Classical genetic studies have identified many cases of pleiotropy where mutations in individual genes alter many different phenotypes. Quantitative genetic studies of natural genetic variants frequently examine one or a few traits, limiting their potential to identify pleiotropic effects of natural genetic variants. Widely adopted community association panels have been employed by plant genetics communities to study the genetic basis of naturally occurring phenotypic variation in a wide range of traits. High-density genetic marker data—18M markers—from 2 partially overlapping maize association panels comprising 1,014 unique genotypes grown in field trials across at least 7 US states and scored for 162 distinct trait data sets enabled the identification of of 2,154 suggestive marker-trait associations and 697 confident associations in the maize genome using a resampling-based genome-wide association strategy. The precision of individual marker-trait associations was estimated to be 3 genes based on a reference set of genes with known phenotypes. Examples were observed of both genetic loci associated with variation in diverse traits , as well as individual loci associated with the same or similar traits across diverse environments. Many significant signals are located near genes whose functions were previously entirely unknown or estimated purely via functional data on homologs. This study demonstrates the potential of mining community association panel data using new higher-density genetic marker sets combined with resampling-based genome-wide association tests to develop testable hypotheses about gene functions, identify potential pleiotropic effects of natural genetic variants, and study genotype-by-environment interaction. Association mapping, initially on a gene-by-gene level and later at a genome-wide scale, has been widely adopted as a tool to identify natural genetic variants controlling variation in both quantitative and qualitative traits. In the plant genetics community, logistical and scientific constraints have driven the development and widespread adoption of community association panels comprising sets of distinct plant genotypes that can be propagated and shared, whether through the use of homozygous inbred lines or clonal propagation. In the earlier era where association mapping was conducted on a gene-by-gene level, the use of community association panels allowed the work of estimating population structure within the population to be conducted once rather than for each independent study. In the later era of genome-wide association studies, the use of community association panels again provided substantial practical benefits: the time-consuming and expensive process of genotyping hundreds of thousands or millions of genetic markers across hundreds of individuals had to be undertaken only once to enable an effectively infinite number of studies on the genes controlling different traits by different research groups.The use of community association panels by many independent research groups to investigate diverse research questions results in data on a wide range of individual traits for genetically identical individuals across 1 or more environments. This provides significant opportunities to investigate both pleiotropy, the effect of a single genetic locus on multiple phenotypes, and genotype-by-environment interactions, where the same allele influences the same phenotype in different ways in different environments. In addition, associations between a given genetic locus and a particular trait that are only marginally significant in several individual studies can often be assigned a higher degree of confidence when the same association is identified across multiple studies. Finally, marginal statistical signals from genome-wide association studies can assist in the interpretation of later mutant mapping, gene expression, or selection scans, but only if the initial studies results are made available in a method that is easy to capture and cross-reference.In maize, an early widely adopted community association panel was the Maize Association Panel (MAP), also referred to variously as the maize 282 panel and the Buckler–Goodman Association panel . MAP iniAs sequencing technologies have improved, new sets of genetic markers have been deployed for existing community association panels with increasing degrees of genetic resolution. The MAP was initially genotyped with a modest number of <100) Simple Sequence Repeat (SSR) markers to estimate population structure as a potential confounder for single-gene association tests . The WiD0 Simple Substantial barriers to the comparison, reuse, and meta-analysis of previously published genome-wide association study (GWAS) results are created by differences in genetic marker data sets as well as the use of different reference genome versions. In this study, we sought to generate a single common genetic marker set spanning multiple association panels with a high marker density. Specifically, we employed a combination of published RNA sequencing (RNA-seq) and resequencing data to generate a common set of  18M genetic markers scored across the union of 1,014 genotypes present in the SAM and/or WiDiv association panels. Given the expected complexity of the genetic architectures controlling many quantitative traits in maize, we employed the Fixed and random model Circulating Probability Unification (FarmCPU) algorithm for genome-wide association . While tThree maize association panels were identified in the literature: the Maize Association Panel (MAP) , the ShoThe set of approximately 200 papers citing either the SAM panel  or eithea priori segregating SNPs from HapMap3 [The total number of unique maize line names observed across these 162 trait data sets was 1,118, which was modestly more than the set of 1,014 unique genotypes present across the WiDiv and SAM mapping populations. We speculate that this difference may result from the inclusion of local checks or lines of interest or changes in naming convention or transcription errors that we were unable to resolve. For the 1,014 unique genotypes named as part of the SAM population  or WiDiv HapMap3 , imputatr2 between 2 SNPs separated by 10 kilobases being approximately 0.18  into distinct peaks based on physical distance and linkage disequilibrium (LD)  reduced the number of associations to 1,466 peaks distributed across phenotypes assigned to 8 categories /ln1 (linoleic acid1). DGAT1-2 substantially increases the seed oil and oleic acid contents [Flowering time trait data sets tended to identify a disproportionately high number of independent GWAS peaks Fig. , potenticontents . Largelycontents ,38  (Zm00001d042315) is a MADS-box transcription factor located between 160,564,021 bp and 160,591,933 bp on maize chromosome 3, which has been shown to function as a flowering activator, with a derived allele conferring earlier flowering in many maize lines relative to its wild progenitor teosinte [ZmMADS69 has been shown to downregulate the expression of ZmRap2.7, which relieves repression of the florigen gene ZCN8, causing/resulting in early flowering [ZmRap2.7 and ZCN8 are located on chromosome 8 [ZCN8  and was associated with anthesis in 3 environments . ZmRap2.7 (Zm00001d010987) is located approximately 10 megabases away from ZCN8 on chromosome 8  and is associated with a peak consisting of 13 SNPs that was detected for 7 measurements of anthesis , 2 approaches to measuring silking in the same environment , and a number of vegetative traits, including extant leaf number (ExtantLeafNumber1_J), leaf width (LeafWidth_J), and plant height (PlantHeight_D).An illustrative example of the potential for genes influencing flowering time to be identified in genome-wide association studies for other traits is the case of teosinte , 39. A pmosome 8 , and botIn addition to the 3 peaks discussed above, 13 other peaks were also associated with both flowering time traits and traits from other categories Fig. . In 2 caliguleless4 (Zm00001d010948)  encodes a knox transcription factor that is highly expressed in the SAM, seed radicle, internode tissues, crown roots, pericarp of seed, and the endosperm of maize [liguleless4 abolishes the ligule and alters the sheath-blade boundary in maize leaves [The potential pleiotropy of genes linked to flowering time was not confined to above-ground traits. In 3 cases, a significant signal for flowering time was also associated with 1 or more data sets describing variation in root phenotypes. A signal on chromosome 5 between 94,710,702 and 94,712,951 bp was associated with flowering time across a wide range of environments , with other above-ground vegetative traits  and with many root architecture traits  . The spe48) Fig. . Ligulelof maize . A domine leaves  likely ve leaves , but pheThe widespread adoption of diverse association panels in plant biology has enabled a wide range of research and discovery by researchers working on diverse phenotypes, species, and research questions . Beyond We identified 21 published papers describing phenotypes or GWAS conducted using 1 or more of 2 widely adopted association panels in maize ,4. In 18One desirable outcome of having access to raw trait data is that it enables reanalysis of existing trait data sets, each of which represents a substantial investment of both finance resources and human effort/suffering, as new higher-resolution genetic marker data sets and new analysis algorithms become available. Here we employed a RMIP-based filter to the FarmCPU GWAS algorithm ,14, 49 aA straightforward way to increase power and utility of reanalyzing data from community association panels is through the integration of data from additional widely employed community association panels. The AM508 panel discussed above  has beenliguleless4/knox11 are an example of an intermediate case between confirmation of known functions and assigning potential functions to previously uncharacterized genes or regions of the genome. Liguleless4 belongs to the knox class I gene family [liguleless4 gene itself was identified via a dominant allele Lg4-O, which alters development at the leaf blade/sheath boundary [liguleless3 gene, a paralog of liguleless4, do not exhibit any obvious phenotype [liguleless4 in determining flowering time and above/below-ground plant architecture is consistent with the reported expression pattern of the wild-type allele in tissues, including the shoot apex, root tips, and developing inflorescence [liguleless4, Zm00001d015513 (Chr5:94710702..94712951), which was associated with variation in anthesis, silking, leaf area index, leaf length, and various traits related to root architecture. The other potential gene for future characterization is Zm00001d008987 (Chr8:28727658..28769198), a previously uncharacterized putative costunolide 3-hydroxylase associated with both flowering time and root depth traits.The significant signals identified for flowering time and both above-ground and below-ground plant architectural traits adjacent to e family , a famile family . The ligboundary  and is aboundary . Loss-ofhenotype . A role rescence . The MADrescence  and indeOne striking observation from the colocalization of association signals across multiple trait data sets was how common the reidentification of shared signals was. In total, 14.5%  of all suggestive associations and 16.2% (76/468) of all confident associations were identified in at least 2 trait data sets. One utility of reanalyzing published trait data sets is that variants with consistent but moderate effects across many studies can be distinguished from, and assigned higher confidence, than signals of equivalent statistical significance, which are identified in only a single study in a single environment. Another lesson to take away from the same colocalization data is how common it was for the same locus to be identified for traits belonging to separate categories of phenotypes. The interpretation of a genetic locus with a significant association with root area will be quite different depending on whether that same locus is also associated with flowering time  Fig. 6D. In bothLogistical and financial limitations often constrain quantitative genetic analyses of plant populations to collecting data on a single phenotype or a small suite of related phenotypes, limiting our capacity to identify and study the ways individual genetic variants can control multiple phenotypic outcomes. The data set described in this article, including 162 traits scored across different subsets of 1,014 immortalized maize inbred genotypes and associated with a high-density marker set of 18M segregating markers, dramatically lowers the barriers for further quantitative genetic studies of both pleiotropy and genotype-by-environment interactions in maize. In addition, by identifying more than 2,000 confident or suggestive genetic associations in the maize genome, this data set means researchers do not have to analyze or interpret GWAS in a vacuum but instead can interpret their results in the context of the rich data sets of previously scored phenotypes and previous GWAS analyses.Papers publishing maize analyses were identified by searching papers citing the initial description of the first iteration of the WiDiv panel , the iniA set of 752 maize genotypes, which were a strict subset of the WiDiv panel and included 254 of 369 genotypes from the SAM diversity panel, was evaluated in a field experiment conducted in Lincoln, Nebraska, in the summer of 2020. The experimental design of this field experiment has been previously described . BrieflyTassel architecture phenotypes were collected once tassels had fully emerged for 3 randomly selected plants per plot, avoiding edge plants. Tassel lengths were measured from the basal primary tassel branch to the tip of the tassel spike. Branch zone length was defined as the length from the basal primary tassel branch to the top primary tassel branch. Tassel spike length was defined as the length from the top primary tassel branch to the tip of the tassel spike. The total number of primary tassel branches was also counted as well as the number of these primary tassel branches that were initiated but later aborted .Male and female flowering times for each plot were scored on the first day that 50% of plants had visible pollen shed or visible silks, respectively. Root and stalk lodging were scored at the end of the growing season as a percentage of extant plants in the plot, following the published Genomes to Fields phenotyping protocol for both traits . Leaf phAll ears were harvested from 8 semi-randomly selected plants per plot with edge plants being excluded when possible. Ear length, ear width, length of fill, kernel row number, and number of kernels per row were hand measured or hand counted for 6 ears per plot or all ears when fewer than 6 ears were present . The aveA single set of markers scored across 1,049 accessions was employed for downstream analyses. These genotypes were determined based on marker data aggregated from 3 published sources: resequencing data for the WiDiv-503 panel  , resequeA priori segregating genetic markers identified in maize HapMap3 [Both genome resequencing data and RNA-seq data were quality trimmed using Trimmomatic (v0.33) . BWA-MEM HapMap3 , 13 were HapMap3 . Missing HapMap3 . The impr2) for all pairs of genetic markers where both genetic markers exhibited minor allele frequency greater than 0.05 and were separated up to a physical distance of less than 600 kilobases using PopLDdecay (v3.41) [A kinship matrix for the complete set of 1,049 genotyped accessions, including all maize lines included in the SAM or WiDiv panels and 35 additional maize lines for which sequence data were generated and released as part of Mazaheri et al. , was cal (v3.41) .Pvalue at 5%. RMIP values for each marker were calculated as the proportion of the 100 analyses in which that marker was significantly associated with the target trait [Marker-trait associations were identified using 100 iterations of the FarmCPU algorithm as implemented in the R package rMVP v1.0.5 with parameter settings maxLoop = 10; method.bin = “FaST-LMM” , 73. Foret trait , 77–79.Linkage disequilibrium was calculated among all genetic markers with RMIP values ≥5 for at least 1 trait. Genetic markers with linkage disequilibrium >0.5 were merged into single peaks for downstream analyses, unless the markers were separated by >1 Mb. When 2 or more markers were merged into a single peak, the marker with the greatest RMIP value was selected as representative of the entire peak.Phenotypic values for all trait data sets employed in this study for all maize accessions evaluated are provided in Supplementary Fig. S1. Phenotyping of tassel architecture. (A) Tassel lengths were measured from the bottom-most primary tassel branch to the tip of the tassel spike (red bars). (B) The branch zone length was defined as the length from the bottom-most primary tassel branch to the upper-most primary tassel branch (red left-facing bracket). (C) The tassel spike length was defined as the length from the upper-most primary tassel branch to the tip of the tassel spike (red right-facing bracket). (D) The total number of primary tassel branches was also counted (example arrowed) as well as the number of these primary tassel branches that were initiated but later aborted (not pictured).Supplementary Fig. S2. Phenotyping of cob traits. (A) Ear lengths were measured as distance from base to the tip of each cob in centimeters, highlighted in green. (B) Ear fill was measured as distance on the cob from base to the tip where the seeds were set, highlighted in red. (C) Ear width was measured as distance of diagonal of the cob, highlighted by blue. (D) Kernels per row correspond to the number of kernels on each line when cobs were set vertically, highlighted in pink. (E) Kernel row number corresponds to the number of rows, highlighted in brown.Supplementary Fig. S3. Multidimension scaling or principal coordinate analysis. (A) Distribution of SNP density across the sorghum genome in 1-megabase sliding windows. (B) Scree plot of eigenvalues for the principal components estimated from the marker data used in this study. (C) Genetic relationship among the accessions used in this study and visualized using multidimensional scaling/principal coordinate analysis of the distance matrix. The x- and y-axes represent first and second principal component coordinates. Each point is color coded by the community association panels each accession belongs to.Supplementary Fig. S4. Allele frequencies of the top SNPs associated with the DGAT-2 gene. (A) The allele frequency for the starch content in each subpopulation of top SNPs associated with the DGAT gene. (B) The allele frequency for the fat content in each subpopulation of top SNPs associated with the DGAT gene. (C) Percentage of alleles in each subpopulation: the starch-promoting allele was more abundant in iodent subpopulations and less abundant in sweet corn subpopulations.Supplementary Fig. S5. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 1 from 102,077,749 to 102,120,437 bp. This peak is associated with the phenotypes belonging to Inflorescence and Flowering Time categories. The phenotypes associated with this group are EarLength_O and Silking_J.Supplementary Fig. S6. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 1 from 276,657,865 to 277,120,054 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are Anthesis1_L, Anthesis_J, ExtantLeafNumber1_J, ExtantLeafNumber2_J, Nodes_M, and Silking_J.Supplementary Fig. S7. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 1 from 280,994,269 to 281,039,919 bp. This peak is associated with the phenotypes belonging to Root and Vegetative categories. The phenotypes associated with this group are EarHeight_M and RootAngle2_O.Supplementary Fig. S8. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 2 from 82,811,901 to 82,811,901 bp. This peak is associated with the phenotypes belonging to Agronomic and Inflorescence categories. The phenotypes associated with this group are BushelAcreEquivalent_J and KernelsPerRow_J.Supplementary Fig. S9. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 2 from 88,191,911 to 88,201,317 bp. This peak is associated with the phenotypes belonging to Agronomic and Inflorescence categories. The phenotypes associated with this group are BushelAcreEquivalent_J, EarFilledLength_J, KernelsPerRow_J, and TotalGrainMassGrams_J.Supplementary Fig. S10. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 3 from 157,317,263 to 157,318,194 bp. This peak is associated with the phenotypes belonging to Root and Vegetative categories. The phenotypes associated with this group are EarHeight_M, RootAngle2_O, and RootWidth1_O.Supplementary Fig. S11. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 3 from 168,948,681 to 168,998,142 bp. This peak is associated with the phenotypes belonging to Cellular/Biochemical, Inflorescence, and Vegetative categories. The phenotypes associated with this group are BranchZoneLength_C, LeafCuticularConductance6_H, SpikeProportion_C, StalkDiamThin_N, and peri_N.Supplementary Fig. S12. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 3 from 218,796,811 to 218,832,925 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are ExtantLeafNumber1_J and Silking_J.Supplementary Fig. S13. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 4 from 28,148,396 to 28,163,192 bp. This peak is associated with the phenotypes belonging to Inflorescence and Vegetative categories. The phenotypes associated with this group are Nodes_M and SkeletonLength_C.Supplementary Fig. S14. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 4 from 78,020,118 to 78,451,569 bp. This peak is associated with the phenotypes belonging to Flowering Time and Inflorescence categories. The phenotypes associated with this group are Anthesis5_H and SpikeLength1_C.Supplementary Fig. S15. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 4 from 190,332,784 to 190,410,017 bp. This peak is associated with the phenotypes belonging to Seed Composition and Vegetative categories. The phenotypes associated with this group are CrudeAsh_K, EarHeight_L, Ncombustion_K, Nkjeltec_K, PlantHeight_L, and Protein_K.Supplementary Fig. S16. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 5 from 4,615,668 to 4,617,726 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are Anthesis1_L, EarHeight_L, and PlantHeight_L.Supplementary Fig. S17. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 5 from 32,944,052 to 32,957,580 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are Anthesis4_H, Anthesis7_H, and ExtantLeafNumber2_J.Supplementary Fig. S18. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 5 from 39,053,843 to 39,077,552 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are Anthesis_J and PlantHeight_G.Supplementary Fig. S19. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 5 from 94,710,702 to 94,712,951 bp. This peak is associated with the phenotypes belonging to Flowering Time, Root, and Vegetative categories. The phenotypes associated with this group are Anthesis1_L, Anthesis7_H, Anthesis_A, Anthesis_J, LeafAreaIndex_J, LeafLength_J, RootArea1_O, RootArea2_O, RootArea4_O, RootWidth4_O, SilkingGDD_L, Silking_J, and Silking_L.Supplementary Fig. S20. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 7 from 164,238,577 to 164,238,577 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are Anthesis5_H and PlantHeight_J.Supplementary Fig. S21. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 7 from 167,280,764 to 167,280,878 bp. This peak is associated with the phenotypes belonging to Cellular/Biochemical and Root categories. The phenotypes associated with this group are LeafCuticularConductance6_H and RootAngle2_O.Supplementary Fig. S22. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 8 from 28,727,658 to 28,769,198 bp. This peak is associated with the phenotypes belonging to Flowering Time and Root categories. The phenotypes associated with this group are Anthesis_J, RootDepth1_O, RootDepth2_O, and Silking_J.Supplementary Fig. S23. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 8 from 86,775,550 to 87,443,426 bp. This peak is associated with the phenotypes belonging to Cellular/Biochemical and Vegetative categories. The phenotypes associated with this group are BiomassYield_G, ExtantLeafNumber1_J, PH-EH_L, PlantHeight_G, and VascularBundleDensity_D.Supplementary Fig. S24. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 8 from 126,593,834 to 126,796,503 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are Anthesis4_H, Anthesis_A, Anthesis_J, LeafAngle_J, Silking_A, and Silking_J.Supplementary Fig. S25. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 8 from 135,045,240 to 135,071,381 bp. This peak is associated with the phenotypes belonging to Inflorescence, Root, and Vegetative categories. The phenotypes associated with this group are LowestBranchAngleAuto_P, PlantHeight_J, RootAngle1_O, and RootWidth4_O.Supplementary Fig. S26. Local Manhattan plot with ±200 kilobases of pleiotropic peak on chromosome 8 from 139,924,627 to 139,925,379 bp. This peak is associated with the phenotypes belonging to Flowering Time and Vegetative categories. The phenotypes associated with this group are ExtantLeafNumber1_J, ExtantLeafNumber2_J, PlantHeight_J, and Silking_A.Supplementary Table S1. Widely used maize community association panels.Supplementary Table S2. Trait values for all phenotypes per accession and associated information per accession. Provided as included Excel file.Supplementary Table S3. Phenotypes and associated information. Provided as included Excel file.Supplementary Table S4. The locations of GWAS peaks, the traits associated with each peak, and the genes adjacent to each peak. Provided as included Excel file.Supplementary Table S5. Individual GWAS hits. Provided as included Excel file.BLUPs: best linear unbiased predictions; bp: base pair; DAG: diacylglycerol; exPVPs: expired plant variety patents; GWAS: genome-wide association study; LD: linkage disequilibrium; MAP: Maize Association Panel; MLM: mixed linear model; mRNA: messenger RNA; NCBI: National Center for Biotechnology Information; PCo: principal coordinate; QTL: quantitative trait locus; RMIP: resampling model inclusion probability; RNA-seq: RNA sequencing; SAM: Shoot Apical Meristem; SNP: single-nucleotide polymorphism; SRA: Sequence Read Archive; TAG: triacylglycerol; WiDiv: Wisconsin Diversity Panel.The Plant Cell. The other authors declare no conflicts of interest.J.C.S. has equity interests in Data2Bio, LLC; Dryland Genetics LLC; and EnGeniousAg LLC. He is a member of the scientific advisory board of GeneSeek and currently serves as a guest editor for This material is based upon work supported by the US Department of Energy Advanced Research Projects Agency–Energy (ARPA-E) under Award Nos. DE-AR0001064 and DE-AR0001367, the National Science Foundation under grants OIA-1557417 and OIA-1826781, and US Department of Agriculture (USDA)–National Institute of Food and Agriculture (NIFA)under the AI Institute for Resilient Agriculture (Award No. 2021-67021-35329) and the Foundation for Food and Agriculture Research (Award No. 602757). This research was supported in part by the USDA, Agricultural Research Service Project #5030-21000-068-00D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer.R.V.M. and J.C.S. conceived of the study. R.V.M., G.S., M.G., M.C.T., H.J., C.S., L.N., A.M.T., and B.S. designed and carried out experiments to generate data. R.V.M., M.G., and G.S. analyzed the data. R.V.M. and J.C.S. wrote the initial draft of the manuscript. All authors read and approved the final manuscript.giac080_GIGA-D-22-00083_Original_SubmissionClick here for additional data file.giac080_GIGA-D-22-00083_Revision_1Click here for additional data file.giac080_Response_to_Reviewer_Comments_Original_SubmissionClick here for additional data file.giac080_Reviewer_1_Report_Original_SubmissionYu Li -- 5/3/2022 ReviewedClick here for additional data file.giac080_Reviewer_1_Report_Revision_1Yu Li -- 5/30/2022 ReviewedClick here for additional data file.giac080_Reviewer_2_Report_Original_SubmissionYingjie Xiao, Ph.D. -- 5/3/2022 ReviewedClick here for additional data file.giac080_Reviewer_2_Report_Revision_1Yingjie Xiao, Ph.D. -- 6/6/2022 ReviewedClick here for additional data file.giac080_Reviewer_3_Report_Original_SubmissionRoberto Pilu -- 5/15/2022 ReviewedClick here for additional data file.giac080_Supplemental_FilesClick here for additional data file.


However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness.In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments  or complex experiments . We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations  based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.The online version contains supplementary material available at 10.1186/s13007-022-00952-5. Based on previous knowledge, and assuming that a grower focuses on the early production of strawberries, an ideal treatment should provide: (i) plants with high water use efficiency that presents a short period between planting and beginning of flowering/harvesting, with low phyllochron values, i.e., a high number of leaves per accumulated thermal sum. Thus, lower values for WUE, NDBF, NDFF, NDBH, and PHYL are desired); (ii) high fruit yield with a higher number of commercial fruits with a high average weight of fruits, which are defined by higher values for NCF, TNF, WCF, TWF, AWCF, OAWF, and FY); (iii) lower number of non-commercial fruits of low average weight, which are defined by lower values for NNCF, WNCF, and AWNCF); and (iv) sweet, firm fruits with lower acidity and ideal external and internal red color, which are defined by lower values for TA and higher values for TSS, TSS/TA, FIRM, L, CHROMA, and H).In this crucial step, we define an Y is an X is an b is an e is an To avoid pseudo-replication, plants within replicates were averaged. The overall effects of cultivar (CUL), origin (ORI), and substrate (SUB) on the accessed traits were analyzed using a 3-way multivariate analysis of variance (MANOVA) using the function manova from the R software, according to the following model.Pillai’s trace was used as the test of significance as suggested by Hand and Taylor . The preideal treatment, we reescaled j after rescaling, respectively; j, respectively, and jth trait of the ith treatment. For NNCF, WNCF, AWNCF, WUE, NDBF, NDFF, NDBH, PHYL, and TA in which lower values are desired, Based on the knowledge of an posed by .2\documeX is a L is a f is a p and f the number of traits and common factors retained, respectively. The eigenvalues and eigenvectors are obtained from the correlation matrix of varimax , the “ideal” treatment would have 100 for all analyzed traits after the rescaling process. Therefore, In experiments accessing several plant traits, it is common that some traits have more importance than others. For example, for wheat experiments, grain yield and grain mass per spike are two common analyzed traits and for both of them, higher values are desired. Given the original MGIDI index, both grain yield and the number of grains per spike have the same weight in the MGIDI computation. It would be desired, however, to give a higher weight to yield, prioritizing treatments with a higher grain yield, even if they have a lesser number of grains per spike. Another example would be the selection of strawberry cultivars based on multiple traits . Total fHere, we propose an optional weighting procedure that will allow giving higher weights for traits that are assumed to have more importance in the treatment ranking. The weighting process is done by simply multiplying each element in mentclass2pt{minimith treatment explained by the jth factor  with Data manipulation and the index computation were performed in the R Software version 4.1.0  using thFigure The multivariate analysis of variance Table  indicateBased on the significance of factors, the MGIDI was computed for (i) a two-way table containing in the rows the combination of cultivars and origin and in columns the traits, and (ii) a two-way table with substrates in the rows and traits in columns. For both cultivar x origin interaction and substrate, three factors (FA) were retained, explaining 100% of the total variance Table . DiffereThe 22 traits were grouped into the factors (FA) as follows  for 16 out of 22 studied traits Fig. . For traAnalyzing the substrate main factor, the 22 traits were grouped into the factors (FA) as follows Table .Fig. 3SeThe better-ranked substrates were S3 and S4. These substrates have as a common ingredient burnt rice husk (70%). The strengths and weaknesses view  to 9.59% (NNCF). For traits in which higher values are desired, the SD ranged from − 2.09% (TSS) to 28.79% (WCF).Our experimental results suggest that most of the productive, phenological, and qualitative traits in strawberries are influenced by the cultivar used, transplant origin, cultivation substrates, and the interaction between cultivar and origin of transplants. Previous studies also reported a significant effect of cultivars for physicochemical traits of fruits , phyllocpost-hoc test—e.g., Tukey’s honest significance test—is used, we have shown here how to rank treatments in agronomic experiments based on the values of several traits  and supply to the consumer market fruits with higher flesh firms and total soluble solids Fig. b. Our reity loss . The conity loss , which city loss . TherefoIn another part of the area, the grower could use National transplants of Camarosa cultivar to take advantage of higher fruit production. The higher number and weight of non-commercial fruits observed in this treatment , we provide the data and necessary scripts to compute the index, which can be easily adapted.The MGIDI can be applied to every situation where rows  need to be ranked based on desired values of multiple columns (traits). An application example to a published paper  shows hoideal treatment. Researchers should then identify a set of key traits to be used, which will certainly vary depending on the goal of the study [The key point underlying MGIDI use is choosing an he study . Comparehe study , it willstrengths and weakness view provided by the MGIDI revealed that researchers should direct efforts on increasing fruit production, improving the water use efficiency, and reducing the total acidy of fruits from the Albion Cultivar originated from Imported transplants. The strengths of this treatment are mainly related to productive precocity, total soluble solids, and flesh firmness. Overall, this study opens the door to the use of MGIDI to analyze plant multivariate data, standing out as a powerful tool to develop better recommendation strategies. The option of including weights for each trait in the selection process will make the index more applicable and provide better treatment recommendations based on several accessed traits. Improving the graphical pipeline and divergent selection for positively correlated traits are key steps to make the method more applicable.Using the multi-trait genotype-ideotype distance index (MGIDI), we have shown how to select superior treatments in plant experiments where multiple traits have been assessed. In our example with strawberry data, the treatments selected by the MGIDI were characterized by the Albion cultivar originating from imported transplants grown in substrates where the main component (70%) is burnt rice husk. The selected treatments provided desired values for most out of the 22 qualitative, physiological, and phenological traits. The Additional file 1. A website with the data, script, and results is available at https://tiagoolivoto.github.io/paper_mgidi_pm/. The source code used to produce the static website and the results in this manuscript have been archived at 10.5281/zenodo.7155173 as manuscript v2.


MegaLMM. Applied to Genomic Prediction, MegaBayesC effectively integrated hyperspectral reflectance data from 620 hyperspectral wavelengths to improve the accuracy of genetic value prediction on grain yield in a wheat dataset. Applied to Genome-Wide Association Studies, we used simulations to show that MegaBayesC can accurately estimate the effect sizes of QTL across a range of genetic architectures and causes of correlations among traits. To apply MegaBayesC to a realistic scenario involving whole-genome marker data, we developed a 2-stage procedure involving a preliminary step of candidate marker selection prior to multivariate regression. We then used MegaBayesC to identify genetic associations with flowering time in Arabidopsis thaliana, leveraging expression data from 20,843 genes. MegaBayesC selected 15 single nucleotide polymorphisms as important for flowering time, with 13 located within 100 kb of known flowering-time related genes, a higher validation rate than achieved by a single-stage analysis using only the flowering time data itself. These results demonstrate that MegaBayesC can efficiently and effectively leverage high-dimensional phenotypes in genetic analyses.Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated traits are challenging. We developed a method for implementing high-dimensional Bayesian multivariate regression to simultaneously analyze genetic variants underlying thousands of traits. As a demonstration, we implemented the BayesC prior in the R package  The advent of high density genome-wide single nucleotide polymorphism (SNP) arrays in the past decades has provided exciting new material for the genetic analysis of complex traits. Linear mixed models that can integrate such large-scale genomic data are widely used for genomic prediction  and genoBSFG and MegaLMM are based on the assumption that the covariances among large sets of traits can be explained by a small set of latent factors (e.g. through gene regulatory networks), which is consistent with the discovery that variation in gene expressions of human diseases is mainly regulated by a few major disease-associated pathways  is a widely used statistical model for the genetic analyses of 2 or more correlated traits . HoweverMegaLMM addressed the statistical and computational challenges of applying MvLMMs to high-dimensional phenotypes, it permits a limited range of models for high-dimensional genotype data. Specifically, MegaLMM incorporates genomic data through 1 (or more) genomic relationship matrices, which imposes specific assumptions about the distribution and effect sizes of the underlying genetic variants, and does not allow direct inference on the identities of causal loci. Whole-genome regression methods  can be obtained simultaneously in parallel across traits and factors, which leads to significant reduction in the computational cost of model fitting.MegaLMM: MegaRRBLUP.Mixture priors are widely used for genetic marker effects in Bayesian regression methods in genome-enabled analysis. In this paper, the BayesC prior  of grain yield and reflectances from 62 wavelength bands collected with an areal hyperspectral camera on each of 10 time-points during the growing season for 1,033 bread wheat lines were downloaded from CIMMYT Research Data : A single-kernel single-trait model with a genomic relationship matrix, A 2-kernel single-trait model with 2 relationship matrices: a genomic relationship matrix GBLUP_K as described in MegaGBLUP with This model implements a multivariate version of MegaBayesC model was specified as described above. MegaBayesC with The MegaRRBLUP model was identical to the MegaBayesC model except that the marker exclusion probability MegaGBLUP except that the prior distribution of MegaRRBLUP, which differs slightly from the Bayesian Horseshoe prior used in MegaGBLUP .We conducted 2 simulation studies and 1 real data analysis to investigate the ability of MegaBayesC greatly reduces the computational burden of Bayesian multivariate regressions by decomposing phenotypic covariances into a set of Although MegaBayesC was compared to a single-trait BayesC model (ST-BayesC) based on the accuracy of estimated marker effects for the focal trait. We induced genetic and nongenetic covariation among the traits through latent factors. The majority of variance in the focal trait was attributed to the latent factors. In Scenario 1, we created latent factors whose variation was primarily determined by the genetic markers (i.e. high-heritability latent factors), and in Scenario 2, the latent factors were predominantly nongenetic (i.e. low-heritability factors).We created a simulated population of MegaBayesC and ST-BayesC: (1) the number of latent factors  using the JWAS package  no fixed factor loadings were included in MegaBayesC and ST-BayesC) by calculating the square root of the mean square error (RMSE) of estimated marker effects.When fitting models to these simulated data, we included the intercept for each trait as the only fixed effect. We compared We created a second set of simulated datasets based on real genotypes from 1003 Arabidopsis thaliana accessions. Genotype data were downloaded from the 1,001 genomes project ; and (3) MegaBayesC implemented in MegaLMM.After creating the simulated data, we applied 3 methods to identify QTL and estimate their effects on the focal trait: (1) single-trait Genome-Wide Association Studies (GWAS) using GCTA  and multivariate genomic prediction (MegaGBLUP) using data from a breeding trial of bread wheat. We compared the genomic value prediction accuracy of MegaBayesC and MegaRRBLUP to MegaGBLUP in this dataset, where we leveraged 620 hyperspectral phenotypes measured on 1,033 bread wheat lines to supplement genotype-based predictions of genomic value for grain yield. As baseline, we performed univariate genomic prediction using either the marker-based genomic relationship matrix (GBLUP_K) or a 2-kernel univariate model using both a genomic relationship matrix and a hyperspectral reflectance relationship matrix (GBLUP_KH). Prediction accuracy was assessed by cross-validation where for each of 20 replicates, grain yield values of 50% of the lines were masked and used as an independent testing set. Estimated genetic correlations between predicted and observed yields in the testing set were used as the cross-validation statistic.We tested if whole-genome regression models could match or exceed the performance of univariate genomic prediction σ^2 . These rMegaBayesC to identify and accurately estimate the effect sizes of genetic variants for a set of correlated traits under different genetic architectures. Specifically, we tested whether MegaBayesC improved the estimation of variant effect sizes of a single focal trait when phenotypes of other correlated traits (i.e. secondary traits) were provided.Next, we ran a set of simulations to evaluate the ability of ST-BayesC  and MegaBayesC . We scored the accuracy of each method by the RMSE of estimated marker effects. In both scenarios, as the genetic architecture increased in complexity , the performance of ST-BayesC decreased (RMSE increased) much more dramatically than MegaBayesC. MegaBayesC and ST-BayesC, with ST-BayesC beyond its effect on the number of QTL. Also, the number of traits linked to each factor  of the covariance structures among traits determine the usefulness of the secondary traits, we considered 2 covariance structures. In both cases, we began by simulating a set of latent factors partially controlled by genetic variation. In Scenario 1, the majority of variation in the focal trait was controlled by latent factors that were dominated by genetic variation. In Scenario 2, the majority of variation in the focal trait was controlled by latent factors dominated by nongenetic sources of variation. We compared the estimation of marker effects between ST-BayesC, the RMSE of estimated marker effects increased significantly as marker-explained variances decreased in both scenarios. Compared to Scenario 1, the increase of RMSE for estimated effects of QTL was greater in Scenario 2, while the increase of RMSE for estimated effects of SNPs were similar between the 2 scenarios. This indicates that the performance of ST-BayesC to identify QTL was affected by the marker-explained variance as well as the variance structure of the focal trait.For MegaBayesC was relatively constant across scenarios as measured by RMSE. In terms of the estimation of effect sizes of QTL, the influence of the variance structure and the marker-explained variance was negligible, which led to a relatively constant RMSE across the simulation settings. At the same time, MegaBayesC was able to shrink most SNPs more effectively toward zero, especially in Scenario 2, when the ratio of number of QTL to number of SNPs was smaller.In contrast, the performance of ST-BayesC and MegaBayesC in Scenario 2, we plotted the estimated marker effects under 1 example simulation with To further explore the differences in the performance of ntrait=2 .ST-BayesC, some QTL were successfully selected by the model and their effect sizes were accurately estimated close to the true value of 0.1. However, for the majority of QTL, the estimated marker effects were shrunk toward 0s. On the other hand, ST-BayesC erroneously estimated effect sizes of null-effect SNPs from MegaBayesC  method, ST-GCTA, that only uses the focal trait, and 2 2-stage methods: ST-BayesC and MegaBayesC, which both rely on a preselection stage to select a set of candidate SNPs using 1 partition of the population, and then an assay stage where the effects of those SNPs on the focal trait are modeled in the second partition of the population. We compared the performance of the models by the RMSE of estimated marker effects and marker-explained variances.To explore the ability of MegaBayesC method achieved the lowest RMSE for both marker effects and marker-explained standard deviations, followed by the 2-stage analysis incorporating ST-BayesC, and then the 1-stage single-trait GWAS (ST-GCTA). The RMSE of the 1-stage single-trait GWAS was around 10 times larger than that of the 2-stage BayesC-based analyses, while the difference between ST-BayesC and MegaBayesC was much smaller. Furthermore, the RMSE of estimated marker-explained standard deviations was generally lower than that of estimated marker effects. The larger RMSE of estimated marker effects is likely due to the selection of linked SNPs rather than the true causal QTL in the preselection stage.ST-BayesC and MegaBayesC in this simulation scenario, we present the relationship between true and estimated marker effects for 1 replicate in ST-GCTA, and only 16 were selected in the preselection stage for the 2-stage methods, ST-BayesC and MegaBayesC. In all 3 cases, the effect sizes of these selected QTL were accurately estimated. However, the effect sizes of many null-effect SNPs were dramatically overestimated by ST-GCTA, leading to an overall high false positive rate. In contrast, although a few true causal QTL were missed in the preselection stage, SNPs with null effects that were moved forward into stage 2 were estimated to have very small effects by both ST-BayesC and MegaBayesC.To further explore the difference in the performance of Note that in some cases, SNPs that are in LD with true QTL were selected instead of the causal QTL. When the linkage phase was negative, the estimated effect sizes for linked SNPs have the opposite sign, which increases the reported RMSE. However, even in these cases, the proportion of variance explained by these linked markers is close to the proportion that would have been explained by the true QTL, so the effect of LD on the RMSE of marker-explained variances is minimized.MegaBayesC and the 1-stage single-trait GWAS (ST-GCTA) to the task of identifying candidate genes that regulate flowering time in Arabidopsis thaliana using actual flowering time measurements and genotype data from 1003 A. thaliana accessions. In MegaBayesC, we included the expression of 20,843 genes measured on 649 of the accessions as secondary traits.We applied the 2-stage MegaBayesC and potentially important SNPs with ST-GCTA were selected as significant SNPs. MegaBayesC was better able to select a limited number of candidate SNPs based on per-marker variance explained  is approximately We therefore developed 2 approximations to greatly reduce the time complexity of the full model. First, we forced the marker effects to affect the secondary traits through the MegaBayesC, and MegaLMM more generally, shows promise in its ability to integrate thousands of traits in genome-wide prediction and association, the tradeoff between the benefit of incorporating secondary traits and the computational cost brought from the increased model complexity must be considered. Based on our simulated study, MegaBayesC can effectively disentangle the genetic and nongenetic sources of covariation among observed traits. When there is an important environmental component in the variation of focal trait, and this environmental component is shared by many other highly correlated traits, we expect MegaLMM whole-genome regression models to provide a large benefit by providing a tool to effectively control for this environmental variation. However, when the secondary traits are not highly correlated with the focal trait, or the heritability of the focal trait is already sufficiently high, MegaLMM whole-genome regression may prove less useful. In this paper, we have focused on 2 versions of MegaLMM: whole-genome regression with the BayesC prior on the marker effects (MegaBayesC), and with a ridge prior on the marker effects (MegaRRBLUP). The extension to other similar Bayesian Alphabet priors such as BayesA and BayesB would require only minor modifications to the code in the MegaLMM package. Other mixture priors such as the BayesR priors could also be implemented in future studies and we anticipate that each version may provide benefits in specific datasets.While iyac183_Supplementary_DataClick here for additional data file.


The vascular bundle of the shank is an important ‘flow’ organ for transforming maize biological yield to grain yield, and its microscopic phenotypic characteristics and genetic analysis are of great significance for promoting the breeding of new varieties with high yield and good quality. In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition at resolutions up to 13.5 μm. Moreover, five categories and 36 phenotypic traits of the shank including related to the cross-section, epidermis zone, periphery zone, inner zone and vascular bundle were analyzed through an automatic CT image process pipeline based on the functional zones. Next, we analyzed the phenotypic variations in vascular bundles at the base of the shank among a group of 202 inbred lines based on comprehensive phenotypic information for two environments. It was found that the number of vascular bundles in the inner zone (IZ_VB_N) and the area of the inner zone (IZ_A) varied the most among the different subgroups. Combined with genome-wide association studies (GWAS), 806 significant single nucleotide polymorphisms (SNPs) were identified, and 1245 unique candidate genes for 30 key traits were detected, including the total area of vascular bundles (VB_A), the total number of vascular bundles (VB_N), the density of the vascular bundles (VB_D), etc. These candidate genes encode proteins involved in lignin, cellulose synthesis, transcription factors, material transportation and plant development. The results presented here will improve the understanding of the phenotypic traits of maize shank and provide an important phenotypic basis for high-throughput identification of vascular bundle functional genes of maize shank and promoting the breeding of new varieties with high yield and good quality. Maize is highly adaptable and widely planted, which is of great significance for meeting global food demand and ensuring world food security . The yieThe phenotypic characterization of vascular bundle traits reported in previous studies was still mainly in the stage of manual detection. However, manual detection of vascular bundle traits was strenuous and time-consuming work. What is more, many characteristics of important biological significance were unable to be detected by the manual test . In the In recent years, with the rapid development of high-density single nucleotide polymorphism (SNP) genotyping and next-generation sequencing (NGS), genome-wide association studies (GWAS) have become a powerful tool for analyzing the genetic basis of the quantitative variations in complex crop traits ,17. For In this study, micro-CT scanning technology was used to obtain the micro images of shanks of inbred maize lines at resolutions that could reach 13.5 um. The phenotypic traits of shank and vascular bundles could be automatically detected using the automatic vascular bundle phenotyping pipeline. The five categories of thirty-six extracted phenotypic traits included traits related to cross-section, epidermis zone, periphery zone, inner zone, and vascular bundle. Based on abundant phenotypic indicators of shank and vascular bundles, we analysed the phenotypic variations in shank vascular bundles among a group of 202 inbred lines and further conducted GWAS to reveal the genetic architecture of the shank vascular bundles. The results presented here will provide a new perspective for high-throughput identification of vascular bundle functional genes of maize shank and promote breeding of new varieties with high yield and good quality.v/v/v, 70% ethanol:100% formaldehyde:100% acetic acid) immediately for later study.The total of 202 inbred maize lines used in this study belong to the maize natural population described by Yang et al., 2011 . AccordiIn order to ensure the reliability and consistency of image collection, the standardized process of maize vascular bundle microscopic data collection independently constructed by our research group was adopted for CT image acquisition and phenotypic analysis of maize shank . The prop < 0.05 level.Microsoft Excel 2013 was used to sort and analyze the experimental data from Beijing and Sanya and calculate for the two locations yield maximums, minimums, averages, standard deviations, etc. A correlation study using Spearman’s correlation as a distance metric was conducted for phenotypic traits. ANOVA and Duncan’s test were used to compare the phenotypic differences among different subgroups of maize shank in 202 inbred lines at yi is the BLUP value of individual i, μ is the grand mean for the two (Beijing and Sanya) environments, fi and ei are the ith genetic effect (inbred lines) and environment effect, respectively, and εi is the random error. Additionally, μ was considered to be a fixed effect, whereas fi and ei were random effects.Locations were regarded as environmental variables. The best linear unbiased prediction (BLUP) for the 36 phenotypic traits was estimated with the following linear mixed model in the lme4 package of the R software:L is the number of locations, R is the number of replications, and gV, GLV and eV represent, respectively, the genotypic variance, interaction between inbred lines and environment variance, and the error variance, respectively. The above analysis was performed in ASReml-R v.3.0 using the ‘asreml’ function of R package asreml [Heritability refers to the percentage of genetic variation . After the genotype data were obtained for 202 inbred lines, PLINK was used to calculate the minimum allele frequency (MAF), the MAF of each SNP was counted, quality control was conducted, SNPs with MAF < 0.05 and call rate <0.9 were removed, and the number of remaining effective SNPs was 797058. PopLDdecay (version 3.41) [p value was set at 1.25 ×10−6 . The default p value of 0.0002 was then used as the filter threshold to determine the significance of the SNPs associated with a given trait. The intersection SNPs obtained by all methods were regarded as significantly related to phenotypic traits and were considered to be more reliable results. All candidate genes are based on EnsemblPlants  in the maize B73 reference genome (B73RefGen_v4) and the NCBI gene database  interpretation. Separately, the significant interactions between the genes and their related phenotypic traits were visualized using Cytoscape v3.7.2 .Genotype data were obtained from the laboratory of Professor Jianbing Yan of Huazhong Agricultural University (download URL: on 3.41)  was usedon 3.41) . The toton 3.41) . Statiston 3.41) . Six metShank, an important developmental step that connects ‘source’ to ‘sink’, is the branch stem connecting stem and ear in maize. The structure of the shank is similar to that of the stem, but it has its own characteristics. High-quality microscopic images of the shank can be obtained through micro-CT scanning at resolutions that reach 13.5 µm. In studying the CT scanning images, it was not difficult to find that the shapes of the shanks are significantly different from those of the stem. In addition to being nearly round A1,A2, thTo explore the relationships between the phenotypic traits of the shank, correlation analysis was carried out of the 36 phenotypic traits . There wHierarchical cluster analysis was performed to explore the relationships between the 36 phenotypic traits using Spearman’s correlation as a distance metric. The 36 accessions were clustered into four major groups, and the resulting dendrogram is shown in 2 to 214.114 mm2 with an average of 108.220 mm2, had the highest maximum variation of 3.310-fold in 202 inbred lines, followed by the total area of the vascular bundles in the inner zone , the convex area of vascular bundles in the inner zone , the shank cross-section area traits , the thickness of the inner zone , and vascular bundle area traits in the inner zone   were observed. The interesting thing was that the area of the inner zone (IZ_A), ranging from 64.688 mm20–fold) .p ≤ 0.05), mainly cross-section-related, inner zone-related and vascular bundle-related traits. For the cross-section-related traits, six phenotypic indicators showed significant differences among different subpopulations: cross-section area (SZ_A), convex area of the cross-section (SZ_CA), circumcircle area of the cross-section (SZ_CCA), short axis length of the cross-section (SZ_SA), perimeter of the cross-section (SZ_P), and long axis length of the cross-section (SZ_LA). Three inner-zone-related phenotypic traits had significant differences among subpopulations, including thickness of the inner zone (IZ_T), area of the inner zone (IZ_A), and density of the vascular bundles in the inner zone (IZ_VB_D). Nine phenotypic traits related to vascular bundles were significantly different among subpopulations: total area of vascular bundles (VB_A), density of vascular bundles (VB_D), average short axis length of vascular bundles (VB_SAave), average perimeter of vascular bundles (VB_Pave), average long axis length of vascular bundles (VB_LAave), average circumcircle area of vascular bundles (VB_ CCAave), average convex area of vascular bundles (VB_CAave), and average area of vascular bundles (VB_Aave). Five phenotypic traits related to periphery zone were significantly different among subpopulations: density of vascular bundles in the periphery zone (PZ_VB_D), convex area of vascular bundles in the periphery zone (PZ_VB_CA), total area of vascular bundles in the periphery zone (PZ_VB_A), thickness of the periphery zone (PZ_T), and area of the periphery zone (PZ_A). There were no significant differences in epidermis zone-related and periphery zone-related phenotypic traits among the four subgroups of the 202 inbred lines. In addition, Duncan’s test showed that the shank cross-section areas in the NSS subgroup were significantly larger than those in the SS, Mixed and TST subgroups, and the inner zone area (IZ_A) and inner zone thickness (IZ_T) in the NSS subgroup were much larger. Meanwhile, the total number of vascular bundles (VB_N) and the number of vascular bundles in the inner zone (IZ_VB_N) in the NSS subgroup were greater than those numbers in the SS, Mixed and TST subgroups, and the total area of the vascular bundles (VB_A) in the NSS subgroup was much higher than that in other subgroups. On the contrary, vascular bundle density (VB_D), vascular bundle density in the inner zone (IZ_VB_D), and vascular bundle density in the periphery zone (PZ_VB_D) in the SS, Mixed and TST subgroups were significantly higher than those in the NSS subgroup.Moreover, we analysed the changes in the vascular bundle traits at the base node of the shanks in the different subgroups of the 202 inbred lines  . Most, 2Here, the heritability of the 36 phenotypic traits of maize shank and vascular bundles based on the data from two environments was calculated. The 36 phenotypic traits showed different heritability patterns, ranging from 0.036 to 0.711 . Nearly p-value < 1.3 × 10−6) were identified for the 30 traits, and 1245 unique candidate genes were annotated according to the maize B73 reference genome (B73 RefGen_v4). In order to reduce the false positives of GWAS and ensure the accuracy of candidate genes, only the SNPs loci and genes co-located by various methods were used in subsequent gene selection. Consequently, 186 significant SNPs were filtered, and 360 unique candidate genes were annotated. Among these genes, 199 genes were unique candidate genes only related to a single trait . A total of 806 significant associated SNPs  were enriched. In addition, a total of 60 GO terms  were enriched after uploading the candidate gene IDs to PlantRegMap. Among the five categories of the shank phenotypic traits, the candidate genes associated with shank cross-section-related traits were only enriched in 1 KEGG pathway, monoterpene biosynthesis , and there were 17 GO terms, among which ‘Peroxidase activity’  and ‘Antioxidant activity’  had the highest significance. A total of 5 KEGG pathways were enriched in candidate genes for the periphery-zone-related traits (p < 0.05), mainly including ‘Biosynthesis of secondary metabolites’  and ‘Metabolic pathways’ . A total of 16 GO items, among which ‘Peroxidase activity’  and ‘Antioxidant activity’  had the highest significance. A total of 10 KEGG pathways were enriched in candidate genes for the inner-zone-related traits (p < 0.05), mainly including ‘Biosynthesis of secondary metabolites’ , ‘ABC transporters’ , and ‘Steroid biosynthesis’ . Among the 29 GO items, ‘Catalytic activity’  and ‘Integral component of membrane’  had the highest significance. The candidate genes associated with vascular-bundle-related traits were enriched in 8 KEGG pathways (p < 0.05), mainly including ‘Biosynthesis of secondary metabolites’ , ‘Metabolic pathway’ , and ‘Brassinolactone biosynthesis’ . Among 18 GO items, ‘Brassinosteroid homeostasis’  had the highest significance  were enriched, including alpha-linolenic acid metabolism , Biosynthesis of secondary metabolites , and Metabolic pathways . 20 GO terms  were enriched, and ’Transferase activity, transferring hexosyl groups’  and ‘Acetylglucosaminyltransferase activity’  had the highest significance . Traits and genes are shown in different shapes and sizes. For the 29 large octagon nodes, the 11 green nodes represent vascular-bundle-related traits , the 6 blue nodes represent cross-section-related traits , the 4 yellow nodes represent periphery zone-related traits , the 7 red nodes represent inner zone-related traits , and the 1 brown node represents an epidermis zone-related trait (EZ_A). Genes are represented by the small round nodes, and different colours indicate different attributes. The light grey nodes are genes that only correlate with one specific trait, and deep purple to light purple in the centre  indicates multi-trait shared genes. There were 100 shared genes among the traits within and between the five categories .In this study, shank CT images were obtained using the standard process for stem micro-CT data acquisition, and its resolution could reach 13.5 µm. Compared with traditional paraffin sectioning and hand-cutting sectioning, CT image acquisition efficiency is greatly improved, and the image quality is guaranteed ,32,33. MAs the vertical axis of the maize plant, the stem is the hub connecting the above-ground organs and the underground parts, and it participates in the transportation and circulation of water and nutrients in the whole plant . Shank, p ≤ 0.05), including SZ_A, VB_A, IZ_VB_A, etc. In addition to the traditional microscopic character indicators, such as SZ_A, VB_N, and VB_D, several new phenotypic traits like IZ_A, IZ_VB_D, IZ_VB_A were significantly different among different subpopulations, and these new phenotypic indexes can be used as essential referenc to distinguish different genotypes. In addition, for the NSS subgroup, the total area of shank vascular bundles was larger, and the total number of vascular bundles was larger, but the density of vascular bundles was smaller. On the contrary, the densities of shank vascular bundles in the SS, Mixed and TST subgroup were larger. These results indicated that the smaller and denser distribution of shank vascular bundles shank in tropical and subtropical inbreeds might be related to climate adaptation.Phenotypic variations of 36 phenotypic indicators were different among subgroups. In this study, 1.001- to 3.310-fold variations in shank micro phenotypes were detected in 202 inbred maize lines. There were 22 phenotypic indexes  generally share the same associated candidate genes. Cross-section size-related traits  and inner zone size-related traits (IZ_A) share associated candidate genes, such as Zm00001d047436, Zm00001d047359, Zm00001d047358, Zm00001d037842, Zm00001d033954, and Zm00001d027392. The total vascular bundle number trait (VB_N) shares candidate genes with the trait of vascular bundle number in the periphery zone (PZ_VB_N), such as Zm00001d039323 and Zm00001d017462. Further, the vascular bundle density trait (VB_D) was negatively correlated with cross-section size-related and functional zone size-related traits, and these traits also had associated candidate genes, such as Zm00001d021936, Zm00001d020580, and Zm00001d002123. These shared genes provide useful information for finding functional links between correlated shank phenotypes.A set of genes encodes proteins that are involved in lignin, cellulose synthesis and cell wall formation, which are associated with vascular bundle number and area traits. Candidate genes GRMZM2G029144 and GRMZM2G156257, which regulate the trait of number of vascular bundles in the periphery zone (PZ_VB_N), has been mapped to chromosome 2 (chr2.S_219749444) and encodes a peroxidase-related protein peroxidase 2-like . AnotherA set of candidate genes is involved in vascular bundle and plant development. The candidate gene GRMZM2G000743, which regulates the trait of density of vascular bundles (VB_D), has been mapped to chromosome 1 (chr1.S_276307427) and is involved in encoding homeobox protein KNOX3. Recent discoveries indicate that KNOX genes promote xylem fibre differentiation during vascular development . A studyCandidate genes related to MYB transcription factor family genes were jointly identified in the third internodes and the shanks. The candidate gene GRMZM2G172327, regulating the trait of area ratio of individual vascular bundles (ARIVB), was mapped to chromosome 7 (chr7.s_155367082), belonging to the MYB transcription factor family. This gene encodes transcription factor MYB21, which has been implicated in the development of flower vascular bundles in Arabidopsis thaliana . In addiThe phenotype-associated genes obtained in our study can provide a reference for related research and offer new ideas for exploring the genetic mechanisms of shank vascular bundle agronomic traits in future studies.


IVW = 1.192; 95% CI 1.078, 1.318, P = 0.0007). We observed suggestive evidence of risky effects of CHR on AMD (P = 0.0034), SS on AD (P = 0.0044), and INS on ALS (P = 0.0123). However, we failed to observe any role of pain. The results were robust on sensitivity analyses. Our study highlighted the role of MP as a risk factor for AMD.Our aim was to determine whether the genetic liability to sleep and pain-related traits have a causal effect on risk of neurodegeneration in individuals of predominantly European ancestry. We selected five neurodegenerative disorders, namely, age-related macular degeneration (AMD), Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), and Parkinson's disease (PD). Sleep duration (SD), short sleep (SS), long sleep (LS), chronotype (CHR), morning person (MP), insomnia (INS), and multisite chronic pain (MCP) were considered as exposures. We conducted Mendelian randomization (MR) using an inverse-variance weighted (IVW) method to compute causal effect estimates using latest available GWAS data sets. The MP phenotype was observed as the strongest risk factor for genetic liability to AMD (OR Patients with neurodegenerative diseases (NDDs) often experience disruptions in circadian rhythmic activities , 2. ManyIt is well-recognized that circadian dysfunction in old age is due to degeneration of the suprachiasmatic nucleus (SCN) in the anterior hypothalamus, directly connected to the light-sensing retina . DiffereSimilar to the involvement of specific brain regions directly influencing circadian rhythms, several brain regions, also referred to as pain matrix, have been shown to be activated during pain perception , 11. TheTo date, the sparse number of large longitudinal studies and clinical trials has limited our progress in understanding the relationship between sleep, pain, and onset or progression of neurodegeneration, necessitating the need for searching alternative approaches for judging the causality. A two-sample Mendelian randomization (MR) is one such approach that employs instruments or proxy markers of risk factor in one population to judge causality of the risk factor with an outcome in an independent population –17.So far, limited studies have employed a genetic instrument-based approach to judge the etiological relationship between sleep, pain, and NDDs. A recent MR study showed an absence of the role of genetic liability to sleep duration (SD) in influencing predisposition to AD . On the −8 to select the genetic instruments. Concerning the outcome data sets, we used the discovery cohort of a recent meta-analysis of GWAS on AD (https://github.com/bulik/ldsc)  that influence circadian rhythm-related traits, including SD , SS 21)21), LS , MP 22)22), INS AS on AD , AMD −8 to selAS on AD . Before ik/ldsc) .Since the study analyzed secondary data (publicly available data) that contained information at the population-level (summary-level data), informed consent and ethical approval were waived off for this study.The prioritized SNP IDs and positions were synchronized with the NCBI GRCh37 assembly. We further checked for the validity of MR assumptions by excluding SNPs with F-statistics <10 and loci known to be directly involved in neurodegeneration based on existing evidence from previously published literature.2) cutoff of 0.001, and used the European population in the 1,000 Genome Phase 3v5 data set to identify the leading SNPs.As the selected genetic instruments could be correlated, we performed clumping of significantly associated SNPs on each GWAS data set with the clump_data function of the TwoSampleMR package (version 0.4.25) in R (version 3.6.1). We employed a clumping window of 10,000 kb and linkage disequilibrium  was used. We further computed the pooled variance (R2) for the respective risk factor using effect estimates (βx) and effect allele frequencies (EAFs) of individual genetic instruments, i.e., R2 = 2*http://cnsgenomics.com/shiny/mRnd). To compute the effect estimates at specific variance for a given outcome, we employed a sample size of each outcome data set, the proportion of patients in the same data set, and a threshold P of 1.42 × 10−3.The leading SNPs were further checked for availability in the respective outcome data sets. When possible, if a specific SNP was not available, a proxy SNP . Heterogeneity was judged using the Cochran's Q-statistic and I2 for the IVW method along with Rucker's Q-statistic, and the intercept deviation test for the MR-Egger's method (https://github.com/CGEatTuebingen/mrpipeline). We used a previously published data set to replicate the findings before employing the package for to this study (We used the inverse variance-weighted (IVW) effect method as the primary method to compute the causal effect estimates, as used previously . We comps method . All theis study . The mrpis study . Lastly,Several approaches were employed to rule out the influence of potential pleiotropic variants on the overall results. We used multiple modern MR methods, including the MR-Egger, weighted median (WME), and weighted mode (MBE) methods, to check the reliability of the estimates, as used in previous studies , 17. Sinhttp://phenoscanner.medschl.cam.ac.uk) to identify genetic variants associated with potential confounders. However, in the absence of knowledge of potential confounders, we adopted a more conservative approach, and all genetic loci known to be associated with non-sleep-related traits were assumed to be pleiotropic loci. We identified such loci by searching for all genetic variants in high LD with genetic instruments prioritized for this study using r2 > 0.9 for previously reported associations in European populations. We used visual approaches, including scatter plots and funnel plots, to identify outlier variants.We further employed a leave-one-out and leave-one-group-out cross-validation approach to rule out the influence of outlier variants known to be associated with confounders of the relationship between the respective exposure and outcome data sets. We specifically employed the Phenoscanner database  and sleep preference  were available, we performed a variable selection procedure to select the optimal variable that represented each category. Such an approach prevented us from conducting an overadjustment and avoided loss of power inherent with multiple variable regression methods. We selected SD to adjust for the quantity of sleep, as a continuous variable is more informative than a binary trait. Similarly, we selected CHR as a variable of choice representing sleep preference. Specifically, the genetic associations of instruments with respective NDDs were regressed on the genetic associations with all the risk factors  in a single regression model using IVW method. Genetic instruments entered into the multivariable regression model were allowed to be associated with any of the risk factor under consideration.https://www.gtexportal.org).We further evaluated the potential biological influence of different brain regions on their respective contribution to the causal effect estimate by analyzing gene expression data for available genetic variants from the Genotype-Tissue Expression Project . Heterogeneity check confirmed the reliability of the observed association with absence of any heterogeneity in the distribution of effect estimates of individual genetic variants . The distribution of individual SNP-level effect estimates and the effect estimates computed with different MR methods for the effect of MP on AMD is further shown as scatter and funnel plots in WME = 1.126; 95% CI = 1.044, 1.214). We also observed a similar trend using a highly correlated but continuous trait, CHR on AMD . The directionality of findings was further confirmed by a significantly higher variance explained by genetic instruments for MP and CHR than that explained by the respective genetic instruments for AMD (PSteiger = 2.1 × 10−98 and PSteiger = 1.65 × 10−24). In contrast, we did not observe any direct role of pain on predisposition to AMD.The data used for computation of causal effect estimates are provided in IVW = 1.256; 95% CI 1.081, 1.459, P = 0.0044). Heterogeneity check further confirmed the reliability of the observed association with absence of any heterogeneity in the distribution of effect estimates of individual genetic variants . A similar directionality in the causal effect estimates was also observed using the WME method . However, we did not observe any role of pain in predisposition to AD.We further observed a suggestive risky causal effect of SS on genetic liability to AD . On the other hand, we failed to observe any role of pain in predisposition to ALS.We also observed a suggestive risky causal effect of INS on genetic liability to ALS . In the reverse MR, PD showed suggestion of a strong protective effect against CHR and MP after the exclusion of overlapping UKB samples (data not shown). Reverse causal estimates for various sleep and pain-related traits using various NDDs as exposure are shown in We failed to observe the predominant influence of any of the single variants on causal the effect estimates of MP with AMD, as shown in The sensitivity analysis using the multivariable MR approach also yielded similar results with the retention of the association of MP and CHR with AMD  .Concerning the influence of specific brain regions, we specifically identified a high proportion of SNPs influencing brain expression in the cerebellum and basal ganglia region . HoweverThe use of GWAS data in MR-based approaches has opened up opportunities to assess and define clinically relevant signatures for a diverse spectrum of diseases. Our study supports the role of a person's underlying circadian rhythm in genetic predisposition to neurodegeneration. We found an association of genetically predicted MP trait with AMD. The correlated trait CHR also had a suggestive risk association with AMD. We also found suggestive evidence for a possible association of genetically predicted SS with AD, and INS with ALS. Surprisingly, however, our study found no evidence to support the association between pain and NDDs.To date, evidence from observational studies has shown a remarkable heterogeneity in the association of different circadian traits with various NDDs. A recent study investigating the incidence of AMD in 108,225 participants observed that patients with INS were 33% more likely to have subsequent AMD  . PreviouIn contrast to previously reported findings from epidemiological studies, we failed to observe any association of INS, SS, and LS with AMD using the genetic data in this study. However, we observed that MP is more likely to be predisposed to AMD . Our study suggests that more prolonged exposure to daylight in such individuals could increase the risk for AMD. Our findings are in contrast to a recent meta-analysis of observational studies demonstrating the absence of an association between sunlight exposure and AMD  . One of Compared to the impact of circadian rhythms on other NDDs, the role of sleep-related traits has been well-investigated in AD but with mixed findings. Previous studies have predominantly focused on sleep-wake rhythmicity, showing higher incidence of sleep fragmentations and lower amplitude of circadian rhythmicity in patients with moderate or severe AD . Concernp = 0.014) . A previous observational study has demonstrated decreased sleep efficiency and fragmented sleep architecture in 59 patients with ALS . Another= 0.014) . In summDespite the consistent findings of excessive daytime sleepiness or altered sleep timing in patients with PD, our MR findings demonstrate the absence of any causal role of sleep-related traits in predisposition to PD . One posWe also failed to observe any causal association of sleep-related traits with MS, although sleep disturbance is a common symptom of MS . It is sAmong all NDDs, high prevalence of pain has been observed in patients with AD and PD . AssessmOur study has several strengths and limitations. We adopted a highly comprehensive approach involving the exploration of several sleep-related traits and pain with commonly prevalent NDD. We further employed multiple MR methods and heterogeneity and sensitivity analysis approaches to confirm the reliability of the observed associations. Concerning limitations, previous observational studies have shown that the impact of sleep and pain-related traits may be dependent on the stage of neurodegeneration or severity of an NDD . HoweverUsing genetic data, we provide strong evidence that being an MP is a causal risk factor for genetic liability to AMD. There is a necessity for conducting large-scale epidemiological cohort studies to confirm our findings. Additional research is also required to understand the biological pathways underlying these associations, including causal analysis with biochemical makers of sleep and correlated traits associated with sleep.The original contributions presented in the study are included in the article/Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent from the participants' legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.SG designed and conceptualized the study, conducted data extraction, analyzed the data, drafted the manuscript, and revised the final draft. MS supervised the overall study and revised the final draft. Both authors contributed to the article and approved the submitted version.www.jpnd.eu) through Germany, BMBF, funding code 01ED1406. MS was further funded by the Michael J. Fox Foundation, USA Genetic Diversity in PD Program: GAP-India Grant ID: 17473 and supported by grants from the German Research Council (DFG/SH 599/6-1 to MS), and MSA Coalition.This study was, in part, supported by the EU Joint Programme-Neurodegenerative Diseases Research (JPND) project under the aegis of JPND (The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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