Distribution and Location of Genetic Effects for Dairy Traits
description
Transcript of Distribution and Location of Genetic Effects for Dairy Traits
2007
Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van TassellBovine Functional Genomics LaboratoryUSDA Agricultural Research Service, Beltsville, MD, USA [email protected]
2009
Distribution and Location of Distribution and Location of Genetic Effects for Dairy TraitsGenetic Effects for Dairy Traits
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Questions of InterestQuestions of Interest
What model best fits our data?
Have we found any genes of large effect?
Can we use marker effects to locate autosomal recessives?
How do we handle the X chromosome?
How can we use marker effects to make better breeding decisions?
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Experimental DesignExperimental Design
Predict April 2008 daughter deviations from August 2003 PTA• Similar to Interbull trend test 3 • 3576 older Holstein bulls• 1759 younger bulls (total = 5335)
Results computed for 27 traits: 5 yield, 5 health, 16 conformation, and Net Merit (NM$)
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Linear and Nonlinear PredictionsLinear and Nonlinear Predictions
Linear model• Infinitesimal alleles model in which
all loci have non-zero effects
Nonlinear models• Model A: infinitesimal alleles with a
heavy-tailed prior• Model B: finite locus model with
normally-distributed marker effects• Model AB: finite locus model with a
heavy-tailed prior
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Regressions for marker allele effects Regressions for marker allele effects
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R-square values comparing linear to R-square values comparing linear to nonlinear genomic predictionsnonlinear genomic predictions
Model
Trait Linear A B AB
Net Merit 28.2 28.4 27.6 27.6
Milk 47.2 48.5 46.7 47.3
Fat 41.8 44.2 41.5 43.6
Protein 47.5 47.0 46.8 46.6
Fat % 55.3 63.3 57.5 63.9
Protein % 51.4 57.7 51.4 56.6
Longevity 25.6 27.4 25.4 26.4
Somatic cell 37.3 38.3 37.3 37.6
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Largest EffectsLargest Effects
Fat %: largest effect on BTA 14 flanking the DGAT1 gene, with lesser effects on milk and fat yield
Protein %: large effects on BTA 6 flanking the ABCG2 gene
Net Merit: a marker on BTA 18 had the largest effect on NM$, in a region previously identified as having a large effect on fertility
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Distribution of Marker Effects (Distribution of Marker Effects (Net MeritNet Merit))
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Distribution of Marker Effects (Distribution of Marker Effects (DPRDPR))
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Marker Effects on WebsiteMarker Effects on Website
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Marker Effects on WebsiteMarker Effects on Website
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Marker Effects on WebsiteMarker Effects on Website
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Dystocia ComplexDystocia Complex
Markers on BTA 18 had the largest effects for several traits:• Dystocia and stillbirth: Sire and
daughter calving ease and sire stillbirth
• Conformation: rump width, stature, strength, and body depth
• Efficiency: longevity and net merit
Large calves contribute to shorter PL and decreased NM$
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Marker Effects for Dystocia ComplexMarker Effects for Dystocia Complex
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Biology of the Dystocia ComplexBiology of the Dystocia Complex
The key marker is ss86324977 at 57,125,868 Mb on BTA 18
Located in a cluster of CD33-related Siglec genes• Many Siglecs are involved in the
leptin signaling system
Preliminary results also indicate an effect on gestation length
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From whom did the bad allele come?From whom did the bad allele come? Round Oak Rag Apple Elevation (7HO00058)
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Locating Causative MutationsLocating Causative Mutations
Genomics may allow for faster identification of causative mutations
Identifies SNP in strong linkage disequilibrium with recessive loci
Tested using BLAD, CVM, and RED
Only a few dozen genotyped carriers are needed
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Marker Effects for Autosomal RecessivesMarker Effects for Autosomal Recessives
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SNP on X ChromosomeSNP on X Chromosome
Each animal has two evaluations• Expected genetic merit of daughters• Expected genetic merit of sons• Difference is sum of effects on X• SD = 0.1 σG, smaller than expected
Correlation with sire’s daughter vs. son PTA difference was significant (P < 0.0001), regression close to 1.0
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X, X, YY, , Pseudo-autosomalPseudo-autosomal SNP SNP
487 SNP
35 SNP
0 SNP
35 SNP
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Chromosomal EBVChromosomal EBV
Sum of marker effects for individual chromosomes• Individual chromosomal EBV sum to
an animal’s genomic EBV
Chromosomal EBV are normally distributed in the absence of QTL
QTL can change the mean and SD of chromosomal EBV
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Distribution of Chromosomal EBVDistribution of Chromosomal EBVfat percent on BTA 14 (fat percent on BTA 14 (DGATDGAT))
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Distribution of Chromosomal EBVDistribution of Chromosomal EBVsire calving ease on BTA 14 (sire calving ease on BTA 14 (no QTLno QTL))
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Positive or Negative TraitsPositive or Negative Traits
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Net Merit by ChromosomeNet Merit by ChromosomeFreddie (1HO08784)Freddie (1HO08784) - highest Net Merit bull - highest Net Merit bull
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Net Merit by ChromosomeNet Merit by Chromosome O Man (7HO06417)O Man (7HO06417) – Sire of Freddie – Sire of Freddie
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Net Merit by ChromosomeNet Merit by ChromosomeDie-Hard (29HO08538) Die-Hard (29HO08538) - maternal grandsire- maternal grandsire
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Net Merit by ChromosomeNet Merit by ChromosomePlanet (7HO08081)Planet (7HO08081) – high Net Merit bull – high Net Merit bull
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New Chromosomal PTA QueryNew Chromosomal PTA Query
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Chromosomal PTA Query ExampleChromosomal PTA Query Example
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Genotype Parents and Grandparents
Manfred
O-Man
Jezebel
O-Style
Teamster
Deva
Dima
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Expected Relationship MatrixExpected Relationship Matrix11
1HO9167 O-Style1HO9167 O-Style
PGS PGD MGS MGD Sire Dam Bull
Manfred 1.0 .0 .0 .0 .5 .0 .25
Jezebel .0 1.0 .0 .0 .5 .0 .25
Teamster .0 .0 1.0 . 0 .0 .5 .25
Dima .0 .0 .0 1.0 .0 .5 .25
O-Man .5 .5 .0 .0 1.0 .0 .5
Deva .0 .0 .5 .5 .0 1.0 .5
O-Style .25 .25 .25 .25 .5 .5 1.0
1Calculated assuming that all grandparents are unrelated
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Pedigree Relationship MatrixPedigree Relationship Matrix1HO9167 O-Style1HO9167 O-Style
PGS PGD MGS MGD Sire Dam Bull
Manfred 1.053 .090 .090 .105 .571 .098 .334
Jezebel .090 1.037 .051 .099 .563 .075 .319
Teamster .090 .051 1.035 .120 .071 .578 .324
Dima .105 .099 .120 1.042 .102 .581 .342
O-Man .571 .563 .071 .102 1.045 .086 .566
Deva .098 .075 .578 .581 .086 1.060 .573
O-Style .334 .319 .324 .342 .566 .573 1.043
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Genomic Relationship Matrix Genomic Relationship Matrix 1HO9167 O-Style1HO9167 O-Style
PGS PGD MGS MGD Sire Dam Bull
Manfred 1.201 .058 .050 .093 .609 .054 .344
Jezebel .058 1.131 .008 .135 .618 .079 .357
Teamster .050 .008 1.110 .100 .014 .613 .292
Dima .093 .135 .100 1.139 .131 .610 .401
O-Man .609 .618 .014 .131 1.166 .080 .626
Deva .054 .079 .613 .610 .080 1.148 .613
O-Style .344 .357 .292 .401 .626 .613 1.157
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Difference (Genomic – Pedigree) Difference (Genomic – Pedigree) 1HO9167 O-Style1HO9167 O-Style
PGS PGD MGS MGD Sire Dam Bull
Manfred .149 -.032 -.040 -.012 .038 -.043 .010
Jezebel -.032 .095 -.043 .036 .055 .004 .038
Teamster -.040 -.043 .075 -.021 -.057 .035 -.032
Dima -.012 .036 -.021 .097 .029 .029 .059
O-Man .038 .055 -.057 .029 .121 -.006 .060
Deva -.043 .004 .035 .029 -.006 .087 .040
O-Style .010 .038 -.032 .059 .060 .040 .114
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O-Style’s Chromosomal PTAO-Style’s Chromosomal PTA
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ConclusionsConclusions
A heavy-tailed model fits the data better than linear or finite loci models
Markers on BTA 18 had large effects on net merit, longevity, calving traits, and conformation
Marker effects may be useful for locating causative mutations for recessive alleles
Results validate quantitative genetic theory, notably the infinitessimal model
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AcknowledgmentsAcknowledgments
Genotyping and DNA extraction:• USDA Bovine Functional Genomics Lab, U.
Missouri, U. Alberta, GeneSeek, Genetics & IVF Institute, Genetic Visions, and Illumina
Computing: • AIPL staff (Mel Tooker, Leigh Walton, Jay
Megonigal) Funding:
• National Research Initiative grants– 2006-35205-16888, 2006-35205-167012006-35205-16888, 2006-35205-16701
• Agriculture Research Service• Holstein, Jersey & Brown Swiss breed associations• Contributors to Cooperative Dairy DNA Repository
(CDDR)