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.

1. 2013 Winter Webinar Series: Breeding Better Sheep & Goats
Advanced Genetic Improvement
SUSAN SCHOENIAN
Sheep & Goat Specialist
University of Maryland Extension
sschoen@umd.edu - www.sheepandgoat.com

2. Advanced genetic improvement
1. 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. 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. 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. 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. 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. Across-flock EPDs/EBVs
Comparing 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. 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. 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. 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. EBVs for body composition traits
determined 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. EBVs for reproductive traits
1) Number of lambs born [NLB]
2) Number of lambs weaned [NLW]
3) Scrotal circumference [SC] (cm)

13. EBVs for parasite resistance
1) Worm egg count [WEC] (%)
At weaning or at early or late post-weaning ages

14. Interpreting data
Trait abbreviation BWT, kg MWWT (mm) WWT, kg PWT, kg YWT, kg
EBV 0.28 1.4 6.4 9.8 6
Accuracy 65 55 72 76 68
Compared to a ram with an 0.14 kg Produce daughters 3.2 kg heavier at 4.9 kg 3 kg heavier at
EBV of 0, this ram’s progeny heavier at birth who wean 0.7 kg weaning heavier at yearling
will be: heavier post weaning
Trait abbreviation NLB, % NLW,% PSC, cm
EBV 2 3 1.2
Accuracy 42 43 55
Compared to a ram with an EBV of 0, Produce 1% more Wean 1.5% more Have 0.6 cm greater
this ram’s progeny will: lambs lambs scrotal circumference
Trait abbreviation PWEC, %
EBV -20
Accuracy 60
Compared to a ram with an EBV of 0, this ram’s progeny will be: 10% more resistant at weaning

15. Breed sire summaries

16. Impacts
Species Annual
Trait
breed progress
Holstein Milk
+ 0.8 %
cattle production
Angus Yearling
+ 0.5 %
cattle weight
Suffolk 120-d
+ 0.3%
sheep weight

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. NSIP/LambPlan Selection Indexes
1) 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. 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. 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. 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. Genetic evaluation of dairy goats incorporates production records from
DHI 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. Different terminology – same technology
1. 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 + progeny
2. 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: type
4. Reliability

24. www.adgagenetics.org

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. 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. What’s available now?
Several genomic-based diagnostic tests
SHEEP GOATS
1) Parentage 1) Parentage
2) Scrapie resistance 2) Alpha S1 Casein
3) Spider lamb syndrome
4) Hairy lamb syndrome
5) OPP susceptibility

28. Scrapie resistance Alpha s1 Casein
• In the PRNP gene, three • Alpha s1 Casein is one of the four
casein proteins found in goat's 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. Genomics in the U.S. sheep and goat industry
Limitations
• 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. The application of genetic markers for selection
of 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. 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