Increasing the accessibility of KASP technology for rice ... technologies/Katherin… · rice...

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Increasing the accessibility of KASP technology for rice improvement Katherine Steele Bangor University

Transcript of Increasing the accessibility of KASP technology for rice ... technologies/Katherin… · rice...

Page 1: Increasing the accessibility of KASP technology for rice ... technologies/Katherin… · rice breeders in Nepal, India and Pakistan The two most common traits for improvement by marker

Increasing the accessibility of KASP technology for rice improvement

Katherine Steele

Bangor University

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Current microsatellites (SSRs) KASP technology

Costly setup and labour intensive; high running costs

KASP from LGC Genomics is about three times cheaper than SSR genotyping (Semagn et al., 2014 Mol. Br. 33: 1-14)

Fewer possible SSR regions in the genome to convert into markers

Numerous SNPs and Indels exist in the genome; high conversion rate into markers

Safety hazards including use of toxic, carcinogenic and environmentally harmful acylamide gels or agarose gels withethidium bromide staining

Safe to use. Unique chemistry and plate fluorescence to detect fluorescent signals

Low throughput, many repetitive steps, difficulty in automation

Automation gives extremely high throughput offering tailor made screens and a full service.

User sends leaf samples and LGC does the DNA extraction, KASP assays and provided genotypic scores in MS Excel

18,000 available in rice 2,055 available in rice

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Limitations• No search option for associated traits or QTLs• Difficult to find polymorphism information/alleles • The Japoinica reference genome was used in their development• 27 % of them do not map to the Indica reference

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Current MAS targets in National Programmerice breeders in Nepal, India and Pakistan

The two most common traits for improvement by marker assisted selection (MAS) are resistance to:

• rice blast

Magnaporthe oryzae

~100 race specific R loci known

• bacterial leaf blight (BLB) Xanthomonas oryzae pv. Oryzae

~40 race specific R loci known

Rice BLB disease comparison with resistant linesPhoto courtesy of IRRI Rice Knowledge Bank

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KASP development for donors and recurrent parents

Field resistance screen of donors and recipients by

NARC and Anmolbiu

KASP ™ technolgyfrom LGC

Novel rice lines from

DFID funding

Improved livelihoods of 1000’s of rice farmers in

Nepal

Initial backcrossing by Nepalese partners

Industrial Research Led by LGC Genomics

2017-2020

Improved livelihoods for millions of rice farmers in other

rice growing countries

Field resistance data from

NARC

Early-Stage FeasibilityLed by Bangor University

2014-2016KASP become avialable to

national programme in Nepal

Marker assisted backcrossing used to improve disease resistance of

new rice varieties for Nepal

Uptake of KASP by breeders across Asia for marker assisted selection to

improve new rice varieties

Innovate UKAgri-Tech Catalyst

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Method 1.

“Conversion” of SSRs using targeted NGS

• Single nucleotide polymorphisms and simple sequence repeats can not be targeted with the same sequences

di-, tri- or tetra-nucleotide repeat CACACACACACACA

• Used SSR primer sequences to target regions either side of primers for sequencing (300 bp paired end Illumina MiSeq) in donor and recurrent parents

• Sequences were aligned with reference genomes• Oryza sativa Japonica Group cultivar Nipponbare• O. sativa Indica Group cultivar 93-11

• SNP and InDel variants identified and filtered to identify variants flanked by regions suitable for KASP assay design

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“Conversion” of SSRs using targeted NGSProof of Concept: Pi33

• Flanking SSR markers RM3507 and RM310 are approx. 0.4 cM either side of Pi33 resistance locus

• 4000 bp was sequenced at RM3507

• This region contained 66 variants of which 15% fitted the criteria for KASP

• Pi33_RM3507 KASP assay was used to screen a mapping population of IR64 x Jumli Marshi.

• It was associated with field resistance to local isolates in a RBD field trial in Jumla, Nepal (Chi Squared = 29.62, P<0.01)

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Xa21

Xa4

Xa4

Xa5

Xa21

Xa4

Xa5

IR64IR71033-4-1-127B

IR65482-4-136-2-2 IRBB-60 Lok Tantra

SS

AM

S-1

K-4

Pir2-3(t)

Pi40Pi9

Pi24(t)-

pi32(t)

Xa4

Xa5

Xa13

Xa20

Xa13

Xa8

Pir2-3(t)

Pir2-3(t)

Pir2-3(t)

R

R

R

R

R

R

R

R

Pi40Pi9

Xa13

Xa20

Xa13

Xa8

Pi40Pi9

Xa13

Xa20

Pi40Pi9

Pi40Pi9

Donor parents

Recu

rren

t pa

rent

s

Boxes: Bacteria leaf

blight (BLB)

resistance alleles

Ovals: Rice blast resistance alleles

Pi 33

XaQTLs

Pi

Pi ?

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Whole genome NGS in nine Indica genotypes

• Nine rice genotypes were selected from the breeding programmes of Anamolbiu and NARC

• They carried a range of Xa and Pi resistance genes.

• Sequencing (100 bp paired end Illumina HiSeq 2000) gave an average sequencing depth of 59 x and average genome coverage of 89%

92.1% Indica

Japonica 87.9%

84.00 85.00 86.00 87.00 88.00 89.00 90.00 91.00 92.00 93.00 94.00

Percentage alignment to reference genome

Method 2.

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Similarity between genomes when aligned against Indica and Japonica reference genomes

(gene sequences)

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Variant calling

• SAMtools was used to calculate genotype likelihoods, and identify SNPs and InDels

• SNPs or insertions with a read depth of less than 5removed

• Any with read depth >200 were discarded due to likelihood of variable copy number repeats

• Variant calling was performed separately against the two O. sativa reference genomes

• Custom Perl scripts were used to identify potential KASP assays using criteria defined by LGC Genomics

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Conversion rate of variants to potentially suitable KASP assays

• Total variants among nine genotypes against the Indica reference: 2,561,351 unique sites.

• Total potential KASP assays after (stringent) filtering: 1,329,314 KASP

• Conversion rate of 52%

• The number of potential KASP assays includes >92,500 potential functional markers located in exons (non synonymous SNPs or frameshift InDels)

• Filtering for variants suitable as KASP assays provided selectable markers

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1491 available KASP align to the Indica genome with 100% identity.

Min. 284 and max. 414 informative in pairwise crosses. There are many gaps.

http://www.lgcgroup.com/products/genotyping-libraries/rice-library/#.V1_zf7grJpg

BEFORE…

Nine genotypes with currently available KASP for rice

Pariasca-Tanaka et al. Euphytica 201.1 (2015): 67-78.

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1,329,325 loci with 2740 times greater mapping density across the Indica genome

Min. 245,367 and max. 511 informative in pairwise crosses. No gaps!

AFTER…

Nine genotypes with all potential new KASP identified in Innovate UK project

Steele et al., In Preparation

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How to identify variants at MAS targets?

If the candidate gene is published for a resistance gene

look at our sequence data for variants in the gene between the donor and recurrent parents of interest.

• Done for Pi9, Pi40, Pi33 and Xa5.

• InDel present in a previously published candidate gene

If there is only a mapped QTL (no published candidate gene)

look for candidate genes with the NBS-LLR motif in the target region between the markers and use variants in the candidate genes or use any SNP within that region.

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Candidate NBS-LRR genes

• Used ‘in-silico PCR’ to find candidate genes:• in the reads of our nine genotypes • In Indica and Japonica reference genomes in sequences where where

our reads aligned

• Only genes annotated as NBS-LRR in Ensembl were listed

• Hits corresponded to 29 Pi loci and four Xa loci

• In some cases susceptible parent did not have the candidate gene sequence present so flanking KASP were identified

246297 188

Japonica Indica

731 total candidate NBS-LRR genes detected in at least one of nine genotypes

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Genotyping with new KASP assays

• So far a sub-set of these newly identified KASP have been used for genotyping in segregating progeny

• Targets include 4 BLB resistance loci and 5 blast resistance loci across ten crosses.

• NARC have set up the capacity to carry out KASP analysis in their own labs

• Both NARC and Anamolbiu are using the KASP genotyping service.

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Next steps in the the “Industrial Phase” project

Two new partners: Agri-Epi Centre and NIBGE, Pakistan

More efficient selection of disease resistant and better adapted varieties is likely to lead to economic benefits in developing country agriculture

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Acknowledgements

• Bangor University

John Witcombe

Mark Quinton-Tulloch

• LGC Genomics

Darshna Vyas

Martin Heine

Knut Mohr

• SKUAST (India)

Asif Bashir Shikari

• NARC (Nepal)

Shambhu Khatiwada

Resham Amgai

• Anmolbiu (Nepal)

Rajeev Dhakal

Anju Pandey

Mahesh Sharma