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

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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