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Selection changes in the United States due to genomics. Coauthors. Janice R. Wright 1 Jana L. Hutchison 1 Jay M. Mattison 2 1 Animal Scientist, Animal Genomic and Improvement Laboratory , Beltsville , Maryland - PowerPoint PPT Presentation
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Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (1)
Dr. H. Duane NormanInterim AdministratorCouncil on Dairy Cattle BreedingBeltsville, MD 20705-2350301-525-2006 (voice)[email protected]
Selection changes in the United States due to genomics
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (2)
Coauthors
Janice R. Wright1 Jana L. Hutchison1 Jay M. Mattison2
1Animal Scientist, Animal Genomic and Improvement Laboratory, Beltsville, Maryland
2Chief Executive Officer, National Dairy Herd Information Association, Verona, Wisconsin
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (3)
Introduction
Genomic testing of dairy cattle accelerated in the United States in early 2008.
Genomics is changing the way breeding programs are operating.
Genomics is changing information available & choice of parents producing replacements.
It is impacting genetic improvement. Some differences in programs will be described.
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (4)
Objectives
Show number of genomic tests across time. Show how age of bulls’ ancestors have
changed. Show genetic merit of bulls entering artificial
insemination (AI) service across time. Determine expected genetic merit of future
animals derived from examining confirmed pregnancies.
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (5)
Traits examined
Milk Fat Protein Somatic cell score (SCS) Productive life (Prod. Life) Daughter Pregnancy Rate (Dau. Preg. Rate) Net Merit Dollars
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (6)
Number of US animals genotyped by year (Holstein)
Year Females Males
2007 77 2389
2008 2740 8810
2009 4445 7083
2010 14,212 6786
2011 37,091 9668
2012 81,382 11,699
2013 125,314 17,417
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (7)
Number of US animals genotyped by year (Jersey)
Year Females Males
2008 90 1123
2009 532 1290
2010 3201 757
2011 7427 1287
2012 12,640 1598
2013 20,206 2829
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (8)
Mean PTA* of Holstein bulls entering AI by year (yield traits)
Year Milk Fat Protein Number bulls
-------------------(kg)--------------------
2005 144 6 5 18182006 175 8 7 17552007 180 9 7 19102008 233 10 8 17972009 249 14 9 17662010 286 16 10 16132011 335 18 13 17312012 466 21 17 18112013 533 27 20 1593 *Based on April 2014 evaluations
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (9)
Mean PTA* of Holstein bulls entering AI by year (other traits)
Year SCS Prod. Life
Dau. Preg. Rate
Net Merit
2005 2.99 -0.2 -0.4 732006 2.94 0.3 -0.4 1332007 2.91 0.4 -0.2 1612008 2.92 0.6 -0.1 1952009 2.88 1.6 0.2 2812010 2.85 2.3 0.2 3352011 2.81 2.9 0.5 4262012 2.80 3.6 0.5 5112013 2.75 4.2 0.8 618 *Based on April 2014 evaluations
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (10)
Mean PTA* of Jersey bulls entering AI by year (yield traits)
Year Milk Fat Protein Number bulls ------------------
(kg)-----------------
2005 88 6 3 1812006 94 8 4 1832007 90 10 5 2162008 138 13 7 2042009 168 14 8 2092010 240 16 10 2092011 313 20 13 2362012 400 24 16 2362013 393 26 16 264
*Based on April 2014 evaluations
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (11)
Mean PTA* of Jersey bulls entering AI by year (other traits)
Year SCS Prod. Life
Dau. Preg. Rate
Net Merit
2005 3.04 0.3 -0.2 682006 3.05 0.4 -0.1 962007 3.05 0.6 0.0 1202008 3.04 0.9 0.0 1582009 3.05 1.4 0.1 2012010 3.02 1.9 0.1 2432011 2.98 2.6 0.1 3292012 2.96 2.7 -0.1 3762013 2.94 3.2 0.1 436 *Based on April 2014 evaluations
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (12)
Age of sire and dam at bull’s birth (all breeds)
2005 2006 2007 2008 2009 2010 2011 2012 20130
1
2
3
4
5
6
7
8
sire age dam age
Year bull entered AI
An
cest
or
ag
e a
t b
ull's
bir
th (
years
)
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (13)
Age of Paternal and Maternal grandsire at bull’s birth
2005 2006 2007 2008 2009 2010 2011 2012 20134
6
8
10
12
14
Paternal grandsire Maternal grandsire
Year bull entered AI
An
cest
or
ag
e a
t b
ull's
bir
th (
years
)
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (14)
Age of Paternal and Maternal granddam at bull’s birth
2005 2006 2007 2008 2009 2010 2011 2012 20134
6
8
10
12
14
Paternal granddam Maternal granddam
Year bull entered AI
An
cest
or
ag
e a
t b
ull's
bir
th (
years
)
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (15)
Mean PTA protein of HO service sires used in matings
2007 2008 2009 2010 2011 2012 20130
2
4
6
8
10
12
0.8 to 3.9 4.0 to 7.9 GE 8.0
Year of breeding
PTA
Prot
ein
(kgs
)
Service sire age groups at breeding (yr):
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (16)
Mean PTA Daughter pregnancy rate of service sires used in matings
2007 2008 2009 2010 2011 2012 2013-0.4
-0.2
0.0
0.2
0.4
0.6
0.80.8 to 3.9 4.0 to 7.9 GE 8.0
Year of breeding
PTA
Dau
ghte
r Pre
gnan
cy R
ate
(%) Service sire age groups at breeding (yr) :
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (17)
Mean PTA Productive life of HO service sires used in matings
2007 2008 2009 2010 2011 2012 20130.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0.8 to 3.9 4.0 to 7.9 GE 8.0
Year of breeding
PTA
Prod
uctiv
e lif
e (m
o)
Service sire age groups at breeding (yr) :
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (18)
Mean Net Merit of HO service sires used in matings
2007 2008 2009 2010 2011 2012 20130
100
200
300
400
500
0.8 to 3.9 4.0 to 7.9 > 8.0
Year of breeding
Net
Mer
it
Service sire age groups at breeding (yr) :
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (19)
Conclusions
Amount of genomic testing has been increasing. Still, it seems risky predicting how the number of tests will change in the next few years.
Bulls age when entering AI has not changed, remaining at 16 mo. All ancestors’ ages when the bull entered AI service have declined.
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (20)
Conclusions
Genetic change for bulls entering AI has been accelerating for most of the important traits. A few of the fitness traits have increased faster than the yield traits.
Confirmed pregnancy from breeding records illustrates rapid improvement due to genomics will continue.
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (21)
Appreciation to all producers in the U.S. dairy industry and the industry support personnel who provided information to the Council on Dairy Cattle Breeding’s national database
Also, to personnel in the Animal Genomics and Improvement Laboratory who have assisted in the transition of operations to the Council on Dairy Cattle Breeding
Acknowledgments
