Edward Buckler USDA-ARS

Cornell University

http://www.maizegenetics.net

Crop design with

genomics and natural

diversity

Goal: Create the global model to

decrease cycle time Make

Crosses

Inbreed

Small Scale Hybrid

Large Area

Hybrid

Trials

Sell or Release Winner Hybrids

5 years

Make Crosses

Doubled Haploid

Genotype

Predict Value

Small Scale Hybrid

Large Area

Hybrid

Trials

The Model

Data From Other Efforts

4 months

4 y

ears

Sell or Release Winner Hybrids

Genomic

Selection (GS) Standard

Breeding

With perfect knowledge it could run 15X faster,

current reality ~3X

GS versus GWAS

• Same data: genome wide marker and

phenotypes, different statistics

• GWAS – Genome Wide Association

Studies are aimed identifying causative

genes and variants

• GS – Genomic Selection aims to predict

phenotype using the complete genotype

Challenges

• Genotype to unite world’s germplasm

resources

• Resolve complex traits so perfect LD

remains for more than 15 meioses.

• Collect and mathematically model

relevant trait and environmental

interaction

• Deploy

The Maize Diversity Project

McMullen & Flint-Garcia, at University

of Missouri

Holland, at North Carolina State Univ.

Ware, at Cold Spring Harbor Lab.

Sun & Kresovich, Cornell University

Doebley, University of Wisconsin

USDA-ARS & NSF Plant Genome

www.panzea.org

Unite world’s

germplasm diversity

Maize has more molecular diversity

than humans and apes combined

Silent Diversity (Zhao PNAS 2000; Tenallion et al, PNAS 2001)

1.34% 0.09%

1.42%

Maize likely has functional variation at every gene. In total, there could

be 100,000s of functional SNPs (Single Nucleotide Polymorphisms)

Only 50% of the maize genome is

shared between two varieties

Fu & Dooner 2002, Morgante et al. 2005, Brunner et al 2005

Numerous PAVs and CNVs - Springer, Lai, Schnable in 2010

50%

Plant 1

Plant 2 Plant 3

99%

Person 1

Person 2 Person 3

Maize Humans

Maize genetic variation has been

evolving for 5 million years

Modern Variation

Begins Evolving

Sister Genus

Diverges

Zea species begin

diverging

Maize domesticated

5mya

4mya

3mya

2mya

1mya

Warm

Pli

ocen

e

Co

ld

Ple

isto

cen

e

Divergence from

Chimps

Ardipithecus

Homo erectus

Modern Humans

Modern Variation Begins

Australopithecus

The Maize HapMapV2 Project Ware, at Cold Spring Harbor Lab.

Ross-Ibarra, Univ. California, Davis

X. Xun & S. Chi, Beijing Genome Inst.

Y. Xu, CIMMYT

J. Lai, Chinese Agri. Univ.

Q. Sun, Cornell Univ.

N. Springer, Univ. of Minnesota

McMullen, at University of Missouri

Doebley & Kaeppler, Univ. of Wisconsin

USDA-ARS, NSF, BGI, JGI

Maize HapMap2 • Increase the breadth of samples (teosinte, landraces, improved lines)

– All inbred lines

• Whole Genome Shotgun, Illumina Paired-End, 76-100bp

• 103 lines, 13 Billion reads, 1Tbp of sequence

• Median 5X coverage

0 200 400 600 800

Sequence Reads (Gbp)

Tripsacum dactyloides

Teosinte (Zea Mays

ssp. Mexicana)

Teosinte (Zea Mays

ssp. Parviglumis)

Maize Landraces

Maize Improved Lines (including

NAM)

60 Inbred lines

23 Inbred lines

17 Inbred lines

2 Inbred lines

1 sample

The Warning & It Applies To

Many Other Studies • CSHL & BGI alignment pipelines only

agree 50% of time with same data

• ~160M SNPs identified – most probably

really exist somewhere

– MOST DO NOT EXIST WHERE ALIGNED

– GENETIC AND EVOLUTIONARY CONTROLS

• >50% errors if accept standard pipelines

• 55M pass various population & genetic

filters

HapMapV2 Results

• 55M SNPs identified

• Domestication & improvement

loci found

• Copy number and PAV

identified

–80-90% of the genome in flux

–Explain many QTL

Genotyping By Sequencing

GBS Reduced representation sequencing

for rapidly genotyping highly diverse

species RJ Elshire, JC Glaubitz, Q Sun, JA Poland, K

Kawamoto, ES Buckler, and SE Mitchell

Institute for

Genomic Diversity

PlosONE 2011

http://www.maizegenetics.net/

What is GBS? • Use next generation sequencing to

genotype a reduced representation

portion of a genome

• RAD, RRL, CROPS, GBS

• Molecularly the most effective

approaches use restriction enzymes

– The first maize HapMap was RRL (Gore

et al 2009 Science)

– Recent efforts are drive price down

Expectation of marker

distribution

Biallelic, 17%

Too Repetitiv

e, 15%

Non-polymor

phic; 18%

Presense/Absense

, 50%

Multiallelic, 34%

Too Repetitiv

e, 15%

Non-polymorphic; 1%

Presense/Absense

, 50%

Biparental population Across the species

GBS 96-plex Protocol http://www.maizegenetics.net/

1. Plate DNA

&

adapter

pair

Barcode

Adapter

―Sticky Ends‖

Barcode

(4-8 bp)

Common

Adapter

primer 1 primer 2

2. Digest DNA with methylation-

sensitive Restriction Enzyme

3. Ligate adapters

(Steps 2 & 3 may be done simultaneously)

ApeKI (5 base-cutter) or PstI (6 base-cutter)

GBS 96-plex Protocol

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. .

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.

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.

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.

. . . . . . . . . . .

Plate DNA &

adapter pair Pool

DNAs

PCR

Primer

s

Digest DNA with RE

Ligate adapters

(may be done simultaneously)

Evaluate

fragment sizes

Clean-up

CTGCAATCTTGGACAATGTATGTAGGGACTAGGGACAGTGATGTAATTAC

CAGCACTAATTCACACAATTTTGTCGGTTGATGTTACTGCAGTGGATCTT

CAGCACTAATTCACACAATTTTGTCGGTTGATGTTACTGCAGTGGATCTT

CAGCACTAATTCATACAATTTTGTTGGTTGATGTTACTGCAGTGGATCTT

CTGCGATCGCCGCGCCGATGAACGGGCCTACCCAGAAGATCCACTGCAGT

CTGCGATCGCCGCGCCGATGAACGGGCCTACCCAGAAGATCCACTGCAGT

CTGCCGTTGCTGGCAGTGCTACAACTCTTCACCTGACTGAAAGCTACTAA

CAGCTAGCGCAAGTGTTTGTGTTGCGCGCGCGCTGTGGAAAAGTGTGCCG

CAGCTAATTTTTTGGTATTTATTTGAAATAAGTTCCCACTACTCGCGGTT

CAGCTAATTTTTTGGTATTTGTTTGAAATAAGTTCCCACTACTCGCGGTT

CAGCCACTTCCCTCATTTGAAACTTTTTGGATCTTTGAAGACCAATAGAT

CAGCTAAGAAGATAGAGCCAAACAAGGTGGGCCTGCCAACGTCTCCTTCC

CAGCTAAGAAGATAGAGCCAAACAAGGTGGGCCTGCCAACGTCTCCTTCC

CTGCGACTCGTGCTTCGCCGCGGCCTGAAGAACCCGGTCTTTCACCGCCG

CTGCTCGGTAGTAAACGGGTACAGAATTTAATCCCGCATCATTTGGAAGC

Sequence (8 x 96 samples

per flowcell)

1.3 million reads per sample

110Mbp (today)

Costs per DNA sample

at various multiplex levels

0

5

10

15

20

25

30

35

48-plex 96-plex 384-plex

Sequencing

Labor

Reagents & Consumables

$33.00

$19.00

$9.00

Co

st

in U

S

Do

llars

GBS has been used in 11

plant species

Molecular Biology Basically Solved Over 30,000 samples run in the last months

Cacao,190Grape,570

Maize,11115

ReedCanaryGrass,1045

Sorghum,3325

Switchgrass,950

The main GBS challenges

currently are

bioinformatics

Bioinformatics Problems

• Massive amounts of data

• Complex genomes with many

unstable parts of a genome

• No reference genome

• Missing data

• Phasing and imputation

Discovery

Tag Counts by Taxa

Map Tags Genetically

Map Tags by Homology

Genetic Logic

Reference Genetic Map

Alleles and synonyms

Alleles to SNPs

Tags by Taxa

QSeq

Assign Tags to Alleles

Alleles to SNPs and locations

Genotypes (HapMap format)

Production

QSeq

Tag Counts by Taxa

Tags by Taxa Reference Genome

GBS Bioinformatic Pipelines

Only 50% of the maize genome is

shared between two varieties

Fu & Dooner 2002, Morgante et al. 2005, Brunner et al 2005

Numerous PAVs and CNVs - Springer, Lai, Schnable in 2010

50%

Plant 1

Plant 2 Plant 3

99%

Person 1

Person 2 Person 3

Maize Humans

Physical and genetic mapping

of 8.7 million GBS alleles Gene candPhysicalAgree

Gene candPhysicalDisagree

NotinPhysical,Gene callymapped

Complexmappingormodestpowercurrently

ConsistentErrororEvenlyrepe ve

Readswithstronggene cand/orBLASTposi on

Readswithweakerposi onhypothesis

Readswithnohypothesis(Errororevenrepe ve)

• Only 29% of alleles are

simple - physical and

genetic agree

• 55% of alleles are easily

genetically mappable

• Many complex alleles are

rarer, so 71% of alleles are

genetic and/or physically

interpretable.

• With more samples and

better error models perhaps

90% will be useable

All

ele

s

Re

ad

s

12 Trillion Data Point

Opportunity/Problem

• By end of 2011:

– GBS on ~30,000 public sample worldwide

– 200M variants known from whole genome

sequencing

– Combine and impute missing data:

2 alleles x 30,000 lines x 200,000,000 variants =

12 trillion data points

Doing the statistics and math will be a

challenge.

Resolve complex

traits

The Hammer: Maize Nested Association Mapping (NAM)

• Crossed and sequenced 25 diverse maize lines to capture a substantial portion of world’s breeding diversity

• Derived 5000 inbred lines from the crosses

• Grew millions of plants

• Largest genetic dissection system ever

Tx303

Mo18W

MS71 Hp301

CML333 CML247

P39

CML228

Ki11

M37W

CML103

NC350

Oh43

Ky21

CML52

Oh7B

M162W

CML69

Tzi8

Ki3

NC358

CML322 CML277

IL14H B97 CML52 B73

F1

RIL2 RIL199 RIL200 RIL1 …

B73

F1

RIL2 RIL199 RIL200 RIL1 …

P39

McM

ullen

et

al 2009 S

cie

nce

P1

P2

P25

B73

Genotyping parents by sequencing to exploit

both recent and ancient recombination

.

.

.

.

.

.

.

.

.

Pop1

Pop2

Pop25

NAM HapMapV1 provides 1.6M SNPs Gore, Chia et al 2009 Science

GWAS for Plant

Density – the leaf

architecture

portion of the

story

There has been 8 fold jump in US

maize yield in the last 80 years

USDA-NASS; Troyer 2006 Crop Sci. 46:528–543; Duvick 2005 Maydica 50:193-202

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

0

20

40

60

80

100

120

140

160

180

1865 1885 1905 1925 1945 1965 1985 2005

Co

rn P

lan

ts p

er

Acre

Avera

ge c

orn

yie

ld (

bu

/ac)

Year

Open pollinated

double cross

single cross

modern

Plant Density

3 fold increase in plant density

Leaf angle, blade length, blade width

•Determine canopy morphology and

light harvest

•Important for high density and yield

Newer hybrids have upright leaves (Duvick 2005)

At least 30-40 genes control each

aspect of the leaf

Upper leaf angle Leaf length Leaf width

93% of significant

alleles: <18mm effect

96% of significant alleles:

<2.5° effect

95% of significant

alleles: <3mm effect

-200

-150

-100

-50

0

50

100

150

200

3 6 9 12 15 18 21 24Fr

eq

ue

ncy

of A

lle

le

Allelic Effect (mm)

Significant alleles

-200

-150

-100

-50

0

50

100

150

200

0.5 1.5 2.5 3.5 4.5

Fre

qu

en

cy o

f All

ele

Allelic Effect (mm)

Significant alleles

Alleles showing positive effects Alleles showing negative effects

-250

-150

-50

50

150

250

0.5 1 1.5 2 2.5 3 3.5 4

Fre

qu

en

cy o

f All

ele

Allelic Effect (°)

Significant allele

Each gene has a small effect

Tian, Bradbury et al 2011 Nature Genetics

liguleless1 and liguleless2 explained the

two ―biggest‖ leaf angle QTL

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

BP

P

0

10

20

30

40

50

60

−lo

g(p

)

Associations with positive effect

Associations with negative effect

Linkage QTL peak

lg1 lg3 lg2 lg4

Upper leaf angle

10

20

1 2 3 4 5 6 7 8 9 10

cM

/Mb

Chromosomes

Tian, Bradbury et al 2011 Nature Genetics

The biggest effect was less than <2°

Effect lg1 lg2

QTL effect -2.4° -2.5°

SNP effect -1.2° -1.7°

Leaf Length

(36 QTL)

Leaf width

(34 QTL)

Upper Leaf Angle

(30 QTL)

2

0.08

3 0.03

3

0.03

Number of

shared QTLs

Phenotypic

correlation (r2)

Days To Silk

(39 QTL)

7

0.30

6

0.20

3 0.04

Low genetic overlap among leaf

architecture traits

Genetic architectures are finely

tuned to each exact

environment with evolution

favoring low pleiotropy

What genes have natural variation

to control Carbon & Nitrogen

metabolism in the field?

Nengyi Zhang

With Stitt & Gibon

groups, sampled 12000

plants in the field for

basic carbon & nitrogen

metabolites across all of

NAM

CA

Direct GWAS hit in the Carbonic anhydrase

(CA) gene

GW

AS

— B

PP

Lin

kag

e —

-lo

g(P

)

CA is the single most important gene

controlling Chlorophyll, Malate, Nitrate,

Glutamine, and overall protein content.

Carbonic anhydrase (CA) is a critical

enzyme in C fixation in C4 plant

Ludwig M. et.al. Plant Physiol. 1998

CA

Mala

• CO2 HCO3-

• CAs are upstream regulators

of CO2-controlled stomatal

movements in guard cells

• Water use efficiency, heat

stress

CA

Hu H. et al. Nature Cell Biology 2010

CA SNP associations: Chla, Mala, Nitr, Glut, Prot, Prin1

Trait SNP BPP (%) Gene AGP Glut 3: 213,890,769 6 carbonic anhydrase 213,888,899-213,896,251

Star 2: 22,808,083 76 invertase 22,804,880-22,809,451

5: 168,868,583 67 invertase 168,865,756-168,868,879

Chla 3: 213,848,077 48 carbonic anhydrase 213,847,057-213,859,958

3: 213,848,298 10 carbonic anhydrase 213,847,057-213,859,958

3: 213,894,582 24 carbonic anhydrase 213,888,899-213,896,251

9: 23,215,157 16 starch synthase 23,213,761-23,217,689

Gluc 5: 167,871,133 5 1,4-alpha-glucan branching enzyme 167,869,465-167,892,914

Fruc 5: 204,526,436 41 endoglucanase 1 (Cellulase) 204,527,678-204,531,175

Mala 3: 213,856,232 29 carbonic anhydrase 213,847,057-213,859,958

3: 214,330,739 23 malate transporter 214,325,927-214,328,710

Prot 8: 117,977,083 8 ribosome protein 117,979,473-117,983,191

3: 213,854,238 11 carbonic anhydrase 213,847,057-213,859,958

Nitr 1: 202,621,762 5 malate dehydrogenase (NADP+) 202,617,705-202,621,864

2: 181,079,834 39 chla,b binding protein 181,076,994-181,079,397

3: 213848077 7 carbonic anhydrase 213,847,057-213,859,958

4: 166,175,217 5 glutamine synthetase 166,172,187-166,175,518

Fuma 1: 195,285,519 22 pyruvate dehydrogenase E1 195,281,414-195,283,531

Significant SNPs either within or very near (<2kb)

candidate genes

Can we make useful

predictions

y = 1.32x – 21.2 R² = 0.93

65

70

75

80

85

90

95

65 70 75 80 85 90 95

Ob

se

rve

d D

ays

To

Flo

we

rin

g o

f P

are

nta

l L

ine

s

NAM QTL Prediction of Days to Flowering

Can we predict?

Predicted Flowering from markers models

Ob

se

rve

d F

low

eri

ng

Tim

e

With a $20 test, we can predict when many

varieties will flower with a couple days

NAM QTLs accurately

predict many traits.

Hence we can breed

with it.

Leaf Length

Leaf width Upper leaf

angle

500

600

700

800

900

1000

650 750 850 950

Ob

serv

ed

Predicted

R2=0.84

55

65

75

85

95

105

115

60 70 80 90 100 110

Ob

serv

ed

Predicted

R2=0.81

25

35

45

55

65

75

85

95

40 50 60 70 80 90

Ob

serv

ed

Predicted

R2=0.78

Taming of NAM

NAM and Ames Yield

Trials

• 1800 NAM lines test crossed on PVP

and trialed in 4 location in 2010 and 6

locations in 2011

• Every inbred in Ames has been

evaluated for basic traits in 2010

• Yield trials for 1200 Ames inbreds on

PVPs in 6 environments in 2011

• Collaborating with breeders to combine

GEBV models

S. Larsson

C. Romay

Evaluate Natural Variation

Mathematically Model Genotype to Phenotype

Predict Phenotype

Facilitates Rapid Breeding Progress

What can genomics do to

accelerate the breeding of

simple and complex traits?

What should and can we do

in the next decades?

• Double yield with same fertilizer and

water (better drought and N utilization)

– Perhaps even more in the developing world.

• Perennialize our crops

• Biofortify crops to improve nutrition in

the developing world

• Do this in 100 species.

Who do I contact to learn more?

• NAM – Jim Holland, Mike McMullen, and Sherry Flint-Garcia

• HapMapV2 – Doreen Ware, Jer-Ming Chia, Jeff Ross-Ibarra

• QTL Mapping on NAM – Peter Bradbury, Zhiwu Zhang, Feng Tian

• Leaf Architecture – Feng Tian

• C & N Metabolites – Nengyi Zhang & Yves Gibon

• GBS Methods & Bioinformatics – Rob Elshire & Sharon Mitchell, Qi Sun, Jeff Glaubitz, James Harriman

Web: www.panzea.org & www.maizegenetics.net

Supported by USDA-ARS & NSF