Genome-Wide Association Mapping and Genomic Prediction ... · natural variation is not analyzed in...

Genome-Wide Association Mapping and GenomicPrediction Elucidate the Genetic Architecture ofMorphological Traits in Arabidopsis1

Rik Kooke, Willem Kruijer, Ralph Bours2, Frank Becker, André Kuhn, Henri van de Geest, Jaap Buntjer,Timo Doeswijk, José Guerra3, Harro Bouwmeester, Dick Vreugdenhil, and Joost J. B. Keurentjes*

Laboratory of Plant Physiology, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, theNetherlands (R.K., R.B., A.K., H.B., D.V.); Laboratory of Genetics, Wageningen University, Droevendaalsesteeg1, 6708 PB Wageningen, the Netherlands (R.K., F.B., J.J.B.K.); Centre for Biosystems Genomics, WageningenCampus, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (R.K., H.v.d.G., D.V., J.J.B.K);Biometris, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (W.K.); PRIBioinformatics, Wageningen University, Droevendaalsesteeg 1, 6708 PB Wageningen, the Netherlands (H.v.d.G.);and Keygene, Agro Business Park 90, 6708 PW Wageningen, the Netherlands (J.B., T.D., J.G.)

ORCID IDs: 0000-0002-9014-9516 (D.V.); 0000-0001-8918-0711 (J.J.B.K.).

Quantitative traits in plants are controlled by a large number of genes and their interaction with the environment. To disentangle thegenetic architecture of such traits, natural variation within species can be explored by studying genotype-phenotype relationships.Genome-wide association studies that link phenotypes to thousands of single nucleotide polymorphism markers are nowadayscommon practice for such analyses. In many cases, however, the identified individual loci cannot fully explain the heritabilityestimates, suggesting missing heritability. We analyzed 349 Arabidopsis accessions and found extensive variation and highheritabilities for different morphological traits. The number of significant genome-wide associations was, however, very low. Theapplication of genomic prediction models that take into account the effects of all individual loci may greatly enhance the elucidationof the genetic architecture of quantitative traits in plants. Here, genomic prediction models revealed different genetic architectures forthe morphological traits. Integrating genomic prediction and association mapping enabled the assignment of many plausiblecandidate genes explaining the observed variation. These genes were analyzed for functional and sequence diversity, and goodindications that natural allelic variation in many of these genes contributes to phenotypic variation were obtained. For ACS11, anethylene biosynthesis gene, haplotype differences explaining variation in the ratio of petiole and leaf length could be identified.

The natural phenomena of mutation and recombi-nation that change the genetic code with each genera-tion have given rise to the enormous genetic diversity

between and within species. Through evolutionaryprocesses, such as drift, migration, and selection, plantshave accumulated a vast number of molecular poly-morphisms that enabled adaptation to a wide range ofenvironments (Kooke and Keurentjes, 2012). With therecent advancements in genetic and genomic tools, thisnucleotide diversity can be fully surveyed to identifycausal polymorphisms for many different plant phe-notypes. This should allow the identification of molec-ular changes that provided evolutionary advantagesand beneficial characteristics in agronomically impor-tant traits.

Through selection on variation in performance,plants have adapted to different environments. Plantperformance is directly determined by life history traits,such as flowering time and growth rate, which in turndepend on genetics, morphology, physiology, and theenvironment (Roff, 2007; Kooke et al., 2015). Under-standing the regulation of plant growth and mor-phology is therefore essential for the comprehension ofplant performance. Arabidopsis has adapted to a widerange of environments and displays an extensive va-riety in morphological and growth-related pheno-types. Its small genome size, the publicly available

1 This work was funded by a grant from the Centre of BioSystemsGenomics (no. AA3-WU-PL). W.K. was partially funded by theLearning from Nature project of the Dutch Technology Foundation,which is part of the Netherlands Organisation for Scientific Research.

2 Present address: Department of Plant Breeding and Genetics,Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, D-50829 Cologne, Germany.

3 Present address: ENZA Zaden, Haling 1E, 1602 DB Enkhuizen,

The Netherlands.* Address correspondence to [email protected] author responsible for distribution of materials integral to the

findings presented in this article in accordance with the policy de-scribed in the Instructions for Authors (www.plantphysiol.org) is:Joost J. B. Keurentjes ([email protected]).

R.K., H.B., D.V., and J.K. conceived the study, and participated in itsdesign and coordination and helped to draft the manuscript; W.K.,H.v.d.G., J.B., T.D., and J.G. participated in the design of the study andperformed the statistical analyses; R.B., F.B., andA.K. participated in theexperiments; and all authors read and approved the final manuscript.

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genome sequences of over 1000 accessions, and itsshort lifecycle make Arabidopsis an excellent model forthe study of natural variation (Horton et al., 2012;Weigel,2012).

Over the last decades, biparental mapping popula-tions have been extremely valuable in the detection ofquantitative trait loci (QTL) responsible for trait varia-tion between segregating progeny of two divergentparents (Alonso-Blanco and Koornneef, 2000; Koornneefet al., 2004; Alonso-Blanco et al., 2005, 2009). Identi-fying the underlying genes, however, remains a labo-rious and time-consuming effort. Moreover, these QTLexplain only the variation between two accessions, butprovide little relevance in an evolutionary context asthe QTL might reflect rare alleles, and the full range ofnatural variation is not analyzed in such studies.Genome-wide association (GWA) studies profitingfrom a wide allelic diversity and high resolution wereexpected to bridge the gap between QTL and candi-date genes, and at the same time lead to the identifi-cation of more evolutionarily relevant variation(Atwell et al., 2010; Bergelson and Roux, 2010). Thecurrently available large populations, dense genotyp-ing, and the advantage of homozygous lines in themainly self-fertilizing species Arabidopsis, ought togreatly enhance the power of such studies. Indeed,GWA studies confirmed many of the previouslyidentified genes in experimental mapping populationsand mutant studies and detected a number of, to ourknowledge, novel candidate genes, although this islimited for quantitative traits (Atwell et al., 2010;Brachi et al., 2010; Li et al., 2010; Todesco et al., 2010;Chan et al., 2011; Chao et al., 2012; Filiault and Maloof,2012; Sterken et al., 2012; Yano et al., 2013; Chao et al.,2014; Meijón et al., 2014; Bac-Molenaar et al., 2015).Similar to the problem of missing heritability in humanGWA studies, where individual variants cannot ex-plain the phenotypic variation despite high herita-bilities (Yang et al., 2010; Gibson, 2011; Makowskyet al., 2011), genetic variants cannot fully explain thehigh heritabilities in plant studies (Brachi et al., 2010;Sasaki et al., 2015). Several reasons, such as popula-tion structure (Filiault and Maloof, 2012), epistasis(Chan et al., 2011; Brachi et al., 2015; Kruijer, 2016),GxE (Sasaki et al., 2015), epigenetics (Johannes et al.,2009; Kooke et al., 2015), sample size (Korte andFarlow, 2013), heterogeneity (Atwell et al., 2010;Barboza et al., 2013), rare alleles (Salomé et al., 2011;Sanchez-Bermajo et al., 2015), and a high number ofsmall-effect loci (Joseph et al., 2013; Verslues et al., 2014)can complicate the association between polymorphismsand phenotypes.

While most GWA analyses for plant traits considerone marker at a time, genomic prediction models useall markers simultaneously, and therefore popula-tion structure is automatically accounted for in theanalysis (Meuwissen et al., 2001). These models wereoriginally proposed to predict breeding values ofunphenotyped individuals, but are increasingly usedto identify causal variants (Shen et al., 2013; Moser

et al., 2015). Important advantages over single locusmethods are that effect estimates are generally moreaccurate, and that the total genetic variance can beattributed to specific genomic regions (Goddardet al., 2009; Yang et al., 2010; Wood et al., 2014).Using similar strategies in maize, whole-genomeprediction was able to predict polygenic traits withvery high accuracy (Riedelsheimer et al., 2012). There-fore, genomic prediction models might also assist in theelucidation of the genetic architecture of highly poly-genic, quantitative traits in the model plant speciesArabidopsis.

Here we present the identification of, to our knowl-edge, novel candidate genes involved in plant growthand architecture through the application of GWAmapping and genomic prediction in a population of 349densely genotyped natural accessions of Arabidopsis.Relating the observed morphological differences be-tween accessions to climatological data from their siteof origin revealed that most traits were to a certain ex-tent adaptive to climate. In-depth analysis of a QTL forthe petiole-to-leaf-length ratio (PL/LL) revealed ACS11,a gene involved in ethylene biosynthesis, as a key playerin determining natural variation of petiole and leafgrowth.

RESULTS

Geographic and Climatic Adaptation ExplainPhenotypic Variation

A collection of 349 natural accessions of Arabidopsis,assembled to contain maximum genetic diversity andleast population structure (Li et al., 2010; Horton et al.,2012), was analyzed to assess the extent of natural vari-ation in shoot morphology. All analyzed traits showedextensive phenotypic variation, globally aswell aswithingeographical classes (Fig. 1 and Supplemental Table S1:H2). The least variation was found on the northernAmerican continent, most likely due to the recent intro-duction of the species and the relatively short period ofadaptation (Platt et al., 2010). Northern European acces-sions,mainly fromSweden, showed the largest deviationfrom other geographical classes in terms of floweringtime (FT), rosette branching (RB), plant height, and pet-iole length (PL).

Because most traits displayed different trait valuedistributions in the analyzed geographical classes, itwas examined whether this variation could be partlydue to adaptation to the local climate. To investigatethis, climatological data from the collection site of theaccessions were obtained (Kistler et al., 2001; Newet al., 2002; Hancock et al., 2011). Because geneticpolymorphisms may be strongly correlated with cli-mate simply due to demographic history, we used amethodology that corrects for population structure(Hancock et al., 2011). Specifically, a Mantel correla-tion matrix based on Spearman’s rank that controlsfor population structure was generated for correla-tions between the morphological traits, and between

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the morphological traits and climatological data(Supplemental Tables S2 and S3).All inflorescence-related traits correlated moder-

ately with FT (r between 0.09 and 0.25, P , 0.001;Supplemental Table S2). The correlations are mostlikely caused by the requirement of long-term ver-nalization to initiate flowering for some accessions.Strong correlations were also found between leaf areaand relative growth rate and among the leaf length(LL) traits. Comparison between Mantel correlationsand uncorrected Spearman correlations revealed thatthe Mantel correlations are somewhat lower andeven opposite for the correlation between relativegrowth rate before vernalization (RGRbv) and leafarea after vernalization (LAav; Mantel r = 0.35,Spearman r = 20.36), indicating that population struc-ture indeed may influence the correlations among mor-phological traits.

Subsequently, the morphological traits were corre-lated to the climatological data. FT, main stem branching(MSB), and plant height at first silique (PH1S) corre-lated significantly with a number of different geo-graphical and climatological factors, such as latitude,number of wet days, temperature, precipitation, andground frost (P , 0.05; Supplemental Table S3).Furthermore, day length and wind speed correlatedsignificantly (P , 0.05) with a large number of mor-phological traits [e.g. FT, PL, PL/LL, RB, and totalplant height (TPH)]. The correlations were generallylow, but comparable to previous reports (Hancocket al., 2011) and, even after a Bonferroni correction formultiple testing, significant (P , 0.05). UncorrectedSpearman correlations were slightly higher than theMantel correlations, especially for the correlationbetween latitude and FT (Mantel r = 0.09, Spearmanr = 0.35), which indicates that population structure

Figure 1. Natural variation in morphology. Box plots of the statistical distribution of morphological trait values divided overgeographical origin of accessions. The blue dot indicates the mean value; the orange stars depict suspected outliers (Tukey test).Abbreviations (number of accessions): BNL, Belgium, and TheNetherlands (21); EUAS, Eastern Europe and Asia (56); FRA, France(61); GESU, Germany, and Suisse (65); GB, Great Britain (48); NA, North America (30); NE, northern Europe (47); SEAF, SouthernEurope, and Africa (19); ALL, all accessions (349, including two accessions from regions outside designated classes).

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GWA Study and Genomic Prediction of Arabidopsis Morphology







Table 1. List of candidate genes from GWA study for different morphological traits

Trait Locus Gene Chromosome Position

Allele

Frequency LOD Effect Documentationa Reference

Indication of

Developmental Trait

LAbv AT1G60980 GA20OX4 1 22,453,259 0.10 3.99 20.0021 5 (Rieu et al., 2008;

Barboza et al.,

2013)

GA20OX1 and

GA20OX2

regulate growth

and development

Labvb AT2G21770 CESA9 2 9,286,277 0.16 4.67 20.0049 4, 5 (Desprez et al.,

2002; Stork

et al., 2010)

Secondary wall

synthesis only

in seed coat

LAav

LAbv At4g10340 LHCB5 4 6,412,715 0.35 4.18 20.0047 4, 5 (Xu et al., 2012) ROS and ABA

signaling

LAbv AT4G16950 RPP5 4 9,546,051 0.21 4.15 0.0048 2 (Parker et al.,

1993; Noel

et al., 1999)

Disease resistance

LAbv AT5G03340 CDC48C 5 807,755 0.31 4.55 0.0050 5 (Park et al., 2008) Mutation of

CDC48A affects

cell divisionand expansion

PL

LAav AT1G22650 A/N-InvD 1 8,015,537 0.33 7.19 20.0104 5 (Qi et al., 2007;

Xiang et al.,

2011)

A/N-InvA and A/

N-InvG are

involved in leaf

growth and

plant

development

RGRav

LAav AT5G65050 MAF2 5 25,984,197 0.17 3.74 20.0022 2 (Caicedo et al.,

2009)

Flowering time

FT AT2G22240 MIPS2 2 9,440,624 0.18 6.79 0.0081 4 (Bruggeman et al.,

2015)

Programmed cell

death

FT At4g28190 ULT1 4 13,996,521 0.25 5.30 20.0051 4 (Fletcher, 2001;

Carles et al.,

2004)

Shoot and floral

meristem size

FT At5g10140 FLC 5 3,188,327 0.21 7.36 20.0070 1 (Koornneef et al.,

1994)

Flowering time

FT At5g45830 DOG1 5 18,603,055 0.42 6.14 20.0063 2 (Bentsink et al.,

2006)

Seed dormancy

PL/LLPL

PL/LL At1g80010 FRS8 1 30,094,960 0.39 4.39 20.0052 4 (Lin and Wang,

2004)

Flowering time

PL

TPH At1g32100 PRR1 1 11,533,638 0.32 4.02 20.0042 4 (Nakatsubo et al.,

2008)

Lignin

biosynthesisPL/LL

PL/LL At2g13810 ALD1 2 5,769,361 0.17 4.21 0.0042 4 (Nie et al., 2011) Senescence

PL

PL/LL At4G08040 ACS11 4 4,912,600 0.49 4.55 0.0041 5 (Tsuchisaka et al.,

2009)

Ethylene signaling,

leaf growth,

flowering

PL/LL At5g26860 LON1 5 9,444,891 0.35 5.38 20.0093 (2), 4 (Rigas et al., 2009;

Solheim et al.,

2012; Verslues

et al., 2014)

Growth, Pro

metabolismPL

PL At1g68870 SOFL2 1 25,882,831 0.43 4.28 0.0050 4 (Zhang et al.,

2009)

Cytokinin, growth

PL At3g29360 UGD2 3 11,227,731 0.48 4.03 0.0052 4, 5 (Reboul et al.,

2011)

Double mutant

with UGD3

shows strongdwarf

phenotype

PL AT4G09800 RPS18C 4 6,170,856 0.44 4.49 0.0067 5 (Vanlijsebettens

et al., 1994)

Mutation of

RSP18A has

strong

developmental

and growth

effects

LL

LL At1g66750 CAK4 1 24,899,279 0.41 4.20 0.0054 5 (Takatsuka et al.,

2009)

Leaf growth

(Table continues on following page.)


Kooke et al.



can have a major influence on these correlations. More-over, the first pair of canonical variates obtained fromcanonical-correlation analysis on the morphologicaltraits and climate variables, was highly correlated

(Spearman r= 0.54,P, 10215;Mantel r= 0.434,P, 1024).Taken together, the results illustrate that adaptation toclimate plays an important role in determining plantmorphology and development.

Table 1. (Continued from previous page.)

Trait Locus Gene Chromosome Position

Allele

Frequency LOD Effect Documentationa Reference

Indication of

Developmental Trait

RB At1g17440 EER4 1 5,994,488 0.12 4.78 0.0050 4 (Kubo et al., 2011) Cytkinin

metabolism,

cell

proliferation

and

differentiation

RB AT1G24150 FH4 1 8,546,658 0.27 5.87 20.0074 6 (Deeks et al.,

2010)

FH4 interacts with

actin and

microtubules

PH1S

RB At1g77760 NIA1 1 29,233,663 0.40 4.16 20.0061 4 (Wilkinson and

Crawford,

1993; North

et al., 2009)

Nitrogen

metabolism

RB AT3G18550 BRANCHED1 3 6,390,880 0.14 4.03 20.0030 3 (Gonzalez-

Grandio et al.,

2013; Niwaet al., 2013)

Branching

RB AT5G35080 OS9 5 13,355,009 0.10 7.14 20.0070 4 (Huttner et al.,

2012)

Growth, mutant

rescues dwarf

phenotype of

bri1

MSB AT1G65480 RSB8 1 24,338,821 0.29 3.59 0.0033 1 (Huang et al.,

2013)

Main stem

branching

MSB At3g63440 CKX6 3 23,419,198 0.25 4.83 20.0052 5 (Werner et al.,

2003)

Over-expression

of CKX3 and

CKX6 increased

branch number

MSB AT4G00430 PIP1;4 4 185,717 0.23 4.07 20.0048 4, 5 (Lee et al., 2012) Temperature-

dependent

growth

LAav

RGRav

PH1S At3g23590 RFR1 3 8,475,104 0.07 5.75 0.0049 3, 5 (Bonawitz et al.,

2014)

Tripe mutant of

rfr1/ref4/ref8

rescues dwarf

phenotype ofref8, lignin

biosynthesis

TPH At3g17360 POK1 3 5,932,358 0.37 5.02 0.0060 3, 5 (Muller et al.,

2006)

Double mutant of

pok1/pok2 is

dwarfed

TPH At4g24620 PGI1 4 12,695,303 0.10 4.78 20.0053 4 (Yu et al., 2000;

Kunz et al.,

2014)

Starch synthesis,

leaf size

flowering time

TPH At5g44790 RAN1 5 18,074,944 0.10 4.90 20.0054 4 (Woeste and

Kieber, 2000)

Ethylene signaling,

development

RGRbv AT2G22125 CSI1 2 9,399,110 0.20 6.59 0.0075 6 (Li et al., 2012) CSI1 interacts with

microtubules

and cellulose

RGRav AT2G18790 PHYB 2 8,139,482 0.39 4.05 20.0051 2 (Filiault et al.,

2008; Filiault

and Maloof,

2012)

Light response,

shade

avoidance

Listed are the trait for which a significant association was detected, the gene identifier of the candidate gene, the position on the chromosome, theallele frequency, significance and effect size of the most associated SNP, and an indication of the developmental trait the gene is involvedin. aDocumentation indicates whether published research identified natural variation or validation of the phenotype: 1, natural variationfor trait of study; 2, natural variation for other (related) developmental traits; 3, mutant phenotype for trait of study; 4, mutant phenotype for otherdevelopmental trait; 5, mutant phenotype for other gene in same gene family; 6, other. bIf multiple traits had the same QTL, the data are givenfor the trait in bold.





Figure 2. GWA analyses of morphological traits. Blue squares indicate SNPs that were detected using the GWA study(2log10(P)-value. 4) and GP (allelic effect. 0.0040) criteria. Red dots indicate SNPs that were detected using the GWA studiesand GP criteria and are described in the candidate gene table (Table 1). Orange triangles indicate SNPs that were linked to goodcandidate genes, but did not surpass the GWA studies and GP criteria.


Kooke et al.



GWA Study Fails to Account for High Heritability

To assess to what extent the morphological variationis defined by the underlying genetic variation, marker-based heritability (h2) was estimated as described be-fore (Kruijer et al., 2015). The value h2 ranged from 0.42for RGRbv to 0.93 for FT, with the majority of traitshaving a heritability higher than 0.70, indicating thatmost of the measured variation could be attributed togenetic factors (Supplemental Table S1). To determinewhether significant associations can be detected be-tween a set of 214,051 genome-wide single nucleotidepolymorphisms (SNPs) and the variation in morpho-logical traits, a linear mixed model that corrects forpopulation structure (EMMAX) was adopted (Kanget al., 2010). With a Bonferroni-corrected threshold of2log10(0.05/214051) = 6.6, only four significant SNPscould be detected: two for FT, one for relative growthrate after vernalization (RGRav), and one for RB (Fig.2; Table I). One of the SNPs for FT was detected in closevicinity of thewell-knownFT gene FLOWERINGLOCUSC (FLC), while the other SNP above the Bonferronithreshold pointed to MIPS2 (AT2G22240), a gene in-volved in inositol biosynthesis and programmed celldeath (Bruggeman et al., 2015). The SNP for RB wasdetected in the vicinity of OS9, a gene involved in theendoplasmic reticulum-associated degradation of gly-coproteins and brassinosteroid signaling (Werner et al.,2003; Hüttner et al., 2012). The SNP for RGRav showed avery strong associationwith a neutral invertaseA/N-INVDand might be involved in regulating growth processesthrough its involvement in carbohydrate metabolism(Xiang et al., 2011).Although only four SNPs were detected above the

stringent Bonferroni threshold, several loci in varioustraits were represented by SNPs with 2log10(p)-valuesthat exceeded the bulk of SNPs tested, suggesting moretrue positive associations to be present (Fig. 2). Thediscrepancy between high heritability and a very lownumber of significantly associated SNPs points tomissing heritability, where little of the phenotypicvariation is explained by significantly associated SNPsin GWA studies. Although there may be many reasonsfor missing heritability, such as epigenetic and epistaticeffects, allelic or genetic heterogeneity (Johanson et al.,2000; Atwell et al., 2010; Barboza et al., 2013), and rarealleles (Gibson, 2011), the quantitative nature and highheritabilities of the traits tested here suggests that a highnumber of loci with too-small effect sizes to exceed thestringent significance threshold in GWA studies con-tribute to the phenotypic variation.

Genomic Prediction Suggests Hidden Heritability

To further investigate the genomic architecture oftraits of genomic prediction (GP), models that presenttraits as a function of all markers simultaneously wereused here. To this end, a heteroscedastic effects model(Shen et al., 2013) was applied to all traits. Predictionaccuracy (r2) in 2-fold cross validation increased with

increasing numbers of randomly chosen SNPs until amaximum was reached between 500 and 25,000 SNPsfor all traits (Supplemental Fig. S1, A and B). Themaximum r2 ranged between 0.02 for PH1S and 0.35 forFT. After the maximum was reached, r2 slightly de-creased and then stabilized for all traits (SupplementalFig. S1A). Prediction performance was much better forFT than for the other traits with reasonable accuracy atonly 2000 randomly chosen SNPs. This may indicatethat FT is determined by numerous genes with largereffects. When using the most significant SNPs fromGWA studies for the prediction, r2 first increased to amaximum value between 5,000 and 25,000 SNPs andthen decreased with increasing numbers of SNPs for alltraits (Supplemental Fig. S1, C and D). The maximumprediction accuracy ranged from 0.86 for TPH to 0.97for MSB, thus predicting the trait values with very highaccuracy. Traits could already be well predicted usingthe 50 most significant SNPs from GWA studies for alltraits, but prediction was substantially improved withincreasing numbers of SNPs (Supplemental Fig. S1D).These results suggest a genetic architecture whereseveral hundreds or thousands of loci contribute to thegenetic variance, but because the effect sizes of indi-vidual SNPs are small, they cannot be detected usingconventional GWA thresholds.

As expected, the estimated allelic substitution effectswere strongly correlated with 2log10(P) values andestimated effect sizes obtained from GWA studies(Supplemental Figs. S2 and S3), indicating that the moststrongly associated SNPs in GWA studies are the bestgenomic predictors. The overrepresentation of negativeeffects for FT suggests that non Columbia-0 (Col-0) al-leles represented by those SNPs delay FT, which isconsistent with the early flowering phenotype of Col-0.

To compare the genetic complexity between traits,the patterns of the allelic effect distributions weresummarized by the first five moments, on whichUPGMA clusteringwas performed (Supplemental Figs.S4 and S5). For more complex traits (i.e., infinitesimalmodel), the effects are expected to follow a uniformdistribution in contrast to traits that are determined by asmall number of large effects. Interestingly, FT clus-tered separately from all other traits. In GWA studies,FT was associated with the highest number of SNPsabove the Bonferroni threshold and above the arbi-trarily set threshold of 2log10(P) = 4 (Fig. 2), and couldbe well predicted with a relatively low number of ran-dom SNPs (Supplemental Fig. S1), suggesting that FT isindeed less complex-regulated than the other morpho-logical traits. Moreover, TPH and RGRbv, whichbranch off after FT, also displayed a higher number ofassociations above the 2log10(P) = 4 threshold thanother traits (TPH = 35; RGRbv = 36). All other mor-phological traits, which cluster closely together, wereassociated with 14 to 30 SNPs above the 2log10(P) = 4threshold. The clustering based on the distribution ofthe allelic effect sizes thus suggests that FT, and to alesser extent TPH and RGRbv, are less complex-regulated than the other morphological traits.
















Identification of Candidate Genes by Integrating GP andGWA Studies

To minimize the number of type I (false-positive)errors for the selection of candidate genes, it was testedwhat candidate gene selection approach (GWA studies,GP, or a combination of the two approaches) wouldgive the highest enrichment among a custom-madecandidate gene list. Because genes involved in FT,growth, and development can affect many develop-mental processes simultaneously and because indi-vidual candidate gene lists per traits are difficult toassemble for the tested traits, all candidate genes weretested against the entire candidate gene list. Althoughnone of the selection approaches demonstrated signif-icant enrichment (P , 0.05), the strongest suggestiveenrichment was found for the combination of GWAstudies and GP (5.0% vs. 3.4%; Supplemental Table S4).All SNPs exceeding the GWA study 2log10(P) = 4threshold and having absolute allelic effect sizes above0.004 in the GP model were thus regarded as candidateSNPs (Fig. 2). For these SNPs, the linkage disequilib-rium (LD) support interval (r2 . 0.3) was determinedand each gene within the support intervals was ana-lyzed for sequence diversity, function, and expressionprofile using publicly available data. This approachenabled us to identify 30 candidate genes. Although thefollowed procedure clearly minimizes the number oftype I (false-positive) errors, it could simultaneouslylead to the rejection of type II (false-negative) errors(Bergelson and Roux, 2010). Therefore, four obviouscandidate genes [RSB8 for MSB, BRANCHED1 for RB;MADS AFFECTING FLOWERING 2 (MAF2) for LAav;and GA20OX4 for leaf area before vernalization (LAbv)]that did not pass the thresholds were added to the can-didate gene list (Table I).

Two of the thirty-four candidate genes have pre-viously been shown to exhibit allelic variation for thestudied trait (Fig. 2; Table I). For FT, FLC was detectedin previous GWA studies and is known to be involvedin regulation of this trait and to display allelic varia-tion explaining the phenotypic variation (Koornneefet al., 1994; Atwell et al., 2010; Brachi et al., 2010).REDUCEDSTEMBRANCHING 8/FLOWERINGLOCUST, a gene involved in FT and reduced stem branchingin the axils of cauline leaves (Koornneef et al., 1998;Huang et al., 2013), was associated with MSB inour study. Sequence variation in the promoter ofREDUCEDSTEMBRANCHING 8/FLOWERINGLOCUST is responsible for variation in FT and stem branchingbetween different accessions of the Arabidopsismultiparent recombinant inbred line population (Huanget al., 2013).

Five other candidate genes have previously beendemonstrated to exhibit allelic variation for develop-mental traits, although these traits were different fromthe trait for which the QTL in our study was detected(Fig. 2; Table I). Nonetheless, the correlations observedbetween developmental traits in our study might wellexplain the detection of these genes. A QTL for FT,

PL, and the PL/LL was identified at the DELAY OFGERMINATION 1 (DOG1) locus, a locus essential fornatural variation in seed dormancy (Bentsink et al.,2006). Interestingly, previous GWA studies on FT alsopointed to the DOG1 locus as a potential candidategene (Atwell et al., 2010; Brachi et al., 2010). For RGRav,a QTL was found at the PHYTOCHROME B (PHYB)locus, which was previously associated with naturalvariation in the response to red light (Filiault et al., 2008;Filiault andMaloof, 2012). Furthermore, MAF2, a geneinvolved in determining FT and the number of rosetteleaves at bolting under both short and long days(Caicedo et al., 2009; Rosloski et al., 2010), was iden-tified for LAav. For LAbv, a QTLwas discovered at theRECOGNITION OF PERONOSPORA PARASITICA 5(RPP5) locus, a gene involved in disease resistanceagainst Peronospora parasitica that causes downy mil-dew on crucifers (Noël et al., 1999). Col-0, but notLandsberg erecta (Ler-1), is sensitive to infection withP. parasitica and the RPP5 locus was identified as thecausal locus by aQTL analysis of a recombinant inbredline population derived from these accessions (Noëlet al., 1999). Finally, LON PROTEASE 1 (LON1) wasdetected as a candidate gene for PL and the PL/LL.LON1 is an ATP-dependent protease and chaperoneinvolved in organic acid metabolism and mutation ofthis gene inhibits growth (Rigas et al., 2009; Solheimet al., 2012). Interestingly, a recent GWA study on Prometabolism also discovered a QTL pointing to LON1as a potential candidate gene for Pro metabolism(Verslues et al., 2014).

One candidate gene, BRANCHED1, for RB waspreviously discovered to display knock-out pheno-types for the trait but natural allelic variation withinthis gene has not been reported before (Fig. 2; Table I;Aguilar-Martínez et al., 2007; Farid et al., 2011;González-Grandio et al., 2013; Niwa et al., 2013). Useof the recently released resequencing data from theArabidopsis 1001 genomes project enabled the com-parison of the gene sequences of 530 accessions (TheArabidopsis Genome Browser; http://signal.salk.edu/atg1001/3.0/gebrowser). For a subset of theseaccessions (150), phenotypic information wasobtained from our study, allowing a detailed linkageanalysis. Strong LD (r2 = 0.8) was observed betweenthe most significant SNPs from the GWA analysesand a high number of polymorphisms, deletions, andinsertions in the first 1.5 kB of the BRANCHED1promoter. Specifically, nine polymorphisms in thepromoter of BRANCHED1 were strongly sharedamong the 25 accessions that contained the non-Col-0haplotype at the position of the SNP from GWAstudies. A linkage analysis based upon the set ofresequenced accessions revealed a very strong associationbetween the BRANCHED1 promoter elements and RB(P , 0.001). To determine whether natural selection actson the locus, we compared the nucleotide diversity (p)among silent, synonymous, and nonsynonymous sites.The nucleotide diversity was rather low (pT = 0.0027) andthe pnon/psyn ratio (pnon/psyn = 0.225) was considerably


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http://signal.salk.edu/atg1001/3.0/gebrowser



lower than the neutral 1:1 ratio, suggesting that thislocus is indeed under purifying selection. This is fur-ther illustrated by a significant negative value forTajima’s D (Dn =22.187, P, 0.01) at nonsynonymoussites, which is considerably lower than the Tajima’s Dgenome-wide distribution (Cao et al., 2011). Valuesbelow 22 are generally considered a significant resultof the effect of rare alleles present at low frequencies,a recent selective sweep or population expansion aftera recent bottleneck. Accessions of the non-Col-0BRANCHED1 haplotype developed on average 1.74more rosette branches than accessions of the Col-0haplotype.In conclusion, strong candidate genes were detected

that were described as being involved in developmen-tal and growth-related processes in previous studies.Although the true involvement of the candidate genescan only be validated through extensive functional andgenetic analyses, the above does give strong additionalsupport for the adopted approach. Moreover, it dem-onstrates that the Bonferroni threshold is in most casestoo stringent for quantitative gene identification.

Validation of ACS11 Regulating Leaf Morphology

To further support our candidate gene discovery ap-proach, a QTL for the ratio between PL and LL (PL/LL)on the top of chromosome 4, coincidingwith an ethylenebiosynthesis gene, 1-AMINOCYCLOPROPANE-1-CARBOXYLATE SYNTHASE 11 (ACS11; Fig. 2), wasanalyzed inmoredetail.ACS enzymes, forminghomo-andheterodimers, are thought to catalyze the rate-limitingstep in ethylene biosynthesis converting S-adenosyl-L-Met into 1-aminocyclopropane-1-carboxylate, theprecursor of ethylene (Bleecker and Kende, 2000).Analyses of single, double, and multiple acs mutantsrevealed that ACS enzymes play essential roles inleaf development, FT, disease resistance, and ethyl-ene production (Tsuchisaka et al., 2009).ACS11 and its most significantly associated SNPs are

in strong LD with a large region of 140 kB in whichmainly transposable elements, pseudogenes, and anumber of small genes are located (Fig. 3A). ExtensiveLD is indicative of a selective sweep suggesting strongadaptive selection of allelic variation. Indeed, withintheACS11 coding region, five nsSNPs can be identified,of which two are in LD (r2 . 0.3) with the most signif-icant GWA SNPs (Fig. 3B). The two most informativeSNPs related to the PL/LL phenotype are located inthe fourth exon, one substituting Asp (negativelycharged) for Gly (nonpolar) and a second SNPsubstituting Gly for Ser (polar). The nucleotide hap-lotype GG, representing these two polymorphismsdisplays the highest PL/LL ratio and differs signifi-cantly from all other haplotypes (Fig. 3B). All acces-sions in the GWA population belonging to thishaplotype originate from Sweden and display a sig-nificant difference in the PL/LL compared to all otheraccessions (P , 0.05; Fig. 3, C and D). In addition,

further analysis of resequenced accessions not in-cluded in the GWA population revealed a similargeographic distribution of the GG haplotype, with theexception of one accession originating from Finland(Fig. 3C). However, Swedish accessions belonging toother haplotypes have, on average, significantly lon-ger petioles relative to their leaves when compared toaccessions from other countries, suggesting conver-gent evolution. Although accessions belonging to theGG haplotype have longer petioles than the otherSwedish accessions (0.34 vs. 0.32), the difference is notsignificant (P , 0.05; Fig. 3D).

These findings indicate a selective sweep in Scandi-navian accessions, suggesting an evolutionary advan-tage of altered PL/LLs at higher latitudes (Fig. 1). Thisis supported by the observation that LD amongSwedish accessions aroundACS11 extends further thanamong all other accessions (Supplemental Fig. S6). Ifselection is limited to geographical regions, this canresult in strong population structure. Correcting forpopulation structure, as done in the applied GWAmodel, can considerably reduce the GWA likelihoodsand lead to false-negative associations. Omitting pop-ulation structure correction in the GWA model indeedincreased the significance of theACS11 locus, exceedingthe Bonferroni threshold (Fig. 3E).

To determine the expression profile of ACS11, anACS11::GUS reporter line (Tsuchisaka and Theologis,2004) was analyzed, revealing that ACS11 is expressedin the mid vein of the petiole, and not in the leaf blade(Fig. 3F). Moreover, application of ethephon, a com-pound that is rapidly degraded into ethylene, to the twomost informative haplotypes suggests ethylene to bethe signal explaining the variation in PL/LL (Fig. 3G).Under controlled conditions, a significant difference(P , 0.001) in PL/LL between the two haplotypes wasdetected, which disappeared 3 d after ethephon appli-cation (Fig. 3G). Subsequently, a subset of accessions,representing different haplotypes, was analyzed fordifferences in gene expression (Fig. 3H). Althoughvariation in gene expression was detected, no signifi-cant difference could be observed between the twomost distinct haplotypes (Fig. 3H). The lack of correla-tion between haplotypes and gene expression differ-ences in addition to the variation in nsSNPs suggeststhat functional variation is a more likely explanation forthe phenotypic differences than expression variation. AT-DNA knockout of ACS11 was analyzed and a minorreduction in PL/LL was detected (Fig. 3I). The lowsignificance might be due to redundancy and/or theCol-0 genetic background. Indeed, the PL and the PL/LLin an acs octuple mutant and in an ethylene insensitivemutant, ein2-1 (Alonso et al., 1999; Tsuchisaka et al.,2009), were significantly reduced, although concomi-tant with a reduction in LL (Fig. 3I). Altogether, thesefindings suggest that ACS11 is the causal gene under-lying a strong association with PL/LLs and that selec-tive pressure favored the proliferation of accessionswith high PL/LLs at northern latitudes, most likelythrough functional diversification.






DISCUSSION

Phenotypic and Geographic Variation

From this and other studies, it is evident that exten-sive variation for morphological traits is present innatural populations of Arabidopsis. Interestingly, theextent of global natural variation is not much largerthan the variation present within geographical regions,especially considering that the number of accessionsper geographical class is about one-tenth of the globalset of accessions (Fig. 1). Similar results were obtainedfor flowering-related traits in French local populationscompared with a global population (Brachi et al., 2013).This argues for GWA studies on regional populations inwhich the confounding effects of population structureand allelic heterogeneity are substantially reduced.Furthermore, the global population used in this studymight not be optimal for gene identification in GWAstudies. The high number of alleles that segregate in thispopulationwith sometimes low allele frequencies causea low QTL detection power, which can result in rela-tively low 2log10(P)-values. The larger allelic diversityin a global population, however, also has its advan-tages. It allows, for instance, the comparison with cli-matic gradients. Moreover, fitness is enhanced throughenvironmental adaptation at loci that are polymorphicin the same environment (Fournier-Level et al., 2011).These loci do not necessarily affect fitness in other en-vironments, suggesting a local genetic basis for adap-tation. Such alleles can only be detected by comparingmultiple local populations or considering a global scaleof adaptation.

When comparing the phenotypic variation betweenthe geographical classes, the Northern American ac-cessions show the least variation for most of the phe-notypes. As the species was introduced only about 300years ago in northern America, it has had a muchshorter time to migrate and evolve than its Eurasiancounterparts (Platt et al., 2010). Although Arabidopsisis common across the entire northern American conti-nent, it shows much less haplotype diversity andweaker isolation by distance compared to accessionsfrom Eurasia (Platt et al., 2010). The northern Europeanaccessions showed the greatest deviation from all otherclasses, which is probably due to the requirement ofvernalization and long days to initiate flowering. Mostdevelopmental phenotypes are thought to be very de-pendent on this transition and FLC, a major floweringlocus, impacts plant development by regulating a greatnumber of developmental genes that are importantthroughout the plant’s life cycle (Deng et al., 2011).Moreover, it was recently found that the floral inte-grators FLC and FRIGIDA regulate stem branching inan epistatic manner (Huang et al., 2013). These obser-vations explain to a large extent why most of the phe-notypes in this study correlate well with FT.

Most of the phenotypes also showed significant cor-relations with climate variables suggesting that localadaptation is (partly) driven by climate. There aremany

positive correlations among latitude, day length(spring), and many of the morphological traits. Thisrelationship is well explained by the delayed FT atnorthern latitudes due to the vernalization requirementand longer day lengths during spring (Hancock et al.,2011). Moreover, the impact of cold temperatures andprecipitation on the genetics of important floweringgenes has been reported before (Méndez-Vigo et al.,2013). However, the correlation between latitude andday length (spring) with PL and the PL/LL is oppositeto earlier findings on amuch smaller panel of accessions(Hopkins et al., 2008). In light of the geographical dis-tribution of ACS11 haplotypes described here, the cor-relations are especially interesting (Fig. 3). Our findingssuggest that a longer PL/LL is adaptive at higher lati-tudes, perhaps to increase light capture. Otherwise,plants growing at higher latitudes might have adaptedto cooler climates and therefore show a lower activationpoint for heat-induced petiole elongation (Koini et al.,2009; van Zanten et al., 2009). It is worthwhile to notehere that mutations in the ACS gene family also affectFLC expression and FT (Tsuchisaka et al., 2009). Somemutations tend to induce flowering while others, con-comitant with a decrease in ethylene production, seemto delay flowering. Besides the effect of ACS genes onFT, the number of branches is significantly reduced andplant height is significantly increased in acs mutants(Tsuchisaka et al., 2009).

Missing Heritability Is Hidden by Small-Effect QTL

All phenotypes tested in this study were highlyheritable, similar to or even slightly higher than theheritabilities measured in many biparental mappingpopulations for the same traits (Ungerer et al., 2002;Bandaranayake et al., 2004; Keurentjes et al., 2007).However, only a very limited number of candidategenes could be assigned in LD with SNPs detectedabove the stringent Bonferroni threshold. AlthoughGWA studies in plants have identified a number of, toour knowledge, novel candidate genes, the detectedindividual loci cannot fully explain the high herita-bility estimates (Brachi et al., 2010; Sasaki et al., 2015).As all phenotypes tested here are likely to be highlypolygenic, it is expected that most of the effects of theunderlying genes are too small to be captured in GWAstudies. As is the case in human GWA studies, weanticipate that the heritability is hidden rather thanmissing (Gibson, 2010). In human genetics, variationfor human height can be explained by the additiveeffects of individual SNPs, indicating that commongenetic variation is able to explain a large part of thevariation for height (Yang et al., 2010; Wood et al.,2014). As the genetic architecture of plants is extremelycomplex (Weigel, 2012; Korte and Farlow, 2013;Alonso-Blanco and Méndez-Vigo, 2014), similar sce-narios might be expected for plant phenotypes. In-deed, the estimates of marker-based heritability andgenomic prediction accuracies suggest that the traits


Kooke et al.



analyzed in our study are determined by small addi-tive effects at a large number of loci. To identify can-didate genes, we combined the results of GWA studiesand GP, which enabled the assignment of many strongcandidate genes to the QTL. Although gene detectionwas not the original purpose of genomic predictionmodels, recent methodological developments in hu-man genetics indicate that integrating discovery andprediction has certain advantages (Moser et al., 2015).For FT, two previously confirmed genes were

identified, FLC and DOG1, illustrating the validity ofthe approach (Koornneef et al., 1994; Bentsink et al.,2006; Atwell et al., 2010; Brachi et al., 2010). Five moregenes for different morphological traits were detectedfor which allelic variation for developmental traits hasbeen described. Interestingly, the majority of thesegenes (RSB8, PHYB, andMAF2) have all been reportedto be involved in the regulation of FT (Guo et al., 1998;Caicedo et al., 2009; Huang et al., 2013), which sug-gests again that FT is a major determinant of othermorphological traits.An interesting QTL was found on chromosome 3 for

variation in the number of branches from the rosette, forwhich an association was found with the BRANCHED1locus. BRANCHED1 is a signal integrator, controllingbud outgrowth and arrest, dependent on differenthormonal pathways and important FT genes, such asFT and PHYB (Aguilar-Martínez et al., 2007; González-Grandio et al., 2013; Niwa et al., 2013). Good indica-tions of purifying selection were found at this locusand many SNPs in the promoter were in high LDwith the most significant GWA SNP. Homologs ofBRANCHED1, known as TB1, are also present in rice,maize, and sorghum and seem to be conserved amongthe angiosperms (Aguilar-Martínez et al., 2007). TheTB1 locus is an extremely important locus involved inbranching architecture by determining the fate of ax-illary meristems. It is one of the loci that discriminatesthe domesticated Zea mays from the wild teosinte, fromwhich maize is derived. Similarly to the results of ourstudy, cis-regulatory variation upstream of the TB1gene is involved in the regulation of TB1 expression(Clark et al., 2006). Follow-up studies should revealwhether the allelic variation at the locus in Arabidopsisis indeed responsible for the natural variation in phe-notypes, and whether it is maintained in local popu-lations across the world.

In-Depth Analysis of a Candidate Gene: ACS11

An ethylene biosynthesis gene from the large ACSgene family, ACS11, was detected to underlie the QTLfor PL/LL. ACS11 expression was high in petioles andethylene nullified the differences in the PL/LL betweentwo distinct haplotypes. From previous studies, it isknown that ethylene can significantly influence PL andangle under different conditions and that ACS enzymesaffect ethylene production (Tsuchisaka et al., 2009; vanZanten et al., 2009; Bours et al., 2013). AllACS genes are

expressed in petioles, but only ACS2, 8, and 11 aredifferentially expressed between petiole and leaf blade(Bours et al., 2013). A T-DNA knockout of ACS11resulted in a small reduction of PL/LL. Possibly, othergenes in the family take over the function of ACS11,abolishing the effect of the T-DNAknockout.Moreover,ACS11 expression is rather low in Col-0 comparedto other accessions, making the study of a T-DNAknockout in the Col-0 background difficult. Naturalvariation in ACS11 functioning might, therefore, havea much stronger effect on the phenotype of distinctaccessions. The PL and the PL/LL in the acs octuplemutant and in the ein2-1 mutant were significantly re-duced, although concomitant with a reduction in LL.Given that most ACS genes are expressed in the leafblade and the petiole, a knockout of multipleACS genesis expected to reduce the length of both petiole and leaf.Similar results were expected and confirmed for theethylene-insensitive mutant, ein2-1.

At the haplotype level, an 8% difference in PL/LLwas observed between two common haplotypes. Ac-cessions belonging to the GG haplotype all originatefrom Sweden and PL/LL correlated positively withlatitude and day length (spring). Longer petioles mightenhance light capture at higher latitudes, and thusprovide plants with an adaptive advantage. Althoughthere is clear variation in ACS11 transcript abundance,it is likely that other mechanisms play a significantrole in the determination of the phenotype. Sequencevariation within the exons gives rise to different aminoacids, possibly changing protein structure and/ orfunction. Given that the ACS enzymes form homo-and heterodimers, the binding between the proteinsmight be affected, leading to changes in ethyleneproduction in petioles, and hence in petiole growth(Tsuchisaka et al., 2009).

This study also presents a clear example of theconsequences of correcting for population structure.Correcting for population structure does reduce thenumber of false positives in GWA studies, but it couldalso lead to over-correction, increasing the number offalse-negative associations (Bergelson and Roux,2010). Since the contrasting haplotype ofACS11 is onlyfound at northern latitudes, correction for populationstructure reduced the association significance of theQTL at the ACS11 locus. Omitting the correction forpopulation structure increased the QTL likelihoodbeyond the Bonferroni threshold. This advocates for acareful application of statistical methods in biologicalstudies, as important findings might be overlooked ifthese are restricted to confined geographical regions.

CONCLUSION

Here we have shown that the high level of variationin morphological traits, observed among a large set ofnatural accessions of Arabidopsis, can be largelyexplained by genetic factors, as represented by rela-tively high heritability estimates. However, significant





associations with SNPs detected by GWA studies failedto explain the majority of this heritable variation. Theapplication of genomic prediction models aided in

establishing the complexity of the genetic architectureof quantitative traits. Moreover, an integrated analysisof GWA studies and GP enabled the assignment of a

Figure 3. Validation of ACS11 as a candidate gene explaining variation in PL/LL. A, LD of surrounding SNPs with the mostsignificant SNPs (red dot) associated to PL/LL, close to ACS11 (AT4G08040). The LD extends up to 140 kB. The arrows indicategenes; the blue bars indicate the exons of ACS11. The numbers under the ACS11 gene indicate the polymorphisms in (B). B,Haplotypes observed in 148 of the 530 resequenced accessions; PL/LL indicates the average PL/LL per haplotype. SNP 7 and 13encircled with a black box are the most informative SNPs. SNPs in bold indicate nonsynonymous polymorphisms, nsSNPs. Thenumber of lines indicates the total number of accessions belonging to each haplotype. C, The site of origin of Arabidopsis ac-cessions with the GG haplotype (see text; green dots) in Sweden (gray), other Swedish accessions not having the GG haplotype(yellowdots), and one accessionwith theGGhaplotype originating from Finland (purple dot). D, PL/LL for all accessions, Swedishaccession (non-GG haplotype, SWEOther), and Swedish accessions with GG haplotype (SWEGG). E, Manhattan plot of a linearGWA study model (LM) for PL/LL. Gray dashed bar indicates2log10(P)-value = 4 threshold; red dashed bar indicates Bonferronithreshold. Each dot represents a SNP and the five different chromosomes are depicted in different gray shades. F, ACS11::GUSexpression in 3-week-old leaves of accession Col-0. G, Ethylene complementation experiment between two haplotypes. Treatedplants were sprayed with 0.5 mM ethephon. Results are shown 3 d after ethephon treatment. Haplotype 0 denotes the average of10 accessionswith 10 replicates of the G haplotype at the fourth nsSNP. Haplotype 1 denotes the average of 10 accessionwith 10replicates of the A haplotype at the fourth nsSNP. H, Relative ACS11 expression in 17 accessions. Light gray bars indicatehaplotype 0; dark gray bars indicate haplotype 1 (n = 3). I, Petiole length (PL, light gray), leaf blade length (BL, dark gray), and totalleaf length (LL = PL + BL) in Col-0WTandmutants. The PL/LL is denoted by the number in the bars, significance is indicated withasterisks (*, P, 0.1; **, P, 0.01; ***, P, 0.001) (n = 30). The upper asterisks indicate the significance of LL; the middle asterisksindicate the significance of PL; and the lowest asterisks indicate the significance in PL/LL.


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larger number of candidate genes, many of which couldbe confirmed by further functional analyses.

MATERIALS AND METHODS

Plant Growth Conditions

Seeds from 349 natural accessions of Arabidopsis, collected worldwide andgenotyped with approximately 215 K single nucleotide polymorphisms (SNPs;Li et al., 2010; Horton et al., 2012), were sown on filter paper with demineralizedwater and stratified at 4°C in dark conditions for 5 d. Subsequently, seeds weretransferred to a culture room (16 h linkage disequilibrium (LD), 24°C) to induceseed germination for 42 h. Three replicates per accession were transplanted inconsecutive order to wet Rockwool blocks of 43 4 cm in a climate chamber (16 hLD, 125mmolm22 s21, 70%RH, 20/18°Cday/night cycle). Two control accessions(Col-0 and Ler-1, each 10 replicates) were transplanted in the middle of a floodingtable. All plants were watered daily for 5 min with 1:1000 Hyponex solution(Hyponex, Osaka, Japan). Nineteen days after germination (DAG), all plantsweremoved to a cold room (12 h light, 4°C) for 6 weeks for vernalization. After thevernalization period plants were transferred back to the same climate chamber inthe same order, but divided over two tables to increase the growth space.

For the confirmation of the 1-AMINOCYCLOPROPANE-1-CARBOXYLATESYNTHASE 11 (ACS11) locus, 21 natural accessions (10 replicates each) weregrown, 10 accessions from each haplotype plus Col-0. Ten replicates of ACS11::GUS, GABI_284B12, acs octuple, and ein2-1 were grown in the same conditionsas listed above. The experiment to test for the effect of the mutations on thepetiole-to-leaf-length ratios was repeated three times, giving a total replicatenumber of 30 plants.

Phenotyping of Morphological Traits

A variety of developmental traits was measured on all individual plants.Rosette images for leaf area (LA) were taken 15, 19 (leaf area before vernali-zation), 63 (leaf area after vernalization), and 68 DAG. Relative growth rate(RGR) was calculated using the following equation: (lnLAx-lnLAy)/dt(x-y). Rel-ative growth rate before vernalization and relative growth rate after vernali-zation were calculated between 15 and 19 DAG and 63 and 68 DAG,respectively. Flowering time was recorded as the time when the first floweropened and images were taken of individual plants two weeks after flowering.Plant height at first silique was measured two weeks after flowering and totalplant height at the end of the growth period. Branching was measured as thenumber of branches that were present on the main inflorescence two weeksafter flowering and as the number of rosette branches at the end of the growthperiod. Leaf length (LL) and petiole length (PL) weremeasured on images takenfrom the longest leaf two weeks after flowering. For the confirmation of theACS11 locus, all plants were phenotyped daily. One leaf (the second leaf of thesecond whorl) of each plant was marked 3 d prior to the start of the experimentwithout damage to the plant. Both PL and LL were measured from 19 DAGwith a caliper for one week. For the ethylene complementation experiment3-week-old rosettes were sprayed with 0.5 mM ethephon and 0.005% Tween20. Control plants were sprayed with mock solutions that lacked the activecomponent. LL and PL were measured daily for three consecutive days.

Climate Data

Climate data for each accession were obtained from the Climate ResearchUnit at the University of East Anglia. Data were extracted for nine climatevariables giving the average per month over a 30-year (1961–1990) period (Newet al., 2002). From these nine variables, most other variables were extracted. Daylength (spring) and relative humidity (spring) from the origin of 306 accessionswere obtained from the NCEP-NCAR climate reanalysis project (Kistler et al.,2001; Hancock et al., 2011) and the FAO GeoNetwork (http://www.fao.org/geonetwork/srv/en/main.home). Canonical correlation analysis was performedwith the R-function Cancor using the 12 morphological trait variables and 14climate variables.

Correlation Analyses

ApartialMantel testwas used to calculate the Spearman correlation betweenthe morphological traits, and between the morphological traits and climate

variables, correcting for population structure byusing a kinshipmatrix based onthe genome-wide SNPs as a covariate in the model (Mantel, 1967). PartialMantel tests were conducted using the Ecodist package in R (Goslee and Urban,2007). Significance was assessed by running 1000 permutations on the depen-dent variable (Smouse et al., 1986).

Descriptive Statistics

The variance components for all the individual traits were used to calculatethe broad-sense heritability, H2, in ANOVA according to the formula

H2 ¼ s 2G

��s 2

G þ s 2E

�; with s 2

G ¼ �MSðGÞ2MSðEÞ�=r; s 2

E ¼ MSðEÞ; ð1Þwhere r is the number of replicates; andMS(G) andMS(E ) are the mean sumsof squares for genotype and residual error, respectively. Marker-based heri-tability, h2, was defined as

h2 ¼ s 2A

��s 2

G þ s 2E

�; ð2Þ

which takes only the additive genetic effects (s 2A) in account. Marker-based

estimates of heritability were obtained from the mixed model,

Yi;j ¼ mþ Gi þ Ei;j; ði ¼ 1; :::; n; j ¼ 1; ::::; rÞG;N�0;s 2

AK�; E i; j;N

�0;s 2

E

�;

ð3Þwhere yi,j is the phenotypic response of replicate j of genotype i; m is the in-tercept; G = (G1,.,Gn) is the vector of random genetic effects; and the errors Ei,jhave independent normal distributions with variance s 2

E, which is the residualvariance for a single individual (Kruijer et al., 2015). The vector G has a mul-tivariate N(0, s 2

AK) distribution, and the genetic relatedness matrix K is esti-mated from standardized SNP-scores.

Coefficient of variation (CVG) was calculated as (sG/X)*100%.

Genome-Wide Association Mapping

Genome-wide association (GWA) mapping on the morphological traits wasperformed on between 335 and 339 accessions, because ofmissing data for someaccessions. All accessions were genotyped with 214,051 SNPs, of which 199,589were used for GWA mapping after removal of SNPs with a minor allele fre-quency , 0.05.

GWA study was performed using an adjusted EMMAX script (Kang et al.,2010; Kruijer et al., 2015).

Genomic Prediction

Traitswere scaled and the completemarker setwas used for the constructionofgenomic prediction (GP)modelsusing the bigRR library (Shenet al., 2013). GPwasperformed for different random and sorted sets of markers based on thehighest2log10(P) value. The similarity in the distribution of effect sizes in all modelswas quantified by calculating pairwise the Euclidian distance over the first fivemoments (R-library moments). A trait-complexity dendrogram was constructedfrom the distancematrix of themoments usingUPGMAclustering (R-library hclust).

Gene Enrichment Analysis

A candidate gene list was assembled based on TAIR10 annotations for“vegetative to reproductive phase transition of meristem”, “photoperiodism,flowering”, “regulation of photoperiodism, flowering”, “flower development”,“leaf development”, “developmental growth”, “plant axis elongation zone”,and “secondary shoot formation”. The total list consisted of 929 genes: 303genes for “flowering time”, 120 genes for “leaf development”, 391 genes for“developmental growth”, 98 genes for “plant axis elongation zone”, and 17genes for “secondary shoot formation”. All genes detected using differentthresholds in GWA studies or GP were tested against the entire candidate genelist. Significance was tested with Fisher’s Exact Test in R (R Development CoreTeam, 2012).

Sequence Analysis

All sequences from the resequenced Arabidopsis accessions were obtainedfrom the Arabidopsis Genome Browser (http://signal.salk.edu/atg1001/3.0/gebrowser). For 525 accessions, 2012 nucleotide variation (�Col-0 TAIR10) files




http://www.fao.org/geonetwork/srv/en/main.home

http://www.fao.org/geonetwork/srv/en/main.home




were downloaded. Custom Perl scripts were developed to select SNPs with anallele frequency . 2% (SNPs must be shared by more than 11 accessions).Another Perl script parsed these positions per accession and outputs either a1 or 0 for compliance or no compliance with Col-0. The resulting data arestored as data frames (.csv file). In order to calculate the LD, required data areextracted from the .csv files with the Gnu program “cut”. The sliced dataframe is read into R (R Development Core Team, 2012) and column-wise, theLD (r2 or correlation coefficient) can be determined by invoking the R functionCor() followed by a quadratic operation. The LD cutoff (r2 , 0.3) was de-termined by a fitted graph over all SNPs. To annotate the genome with SNPs,we applied the tool Snpeff (Cingolani et al., 2012). With the output of this tool,which is stored in a mySQL database, we are able to predict the effect of eachmutation.

Nucleotide Diversity Analysis

Nucleotide diversity was measured with Tajima’s p (Tajima, 1983) usingDnaSP software v. 4.0 (Rozas et al., 2003). Tajima’s pwas calculated for all sites,synonymous, nonsynonymous and silent sites (synonymous plus noncodingsites) for each candidate gene. For deviation from neutrality, we tested usingTajima’s D statistic (Tajima, 1989) and Fu and Li’s D and F statistic (Fu and Li,1993) using DnaSP v. 4.0 (Rozas et al., 2003).

GUS Assays

ACS11::GUS lines were grown for three weeks and complete plants wereharvested in 50mL tubes containing cold acetone (4°C) for 20min,washed twicewith rinsing solution [50 mM NaPO4, pH 7.2, 0.5 mM K3Fe(CN)6 and 0.5 mMK4Fe(CN)6], and then placed in staining solution [50 mMNaPO4, pH 7.2, 0.5 mMK3Fe(CN)6, 0.5 mM K4Fe(CN)6, and 2 mM X-Glc]. The plants were vacuum-infiltrated twice for 30 s and then wrapped in aluminum foil and incubated at37°C for 24 h. The staining solution was then removed, plants were rinsed twicewith water, and 30% ethanol was added to the plants. To completely remove thechlorophyll, this was followed by washes with graded ethanol series from 30 to98% ethanol. Then, individual leaves were photographed.

Quantitative Real-Time PCR

RNA extraction was performed as described (Oñate-Sánchez and Vicente-Carbajosa, 2008). Remaining DNA was removed using RNA-free DNase I(Qiagen, Hilden, Germany). cDNA synthesis was performed using the iScriptcDNA synthesis Kit (Bio-Rad, Hercules, CA). For each qPCR, 5 mL of sample,10 mL of iQ SYBR Green Supermix (Bio-Rad), and 0.5 mL of each primer (10 mM)were mixed and mQ water was added to a total volume of 20 mL. The RT-PCRwas performed on theMyiQ (Bio-Rad). The programwas started with a cycle of95°C for 3 min, then 50 cycles of 15 s at 95°C and 1 min at 60°C followed by acycle of 95°C for 1min and one cycle at 55°C for 1min and then 80 cycles at 55°Cfor 10 s, raising the temperature by 0.5°C each cycle. The primers used are listedin Supplemental Table S4.

RefGenes in Genevestigator (https://genevestigator.com/gv/) was used tofind neutral reference genes. The top 20 reference genes from leaf material werechosen and then checked for their stability in petiole tissue. The four highestscoring genes were selected. Additionally, UBQ10 was used (Hong et al., 2010).All reference genes are listed in Supplemental Table S5, together with theprimers used in RT-PCR. BestKeeper (http://www.gene-quantification.com/bestkeeper.html) was employed to combine the reference genes into an indexthat was used to normalize the RT-qPCR data (Pfaffl et al., 2004).

Supplemental Data

The following supplemental materials are available.

Supplemental Fig. S1. Genomic prediction (GP).

Supplemental Fig. S2. Comparison of GWA studies and GP.

Supplemental Fig. S3. Comparison of effect sizes in GWA studies and GP.

Supplemental Fig. S4. Histograms of SNP effects from GP models.

Supplemental Fig. S5. Trait clustering based on allelic effects distribution.

Supplemental Fig. S6. Linkage disequilibrium of Swedish and non-Swedish accessions around ACS11.

Supplemental Table S1. Descriptive statistics for morphological traits.

Supplemental Table S2. Mantel (Spearman) correlations of morphologicaltraits.

Supplemental Table S3. Mantel correlations between morphological traitsand climate variables.

Supplemental Table S4. Gene enrichment table for GWA studies and GPcandidate gene lists.

Supplemental Table S5. Primers for RT-PCR.

Supplemental Table S6. Morphological data for all 349 accessions.

ACKNOWLEDGMENTS

We thank Bob Schmitz (The University of Georgia) and Huaming Chen(SALK institute) for kindly providing us with the resequencing data for 525accessions from the 1001 genome project in Arabidopsis.

Received June 30, 2015; accepted February 11, 2016; published February 11,2016.

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Genome-Wide Association Mapping and Genomic Prediction ... · natural variation is not analyzed in...

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