Genetic and environmental factors collectively determine plant growth and yield. In the past 20 years, genome-wide association studies (GWAS) have been conducted on crops to decipher genetic loci that contribute to growth and yield, however, plant genotype appears to be insufficient to explain the trait variations. Here, we unravel the associations between genotypic, phenotypic, and rhizoplane microbiota variables of 827 foxtail millet cultivars by an integrated GWAS, microbiome-wide association studies (MWAS) and microbiome genome-wide association studies (mGWAS) method. We identify 257 rhizoplane microbial biomarkers associated with six key agronomic traits and validated the microbial-mediated growth effects on foxtail millet using marker strains isolated from the field. The rhizoplane microbiota composition is mainly driven by variations in plant genes related to immunity, metabolites, hormone signaling and nutrient uptake. Among these, the host immune gene FLS2 and transcription factor bHLH35 are widely associated with the microbial taxa of the rhizoplane. We further uncover a plant genotype-microbiota interaction network that contributes to phenotype plasticity. The microbial-mediated growth effects on foxtail millet are dependent on the host genotype, suggesting that precision microbiome management could be used to engineer high-yielding cultivars in agriculture systems. Plant genotype alone appears to be insufficient to explain trait variations. This study integrates GWAS, MWAS and mGWAS in 827 foxtail millet cultivars, revealing that root-associated microbiota affect plant phenotypes in a host genotype-dependent manner. In contrast, most studies on plants are focused on how individual genetic loci contribute to trait variation across populations. Over the past decade, several high-quality genotype-phenotype associations have been identified in important crops, including maize2, rice3, sorghum4, cotton5 and soybean6. However, isolating the genes that control complex yield traits has been difficult, thereby limiting the yield and quality of plants obtained through genetic engineering.Genome-wide association studies (GWAS) are widely used to dissect the genetic basis of phenotypic variation8. The plant genotype can alter the root microbiome, revealing an indirect mechanism for host genes to modulate the phenotypic plasticity of plants that is dependent on environmental exposure10. For instance, the root microbiota can alleviate the effects of environmental stress, regulate plant development and control defense responses9. Identifying the bacterial taxa that promote plant growth and health, and elucidating their interactions with host plants, could transform sustainable agricultural practice. Plant nutrient starvation and plant immunity affect plant-microbe associations and, in turn, the adaptability of plants to the environment15. Further, root-associated microbes can promote their colonization by inducing the root-specific transcription factor MYB72 and excretion of scopoletin16. Microbiome genome-wide association studies (mGWAS) are used to elucidate the interaction of host genetic variation with the microbiome in human gut17 and Arabidopsis thaliana18. Nevertheless, the identity and relative importance of factors that shape host-microbe interactions and the resulting phenotypes remain poorly understood. Moreover, the beneficial effects of bacterial strains on their host are often cultivar- and species-specific, posing a challenge to their general application20.Plants establish relationships with soil-borne microbiota, which can fix nitrogen or protect plants from pathogens or stressSetaria italica) is an important regional crop in arid and semi-arid areas, especially in East Asia, because of its highly efficient water-use and drought tolerance21. Foxtail millet also provides an excellent model for studying abiotic stress as most of the foxtail millet accessions are primarily abiotic stress tolerant particularly to drought and salinity. Therefore, unraveling the genetic variability for agronomic traits can broaden the gene pool for marker-aided breeding programs as well as enhance the efficacy of genetic engineering for abiotic stress tolerance22. In addition, the yield component traits like main stem panicle weight (MSPW) and per plant grain weight (PGW) in foxtail millet exhibit highly significant positive correlations with the growth indices such as plant height, leaf width, panicle diameter, and main stem diameter22, indicating the importance of growth promotion for yield increment. Virtually all yield component traits and most agronomic traits of foxtail millet are quantitatively inherited23. Although GWAS has revealed key loci for early and late flowering times and blast-resistance in foxtail millet25, the loci associated with plant growth or yield are still not known. Through large-scale sampling and analysis, microbial composition of the root zone microbiota of foxtail millet and its correlation with the yield trait were identified in our previous study. The findings provided insights into potential agricultural improvement of foxtail millet by root microbiota modification26. Here, we performed GWAS and microbiome-wide association studies (MWAS) on growth and yield traits of foxtail millet and quantified the effects of marker SNPs and marker microbes on these agronomic traits. We identified marker strains associated with agronomic traits and validated their growth-promoting or growth-suppressing effects on foxtail millet. We then performed mGWAS on root microbiota and uncovered the host genetic variations that impact the assembly of the root-associated microbiota. We found that root microbes affect agronomic phenotypes in a host genotype-dependent manner. Collectively, this work reveals a reciprocal interplay among host genetic variations, the root-associated microbiota and the agronomic traits of crops.Foxtail millet 27. In total, 161,562 SNPs were detected after stringent steps of quality control, including population stratification and pedigree filtering, individual- and site-level call-rate filtering, and minor allele frequency (MAF) filtering. The SNPs were evenly distributed along chromosomes and the genetic distance for linkage disequilibrium (LD) decay to its half maximum was 9 kb  based on a ~423 Mb H2 = 0.006, P = 0.15). Growth traits exhibited higher heritability than yield traits, for example, MSPD showed the highest heritability  while PGW showed the lowest heritability (H2 = 0.16) , some of which were for multiple traits   whose expression are frequently upregulated and contribute to drought resistance in drought-stressed seedlings, were found to be associated with the growth and yield traits (Supplementary Data si2:49320133 that was associated with MSPD was located within the genetic region of PP2C (phosphatidylinositol-specific phospholipase C)  near the peak SNP (si4:27590764) of MSPD, which functioned as an ABA importer, is important for the regulation of stomatal aperture in inflorescence stems of Arabidopsis35. Another candidate gene SAPK9 (serine/threonine-protein kinase SAPK9) near the significant SNP (si3:44029863) of PGW improves drought tolerance and grain yield in rice by modulating cellular osmotic potential, stomatal closure and stress-responsive gene expression36  and tolerant genes  and phosphate transporter (PT) were also observed  as inputs of the linear regression models35 - and yield -related traits of the 827 different foxtail millet cultivars. Considering no SNPs associations with phenotypes TSLW and MSPW under suggestive thresholds, we extended the candidate SNPs  with a 70% occurrence in all samples (here defined as common OTUs), covering an average of 61.30% of total abundances were used as the input data as these OTUs commonly exist in the root zone of foxtail millet cultivars. The common sub-community (1004 common OTUs) showed higher evenness and correlations with the growth traits than the whole microbial community , Pevenness = 2.58e−30, Supplementary Fig. P < 0.001, Supplementary Fig. P < 0.05) were selected from the common sub-community and used as the input of the predicting models  as inputs of the linear regression models40 to predict phenotypic variations (Supplementary Data P < 0.001) except for the trait MSPL  and four negative marker OTUs  with top beta estimation in the regression model were selected for the validation experiments  at Shenzhen, China) for 7-days in sterilized plates, and observed altered root lengths and plant heights compared with the control  promoted both root and stem growth, whereas Baci299 (Bacillus OTU_22228) and Acid550 (Acidovorax OTU_46) only promoted shoot growth compared to the control  and Baci81 (Bacillus OTU 54) exhibited growth-promoting effects, they may only function in special root microbial flora in collaboration with other strains or be mistakenly identified as representative strains due to high 16 S rDNA sequence similarities with negative marker OTU 37 and 54.A cultured strain was considered a representative OTU if its 16 S rRNA gene had 97% similarity with the rhizoplane microbiota OTU  and two negative marker strains (Baci173 and Baci554) with good promoting or suppressing performances on plant growth in plate experiment by watering millet seedlings grown in sterilized soil with these bacterial suspensions separately. Consistently, the seedlings watered with suspensions of the promoting bacterial strains Kita594, Baci299, Baci154 and Acid550 showed significantly increased plant height and root length compared with the control, whereas the seedlings watered with suspensions of the suppressing bacteria Baci173 showed shorter roots , auxin-responsive protein IAA , L-glutamine synthetase (K01915) and branched-chain amino acid synthetase  . Subsequently, to investigate host genotype-dependent variation in the foxtail millet rhizoplane microbiota, the heritable microbes were identified based on a common rhizoplane OTUs data set, which covered 17 phylum and 52 orders. Using an SNP-based approach, the heritability for individual OTU was calculated. 281 OTUs with H2 (the broad sense heritability) more than 0.15 were defined as highly heritable and the others as lowly heritable  at the genome-wide suggestive significance threshold of si7:13687399 located in the genic region of bHLH35 was associated with 39 common OTUs from different microbial taxonomies such as Acidobacteria (28), Proteobacteria (8) and Bacteroidetes (3). bHLH35 proteins are transcription factors induced by effector-triggered immunity (ETI), and also involved in tolerance to abiotic stresses51. The SNP si1:32157654 located in the generic region of WAK2  was associated with 30 common OTUs, including Acidobacteria (21), Bacteroidetes (4), Proteobacteria (4) and Actinobacteria (1). The WAK2 protein bound to pectin, is required for cell expansion and is induced by a variety of environmental stimuli, including pathogens and wounding52. Similarly, the 50 common OTUs were found to be associated with FLS2 , metabolites , nutrient uptake , plant hormone signal transduction  and others (E3 ubiquitin protein ligase) are perhaps common host genetic factors with function to modulate root microbial composition assembly  and closeness centrality (>0.5) in the network as described in a previous study56, resulting in 102 hub OTUs. We identified that 90 hub OTUs and 748 non-hub OTUs had significant associations with the host genetic SNP loci  with the 257 marker OTUs (MWAS identified). We discovered that 219 of the SNP-associated OTUs overlapped with the marker OTUs in our data sets, covering 85.2% of 257 marker OTUs. , MAPK signaling (ko04016), Steroid biosynthesis ko00100) and so on (Supplementary Data 0 and so si7:2994337), transcription factor bHLH35 (si7:13687399) and WAK2 (si:2:5642650) had a dominant impact on the marker OTUs from the phylum of Acidobacteria , triterpene synthase , BGLU12  and RXW8 , mainly associated with marker OTUs from Actinobacteria, Bacteroidetes and Proteobacteria, which mostly contribute positively to the growth and yield traits of foxtail millet , and between positive marker Kitasatospora OTU_8 and FaQR  , Baci299 (Bacillaceae OTU_22228), Baci173 (Bacillaceae OTU_19835) and Acid550 (Acidovorax OTU_46) compare to control, respectively  Fig. . To explLH35 si7:687399 anP < 0.05 by ANOVA-LSD), supporting that plant-growth promoting rhizobacteria support genotype-dependent cooperation with the plant. We observed strong root growth inhibition in seedlings inoculated with the growth-suppressing strain Baci173 , and a more significant suppressing effect on root length was observed in the allele cultivars (C1296 and C1021) compared to the reference cultivars (C946 and C306). Significant effects of the interaction between the genotype and strain Kita594 and strain Baci173 on the shoot and root length were also detected by PERMANOVA, respectively 59. The suppressing strain also induced branched-amino acid and L-glutamine, which inhibit root and seedling elongation44. In addition, the genes induced by growth-promoting bacteria varied among marker strains, indicating that plants have customized responses to different bacteria.We further validated the growth-promotion and -suppression effects of microbial markers predicted by MWAS, supporting that MWAS can be used to discover rhizobacteria that modulate plant growth. The pathways induced by all marker strains were mainly related to metabolites, hormone biosynthesis , immune responses and nutrient uptake, suggesting these functions generally regulate plant-microbe interactions. Auxin and auxin-responsive genes that control plant root and shoot growth were induced by suppressing strains, consistent with recent observationsArabidopsis thaliana18 and Sorghum bicolor45. Numerous potential genes governing the microbial effect in this study were at first excavated. Notably, among the 257 marker microorganisms, 219 were associated with the host genetic variations. The direct GWAS analysis of the relationship between the corresponding SNPs and millet traits were mostly not detected at the suggestive genome-wide significant cutoff. The marker OTU-associated SNPs did not overlap with marker SNPs, suggesting that host genetic variations and the microbiota affect the plant phenotypes independently. Moreover, the SNP-associated marker OTUs had higher correlations with plant growth traits than non SNP-associated marker OTUs. These results indicate that host genetics might shape the composition of the root microbiota which in turn shapes the agronomic traits of foxtail millet, similar to the Mendelian randomization relationships among the gut microbiome, short-chain fatty acids and metabolic diseases60.The heritable taxa from foxtail millet strongly overlapped with sorghum and maize, implying a similar co-evolution pattern of plant and their microbiota across hosts. mGWAS was applied to reveal the genetic loci correlated with rhizoplane microbial abundance, which has been only conducted in 61. Of course, numerous genes governing the microbial effect in this study were excavated for the first time and need yet to be proved by further experiments. In the future, independent GWAS performed in multiple environments will dissect shared genetic loci which affect root microbial communities. Our study outlines a reciprocal interplay among host genetic variations, root microbiota and crop agronomic traits. We propose that the exclusive coupling of plant-bacteria interactions in agricultural practice, or precision microbiome engineering, will be crucial for efficient and sustainable agriculture.We further experimentally validated the genotype-dependent microbe-mediated growth promotion of foxtail millet in cultivars, SUZ12 and FaQR. We found the promoting or suppressing functions of the target strains in foxtail subspecies were associated with the cultivar genotypes. The differences in the field environments may cause distinct loci or host processes to shape the root microbiota. Independent GWAS in multiple environments or single environment could have both the same and different association features, but with different emphasis. In this study, numerous potential genes associated with the root microbiota were identified by mGWAS based on 827 foxtail millet cultivars in a single environment, and changes in effects (genotype-by-environment interactions) are consistent across the environments. Among these, the genes such as FLS2, WAK2 and GDSL-type lipase have been reported to function in structuring the plant microbiota assembly or mediate pathogen and stress responses26. Genotyping data of 1127 cultivars were generated by restriction site associated DNA sequencing (RAD sequencing) as described in the previous study62. Briefly, genomic DNA was extracted from young leaves using a standard cetyltrimethylammonium bromide (CTAB) method. The genomic DNA of each sample was digested with TaqI which recognizes the sequence T^CGA. The unique index was then added to each sample and 400–600 bp DNA fragments were selected and amplified. Samples were sequenced on Illumina HiSeq2000 platform, generating 90-bp paired-end reads.Thousands of foxtail millet cultivars were planted in the natural fields at Yangling Agricultural Hi-tech Industrial Demonstration Zone  in September 2013, with 20 repeats for each cultivar. For each cultivar, an extensive dataset of root associated microbiota and agronomic traits information was collected and has been described in detail previouslySetaria italica cv. Zhanggu reference genome (v.2.3) (https://ftp.cngb.org/pub/CNSA/data2/CNPhis0000549/Foxtail_millet/)27 using BWA-MEM (v.0.7.12-r1039)63 with the parameter (-M -R). The generated SAM files were converted into BAM files using SAMtools (v.0.1.19–44428 cd)64, and then sorted by reference position using SortSam.jar in the Picard package . To improve the accuracy of alignment around InDels, realignment was conducted using RealignerTargetCreator and IndelRealigner in GATK (v.3.6)65 with default parameters. SNPs were then called using HaplotypeCaller and filtered using VariantFiltration with parameter . Only bi-allelic SNPs were kept using bcftools . The imputation step was performed using BEAGLE (v.4.1) with default parameters66. The SNP markers with an estimated allelic R-squared value (AR2) of more than 0.3 were retained for subsequent analysis.The raw reads were filtered using SOAPnuke (v.1.5.6) based on the percentage of low-quality bases and ambiguous bases (Ns) or adapter sequences with parameter (-l 5 -q 0.5 -n 0.03-f GATCGGAAGAGCACACGTCTGAACTCCAGTCAC-r-AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA). Clean reads were mapped to the 67. To remove duplicated or related individuals, we kept only one with a high calling rate between two individuals with IBD of more than 0.185. To further control population stratification, we performed multidimensional scaling (MDS) analysis on the genome-wide IBS pairwise distances and removed outlier individuals, and finally, 827 samples remained. The SNPs were further kept with minor allele frequency (MAF) ≥ 0.01, genotype calling rate >0.7 and individual-level genotype calling rate >0.7 with the command (plink -maf 0.01 -geno 0.3 -mind 0.3). The genomic location and putative function of SNPs were predicted according to the gene model on the S. italica reference genome using snpEff (v.4.3t)68.All pairwise identity-by-state (IBS) distances between individuals were calculated for the filtered SNP data using PLINK (v.1.90) with the command (plink -genome)69. To determine the genome-wide significant cutoff for GWAS results, we estimated the number of genome-wide effective SNPs by pruning SNPs within 500 bp and with an R2 ≥ 0.2 by a slide window approach with a window size of 500 bp and step of 100 bp using PLINK, and the number of effective SNPs were determined to be 49,512. We then selected 2.01e−5 (1/effective SNP number) as the suggestive genome-wide significant cutoff, resulting in 13, 18, 8, 20, 5, 3, 15, 3,1 and 5 significant loci for MSPD, MSW, TSLL, FNL, MSH, PGW, MSPL,HKW, MSSN, SGN, respectively (Supplementary Data R2 > 0.4) around the significantly associated SNPs were identified as the candidate genes. Enrichment of Gene Ontology (GO) term and KEGG pathway were performed for candidate genes according to EnrichmentPipeline (v.1.01).Twelve agronomic traits namely top second leaf length (TSLL), top second leaf width (TSLW), main stem height (MSH), main stem width (MSW), panicle diameter of the main stem (MSPD), fringe neck length (FNL), panicle length of the main stem (MSPL), per plant grain weight (PGW), main stem panicle weight (MSPW), hundred kernel weight (HKW), spikelet number of the main stem(MSSN) and grain number per spike(SGN) were recorded for 827 foxtail millets  served as input data. Five-fold cross-validation was performed to estimate the ability of the model to predict new data. The original 827 samples were randomly partitioned into five equal-sized groups, of which four groups were randomly selected to train the prediction model and the remaining one group was used to test the model fitness. The cross-validation process is then repeated five times, with each of the five sub-group samples used exactly once as the validation data. Specifically, the prediction model selection for each trait was performed according to the Akaike’s Information Criterion (AIC) value based on the training data set. The fitness of the selected model was then estimated according to correlation efficiency (R2) of the observation and prediction value of the trait in the testing data set. The predictive models generated from different sample groups show different performances in the prediction of the testing data. To reduce the noise in the estimated model performance, we repeat the five-fold cross-validation process 30 times and report the mean performance across all folds and all repeats.For each trait, the effective SNPs  were selected as input of the linear regression model. Then five-fold cross-validation was performed and 827 samples were randomly divided into five equal-sized groups, of which four groups were randomly selected to train the prediction model according to the Akaike’s Information Criterion (AIC) value and the other samples were used to test the fitness. To reduce the noise in the estimated model performance, we repeat the five-fold cross-validation process 30 times and report the mean performance across all folds and all repeats.The OTU table of 827 rhizoplane samples was extracted from the 16 S variable region V4-V5 dataset of root microbiota of foxtail milletSimilarly, the combined effects of host genetic SNPs and root microbiota on plant traits were calculated by the mixed linear model, and 10 genetic PC were used as covariables Eq. :3\documeP < 1.0e−4) and microbial OTUs from the model mentioned above were screened as the input data of the mixed linear model. We firstly added the effective SNPs in the model to stepwise regress against the phenotype, then the microbial OTUs were added, followed by interactions of SNPs and OTUs. Similarly, five-fold cross-validation was performed using the same sample groups as above. To reduce the noise in the estimated model performance, the five-fold cross-validation process was repeated 30 times and reported the mean performance across all folds and all repeats. The predictive models with the best prediction accuracy for the phenotypes using the SNP and OTU variables were selected, resulting in the identification of marker OTUs , whereas the OTUs with relative abundances <0.01% across more than 30% of samples but never abundant (≥0.1%) more than 30% samples were defined as rare OTUs (RT). Those OTUs neither belonging to abundant taxa nor rare taxa were defined as moderate OTUs (MT). The distributions of AT, MT and RT in whole and common-sub communities were analyzed. The rare abundant OTUs (RT) have most of the OTU numbers (81.20%) in the whole community, but covered an average 39.26% sequence abundance, similar to the previous study70. The moderate OTUs have the highest numbers (83.67%) in the common-sub community, followed by abundant OTUs (12.85%) and rare OTUs (3.48%)  index, which is used as an indicator of soil microbiota stabilitytion Eq. .4\documexi is the rarefied abundance of the OTU in one sample, i is the average rarefied abundance of the OTU in one sample group, and δi is the standard. k is the number of samples in one sample group, n is the number of OTUs in each sample group). A lower AVD value indicates higher microbiota stability. To compare the structure stability with the common OTU community, the OTU sub-tables with 30 and 50% occurrence frequency were extracted from the whole communities, and the AVD values were calculated according to the above equation, respectively.(r > 0.4 or r < −0.4) and statistically significant (adjusted P < 0.05) correlations were kept. The topology characteristics  of the network were calculated using R package igraph 1.2.11 to quantitatively describe the species’ role in communities. The significance of the differences between the groups was detected using Kruskal-Wallis rank sum test (one-way) with P < 0.05. The “hub species” was defined as OTU with high values of degree (>400) and closeness centrality (>0.5) in the network56. Thus 102 hub OTUs were identified in 1004 common OTUs.The network based on Spearman correlation scores was calculated using ‘corr.test’ function in R package lsr 0.5.2 and only robust . A total of 281 highly heritable microbes were observed, giving H2 values in the range of 0.15 to 0.32.To estimate OTU heritability, their counts of the 1004 common microbes in each sample were normalized by the CSS  method. Relative abundance was log-transformed to fit a normal distribution and genetics complex trait analysis (GCTA) (v.1.92.2) was used to estimate the phenotypic variance explained by all SNPs with a genome-based restricted maximum likelihood (GREML) method75. Rank-based INT entails creating a modified rank variable and then computing a new transformed value of the phenotype for the ith subject. A detailed description of the calculating formula is given below  function. The value of c = 3/8 is recommended by Blom76. The mGWAS of normalized OTU abundance was performed based on a linear mixed model with correction of the first ten PCs and kinship using GEMMA (v.0.98). The Q-Q plot of the mGWAS results was generated to detect the existence of the systematic bias. SNPs with a suggestive p-value <2.01e−5 were considered to have putative associations with microbes.To evaluate the impact of host genetic variations on root microbiota abundance, we ran mGWAS on 1004 common microbes of foxtail millet. To make the CSS value to be normally distributed, the rank-based inverse normal transformations (Rank-based INT) were applied to transform the distribution of each OTU to make it appear more normally distributedR2 > 0.4) around the significantly associated SNPs were identified as the candidate genes, and the GO term and KEGG pathway enrichment for these genes were also performed.2108 significant SNP loci associated with 838 OTUs were identified (Supplementary Data g. The supernatants were diluted from 10−1 to 10−7 and then 10−4 and 10−6 dilutions were distributed and cultivated in 96-well microtiter plates in Nutrient Agar (NA) media (BD Company) for 48–72 h at 28 °C. After purification by three consecutive platings on solidified media, 644 bacterial clones were obtained. The full length of 16 S rRNA gene for each strain was amplified with bi-barcoded universe primers   cultivar bred by BGI Institute of Applied Agriculture . The seeds of foxtail millet Huagu12 were sterilized with 0.1% NaClO for 5 min, washed with sterilized water 5 times, and transferred into plates with sterilized filter paper. For each treatment, 2 ml bacterial suspension was added to the plate with sterilized seeds, and three repeats were included. The control was added with 2 ml of sterilized water. Seeds were grown at 22 °C, 12-h light/12-h dark and 21% humidity. After a 7-day co-culture, plant height and root length were measured to evaluate plant growth conditions.To validate the promoting or suppressing effect of marker microbes on foxtail growth, 10 marker OTUs with representative strain and with top beta estimation in the regression model were selected for the validation experiment, including positive marker OTUs: P-value adjusted by the BH method.To further validate the promoting or suppressing effect of marker microbes on foxtail millet growth conditions, we sowed the germfree Huagu12 seeds into the small basin with the sterilized soil collected from the planting field of foxtail millet in the greenhouse of CNGB, Shenzhen. Then the suspension of marker strain was used to water the 5-day old seedlings in the basin, with three repeats for each strain. The control was watered with sterilized water. After 14 days of inoculation, the plant height and root length were measured and assessed by t-test with The foxtail millet seeds with reference genotype and allele genotype were sterilized and put on the filter paper in the germ-free plate, respectively. Then the 2 ml suspension of marker strain was inoculated into the plate. The control was added with 2 ml of sterilized water. After 7-days of co-culture, the difference in root length and plant height compared with control were determined by ANOVA with LSD test. The promoting effect and suppressing effect on the root and plant height were evaluated by subtracting the average of the control. The difference between the effects of the strain Baci173 and Kita594 on reference genotype cultivars and mutant genotype cultivars were detected by Wilcoxon rank test, respectively. The impacts of genotype and strain on seedling’s height and root length were calculated with adonis function in the R package (v.4.0.3).77. Then the library was sequenced on BGISEQ platform using 100 bp pair-ended strategy. Eight Gigabase sequencing data for each sample was generated.As strains Kita594, Baci299, Acid550 and Baci173 exhibited significant effects on foxtail millet phenotypes, their inoculated seedlings and control seedlings were selected to perform transcriptomic sequencing, separately. Five seedlings of foxtail millet from each treatment of the sterilized soil experiment were pooled and flash frozen and stored at −80 C until processing. RNA from the foxtail millet was extracted using Column Plant RNAout Kit according to the instructions . The RNA extraction was quantified and assessed for integrity using the NanoDrop  and 2100 Agilent Bioanalyzer  before subsequent experiments. The 1ug qualified RNA from each sample was used to construct the BGI-based mRNA-seq library. In general, the mRNA molecules were purifed using oligo(dT)-attached magnetic beads and then fragmented into small pieces. The cDNA was generated using random hexamer-primed reverse transcription and adaptors were ligated to the ends of these 3′ adenylated cDNA fragments78. Then the resulting high quality reads from each sample were mapped against the Setaria italica cv. Zhanggu reference genome (v.2.3) (https://ftp.cngb.org/pub/CNSA/data2/CNPhis0000549/Foxtail_millet/) using hisat2 (v.2.0.4)7279. FeatureCounts from the package of the Subread (v.1.6.4)80 were used to count the reads that mapped to each one of the protein-coding sequences. Then R-package ballgown (v.2.22.0) was used to calculate the FPKM value of the genes81. The differential expression genes were identified using DEseq (v.1.38.0)82 based on the reads matrix. The differential genes were enriched into pathways using the Fisher’s exact test.The raw reads were filtered by SOAPfilter, an application included in the SOAPdenovo package(v.2.0)Further information on research design is available in the Supplementary informationDescription of Additional Supplementary FilesReporting SummaryPeer Review FileSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 8Supplementary Data 8Supplementary Data 9Supplementary Data 10


Climate change will fundamentally reshape life on Earth in the coming decades. Therefore, understanding the extent to which species will cope with rising temperatures is of paramount importance. Phenotypic plasticity is the ability of an organism to change the morphological and functional traits encoded by its genome in response to the environment. I show here that plasticity pervades not only natural but also artificial systems that mimic the developmental process of biological organisms, such as self-replicating and evolving computer programs—digital organisms. Specifically, the environment can modify the sequence of instructions executed from a digital organism’s genome (i.e. its transcriptome), which results in changes in its phenotype . This genetic-based pathway for plasticity comes at a fitness cost to an organism’s viability and generation time: the longer the transcriptome (higher fitness cost), the more chances for the environment to modify the genetic execution flow control, and the higher the likelihood for the genome to encode novel phenotypes. By studying to what extent a digital organism’s phenotype is influenced by both its genome and the environment, I make a parallelism between natural and artificial evolving systems on how natural selection might slide trait regulation anywhere along a continuum from total environmental control to total genomic control, which harbours lessons not only for designing evolvable artificial systems, but also for synthetic biology. Plasticity without a genetic basis can be considered a by-product of the physics and chemistry of the developmental process. For example, temperature affects the underlying chemical and metabolic processes of development directly, without the intervention of any genetic mechanism . HeritabWoltereck  coined tChanges in transcription (e.g. an increase in the level of gene expression) can impact the fitness of an organism. There is evidence that transcription is costly, and that this cost is important during evolution . But, unQuantifying the cost of plasticity requires a biologist to meet the challenge of finding distinct genotypes that have the same phenotype in a particular environment but differ in their plastic responses to a variable environmental factor. In well-studied microbes, most traits are plastic and many are amenable to environmental manipulations using laboratory culture techniques (e.g. ). One adThe concept of digital twins as software replicas of biological processes  may helpIn this study, I quantify to what extent a digital organism’s phenotype is influenced by both its genome and the environment, meaning that selection might slide trait regulation anywhere along a continuum from total environmental control to total genomic control. To this end, I first quantify the prevalence of plasticity in digital organisms. Second, I identify the genetic basis of phenotypic plasticity and disentangle the genetic and non-genetic mechanisms responsible for plasticity. Third, I quantify the fitness costs of plasticity in terms of reduced organism’s viability and longer generation times and explore the reasons underlying the trade-off between fitness costs and phenotypic plasticity. Lastly, I compute the uncertainty of finding a particular phenotype in a novel environment as a function of the mechanism responsible for plasticity. Although my focus here is on how phenotypic variation arises (as opposed to a focus based on the consequences of that variation), I also discuss the fitness implications of plasticity under different selective pressures.. 2. 2.1Digital evolution is a form of evolutionary computation in which self-replicating computer programs—digital organisms—evolve within a user-defined computational environment. Avida is the most widely used software platform for research in digital evolution . It sati. 2.2The genome of a digital organism is a circular sequence of instructions taken from a 26-instruction alphabet ,31. Some. 2.3The genetic language of Avida contains instructions for copying an organism’s genome as well as for storing and manipulating 32-bit binary numbers in buffers (input-1 and input-2), stacks, and registers ; AND, which returns 1 if and only if both inputs are 1 (otherwise it returns 0); OR_N (or-not), which returns 1 if for each input bit pair one input bit is 1 or the other is 0 (otherwise it returns 0); OR, which returns 1 if either the first input, the second input, or both are 1 (otherwise it returns 0); AND_N (and-not), which only returns 1 if for each bit pair one input is 1 and the other input is 0 (otherwise it returns 0); NOR (not-or), which returns 1 only if both inputs are 0 (otherwise it returns 0); XOR (exclusive or), which returns 1 if one but not both of the inputs are 1 (otherwise it returns 0); EQU , which returns 1 if both bits are identical, and 0 if they are different.The phenotype of an organism is defined by the combination of the following nine logic operations that it can compute when executing input–output instructions: . 2.4I hereby refer to phenotypic plasticity as the ability of a digital organism’s genome to perform distinct logic operations in different environments. The environment may modify the phenotype encoded by an organism’s genome every time the organism’s CPU executes an input–output instruction. Since this instruction takes the content of the organism’s BX register and outputs it, checking it for any logic operations that may have been performed on the two 32-bit binary numbers stored in its input buffers, an organism’s phenotype depends on the content of the organism’s BX register when an input–output instruction is executed, and this content depends initially—and later on, every time an input–output instruction is executed—on the environment.The genome of a digital organism can harbour one or several input–output instructions that can be executed either only once or many times during the time it takes to generate an offspring. This means that an organism can take input numbers from the environment more than once before replicating and can compute the result of more than one logic operation. Note that a particular environment is associated with a unique set of 32-bit binary numbers that can be supplied to the BX register . Therefore, two identical genomes may encode different phenotypes in different environments because, although they could execute the same sequence of instructions, their BX registers might contain different binary numbers and hence, the input–output instruction would output different binary numbers that might not be the result of computing the same logic operation.nand), (ii) adding (substracting) to its content the content of the CX register , (iii) incrementing (decrementing) by one its content , (iv) swapping its content with the content of the CX register (swap), (v) moving numbers between the active stack  and the BX register  and (vi) shifting all of the bits in the BX register by one to the left (shift-l) or right (shift-r). Therefore, two different genomes in the same environment  might encode distinct phenotypes if one of them executes any of these instructions before an input–output instruction.An organism can further change the content of its BX register by executing other instructions  that might be contained in its genome. This additional change can happen by (i) performing a bitwise NAND operation on the BX and CX registers and placing the result in the BX register  and (iii) moving the flow-head to the memory position specified by the CX register (set-flow). I will hereby refer to these instructions as regulatory because of their role in shaping the execution flow of an organism’s genome. Therefore, two identical genomes may also encode different phenotypes in different environments because they might execute different sequences of instructions and hence, the input–output instruction would output different binary numbers that might not be the result of computing the same logic operation , the content of an organism’s BX register may change the sequence of instructions to be executed by the organism’s CPU during the time it takes to generate an offspring  and sampled 1000 distinct genomes encoding the same phenotype, the database contained 512 000 distinct genomes [I built a database of digital organisms by randomly sampling the genotype space of 100-instruction long genomes encoding the same phenotype in the same single environment. Since I covered the entire phenotype space comprising nine Boolean logical operations . Changes in transcription mediated by the environment that did not produce changes in the phenotype were also quantified as a source of canalization. That is, I also classified organisms according to the number of distinct transcriptomes they executed  in the environments in which they were viable (i.e. if they were capable of producing an offspring during its replication cycle).input–output instructions executed during the replication cycle was quantified using a generalized linear model . Lastly, I used a generalized linear model  to quantify the effect of the sensitivity of the genome to changes in transcription  on the uncertainty of computing a logic operation in a particular environment measured as the Shannon entropy (base-2 logarithm). The coefficient estimates of the generalized linear models are on the log odds  and log scale , but I explain the results on the original scale , which is more intuitive to understand. To calculate these probabilities, I applied the inverse logit, 1/(1 + exp β−), and the inverse log, expβ, estimates to the regression models reported in the main text.Then, I used a generalized linear model  to quantify the effect of the sensitivity of an organism’s genome to environmental-mediated changes in transcription (robust- or sensitive-transcriptome organism) on the probability of an organism to show phenotypic plasticity (single- or plastic-phenotype organism). I also used a generalized linear model  to quantify the effect of genetic-based phenotypic plasticity (i.e. changes in transcription) on the costs to an organism’s viability. I included phenotypic plasticity (single- or plastic-phenotype organisms) of only the sensitive-transcriptome organisms as fixed effect, and the probability for an organism to be viable in all environments as the binary response variable. To quantify the effects of the number of distinct transcriptomes executed by the same genome in different environments and the viability costs of the changes in transcription on the number of distinct phenotypes encoded by that genome in those environments, I used a zero-truncated negative binomial model link function = log. I categorized the costs of changes in transcription on an organism’s viability as a binary variable . I also scaled the logarithm of the number of distinct transcriptomes  to aid the interpretation of both main effects and statistical interaction in my analysis . The eff. 31.04+0.66 = 5.5 and e1.04 = 2.8, respectively; Only slightly more than half of organisms' genomes (55%) encoded the same phenotype in all environments where they were viable (i.e. non-plastic organisms), and almost all of them (95%) did it by executing the same transcriptome; see Some genomes encoded non-viable organisms in at least one environment (15%). This might influence the results of the logistic regression model reported above . To rule this possibility out, I repeated the analysis by considering only the genomes encoding viable organisms in all environments (85%). The results did not change qualitatively (see Data availability).Phenotype does not affect an organism’s viability in this study because there is no selective pressure favouring one phenotype over another (i.e. the faster replicator will dominate the population). If a digital organism is viable is one environment (i.e. it can produce an offspring), there is no reason for the organism to not be viable in any other environment unless its transcriptome changes. That is, if the content of the BX register does not change the instructions that a digital organism executes during its replication cycle (i.e. if there is no genetic basis for plasticity), the same sequence of instructions will always be executed and the digital organism will never enter in an infinite execution loop that might jeopardize its viability. Therefore, the organisms excluded from the previous analysis were all sensitive-transcriptome organisms. This means that the genetic basis of phenotypic plasticity comes at a cost to an organism’s viability. In fact, the probability for a sensitive-transcriptome organism to be viable in all environments is lower  = 0.7 and 1/(1 + e−2.3) = 0.9, respectively, within a range between 0 and 1; Let us focus now on the genetic basis of phenotypic plasticity. We explored first to what extent the effect of the sensitivity of a genome to changes in transcription on plasticity is mediated by the cost to an organism’s viability. The number of distinct phenotypes at the average number of distinct transcriptomes was 75% higher when there were no costs on viability than when there were costs  that would prevail under natural selection is out of the scope of this paper. Yet, the selective pressures that could favour one type of plasticity over the other were explored in a previous work  where thThe difficulty of disentangling the costs of plasticity from the costs of phenotype might explain why plasticity costs have been found to be negligible in most studies  (e.g. thWhen two plastic genotypes produce the same phenotype in a particular environment, the organism sensitive to changes in transcription might have reduced fitness compared with the robust-transcriptome organism because of its lower viability and longer generation time. The costs in viability found when plasticity results from genetic-based mechanisms might be analogous to the developmental instability leading to a reduction in the performance traits that can result in low fitness . On the The potential of digital organisms to carry out studies on plasticity allows direct testing of theoretical models, particularly the prediction that variable versus static environments indeed select for or against plasticity, respectively. It is also possible to obtain insights to make a parallelism on, for example, the genes, sensory proteins, enzymes, and biochemical pathways—in short, the molecular machinery responsible for phenotypic plasticity. In fact, the two mechanisms for phenotypic plasticity reported here, analogous to the developmental process of biological organisms, support, once more, the parallelism between artificial and natural evolving systems. Simultaneously, empirical data about population sizes or mutation rates in digital organisms make it possible to address the effect of selection, mutation, and genetic drift on the maintenance or loss of plasticity and the associated mechanisms regulating that plasticity.Phenotypic plasticity can eventually be lost through genetic assimilation. Genetic assimilation occurs when a trait that was originally triggered by the environment loses this environmental sensitivity and ultimately becomes fixed or expressed in a population ,39. ThisLastly, phenotypic plasticity plays a key role in the genotype-phenotype map. Polyphenisms in digital organisms increase the overlap between genotype networks because plastic genotypes will act as bridges connecting non-plastic genotypes encoding different phenotypes ,41. That. 5This work on the phenotypic plasticity of digital organisms opens up new avenues for further research in many areas of evolutionary biology. Future studies will shed light on the role of phenotypic plasticity in promoting or hindering adaptation, characterizing the genotype–phenotype map, fostering the navigability of fitness landscapes, shaping the relationship between robustness and evolvability, promoting genetic assimilation, driving demographic changes of invasive species, and accelerating the rate of coevolution.


The majority of Genome Wide Associate Study (GWAS) loci fall in the non-coding genome, making causal variants difficult to identify and study. We hypothesized that the regulatory features underlying causal variants are biologically specific, identifiable from data, and that the regulatory architecture that influences one trait is distinct compared to biologically unrelated traits.To better characterize and identify these variants, we used publicly available GWAS loci and genomic annotations to build 17 Trait Specific Annotation Based Locus (TSABL) predictors to identify differences between GWAS loci associated with different phenotypic trait groups. We used a penalized binomial logistic regression model to select trait relevant annotations and tested all models on a holdout set of loci not used for training in any trait. We were able to successfully build models for autoimmune, electrocardiogram, lipid, platelet, red blood cell, and white blood cell trait groups. We used these models both to prioritize variants in existing loci and to identify new genomic regions of interest.We found that TSABL models identified biologically relevant regulatory features, and anticipate their future use to enhance the design and interpretation of genetic studies.The online version contains supplementary material available at 10.1186/s12864-022-08654-x. Genome-wide association studies (GWAS) are a widely used method to identify genomic regions associated in a population with a phenotype of interest, and have successfully detected thousands of loci across the human genome, each of which contains a set of linked variants that are potentially causal –16. UnfoSORT1 [NOS1AP [specific sets of traits relative to all others. Ideally, such a predictor would learn and select biologically relevant annotations from a set of all possible genomics features, thereby also providing informative hypotheses for why particular loci or variants are predicted to be “trait-relevant”. Current methods to predict if a variant is likely functional for any trait . Presum as CADD , GWAVA [ as CADD , and Dee as CADD ) providePreviously, we built platelet and red blood cell models using those GWAS as positives and random matched genomic regions as controls . While tR2 > 0.7). Next, for each trait group we defined positive loci as those which contained at least one variant with an associated P-value < 5 × 10–8 with at least one of the phenotypes in the trait group. Negative loci for each trait group were defined as those without any variant even modestly associated  with one or more of the phenotypes in the trait group. We utilized genome-wide annotations from multiple public resources, including nearest gene tissue-specific expression and epigenomic features, tagging these features across positive and negative examples if the features overlapped with any variant in the locus.To develop our approach, we needed to define the set of traits, identify positive and negative loci for those traits, and obtain a collection of genomic features to discriminate between these labels. For traits to study, we focused on 42 phenotypes obtained from curated GWAS and the NHGRI-EBI GWAS catalog that we collapsed into 17 related trait groupings  , using cTo assess why these 6 traits produced successful models while the other 11 failed, we considered whether trait models with more loci with coding variants, more positive loci or more features were more predictive  (Supplementary Table R2 LD > 0.7) from the dataset and reassessed enrichment. We found that the lipids, platelets, and WBC models were significant in all individual phenotype association data, and the RBC model was significant for 3 of 4 phenotype GWAS  or Type 2 Diabetes (T2D), both of which involve many tissues and have disease subtypes that are not well captured in current GWAS analyses –38. We ePleiotropy presents another challenge to our modeling approach, but is inherently captured in any genomic data set. It is plausible that more sophisticated approaches with this in mind might be anticipated to improve performance . Moreover, approaches that captured uncertainty around labelled examples  might also be helpful to address these challenges as well as expand the use of both positive and negative examples. Additional processing of genome wide features, both to account for correlation and to reduce the feature space prior to model building  may also improve results.An important limitation to acknowledge concerning our locus discovery and fine mapping applications is that both are applied to variant level data, while our models were trained on locus level data that incorporates feature overlaps from multiple variants within a locus. While ideally we would train a model on individual variants before applying it to individual variants, the reality of GWAS data means that the pool of validated causal variants is still inadequate to this task. Identifying causal variants from other sources introduces additional sources of bias which we judged to be larger than that introduced by shifting from locus to variant.A logical point of comparison for TSABL is to another strategy used for fine mapping or locus discovery. The most similar is the fGWAS method, which is designed to jointly consider GWAS and genomic annotations . The maiWith current pitfalls in fine mapping and locus discovery, our approach can distinguish amongst complex trait-related groups applied to 17 sets of traits. Our modeling process, built using publicly available GWAS and annotation data, selects and outputs features and variants relevant to underlying disease biology. These models predict differences between traits that currently available SNP ranking schemes do not, and the overlaps we see among models are consistent with known biological links between the traits assessed. Our modelling strategy makes no assumptions about number of causal variants in a GWAS locus, is expandable to include new genome wide features as they are developed, and can be applied to locus discovery problems or to prioritization after fine-mapping for functional validation efforts.http://grch37.ensembl.org/biomart/ on 10/24/17, and used the extreme exon boundaries as gene boundaries. We assessed annotations for rsIDs of interest using ANNOVAR version 2016Feb01 [All chromosome:position coordinates given in this paper refer to genome build hg19. We downloaded refseq gene exon coordinates for build hg19 from ENSEMBL  at http:016Feb01  and regu016Feb01 .Full listing of GWAS phenotypes included in each of the 17 trait groups are found in Supplementary Table https://www.ebi.ac.uk/gwas/docs/file-downloads on 06/21/19 and processed GWAS results by removing the following associations:P-value or a P-value > 5e-8with no listed with no rsID listed or multiple rsIDs listedlisted as on the X chromosome, no chromosome, or multiple chromosomeswith descriptions indicating they were interaction effects with another locuslocated in the HLA region We downloaded the NHGRI-EBI GWAS catalog v1.0 2019–06-20 from Remaining associations were grouped by rsIDs, resulting in 53,640 variants.We also included lead variants from GWAS for specific phenotypes associated with our 17 trait groups, regardless of their inclusion in the GWAS catalog  to create a negatives pool. Finally, we used only positive loci where we could match 6 (5 for height) negative loci with similar locus variant counts: within 10 of a locus with variant count < 100 or within 50 of a locus with variant count >  = 100. This ensured that our positive and negative locus pools were roughly matched for locus size. Using these processed positive and negative locus sets for each trait, we built models using the glmnet28 package in R, using binomial logistic regression with LASSO regularization and maximizing the AUC. The feature pool included 2305 ENCODE and Epigenome Roadmap annotations, 53 tissue-specific nearest gene expression annotations, the 100way Phastcon score, and normalized variant number. We used tenfold cross validation to select lambda.s1e, performed 15 trials of model building, and selected the median training AUC model as the trait model for analysis. We reasoned that selecting the median model preserved the goal of running multiple trials of cross validation modelling – reducing overfitting and bias due to data imbalances.For each trait, we processed the training and holdout sets to identify positive and negative loci. First, we identified positive loci where at least one variant had General model performance was assessed using the ROCR package  in R on Due to the nature of GWAS, there are loci tagged as positive across multiple traits. It is possible that these loci were driving the correlation between models for different traits. To test this, we removed all loci that are positive in the holdout set for the trait of interest and any other well modeled trait, creating a non-overlapping holdout set. We then compared ROC plots of the prediction of different models on the all and no overlap holdouts Fig. .ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/. Removed all variants that were not SNPs or that did not have rsID, leaving 78,017,615 SNPs. Calculated scores for individual SNPs via bedtools intersect using bed files used to make models, and recalculated gene expression annotation using single SNPs instead of loci. We then identified SNPs  with FDR ≤ 0.01 for each model, excluding all SNPs used as positives in model building . We ran fGWAS using distance intervals of 25, 50 and 500 kb (ranging from most to least stringent) and found 25 kb to have the best enrichment for all phenotypes , a random subset of 150,000 was submitted. To assess if enrichments changed when variants linked to known GWAS variants were removed, we removed any variants within 500 KB of a lead GWAS hit for the trait, again submitting a random 150,000 if the SNP list exceeded (ECG) this threshold. For all analysis, we recorded the GO biological processes and mouse single KO phenotype results. We note that the analyses that returned no results were all assessing < 7,000 SNPs, and expect that they failed due to having too few input regions. Full lists of variants and results can be found in Supplementary Table We used the Genomic Regions Enrichment of Annotations Tool (GREAT)  to interR2 ≥ 0.5 to the lead variant in European 1000 genomes phase III [We used available summary statistics  to V2.2 annot files. Removed 11,608 SNPs on chromosome 12 containing ssIDs instead of rsIDs from both annot and plink files. Recalculated LD Scores [https://github.com/bulik/ldsc/wiki/LD-Score-Estimation-Tutorial. Partitioned heritability for 20 relevant GWAS , we also removed SNPs in LD R2 > 0.7 with lead SNPs  number of features selected by the model B) number of positives groups used for training C) % total groups containing a coding variant and D) % training groups containing a coding variant. Panels C and D include a linear model best fit line as well as significance. In all panels, models which passed selection criteria  are green while models which were not selected are purple. Supplementary Figure 3. Model AUCs on same holdout sets. Each panel shows the named holdout set: A) Autoimmune, B) Lipids, C) Red Blood Cells and D) White Blood Cells. AUCs for all six selected models are shown on all plots, with the left (All) plot having all loci in that holdout and the right (No Overlap) having only those loci not positive in multiple models. Supplementary Figure 4. Selected Model Feature Coefficients. A) Autoimmune, B) Red Blood Cells, and C) White Blood Cells model coefficients, sorted by tissue type and value. GE – Gene Expression. Supplementary Figure 5. Enrichment of FDR < 0.01 SNPs within 25kb of GWAS variants. GWAS tested for platelets model: mean platelet volume (MPV) and platelet count (PLT); for RBC model: hematocrit (HCT), mean corpuscular volume (MCV), red blood cell count (RBC), and red cell distribution width (RDW); for WBC model: basophil count (BAS), eosinophil count (EOS), monocyte count (MON) and neutrophil count (NEU).


Diseases caused by necrotrophic fungi, such as the cosmopolitan Sclerotinia sclerotiorum and the Diaporthe/Phomopsis complex, are among the most destructive diseases of sunflower worldwide. The lack of complete resistance combined with the inefficiency of chemical control makes assisted breeding the best strategy for disease control. In this work, we present an integrated genome-wide association (GWA) study investigating the response of a diverse panel of sunflower inbred lines to both pathogens. Phenotypic data for Sclerotinia head rot (SHR) consisted of five disease descriptors . Two disease descriptors (DI and DS) were evaluated for two manifestations of Diaporthe/Phomopsis: Phomopsis stem canker (PSC) and Phomopsis head rot (PHR). In addition, a principal component (PC) analysis was used to derive transformed phenotypes as inputs to a univariate GWA (PC-GWA). Genotypic data comprised a panel of 4269 single nucleotide polymorphisms (SNP), generated via genotyping-by-sequencing. The GWA analysis revealed 24 unique marker–trait associations for SHR, 19 unique marker–trait associations for Diaporthe/Phomopsis diseases, and 7 markers associated with PC1 and PC2. No common markers were found for the response to the two pathogens. Nevertheless, epistatic interactions were identified between markers significantly associated with the response to S. sclerotiorum and Diaporthe/Phomopsis. This suggests that, while the main determinants of resistance may differ for the two pathogens, there could be an underlying common genetic basis. The exploration of regions physically close to the associated markers yielded 364 genes, of which 19 were predicted as putative disease resistance genes. This work presents the first simultaneous evaluation of two manifestations of Diaporthe/Phomopsis in sunflower, and undertakes a comprehensive GWA study by integrating PSC, PHR, and SHR data. The multiple regions identified, and their exploration to identify candidate genes, contribute not only to the understanding of the genetic basis of resistance, but also to the development of tools for assisted breeding. Helianthus annuus var. macrocarpus, Asteraceae: Heliantheae) is nowadays the fourth most important oilseed crop in the world, with approximately 57 million tons of seeds produced in 2021 [S. sclerotiorum (Lib) de Bary and the Diaporthe/Phomopsis complex, are among those with the highest latent risk [Originally from North America, sunflower . It co weeds, ). The hio weeds, . In factjectives . In recejectives ,12,13,14S. sclerotiorum, diseases caused by Diaporthe/Phomopsis have historically had a great, but localized, impact. This way, since its initial report, it has been a major problem in Europe, where the first efforts were made to characterize the pathogen and identify sources of resistance [The Diaporthe/Phomopsis complex comprises multiple species involved in disease development in sunflower, with Diaporthe helianthi being the most widespread and the first to be reported ,16. Of tsistance ,20,21. Isistance . In factsistance  reportedsistance  and Canasistance . In Argesistance ). Howevesistance . A recensistance .S. sclerotiorum, head rot (SHR) has the greatest damage potential, determined not only as yield reduction, but also as a decrease in the quality of the oil. As mentioned before, PSC is the most frequent disease of the Diaporthe/Phomopsis complex, although head rot (PHR) is becoming more common, at least in Argentina [S. sclerotiorum and Diaporthe/Phomopsis-associated diseases [Despite their different prevalence and distribution history, the two pathogens have common characteristics that make them comparable to some extent. Both pathogens generate a primary infection in the plant, caused by ascospores, existing as a strong environmental component in epidemic development. In addition, both pathogens are able to infect different parts of the plant  and cause different diseases ,29. In trgentina . In bothrgentina . Biparendiseases ,28. On tdiseases ,28,31. IIn this work, we conduct a genome-wide association mapping (GWA) study for SHR, PSC, and PHR based on the association mapping population (AMP) of the INTA, Argentina . In addiOur AMP includes 135 inbred lines of sunflower developed and preserved by the Active Germplasm Bank of the Instituto Nacional de Tecnología Agropecuaria, Argentina (INTA). These inbred lines were selected to achieve a balance between genetic diversity ,32,34 anInformation on the SHR response data used here can be found in Filippi et al. . BrieflyThe response of the AMP inbred lines against SHR was estimated as disease incidence (DI), disease severity (DS), area under the disease progress curve for disease incidence (AUDPCI) and disease severity (AUDPCS), and incubation period (IP). It should be noted here that Filippi et al.  also conTo estimate the response of the AMP inbred lines to Diaporthe/Phomopsis infection, two successive FTs were conducted in naturally infested fields containing a range of Diaporthe/Phomopsis populations. In addition, inoculum titer was increased by aggregates of infected crop debris in the V2 stage. These FTs, consisting of a randomized complete block design with two blocks and no irrigation, were conducted in General Pico  in 2017 (sowing date 30 October 2017) and 2018 (sowing date 21 October 18) growing seasons. This location was selected based on reports of high incidence of the disease. The experimental unit was a row of 0.52 m in length and 5 m wide, with a planting distance of 0.52 m. Two manifestations of the disease were evaluated: Phomopsis head rot (PHR) and Phomopsis stem canker (PSC). In PHR, the symptoms are brown, rotten areas affecting the receptacles and achenes (discolored seeds), necrotic bracts, and expanded V-shaped necrosis pointing toward the peduncles. In PSC, the symptoms on the stems are pale brown cankers that develop around petiole insertions. For each of these diseases, both DI  and DS  were visually registered, at the R7-R8 sunflower stage , on at lSpearman rank correlation analyses of the standardized adjusted means of all phenotypic variables, as well as the correlation plot, were performed using corrplot v0.92 , while phttp://github.com/cfilippi/GWAS_Han.The ddRADseq sequencing data generated by Filippi et al.  for the The association between SNPs and phenotypes was evaluated using statgenGWAS v1.0.9  in R 4343, folloTo investigate the existence of epistatic interactions between significant and non-significant SNPs, we relied on the WISH-R package v1.0 , which iManhattan plots and marker effect plots were depicted using statgenGWAS v1.0.9  and ggplGenes in linkage disequilibrium with the associated SNPs  were rep < 0.05) were observed between inbred lines. The PSC susceptible accession HA89, used as a control in other studies , showedp < 0.05). Again, this correlation was positive for all recorded traits except PI, which was negative. This can be observed in both As expected, the correlation between traits recorded for the same disease was stronger than the correlation between traits for different diseases. Thus, the different traits associated with SHR showed a strong and significant correlation , whereas DI and DS were strongly correlated in PHR and PSC. No correlation was observed for DI between PSC and PHR, but the correlation was significant for DS between PSC and PHR. This suggests that, although the incidence to both Diaporthe/Phomopsis diseases may be uncoupled, once the disease is established, the severity is subject to similar responses. Overall, the traits associated with SHR showed a weak but significant correlation with the response to PHR and PSC . After initial filtering, a total of 16,705 SNPs were considered for population analysis, as PCA (to control population structure) and kinship estimation. The first PCs did not explain a high percentage of genotypic variance  epistatic interactions emerged as candidates for further studies . As an eExamination of the 200 Kb region around each significantly associated SNP allowed the identification of 364 physically close genes. Gene enrichment analysis showed that biological processes, such as hydrogen peroxide signaling pathways, response to molecules of fungal origin, response to molecules of bacterial origin, and others, were significantly enriched in the 364 genes physically closest to the associated markers . The GO In addition, prediction of disease resistance genes using DRAGO2  revealedS. sclerotiorum, and the re-emerging Diaporthe/Phomopsis complex are currently the necrotrophic fungi with the greatest impact on global sunflower production. Although natural variability is observed, the lack of complete resistance combined with the inefficiency of chemical control has made assisted breeding the best disease control strategy. In this work, we present an integrated study of the response of a diverse panel of sunflower inbred lines, the AMP of the INTA (Argentina), to both fungal pathogens.The devastating and cosmopolitan Diaporthe/Phomopsis were erratic and tended to be low for both DI and DS. This fact reflects the differences in the way the FTs were conducted. SHR FTs were conducted with irrigation and assisted inoculation to support conditions for ideal infection. Indeed, traits evaluated after artificial inoculation usually show higher heritabilities than those obtained in semi-natural infection conditions [Diaporthe/Phomopsis FTs were performed in naturally infested fields. One of the major drawbacks of relying on non-assisted infection is the high dependence not only on the availability of a viable inoculum, but also on the occurrence of climatic conditions that allow the disease to establish and develop [Disease levels achieved for SHR were consistent across field trials (FTs). In contrast, disease levels for nditions . In the  develop .Diaporthe/Phomopsis benefits from abundant moisture and temperatures between 23–25 °C during the growing stage [Diaporthe/Phomopsis involve inoculation with fungal mycelium. While these methods allow the study of the kinetics of symptom development, they cannot reveal the resistance to penetration by ascospores.ng stage  for PSC ng stage . This alng stage . On the Analysis of the genotypic matrix revealed the importance of hard filtering SNP data for good data quality. Here, only ~5.7% of the original SNP matrix was considered for marker–phenotype association testing . The main reasons for filtering out the markers were the high percentage of missing data, low MAF, and the presence of strongly linked markers. This was not unexpected as the strategy used for data generation, ddRADseq, typically results in a high percentage of missing data . FurtherDiaporthe/Phomopsis, with CHR05, CHR10, and CHR15 having the highest number of associated loci. No common markers were found for the response to the two pathogens studied. This is not unexpected considering the complex genetic base of the resistance, the size of our AMP , and the weak correlation between phenotypic SHR and PSC traits. The observed correlation values are comparable to those reported by Bert et al. [S. sclerotiorum and Diaporthe/Phomopsis phenotypic traits (r~0.20). In an independent work, Talukder et al. [r = 0.52). However, the GWA study conducted on the basis of Talukder’s phenotypic data, and using > 200,000 SNPs as genotypic data, found only a single common associated region for SHR and PSC [Diaporthe/Phomopsis in sunflower.The GWAS analysis revealed 24 unique marker–trait associations for SHR and 19 unique marker–trait associations for t et al.  for S. sr et al.  reported and PSC . These wAs a complementary approach, a GWAS using PC scores as dependent variables was performed. The effectiveness of PC-GWAS relies on (a) the ability of this strategy to decrease the type I error rate ; (b) theWhile each SNP associated via GWA is expected to have a significant effect in determining a phenotypic trait, non-significant markers interacting with these associated SNPs could also have an influence on such traits . ConsideS. sclerotiorum and/or Diaporthe/Phomopsis on this chromosome  genes using a tool developed specifically for this purpose  yieldedomosome  in sunflower. The combined evaluation of phenotypic data obtained for PSC and PHR, together with data previously generated for SHR [Diaporthe/Phomopsis species causing PHR and PSC at our screening site over the years . We anticipate that these studies will have a concrete application in the near future.This paper presents the first simultaneous large-scale evaluation of two manifestations of  for SHR , allowedS. sclerotiorum and the Diaporthe/Phomopsis complex is evidenced by the multiple associated SNPs found, in addition to the identification of epistatic interactions. Although no common SNPs were found to be associated with resistance to both pathogens, the study of epistatic interactions allowed the identification of two markers significantly associated with the response to S. sclerotiorum and Diaporthe/Phomopsis. The posterior search for genes physically linked to the associated SNPs revealed in all cases pathogenesis-related genes, mainly of the “receptor-like kinase” class. In-depth analysis of these genes may help to dissect the underlying mechanism of resistance to both pathogens in sunflower. It is expected that the associated regions, and the identified sources of resistance, will contribute to the development of tools for assisted breeding in crops.The complex nature of the genetic basis of sunflower resistance to diseases caused by


Sorghum bicolor) is a model C4 crop madeexperimentally tractable by extensive genomic and genetic resources. Biomass sorghum isstudied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducingstomatal conductance (sg) could improve sorghum intrinsicwater use efficiency (iWUE) and biomass production. Phenotyping to discovergenotype-to-phenotype associations remains a bottleneck in understanding the mechanisticbasis for natural variation in sg and iWUE. This studyaddressed multiple methodological limitations. Optical tomography and a machine learningtool were combined to measure stomatal density (SD). This was combined with rapidmeasurements of leaf photosynthetic gas exchange and specific leaf area (SLA). Thesetraits were the subject of genome-wide association study and transcriptome-wideassociation study across 869 field-grown biomass sorghum accessions. The ratio ofintracellular to ambient CO2 was genetically correlated with SD, SLA,sg, and biomass production. Plasticity in SD and SLA wasinterrelated with each other and with productivity across wet and dry growing seasons.Moderate-to-high heritability of traits studied across the large mapping populationvalidated associations between DNA sequence variation or RNA transcript abundance andtrait variation. A total of 394 unique genes underpinning variation in WUE-related traitsare described with higher confidence because they were identified in multiple independenttests. This list was enriched in genes whose Arabidopsis  putative orthologs have functions related to stomatal or leafdevelopment and leaf gas exchange, as well as genes with nonsynonymous/missense variants.These advances in methodology and knowledge will facilitate improving C4 crop WUE.Sorghum ( Rapid phenotyping and machine learning-enabled GWAS and TWAS for 869 biomass sorghumaccessions reveal relationships between water use efficiency traits and biomass in bothwet and dry years. However, less research has been directed towardunderstanding and improving WUE and its component traits in C4 crops, possiblybecause they already achieve high WUE as a result of the CO2 concentratingmechanism they possess (sg) while maintainingrates of net CO2 assimilation (NA) can increasebiomass production in C4 as well as C3 species across a broad range ofenvironmental conditions  plus stomataldensity (SD) and specific leaf area (SLA) since these anatomical and allometric traits areknown to influence leaf physiology.Sorghum is a model C2 and water vapor exchangebetween leaves and the atmosphere  gene has been targeted to reduce sgthrough reduced SD, thereby improving WUE in barley  are the popular methods that canidentify genomic regions or genes for which variation in DNA sequence or gene expression areassociated with quantitative variation in a trait of interest . The chaof cases . But, evof cases . Larger of cases . However4 grasses, this study evaluated a diverse population of 869biomass sorghum accessions grown in replicated trials over two growing seasons. To achievethis goal, a set of tools were developed, tested, and integrated. To break the phenotypingbottleneck for SD, optical tomography was adapted and tested as an imaging technology, and acustom machine learning software platform was developed to automatically identify and countstomatal complexes. This was combined with a rapid method to measure leaf-level gas exchangeand SLA. Trait correlations were evaluated, and genes putatively underlying geneticvariation in iWUE and related traits were identified through GWAS, TWAS, and an ensembleassociation mapping approach. Genes identified with the greatest confidence were tested forthe presence of nonsynonymous/missense variants.In summary, to address knowledge gaps about the physiology and genetics of naturalvariation in iWUE in CN = 2; Fisher and EnergyFarms) and 2017 . Mean daytime maximumtemperature was similar between 2016 (28.9°C) and 2017 (28.8°C). But, compared to theaverage growing season rainfall of 396 mm , along with tissuesampling for SLA and SD, was performed on ∼220 leaves per day, allowing two leaves perreplicate plot of every genotype in the population to be sampled through 9–10 d of workfor each replicate field in a given year.A high-throughput approach for measurement of photosynthetic gas exchange with the medianSD per leaf from human counting of 228 randomly selected ground truth samples  was used to rapidly image 4–6 fields of view (FOV) from theabaxial surface of 4,169 leaves in 2016, and 3,211 leaves in 2017 without the need forsample preparation beyond adhesion to microscope slides with double-sided tape , SLA (gr =−0.52), sg (gr = 0.65), andbiomass , sg(pr = 0.72), NA(pr = 0.38), and biomass (pr =−0.08), although to a weaker degree , and the genetic correlation between iWUE and SD was justoutside the significance threshold (r = 0.38) and phenotypic basis (r = 0.08). Height waspresented as adjusted genotypic means calculated from a mixed model incorporating datafrom both 2016 and 2017 because results were so highly correlated between years.Data from 2017 allowed correlation analysis across the complete set of anatomical,physiological, and agronomic traits investigated . On a rendci/ca .ci/ca wFigure2;. iWUE anhreshold . SD wasThe associations observed when SD, SLA, and agronomic traits were measured in 2016 weregenerally consistent with those reported above for 2017, although phenotypic correlationswere generally stronger in 2016 ,consistgr = 0.78) and SD (rg =0.87), phenotypic correlations between years were weaker  andplasticity in response to drought stress was clearly apparent. Under the droughtconditions of 2017, SD was 40% lower on average than in wetter conditions of 2016 (pr = −0.10),biomass production (pr = 0.11), and height between growing seasons as well as biomass(pr = 0.19), height (pr =0.37), SD in 2016 (pr = 0.10), sg(pr = −0.011), and iWUE .Generalized heritability was relatively high for SD and SLA . HoweverNA in 2017  across test types for a singletrait  and (2) Figure 6A;The smallest number of “high confidence” genes was found by being identified in TWAStests for both tissues, without evidence for genotype to phenotype associations from GWAS. For exaFigure 7B;Candidate genes were also consistently identified by two or more tests that spanned keytrait groups . For exawww.arabidopsis.org) as having somefunction related to leaf development or WUE .Repeating the same strategy would not be desirable in sorghum, assuming that highproductivity under water-limited conditions is a priority. Notably, the genetic variationobserved in iWUE (or the closely related i/cac) wasmore a factor of variation in sg like that in biomass sorghum, the side-effect of increased light transmission into thecanopy may ameliorate losses in carbon gain at the canopy level , which controls stomatal patterning relativeto CO2 concentration (HOMEOBOX-7 (HB-7), which regulatesstomatal size relative to water availability  that is documented to positively regulatethe expression of CRYPTOCHROME 2 anion transporter  protein identified via GWAS and TWAS that facilitate immunity  was also identified via GWAS and TWAS forsg, but not for NA, whereconfidence in its role for sg is imparted by the presence ofmultiple nonsynonymous and two deleterious coding sequence mutations across sorghumnatural variants (MAP KINASE 4 (MAPK4) is another geneidentified via GWAS for gs that is known to be involved in the responseto pathogen recognition  gene via multipleNA mapping approaches H pools, thereby facilitating efficientphotosynthetic functioning was identified via mapping for NA andsg , a master regulator of stomatal development accessions . The SLA data collected in 2016 were previously reported  data store  at ×20 magnification with a standardized areaof 800 × 800 µmta store . The cus2NA, sg, iWUE,and the ratio of intracellular and atmospheric CO2(ic/ac) were obtained byaveraging data from the last 2 min of a 4-min autolog program  wereharvested predawn as described for the 2016 SLA and SD leaf sampling. Upon returning theleaves to the lab, stable rates of light-saturated gas exchange were measured by followingthe experimental protocol described previously for maize . This ap program . After tImage processing and machine learning methods were combined to produce a software toolthat automatically detected stomata in 33,355 grayscale images of sorghum leaf surfaces.Constructing the method required a set of training data based on circular image disks 80pixels in diameter centered where human experts had registered the locations of stomata inmany 512×512 raw images. Each 80-pixel disk was subjected to circular fast Fouriertransformation (FFT) to produce a radial series of phase and amplitude values that provedto be predictive of stomata. The radial FFT results were recast by principal components(PCs) analysis into a lower dimensional form that served as the feature set used to trainnine different machine learning methods. The nine methods were: An Artificial NeuralNetwork, Linear Discriminant Analysis, a Convolution Neural Network, three GeneralizedLinear models, two Regularization (Ridge and Lasso), and one without, Partial LeastSquares Regression, Stepwise Linear Regression, and a Decision Tree. Each method produceda version of the original image in which each pixel value was a probability of thatlocation belonging to a stoma. Next, a fusion process filtered and combined theindependent probability maps such that local probability peaks in excess of a heightthreshold optimally coincided with the locations of human-verified stomata. Specifically,a Nelder–Mead optimization process adjusted the filter and threshold parameters tomaximize the agreement between the machine-labeled stomata and the human-identifiedstomata as quantified by the Matthews correlation coefficient. 2) to give SD. Themedian of the 4–6 SD estimates was calculated for each sample and used for subsequentanalyses. To benchmark the efficiency of the automated stomatal counting, we manuallycounted and estimated SD for randomly selected samples, which represented 1,056 individualFOV. A linear model predicting manually counted SD from automatic SD was subsequently fit is the vectorof phenotypes for j environments (year × location combination) with 1 (n × 1) is a vector ofones; µ is the overall mean; X1(n × j) is the incidence matrix associated with the vector of fixedeffect environments t (j × 1); X2(n × v) is the incidence matrix associated with the vector of fixedeffect covariates q  is the incidence matrix associated with the vector of randomeffect set within environment s (f × 1) with Z2(n × r) is the incidence matrix associated with thevector of random block within set within environment effects b(r × 1) with Z3 (n × l) is theincidence matrix associated with the vector of random genotype effects g(l × 1) with Z4 (n × w) is theincidence matrix associated with the vector of random genotype-by-environment effectsge (w × 1) with e (n × 1) is the vectorof residuals with S, B,G, K, and R are the variance–covariance matricesfor set within environment, block within set within environment, genotype,genotype-by-environment, and residual effects, respectively. For each genotype, weobtained predictions from model 1, and these were used for downstream analysis. Thegeneralized heritability was estimated as proposed by  using the splice-aware aligner,Spliced Transcripts Alignment to a Reference (version 2.4.2). Feature counts were thengenerated using HTSeq (version 0.6.1) from previously generated alignment files. Finally,count normalization was performed using the R package, DESeq2 via size factorestimation.A subset of the full diversity panel was grown under controlled experimental conditionsfor 3′ RNA-sequencing (RNA-seq) analysis of genes potentially involved in regulating leafdevelopment, including stomatal patterning. The abundances of transcripts for putativeorthologs of known stomatal patterning genes were initially screened across —three toseven separate tissues at each of four developmental stages during the day and night insix accessions. Based on that screen, the base of leaf three and the shoot GP at the3-leaf stage were targeted for sample collection during the day from a subset of 229accessions from the full population . Samplesr2 > 0.9) using Plink options“–indep-pairwise 50 10 0.9”. The final dataset consisted of 454,393 SNPs scored in 836sorghum lines.For conducting GWAS, we imputed the 100,435 GBS SNPs from  using asThe association analysis was conducted using the unified MLM  implemenA TWAS was performed on a subset (229) of the total accessions  and condP-values were calculated similarly as described byP-values of the top 10% significant GWAS SNPs were assigned to theirnearest gene. Assigned GWAS P-values were then combined with theirrespective TWAS P-values via the Fisher’s combined test as using thesumlog function within the R package, metap.TWAS-GWAS combined To further explore the results of all genetic mapping approaches, we queried commonalitybetween specific gene sets, for example, genes identified for SD and SLA, SD genes commonto 2016 and 2017, Genes identified via GWAS and Fisher’s combined test, etc. . For the10 were identified. The LD blocks these SNPs associated withwere determined, and all genes within these LD blocks or spanning their borders wereextracted. LD blocks were estimated based on the method proposed by  were identified for each result. This approach of selecting apercentage of the top hits for each method is necessary when comparing results fromorthogonal methods (GWAS/TWAS) that are differently powered and structured, and thereforea direct comparison of P-values is not appropriate /2017SLA×100. SD decreased in allaccessions across the two years. Thus, the percentage change in SD was calculated as(2016SD–2017SD)/2016SD×100. Tests for associationsbetween all pairwise trait interactions were performed using adjusted means from allmodels and Pearson’s product-moment correlation coefficient. Pairwise interactions betweenspecific leaf-level gas exchange traits were further investigated by fitting second-orderpolynomial equations between traits. Except for Manhattan plots, all figures weregenerated using the R package ggplot2  are available at:The following materials are available in the online version of this article.2.sg, NA,ic/ac, iWUE, and SLA.kiab346_Supplementary_DataClick here for additional data file.