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Genomic Correlation: Harnessing the benefit of combining two unrelated populations for genomic selection
CSIRO AGRICULTURE
Toni Reverter – OCE Symposium, Genome to Phenome, 25-27 March 2015.
LR Porto-Neto, W Barendse, JM Henshall, SM McWilliam,SA Lehnert and A Reverter Currently under review in
Genetics Selection Evolution
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Introduction
• Genomic selection uses a reference population to build & calibrate the genomic-based predictions of genetic value (GEBV).
• The larger the reference population, the more accurate the predictions.
• Also impacting accuracy:
• Heritability• LD decay• Relationship with target population• Selection of markers
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Introduction
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Introduction
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Introduction
• One way to increase the size of the reference population is to merge data from different populations (breeds) Mixed Results
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Introduction
• Assumption #1: A genetic correlation (>0!) exists for the phenotype of interest in the two populations
The stronger this correlation the higher the benefit.
• Assumption #2: The genomic relationship matrix (GRM) allows for estimation of this “genomic” correlation.
• Assumption #3: This genomic correlation can be manipulated by selecting (via GWAS) which SNP to use when building the GRM.
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Objectives
• Estimate the genomic correlation for 5 phenotypes related to tropical adaptation in beef cattle
• To show that a careful selection of the SNP based on LD phase (same or different) and having highly significant effect impacts on the estimates of genomic correlation and accuracy of GEBV
• Data: 1,829 Brahman (BB) and 1,973 Tropical Composite (TC) cows and bulls genotyped for 71,726 SNP highly polymorphic in Bos indicus cattle.
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Methods
Tropical Composite (TC) Brahman (BB)
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Flowchart
REFERENCE POPULATION817 BB + 1,028 TC Cows
5 Phenotypes71,726 SNPs
GWAS AnalysesNRM + SNP
1 2
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
REFERENCE POPULATION817 BB + 1,028 TC Cows
5 Phenotypes71,726 SNPs
Whole GRM: GW
GWAS AnalysesNRM + SNP
Same Effect GRM: GS Different Effect GRM: GD
1 2
Flowchart
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
REFERENCE POPULATION817 BB + 1,028 TC Cows
5 Phenotypes71,726 SNPs
Whole GRM: GW
Bi-Variate AnalysesNRM + GRM
GWAS AnalysesNRM + SNP
Same Effect GRM: GS Different Effect GRM: GD
GenomicCorrelations
Missing Heritability
1 2
3
Flowchart
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
REFERENCE POPULATION817 BB + 1,028 TC Cows
5 Phenotypes71,726 SNPs
Whole GRM: GW
Bi-Variate AnalysesNRM + GRM
GWAS AnalysesNRM + SNP
Same Effect GRM: GS Different Effect GRM: GD
VALIDATION POPULATION1,012 BB + 945 TC Bulls
5 Phenotypes
GenomicCorrelations
Missing Heritability
Uni-Variate GBLUP AnalysesGRM only
GEBVAccuracies
1 2
3 4
5
Flowchart
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Relationship Matrices
Brahman
Tropical Composite
NRM
GRM
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Models
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
e
e
u
u
M0
0M
a
a
Z0
0Z
β
β
X0
0X
y
y
REML Estimation of genetic parameters and fraction of missing heritability
I00000
0I0000
00GG00
00GG00
0000A0
00000A
e
e
u
u
a
a
2e
2e
2uu
u2u
2a
2a
TC
BB
TC
BB
TC
BB
TC
BB
TCTCBB,
TCBB,BB
TC
BB
V
Fixed Effects Pedigree (NRM) Genotype (GRM)
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Models
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
e
e
u
u
M0
0M
a
a
Z0
0Z
β
β
X0
0X
y
y
REML Estimation of genetic parameters and fraction of missing heritability
I00000
0I0000
00GG00
00GG00
0000A0
00000A
e
e
u
u
a
a
2e
2e
2uu
u2u
2a
2a
TC
BB
TC
BB
TC
BB
TC
BB
TCTCBB,
TCBB,BB
TC
BB
V2u
2u
u
G
TCBB
TCBB,
r
Genomic Correlation
Fixed Effects Pedigree (NRM) Genotype (GRM)
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Models
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
TC
BB
e
e
u
u
M0
0M
a
a
Z0
0Z
β
β
X0
0X
y
y
REML Estimation of genetic parameters and fraction of missing heritability
I00000
0I0000
00GG00
00GG00
0000A0
00000A
e
e
u
u
a
a
2e
2e
2uu
u2u
2a
2a
TC
BB
TC
BB
TC
BB
TC
BB
TCTCBB,
TCBB,BB
TC
BB
V2u
2u
u
G
TCBB
TCBB,
r
Genomic Correlation
2a
2u
2uBB
BBBB
BB1
missC
Missing h2
Fixed Effects Pedigree (NRM) Genotype (GRM)
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
GRMs
1. GW = the GRM built with the Whole set of 71,726 SNP genotypes;
2. GS = the GRM built with 16,207 SNP having effects in the Same direction in both breeds and across all five phenotypes;
3. GD = the GRM built with 16,951 SNP having effects in Different direction in both breeds and across all five phenotypes.
1 2 3 4 5
16,207 Same 75.18 19.70 4.15 0.89 0.08
16,951 Diff. 76.91 19.26 3.41 0.38 0.05
% SNPs affecting 1 to 5 phenotypes
For each phenotype select the 10% (or 7,200) most significant SNP and in the same direction in both breeds. Merge the 5 lists into a single list, namely “list-of-same”.
Similarly, create the “list-of-different”. Finally, remove any SNP in the overlap of “list-of-same” and “list-of-different”.
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Results
GW
GD
GS
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Results: ACC of GEBV
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Results: ACC of GEBV
Toni Reverter – OCE Symposium: Genome to Phenome, March 2015, UQ Brisbane.
Conclusions
1. For the merging of two populations into a single reference to be beneficial, there must be a correlation between the same phenotype in the two populations.
2. There is benefit in including relevant markers and excluding irrelevant ones.
3. This careful selection of markers impacts on: • The estimate of the genomic correlation.• The accuracy of GEBV.
4. Future work:• Mechanism for “careful” selection• Allele frequencies used in GRM• What if highly unbalanced population sizes?
CSIRO AGRICULTURE
Thank you!