• Loading metrics

Allelic Heterogeneity and Trade-Off Shape Natural Variation for Response to Soil Micronutrient

Allelic Heterogeneity and Trade-Off Shape Natural Variation for Response to Soil Micronutrient

  • Seifollah Poormohammad Kiani, 
  • Charlotte Trontin, 
  • Matthew Andreatta, 
  • Matthieu Simon, 
  • Thierry Robert, 
  • David E. Salt, 
  • Olivier Loudet


As sessile organisms, plants have to cope with diverse environmental constraints that may vary through time and space, eventually leading to changes in the phenotype of populations through fixation of adaptive genetic variation. To fully comprehend the mechanisms of evolution and make sense of the extensive genotypic diversity currently revealed by new sequencing technologies, we are challenged with identifying the molecular basis of such adaptive variation. Here, we have identified a new variant of a molybdenum (Mo) transporter, MOT1, which is causal for fitness changes under artificial conditions of both Mo-deficiency and Mo-toxicity and in which allelic variation among West-Asian populations is strictly correlated with the concentration of available Mo in native soils. In addition, this association is accompanied at different scales with patterns of polymorphisms that are not consistent with neutral evolution and show signs of diversifying selection. Resolving such a case of allelic heterogeneity helps explain species-wide phenotypic variation for Mo homeostasis and potentially reveals trade-off effects, a finding still rarely linked to fitness.

Author Summary

Plants are studied for their ability to adapt to their environment and especially to the physical constraints to which they are subjected. It is expected that they evolve in promoting genetic variants favorable under their native conditions, which could lead to negative consequences in other conditions. One approach to study the mechanisms and dynamics of these adaptations is to discover genetic variants that control potentially adaptive traits, and to study directly these variants in wild populations to try to reveal their evolutionary trajectory. We have identified a new polymorphism in a gene coding for a transporter of molybdenum (an essential micronutrient for the plant) in Arabidopsis; we show that this variant has strong phenotypic consequences at the level of plant growth and reproductive value in specific conditions, and that it explains a lot of the species diversity for these traits. Especially, the variant is associated with a clear negative effect under molybdenum-deficient conditions (caused by soil acidity) and with a subtle positive effect under molybdenum-plethoric conditions. Interestingly, the landscape distribution of the variant is not random among Asian populations and correlates well with the availability of molybdenum in the soil at the precise location where the plants are growing in the wild.


Some of the most important constraints that plants have to adapt to are those related to soil properties [1], [2]. These are also possibly some of the least well studied constraints, because they are spatially heterogeneous, thus not prone to typical geographic clines [3], [4], and require analysis at the local/population scale [5]. In this context, quantitative genetics approaches hold great promise to reveal the genetic basis of adaptation by enabling the identification of the molecular origin of phenotypic differences between populations or even between species [6], [7]. One of the benefits of identifying the causative polymorphism(s) and/or gene(s) explaining natural phenotypic variation is that it allows direct testing for correlations between environmental factors populations may be responding to and the occurrence of the target genetic polymorphism. This contrasts with working indirectly through populations phenotype, which may reflect contradictory patterns and trade-offs, if not genetic drift [5], [8]. Moreover, this approach also enables direct testing for fitness advantages or potential cost of adaptation (i.e. antagonistic pleiotropy, an expected argument for local adaptation), that may be masked by linkage to deleterious mutations or genetic drift in reciprocal transplant experiments [9], [10]. Molecularly identified examples of potentially-adaptive variation are still largely lacking and the debate is open as to the scale and rate of adaptive evolution [11], [12].

In this work, we aimed at identifying the molecular bases of natural variation in accumulation of an essential micronutrient and understanding the ecological significance of this diversity. We describe both the fitness trade-offs of this variation and its potential adaptive advantage in the environment, revealing a system that is unlikely to have remained neutral.


Bay-0 and Shahdara, two strains (accessions) derived from wild populations of Arabidopsis thaliana, show contrasted growth behavior when grown on acidic peatmoss substrate (Figure 1). Using a segregating population derived from the cross of these two accessions, we determined the Shahdara growth defect to be segregating from a major-effect recessive locus on chromosome 2, as confirmed by a near-isogenic line derived from a residual heterozygous interval in one of the recombinant inbred lines (HIF084; Figure 1). Additional recombinant lines were phenotyped and genotyped to pinpoint the causative interval to 80 kb (Figure S1), covering 19 annotated genes. Among these, one gene appeared as a good functional candidate: MOT1 was previously linked to the transport and homeostasis of the essential micronutrient molybdenum (Mo) in the plant [13], [14], an element which availability is known to vary with soil pH [15]. Indeed, a T-DNA insertion mutant in the MOT1 gene (mot1.1; Figure S1) shows a phenotype similar to Shahdara on peatmoss and –contrary to the Bay and Col alleles– the Sha allele is not able to restore wild-type growth when combined with the mutant allele in F1 hybrids (Figure 2). Moreover, we show that defective growth is complemented by either increasing soil pH with additional CaCO3 mixed to the peatmoss (Figure S2) or increasing Mo availability (without altering soil pH) by adding Mo in the watering solution (Figure 3). Hence, genetic and chemical complementation shows that acidic soil pH is responsible for reducing Mo-bioavailability and that, combined with a defective allele at MOT1, this results in the typical Mo-deficiency syndromes of reduced leaf Mo contents, strongly altered growth and development, necrosis [16]. These observations of a significant phenotypic consequence of variation at MOT1 provide a model for the potential adaptive significance of this variation that goes beyond the simple variation in Mo content revealed previously [13], [14].

Figure 1. Acidic peatmoss substrate induces severe growth defect linked to the Shahdara allele.

When grown on peatmoss at a pH close to 5, Shahdara is subject to severe growth and developmental arrest, necrosis and death, contrary to Bay-0 which develops normally. In the cross between these two strains, this phenotype is entirely controlled by a single locus (Figure S1), as confirmed by near-isogenic line ‘HIF084’ segregating solely for a region of chromosome 2.

Figure 2. Mutant analysis and allelic complementation confirms MOT1 as the likely causative gene.

Peatmoss phenotype of diverse genotypes is shown, including Bay-0 and Shahdara parental lines, mot1.1 mutant and its wild-type genetic background (Col-0), and F1 plants from complementation crosses between these genotypes. Unlike other alleles, the Shahdara allele at MOT1 is not able to rescue the mutant phenotype.

Figure 3. Chemical complementation links growth defect with Mo shortage.

Accessions with potentially functional (Bay-0, Col-0) and defective MOT1 alleles (Ler, Shahdara, mot1.1) were grown on peatmoss substrate watered with nutrient solution containing either traces of Mo (‘Control’) as in Figure 1, or 1 mM Na2MoO4 (‘+Na2MoO4’). pH was checked to remain unchanged across treatments at ∼5.

Although we find that Landsberg erecta (Ler) has a similar behaviour than Shahdara in our conditions (Figure 3), this defective allele (MOT1Ler) used initially to reveal the gene's activity [13], [14] is functionally different from the MOT1Sha defective allele. MOT1Sha doesn't bear the promoter 53 bp-deletion as in Ler (Figure S1) and in fact is not showing MOT1Ler-like transcriptional down-regulation compared to MOT1Bay or MOT1Col (Figure S3). Instead, MOT1Sha seems defined by a single amino-acid change in the protein relative to Bay-0 and Col-0 (Figure S1), strongly suggesting that MOT1Sha is hypofunctional. However, the MOT1 protein produced from the Sha allele is still able to increase Mo accumulation when heterologously expressed in yeast (Figure S4).

We then genotyped a random worldwide sample of ∼300 accessions for the Sha-like amino-acid change and the Ler-like 53-bp deletion and find that these alleles are both present at intermediate frequencies (15–20%) among the populations. Sequencing 102 of these accessions for the whole gene and promoter region revealed that the very conserved MOT1Sha haplotype is indeed clearly defined solely by the D104Y amino-acid change, while the MOT1Ler genotype is more complex and diverse (Table S1). All Sha-like and Ler-like accessions that have been phenotyped show that both MOT1Sha and MOT1Ler haplotypes are perfectly associated with defective growth under acidic soil conditions (Table S1) and complementation crosses with five additional Sha-like accessions confirm allelism to mot1.1 (Figure S5). Taking into account this allelic heterogeneity now explains most of the species variation toward low-Mo contents revealed in previous work [13] ( This form of complexity –in addition to genetic heterogeneity– is probably more frequent than previously thought in many organisms and is likely to help explain part of the missing heritability [17], [18].

Regarding MOT1 defective haplotypes, MOT1Sha is confined to ‘West-Asia’ (including Russia) with a high frequency among these populations (Figure S6) and displays a very low polymorphism level (πSha = 0,00016) in comparison to other haplotype clusters, including the worldwide-distributed MOT1Ler allele (πLer = 0,0017; Table S1). This may translate a recent and rapid expansion of the Sha allele through ‘West-Asia’, which could be due to neutral processes such as gene surfing associated with post-glaciation recolonization events from Central Asia [19]. This may also witness local positive selection events in favour of the Sha allele. Indeed, patterns of nucleotide polymorphisms at MOT1 in the sample of 102 accessions strongly deviate from the expectation under the strict neutrality model, contrarily to two control loci, PI and COI1 (Table S2). Negative values of Tajima's D reveal an excess of rare alleles at MOT1, suggesting the possible occurrence of at least one past selective sweep that has targeted this locus. Other well documented evolutionary processes such as population expansion after the last glaciation event [20] and population genetic structure [21] could also have contributed to the excess of rare alleles observed at the genome-wide level [22], as well as at the MOT1 locus in A. thaliana. Nevertheless, the HKA and McDonald-Kreitman tests, which do not rely on the frequency spectrum, support the hypothesis of diversifying selection at the species level (Tables S2 and S3). The excess of within-species polymorphisms relatively to inter-specific divergence and the excess of non-synonymous polymorphisms observed at MOT1 may result from the selection of different haplotypes at the worldwide scale. Interestingly, this trend is also clear when considering only accessions from ‘West Asia’, suggesting that the selection process could happen at different geographical scales.

Our own documented collection of wild populations from diverse regions in ‘West-Asia’ allowed us to investigate potential relationships between MOT1 alleles and environmental parameters described precisely at the population site, especially soil properties. We saw no relationship with soil pH (indeed, none of the described populations were facing acidic soil conditions), but there was an obvious trend for populations with the defective MOT1Sha allele to grow on soils with high water-extractable Mo content (Figure 4). This may indicate that the defective Sha allele is a protective response to Mo accumulation in environments with excess Mo. Indeed, under such conditions in the laboratory, we observe a strong decrease in fitness (through the total number of seeds produced per plant) in all genotypes (Figure S7), indicating that plants have to find the right balance between Mo deficiency and Mo toxicity, a trade-off that could be resolved partly through variation in function of the Mo transporter MOT1. Moreover, we show that a defective MOT1 allele (either mot1.1 or MOT1Sha) is accompanied by a slightly increased average seed mass (another component of fitness) specifically under Mo-toxic conditions (Figure S7), the outcome of which is difficult to estimate in nature [23]. It is however worth noting that previous studies in A. thaliana have shown positive effects of increased seed size for example on subsequent root and shoot growth [24] or seedling survival under limiting conditions [25].

Figure 4. West-Asian populations highlight the correlation between MOT1Sha allele and high native soil Mo content.

Soil parameters (pH, extractable Mo) from the precise original collection site are represented for independent populations carrying (red dots) or not (black dots) the MOT1Sha allele.

In summary, we have identified a new functional variant at MOT1 that contributes to explain most of the species' diversity in Mo homeostasis, and associated phenotypes that provide likely explanations for its non neutral evolution and its correlation to native soil. It is still a rare finding to be able to relate functional genetic variants to fitness or trade-off effects [26][28], and even more to associate this variation to the environment [3], [7]. Our work indicates that environmental parameters of importance, such as soil properties, may be heterogeneously distributed and therefore require local description [5] and study of local adaptation [9], [11], which is greatly facilitated by the identification of the causative locus.

Materials and Methods

All accessions used and the Bay-0×Shahdara RIL set [29] were obtained from Versailles Arabidopsis Stock Centre ( Heterogeneous Inbred Family ‘HIF084’ was derived from RIL084 (segregating for the region of interest) as previously described [30]. New collections of A. thaliana accessions were partly described previously [31] and are shown at T-DNA insertion mutant mot1.1 corresponds to line SALK_118311 as described [13], [14]. Genetic complementation tests were performed on F1 plants issued from the cross of diverse accessions to mot1.1 or its wild-type background.

Acidic soil assays were performed on ‘Floratorf’ peatmoss (Floragard, Germany) mixed with CaCO3 (4 g per liter of dry peatmoss) to maintain a soil pH∼5, watered with classical nutrient solution and grown under typical long-day conditions at 20°C. Chemical complementations were achieved in the same condition but, either with 8 g CaCO3 per liter of peatmoss to reach a pH∼6, or using a watering solution supplemented with 1 mM Na2MoO4. Mo toxicity was tested on regular fertilised soil mix (pH = 6) watered with nutrient solution supplemented with 7 mM Na2MoO4, or not (control).

MOT1 sequencing, qPCR analysis of expression (normalised against GAPDH and PP2A), functional characterisation in yeast were performed as previously described [13]. Extractable Mo was determined in soils by the method of Soltanpour and Schwab [32] using ICP-MS as the detector.

A total of 102 A. thaliana accessions (including 48 accessions known to maximize A. thaliana diversity [33] and 44 accessions from ‘West-Asia’) and 5 A. halleri accessions (I-14, I-16, F-1, PL-22 and TZC; obtained from H. Frérot at Univ. Lille [34]) were sequenced at MOT1 (including 1 kb upstream and 0.3 kb downstream for A. thaliana accessions) and at two reference loci, COI1 (At2g39940; 2,600 bp coding sequence) and PI (At5g20240; 2,150 bp coding sequence). Those genes were either used previously as reference or shown to have a neutral pattern of polymorphisms in A. thaliana [35], [36]. Sequences were aligned using Codoncode Aligner v3.7.1. and subsequent alignments were improved visually.

Intraspecific analyses i.e. nucleotide diversity estimated by π [37] and θw [38], and Tajima's D statistics [39] were calculated using DNAsp v5.10.01 on the whole region sequenced. Ten thousands coalescent simulations under the strict Wright-Fisher neutral model assuming no recombination and conditioning on S were performed to estimate statistical significance of Tajima's D.

For interspecific analyses, the orthologous of MOT1 and of the two control loci in A. halleri were used. The McDonald-Kreitman test [40] was performed by using DNAsp v5.10.01 in order to test for possible excess or deficiency in replacement substitutions at MOT1. Singletons were discarded for this analysis in order to reduce the contribution of slightly deleterious mutations (expected at very low frequencies and unlikely to become fixed). Neutral index was calculated as previously described [41]. Divergence between A. thaliana and A. halleri, defined as the average number of nucleotide differences between populations per gene, was calculated using DNAsp v5.10.01 and used to perform HKA tests with the multilocus HKA program available from J. Hey laboratory (

Supporting Information

Figure S1.

Fine-mapping the causative locus identifies MOT1 as a candidate gene. A. The physical region of chromosome 2 found to be linked to the growth defect phenotype is shown (physical positions are given in Mb). B. Zooming in the candidate region highlights recombinants within the inbred lines that allow to fine-map the causative locus, thanks to additional markers (vertical dashed lines). Individual lines' genotype are depicted in horizontal coloured boxes (green = Bay allele; purple = Sha allele ; dashed = heterozygous) and their phenotype on peatmoss are indicated (S = Sensitive; R = Resistant). C. This allows to narrow down the causative region to 80 kb, a region containing 19 predicted genes including the candidate At2g25680 (MOLYBDATE TRANSPORTER 1). D. MOT1 has been sequenced in parental accessions and polymorphisms between Col-0, Bay-0 and Shahdara are represented along the single-exon gene model, including an amino-acid change specific to Shahdara (D104Y). The position of the insertion of a T-DNA in the mot1.1 mutant (SALK_118311) is indicated.


Figure S2.

Chemical complementation links growth defect with soil pH. Increasing soil pH from ∼5 (‘Control’) to ∼6 (‘+CaCO3’) by doubling the amount of CaCO3 mixed to the peatmoss substrate rescues normal vegetative growth of the Shahdara strain.


Figure S3.

MOT1 transcript accumulation does not explain MOT1Sha defective allele. MOT1 transcript accumulation relative to GAPDH and PP2A controls is shown from roots of diverse genotypes as in Figure 3. Contrary to Ler and mot1.1, Shahdara accumulates normal levels of transcript. Standard errors are shown.


Figure S4.

MOT1Sha is able to transport Mo in yeast. The Sha allele of MOT1 was overexpressed in yeast heterologous system (Sha/p416) and shown to lead to Mo accumulation compared to the empty vector (p416, used as reference) or the yeast wild-type strain (BY4741), to an extent not significantly different from the Col MOT1 allele (Col/p416). Error bars represent interquantile range of medians.


Figure S5.

Multiple accessions sharing the MOT1Sha haplotype confirm the causative gene and polymorphism. Peatmoss phenotype of diverse genotypes is shown: 5 independent Sha-like accessions (Stw-0, Kly-2, Sij-4, Kondara and Zal-3) and F1 plants from complementation crosses between each of these accessions and either the mot1.1 mutant or its wild-type genetic background (Col-0). As in Figure 2, all accessions sharing the MOT1Sha haplotype are both sensitive and unable to rescue the mutant phenotype.


Figure S6.

Worldwide distribution of functionally contrasted alleles at MOT1. Original collection site and functional MOT1 haplotype (Sha-like in red dots, Ler-like in yellow, Col-like in blue) is shown on a world map for the 102 accessions sequenced in Table S1. Ler-like accessions are found across the whole species known distribution range, while Sha-like accessions are restricted to Asia and Russia (‘West-Asia’).


Figure S7.

Effect of Mo toxicity on fitness components -seed number and weight- of contrasted MOT1 genotypes. The fitness consequences of Mo toxicity was tested on regular (non-acidic) soil mix with different nutrient solutions containing either traces of Mo (‘Control’) or 7 mM Na2MoO4 (‘Na2MoO4 (7 mM)’). The assay was performed to compare (A) the mot1.1 mutant and its wild-type (‘WT’) genetic background, (B) the Bay and Sha allele in the HIF084 background (‘HIF[Bay]’ vs ‘HIF[Sha]’). In both cases, the defective MOT1 allele is represented with black bars. To avoid heterogeneity/effects on descendance conveyed through the maternal plant, the mutant assays were performed as a progeny testing from a mother plant segregating for the T-DNA insertion. Two fitness parameters are represented: the total number of seeds produced per plant (on the left) and the weight of 1,000 seeds (on the right). Error bars show 95% confidence interval of the mean. For the weight of 1,000 seeds, there is no significant difference between genotypes under ‘control’ treatment, while defective MOT1 alleles have significantly larger seeds under Mo excess (t-test; p<0.017 when comparing mot1.1 and WT; p<0.0018 when comparing HIF084[Bay] and HIF084[Sha]).


Table S1.

Haplotype diversity at MOT1 among 102 accessions. Polymorphisms detected across 102 A. thaliana accessions for the MOT1 locus, including 1 kb upstream (promoter region) and 0.3 kb downstream of the coding region (between blue vertical double-lines). The position (coordinates) of polymorphic bases (regions) are indicated in bp from TAIR10 reference. Synonymous polymorphisms are highlighted in light grey, non-synonymous polymorphisms in medium grey and missing data in dark grey. A. lyrata and A. halleri serve as outgroups. Accessions individually phenotyped on acidic peatmoss substrate are indicated (S = Sensitive; R = Resistant). The main functional haplotypes highlighted with colours (Sha-like in red, Ler-like in yellow, Col-like in blue) are those represented on Figure S6, including the Sha-like haplotype defined by the ‘D104Y’ polymorphism, and the Ler-like haplotype associated to the “53 bp-deletion.”


Table S2.

Genetic diversity and tests of selection at MOT1 and two reference loci (COI1 and PI).


Table S3.

Detailed results of HKA simulations.



We thank I. Baxter, C. Toomajian, F. Roux, C. Camilleri, and F. Budar for discussions at various stages during this work and/or comments on the manuscript.

Author Contributions

Conceived and designed the experiments: OL SPK CT DES. Performed the experiments: SPK CT MA MS. Analyzed the data: SPK CT MS TR. Wrote the paper: OL SPK.


  1. 1. Karrenberg S, Widmer A (2008) Ecologically relevant genetic variation from a non-Arabidopsis perspective. Curr Opin Plant Biol 11: 156–162.
  2. 2. Nord EA, Lynch JP (2009) Plant phenology: a critical controller of soil resource acquisition. J Exp Bot 60: 1927–1937.
  3. 3. Baxter I, Brazelton JN, Yu D, Huang YS, Lahner B, et al. (2010) A coastal cline in sodium accumulation in Arabidopsis thaliana is driven by natural variation of the sodium transporter AtHKT1;1. PLoS Genet 6: e1001193.
  4. 4. Stinchcombe JR, Weinig C, Ungerer M, Olsen KM, Mays C, et al. (2004) A latitudinal cline in flowering time in Arabidopsis thaliana modulated by the flowering time gene FRIGIDA. Proc Natl Acad Sci USA 101: 4712–4717.
  5. 5. Trontin C, Tisné S, Bach L, Loudet O (2011) What does Arabidopsis natural variation teach us (and does not teach us) about adaptation in plants? Curr Opin Plant Biol 14: 225–231.
  6. 6. Hanikenne M, Talke IN, Haydon MJ, Lanz C, Nolte A, et al. (2008) Evolution of metal hyperaccumulation required cis-regulatory changes and triplication of HMA4. Nature 453: 391–395.
  7. 7. Turner TL, Bourne EC, Von Wettberg EJ, Hu TT, Nuzhdin SV (2010) Population resequencing reveals local adaptation of Arabidopsis lyrata to serpentine soils. Nat Genet 42: 260–263.
  8. 8. Alonso-Blanco C, Aarts MG, Bentsink L, Keurentjes JJ, Reymond M, et al. (2009) What has natural variation taught us about plant development, physiology, and adaptation? Plant Cell 21: 1877–1896.
  9. 9. Anderson JT, Willis JH, Mitchell-Olds T (2011) Evolutionary genetics of plant adaptation. Trends Genet 27: 258–266.
  10. 10. Hereford J (2009) A quantitative survey of local adaptation and fitness trade-offs. Am Nat 173: 579–588.
  11. 11. Fournier-Level A, Korte A, Cooper MD, Nordborg M, Schmitt J, et al. (2011) A map of local adaptation in Arabidopsis thaliana. Science 334: 86–89.
  12. 12. Hancock AM, Brachi B, Faure N, Horton MW, Jarymowycz LB, et al. (2011) Adaptation to climate across the Arabidopsis thaliana genome. Science 334: 83–86.
  13. 13. Baxter I, Muthukumar B, Park HC, Buchner P, Lahner B, et al. (2008) Variation in molybdenum content across broadly distributed populations of Arabidopsis thaliana is controlled by a mitochondrial molybdenum transporter (MOT1). PLoS Genet 4: e1000004.
  14. 14. Tomatsu H, Takano J, Takahashi H, Watanabe-Takahashi A, Shibagaki N, et al. (2007) An Arabidopsis thaliana high-affinity molybdate transporter required for efficient uptake of molybdate from soil. Proc Natl Acad Sci USA 104: 18807–18812.
  15. 15. Mengel K, Kirkby EA (2001) Principles of plant nutrition; Springer, editor. Berlin: Springer.
  16. 16. Mendel RR (2011) Cell biology of molybdenum in plants. Plant Cell Reports 30: 1787–1797.
  17. 17. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, et al. (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467: 832–838.
  18. 18. Wood AR, Hernandez DG, Nalls MA, Yaghootkar H, Gibbs JR, et al. (2011) Allelic heterogeneity and more detailed analyses of known loci explain additional phenotypic variation and reveal complex patterns of association. Hum Mol Genet 20: 4082–4092.
  19. 19. Beck JB, Schmuths H, Schaal BA (2008) Native range genetic variation in Arabidopsis thaliana is strongly geographically structured and reflects Pleistocene glacial dynamics. Mol Ecol 17: 902–915.
  20. 20. Francois O, Blum MG, Jakobsson M, Rosenberg NA (2008) Demographic history of european populations of Arabidopsis thaliana. PLoS Genet 4: e1000075.
  21. 21. Platt A, Horton M, Huang YS, Li Y, Anastasio AE, et al. (2010) The scale of population structure in Arabidopsis thaliana. PLoS Genet 6: e1000843.
  22. 22. Nordborg M, Hu TT, Ishino Y, Jhaveri J, Toomajian C, et al. (2005) The pattern of polymorphism in Arabidopsis thaliana. PLoS Biol 3: e196.
  23. 23. Bergelson J, Roux F (2010) Towards identifying genes underlying ecologically relevant traits in Arabidopsis thaliana. Nat Rev Genet 11: 867–879.
  24. 24. Elwell AL, Gronwall DS, Miller ND, Spalding EP, Durham Brooks TL (2011) Separating parental environment from seed size effects on next generation growth and development in Arabidopsis. Plant Cell Environ 34: 291–301.
  25. 25. Krannitz PG, Aarssen LW, Dow JM (1991) The effect of genetically based differences in seed size on seedling survival in Arabidopsis thaliana (Brassicaceae). Am J Bot 78: 446–450.
  26. 26. Kroymann J, Donnerhacke S, Schnabelrauch D, Mitchell-Olds T (2003) Evolutionary dynamics of an Arabidopsis insect resistance quantitative trait locus. Proc Natl Acad Sci USA 100: 14587–14592.
  27. 27. Todesco M, Balasubramanian S, Hu TT, Traw MB, Horton M, et al. (2010) Natural allelic variation underlying a major fitness trade-off in Arabidopsis thaliana. Nature 465: 632–636.
  28. 28. Zhen Y, Dhakal P, Ungerer MC (2011) Fitness benefits and costs of cold acclimation in Arabidopsis thaliana. Am Nat 178: 44–52.
  29. 29. Loudet O, Chaillou S, Camilleri C, Bouchez D, Daniel-Vedele F (2002) Bay-0×Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theor Appl Genet 104: 1173–1184.
  30. 30. Loudet O, Gaudon V, Trubuil A, Daniel-Vedele F (2005) Quantitative trait loci controlling root growth and architecture in Arabidopsis thaliana confirmed by heterogeneous inbred family. Theor Appl Genet 110: 742–753.
  31. 31. Kronholm I, Loudet O, de Meaux J (2010) Influence of mutation rate on estimators of genetic differentiation–lessons from Arabidopsis thaliana. BMC Genet 11: 33.
  32. 32. Soltanpour PP, Schwab AP (1977) A new soil test for simultaneous extraction of macro- and micronutrients in alkaline soils. Comm Soil Sci Plant Anal 8: 195–207.
  33. 33. McKhann HI, Camilleri C, Berard A, Bataillon T, David JL, et al. (2004) Nested core collections maximizing genetic diversity in Arabidopsis thaliana. Plant J 38: 193–202.
  34. 34. Gode C, Decombeix I, Kostecka A, Wasowicz P, Pauwels M, et al. (2012) Nuclear microsatellite loci for Arabidopsis halleri (Brassicaceae), a model species to study plant adaptation to heavy metals. Am J Bot.
  35. 35. Caldwell KS, Michelmore RW (2009) Arabidopsis thaliana genes encoding defense signaling and recognition proteins exhibit contrasting evolutionary dynamics. Genetics 181: 671–684.
  36. 36. Cork JM, Purugganan MD (2005) High-diversity genes in the Arabidopsis genome. Genetics 170: 1897–1911.
  37. 37. Nei M (1987) Molecular Evolutionary Genetics; Press CU, editor. New York: Columbia Univ. Press.
  38. 38. Watterson GA (1975) On the number of segregating sites in genetical models without recombination. Theor Popul Biol 7: 256–276.
  39. 39. Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123: 585–595.
  40. 40. McDonald JH, Kreitman M (1991) Adaptive protein evolution at the Adh locus in Drosophila. Nature 351: 652–654.
  41. 41. Rand DM, Kann LM (1996) Excess amino acid polymorphism in mitochondrial DNA: contrasts among genes from Drosophila, mice, and humans. Mol Biol Evol 13: 735–748.