Presented by Raphael Mrode, Julie Ojango, John Gibson and Okeyo Mwai at the 7 All Africa Conference on Animal Agriculture (AACAA), Accra , Ghana 29 July– 2 August 2019

1. SRUCLogo
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Improving the accuracy of genomic predictions
in small holder crossed-bred dairy cattle
Raphael Mrode, Julie Ojango, John Gibson and Okeyo Mwai
7 All Africa Conference on Animal Agriculture (AACAA), Accra , Ghana
29 July– 2 August 2019

2. Genomic system in developed countries
• Rapid rate of genetic progress due to genomic selection with
higher proportion of active AI bulls being gnomically
evaluated
• Genomic systems in developed countries are characterised
• With large reference populations
• Well defined phenotypes
• But mostly within pure breeds
• High accuracies – over 70 % for milk traits

3. Characteristics of genomic data in small
holder systems
• Challenges:
• Small data sets
• Difficult to define good reference and validation populations
• Little data on pure breeds
• Mostly on cross breeds animals
• Most genotyped animals are females
• Low accuracies – about 0.1 to 0.40

4. Characteristics of genomic data in small
holder systems
• Lack of pedigree, predictions uses the G matrix
• However in G markers are weighted by their expected
variances - solely function of their allele frequencies
• Crossbred data - some animals with >87% exotic genes and
others less than 36%.
• Allele frequencies differ among these categories of animals
• Usually more cows with >50 exotic genes in the data and
dominate allele frequencies estimates.
• Use of frequencies computed across all cross-bred data in GBLUP
tends to produce top cow lists to be dominated with cows of high
degree of exotic genes

5. Characteristics of genomic data in small
holder systems
• Marker frequencies computed for breeds of origin in the
crosses (breed-wise frequencies)
• Small data sets, no A matrix and estimation of breed-wise alleles
not feasible.
• Can we optimize the use of across-breed frequencies in terms
of accuracy? We can examine approaches that
• Standardizes allele frequencies of markers and therefore equalizes
their relative contribution
• Weights markers on the basis of their effects on traits of interest

6. Objectives of the study
• Examine genomic models attempting to optimize the use of
across breed frequencies with aim of improving accuracies.
• GBLUP
• Regular G matrix
• Gstd from standardized allele frequencies
• G0.5 with allele frequencies set to intermediate (0.5)
• Weighted G matrices
• GwtA -- G weighted by SNP effects from BayesA from all data
• GwtA-exo -- weighted with effects from only animals with > 0.65
exotic genes
• GwtA-ind -- weighted with effects from only animals with < 0.65
exotic genes
• Correspondingly : GwtB , GwtB-exotic, GwtB-ind from BayesB

7. Genotypic data
• Genotypic data consisted of 1038
• Data consisted of 1038 cows genotyped with the 777K Illumina
High density chip
• Cows from 5 random sites in dairy production areas in Kenya
• Crossbred cows between indigenous African breeds which
(N’dama and Nellore) and 5 exotic dairy breeds (Ayrshire,
Friesian, Holstein, Guernsey and Jersey).
• Breed composition determined using admixture analysis
• Cows classified into 4 classes based on percentage exotic
genes: > 87.5% (C1), 61−87.5% (C2), 36−60% (C3), and < 36%
(C4) exotic gene.

8. The DGEA Phenotypic data
• Test day milk records were initially analysed with a fixed
regression model obtaining a heritability of 0.19±0.05.
• Yield deviations for milk yield generated from above models
were used for all genomic predictions
• Various G used were computed as follows:
G =
• G0.05 = same with frequencies set 0.5
• Gstd = Z*Z*'/m, Z* is Z*j = Zj / .1

9. The DGEA Phenotypic data
• GwtA or GwtB =
• D = SNP effects from either BayesA or B and was estimated
from 3 different analyses
• All cows, 669 cows >= 0.65 (exotic) and 335 cows <0.65
(Ind)
• Accuracies of GEBV = correlation GEBV & YD for groups
of animals with YD excluded (cross-validation)

10. Mean Allele Frequencies
Variable Percentage exotic genes
All cows
(1038)
>87.5 (304) 61 – 87.5
(457)
36-60
(212)
<36 (61)
Means 0.527 0.519 0.526 0.536 0.544
STD 0.263 0.283 0.265 0.260 0.279
0
10
20
30
40
50
60
70
80
90
100
InThousands
Allele frequencies
C1
C2
C3
C4
All

11. Correlation among allele frequencies for
different cows of different breed proportion.
Categor
y of
cows
No. All > 0.875 0.61-0.875 0.36-0.60 <0.36
All 1034 1.00
>
0.875
304 0.97 1.00
0.61 -
0.875
457 1.00 0.98 1.00
0.36
– 0.60
212 0.96 0.86 0.94 1.00
< 0.36 61 0.85 0.67 0.82 0.95 1.00

12. Accuracy of Genomic Predictions –GBLUP
and standardizing allele frequencies
frequen

13. Accuracy of Genomic Predictions-BayesA
and weighted Analyses

14. Accuracy of genomic prediction – BayesB
and weighted analyses

15. • Ranking of indigenous cows in the top 40% increased by 14%
with using SNP effects from indigenous or combined
Weighted analysis using BayesA

16. Other options to improve genomic accuracies in
small holder systems

17. Larger reference data: African Dairy Genetics
Gain – genomic accuracies from forward validation
Commenced analysis of ADGG data and some summary of some
results using G. About 2000 genotyped cows with 9000 test days
records: h2=0.22
FRM = Fixed regression model; RRM –random regression model

18. Pooling data and genotype exchange
• Pool data across countries (increases up to 70% in accuracy)
• Exchange (trading) of genotypes
• This is the trend world-wide : Euro-Genetics and North
America Consortium Plus UK and Italy:
• Close to 40,000 bulls in reference populations
• Need good protocol for data exchange ensuring
confidentiality
• Incorporation foreign sires with genotypes abroad but with
only daughters in small holder systems:
• About 2 to 45% improvement in genomic accuracy in Brazil

19. Conclusions
• SNP allele frequencies for markers differ between animals of
high and low exotic genes.
• Frequencies were more towards fixation in cows with low
exotic genes. Need to investigate in larger data set and
different types of chips
• While GBLUP seems very robust in genomic prediction
across the range of crossbred animals. Methods that
account for variation of allele frequencies and effects are
• slightly better in prediction of cows with more indigenous proportions.
• Increases their frequency in the top list
• Larger data sets through cooperation and data exchange is
critical

20. Dairy Farmers & Farmer
organizations
National/regional
Institutions/govts.
Acknowledgements

21. CRP and CG logos

22. better lives through livestock
ilri.org
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