# The use of markers and perspectives for genomic selection in maritime pine

Laurent Bouffier

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## To cite this version:

Laurent Bouffier. The use of markers and perspectives for genomic selection in maritime pine. Journées thématiques "Sélection génétique de Hévéa", CIRAD, Sep 2020, Paris, France. hal-04712245

HAL Id: hal-04712245

https://hal.inrae.fr/hal-04712245v1

Submitted on 27 Sep 2024

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Distributed under a Creative Commons Attribution 4.0 International License
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# The use of markers and perspectives for genomic selection in maritime pine
## (Pinus pinaster)

Laurent Bouffier

INRAE, UMR BIOGECO (Bordeaux)

laurent.bouffier@inrae.fr
Outlines

1/ French maritime pine breeding programme

2/ Molecular markers available

3/ Identity and pedigree checking

4/ Mating designs based on pedigree recovery

5/ GS proof-of-concept in maritime pine

6/ Perspectives for GS in maritime pine
# 1/ French maritime pine breeding programme

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# Maritime pine forests in southwestern France

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## Maritime pine forest established at the end of the 19th century

- Native species

- Poor soils (sandy podzol)

- Dry summers / wet winters (hydromorphic soils)

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## 0.8 million hectares forest (7% of the French forest)

- Maritime pine plantations (>90% improved seedlings) except in sand dunes (natural regeneration)

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## 24% of French wood harvest

- 11 m3/ha/year in average

- 8.5 million m³ harvested /year

- 60% saw timber / 40% industrial wood
Maritime pine = 1st plantation species in France

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Breeding program managed by a consortium

➤ Base population established in 1960's by INRA

➤ Two breeding programs (INRA, FCBA)

➤ Consortium since 1995 = “ Groupe Pin Maritime du Futur ”

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Groupe
Pin Maritime
du Futur

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INRA
SCIENCE & IMPACT

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Office National des Forêts

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CNPF

« Recurrent » fundings = Ministry of Agriculture + Région « Nouvelle Aquitaine »
Specific studies = European Union + Ministry of Education and Research (ANR projects)
# Stakeholders for maritime pine breeding

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## Maritime pine breeding program

Managed by GPMF (« Groupe Pin Maritime du Futur »)
IP = INRA + FCBA

INRA
SCIENCE & IMPACT

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royalties

### Seed Orchards

**Composition + Design**

GPMF but SO must be validated by
a national public authority (CTPS)

**Ownership + Management**

Public (ONF) and private companies

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### Nurseries

Private companies
(Forelite, Planfor, Naudet, Robin)
Recurrent selection scheme

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➤ Selection criteria:

- Volume (girth, height)

- Stem straightness

- Rust resistance

- Wood quality traits (branching quality, wood density, spiral grain)

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# Maritime pine breeding: the latest advances

* **BLUP evaluation** (individual mixed model based on pedigrees and phenotypes)

* database = 600,000 trees including historical trials (first one in 1962)

* powerful genetic evaluation

* **Seed orchards renewed more often**

* previously: one new SO composition every 15 years based on backward selections

* currently: faster turnover (~3 years) based on backward and forward selections

* **New selection criteria under study**

* pest resistance (pine wood nematode)

* climate change (drought resistance)

* **New provenances**

* Genetic diversity infusion with southern provenances for drought resistance

* **Rolling front strategy**

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## 2/ Molecular markers available

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High-density SNP assay development for genetic analysis in
maritime pine (*Pinus pinaster*)

C. PLOMION,\*† J. BARTHOLOMÉ,\*† I. LESUR,\*‡ C. BOURY,\*† I. RODRÍGUEZ-QUILÓN,§

H. LAGRAULET,\*† F. EHRENMANN,\*† L. BOUFFIER,\*† J. M. GION,\*¶ D. GRIVET,§ M. DE MIGUEL,\*†

N. DE MARÍA,§ M. T. CERVERA,§ F. BAGNOLI,** F. ISIK,†† G. G. VENDRAMIN** and

S. C. GONZÁLEZ-MARTÍNEZ§

➤ Currently: **9,000** Illumina Infinium SNP array (transcriptome data + candidate genes)

➤ In 2020: **16,000** SNPs available (B4EST project) with a multi-species Axiom array (ThermoFischer) ~50€/sample
Low density genotyping (80 SNPs)

<figure><img src="image_33.png" /><figcaption>Genotyping using Sequenom's Mass Array
technology (mass spectroscopy)</figcaption></figure>

(cost ~4€ per sample for 1 plex)

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# Applications High / Low density genotyping

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## High density genotyping for:

- linkage mapping

- association studies

- genomic selection

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## Low density genotyping for:

- id checking

- pedigree recovery

- pollen contamination / parental contribution in SO

- seedlot certification (provenance)
3/ Identity and pedigree checking

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Various sources of id errors

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(from Marjorie Vidal PhD, 2016)

➤ Cumulative process over generations

➤ Markers = the only way to check id
Id errors in progeny trials and clonal archives

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Id errors in progeny trials and clonal archives

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# Id errors in progeny trials and clonal archives

## Estimation of pedigree errors in the maritime pine
breeding population (80 SNPs)

<figure><img src="image_43.png" /><figcaption>**G1 clonal archives**
(1061 G1 trees x 3-5 ramets / tree)</figcaption></figure>

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Consequences of id errors

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Consequences of id errors

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Consequences of id errors

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Consequences of id errors

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Evaluation of id errors consequences through
simulations

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Higher genetic gains when the pedigree is cleansed
# « Id card » in clonal archives

* 3-year-project (2018-2020) to genotype 8,000 trees with 80 SNPs :

* « id card » for each genotype in clonal archives

* correct (when possible) for pedigree mistakes in the database and in the field

* field labelling with bar codes

* Easier to implement during the first generations

* First mandatory step to go further with markers implementation in the breeding programme
4/ Alternative mating design based
on pedigree recovery

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# Mating design based on pedigree recovery

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* Comparisons through simulations: no advantages to generate a lot of families

* Simulations in progress to carry out comparison including the costs of crosses and genotyping
5/ GS proof-of-concept in maritime pine

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2 GS populations (sampled from the breeding pop)

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➤ 661 individuals (Ne≈100)

➤ G0 + G1

➤ G1: 191 HS families (≈ 2.5 individuals)

➤ 2,500 SNPs

➤ Pseudo-phenotypes (BLUP)

➤ Growth, sweep

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➤ 818 individuals (Ne=24)

➤ G2 + all their ancestors (G0 + G1)

➤ G2: 32 HS families (≈ 22 individuals)

➤ 4,300 SNPs

➤ Pseudo-phenotypes (BLUP)

➤ Growth, sweep
Prediction accuracy

**Population 1**

Training: G0 + 90% G1

Validation: 10% G1

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**Population 2**

Training: 80% G2

G0 + G1 + 80% G2

Validation: 20% G2

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➤ Accuracy from 0.5 to 0.75 despite low linkage disequilibrium and low marker coverage of the genome (2.5 SNPs/cM in Pop2)

➤ Higher accuracy in Pop2 (lower effective size, all ancestors genotyped)

➤ Similar results whatever the method (GBLUP vs Bayesian methods)

➤ Similar accuracy for the pedigree-based method (A-BLUP) and marker-based methods
Mendelian sampling prediction: A-BLUP vs G-BLUP

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* A-BLUP evaluation: all trees from one family have the same BV if no individual phenotype / progeny (= mid-parent value)

* GS models: can predict a different BV for each tree of a family even with no phenotype (variation around mid-parent value = Mendelian sampling)
# Mendelian sampling prediction: A-BLUP vs G-BLUP

GS predictive accuracy is not directly linked with the ability to predict the Mendelian sampling (GS accuracy comes from relatedness and LD markers-QTLs)

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Depending on the population studied, GS models can have high accuracy even without any ability to predict Mendelian sampling
# Mendelian sampling prediction: A-BLUP vs G-BLUP

## In the forest trees litterature:

* Ability to predict Mendelian sampling is generally poorly discussed

* GS accuracy mainly based on relatedness (and not LD markers-QTLs)

* G-BLUP > A-BLUP when G-matrix allows correcting (pedigree errors) or completing kinship (full pedigree unknown, structuration in the base population not taken into account with A-BLUP)

## For maritime pine, the similar accuracy from A-BLUP and G-BLUP can be explained by:

* a limited number of markers in comparison to the large size of conifer genome (more than 20 Gb)

* the reliability of the pseudo-phenotypes considered as the reference to estimate the genomic selection accuracy

* the design of the training population, generally with a low number of trees per family
6/ Perspectives for GS in maritime pine

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A/ From pedigree based matrix (A-matrix) to markers based
matrix (G-matrix)

- Keep the same BLUP methodology but substitute G-matrix (or H-matrix) for A-matrix

- Increase of accuracy as:

* realized relationship is substituted to expected relationship (or selection earlier for the same accuracy)

* Particularly efficient for hidden relationship (or incomplete pedigree, or structure based population)

- This arises 2 issues:

* Merge genotyping data with various number of SNPs (-> implementation)

* Strategies to genotype the « key » trees
B/ Selection without phenotyping (decrease breeding cycle length)

- The more attractive perspective but depends on the sexual maturity age (or associated with flowering induction)

- Can be used for pre-screening (ie selection intensity increase)

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C/ Selection for costly traits

- For instance: pest resistance, drought resistance...

- Phenotyping focused in the training population

- Multitrait evaluation

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➤ Even without GS, use of markers increases genetic gains and open opportunities for new breeding strategies

➤ Main GS objectives in maritime pine:
- shortening of breeding cycle duration
- selection for costly traits (disease resistance, climate change)

➤ Main research area in GS:
ability to predict the variability within families i.e. Mendelian sampling (design of the training population, phenotypes reliability, number of markers)

➤ Optimal breeding strategy vs. the one we can effectively apply!

• GS implies: new field work, new databases, new skills...

• implementation of markers as a first step (id checking, pedigree recovery)
Acknowledgment

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Christophe Plomion, Jérôme
Bartholomé, Marjorie Vidal,
Marc Kleinhentz, Marie
Pégard, Geoffrey Haristoy

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UNITÉ EXPÉRIMENTALE
FORÊT PIERROTON

Annie Raffin, Patrick Pastuszka,
Frédéric Bernier

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Erwan Guichoux, Christophe Boury,
Marie Massot

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