Genebanks are responsible for collecting, maintaining, characterizing, documenting, and distributing plant genetic resources for research, education, and breeding purposes. The rationale for requests of plant materials varies highly from areas of anthropology, social science, small-holder farmers, the commercial sector, rehabilitation of degraded systems, all the way to crop improvement and basic research. Matching “the right” accessions to a particular request is not always a straightforward process especially when genetic resource collections are large and the user does not already know which accession or even which species they want to study. Some requestors have limited knowledge of the crop; therefore, they do not know where to begin and thus, initiate the search by consultation with crop curators to help direct their request to the most suitable germplasm. One way to enhance the use of genebank material and aid in the selection of genetic resources is to have thoroughly cataloged agronomic, biochemical, genomic, and other traits linked to genebank accessions. In general, traits of importance to most users include genotypes that thrive under various biotic and abiotic stresses, morphological traits , plant architecture, disease resistance, nutrient content, yield, and crop specific quality traits. In this review, we discuss methods for linking traits to genebank accessions, examples of linked traits, and some of the complexities involved, while reinforcing why it is critical to have well characterized accessions with clear trait data publicly available. Ex situ collections such as genebanks serve to maintain genetic diversity for current and future use in crop improvement, research, and educational programs. Maintenance of genetic resources into perpetuity along with making this material available to researchers worldwide helps ensure food security for the future. The underlying genetic diversity in the plant germplasm is the lifeblood of plant breeding, making conservation of the diversity of major crops critical, and mining these collections for useful traits.1 However, the major obstacle to enhancing genebank materials is the lack of adequate evaluation data, and thus, the inability to adequately respond to inquiries for those particular accessions that directly meet the needs of the user.2 For the majority of germplasm accessions, only basic passport data  is available and data on unique proprieties/traits is generally lacking.4 Even newly acquired material rarely has more information associated with it other than basic passport information. While passport information is important, it often does not help a user of the genebank to discern which of the thousands of accessions in a database potentially contain the trait they want.Collecting trait data is labor intensive, costly, and requires multiple sites/years to evaluate the magnitude and structure of genotype-by-environment (GxE) variation in the expression of a given trait that is influenced by the population and the environments under study. Environments should cover a wide range of geographical locations and seasons for better decision making on which accession performs optimally in varying environments. A minimum of three locations are needed to evaluate GxE for most agronomic traits, but more locations may be needed to fully predict how the trait is influenced by the environment. Even when extensive phenotyping is performed with multiple locations evaluated, it is often difficult to display the information succinctly in a database (dbase) or predict how the accession would perform in a new environment.5 it was estimated that there are over 1750 genebanks worldwide, holding ∼7.4 million accessions; yet, the report claims that only 25%–30% of these accessions are genetically unique. Furthermore, the majority of these collections are securely preserved, but largely unused. Even though ex situ collections have increased over the last few decades in a global effort to conserve plant genetic resources, the size of these collections complicate the maintenance and evaluation of these genetic resources.6 Moreover, the lack of publicly available information has resulted in low use of these resources.7In the Second Report on The State of the World's Plant Genetic Resources for Food and Agriculture,Historically, many of the accessions added to genebank collections came without information on specific traits or sometimes even lacking basic passport information. In addition, some genetic resources were collected by eager scientists on mission trips to find new species or explore new geographical locations for their crop of interest and sometimes only basic information such as the collecting site or putative species was noted due to time constraints of the mission. Also, older plant collection trips did not always have easy access to GPS locators or GPS applications on cellular phones. In other cases, material was collected in open air markets or donated from other collaborators and arrived in the genebank without any information on its parentage or unique attributes. Evaluating, accurately scoring, and documenting most traits of agronomic, nutritional, or other interest often requires several years of intensive field phenotyping and/or specialized equipment for laboratory measurements along with bona fide documentation so the information becomes available to the community.ex situ for future generations to utilize. The plants and seeds maintained in these genebanks hold various genes and traits that may be the keys to solving current or future biotic and abiotic challenges that arise, especially important considering the pressures from a changing climate. Genetic resources, however, are often “diamonds in the rough” and need further work, such as extensive evaluation to uncover their true nature or prebreeding efforts, to elucidate their value. The value of some accessions may never be realized especially if they remain uncharacterized and unutilized like many of the holdings in global genebank.Genebanks are often referred to as the crown jewels in organizations to which they belong. They house numerous plants that have been collected worldwide and preserved Arachis hypogaea), which was collected in 1952 in a market in Porto Alegre, Brazil and added as an accession to the peanut germplasm collection in the United States Department of Agriculture (USDA) genebank in Griffin, GA USA. PI 203396 sat mainly unused except for regular regeneration cycles needed to keep the seed viable. Yet, this accession contained gene(s) for resistance to tomato spotted wilt virus (TSWV) resistance and the incorporation of these alleles into peanut varieties has now been estimated to have an economic value of more than $200 million annually.8 Even today, the majority of the peanut varieties released have some ancestry linked back to PI 203396 or its derivatives to confer TSWV resistance. This diamond in the rough likely would have never been discovered if it hadn't been for TSWV devastating the peanut production in the Southeast of the United States starting in 1987 with little to no resistance in commercially grown varieties at that time. Breeders raced to find a solution and it came from a single accession. Even though it is well known that this accession confers TSWV resistance and its alleles are integrated into most of the commercial peanut varieties grown today, the information is not documented or associated with this accession in the observational data in the USDA Genetic Resources Information Network (GRIN) public dbase. This makes it hard to impossible for worldwide researchers or those new to peanut breeding to find information and order this accession that contains TSWV resistance. Notably, in GRIN this accession is listed as being resistant to leaf spot.A great example of a “diamond in the rough” is PI 203396  in breeding programs. Many ex situ banks are still documenting information on paper or Excel sheets and do not have a publicly accessible dbase. As a whole, genebanks have characterized and evaluated their collections, but on average only 64% are characterized morphologically, 51% agronomically, 14% biochemically, 14% for abiotic traits, and 22% for biotic traits. One of the main recommendations from this report5 was to strengthen the characterization and evaluation of these genetic resources to encourage and increase the use of germplasm. Further, search tools and public dbases are needed to hold and make accessible all the information. Even in well funded genebanks with public dbases significant gaps appear for various traits where some accessions will have data and the rest are uncharacterized. For example, the International Potato Center (CIP) has over 5800 accessions with morphological or trait data for potato, but the number of accessions characterized for each trait vary from 2 to over 5000 accessions with morphological descriptor data being the most common. The sweetpotato collection at CIP, which started later than potato, has over 3400 accessions with some associated data yet, some traits are only evaluated for less than 10 accessions.The peanut accession PI 203396 is not a unique case of undocumented material in genebanks. In the Second Report on the state of the world's plant genetic resources for food and agriculture,Many accessions maintained in genebanks were originally collected as populations and not as individuals, meaning the genotype and phenotype within an accession can be vastly different from plant to plant; therefore, purification of seed lots that are heterogeneous needs to be a priority to make these accessions of value to breeders or for molecular/genomic analyses. Once a seed lot is purified then specific traits can be tagged to an accession and made available to others. The major limitation with this philosophy is that if you purify each accession with an average of 10 different phenotypes then you increase the size of the collection 10-fold and if you multiple this by thousands of accessions, the number of accessions to maintain becomes overwhelming for genebanks that are already limited in terms of resources. Also, some crops are not amenable to selfing, and thus, cannot be easily purified so clonal maintenance is the only solution to maintain specific genotypes. Maintaining clones is considerably more expensive than seed collections.9 Some curators of genebank crop collections have a background in breeding and therefore can facilitate prebreeding work to aid crop breeders and provide improved material. Many genebanks, however, do not have dedicated prebreeders, and thus, scientists outside of the genebank or individual breeders carry out this work independently, which usually means the information gained is not deposited back to the public genebank dbase. In addition, the information obtained is often decoupled from the original accession because the focus for phenotyping is on the new progeny or progenies produced from a cross and not the original accession from the genebank. Although genebanks always encourage feedback of data from all users of genetic resources, seldom does data return to the genebank to add value back to the original accession.In addition to identifying and purifying heterogenous accessions, prebreeding may be required to produce a line worthy of future crosses for a breeder. Prebreeding identifies and captures desirable characteristics from unadapted plants that cannot be directly used in breeding populations and transfers these genes into an intermediate stage that can be directly used by a breeder. This is necessary for accessions not adapted to a particular target environment, closely related wild species that can cross to the cultivated form, and for distant wild species that are difficult to cross.3 and often are of little value to breeders. Generally, the morphological descriptors selected for characterizing genebank accessions are chosen based on characters that do not vary dramatically between environments yet have a strong genetic component so that one accession can be differentiated from another over several environments. Characterization is normally done in very irregular cycles over multiple years as the accession needs regenerating due to low viability, seed stocks become low, and with different sets of accessions in each regeneration cycle. Next generation phenotyping and genotyping have emerged as methods to capture quality data on genetic resources, but these techniques are often quite costly for a genebank to procure and require considerable IT infrastructure to implement these systems.4 For genebanks to stay relevant in the 21st century, it is necessary to embrace the digital information age and invest in the infrastructure to provide useful data (genomics and phenomics) to its users.Curators do regularly measure morphological descriptors on the accessions they maintain and regenerate, which in effect describe particular attributes of the accession such as flower color, leaf shape, and so on; however, these traits are largely ignoredTo promote and enhance germplasm use, value needs to be added in the form of comprehensive cataloging and association of important traits to each accession. Users of genetic resources are interested in various traits, though yield quality traits, nutrients, and disease resistance are among the most commonly requested. There are several methods that can be utilized to link or discover traits in accessions, such as standard phenotyping, marker assisted selection (MAS), genome wide association studies (GWAS), prediction based on known data, core/mini core collections, and mining publications and public datasets for information. These various methods along with a few select examples of each method are discussed in this review . This is* and CGspace**, respectively are the current mandatory data repositories. Scientific journals are also now requiring depositing of data sets in supplemental links to a published article. Once potential publications or datasets are identified with genebank accessions, the trait data collected can be summarized and tagged to an accession. In the last 2 years, the International Potato Center (CIP) genebank has been using this method to expand the available trait information. This method has provided data collected by other researchers on 26 traits for 2995 accessions from the genebank of which 529 and 2466 were sweetpotato and potato, respectively. Information collected for potato included traits for resistance to late blight and bacterial wilt, vitamin C content, anthocyanin, dry matter, tuber bulking maturity, sugar content, glycoalkaloids, cooking and post cooking quality measurements, chipping color, and drought index. In sweetpotato, traits such as drought tolerance, sweetpotato virus disease resistance, yield, β-carotene, dry matter, total sugars, starch, and protein were associated with accessions to aid in germplasm selection. Overall, this has proven to be a low-cost method of gaining information from previously conducted studies on genebank accessions.One fairly easy, low cost way to link traits to accessions is by mining publicly available data. The only requirement is staff time needed to search for the information and format it appropriately. Searches can be made for the crop of interest to discover publications and evaluate the samples chosen in each study that were distributed from the genebank. Open access datasets that have now become a fairly standard requirement can be mined to find genebank accessions. In the case of CGIAR  datasets and publications, dataverse3 Breeders are generally willing to support the genebank with trait information on accessions; however, the main limitation is that breeders request material, make a cross, and then characterize the progeny/derivatives of genebank accessions with the resulting information pertaining to a new genotype which is not easily linked to the original genebank accession requested.Another potential strategy is to request and encourage researchers and breeders to provide data back to the genebank from material they have requested and evaluated. This can be successful and genebanks receive the information usually in the form of a publication and can make the data publicly available without the expense of having to collect the data. In reality, most of the time, no information is returned even when stakeholders request the germplasm to screen for a trait of interest.3 Thus, evaluation of genebank collections is often only a feasible strategy for smaller genetic resource collections containing a few dozen to a few hundred accessions. The biggest limitations, therefore, are resources and balancing the goals for maintaining collections with the number of available staff, financial support, number of accessions that can be evaluated, and a reasonable time period to complete a project. Further, a trait needs to be fairly easy and inexpensive to measure without destroying a lot of the plant material, otherwise considerable effort will also be needed to regenerate the accession(s).Another straight forward way to link traits to germplasm accessions is to put the effort into phenotyping for a trait of interest. This approach often requires considerable effort from staff, multiple years of planning, and separate financial support to accomplish. Funding to genebanks usually only covers the basic maintenance of the collection and not evaluation work, and several studies have demonstrated that the majority of genebanks lack sufficient funds to cover basic facilities and staff to maintain their collections.10 This is because plants display phenotypic plasticity where one genotype can produce multiple phenotypes due to the environmental conditions such as response to shade or light, architectural changes above ground due to nutrients or changes to root structure in differing soil types affecting access to water.11 This further reinforces that evaluation data needs to be collected at multiple locations to understand the level of plasticity for a particular trait. Because traits can be influenced by the environment, it is critical that GxE is addressed by collecting trait data in multiple environments. While evaluation of a trait(s) in a single environment over a single year does provide some baseline information on the range of variation among different genotypes, it does not provide any information on how that accession may respond in an environment different to the one in which it was evaluated.In this approach, the evaluation needs to be mainly restricted to agronomic traits showing high heritability.12 Castor seeds contain a toxin and are unsafe for consumption; however, the oil is edible, highly viscous, and is used in various cosmetics and lubricants for high speed engines. Total oil content was measured by nuclear magnetic resonance (NMR), which is a nondestructive approach and does not require many seeds. The variation in the collection ranged from 37.2% to 60.6% oil content. Unlike NMR, gas chromatography (GC) is a destructive process, but it is fairly cost efficient, can be automated, and requires only part of a single seed up to a few seeds for analysis. The GC analysis of the castor bean collection demonstrated significant variability for the following fatty acids: ricinoleic acid (C18:1-1OH), linoleic (C18:2), oleic (C18:1), and stearic (C18:0), while the range of variability for the remaining fatty acids was rather small.12 The evaluation information collected in such studies is a long-term resource for breeders and researchers to select ideal germplasm for improving total oil or fatty acid profiles in castor bean.One example of phenotyping an entire collection by brute force is from the USDA castor bean germplasm collection (1033 accessions) that was measured for total oil content and fatty acid composition.13 The entire USDA cucumber germplasm collection was evaluated for three years for fruit yield and quality traits. Interestingly, the environment did not play a significant role for any of the traits evaluated. Accessions with the highest fruit number were identified and some had higher yields than the checks included in the study.13 All of the data from this study was made available on GRIN†, making it easy for users to evaluate this data and make selections for their breeding or research programs.Cucumber is an old crop that has been cultivated for 5000 years and grown throughout the world for the fresh or processed vegetable market.14 In rice blast, it is critical to look for multiple forms of resistance because the causal agent (fungi) rapidly overcomes any single form of resistance after only a few years of agricultural use; therefore, continuous searches are ongoing for new germplasm containing resistance. A total of 4246 accessions were screened from the International Rice Research Institute (IRRI) genebank and 74.8% of these accessions were found to be resistant. However, only 289 genotypes (7%) showed resistance to all five rice blast isolates.14Phenotyping an entire collection is noteworthy, although this is not always feasible due to the cost involved for large genetic resources collections. Therefore, stratification strategies can be applied to choose a manageable number of accessions with knowledge gained on a select set by phenotyping a portion of the collection. One example of this is resistance to rice blast that causes significant yield losses in this major cereal crop.15 over half (55%) of the watermelon genebank from the USDA, which included three different species and material originating from 57 different countries, were examined for tolerance to drought stress, an important trait due to the growing threat of climate change. This screen demonstrated that the most drought tolerant material originated from desert regions in Africa. Of these, two identified drought tolerant accessions also had resistance to papaya ringspot type-W and zucchini yellow mosaic virus.15–17 These accessions containing drought and virus resistance are ideal candidates as parents in a breeding program for stacking multiple traits.In another study18Oil of palm is an important source of edible oil; however, these oils can oxidize, which affects the overall quality of the oil. If endogenous enzymes (lipase) are reduced in a particular genotype, then the shelf life of a product is improved and lengthened. Palms from the Malaysian Palm Genebank (148 accessions) were screened for lipase activity. Low and high lipase materials were identified and found to be correlated with geographic origin with low lipase palms coming from countries bordering the Sahara desert and high lipase palms derived from areas with higher rainfall, which was consistent with the biology in which the enzyme needs water to hydrolyze the oil.Citrus medica), accessions with citron pedigrees, and the wild relatives all of which had low or no symptoms of HLB.19 These accessions can be candidate accessions used by the breeders to develop more tolerant cultivars.Huanglongbing (HLB) is a destructive disease to the citrus industry that has spread in the primary growing areas of the United States since the mid 2000s. The focus in the citrus world is to find resistant and/or tolerant cultivars. Eighty-three accessions representing 85% of the genetic diversity of citrus and its wild relatives were evaluated under field conditions to determine tolerance to HLB. Of the accessions evaluated, the best performing ones under HLB pressure included citrons . Data such as geographic origin, specific plant characteristics, trait data, and molecular data are utilized to develop core subsets. Because a core is a much smaller subset, it facilitates evaluation and characterization more efficiently and effectively.6 The core collection concept allows researchers to screen a smaller set of samples, generally 10% of the total collection that approximately captures 70% of the total genetic diversity, to save time and resources to find the trait(s) of interest. Certainly this is not the case with crops such as maize, wheat, and rice with extremely large holdings >25,000–120,000 accessions, where a 10% set will still be a very large number of accessions to screen. In these cases, a core collection would generally be too large to evaluate at multiple locations with replication21 without extensive resources.The concept of core collections was originally proposed by Brown.22 PowerCore,23 ccChooser,24 CoreHunter,25 and GenoCore26 among others that now are available to help construct a core or mini core collection. Many of these programs can use molecular marker data, genetic distances, phenotypic traits, geographic origin, or integration of these various data types to select a core set.There are many methods available and free software packages such as MSTRAT,27 Additionally, the pearl millet core collection from the International Crops Research Institute for Semi-Arid Tropics (ICRISAT) was modified to add 501 accessions from accessions that were characterized after the original construction of the initial core set.28A further point of consideration is that core collections should be dynamic, not static. A periodic review and modification of the core collection may be warranted as genebank holdings increase and new diversity is added or when new information on accessions becomes available. As an example, the core subset in yam was revised from 371 to 843 accessions because the International Institute of Tropical Agriculture (IITA) genebank had increased its collection over time by acquiring material from Benin and Togo, and new information that had been collected on duplicates within the collection and sorting of genetic identity issues.29 Universidad Nacional del Altiplano (UNAP) collection of quinoa,30 IITA germplasm collection of yam,31 USDA peanut collection,32 ICRISAT groundnut collection,33 Beijing Vegetable Research Center (BVRC) watermelon germplasm,34 ICRISAT chickpea germplasm,35 and the Worldwide Olive Germplasm bank (OWGB)36 to name a few. One element often missing, however, is a comparison of core collections between genebank collections that hold the same crop. A comparison was made between the peanut mini core from China and the ICRISAT mini core, which were both evaluated using simple sequence repeat (SSR) markers. The genetic distance between the two subsets was larger than the distance within a single mini core, suggesting that the material was fairly unique. Overall, the diversity was higher in the Chinese mini core than the ICRISAT mini core.37Core collections have been constructed for numerous crops from many genebank collections worldwide. For example, core collections have been developed from the USDA chile pepper germplasm,38 and genetic variability of iron and zinc concentrations ranged from 34 to 89 and 21 to 54 mg/kg, respectively. Tannins were also evaluated in this study because high levels of tannins tend to reduce the availability of iron levels in food preparation or digestion. Colored seeds are often associated with higher tannin levels, but this study demonstrated that colored seeds had a large range of variation, suggesting it is possible to reduce tannin levels even for the darker colored seeds.38Evaluations of these core collections from genetic resource collections has led to comprehensive cataloging of germplasm, and, important discoveries of traits that breeders can use to make selections for improving crops of interest. A core collection for the common bean was evaluated for trace minerals,39 The wheat core from this collection had a large range in protein content (10.9%–19.2%), which is important for determining the nutritional value. Preharvest sprouting ranged from 0% to 61.3% and the quality of the wheat for bread making ranged from tough, inelastic dough to high quality dough for bread. Bordes et al.39 found that the modern varieties (1960–2000) in the core collection typically had a smaller range of variation than the landrace or older varieties for several of the traits evaluated in the core. This comprehensive evaluation of wheat allows breeders to select accessions for breeding programs with the traits they desire.In wheat, a core collection of 372 accessions from the Clermont-Ferrand Genetic Resources Center, France, was chosen based on passport and microsatellite data. Various agronomic and quality traits in the core were evaluated and compared to modern varieties to assess the diversity within the core subset.40 All of the core lines and other accessions assayed showed the presence of P34, indicating that this protein is highly conserved in soybean, which suggests breeding to eliminate this major allergen will be difficult.40 These results further suggest that without genetic modification to knock out the gene(s) involved in P34 synthesis, there likely will be no solution to obtaining a nonallergenic soybean.While core collections can often lead to the identification of accessions with specific traits needed by breeders for crop improvement, sometimes this strategy does not work to identify a trait of interest. Food allergens are a significant problem around the world. In soybeans, the seeds are a major source of human allergens (i.e-P34-cysteine protease). Soy is used in processed foods making those with food allergies vigilant in checking all the ingredients from any of the products they consume. The soybean core collection and a group of wild relatives were evaluated for P34 and other seed allergens.41 because for screening of certain traits, a core collection is still too large. A mini core is ∼1% of the entire germplasm collection and derived from accessions within a core collection and is thus a subsample of the core collection. The advantage of the core and mini core strategy is that once accessions are found with a particular trait of interest, a user can back track to the clusters these accessions originally were grouped in and screen more accessions from that particular cluster to find additional individuals with a trait of interest. Because accessions are grouped together based on some commonality, in general the accessions in the germplasm collection that were not selected for inclusion in the core, but reside in the same cluster as the accession identified with a unique trait, often may contain the trait of interest. This particular method has been successful in identifying additional accessions from a collection with the desired trait(s).The mini core concept was proposed in the early 2000s42 In chickpea, fungal diseases hamper yield potential of this important pulse crop. To find resistant material for breeding programs, the mini core collection (211 accessions) was screened for resistance to several fungal diseases under controlled conditions. None of the accessions were found to be resistant to the multiple diseases screened. However, 25 accessions were resistant to Fusarium wilt, three moderately resistant to Ascochyta blight, 55 moderately resistant to Botrytis gray mold, and six accessions to dry root rot.43 Fusarium wilt and sterility mosaic disease affect pigeonpea production. The pigeonpea mini core subset (146 accessions) was found to contain six accessions with resistance to Fusarium wilt and 24 accessions with resistance to sterility mosaic disease.44Diseases are one of the main limitations to yield potential in all crop plants, thus, identifying new sources of resistance that can be used in breeding programs is always needed. Mini cores have been an effective tool to mine germplasm for needed resistant accessions. Grain mold and downy mildew are diseases that affect the yield of sorghum, an important cereal crop worldwide. A mini core collection was screened for resistance to these diseases and a total of 50 accessions were identified as resistant to grain mold and six accessions were resistant to downy mildew from the ICRISAT genebank collection. One accession was identified that was resistant both to downy mildew and grain mold.45 This approach is supposed to provide a better alternative to random sampling and use of core collections since it is specific to each trait and is selecting manageable size subsets with higher probability of finding the desired traits. FIGS has demonstrated its relevance and efficiency in identifying specific traits for breeders rapidly and precisely. In recent years, it has allowed the identification of new allelic variation and novel genes for traits that researchers have been looking for, unsuccessfully, for a number of years (The Focused Identification of Germplasm Strategy (FIGS) has been developed jointly by the International Center for Agricultural Research in the Dry Areas (ICARDA) with the Vavilov Institute (Russia) and the Australian Winter Cereals Collection as an approach to address utilization of genetic resources. The premise behind the FIGS approach is that the environment under which wild and landrace material grows will drive the evolution and selection of adaptive traits that could be of use to plant breeders. The method seeks to determine a potential relationship between collection site (agro-climatic conditions) and the presence of specific traits, such as resistance to biotic stresses and tolerance to abiotic stresses. The process identifies candidate collection sites that are likely to have imposed selection pressure for the trait of interest, which in turn allows the germplasm curator to identify a best-bet set of germplasm for evaluation.of years .During the last 10 years, ICARDA, with the financial support of GRDC/Australia, is taking the lead to further develop and improve the FIGS pathways through testing different modeling processes and generating long-term layers for main climatic variables and onset layers for ICARDA main crops . Fine-tuning the FIGS approach is a continuous process since it is model based. ICARDA and its collaborators are evaluating FIGS subsets and generating feedback to improve the models and algorithms used for sub-setting. This improvement is also enriched by access to evaluation data around the globe. Furthermore, the availability of molecular data will be an added-value for FIGS processing by integrating marker-trait association and maximizing genetic diversity in the selection of trait subsets. Environmental data is a limiting factor in FIGS processing and more precision in daily climatic data for crops is needed in order that effective modeling can occur to evaluate traits that are influenced by specific growing periods. Another limiting factor is the lack of information of virulence spectra of pests.in situ populations over time. Developing a FIGS filtering strategy requires deep understanding of the ecology and the optimal conditions of the expression of the trait under study, how these conditions affect the crop, and how this will relate to a selection pressure on an in situ population. Filters can be applied in the search process, such as excluding regions where a particular disease has not been reported or restricting a search to collection sites where stresses have occurred. Since most biotic and abiotic stresses happen at a very specific growing stage(s), daily long-term data together with the crop onset information are required to zoom into the crop growing stages using growing degree days.FIGS follows two distinct pathways: filtering and modeling, both of which select best-bet environments that are likely to have imposed selection pressure for specific traits on Y) depends on the environment (X), where X =  are the covariates. The quantification process leads to the generation of a priori information, which is used in the prediction of accessions that would carry the desired trait.When evaluation data is available or a user has a clear idea about the classification of an adaptive trait based on knowledge such as heat, drought, or frost, FIGS can explore the mathematical relationship between the adaptive trait of interest and the long-term climatic and/or soil characteristics of collection sites. The mathematical conceptual framework of FIGS is based on the paradigm that the trait as a response variable (n by n (n number of classes in the trait) matrix presenting the percentage of true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). Accuracy (how often the classification is right), Kappa (accuracy vs. random chance), sensitivity (proportion of truly positives cases), and specificity (proportion of truly negative cases) are the metrics used in addition to the area under the curve to discriminate between different models and assess the trait/environment association. The high accuracy of the models is an indication of the presence of the trait environment association. This information is used in predicting accessions that could carry the trait at a higher frequency than a random selection of accessions.The performance of the models/classifiers is measured using the metric parameters derived from a confusion or error matrix, an 46 support vector machine (SVM),47 and random forest (RF).48 R language49 is used as an open source platform for FIGS development and is the most appropriate for packaging FIGS steps to conduct research and to communicate the results to the global plant genetics community. The first version of the R-FIGS package will be published in GitHub and will be available in 2018.FIGS uses several machine learning techniques including nearest neighbors k-nearest neighbors (kNN),51—explained more than 80% of the total climatic variation, but failed at classifying the durum landraces into short or long GFPs  was then estimated as the difference between days to maturity and days to heading. The evaluation was done at the ICARDA station TelHadya in Syria during the 1991 growing season. The distribution of the GFP trait shows a bimodal distribution  for the ong GFPs . Climatik number of neighbors for kNN, and C and gamma for SVM. Finally, the three models were trained using the training set with the optimal tuning parameters and extraction of the metrics. RF was the most accurate model with good Kappa, sensitivity, and specificity values (ex situ collection.The data was subsequently split into two sets: training (75%) and validation (25%). Three machine learning classification algorithms kNN, RF, and SVM were run. First the algorithms were tuned (adjustments to the model) to choose the best parameters for each algorithm, tree numbers and the number of predictors (mtry for RF), y values . High mey values  validatey values  and 4. Tin situ to guide future collecting missions. The ultimate goal is to develop a friendly package in R that could be available to users worldwide for efficient mining of genebank collections.The focused identification of the germplasm strategy is a powerful tool aiming to reduce the number of accessions that breeders need to screen and maximizing the chance of finding novel alleles for the targeted adaptive trait in a predicted subset. Similar approaches are also being investigated for searching useful traits 52 Further, MAS is only effective with major QTLs that have limited environmental or epistatic interactions.53 Lastly, the markers need to be highly reliable and reproducible among various labs and populations for this approach to be consistently successful.MAS has been utilized as a means to gain valuable information in segregating populations to rapidly identify undesirable genotypes or to classify a set of genebank accessions for a trait of interest. Utilization of markers saves a lot of valuable time by identifying material with a particular trait of interest at an early stage of development rather than waiting for full maturity of a plant to be able to phenotype for that trait. This approach is especially useful in crops with long periods of juvenility. If a marker is tightly linked to a trait or if it is designed to detect a functional mutation within a gene, accessions can be interrogated with the marker(s) and the trait can be inferred and/or validated by phenotyping. The main limitation to MAS is it is only an effective tool when the trait is controlled by one or two genes or if the trait is under the control of few quantitative trait loci (QTLs) with large contributions to phenotypic variation.ahFAD2A and 442insA in ahFAD2B were necessary to produce a high oleic peanut. Both of these mutations are required in the homozygous recessive state to significantly affect the function of the enzymes that convert oleic acid (18:1 monounsaturated fatty acid) to linoleic acid (18:2 polyunsaturated acid).54–57 Markers were developed to track these important mutations and the underlying trait59 in germplasm. Ninety-four accessions from the USDA mini core peanut collection were evaluated with these markers60 showing that the ahFAD2A mutation naturally existed in a homozygous state in 41% of the population whereas the ahFAD2B functional mutation was not detected.61 The alleles were also screened in 39 wild peanut species from the genebank to track the ancestry of these mutations; however, no functional mutations were detected in the wild accessions.62 Further, a study tracking the genotypes (ahFAD2) and the resulting phenotypes (fatty acid profiles) in segregating populations demonstrated that this trait was not controlled by dominant gene action as previously determined, but was quantitative in nature with much of the variability for three fatty acids  being controlled by the two key genes (ahFAD2A and ahFAD2B), even though segregation patterns were typical of Mendelian inheritance from the two homoeologs. Another line of evidence of their quantitative nature was that several of the fatty acids were significantly positively and negatively correlated with one another.63The high oleic trait in peanut is an important seed quality trait. This trait gives peanut seed longer shelf stability (prevention of the oils going rancid) that is desired by manufacturers and provides the consumer with the health benefit of more monounsaturated fat in their diet with a fatty acid profile similar in composition to olive oil. Previous work has shown that two functional mutations G448A in Vitis germplasm bank from IMIDRA, Spain.53 Although the authors discuss the value of genotypic selection being superior over phenotypic selection, there were a few cases of false positives and negatives that were assumed to be caused by recombination between the marker and the gene, phenotypic misclassification, or a minor effect QTL. However, in most cases the markers were effective in the detection of the desired trait greatly speeding up the identification of seedlessness for breeders.Where molecular markers can really expedite trait discovery is in crops with long periods of juvenility where years are required to produce the first fruits and/or significant land is needed for growing the crop. For instance, table grapes can take two to four years to produce fruit and then phenotyping would be required for several seasons after fruit production to evaluate a particular trait. Microsatellite markers linked to the seedlessness trait in grapes were employed to evaluate material in the 10 FIGS was employed to define a subset of landrace accessions that were manageable for a molecular screening study in wheat.64 FIGS selected 1320 accessions from 323 different geographic origins that showed high selection pressure for powdery mildew resistance. These accessions were tested with isolates of powdery mildew and a total of 211 accessions showed complete or intermediate resistance to at least one race. The resistant accessions (56) were screened for the Pm3 gene that is a known resistance gene and new allelic variants were identified by cloning and sequencing the gene from wheat accessions. Sequence data demonstrated 16 new allelic variants for the Pm3 resistance gene. Bhullar et al.64 found that some of the resistant accessions had Pm3 gene sequences identical to the susceptible alleles suggesting that there are more resistant genes in the genome to be discovered. Some of the new allelic variants identified were from accessions largely derived from Eastern Turkey. To verify whether these new allelic variants were linked to powdery mildew resistance, virus induced gene silencing VIGS was employed. This technique demonstrated that some of the new variants were indeed conferring the observed resistance and other variants either had another gene conferring resistance or the resistance was from a combination of Pm3 and other genes. Overall, seven new Pm3 alleles were described that represents a large allelic series of resistance genes with 14 allelic variants now described. Clearly, as demonstrated here, the diversity in genebank accessions can be utilized to identify important alleles from known resistance genes.64Another useful molecular approach in linking traits to genebank accessions is to sequence known functional genes from different individuals to identify the effect of different allelic variants. The most effective strategy for determining allelic richness is to sequence a collection of individuals to find the variants.65 This technique relies on linkage disequilibrium, which is nonrandom association of alleles in a population. The phenotypes collected for GWAS can be quantitative or qualitative. The potential for success in GWAS depends on the number of loci affecting the trait that are segregating in the population, allele frequency at these loci (genetic architecture), sample size, panel of markers used, and the heterogeneity of the trait.66 Further, finding an association between a genetic marker and a trait of interest is dependent on the variance of the phenotype in the population explained by that marker.65GWAS have emerged in the last 10 years as a powerful tool to link genetic markers to phenotypic variables in populations and further to discover genes and alleles for agricultural traits. It provides a connection between a trait and its underlying genetics. GWAS either identifies causative/predictive factors for a particular trait or it can provide information on the genetic architecture such as the number of loci and their contribution to the phenotype.67 Phenotyping was performed for 34 different traits under drought and heat stress over multiple years because drought can severely affect crop production. A combined approach of SSRs, diversity arrays technology, and single nucleotide polymorphisms (SNPs) were evaluated on the reference set along with sequence characterization of 10 drought related candidate genes, producing a total of 1872 markers. In total, 312 marker trait associations were found and 18 of the SNPs located in genes were significantly associated to the traits measured.67GWAS and candidate gene sequencing-based association approaches were both utilized to evaluate marker trait associations in a chickpea reference set of 300 accessions derived from the ICRISAT genebank.68 A total of 174 genebank accessions were evaluated for curd traits using over 120,000 SNP markers. A total of 24 SNPs were significantly associated with the curd traits.68 GWAS was also utilized to evaluate genotyping data produced from the “iCore” or informative core of 1860 barley accessions using three phenotypes deposited in the GRIN dbase.69 Significant SNPs were detected for the hull cover that were associated with the NUD locus and major genes determining spike row number.69In another study, GWAS was employed to locate SNP markers that are associated with variation in curd traits (edible inflorescence) in cauliflower, which is an important trait for yield.70Soybean is an important source of oil and protein. However, salinization of land can affect soybean yields and phenotyping for salt tolerant lines in the greenhouse is expensive and time consuming whereas field selections can vary since salt concentration can range vastly in a particular field. Therefore, GWAS was employed using 33K SNP markers on a set of 283 accessions from the USDA Soybean germplasm collection. Soybeans from 29 different countries were utilized to avoid spurious associations from population structure and relatedness. Plants were treated with salt in the greenhouse. Chlorophyll concentrations were measured and chloride was extracted from the harvested dried leaves. This study demonstrated 45 SNPs from nine regions of the chromosomes associated with leaf chloride and leaf chlorophyll concentrations. Additionally, major QTLs associated with salt tolerance were also identified.66 This means that individuals carry different alleles at multiple loci can increase and decrease the frequency or occurrence of the trait. In a population, there are many combinations of these alleles so that each individual can have a unique combination. Traits are often associated with variants at hundreds to thousands of loci and there is evidence of widespread pleiotropy for complex traits, implying that some variants affect more than one trait.66 Hence, large population sizes are needed for GWAS studies to determine the effect of each allelic combination on the particular trait being studied. Further, spurious signals can occur due to population structure or relatedness within the population. Signals determined from GWAS are often markers of putative risk and not the underlying functional genetic variant culprits, so caution should be taken before claiming variants have been identified.71 GWAS also does not provide information on the mechanism of how the genetic variant is associated with phenotypic differences or the target gene that controls the trait; however, new technologies are providing opportunities to bridge this knowledge gap.66 Another key point of all GWA studies is that after associations are identified, they need to be validated because there have been problems confirming the results.72 These associations also need to be generalized to other populations. Because a large amount of marker data is collected, a GWA study can identify numerous associations that are likely to be overestimates and unbiased effects can only be made in a data set not used in the discovery process.73 In summary, validation of GWAS results is an important factor to ensure linkage between a marker and a trait of interest.Even though GWAS has brought about advancements in linking markers to traits for rapid selection, there are still some potential pitfalls. Complex traits are typically polygenic, and thus, have many loci contributing to the genetic variation observed; therefore, polymorphism in many genes play a part in the genetic variation observed in the population, so that the proportion of variance at the individual level is small.There are several ways in which important agronomic and quality traits can be linked to accessions. From predictive machine learning, molecular techniques such as MAS/GWAS, to brute force phenotyping, all of which can lead to uncovering the range of diversity in a set of accessions and reveal critical traits of interest for crop improvement. Once accessions with traits of interest are identified and made available, breeders can use this information to select parents for crossing and move forward on releasing new varieties to meet current needs. Overall, comprehensive characterization of genetic resources is critical to add value to accessions and to further help guide users on selection of appropriate germplasm for their specific needs.


Little is known about the etiology of developmental change and continuity in educational achievement. Here, we study achievement from primary school to the end of compulsory education for 6000 twin pairs in the UK-representative Twins Early Development Study sample. Results showed that educational achievement is highly heritable across school years and across subjects studied at school ; achievement is highly stable (phenotypic correlations ~0.70 from ages 7 to 16). Twin analyses, applying simplex and common pathway models, showed that genetic factors accounted for most of this stability (70%), even after controlling for intelligence (60%). Shared environmental factors also contributed to the stability, while change was mostly accounted for by individual-specific environmental factors. Polygenic scores, derived from a genome-wide association analysis of adult years of education, also showed stable effects on school achievement. We conclude that the remarkable stability of achievement is largely driven genetically even after accounting for intelligence. Twin studies have shown that individual differences in school achievement are to a large extent (around 60%) explained by genetic differences. However, little is known about age-to-age stability and change in school achievement. A team led by Kaili Rimfeld used school achievement data from primary school to the end of compulsory education for 6000 twin pairs in the UK-representative Twins Early Development Study sample. Results showed that school achievement is highly heritable across the school years and across subjects studied at school, that school achievement is highly stable, and that this stability is largely explained by genetic factors. The finding of genetically driven stability of school achievement should provide additional motivation to identify children in need of interventions as early as possible, as the problems are likely to remain throughout the school years. Compulsory education in the UK culminates with standardized nation-wide exams, the General Certificate of Secondary Education (GCSE). GCSE grades are a gateway to further education, university acceptance, and even later employment, shaping individuals’ life-long educational and professional trajectories. Previous twin research has shown that GCSE performance is highly heritable, and to a lesser extent explained by environmental factors.6 However, little is known about whether the same or different genetic and environmental effects contribute to individual differences in achievement over the course of compulsory education. In the present paper, quantitative (twin) and molecular genetic (DNA) methods are used to examine the etiology and developmental course of educational achievement during the primary and secondary education period, culminating in GCSE grades.Educational achievement is important to society and to children as individuals. In fact, educational achievement has been shown to be a good predictor of many life outcomes, such as occupational status, happiness, health, and even life expectancy.16 This holds when considering overall achievement scores as well as separate school subjects, from Sciences to Humanities.12 It is also possible to estimate heritability using DNA of unrelated individuals, where small DNA differences between individuals (single-nucleotide polymorphisms (SNPs)) are associated with the individuals’ scores in a trait of interest. Rather than estimating the association between each SNP and the trait, this method estimates the association between the trait and all the SNPs combined.18 This so-called SNP heritability for educational achievement has been shown to be around 20–30%.21 The SNP heritability is less than that estimated by twin studies partly because SNP heritability is limited to additive effects of common SNPs that are included in current arrays used to genotype SNPs. Because genome-wide association (GWA) studies have the same limitations as SNP heritability, SNP heritability is the current ceiling for the phenotypic variance that GWA studies can explain.There is now converging evidence for the heritability of educational achievement across school years using family designs, such as twin and adoption studies, and DNA-based methods. Twin studies have shown that around 60% of individual differences in school achievement are explained by inherited differences in children’s DNA sequence.9 However, that conclusion is agnostic about the extent to which the same or different genetic factors contribute to individual differences in educational achievement longitudinally from age to age, that is, to stability and change. Understanding the developmental etiology of educational achievement in this way has considerable potential for illuminating the mechanisms that trigger differences in GCSE performance and, consequently, in educational and professional outcomes.These univariate genetic analyses have shown that the heritability of educational achievement is substantial and consistent across school years, from primary to secondary education and culminating in the GCSEs.9 However, the genetic and environmental etiology of stability and change of educational achievement across the longer span of school years, from primary school to secondary education and beyond, remains unexplored. Only a few longitudinal studies of reading ability have been reported. In one study, the stability of reading, measured as word recognition, was explained largely by genetic factors (around 70%) from primary through secondary school.22 Another study found that the etiology of reading fluency across the first five years of schooling, an important developmental time when students transition from ‘learning to read’ to ‘reading to learn’, was characterized by stable genetic and shared environmental influences.23 Two additional longitudinal analyses of reading comprehension in two different samples from the UK24 and US25 also showed substantial genetic stability. However, school achievement involves much more than reading.Multivariate genetic methods can be used to address this question of the etiology of age-to-age stability and change. Using a multivariate twin design we have previously demonstrated that, during the primary school years, genetic and shared environmental factors show substantial stability in English, Mathematics, and Science, while non-shared environmental factors contribute to change.26To our knowledge, no longitudinal analysis has been conducted to assess the genetic and environmental etiology of continuity and change of educational achievement throughout compulsory education, for specific subjects as well as for general educational achievement. This is the purpose of the current study, which uses longitudinal data from age 7 to 16 on educational achievement from a UK-representative sample of 6000 twin pairs participating in the Twins Early Development Study (TEDS).27 with effects weighted by results from the GWA discovery sample. A GPS can be used to predict variance in a trait for unrelated individuals in a sample independent of the GWA discovery sample. We will refer to this estimate as GPS heritability. It explains less variance than SNP heritability or twin study heritability because GPS heritability predicts educational achievement from specific SNPs.We also addressed the issue of stability and change in school achievement, for the first time using DNA-based analyses. In addition to SNP heritability, which was described earlier, another recently developed method predicts academic achievement directly from DNA using specific SNPs that have been shown to be associated with the trait in GWA analyses. This method aggregates thousands of SNP associations, which individually have very small effects, into a genome-wide polygenic score (GPS)EduYears GPS was derived from a GWA study of years of education for 300,000 individuals.28 We used the GWA summary data to create an EduYears GPS for each of 6000 unrelated individuals (one member of a twin pair) in our TEDS sample 26 in the UK. We correlated EduYears GPS with achievement measures at ages 7, 9, 12, and 16. We have previously shown that EduYears GPS predicts up to 9% of the variance in GCSE scores;29 here we extend this analysis and investigate results for specific subjects in addition to general achievement. The focus of our present analyses is the extent to which the EduYears GPS contributes to stability of educational achievement.Our g), which has also been shown to be substantially heritable31 and developmentally stable,32 and is one of the strongest predictors of school achievement.36 Moreover, the links between achievement and g have shown to be explained by genetic factors.37 Because g is a likely candidate to explain stability of school achievement across compulsory education, we also investigate the role of g in the stability of educational achievement, using both the twin design and DNA-based methods.Genetic stability of school achievement might be explained fully or in part by general cognitive ability  males, dizygotic (DZ) males, MZ females, DZ females, and DZ opposite-sex twin pairs. One twin per pair was randomly selected for phenotypic analyses to maintain independence of data. Analyses of variance (ANOVA) were used to test the significance of these group differences. ANOVA results showed some significant sex differences, however, sex and zygosity together explain only 1% of variance in achievement on average  was controlled for by means of linear regression . Figure g only slightly reduced the phenotypic stability (range = 0.50–0.78) and genetic stability (range = 0.53–0.82) of the correlations  was highly stable, with age-to-age correlations ranging from 0.66 to 0.85 Fig. . In bivaons Fig. . The phe39 .  and the age-specific genetic path squared (As) by the overall heritability. For example, for GCSE in Fig. 2/0.58) of the heritability of core GCSE performance is innovation (there is no age-specific genetic path); the rest of the heritability (83%) is transmitted from previous achievement ages. For KS3 core achievement, 78%  ×0.842 (genetic transmission)/0.63 (heritability of KS3)) of the genetic variance was transmitted from KS2, and for KS2 77% (0.73 × 0.862/0.70) of the genetic variance was transmitted from KS1. There was substantial innovative genetic influence at each age (Ai)—24%, 15%, and 17% at ages 12, 14, and 16, respectively. To investigate whether the new genetic influence was due to increasing use of test assessments and decreasing use of teacher assessments across the four ages, we repeated the analyses using only standardized test scores across the school years , in other words, not operating at the previous age and not transmitted to the next age. These paths were not significant as indicated by their 95% confidence intervals.The proportion of heritability at each age that is accounted for by genetic effects different from those operating at the previous age can be calculated by dividing the sum of the innovation path squared  at each stage of assessment, but, consistent with an overall pattern of stability, all of these new genetic influences were transmitted to the next achievement stage.In addition, the nature of educational achievement also changes during the school years as children are exposed to more subjects and more complex subjects. For reading, children move from learning to read to using reading to learn. The present twin analyses address, for the first time, the etiology of the stability of academic outcomes over compulsory education, showing that genetic factors are largely responsible for this stability. In other words, the same genetic factors largely shape individual differences in achievement from primary through secondary school. Shared environmental factors were also largely stable, although they explained a smaller proportion of overall variance in achievement. However, it has been suggested that shared environmental effects might actually be driven genetically.g, intelligence). Importantly, however, we showed that the heritability of educational achievement over school years and its stability is not explained by g alone. The results of our twin analyses showed that when g was controlled for, educational achievement remained highly heritable and stable and the stability of educational achievement independent of g was still explained by genetic factors. Although there was evidence for some specific (new) genetic influence at each age, again these new genetic influences were not age specific but were transmitted to the next assessment stage. This is in line with our earlier reports in which we showed that educational achievement at age 16 is not explained by intelligence alone.12 The EduYears GPS regression analysis yielded similar results showing genetic stability, even after controlling for g. This GPS result is not exactly analogous to the twin study results, as we tested the effect of the same genetic variants over time. Nevertheless, our multi-method approach yielded similar results indicating that the substantial stability of educational achievement is to a large extent explained by genetic factors.A reasonable assumption is that the substantial genetic stability observed here is explained by general cognitive ability 28 in independent samples, but it predicts more than twice as much variance in GCSE scores at age 16. We are not aware of any other example in which a GPS predicts less variance in the GWA target trait  than in another trait (GCSE scores). We suggest that the reason for this unusual finding is that educational attainment is a much coarser measure than GCSE scores, which are the result of hours of standardized assessment. In support of this hypothesis, we find that EduYears GPS also predicts 4% of the variance when we analyzed a similarly coarse dichotomous item about whether or not TEDS participants planned to go to university. Furthermore, EduYears GPS also predicts 4% of the variance in a cruder measure of GCSE achievement—5 passes at grades A* to C, which is often used in government statistics, and used for selection purposes by many employers and educational institutions .48 In the twin design, assortative mating increases DZ correlations relative to MZ correlations and could therefore lead to underestimating heritability and overestimating shared environmental influence; in effect this makes the present findings concerning heritability conservative. GCTA and GPS methods also have their limitations. Notably, both of these DNA-based methods rely on the additive effects of common SNPs genotyped on SNP arrays, and do not capture gene-gene or gene-environment interplay or the effects of less common SNPs.49 However, since the main limitations are different for each method used in the current study, the fact that our multi-method approach yielded similar results is a strength.The limitations of this study include the usual assumptions of the twin design, which are described in detail elsewhere.g.Our multi-method analyses corroborated previous findings showing that individual differences in educational achievement are largely explained by inherited differences in DNA sequence. The novel contribution of our study is to show that the substantial stability of educational achievement across compulsory education is to a large extent explained by genetic factors, even after controlling for 51 For example, GPS could be used to identify children at birth at genetic risk for developing reading problems, thus enabling early intervention. As preventive interventions have greater chances of succeeding early in life, a great strength of GPS is that they can predict at birth just as well as later in life, which enables early intervention, particularly for those children who are likely to struggle the most.Our finding of genetically driven stability of educational achievement should provide additional motivation to identify children in need of interventions as early as possible, as the problems are likely to remain throughout the school years. GPS prediction, specifically, might in the future provide a tool to identify children with educational problems very early in life and aid in providing both individualized prevention and individualized learning programs. We hope that with GPS, we can move towards precision education, just as medicine is moving towards precision medicine.52 Ethical approval for this study was received from King’s College London Ethics Committee.The present study used the TEDS sample. TEDS is a large twin study that recruited over 16,000 twin pairs born between 1994 and 1996 in England and Wales. More than 10,000 twin pairs are still actively involved in the study. Rich cognitive and behavioral data, including educational achievement, have been collected from the twins, their parents and teachers, over compulsory education and beyond. Importantly, TEDS was a representative sample of the UK population at first contact, and remains representative in terms of family socioeconomic status and ethinicity.53 Nevertheless, DNA testing was conducted when zygosity was unclear from the questionnaire. The sample size per academic achievement measure is shown in Supplementary Table The sample for the present study included all twins with available academic achievement measures over the school years. Participants who had major medical or psychiatric conditions, or those with severe perinatal complications, were removed from the analyses. Zygosity was assessed by the parent-reported questionnaire of physical similarity. This measure has been shown to be highly reliable.54 (see Supplementary Methods). We then matched the individuals with genotyped data to those participants with available academic achievement data.DNA has been genotyped for a subsample of unrelated individuals from TEDS (one twin per pair). We processed genotypes for 6710 individuals using the standard quality control procedure followed by imputation of genetic variants to the Haplotype Reference Consortiumhttp://www.nfer.ac.uk/index.cfm; QCA: http://www.qca.org.uk). Data were obtained directly from teachers. At age 7 data are available for English and Mathematics; at ages 12 and 14 data are available for English, Mathematics, and Science. The teacher rating of English used a combined rating of students’ reading, writing, and speaking and listening; Mathematics used a combined score of knowledge in numbers, shapes, space, using and applying mathematics, and measures; and Science used a score combining life process, scientific enquiry, and physical process. These teacher ratings were found to be highly reliable when compared to the achievement measures collected by the UK National Pupil Database (NPD), as described later.TEDS has obtained assessments of academic achievement directly from the twins’ teachers who reported grades following the UK National Curriculum guidelines, a standardized core academic curriculum formulated by the National Foundation for Educational Research (NFER) and the Qualifications and Curriculum Authority (QCA) , Science , and Mathematics.https://www.gov.uk/government/collections/national-pupil-database). Data are available for each Key Stage (KS) completed in the UK following the National Curriculum (NC). Teachers provide NC ratings for every student at the end of each KS . Exam scores as well as teacher ratings are available from KS1–KS3; and exam scores only are available for KS4 and KS5. Children’s ages for KS1, KS2, and KS3 are about 7, 11, and 14 years. KS4 marks the end of compulsory education with GCSE testing at about age 16. Sample size and descriptive characteristics for each measure are provided in Supplementary Table The TEDS dataset was linked to the NPD for every participant for whom we received written informed consent from either the twin or the parent. NPD is a rich UK database collecting data about students’ academic achievement across the school years  with the exam scores for English, Mathematics, and Science separately by taking a mean of the three scores. The average correlation between NPD and TEDS teacher ratings was 0.70  was calculated at each KS by taking a mean of English NC teacher ratings, Mathematics NC teacher rating (for both NPD and TEDS), English exam score, and Mathematics exam score. We did not include Science grades in overall achievement scores to make a more direct comparison across age because Science is not part of the National Curriculum at KS1.g; intelligence) was assessed in TEDS at ages 7, 9, 10, 12, 14, and 16. For the present analyses we created a longitudinal composite measure of g as a mean of these six assessments. See Supplementary Methods for a more detailed description of g measures.General cognitive ability , shared environmental (C), and non-shared environmental (E) effects on the variance and covariance of the phenotypes, by comparing MZ correlations to DZ correlations. Heritability can be roughly calculated by doubling the difference between MZ and DZ correlations, C can be calculated by deducting heritability from MZ correlation and E can be estimated by deducting MZ correlation from unity .rG), which is an index of pleiotropy, indicating the extent to which the same genetic variants influence two traits or measures of the same trait at two times. The shared environmental correlation (rC) and non-shared environmental correlation (rE) are estimated in a similar manner.47These univariate analyses can be extended to multivariate analyses to study the etiology of covariance between multiple traits. Multivariate genetic method decomposes the covariance between traits into additive genetic (A), shared environmental (C), and non-shared environmental (E) components by comparing the cross-trait cross-twin correlations between MZ and DZ twin pairs. This method also enables estimation of the genetic correlation  is explained by A, C, and E. The common pathway model is illustrated in Supplementary Figure The common pathway model is a multivariate genetic model in which the variance common to all measures included in the analysis can be reduced to a common latent factor, for which the A, C, and E components are estimated. As well as estimating the etiology of the common latent factor, the model allows for the estimation of the A, C, and E components of the residual variance in each measure that is not captured by the latent construct.60 This estimate is often called SNP heritability. This method does not use known genetic relatedness coefficients but estimates heritability from DNA using only unrelated individuals. SNP heritability is calculated using restricted maximum likelihood and the variance and covariance is decomposed using mixed linear models.The genome-wide complex trait analysis (GCTA) software package enables estimates of the proportion of phenotypic variance or covariance that is explained by all SNPs that are available on genotype arrays, without testing the association of any single SNP individually.61 The matrix of pair-by-pair genetic similarity is compared to the matrix of pair-by-pair phenotypic similarity using the residual maximum likelihood estimation. SNP heritabilities were calculated for overall achievement across compulsory education, as well as for specific subjects.First, the genetic relatedness matrix is calculated by weighting genetic similarities between all possible pairs of individuals with the allele frequencies across all SNPs on the DNA array. Individuals who are found to be even remotely related (greater than fifth cousins) are removed from the analyses as they would otherwise bias the results, which rely on chance genetic similarity between pairs of individuals.62 GPSs were calculated for 6710 participants using summary statistics from Okbay et al.28 GWA analysis of years of education (EduYears).28 Of the 293,723 participants in the EduYears GWA discovery sample, the summary statistics excluded 23andMe participants, for legal reasons. Polygenic scores were constructed as the weighted sums of each individual’s genotype across all SNPs using the LDpred method63 (see Supplementary Methods for details). Delta R2 is reported as the estimate of variance explained by the GPS. These delta R2 estimates were obtained by comparing the incremental increase in the model R2 after adding the GPS to the regression model, and comparing this to the model that included 10 principal components in order to control for population stratification. See Supplementary Methods for genetic quality control and further information about GPS calculation.GPSs aggregate the effects of individual SNPs shown to be associated with the trait in a GWA study.EduYears with general educational achievement composites, as well as with performance in specific subjects at each age to estimate EduYears GPS heritability. Delta R2 are reported as the estimates of variance explained by adding the GPS to the regression model that included the academic achievement from all earlier ages to assess the extent to which EduYears contributes to age-to-age stability.We correlated Supplemental MaterialSupplementary Methods


Maize is a crop in high demand for food purposes and consumers worldwide are increasingly concerned with food quality. However, breeding for improved quality is a complex task and therefore developing tools to select for better quality products is of great importance. Kernel composition, flour pasting behavior, and flour particle size have been previously identified as crucial for maize-based food quality. In this work we carried out a genome-wide association study to identify genomic regions controlling compositional and pasting properties of maize wholemeal flour.dull endosperm 1 gene that encodes a starch synthase and is determinant on the starch endosperm structure in maize.A collection of 132 diverse inbred lines, with a considerable representation of the food used Portuguese unique germplasm, was trialed during two seasons, and harvested samples characterized for main compositional traits, flour pasting parameters and mean particle size. The collection was genotyped with the MaizeSNP50 array. SNP-trait associations were tested using a mixed linear model accounting for genetic relatedness. Fifty-seven genomic regions were identified, associated with the 11 different quality-related traits evaluated. Regions controlling multiple traits were detected and potential candidate genes identified. As an example, for two viscosity parameters that reflect the capacity of the starch to absorb water and swell, the strongest common associated region was located near the This study allowed for identifying relevant regions on the maize genome affecting maize kernel composition and flour pasting behavior, candidate genes for the majority of the quality-associated genomic regions, or the most promising target regions to develop molecular tools to increase efficacy and efficiency of quality traits selection (such as “breadability”) within maize breeding programs.The online version of this article (10.1186/s12870-019-1729-7) contains supplementary material, which is available to authorized users. Maize is one of the most important food crops worldwide, and a staple crop for large populations in Latin America, Africa, and Asia . Maize bThe maize kernel’s high nutritional value is mainly due to its starch (~ 72% of the kernel dry matter), protein (~ 10% of the kernel dry matter), and fat in the form of oil (~ 4% of the kernel dry matter) . The intMany maize mutants have been widely used to isolate genes encoding key enzymes in starch metabolism, as well as genes regulating zeins, maize primary storage proteins, synthesis and deposition , 10. Curbrittle endosperm2 (bt2), shrunken1 (sh1), and shrunken2 (sh2) that were associated with several kernel compositional traits, and haplotypes of amylose extender1 (ae1) and sh2 that were associated with starch pasting properties.Linkage mapping and association mapping studies have shown that kernel main components are controlled by many genes having complex patterns of inheritance. Examples are the works of Li et al.  and Li ebroa )operties ; brittlein maize , 29. ThiGRMZM2G1499, du1, DGAT1–2) . Indeed,GRMZM2G1499, du1, The rapid rate of LD decay observed in the present study in the SNPs associated with the quality-related traits evaluated suggests that a higher marker density would have been beneficial in the detection of other regions putatively linked to maize flour’s quality. Moreover, it is important to mention that the size of the collection of maize inbred lines used in this work most likely affected the power to detect significant marker-trait associations and the subsequent identification of genomic regions controlling the analyzed traits association. As reported in Yang et al. , using sThis work reports the identification of 57 genomic regions associated with 11 different quality-related traits evaluated in wholemeal maize flour, highlighting candidate genes for the majority of those regions. However, novel regions, with no clear candidate genes, were also identified, which were not previously acknowledged using other germplasm collections studies.Findings from GWAS provide valuable genetic information of trait architecture or candidate loci for subsequent validation . PrelimiNevertheless, prior to that, the significant marker-trait associations detected in the current work need to be validated. Future work will concentrate on the validation of the results retrieved in this work by sequencing those regions on contrasting maize populations for the given trait. Since the actual materials used for the manufacturing of maize-based bread are the traditional maize populations, these are the ideal independent materials to proceed with the sequencing validation.broa production.In this work, a genome-wide association approach was carried out to identify genomic regions controlling the variation of maize kernel major constituents  and parameters affecting the maize flour “breadability” (starch pasting properties and flour’s mean particle size). For that, a unique maize inbred collection containing lines partially derived from Portuguese maize populations was used given that Portuguese traditional maize populations have been developed through centuries adapted both to the local environment and food uses, in particular, for The inbred lines derived from Portuguese maize populations were overall characterized by having a low breakdown and peak viscosity, and starch content; and a high fiber and protein content. A putative positive selection toward the presence of the favorable alleles for protein content, for fiber content, and mean particle size, and for alleles associated with a decrease in breakdown and peak viscosities values and a decrease in starch content was observed in the inbred lines derived from Portuguese traditional maize populations.This work allowed for identifying relevant regions on the maize genome affecting maize kernel composition and flour pasting behavior, and identifying candidate genes for the majority of the flagged genomic regions. A total of 57 genomic regions were detected and candidate genes underlying the majority of those regions were identified. Moreover it was observed that the majority of the detected genomic regions were associated with a single trait. The traits for which the strongest and stable associations were found were protein, breakdown viscosity and peak viscosity. For those the strongest SNP-trait association could result in changes from more than 4% in protein content to more than 10% in the case of the viscosity parameters. Importantly, this work allowed reducing the gap towards the development of selection tools to support breeding for these complex quality traits, such as “breadability”.In this work, we took advantage of the diverse inbred lines developed in the past by the extinct NUMI (Núcleo de Melhoramento de Milho) combining Portuguese germplasm with foreign (mainly US) germplasm. These inbred lines (developed by single seed descent) are currently conserved at the National Portuguese Plant Germplasm Bank . The maize inbred line collection used in this study was assembled observing a significant representation of lines selected from Portuguese traditional maize populations (29 lines) and lines with a mixed Portuguese × foreign origin  in Portugal during the 2011 and 2012 growing seasons, using an organic agriculture converted field. The conversion started in 2011 and the field was considered to be fully managed under an organic agriculture system by 2012. This site is part of the Mondego River irrigation perimeter, a very high-yielding maize area where the average maize hybrids yield is 14.5 Mg.ha− 1 . It is l− 1. Plots were both mechanically weeded and hand-weeded when needed and managed following common agricultural practices for maize in the region. Pollination was controlled within each plot. All the plots were harvested by hand. After harvest, ears were dried at 30–35 °C in an oven  until a ~ 15% in moisture was reached. The ears were then shelled, and the kernel collected per plot basis was packed in paper bags and kept at 4 °C until further analysis.In each year, the maize inbred lines were evaluated using a randomized complete block design, with two blocks (replicates). Information on the spatial distribution of the plots was also recorded (field row and columns coordinates). Each plot consisted of two rows 7.2 m long (6.4 m planted row plus 0.8 m border space between two planted rows), with an inter-row distance of 0.75 m. Each plot was overplanted by hand and thinned at the V7 growth developmental stage to achieve a plant density of approximately 50,000 plants haA seed sample from each of the harvested plots was used for quality determinations in the two years/growing seasons. Eleven nutritional-related and technological-related traits were measured in wholemeal maize flour. Wholemeal maize flour was obtained from all the seed samples using a Falling number 3100 mill  with a 0.8 mm screen. In order to prevent/minimize the enzymatic action and subsequent alteration of the flour properties, flour samples were also lyophilized using Cientificolab® equipment built for pilot-scale lyophilization of food commodities. For that, each sample was individually placed in a flask  and then freeze-dried for long-term preservation.Flour protein (PR), fiber (FI), and fat (FT) content were determined for each non-lyophilized sample by near-infrared reflectance (NIR) spectroscopy , with calibrations for non-lyophilized samples supplied by the manufacturer. Values for protein, fiber, and fat corresponded to the mean value of up to two technical replicates. The total starch content was determined in lyophilized (STL) (both 2011 and 2012 growing seasons) and non-lyophilized (ST) (only the 2012 growing season) samples using Fourier Transform Near-Infrared Reflectance (FT-NIR) spectroscopy , with calibrations for non-lyophilized samples supplied by the manufacturer. Values for total starch content obtained from the 2012 growing season lyophilized and non-lyophilized samples were further used to test whether both datasets were correlated (phenotypic correlation between datasets). Values for total starch content (non-lyophilized (ST) and lyophilized (STL) samples) corresponded to the mean value of two to four technical replicates. Protein, fiber, fat, and starch content was expressed as a percentage (%).The maize flour Particle Size Index (PSI) was also determined using FT-NIR spectroscopy . For the 2011 growing season, only the mean for particle size in lyophilized samples (SIZEL) was measured. For the 2012 growing season, both mean particle size in non-lyophilized (SIZE) and lyophilized flours (SIZEL) were determined. Values for mean particle size (non-lyophilized (SIZE) and lyophilized (SIZEL) samples) corresponded to the mean value of two to four technical replicates. The calibration models for PSI FT-NIR analysis were obtained using the particle size values measured in a subset of 30 non-lyophilized samples according to the AACC method 55–40.01:1999 , with a Maize flour pasting properties were evaluated by recording their viscosity profiles using a Rapid Visco Analyser (RVA) . The viscosity profiles were obtained on non-lyophilized samples according to  at 15% sQuality control was performed for each year/growing season, on the data collected per genotype individually from each of the two replicated plots. Graphical inspection of residuals was used to assess normality (Q-Q plot), homogeneity of variance , and identify outliers. Observations were flagged for closer inspection when they exceeded 1.5 times the interquartile range, and when the standardized residuals after mixed model analysis were extreme. One of the traits  required a squared-root-transformation to stabilize the variance. All analyses were done using Breeding View software , availabA phenotypic analysis was performed per single environment trial: 1) the estimates of genetic variances (and covariances between traits) and of heritability were obtained according to Oakey et al. , and 2) In detail, single trait-single growing season analysis, using mixed models, was performed using the “Single trait field trial analysis” pipeline of Breeding View, selecting the model for resolvable row-column design as implemented in the software. The statistical model includes an intercept, a fixed block effect, a random row and column effects (nested within blocks), a genotypic effect , and a residual. The Field trial analysis node in Breeding View performs two mixed model analyses: In the first step (Step 1) the inbred lines (genotypes) were fitted as a random term, while in the second step (Step 2) the inbred lines were fitted as a fixed term. The Step 1 model is used to obtain estimates of variance parameters. From Step 1 the heritability, as well as the best linear unbiased predictors (BLUPs), was calculated for each inbred line (and correlations between BLUPs of different traits used to obtain estimates of genetic correlations between traits). In Step 2, structural variance components (rows and column variances) are fixed to those estimated in Step 1, and by including the inbred lines as a fixed term, the best linear unbiased estimators (BLUEs) for each inbred line were produced.For each quality trait, a multi-environment trial analysis was also performed to assess the consistency across growing seasons. The analysis of variance was carried out using the residual maximum likelihood (REML) variance components analysis procedure in Genstat software . The miDNA was isolated from adult leaves from each maize inbred line using a modified CTAB procedure described in . DNA quawww.maizegdb.org).The position of each marker along the maize B73 reference genome was updated from the markers’ coordinates available when the MaizeSNP50 BeadChip was originally designed (B73 reference genome version 1) to the coordinates in the released B73 reference genome version 3. These coordinates were taken from the maize genome browser, via the MaizeGDB database . Moreover, markers with a minor allele frequency (MAF) smaller than 5% were removed. After this filter, a total of 48,772 SNPs remained and were used for the association mapping analysis.A subset of 1821 SNPs, evenly distributed across the genome , was used to calculate principal components to study the population structure among inbred lines and to calculate the kinship matrix to estimate pairwise genetic relatedness among inbred lines as implemented in Genstat software .2g × y) were much smaller than the genotype variance component (σ2g), univariate association analysis was carried out using the adjusted means for field trial design (BLUEs) obtained across growing seasons. Genome-wide association studies were conducted with the Genstat software using the available genotypic (SNPs scored with the MaizeSNP50 BeadChip array) and quality data  measured in 132 maize inbred lines. The Genstat software performs association mapping in the mixed model framework, fitting markers as fixed and inbred lines as random terms using REML , that neither accounts for population structure nor familial relatedness; a model accounting for population structure (Q) using 15 principal components from PCA [Phenotype = Q + SNP + (Genotype + Error)]; and a model accounting for familiar relatedness (K) [Phenotype = SNP + Genotype + Error] with Genotype random effects structured following a kinship matrix K. For each chromosome, a different kinship matrix was calculated where only the SNPs located on the other nine maize chromosomes were used to calculate the kinship matrix [Three different models were tested to detect significant marker-trait associations: the naïve model [p matrix , 47.P-values allowed to informally assess to what extent the models accounted for genetic structure/relatedness among the inbred lines and therefore guarded against false discoveries. Models resulting in an inflation factor near 1 perform better, and quantile-quantile (Q-Q) plots should show few P-values that deviate from the expected uniform distribution that holds under the null hypothesis . The observed P-values  of each SNP were plotted against their chromosomal positions so they produce Manhattan plots. A threshold of –log10  = 4 was set to identify significant marker-trait associations. Given that the association panel used was not that large and looking at the background noise in the obtained Manhattan plots, the threshold of -log10  = 4 seemed reasonable not to avoid losing potentially interesting regions, at the expense of applying a more conservative type of correction such as Bonferroni. Examples of other maize studies with similar association panel sizes and number of markers using the same threshold are the works of Liu et al. [The inspection of the inflation values for each model and the quantile-quantile (Q-Q) plots of the respective u et al. , 49. TheA local linkage disequilibrium (LD) study was performed to define chromosomal regions to search for candidate genes for the traits under analysis.2, after correcting for population structure using the principal component scores from Eigenanalysis, as implemented in Genstat software. For this calculation, the same subset of 1821 SNPs previously used for the genetic structure analysis was employed. LD decay was visualized per chromosome by plotting r2 against the physical mapping distance in Mb. A threshold for LD decay (r2 = 0.1) was used to estimate the average genetic distance for which markers were considered to be no longer correlated. In Step 2, a genomic window around each SNP location significantly associated with the traits analyzed was established by subtracting and adding the average genetic distance for LD decay (r2 > 0.1), estimated in Step 1. All the SNP markers located within those windows were then used to estimate the local LD decay. At this point, a stricter threshold of r2 = 0.2 was considered. The markers’ positions flanking each local LD block were further used as query positions on the maize genome browser, via MaizeGDB (https://www.maizegdb.org/gbrowse/), to retrieve the list of candidate genes mapped within those genomic regions.This procedure was done in two steps: In Step 1, the average intra-chromosomal LD was estimated as the squared correlation coefficient rZea mays B73 RefGen_v3) was used as the reference genome for candidate gene analyses [Zea mays Ensembl-18) using the gene model identifier as the query. KEGG: Kyoto Encyclopedia of Genes and Genomes database [The genome sequence of the maize inbred line B73 Additional file 2:Table S2. Estimated genetic correlations among quality traits measured in wholemeal flour of a collection of 132 maize inbred lines. In Table S2 one can find the estimated pairwise genetic correlation between the 11 quality trait evaluated for each growing season. (DOCX 22 kb)Additional file 3:Table S3. Phenotypic values  for 11 quality traits measured in 132 maize inbred lines. In Table S3 one can find for the 11 quality trait evaluated the summary statistics on phenotypic data for each growing season and across growing seasons. (DOCX 24 kb)Additional file 4:Table S4. Observed inflation factors for the models tested in genome-wide association (GWAS) analysis. In Table S4 one can find the inflation factors for each of the three different models that were tested to detect significant marker-trait associations: the naïve model, a model accounting for population structure; and a model accounting for familiar relatedness. (DOCX 21 kb)Additional file 5:Table S5. Significant SNP-trait associations from a genome-wide association study for 11 quality traits in wholemeal maize flour. In Table S5 one can find the genome-wide association study results for the 11 quality trait evaluated based on the information combining the phenotypic data obtained in two growing seasons with the genotypic data obtained from the Illumina MaizeSNP50 BeadChip array using 132 maize inbred lines. Additionally, Table S5 also encloses information on the genomic regions associated to quality-related traits previously identified by other authors. (XLSX 22 kb)Additional file 6:Table S6. Percentage of associated SNPs with opposite effects on each traits value, and the range of phenotypic variance explained. In Additional file Additional file 7:Table S7. Candidate genes underlying the genomic regions associated with 11 quality traits in wholemeal maize flour. In Additional file Additional file 8:Table S8. Maize inbred lines with available quality data, known pedigree, kernel color, and endosperm type. In Additional file Additional file 9:Table S9. Number of maize inbred lines grouped accordingly to their kernel color and endosperm type. In Additional file