Genotyping-by-sequencing reveals the genetic diversity and population structure of Peruvian highland maize races

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Arbizu, Isamar Bazo Soto, Joel Flores, Rodomiro Ortiz, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4486762/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 Peruvian maize exhibits abundant morphological diversity, with landraces cultivated from sea level (sl) up to 3,500 m above sl. Previous research based on morphological descriptors, defined at least 52 Peruvian maize races, but its genetic diversity and population structure remains largely unknown. Here we used genotyping-by-sequencing (GBS) to obtain single nucleotide polymorphisms (SNPs) that allow inferring the genetic structure and diversity of 423 maize accessions from the genebank of Universidad Nacional Agraria la Molina (UNALM) and Universidad Nacional Autónoma de Tayacaja (UNAT). These accessions represent nine races and one sub-race, along with 15 open-pollinated lines (purple corn) and two yellow maize hybrids. It was possible to obtain 14,235 high-quality SNPs distributed along the 10 maize chromosomes of maize. Gene diversity ranged from 0.33 (sub-race Pachia) to 0.362 (race Ancashino), with race Cusco showing the lowest inbreeding coefficient (0.205) and Ancashino the highest (0.274) for the landraces. Population divergence (F ST ) was very low (mean = 0.017), thus depicting extensive interbreeding among Peruvian maize. Population structure analysis indicated that these 423 distinct genotypes can be included in 10 groups, with some maize races clustering together. Peruvian maize races failed to be recovered as monophyletic; instead, our phylogenetic tree identified two clades corresponding to the groups of the classification of the races of Peruvian maize based on their chronological origin, i.e., anciently derived or primary races and lately derived or secondary races. Additionally, these two clades are also congruent with the geographic origin of these maize races, reflecting their mixed evolutionary backgrounds and constant evolution. Peruvian maize germplasm needs further investigation with modern technologies to better use them massively in breeding programs that favor agriculture mainly in the South American highlands. We also expect this work will pave a path for establishing more accurate conservation strategies for this precious crop genetic resource. Biological sciences/Genetics Biological sciences/Plant sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Maize is a major global crop that is cultivated on around 200 million hectares, and is considered a key component of food security 1 . In several regions of the world (western South America, Mesoamerica, sub-Saharan Africa), this crop has an important social and economic value because maize is consumed daily, mainly by the poor in their single daily meal. Floury maize grows in about one million hectares in the South American Andes, where it is consumed with very little processing; i.e., boiled grain (mote), fried grain without oil (cancha) or boiled ear as green corn (choclo). Migration of rural populations to urban areas dropped maize consumption by switching to more expensive products 2 . Since the publication “The Races of Maize in Mexico” 3 , each country, where maize thrives, released similar books. The South American Andean races were described for Colombia 4 , Brazil and other eastern South American countries 5 , Bolivia 6 , Perú 7 , Chile 8 Ecuador 9 and Venezuela 10 . These investigations described 132 races in the South American Andes; i.e., about 52% of the known 260 maize races 11 . Due to the methodology of sampling for collecting maize in the farms or rural households, maize landraces were recognized together with farmers according to their assessment for different purposes and under the name of their native language. More than half a century than these maize race books were published, we recognize the importance of the race concept to classify the diversity of this crop. Race has been the unit of maize diversity for more than 60 years and provides means for its monitoring. For example, to the best of our knowledge, all described races remain available in Perú 12 . To assess the current maize diversity, a new racial classification of the Peruvian maize is being conducted 12 . Race diversity is not an indicator of genetic diversity in maize because races were described using only a few morphological characters, mostly from the ear and grain. Likewise, there are ecological and cultural criteria for classification that are not well understood. Maize farming began in Mexico about 9,000 years ago 13 . According to Kistler et al 14 , a wild teosinte ( Zea mays ssp. parviglumis ) was domesticated in Mexico, and thereafter spread towards the south way and arrived in Peru, where it continued to expand to the Andean region, and later to the Amazon region. The southwestern Amazon is, however, considered now another center for partially domesticated maize 14 . Maize was grown in Peru about 6,700 years ago in the Chicama Valley, where a sample of maize with well conserved cobs, husks, stalks and tassels was found 15 . The macrofossils records indicated that maize included various races at that time. Grobman 16 indicated that maize diversification began very early in human settlements. There is evidence of massive use of maize as food at Los Gavilanes –an archeological site in Huarmey, north of Lima 17 . Andean possesses many beneficial alleles to address key constrains affecting its production and productivity across the South American highlands 18 . Some of these alleles, along with adaptive genes, may be present today in low frequency 19 , thus they are not detected while evaluating maize accessions in field trials. The success of plant breeding depends on both, genetic diversity and related useful variation, but diversity must be organized, tested and evaluated. To maintain the total maize diversity, every race of this crop should be improved. This breeding strategy ensures both variety adaptation and further adoption. Therefore, accurate classification is essential; all diversity belonging to a maize race should be understood, and all races properly defined. Precise and objective techniques and tools for these research tasks are urgently needed, as classification based on morphology, adaptation, and cultural criteria is insufficient. Genetic structure and diversity of the races of Peruvian maize remains largely unknown. On the other hand, Vigouroux et al 20 used a set of almost 350 races of maize to assess their genetic and population parameters with 96 microsatellite markers, detecting four main groups: (i) highland Mexican, (ii) northern United States of America, (iii) tropical lowland, (iv) and Andean maize races. They suggested that isolation by distance could be the main factor accounting for the historical maize diversification. Further research with microsatellites suggested that the Andean group of maize displayed little mixing with other races 21 . Genotyping-by-sequencing (GBS) is now a feasible technique for describing highly diverse and large genomes as maize 22 . This approach is increasingly important as a cost effective and unique tool for genomic diversity research and gene discovery in maize and provides more single nucleotide polymorphisms (SNPs) markers than SNP arrays 23 . Maize evolved rapidly owing to human selection, leading to a significant phenotypic changes and adaptation to various environments, such as those in Mesoamerica and the Andes. There are, however, some unanswered questions regarding maize domestication and its further evolution. The main aim of this investigation was to determine the genetic diversity and population structure of nine races and one subrace of maize grown in the highlands of Peru using SNPs covering all its 10 chromosomes generated by GBS, thus providing consistent means for understanding its classification and spread in the Peruvian Andes. Methods Plant material We examined (i) 406 accessions of nine races and one sub-race of Peruvian maize that are currently cultivated in 10 Andean geographic departments of Peru, (ii) 15 open-pollinated (OP) purple maize lines and (iii) two yellow maize hybrids (423 individuals in total) (Fig. 1 ). Maize accessions were obtained from the Maize Research Program Germplasm Bank maintained at the Universidad Nacional Agraria la Molina (UNALM) in Lima, except for 19 accessions that were obtained from the corn research project of UNAT. One yellow dent hybrid and all purple maize were donated by Associate Prof. Hugo E. Huanuqueño (Maize Research Unit, UNALM); the other maize hybrid was a local cultivar. All possible accessions available as germplasm were employed. Further details of the maize genotypes examined in this study are available at the Table S1 . Genotyping-by-sequencing All 423 maize individuals (one seed per accession) were planted at UNALM and three weeks after germination leaf samples from one plant of each sample were collected and total genomic DNA was extracted following Doyle and Doyle 24 protocol with some modifications. The concentration and purity of DNA samples were determined with a NanoDrop 1000 spectrophotometer. DNA samples showing absorbance ratios above 1.8 at 260/280 nm were used for further analysis. For quality determination, DNA samples were electrophoresed on 1% agarose gel, and 50 random samples were digested with ApeKI following the manufacturer's protocol. Samples were sent to the University of Minnesota-Biotechnology Center for DNA sequencing. Genotyping by sequencing libraries were developed following Elshire et al. 25 protocol. Genomic DNA was digested with the ApeKI enzyme and fragments were ligated to Illumina sequencing adapters and with sequence barcodes that are unique to each sample, which allows the recovery of sample identity for each sequenced DNA fragment after multiplexing. The pooled samples were sequenced on the Illumina NovaSeq 600 platform from which 100 bp single-end sequences reads were obtained. Quality of the raw data was examined with FastQC v0.11.7 software 26 . Thereafter, we employed the TASSEL v5.2.42 bioinformatic pipeline 27 , 28 for SNPs calling with maize Zm-B73-REFERENCE-NAM-5.0 29 as the reference genome. Parameters employed in this pipeline were the same as in the study of Huaringa et al 30 . Data curation was performed using software VCFtools v0.1.16 31 with the following criteria of retention: (i) minimum minor allele frequency of 0.1, (ii) number of alleles less than or equal to 2, and (iv) maximum missing data of 0.1. Additional filtering was conducted by removing SNPs in linkage disequilibrium (LD) at a threshold of r 2 = 0.2 with function snpgdsLDpruning of SNPRelate package 32 in R v4.2.2 33 program. Lastly, TASSEL software was employed to convert the .vcf file to PHYLIP format with argument -exportType Phylip_Inter. Genetic diversity and population structure Genetic diversity indices were calculated for each race of maize and for the OP and hybrid cultivars using the adegenet v2.1.10 34,35 and HIERFSTAT v0.5-11 36 R packages. A maximum likelihood (ML) tree was constructed using the .phy file with the multi-threaded version of the program RAxML v8.2.11 37 , raxmlHPC-PTHREADS , with the rapid bootstrapping algorithm and a total of 100 nonparametric bootstrap (BS) inferences. Model ASC_GTRGAMMA with the ascertainment correction of Lewis 38 was also considered, and the resulting tree was plotted with ggtree 39 R package. A principal coordinate analysis (PCoA) was performed with the dudi.pco function of ade4 v1.7.22 40 package in R. To determine the population structure, first the filtered .vcf file was converted into .str format with VCFtools and PGDSpider v2.1.1.5 41 programs. We then employed the Bayesian clustering program STRUCTURE v2.3.4 42 with populations (K) of 1 to 25 and 10 replicates. A burn-in length of 50,000 with 100,000 Monte Carlo iterations was considered, and the optimal K value was estimated by the Evanno method 43 . Population structure was visualized with POPHELPER v2.3.1 44 R package. An analysis of molecular variance (AMOVA) with the poppr v1.1.4 45,46 package in R to determine the sources of genetic variance within and among the races of maize was also conducted. Finally, a pairwise fixation index (F ST ) was estimated using R package HIERFSTAT, according to Weir and Cockerman 47 . Results Sequencing analysis and SNPs distribution After filtering out the raw reads, the total demultiplexed reads for all 423 genotypes were 1,566.7 M with good barcoded reads representing 99.9%, and the average read per accession was 3.7 M. A total of 5,010,502 tags were identified, of which 86.4% uniquely aligned to the maize reference genome. Next, we detected a total of 1,002,078 raw SNPs after using the TASSEL software, and we kept 31,132 SNPs after filtering with VCFtools program. A set of 14,235 SNPs distributed across the 10 chromosomes of maize were selected after LD pruning in R, which was used for subsequent genetic structure and diversity analysis. The highest and lowest number of physically mapped SNPs were identified in chromosome 1 (2170, 15.2%) and 10 (954, 6.7%), respectively (Table 1). The 10 maize chromosomes exhibited a very consistent distribution of SNP markers spanned virtually the whole genome, showing a low SNP density near the centromeres, whereas the telomere region exhibited a high density of SNPs (Fig. 2). Chromosome 1 possessed the highest density (7.04 SNPs/Mb) and chromosome 10, with 6.26 SNPs/Mb, the lowest (Fig. 2). Genetic diversity The nine races and one subrace of Peruvian maize examined in this work showed very similar number of different alleles; the allelic richness ranged from 1.33 (subrace Pachia) to 1.36 (race Ancashino). In addition, sub-race Pachia possessed the lowest observed heterozygosity (H O ) (0.25), whereas race Cusco the highest (0.28), and the expected heterozygosity (H E , genetic diversity) ranged from 0.33 (Pachia) to 0.36 (Ancashino). On the other hand, Cusco (0.21) exhibited the lowest inbreeding coefficient (F IS ), while Ancashino the highest (0.27) (Table 2). Population structure The PCoA showed that the first and second axis explained 2.0% and 1.5% of the variance, respectively. Sub-race Pachia and the two types of improved maize (purple and yellow cultivars) are separated into two well-distinctive groups. On the contrary, there is not a consistent grouping of the other amylaceous maize. Accessions of race Cusco Gigante are closely related to some individuals of Cusco, and races Ancashino, Chullpi, Huayleño and Paro are grouping together, but without a clear resolution (Fig. 3). Deletion of improved maize slightly changed the structure in the PCoA, showing that most accessions of race Chullpi are separated, and race Cusco Gigante and most accessions of Cusco are roughly grouping together (Fig. S1). Accessions of race Ancashino and Huayleño are grouping together, but some other accessions are intermixed in this group. The same feature was shown by other group of some accessions of race Pisccorunto. However, most accessions cannot be clearly separated by race criteria. Our ML tree recovered two main clades containing the following maize landraces: (CR1) almost all accessions of Ancashino, Huayleño and Paro, some accessions of Chullpi, and few of San Gerónimo and San Gerónimo Huancavelicano, and (CR2) all accessions of Cusco Gigante, Cusco and Pisccorunto, some accessions of Chullpi, San Gerónimo and San Gerónimo Huancavelicano. In addition, all individuals of improved maize were placed in a subcluster within CR1. Interestingly, the CR1 mainly comprises the anciently derived or primary races (ADPR) of maize in Peru, and CR2 consists of the lately derived or secondary races (LDSR), according to the classification described by Grobman et al. 7 (Fig. 4). There are, however, few exceptions: (i) races Cusco and Pisccorunto are considered an ADPR but they are not within CR1 clade but in CR2 clade, (ii) initially defined as imperfectly defined race by Grobman et al., (1961), San Gerónimo should be considered as a LDSR. A clade of two accessions of Ancashino (PM-005, PM-012) and one of Paro (PM-165) races is sister to those two clades, and sister to them there is a grade comprising sub-race Pachia. Our ML tree also resolved a subclade within CR2 containing almost all accession of race Cusco Gigante and Cusco. Moreover, we observed three subclusters of race Chullpi, one of them with above 90% BS within CR1. One grade comprising 10 accession of race San Gerónimo Huancavelicano including one Chullpi maize (PM-427) was also detected. For most accessions labelled by their race, a consistent grouping pattern was not noticed. A clear grouping was not observed when maize accessions were labelled based on their Peruvian geographic department of origin, except for accessions from Tacna who form a grade with above 90% BS. Maize from Ancash also showed another grade, but accessions from other locations are also intermingled; similarly with another grade formed by maize from Huancavelica and Junín (Fig. S2). Interestingly, when accessions were labelled according to their geographic zone of origin (north, center, south) in Peru, our ML tree revealed the following two clades: (CZ1) individuals from the northern Andes (Ancash and Cajamarca) and purple maize OP lines obtained in Lima, (CZ2) individuals from the center (Huancavelica, Huánuco, Junín) and southern (Apurímac, Ayacucho, Cusco, Moquegua) of the Peruvian Andes; both also contained within their corresponding subclades. These two clades possess a BS < 90% and very few accessions from other geographic zones are intermingled. A similar pattern was detected in our PCoA (Fig. S3). The Evanno method determined that the best K (number of populations) is two for our data set, and the next two largest peaks are at K = 4 and K = 10 (Fig. S4). Previous research in capirona 48 , carrot 49,50 and maize 20 found a false highest peak at K = 2 in population structure analysis as the null hypothesis of no structure (K = 1) was strongly rejected. In addition, Waples and Gaggiotti 51 , Frantz et al. 52 and Janes et al. 53 indicated that the Evanno method tends to underestimate the number of genetic clusters. Hence, it is very likely the second highest peak obtained with our dataset of 14,235 SNPs (K = 4) is caused by a strong rejection of the hypothesis of three clusters only. Furthermore, the maximum likelihood value was obtained at K = 10 (Fig. S5), which is concordant also with our ML analyses of the Peruvian maize. Hence, we decided to discuss our results with K = 10. STRUCTURE analysis showed abundant admixture, except for accession of sub-race Pachia, whose accessions were placed in cluster 7, thus exhibiting very low admixture (Fig. 5). Furthermore, Cusco Gigante and Cusco races are clustering together; i.e., most of those accessions are within cluster 4. Like Ancashino and Huayleño, which are forming a group (cluster 5) but with some degree of admixture, improved maize is also clustering together (cluster 2). Chullpi accessions are mainly distributed between clusters 1 and 9, and race Paro were placed in clusters 1, 3 and 5. San Gerónimo Huancavelicano is grouped within clusters 1 and 9 mainly, whereas race San Gerónimo was placed mainly in cluster 1. There was not a clear cluster assignation when accessions were labelled according to their geographic origin, except for maize from Ancash, Huánuco, Moquegua, Tacna and Lima (bred maize). Geographic zone criterion of clustering exhibited that most accessions from northern Peru are grouped together (cluster 5), while those from Lima were placed in cluster 2. Maize accessions from the center of Peru were mainly grouped within clusters 1, 4 and 9. Clusters 1, 4 and 7 contained accessions of maize from southern Perú (Table S2, Fig. S6). Fixation indices (F ST ) were very low in general. Population divergence between yellow maize (improved maize) and Cusco Gigante revealed the highest genetic difference (0.18), while races Huayleño and Ancashino exhibited the lowest (0.001) (Table S3). Furthermore, the greatest genetic variation was observed within races of Peruvian maize (95.48%), while 4.52% was reported for between races, according to our AMOVA (Table 3). Discussion The foundation for crop improvement lies in genetic diversity 54,55 , which can be assessed by DNA (molecular) markers like SNPs. Analyzing the molecular genetic variation in germplasm provides valuable insights into allelic richness, population structure and diversity parameters. This information helps plant breeders utilize genetic resources more effectively, reducing for extensive pre-breeding tasks when developing new cultivars 56 . In recent years, due to the advances in next-generation sequencing (NGS), GBS has emerged as a promising genomic approach for estimating plant genetic diversity and population structure on a genome-wide scale, and has been successfully employed, inter alia , in Brassica 57 , Daucus 50,58,59 , finger millet 60 , maize 61,62 , spruce 63 , wheat 64 , and watermelon 65 . However, GBS has not been used so far for genotyping Peruvian races of maize, whose morphological diversity seems to be the largest worldwide 7,12 . Studies on Peruvian maize have primarily focused on morpho-agronomic characteristics 66–76 , leaving their molecular composition largely unexplored. Herein, we determined the gene diversity and composition of Peruvian maize races from the Andean highlands by means of SNP markers spanning each chromosome. Unfortunately, despite the significant diversity within Peruvian maize germplasm, knowledge of its genetic components remains very limited. Catalán et al 77 reported a high level of variability using eight microsatellites in 83 accessions of six races of maize from Cusco. The genetic diversity of the nine races and one subrace of maize assessed in this study is very high, which is concordant with its improvement status (i.e., landraces), as reported for other landraces of beans 78 , peas 79 , squash 80 , tarwi 30 , or wheat 81 , among others. Our genetic diversity indices align with other studies on maize landraces. For example, Warburton et al. 82 examined the genetic diversity of 24 maize landraces from Mexico with 25 simple sequence repeats (SSR) and reported a total gene diversity of 0.61 across all populations. Similarly, Herrera-Saucedo et al 83 determined the genetic variability of 63 native maize accessions from northern Mexico using 31 SSR, reporting an expected heterozygosity of 0.68. A study of 30 maize landrace accessions from the southern Andean region of South America using 22 SSR showed a genetic diversity of 0.72 84 . In a more comprehensive study 20 employing 96 SSR that encompasses most of the described races in the American continent, 136 accessions of 47 races of Peruvian maize were included, and these together with other maize from Ecuador and Bolivia (total of 235 plants) possessed a total gene diversity of 0.71. Here we determined that the genetic diversity of the Peruvian maize (0.35) from more diverse geographic regions is higher than the value reported for 46 Mexican landraces (161 accessions) of maize using SNPs (0.311 85 ), demonstrating that Peru possesses one of the largest genetic diversities of amylaceous maize, pointing to the fact that the central Andean region possesses abundant maize genetic variability. A recent study 86 found that gene diversity of landraces from seven countries from South America, assessed with 23,412 SNPs, was slightly lower (0.323 ± 0.007) than landraces from Central America and Mexico (0.328 ± 0.006). The higher gene diversity reported with SSR compared to SNP markers may be due to the multi-allelic nature and higher level of polymorphism of SSR compared to bi-allelic SNP. However, SNPs are more reliable for inferring genome-wide genetic diversity, as demonstrated by previous work 87,88 . Consistent with previous investigations 18,82 , bred maize is clearly separated from Peruvian maize landraces, which is explained by their intensity of selection. The well-defined grouping of accessions of sub-race Pachia may be explained in the light of its cultivation in a restricted area in southern Peru (Valley of Pachia, Tacna). Although Grobman et al. 7 indicated that this sub-race derived from the race Arequipeño which is a lately derived race, our molecular data however does not support this fact, as Pachia was not placed within the CR2 clade. Instead, it is more likely related to race Coruca, which also grows in Tacna and is similar to a floury maize landrace Choclero from Chile 7 . Further research is needed, including maize samples from other southern Peruvian regions (Arequipa, Moquegua, Puno, Tacna) as landraces of maize cultivated in Tacna show tolerance to high levels of boron 74,89 , which is a trait of interest for breeding maize for locations with high levels of this element in soil and irrigation water. Landrace Cabanita, widely grown in Arequipa, also shows potential as a source of phenolic compounds with in vitro antioxidant capacity 90 . Races Cusco Gigante and Cusco tend to group together as they are mainly cultivated in Cusco. Additionally, races Ancashino and Huayleño, both sympatrically distributed in northern Peru (Ancash), group together and possess a very low F ST , suggesting they evolved simultaneously. Hybridization likely plays a role in the grouping of these races, as noted by Grobman et al 7 . Even though the other Peruvian maize races are morphologically distinct, our GBS dataset failed to support them as monophyletic, which agrees with other research that evaluated Peruvian germplasm 20,21,86,91,92 . Similarly, Mexican maize races do not form distinct cluster 85 . However, Caldu-Primo et al. 93 were able to distinguish Mexican maize races based on a high F ST SNP dataset. Population structure analysis clustered maize races from the American continent based on geographic origin, with the Peruvian germplasm contained within a clade named “Andean” 20,21,86,91,92 . More consistent clustering was observed when Peruvian races of maize were labelled according to their geographical zones of origin, identifying CZ1 and CZ2. This mixing among maize landraces is likely due to extensive gene flow within these zones explained by their proximities, and frequent seed exchange which is a common practice in the Peruvian Andes. However, Peruvian maize farmers in the Andes usually dynamize seed flow between families and rural communities, and conduct selection within their populations to maintain the morphological characteristics of their landraces. Our ML mostly agrees with the classification of Peruvian maize races based on the chronological origin described by Grobman et al. 7 as it was possible to reconstruct the ADPR (CR1) and LDSR (CR2) clades. Even though Chullpi and Paro were considered very closely related 7 , these ADPR races developed independently from different ancestors. Moreover, the polyphyletic status of race Chullpi reflects a mixed evolutionary origin; that is, more than one ancestor was involved in its development. Thus, the similar traits that race Chullpi exhibits is very likely due to environmental pressures as the climatic gradients of the Andes are laboratories of constant plant evolution. Similarly, the paraphyletic pattern depicted by Cusco Gigante, Cusco, Pisccorunto, San Gerónimo and San Gerónimo Huancavelicano is a result of evolution in the Andes of Peru of a set of novel or derived traits. A more detailed morphological evaluation is needed for the races of Peruvian maize to identify morphotypes and determine their phenetic plasticity. It is very likely that two accessions of Ancashino (PM-005, PM-012) and one of Paro (PM-165) contributed to the origin of other maize races evaluated in this work, as these individuals form a sister clade to CR1 and CR2. Both races are considered ADPR, directly derived from the primitive races (PR), as described by Grobman et al. 7 . Therefore, it is very likely these three accessions may still possess genetic signatures of PR. However, further research is necessary, including samples from a wider geographical area, for more conclusive results. The position of purple maize OP cultivars within CR1 is explained by its origin on race Kculli, classified as ADPR by Grobman et al. 7 . The close relation among purple (OP) and yellow maize (hybrid) is due the use of the latter at UNALM to enhance yield performance of OP lines. The vast diversity exhibited by maize races in Peru is crucial for research in plant genetic resources 95 . However, the lack of relevant information on the genetic diversity of conserved plant material hinders the use of accessions preserved in germplasm banks 96,97 . To address this gap for Peruvian maize, we suggest that genomic tools can facilitate the characterization and utilization of this invaluable plant genetic resources, as emphasized by Mascher et al 98 . This approach is particularly important considering that amylaceous maize in Peru are landraces, dynamic populations in constant evolution in the Andes. Moreover, maize Peruvian maize germplasm requires a special attention as a ~5500 cal. BP maize cob from northern Peru 99 was the only sample without the Zea mays ssp. mexicana ancestry in a recent study 100 , shedding lights on the origin of maize in the Central Andean region. On the other hand, we expect our study will provide useful guides to researchers and decision makers for establishing a strong conservation strategy and dynamic utilization soon for Peruvian maize germplasm. Declarations Data availability The data that supports the findings of this study are openly available in Dryad: https://datadryad.org/stash/share/4OI0AXLoEIDsbSSLvoOdXrhzSyMUAow9x0elGjhyPmQ Acknowledgements The authors wish to acknowledge Fabiola Catalán, Hugo Huanuqueño, Gilberto Rodriguez and Fatima Silva for providing germplasm and technical support. We thank the University of Minnesota Genomics Center for providing facilities and services. The authors also thank the Bioinformatics High-performance Computing server of Universidad Nacional Agraria la Molina (BioHPC-UNALM) for providing resources to perform the analyses. C.I.A. thanks Vicerrectorado de Investigación of UNTRM. Author contributions statement C.I.A.: conceptualization, methodology, data curation, validation, formal analysis, resources, writing-original draft, writing-review and editing, funding acquisition. I.B.: investigation, data curation, validation, formal analysis, writing-original draft, writing-review and editing. J.F. investigation, data curation, validation, writing-original draft. R.O.: conceptualization, investigation, supervision, writing-original draft, writing-review and editing, funding acquisition. R.B.: conceptualization, investigation, supervision, resources, writing-review and editing, funding acquisition. P.J.G.-M.: investigation, validation, writing-review and editing, funding acquisition. R.S.: conceptualization, investigation, writing-review and editing. J.C.: investigation, methodology, writing-review and editing. A.G.: investigation, validation, writing-review and editing. All authors read, revised and approved the final manuscript. Funding This work was funded by project N°03-2017 of STC-CGIAR (Ministry of Agrarian Development and Irrigation of the Peruvian Government). We acknowledge project “Mejoramiento de nuevas variedades de maíz amiláceo explotando el germoplasma: uso de secuenciamientos de ADN de última generación y fenotipado en campos experimentales”. Competing interests The authors declare they have no competing interests References Erenstein, O., Jaleta, M., Sonder, K., Mottaleb, K. & Prasanna, B. M. Global maize production, consumption and trade: trends and R&D implications. Food Secur 14, 1295–1319 (2022). Sevilla, R. Variation in Modern Andean Maize and Its Implications for Prehistoric Patterns. in Corn and culture in the prehistoric New World (eds. 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C.) 19–25 (International Plant Genetic Resources Institute (IPGRI), Rome, Italy, 2018). McCouch, S. et al. Mobilizing Crop Biodiversity. Mol Plant 13, 1341–1344 (2020). Mascher, M. et al. Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding. Nat Genet 51, 1076–1081 (2019). Grobman, A. et al. Preceramic maize from Paredones and Huaca Prieta, Peru. Proceedings of the National Academy of Sciences 109, 1755–1759 (2012). Yang, N. et al. Two teosintes made modern maize. Science (1979) 382, (2023). Tables Table 1. Genome-wide distribution and density of 14,235 single nucleotide polymorphisms (SNPs) across the 10 chromosomes of maize. Chromosome SNP # SNP % Total length (Mb) Density (SNPs/Mb) 1 2170 15.2 308.45 7.04 2 1793 12.6 243.68 7.36 3 1631 11.5 238.02 6.85 5 1615 11.3 226.35 7.13 4 1410 9.9 250.33 5.63 7 1260 8.9 185.81 6.78 8 1229 8.6 182.41 6.74 6 1109 7.8 181.36 6.12 9 1064 7.5 163 6.53 10 954 6.7 152.44 6.26 Table 2. Genetic diversity indices of Peruvian maize germplasm based on 14,235 single nucleotide polymorphisms (SNPs). Race (or cultivar type) Number of accessions N A A R H O H E F IS Ancashino 35 2.000 1.360 0.263 0.362 0.274 Chullpi 43 1.999 1.353 0.264 0.354 0.254 Cusco 44 2.000 1.349 0.278 0.350 0.205 Cusco Gigante 41 1.996 1.336 0.262 0.337 0.223 Huayleño 28 1.998 1.355 0.265 0.357 0.257 Pachia 43 1.988 1.332 0.249 0.333 0.253 Paro 40 2.000 1.356 0.277 0.357 0.225 Pisccorunto 41 1.999 1.346 0.274 0.347 0.211 Purple corn (OP) 15 1.914 1.313 0.190 0.318 0.401 San Geronimo 41 1.999 1.350 0.267 0.351 0.238 San Geronimo Huancavelicano 50 2.000 1.354 0.270 0.355 0.239 Yellow maize (hybrid) 2 1.440 NA 0.230 0.297 0.227 N A : number of different alleles, A R : allelic richness, H O : observed heterozygosity, H E : expected heterozygosity, F IS : inbreeding coefficient Table 3. Analysis of molecular variance of the genetic variation for 423 accessions of Peruvian maize germplasm using 14,235 SNPs. Source of variation df SS MS Est. var. % Between races 11 179,018.3 16,274.39 290.99 4.52 Within races 411 2,526,998.7 6148.42 6148.42 95.48 Total 422 2,706,017.0 6412.36 6439.41 100 df: degree of freedom, SS: sum of squares, MS: mean squares, Est. Var.: estimated variance, %: percentage of genetic variation Additional Declarations No competing interests reported. Supplementary Files FigS1.pdf FigS2.pdf FigS3.pdf FigS4.pdf FigS5.pdf FigS6.png Suppltable.xls 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-4486762","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312084043,"identity":"834de3e9-1ecc-4716-b226-42b551329a62","order_by":0,"name":"Carlos I. 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Only accessions with coordinate information available are shown.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/18c5b41e019bc365bacc1e25.png"},{"id":58023324,"identity":"74cafb04-5b64-48fb-af0c-d55d6273d441","added_by":"auto","created_at":"2024-06-10 05:48:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90790,"visible":true,"origin":"","legend":"\u003cp\u003eDensity and\u003cstrong\u003e \u003c/strong\u003edistribution of 14,235 single nucleotide polymorphism (SNP) markers on the 10 maize chromosomes. Red bars denote the end of chromosome; nt refers to nucleotide.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/d639c11e4f9698b86d796240.png"},{"id":58023325,"identity":"73b44460-66ca-41b8-923d-b5f051cf5afd","added_by":"auto","created_at":"2024-06-10 05:48:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93003,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal coordinate analysis of 423 accessions of Peruvian maize germplasm using 14,235 single nucleotide polymorphisms (SNPs). Percentages on the axis represent the variance explained by each coordinate.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/c9cb8c57317dc783ebbe8603.png"},{"id":58022806,"identity":"39ed857c-031b-4796-8f1d-a98a7357e222","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":481984,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum likelihood reconstruction of 423 accessions of Peruvian maize germplasm using 14,235 single nucleotide polymorphisms (SNPs). Round symbol on nodes represents bootstrap support, with only values higher than 90% shown.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/f581d45be1ce8b899379e8ec.png"},{"id":58022804,"identity":"fac0aae5-371d-4778-996d-5878c71e44d6","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":106316,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure of 423 maize accessions based on 14,235 single nucleotide polymorphisms (SNPs). Each accession is represented by a horizontal bar, and each color corresponds to a population (10 in total).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/e8381e2c7bfa8cd365623084.png"},{"id":66872986,"identity":"d30dfc00-3313-4b8b-abd7-f7732410211d","added_by":"auto","created_at":"2024-10-17 10:02:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1930984,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/88fd4885-13d7-4e26-b917-d596e558cbc6.pdf"},{"id":58022802,"identity":"b60f129e-3a4a-4d2a-a99a-dbc39e9b79f0","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":192371,"visible":true,"origin":"","legend":"","description":"","filename":"FigS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/10724b784ffeae5d27d1e11c.pdf"},{"id":58022801,"identity":"38f994e4-1c5b-4b0d-83a0-e6896f1a424f","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":600591,"visible":true,"origin":"","legend":"","description":"","filename":"FigS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/8734e34f8b473b04a7c433e8.pdf"},{"id":58022809,"identity":"b4570890-474a-47b1-a7b0-75bb8f4159b0","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":636373,"visible":true,"origin":"","legend":"","description":"","filename":"FigS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/e54cb01e9fccb52f0f83b37e.pdf"},{"id":58022805,"identity":"a415ae0c-1215-4bed-b900-bb2437bc33c3","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":97340,"visible":true,"origin":"","legend":"","description":"","filename":"FigS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/668228f8473cb181056404bf.pdf"},{"id":58022811,"identity":"556de9df-d222-40a0-96bf-e60c4883417e","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":122739,"visible":true,"origin":"","legend":"","description":"","filename":"FigS5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/b863a5df5d4cf70becc347ac.pdf"},{"id":58023326,"identity":"1f5596df-9a92-450c-b4dc-95665ae73d8e","added_by":"auto","created_at":"2024-06-10 05:48:00","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":848892,"visible":true,"origin":"","legend":"","description":"","filename":"FigS6.png","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/ec39c6e043f439493884f140.png"},{"id":58022812,"identity":"3a482340-5117-4b8c-b32f-86a0c969a270","added_by":"auto","created_at":"2024-06-10 05:40:00","extension":"xls","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":121856,"visible":true,"origin":"","legend":"","description":"","filename":"Suppltable.xls","url":"https://assets-eu.researchsquare.com/files/rs-4486762/v1/101467ee5bf85675ac8d89f6.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genotyping-by-sequencing reveals the genetic diversity and population structure of Peruvian highland maize races","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize is a major global crop that is cultivated on around 200\u0026nbsp;million hectares, and is considered a key component of food security\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In several regions of the world (western South America, Mesoamerica, sub-Saharan Africa), this crop has an important social and economic value because maize is consumed daily, mainly by the poor in their single daily meal. Floury maize grows in about one million hectares in the South American Andes, where it is consumed with very little processing; i.e., boiled grain (mote), fried grain without oil (cancha) or boiled ear as green corn (choclo). Migration of rural populations to urban areas dropped maize consumption by switching to more expensive products\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSince the publication \u0026ldquo;The Races of Maize in Mexico\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, each country, where maize thrives, released similar books. The South American Andean races were described for Colombia\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, Brazil and other eastern South American countries\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, Bolivia\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, Per\u0026uacute;\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, Chile\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Ecuador\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and Venezuela\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These investigations described 132 races in the South American Andes; i.e., about 52% of the known 260 maize races\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Due to the methodology of sampling for collecting maize in the farms or rural households, maize landraces were recognized together with farmers according to their assessment for different purposes and under the name of their native language. More than half a century than these maize race books were published, we recognize the importance of the race concept to classify the diversity of this crop. Race has been the unit of maize diversity for more than 60 years and provides means for its monitoring. For example, to the best of our knowledge, all described races remain available in Per\u0026uacute;\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. To assess the current maize diversity, a new racial classification of the Peruvian maize is being conducted\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRace diversity is not an indicator of genetic diversity in maize because races were described using only a few morphological characters, mostly from the ear and grain. Likewise, there are ecological and cultural criteria for classification that are not well understood. Maize farming began in Mexico about 9,000 years ago\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. According to Kistler et al\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, a wild teosinte (\u003cem\u003eZea mays\u003c/em\u003e ssp. \u003cem\u003eparviglumis\u003c/em\u003e) was domesticated in Mexico, and thereafter spread towards the south way and arrived in Peru, where it continued to expand to the Andean region, and later to the Amazon region. The southwestern Amazon is, however, considered now another center for partially domesticated maize\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Maize was grown in Peru about 6,700 years ago in the Chicama Valley, where a sample of maize with well conserved cobs, husks, stalks and tassels was found\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The macrofossils records indicated that maize included various races at that time. Grobman\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e indicated that maize diversification began very early in human settlements. There is evidence of massive use of maize as food at Los Gavilanes \u0026ndash;an archeological site in Huarmey, north of Lima\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAndean possesses many beneficial alleles to address key constrains affecting its production and productivity across the South American highlands\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Some of these alleles, along with adaptive genes, may be present today in low frequency\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, thus they are not detected while evaluating maize accessions in field trials. The success of plant breeding depends on both, genetic diversity and related useful variation, but diversity must be organized, tested and evaluated. To maintain the total maize diversity, every race of this crop should be improved. This breeding strategy ensures both variety adaptation and further adoption. Therefore, accurate classification is essential; all diversity belonging to a maize race should be understood, and all races properly defined. Precise and objective techniques and tools for these research tasks are urgently needed, as classification based on morphology, adaptation, and cultural criteria is insufficient.\u003c/p\u003e \u003cp\u003eGenetic structure and diversity of the races of Peruvian maize remains largely unknown. On the other hand, Vigouroux et al\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e used a set of almost 350 races of maize to assess their genetic and population parameters with 96 microsatellite markers, detecting four main groups: (i) highland Mexican, (ii) northern United States of America, (iii) tropical lowland, (iv) and Andean maize races. They suggested that isolation by distance could be the main factor accounting for the historical maize diversification. Further research with microsatellites suggested that the Andean group of maize displayed little mixing with other races\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Genotyping-by-sequencing (GBS) is now a feasible technique for describing highly diverse and large genomes as maize\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. This approach is increasingly important as a cost effective and unique tool for genomic diversity research and gene discovery in maize and provides more single nucleotide polymorphisms (SNPs) markers than SNP arrays\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMaize evolved rapidly owing to human selection, leading to a significant phenotypic changes and adaptation to various environments, such as those in Mesoamerica and the Andes. There are, however, some unanswered questions regarding maize domestication and its further evolution. The main aim of this investigation was to determine the genetic diversity and population structure of nine races and one subrace of maize grown in the highlands of Peru using SNPs covering all its 10 chromosomes generated by GBS, thus providing consistent means for understanding its classification and spread in the Peruvian Andes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003eWe examined (i) 406 accessions of nine races and one sub-race of Peruvian maize that are currently cultivated in 10 Andean geographic departments of Peru, (ii) 15 open-pollinated (OP) purple maize lines and (iii) two yellow maize hybrids (423 individuals in total) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Maize accessions were obtained from the Maize Research Program Germplasm Bank maintained at the Universidad Nacional Agraria la Molina (UNALM) in Lima, except for 19 accessions that were obtained from the corn research project of UNAT. One yellow dent hybrid and all purple maize were donated by Associate Prof. Hugo E. Huanuque\u0026ntilde;o (Maize Research Unit, UNALM); the other maize hybrid was a local cultivar. All possible accessions available as germplasm were employed. Further details of the maize genotypes examined in this study are available at the Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping-by-sequencing\u003c/h2\u003e \u003cp\u003eAll 423 maize individuals (one seed per accession) were planted at UNALM and three weeks after germination leaf samples from one plant of each sample were collected and total genomic DNA was extracted following Doyle and Doyle\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e protocol with some modifications. The concentration and purity of DNA samples were determined with a NanoDrop 1000 spectrophotometer. DNA samples showing absorbance ratios above 1.8 at 260/280 nm were used for further analysis. For quality determination, DNA samples were electrophoresed on 1% agarose gel, and 50 random samples were digested with \u003cem\u003eApeKI\u003c/em\u003e following the manufacturer's protocol. Samples were sent to the University of Minnesota-Biotechnology Center for DNA sequencing.\u003c/p\u003e \u003cp\u003eGenotyping by sequencing libraries were developed following Elshire et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e protocol. Genomic DNA was digested with the \u003cem\u003eApeKI\u003c/em\u003e enzyme and fragments were ligated to Illumina sequencing adapters and with sequence barcodes that are unique to each sample, which allows the recovery of sample identity for each sequenced DNA fragment after multiplexing. The pooled samples were sequenced on the Illumina NovaSeq 600 platform from which 100 bp single-end sequences reads were obtained. Quality of the raw data was examined with FastQC v0.11.7 software\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Thereafter, we employed the TASSEL v5.2.42 bioinformatic pipeline\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e for SNPs calling with maize Zm-B73-REFERENCE-NAM-5.0\u003csup\u003e29\u003c/sup\u003e as the reference genome. Parameters employed in this pipeline were the same as in the study of Huaringa et al\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Data curation was performed using software VCFtools v0.1.16\u003csup\u003e31\u003c/sup\u003e with the following criteria of retention: (i) minimum minor allele frequency of 0.1, (ii) number of alleles less than or equal to 2, and (iv) maximum missing data of 0.1. Additional filtering was conducted by removing SNPs in linkage disequilibrium (LD) at a threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.2 with function \u003cem\u003esnpgdsLDpruning\u003c/em\u003e of SNPRelate package\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e in R v4.2.2\u003csup\u003e33\u003c/sup\u003e program. Lastly, TASSEL software was employed to convert the .vcf file to PHYLIP format with argument \u003cem\u003e-exportType\u003c/em\u003e Phylip_Inter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity and population structure\u003c/h2\u003e \u003cp\u003eGenetic diversity indices were calculated for each race of maize and for the OP and hybrid cultivars using the \u003cem\u003eadegenet\u003c/em\u003e v2.1.10\u003csup\u003e34,35\u003c/sup\u003e and HIERFSTAT v0.5-11\u003csup\u003e36\u003c/sup\u003e R packages. A maximum likelihood (ML) tree was constructed using the .phy file with the multi-threaded version of the program RAxML v8.2.11\u003csup\u003e37\u003c/sup\u003e, \u003cem\u003eraxmlHPC-PTHREADS\u003c/em\u003e, with the rapid bootstrapping algorithm and a total of 100 nonparametric bootstrap (BS) inferences. Model \u003cem\u003eASC_GTRGAMMA\u003c/em\u003e with the ascertainment correction of Lewis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e was also considered, and the resulting tree was plotted with ggtree\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e R package. A principal coordinate analysis (PCoA) was performed with the \u003cem\u003edudi.pco\u003c/em\u003e function of ade4 v1.7.22\u003csup\u003e40\u003c/sup\u003e package in R. To determine the population structure, first the filtered .vcf file was converted into .str format with VCFtools and PGDSpider v2.1.1.5\u003csup\u003e41\u003c/sup\u003e programs.\u003c/p\u003e \u003cp\u003eWe then employed the Bayesian clustering program STRUCTURE v2.3.4\u003csup\u003e42\u003c/sup\u003e with populations (K) of 1 to 25 and 10 replicates. A burn-in length of 50,000 with 100,000 Monte Carlo iterations was considered, and the optimal K value was estimated by the Evanno method\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Population structure was visualized with POPHELPER v2.3.1\u003csup\u003e44\u003c/sup\u003e R package. An analysis of molecular variance (AMOVA) with the \u003cem\u003epoppr\u003c/em\u003e v1.1.4\u003csup\u003e45,46\u003c/sup\u003e package in R to determine the sources of genetic variance within and among the races of maize was also conducted. Finally, a pairwise fixation index (F\u003csub\u003eST\u003c/sub\u003e) was estimated using R package HIERFSTAT, according to Weir and Cockerman\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSequencing analysis and SNPs distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter filtering out the raw reads, the total demultiplexed reads for all 423 genotypes were 1,566.7 M with good barcoded reads representing 99.9%, and the average read per accession was 3.7 M. A total of 5,010,502 tags were identified, of which 86.4% uniquely aligned to the maize reference genome. Next, we detected a total of 1,002,078 raw SNPs after using the TASSEL software, and we kept 31,132 SNPs after filtering with VCFtools program. A set of 14,235 SNPs distributed across the 10 chromosomes of maize were selected after LD pruning in R, which was used for subsequent genetic structure and diversity analysis. The highest and lowest number of physically mapped SNPs were identified in chromosome 1 (2170, 15.2%) and 10 (954, 6.7%), respectively (Table 1). The 10 maize chromosomes exhibited a very consistent distribution of SNP markers spanned virtually the whole genome, showing a low SNP density near the centromeres, whereas the telomere region exhibited a high density of SNPs (Fig. 2). Chromosome 1 possessed the highest density (7.04 SNPs/Mb) and chromosome 10, with 6.26 SNPs/Mb, the lowest (Fig. 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nine races and one subrace of Peruvian maize examined in this work showed very similar number of different alleles; the allelic richness ranged from 1.33 (subrace Pachia) to 1.36 (race Ancashino). In addition, sub-race Pachia possessed the lowest observed heterozygosity (H\u003csub\u003eO\u003c/sub\u003e) (0.25), whereas race Cusco the highest (0.28), and the expected heterozygosity (H\u003csub\u003eE\u003c/sub\u003e, genetic diversity) ranged from 0.33 (Pachia) to 0.36 (Ancashino). On the other hand, Cusco (0.21) exhibited the lowest inbreeding coefficient (F\u003csub\u003eIS\u003c/sub\u003e), while Ancashino the highest (0.27) (Table 2). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PCoA showed that the first and second axis explained 2.0% and 1.5% of the variance, respectively. Sub-race Pachia and the two types of improved maize (purple and yellow cultivars) are separated into two well-distinctive groups. On the contrary, there is not a consistent grouping of the other amylaceous maize. Accessions of race Cusco Gigante are closely related to some individuals of Cusco, and races Ancashino, Chullpi, Huayle\u0026ntilde;o and Paro are grouping together, but without a clear resolution (Fig. 3). Deletion of improved maize slightly changed the structure in the PCoA, showing that most accessions of race Chullpi are separated, and race Cusco Gigante and most accessions of Cusco are roughly grouping together (Fig. S1). Accessions of race Ancashino and Huayle\u0026ntilde;o are grouping together, but some other accessions are intermixed in this group. The same feature was shown by other group of some accessions of race Pisccorunto. However, most accessions cannot be clearly separated by race criteria. Our ML tree recovered two main clades containing the following maize landraces: (CR1) almost all accessions of Ancashino, Huayle\u0026ntilde;o and Paro, some accessions of Chullpi, and few of San Ger\u0026oacute;nimo and San Ger\u0026oacute;nimo Huancavelicano, and (CR2) all accessions of Cusco Gigante, Cusco and Pisccorunto, some accessions of Chullpi, San Ger\u0026oacute;nimo and San Ger\u0026oacute;nimo Huancavelicano.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, all individuals of improved maize were placed in a subcluster within CR1. Interestingly, the CR1 mainly comprises the anciently derived or primary races (ADPR) of maize in Peru, and CR2 consists of the lately derived or secondary races\u003cem\u003e\u0026nbsp;\u003c/em\u003e(LDSR), according to the classification described by Grobman et al.\u003csup\u003e7\u003c/sup\u003e (Fig. 4). There are, however, few exceptions: (i) races Cusco and Pisccorunto are considered an ADPR but they are not within CR1 clade but in CR2 clade, (ii) initially defined as imperfectly defined\u003cem\u003e\u0026nbsp;\u003c/em\u003erace by Grobman et al., (1961), San Ger\u0026oacute;nimo should be considered as a LDSR. A clade of two accessions of Ancashino (PM-005, PM-012) and one of Paro (PM-165) races is sister to those two clades, and sister to them there is a grade comprising sub-race Pachia. Our ML tree also resolved a subclade within CR2 containing almost all accession of race Cusco Gigante and Cusco. Moreover, we observed three subclusters of race Chullpi, one of them with above 90% BS within CR1. One grade comprising 10 accession of race San Ger\u0026oacute;nimo Huancavelicano including one Chullpi maize (PM-427) was also detected. For most accessions labelled by their race, a consistent grouping pattern was not noticed.\u003c/p\u003e\n\u003cp\u003eA clear grouping was not observed when maize accessions were labelled based on their Peruvian geographic department of origin, except for accessions from Tacna who form a grade with above 90% BS. Maize from Ancash also showed another grade, but accessions from other locations are also intermingled; similarly with another grade formed by maize from Huancavelica and Jun\u0026iacute;n (Fig. S2). Interestingly, when accessions were labelled according to their geographic zone of origin (north, center, south) in Peru, our ML tree revealed the following two clades: (CZ1) individuals from the northern Andes (Ancash and Cajamarca) and purple maize OP lines obtained in Lima, (CZ2) individuals from the center (Huancavelica, Hu\u0026aacute;nuco, Jun\u0026iacute;n) and southern (Apur\u0026iacute;mac, Ayacucho, Cusco, Moquegua) of the Peruvian Andes; both also contained within their corresponding subclades. These two clades possess a BS \u0026lt; 90% and very few accessions from other geographic zones are intermingled. A similar pattern was detected in our PCoA (Fig. S3).\u003c/p\u003e\n\u003cp\u003eThe Evanno method determined that the best K (number of populations) is two for our data set, and the next two largest peaks are at K = 4 and K = 10 (Fig. S4). Previous research in capirona\u003csup\u003e48\u003c/sup\u003e, carrot\u003csup\u003e49,50\u003c/sup\u003e and maize\u003csup\u003e20\u003c/sup\u003e found a false highest peak at K = 2 in population structure analysis as the null hypothesis of no structure (K = 1) was strongly rejected. In addition, Waples and Gaggiotti\u003csup\u003e51\u003c/sup\u003e, Frantz et al.\u003csup\u003e52\u003c/sup\u003e and Janes et al.\u003csup\u003e53\u003c/sup\u003e indicated that the Evanno method tends to underestimate the number of genetic clusters. Hence, it is very likely the second highest peak obtained with our dataset of 14,235 SNPs (K\u0026thinsp;=\u0026thinsp;4) is caused by a strong rejection of the hypothesis of three clusters only. Furthermore, the maximum likelihood value was obtained at K\u0026thinsp;=\u0026thinsp;10 (Fig. S5), which is concordant also with our ML analyses of the Peruvian maize. Hence, we decided to discuss our results with K\u0026thinsp;=\u0026thinsp;10.\u003c/p\u003e\n\u003cp\u003eSTRUCTURE analysis showed abundant admixture, except for accession of sub-race Pachia, whose accessions were placed in cluster 7, thus exhibiting very low admixture (Fig. 5). Furthermore, Cusco Gigante and Cusco races are clustering together; i.e., most of those accessions are within cluster 4. Like Ancashino and Huayle\u0026ntilde;o, which are forming a group (cluster 5) but with some degree of admixture, improved maize is also clustering together (cluster 2). Chullpi accessions are mainly distributed between clusters 1 and 9, and race Paro were placed in clusters 1, 3 and 5. San Ger\u0026oacute;nimo Huancavelicano is grouped within clusters 1 and 9 mainly, whereas race San Ger\u0026oacute;nimo was placed mainly in cluster 1. There was not a clear cluster assignation when accessions were labelled according to their geographic origin, except for maize from Ancash, Hu\u0026aacute;nuco, Moquegua, Tacna and Lima (bred maize). Geographic zone criterion of clustering exhibited that most accessions from northern Peru are grouped together (cluster 5), while those from Lima were placed in cluster 2. Maize accessions from the center of Peru were mainly grouped within clusters 1, 4 and 9. Clusters 1, 4 and 7 contained accessions of maize from southern Per\u0026uacute; (Table S2, Fig. S6).\u003c/p\u003e\n\u003cp\u003eFixation indices (F\u003csub\u003eST\u003c/sub\u003e) were very low in general. Population divergence between yellow maize (improved maize) and Cusco Gigante revealed the highest genetic difference (0.18), while races Huayle\u0026ntilde;o and Ancashino exhibited the lowest (0.001) (Table S3). Furthermore, the greatest genetic variation was observed within races of Peruvian maize (95.48%), while 4.52% was reported for between races, according to our AMOVA (Table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe foundation for crop improvement lies in genetic diversity\u003csup\u003e54,55\u003c/sup\u003e, which can be assessed by DNA (molecular) markers like SNPs. Analyzing the molecular genetic variation in germplasm provides valuable insights into allelic richness, population structure and diversity parameters. This information helps plant breeders utilize genetic resources more effectively, reducing for extensive pre-breeding tasks when developing new cultivars\u003csup\u003e56\u003c/sup\u003e. In recent years, due to the advances in next-generation sequencing (NGS), GBS has emerged as a promising genomic approach for estimating plant genetic diversity and population structure on a genome-wide scale, and has been successfully employed, \u003cem\u003einter alia\u003c/em\u003e, in \u003cem\u003eBrassica\u003c/em\u003e\u003csup\u003e57\u003c/sup\u003e, \u003cem\u003eDaucus\u003c/em\u003e\u003csup\u003e50,58,59\u003c/sup\u003e, finger millet\u003csup\u003e60\u003c/sup\u003e, maize\u003csup\u003e61,62\u003c/sup\u003e, spruce\u003csup\u003e63\u003c/sup\u003e, wheat\u003csup\u003e64\u003c/sup\u003e, and watermelon\u003csup\u003e65\u003c/sup\u003e. However, GBS has not been used so far for genotyping Peruvian races of maize, whose morphological diversity seems to be the largest worldwide\u003csup\u003e7,12\u003c/sup\u003e. Studies on Peruvian maize have primarily focused on morpho-agronomic characteristics\u003csup\u003e66\u0026ndash;76\u003c/sup\u003e, leaving their molecular composition largely unexplored. Herein, we determined the gene diversity and composition of Peruvian maize races from the Andean highlands by means of SNP markers spanning each chromosome.\u003c/p\u003e\n\u003cp\u003eUnfortunately, despite the significant diversity within Peruvian maize germplasm, knowledge of its genetic components remains very limited. Catal\u0026aacute;n et al\u003csup\u003e77\u003c/sup\u003e reported a high level of variability using eight microsatellites in 83 accessions of six races of maize from Cusco. The genetic diversity of the nine races and one subrace of maize assessed in this study is very high, which is concordant with its improvement status (i.e., landraces), as reported for other landraces of beans\u003csup\u003e78\u003c/sup\u003e, peas\u003csup\u003e79\u003c/sup\u003e, squash\u003csup\u003e80\u003c/sup\u003e, tarwi\u003csup\u003e30\u003c/sup\u003e, or wheat\u003csup\u003e81\u003c/sup\u003e, among others. Our genetic diversity indices align with other studies on maize landraces. For example, Warburton et al.\u003csup\u003e82\u003c/sup\u003e examined the genetic diversity of 24 maize landraces from Mexico with 25 simple sequence repeats (SSR) and reported a total gene diversity of 0.61 across all populations. Similarly, Herrera-Saucedo et al\u003csup\u003e83\u003c/sup\u003e determined the genetic variability of 63 native maize accessions from northern Mexico using 31 SSR, reporting an expected heterozygosity of 0.68. A study of 30 maize landrace accessions from the southern Andean region of South America using 22 SSR showed a genetic diversity of 0.72\u003csup\u003e84\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn a more comprehensive study\u003csup\u003e20\u003c/sup\u003e employing 96 SSR that encompasses most of the described races in the American continent, 136 accessions of 47 races of Peruvian maize were included, and these together with other maize from Ecuador and Bolivia (total of 235 plants) possessed a total gene diversity of 0.71. Here we determined that the genetic diversity of the Peruvian maize (0.35) from more diverse geographic regions is higher than the value reported for 46 Mexican landraces (161 accessions) of maize using SNPs (0.311\u003csup\u003e85\u003c/sup\u003e), demonstrating that Peru possesses one of the largest genetic diversities of amylaceous maize, pointing to the fact that the central Andean region possesses abundant maize genetic variability. A recent study\u003csup\u003e86\u003c/sup\u003e found that gene diversity of landraces from seven countries from South America, assessed with 23,412 SNPs, was slightly lower (0.323 \u0026plusmn; 0.007) than landraces from Central America and Mexico (0.328 \u0026plusmn; 0.006). The higher gene diversity reported with SSR compared to SNP markers may be due to the multi-allelic nature and higher level of polymorphism of SSR compared to bi-allelic SNP. However, SNPs are more reliable for inferring genome-wide genetic diversity, as demonstrated by previous work\u003csup\u003e87,88\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eConsistent with previous investigations\u003csup\u003e18,82\u003c/sup\u003e, bred maize is clearly separated from Peruvian maize landraces, which is explained by their intensity of selection. The well-defined grouping of accessions of sub-race Pachia may be explained in the light of its cultivation in a restricted area in southern Peru (Valley of Pachia, Tacna). Although Grobman et al.\u003csup\u003e7\u003c/sup\u003e indicated that this sub-race derived from the race Arequipe\u0026ntilde;o which is a lately derived race, our molecular data however does not support this fact, as Pachia was not placed within the CR2 clade. Instead, it is more likely related to race Coruca, which also grows in Tacna and is similar to a floury maize landrace Choclero from Chile\u003csup\u003e7\u003c/sup\u003e. Further research is needed, including maize samples from other southern Peruvian regions (Arequipa, Moquegua, Puno, Tacna) as landraces of maize cultivated in Tacna show tolerance to high levels of boron\u003csup\u003e74,89\u003c/sup\u003e, which is a trait of interest for breeding maize for locations with high levels of this element in soil and irrigation water. Landrace Cabanita, widely grown in Arequipa, also shows potential as a source of phenolic compounds with \u003cem\u003ein vitro\u003c/em\u003e antioxidant capacity\u003csup\u003e90\u003c/sup\u003e. Races Cusco Gigante and Cusco tend to group together as they are mainly cultivated in Cusco. Additionally, races Ancashino and Huayle\u0026ntilde;o, both sympatrically distributed in northern Peru (Ancash), group together and possess a very low F\u003csub\u003eST\u003c/sub\u003e, suggesting they evolved simultaneously. Hybridization likely plays a role in the grouping of these races, as noted by Grobman et al\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEven though the other Peruvian maize races are morphologically distinct, our GBS dataset failed to support them as monophyletic, which agrees with other research that evaluated Peruvian germplasm\u003csup\u003e20,21,86,91,92\u003c/sup\u003e. Similarly, Mexican maize races do not form distinct cluster\u003csup\u003e85\u003c/sup\u003e. However, Caldu-Primo et al.\u003csup\u003e93\u003c/sup\u003e were able to distinguish Mexican maize races based on a high F\u003csub\u003eST\u003c/sub\u003e SNP dataset. Population structure analysis clustered maize races from the American continent based on geographic origin, with the Peruvian germplasm contained within a clade named \u0026ldquo;Andean\u0026rdquo;\u003csup\u003e20,21,86,91,92\u003c/sup\u003e. More consistent clustering was observed when Peruvian races of maize were labelled according to their geographical zones of origin, identifying CZ1 and CZ2. This mixing among maize landraces is likely due to extensive gene flow within these zones explained by their proximities, and frequent seed exchange which is a common practice in the Peruvian Andes. However, Peruvian maize farmers in the Andes usually dynamize seed flow between families and rural communities, and conduct selection within their populations to maintain the morphological characteristics of their landraces. Our ML mostly agrees with the classification of Peruvian maize races based on the chronological origin described by Grobman et al.\u003csup\u003e7\u003c/sup\u003e as it was possible to reconstruct the ADPR\u003cem\u003e \u003c/em\u003e(CR1) and LDSR (CR2) clades. Even though Chullpi and Paro were considered very closely related\u003csup\u003e7\u003c/sup\u003e, these ADPR races developed independently from different ancestors. Moreover, the polyphyletic status of race Chullpi reflects a mixed evolutionary origin; that is, more than one ancestor was involved in its development. Thus, the similar traits that race Chullpi exhibits is very likely due to environmental pressures as the climatic gradients of the Andes are laboratories of constant plant evolution. Similarly, the paraphyletic pattern depicted by Cusco Gigante, Cusco, Pisccorunto, San Ger\u0026oacute;nimo and San Ger\u0026oacute;nimo Huancavelicano is a result of evolution in the Andes of Peru of a set of novel or derived traits.\u003c/p\u003e\n\u003cp\u003eA more detailed morphological evaluation is needed for the races of Peruvian maize to identify morphotypes and determine their phenetic plasticity. It is very likely that two accessions of Ancashino (PM-005, PM-012) and one of Paro (PM-165) contributed to the origin of other maize races evaluated in this work, as these individuals form a sister clade to CR1 and CR2. Both races are considered ADPR, directly derived from the primitive races\u003cem\u003e \u003c/em\u003e(PR), as described by Grobman et al.\u003csup\u003e7\u003c/sup\u003e. Therefore, it is very likely these three accessions may still possess genetic signatures of PR. However, further research is necessary, including samples from a wider geographical area, for more conclusive results. The position of purple maize OP cultivars within CR1 is explained by its origin on race Kculli, classified as ADPR by Grobman et al.\u003csup\u003e7\u003c/sup\u003e. The close relation among purple (OP) and yellow maize (hybrid) is due the use of the latter at UNALM to enhance yield performance of OP lines.\u003c/p\u003e\n\u003cp\u003eThe vast diversity exhibited by maize races in Peru is crucial for research in plant genetic resources\u003csup\u003e95\u003c/sup\u003e. However, the lack of relevant information on the genetic diversity of conserved plant material hinders the use of accessions preserved in germplasm banks\u003csup\u003e96,97\u003c/sup\u003e. To address this gap for Peruvian maize, we suggest that genomic tools can facilitate the characterization and utilization of this invaluable plant genetic resources, as emphasized by Mascher et al\u003csup\u003e98\u003c/sup\u003e. This approach is particularly important considering that amylaceous maize in Peru are landraces, dynamic populations in constant evolution in the Andes. Moreover, maize Peruvian maize germplasm requires a special attention as a ~5500 cal. BP maize cob from northern Peru\u003csup\u003e99\u003c/sup\u003e was the only sample without the \u003cem\u003eZea mays \u003c/em\u003essp. \u003cem\u003emexicana\u003c/em\u003e ancestry in a recent study\u003csup\u003e100\u003c/sup\u003e, shedding lights on the origin of maize in the Central Andean region. On the other hand, we expect our study will provide useful guides to researchers and decision makers for establishing a strong conservation strategy and dynamic utilization soon for Peruvian maize germplasm.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that supports the findings of this study are openly available in Dryad: https://datadryad.org/stash/share/4OI0AXLoEIDsbSSLvoOdXrhzSyMUAow9x0elGjhyPmQ\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge Fabiola Catal\u0026aacute;n, Hugo Huanuque\u0026ntilde;o, Gilberto Rodriguez and Fatima Silva for providing germplasm and technical support. We thank the University of Minnesota Genomics Center for providing facilities and services. The authors also thank the Bioinformatics High-performance Computing server of Universidad Nacional Agraria la Molina (BioHPC-UNALM) for providing resources to perform the analyses. C.I.A. thanks Vicerrectorado de Investigaci\u0026oacute;n of UNTRM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.I.A.: conceptualization, methodology, data curation, validation, formal analysis, resources, writing-original draft, writing-review and editing, funding acquisition. I.B.: investigation, data curation, validation, formal analysis, writing-original draft, writing-review and editing. J.F. investigation, data curation, validation, writing-original draft. R.O.: conceptualization, investigation, supervision, writing-original draft, writing-review and editing, funding acquisition. R.B.: conceptualization, investigation, supervision, resources, writing-review and editing, funding acquisition. P.J.G.-M.: investigation, validation, writing-review and editing, funding acquisition. R.S.: conceptualization, investigation, writing-review and editing. J.C.: investigation, methodology, writing-review and editing. A.G.: investigation, validation, writing-review and editing. All authors read, revised and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by project N\u0026deg;03-2017 of STC-CGIAR (Ministry of Agrarian Development and Irrigation of the Peruvian Government). We acknowledge project \u0026ldquo;Mejoramiento de nuevas variedades de ma\u0026iacute;z amil\u0026aacute;ceo explotando el germoplasma: uso de secuenciamientos de ADN de \u0026uacute;ltima generaci\u0026oacute;n y fenotipado en campos experimentales\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare they have no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eErenstein, O., Jaleta, M., Sonder, K., Mottaleb, K. \u0026amp; Prasanna, B. M. Global maize production, consumption and trade: trends and R\u0026amp;D implications. Food Secur 14, 1295\u0026ndash;1319 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSevilla, R. Variation in Modern Andean Maize and Its Implications for Prehistoric Patterns. in \u003cem\u003eCorn and culture in the prehistoric New World\u003c/em\u003e (eds. Johannessen, S. \u0026amp; Hastorf, C. A.) 219\u0026ndash;244 (Westview Press, Colorado, 1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWellhausen, E. J., Roberts, L. M., Hern\u0026aacute;ndez Xolocotzi, E. \u0026amp; Mangelsdorf, P. C. \u003cem\u003eRaces of Maize in Mexico: Their Origin, Characteristics and Distribution\u003c/em\u003e. (The Bussey Institution of Harvard University, Massachusetts, 1952).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoberts, L. M. \u003cem\u003eet al. Races of Maize in Colombia\u003c/em\u003e. (National Academy of Sciences, National Research Council, Washington D.C., 1957).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrieger, F. G., Gurgel, J. T. A., Paterniani, E., Blumenschein, A. \u0026amp; Alleoni, M. R. \u003cem\u003eRaces of Maize in Brazil and Other Eastern South American Countries\u003c/em\u003e. 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Front Ecol Evol 2, (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz, R., Crossa, J., Franco, J., Sevilla, R. \u0026amp; Burgue\u0026ntilde;o, J. Classification of Peruvian highland maize races using plant traits. Genet Resour Crop Evol 55, 151\u0026ndash;162 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrtiz, R. \u0026amp; Engels, J. Genebank management and the potential role of molecular genetics to improve the use of conserved genetic diversity. in \u003cem\u003eThe evolving role of genebanks in the fast-developing field of molecular genetics\u003c/em\u003e (ed. de Vicente, M. C.) 19\u0026ndash;25 (International Plant Genetic Resources Institute (IPGRI), Rome, Italy, 2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCouch, S. \u003cem\u003eet al.\u003c/em\u003e Mobilizing Crop Biodiversity. Mol Plant 13, 1341\u0026ndash;1344 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMascher, M. \u003cem\u003eet al.\u003c/em\u003e Genebank genomics bridges the gap between the conservation of crop diversity and plant breeding. Nat Genet 51, 1076\u0026ndash;1081 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrobman, A. \u003cem\u003eet al.\u003c/em\u003e Preceramic maize from Paredones and Huaca Prieta, Peru. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e 109, 1755\u0026ndash;1759 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, N. \u003cem\u003eet al.\u003c/em\u003e Two teosintes made modern maize. Science (1979) 382, (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Genome-wide distribution and density of 14,235 single nucleotide polymorphisms (SNPs) across the 10 chromosomes of maize.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"460\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e\u003cstrong\u003eChromosome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP #\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP %\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal length (Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDensity (SNPs/Mb)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e2170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e308.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e243.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e7.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e238.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e11.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e226.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e250.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e5.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e185.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e182.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e181.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e1064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.739696312364426%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.136659436008678%\"\u003e\n \u003cp\u003e954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.629067245119305%\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.945770065075923%\"\u003e\n \u003cp\u003e152.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.54880694143167%\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Genetic diversity indices of Peruvian maize germplasm based on 14,235 single nucleotide polymorphisms (SNPs).\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"629\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace (or cultivar type)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of accessions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003csub\u003eA\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003csub\u003eR\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003eO\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003csub\u003eE\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eAncashino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e2.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eChullpi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eCusco\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e2.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eCusco Gigante\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eHuayle\u0026ntilde;o\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003ePachia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eParo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e2.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003ePisccorunto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003ePurple corn (OP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eSan Geronimo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eSan Geronimo Huancavelicano\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e2.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.239\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.58730158730159%\"\u003e\n \u003cp\u003eYellow maize (hybrid)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.61904761904762%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e1.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.158730158730158%\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\"\u003e\n \u003cp\u003eN\u003csub\u003eA\u003c/sub\u003e: number of different alleles, A\u003csub\u003eR\u003c/sub\u003e: allelic richness, H\u003csub\u003eO\u003c/sub\u003e: observed heterozygosity, H\u003csub\u003eE\u003c/sub\u003e: expected heterozygosity, F\u003csub\u003eIS\u003c/sub\u003e: inbreeding coefficient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eAnalysis of molecular variance of the genetic variation for 423 accessions of Peruvian maize germplasm using 14,235 SNPs.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"415\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.650602409638555%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource of variation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e\u003cstrong\u003eEst. var.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.650602409638555%\"\u003e\n \u003cp\u003eBetween races\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\"\u003e\n \u003cp\u003e179,018.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e16,274.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e290.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e4.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.650602409638555%\"\u003e\n \u003cp\u003eWithin races\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\"\u003e\n \u003cp\u003e2,526,998.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e6148.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e6148.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e95.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.650602409638555%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.662650602409638%\"\u003e\n \u003cp\u003e2,706,017.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e6412.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e6439.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.421686746987952%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"6\"\u003e\n \u003cp\u003edf: degree of freedom, SS: sum of squares, MS: mean squares, Est. Var.: estimated variance, %: percentage of genetic variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4486762/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4486762/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeruvian maize exhibits abundant morphological diversity, with landraces cultivated from sea level (sl) up to 3,500 m above sl. Previous research based on morphological descriptors, defined at least 52 Peruvian maize races, but its genetic diversity and population structure remains largely unknown. Here we used genotyping-by-sequencing (GBS) to obtain single nucleotide polymorphisms (SNPs) that allow inferring the genetic structure and diversity of 423 maize accessions from the genebank of Universidad Nacional Agraria la Molina (UNALM) and Universidad Nacional Aut\u0026oacute;noma de Tayacaja (UNAT). These accessions represent nine races and one sub-race, along with 15 open-pollinated lines (purple corn) and two yellow maize hybrids. It was possible to obtain 14,235 high-quality SNPs distributed along the 10 maize chromosomes of maize. Gene diversity ranged from 0.33 (sub-race Pachia) to 0.362 (race Ancashino), with race Cusco showing the lowest inbreeding coefficient (0.205) and Ancashino the highest (0.274) for the landraces. Population divergence (F\u003csub\u003eST\u003c/sub\u003e) was very low (mean\u0026thinsp;=\u0026thinsp;0.017), thus depicting extensive interbreeding among Peruvian maize. Population structure analysis indicated that these 423 distinct genotypes can be included in 10 groups, with some maize races clustering together. Peruvian maize races failed to be recovered as monophyletic; instead, our phylogenetic tree identified two clades corresponding to the groups of the classification of the races of Peruvian maize based on their chronological origin, i.e., anciently derived or primary races and lately derived or secondary races. Additionally, these two clades are also congruent with the geographic origin of these maize races, reflecting their mixed evolutionary backgrounds and constant evolution. Peruvian maize germplasm needs further investigation with modern technologies to better use them massively in breeding programs that favor agriculture mainly in the South American highlands. We also expect this work will pave a path for establishing more accurate conservation strategies for this precious crop genetic resource.\u003c/p\u003e","manuscriptTitle":"Genotyping-by-sequencing reveals the genetic diversity and population structure of Peruvian highland maize races","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-10 05:39:55","doi":"10.21203/rs.3.rs-4486762/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":"c3e6013f-31cd-493f-b85c-5fb02fd19e44","owner":[],"postedDate":"June 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":32988809,"name":"Biological sciences/Genetics"},{"id":32988810,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2024-10-17T09:54:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-10 05:39:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4486762","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4486762","identity":"rs-4486762","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}