cross selection through genomic prediction in two … · 2017-04-30 · cross selection through...
TRANSCRIPT
CROSS SELECTION THROUGH GENOMIC
PREDICTION IN TWO WHEAT BREEDING
PROGRAMS
Bettina Lado, Sarah Battenfield, Carlos Guzman, Martín Quincke, Ravi P.
Singh, Susanne Dreisigacker, R. Javier Peña, Allan Fritz, Paula Silva, Jesse
Poland and Lucía Gutiérrez
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Critical step in a breeding program.
Many possible crosses but not all feasible to do.
Not enough information to make a decision of which crosses to
do.
Mean of the progeny was well predicted using mid-parents
value.
Variance of crosses was difficult to predict using either
morphological data, pedigree and few molecular markers.
OVERVIEW
CROSS SELECTION
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Recently better prediction of variance was obtained using high
throughput genotyping:
- Variance could be predicted adding the markers effects to
account for parental differences (Endelman, 2011).
- Variances could be predicted through RILs simulation
predicting the variance through individual progeny values
(Mohammadi et al. 2015).
OVERVIEW
Is not clear what is the relevant information that the
breeders need to use to select crosses.
VARIANCE PREDICTION
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Compare methodologies for cross prediction and their
implications in cross selection.
1. Predict variance assuming linkage equilibrium (VLE) and
accounting for linkage disequilibrium (VLD).
2. Compare best crosses and parents selected by mid-parent
value and by mean of the top 10% of the progeny
calculated using VLE and VLD.
3. Increase the weight of variance in selection and compare
the mean of the top 10% of the progeny.
OBJECTIVES
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Population
INIA (Uruguay)
1,465 wheat lines evaluated for grain yield
3,884 SNPs identified by genotyping by sequencing
CIMMYT (Mexico)
5
5,984 wheat lines evaluated forbaking quality:
- Grain protein
- Loaf volume
- Mixing time
1,164 SNPs identified by genotyping by sequencing
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Variance prediction assuming Linkage Equilibrium (VLE)
Endelman, 2011
𝑉𝐿𝐸𝑖𝑗 =
𝑘=1
𝑘=𝑀
1 − (𝑝𝑘+ 𝑖𝑗 − 𝑝𝑘− 𝑖𝑗 )2 ∗ 𝑢𝑘
2
Term to account fordifferences betweenparents𝑝𝑘+(𝑖𝑗): frequency of biallele +1
𝑝𝑘−(𝑖𝑗): frequency of biallele -1
Marker effect
Variances are predicted accounting for the effect of markers which are different between parents
Markers do not require map positions
1000 Progeny performance values are simulated: ~ N(MP, VLE)
i: i-th parent 1
𝑗: j-th parent 2
𝑘: k-th SNP
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Parent A
• AAAAAAAAAAAAAAAAAAAAAAA
Parent B
• BBBBBBBBBBBBBBBBBBBBBBBB
Random recombination points
Generic progeny
AAAAAABBBBBBBBBBBBBAAAA
BBBBBBAAAAAAAAAAAAABBBB
AAAAAABBBBBBBBBBBBBBBBBB
AAAAAAAAAAAAAAAAAAABBBB
Variance prediction accounting for Linkage Disequilibrium (VLD)
1000 RILs were simulated
Requires annotated markers
with positions to simulate
recombination points
𝑉𝐿𝐷𝑖𝑗 = 𝑣𝑎𝑟(1000 RILs)
‘PopVar’ R package.Mohammadi et al. 2015
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Correlations between variance prediction assuming linkage
equilibrium (VLE) and accounting for linkage disequilibrium (VLD).
RESULTS
Lado, Battenfield et al. 2017
1
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VLD VLE
Variance vs. mid-parents values for a cross between two lines withlow and high performance values
9Lado, Battenfield et al. 2017
RESULTS1
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Selection criteria
• Mid-parents (MP) value.
• Mean of top 10% of progeny within the cross
predicted using VLE(T10_LE).
• Mean of top 10% of progeny within the cross
predicted using VLD (T10_LD).
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Most crosses were common between three strategies of selectingcrosses.
Lado, Battenfield et al. 2017
RESULTS2
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Bread Quality Traits
There are many common crosses and parents
Variance had more impact on cross selection for quality traits
Lado, Battenfield et al. 2017
RESULTS2
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Weighting progeny variance on cross selection
13
Mean of all progenies and the mean of the top 10% of the progeny
for the 100 selected crosses were calculated.
Performance thresholds:
Max(MP) – f*[Max(MP) – Mean(MP)]
f = 1, 0.8, 0.6, 0.4 and 0.2
blue points: 100 crosses selected
above the threshold and maximum
variance.
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Mean of the top 10% of superior progeny seems to increase when
threshold increases, regardless of cross variance
With lower thresholds crosses in common between VLD and VLE
decreased
Lado, Battenfield et al. 2017
RESULTS3
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CONCLUSIONS
• Modelling the variance to select crosses has less impact for
grain yield than for the baking quality traits.
• There are no differences in selection using variance accounting
for LD and assuming LE for all traits. In addition, calculate VLE is
less computational intensive.
• Best mean parent value is the best approach to select by grain
yield. However, to sustain genetic gain genetic diversity should
be considered.
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BIBLIOGRAPHY
Endelman, J.B. 2011. Ridge Regression and Other Kernels for GenomicSelection with R Package rrBLUP. Plant Genome J. 4(3): 250–255.
Lado B., S. Battenfield, C. Guzman, M. Quincke, R. P. Singh, S.Dreisigacker, R. Javier Peña, A. Fritz, P. Silva, J. Poland and L.Gutiérrez. 2017. Comparing Strategies to Select Crosses UsingGenomic Prediction in Two Wheat Breeding Programs.The Plant Genome.
Mohammadi, M., T. Tiede, and K.P. Smith. 2015. PopVar: A Genome-WideProcedure for Predicting Genetic Variance and Correlated Response inBiparental Breeding Populations. Crop Sci. 55(5): 2068.
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FUNDING AND PARTCIPANT INSTITUTIONS
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Thanks for your attention