Genotyping-By-Sequencing (GBS): a simple method for a multitude of markers

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Genotyping-By-Sequencing (GBS): a simple method for a multitude of markers

Philippe Barre, Tom Ruttink

To cite this version:

Philippe Barre, Tom Ruttink. Genotyping-By-Sequencing (GBS): a simple method for a multitude of markers. Training workshop “Genotyping, phenotyping, data management and analysis in plant breeding”, 2019. �hal-02789479�

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In the frame of the H2020 EUCLEG project “Breeding forage and grain legumes to increase EU’s and China’s protein self-sufficiency”

This project has received funding from the European Union’s Horizon 2020 Programme for Research & Innovation under grant agreement n°727312.

Genotyping-By-Sequencing (GBS):

a simple method

Training workshop “Genotyping, phenotyping, data management and analysis in plant breeding” 31 October & 1 November 2019 – Brno, Czech Republic

a simple method

for a multitude of markers

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Sequencing as a tool for genotyping

Getting the genotypic classes or the allele frequencies at different

loci of a sample

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Training workshop “Genotyping, phenotyping, data management and analysis in plant breeding”– 31 October & 1 November 2019 – Brno, Czech Republic

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Necessity to reduce the complexity

of the genome

• Possibility to re-sequence the whole genome if you

have enough money:

–

1000 $ for 2X = 6.4 10

9 _{bp for human}

–

562 $ for 4X = 3.6 10

9 _{bp for alfalfa}

Once you have a reference

sequence

3

–

562 $ for 4X = 3.6 10

9 _{bp for alfalfa}

–

93 $ for 2X = 0.6 10

9 _{bp for flax}

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Necessity to reduce the complexity

of the genome

• How to reduce the complexity

–

Genes : sequencing of mRNA

• Choice of plant material

–

Targeting specific loci : amplicon sequencing or capture

• Design of primers or probes

4

• Design of primers or probes

–

Random sequences : use of restriction enzymes

BUT Patents from KEYGENE : SNP identification by using restriction enzymes and high throughput sequencing methods

EP 1910562, EP 1966393, EP 2002017, EP 2292788, EP 2789696, EP 2963,127, EP 2,302,070

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Principle of GBS

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Principle of GBS

Sample 1

Restriction enzyme site

2/ Ligation of adapters _primer

A

Single Nucleotide Polymorphism (SNP)

1/ Digestion Genomic DNA

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3/ Amplification < 450 bp

Sample 2

G

Loss of a restriction site : MD 4/ Sequencing

Barcode specific of sample 1

AFLP (Amplified Fragment length Polymorphism) → Electrophoresis:

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Principle of GBS

5/ Demultiplexing with barcodes Many sequences for each sample

Files .fastq

6/ Cleaning

header sequence quality

7

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Principle of GBS

Reference

7/ Alignment on a reference sequence files .sam and .bam

Read depth Coverage

CIGAR: alignment

Reference name Position Sequence name

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HISEQ8:C5VAYACXX:4:1101:9138:7833 16 MsNRG000127 383005 0 88M *

0 0

CTGCGCTGATTAGCCTGAGAAGAGCACTGACTTGTAGCAGTCTGAACTGCATATGAGCACACTGGGTCCGAGCAGGAAACCAGCG CAG DDDDCDDDDCDDCCEDCEEEFFFFFFHHHEGHJJJIJIJJJJJJJJJIIGJJJJJIHFBGHGGHIHGJJJIJJGIJIIGGHHHGFFFF

RG:Z:readscleanadpt.sai XT:A:R NM:i:1 X0:i:2 X1:i:0 XM:i:1 XO:i:0 XG:i:0

MD:Z:4A83 XA:Z:MsNRG000506,+909328,88M,1;

HISEQ8:C5VAYACXX:4:1101:9015:7835 16 MsNRG000138 746536 0 97M *

0 0

ATGACATTTGTTCTTGACCGTCGGAAAGATTAAGTACGGCATTTTCCAACAGATAGACTAAATGCTTGATATTATTTTCTTCTGATACT GTGTGCTG DEDCDDDCDDDCDDDDDDDFEFGHHHHIE@GJJJIIJJJIHF<JIJJJJJJJIIJJIJJJIHAJJJJJGGJJIJJJJIHHEJIJIIGIHHHHHBFFF

RG:Z:readscleanadpt.sai XT:A:R NM:i:1 X0:i:3 X1:i:0 XM:i:1 XO:i:0 XG:i:0

MD:Z:93T3 XA:Z:MsNRG000119,+373466,97M,1;MsNRG000673,-603245,97M,1;

CIGAR: alignment

Reference name Position Sequence name

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Principle of GBS

8/ Variant calling files .vcf

= ref = ref MD = ref MD

Not enough reads Min. number of reads to

be defined 1=ref 4=Alt Position 452 Interpretation of the allele frequencies Focus on biallelic SNP 9

Variant calling : sample 1 at position 452 ref: A alt: G, 1 ref, 4 alt, genotype: GG or 0/0 What about the position with enough reads but identical to the reference ? file .gvcf

508 218848 . A G 3834.26 . BaseQRankSum=0.913;ClippingRankSum=-0.308;DP=302;MLEAC=2,0;MLEAF=0.500,0.00;MQRankSum=-2.242;RAW_MQ=413438.00;ReadPosRankSum=-6.469 GT:AD:DP:GQ:PL:SB 1/1:187,115:302:18:3861,18,0,356,6642,4094,346,559,6786,4421,905,6989,4980,7335,11410:115,72,115,0 508 218849 . C . . END=218873 GT:DP:GQ:MIN_DP:PL 0/0:304:50:278:0,50,120,241,1800

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Principle of GBS

6=ref 0=Alt 5 = ref 0=Alt 5=ref 0=Alt

MD MD 1=ref 4=Alt Position 452 Position 400 Sample 1 MD 1=ref 4=Alt MD MD MD MD .vcf .gvcf 10

6=ref 0=Alt 5 = ref 0=Alt 5=ref 0=Alt

Sample 2

1=ref 4=Alt MD MD

.gvcf

O = ref 5=Alt 5=ref 0=Alt

MD MD 1=ref 4=Alt MD 1=ref 4=Alt MD MD MD .vcf .gvcf O = ref 5=Alt MD

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Principle of GBS

9/ Merge variant calling files for all samples files .vcf

scaffold_0|ref0040988 166641 . A C . . .

GT:AD:DP:GC 0/0:146,0:146:85.5387987028

0/1:106,180:286:228.995630731 0/0:345,0:345:204.97487344 1/1:0,205:205:120.911257016 1/1:0,202:202:119.11357202

0/0:11,0:11:5.32928395111 0/0:231,0:231:136.513314158 1/1:22,973:995:999

chromosome position ref alt Sample 1 Sample 4

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0/0:11,0:11:5.32928395111 0/0:231,0:231:136.513314158 1/1:22,973:995:999 0/0:100,0:100:58.0077078195 ./.:3,0:3:. scaffold_0|ref0040988 166656 . A T . . . GT:AD:DP:GC 0/0:145,0:145:84.9367387115 0/0:287,0:287:170.134951164 0/0:347,0:347:206.176493884 0/0:207,0:207:122.11119102 0/0:202,0:202:119.11357202 0/0:11,0:11:5.32928395111 0/0:231,0:231:136.513314158 0/0:1001,1:1002:57.4056478282 0/0:100,0:100:58.0077078195 ./.:3,0:3:.

Ready for genetic analyses !!!

10/ Filtering: Check the missing data per SNP and per sample

Chro_position1 Chro_position2

Sample 1 0 1

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Principle of GBS

2/ Ligation of adapters 3/ Amplification

1/ Digestion 5/ Demultiplexing files .fastq per sample

6/ Cleaning = Trimming files .fastq per sample

7/ Alignment on a reference sequence files .sam and .bam per sample

Libraries preparation

Bioinformatics

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4/ Sequencing file .fastq for all samples

.sam and .bam per sample

8/ Variant calling files .vcf or gvcf per sample

9/ Merge variant calling file .vcf for all samples

10/ Filtering file .csv or .txt for all samples

Sequencing

To be done by or with the geneticist To be done by or with the geneticist

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Library preparation

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Library preparation (Elshire et al., 2011)

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Several PCR could be performed on

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Library preparation (Elshire et al., 2011)

15

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Library preparation (Elshire et al., 2011)

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Essential links between :

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Sequencing Illumina Hiseq 2000

O2

17

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Sequencing Illumina Hiseq 2000

Single read

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Read 2

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Library preparation : an example of

adapters and primers

Barcode

Insert

Adapter P1

Adapter P2 Primer P1 O1 Index 5

Primers for sequencing

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Barcode Adapter P1

Primer for sequencing

Index 7 For dactylis: - 29 barcodes - 5 i7 - 2 i5 290 samples Have to be compatible 1 lane of Novaseq (about 3.9 109 _reads) (about 11 k€) In average 13 109 _reads From 2 – 25 M Sequence again some samples

Per sample : if target 10 M reads Library + sequencing about 60 € Without staff

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Library preparation (Elshire et al., 2011)

Amount of adapters Clean: discard small fragments 20

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Choice of restriction enzyme and

number of reads

N u m b e r o f lo ci ApeKI (5 bp) PstI (6 bp) W= A or T Read depth About 50000 loci with few MD About 200000 loci with many MD 21

Number of reads (millions)

N u m b e r

Citation: Byrne S, Czaban A, Studer B, Panitz F, Bendixen C, Asp T (2013) Genome Wide Allele Frequency Fingerprints (GWAFFs) of Populations via Genotyping by Sequencing. PLoS ONE 8(3): e57438.

https://doi.org/10.1371/journal.pone.0057438 Sample 1 Sample 2 MD MD MD MD stack

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Choice of restriction enzyme and number

of reads

Depends on :

- the number of markers needed - the capacity to predict MD

- the funding re a d d e p th > 1 0 Dactylis _Alfalfa PstI-MseI : 17000 stacks with 10 M 22

N u m b e r o f st a ck s w it h re a d Number of reads

Test of different restriction enzymes and pairs of enzymes PstI : 10000 stacks

with 10 M reads

stacks with 10 M reads

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GBS in order to estimate allele

frequencies on pools

Allele frequencies estimated on 48 individuals Allele frequencies estimated on a pool of DNA from 48 Not a good estimation for AF<0.05 or >0.95 23

Y=0.93X + 0.05 R²= 0.92 prediction interval: ± 0.12

Y=0.96X + 0.02 R²= 0.98 prediction interval: ± 0.10

48 individuals

Allele frequencies estimated on a pool of leaves from 48 individuals from 48

individuals

Good estimation of allele frequencies on pools

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Example of using GBS on pools of

leaves in alfalfa

Estonia Grazing Germany F_ST: 0.075 to 0.203, P < 0.05 France Julier, B. et al. (2018) Use of GBS markers to distinguish among lucerne varieties,with comparison to morphological traits.

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Canada Turf

to morphological traits. Molecular Breeding 38 : 133-145

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Example of using GBS on pools of

leaves in alfalfa

25

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Example of using GBS on pools of

leaves in alfalfa

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Bioinformatics

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Bioinformatics : a multitude of softwares

on Linux

• Demultiplexing : often performed

by the sequences provider

sabre,

GBSX, Stacks (process_radtags)

…

• Cleaning :

Possibility with Windows : CLC genomics but pb to merge many samples Sequences per sample

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–

Remove adapters : cutadapt, scythe

–

Remove bad quality reads or part of

reads: sickle

–

Remove too short reads: sickle

1 file of 10 M reads 2.5 Go

(800 Mo compressed)

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Bioinformatics : a multitude of softwares

on Linux

• Alignment on a reference sequence

–

BWA aln or MEM, GSNAP

–

Samtools, Picard for files management

Importance of the reference sequence: 30% reads of alfalfa mapped on M.truncatula Unified 29

M.truncatula

80% mapped on

M.sativa

Veeckman et al. (2019) DNA Research, 2019, 26(1), 1–12 doi: 10.1093/dnares/dsy033 Possibility without a reference sequence Unified Genotyper Haplotype Caller Garsmeur et al. (2018) Nature communications 2638

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Bioinformatics : a multitude of softwares

on Linux

• Variant calling

–

GATK, freebayes,

mpileup, SNAPE

for pools

–

bamread count

VariantMetaCaller

Gézsi et al. BMC Genomics (2015) 16:875 DOI 10.1186/s12864-015-2050-y

33%

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Necessity of many CPU (central processing unit) and large RAM

(random access memory)

Veeckman et al. (2019) DNA Research, 2019, 26(1), 1–12 doi: 10.1093/dnares/dsy033

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Bioinformatics : Stack Mapping Anchor

Point (SMAP)

• For individuals and pools

• Polymorphism of RE sites

• Polymorphism of insertion / deletion

Polymorphism of SNP

T. Ruttink et al. In progress

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• Polymorphism of SNP

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Bioinformatics : SMAP

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Stack anchor point SNP

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Bioinformatics : SMAP Haplotype

A A G A T G C T C Hap. 1 Hap. 2 Hap. 3 SNP 1 SNP 2 SNP 3 33

C A C

Hap. 3 Hap. 4

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Bioinformatics : SMAP Haplotype

Calculate haplotype frequencies Haplotype count Discrete call

Filter for diploid Table with haplotype counts per sample

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Re-Calculate haplotype frequencies Filter min. haplotype frequencies

Remove noise

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Variants of GBS

Davey et al. (2011) Genome-wide genetic marker discovery and

RRL RAD-seq GBS Random sheer Pool Digest Ligate adapters

RRL: reduced representation libraries

RAD-seq: restriction site associated DNA sequencing GBS: genotyping by sequencing

35

genetic marker discovery and genotyping using next-generation sequencing. Nature review Genetics (2011) 12: 499-510 Size selection sheer Ligate adaptors PCR RRL RAD-seq GBS Pair-ends sequencing

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Conclusions

• GBS could provide a multitude of markers

• For a relatively low cost

• For any species

Should decrease but Patent ! If the restriction enzyme is adapted But not targetted 36

• Without too many missing data

• Relatively quickly

enzyme is adapted to the number of reads (budget) With a good sequence provider and a bioinformatic team / pipeline

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Horizon 2020 of European Union:

In the frame of the H2020 EUCLEG project

Horizon 2020 of European Union:

Call 2016, SFS 44: “A joint plant breeding programme to decrease the EU's and China's dependency on protein imports”

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