:1 or 1:2 production: type4. Reliability
  24. 24. www.adgagenetics.org
  25. 25. Genomics• The genome is an organisms complete genetic make-up.• The sheep genome is more than 90 percent mapped and has ~90% homology with the cattle sequence, leaving few gaps. – Ovine SNP50 BeadChip (~$200) is a cutting-edge genetic tool that allows researchers to characterize the genetic variation at more than 50,000 SNPs in the sheep genome.• Gene mapping in goats is much less advanced, containing only half the number of markers as sheep.
  26. 26. Detection of genetic markers QUALITATIVE TRAITS QUANTITATIVE TRAITS Single gene 1. Major genes with 2. Many genes with small effects large effects on trait. individual effects. Examples Examples Examples 1) Spider lamb disease 1) Booroola fecundity Most traits of 2) Hairy lamb syndrome 2) Inverdale fecundity economic importance 3) Myostatin 1) Reproduction 3) Polled intersex 4) Footrot 2) Growth 4) Horns 5) Cold tolerance 3) Milk production 5) Callipyge 6) Scrapie resistance 4) Carcass 6) Muscular hypertrophy 7) OPP resistance 5) Disease resistance Genome-wide association studies Genetic linkage analysis SNP50 Chip
  27. 27. What’s available now?Several genomic-based diagnostic testsSHEEP GOATS1) Parentage 1) Parentage2) Scrapie resistance 2) Alpha S1 Casein3) Spider lamb syndrome4) Hairy lamb syndrome5) OPP susceptibility
  28. 28. Scrapie resistance Alpha s1 Casein• In the PRNP gene, three • Alpha s1 Casein is one of the four casein proteins found in goats milk codons affect scrapie and is the most important of the four susceptibility: 171, 154, for cheese making. and 136. • The Alpha s1 Casein gene (CSN1S1) that produces the protein shows• Codon 171 is the major polymorphisms which affect the determinant of scrapie amount of protein and fat produced. susceptibility in the U.S. Variant Production levels Genotype Scrapie susceptibility A High content of alpha B s1 casein in milk RR Resistant QR Rarely susceptible E Low amount of alpha QQ Susceptible F s1 casein in milk N
  29. 29. Genomics in the U.S. sheep and goat industryLimitations• Mapping is less advanced in sheep and goats.• There is less investment in sheep and goat research.• Most sheep and goat farms are small and many are limited resource.• Lack of large number of performance tested animals• Genomic selection criteria developed in other countries may not be accurate in the U.S.• Will most likely focus on single and major gene traits.
  30. 30. The application of genetic markers for selectionof quantitative traits is still in the distant future.Most producers don’t use the tools we already have. 1. On-farm performance evaluation 2. Central performance testing 3. Across-flock genetic evaluation a) NSIP/LambPlan (EBVs) b) AGIL/DHI/ADGA
  31. 31. Is she finally done? She talks too much!Thank you for participating in the UME 2013 webinar series.All recordings and PowerPoint presentations are available at http://www.sheepandgoat.com/recordings.html

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