\\csng14p20101\CovSEA$\IBDUSER\INDONESIA\Sampoerna\Project P3\Roadshow\P3 Roadshow FINAL (Print) Part 1 of 2.ppt /1

PT Sampoerna Agro TbkSampoerna Strategic Square North Tower, 28th Floor

Jl. Jend. Sudirman Kav. 45Jakarta , Indonesia ,12930

Development of Marker Assisted Selection through association

mapping in Oil Palm

Javier Herrero, PhD

Background

THE FACT: Nowadays African oil palm (Elaeis guineensis Jacq.) is the major source of vegetable oil. The application of genomic knowledge and tools in oil palm breeding is highly suitable due to the crop’s long generation cycle and large space requirement for field testing.

OBJECTIVE:As part of Sampoerna Agro breeding program improvement.

AIMS to improve and accelerate its oil palm breeding process

Molecular breeding laboratory (Palembang)

Biotechnology Department (Vitoria, Spain)

Research collaboration

Background

THE CHOICE: CANDIDATE-GENE ASSOCIATION MAPPING

DEFINITION: “Association mapping is a statistical method to analyze the effects of existing alleles of

candidate genes based on linkage disequilibrium (LD) and allows the discovery of strong associations

between specific allele marker-trait expression in a set of genotypes, instead of in a designed mapping

population” (Breseghello and Sorrells 2005; Ingvarsson 2005; Zhu et al., 2008).

It involves finding marker-trait associations

QTL-Mapping(Bi-parental population)

Association Mapping(Germoplasm or natural population)

• Less genetic variation (2n alleles).

• Limited number of recombination events.

• Much work-time and cost.

• Many alleles evaluated simultaneously.

• Uses majority recombination events.

• Timesaving and cost effective.

Phenotypic data

Interesting traits

BN : Bunch Number (bunch)

BW : Bunch Weight (kg)

FN : Fruit Number (fruit)

FW : Fruit Weight (g)

HI : Height Increment (cm)

MF : Mesocarp to fruit (%)

OWM : Oil to Wet Mesocarp (%)

CPO : Oil Yield CPO (ton/ha)

Candidate genes (CG)

ASSOCIATION

Phenotyping

PLANT MATERIAL:

Derived from Dura x Pisifera selected parents.

Samples are selected based on 8 interesting traits.

Extensively characterized (10 years) .

Every trait consists of average taken from 17

progenies with 2 GT which are segregated as best and

worst performance.

Set of 250 Genotypes (GT)

a)

Genotypic data

Interesting traits

Candidate genes (CG)

Phenotyping Genotyping

Identification of trait-specific CG:

Two contrasting bulks PER trait

1 BSA-cDNA-AFLP

Quantitative trait lociVS

Annotated sequences

mEgCIR0059 linked to a QTL for MF in Lg 16 with

high homology to a Transcription factor

(Auxin Response factor)

2 Co-location analysis 3 Known genes

DATABASES

PUBLICATIONS

With relevant biological meaning and related to

the trait

ASSOCIATION

Set of 250 Genotypes (GT)

b)

Direct homology search

Genotypic data

Interesting traits

Candidate genes (CG)

Phenotyping Genotyping

Identification of trait-specific CG:

ASSOCIATION

Set of 250 Genotypes (GT)

Nº REF Origin Method TraitAccession/ Sequence

Biological meaning Fw Primer Rw PrimerExpected product (pb)

Validated

1 CDA31 Eleais guinensis BSA-cDNA-AFLP FW B54 cell wall-associated hydrolase GCTAAGCGATCTGCCGAAG GTTTTCGGGGCATTGGAT 160 YES2 CDA34 Eleais guinensis BSA-cDNA-AFLP MF B57b conserved hypothetical protein GGACGGGTGAGTAATGCCTA GGCCTTTACCCCACCAACTA 162 YES3 CS26 mEgCIR0059_2 co-loc SSR DB Bn XM_002268813 Auxin Response factor 2 TGCAGGGGATGCTTTTATT CCCTTAATTCCTGCCTTATT 200 YES4 CS31 mEgCIR0775_2 co-loc SSR DB IV NM_001185577 Phosphatidylserine synthase CATATGGCTAAGAAAGCCAACAGAATA CGTGGTTGTGTATAGGCTGATTCTA 250 YES5 CS11 mEgCIR0778_1 co-loc SSR DB PO yield AJ236914 Metal thionine (mt3-B) TTCGTTATCCAAACCATATCTTAT CCTCAAAGAGTATTGGATGATCTAT 300 YES6 KG13 Eleais guinensis Known G. -DB_ PDK_ col. Brugis Fwt XM_002510396 glycosyltransferase, putative GCAGAAATCAGGGTGACCTC CACGACGGAGATTGTTGTGA 174 YES7 KG18 Eleais guinensis Known G. -DB_ PDK_ col. Brugis BW, SPIKE XM_002514230 sugar transporter TCCATTCTCTTTTCGTGCAA CGATGCGAAGCATCAAGG 168 YES

Total = 188 potential candidate genes

1 2 3BSA-cDNA-AFLP Co-location analysis Known genes

*Example CG database

Genotyping

Interesting traits

Candidate genes (CG)

Phenotyping Genotyping

Identification of CG (3 aproaches):

1. BSA-cDNA-AFLP.

2. Co-location analysis.

3. Known genes.

ASSOCIATION

Amplicon sequencing in Set of GTs

(ION Torrent)

Set of 250 Genotypes (GT)

filter and separate output amplicon sequencing

Identification of CG’s allele patterns in the GTs

ASSOCIATION MAPPING

Statistical analysis

ASPAM software

1 2 3

Pool of PCR products

……

Amplicon processing

1. Identification of allelic diversity for each CG.

2. Assignation of allele composition to each GT.

1

2

37 bp 187 bp

Via Clustal: 2 SNPs.

Pattern 1: 99 GTs

Pattern 2: 60 GTs

Sequence SELECTION

PATTERN – GT association

Short sequences and sequencing errors are discarded.

Example: One batch consisted of 52 candidate genes in 250 bar-coded genotypes sent for sequencing using the ION Torrent system.

It separates per CG and GT via several steps of local BLAST

CG GT

*OUTPUT = 5 million sequences.

*Desired output product.

*Example of CG sequence analysis. Alignment file of repeated patterns.

Association

Pat S1 S2 S3 S4 Code Cultivar $ BN BW CPO HT FN FW MF OWM 2 2 1 1 1 1111 403/14 8.5 15.6 3.8 47 1001 9 92.9 49.5 2 2 1 1 1 1111 403/14 8.5 15.6 3.8 47 1001 9 92.9 49.5 3 2 2 2 2 1112 403/16 10.9 14.5 3.4 58 1230 7.8 71.2 45.2 3 2 2 2 2 1112 403/16 10.9 14.5 3.4 58 1230 7.8 71.2 45.2 2 2 1 1 1 1113 405/5 11 15.2 5.6 49 2515.0 4.5 76.6 61 3 2 2 2 2 1113 405/5 11 15.2 5.6 49 2515.0 4.5 76.6 61 2 2 1 1 1 1122 405/33 11.4 12.2 3 45 792.0 8.6 68.9 53.4 2 2 1 1 1 1122 405/33 11.4 12.2 3 45 792.0 8.6 68.9 53.4 ……………

*Example for a particular CG “Pattern Code Trait File”.

ASPAM generates SAS files for Pattern and SNP analyses.

Association MappingSTSTISTICAL ANALYSES

mixed-model approach Stich et al. (2008)

Pattern/SNPs GT/CODE Trait/Phenotype

1. Identification of significant associations.

2. Effects of specific allel combination on traits (So far, 89 CG analyzed).

3 PATTERN – GT – TRAIT association

ResultsTable b): Pattern analysis.Table a): Example for Results of SNP analysis.

CG16: PYRKIN CG19: M6ASA PAT: 5 0,0063

Trait SNP SL Prob ML CS L S ML CS L S BN CP PNº S

BW S1 2 0,023 16,28 11 2 A 14,56 69 1 B 11,48 55 1 A

HT S1 2 0,027 70 11 2 A 60,89 69 1 B 10,70 51 5 BA

10,38 162 3 B

10,06 108 4 B

9,68 16 2 B

CPO PAT: 5 0,0024

4,56 55 1 A

4,02 162 3 BA

3,87 51 5 BA

3,83 108 4 B

2,90 16 2 C

OWM PAT: 5 0,0058

53,36 55 1 A

49,98 162 3 BA

49,30 51 5 BA

48,51 108 4 B

44,40 16 2 C

b): CG INFLUENCES traits BN, CPO and OWM with indicates significances (0,0063, 0,0024 and

0,0058, respectively) with 5 patterns. For CPO Allele 1 (present in 55 GTs) yields the highest

value with 4,5 (Ton/he), which is significantly better than that of pattern 2.

a): SNP 1 of a specific CG has a significant effect on BW and HT. Level 2 of S1 (present in 11

GTs) leads to a significant higher bunch weight and Height increment than Level 1.

Legend:

SL: Number of levels of the SNP

ML: Trait mean of the particular level

CS / CP: number of cases with the SNP or Pattern

L: Level Number

S: Significance Class

Prob: errror probability

PNº: Number of Pattern

PM: Trait Mean of the Pattern

Significant effects for 12 CG alleles on trait expression for 7 different characters were identified.

(So far, 89 potential CGs analyzed OUT OF 188).

SUMMARY

Interesting traits

Candidate genes (CG)

Phenotyping Genotyping

Identification of CG (3 aproaches):

1. BSA-cDNA-AFLP.

2. Co-location analysis.

3. Known genes.

Alleles of CG / Allele composition of GT

Effects of specific allel combination on traits

ASSOCIATION

Amplicon sequencing in Set of GTs

(ION Torrent)

Set of 250 Genotypes (GT)

Data Analysis (Association Mapping)

Phenotypic values of 8 interesting oil palm traits:

BN, BW, FN, FW, HI, MF, OWM & CPO

What’s next?

Interesting traits

Candidate genes (CG)

Phenotyping Genotyping

ASSOCIATION

Association Mapping

MORE TRAITs!! Sh gene

VirescensDrought ToleranceFertilizer efficiency

MORE CGs!! Involved in the

desired trait performance

Obtained CGs

WITH SIGNIFICANT EFFECTS

Design of allel-specific primers

GENOTYPING

&

VALIDATION

(in independent set of Genotypes)

Selection of parents with useful alleles

GERMOPLASMIncrement

(New varieties)

MAS ModelBuilding

(New crosses)

Plant material selection:

Thanks for your attention

Terima kasih