SlideShare a Scribd company logo
1 of 20
J. B. ColeJ. B. Cole**
and P. M. VanRadenand P. M. VanRaden
Animal Improvement Programs Laboratory
Agricultural Research Service, USDA
Beltsville, MD 20705-2350
john.cole@ars.usda.gov
Use of Haplotypes to Predict
Selection Limits and Mendelian
Sampling
ADSA, July 2010 (2) Cole and VanRaden
Introduction
• Mendelian sampling is the difference
between an individual's PTA and its PA
• Sustained genetic gain under selection
depends on Mendelian sampling
variance (Woolliams et al., 1999)
• Increased reliabilities from genomic
selection due to better estimates of
Mendelian sampling (Hayes et al., 2009)
ADSA, July 2010 (3) Cole and VanRaden
Introduction (cont’d)
• You do not benefit substantially
from genomic selection until you
have a large enough pool of
genotyped animals to provide good
estimates of marker effects
• Good marker effects are essential
for reliable prediction of
Mendelian sampling
ADSA, July 2010 (4) Cole and VanRaden
Objectives
• Describe the predicted Mendelian
sampling (MS) variation in Brown
Swiss, Holstein, and Jersey cattle
• Calculate selection limits based on
haplotypes present in the genotyped
population
• Examine adjustments to breeding
values for changes in heterozygosity
ADSA, July 2010 (5) Cole and VanRaden
Materials
• 43,382 SNP from the Illumina BovineSNP50
• SNP solutions from the June, 2010 evaluation
• Three breeds
• 1,455 Brown Swiss males and females
• 40,351 Holstein males and females
• 4,064 Jersey males and females
• Three traits
• Daughter pregnancy rate (DPR)
• Milk yield (Milk)
• Lifetime net merit (NM$)
ADSA, July 2010 (6) Cole and VanRaden
Methods
• Haplotypes imputed with findhap.f90
(VanRaden et al., 2010)
• Calculations and simulations performed
with SAS 9.2
• Plots produced with R 2.10.1 and
ggplot2 0.8.7 (Wickham, 2009)
• Operating system is 64-bit RedHat
Enterprise Linux 5
ADSA, July 2010 (7) Cole and VanRaden
Predicted Mendelian sampling
• Lower bound assumes all SNP on a
chromosome are unlinked
• Upper bound assumes all SNP on a
chromosome are perfectly linked
• “I have discovered a truly marvelous
proof…this slide is too narrow to
contain it”
• Also could be calculated from linkage
disequilibrium blocks
ADSA, July 2010 (8) Cole and VanRaden
Distribution of Mendelian sampling variance
ADSA, July 2010 (9) Cole and VanRaden
Predicted Mendelian sampling variance
Trait Breed Lower Expected Upper
DPR BS 0.09 1.45 1.57
HO 0.57 1.45 4.02
JE 0.09 0.98 1.27
Milk BS 35,335 215,168 507,076
HO 228,011 261,364 1,069,741
JE 150,076 205,440 601,979
NM$ BS 2,539 19,602 40,458
HO 16,601 19,602 87,449
JE 3,978 19,602 44,552
ADSA, July 2010 (10) Cole and VanRaden
Why are Holsteins so different?
• There are many more HO haplotypes represented
• There are multiple QTL affecting NM$ in HO
ADSA, July 2010 (11) Cole and VanRaden
Sampling variance over time — DPR
BS HO JE
ADSA, July 2010 (12) Cole and VanRaden
Sampling variance over time — Milk
BS HO JE
ADSA, July 2010 (13) Cole and VanRaden
Sampling variance over time — NM$
BS HO JE
ADSA, July 2010 (14) Cole and VanRaden
Correlations with inbreeding
Lower Upper
Trait Breed Genomic Pedigree Genomic Pedigree
DPR BS -0.73 -0.38 -0.02 0.09
HO -0.77 -0.40 -0.11 -0.03
JE -0.83 -0.53 -0.01 0.06
Milk BS -0.86 -0.55 -0.05 0.03
HO -0.12 -0.05 -0.10 -0.03
JE -0.01 0.03 -0.04 0.04
NM$ BS -0.85 -0.49 0.03 0.13
HO -0.21 -0.12 -0.11 -0.03
JE -0.86 -0.53 -0.11 -0.02
ADSA, July 2010 (15) Cole and VanRaden
Selection limits
• Lower bound found by summing
the best chromosomes
• Upper bound found by summing
the best alleles at each locus
• NM$ was adjusted to account for
changes in heterozygosity
ADSA, July 2010 (16) Cole and VanRaden
Calculated selection limits
Trait Breed Lower Upper Largest DGV
DPR BS 20 53 8
HO 40 139 8
JE 19 53 5
Milk BS 14,193 34,023 4,544
HO 24,883 77,923 7,996
JE 16,133 40,249 5,620
NM$ BS 3,857 9,140 1,102
HO 7,515 23,588 2,528
JE 4,678 11,517 1,556
ADSA, July 2010 (17) Cole and VanRaden
Adjusting for heterozygosity
• Chromosomal EBV for NM$ were
adjusted by adding or subtracting
6% of an additive genetic standard
deviation ($11.88) per 1% change
in heterozygosity (Smith et al.,
1998)
• Only 4 chromosomes (BTA 6, 10,
14, and 16) differed between the
adjusted and unadjusted groups
ADSA, July 2010 (18) Cole and VanRaden
What’s the best cow we can make?
A “Supercow” constructed from the best haplotypes in the
Holstein population would have an EBV(NM$) of $7515
ADSA, July 2010 (19) Cole and VanRaden
Conclusions
• Selection limits and sampling variances
differ among breeds
• Sampling variance has decreased
slightly over time
• The top animals in each breed are well
below predicted selection limits
• Adjustment for heterozygosity had
little effect on breeding values for NM$
ADSA, July 2010 (20) Cole and VanRaden
Questions?

More Related Content

Viewers also liked

Viewers also liked (7)

Overview of Animal Improvement Programs Laboratory
Overview of Animal Improvement Programs LaboratoryOverview of Animal Improvement Programs Laboratory
Overview of Animal Improvement Programs Laboratory
 
Hills application
Hills  applicationHills  application
Hills application
 
Genomic selection and systems biology – lessons from dairy cattle breeding
Genomic selection and systems biology – lessons from dairy cattle breedingGenomic selection and systems biology – lessons from dairy cattle breeding
Genomic selection and systems biology – lessons from dairy cattle breeding
 
Visualization of Results from Genomic Predictions
Visualization of Results from Genomic PredictionsVisualization of Results from Genomic Predictions
Visualization of Results from Genomic Predictions
 
باب 7
باب 7باب 7
باب 7
 
Genetic Evaluation of Stillbirth in US Holsteins Using a Sire-maternal Grands...
Genetic Evaluation of Stillbirth in US Holsteins Using a Sire-maternal Grands...Genetic Evaluation of Stillbirth in US Holsteins Using a Sire-maternal Grands...
Genetic Evaluation of Stillbirth in US Holsteins Using a Sire-maternal Grands...
 
Estimation of yields for long lactations using best prediction
Estimation of yields for long lactations using best predictionEstimation of yields for long lactations using best prediction
Estimation of yields for long lactations using best prediction
 

Similar to Use of Haplotypes to Predict Selection Limits and Mendelian Sampling

Status of trace elements in mysore subjects - dr. g. nagaraj
Status  of  trace  elements  in mysore subjects - dr. g. nagarajStatus  of  trace  elements  in mysore subjects - dr. g. nagaraj
Status of trace elements in mysore subjects - dr. g. nagaraj
gnriem
 
16 apple germplasm strcture and tools for germplasm curators durel charles eric
16 apple germplasm strcture and tools for germplasm curators durel charles eric16 apple germplasm strcture and tools for germplasm curators durel charles eric
16 apple germplasm strcture and tools for germplasm curators durel charles eric
fruitbreedomics
 
Haematological values of apparently healthy indigenous goats in Malaysia: A c...
Haematological values of apparently healthy indigenous goats in Malaysia: A c...Haematological values of apparently healthy indigenous goats in Malaysia: A c...
Haematological values of apparently healthy indigenous goats in Malaysia: A c...
Mohammed Muayad TA
 

Similar to Use of Haplotypes to Predict Selection Limits and Mendelian Sampling (20)

What can we do with dairy cattle genomics other than predict more accurate br...
What can we do with dairy cattle genomics other than predict more accurate br...What can we do with dairy cattle genomics other than predict more accurate br...
What can we do with dairy cattle genomics other than predict more accurate br...
 
Challenges and successes in dairy cattle genomics
Challenges and successes in dairy cattle genomicsChallenges and successes in dairy cattle genomics
Challenges and successes in dairy cattle genomics
 
New Tools for Genomic Selection of Livestock
New Tools for Genomic Selection of LivestockNew Tools for Genomic Selection of Livestock
New Tools for Genomic Selection of Livestock
 
Estimation of Stillbirth (Co)variance Components and Development of a Calving...
Estimation of Stillbirth (Co)variance Components and Development of a Calving...Estimation of Stillbirth (Co)variance Components and Development of a Calving...
Estimation of Stillbirth (Co)variance Components and Development of a Calving...
 
Indigenous Life Expectancy Using Multiple Australian Data Sources_PVERConf_Ma...
Indigenous Life Expectancy Using Multiple Australian Data Sources_PVERConf_Ma...Indigenous Life Expectancy Using Multiple Australian Data Sources_PVERConf_Ma...
Indigenous Life Expectancy Using Multiple Australian Data Sources_PVERConf_Ma...
 
Status of trace elements in mysore subjects - dr. g. nagaraj
Status  of  trace  elements  in mysore subjects - dr. g. nagarajStatus  of  trace  elements  in mysore subjects - dr. g. nagaraj
Status of trace elements in mysore subjects - dr. g. nagaraj
 
Breed Composition Codes for Crossbred Dairy Cattle with an Application to Cal...
Breed Composition Codes for Crossbred Dairy Cattle with an Application to Cal...Breed Composition Codes for Crossbred Dairy Cattle with an Application to Cal...
Breed Composition Codes for Crossbred Dairy Cattle with an Application to Cal...
 
Jie Zheng at #ICG12: PhenoSpD: an atlas of phenotypic correlations and a mult...
Jie Zheng at #ICG12: PhenoSpD: an atlas of phenotypic correlations and a mult...Jie Zheng at #ICG12: PhenoSpD: an atlas of phenotypic correlations and a mult...
Jie Zheng at #ICG12: PhenoSpD: an atlas of phenotypic correlations and a mult...
 
What are the sources of bacteria in your watershed? They may not be what you ...
What are the sources of bacteria in your watershed? They may not be what you ...What are the sources of bacteria in your watershed? They may not be what you ...
What are the sources of bacteria in your watershed? They may not be what you ...
 
16 apple germplasm strcture and tools for germplasm curators durel charles eric
16 apple germplasm strcture and tools for germplasm curators durel charles eric16 apple germplasm strcture and tools for germplasm curators durel charles eric
16 apple germplasm strcture and tools for germplasm curators durel charles eric
 
Distribution and Location of Genetic Effects for Dairy Traits
Distribution and Location of Genetic Effects for Dairy TraitsDistribution and Location of Genetic Effects for Dairy Traits
Distribution and Location of Genetic Effects for Dairy Traits
 
Heat tolerance, real-life genomics and GxE issues
Heat tolerance, real-life genomics and GxE issuesHeat tolerance, real-life genomics and GxE issues
Heat tolerance, real-life genomics and GxE issues
 
RecSys2018論文読み会 資料
RecSys2018論文読み会 資料RecSys2018論文読み会 資料
RecSys2018論文読み会 資料
 
Comparing the Amount and Quality of Information from Different Sequencing Str...
Comparing the Amount and Quality of Information from Different Sequencing Str...Comparing the Amount and Quality of Information from Different Sequencing Str...
Comparing the Amount and Quality of Information from Different Sequencing Str...
 
Microtask crowdsourcing for annotating diseases in PubMed abstracts (ASHG 2014)
Microtask crowdsourcing for annotating diseases in PubMed abstracts (ASHG 2014)Microtask crowdsourcing for annotating diseases in PubMed abstracts (ASHG 2014)
Microtask crowdsourcing for annotating diseases in PubMed abstracts (ASHG 2014)
 
Eq 5 d distribution pro-ms oxford bzamora_07062017+comments + nd 070617
Eq 5 d distribution pro-ms oxford bzamora_07062017+comments + nd 070617Eq 5 d distribution pro-ms oxford bzamora_07062017+comments + nd 070617
Eq 5 d distribution pro-ms oxford bzamora_07062017+comments + nd 070617
 
New Methods for Analysing the Distribution of 5Q 5D Observations
New Methods for Analysing the Distribution of 5Q 5D ObservationsNew Methods for Analysing the Distribution of 5Q 5D Observations
New Methods for Analysing the Distribution of 5Q 5D Observations
 
Vivo vitrothingamajig
Vivo vitrothingamajigVivo vitrothingamajig
Vivo vitrothingamajig
 
Dr. Igor Paploski - Making Epidemiological Sense Out of Large Datasets of PRR...
Dr. Igor Paploski - Making Epidemiological Sense Out of Large Datasets of PRR...Dr. Igor Paploski - Making Epidemiological Sense Out of Large Datasets of PRR...
Dr. Igor Paploski - Making Epidemiological Sense Out of Large Datasets of PRR...
 
Haematological values of apparently healthy indigenous goats in Malaysia: A c...
Haematological values of apparently healthy indigenous goats in Malaysia: A c...Haematological values of apparently healthy indigenous goats in Malaysia: A c...
Haematological values of apparently healthy indigenous goats in Malaysia: A c...
 

More from John B. Cole, Ph.D.

Using genotypes to construct phenotypes for dairy cattle breeding programs an...
Using genotypes to construct phenotypes for dairy cattle breeding programs an...Using genotypes to construct phenotypes for dairy cattle breeding programs an...
Using genotypes to construct phenotypes for dairy cattle breeding programs an...
John B. Cole, Ph.D.
 
Genomic evaluation of dairy cattle health
Genomic evaluation of dairy cattle healthGenomic evaluation of dairy cattle health
Genomic evaluation of dairy cattle health
John B. Cole, Ph.D.
 

More from John B. Cole, Ph.D. (19)

Crv 2015 jbc
Crv 2015 jbcCrv 2015 jbc
Crv 2015 jbc
 
Using genotypes to construct phenotypes for dairy cattle breeding programs an...
Using genotypes to construct phenotypes for dairy cattle breeding programs an...Using genotypes to construct phenotypes for dairy cattle breeding programs an...
Using genotypes to construct phenotypes for dairy cattle breeding programs an...
 
If we would see further than others: research & technology today and tomorrow
If we would see further than others: research & technology today and tomorrowIf we would see further than others: research & technology today and tomorrow
If we would see further than others: research & technology today and tomorrow
 
Using genotyping and whole-genome sequencing to identify causal variants asso...
Using genotyping and whole-genome sequencing to identify causal variants asso...Using genotyping and whole-genome sequencing to identify causal variants asso...
Using genotyping and whole-genome sequencing to identify causal variants asso...
 
Genetic improvement programs for US dairy cattle
Genetic improvement programs for US dairy cattleGenetic improvement programs for US dairy cattle
Genetic improvement programs for US dairy cattle
 
The hunt for a functional mutation affecting conformation and calving traits ...
The hunt for a functional mutation affecting conformation and calving traits ...The hunt for a functional mutation affecting conformation and calving traits ...
The hunt for a functional mutation affecting conformation and calving traits ...
 
An updated version of lifetime net merit incorporating additional fertility t...
An updated version of lifetime net merit incorporating additional fertility t...An updated version of lifetime net merit incorporating additional fertility t...
An updated version of lifetime net merit incorporating additional fertility t...
 
An updated version of lifetime net merit incorporating additional fertility t...
An updated version of lifetime net merit incorporating additional fertility t...An updated version of lifetime net merit incorporating additional fertility t...
An updated version of lifetime net merit incorporating additional fertility t...
 
Stillbirth, Longevity and Fertility Update
Stillbirth, Longevity and Fertility UpdateStillbirth, Longevity and Fertility Update
Stillbirth, Longevity and Fertility Update
 
New tools for genomic selection in dairy cattle
New tools for genomic selection in dairy cattleNew tools for genomic selection in dairy cattle
New tools for genomic selection in dairy cattle
 
Genomic evaluation of dairy cattle health
Genomic evaluation of dairy cattle healthGenomic evaluation of dairy cattle health
Genomic evaluation of dairy cattle health
 
Uso e valore economico dei test genomici in azienda
Uso e valore economico dei test genomici in aziendaUso e valore economico dei test genomici in azienda
Uso e valore economico dei test genomici in azienda
 
Genomic evaluation of low-heritability traits: dairy cattle health as a model
Genomic evaluation of low-heritability traits: dairy cattle health as a modelGenomic evaluation of low-heritability traits: dairy cattle health as a model
Genomic evaluation of low-heritability traits: dairy cattle health as a model
 
New applications of genomic technology in the US dairy industry
New applications of genomic technology in the US dairy industryNew applications of genomic technology in the US dairy industry
New applications of genomic technology in the US dairy industry
 
PyPedal, an open source software package for pedigree analysis
PyPedal, an open source software package for pedigree analysisPyPedal, an open source software package for pedigree analysis
PyPedal, an open source software package for pedigree analysis
 
Applications of haplotypes in dairy farm management
Applications of haplotypes in dairy farm managementApplications of haplotypes in dairy farm management
Applications of haplotypes in dairy farm management
 
Fine-mapping of QTL using high-density SNP genotypes
Fine-mapping of QTL using high-density SNP genotypesFine-mapping of QTL using high-density SNP genotypes
Fine-mapping of QTL using high-density SNP genotypes
 
Genomics Beyond EBVs
Genomics Beyond EBVsGenomics Beyond EBVs
Genomics Beyond EBVs
 
Validation of Producer-Recorded Health Event Data and Use in Genetic Improvem...
Validation of Producer-Recorded Health Event Data and Use in Genetic Improvem...Validation of Producer-Recorded Health Event Data and Use in Genetic Improvem...
Validation of Producer-Recorded Health Event Data and Use in Genetic Improvem...
 

Recently uploaded

Human genetics..........................pptx
Human genetics..........................pptxHuman genetics..........................pptx
Human genetics..........................pptx
Silpa
 
Conjugation, transduction and transformation
Conjugation, transduction and transformationConjugation, transduction and transformation
Conjugation, transduction and transformation
Areesha Ahmad
 
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 bAsymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Sérgio Sacani
 
POGONATUM : morphology, anatomy, reproduction etc.
POGONATUM : morphology, anatomy, reproduction etc.POGONATUM : morphology, anatomy, reproduction etc.
POGONATUM : morphology, anatomy, reproduction etc.
Silpa
 
Bacterial Identification and Classifications
Bacterial Identification and ClassificationsBacterial Identification and Classifications
Bacterial Identification and Classifications
Areesha Ahmad
 
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
Scintica Instrumentation
 

Recently uploaded (20)

FAIRSpectra - Enabling the FAIRification of Analytical Science
FAIRSpectra - Enabling the FAIRification of Analytical ScienceFAIRSpectra - Enabling the FAIRification of Analytical Science
FAIRSpectra - Enabling the FAIRification of Analytical Science
 
300003-World Science Day For Peace And Development.pptx
300003-World Science Day For Peace And Development.pptx300003-World Science Day For Peace And Development.pptx
300003-World Science Day For Peace And Development.pptx
 
Human genetics..........................pptx
Human genetics..........................pptxHuman genetics..........................pptx
Human genetics..........................pptx
 
Clean In Place(CIP).pptx .
Clean In Place(CIP).pptx                 .Clean In Place(CIP).pptx                 .
Clean In Place(CIP).pptx .
 
Conjugation, transduction and transformation
Conjugation, transduction and transformationConjugation, transduction and transformation
Conjugation, transduction and transformation
 
GBSN - Microbiology (Unit 3)
GBSN - Microbiology (Unit 3)GBSN - Microbiology (Unit 3)
GBSN - Microbiology (Unit 3)
 
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIACURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA
 
Use of mutants in understanding seedling development.pptx
Use of mutants in understanding seedling development.pptxUse of mutants in understanding seedling development.pptx
Use of mutants in understanding seedling development.pptx
 
Velocity and Acceleration PowerPoint.ppt
Velocity and Acceleration PowerPoint.pptVelocity and Acceleration PowerPoint.ppt
Velocity and Acceleration PowerPoint.ppt
 
Dr. E. Muralinath_ Blood indices_clinical aspects
Dr. E. Muralinath_ Blood indices_clinical  aspectsDr. E. Muralinath_ Blood indices_clinical  aspects
Dr. E. Muralinath_ Blood indices_clinical aspects
 
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 bAsymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b
 
POGONATUM : morphology, anatomy, reproduction etc.
POGONATUM : morphology, anatomy, reproduction etc.POGONATUM : morphology, anatomy, reproduction etc.
POGONATUM : morphology, anatomy, reproduction etc.
 
COMPUTING ANTI-DERIVATIVES (Integration by SUBSTITUTION)
COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)COMPUTING ANTI-DERIVATIVES(Integration by SUBSTITUTION)
COMPUTING ANTI-DERIVATIVES (Integration by SUBSTITUTION)
 
module for grade 9 for distance learning
module for grade 9 for distance learningmodule for grade 9 for distance learning
module for grade 9 for distance learning
 
Introduction of DNA analysis in Forensic's .pptx
Introduction of DNA analysis in Forensic's .pptxIntroduction of DNA analysis in Forensic's .pptx
Introduction of DNA analysis in Forensic's .pptx
 
An introduction on sequence tagged site mapping
An introduction on sequence tagged site mappingAn introduction on sequence tagged site mapping
An introduction on sequence tagged site mapping
 
Factory Acceptance Test( FAT).pptx .
Factory Acceptance Test( FAT).pptx       .Factory Acceptance Test( FAT).pptx       .
Factory Acceptance Test( FAT).pptx .
 
Climate Change Impacts on Terrestrial and Aquatic Ecosystems.pptx
Climate Change Impacts on Terrestrial and Aquatic Ecosystems.pptxClimate Change Impacts on Terrestrial and Aquatic Ecosystems.pptx
Climate Change Impacts on Terrestrial and Aquatic Ecosystems.pptx
 
Bacterial Identification and Classifications
Bacterial Identification and ClassificationsBacterial Identification and Classifications
Bacterial Identification and Classifications
 
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
(May 9, 2024) Enhanced Ultrafast Vector Flow Imaging (VFI) Using Multi-Angle ...
 

Use of Haplotypes to Predict Selection Limits and Mendelian Sampling

  • 1. J. B. ColeJ. B. Cole** and P. M. VanRadenand P. M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 20705-2350 john.cole@ars.usda.gov Use of Haplotypes to Predict Selection Limits and Mendelian Sampling
  • 2. ADSA, July 2010 (2) Cole and VanRaden Introduction • Mendelian sampling is the difference between an individual's PTA and its PA • Sustained genetic gain under selection depends on Mendelian sampling variance (Woolliams et al., 1999) • Increased reliabilities from genomic selection due to better estimates of Mendelian sampling (Hayes et al., 2009)
  • 3. ADSA, July 2010 (3) Cole and VanRaden Introduction (cont’d) • You do not benefit substantially from genomic selection until you have a large enough pool of genotyped animals to provide good estimates of marker effects • Good marker effects are essential for reliable prediction of Mendelian sampling
  • 4. ADSA, July 2010 (4) Cole and VanRaden Objectives • Describe the predicted Mendelian sampling (MS) variation in Brown Swiss, Holstein, and Jersey cattle • Calculate selection limits based on haplotypes present in the genotyped population • Examine adjustments to breeding values for changes in heterozygosity
  • 5. ADSA, July 2010 (5) Cole and VanRaden Materials • 43,382 SNP from the Illumina BovineSNP50 • SNP solutions from the June, 2010 evaluation • Three breeds • 1,455 Brown Swiss males and females • 40,351 Holstein males and females • 4,064 Jersey males and females • Three traits • Daughter pregnancy rate (DPR) • Milk yield (Milk) • Lifetime net merit (NM$)
  • 6. ADSA, July 2010 (6) Cole and VanRaden Methods • Haplotypes imputed with findhap.f90 (VanRaden et al., 2010) • Calculations and simulations performed with SAS 9.2 • Plots produced with R 2.10.1 and ggplot2 0.8.7 (Wickham, 2009) • Operating system is 64-bit RedHat Enterprise Linux 5
  • 7. ADSA, July 2010 (7) Cole and VanRaden Predicted Mendelian sampling • Lower bound assumes all SNP on a chromosome are unlinked • Upper bound assumes all SNP on a chromosome are perfectly linked • “I have discovered a truly marvelous proof…this slide is too narrow to contain it” • Also could be calculated from linkage disequilibrium blocks
  • 8. ADSA, July 2010 (8) Cole and VanRaden Distribution of Mendelian sampling variance
  • 9. ADSA, July 2010 (9) Cole and VanRaden Predicted Mendelian sampling variance Trait Breed Lower Expected Upper DPR BS 0.09 1.45 1.57 HO 0.57 1.45 4.02 JE 0.09 0.98 1.27 Milk BS 35,335 215,168 507,076 HO 228,011 261,364 1,069,741 JE 150,076 205,440 601,979 NM$ BS 2,539 19,602 40,458 HO 16,601 19,602 87,449 JE 3,978 19,602 44,552
  • 10. ADSA, July 2010 (10) Cole and VanRaden Why are Holsteins so different? • There are many more HO haplotypes represented • There are multiple QTL affecting NM$ in HO
  • 11. ADSA, July 2010 (11) Cole and VanRaden Sampling variance over time — DPR BS HO JE
  • 12. ADSA, July 2010 (12) Cole and VanRaden Sampling variance over time — Milk BS HO JE
  • 13. ADSA, July 2010 (13) Cole and VanRaden Sampling variance over time — NM$ BS HO JE
  • 14. ADSA, July 2010 (14) Cole and VanRaden Correlations with inbreeding Lower Upper Trait Breed Genomic Pedigree Genomic Pedigree DPR BS -0.73 -0.38 -0.02 0.09 HO -0.77 -0.40 -0.11 -0.03 JE -0.83 -0.53 -0.01 0.06 Milk BS -0.86 -0.55 -0.05 0.03 HO -0.12 -0.05 -0.10 -0.03 JE -0.01 0.03 -0.04 0.04 NM$ BS -0.85 -0.49 0.03 0.13 HO -0.21 -0.12 -0.11 -0.03 JE -0.86 -0.53 -0.11 -0.02
  • 15. ADSA, July 2010 (15) Cole and VanRaden Selection limits • Lower bound found by summing the best chromosomes • Upper bound found by summing the best alleles at each locus • NM$ was adjusted to account for changes in heterozygosity
  • 16. ADSA, July 2010 (16) Cole and VanRaden Calculated selection limits Trait Breed Lower Upper Largest DGV DPR BS 20 53 8 HO 40 139 8 JE 19 53 5 Milk BS 14,193 34,023 4,544 HO 24,883 77,923 7,996 JE 16,133 40,249 5,620 NM$ BS 3,857 9,140 1,102 HO 7,515 23,588 2,528 JE 4,678 11,517 1,556
  • 17. ADSA, July 2010 (17) Cole and VanRaden Adjusting for heterozygosity • Chromosomal EBV for NM$ were adjusted by adding or subtracting 6% of an additive genetic standard deviation ($11.88) per 1% change in heterozygosity (Smith et al., 1998) • Only 4 chromosomes (BTA 6, 10, 14, and 16) differed between the adjusted and unadjusted groups
  • 18. ADSA, July 2010 (18) Cole and VanRaden What’s the best cow we can make? A “Supercow” constructed from the best haplotypes in the Holstein population would have an EBV(NM$) of $7515
  • 19. ADSA, July 2010 (19) Cole and VanRaden Conclusions • Selection limits and sampling variances differ among breeds • Sampling variance has decreased slightly over time • The top animals in each breed are well below predicted selection limits • Adjustment for heterozygosity had little effect on breeding values for NM$
  • 20. ADSA, July 2010 (20) Cole and VanRaden Questions?