Genomic selection and systems biology – lessons from dairy cattle breedingJohn B. Cole, Ph.D.
Presentation made to the staff of Keygene, NV, in Wageningen, The Netherlands.
(I don't know what the problem is with the template here. It looks fine if you use a dark background.)
Using genotypes to construct phenotypes for dairy cattle breeding programs an...John B. Cole, Ph.D.
Modern dairying uses sophisticated data collection systems to maximize farm profitability. This has traditionally included information on cows and their environments, and now commonly includes genotype information from high-density single nucleotide polymorphism (SNP) panels. The US national database alone contains genotypes for 924,543 bulls and cows as of March 23, 2015, and many other countries are also genotyping animals. As the data continue to grow, the prospect of using genotypes to construct phenotypes directly, instead of measuring phenotypes on animals, becomes more attractive. There are many applications for this genomic information other than the prediction of breeding values. A notable recent application is the use of haplotypes in combination with next-generation sequencing data to identify causal variants associated with recessives. The methodology for identifying recessive haplotypes by searching for a deficit of homozygotes was first used in combination with sequence data to identify the causal variant (APAF1) associated with the HH1 haplotype. The US currently tracks 24 recessive haplotypes in four cattle breeds, and thanks to the work of several teams around the world the causal variants for 17 of them are known. The haplotypes include lethal recessive conditions, such as brachyspina, as well as hair coat color and polledness. There is growing interest in the latter to improve animal welfare and increase economic efficiency, but the polled haplotype has a very low frequency (0.41%, 0.93%, and 2.22% in Brown Swiss, Holstein, and Jersey, respectively). Increasing haplotype frequency by index selection requires known status for all animals. Gene content (GC) for non-genotyped animals was computed using records from genotyped relatives. Prediction accuracy was checked by comparing polled status from recessive codes and animal names to GC for 1,615 non-genotyped Jerseys with known status. 97% (n = 675) of horned animals were correctly assigned GC near 0, and 3% (n = 19) were assigned GC near 1. Heterozygous polled animals had GC near 0 (52%, n = 474) and near 1 (47%; n = 433), although 3 animals were assigned a GC near 2. All homozygous polled animals (n = 11) were assigned GC near 2. Genotype information can also be combined with other data, such as milk spectral data, to predict phenotypes for traits that are expensive or difficult to measure directly. These data can be used for precision farm management, including early culling decisions, monitoring of animals at risk for health problems, and identification of efficient and inefficient cows. The most substantial challenge faced by many dairy managers will be the effective use of the new phenotypes that now are available.
Genomic selection and systems biology – lessons from dairy cattle breedingJohn B. Cole, Ph.D.
Presentation made to the staff of Keygene, NV, in Wageningen, The Netherlands.
(I don't know what the problem is with the template here. It looks fine if you use a dark background.)
Using genotypes to construct phenotypes for dairy cattle breeding programs an...John B. Cole, Ph.D.
Modern dairying uses sophisticated data collection systems to maximize farm profitability. This has traditionally included information on cows and their environments, and now commonly includes genotype information from high-density single nucleotide polymorphism (SNP) panels. The US national database alone contains genotypes for 924,543 bulls and cows as of March 23, 2015, and many other countries are also genotyping animals. As the data continue to grow, the prospect of using genotypes to construct phenotypes directly, instead of measuring phenotypes on animals, becomes more attractive. There are many applications for this genomic information other than the prediction of breeding values. A notable recent application is the use of haplotypes in combination with next-generation sequencing data to identify causal variants associated with recessives. The methodology for identifying recessive haplotypes by searching for a deficit of homozygotes was first used in combination with sequence data to identify the causal variant (APAF1) associated with the HH1 haplotype. The US currently tracks 24 recessive haplotypes in four cattle breeds, and thanks to the work of several teams around the world the causal variants for 17 of them are known. The haplotypes include lethal recessive conditions, such as brachyspina, as well as hair coat color and polledness. There is growing interest in the latter to improve animal welfare and increase economic efficiency, but the polled haplotype has a very low frequency (0.41%, 0.93%, and 2.22% in Brown Swiss, Holstein, and Jersey, respectively). Increasing haplotype frequency by index selection requires known status for all animals. Gene content (GC) for non-genotyped animals was computed using records from genotyped relatives. Prediction accuracy was checked by comparing polled status from recessive codes and animal names to GC for 1,615 non-genotyped Jerseys with known status. 97% (n = 675) of horned animals were correctly assigned GC near 0, and 3% (n = 19) were assigned GC near 1. Heterozygous polled animals had GC near 0 (52%, n = 474) and near 1 (47%; n = 433), although 3 animals were assigned a GC near 2. All homozygous polled animals (n = 11) were assigned GC near 2. Genotype information can also be combined with other data, such as milk spectral data, to predict phenotypes for traits that are expensive or difficult to measure directly. These data can be used for precision farm management, including early culling decisions, monitoring of animals at risk for health problems, and identification of efficient and inefficient cows. The most substantial challenge faced by many dairy managers will be the effective use of the new phenotypes that now are available.
Talk on the genetic and genomic evaluation system for US dairy cattle made to scientists at Embrapa Gado de Leite in Juiz de Fora, MG, Brasil, on September 10, 2014.
JGI: Genome size impacts on plant adaptationjrossibarra
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Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
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The present study was conducted with the aim of reducing the cost of implementing Genomic Selection(GS) by using Genotype imputation methodology in Gir cattle. Application of GS mainly depends upon the cost of genotyping and reduce its cost, imputation approaches have been used. Imputation strategies and GS have been comprehensively studied in several taurine dairy cattle populations but very limited information is available on indigenous populations. Factors that affect the efficiency of imputation and GS are population structure, linkage disequilibrium between markers and differing marker density between indigenous and taurine breeds. The objective of the study was to evaluate the performance of INDUSCHIP-1, a customized Illumina bovine microarray chip for indigenous cattle breeds, designed by National Dairy Development Board, Anand and design one (7-15K) LD panel, and evaluate the performance of two panels of INDUSCHIP-1, and a 13K subset of the same for its imputation accuracy to HD (777K or INDUSCHIP-1 level). Thus, the study was planned with the aim to design LD panel for genotype imputation to INDUSCHIP-1 level with the strategy to maximize the accuracy of imputation in Gir cattle.
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Genomic selection changing Breeding programe around the world, talk consist of concept of Breeding, breeding value, Genomic breeding value, Genotype imputation, male calf procurement on basis of GEBV under SAG PT Project and 1000 bull genome project.
Potential for genomic selection in indigenous cattle breeds and results of GWAS in Gir dairy cattle of Gujrat by Dr.Pravin Kandhani and Dr. Vijay Trivedi KAMDHENU UNIVERSITY GANDHINAGAR
Using genotyping and whole-genome sequencing to identify causal variants asso...John B. Cole, Ph.D.
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The national genetic evaluation program
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Talk on the genetic and genomic evaluation system for US dairy cattle made to scientists at Embrapa Gado de Leite in Juiz de Fora, MG, Brasil, on September 10, 2014.
JGI: Genome size impacts on plant adaptationjrossibarra
Genome size may impact how plant genomes adapt, offering larger mutational targets leading to more adaptation from standing variation and more adaptation in noncoding regions.
Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org
The present study was conducted with the aim of reducing the cost of implementing Genomic Selection(GS) by using Genotype imputation methodology in Gir cattle. Application of GS mainly depends upon the cost of genotyping and reduce its cost, imputation approaches have been used. Imputation strategies and GS have been comprehensively studied in several taurine dairy cattle populations but very limited information is available on indigenous populations. Factors that affect the efficiency of imputation and GS are population structure, linkage disequilibrium between markers and differing marker density between indigenous and taurine breeds. The objective of the study was to evaluate the performance of INDUSCHIP-1, a customized Illumina bovine microarray chip for indigenous cattle breeds, designed by National Dairy Development Board, Anand and design one (7-15K) LD panel, and evaluate the performance of two panels of INDUSCHIP-1, and a 13K subset of the same for its imputation accuracy to HD (777K or INDUSCHIP-1 level). Thus, the study was planned with the aim to design LD panel for genotype imputation to INDUSCHIP-1 level with the strategy to maximize the accuracy of imputation in Gir cattle.
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Genomic selection changing Breeding programe around the world, talk consist of concept of Breeding, breeding value, Genomic breeding value, Genotype imputation, male calf procurement on basis of GEBV under SAG PT Project and 1000 bull genome project.
Potential for genomic selection in indigenous cattle breeds and results of GWAS in Gir dairy cattle of Gujrat by Dr.Pravin Kandhani and Dr. Vijay Trivedi KAMDHENU UNIVERSITY GANDHINAGAR
Using genotyping and whole-genome sequencing to identify causal variants asso...John B. Cole, Ph.D.
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The national genetic evaluation program
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What can we do with dairy cattle genomics other than predict more accurate br...John B. Cole, Ph.D.
Presentation on applications of genomic information in additional to estimation of breeding values made to the Department of Animal Science at North Carolina State University at 2010.
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This presentation describes recent changes to the national genetic evaluation system, as well as new research undertaken by AGIL scientists. Topics covered include the 2014 genetic base change, updates to the lifetime net merit selection index, and introduction of the grazing merit index, and the redefinition of daughter pregnancy rate. New research on the use of gene content to predict polled status, and statistical models for accommodating genotype-by-environment interactions also are described.
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1. John B. Cole
Animal Improvement Programs Laboratory
Agricultural Research Service, USDA
Beltsville, MD 20705-2350
john.cole@ars.usda.gov
New tools for genomic
selection in dairy cattle
2. Department of Animal Sciences, Purdue University, October 23, 2013 (2) Cole
Why genomic selection works in dairy
Extensive historical data available
Well-developed genetic evaluation
program
Widespread use of AI sires
Progeny test programs
High-valued animals, worth the cost of
genotyping
Long generation interval which can be
reduced substantially by genomics
3. Department of Animal Sciences, Purdue University, October 23, 2013 (3) Cole
Illumina genotyping arrays
• BovineSNP50
• 54,001 SNPs (version 1)
• 54,609 SNPs (version 2)
• 45,187 SNPs used in evaluation
• BovineHD
• 777,962 SNPs
• Only BovineSNP50 SNPs used
• >1,700 SNPs in database
• BovineLD
• 6,909 SNPs
• Allows for additional SNPs
BovineSNP50 v2
BovineLD
BovineHD
4. Department of Animal Sciences, Purdue University, October 23, 2013 (4) Cole
Genotyped animals (April 2013)
Chip
Traditional
evaluation?
Animal
sex Holstein Jersey
Brown
Swiss Ayrshire
50K Yes Bulls 21,904 2,855 5,381 639
Cows 16,062 1,054 110 3
No Bulls 45,537 3,884 1,031 325
Cows 32,892 660 102 110
<50K Yes Bulls 19 11 28 9
Cows 21,980 9,132 465 0
No Bulls 14,026 1,355 90 2
Cows 158,622 18,722 658 105
Imputed Yes Cows 2,713 237 103 12
No Cows 1,183 32 112 8
All 314,938 37,942 8,080 1,213
362,173
5. Department of Animal Sciences, Purdue University, October 23, 2013 (5) Cole
Marketed Holstein bulls
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2007 2008 2009 2010 2011
%oftotalbreedings
Breeding year
Old non-G
Old G
First crop non-G
First crop G
Young Non-G
Young G
6. Department of Animal Sciences, Purdue University, October 23, 2013 (6) Cole
What’s a SNP genotype worth?
For the protein
yield (h2=0.30), the
SNP genotype
provides
information
equivalent to an
additional 34
daughters
Pedigree is equivalent to information on about 7 daughters
7. Department of Animal Sciences, Purdue University, October 23, 2013 (7) Cole
And for daughter pregnancy rate (h2=0.04), SNP = 131 daughters
What’s a SNP genotype worth?
8. Department of Animal Sciences, Purdue University, October 23, 2013 (8) Cole
Genotypes and haplotypes
• Genotypes indicate how many copies of each
allele were inherited
• Haplotypes indicate which alleles are on
which chromosome
• Observed genotypes partitioned into the two
unknown haplotypes
• Pedigree haplotyping uses relatives
• Population haplotyping finds matching allele
patterns
9. Department of Animal Sciences, Purdue University, October 23, 2013 (9) Cole
Haplotyping program – findhap.f90
• Begin with population haplotyping
• Divide chromosomes into segments, ~250
to 75 SNP / segment
• List haplotypes by genotype match
• Similar to fastPhase, IMPUTE
• End with pedigree haplotyping
• Detect crossover, fix noninheritance
• Impute nongenotyped ancestors
10. Department of Animal Sciences, Purdue University, October 23, 2013 (10) Cole
Example Bull: O-Style (USA137611441)
• Read genotypes and pedigrees
• Write haplotype segments found
• List paternal / maternal inheritance
• List crossover locations
11. Department of Animal Sciences, Purdue University, October 23, 2013 (11) Cole
O-Style Haplotypes Chromosome 15
12. Department of Animal Sciences, Purdue University, October 23, 2013 (12) Cole
Loss-of-function mutations
• At least 100 LoF per human genome
surveyed (MacArthur et al., 2010)
• Of those genes ~20 are completely
inactivated
• Uncharacterized LoF variants likely to have
phenotypic effects
• How should mating programs deal with
this?
• Can we find them?
13. Department of Animal Sciences, Purdue University, October 23, 2013 (13) Cole
Recessive defect discovery
• Check for homozygous haplotypes
• 7 to 90 expected but none observed
• 5 of top 11 are potentially lethal
• 936 to 52,449 carrier sire by carrier MGS
fertility records
• 3.1% to 3.7% lower conception rates
• Some slightly higher stillbirth rates
• Confirmed Brachyspina same way
15. Department of Animal Sciences, Purdue University, October 23, 2013 (15) Cole
Precision mating
Eliminate undesirable haplotypes
Detection at low allele frequencies
Avoid carrier-to-carrier matings
Easy with few recessives, difficult with
many recessives
Include in selection indices
Requires many inputs
Use a selection strategy for favorable
minor alleles (Sun & VanRaden, 2013)
16. Department of Animal Sciences, Purdue University, October 23, 2013 (16) Cole
Sequencing successes at AIPL/BFGL
• Simple loss-of-function mutations
• APAF1 (HH1) – Spontaneous abortions in
Holstein cattle (Adams et al., 2012)
• CWC15 (JH1) – Early embryonic death in
Jersey cattle (Sonstegard et al., 2013)
• Weaver syndrome – Neurological
degeneration and death in Brown Swiss
cattle (McClure et al., 2013)
17. Department of Animal Sciences, Purdue University, October 23, 2013 (17) Cole
Modified pedigree & haplotype design
Bull A (1968)
AA, SCE: 8
Bull B (1962)
AA, SCE: 7
MGS
Bull H (1989)
Aa, SCE: 14
Bull I (1994)
Aa, SCE: 18
Bull E (1982)
Aa, SCE: 8
Bull F (1987)
Aa, SCE: 15
Bull C (1975)
AA, SCE: 8
δ = 10 Bull E (1974)
Aa, SCE: 10
MGS
Bull J (2002)
Aa, SCE: 6
Bull K (2002)
Aa, SCE: 15
Bull K (2002)
aa, SCE: 15
These bulls carry
the haplotype with
the largest, negative
effect on SCE:
Bull D (1968)
??, SCE: 7
Couldn’t obtain DNA:
18. Department of Animal Sciences, Purdue University, October 23, 2013 (18) Cole
Things can move quickly!
● Dead calves will be
genotyped for BH2
status
● If homozygous, we
will sequence in a
family-based design
● Austrian group also
working on BH2
(Schwarzenbacher
et al., 2012)
● Strong industry
support!
Semen
in
CDDR
Tissue samples (ears)
being processed for DNA
Owner will collect blood
samples when born
Owner will collect
blood samples
AI firm
sending
10 units
of semen
Brown Swiss family with possible
BH2 homozygotes (dead)
19. Department of Animal Sciences, Purdue University, October 23, 2013 (19) Cole
Our industry wants new genomic tools
20. Department of Animal Sciences, Purdue University, October 23, 2013 (20) Cole
We already have some tools
https://www.cdcb.us/Report_Data/Marker_Effects/marker_effects.cfm`
21. Department of Animal Sciences, Purdue University, October 23, 2013 (21) Cole
Chromosomal DGV query
https://www.cdcb.us/CF-
queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm
22. Department of Animal Sciences, Purdue University, October 23, 2013 (22) Cole
Now we have a new haplotype query
https://www.cdcb.us/CF-
queries/Bull_Chromosomal_EBV/bull_chromosomal_ebv.cfm
23. Department of Animal Sciences, Purdue University, October 23, 2013 (23) Cole
Paternal and maternal DGV
• Shows the DGV for the paternal and
maternal haplotyles
• Imputed from 50K using findhap.f90 v.2
• Can we use them to make mating
decisions?
• People are going to do it – we need to help
them!
• Who is actually making planned matings?
24. Department of Animal Sciences, Purdue University, October 23, 2013 (24) Cole
Top net merit bull August 2013
COOKIECUTTER PETRON HALOGEN
(HO840003008710387, PTA NM$ +926, Rel 68%)
25. Department of Animal Sciences, Purdue University, October 23, 2013 (25) Cole
Pluses and minuses
23 positive chromosomes 19 negative chromosomes
26. Department of Animal Sciences, Purdue University, October 23, 2013 (26) Cole
Breeders need MS variance
27. Department of Animal Sciences, Purdue University, October 23, 2013 (27) Cole
The good and the bad Chromosome 1
28. Department of Animal Sciences, Purdue University, October 23, 2013 (28) Cole
The best we can do DGV for NM$ = +2,314
29. Department of Animal Sciences, Purdue University, October 23, 2013 (29) Cole
The worst we can do DGV for NM$ = -2,139
30. Department of Animal Sciences, Purdue University, October 23, 2013 (30) Cole
Dominance in mating programs
Quantitative model
Must solve equation for each mate pair
Genomic model
Compute dominance for each locus
Haplotype the population
Calculate dominance for mate pairs
Most genotyped cows do not yet have
phenotypes
31. Department of Animal Sciences, Purdue University, October 23, 2013 (31) Cole
Inbreeding effects
Inbreeding alters transcription levels and
gene expression profiles (Kristensen et al.,
2005).
Moderate levels of inbreeding among
active bulls (7.9 to 18.2)
Are inbreeding effects distributed
uniformly across the genome?
Can we find genomic regions where
heterozygosity is necessary or not using
the current population?
32. Department of Animal Sciences, Purdue University, October 23, 2013 (32) Cole
Precision inbreeding
• Runs of homozygosity may indicate
genomic regions where inbreeding is
acceptable
• Can we target those regions by
selecting among haplotypes?
Dominance
RecessivesUnder-dominance
33. Department of Animal Sciences, Purdue University, October 23, 2013 (33) Cole
Challenges with new phenotypes
Lack of information
Inconsistent trait definitions
Often no database of phenotypes
Many have low heritabilities
Lots of records are needed for
accurate evaluation
Genetic improvement can be slow
Genomics may help with this
34. Department of Animal Sciences, Purdue University, October 23, 2013 (34) Cole
Reliability with and without genomics
Event EBV Reliability GEBV Reliability Gain
Displaced
abomasum
0.30 0.40 +0.10
Ketosis 0.28 0.35 +0.07
Lameness 0.28 0.37 +0.09
Mastitis 0.30 0.41 +0.11
Metritis 0.30 0.41 +0.11
Retained placenta 0.29 0.38 +0.09
Average reliabilities of sire PTA computed with pedigree information and
genomic information, and the gain in reliability from including genomics.
Example: Dairy cattle health (Parker Gaddis et al.,
2013)
35. Department of Animal Sciences, Purdue University, October 23, 2013 (35) Cole
Some novel phenotypes being studied
Age at first calving (Cole et al., 2013)
Dairy cattle health (Parker Gaddis et al., 2013)
Methane production (de Haas et al., 2011)
Milk fatty acid composition (Bittante et al., 2013)
Persistency of lactation (Cole et al., 2009)
Rectal temperature (Dikmen et al., 2013)
Residual feed intake (Connor et al., 2013)
36. Department of Animal Sciences, Purdue University, October 23, 2013 (36) Cole
What do we do with novel traits?
• Put them into a selection index
• Correlated traits are helpful
• Apply selection for a long time
• There are no shortcuts
• Collect phenotypes on many daughters
• Repeated records of limited value
• Genomics can increase accuracy
39. Department of Animal Sciences, Purdue University, October 23, 2013 (39) Cole
What does it mean to be the worst?
• Large body size
• Eats a lot of expensive feed
• Average fertility…or worse!
• Begin first lactation with dystocia
• Bull calf (sexed semen?)
• Retained placenta, metritis, etc.
• Mediocre production
• Uses many resources, produces very little
40. Department of Animal Sciences, Purdue University, October 23, 2013 (40) Cole
Dissecting genetic correlations
• Compute DGV for 75-SNP segments
• Calculate correlations of DGV for traits
of interest for each segment
• Is there interesting biology associated
with favorable correlations?
• …and what about linkage
disequilibrium?
41. Department of Animal Sciences, Purdue University, October 23, 2013 (41) Cole
SNP segment correlations Milk with DPR
Unfavorable associations
Unfavorable associationsFavorable associations
Favorable associations
42. Department of Animal Sciences, Purdue University, October 23, 2013 (42) Cole
SNP segment correlations Dist’n over genome
44. Department of Animal Sciences, Purdue University, October 23, 2013 (44) Cole
Conclusions
Non-additive effects may be useful for
increasing selection intensity while
conserving important heterozygosity
Whole-genome sequencing has been very
successful at helping economically
important loss-of-function mutations
Novel phenotypes are necessary to address
global food security and a changing climate
45. Department of Animal Sciences, Purdue University, October 23, 2013 (45) Cole
Acknowledgments
Paul VanRaden, George Wiggans, Derek Bickhart, Dan Null, and Tabatha
Cooper
Animal Improvement Programs Laboratory, ARS, USDA Beltsville, MD
Tad Sonstegard, Curt Van Tassell, and Steve Schroeder
Bovine Functional Genomics Laboratory, ARS, USDA, Beltsville, MD
Chuanyu Sun
National Association of Animal Breeders
Beltsville, MD
Dan Gilbert
New Generation Genetics Inc., Fort Atkinson, WI
46. Department of Animal Sciences, Purdue University, October 23, 2013 (46) Cole
Questions?
http://gigaom.com/2012/05/31/t-mobile-pits-its-math-against-verizons-the-loser-common-sense/shutterstock_76826245/