Dario Grattapaglia, Lucileide V. Resende, Pedro Tanno,

Alan Andrade and Orzenil B. Silva-Junior

A Five-Species 50K Axiom SNP Microarray Allows High Quality Genotyping of Coffee, Cashew,

Cassava, Brazilian Pine and Eucalyptus

• The identification of new sources of genetic variation is a key component of any long-term breeding strategy to increase the productivity, resilience and sustainability of crop varieties

• Over six million crop accessions are currently being conserved worldwide in still largely untapped genebanks

• High throughput, low cost and robust SNP genotyping technologies are necessary to unlock this wealth of diversity

• SNP data has to be simple, robust, reproducible and interchangeable across organizations to allow user friendly adoption by germplasm curators and breeders

Affordable SNP data needed for hundreds of crops, fruit and forest trees

Davey et al. Nat. Genet. Rev. 2011

GbS Genotyping-by-sequencing

methods

Uneven distribution of GbS tags coverage results in unacceptable levels of missing data

In house GbS experiment in Eucalyptus 24 individuals replicated in two lanes

Chromosome Lane 1 Lane 2 # Unique SNPs # Repeated SNPs % SNPs repeated

1 1.906 2.036 2.422 1.520 62,8%

2 2.389 2.650 3.093 1.944 62,9%

3 2.019 2.337 2.673 1.682 62,9%

4 1.491 1.716 1.934 1.271 65,7%

5 2.008 2.228 2.598 1.636 63,0%

6 2.188 2.409 2.805 1.791 63,9%

7 1.903 2.143 2.462 1.581 64,2%

8 2.792 3.088 3.579 2.301 64,3%

9 1.819 2.046 2.352 1.512 64,3%

10 2.047 2.228 2.581 1.694 65,6%

11 1.968 2.178 2.518 1.627 64,6%

TOTAL 22.530 25.059 29.017 18.559 64,0%

Chromosome Chr 1 Chr 2 Chr 3 Chr 4 Chr 5 Chr 6 Chr 7 Chr 8 Chr 9 Chr 10 Chr 11 TOTAL

Total # SNPs 3.896 4.564 4.531 3.058 4.669 3.907 5.736 6.042 3.208 3.486 3.926 47.023

# SNP call rate >95% 305 546 343 179 374 209 979 400 172 199 190 3.896

# SNP unique into tag 65 114 74 49 70 52 103 91 61 62 50 791

# SNP > 2 in the same tag 240 432 269 130 304 157 876 309 111 137 140 3.105

# SNP call rate >95% and

MAF > 0.05

122 265 177 77 173 118 470 208 88 104 95 1.897

Our analysis pipeline - E. globulus Population

Faria et al. PAG 2012

Repeatability of SNP markers: Sampling the same SNPs: impact on call rate Declaring the same genotype

Results of Cornell GbS in Cashew (no reference genome)

A total of 11,289 SNPs were genotyped at call rate > 60%, i.e. 40% missing data (Stacks)

Average call rate in these 11,289 selected SNP was 64.7% ± 2.7%

0

20

40

60

80

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0 20 40 60 80 100

% m

issi

ng

dat

a

sample #

0

1000

2000

3000

4000

5000

6000

7000

# SN

Ps

SNP Call Frequency (%)

Parameter EMB51 END189 G12 G235 G5 G98 Mean

# Consistent SNP genotype calls 5623 10103 35 9098 8919 553 5721.8

# Different SNP genotype calls 2056 116 2 1826 218 46 710.6

# Missing SNP genotype calls 3610 1070 11252 365 2152 10690 4856.5

Total genotypes 7679 10219 37 10924 9137 599 6432.5

% SNP reproducibility 68.0 90.5 0.3 96.8 80.9 5.3 56.9

% Genotype reproducibility 73.2 98.9 94.6 83.3 97.6 92.3 89.9

• 76% of the predicted ApeKI cut sites were not sampled • In the maize genome with 23 million ApeKI GbS reads/individual = 30% missing data • GbS highly dependent on data imputation (not an option for highly variable trees) • Solution: much higher target coverage necessary – higher cost per sample

Beissinger et al. Genetics 2013

Myles S. 2013 Trends in Genetics

Examples of the challenges associated with calling genotypes from genotyping-by-sequencing (GBS) data

• Fast and affordable to discover and genotype SNPs and additional variants

• Large proportions of missing data (30-80%) that increases considerably as more

samples are typed and/or more stringent call rate threshold is applied

• Interchange of SNP data within and across labs and studies is very challenging

• Impacted by DNA quality, library prep and sequencing run

• GbS more acceptable for autogamous species (SNPs largely homozygous)

• GbS in heterozygous genomes:

Inconsistent genotype calls; reproducibility is a problem

SNP imputation very difficult with high heterozygosity & large effective size populations

Higher sequence depth needed to reach acceptable data (call rate & accuracy) (15X +) (Cost??)

• GbS useful for:

“Never-to -be-repeated” studies: no need to go back to the same markers (most

linkage mapping studies)

Estimate coarse population level parameters not for individual level inference

No need for long term, multiple organization SNP data interchange

• However methods are constantly improving and seq.costs dropping

Some considerations on current GbS methods

Eucalyptus genome

Myburg, Grattapaglia, Tuskan et al. Nature 2014

EuCHIP60k development

• For robust, long term, breeder-friendly data interchangeble implementation of GS in Eucalyptus we needed:

– Genome-wide marker density

– High assay reproducibility

– Informative SNPs across the BIG TEN Eucalyptus sp.

– Speed of data delivery

– Public access, world use and platform transferable

– Low cost per sample

• “Crowd funding”: eucalypt forest companies worldwide

• 240 tree genomes of 13 species resequenced at 3.5X/tree

• 46M SNPs – 64K on chip, 61K converted

Density distribution of the 59,299 converted SNPs of the EUCHIP60K along the 11 Eucalyptus chromosomes

Silva-Junior et al. 2015 New Phytologist

Average number of SNPs per 50kb bin:

5.01

Average number of genes per 50 kb bin:

2.97

568 bins with zero SNPs

(28.4 Mbp , ~4%) Correspond to N’s

regions in reference genome

• Most crops are still genomic-orphan with usually a handful of microsatellites

• NGS based SNP discovery using different forms of complexity reduction, low pass sequencing of multiple accessions is accessible

• For most applications in breeding and germplasm characterization, only a few thousand markers are actually needed

• Genetic structure analyses

• Pedigree reconstruction

• Marker Assisted Introgression

• Genomic Selection

• SNP data has to be fast, cheap very robust and user friendly

• We don’t need hundreds of thousands of SNPs for a few species but a few thousands SNPs for hundreds of species

How many SNPs do we really need for breeding and germplasm studies ?

Germplasm Inventory

Total: 152,873 accessions (141 Active Germplasm Banks) 700 species 300 genera Largest collections (# accessions): Rice: 27,050 Bean: 18,447 Soybean: 18,024 Wheat: 15,118 Sorghum: 7,215 Cowpea: 3,942 Maize: 3,922 ... Basic Seed collection: 122,000 accessions

Glaszmann et al. 2010 COPB

Glaszmann et al. 2010 COPB

• Underutilized sources of genetic variation • Selection imprints • Heterotic patterns (maize) • Hidden translocations (wheat) • Rare recombinants

• Breeder-ready lines and populations with new, beneficial alleles for priority characters in elite genetic backgrounds

• Molecular markers linked to beneficial alleles and statistical models for estimating breeding values to accelerate genetic progress in breeding programs

• Novel, beneficial alleles, haplotypes • Markers linked to loci and alleles that

control priority traits • Genetically distinct ‘donor accessions’

Molecular atlases Asociación genómica

Novel alleles and allele donors

‘Bridging germplasm’

1 2

3 Elite germplasm

selected by breeders

New breeding approaches and

technologies; new tools such as GS

Seeds of Discovery (SeeD)

Slide kindly provided by

Dr. Peter Wenzl

• Coffee (Coffea canephora) • Genome size: 710 Mb – Genome assembly available

• SNP discovery from pooled whole-genome resequencing of 80 accessions

• Cashew (Anacardium occidentale) • Genome size: 488 Mb – Draft reference genome built by our group (Nut & Fruit Workshop

PAG2017)

• SNP discovery from whole-genome individual resequencing of 25 diverse accessions

• 18 M GATK SNP; 9.9 M filtered and recalibrated

• Cassava (Manihot esculenta) • Genome size: 742 Mb - Draft reference genome available

• SNP discovery from 20 resequenced accessions by JGI (NCBI data)

• Brazilian Pine (Araucaria angustifolia) • Genome size 24 Gbp; no reference genome

• SNP discovery from reduced representations RAD and RNAseq data

• Eucalyptus (Eucalyptus grandis) • Genome size: 640 Mb; Draft reference genome available

• Validated SNPs from Infinium chip

Species, genomes and SNPs for multi-species Axiom array

SNP categories in Axiom suite analysis

Axiom array conversion statistics

Metric Coffee Cashew Cassava Pine Eucalypt TOTAL

# SNPs selected for array 25,456 16,504 3,417 3,400 2,000 50,777

# individuals genotyped 767 671 192 192 96 1918

# individuals passed 744 602 191 185 95 1817

High resolution polymorphic 19,586 12,437 2,382 1,650 1,572 37,627

No minor homozygous 2,603 560 423 57 5 3,648

High resolution monomorphic 1,396 341 409 814 1 2,961

Call rate below threshold 241 539 41 178 250 1,249

Off target variants 488 678 44 33 34 1,277

Other 1,142 1949 118 668 138 4,015

# Converted SNPs 23,585 13,338 3,214 2,521 1,578 44,236

Effective conversion rate (%) 93 81 94 74 79 88

Reproducibility (%) 99.97 99.92 99.98 99.92 99.94 99.95

The Cashew fruit

Pseudo-fruit (hypertrophied pedicel)

a.k.a Apple

True fruit (kidney shaped achene)

Cashew Nuts Seed kernel after shelling

Cashew progeny trial Cashew grafted plants

Cashew fruit and nut diversity Cashew fruit and nut products

Cashew plantation

Antrachnose Colletotrichum gloeosporioides

Black Mold Pilgeriella anacardii

Resinosis Lasiodiplodia theobromae

Bacteriosis (Xanthomonas campestris

pv. Mangiferaeindicaeanacardii)

Powdery mildew Oidium anacardii

Bacterial blight Septoria anacardii

Main cashew diseases

SNP linkage data allowed anchoring scaffolds into pseudo-chromosomes

Cashew pseudochromosomes

20 PCs One missing

349 Mb anchored 72% of genome

Cashew linkage maps to pseudochromosomes statistics

Metric BRS226 CCP1001 CCP76_1 CCP76_2 EMBRAPA51 MICROCARPUM

Unique SNPs 736 1,270 1,294 852 731 693

Linkage Groups 24 21 19 22 31 23

SNPs/Mb 2.3 3.9 4.0 2.6 2.5 3.0

N50 Scaffolds 34 34 32 33 31 27

# Scaffolds 134 134 134 139 113 78

Total bp anchored 319.9 329.2 319.5 328.7 295.7 233.2

% anchored 85.4 87.8 85.3 87.7 78.9 62.2

DWARF TYPES A. microcarpum

Metric Anchored Oriented Unplaced

Unique SNPs 3,876 3,659 21

SNPs/Mb 11.1 11.4 0.8

# Scaffolds 180 113 469

Scaffolds with 2> SNPs 145 113 1

Total bp 348.7 320.1 26.1

% anchored 92.5 85.4 6.9

CONSENSUS LINKAGE MAP – 20 LINKAGE GROUPS

SNP data provide higher resolution and precision and less biased population structure inferences in Araucaria natural populations

Marker type

# Markers

Fst Gst (Hedrick standard.)

AMOVA (% variation among)

SSR 30 0.083 ± 0.015 0.148 ± 0.028

13.3

SNP 30 0.165 ± 0.030 0.292 ± 0.045

27.9

SNP 300 0.221 ± 0.012 0.371 ± 0.017

35.4

SNP 1643 0.186 ± 0.004 0.290 ± 0.006

31.0

Collection sites

Germplasm collection across 15 natural

populations

Need to estimate variation within and among

populations for conservation management

purposes

Microsatellites lead to substantial underestimates

of variation among populations despite using

corrected estimators (Gst)

Numerator relationship matrix (A matrix) EXPECTED PEDIGREES

Genomic relationship matrix (G matrix) REALIZED PEDIGREES

SNP based genomic relationship matrix resolved pedigree errors and revealed cryptic relatedness among selected cultivars

Cassava SNPs for Pedigree reconstruction

Cassava breeding advanced by open pollination in “Hybridization fields” Pedigrees are “expected” and no actual control of inbreeding and relatedness is possible SNP data used for parentage analysis and pedigree reconstruction (300 SNPs) SNP data planned for Genomic Selection and low resolution GWAS for disease traits

The (genomic) breeder`s equation

DG = i * r * sA

L *genotyping cost

DG = Response to selection i = selection intensity r = selection accuracy(correlation betweene stimated breeding value and true BV) sA = additive genetic standard deviation (additive genetic variation available) L = breeding cycle length

Genomic Selection (GS) or Genome-Wide Selection a quick and easy sketch to communicate to breeders

GS is the simultaneous selection for hundreds or thousands of SNPs covering most of the genome such that all “genes”of interest will be

associated one or more markers

QTLs SNPs

chrom. 1

chrom. 2

chrom. 3

chrom. 4

chrom. 5

SNPs for breeding perennial plants by Genomic Selection

• Perennial crops (forest and fruit trees) are largely undomesticated (lots of unexploited variation) with shallow pedigrees

• Long breeding cycles, poor juvenile mature correlations

• Logistically complex, large areas, multiple sites

• Late expressing traits and delayed flowering

• Use of BLUP is common practice (overlapping generations, multiple sites, missing data, mixtures of half-sib, full-sib and clonal trials

• Controlled pollination is difficult and expensive; pedigree errors are common

• Perennials can benefit from SNP-based Genomic Prediction much more than annual crops

Coffee Cashew Eucalyptus

• SNP data GENOTYPES

• Trait data PHENOTYPES

Predictive model

Y = Xb + Zh + e

Cross validation

SELECTION CANDIDATES (Young seedlings genotyped but not phenotyped) (e.g. 100 full or half-sib families of 100 offspring each = 10,000 seedlings)

• SNP data GENOTYPES

Predictive model updating

GENOMIC SELECTION CYCLE

Elite clones

Selected seedlings (top 5% ranked by GS)

Field trial and phenotype

Clonal trial of top ranked GEGV seedlings

Flower induction of top ranked GEBV seedlings

BREEDING

DEVELOPMENT OF PREDICTIVE MODEL

(e.g. progeny trial N ~ 2,000 of a breeding population with Ne ~ 60)

Training population

Validation population

Breeding population

Breeding

Selection candidates

SNP data GS model

Selected seedlings

GEBV Flower induction

Improved population

GEGV

Breeding population

Breeding

Selection candidates

Progeny test

Year 0

1

2

3

4

5

6

7

8

Selection of elite clones

Breeding

Selection candidates

Selected seedlings

Phenotypes for model updating

9

10

11

12

13

14

Selected trees GV

15

BV

Grafted orchard

Improved population

GENOMIC SELECTION BREEDING PHENOYTPIC SELECTION BREEDING

Field plant in exper. design

Phenotypes for model updating

Breeding

Selection candidates

Progeny test

GEGV

SNP data GS model

GEBV Flower induction

Improved population

Breeding

Selection candidates

Selected seedlings

SNP data GS model

GEBV Flower induction

Improved population

Field plant in exper. design

GEGV

Verification clonal trial

Verification clonal trial

Verification clonal trial

Verification clonal trial

Nursery

Nursery

Selection of elite clones

Selection of elite clones

Grattapaglia 2014. Advances in Genomics of Plant Genetic Resources. Chapter 26. Springer. DOI 10.1007/978-94-007-7572-5_26

Summary • There is an urgent need to develop robust, high throughput SNP

platforms for hundreds of plant species

• SNP discovery is currently very affordable and accessible using NGS with or without reference genome

• SNP data has to be genome-wide, affordable, simple, fast, interchangeable across institutions

• SNP data will allow a vast array of applications in germplasm characterization, genomic prediction, gene discovery, operational breeding

• Multi-species arrays allow sharing the upfront cost of array development and provide great flexibility, low cost and high quality data

Genomic Selection to boost existing breeding programs of Brazilian tropical fruit crops

Passion Fruit Passiflora edulis Passifloraceae

Genome: 1500 Mb Diploid

Mangaba Hancornia speciosa

Apocynaceae Genome: 500 Mb

Diploid

Guava Psidium guyava

Myrtaceae Genome: 240 Mb

Diploid

Pitanga Eugenia uniflora

Myrtaceae Genome: 240 Mb

Diploid

• Draft assembly

• Resequencing pool of diversity

Genome

• SNP discovery

• Multispecies chip

SNP Resource

• Model development

• Operational use in breeding

Genomic Selection

Cashew Anacardium

Anacardiaceae Genome: 500 Mb

Diploid

Acknowledgments

Funding

Brazilian National Research Council

Brazilian Corporation of Agricultural Research

Federal District Research Foundation

Service providers

part of Thermo Fisher Scientific