Water shortages caused by droughts lead to crop losses that affect billions of people around the world each year. By discovering how wild plants adapt to drought, it may be possible to identify traits and genes that help to improve the growth of crop plants when water is scarce. It has been suggested that plants have adapted to droughts by flowering at times of the year when droughts are less likely to occur. For example, if droughts are more likely to happen in spring, the plants may delay flowering until the summer.

Arabidopsis thaliana is a small plant that is found across Eurasia, Africa and North America, including in areas that are prone to drought at different times of the year. Individual plants of the same species may carry different versions of the same gene (known as alleles). Some of these alleles may not work properly and are referred to as loss-of-function alleles. Monroe et al. investigated whether A. thaliana plants carry any loss-of-function alleles that are associated with droughts happening in the spring or summer, and whether they are linked to when those plants will flower.

Monroe et al. analyzed satellite images collected over the last 30 years to measure when droughts have occurred. Next, they searched genome sequences of Arabidopsis thaliana for alleles that might help the plants to adapt to droughts in the spring or summer. Combining the two approaches revealed that loss-of-function alleles associated with spring droughts were strongly predicted to be associated with the plants flowering later in the year. Similarly, loss-of-function alleles associated with summer droughts were predicted to be associated with the plants flowering earlier in the year.

These findings support the idea that plants can adapt to drought by changing when they produce flowers, and suggest that loss-of-function alleles play a major role in this process. New techniques for editing genes mean it is easier than ever to generate new loss-of-function alleles in specific genes. Therefore, the results presented by Monroe et al. may help researchers to develop new varieties of crop plants that are better adapted to droughts.

Discovering the environmental drivers and functional genetics of adaptation in nature is a key goal of evolutionary biology and valuable to advance applied genetics in agriculture. Understanding the genetics of drought adaptation in plants is particularly important as crop losses resulting from droughts affect billions of people each year, posing the greatest threat to global food stability. Because droughts also impose strong selection on natural plant populations, investigating drought adaptation in wild species is both useful for addressing fundamental questions of evolutionary biology, such as determining whether adaptation proceeds by few or many alleles, and informative for efforts to reverse engineer drought tolerance in crops (Mickelbart et al., 2015). Such an evolutionary research program is motivated by the need to understand adaptive drought tolerance strategies for different types of drought conditions, which can vary in severity and timing (Tardieu, 2012). Furthermore, previous limitations of single gene approaches have reinforced the necessity of developing methods to identify beneficial alleles at genomic scales and functional molecular resolutions (Dean and Thornton, 2007; Passioura, 2010).

Drought stress can occur throughout the year and drought timing is forecast to change over the next century (Trenberth et al., 2014). While dramatic evolutionary responses to drought events have been documented, (e.g. Franks et al., 2007), little is known about the relationship between drought timing and adaptation. However, the observation both in nature and agriculture that plants are particularly susceptible to drought while flowering (Nam et al., 2001; Dietrich and Smith, 2016) has contributed to the longstanding hypothesis that adaptive flowering time should reflect patterns in the seasonal timing of drought events (Passioura, 1996). Detailed studies of life history also reveal that locally adapted Arabidopsis thaliana (Arabidopsis hereafter) populations begin flowering in their home environments just prior to and after periods of increased historical drought frequency (Mojica et al., 2016).

Flowering time in Arabidopsis is correlated with other drought tolerance traits such as water use efficiency and can serve as a proxy for alternative drought tolerance strategies, with early flowering genotypes being associated with low water use efficiency (drought escape strategy) and late flowering genotypes with high water use efficiency (dehydration avoidance strategy) (McKay et al., 2003; Lovell et al., 2013; Kenney et al., 2014). Thus, the historical timing of drought experienced by locally adapted populations may explain the evolution of these strategies and the distribution of alleles responsible for natural flowering time variation. This hypothesis motivated our investigation to identify alleles associated with drought timing and test the prediction that they contribute to adaptive flowering time evolution.

Identifying functionally relevant genetic variation contributing to adaptation is needed to understand fundamental evolutionary processes. In contrast to early theoretical predictions and popular assumptions, loss-of-function (LoF) alleles, those that eliminate or ‘knockout’ a gene’s molecular function, are overrepresented among alleles reported as responsible for crop improvement and often produce adaptive phenotypes in wild species (Hoekstra et al., 2006; Rausher, 2008; Olsen and Wendel, 2013; Alonso-Blanco and Méndez-Vigo, 2014; Weigel and Nordborg, 2015b; Torkamaneh et al., 2018). Indeed, a number of individual genes exhibiting evidence of locally adaptive loss-of-function have been documented in Arabidopsis (Grant et al., 1998; Johanson et al., 2000; Kliebenstein, 2001; Kroymann et al., 2003; Mouchel et al., 2004; Aukerman, 1997; Hauser et al., 2001; Mauricio et al., 2003; Alonso-Blanco et al., 2005; Werner et al., 2005; Barboza et al., 2013; Xiang et al., 2014).

Discovering adaptive LoF alleles is particularly valuable for inspiring targeted molecular breeding because functionally similar mutations can be mined from the breeding pool or generated directly by non-transgenic native gene editing. Unfortunately, traditional genome-wide association scans based on the one-locus two-allele model perform poorly at detecting adaptive LoF alleles, which because of the large number of mutations that can create them, are likely to arise through parallel molecular evolution (Pennings and Hermisson, 2006; Barboza et al., 2013; Kerdaffrec et al., 2016). Species-wide whole genome sequences however, present the opportunity to advance beyond previous mapping and scanning methods that relied on linked polymorphisms by instead characterizing and contrasting functionally defined alleles.

Here, we combined long-term satellite-detected drought histories, whole genome sequence scans based on allele function, and transgenic knockout experiments in Arabidopsis to test historical predictions about how drought timing shapes the evolution of flowering time and outline a broadly scalable approach for discovering loss-of-function gene variants contributing to plant climate adaptation.

To study global seasonal drought timing, satellite-detected measurements offer a valuable historical record. One such measurement, the Vegetative Health Index (VHI) has been used for decades to monitor drought, including in many places across the natural range of Arabidopsis (Kogan, 1997). Though primarily used as a tool to predict crop productivity, by quantifying drought induced vegetative stress this index also provides a resource for evolutionary ecologists to study seasonal patterns in drought-related episodes of natural selection. We analyzed 34 years of VHI data to characterize drought regimens at the home environments of Arabidopsis ecotypes (Figure 1, Supplementary file 1). We found that drought frequency during the spring (ß = 50.016, p < 2×10⁻¹⁶) and summer (ß = −28.035, p = 4.4×10⁻⁷) significantly predict flowering time among Arabidopsis ecotypes (Supplementary file 2A). We then generated a drought-timing index that quantifies the relative frequency of drought between spring and summer over the typical reproductive growing season and observed substantial differences in drought timing experienced by ecotypes (Figure 1—figure supplement 1). This environmental variation presented a useful cline to address classical hypotheses about the evolution of flowering time in relation to drought timing and identify LoF alleles potentially contributing to this evolution.

Figure 1 with 1 supplement see all

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To identify candidate LoF alleles underlying drought adaptation and flowering time evolution, we analyzed whole genome sequences in Arabidopsis. We first surveyed the genomes of 1135 ecotypes (1001 Genomes Consortium, 2016) for LoF alleles in protein coding genes predicted to encode truncated amino acid sequences (Supplementary file 3A). To overcome the likely parallel evolutionary origins of LoF alleles that would have challenged previous methods, we classified alleles based functional allele state rather than individual polymorphisms for association testing. After filtering to reduce the likelihood of false positives (see materials and methods), we thus tested 2088 genes for LoF allele associations with drought timing (Figure 2A) and flowering time (Figure 2B). These analyses identified 247 genes in which LoF alleles are significantly associated with drought timing and/or flowering time after accounting for population structure and multiple testing (Supplementary file 3B). In contrast, when we performed these analyses on a permuted LoF genotype matrix, we found no genes that were significantly associated with drought timing or flowering time (Figure 1—figure supplement 1).

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It should be noted that the 2088 genes tested for associations to flowering time and drought timing are not a complete representation of LoF alleles in Arabidopsis. In some cases, previously studied LoF alleles did not pass filtering steps (Supplementary file 3D,E). This was primarily because the frequency or quality of LoF allele calls in these genes fell below our filtering requirements (see materials and methods). In other cases, the Col-0 reference genome already has a documented LOF allele. Finally, we expect LoF alleles to be undetectable if they are the product of large insertions or deletions which cannot be properly identified with currently available resequencing data. Thus, while the methods used here are designed to minimize false positives (alleles classified as LoF, but which are actually functional), the likely occurrence of false negatives (undetected LoF alleles) in available data motivates the need for more sophisticated species wide genome sequencing efforts including a greater diversity of de-novo quality genomes for comprehensive detection of functionally relevant genetic variation across the species.

Associations to drought timing predicted associations of LoF alleles to flowering time directly. Together, summer drought and earlier flowering associated genes (Figure 2C), and spring drought and later flowering associated genes (Figure 2D) overlapped seven times more often than expected by chance (χ²=492, p < 2 × 10⁻¹⁶) and no shared associations were observed in the opposite direction. The strengths of the associations between LoF alleles and drought timing (P values) was also strongly correlated with the strengths of the associations to flowering time (r² = 0.48. Figure 2—figure supplement 1E, Figure 2C,D). This result is comparable to overlapping peaks in a ‘Manhattan plot’ generated from a traditional genome wide association scan (e.g. Bosse et al., 2017). In contrast, these associations were weakly correlated when genotypes were permuted (r² = 0.01 Figure 2—figure supplement 1F), indicating that the result is not simply explained as an artifact of allele frequencies or by the relationship between drought timing and flowering time (i.e. Supplementary file 1A). Thus, satellite-detected drought histories and a functional genome-wide scanning approach prove useful for predicting the direction and molecular targets of phenotypic evolution. Similar investigations with ecologically meaningful environmental variation could be valuable for discovering candidates underlying other important traits that are especially difficult to measure.

These results further support the classical hypothesis that the relationship between phenology and drought timing is the most important feature of plant drought tolerance (Passioura, 1996), indicating the evolution of ‘drought escape’ through earlier flowering in summer drought environments, and ‘dehydration avoidance’ by later flowering genotypes in spring drought environments. Because most Arabidopsis populations appear to exhibit a winter annual life habit, germinating in the fall and overwintering as a rosette (Ratcliffe, 1961; Thompson, 1994; Burghardt et al., 2015), late flowering genotypes in spring drought environments are expected to still encounter drought conditions. However, delayed flowering may ensure that droughts co-occur with vegetative growth rather than during the drought sensitive reproductive phase. This pattern is also consistent with hypotheses explaining the more water conservative water use and stomatal traits observed in late flowering genotypes (McKay et al., 2003; Lovell et al., 2013; Kenney et al., 2014; Kooyers, 2015) and those from spring drought environments (Dittberner et al., 2018). Future experimental work will be valuable to identify other plant physiological traits affected by the LoF alleles associated with drought timing.

These results provide new insight into the ecology and genetics of Arabidopsis life history evolution, but the complex ecological reality of these processes is undoubtedly beyond the scope of this study. We found that drought timing remains a significant predictor of allele associations to flowering time when controlling for allele associations with latitude and minimum temperature (slope estimate in multiple linear regression, p < 2×10⁻¹⁶, Supplementary file 2B). However, other unknown climatic variables or environmental interactions and non-linearities likely contribute to the flowering time adaptation as well. Flowering time is only one component of phenology and other adaptive life history transitions such a germination timing (Donohue, 2002) may also be influenced by drought timing and could change how drought timing affects the evolution of flowering time, a hypothesis that warrants further investigation. Furthermore, measuring flowering time in other environments, such alternate light regimes, may yield a different set of candidate genes using similar approaches.

Signatures of selection in the genes identified differ from the genome average and neutral expectations. As expected for genes harboring LoF alleles, these show parallel evolution of LoF and accelerated amino acid sequence evolution among Arabidopsis ecotypes (Figure 2—figure supplement 2A,B, Supplementary file 2C). We also found evidence of positive selection for LoF alleles in genes associated with drought timing and/or flowering time. While these genes have similar global frequencies of LoF alleles compared to genes not showing associations with drought timing and/or flowering time (Figure 2—figure supplement 2C), they tend to have significantly fewer unique LoF alleles (Figure 2—figure supplement 2D) and greater frequencies of each independent LoF allele (Figure 2E). This pattern is consistent with theoretical predictions and results from simulations of adaptation by parallel molecular evolution involving recurrent mutation combined with more rapid local fixation of alleles experiencing positive selection (Pennings and Hermisson, 2006). In cases where adaptation proceeds through the fixation of a single adaptive allele, traditional genome scanning approaches may be sufficient to detect causal loci. However, when genetic variation consists of multiple independent alleles, as is often the case for the genes examined here (Figure 2—figure supplement 2D), classifying alleles functionally before testing for associations is likely necessary.

The extent of LoF responsible for adaptive phenotypic evolution is much greater than once assumed (Smith, 1970; Albalat and Cañestro, 2016). LoF alleles identified were overwhelmingly associated with spring drought or later flowering rather than summer drought or earlier flowering (χ² = 132, p < 2 × 10⁻¹⁶, Figure 2). Because the reference genome and gene models are from an early flowering Arabidopsis line, Col-0, this is consistent with the hypothesis that LoF alleles are particularly important in the evolution of phenotypic divergence (Rausher, 2008). This result also highlights the need to develop functional genomics resources informed by multiple de-novo quality reference genomes. We found that flowering time is strongly predicted by the accumulation of LoF alleles across the 214 candidate genes associated to spring drought and/or later flowering time (Figure 3A–E), estimating a 1 day increase for every three additional LoF alleles across these candidate genes (Figure 3F). This relationship is best represented as a simple linear regression; the addition of a non-linear quadratic predictor variable did not significantly improve the fit of the model (F = 0.7005, p = 0.4028). Importantly, we did not find a broader overabundance of LoF alleles in later flowering ecotypes or those from spring drought environments that would explain this relationship (e.g. Figure 2—figure supplement 3). Rather, these findings support a model of climate-associated evolution in complex traits that includes a substantial contribution from widespread genetic LoF and give promise to targeted LoF for directed phenotypic engineering.

Figure 3

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Experimental knockout lines confirmed the later flowering times predicted from natural allele associations. To test phenotypic effects, we screened a panel of confirmed T-DNA insertion mutants representing a sample of candidate LoF alleles associated with spring drought and/or later flowering. As predicted by variation among Arabidopsis ecotypes (Figure 2D), the vast majority of knockout lines in these candidate genes (57 of 59, χ² = 51, p = 8.045e-13) flowered later on average than the wild type genotype (Figure 3G, Supplementary file SF). LoF alleles identified through these analyses and experiments include those previously linked to flowering time (Cui et al., 2007) and drought responses (Aghdasi et al., 2012; Qin et al., 2017). Implementing a functional genome-wide association scan, we find that allele associations with ecologically meaningful environmental variation (drought timing) accurately predict associations with adaptive phenotypes directly (flowering time).

Together with validation in transgenic lines, these findings outline a scalable model for gaining deeper insights into the functional genomics of climate adaptation in nature. Combining large scale knockout experiments with functional genome wide association scans may be a valuable approach for future research to quantify the power to predict LoF allele effects. These results also further challenge historical assumptions about molecular adaptation that have implications for influencing evolutionary theory and public attitudes toward emerging molecular breeding approaches.

Groundbreaking yield increases during the green revolution of the 1960 s were largely attributable to semi-dwarf phenotypes caused by LoF alleles in both rice and barley (Spielmeyer et al., 2002; Jia et al., 2009). Later it was found that natural LoF alleles of the same gene in wild Arabidopsis produce similar phenotypes (Barboza et al., 2013), suggesting the potential to mine ecological species for information directly useful for crop improvement. Visions of a second green revolution powered and informed by such natural variation call for discoveries in evolutionary functional genomics at scales that have now become possible. The genes identified here could inspire future molecular breeding of climate resilient crops and this work more broadly highlights the value of integrating diverse disciplines to scale up the discovery of the climatic drivers of adaptation and functionally significant genetic variation at molecular resolutions.

All data generated or analyzed during this study are included in the manuscript and supporting files.

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Author details

1. J Grey Monroe

   1. Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, United States
   2. Graduate Degree Program in Ecology, Colorado State University, Fort Collins, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   greymonroe@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4025-5572

2. Tyler Powell

   1. Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, United States
   2. Department of Biology, Colorado State University, Fort Collins, United States

   Contribution

   Validation, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Nicholas Price

   Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, United States

   Contribution

   Data curation, Formal analysis, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6672-2952

4. Jack L Mullen

   Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, United States

   Contribution

   Supervision, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

5. Anne Howard

   Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, United States

   Contribution

   Supervision, Investigation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

6. Kyle Evans

   Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, United States

   Contribution

   Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9895-5010

7. John T Lovell

   HudsonAlpha Institute for Biotechnology, Huntsville, United States

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8938-1166

8. John K McKay

   1. Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, United States
   2. Graduate Degree Program in Ecology, Colorado State University, Fort Collins, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

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