Individual plant genetics reveal the control of local adaption in European maize landraces

Individual plant genetics reveal the control of local adaption in European maize landraces | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Individual plant genetics reveal the control of local adaption in European maize landraces Leke Victor Aiyesa, Timothy Beissinger, Stefan Scholten, Wolfgang Link, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4925882/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background European maize landraces encompass a large amount of genetic diversity, allowing them to be well-adapted to their local environments. This diversity can be exploited to improve the fitness of elite material in the face of a changing climate. Results We characterized the genetic diversity of 333 individual plants from 40 European maize landrace populations (EMLPs). We identified five genetic groups that mirrored the proximities of their geographical origins. Fixation indices showed moderate differentiation among genetic groups (0.034 to 0.093). More than half of the genetic variance was observed to be partitioned among individuals. Nucleotide diversity of EMLPs decreased significantly as latitude increased (from 0.16 to 0.04), suggesting serial founder events during maize expansion in Europe. GWAS with latitude, longitude, and elevation as response variables identified 28, 347, and 68 significant SNP positions, respectively. We pinpointed significant SNPs near dwarf8, tb1, ZCN7, ZCN8, and ZmMADS69, and identified 137 candidate genes with ontology terms indicative of local adaptation in maize, regulating the adaptation to diverse abiotic and biotic environmental stresses. Conclusions This study suggests a quick and cost-efficient approach to identifying genes involved in local adaptation without requiring field data. The EMLPs used in this study have been assembled to serve as a continuing resource of genetic diversity for further research aimed at improving agronomically relevant adaptation traits. Individual plants nucleotide diversity founder events GWAS selection signatures candidate genes. Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND The domestication of maize took place approximately 9,000 years ago in the Balsas district valley of Mexico, and its first introduction to Europe was in 1493 through the Caribbean by Columbus (Ranere et al., 2009 ). The cultivation of maize in northern European regions was first reported in Germany in 1539, followed by a rapid expansion that led to immense diversification and adaptation to long days and low temperatures (Tenaillon & Charcosset, 2011). There are also claims of secondary introductions to different northern parts of Europe from North America (Finan, 1948 ; Tenaillon & Charcosset, 2011). Currently, maize landrace populations are grown over a wide range of latitudes from 25°N to 53°N and at elevations up to 3000 m (X. Wang et al., 2023 ; Navarro et al., 2017 ). Previous reports have characterized European maize landrace populations (EMLPs) within and across countries utilizing morphological differences, such as the number of days to flowering (Gouesnard et al., 1997 ; Rebourg et al., 2001 ; Gauthier et al., 2002). EMLPs collected from northeastern Europe for instance were noted for earlier flowering compared to those from southern Europe (Rebourg et al., 2001 , P. Gauthier et al., 2002). Despite this, morphological characterizations became inconsistent due to environmental interactions (Rebourg et al., 2001 ). The extensive adoption of molecular markers, such as isozyme, restriction fragment length polymorphisms (RFLP), and single sequence repeats (SSRs) (Revilla et al., 1998 ; Dubreuil et al., 1996; Rebourg et al., 1999, Reif et al., 2006), and more recently, single nucleotide polymorphisms (SNPs) from SNP arrays (Ganal et al., 2011 ; Unterseer et al., 2014 ; Mayer et al., 2022 ), largely mitigated this issue. However, discovering large and novel polymorphisms, especially through genotyping-by-sequencing (GBS), should provide a broad genetic base and prevent misinterpretation of diversity studies through ascertainment bias (Inghelandt et al., 2011; Frascaroli et al., 2012 ). Studies have also revealed the effect of geographic origin on the genetic diversity of maize populations concerning altitude (Tenaillon & Charcosset, 2011; Navarro et al., 2017 ), longitude, and latitude variations (Gauthier et al., 2002; Revilla et al., 2002 ; Wang et al., 2017; Diaw et al., 2021 ). This diversity can be harnessed to improve the fitness of elite materials in response to changing climates (Navarro et al., 2017 ; Kawecki & Ebert, 2004 ), because not all geographic and genetic diversity was captured in selecting the few parental landraces used to develop the elite lines available in Europe today (Strigens et al., 2019), leading to loss of some favorable alleles from the gene pool and limiting their potential for adaptation to extreme climatic conditions (Mayer et al., 2020 ). Moreover, as the climate changes, new and currently neutral or negative alleles may become desirable for local adaptation, altering the value of populations previously considered less important (Mayer et al., 2020 ). Local adaptation is assumed when a local population exhibits higher fitness trait values than non-local populations (Janzen et al., 2022 ). This phenomenon in maize landraces is complex, and the understanding of it is not fully established (Millet et al., 2016 ; Janzen et al., 2022 ). Studies have explored methods including multi-environment trials (Navarro et al., 2017 ), reciprocal transplantation (Nuismer & Gandon, 2008 ; Gibson et al., 2016 ; Janzen et al., 2022 ), and common garden experiments (Clazsen et al., 1940; Fraser et al., 2011; Savolainen et al., 2013 ) to observe phenotypic variation and functional variants for maize adaptation. While these experimental methods have been useful, they are laborious, costly, and time-consuming. In this study, we carried out extensive genetic screening of EMLPs to understand their population structure and investigated patterns of genetic diversity across geographic regions. We further identified genomic loci under selection for adaptation to local environments, leveraging each population’s geographical information. RESULTS SNP dataset We genotyped 340 individual plants from 40 landrace populations across nine countries of origin, representing the diversity of maize in Europe (Fig. S1). After filtering, we retrieved 152,671 SNPs from 333 individual plants. These SNPs were distributed across the 10 chromosomes, ranging from 10,677 to 23,543 SNPs for chromosomes ten and one, respectively. SNPs were well distributed within chromosomes, except for centromeric regions, which showed a lower SNP density. This result is consistent with reported GBS SNP-data for maize populations and the relatively low SNP density at centromeres and peri-centromeres is expected due to low recombination (Bauer et al., 2013 ; Wolfgruber et al., 2016 ). The distribution of genome-wide minor allele frequencies (MAF) and heterozygosities were as expected (Fig. S2). Genetic clustering and genetic differentiation of EMLPs Analyzing diverse landraces across a wide range of geographical origins captures the broad genetic diversity available in maize (Aguirre-Liguori et al., 2019 ; Arteaga et al., 2016 ). To confirm this, we examined the relationship between the geographic distance (using latitude, longitude, and elevation of origin) and genetic distance (determined by SNP markers) of EMLPs. The Mantel test (Mantel, 1967 ) revealed a significant correlation of 0.41 (p-value < 0.001) between pairwise geographic and genetic distances. We further identified genetic groups based on hierarchical clustering analysis at k = 5 (Fig. S3). These groups closely mirrored the geographical proximities of EMLPs, as presented in the principal coordinate analysis (Fig. 2 ). This provides a broader view of reported classifications of EMLPs into northeastern populations (group 1), southeastern populations (group 2 and group 4), south-Italian populations (group 3), and the Pyrenees populations (group 5) (Gauthier et al., 2002; Tenaillon et al., 2011; Galic et al., 2023). Group 2 exhibited the largest spread of populations across three southeastern countries and southern Italy, possibly due to the hybridization of tropical dents and northern flint populations that occurred in this region, reported to be the European corn belt (Tenaillon et al., 2011; Galic et al., 2023). We further assessed genetic differentiation between the groups using their pairwise fixation indices (Fst) (Weir and Cockerham, 1984 ). Fst ranged from 0.034 between Group 2 and Group 3 to 0.093 between Group 4 and Group 5 (Fig. 2 ). We observed a high Fst between the northern and southern groups, consistent with previous reports for European maize landraces (McLean-Rodríguez et al., 2021 ; ) and between the Pyrenees (west) and southeastern groups. Interestingly, for three out of the nine countries of origin (Bulgaria, Croatia, and Italy), we found populations belonging to different groups, contrary to our expectations. The mixtures of group 2 and group 3 found among south Italian populations could be attributed to their elevational differences. While group 2 south Italian populations were found in the lowlands with an average elevation of 245.6m, group 3 populations belonged to relatively higher elevations with an average elevation of 690m (Fig. S4). Nucleotide diversity of EMLPs and founder effects Genetic variation between maize populations is low compared to within populations as observed in the analysis of molecular variance (AMOVA) which partitioned 51% of the genetic variance to individuals within populations, consistent with reported estimates (Reif et al., 2003 ; Mir et al., 2013 ), 36.4% between populations, and 12.6% between groups (Fig. S6). Each population in our EMLP had 5–10 genotyped individuals, except for one population (TUR_3602) with 3 individuals. These individuals were used to estimate the population’s nucleotide diversity (π), measuring the average pairwise difference between all possible pairs of individuals in a population. A high π value would indicate that such a population consists of more genetically distant individuals vis-a-vis (Fig. S7). The values of π ranged from 0.04 for a Hungarian population (HUN_116) to 0.16 for a southeastern population (TUR_3602), while the average π value was 0.106 across populations (Fig. S9). Our values are comparable to reported π for maize landraces (Brandenburg et al., 2017 ; Wang et al., 2017; Beissinger et al., 2016; Gauthier et al., 2002). Interestingly, we observed a significant decline (p-value = 0.005) in π as latitude increases, suggesting a serial founder effect resulting in the northeastern populations. This aligns with reports on domestication events that led to major expansion and selection of EMLPs from the tropical south to the temperate north and northeastern regions (Galic et al., 2023; Diaw et al., 2021 ; Tenaillon et al., 2011; Rebourg et al., 2001 ). The impact of longitude and elevation on EMLP’s diversity was insignificant (p-values > 0.5) (Fig. 1 E), GWAS identifies genomic regions under selection Maize landraces are genetically diverse materials that have since long adapted to their local environments, enabling researchers to explore their geographical properties in identifying genetic controls for local adaptation (Navarro et al., 2017 ). This inspired us to perform GWAS using the latitude, longitude, and elevation of origin of our EMLPs as response variables and the 152,671 SNPs as explanatory variables. We corrected the population structure using the first three principal coordinates, explaining 22.8% of the total variation and a Fixed and Random Model Circulating Probability Unification (FarmCPU) algorithm (J. Wang & Zhang, 2021). We identified 28, 347, and 68 significant SNPs, respectively, flagged at p < 0.00001 (-log10 5 ) to allow only two to three false positives (Fig. 3 ). We compared our significant SNPs with reported genomic positions for flowering time and plant height, which are indicator traits of adaptation in maize (Janzen et al., 2022 ; Bouchet et al., 2013 ; Sunoj et al., 2016 ; Liu et al., 2021 ), and found overlapping peaks. Notably, on chromosome 4, we identified two significant associations for elevation between 150 Mb and 200 Mb. This chromosome region has been reported for In4vm - an introgression from highland Mexicana to highland maize consisting of floral genes (Hufford et al., 2013; Navarro et al., 2017 ). We also found five significant SNPs located in the vicinity of dwarf8 (221–225 Mb) for latitude, and two significant SNPs close to tb1 (261 Mb and 267 Mb) on chromosome 1 for longitude. ZCN7 (Chromosome 6, 171.3 Mb) and ZCN8 (Chromosome 8, 128.5 Mb) were identified for longitude. These genes have been reported to regulate floral formation in maize and to be strongly associated with flowering time variation (Brandenburg et al., 2017 ; Camus-Kulandaivelu et al., 2006; Meng et al., 2011 ) (Table S2). Candidate genes and GO enrichment analysis A search for candidate genes across all significant associated SNPs using a window of ± 50 Kb, led to the identification of 42, 49, and 46 associated candidate genes for latitude, longitude, and elevation, respectively. Leveraging the gene ontology (GO) files for B73 v4.0, we elucidated their biological functions, some of which describe local adaptation properties in maize (see Table S2). Upon inspecting the topmost significant SNP for longitude on chromosome 5 at 171.3Mb (p-value 1.32e-84), we found gene model Zm00001d016653 , 17 Kb away, to be an ortholog of AT1G12910 in A. thaliana , encoding for LIGHT-REGULATED WD1 (LWD1), a clock protein regulating the circadian period length and photoperiodic flowering. For elevation, the topmost SNP (p-value 1.4e-123) on chromosome 3 at 26.4 Mb was located 60 Kb away from Zm00001d040082 , also an ortholog of AT2G01130 in A. thaliana. This gene model is a member of the DEA(D/H)-box RNA helicase family protein expressed during petal differentiation. Additionally, we conducted GO enrichment analysis to describe conserved GO terms of the identified genes. GO enrichment was successful for genes associated with latitude, as presented in Fig. 4 . However, elevation and longitude had no significantly enriched GO terms. The most enriched terms for latitude, namely "sulphur amino acid metabolic process" and "monoatomic anion transport," play active roles in Anion channels/transporters, which are crucial to signaling pathways leading to the adaptation of plant cells to abiotic and biotic environmental stresses, as well as in the control of metabolism and maintenance of electrochemical gradients (de Angeli et al., 2007 ; Barbier-Brygoo et al., 2000 ). DISCUSSION Genetic grouping and genetic differentiation of EMLPs Recent diversity studies on European maize have primarily focused on inbred lines derived from a limited number of landraces (Mayer et al., 2022 ; Galic et al., 2023; Diaw et al., 2021 ). Earlier reports explored the diversity of maize landraces in Europe, either from one or a few countries of origin or a restricted geographical region (Mayer et al., 2022 ; Galic et al., 2023; Diaw et al., 2021 ). Many of these studies relied on marker types such as SSR, RFLPs, and Isozymes, and often utilized bulk genotyping of multiple individuals within a population (Rebourg et al., 2001 ; Gauthier et al., 2002; Revilla et al., 2002 ; Reif et al., 2003 ; Dubreuil et al., 1998; Mir et al., 2013 ; Bennetzen et al., 2018 ). In our study, we assessed 333 individual plants from 40 European maize landrace populations (EMLPs) across nine countries of origin using 152,671 SNP markers. This provides a broad genetic base for evaluating European maize populations. The diversity in the geographic origins of these landraces, explaining 41% of the genetic variation as presented in the results, is comparable to 46% reported by Navarro et al. ( 2017 ), although less than the 0.71 reported by Gauthier et al. (2002), possibly due to differences in sample size and the type of genetic markers used. Nevertheless, these values underscore the significant impact of geographical spread on maize genetic diversity. The use of genetic groups for the classification of EMLPs into northeastern, southeastern, south Italian, and the Pyrenees aligns with previous attempts at molecular classification of European maize (Brandenburg et al., 2017 ; Galic et al., 2023; Gauthier et al., 2002). In addition, we observed a mixture of the identified groups within countries and regions of origin. We anticipated that populations within a country would exhibit more similarities than populations from different countries, however, this was not the case. For instance, the mixture of two groups among the south Italian populations, attributed to their elevational differences, suggests that there exists remarkable genetic differentiation for adaptation to variable elevations of EMLPs. This, coupled with admixture observed in Bulgaria and Croatia, suggests that geographical proximity does not always guarantee genetic similarities. In other words, EMLPs expected to be more genetically similar across much of their genomes might differ significantly in some strongly divergent regions which may contain genes enabling populations to adapt to slightly different environments. This underscores that the selection of EMLPs for diversity representation is less effective when based solely on the country of origin, but optimal if combined with variations in latitude, longitude, and elevation of origin. Conversely, group 2 comprises several closely related populations spanning four countries of origin. This mixture of countries within a group reveals the extent of serial exchange of materials and hybridization between tropical dents and temperate flints in the European corn-belt region (Tenallion et al., 2011; Galic et al., 2023). The insights gained from the clustering analysis should guide researchers and breeders in making informed selections of populations to study. Individual-plant-based nucleotide diversity estimates Due to the heterogeneity observed in maize landraces, as evident from the AMOVA result partitioning more than half of the genetic variance within the population, characterization should be carried out based on representative sets of individuals to efficiently capture the population’s diversity (Diaw et al., 2021 ; Reyes-Valdés et al., 2013 ; Dubreuil and Charcosset, 1998 ). This approach prevents the loss of information about individual plant genetic variation (Rebourg et al., 2001 ) and aids in identifying selection scans caused by linkage disequilibrium, which is limited in bulk genotyping (Hirsch et al., 2014 ). Gouda et al. ( 2020 ) suggested a minimum of 5 individuals per population, and studies have used up to 30 individuals for estimating maize population diversity using SSRs (Reif et al., 2003 ). While cost considerations are relevant (which may become highly comparable in the near future), genotyping individual plants is recommended over bulk genotyping for an optimal estimate of population diversity. Our EMLP individual plant panel allows us to explore pairwise differences among individuals for estimating population diversity using π and assessing the degree of heterogeneity within populations. This measure of diversity has been reported as a reliable estimator, particularly when dealing with a large genome-wide dataset and a limited number of individuals per population (Brandenburg et al., 2017 ; Hufford et al., 2012 ). Geographical variation in genetic diversity reveals serial founder effect The gradual decline in π as latitude increases suggests a serial founder effect as maize expanded from the tropical south to the temperate north of Europe, possibly due to selection for adaptation to long days and lower temperatures (Tenaillon et al., 2001). Founder populations are a product of recurrent sub-sampling of diversity from preceding populations (Slatkin & Excoffier, 2012 ; Austerlitz et al., 1997 ). A similar pattern was observed for American maize according to Wang et al., (2017) where an extreme founder effect was observed for Andean populations based on their distance from the maize domestication center in south-western Mexico. Gauthier et al. (2002) reported two genomic regions driving European maize latitude variation with SSRs and several other reports have documented the close population structure of Europe's northern populations compared to the southern populations (Rebourg et al., 2001 ; Revilla et al., 2002 ; Mir et al., 2013 ). These suggest that as maize migrates from centers of domestication across latitudinal gradients, genetic diversity is impaired due to selection (Ramachandran et al., 2005 ; Henn et al., 2015 ). The report of a second introduction from North America (Rebourg et al., 2002; Dubreuil et al., 1998) is not in contrast since the diversity of European maize represents ∼75% of the Americas, such that several landraces cultivated in southwestern Europe are related to that of Mesoamerican, and landraces from northern Europe are similar to north American flint varieties (Camus-Kulandaivelu et al., 2006; Tenaillon et al., 2001). This study can therefore be extensively adapted to understand the genetic dimensions of maize diversity from the Mesoamerican to North American regions with similar latitudes. This pattern was also observed in the Fst result where the northeastern group had the highest differentiation from other populations. Detailed Fst results (Fig. S8 and Fig. S9) showed that the farthest northeastern population from Hungary with the lowest π had the greatest differentiation (Holsinger & Weir, 2009 ). Signatures of selection for local adaptation in maize The most commonly used statistical methods for identifying selection signatures are Fst outlier analysis and genetic environment association analysis (GEA) (Ahrens et al., 2018 ; Galic et al., 2023). Here, we adopted the GEA approach, focusing on the association between SNPs and environmental variables under the assumption that genome-wide diversity primarily reflects the action of divergent selection, in this case, for local adaptation (Navarro et al., 2017 ). We employed a straightforward and rapid approach using the latitude, longitude, and elevation of the origin of geographically and genetically diverse populations to identify location-specific adaptation loci, especially in cases where cost constraints exist. The 443 significant associations identified were broadly distributed within and across chromosomes, affirming the polygenicity of local adaptation traits in maize (Navarro et al., 2017 ). Some of these loci identified align with reported loci for flowering time and plant height, as shown in Table S1, supporting their strong relationship with maize adaptation (Hufford et al., 2012 ; Navarro et al., 2017 ). Agronomic traits, such as plant height and flowering time, have served as indicators of fitness for geographic and climatic variation (Bouchet et al., 2013 ; Liu et al., 2021 ). Janzen et al. (2021) confirmed the relative home-site advantage displayed by local maize populations for various agronomic and vegetative traits, including plant height and flowering time, in a lowland versus highland site cross-reaction norm experiment. Hufford et al. ( 2012 ) untangled the genomic targets for highland adaptation in maize on chromosome 4 ( In4vm , between 150 Mb − 200 Mb) as a result of gene introgression from the Mexicana wild relative, a finding later confirmed to be significantly associated with flowering time by Navarro et al. ( 2017 ). We found two significant associations within this 50 Mb region for elevation, contributing to the adaptation of European maize to higher elevations. Longitudinal variation of European maize accounted for the majority of the associations, a discovery earlier reported by Gauthier et al. (2002), who detected 5 SSR alleles for longitude compared to 2 SSR alleles for latitude, emphasizing the critical role of longitudinal distance in understanding European maize local adaptation. During the domestication process, the tb1 locus experienced a significant reduction in diversity, and the Dwarf8 locus revealed signs of purifying selection accompanied by substantial diversity loss (Studer et al., 2011 ; Wang et al., 1999 ). While unverified, certain Northern Flint germplasms, such as sweet corn, exhibit a morphology resembling the undomesticated tb1 phenotype. It is possible that the region encompassing Dwarf8 and tb1 underwent a bottleneck with multiple selective sweeps, leading to the formation of extended haplotype blocks for this region (Larsson et al., 2013 ). ZCN7 and ZCN8 are found in the photoperiod pathway and play a role in regulating flowering time (Shi et al., 2022; Guo et al., 2018 ; Brandenburg et al., 2017 ; Dong et al., 2012 ). However, environmental variables such as soil type, and soil microbes, among others, would have possibly introduced additional location-specific alleles and genes (e.g. Zm00001d016653 and Zm00001d040082 ) that we uncovered in the study. CONCLUSION This study of European maize landrace populations (EMLPs) covers more geographical regions than previous reports, providing a valuable resource for selecting representative populations that largely capture the gene pools of European maize diversity for breeding, conservation, and research. We conclude that latitude, elevation, and longitude are key factors in the genetic groupings of EMLPs, even within their country of origin. We propose genotyping multiple individuals per population for a thorough examination of the genetic parameters of the EMLP. We further revealed serial founding events that likely occurred during maize expansion in Europe due to selection. By using latitude, elevation, and longitude of origin as response variables, we identified both reported and novel SNP associations and genes linked to local adaptation. Thus, in the absence of phenotypic information from field experiments with multi-environment replicates, this approach proved adequate for making informative associations for local adaptation traits. We have demonstrated the potential to use individual plants from maize landrace populations as a resource to gain insight into EMLPs' genetic structure, selection events, and the genetic basis of their location-specific adaptation. MATERIALS AND METHODS Plant material and experiment Seeds from 40 EMLP were collected from the Leibniz Institute of Plant Genetics and Crop Plant Research IPK ( https://www.ipk-gatersleben.de/ ) germplasm repository. These landraces were from nine countries of origin, namely Germany, France, Spain, Hungary, Croatia, Austria, Bulgaria, Turkey, and Italy (Fig. 1 . Supplementary Table 1). Fifty kernels per population were sown in two rows on each plot at a distance of 15 cm by 95 cm. Plots were separated by a commercial German hybrid - SY-telias . Ten individual plants were sampled six weeks after sowing from each population, to be tracked through genotyping and phenotyping. The experiment was conducted at Rosdorf, Goettingen, Germany (coordinates, 51.512679, 9.886327). Genotyping Leaf tissues were collected from a total of 340 individual plants across the 40 populations. These were collected from the youngest visible leaf 9 weeks after sowing and were lyophilized and submitted to the University of Wisconsin-Madison Biotechnology Center for DNA extraction using the Qiagen DNeasy 96 plant kit. DNA yield was quantified with Promega QuantiFluor on a Tecan Spark 10 M. DNA concentration was verified using the Quant-iT™ PicoGreen dsDNA kit (Life Technologies, Grand Island, NY). Libraries were prepared as in Elshire et al ( 2011 ) with minimal modification; in short, 150 ng of DNA was digested with ApeK I (New England Biolabs, Ipswich, MA) after which barcoded adapters amenable to Illumina sequencing were added by ligation with T4 ligase (New England Biolabs, Ipswich, MA). The 96 adapter-ligated DNA samples were pooled and amplified to provide library quantities amenable for sequencing, and adapter dimers were removed by SPRI bead purification. The quality and quantity of the finished libraries were assessed using the Agilent Bioanalyzer High Sensitivity Chip (Agilent Technologies, Inc., Santa Clara, CA) and Qubit dsDNA HS Assay Kit (Life Technologies, Grand Island, NY), respectively. Libraries were sequenced targeting about 400 million reads on a NovaSeq6000 (Illumina Inc.). Variant calling and filtering Demultiplexing of the pooled raw reads dataset was done with sabre tools (Najoshi 2013) using the barcode information. Demultiplexed reads were mapped to the B73 maize reference genome (Jiao et al., 2017) using the Burrow Alignment tools (BWA-mem) (Li & Durbin, 2009 ). Mapped reads were further trimmed and sorted using samtools (Li et al., 2009). Variants were called using bcftools (Danecek et al., 2021 ). Further filtering of the SNP data was performed using vcftools (Danecek et al., 2021 ) to remove multi-allelic loci, and structural variants and retain only SNP with minor allele frequencies (MAF) greater than 0.01, QUAL scores > 30, minimum depth > 5, maximum depth < 500, and average missingness proportion was 0.14. Imputation of missing value was done using Beagle v5.4 (Browning et al., 2021 ) and imputation accuracy was 0.96. SNP density across and within chromosomes was inspected using CMplot r-package (Yin et al., 2015). Distribution of MAF and heterozygosity were performed using snpReady r-package (Granato and Fritsche-Neto 2017 ) (Fig. S2, Fig. S3 ). Genetic diversity and population structure Pairwise genetic and geographic distances among individuals were estimated using their marker dataset and geographical data (latitude, longitude, and elevation) respectively as described in Chu et al. ( 2020 ). Mantel test (Mantel, 1967 ) was used to check the correlation between genetic and geographic distance. Hierarchical clustering analysis was performed using the genetic distance matrix for population structure analysis as documented in the r-package ‘ape’ v5.0 (Paradis & Schliep, 2018 ). Multidimensional scaling analysis was performed using principal coordinates to inspect the clustering of individuals and populations (Chu et al., 2020 ). Country of origin, year of collection, collection site, latitude, longitude, and elevation of the collection sites as contained in the passport data ( https://eurisco.ipk-gatersleben.de/ ) for each population were further examined to explain the population structure. Analysis of molecular variance (AMOVA) and fixation indices ( F ST ) among groups and populations were estimated using the poppr r-package v 2.9.4 (Kamvar et al., 2014) and Hierfstat r-package v0.5-11 (Goudet, 2004 ) respectively. For the population’s nucleotide diversity ( π ), the average pairwise genetic distance between individuals within a population was used. GWAS, selection signature, and search for candidate genes Genome-wide association analysis was conducted by adopting the latitude, elevation, and longitude of origin of the populations as response variables (assuming genetic adaptation of the population to elevation, latitude, and longitude of the collection sites as real traits) and the SNP markers as explanatory variables. Fixed and Random Model Circulating Probability Unification (FarmCPU), implemented in the GAPIT3 R-package (J. Wang & Zhang, 2021) was used to scan the SNP markers for association using the first three principal coordinates as covariates. FarmCPU performs a single marker scan with associated markers as cofactors in a fixed effect model and independently optimizes the associated cofactors in a random effect model. This helps to correct for multiple testing errors and reduce the risk of compromising the true positives as is the case in mixed-linear models (MLM) (J. Wang & Zhang, 2021). P-values of the GWAS results were corrected using 0.00001 and plotted as Manhattan plots and as QQ plots. The distribution of allele frequency of conserved significant SNPs was investigated for latitude, longitude, and elevation. Genes within a ± 50 Kb window of significant SNPs were investigated and functionally annotated with the gostprofiler R-package (Kolberg et al., 2020 b) using gff3 file (v4) from Ensembl ( https://ensembl.gramene.org/Zea_mays/Info/Index ) and the maize genome database- https://www.maizegdb.org/genome/assembly/Zm-B73-REFERENCE-GRAMENE-4.0 Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE Not applicable. CONSENT FOR PUBLICATION Not applicable. DATA AND CODE Data available at https://figshare.com/account/home#/projects/216463 Code for all analysis are available at https://github.com/Aiyesa/EMLP-local-adaptation FUNDING This project was funded by the University of Göttingen. AUTHORS' CONTRIBUTIONS Leke Victor Aiyesa, contributed to the experiment design and data collection, performed all data analysis, and wrote the original draft of the manuscript. Timothy Beissinger, obtain the funding for the project, obtained the plant materials used from the genebank, designed the scope of the experiment, supervised data analysis, contributed to the review and writing of the manuscript. Stefan Scholten and Wolfgang Link supervised the experiment, data analysis, contributed to the review and writing of the manuscript. Birgit Zumbach supervised the data analysis, contributed to the review and writing of the manuscript. Dietrich Kaufmann, managed the field and greenhouse experiments. ACKNOWLEDGEMENTS We would like to thank Gesellschaft für Wissenschaftliche Datenverarbeitung GmbH (GWDG), Göttingen, for providing high-performing cluster service for the computational analysis, Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK) for providing the plant materials used for this study. Centre for Integrated Breeding Research (CiBreed), Georg-August-University of Goettingen, Germany. The University of Wisconsin–Madison Biotechnology Center’s DNA Sequencing Facility (Research Resource Identifier – RRID: SCR_017759) was used for DNA extraction, generating GBS libraries, and sequencing GBS libraries. AUTHORS' INFORMATION AUTHOR - AFFILIATIONS Leke Victor Aiyesa - Division of Plant Breeding Methodology, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany. Timothy Beissinger - Google X, The Moonshot Factory, Mountain View, California, United States. Stefan Scholten - Division of Crop Plants Genetics, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany Wolfgang Link - Division of Plant Breeding Methodology, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany. Brigit Zumbach - Division of Plant Breeding Methodology, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany. ETHICS DECLARATION The authors declare no competing interests. References Aguirre-Liguori, J. A., Ramírez-Barahona, S., Tiffin, P., & Eguiarte, L. E. (2019). Climate change is predicted to disrupt patterns of local adaptation in wild and cultivated maize. Proceedings of the Royal Society B: Biological Sciences , 286 (1906), 20190486. https://doi.org/10.1098/rspb.2019.0486 Ahrens, C. W., Rymer, P. D., Stow, A., Bragg, J., Dillon, S., Umbers, K. D. L., & Dudaniec, R. Y. (2018). 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Identification of a functional transposon insertion in the maize domestication gene tb1. Nature Genetics, 43(11), 1160–1163. https://doi.org/10.1038/ng.942 Sunoj, V. S. J., Shroyer, K. J., Jagadish, S. V. K., & Prasad, P. V. V. (2016). Diurnal temperature amplitude alters physiological and growth response of maize (Zea mays L.) during the vegetative stage. Environmental and Experimental Botany , 130 , 113–121. https://doi.org/10.1016/j.envexpbot.2016.04.007 Tenaillon M I. ,Sawkins M C, Long A D., Gaut R L., Doebley J F., and Gaut B S.. Patterns of DNA sequence polymorphism along chromosome 1 of maize (Zea mays ssp. mays L.). https://doi.org/10.1073/pnas.151244298 Tenaillon, M. I., & Charcosset, A. (2011a). A European perspective on maize history. Comptes Rendus Biologies , 334 (3), 221–228. https://doi.org/10.1016/j.crvi.2010.12.015 Thornsberry, J. M., Goodman, M. M., Doebley, J., Kresovich, S., Nielsen, D., & Buckler, E. S., IV. (2001). Dwarf8 polymorphisms associate with variation in flowering time. Nature Genetics, 28(3), 286–289. https://doi.org/10.1038/90135 Unterseer, S., Bauer, E., Haberer, G., Seidel, M., Knaak, C., Ouzunova, M., Meitinger, T., Strom, T. M., Fries, R., Pausch, H., Bertani, C., Davassi, A., Mayer, K. F., & Schön, C.-C. (2014). A powerful tool for genome analysis in maize: Development and evaluation of the high density 600 k SNP genotyping array. BMC Genomics, 15(1). https://doi.org/10.1186/1471-2164-15-823 Wang, J., & Zhang, Z. (2021). GAPIT version 3: Boosting power and accuracy for genomic association and prediction. Genomics, Proteomics & Bioinformatics, 19(4), 629–640. https://doi.org/10.1016/j.gpb.2021.08.005 Wang, R.-L., Stec, A., Hey, J., Lukens, L., & Doebley, J. (1999). The limits of selection during maize domestication. Nature, 398(6724), 236–239. https://doi.org/10.1038/18435 Wang, X., Han, J., Li, R., Qiu, L., Zhang, C., Lu, M., Huang, R., Wang, X., Zhang, J., Xie, H., Li, S., Huang, X., & Ouyang, X. (2023). Gradual daylength sensing coupled with optimum cropping modes enhances multi-latitude adaptation of rice and maize. Plant Communications, 4(1), 100433. https://doi.org/10.1016/j.xplc.2022.100433 Weir, B. S., & Cockerham, C. C. (1984). Estimating f-statistics for the analysis of population structure. Evolution, 38(6), 1358. https://doi.org/10.2307/2408641 Wolfgruber, T. K., Nakashima, M. M., Schneider, K. L., Sharma, A., Xie, Z., Albert, P. S., Xu, R., Bilinski, P., Dawe, R. K., Ross-Ibarra, J., Birchler, J. A., & Presting, G. G. (2016). High-quality maize centromere 10 sequence reveals evidence of frequent recombination events. Frontiers in Plant Science , 7 . https://doi.org/10.3389/fpls.2016.00308 Zhou, Z., Lu, X., Zhang, C., Li, M., Hao, Z., Zhang, D., Yong, H., Han, J., Li, X., & Weng, J. (2023). A differentially methylated region of the ZmCCT10 promoter affects flowering time in hybrid maize. The Crop Journal, 11(5), 1380–1389. https://doi.org/10.1016/j.cj.2023.05.006 Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYFIGURES.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4925882","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346809683,"identity":"34c62051-8a16-41b9-a8d5-11e263ec7e32","order_by":0,"name":"Leke Victor Aiyesa","email":"data:image/png;base64,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","orcid":"","institution":"University of Göttingen","correspondingAuthor":true,"prefix":"","firstName":"Leke","middleName":"Victor","lastName":"Aiyesa","suffix":""},{"id":346809686,"identity":"10c8d92a-b6db-4596-acc9-41c3ec47abfa","order_by":1,"name":"Timothy Beissinger","email":"","orcid":"","institution":"Google X, The Moonshot Factory","correspondingAuthor":false,"prefix":"","firstName":"Timothy","middleName":"","lastName":"Beissinger","suffix":""},{"id":346809689,"identity":"7bf48753-8d96-45e3-8026-be720d665ec7","order_by":2,"name":"Stefan Scholten","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Scholten","suffix":""},{"id":346809693,"identity":"aa62fe96-34f8-4fab-9a32-edb93e44983d","order_by":3,"name":"Wolfgang Link","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Wolfgang","middleName":"","lastName":"Link","suffix":""},{"id":346809698,"identity":"cbab0a89-3700-4a50-b888-69dd7da5b45e","order_by":4,"name":"Birgit Zumbach","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Birgit","middleName":"","lastName":"Zumbach","suffix":""},{"id":346809699,"identity":"d84e0545-b164-4dd1-95b1-7c3062f12e24","order_by":5,"name":"Dietrich Kaufmann","email":"","orcid":"","institution":"University of Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Dietrich","middleName":"","lastName":"Kaufmann","suffix":""}],"badges":[],"createdAt":"2024-08-16 15:15:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4925882/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4925882/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64356590,"identity":"02b47cc9-9079-4507-917a-e2ad4e42be36","added_by":"auto","created_at":"2024-09-12 06:01:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":86209,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of SNPs within and across chromosomes showing SNP density per 1 Mb.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4925882/v1/1b813cc869ff046140de677e.png"},{"id":64357086,"identity":"d77d2427-f441-4b48-aaf9-6b782ffbd426","added_by":"auto","created_at":"2024-09-12 06:09:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":184283,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of European maize landrace populations (A) \u0026nbsp;and principal coordinate analysis (PCoA) (B) coloured by the optimal number of genetic groupings (at K = 5). Genetic differentiation using Fixation indices (Fst) among the groups (C).Regression of latitude (D) and elevation (E) on nucleotide diversity (π) across populations and groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4925882/v1/8cab1e4b217015eae19004f0.png"},{"id":64356593,"identity":"3bead3dc-3df0-4419-bb58-c405fcc9b819","added_by":"auto","created_at":"2024-09-12 06:01:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100672,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot displaying the GWAS results for latitude, longitude, and elevation. The y-axis represents the negative logarithmic transformation of the p-values, while the x-axis corresponds to the maize chromosomes ('chr'). The red horizontal line at -log10\u003csup\u003e5\u003c/sup\u003e (p-value = 0.00001) serves as the threshold for identifying significantly associated SNPs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4925882/v1/949f23ba8eea895c333b00ea.png"},{"id":64356591,"identity":"98fdb858-fdfa-484d-8993-4f8ce6309e1e","added_by":"auto","created_at":"2024-09-12 06:01:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":232349,"visible":true,"origin":"","legend":"\u003cp\u003eEnriched GO terms for 42 candidate genes associated with latitude, and significantly enriched at p-value \u0026lt; 0.05 (A). Other GO terms that are not enriched indicate local adaptation across latitude, longitude, and elevation (B).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4925882/v1/9df420cefdb060ffa68f2808.png"},{"id":64744761,"identity":"3ef098e7-16c0-4b69-aeac-22825cdca80c","added_by":"auto","created_at":"2024-09-18 09:30:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1193407,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4925882/v1/8f7fc42b-99e2-4e59-94e5-c64d2793e272.pdf"},{"id":64357087,"identity":"442f7e33-950f-4504-a20f-adf6fb36b1cf","added_by":"auto","created_at":"2024-09-12 06:09:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1313631,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYFIGURES.docx","url":"https://assets-eu.researchsquare.com/files/rs-4925882/v1/d45cb1edb5a945d784f35bd0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Individual plant genetics reveal the control of local adaption in European maize landraces","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eThe domestication of maize took place approximately 9,000 years ago in the Balsas district valley of Mexico, and its first introduction to Europe was in 1493 through the Caribbean by Columbus (Ranere et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The cultivation of maize in northern European regions was first reported in Germany in 1539, followed by a rapid expansion that led to immense diversification and adaptation to long days and low temperatures (Tenaillon \u0026amp; Charcosset, 2011). There are also claims of secondary introductions to different northern parts of Europe from North America (Finan, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1948\u003c/span\u003e; Tenaillon \u0026amp; Charcosset, 2011). Currently, maize landrace populations are grown over a wide range of latitudes from 25\u0026deg;N to 53\u0026deg;N and at elevations up to 3000 m (X. Wang et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrevious reports have characterized European maize landrace populations (EMLPs) within and across countries utilizing morphological differences, such as the number of days to flowering (Gouesnard et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Rebourg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gauthier et al., 2002). EMLPs collected from northeastern Europe for instance were noted for earlier flowering compared to those from southern Europe (Rebourg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, P. Gauthier et al., 2002). Despite this, morphological characterizations became inconsistent due to environmental interactions (Rebourg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The extensive adoption of molecular markers, such as isozyme, restriction fragment length polymorphisms (RFLP), and single sequence repeats (SSRs) (Revilla et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Dubreuil et al., 1996; Rebourg et al., 1999, Reif et al., 2006), and more recently, single nucleotide polymorphisms (SNPs) from SNP arrays (Ganal et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Unterseer et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mayer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), largely mitigated this issue. However, discovering large and novel polymorphisms, especially through genotyping-by-sequencing (GBS), should provide a broad genetic base and prevent misinterpretation of diversity studies through ascertainment bias (Inghelandt et al., 2011; Frascaroli et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies have also revealed the effect of geographic origin on the genetic diversity of maize populations concerning altitude (Tenaillon \u0026amp; Charcosset, 2011; Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), longitude, and latitude variations (Gauthier et al., 2002; Revilla et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Wang et al., 2017; Diaw et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis diversity can be harnessed to improve the fitness of elite materials in response to changing climates (Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kawecki \u0026amp; Ebert, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), because not all geographic and genetic diversity was captured in selecting the few parental landraces used to develop the elite lines available in Europe today (Strigens et al., 2019), leading to loss of some favorable alleles from the gene pool and limiting their potential for adaptation to extreme climatic conditions (Mayer et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, as the climate changes, new and currently neutral or negative alleles may become desirable for local adaptation, altering the value of populations previously considered less important (Mayer et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Local adaptation is assumed when a local population exhibits higher fitness trait values than non-local populations (Janzen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This phenomenon in maize landraces is complex, and the understanding of it is not fully established (Millet et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Janzen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Studies have explored methods including multi-environment trials (Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), reciprocal transplantation (Nuismer \u0026amp; Gandon, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gibson et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Janzen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and common garden experiments (Clazsen et al., 1940; Fraser et al., 2011; Savolainen et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to observe phenotypic variation and functional variants for maize adaptation. While these experimental methods have been useful, they are laborious, costly, and time-consuming.\u003c/p\u003e \u003cp\u003eIn this study, we carried out extensive genetic screening of EMLPs to understand their population structure and investigated patterns of genetic diversity across geographic regions. We further identified genomic loci under selection for adaptation to local environments, leveraging each population\u0026rsquo;s geographical information.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSNP dataset\u003c/h2\u003e \u003cp\u003eWe genotyped 340 individual plants from 40 landrace populations across nine countries of origin, representing the diversity of maize in Europe (Fig. S1). After filtering, we retrieved 152,671 SNPs from 333 individual plants. These SNPs were distributed across the 10 chromosomes, ranging from 10,677 to 23,543 SNPs for chromosomes ten and one, respectively. SNPs were well distributed within chromosomes, except for centromeric regions, which showed a lower SNP density. This result is consistent with reported GBS SNP-data for maize populations and the relatively low SNP density at centromeres and peri-centromeres is expected due to low recombination (Bauer et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wolfgruber et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The distribution of genome-wide minor allele frequencies (MAF) and heterozygosities were as expected (Fig. S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenetic clustering and genetic differentiation of EMLPs\u003c/h2\u003e \u003cp\u003eAnalyzing diverse landraces across a wide range of geographical origins captures the broad genetic diversity available in maize (Aguirre-Liguori et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Arteaga et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To confirm this, we examined the relationship between the geographic distance (using latitude, longitude, and elevation of origin) and genetic distance (determined by SNP markers) of EMLPs. The Mantel test (Mantel, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) revealed a significant correlation of 0.41 (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between pairwise geographic and genetic distances. We further identified genetic groups based on hierarchical clustering analysis at k\u0026thinsp;=\u0026thinsp;5 (Fig. S3). These groups closely mirrored the geographical proximities of EMLPs, as presented in the principal coordinate analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This provides a broader view of reported classifications of EMLPs into northeastern populations (group 1), southeastern populations (group 2 and group 4), south-Italian populations (group 3), and the Pyrenees populations (group 5) (Gauthier et al., 2002; Tenaillon et al., 2011; Galic et al., 2023). Group 2 exhibited the largest spread of populations across three southeastern countries and southern Italy, possibly due to the hybridization of tropical dents and northern flint populations that occurred in this region, reported to be the European corn belt (Tenaillon et al., 2011; Galic et al., 2023).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further assessed genetic differentiation between the groups using their pairwise fixation indices (Fst) (Weir and Cockerham, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Fst ranged from 0.034 between Group 2 and Group 3 to 0.093 between Group 4 and Group 5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We observed a high Fst between the northern and southern groups, consistent with previous reports for European maize landraces (McLean-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; ) and between the Pyrenees (west) and southeastern groups. Interestingly, for three out of the nine countries of origin (Bulgaria, Croatia, and Italy), we found populations belonging to different groups, contrary to our expectations. The mixtures of group 2 and group 3 found among south Italian populations could be attributed to their elevational differences. While group 2 south Italian populations were found in the lowlands with an average elevation of 245.6m, group 3 populations belonged to relatively higher elevations with an average elevation of 690m (Fig. S4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eNucleotide diversity of EMLPs and founder effects\u003c/h2\u003e \u003cp\u003eGenetic variation between maize populations is low compared to within populations as observed in the analysis of molecular variance (AMOVA) which partitioned 51% of the genetic variance to individuals within populations, consistent with reported estimates (Reif et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Mir et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), 36.4% between populations, and 12.6% between groups (Fig. S6). Each population in our EMLP had 5\u0026ndash;10 genotyped individuals, except for one population (TUR_3602) with 3 individuals. These individuals were used to estimate the population\u0026rsquo;s nucleotide diversity (π), measuring the average pairwise difference between all possible pairs of individuals in a population. A high π value would indicate that such a population consists of more genetically distant individuals vis-a-vis (Fig. S7). The values of π ranged from 0.04 for a Hungarian population (HUN_116) to 0.16 for a southeastern population (TUR_3602), while the average π value was 0.106 across populations (Fig. S9). Our values are comparable to reported π for maize landraces (Brandenburg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., 2017; Beissinger et al., 2016; Gauthier et al., 2002). Interestingly, we observed a significant decline (p-value\u0026thinsp;=\u0026thinsp;0.005) in π as latitude increases, suggesting a serial founder effect resulting in the northeastern populations. This aligns with reports on domestication events that led to major expansion and selection of EMLPs from the tropical south to the temperate north and northeastern regions (Galic et al., 2023; Diaw et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tenaillon et al., 2011; Rebourg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The impact of longitude and elevation on EMLP\u0026rsquo;s diversity was insignificant (p-values\u0026thinsp;\u0026gt;\u0026thinsp;0.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE),\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGWAS identifies genomic regions under selection\u003c/h2\u003e \u003cp\u003eMaize landraces are genetically diverse materials that have since long adapted to their local environments, enabling researchers to explore their geographical properties in identifying genetic controls for local adaptation (Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This inspired us to perform GWAS using the latitude, longitude, and elevation of origin of our EMLPs as response variables and the 152,671 SNPs as explanatory variables. We corrected the population structure using the first three principal coordinates, explaining 22.8% of the total variation and a Fixed and Random Model Circulating Probability Unification (FarmCPU) algorithm (J. Wang \u0026amp; Zhang, 2021). \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe identified 28, 347, and 68 significant SNPs, respectively, flagged at p\u0026thinsp;\u0026lt;\u0026thinsp;0.00001 (-log10\u003csup\u003e5\u003c/sup\u003e) to allow only two to three false positives (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3\u003c/span\u003e). We compared our significant SNPs with reported genomic positions for flowering time and plant height, which are indicator traits of adaptation in maize (Janzen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bouchet et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sunoj et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and found overlapping peaks. Notably, on chromosome 4, we identified two significant associations for elevation between 150 Mb and 200 Mb. This chromosome region has been reported for \u003cem\u003eIn4vm\u003c/em\u003e - an introgression from highland Mexicana to highland maize consisting of floral genes (Hufford et al., 2013; Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We also found five significant SNPs located in the vicinity of dwarf8 (221\u0026ndash;225 Mb) for latitude, and two significant SNPs close to \u003cem\u003etb1\u003c/em\u003e (261 Mb and 267 Mb) on chromosome 1 for longitude. \u003cem\u003eZCN7\u003c/em\u003e (Chromosome 6, 171.3 Mb) and ZCN8 (Chromosome 8, 128.5 Mb) were identified for longitude. These genes have been reported to regulate floral formation in maize and to be strongly associated with flowering time variation (Brandenburg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Camus-Kulandaivelu et al., 2006; Meng et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) (Table S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCandidate genes and GO enrichment analysis\u003c/h2\u003e \u003cp\u003eA search for candidate genes across all significant associated SNPs using a window of \u0026plusmn;\u0026thinsp;50 Kb, led to the identification of 42, 49, and 46 associated candidate genes for latitude, longitude, and elevation, respectively. Leveraging the gene ontology (GO) files for B73 v4.0, we elucidated their biological functions, some of which describe local adaptation properties in maize (see Table S2). Upon inspecting the topmost significant SNP for longitude on chromosome 5 at 171.3Mb (p-value 1.32e-84), we found gene model \u003cem\u003eZm00001d016653\u003c/em\u003e, 17 Kb away, to be an ortholog of \u003cem\u003eAT1G12910\u003c/em\u003e in \u003cem\u003eA. thaliana\u003c/em\u003e, encoding for LIGHT-REGULATED WD1 (LWD1), a clock protein regulating the circadian period length and photoperiodic flowering. For elevation, the topmost SNP (p-value 1.4e-123) on chromosome 3 at 26.4 Mb was located 60 Kb away from \u003cem\u003eZm00001d040082\u003c/em\u003e, also an ortholog of \u003cem\u003eAT2G01130\u003c/em\u003e in A. thaliana. This gene model is a member of the DEA(D/H)-box RNA helicase family protein expressed during petal differentiation. Additionally, we conducted GO enrichment analysis to describe conserved GO terms of the identified genes. GO enrichment was successful for genes associated with latitude, as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4\u003c/span\u003e. However, elevation and longitude had no significantly enriched GO terms. The most enriched terms for latitude, namely \"sulphur amino acid metabolic process\" and \"monoatomic anion transport,\" play active roles in Anion channels/transporters, which are crucial to signaling pathways leading to the adaptation of plant cells to abiotic and biotic environmental stresses, as well as in the control of metabolism and maintenance of electrochemical gradients (de Angeli et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Barbier-Brygoo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenetic grouping and genetic differentiation of EMLPs\u003c/h2\u003e \u003cp\u003eRecent diversity studies on European maize have primarily focused on inbred lines derived from a limited number of landraces (Mayer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Galic et al., 2023; Diaw et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Earlier reports explored the diversity of maize landraces in Europe, either from one or a few countries of origin or a restricted geographical region (Mayer et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Galic et al., 2023; Diaw et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Many of these studies relied on marker types such as SSR, RFLPs, and Isozymes, and often utilized bulk genotyping of multiple individuals within a population (Rebourg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Gauthier et al., 2002; Revilla et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Reif et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Dubreuil et al., 1998; Mir et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bennetzen et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In our study, we assessed 333 individual plants from 40 European maize landrace populations (EMLPs) across nine countries of origin using 152,671 SNP markers. This provides a broad genetic base for evaluating European maize populations. The diversity in the geographic origins of these landraces, explaining 41% of the genetic variation as presented in the results, is comparable to 46% reported by Navarro et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), although less than the 0.71 reported by Gauthier et al. (2002), possibly due to differences in sample size and the type of genetic markers used. Nevertheless, these values underscore the significant impact of geographical spread on maize genetic diversity.\u003c/p\u003e \u003cp\u003eThe use of genetic groups for the classification of EMLPs into northeastern, southeastern, south Italian, and the Pyrenees aligns with previous attempts at molecular classification of European maize (Brandenburg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Galic et al., 2023; Gauthier et al., 2002). In addition, we observed a mixture of the identified groups within countries and regions of origin. We anticipated that populations within a country would exhibit more similarities than populations from different countries, however, this was not the case. For instance, the mixture of two groups among the south Italian populations, attributed to their elevational differences, suggests that there exists remarkable genetic differentiation for adaptation to variable elevations of EMLPs. This, coupled with admixture observed in Bulgaria and Croatia, suggests that geographical proximity does not always guarantee genetic similarities. In other words, EMLPs expected to be more genetically similar across much of their genomes might differ significantly in some strongly divergent regions which may contain genes enabling populations to adapt to slightly different environments. This underscores that the selection of EMLPs for diversity representation is less effective when based solely on the country of origin, but optimal if combined with variations in latitude, longitude, and elevation of origin. Conversely, group 2 comprises several closely related populations spanning four countries of origin. This mixture of countries within a group reveals the extent of serial exchange of materials and hybridization between tropical dents and temperate flints in the European corn-belt region (Tenallion et al., 2011; Galic et al., 2023). The insights gained from the clustering analysis should guide researchers and breeders in making informed selections of populations to study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIndividual-plant-based nucleotide diversity estimates\u003c/h2\u003e \u003cp\u003eDue to the heterogeneity observed in maize landraces, as evident from the AMOVA result partitioning more than half of the genetic variance within the population, characterization should be carried out based on representative sets of individuals to efficiently capture the population\u0026rsquo;s diversity (Diaw et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reyes-Vald\u0026eacute;s et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Dubreuil and Charcosset, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This approach prevents the loss of information about individual plant genetic variation (Rebourg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and aids in identifying selection scans caused by linkage disequilibrium, which is limited in bulk genotyping (Hirsch et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Gouda et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) suggested a minimum of 5 individuals per population, and studies have used up to 30 individuals for estimating maize population diversity using SSRs (Reif et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). While cost considerations are relevant (which may become highly comparable in the near future), genotyping individual plants is recommended over bulk genotyping for an optimal estimate of population diversity. Our EMLP individual plant panel allows us to explore pairwise differences among individuals for estimating population diversity using \u003cem\u003eπ\u003c/em\u003e and assessing the degree of heterogeneity within populations. This measure of diversity has been reported as a reliable estimator, particularly when dealing with a large genome-wide dataset and a limited number of individuals per population (Brandenburg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hufford et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeographical variation in genetic diversity reveals serial founder effect\u003c/h2\u003e \u003cp\u003eThe gradual decline in π as latitude increases suggests a serial founder effect as maize expanded from the tropical south to the temperate north of Europe, possibly due to selection for adaptation to long days and lower temperatures (Tenaillon et al., 2001). Founder populations are a product of recurrent sub-sampling of diversity from preceding populations (Slatkin \u0026amp; Excoffier, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Austerlitz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). A similar pattern was observed for American maize according to Wang et al., (2017) where an extreme founder effect was observed for Andean populations based on their distance from the maize domestication center in south-western Mexico. Gauthier et al. (2002) reported two genomic regions driving European maize latitude variation with SSRs and several other reports have documented the close population structure of Europe's northern populations compared to the southern populations (Rebourg et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Revilla et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mir et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These suggest that as maize migrates from centers of domestication across latitudinal gradients, genetic diversity is impaired due to selection (Ramachandran et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Henn et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe report of a second introduction from North America (Rebourg et al., 2002; Dubreuil et al., 1998) is not in contrast since the diversity of European maize represents \u0026sim;75% of the Americas, such that several landraces cultivated in southwestern Europe are related to that of Mesoamerican, and landraces from northern Europe are similar to north American flint varieties (Camus-Kulandaivelu et al., 2006; Tenaillon et al., 2001). This study can therefore be extensively adapted to understand the genetic dimensions of maize diversity from the Mesoamerican to North American regions with similar latitudes. This pattern was also observed in the Fst result where the northeastern group had the highest differentiation from other populations. Detailed Fst results (Fig. S8 and Fig. S9) showed that the farthest northeastern population from Hungary with the lowest π had the greatest differentiation (Holsinger \u0026amp; Weir, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSignatures of selection for local adaptation in maize\u003c/h2\u003e \u003cp\u003eThe most commonly used statistical methods for identifying selection signatures are Fst outlier analysis and genetic environment association analysis (GEA) (Ahrens et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Galic et al., 2023). Here, we adopted the GEA approach, focusing on the association between SNPs and environmental variables under the assumption that genome-wide diversity primarily reflects the action of divergent selection, in this case, for local adaptation (Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We employed a straightforward and rapid approach using the latitude, longitude, and elevation of the origin of geographically and genetically diverse populations to identify location-specific adaptation loci, especially in cases where cost constraints exist.\u003c/p\u003e \u003cp\u003eThe 443 significant associations identified were broadly distributed within and across chromosomes, affirming the polygenicity of local adaptation traits in maize (Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Some of these loci identified align with reported loci for flowering time and plant height, as shown in Table S1, supporting their strong relationship with maize adaptation (Hufford et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Navarro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Agronomic traits, such as plant height and flowering time, have served as indicators of fitness for geographic and climatic variation (Bouchet et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Janzen et al. (2021) confirmed the relative home-site advantage displayed by local maize populations for various agronomic and vegetative traits, including plant height and flowering time, in a lowland versus highland site cross-reaction norm experiment. Hufford et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) untangled the genomic targets for highland adaptation in maize on chromosome 4 (\u003cem\u003eIn4vm\u003c/em\u003e, between 150 Mb \u0026minus;\u0026thinsp;200 Mb) as a result of gene introgression from the Mexicana wild relative, a finding later confirmed to be significantly associated with flowering time by Navarro et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We found two significant associations within this 50 Mb region for elevation, contributing to the adaptation of European maize to higher elevations. Longitudinal variation of European maize accounted for the majority of the associations, a discovery earlier reported by Gauthier et al. (2002), who detected 5 SSR alleles for longitude compared to 2 SSR alleles for latitude, emphasizing the critical role of longitudinal distance in understanding European maize local adaptation. During the domestication process, the \u003cem\u003etb1\u003c/em\u003e locus experienced a significant reduction in diversity, and the \u003cem\u003eDwarf8\u003c/em\u003e locus revealed signs of purifying selection accompanied by substantial diversity loss (Studer et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). While unverified, certain Northern Flint germplasms, such as sweet corn, exhibit a morphology resembling the undomesticated \u003cem\u003etb1\u003c/em\u003e phenotype. It is possible that the region encompassing \u003cem\u003eDwarf8\u003c/em\u003e and \u003cem\u003etb1\u003c/em\u003e underwent a bottleneck with multiple selective sweeps, leading to the formation of extended haplotype blocks for this region (Larsson et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). \u003cem\u003eZCN7\u003c/em\u003e and \u003cem\u003eZCN8\u003c/em\u003e are found in the photoperiod pathway and play a role in regulating flowering time (Shi et al., 2022; Guo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Brandenburg et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dong et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, environmental variables such as soil type, and soil microbes, among others, would have possibly introduced additional location-specific alleles and genes (e.g. \u003cem\u003eZm00001d016653 and Zm00001d040082\u003c/em\u003e) that we uncovered in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study of European maize landrace populations (EMLPs) covers more geographical regions than previous reports, providing a valuable resource for selecting representative populations that largely capture the gene pools of European maize diversity for breeding, conservation, and research. We conclude that latitude, elevation, and longitude are key factors in the genetic groupings of EMLPs, even within their country of origin. We propose genotyping multiple individuals per population for a thorough examination of the genetic parameters of the EMLP. We further revealed serial founding events that likely occurred during maize expansion in Europe due to selection. By using latitude, elevation, and longitude of origin as response variables, we identified both reported and novel SNP associations and genes linked to local adaptation. Thus, in the absence of phenotypic information from field experiments with multi-environment replicates, this approach proved adequate for making informative associations for local adaptation traits. We have demonstrated the potential to use individual plants from maize landrace populations as a resource to gain insight into EMLPs' genetic structure, selection events, and the genetic basis of their location-specific adaptation.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and experiment\u003c/h2\u003e \u003cp\u003eSeeds from 40 EMLP were collected from the Leibniz Institute of Plant Genetics and Crop Plant Research IPK \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e(\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ipk-gatersleben.de/\u003c/span\u003e\u003cspan address=\"https://www.ipk-gatersleben.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e germplasm repository. These landraces were from nine countries of origin, namely Germany, France, Spain, Hungary, Croatia, Austria, Bulgaria, Turkey, and Italy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Supplementary Table\u0026nbsp;1). Fifty kernels per population were sown in two rows on each plot at a distance of 15 cm by 95 cm. Plots were separated by a commercial German hybrid - \u003cem\u003eSY-telias\u003c/em\u003e. Ten individual plants were sampled six weeks after sowing from each population, to be tracked through genotyping and phenotyping. The experiment was conducted at Rosdorf, Goettingen, Germany (coordinates, 51.512679, 9.886327).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping\u003c/h2\u003e \u003cp\u003eLeaf tissues were collected from a total of 340 individual plants across the 40 populations. These were collected from the youngest visible leaf 9 weeks after sowing and were lyophilized and submitted to the University of Wisconsin-Madison Biotechnology Center for DNA extraction using the Qiagen DNeasy 96 plant kit. DNA yield was quantified with Promega QuantiFluor on a Tecan Spark 10 M. DNA concentration was verified using the Quant-iT\u0026trade; PicoGreen dsDNA kit (Life Technologies, Grand Island, NY). Libraries were prepared as in Elshire et al (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) with minimal modification; in short, 150 ng of DNA was digested with \u003cem\u003eApeK\u003c/em\u003eI (New England Biolabs, Ipswich, MA) after which barcoded adapters amenable to Illumina sequencing were added by ligation with T4 ligase (New England Biolabs, Ipswich, MA). The 96 adapter-ligated DNA samples were pooled and amplified to provide library quantities amenable for sequencing, and adapter dimers were removed by SPRI bead purification. The quality and quantity of the finished libraries were assessed using the Agilent Bioanalyzer High Sensitivity Chip (Agilent Technologies, Inc., Santa Clara, CA) and Qubit dsDNA HS Assay Kit (Life Technologies, Grand Island, NY), respectively. Libraries were sequenced targeting about 400\u0026nbsp;million reads on a NovaSeq6000 (Illumina Inc.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eVariant calling and filtering\u003c/h2\u003e \u003cp\u003eDemultiplexing of the pooled raw reads dataset was done with sabre tools (Najoshi 2013) using the barcode information. Demultiplexed reads were mapped to the B73 maize reference genome (Jiao et al., 2017) using the Burrow Alignment tools (BWA-mem) (Li \u0026amp; Durbin, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Mapped reads were further trimmed and sorted using samtools (Li et al., 2009). Variants were called using bcftools (Danecek et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Further filtering of the SNP data was performed using vcftools (Danecek et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to remove multi-allelic loci, and structural variants and retain only SNP with minor allele frequencies (MAF) greater than 0.01, QUAL scores\u0026thinsp;\u0026gt;\u0026thinsp;30, minimum depth\u0026thinsp;\u0026gt;\u0026thinsp;5, maximum depth\u0026thinsp;\u0026lt;\u0026thinsp;500, and average missingness proportion was 0.14. Imputation of missing value was done using Beagle v5.4 (Browning et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and imputation accuracy was 0.96. SNP density across and within chromosomes was inspected using CMplot r-package (Yin et al., 2015). Distribution of MAF and heterozygosity were performed using snpReady r-package (Granato and Fritsche-Neto \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) (Fig. S2, Fig.\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity and population structure\u003c/h2\u003e \u003cp\u003ePairwise genetic and geographic distances among individuals were estimated using their marker dataset and geographical data (latitude, longitude, and elevation) respectively as described in Chu et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Mantel test (Mantel, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1967\u003c/span\u003e) was used to check the correlation between genetic and geographic distance. Hierarchical clustering analysis was performed using the genetic distance matrix for population structure analysis as documented in the r-package \u0026lsquo;ape\u0026rsquo; v5.0 (Paradis \u0026amp; Schliep, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Multidimensional scaling analysis was performed using principal coordinates to inspect the clustering of individuals and populations (Chu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Country of origin, year of collection, collection site, latitude, longitude, and elevation of the collection sites as contained in the passport data (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eurisco.ipk-gatersleben.de/\u003c/span\u003e\u003cspan address=\"https://eurisco.ipk-gatersleben.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e for each population were further examined to explain the population structure. Analysis of molecular variance (AMOVA) and fixation indices (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e) among groups and populations were estimated using the poppr r-package v 2.9.4 (Kamvar et al., 2014) and Hierfstat r-package v0.5-11 (Goudet, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) respectively. For the population\u0026rsquo;s nucleotide diversity (\u003cb\u003eπ\u003c/b\u003e), the average pairwise genetic distance between individuals within a population was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eGWAS, selection signature, and search for candidate genes\u003c/h2\u003e \u003cp\u003eGenome-wide association analysis was conducted by adopting the latitude, elevation, and longitude of origin of the populations as response variables (assuming genetic adaptation of the population to elevation, latitude, and longitude of the collection sites as real traits) and the SNP markers as explanatory variables. Fixed and Random Model Circulating Probability Unification (FarmCPU), implemented in the GAPIT3 R-package (J. Wang \u0026amp; Zhang, 2021) was used to scan the SNP markers for association using the first three principal coordinates as covariates. FarmCPU performs a single marker scan with associated markers as cofactors in a fixed effect model and independently optimizes the associated cofactors in a random effect model. This helps to correct for multiple testing errors and reduce the risk of compromising the true positives as is the case in mixed-linear models (MLM) (J. Wang \u0026amp; Zhang, 2021). P-values of the GWAS results were corrected using 0.00001 and plotted as Manhattan plots and as QQ plots. The distribution of allele frequency of conserved significant SNPs was investigated for latitude, longitude, and elevation. Genes within a\u0026thinsp;\u0026plusmn;\u0026thinsp;50 Kb window of significant SNPs were investigated and functionally annotated with the gostprofiler R-package (Kolberg et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003eb) using gff3 file (v4) from Ensembl (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ensembl.gramene.org/Zea_mays/Info/Index\u003c/span\u003e\u003cspan address=\"https://ensembl.gramene.org/Zea_mays/Info/Index\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e and the maize genome database- \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maizegdb.org/genome/assembly/Zm-B73-REFERENCE-GRAMENE-4.0\u003c/span\u003e\u003cspan address=\"https://www.maizegdb.org/genome/assembly/Zm-B73-REFERENCE-GRAMENE-4.0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCONSENT FOR PUBLICATION\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eDATA AND CODE\u003c/p\u003e\n\u003cp\u003eData available at https://figshare.com/account/home#/projects/216463\u003cbr\u003eCode for all analysis are available at https://github.com/Aiyesa/EMLP-local-adaptation\u003c/p\u003e\n\u003cp\u003eFUNDING\u003c/p\u003e\n\u003cp\u003eThis project was funded by the University of G\u0026ouml;ttingen.\u003c/p\u003e\n\u003cp\u003eAUTHORS\u0026apos; CONTRIBUTIONS\u003c/p\u003e\n\u003cp\u003eLeke Victor Aiyesa, contributed to the experiment design and data collection, performed all data analysis, and wrote the original draft of the manuscript. Timothy Beissinger, obtain the funding for the project, obtained the plant materials used from the genebank, designed the scope of the experiment, supervised data analysis, contributed to the review and writing of the manuscript. Stefan Scholten and Wolfgang Link supervised the experiment, data analysis, contributed to the review and writing of the manuscript. Birgit Zumbach supervised the data analysis, contributed to the review and writing of the manuscript. Dietrich Kaufmann, managed the field and greenhouse experiments.\u003c/p\u003e\n\u003cp\u003eACKNOWLEDGEMENTS\u003c/p\u003e\n\u003cp\u003eWe would like to thank Gesellschaft f\u0026uuml;r Wissenschaftliche Datenverarbeitung GmbH (GWDG), G\u0026ouml;ttingen, for providing high-performing cluster service for the computational analysis, Leibniz-Institut f\u0026uuml;r Pflanzengenetik und Kulturpflanzenforschung (IPK) for providing the plant materials used for this study. Centre for Integrated Breeding Research (CiBreed), Georg-August-University of Goettingen, Germany. The University of Wisconsin\u0026ndash;Madison Biotechnology Center\u0026rsquo;s DNA Sequencing Facility (Research Resource Identifier \u0026ndash; RRID: SCR_017759) was used for DNA extraction, generating GBS libraries, and sequencing GBS libraries.\u003c/p\u003e\n\u003cp\u003eAUTHORS\u0026apos; INFORMATION\u003cbr\u003eAUTHOR - AFFILIATIONS\u003c/p\u003e\n\u003cp\u003eLeke Victor Aiyesa - Division of Plant Breeding Methodology, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany.\u003c/p\u003e\n\u003cp\u003eTimothy Beissinger - Google X, The Moonshot Factory, Mountain View, California, United States.\u003c/p\u003e\n\u003cp\u003eStefan Scholten - Division of Crop Plants Genetics, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany\u003c/p\u003e\n\u003cp\u003eWolfgang Link - Division of Plant Breeding Methodology, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany.\u003c/p\u003e\n\u003cp\u003eBrigit Zumbach - Division of Plant Breeding Methodology, Department of Crop Sciences, Faculty of Agriculture, Georg-August-University of Goettingen, Germany.\u003c/p\u003e\n\u003cp\u003eETHICS DECLARATION\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAguirre-Liguori, J. 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The Crop Journal, 11(5), 1380\u0026ndash;1389. https://doi.org/10.1016/j.cj.2023.05.006\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Individual plants, nucleotide diversity, founder events, GWAS, selection signatures, candidate genes.","lastPublishedDoi":"10.21203/rs.3.rs-4925882/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4925882/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEuropean maize landraces encompass a large amount of genetic diversity, allowing them to be well-adapted to their local environments. This diversity can be exploited to improve the fitness of elite material in the face of a changing climate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nWe characterized the genetic diversity of 333 individual plants from 40 European maize landrace populations (EMLPs). We identified five genetic groups that mirrored the proximities of their geographical origins. Fixation indices showed moderate differentiation among genetic groups (0.034 to 0.093). More than half of the genetic variance was observed to be partitioned among individuals. Nucleotide diversity of EMLPs decreased significantly as latitude increased (from 0.16 to 0.04), suggesting serial founder events during maize expansion in Europe. GWAS with latitude, longitude, and elevation as response variables identified 28, 347, and 68 significant SNP positions, respectively. We pinpointed significant SNPs near dwarf8, tb1, ZCN7, ZCN8, and ZmMADS69, and identified 137 candidate genes with ontology terms indicative of local adaptation in maize, regulating the adaptation to diverse abiotic and biotic environmental stresses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cbr\u003e\nThis study suggests a quick and cost-efficient approach to identifying genes involved in local adaptation without requiring field data. The EMLPs used in this study have been assembled to serve as a continuing resource of genetic diversity for further research aimed at improving agronomically relevant adaptation traits.\u003c/p\u003e","manuscriptTitle":"Individual plant genetics reveal the control of local adaption in European maize landraces","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-12 06:01:21","doi":"10.21203/rs.3.rs-4925882/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0dbb3369-d381-4f36-966d-b36d35e036d7","owner":[],"postedDate":"September 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-24T07:53:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-12 06:01:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4925882","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4925882","identity":"rs-4925882","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}