Association Mapping of Drought Tolerance

Genes

Jianbing Yan – CIMMYT

Tim Setter – Cornell University

Marilyn Warburton – USDA-ARS

Candidate gene association mapping: precedence

Qualitative traits: flowering time, plant height, seed quality

Quantitative traits: pro Vitamin A accumulation in grain

Candidate gene association mapping works for quantitative traits!

Yan et al., “Rare Genetic Variation at Zea mays crtRB1 Increases β -carotene in Maize Grain” Nature Genetics in press.

Harjes, C. E. et al. 2008. Natural genetic variation in lycopene epsilon cyclase tapped for maize biofortification. Science 319, 330-333.

Matthews and Wurtzel, 2007 Chemical and Functional Properties, p347-398

LCYElycopene

δ-carotene

α-caroteneLCYB

HYDb

zeinoxanthin

lutein

HYDE

ABA

HYDb1

LCYB

β-cryptoxanthin

γ-carotene

β-caroteneLCYB

zeaxanthin

HYDb

GGPPPSYPDSZ-ISOZDS/CRISTO

LCYElycopene

δ-carotene

α-caroteneLCYB

HYDb

zeinoxanthin

lutein

HYDE

ABA

HYDb1

LCYB

β-cryptoxanthin

γ-carotene

β-caroteneLCYB

zeaxanthin

HYDb

GGPPPSYPDSZ-ISOZDS/CRISTO

Ratio =(lutein + α-carotene)(zeaxan+ BC+ β-crypt)

BC = β-carotene

BC/ALL =β-caroteneAll five components

Pro VA =β-carotene + ½(α-carotene + β-cryptoxanthin)

Maize: allele mining of pro-Vitamin A content

Objectives of the drought study:

Identify SNPs/Genes associated with yield

related traits under well watered and

water stress conditions by candidate

gene association mapping

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Flowering Group

No.

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s

earlyMidLate

350 lines selected from a global maize diversity collection and divided into 3 groups based on flowering time

CIMMYTCIMMYT & KARI CIMMYT/IRRI

Cornell University

NCFCRC

SIRDC

Sichuan Agriculture University

350 lines were crossed with a common tester (CML312) and phenotyped under replication in 5 countries over two years

Phenotyping siteGenotyping site

Category Trait Trait description

Plant architecture

PH Plant height

EHT Ear height

Flowering time

MFLW Male flowering timeFFLW Female flowering timeASI Anthesis-silking interval

Yield components

GY Grain yieldKNO Kernel number ENO Ear number per plantHKW Hundred kernels weight

Drought related

YLCH4W Leaf chlorophyll content at the end of stress ELCH4W Ear chlorophyll content at the end of stress

SEN Leaf senescence (0-10) scored 20 days after female flowering

Traits measured in each environment

Category Trait TreatmentAverage ±

SDRange WS/WW Heritabil.

Plant architecture

PHWW 219.9±8.4 184.7-240.3

0.790.81

WS 173.4±4.7 144.5-185.7 0.53

EHTWW 107.8±8.6 85.2-133.8

0.850.71

WS 92.0±5.4 76.4-112.0 0.74

Flowering time

MFLWWW 66.1±1.8 61.3-70.3

1.060.93

WS 70.2±2.1 64.8-74.9 0.88

FFLWWW 72.5±2.2 67.2-78.0

1.050.92

WS 76.4±2.0 70.8-82.2 0.81

ASIWW 2.2±0.6 0.2-3.7

2.820.80

WS 6.1±0.7 4.3-8.2 0.53

Yield components

GYWW 6.9±0.4 5.6-7.9

0.460.62

WS 3.1±0.3 2.2-4.0 0.54

KNOWW 408.6±25.4 310.2-508.3

0.740.81

WS 300.4±19.9 226.2-352.8 0.52

ENOWW 0.98±0.01 0.94-1.01

0.660.25

WS 0.64±0.05 0.47-0.78 0.42

HKWWW 33.4±1.9 28.5-39.5

0.850.52

WS 28.4±1.6 23.4-34.6 0.70

Drought related

YLCH4WWW 40.2±0.6 38.0-42.2

0.780.23

WS 31.5±1.4 24.6-25.7 0.53

ELCH4WWW 45.7±1.0 42.1-48.8

0.830.39

WS 37.9±1.5 30.6-41.5 0.50

SENWW 3.5±0.1 3.3-3.9

1.290.77

WS 4.5±0.1 4.4-5.0 0.23

Trait performance based on the BLUP value across 14 environments

Trait GY KNO ENO HKW PHT EHT MFLW FFLW ASI YLCH4W ELCH4W SEN

GY 1 0.39** 0.51** 0.29** 0.40** 0.30** 0.08 0.05 -0.05 0.08 0.14** 0.01

KNO 0.66** 1 0.22** -0.47** 0.36** 0.36** 0.27** 0.28** 0.08 -0.04 -0.06 -0.01

ENO 0.71** 0.54** 1 -0.14** 0.18** 0.18** -0.02 -0.12* -0.22** 0.08 0.14** -0.02

HKW 0.42** -0.04 0.07 1 0.09 -0.04 -0.11* -0.09 0.02 0.03 0.06 0.01

PHT 0.30** 0.22** 0.20** 0.18** 1 0.77** 0.49** 0.47** 0.05 -0.13* -0.12* -0.05

EHT 0.22** 0.19** 0.10 0.10 0.64** 1 0.60** 0.55** -0.04 -0.19** -0.20** -0.09

MFLW -0.23** -0.19** -0.29** -0.01 0.23** 0.47** 1 0.92** 0.02 -0.27** -0.37** -0.18**

FFLW -0.44** -0.35** -0.53** 0.00 0.15** 0.30** 0.84** 1 0.37** -0.28** -0.39** -0.19**

ASI -0.49** -0.43** -0.56** -0.03 -0.21** -0.20** -0.03 0.48** 1 0.00 -0.07 -0.01

YLCH4W 0.38** 0.30** 0.43** 0.10 -0.08 -0.19** -0.56** -0.53** -0.18** 1 0.62** -0.12*

ELCH4W 0.28** 0.25** 0.36** -0.02 -0.08 -0.24** -0.53** -0.47** -0.12* 0.86** 1 -0.11*

SEN -0.07 -0.05 -0.16** 0.01 -0.08 0.05 0.00 0.01 0.07 -0.14** -0.19** 1

Correlations among traits

* significant at p<0.05; ** significant at p<0.01.

ASI + ENO/KNO GY

(WS, under diagonal; WW, above diagonal)

Illumina BeadStation 500GX• Custom panels up to 1536-plex (GoldenGate technology)

1536 SNPs from 582 genes, half drought candidate gene

1/96 plot with1536 SNPs

Distribution of drought candidate genes

Chr.SNP Number

Unigene Minor Allelic Frequency1+SNPs 1SNP ≥0.05 ≥0.1 ≥0.2

1 211 61 19 178 148 982 166 37 33 124 93 603 131 33 30 92 77 524 132 36 26 101 75 455 128 36 30 102 79 436 65 18 14 50 38 257 112 22 17 90 75 478 102 26 13 82 75 489 83 21 13 63 56 3810 73 16 13 61 45 34Unknown 26 21 3Total 1229 327 211 943 761 Total

Summary of SNPs in the chip

Category TraitWW WS

P<0.00006P = 0.001-0.00006

P<0.00006P = 0.001-0.00006

Plant architecture

PHT 0 1 0 2EHT 1 1 0 1

Flowering time

MFLW 0 1 0 1FFLW 0 2 0 1ASI 0 1 0 1

Yield components

GY 0 1 0 1KNO 0 4 0 0ENO 0 1 0 0HKW 0 4 0 1

Drought related

ELCH4W 0 0 0 1YLCH4W 0 3 0 0

SEN 0 1 1 5Summary 1 20 1 14

Summary of SNPs associated with the yield related traits

Summary of SNPs associated with yield related traits

SNP FDR

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Quantile-quantile plot combining all the agronomic traits and markers

Why no SNPs associated with yield?

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Sample size

Effect=0.05

Effect=0.04

Effect=0.03

Effect=0.02

Effect=0.01

GWAS in maize, we need >10M markers; 100 markers/gene(Myles et al, Plant Cell, 2009)

Complex traits controlled by a huge number of genes, each gene can only explain a small effect (<3%)(Buckler et al, Science, 2009)

More markers + Bigger sample size

What can we do for drought improvement?

Genomewide Selection (GWS) is an alternative

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2 2.5 3 3.5 4

Obs

erve

d va

lue

Predicted value

GY_WS, R2=0.91

4.5

5.5

6.5

7.5

4 5 6 7 8O

bser

ved

valu

ePredicted value

ASI_WS, R2=0.94

Metabolites assayed:- ABA and metabolites: ABA-glucose ester, phaseic acid- carbohydrates: glucose, sucrose, starch - proline

Tissues sampled:- leaf at 2 and 4 weeks after irrigation stopped- ear tip and silk at 0 and 7 d after anthesis

5000 tissue samples each year (2005-2006); assayed in duplicate1200 samples in 2007-2008; assayed in duplicate

What we can do for drought improvement?

Looking for secondary traits is another alternative

Tim setter/Cornell University

Metabolic traits measured

11 metabolic characteristics: tissue weights, sucrose, glucose, derived sugar traits (2) starch, ABA, and ABA glucose ester (ABA-GE), phaseic acid (PA), and proline

3 tissues (ear tips, silks and leaves)

2 time points (0 and 7 days after anthesis)

two year’s data for water stressed treatment, one year’s data for well watered treatment

= 66 traits in well watered and 132 traits in drought stressed

Correlations between metabolites and yield

ABA –ASI 0.32 – 0.39

ABA –GY -0.17 – -0.22

Associations to metabolic traits:

35 traits in the well watered and 66 traits in the water stressed treatments were significantly associated with at least one SNP (at p=0.001 sig. level)

Some traits had more than one SNP associated with them

Most SNPs were significantly associated with more than one trait (average = 3, and ranged from 1 – 16)

Summary of SNPs associated with the metabolite traits

SNP FDR P Value Q Value

7 0.05 <1.86E-06 <0.04

11 0.1 <6.52E-06 <0.09

16 0.2 <1.55E-05 <0.15

Quantile-quantile plot combining all the metabolite traits and markers

11 SNPs were identified associated with 6 traits at P<6.52E-6

SNP Name Chr. SNP MAF N Traits P Near GenePZB01400.2 1 A/G 0.063 303 S.Aba7_SS_06 4.09E-10 aldehyde oxidase, ZmAO1PZB01403.4 1 A/G 0.054 332 S.Aba7_SS_06 3.02E-08 aldehyde oxidase, ZmAO3

PZB02017.1 2 A/T 0.085 342 E.Suc7_SS_05 1.77E-06casein kinase II, regulatory subunit

PZA03635.1 2 C/T 0.085 342 E.Suc7_SS_05 1.86E-06SET domain-containing protein

PZD00027.3 3 A/C 0.09 345 E.Pa.0_WW_06 4.87E-11MADS-domain transcription factor

PZD00027.3 3 A/C 0.09 345 E.Pa.7_WW_06 1.58E-08MADS-domain transcription factor

PZB01223.1 3 T/C 0.101 338 E.Glc0_SS_05 3.30E-06AT Hook transcription factor

PZA03368.1 7 C/T 0.074 350 S.Glc7_SS_06 1.78E-06histidine kinase-related protein

PZA03368.1 7 C/T 0.074 350 S.TS.ug7_SS_06 3.96E-06histidine kinase-related protein

PZA03583.1 7 A/G 0.437 316 S.Abage7_SS_06 5.80E-06 ZnF, Me CpG DNA bindingPZA03569.2 10 T/G 0.063 335 E.Pa.7_WW_06 6.52E-06 aquaporin 2, MIP

Lycopene

δ-carotene

lycopene cyclase, epsilon

neoxanthin

NCED

ABA aldehyde

aldehyde oxidase

ABA

phytoene synthase(PSY)

Phaseic acid ABA-GE

ABA synthesis pathway

Silk_ABA7_WSP= 4.09E-10 n=303R2=7.1%

Lessons Learned: marker assisted breeding of quantitative traits

No candidate gene for a quantitative trait is going to be very big, even if it is indeed an important gene for the trait

Phenotypic variation associated with any given gene must be small. Phenotypic variation associated with error in measuring may be large

Choosing the wrong candidate genes provide no useful data; waste resources; and complicate the analysis

Lessons Learned: marker assisted breeding of quantitative traits

Candidate gene method works better when you can break complex traits into pathways, because you have an obvious place to look for candidate genes

Error associated with phenotypic measurements tends to be smaller when measuring metabolites, which can be more precisely measured than yield. Many replications still need to be measured

Environmental variation still a problem, and must be overcome using many different testing environments

Whole genome scanning may work better but requires an economical platform for genotyping (nearly available!) and computing methods for dealing with huge numbers of multiple comparisons

Will candidate gene based association mapping work on other

quantitative traits?

Quantitative disease resistance: A. flavus example

A. flavus is an opportunistic saprophyte of plants that can become an economic problem in oil seed crops.

A. flavus appears to respond to oxidative stress by producing aflatoxin as an antioxidant

High temperature and drought cause oxidative stress in the cob

These stresses trigger aflatoxin production by A. flavuspresent in the cobTherefore, preventing oxidative stress in the ear by have protective abiotic stress proteins may prevent aflatoxin accumulation

Preventing aflatoxin production

Phenylpropanoid pathway enzymes are more abundant in resistant cobs

Phenylpropanoid compoundsAntioxidantsAntifungalPrecursors of lignin

Phenylalanine ammonia lyase (PAL)Rate limiting step of the pathway

Caffeoyl CoA O-methyl traferase (CCoAOMT) levels higher in resistant cobs

Key enzyme in lignin biosynthesisFive CCoAOMT genes in maize

Chromosomal Locations of PAL Genes

Pal1 – chromosome 5, bin 5.05

Pal2 – chromosome 2, bin 2.03

Pal3 – chromosome 4, bin 4.o5

Near known QTL for A. flavus resistance

Would these be good candidate genes?

Chromosomal Locations ofCCoAOMT Genes

CCoAOMT1 – chromosome 6, bin 6.02

CCoAOMT2 – chromosome 9, bin 9.02

CCoAOMT3 – chromosome 2, bin 2.07

CCoAOMT4 – chromosome 4, bin 4.06

CCoAOMT5 – chromosome 4, bin 4.07

How about these as candidates?

Near known QTL for A. flavusresistance

Association Mapping progress to date:

300 diverse inbreds testcrossed to Va35

4 field locations, two years, three replications (first year has been harvested and is currently being phenotyped)

Ears inoculated with A. flavus after flowering

Phenotypes to be measured: quantification of grain aflatoxin, quantification of A. flavus in the ear via q-PCR and NIRS, flowering time, ear worm damage, husk coverage.

Inbreds will be genotyped in order to associate the change in the DNA sequence (genotype) with aflatoxin levels (phenotype)

Additional markers will be used to measure population substructure and genetic relationships

AcknowledgmentsAnd Co-PI’s:

DroughtJianbing YanMark SawkinsJean-Marcel RibautEd Buckler Michael GoreMichael McMullenTim SetterZhiwu ZhangYunbi XuPichet GrudloymaJames GethiEster KhosaWanchen Li

CarotenoidsTorbert RochefordCatherine Bermudez KandianisCarlos E. HarjesLing BaiEun-Ha KimXiaohong YangDebra SkinnerZhiyuan FuSharon MitchellQing LiMaria Guadalupe Salas FernandezMaria ZaharievaRaman BabuYang FuNatalia PalaciosJiansheng LiDean DellaPennaThomas Brutnell

AflatoxinW. Paul WilliamsGary WindhamJack HaynesBrien HenryLeigh HawkinsRowena KelleySantiago MiderosSeth MurrayChris DavesKerry MayfieldMatt KrakowskiWenwei XuJack HaynesLadonna OwensRebecca NelsonDawn LutheJeff WilkinsonErik MylroieWanchen LiSusan BridgesJonathan HarperSeval Ozkan