Inbreeding Management and Optimization of Genetic Gain with Phenotypic and Genomic Selection in Oil Palm (Elaeis guineensis)

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Inbreeding Management and Optimization of Genetic Gain with Phenotypic and Genomic

Selection in Oil Palm (Elaeis guineensis)

David CROS, Billy TCHOUNKE, Leopoldo SANCHEZ david.cros@cirad.fr

Palm genomics and genetics Workshop

January 12, 2020 – San Diego, CA

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1500 15020

1500

Progeny tests

1500 15020

1500

A B

2/ Progeny tests in A x B hybrid trials to estimate GCAs

3/ Selection of individuals with highest GCAs within each parental group

2 heterotic groups, A (mostly Deli) and B (eg La Mé)

 hybrid vigor for bunch production (FFB) in A x B crosses

 conventional oil palm breeding for yield = reciprocal recurrent selection (RRS):

1/ Selection candidates

4/ Selfings and intra-group crossings to produce next generation (used to start following breeding cycle and to produce commercial hybrid seeds)

Oil palm breeding

Commercial

varieties

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Why managing inbreeding in oil palm ?

Previous studies indicated GS could increase inbreeding per year and per cycle in plants (Lin et al 2017)

 risks associated with inbreeding depression (low germination rate, etc)

 the faster the increase in inbreeding, the higher the risks of losing favorable alleles (in particular with small populations, like in oil palm)

Simulations in oil palm showed higher annual increase in inbreeding over generations with GS than with RRS (Cros et al 2015)

(the more candidates  the larger FS families, as number of crosses is limited  the higher the risks to select related individuals)

 a method to limit the increase in inbreeding in oil palm is required, in particular for GS

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 simulation study comparing inbreeding management strategies in La Mé population (computational reasons) :

- current method without inbreeding management

- simple inbreeding management (number FS selected < user- defined threshold and / or no selfing)

- mate selection (Toro et Perez-Enciso, 1990; Sánchez et al., 1999)

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• Simulation of initial breeding populations (G0) to match actual breeding populations (see Cros et al. 2018 Mol Breeding)

• 2 correlated traits (BW, BN; with FFB = BW x BN)

• 800 QTLs per trait, 70% pleiotropic QTLs – 27 replicates Simulation procedure:

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From G0, simulation of 4 cycles of RRGS versus RRS to increase bunch production (FFB = BN x BW) in Deli x La Mé hybrids:

76 years

Generation 0

50 y

(-34.2%)

Conventional RRS RRGS

(training every 2 generation)

C1

C2

C3

C4

C1

C2 C3

C4

Deli La Mé

Simulation procedure:

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progeny tests C1

C2

C3

C4

5040

• Progeny-tests:

‐ 120 Deli x 120 La Mé possible crosses

‐ 252 random Deli x La Mé crosses

‐ 20 individuals per Deli x La Mé cross

• Selection:

‐ 16 individuals selected per generation and population

‐ number of selection candidates n

_c

= 120 in RRS and, in RRGS, 120 or 330 per generation and population

Simulation procedure:

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• Method without inbreeding management:

- Selection and recombinations steps: (per population and generation)

1/ select the 16 individuals giving highest expected FFB in

hybrids with other population (according to their BN and BW GCA / GEBV)

Let’s FFB

_DixLMj

the expected FFB performance of Di x LMj hybrids:

FFB

_DixLMj

= BN

_DixLMj

x BW

_DixLMj

= (µBN + GCA BN of Di + GCA BN of LMj) x (µBW + GCA BW of Di + GCA BW of LMj)

2/ make 32 crosses among the selected individuals in each population, with:

- random mating with balanced number of crosses per

individual (each individual involved in 4 crosses, ie 25% of partners used)

- selfings allowed

 mating design (incomplete diallel) = matrix of crosses (X)

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n

_c

120 330 Mean FS family size 3.75 10.31

- METHOD1: FS_THRESHOLD =

limiting the number of individuals selected per FS family to 1

• Methods with inbreeding management:

- METHOD2: NO_SELFINGS =

Selected individuals can not be selfed

- METHOD3: FS_THRESHOLD+NO_SELFINGS

= combining the 2 approaches

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- METHOD4: MATE_SELECTION

{ X }

_n

= set of all possible matrices of matings between best n

_presel

La Mé candidates at generation n, comprising 0s (crosses not made) and 1s

(crosses made) , with the following restrictions:

- 16 individuals selected among the candidates - 32 matings ^:

- selfings allowed

- limited increase in inbreeding per generation between La Mé candidates and the progeny of the selected La Mé, , so that:

with matrix of coancestry among the n

_presel

LM, mean inbreeding of LM individuals of generation n and max ΔF

LM mate selection(n)

computed based on the ΔF

_LM

that would be obtained without inbreeding management

• Methods with inbreeding management:

like without inbreeding management

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- METHOD4: MATE_SELECTION

• n

_presel

La Mé used instead of n

_c

for computational reasons (to reduce

the number of possible Xs)

• Methods with inbreeding management:

Example X matrix for n

_presel

= 60 (upper triangular with diagonal elements):

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 retained X matrix = the one giving the highest mean expected hybrid cross value in terms of FFB, computed from the parental GCAs/GEBVs for BN and BW

= retained X matrix indicates which La Mé to select and how to mate them to obtain the best hybrid crosses while limiting the inbreeding in La Mé population

How to identify this optimized X matrix ? too many Xs for a simple loop…

 optimization algorithm required – here, simulated annealing

 preliminary investigations indicated many random starting points were necessary

 parallel simulated annealing

(96 cores and a random initial X / core)

- METHOD4: MATE_SELECTION

• Methods with inbreeding management:

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• Genomic predictions:

‐ Statistical method = GBLUP

‐ SNP data used = parental genotypes for 2250 random SNPs

‐ Phenotypic data used = hybrid phenotypes Simulation procedure:

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- Pedigree-based inbreeding (back to the 19 La Mé founders) ,

Simulation procedure:

- Annual genetic gain:

FFB

Deli x La Mé hybrids(G4)

− FFB

Deli x La Mé hybrids(G0)

number of years

• Monitoring target parameters:

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Simulations and analyses made with R, using in particular HaploSim (haplotypes and recombinations), R-ASReml (BLUP) and snow / doSNOW (parallel computing)

packages

Simulation procedure:

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Results…

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Results: INBREEDING IN LA ME

• All methods slowed down the increase in parental inbreeding:

RRGS / 330 candidates :

RRS:

no selfings

no inbreeding management

max selected La Mé per full-sib family = 1 max selected La Mé per full-sib family = 1

& no selfings mate selection

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Results: GENETIC GAIN

• Mate selection reduces inbreeding in parents, but also increases genetic progress:

no selfings

no inbreeding management

max selected La Mé per full-sib family = 1 max selected La Mé per full-sib family = 1

& no selfings mate selection

RRGS / 330 candidates:

RRS:

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+10.6% +5.7%

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Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

- Selection of best individuals but also of individuals of lower value

Mate selection reduces inbreeding in parents, but also increases genetic progress, by optimizing selection and matings between selected individuals:

Mate selection

No inbreeding management

RRGS / 330 candidates:

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- Selection of best individuals but also of individuals of lower value

- Decrease in the proportion of selfings Mate selection reduces inbreeding in parents, but also increases

genetic progress, by optimizing selection and matings between selected individuals:

M ate se lec tio n

No in br ee din g ma na ge me nt

No se lfin gs

RRGS / 330 candidates:

Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

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- Selection of best individuals but also of individuals of lower value

- Decrease in the proportion of selfings - Selfings preferentially made on top

individuals

Mate selection reduces inbreeding in parents, but also increases genetic progress, by optimizing selection and matings between selected individuals:

Mate selection

No inbreeding management

RRGS / 330 candidates:

Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

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- Selection of best individuals but also of individuals of lower value

- Decrease in the proportion of selfings - Selfings preferentially made on top

individuals

- Highly variable number of crosses per individual

Mate selection reduces inbreeding in parents, but also increases genetic progress, by optimizing selection and matings between selected individuals:

Mate selection

No inbreeding management

RRGS / 330 candidates:

Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

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- Selection of best individuals but also of individuals of lower value

- Decrease in the proportion of selfings - Selfings preferentially made on top

individuals

- Highly variable number of crosses per individual / the better an individual, the higher the number of crosses Mate selection reduces inbreeding in parents, but also increases genetic progress, by optimizing selection and matings between selected individuals:

Mate selection

No inbreeding management

RRGS / 330 candidates:

Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

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- Selection of best individuals but also of individuals of lower value

- Decrease in the proportion of selfings - Selfings preferentially made on top

individuals

- Highly variable number of crosses per individual / the better an individual, the higher the number of crosses - Reduction in the number of

individuals selected per family

Mate selection reduces inbreeding in parents, but also increases genetic progress, by optimizing selection and matings between selected individuals:

M ate se lec tio n

No in br ee din g ma na ge m en t M ax se lec te d p er

fu ll-s ib fam ily = 1

RRGS / 330 candidates:

Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

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- Selection of best individuals but also of individuals of lower value

- Decrease in the proportion of selfings - Selfings preferentially made on top

individuals

- Highly variable number of crosses per individual / the better an individual, the higher the number of crosses

- Reduction in the number of individuals selected per family

- Disassortative mating

Mate selection reduces inbreeding in parents, but also increases genetic progress, by optimizing selection and matings between selected individuals:

Mate selection

No inbreeding management

RRGS / 330 candidates:

Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

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 Identification of rules that can easily be implemented by breeders

Mate selection reduces inbreeding in parents, but also increases genetic progress, by optimizing selection and matings between selected individuals

(similar results for RRS and RRGS)

Results: BREEDING LESSONS FROM THE SIMULATED ANNEALING ALGORITHM

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