1. BEEF AUSTRALIA 2012
INTERNATIONAL BEEF CATTLE GENETICS CONFERENCE
Brazilian Experience in Selection for Fertility in
Zebu Breeds including Incorporation of DNA
Marker Technology
Raysildo B. Lôbo, Cláudio Magnabosco, Luís G. G. Figueiredo, Daniel P. Lôbo,
Thereza C. Bittencourt
USP, ANCP, EMBRAPA, ECOLOG, UFBA

2. Brazilian Population (2011)
192.4 million people
7 billion worldwide
Regions
• Brazil is divided into 5 major regions with 26 states:
• North (16.1 million people)
• Northeast (53.5 million people)
• Midwest (14.2 million people)
• Southeast (81.0 million people),
• South (27.6 million people)

3. Macuni do Salto MGT = 13,55 (Top 2%)
Birth date. Sept/2002 3

4. HERD, SLAUGHTERING AND CARCASS WEIGHT OF
CATTLE IN BRAZIL

5. Brazilian Exports
(US$ bilhões)
HISTORICAL EVOLUTION
256.0
Total Exports
Total Agribusiness
197.9 201.9
160.6
153.0
137.8
118.5
96.7 95.0
73.2 71.8 76.4
60.4 64.8
58.3 58.4
43.6 49.5
39.0
24.8 30.6
23.9
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

6. The Genetics of Beef Cattle Reproduction
Reproductive performance and longevity
are economically relevant traits that
determine the number of beef cattle
available each year as replacements and
for slaughter in beef cattle production.

7. The beef cattle industry is considered to
be one of the most important economic
activities in Brazilian agribusiness and
Zebu breeds; the Nelore is the most
important beef cattle breed in Brazil.

8. In this presentation, we will be stressing
on our results of fertility selection in zebu
breeds, and make application on how to
integrate of genomic information into
genetic evaluation of Brazilian Nellore
cattle.

9. Fertility is a complex trait in beef
production systems and genetic
evaluation procedures.

10. The ANCP has initiated a
program of selection for sexual
precocity in females, challenging
heifers from 14 to 17 months old in
the Nellore breed.

11. Results of the program of sexual precocity in Rancho da
Matinha, ANCP Associate.
Number Number
Birth Year % Pregnancy
Exposed Pregnant
1998 77 6 7.79
1999 122 1 0.82
2000 122 16 13.11
2001 142 27 19.01
2002 159 86 54,09
2003 159 77 48,43
2004 149 77 51,68
2005 162 57 35,19
2006 113 81 71,68
2007 151 110 72,85
2008 141 103 73,05
2009 166 124 74,70
2010 177 138 77,97
Source: Rancho da Matinha, 2012

12. Rancho da Matinha
Mandarin FIV Matinha Maia FIV Matinha
Birth date.: Oct/2008 Birth date: Sept/2008
12

13. Genetic Trends (mo) in Nellore Cattle for AFC
-0.40
-0.48
-0.50
IPP (meses)
-0.52
-0.57
-0.60
-0.61
-0.62
-0.66
-0.70
-0.74
-0.80
2001 2002 2003 2004 2005 2006 2007
Source: ANCP/CTAG, 2012

14. Genetic Trends in Nellore Cattle for Heifer
Pregnancy Rate (%)
48.35 48.32
48.30
48.27 48.27
48.25
48.20 48.18
48.16
D3P (%)
48.15
48.10
48.05
48.00 47.98
47.95 47.93
47.90
2001 2002 2003 2004 2005 2006 2007
Source: ANCP/CTAG, 2012

15. Genetic Trends in Nellore Cattle for Stayability (%)
54.00
53.75
53.60
53.53
STAY (%)
53.20
53.16
52.87
52.80
52.84
52.72
52.53
52.40
2003 2004 2005 2006 2007 2008 2009
Source: ANCP/CTAG, 2012

16. Genetic Trends (cm) in Nellore Cattle SC365
0.20
0.17
0.15
0.13
PE365 (cm)
0.10
0.07
0.05
0.03
0.02
0.00
0.00
-0.02
-0.05
2003 2004 2005 2006 2007 2008 2009
Source: ANCP/CTAG, 2012

17. Implementation of DNA Markers to
Produce Genomic EPDs
• One way to implement the genomic information into
genetic improvement programs is to simply integrate
the genomic predictions into traditional genetic
evaluation.
• This approach combines traditional EBVs based on
phenotype and pedigree with the genomic breeding
values based on the markers.

18. Implementation of DNA Markers to Produce Genomic
EPDs
• The Nellore Genetic Evaluation of the Brazilian
Association of Farmers and Researchers (ANCP)
published last year (2011) enhanced EPDs for growth
and reproduction traits.
• The molecular markers (Clarifide® - Pfizer) were
analyzed as a correlated trait.

19. Analysis of Nellore Data
• Last April (2012) we launched for Nellore Brazil Program the genetic
evaluation for 1.1 million animals with Enhanced Genomic EPDs.
(EBV:2)
• We analyzed 12 (twelve) traits:
Age at first calving (AFC);
Heifer pregnancy rate (HPR);
Stayability (STAY);
Accumulated production (CAP);
120-day weight (WT120);
Weights at 365/450 days (WT365/450);
Scrotal circumference at 365/450 days (SC365/450);
Rib eye area (REA);
Fat Thickness (FAT).

20. Statistical Analysis
The data were analyzed using two animal
models: a traditional single trait model, and a
three-traits model where the Pfizer molecular
value was included as a correlated trait.
All mixed model analysis were performed using
the statistical software ASREML 3.0, and SELMA
(CTAG, 2011).

21. In the next figures are illustrated the benefits of
including the Pfizer MVP into actual accuracy.
The green portion of the bars shows the accuracy
before the inclusion of genomic markers and the
blue shows the increase in accuracy after the
inclusion of the MVP.

22. Nellore Cattle
AGE AT FIRST CALVING ACTUAL ACCURACY
1 0.00%
0.9 1.00%
0.8
4.00%
11.00%
0.7
16.00%
A
c 0.6 28.00%
c 49.00%
u
0.5
80.00%
r 145.00%
a
c 0.4
y
0.3
0.2
0.1
0
0.11 A 0.20 0.21 A 0.30 0.31 A 0.40 0.41 A 0.50 0.51 A 0.60 0.61 A 0.70 0.71 A 0.80 0.81 A 0.90 0.91 A 0.99
Accuracy Class

23. Nellore Cattle
HEIFER PREGNANCY RATE ACTUAL ACCURACY
1
0.00%
0.9 1.00%
0.8 2.00%
0.7 5.00%
A 9.00%
c 0.6
c 16.00%
u
0.5
r 29.00%
a 49.00%
c 0.4 102.00%
y
0.3
0.2
0.1
0
0.11 A 0.20 0.21 A 0.30 0.31 A 0.40 0.41 A 0.50 0.51 A 0.60 0.61 A 0.70 0.71 A 0.80 0.81 A 0.90 0.91 A 0.99
Accuracy Class

24. Nellore Cattle
YEARLING SCROTAL CIRCUMFERENCE (ACTUAL ACC)
1 0.00%
0.9 0.00%
0.8
2.00%
0.7 3.00%
A 6.00%
c 0.6
c 11.00%
u
0.5
r
23.00%
a
c 0.4
40.00%
y 83.00%
0.3
0.2
0.1
0
0.11 A 0.20 0.21 A 0.30 0.31 A 0.40 0.41 A 0.50 0.51 A 0.60 0.61 A 0.70 0.71 A 0.80 0.81 A 0.90 0.91 A 0.99
Accuracy Class

25. Nellore Cattle
STAYABILITY (ACTUAL ACC)
1 0.00%
0.9 1.00%
0.8 3.00%
0.7 8.00%
12.00%
A
c 0.6
23.00%
c
u 35.00%
0.5
r 67.00%
a 137.00%
c 0.4
y
0.3
0.2
0.1
0
0.11 A 0.20 0.21 A 0.30 0.31 A 0.40 0.41 A 0.50 0.51 A 0.60 0.61 A 0.70 0.71 A 0.80 0.81 A 0.90 0.91 A 0.99
Accuracy Class

26. The early graphs show that animals
with lower accuracy have more benefit
with the inclusion of genomic markers. On
the other hand, the benefits decline as the
actual accuracy increases. The benefits
dissipate for actual accuracy great than
0.5.

27. Change in BIF accuracy (ACCBIF) from the incorporation of Pfizer MVP into young sires with no
other phenotypic information (i.e. parent-average EPDs; ACCBIF = 0.08), and average number of
progeny records (Approx. progeny equivalents) it would require to obtain a similar ACCBIF increase
based on progeny test records.
Change in Approx.
TRAITS h2 r ACCBIF Progeny
(from 0.08*) Equivalents
AFC 0.11 0.40 0.15 14
HPR 0.24 0.32 0.07 6
SC365 0.38 0.28 0.12 3
STAY 0.12 0.38 0.11 9
CAP 0.20 0.27 0.11 10
*These changes are less for higher initial accuracy values; h2 =
Heritability; r = Genetic correlation estimates between target trait
and respective MVPs.

28. Genetic correlations among Pfizer MVP and
reproductive traits a
Traits h2 r(%) Vg(%)
Age at First Calving 0.11 40 16
Heifer Pregnancy Rate 0.24 32 10
Scrotal Circumference at 365 days 0.38 28 8
Stayability 0.12 38 14
Accumulated production 0.20 27 7
aNellore breed; h2 Heritability; r=Genetic correlation estimates
between target trait and MVPs; Vg = Portion of genetic variance
explained by MVP.
Source: ANCP & Pfizer (2012)

29. Conclusions
Results showed that the incorporation of
molecular markers information into genetic
evaluation resulted in increased accuracy of
young sires as compared to the accuracy of
traditional genetic evaluation.

30. Conclusions
The effect of the inclusion of genomic markers
decline as the actual accuracy increases in Nellore
breed.
The benefits of including genomic information
dissipate when accuracy is over 0.5 for most traits
studied.
 GE-EPD will be an effective tool to enable faster
genetic improvement of fertility and growth traits in
Nellore cattle in Brazil.

31. Thank you so much!