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2014 
Genetic improvement 
programs for US dairy 
cattle 
John B. Cole 
Animal Genomics and Improvement Laboratory 
Agricultural Research Service, USDA 
Beltsville, MD 
john.cole@ars.usda.gov
U.S. dairy population and milk yield 
10,000 
8,000 
6,000 
4,000 
2,000 
0 
30 
25 
20 
15 
10 
5 
0 
40 50 60 70 80 90 00 10 
Milk yield (kg/cow) 
Cows (millions) 
Year 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (2) Cole
U.S. DHI dairy statistics (2011) 
l 9.1 million U.S. cows 
l ~75% bred AI 
l 47% milk recorded through Dairy Herd Information (DHI) 
w 4.4 million cows 
− 86% Holstein 
− 8% crossbred 
− 5% Jersey 
− <1% Ayrshire, Brown Swiss, Guernsey, Milking 
Shorthorn, Red & White 
w 20,000 herds 
w 220 cows/herd 
w 10,300 kg/cow 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (3) Cole
Collaboration with industry 
l Council on Dairy Cattle Breeding (CDCB) 
responsible for receiving data and for 
computing and delivering US genetic 
evaluations for dairy cattle 
l AIP responsible for research and 
development to improve the evaluation 
system 
l CDCB and AIP employees co-located in 
Beltsville 
l Dr. João Dürr is CDCB CEO 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (4) Cole
Council on Dairy Cattle Breeding 
CDCB 
PDCA NAAB DRPC DHIA 
Purebred Dairy 
Cattle Association 
National Association of 
Animal Breeders 
Dairy Records 
Processing Centers 
Information Association 
l 3 board members from each 
organization 
l Total of 12 voting members 
l 2 nonvoting industry members 
Dairy Herd 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (5) Cole
Genetic evaluation advances 
Year Advance Gain, 
% 
1862 USDA established 
1895 USDA begins collecting dairy records 
1926 Daughter-dam comparison 100 
1962 Herdmate comparison 50 
1973 Records in progress 10 
1974 Modified contemporary comparison 5 
1977 Protein evaluated 4 
1989 Animal model 4 
1994 Net merit, productive life, and somatic cell 
score 
50 
2008 Genomic selection >50 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (6) Cole
Animal model 
1989 to present 
Introduced by Wiggans and VanRaden 
Advantages 
Information from all relatives 
Adjustment for genetic merit of mates 
Uniform procedures for males and females 
Best prediction (BLUP) 
Crossbreds included (2007) 
Genomic information added (2008) 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (7) Cole
Traits evaluated 
Year Trait Year Trait 
1926 Milk & fat yields 2000 Calving ease1 
1978 Conformation (type) 2003 Daughter pregnancy rate 
1978 Protein yield 2006 Stillbirth rate 
1994 Productive life 2006 Bull conception rate2 
1994 Somatic cell score 
(mastitis) 
2009 Cow and heifer 
conception rates 
1Sire calving ease evaluated by Iowa State University (1978–99) 
2Estimated relative conception rate evaluated by DRMS in Raleigh, 
NC (1986–2005) 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (8) Cole
Evaluation methods for traits 
Heritability 
Animal model (linear) 
Yield (milk, fat, protein) 
Type (AY, BS, GU, JE) 
Productive life 
Somatic cell score 
Daughter pregnancy rate 
Heifer conception rate 
Cow conception rate 
Sire–maternal grandsire model (threshold) 
Service sire calving ease 
Daughter calving ease 
Service sire stillbirth rate 
Daughter stillbirth rate 
25 – 40% 
7 – 54% 
8.5% 
12% 
4% 
1% 
1.6% 
8.6% 
3.6% 
3.0% 
6.5% 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (9) Cole
Type traits 
Stature 
Strength 
Body depth 
Dairy form 
Rump angle 
Thurl width 
Rear legs (side) 
Rear legs (rear) 
Foot angle 
Feet and legs 
score 
Fore udder 
attachment 
Rear udder height 
Rear udder width 
Udder cleft 
Udder depth 
Front teat placement 
Rear teat placement 
Teat length 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (10) Cole
Holstein milk (kg) 
1,000 
0 
-1,000 
-2,000 
-3,000 
-4,000 
Phenotypic base = 11,828 kg 
Cows 
Sires 
1960 1970 1980 1990 2000 2010 
Breeding value (kg) 
Birth year 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (11) Cole
Holstein productive life (mo) 
2 
0 
-2 
-4 
-6 
-8 
-10 
Phenotypic base = 27.2 mo 
Sires 
Cows 
1960 1970 1980 1990 2000 2010 
Breeding value (mo) 
Birth year 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (12) Cole
Holstein somatic cell score (log2) 
3.10 
3.00 
2.90 
2.80 
2.70 
Sires 
Cows 
Phenotypic base = 3.0 
1984 1988 1992 1996 2000 2004 2008 
Breeding value (log2) 
Birth year 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (13) Cole
Holstein daughter pregnancy rate (%) 
8.0 
6.0 
4.0 
2.0 
0.0 
-2.0 
Sires 
Cows 
Phenotypic base = 22.6% 
1960 1970 1980 1990 2000 2010 
Breeding value (%) 
Birth year 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (14) Cole
Holstein calving ease (%) 
11.0 
10.0 
9.0 
8.0 
7.0 
6.0 
Daughte 
r 
Service-sire 
phenotypic base = 7.9% 
Daughter 
phenotypic base = 7.5% 
Service 
sire 
0.01%/yr 
1980 1985 1990 1995 2000 2005 2010 
PTA 
(% difficult births in heifers) 
Birth year 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (15) Cole
Genetic-economic indices (2010) 
Trait 
Relative value (%) 
Net 
Cheese 
merit 
merit 
Fluid 
merit 
Milk (lb) 0 –15 19 
Fat (lb) 19 13 20 
Protein (lb) 16 25 0 
Productive life (PL, mo) 22 15 22 
Somatic cell score (SCS, log2) –10 –9 –5 
Udder composite (UC) 7 5 7 
Feet/legs composite (FLC) 4 3 4 
Body size composite (BSC) –6 –4 –6 
Daughter pregnancy rate (DPR, %) 11 8 12 
Calving ability (CA$, $) 5 3 5 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (16) Cole
Index changes 
Trait 
Relative emphasis on traits in index (%) 
PD$ 
1971 
MFP$ 
1976 
CY$ 
1984 
NM$ 
1994 
NM$ 
2000 
NM$ 
2003 
NM$ 
2006 
NM$ 
2010 
Milk 52 27 –2 6 5 0 0 0 
Fat 48 46 45 25 21 22 23 19 
Protein … 27 53 43 36 33 23 16 
PL … … … 20 14 11 17 22 
SCS … … … –6 –9 –9 –9 –10 
UDC … … … … 7 7 6 7 
FLC … … … … 4 4 3 4 
BDC … … … … –4 –3 –4 –6 
DPR … … … … … 7 9 11 
SCE … … … … … –2 … … 
DCE … … … … … –2 … … 
CA$ … … … … … … 6 5 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (17) Cole
Traditional evaluation summary 
Evaluation procedures have improved 
Fitness traits have been added 
Effective selection has produced substantial 
annual genetic improvement 
Indices enable selection for overall economic 
merit 
Fertility evaluations prevent continued decline 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (18) Cole
Genomic evaluation system 
Provides timely evaluations of young 
bulls for purchasing decisions 
Increases accuracy of evaluations of bull 
dams 
Assists in selection of service sires, 
particularly for low-reliability traits 
High demand for semen from 
genomically evaluated 2-year-old bulls 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (19) Cole
Genomic data flow 
Dairy Herd Improvement 
(DHI) producer 
DNA samples 
genotypes 
Council on Dairy Cattle 
Breeding (CDCB) 
DNA laboratory 
AI organization, 
breed association 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (20) Cole
Progression of chips 
Bovine3K 
BeadChip 
(3K) 
Jul 
BovineHD 
BeadChip 
(777K) 
Jan 
2008 2009 2010 
Dec 
Official 3K 
evaluations 
Sep 
Unofficial 3K 
evaluations 
Aug 
Official 50K 
Brown Swiss 
evaluations 
Jan 
Official 50K 
Holstein & Jersey 
evaluations 
BovineSNP50 
BeadChip 
Apr 
(50K) 
Jan 
Unofficial 50K 
evaluations 
Zoetis LD 
BeadChip 
(12K) 
Sep 
GGP HD 
BeadChip 
(77K) 
GGP v2 BeadChip 
(19K) 
May 
Dec 
GeneSeek Genomic 
Profiler (GGP) 
BeadChip (8K) 
Feb 
BovineLD 
BeadChip 
(7K) 
Sep 
2011 2012 2013 
Oct 
Official 12K 
evaluations 
May 
Official 19K 
evaluations 
Jan 
Official 77K 
evaluations 
Mar 
Official 8K 
evaluations 
Dec 
Official 
7K & 648K 
evaluations 
Aug 
Affymetrix BOS 1 
Official 777K 
evaluations 
Plate Array 
(648K) 
Jan 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (21) Cole
Evaluation flow 
Animal nominated for genomic evaluation 
by breed association or AI organization 
Hair or other DNA source sent to 
genotyping lab 
DNA extracted and placed on chip for 3-day 
genotyping process 
Genotypes sent from genotyping lab to AIPL 
for accuracy review 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (22) Cole
Laboratory quality control 
Each SNP evaluated for 
Call rate 
Portion heterozygous 
Parent-progeny conflicts 
Clustering investigated if SNP exceeds limits 
Number of failing SNPs indicates genotype 
quality 
Target of <10 SNPs in each category 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (23) Cole
Evaluation flow (continued) 
Genotype calls modified as necessary 
Genotypes loaded into database 
Nominators receive reports of parentage 
and other conflicts 
Pedigree or animal assignments corrected 
Genotypes extracted and imputed to 45K 
SNP effects estimated 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (24) Cole
Imputation 
Based on splitting genotype into individual 
chromosomes (maternal and paternal 
contributions) 
Missing SNPs assigned by tracking inheritance 
from ancestors and descendants 
Imputed dams increase predictor population 
Genotypes from all chips merged by imputing 
SNPs not present 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (25) Cole
findhap 
Developed by Dr. Paul VanRaden, ARS, USDA 
Divides chromosomes into segments 
Allows for successively shorter segments (usually 3 
runs) 
Long segments lock in identical by descent 
Shorter segments fill in missing SNPs 
Separates genotype into maternal and paternal 
contribution, haplotypes (phasing) 
Builds haplotype library sequenced by frequency 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (26) Cole
Evaluation flow (continued) 
Final evaluations calculated 
Evaluations released to dairy industry 
Download from CDCB FTP site with 
separate files for each nominator 
Monthly release for new animals 
All genomic evaluations updated 3 times 
each year with traditional evaluations 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (27) Cole
Genomic evaluation results 
Source: https://www.cdcb.us/Report_Data/Marker_Effects/marker_effects.cfm?Breed=HO&Trait=Net_Merit 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (28) Cole
Information sources for evaluations 
Traditional evaluations of genotyped bulls 
and cows used to estimate SNP effects 
Combined final evaluation 
Sum of SNP effects for an animal’s alleles 
Polygenetic effect 
Traditional evaluation 
Pedigree data used and validated by 
genotypes 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (29) Cole
Genotypes received since July 2013 
Breed Female Male 
All 
animals 
% 
female 
Ayrshire 1,359 229 1,588 86 
Brown Swiss* 892 6,253 7,145 12 
Holstein 172,956 31,657 204,613 85 
Jersey** 26,434 4,804 31,238 85 
All 201,641 42,943 244,584 82 
*Includes >5,000 bulls added from Interbull in June 2014 
**Includes 1,068 Danish bulls added in November 2013 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (30) Cole
Genotypes evaluated 
400,000 
350,000 
300,000 
250,000 
200,000 
150,000 
100,000 
50,000 
0 
Jun 
A 
O 
Jan 
Young imputed 
Old imputed 
Female Young <50K 
Male Young <50K 
Female Old <50K 
Male Old <50K 
Female Young >=50K 
Male Young >=50K 
Female Old >=50K 
Male Old >=50K 
F 
A 
M 
J 
J 
A 
S 
O 
N 
D 
Jan 
F 
M 
A 
M 
J 
J 
A 
S 
O 
N 
D 
Jan 
F 
M 
A 
M 
J 
J 
A 
S 
O 
N 
D 
Jan 
F 
M 
A 
M 
J 
J 
A 
S 
Animals genotyped (no.) 
2009 2010 2011 2012 2013 
Evaluation date 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (31) Cole
Growth in bull predictor population 
Breed May 2014 
12-mo 
gain 
Ayrshire 678 30 
Brown Swiss 5,862 366 
Holstein 25,276 2,361 
Jersey 4,262 1,391 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (32) Cole
Reliabilities for young Holsteins* 
50K genotypes 
3K genotypes 
40 45 50 55 60 65 70 75 80 
Reliability for PTA protein (%) 
*Animals with no traditional PTA in April 2011 
9000 
8000 
7000 
6000 
5000 
4000 
3000 
2000 
1000 
0 
Number of animals 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (33) Cole
Holstein prediction accuracy 
Trait Bias* 
Final score 0.1 58.8 22.7 
Stature −0.2 68.5 30.6 
Dairy form −0.2 71.8 34.5 
Rump angle 0.0 70.2 34.7 
Rump width −0.2 65.0 28.1 
Feed and legs 0.2 44.0 12.8 
Fore udder attachment −0.2 70.4 33.1 
Rear udder height −0.1 59.4 22.2 
Udder depth −0.3 75.3 37.7 
Udder cleft −0.2 62.1 25.1 
Front teat placement −0.2 69.9 32.6 
Teat length −0.1 66.7 29.4 
*2013 deregressed value – 2009 genomic evaluation 
Reliability 
(%) 
Reliability 
gain (% 
points) 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (34) Cole
Parent ages of marketed Holstein bulls 
140 
120 
100 
80 
60 
40 
20 
0 
Sire 
Dam 
2007 2008 2009 2010 2011 2012 2013 
Parent age (mo) 
Bull birth year 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (35) Cole
Marketed Holstein bulls 
Year 
entered 
AI 
Traditional 
progeny-tested 
Young 
genotyped All bulls 
2008 1,798 0 1,798 
2009 1,909 337 2,246 
2010 1,827 376 2,203 
2011 1,441 467 1,908 
2012 1,376 555 1,931 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (36) Cole
Genetic merit of marketed Holstein bulls 
800 
700 
600 
500 
400 
300 
200 
100 
0 
-100 
Average gain: 
$19.77/year 
Average gain: 
$52.00/year 
Average gain: 
$85.60/year 
00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 
Average net merit ($) 
Year entered AI 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (37) Cole
Genomic prediction of progeny test 
0 1 2 3 4 5 
Select parents, 
transfer embryos 
to recipients 
Calves 
born and 
DNA 
tested 
Calves born 
from DNA-selected 
parents 
Bull 
receives 
progeny 
test 
Reduce generation interval from 5 to 2 years 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (38) Cole
Genetic choices 
Before genomics: 
Proven bulls with daughter records 
(PTA) 
Young bulls with parent average (PA) 
After genomics: 
Young animals with DNA test (GPTA) 
Reliability of GPTA ~70% compared to 
PA ~35% and PTA ~85% for Holstein NM$ 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (39) Cole
Young bulls: 2013 NM$ vs. 2010 PA 
900 
700 
500 
300 
100 
-100 
-300 
-500 
-500 -300 -100 100 300 500 700 900 
Net Merit, Dec. 2013 
PA Net Merit, April 2010 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (40) Cole
Proven bulls: 2013 vs. 2010 NM$ 
900 
700 
500 
300 
100 
-100 
-300 
-500 
-500 -300 -100 100 300 500 700 900 
Net Merit, Dec. 2013 
Net Merit, April 2010 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (41) Cole
Young bulls: 2013 vs. 2010 NM$ 
900 
700 
500 
300 
100 
-100 
-300 
-500 
-500 -300 -100 100 300 500 700 900 
Net Merit, Dec. 2013 
Net Merit, April 2010 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (42) Cole
% genotyped mates of top young bulls 
100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
0 
Numero Uno 
Mogul 
Maurice 
Elvis ISY Altatrust 
Garrold 
Fernand 
Supersire 
S S I Robust Topaz 
700 725 750 775 800 825 850 875 900 925 
Net Merit (Aug 2013) 
Percentage of mates genotyped 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (43) Cole
Why genomics works for dairy 
cattle 
Extensive historical data available 
Well-developed genetic evaluation program 
Widespread use of AI sires 
Progeny-test programs 
High-value animals worth the cost of 
genotyping 
Long generation interval that can be 
reduced substantially by genomics 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (44) Cole
Key issues for the dairy industry 
Inbreeding and genetic diversity 
(including across breeds) 
Sequencing, new genes, and mutations 
Novel traits, resource populations 
(feed efficiency, health, milk properties) 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (45) Cole
Application to more traits 
Animal’s genotype good for all traits 
Traditional evaluations required for accurate 
estimates of SNP effects 
Traditional evaluations not currently available 
for heat tolerance or feed efficiency 
Research populations could provide data for 
traits that are expensive to measure 
Will resulting evaluations work in target 
population? 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (46) Cole
Parentage validation and discovery 
Parent-progeny conflicts detected 
Animal checked against all other genotypes 
Reported to breeds and requesters 
Correct sire usually detected 
Maternal grandsire (MGS) checking 
SNP at a time checking 
Haplotype checking more accurate 
Breeds moving to accept SNPs 
in place of microsatellites 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (47) Cole
Haplotypes affecting fertility 
Rapid discovery of new recessive defects 
Large numbers of genotyped animals 
Affordable DNA sequencing 
Determination of haplotype location 
Significant number of homozygous 
animals expected, but none observed 
Narrow suspect region with fine mapping 
Use sequence data to find causative 
mutation 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (48) Cole
Haplotypes affecting fertility 
frequency (%) Earliest known ancestor 
HH1 5 63.2* 4.5 Pawnee Farm Arlinda Chief 
HH2 1 94.9–96.6 4.6 Willowholme Mark Anthony 
HH3 8 95.4* 4.7 Glendell Arlinda Chief, 
Gray View Skyliner 
HH4 1 1.3* 0.7 Besne Buck 
HH5 9 92.4–93.9 4.4 Thornlea Texal Supreme 
JH1 15 15.7* 23.4 Observer Chocolate Soldier 
BH1 7 42.8–47.0 14.0 West Lawn Stretch 
BH2 19 10.6–11.7 15.4 Rancho Rustic My Design 
AH1 17 65.9–66.2 23.6 Selwood Betty’s 
*Causative mutation known 
Name 
Chromo-some 
Location 
(Mbp) 
Carrier 
Improver 
Commander 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (49) Cole
Haplotypes to track known recessives 
Concord-ance 
(%) 
New 
carriers 
(no.) 
BLAD HHB 1* 11,782 99.9 314 
CVM HHC 3* 13,226 — 2,716 
DUMPS HHD 1* 3,242 100.0 3 
Mule foot HHM 15* 87 97.7 120 
Horned HHP 1 345 — 2,050 
Red coat 
HHR 18* 4,137 — 5,927 
color 
SDM BHD 11* 108 94.4 108 
SMA BHM 24* 568 98.1 111 
Weaver BHW 4 163 96.3 32 
*Causative mutation known 
Recessive Haplotype 
Chromo-some 
Tested 
animals 
(no.) 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (50) Cole
International dairy breeding 
Genotype alliances 
North America (US, Canada, UK, Italy) 
Ireland, New Zealand 
Netherlands, Australia 
Eurogenomics (Denmark/Sweden/Finland, 
France, Germany, Netherlands/Belgium, 
Spain, Poland) 
Interbull genomic multitrait across-country 
evaluation (GMACE) 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (51) Cole
Impact on breeders 
Haplotype and gene tests in selection and 
mating programs 
Trend towards a small number of elite 
breeders that are investing heavily in 
genomics 
About 30% of young males genotyped 
directly by breeders since April 2013 
Prices for top genomic heifers can be 
very high (e.g., $265,000 ) 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (52) Cole
Impact on dairy producers 
General 
Reduced generation interval 
Increased rate of genetic gain 
More inbreeding/homozygosity? 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (53) Cole
Impact on dairy producers (continued) 
Sires 
Higher average genetic merit of available 
bulls 
More rapid increase in genetic merit for 
all traits 
Larger choice of bulls in terms of traits 
and semen price 
Greater use of young bulls 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (54) Cole
Conclusions 
Genomic evaluation has dramatically changed 
dairy cattle breeding 
Rate of gain is increasing primarily because of 
a large reduction in generation interval 
Genomic research is ongoing 
Detect causative genetic variants 
Find more haplotypes affecting fertility 
Improve accuracy through more SNPs, more 
predictor animals, and more traits 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (55) Cole
U.S. genomic evaluation team 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (56) Cole
Questions? 
Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (57) Cole

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Genetic improvement programs for US dairy cattle

  • 1. 2014 Genetic improvement programs for US dairy cattle John B. Cole Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville, MD john.cole@ars.usda.gov
  • 2. U.S. dairy population and milk yield 10,000 8,000 6,000 4,000 2,000 0 30 25 20 15 10 5 0 40 50 60 70 80 90 00 10 Milk yield (kg/cow) Cows (millions) Year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (2) Cole
  • 3. U.S. DHI dairy statistics (2011) l 9.1 million U.S. cows l ~75% bred AI l 47% milk recorded through Dairy Herd Information (DHI) w 4.4 million cows − 86% Holstein − 8% crossbred − 5% Jersey − <1% Ayrshire, Brown Swiss, Guernsey, Milking Shorthorn, Red & White w 20,000 herds w 220 cows/herd w 10,300 kg/cow Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (3) Cole
  • 4. Collaboration with industry l Council on Dairy Cattle Breeding (CDCB) responsible for receiving data and for computing and delivering US genetic evaluations for dairy cattle l AIP responsible for research and development to improve the evaluation system l CDCB and AIP employees co-located in Beltsville l Dr. João Dürr is CDCB CEO Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (4) Cole
  • 5. Council on Dairy Cattle Breeding CDCB PDCA NAAB DRPC DHIA Purebred Dairy Cattle Association National Association of Animal Breeders Dairy Records Processing Centers Information Association l 3 board members from each organization l Total of 12 voting members l 2 nonvoting industry members Dairy Herd Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (5) Cole
  • 6. Genetic evaluation advances Year Advance Gain, % 1862 USDA established 1895 USDA begins collecting dairy records 1926 Daughter-dam comparison 100 1962 Herdmate comparison 50 1973 Records in progress 10 1974 Modified contemporary comparison 5 1977 Protein evaluated 4 1989 Animal model 4 1994 Net merit, productive life, and somatic cell score 50 2008 Genomic selection >50 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (6) Cole
  • 7. Animal model 1989 to present Introduced by Wiggans and VanRaden Advantages Information from all relatives Adjustment for genetic merit of mates Uniform procedures for males and females Best prediction (BLUP) Crossbreds included (2007) Genomic information added (2008) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (7) Cole
  • 8. Traits evaluated Year Trait Year Trait 1926 Milk & fat yields 2000 Calving ease1 1978 Conformation (type) 2003 Daughter pregnancy rate 1978 Protein yield 2006 Stillbirth rate 1994 Productive life 2006 Bull conception rate2 1994 Somatic cell score (mastitis) 2009 Cow and heifer conception rates 1Sire calving ease evaluated by Iowa State University (1978–99) 2Estimated relative conception rate evaluated by DRMS in Raleigh, NC (1986–2005) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (8) Cole
  • 9. Evaluation methods for traits Heritability Animal model (linear) Yield (milk, fat, protein) Type (AY, BS, GU, JE) Productive life Somatic cell score Daughter pregnancy rate Heifer conception rate Cow conception rate Sire–maternal grandsire model (threshold) Service sire calving ease Daughter calving ease Service sire stillbirth rate Daughter stillbirth rate 25 – 40% 7 – 54% 8.5% 12% 4% 1% 1.6% 8.6% 3.6% 3.0% 6.5% Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (9) Cole
  • 10. Type traits Stature Strength Body depth Dairy form Rump angle Thurl width Rear legs (side) Rear legs (rear) Foot angle Feet and legs score Fore udder attachment Rear udder height Rear udder width Udder cleft Udder depth Front teat placement Rear teat placement Teat length Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (10) Cole
  • 11. Holstein milk (kg) 1,000 0 -1,000 -2,000 -3,000 -4,000 Phenotypic base = 11,828 kg Cows Sires 1960 1970 1980 1990 2000 2010 Breeding value (kg) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (11) Cole
  • 12. Holstein productive life (mo) 2 0 -2 -4 -6 -8 -10 Phenotypic base = 27.2 mo Sires Cows 1960 1970 1980 1990 2000 2010 Breeding value (mo) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (12) Cole
  • 13. Holstein somatic cell score (log2) 3.10 3.00 2.90 2.80 2.70 Sires Cows Phenotypic base = 3.0 1984 1988 1992 1996 2000 2004 2008 Breeding value (log2) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (13) Cole
  • 14. Holstein daughter pregnancy rate (%) 8.0 6.0 4.0 2.0 0.0 -2.0 Sires Cows Phenotypic base = 22.6% 1960 1970 1980 1990 2000 2010 Breeding value (%) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (14) Cole
  • 15. Holstein calving ease (%) 11.0 10.0 9.0 8.0 7.0 6.0 Daughte r Service-sire phenotypic base = 7.9% Daughter phenotypic base = 7.5% Service sire 0.01%/yr 1980 1985 1990 1995 2000 2005 2010 PTA (% difficult births in heifers) Birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (15) Cole
  • 16. Genetic-economic indices (2010) Trait Relative value (%) Net Cheese merit merit Fluid merit Milk (lb) 0 –15 19 Fat (lb) 19 13 20 Protein (lb) 16 25 0 Productive life (PL, mo) 22 15 22 Somatic cell score (SCS, log2) –10 –9 –5 Udder composite (UC) 7 5 7 Feet/legs composite (FLC) 4 3 4 Body size composite (BSC) –6 –4 –6 Daughter pregnancy rate (DPR, %) 11 8 12 Calving ability (CA$, $) 5 3 5 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (16) Cole
  • 17. Index changes Trait Relative emphasis on traits in index (%) PD$ 1971 MFP$ 1976 CY$ 1984 NM$ 1994 NM$ 2000 NM$ 2003 NM$ 2006 NM$ 2010 Milk 52 27 –2 6 5 0 0 0 Fat 48 46 45 25 21 22 23 19 Protein … 27 53 43 36 33 23 16 PL … … … 20 14 11 17 22 SCS … … … –6 –9 –9 –9 –10 UDC … … … … 7 7 6 7 FLC … … … … 4 4 3 4 BDC … … … … –4 –3 –4 –6 DPR … … … … … 7 9 11 SCE … … … … … –2 … … DCE … … … … … –2 … … CA$ … … … … … … 6 5 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (17) Cole
  • 18. Traditional evaluation summary Evaluation procedures have improved Fitness traits have been added Effective selection has produced substantial annual genetic improvement Indices enable selection for overall economic merit Fertility evaluations prevent continued decline Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (18) Cole
  • 19. Genomic evaluation system Provides timely evaluations of young bulls for purchasing decisions Increases accuracy of evaluations of bull dams Assists in selection of service sires, particularly for low-reliability traits High demand for semen from genomically evaluated 2-year-old bulls Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (19) Cole
  • 20. Genomic data flow Dairy Herd Improvement (DHI) producer DNA samples genotypes Council on Dairy Cattle Breeding (CDCB) DNA laboratory AI organization, breed association Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (20) Cole
  • 21. Progression of chips Bovine3K BeadChip (3K) Jul BovineHD BeadChip (777K) Jan 2008 2009 2010 Dec Official 3K evaluations Sep Unofficial 3K evaluations Aug Official 50K Brown Swiss evaluations Jan Official 50K Holstein & Jersey evaluations BovineSNP50 BeadChip Apr (50K) Jan Unofficial 50K evaluations Zoetis LD BeadChip (12K) Sep GGP HD BeadChip (77K) GGP v2 BeadChip (19K) May Dec GeneSeek Genomic Profiler (GGP) BeadChip (8K) Feb BovineLD BeadChip (7K) Sep 2011 2012 2013 Oct Official 12K evaluations May Official 19K evaluations Jan Official 77K evaluations Mar Official 8K evaluations Dec Official 7K & 648K evaluations Aug Affymetrix BOS 1 Official 777K evaluations Plate Array (648K) Jan Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (21) Cole
  • 22. Evaluation flow Animal nominated for genomic evaluation by breed association or AI organization Hair or other DNA source sent to genotyping lab DNA extracted and placed on chip for 3-day genotyping process Genotypes sent from genotyping lab to AIPL for accuracy review Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (22) Cole
  • 23. Laboratory quality control Each SNP evaluated for Call rate Portion heterozygous Parent-progeny conflicts Clustering investigated if SNP exceeds limits Number of failing SNPs indicates genotype quality Target of <10 SNPs in each category Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (23) Cole
  • 24. Evaluation flow (continued) Genotype calls modified as necessary Genotypes loaded into database Nominators receive reports of parentage and other conflicts Pedigree or animal assignments corrected Genotypes extracted and imputed to 45K SNP effects estimated Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (24) Cole
  • 25. Imputation Based on splitting genotype into individual chromosomes (maternal and paternal contributions) Missing SNPs assigned by tracking inheritance from ancestors and descendants Imputed dams increase predictor population Genotypes from all chips merged by imputing SNPs not present Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (25) Cole
  • 26. findhap Developed by Dr. Paul VanRaden, ARS, USDA Divides chromosomes into segments Allows for successively shorter segments (usually 3 runs) Long segments lock in identical by descent Shorter segments fill in missing SNPs Separates genotype into maternal and paternal contribution, haplotypes (phasing) Builds haplotype library sequenced by frequency Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (26) Cole
  • 27. Evaluation flow (continued) Final evaluations calculated Evaluations released to dairy industry Download from CDCB FTP site with separate files for each nominator Monthly release for new animals All genomic evaluations updated 3 times each year with traditional evaluations Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (27) Cole
  • 28. Genomic evaluation results Source: https://www.cdcb.us/Report_Data/Marker_Effects/marker_effects.cfm?Breed=HO&Trait=Net_Merit Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (28) Cole
  • 29. Information sources for evaluations Traditional evaluations of genotyped bulls and cows used to estimate SNP effects Combined final evaluation Sum of SNP effects for an animal’s alleles Polygenetic effect Traditional evaluation Pedigree data used and validated by genotypes Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (29) Cole
  • 30. Genotypes received since July 2013 Breed Female Male All animals % female Ayrshire 1,359 229 1,588 86 Brown Swiss* 892 6,253 7,145 12 Holstein 172,956 31,657 204,613 85 Jersey** 26,434 4,804 31,238 85 All 201,641 42,943 244,584 82 *Includes >5,000 bulls added from Interbull in June 2014 **Includes 1,068 Danish bulls added in November 2013 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (30) Cole
  • 31. Genotypes evaluated 400,000 350,000 300,000 250,000 200,000 150,000 100,000 50,000 0 Jun A O Jan Young imputed Old imputed Female Young <50K Male Young <50K Female Old <50K Male Old <50K Female Young >=50K Male Young >=50K Female Old >=50K Male Old >=50K F A M J J A S O N D Jan F M A M J J A S O N D Jan F M A M J J A S O N D Jan F M A M J J A S Animals genotyped (no.) 2009 2010 2011 2012 2013 Evaluation date Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (31) Cole
  • 32. Growth in bull predictor population Breed May 2014 12-mo gain Ayrshire 678 30 Brown Swiss 5,862 366 Holstein 25,276 2,361 Jersey 4,262 1,391 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (32) Cole
  • 33. Reliabilities for young Holsteins* 50K genotypes 3K genotypes 40 45 50 55 60 65 70 75 80 Reliability for PTA protein (%) *Animals with no traditional PTA in April 2011 9000 8000 7000 6000 5000 4000 3000 2000 1000 0 Number of animals Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (33) Cole
  • 34. Holstein prediction accuracy Trait Bias* Final score 0.1 58.8 22.7 Stature −0.2 68.5 30.6 Dairy form −0.2 71.8 34.5 Rump angle 0.0 70.2 34.7 Rump width −0.2 65.0 28.1 Feed and legs 0.2 44.0 12.8 Fore udder attachment −0.2 70.4 33.1 Rear udder height −0.1 59.4 22.2 Udder depth −0.3 75.3 37.7 Udder cleft −0.2 62.1 25.1 Front teat placement −0.2 69.9 32.6 Teat length −0.1 66.7 29.4 *2013 deregressed value – 2009 genomic evaluation Reliability (%) Reliability gain (% points) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (34) Cole
  • 35. Parent ages of marketed Holstein bulls 140 120 100 80 60 40 20 0 Sire Dam 2007 2008 2009 2010 2011 2012 2013 Parent age (mo) Bull birth year Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (35) Cole
  • 36. Marketed Holstein bulls Year entered AI Traditional progeny-tested Young genotyped All bulls 2008 1,798 0 1,798 2009 1,909 337 2,246 2010 1,827 376 2,203 2011 1,441 467 1,908 2012 1,376 555 1,931 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (36) Cole
  • 37. Genetic merit of marketed Holstein bulls 800 700 600 500 400 300 200 100 0 -100 Average gain: $19.77/year Average gain: $52.00/year Average gain: $85.60/year 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14 Average net merit ($) Year entered AI Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (37) Cole
  • 38. Genomic prediction of progeny test 0 1 2 3 4 5 Select parents, transfer embryos to recipients Calves born and DNA tested Calves born from DNA-selected parents Bull receives progeny test Reduce generation interval from 5 to 2 years Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (38) Cole
  • 39. Genetic choices Before genomics: Proven bulls with daughter records (PTA) Young bulls with parent average (PA) After genomics: Young animals with DNA test (GPTA) Reliability of GPTA ~70% compared to PA ~35% and PTA ~85% for Holstein NM$ Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (39) Cole
  • 40. Young bulls: 2013 NM$ vs. 2010 PA 900 700 500 300 100 -100 -300 -500 -500 -300 -100 100 300 500 700 900 Net Merit, Dec. 2013 PA Net Merit, April 2010 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (40) Cole
  • 41. Proven bulls: 2013 vs. 2010 NM$ 900 700 500 300 100 -100 -300 -500 -500 -300 -100 100 300 500 700 900 Net Merit, Dec. 2013 Net Merit, April 2010 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (41) Cole
  • 42. Young bulls: 2013 vs. 2010 NM$ 900 700 500 300 100 -100 -300 -500 -500 -300 -100 100 300 500 700 900 Net Merit, Dec. 2013 Net Merit, April 2010 Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (42) Cole
  • 43. % genotyped mates of top young bulls 100 90 80 70 60 50 40 30 20 10 0 Numero Uno Mogul Maurice Elvis ISY Altatrust Garrold Fernand Supersire S S I Robust Topaz 700 725 750 775 800 825 850 875 900 925 Net Merit (Aug 2013) Percentage of mates genotyped Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (43) Cole
  • 44. Why genomics works for dairy cattle Extensive historical data available Well-developed genetic evaluation program Widespread use of AI sires Progeny-test programs High-value animals worth the cost of genotyping Long generation interval that can be reduced substantially by genomics Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (44) Cole
  • 45. Key issues for the dairy industry Inbreeding and genetic diversity (including across breeds) Sequencing, new genes, and mutations Novel traits, resource populations (feed efficiency, health, milk properties) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (45) Cole
  • 46. Application to more traits Animal’s genotype good for all traits Traditional evaluations required for accurate estimates of SNP effects Traditional evaluations not currently available for heat tolerance or feed efficiency Research populations could provide data for traits that are expensive to measure Will resulting evaluations work in target population? Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (46) Cole
  • 47. Parentage validation and discovery Parent-progeny conflicts detected Animal checked against all other genotypes Reported to breeds and requesters Correct sire usually detected Maternal grandsire (MGS) checking SNP at a time checking Haplotype checking more accurate Breeds moving to accept SNPs in place of microsatellites Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (47) Cole
  • 48. Haplotypes affecting fertility Rapid discovery of new recessive defects Large numbers of genotyped animals Affordable DNA sequencing Determination of haplotype location Significant number of homozygous animals expected, but none observed Narrow suspect region with fine mapping Use sequence data to find causative mutation Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (48) Cole
  • 49. Haplotypes affecting fertility frequency (%) Earliest known ancestor HH1 5 63.2* 4.5 Pawnee Farm Arlinda Chief HH2 1 94.9–96.6 4.6 Willowholme Mark Anthony HH3 8 95.4* 4.7 Glendell Arlinda Chief, Gray View Skyliner HH4 1 1.3* 0.7 Besne Buck HH5 9 92.4–93.9 4.4 Thornlea Texal Supreme JH1 15 15.7* 23.4 Observer Chocolate Soldier BH1 7 42.8–47.0 14.0 West Lawn Stretch BH2 19 10.6–11.7 15.4 Rancho Rustic My Design AH1 17 65.9–66.2 23.6 Selwood Betty’s *Causative mutation known Name Chromo-some Location (Mbp) Carrier Improver Commander Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (49) Cole
  • 50. Haplotypes to track known recessives Concord-ance (%) New carriers (no.) BLAD HHB 1* 11,782 99.9 314 CVM HHC 3* 13,226 — 2,716 DUMPS HHD 1* 3,242 100.0 3 Mule foot HHM 15* 87 97.7 120 Horned HHP 1 345 — 2,050 Red coat HHR 18* 4,137 — 5,927 color SDM BHD 11* 108 94.4 108 SMA BHM 24* 568 98.1 111 Weaver BHW 4 163 96.3 32 *Causative mutation known Recessive Haplotype Chromo-some Tested animals (no.) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (50) Cole
  • 51. International dairy breeding Genotype alliances North America (US, Canada, UK, Italy) Ireland, New Zealand Netherlands, Australia Eurogenomics (Denmark/Sweden/Finland, France, Germany, Netherlands/Belgium, Spain, Poland) Interbull genomic multitrait across-country evaluation (GMACE) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (51) Cole
  • 52. Impact on breeders Haplotype and gene tests in selection and mating programs Trend towards a small number of elite breeders that are investing heavily in genomics About 30% of young males genotyped directly by breeders since April 2013 Prices for top genomic heifers can be very high (e.g., $265,000 ) Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (52) Cole
  • 53. Impact on dairy producers General Reduced generation interval Increased rate of genetic gain More inbreeding/homozygosity? Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (53) Cole
  • 54. Impact on dairy producers (continued) Sires Higher average genetic merit of available bulls More rapid increase in genetic merit for all traits Larger choice of bulls in terms of traits and semen price Greater use of young bulls Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (54) Cole
  • 55. Conclusions Genomic evaluation has dramatically changed dairy cattle breeding Rate of gain is increasing primarily because of a large reduction in generation interval Genomic research is ongoing Detect causative genetic variants Find more haplotypes affecting fertility Improve accuracy through more SNPs, more predictor animals, and more traits Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (55) Cole
  • 56. U.S. genomic evaluation team Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (56) Cole
  • 57. Questions? Embrapa Gado de Leite, Juiz de Fora, MG, Brasil 10 September 2014 (57) Cole