Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for maximizing the knowledge dividend from human genome-wide association studies (GWAS). Recently, diverse machine learning-based methods have been used for functional SNP identification; however, this task remains a fundamental challenge in computational biology. We report CERENKOV3, a machine learning pipeline that leverages clustering-derived and molecular network-derived features to improve prediction accuracy of regulatory SNPs (rSNPs) in the context of post-GWAS analysis. The clustering-derived feature, locus size (number of SNPs in the locus), derives from our locus partitioning procedure and represents the sizes of clusters based on SNP locations. We generated two molecular network-derived features from representation learning on a network representing SNP-gene and gene-gene relations. Based on empirical studies using a ground-truth SNP dataset, CERENKOV3 significantly improves rSNP recognition performance in AUPRC, AUROC, and AVGRANK (a locus-wise rank-based measure of classification accuracy we previously proposed). GWAS have uncovered variant-to-trait associations in thousands of studies collectively involving millions of individuals.15 evidence from our work17 and others’21 suggests that supervised approaches in general have superior rSNP detection accuracy. In addition, the growth of literature-curated databases of experimentally validated rSNPs24 has stimulated the development of supervised approaches. A variety of supervised classification algorithms have been proposed, including the SVM,25 naïve Bayes,26 ensemble decision tree algorithms,27 probabilistic graphical models,28 deep neural networks,29 weighted sum of feature ranks,30 and our work using regularized gradient boosted decision trees17 and deep residual networks.31 Recently, several hybrid methods have been proposed such as combining recurrent and convolutional neural networks21 and combining deep neural networks with regularized gradient boosted decision trees.29 In addition to binary classification approaches, regression-based approaches have been used for the rSNP detection problem.33With the tremendous increase in genomic and functional genomic datasets, computational data-driven approaches have become a mainstay of functional rSNP prioritization, although the rSNP identification problem remains fundamentally challenging. While some unsupervised approaches, which do not involve training based on an example set of experimentally validated rSNPs, have been proposed,34 for example, phylogenetic sequence conservation and expression quantitative trait locus  association35 scores. Furthermore, studies have shown that increasing the diversity of SNP annotation features improves rSNP detection, and thus there has been a steady increase in the number of features used in machine-learning approaches for this problem.29 In our previous work17 we reported a model  with a 246-dimensional feature space that clearly outperformed some models29 with significantly higher-dimensional feature spaces. This suggested that feature correlation within, and sparsity of, high-dimensional feature space may weaken the improvement of rSNP detection accuracy. Therefore, how to identify and integrate various types of rSNP correlates remains a key challenge for accurate rSNP detection.For rSNP detection, as with other machine-learning problems, the features  are as important as the classification algorithm. Consequently, various types of SNP annotations that correlate with functional rSNPs have been used,17 had four key innovations. First, we selected a reference set of SNPs to represent noncoding loci that would be expected to be encountered in a post-GWAS analysis, based on population minor allele frequency.17 Second, we used a regularized gradient boosted decision tree (XGBoost) classification algorithm,36 which we found has superior rSNP recognition performance to Random Forest and Kernel SVM. Third, we engineered 246 SNP-level features from phylogenetic, genomic, epigenomic, chromatin structural, cistromic, population genetic, replication-timing, and functional genomic datasets. Fourth, CERENKOV incorporated a locus-wise rank-based measure of classification accuracy, AVGRANK,17 which more realistically models the costs associated with incorrect predictions in post-GWAS analysis than typical measures like area under the receiver operating characteristic curve (AUROC) or area under the precision-recall curve (AUPRC). We compared the accuracy of CERENKOV to nine previously published rSNP recognition models17 and found that CERENKOV’s performance significantly improved upon the nine other models, by AUPRC, AUROC, and AVGRANK.Our previous classifier, CERENKOV,16 which improved performance over CERENKOV by leveraging insights into the data-space geometry of the problem. In addition to using a 2.5-fold expanded reference set of SNPs , we incorporated new features that are based on likelihood ratios of average SNP-to-neighboring-SNPs distances for various types of distance measures. By taking account geometric properties of the distribution of SNPs in data space, CERENKOV2 achieved significantly better rSNP recognition performance than CERENKOV and (as with CERENKOV) it outperformed the next-strongest rSNP detection tool, GWAVA.19More recently, we reported on CERENKOV2,37 To find useful SNP partitions, one option is to use specific domain knowledge to group the SNPs, for example by target genes and/or functional pathways. Another option is to use hierarchical clustering methods which rely on a distance measure between the SNPs. For example, SNPs can be clustered based on their pairwise relation given by stagewise regression coefficients using average linkage and the result of clustering helps alleviate the dimensionality problem when training deep Boltzmann machines.38 Therefore, we hypothesized that deriving a feature to explicitly account for SNP clustering could improve performance for rSNP detection in the context of supervised classification.Clustering is a widely used technique for statistical data analyses. In GWAS, SNP clustering can help detect groups of similar SNPs that are amenable to classification using group-wise models.42 However, although biologically intuitive, the complex interactions reflected by the underlying GRNs in which noncoding rSNPs take effect, namely, interactions among transcription factors and their target genes, are largely not taken into account in existing algorithms for functional SNP identification.43 We endeavored to capture such network-contextual information as new SNP-level features in CERENKOV3.Molecular networks, especially gene regulatory networks (GRN), are also key to the rSNP detection problem because molecular networks mediate the effects exerted by rSNPs on trait population variation. Therefore we hypothesized that mapping SNPs to a molecular network, and deriving features from the vantage points of SNPs in the network, would benefit rSNP detection. Construction of such a network is greatly aided by the recent availability of datasets from studies of direct DNA contacts utilizing assays such as Hi-C or chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). More-over, molecular networks have been successfully used to improve the inference accuracy of causal coding variants.17 and CERENKOV2.16 As in our previous approaches, in CERENKOV3 we use regularized gradient boosted decision trees (XGBoost) as the base classifier due to its superior speed and performance. We combined our original 248-dimensional feature matrix with two new types of features derived from clustering and molecular networks, respectively: (1) “locus size”, a static feature based on the number of SNPs within a locus; and (2) a pair of dynamic features extracted by node2vec,44 an algorithm for learning continuous feature representations for nodes based on network random walks. We constructed a weighted molecular network using the the data sources 4DGenome,45 Encyclopedia of DNA Elements (ENCODE), and Genotype Tissue Expression (GTEx) for SNP-gene connections; and BioGRID,46 Coexpedia47 and HumanNet48 for gene-gene connections. We treated the edge weights as hyperparameters that we tuned in the method  folds by locus. After we analyzed the repeated locus-sampling based CV performance, we found that, whichever fold it was assigned, the locus with ID chr5_30  would hinder the validation performance. From the OSU18 SNPs we pruned one locus (chr5_30) because it contained an anomalously high number of rSNPs (143) that lack supporting documentation in the source database (ClinVar). With that exclusion, the overall class balance of the remaining 38,795 OSU18 SNPs is ~15.26 . As our baseline set of features, we obtained the 248 SNP annotation features from the CERENKOV216 feature pipeline. For comparison purposes, we also extracted 175 SNP-level features from the GWAVA19 software.A set of experimentally validated SNPs is fundamental to our supervised-learning method. In this work, we used the OSU18 SNP set that we first used in CERENKOV2r2 ≥ 0.8) with, and located no more than 50 kbp distance from, an rSNP. This procedure provided us a natural way of clustering: we first sorted all the OSU18 SNPs by their locations per chromosome. Then, for any pair of neighboring SNPs on the same chromosome, if the distance between their chromosome positions is greater than 50 kbp, we divided them into two separate loci. In this way, we partitioned the OSU18 SNP set into 1290 loci, each containing approximately 30 SNPs on average. Then for each SNP, the number of SNPs within its locus is computed as the “locus size” feature.As mentioned above, when collecting the negative examples, i.e. the cSNPs, we only chose those that were in strong linkage disequilibrium  and gene-gene interactions (three sources) in order to maximize the connectedness of the network. Third, under the premise that different data sources are likely to have different degrees of relevance/informativeness for the rSNP prediction task, we assigned edges numerical weights according to the relation types they represented  lies closest downstream to that gene, we drew an edge between s and g. We call this a “nearest-gene” SNP-gene relation, as it is based on SNP-TSS proximity.For any single SNP 49 studies. If a SNP s and the TSS of a gene g exclusively located in two interacted regions reported by 4DGenome, we added a s–g connection in the network. Furthermore, for any gene g, we defined the promoter region to span the range from 2000 bp upstream to 500 bp downstream of its TSS. Similarly, for any pair of interacted regions reported by 4DGenome, if the genomic region contains a SNP s and partially overlaps with a gene g’s promoter section exclusively, such an s-g edge will also be included in our CERENKOV3 network. We call these two types of relations 4DGt (for TSS proximity) and 4DGp (for promoter proximity), respectively.4DGenome is a public database of chromatin interaction records that contains over three million human chromatin interactions curated from a comprehensive collection of 3C, 4C, 5C, ChIA-PET, Hi-C and IM-PETcis significantly associated, at a false discovery rate (FDR) of ≤ 0.05, with expression differences of that gene. We used single-tissue cis-eQTL data from GTEx Analysis V7 and we incorporated all SNP-eGene associations into our CERENKOV3 network as edges.GTEx is a comprehensive public resource to study tissue-specific gene expression and regulation. GTEx defines “eGenes” as genes with at least one SNP in All SNP (build 146) and Transcription Factor ChIP-seq Clusters V3 tables of the GRCh37 assembly from the UCSC Genome Browser to obtain all TFBS symbols overlapping with our pruned OSU18 SNP set; then we used MyGene.info API in order to find all genes that are translated into the corresponding transcription factors.The last set of SNP-gene edges were obtained from connections through overlapping transcription factor binding sites (TFBS) using the UCSC Genome Browser and MyGene.info application programming interface (API). First, we used an inner join between the 46 Coexpedia47 and HumanNet.48We directly obtained gene-gene edges from BioGRID,BioGRID is an online biological interaction repository with data compiled through comprehensive curation efforts. We used version 3.5.171 to extract all gene-gene pairs which participates in the interactions reported and augmented our network.Coexpedia and HumanNet (v2) are two gene co-expression databases and serve as a natural source of gene-gene edges.node2vec44 to extract low-dimension continuous representations for each network vertex. Specifically, through a set of parameters controlling the usage of breadth-first and depth-first searches, node2vec provides a way of balancing the exploration-exploitation tradeoff when generating random walks for each vertex. Once the random walks are completed, node2vec calls word2vec,50 a word embedding algorithm, to generate embeddings on the string representations of the random walks. The dimension of node2vec’s output, i.e., the number of network-derived features, is not determined in advance; instead we optimized it within the hyperparameter search.Once the molecular network was constructed and a set of edge weight hyperparameters assigned , we used The unweighted network is saved in edge-list format and then assigned weights dynamically according to the types of edges, i.e., the types of SNP-gene or gene-gene relations.node2vec and the embeddings are generated and output as new features for the classifier.The weighted network is sent to The network-derived features and clustering-derived feature, locus sizes, are integrated with the baseline 248-dimension SNP features. The combined feature matrix is input to the XGBoost classifier within the context of a replicated, locus sampling-based, five-fold cross-validation training process, with performance measures obtained on the validation sets.The whole pipeline is wrapped into a custom scikit-learn estimator object, whose three sets of hyperparameters are as follows:wNG (for the nearest-gene relations), wDGt4, wDGp4 (for SNP-gene relations extracted from 4DGenome data), wGTEx, wTFBS (for SNP-gene relations extracted through overlapping TFBS), wbg, wcoexp and whn .Edge weights, i.e., node2vec, including d (the number of output dimensions), r , l , k , p  and q .The hyperparameters of The hyperparameters of XGBoost as below: max_depth (the maximum tree depth for base learners), learning_rate, n_estimators (the number of trees to fit), gamma (the minimum loss reduction required to make a further partition on a leaf node of the tree), subsample (subsample ratio of the training instance) and colsample_bytree (the subsample ratio of columns when constructing each tree).As shown in 51 to approximate the optimal configuration. The random search works on the assumption that 1% of the hyperparameter configuratoins will lead to close-to-optimal performance. Based on this assumption, with n ≥ 240 trials, we would expect to find a close-to-optimal configuration with a high probability of 1 − (1 − 0.01)n > 0.99.Considering high dimensionality (20) of the hyperparameter space, we used a random search method19 program and then applied Random Forest algorithm with R package ranger version 0.6.0 with the published hyperparameters. To make a fair comparison, we adapted the same cross-valiation settings, fold assignments, and performance measurements for all classifiers.For the CERENKOV3 machine learning pipeline, we used a combination of bash, bedtools (v2.25.0), the R statistical computing environment (version 3.4.4), scikit-learn (version 0.21.2) and Python 3.5.2, all under Ubuntu 16.04. In addition, for the purpose of comparison, we also generated features for the pruned OSU18 SNP set with the GWAVA3.We carried out a 240-trial random search on hyperparameters with XGBoost on a basis of ten-fold replicated, locus sampling-based, five-fold cross-validation and esetimated the optimal hyperparameters as shown below.wNG = 0.1, wDGt4 = 0.3, wDGp4 = 3.0, wGTEx = 0.3, wTFBS = 0.1, wbg = 3.0, wcoexp = 0.3 and whn = 0.3.For edge weights, node2vec, d = 2, r = 12, l = 6, k = 4, p = 4 and q = 8.For For XGBoost, max_depth=10, learning_rate=0.1, n_estimators=100, gamma=10, subsample=1.0 and colsample_bytree=0.3.node2vec parameters, we found in general that lower dimensions of output (d) and longer distances of random walks (l) perform best. In addition, the optimal combination of p = 4 and q = 8 means probablistically in our constructed network, more breadth-first searches were carried out than depth-first ones in the optimal configuration.For edge weights, we set the options for random search within a set of real numbers {0.0, 0.1, 0.3, 1.0, 3.0}. The optimized edge weights appear to emphasize snp-gene promoter-proximity edges through 4DGenome data and gene-gene edges from BioGRID. In terms of node2vec. We first analyzed these three new features for the two SNP classes (rSNPs and cSNPs) using kernel density estimation for feature values conditioned on the class label (rSNP or cSNP) of the reference SNP. As seen in For each SNP, we obtained the locus size and the optimal two-dimensional embedding returned by 17). Comparing the performance of CERENKOV3 with CERENKOV2, we see that the inclusion of the three new features improved validation-set AUPRC from 0.418 to 0.459 (p < 10−35) and AUROC from 0.858 to 0.870 (p < 10−19); in terms of AVGRANK, although the improvement is not statistically significant (p = 0.30) when compared to that of CERENKOV2, AVGRANK decreased from CERENKOV’s 7.873 to a lower 7.726 in CERENKOV3 . These results indicate that the addition of the three SNP clustering and molecular network-based features significantly improve rSNP recognition performance.Using the above-described optimal hyperparameters and cross-validation framework, we compared the performance of GWAVA, CERENKOV, CERENKOV2, and CERENKOV3 in terms of AUPVR, AUROC, and AVGRANK . CERENKO4.We have demonstrated, using side-by-side comparisons on identical assignments of SNPs to cross-validation folds, that CERENKOV3’s performance exceeds that of our previous CERENKOV, by both classical global rank-based measures (AUPRC and AUROC) and by the GWAS-oriented performance measure, AVGRANK. In particular, CERENKOV3’s validation-set AUPRC performance, 0.459, is a significant improvement over CERENKOV2’s AUPRC of 0.418 on the same pruned reference SNP set. These results reveal CERENKOV3’s ability, by virtue of its novel features based on clustering and molecular networks, to contribute to solving the problem of identifying candidate causal noncoding SNPs in GWAS summary regions.52), with CERENKOV3’s current set of 251 SNP features. Our previous work31 has demonstrated that a classifier (Res2s2am) based on a deep residual network architecture has state-of-the-art performance on the related problem of discriminating trait-associated noncoding SNPs from control noncoding SNPs. Another direction of improvement is to continue feature engineering from clustering and networks. For example, currently, graph neural networks (GNN) are showing promise for integrating the SNP annotaion features and the connections between them. With GNN, it is possible to carry out representation learning on annotation features through graph embedding. The complete source code for CERENKOV3 is publicly available under an open-source license via GitHub at https://github.com/ramseylab/cerenkov3.We anticipate that CERENKOV3’s performance may be further improved through several possible enhancements. An appealing extension would be to combine deep neural network-based approaches based on the local 1 kbp sequence haplotype (recognizing that the local haplotype provides important correlates of functional SNP alleles


Brassica napus), the genetic basis of that resistance is poorly understood. In this study, we used an associative transcriptomics approach to elucidate the genetic basis of resistance to clubroot pathotype ECD 17/31/31 across a genetic diversity panel of 245 accessions of B. napus. A single nucleotide polymorphism (SNP) association analysis was performed with 256,397 SNPs distributed across the genome of B. napus and combined with transcript abundance data of 53,889 coding DNA sequence (CDS) gene models. The SNP association analysis identified two major loci (on chromosomes A2 and A3) controlling resistance and seven minor loci. Within these were a total of 86 SNP markers. Altogether, 392 genes were found in these regions. Another 21 genes were implicated as potentially involved in resistance using gene expression marker (GEM) analysis. After GO enrichment analysis and InterPro functional analysis of the identified genes, 82 candidate genes were identified as having roles in clubroot resistance. These results provide useful information for marker-assisted breeding which could lead to acceleration of pyramiding of multiple clubroot resistance genes in new varieties.Clubroot is a destructive soil-borne pathogen of Brassicaceae that causes significant recurrent reductions in yield of cruciferous crops. Although there is some resistance in oilseed rape (a crop type of the species The online version of this article (10.1007/s11032-019-1021-4) contains supplementary material, which is available to authorized users. Plasmodiophora brassicae is one of the most serious diseases of cruciferous crops, especially the allotetraploid Brassica napus  and its diploid progenitor species Brassica rapa  and Brassica oleracea   Dixon . Two-phaC) Dixon . CreatioC) Dixon . The symC) Dixon . The aveP. brassicae. The most commonly used is the Williams classification , namely Gelria R, Siloga, Debra and Milan White  genes were found in European fodder turnip cultivars . Previous studies have presumed that there are much less dominant CR genes in C genome and CR is quantitative under polygenic control there  platform  and an 8-h night (18 °C) photoperiod over a period of 7 weeks. Indication of infection was observed 3 weeks after sowing. The plants were fertilized weekly using a solution of Kristalon Start  (0.5 g per 10 l of water).Cultivation trays with a cell size of 4 × 4 cm were filled with a mixture of coarse pearlite and conventional growing medium for vegetables  with a pH 6.5 in the ratio 1:1. The seeds of tested genotypes were sown in each pot on the surface of the growth substrate. On every seed was applied by micropipette 0.5 ml of inoculum at a concentration of 100 M spores mlDisease severity was assessed 7 weeks after inoculation from roots using a standard 0–3 scale, where 0 = no visual symptoms, 1 = clubs only on the lateral roots, 2 = main root clubs and 3 = deformed entire root system /(NT × 3)] × 100, where n1 to n3 were the number of plants with different disease severity of 1–3 scale and NT represented the total number of identified plants, respectively . The minor loci were called as a potential associated loci, if a minimum of one SNP in the peak has significance 10−5 < − log10P < 10−4 (pSNP) and a locus has a distinguishable shape of peak as well.Plant material was a subset of the genetically diverse AT panel of 383 rapeseed accessions described by Havlickova et al. . The groP > 4) which r2 > 0.4 .AT analysis of DI identified 9 SNP association peaks with trait variation Fig. . They weP < 10−4 threshold line without any close sSNPs or pSNPs to create a distinguishable peak, therefore excluded from further investigation. The rest of the associated SNPs were clustered in small loci and formed clear peaks. Graphic representation of SNP association analysis in Manhattan plots for individual chromosomes is seen in Supplemental Figure BnA03_0263 with predominant simple sSNPs (− log10P > 7) that can be assigned with confidence to a genome was discovered on chromosome A03  shown in Supplemental Figure BnA02_0265 and BnA03_0186 were found on chromosomes A02 and A03 respectively  of DI 0.469 suggests that genetic variability may play a substantial role in CR resistance. In total, 63 candidate genes have been identified from SNP analysis . This suggests that the clubroot resistance is probably quantitatively inherited trait controlled by multiple loci.In total, we identified 86 SNPs to be highly associated with clubroot resistance. Twenty-nine of them with significance of BnA03_0263 . This gene shows the greatest similarity with the already cloned CRa clubroot resistance gene in the B. rapa genome. However, in the study by Zhang et al.  and defence response CAP-gly domain linker.A second locus on A02 chromosome BnA02_0265 and BnA02_0286 overlap a relatively large locus designed as Rcr8  COMPROMISED RECEPTOR-LIKE CYTOPLASMIC KINASE 1 and PCRK1. This gene is important for immunity induced by damage associated molecular pattern—DAMPs Transcript abundance with clubroot resistance. The gene models are positioned on the x-axis based on their genomic order, with the significance of the trait association, as –log10Supplemental Figure 2P, on the y-axis. Hemi-SNP markers  for which the genome of the polymorphism cannot be assigned are shown as light points whereas simple SNP markers (i.e. polymorphisms between resolved bases) and hemi-SNPs that have been directly linkage mapped, both of which can be assigned to a genome, are shown as dark points. The broken blue and red horizontal lines mark significance -log10P = 5 and -log10P = 4, respectively (PDF 1009 kb)Transcriptome SNP association analysis for clubroot resistance displayed individually for all chromosomes. The SNP markers are positioned on the x-axis based in the genomic order of the gene models in which the polymorphism was scored, with the significance of the trait association, as –log10Supplemental Figure 3P > 4) a - BnA01_0308, c - BnA02_0286, e - BnA08_0009, f - BnC02_0414 and significant loci (-log10P > 5) b - BnA02_0265, d - BnA03_0186, g - BnC07_0238, h - BnC07_0421. The SNP are positioned on the x-axis based on their location (units 105), the positions of the candidate genes for this locus are further indicated on x-axis for each loci. On the y-axis are values of the trait association significance (–log10P). The black signs represent simple SNPs and grey hemi-SNPs. The dashed blue and red lines mark significance -log10P = 5 and -log10P = 4, respectively (PDF 264 kb)SNP association analysis for clubroot resistance focus on the parts of pseudomolecule with clubroot protentional loci Supplemental Figure 5Polymorphism for marker design. The table summarizes polymorphism details as used in the Associative Transcriptomics analysis. For each of the six polymorphisms, a plot is presented for visualization of the correlation between allele and disease index (DI) and both CDS and genomic (gDNA) sequences with the polymorphic base indicated for which a suitable assay should be developed for high throughput screening during breeding. (PDF 502 kb)Supplemental table 1B. napus: Supplemental_Table_1_Disease _Index.xlsx (XLSX 21 kb)Disease Index (DI) was assessed in 245 accession of Supplemental table 2SNP markers and genomic regions showing association with variation in DI: Supplemental_Table_2_DI_associated_regions_based_on_SNPs.xlsx (XLSX 282 kb)Supplemental table 3Gene expression markers showing association with variation in DI: Supplemental_Table_3_DI_associated_regions_based_on_GEMs.xlsx (XLSX 23 kb)


Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.Oat ( Avena sativa L.) is a nutrient-rich human and animal food source. Recent studies have revealed the numerous beneficial effects of oat consumption on human health, from reduction in cardiovascular diseases risk  Merr.; To date, researchers have primarily investigated the genetic basis of variation in fatty acid composition by mapping quantitative trait loci (QTL) in biparental populations . A genomHistorically, a GWAS considered a single-trait or multiple traits independently. To capitalize on the increasing quantity and complexity of phenotypic data, GWAS methods were developed to analyze multiple traits simultaneously. The primary benefit of multi-trait GWAS is that phenotypic correlations can increase statistical power to detect association signals relative to univariate methods . SeveralMultivariate GWAS approaches are well-suited for genetically dissecting metabolic networks, because the abundances of precursors, intermediates, and products are likely correlated due to metabolic flux. Here, we present a GWAS of seed fatty acid content and composition in a diverse germplasm collection of cultivated oats. We compared single-trait, univariate GWAS on the abundance of ten fatty acids and their total with two multivariate GWAS methods: GWAS of principal components (PC-GWAS) and GWAS with a multivariate model that accounts for the abundance of all fatty acids simultaneously (multi-GWAS). These multivariate GWAS methods detected several novel loci associated with distinct effects on fatty acid composition and previously identified loci. Conservation of fatty acid metabolism across plant species  were performed at Kansas State University . Brieflyr) for all pairwise combinations of SNPs, where missing data were coded as a fourth state. Only one SNP was retained in a group of SNPs with 2r = 1, leaving 29,320 SNPs. For the GWAS, we imputed missing data with the mean genotype.Genotypes were called using the genotyping pipeline Haplotag, as this pipeline does not require a reference genome sequence  with low missing data (<20%) with the e rrBLUP . Prior tDehulled oat seeds were stored at -20° prior to gas chromatography-mass spectrometry (GC-MS), performed at the Proteomics and Metabolomics Facility at Colorado State University. Oat seeds (300 mg) were ground to a fine powder in a 5 mL polypropylene vial containing a tungsten bead. A 6:3:1 (by volume) solution of cold methyl tert-butyl ether, methanol, and water (3 mL) was added to the vial prior to one hour of shaking at 4°. Subsequently, 750 µL of water was added, the vial was vortexed again and centrifuged for 15 min at 4° to induce phase separation. The upper organic layer, containing non-polar lipids, was transferred to a new glass vial and stored at -80° until analysis.Thawed samples were centrifuged at 3,750 rpm at 4° for 10 min. The solvent was removed from 100 µL aliquots of each sample by nitrogen evaporation at room temperature. Once dried, 200 µL of toluene containing 1.25 mg/mL of internal standard (glyceryl triheptadecanoate), 200 µL of methanol, and 200 µL of 3N methanolic HCl, were added to the sample, and the mixture was incubated at 60° for 1 h. Then, 1 ml of hexane and 300 µL of water were added to the cooled sample. After brief vortexing, the sample was centrifuged at 2,000 rpm for 5 min at 4°.-1). The oven initial temperature was 200° and held for 1 min, then increased to 260° at 10° min-1 and held for 3 min. Detection was completed under electron impact mode, with a scan range of 50-650 atomic mass units and scan rate of 5 scans s-1. Transfer line and source temperatures were 260° and 230°, respectively.One microliter of the upper hexane layer containing the fatty acid methyl esters (FAMEs) was injected onto a TG-WaxMS column  in a TRACE 1310 Gas Chromatograph (Thermo) coupled to a Thermo ISQ LT GC-MS. The injector temperature and split ratio were 260° and 30:1, respectively. The column was eluted with a constant flow of He carrier gas  , 18:2, and 18:3. Two standard FAME mix samples (Nucheck GLC-85 and Sigma 47080-U) were used to confirm the retention times. In the case of 18:1, two peaks were consistently observed. The major peak in all samples corresponded to 18:1(9) based on retention time and authentic standards. The minor peak we designate 18:1* since assignment of double bond position was not possible based on spectra or authentic standards , although 18:1* is most likely the methyl ester of Ten fatty acids were measured as their respective FAMEs from the 500-line diversity panel ij is the effect of the ith genotype in the jth environment; heading(env)ij is the effect of days to heading of the ith genotype within the jth environment; block(env)jk is the effect of the kth block within the jth environment; batchl is the effect of the lth GC-MS batch; and, eijkl is the random error term, assumed to follow a normal distribution with mean zero and variance 2σ. Only heading(env)ij was fitted as a (continuous) fixed effect; all other terms were fitted as random effects.As the FAME concentrations were approximately normally distributed, we used the raw phenotype values to estimate a best linear unbiased predictor (BLUP) for each line in ASReml-R. Our BLUP model  incorporl2h as per Variance component estimates from the fitted model were used to calculate line-mean heritability for each trait  BLUPs using the R package r) between each trait with the R package Hmisc ) / 2)), where m is equal to the number of metabolites (m = 10). We visualized both the r and pr networks with the R packages network and ggplot2 , hereafter referred to as FAME BLUPs, we first estimated pairwise Pearson’s correlation coefficients . In each association test, we employed the univariate analog of our multivariate GWAS model in GEMMA. To account for multiple testing, we computed a Bonferroni correction . Alternatively, we controlled the false-discovery rate (FDR) at 5% and 10% , excluding missing and double heterozygous genotypes  with at least two other significant SNPs.Pairwise LD between SNPs was estimated using squared allele frequency correlations (enotypes . We defiR2 statistic (R2LR), as defined by R2LR value of models with or without a significant SNP was calculated using the maximum log-likelihood of the model of interest fitted in GEMMA and the maximum log-likelihood of an intercept-only model fitted with the lm function in R. For the SNPs detected to be significant in the multi-GWAS, we approximated the relative contribution of each trait to the multivariate signal by calculating R2LR for each SNP in each of the ten respective univariate models developed for the quantile-transformed FAME BLUPs (Table S3).The amount of phenotypic variation explained by each SNP in the univariate analyses was approximated using a likelihood-ratio-based t-test (pairwise.t.test function in R) for each SNP. Phenotypes were mean centered to zero and standardized to unit variance prior to testing. We excluded heterozygous and imputed genotypes.To qualitatively summarize the effects of SNPs identified as statistically significant in the multi-GWAS on individual FAME levels, we compared the BLUP phenotypic means of the distinct homozygous classes (major and minor) and conducted a pairwise https://triticeaetoolbox.org/oat/). Raw GBS sequence reads are available as NCBI BioProject PRJNA555603. Supplemental material available at FigShare: https://doi.org/10.25387/g3.7889747.All genotype and phenotype data are freely available from the Triticeae Toolbox/Oat database . We genotyped this panel with genotyping-by-sequencing (GBS), a reduced-representation sequencing technique  revealed minimal structure with respect to line origin .We assembled a panel of 1,012 oat lines from three existing germplasm collections. This panel consists of inbred lines from around the world, some of which were selected based on variability of beta-glucan, including selections from a beta-glucan improvement program . In both environments, 18:2, 18:1(9), and 16:0 were the most abundant FAME species , in accordance with previous reports . To assess the impact of this consistent environmental difference on the statistical relationships between traits with respect to environment, we calculated Spearman’s rank correlations between each trait-environment combination. Hierarchical clustering revealed grouping with respect to environment and to a lesser extent compound . For example, the three FAMEs in highest abundance, 18:2, 18:1(9), and 16:0, clustered together in each environment, along with 20:1. In addition, we observed clustering between 20:0 and 18:0, and 16:1 and 18:1*, respectively.FAME concentrations were consistently higher in ENV1 compared to ENV2 . The proportions of other compounds, and the sum of the saturated FAMEs, were comparable across environments.Having observed that trait-environment clusters were predominately defined by environment (and secondarily by compound), we were interested in assessing the differential effect of environment on FAME composition, defined as the percent contribution of each FAME to total concentration. We observed significantly higher proportions of 14:0, 18:0, 18:2, and 20:0, and lower proportions of 18:1(9), 18:3, and 20:1 in ENV1 as compared to ENV2 , trait rather than environment defined clustering . Yet, we still observed positive correlations between all trait pairs, excluding the pairwise comparisons that featured 16:1.Assessing the correlation structure in this compositional sample space necessitated relieving the unit-sum constraint imposed by the fact that proportions must sum to one. After applying the log-contrast transformation  to FAME Consistent effects of environment on concentrations across lines corresponded to high line-mean heritabilities for all FAMEs, ranging from 0.67 to 0.91 (Table S1). Thus, we conclude that genetic determinants have a greater impact than environmental factors on variation in FAME concentrations between lines.Using data from the two environments, we calculated BLUPs of each FAME for the 492 lines in the diversity panel . Consistprs), defined as the correlation between the residuals of two compounds after accounting for the other n-2 compounds. As hypothesized, the pr distribution was shifted toward zero relative to its r counterpart . This translated to a far sparser network, such that every node was no longer connected to nearly every other node by an edge, and to the introduction of negative correlations , persisted in the pr network. While the significant pairwise prs were not restricted to only precursor/product relationships in fatty acid biosynthesis, some of the strongest correlations in this network are between such pairs . Thus, most of the variation in individual FAME concentrations can be explained by variation in fatty acid totals. The second PC (explaining 15.2% of variance) primarily distinguished saturated from unsaturated FAMEs, whereas the third and fourth PCs differentiated 14:0 and 18:3 from all other FAMEs, respectively. While 20:1 contributed disproportionately to both the fifth and sixth PCs, these two PCs were less obviously interpretable.To identify the contribution of each fatty acid to the major axes of variation in the phenotypic data set, we performed PCA on the centered and scaled BLUPs for the ten FAMEs . All FAMEs exhibited negative loadings along the first PC , which tracked with variation in total fatty acids (adjusted n x m = 492 lines × 10 traits) as phenotypes (PC-GWAS). Third, we implemented univariate tests of association for each FAME separately, as estimated by their BLUPs. To account for population structure, we included the first three PCs of the non-imputed genotype matrix as covariates in all of our GWAS models. These PCs were selected based on application of the BIC model selection procedure in the multi-GWAS, and explained 6.7, 4.3, and 3.2% of the total variance, respectively.We employed several mapping approaches to capitalize on the correlations between traits. First, we performed a multivariate GWAS of all ten FAME concentrations simultaneously (multi-GWAS). Second, we conducted GWAS with each PC derived from decomposition of the phenotypic data matrix . We also considered SNPs passing the two less conservative significance thresholds of genome-wide (FDR of 5% and 10%). At these thresholds, 93 and 148 SNPs were detected, respectively.In the multi-GWAS, a total of 25 SNPs were significant after accounting for multiple testing with a 5% Bonferroni significance threshold . All but two of these SNPs were also found in GWAS of the non-quantile transformed BLUPs, suggesting that deviations from normality in the untransformed BLUPs had minimal influence on the results .In the univariate GWAS of the ten FAMEs and total FAME concentration, we identified 73 significantly associated SNPs at a 10% FDR – the smallest number of significant associations among the three approaches (r2 > 0.5 with at least two other significant SNPs (at a 10% FDR threshold) across all analyses. This high confidence SNP set consisted of a 152 SNP subset of the 237 significant SNPs. The majority of SNPs in the high confidence SNP set (101 SNPs) spanned eight of the 21 consensus linkage groups, while 51 SNPs were unmapped . Although the two other SNPs in this cluster were unanchored, the overall peak SNP in the multi-GWAS (P = 1.1 × 10−33) was mapped to a nearby position (40.9 cM) on this linkage group but excluded from the high confidence SNP set by our r2 clustering criteria. This SNP and the LD cluster SNPs were associated with 14:0 and PC3 , suggesting at least one gene in this Mrg12 region underlies variation in seed 14:0 content.Using these criteria, we identified six main clusters, consisting of 124 SNPs in the high confidence SNP set a-f in . Five ofa) and 67.4 to 85.3 cM (cluster b), respectively. These large intervals are consistent with previous reported of long-range LD in this linkage group (a and b) had markedly different minor allele frequencies . As such, the r2 for these two SNPs is constrained to a maximum value of r2 = 0.19, reflecting the sensitivity of r2 to differences in allele frequency (r2 = 0.14) resulted in assignment to different clusters by our criteria (a and b). SNPs in cluster a were also associated with higher levels of 20:0, 18:2, 18:3, and 16:0, while SNPs in cluster b also exhibited association with higher levels of 18:1*. Notably, in the univariate GWAS, the SNPs within these two clusters were only associated with 20:1.We observed two primary clusters in Mrg02, spanning 37.5 to 107.5 cM  and 114.7 to 122.7 cM (cluster e). The minor alleles in cluster c were associated with significant decreases in most FAMEs as well as total concentration. All three approaches  detected significant SNPs in the interval associated with cluster c. In contrast, cluster e was only detected by the multi-GWAS. In this cluster, the minor allele was associated with higher total FAMEs and concentrations of the most abundant FAMEs, as well as, decreases in 18:3 and 16:1. The correlated estimated effects of SNPs in cluster e on 16:1 and 18:3 were not expected a priori based on known biochemical pathways . By this analysis, detection of cluster e SNPs associate with 18:3 variation, despite the fact that we failed to detect significant associations in the univariate GWAS. Thus, it appears that unanticipated correlations between 18:3 and other fatty acids, such as 16:1, underlie the enhanced sensitivity of the multi-GWAS model.Two clusters were also observed in Mrg05, mapping to non-overlapping intervals between 124.1 and 135.5 cM (cluster pathways  or the opathways . To furtf) contained two SNPs identified in the univariate analyses of total and 18:1(9) FAMEs, and four SNPs uniquely identified in the multi-GWAS. The main effects associated with the peak SNP in this cluster were increased 16:1 and 18:1* and decreased 18:0 and 18:3  consisted of 29 SNPs of low minor allele frequency (< 0.05). The only mapped SNP in cluster d was located at 80.3 cM on Mrg03 of the consensus map and a significant association of this SNP was only detected in GWAS of the last PC. SNPs in this cluster were predominantly associated with higher levels of saturated fatty acids .The final cluster  activity. In the case of cluster b, an increase in AAD activity could lead to increases in both 18:1(9) and 18:1* as well the elongation of 18:1(9) to 20:1. The correlated change in 16:1 and 18:1* (assumed to be 18:1(11)), on the other hand, would be consistent with an altered substrate specificity for AAD. In plants, AAD typically has a strong preference for 18:0-ACP as its substrate, and the conversion of 16:0-ACP to 16:1-ACP is typically a minor side reaction (Doxantha unguis-cati L.), AAD isoforms with preference for 16:0-ACP as a substrate can lead to substantial production of 16:1(9) and its elongation product, 18:1(11) , this still relies on quantile-normalized traits that obscure a SNP’s true effect size. Our best approximation of a given SNP’s contribution to each trait comes from ∆R2LR statistics for SNPs that were detected in univariate GWAS of non-transformed individual traits . By this measure, significantly associated SNPs explained approximately 2–16% of phenotypic variance within each of the 11 traits in our population. For total fatty acid content, significant SNPs explained approximately 3–7% of the phenotypic variance. The peak cluster c SNP, for example, has a ∆R2LR of 6.6% for GWAS of total fatty acid content in our population, and is likely a useful GBS marker for tagging ACCaseA alleles. We have also identified a new marker for total fatty acid content, avgbs2_90110.1.11, that has a similarly large effect size but appears to not be linked to ACCaseA . This marker is presently unmapped and the allele that confers high fatty acid content has a frequency of just 0.025 in our population, suggesting that it marks a locus for oil content that is currently underutilized in oat breeding programs.For SNPs identified by GWAS to be useful for MAS, it is important that they exhibit relatively large effects on the trait(s) of interest. Unfortunately, the increased sensitivity of multivariate GWAS is accompanied by difficulty in accurately assigning effect sizes of a given SNP on each trait. While we have attempted to deconvolute the drivers of our multivariate GWAS signal using ∆In conclusion, we have demonstrated the utility of multivariate GWAS methods for identifying genetic associations with metabolites that share a biosynthetic pathway. By analyzing all ten fatty acids simultaneously with multi-trait GWAS methods, we were able to detect SNPs that were not detected by traditional, univariate GWAS. The increased sensitivity of multivariate GWAS may be particularly advantageous for polyploid species like oat, where the effect of a single locus may be buffered by the activity of its homeologs (


Puccinia striformis f. sp tritici Eriks (Pst), is a major challenge to resistance breeding in wheat. A genome wide association study (GWAS) was performed using 22,415 single nucleotide polymorphism (SNP) markers and 591 haplotypes to identify genomic regions associated with resistance to YR in a subset panel of 419 pre-breeding lines (PBLs) developed at International Center for Maize and Wheat Improvement (CIMMYT). The 419 PBLs were derived from an initial set of 984 PBLs generated by a three-way crossing scheme (exotic/elite1//elite2) among 25 best elites and 244 exotics  from CIMMYT’s germplasm bank. For the study, 419 PBLs were characterized with 22,415 high-quality DArTseq-SNPs and phenotyped for severity of YR disease at five locations in Mexico. A population structure was evident in the panel with three distinct subpopulations, and a genome-wide linkage disequilibrium (LD) decay of 2.5 cM was obtained. Across all five locations, 14 SNPs and 7 haplotype blocks were significantly (P < 0.001) associated with the disease severity explaining 6.0 to 14.1% and 7.9 to 19.9% of variation, respectively. Based on average LD decay of 2.5 cM, identified 14 SNP–trait associations were delimited to seven quantitative trait loci in total. Seven SNPs were part of the two haplotype blocks on chromosome 2A identified in haplotypes-based GWAS. In silico analysis of the identified SNPs showed hits with interesting candidate genes, which are related to pathogenic process or known to regulate induction of genes related to pathogenesis such as those coding for glunolactone oxidase, quinate O-hydroxycinnamoyl transferase, or two-component histidine kinase. The two-component histidine kinase, for example, acts as a sensor in the perception of phytohormones ethylene and cytokinin. Ethylene plays a very important role in regulation of multiple metabolic processes of plants, including induction of defense mechanisms mediated by jasmonate. The SNPs linked to the promising genes identified in the study can be used for marker-assisted selection.Yellow rust (YR) or stripe rust, caused by  Triticum aestivum L.) is one of the most important cereal crops with an estimated production of 736.1 million tons in 2019  provides highly effective protection against pathogenic races , a leading approach for complex trait dissection and identification of novel and superior alleles to be utilized for breeding  in crops. Wheat has particularly benefited from these advancements, in which marker number and density have been the limiting factors for a long time . Dense sbreeding . Use of breeding . In wheabreeding . For YR,breeding .The objective of this research was to identify genetic loci associated with resistance to YR in pre-breeding lines (PBLs) developed at International Maize and Wheat Improvement Center (CIMMYT). These lines were derived by a three-way crossing scheme (exotic/elite1//elite2) among CIMMYT’s 25 best elites and exotics  from gene bank .Aegilops squarrosa followed by KACHU in 419 lines.Out of an initial set of 984 PBLs , 419 werThe 419 PBLs were evaluated in an experimental alpha lattice design with two replications in five locations in Mexico. Three of these locations were Villagrán, Guanajuato , Celaya, Guanajuato , and La Barca, Jalisco  where evaluations were done in fall–winter (FW) cycle of 2015–2016. The remaining two locations were Texcoco, State of Mexico  and Nanacamilpa, Tlaxcala  where evaluations were done in spring–summer (SS) cycle of 2016.Pst across the experimental trial, seeds of ‘Morocco’ were also planted as border rows all around the trial. The sowing density of experimental plots was 46 kg ha−1. A total of five irrigations were applied at 0, 35, 65, 85, and 105 days after sowing and plots were fertilized with the dose 240-60-00 N-P-K; all the phosphorus and half of the nitrogen at sowing and the rest in the first auxiliary irrigation. For inoculation, urediniospores were collected from previous cycles with a mechanical collector and kept at –55°C in a freezer. Before use, frozen urediniospores received a thermal shock with water at 60°C for 7 min and then rehydrated for 4 h in a humid chamber  for traits in each location and across combined locations. For individual location, h2 was calculated as follows:Best linear unbiased predictors (BLUPs) were calculated from two replications data for each of the five environments separately and across combined environments using Meta-R program V.6.0 . The folVg is genotypic variance and Verr is the error variance and r = number of replications in a location. For combined locations, h2 was calculated as follows:where Vg is genotypic variance, Vg×e is genotype × environment interaction variance, Verr is the error variance, n = number of environments, and r = number of replications.where Histograms showing the dispersion of disease scores of the 419 lines were made for each location with a script executed in R version 3.5.1.−1) in 96-well plates. The FASTQ files (full reads of 77 bp) were quality filtered using a Phred quality score of 30, which represent a 90% of base call accuracy for at least 50% of the bases. More stringent filtering was also performed on barcode sequences using a Phred quality score of 10, which represent 99.9% of base call accuracy for at least 75% of the bases. A proprietary analytical pipeline developed by DArT P/L was used to generate allele calls for SNP and presence/absence variation (PAV) markers. Then, a set of filtering parameter was applied to select high quality markers for this specific study. One of the most important parameters is the average reproducibility of markers in technical replicates for a subset of samples which was set at 99.5%. Another critical quality parameter is call rate. This is the percentage of targets that could be scored as “0” or “1”, the threshold was set at 50%. SNPs with missing data more than 20% and minor allele frequency <0.05 were eliminated. To obtain physical positions of SNPs, sequence reads of the SNPs were blasted to the reference genome of RefSeq V.1.0 with E value <10−8 and identity >95%.Genomic DNA was extracted from fresh leaves collected from the 419 individuals using the CTAB (cetyltrimethylammonium bromide) method but modified as described in K) ranging from 2 to 10 were chosen and five independent runs were conducted for each K. A zip archive containing all of the results-f files was created and used as an input in Structure Harvester program to estimate delta K (∆K). Additionally, the principal component analysis (PCA) was performed with the TASSEL software , using 22,415 high quality and filtered DArTseq markers . The population structure was corroborated by a cladogram made with the TASSEL program. The kinship matrix was also estimated using the 22,415 markers using TASSEL. The PCA was drawn using the rgl package in R. GAPIT version 2.0 was used to obtain correlation estimates of the frequency of the squared allele of LD (r2) for all pairwise comparisons. Pattern of LD decay was visualized by plotting pair-wise r2 values against the distance (cM) between markers for A, B, and D genomes separately and for whole genome. A smooth line was fit to the data using second-degree locally weighted scatterplot smoothing . A mixed linear model was used with PCA as a fixed variate and kinship as random. The quality of the association was analyzed by drawing Q–Q plots . Based on BLAST in the server, quantitative trait loci (QTL) were located on short and long arms of chromosomes and candidate gene hits were reported.The minimum and maximum YR scores for all individual locations and across five locations are listed in 51,232 SNPs and PAV were generated across 419 lines after allele calling, of which 22,415 filtered SNPs were utilized for further analysis. PAV markers were not used in any analysis. 6,838 SNPs were found in genome A, 7,605 in genome B, and 7,972 in genome D. The chromosome with the highest number of SNPs was 7D with 1,718 SNPs, followed by chromosome 2B with 1,453 SNPs. Chromosomes with fewer SNPs were 6A with 790, 4D with 635, and 4B with 603 SNPs. A total of 591 genome-wide haplotype blocks were obtained, of which 253 were detected in genome A, 260 in genome B, and 78 in genome D. The number of SNPs in haplotype blocks ranged from 2 to 8.K (K = 3) by method described in The fastStructure analysis showed three subpopulations in the panel determined by the best r2) among all possible pairs of markers for each of the 21 chromosomes. Obtained r2 values were then plotted against genetic distance (cM) for each of the three genomes separately and across whole genome  ranging from 6.0 to 14.2%. The following 10 SNPs from chromosome 2A were significantly associated; 100272191 and 1177572 at 8.29 cM, 1206128 at 8.6 cM, 1028859 and 1088511 at 9.93 cM, 1092886 at 9.97 cM, 7940374 at 11.57 cM, 3951942 at 13.34 cM, 100247987 at 17.84 cM, and 2293684 at 75.64 cM. Based on an average LD decay of 2.5 cM, these 10 marker–trait associations were classified into four QTLs  with YR resistance  was 2.5 cM for whole genome . Four QTL were identified on chromosome 2A on short arm in the present study. Of these, Yr2A.1PBL and Yr2A.2PBL overlapped with the metaQTL QRYr2A.1 on 2AS  on short arm reduced YR severity by 43%. There are three metaQTL on 2BS, of which metaQTL QRYr2B.2 particularly is a gene rich region containing a number of seedling and APR genes, for example, riticina . Hence, Yr16 and Yr54 are located on 2DL. Lines having Yr2D.2PBL had on an average 9.2% of rust severity against 16.25% of those that do not have it, indicating the effectiveness of this new QTL. BLAST analysis of SNP 1030280 identified a gene that produces the two-component histidine kinase related to the ethylene response sensor 2, which elicits the response to ethylene. This alkene plays a very important role in the final regulation of multiple metabolic processes of plants, including the induction of defense mechanisms mediated by jasmonate (There are three metaQTL regions associated with YR resistance on chromosome 2D. The one reported on 2DS was identified in a single study . Naruokaasmonate .R2) explained by haplotypes ranged from 7.9 to 19.9% in different locations, while for the associated 14 significant SNPs it ranged from 6.0 to 14%, resulting in an average 6.5% higher R2 by haplotypes compared to single SNPs. This clearly demonstrates the advantage provided by the former approach in identifying genomic regions that control YR resistance.Use of multi-allelic haplotypes has significantly improved the power and robustness of GWAS studies in major crops including soybean , barley In silico analysis of the SNPs revealed four candidate genes known to regulate induction of genes related to pathogenesis. In depth upstream analysis of these genes can help dissect the underlying mechanism of YR resistance in PBLs.This research identified 14 SNP markers and 7 haplotypes associated with YR resistance in a diverse set of pre-breeding lines. These associations were delimited to seven QTLs based on average LD decay of 2.5 cM. Three of these QTLs are likely to be novel and represent attractive targets for marker-assisted selection. https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10548313.The datasets generated for this study can be found in the CIMMYT Research Data & Software Repository Network Conceived and designed the study: SS, ES-M. Performed the study: LL-R, ES-M. Analyzed the data: DS, LL-R, SS, ES-M, GI. Contributed materials: MR-V, VM-T, CS, JB, CO, CA-M, JR-P, PV. Genotyping/analysis tools: SS, PV, ES-M, CS, DS. Interpretation of results: LL-R, DS, JB, SS, ES-M, VM-T. Wrote first draft of the paper: LL-R, DS, SS, ES-M. Revised the manuscript: DS, LL-R, SS, DS, ES-M, GI, MR-V, VM-T, CS, JB, CO, CA-M, JR-P, PV.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.


Additionally, based on single-marker analysis, a SNP in RYR3 ranked at the top of all SNPs associated with variation in shear force. Our data suggest a role for RYR3 in muscle firmness that may be considered for genomic- and marker-assisted selection in breeding programs of rainbow trout.Filet quality traits determine consumer satisfaction and affect profitability of the aquaculture industry. Soft flesh is a criterion for fish filet downgrades, resulting in loss of value. Filet firmness is influenced by many factors, including rate of protein turnover. A 50K transcribed gene SNP chip was used to genotype 789 rainbow trout, from two consecutive generations, produced in the USDA/NCCCWA selective breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform genome-wide association (GWA) analyses to identify quantitative trait loci affecting filet firmness and protein content. Applying genomic sliding windows of 50 adjacent SNPs, 212 and 225 SNPs were associated with genetic variation in filet shear force and protein content, respectively. Four common SNPs in the ryanodine receptor 3 gene (RYR3) affected the aforementioned filet traits; this association suggests common mechanisms underlying filet shear force and protein content. Genes harboring SNPs were mostly involved in calcium homeostasis, proteolytic activities, transcriptional regulation, chromatin remodeling, and apoptotic processes. RYR3 harbored the highest number of SNPs ( Despite a well-developed understanding of meat tenderization that has been studied for decades in mammals, the need exists for genetic markers of the fish “gaping” and filet softness phenotypes  analysis compares allele frequencies at candidate loci with respect to the studied trait, and takes advantage of linkage disequilibrium (LD) between SNP marker and trait loci . GWA anaA 50K transcribed gene SNP chip of average 1 SNP per 42.7 Kb, was recently developed for rainbow trout. About 21K SNPs showing potential association with important traits, including fish growth, muscle yield/quality and filet softness, were used to build the chip. In addition, 29K SNPs were added to the chip following a strategy of 2 SNPs/ gene to randomize the SNP distribution. The recent release of rainbow trout genome  helped in assigning SNPs to chromosomes. Recently, the chip was successfully used to identify several QTL markers associated with muscle yield . The objInstitutional Animal Care and Use Committee of the United States Department of Agriculture, National Center for Cool and Cold Water Aquaculture  specifically reviewed and approved all husbandry practices used in this study .Fish population and tissue sampling were previously described in detail . BrieflySD = 239 g), whereas those from YC 2012 were harvested between 446- and 481-days post-hatch . Muscle shear force and protein content showed low regression coefficient (R2) values of 0.05 and 0.04 with body weight, respectively. Fish were euthanized in a lethal dose of tricaine methane sulfonate , harvested, and eviscerated. Head-on gutted carcasses were packed in ice, transported to the West Virginia University Muscle Foods Processing Laboratory , and stored overnight. Carcasses were manually processed into trimmed, skinless filets , equipped with a 50 kg load cell; tests were performed at a crosshead speed of 127 mm/min (2h (2pedh) for protein content and shear force were estimated according to Whole body weight (WBW) was measured in fish belonging to each family and families were sorted according to their WBW. The 2nd or 3rd fish from each family was selected for muscle sampling to keep the distribution of WBW consistently adjusted around the median of each family. For each harvest year, selected fish were randomly assigned to one of five harvest groups (∼100 fish each) allowing one fish per family per harvest group. The five groups were sampled in five consecutive weeks (one group/week) each YC. Fish from the YC 2010 were harvested between 410- and 437-days post-hatch  as we previously described . In briey is the vector of the phenotypes, b is the vector of fixed effects including harvest group and hatch year, a is the vector of additive direct genetic effects , w is the vector of random family effect, and e is the residual error. The matrices X, Z1, and Z2 are incidence matrices for the effects contained in b, a, and w, respectively. The model combines all the relationship information (based on pedigree and genotypes) into a single matrix (H-1).Where H-1 is the inverse of the realized relationship matrix (H), A-1 is the inverse of the relationship matrix based on pedigree information, -1 is the inverse of the genomic relationship matrix. The random family effect is uncorrelated and just accounts for the fact the animals within the same family were raised in a common environment, and the covariance structure is given by II is an identity matrix and where AIREMLF90  was used∧) assessed in the first iteration asu∧2p(1 -p), where p represents the current allele frequency. Three steps were performed in each iteration: (1) weight was assigned to the SNPs. (2) genomic estimated breeding values (GEBV) were computed using BLUPF90 based on H-1 (n = 50 SNPs) instead of physical size . A Manhattan plot showing the proportion of additive genetic variance explained by the 50 SNP windows was generated in R using the qqman package . For the second iteration, weights were calculated according to the SNP effects (ud on H-1 . (3) SNPd on H-1  based on package .R-squared values of association.Single marker association analysis was conducted using PLINK . The pheTo retrieve SNP annotations, SNPs bed file was intersected with the rainbow trout genome gff/gtf file using Bedtools as described before . SNPs loR2 = 0.18; p-value < 0.0001). Therefore, we used a 50K SNP chip to perform GWA analyses to identify QTL associated with both traits based on 50 SNP sliding windows using WssGBLUP and single-marker association analyses using PLINK. The chip contains SNPs potentially associated with muscle quality traits including filet softness as we previously described  in windows explaining additive genetic variance for shear force (n = 80) were located within the 3′UTR of their genes suggesting a role for these SNPs in microRNA, post-transcriptional regulation of gene expression. Among those 80 SNPs, 32 SNPs created or deleted binding sites for 56 microRNAs  of the additive genetic variance for shear force . Genomicar force . Many oficroRNAs . All QTLicroRNAs , 2. EnriicroRNAs .Ten genes necessary for calcium metabolism harbored 47 SNPs affecting the genetic variation in shear force . RyanodiTwo SNPs were non-synonymous, and one of these SNPs exists in the third structural repeat that is conserved in all RYR isoforms; it is located in the N-terminal part of the cytoplasmic region of the RYRs. Several studies reported a correlation between development of pale, soft and exudative (PSE) meat and abnormality in calcium release mechanism of porcine skeletal muscle as a result of a point mutation in the porcine RYR1 that led to a substitution of cysteine for arginine (Arg615Cys) . Poor re2+ transients , that had 7 SNPs explaining at least 2.0% of the additive genetic variance . MMP14 hcollagen . Our reccollagen . In addi in fish , were ov in fish . The rel in fish . TogetheSix SNP-harboring genes involved in proteolytic/ catabolic and apoptotic processes were identified on chromosomes 10, 13, and 28 . ChromosTwo SNP-harboring genes were mapped to the autophagy pathway; immunoglobulin-binding protein 1 (IGBP1) and zinc finger FYVE domain-containing protein 1 (ZFYVE1). ZFYVE1, has been used as a marker of omegasomes (exists only during autophagosome formation) . Three S2+-independent cell-adhesion activity -ATPases acidify the intracellular compartments and play an important role in protein degradation (Ten genes with proteolytic activities were identified that affected genetic variability in protein content . A singlradation . RHOB isradation . Three S2+ to the cytosol (2+ level and plays a role in Ca2+ homeostasis (2+ handling (release and re-sequestration). In mammals, proteolysis by calcium-dependent proteases  in the early postmortem period greatly affects muscle texture and meat tenderization  and RCN2 were ranked at the top of this group. MEF2 plays diverse roles in muscle to control myogenesis .N-methyltransferase KMT5B (KMT5B) had a single SNP located in a window explaining the maximum variance in protein content in this group (3.97%). KMT5B is a histone methyltransferase that trimethylates ‘Lys-20’ of histone H4  and plays a role in myogenesis  and chromosome 7 (n = 4). However, the most significant SNP explaining 3.4% of the phenotypic variability in shear force, was located on chromosome 8 in a gene coding for RYR3. This result was in agreement with the WssGBLUP 50 SNP-window analysis and suggests an essential role for RYR3 in regulating filet firmness in trout. Cytochrome c oxidase subunit 6C-1 (COX6C1), 14-3-3B1 protein, and rho GTPase-activating protein 15 (ARHGAP15) were ranked next to RYR3 in impacting phenotypic variability in shear force. COX6 was rapidly degraded under endoplasmic reticulum stress conditions induced by Ca2+ depletion (D’ = 1), with two SNPs located in COX6C and 14-3-3B1 protein. In addition to 14-3-3B1 protein, a gene coding for disabled homolog 2 (DAB2) was also involved in apoptotic process (2+-dependent phospholipid-binding proteins that have an important role in the cell cycle and apoptosis (Most of the significant SNPs were located on chromosome 5 (epletion  and upreepletion . 14-3-3Bepletion . Previouepletion . Overexpepletion . ARHGAP1 process . Two SNP process . Annexinpoptosis . The lispoptosis . Single-Altogether, results obtained from the single SNP analyses provided additional evidence of RYR3 role in regulating phenotypic variability in filet firmness. Also, single-marker analysis highlighted a role for a few more genes in filet firmness. However, estimating the effect of each SNP individually does not allow the detection of small effects of multiple joint SNPs. This may explain the inconsistency in the significant peaks between the single-marker analysis and the WssGBLUP approach. Several studies indicated that the SNP-joint analysis is more successful than the single-SNP analysis in GWA studies of complex traits . TherefoThe current GWA analyses identified novel genomic loci with a role in regulating muscle firmness and protein content. These genomic loci code for proteins involved in calcium homeostasis, transcriptional and chromatin regulators, cell adhesion, protein synthesis/degradation, and apoptotic processes. The top windows affecting the additive genetic variance in protein content and shear force appeared on chromosome 4 and 13, respectively. RYR3 was the major gene harboring the largest number of SNPs located within windows affecting the additive genetic variance in shear force and protein content. Abnormal calcium homeostasis in muscle cells accelerates postmortem protein degradation, and meat softness . The curAll datasets generated for this study are included in the manuscript and/or the MS, TL, and BK conceived and designed the experiments. RA-T, MS, TL, and BK performed the experiments. RA-T, AA, DL, BK, and MS analyzed the data. AA and MS wrote the manuscript. All authors reviewed and approved the publication.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.


Genome-wide association studies of six indices for CC and SG identified a large number of association signals, among which 14 were identified as pleiotropic regions for CC and SG. Twenty-five known genes and pleiotropic candidate gene OsSG1 accounted for natural variation in CC and SG. Further analysis indicated that 20 large-effect, non-synonymous SNPs within six known genes around GWAS signals and three SNPs in the promoter of OsSG1 could be functional causing significant phenotypic differences between alleles. Superior haplotypes were identified based on these potentially functional SNPs. Population analyses of 368 cultivated accessions and 446 wild accessions based on SNPs within genes for CC and SG suggested that these genes had been subjected to strong positive selection in japonica in the process of spreading from its subtropical origin to the North China temperate zone. Our studies point to important genes that account for natural variation and provide superior haplotypes of possible functional SNPs that will be beneficial in breeding for high-photosynthetic efficiency in rice.Higher chlorophyll content (CC) and strong stay-green (SG) traits are conducive for improvement of photosynthetic efficiency in plants. Exploration of natural elite alleles for CC and SG, and highly resolved gene haplotypes are beneficial to rational design of breeding for high-photosynthetic efficiency. Phenotypic analysis of 368 rice accessions showed no significant correlation between CC and SG, and higher CC and stronger SG in  Chlorophyll content (CC) is closely related to crop yield as a consequence of photosynthetic efficiency, but degradation of chlorophyll during maturation limits yield potential because of leaf senescence2. Breeding crop genotypes with prolonged active photosynthetic duration is a practical approach to increase yield3, but requires strong stay-green (SG) phenotypes. Rice is a stable food source that feeds more than half of the world population, and insight into the genetic basis of CC and SG could provide valuable information for breeding.Chlorophylls is essential for photosynthesis and mainly functions in light energy harvesting and as a transfer center6. Hundreds of genes related to CC have been reported in rice. Among them, a large number of genes were detected using rice mutants exhibiting yellow-green (chlorina) or albinic leaves, such as YGL8, OsDVR, OsValRS2, YGL138(t), OsNUS1, v2, PAPST1, GIC, RNRL2, RNRL1, RNRS1 and CHR7217. Some genes affecting chlorophyll content were identified using SG mutants, such as NYC1, NOL and SGR19. These studies identified functional genes controlling CC and SG, but natural variation in those genes was not revealed. Therefore, detailed knowledge of the natural variation in genes underlying CC and SG is required in order to identify or design superior cultivars with highly efficient photosynthetic capacity.Chlorophyll biosynthesis and degradation have been studied biochemically and genetically in various organisms24. These natural variations underlie adaptability to different light and temperature conditions that range from tropical to temperate zones, and also lay a genetic basis for breeding specifically adapted varieties with high and stable yields. Compared with conventional linkage mapping in biparental populations, genome-wide association studies (GWAS) explore a wider range of natural variation and enable identification of numerous SNPs associated with targeted traits. Using diverse rice accessions, the genetic architecture of natural variation in rice CC was investigated through GWAS, and genes Ghd7 and NAL were found to be associated with CC25. Given that molecular breeding relies on precise genetic dissection of agronomic traits and high-resolution chromosome haplotypes26, further identification of elite alleles underlying CC and SG will be beneficial in gaining insights into the molecular basis of variation in CC and SG and in breeding photosynthetically efficient varieties.Rich genetic diversity and clear subspecies and population differentiation are established characteristics of Asian cultivated riceOsSG1 accounted for natural variation in CC and SG. We scanned for possible functional non-synonymous SNPs within these genes, and observed diverse effects of the major haplotypes. In addition, we investigated the signatures of natural selection on genes underlying variation in CC and SG within and between indica and japonica subpopulations. The results provide insight into how domestication has affected CC and SG genes as well as information that may be useful for future molecular applications of these genes in breeding for high photosynthetic efficiency.In the present study, we identified ontological gene categories and overview of 152 known genes controlling CC and SG. The genetic architecture of natural variation in CC and SG was studied through GWAS using 368 cultivated Asian rice accessions. Twenty-five known genes and candidate gene 28. For phylogenetic analysis, we added 446 wild rice accessions, having publicly available sequencing data from a previous report20.Three hundred and sixty-eight rice accessions from 32 countries were used as materials for identification of CC and SG genes. The sequence data of all accessions were obtained from the 3000 Rice Genome Project (3KRGP)All 368 rice accessions were used in phenotyping CC and SG. Field experiments were performed at the China Agricultural University Shangzhuang Experimental Station in Beijing in the summer of 2014. Two replicates were grown in each of two fields and each accession was transplanted 30 days after sowing in three row plots with 20 cm between plants and 26 cm between rows. Three central plants from the middle row of each plot were used to assess CC and SG. We measured the CC in the flag leaf, and second and third upper leaves of two tillers of each plant by a SPAD  meter  at heading and 30 days after heading. Average SPAD values across the two replicates were used for analysis.We adopted six indices to evaluate the CC and SG of all materials. These included SPAD of the flag leaf at heading (SFH), total SPAD for the three upper leaves at heading (TSH), absolute difference value of SPADs of the flag leaf at heading and 30 days post heading (ADSF), relative difference value of SPAD of the flag leaf at heading and 30 days post heading (RDSF), cumulative SPAD of the flag leaf at heading and 30 days post heading (CSF), and total cumulative SPAD for the three upper leaves at heading and 30 days post heading (TCS). The formulae of ADSF, RDSF, CSF and TCS were: ADSF = SPAD of the flag leaf at heading − SPAD of the flag leaf at 30 days post heading, RDSF = ADSF/SPAD of the flag leaf at heading, CSF = SPAD of the flag leaf at heading + SPAD of the flag leaf at 30 days post heading, and TCS = total SPAD for the three upper leaves at heading + total SPAD for the three upper leaves at 30 days post heading. Among these indices, SFH and TSH were used as CC indices, and ADSF and RDSF indicated the difference and degradation rate of CC at two growth stages. We applied the two indices to assess the SG of each accession. We also considered CSF and TCS as indices to evaluate ability including CC and SG, which to a certain extent, reflect the accumulation of chlorophyll (ACC) during the heading and 30 days post heading stages.29 to evaluate population structure and relative kinship of the 368 rice accessions. The first three PCs were used to construct a PC matrix. To control spurious associations, we performed GWAS on 6 indices for CC, SG and ACC using the compressed mixed linear model (CMLM) with PC and kinship matrices, that account for population structure and identify the optimal group kinship matrix30. A significance threshold was calculated using the formula “-log10(1/the effective number of independent SNPs)” as described previously31, and effective numbers of independent SNPs were determined by PLINK to be 144605, 172233 and 95342 in the full population, and indica and japonica subpopulations, respectively32. The suggestive P values were 6.9 × 10−6, 5.8 × 10−6 and 1.0 × 10−5, respectively. Finally, the threshold was set at −log(P) = 5 to identify significant association signals. Due to different genome-wide linkage disequilibrium (LD) decay rates in indica and japonica at 123 kb and 167 kb33, adjacent significant SNP with distances less than 170 kb were merged into single association signals. The SNP with the minimum P value in a signal region was considered to be the lead SNP. In order to identify candidate genes in the signal region, LD heatmaps surrounding peaks in the GWAS were constructed using the R package “LD heatmap”34.More than 3.3 million SNPs with minor allele frequencies (MAF) >0.05 and missing rates <0.5 were used in population genetic analysis and GWAS. Principal component (PC) and kinship analyses were performed using GAPIT35. According to the default parameters, a two-sided hypergeometric test and Bonferroni stay-down correction were used to identify enriched GO terms and pathways. Significant enrichment was detected with a corrected P value of <0.05.A cytoscape plug-in ClueGO v2.3.5 was used to analyse GO and pathway enrichmentP < 0.05).Based on information on coding sequence (CDS) coordinates and the transcript from MSU RGAP 7, we separated non-synonymous SNPs from all SNPs across the 368 accessions using an in-house Perl script. Differences in phenotypic values between alleles of each non-synonymous SNP were examined by Student’s t-tests. Sequence alignment of each gene was performed using non-synonymous SNPs associated with CC or SG, and differences in phenotypic values among haplotypes of each gene were calculated by one-way ANOVA or Student’s t-tests. Duncan’s multiple range tests were conducted to make comparisons if the results of the one-way ANOVA were significant (37. Nucleotide diversity (π)38 and Tajima’s D39 were calculated using an in-house Perl script.A phylogenetic tree for all 368 cultivated and 446 wild accessions was constructed using the neighbor-joining method in TASSEL 5 and MEGA 5indica and 169 japonica accessions . Thirty-five, 15, 13, 12, 28 and 10 significant signals were obtained for SFH, TSH, ADSF, RDSF, CSF and TCS, respectively, in the full population  database and more recent reports42. The gene ontology (GO) categories significantly enriched in this protein group were located in chloroplasts  or SG in rice were selected from the China Rice Data Center , NYC1 (LOC_Os01g12710), RDD1 (LOC_Os01g15900), PAPST1 (LOC_Os01g16040), YGL8 (LOC_Os01g17170), OsWAK25 (LOC_Os03g12470), OsMTP8.1 (LOC_Os03g12530), NOL (LOC_Os03g45194), OsNUS1 (LOC_Os03g45400), GIC (LOC_Os04g57920), RNRL1 (LOC_Os06g07210), RNRS1 (LOC_Os06g14620), OsValRS2 (LOC_Os07g06940), Ghd7 (LOC_Os07g15770), OsPHR2 (LOC_Os07g25710), CK2β3 (LOC_Os07g31280) and CHR729 (LOC_Os07g31450) were located in the association regions for two CC indices, indicating that these genes could contain important loci involved in natural variation of CC; NYC1 (LOC_Os01g12710), Cga1 (LOC_Os02g12790), RNRL2 (LOC6_Os02g56100), OsMTP8.1 (LOC_Os03g12530), v2 (LOC_Os03g20460), OsDVR (LOC_Os03g22780), OsGGPPS1 (LOC_Os07g39270) and YGL138(t) (LOC_Os11g05552) were located in association regions for two SG indices, suggesting that they could be related to natural variation in SG; NYC1 (LOC_Os01g12710), RDD1 (LOC_Os01g15900), PAPST1 (LOC_Os01g16040), YGL8 (LOC_Os01g17170), OsFRDL1 (LOC_Os03g11734), OsWAK25 (LOC_Os03g12470), OsMTP8.1 (LOC_Os03g12530), YSA (LOC_Os03g40020), NOL (LOC_Os03g45194), OsNUS1 (LOC_Os03g45400) and RNRS1 (LOC_Os06g14620) were in association regions for two ACC indices, implying that these genes could be involved in natural variation of ACC  associated with SFH, TSH, ADSF, RDSF, CSF and TCS, respectively. Eight, 8, 1, 1, 1 and 1 non-synonymous SNPs showed significant associations with SFH, TSH, ADSF, RDSF, CSF and TCS in indica, respectively  with three transmembrane domains, and mutation in which produced an SG phenotype43. These SNPs showed significant associations with four indices for CC and ACC   values of 2.35, 3.15, 3.36 and 5.34, respectively  within the ica Figs . Six hapice Fig. . NOL-1 apes Fig. . NOL-4 wely Fig. . In indiL-4 Fig. . AccessiSSG4, mutation of which affected the size of chloroplasts and amyloplasts and produced a variegated phenotype44. These SNPs showed significant associations with two CC indices in japonica    in known genes around GWAS signals for CC were detected within p.) Figs . Three hp.) Figs . Haploty01) Fig. .CHR729, mutation of which caused a number of morphological and growth defects, including reduced CC45. The eight non-synonymous SNPs showed significant associations with 2 CC indices in indica (SFH_Ind. and TSH_Ind.)   were detected within d.) Figs . Three hice Fig. . SFH and01) Fig. .OsFRDL1. Knockout of this gene resulted in leaf chlorosis46. Both SNPs were significantly associated with two ACC indices in indica   values of 7.08 and 9.12 in indica by Student’s t-tests  were identified within d.) Figs . Three hica Fig. . Haplotysts Fig. .NYC1 and YGL138(t) showed significant associations with CC or SG, respectively  mutant exhibits a distinct yellow-green leaf phenotype throughout development47. There were two haplotypes within NYC and YGL138(t) in each subpopulation due to rare non-synonymous SNPs and obvious differentiation of indica and japonica. In conclusion, the 20 non-synonymous SNPs could be possible functional SNPs within six known genes responsible for natural variation in CC and SG; natural elite alleles/haplotypes were identified for the six known genes with larger effects on CC and SG.Two (Chr1_7024543 and Chr1_7027178) and one (Chr11_2514115) non-synonymous SNPs within indica were associated with SFH, TSH, ADSF, RDSF, CSF and TCS with −log(P) values of 8.66, 7.60, 6.57, 5.14, 5.82 and 5.89, respectively  values of 5.99 and 5.76, respectively 49, we estimated a candidate region from 15.8 Mb to 16.3 Mb  of OsSG1. Three non-synonymous SNPs were identified but their MAFs were lower than 0.05 in indica  and ACC (CSF = 69.7 and TCS = 191), but weaker stay-green capacity (ADSF = 11.6 and RDSF = 0.27) than indica varieties carrying OsSG1-2 or OsSG1-3. The results suggested the sequences in OsSG1 for maintaining protein function were highly conserved, and that phenotypic differences between the three haplotypes could be caused by the differences in expression level in indica.To explore possible functional sequences within ica Fig. . ConsideACC Fig. ; three aACC Fig. . The hap1-2 Fig. . Clear dVOA Fig. . Varietiindica and japonica, we made a phylogenetic analysis and signature identification of selection using 368 cultivated and 446 wild rice accessions. According to the phylogenetic tree calculated from SNPs in 152 known genes and OsSG1, there was a distinct differentiation between japonica and indica  group from southern China, and indica accessions were close to Or-I (indica-like wild rice) , respectively (Table indica (πW/πI > 3) and japonica (πW/πJ > 3), respectively. After considering the values of Tajima’s D (Tajima’s D < −2) of these genes in their respective subpopulations, we found that nine genes had been strongly positively selected in indica, whereas 43 genes were strongly selected in japonica , and distributed in areas with long days and low light intensity  Fig. . Thus thly Table . Twenty-ity Fig. . We ther26. Despite these research results the practice of breeding by molecular design is still difficult and requires more precise genetic dissection of agronomic traits and precisely identified chromosome haplotypes.With development of functional genomics, high throughput genotyping and phenotyping technologies, more than 2,200 genes have been cloned and functionally identified in rice by forward or reverse genetic strategies. Based on those studies, molecular knowledge has been increasingly applied to the breeding of high yielding, superior-quality rice. This is considered to be a powerful strategy to meet the challenges of future crop breeding, particularly in pyramiding multiple complex traits50. In this study, we conducted GWAS using a diverse worldwide population of 368 rice accessions, following a comparison of GWAS results and 152 known genes for CC or SG. Twenty-five known genes were around GWAS signals in GWAS, implying that these genes could be involved in genetic variation of CC or SG, and could be used in molecular breeding for high photosynthetic efficiency.High throughput genotyping and GWAS provide strong support for determining the effect of known functional genes in natural populations and exploration of superior natural variation48. Based on high-density SNPs from the 3KRGP, we extracted 811 non-synonymous SNPs within known genes for CC or SG. After removing SNPs with MAF < 0.05, 20 non-synonymous SNPs within 6 of 25 genes ) were associated with at least one of six indices, implying that the 20 SNPs could be real functional SNPs accounting for natural variation in CC or SG. The results of haplotype analysis using the 20 non-synonymous SNPs can provide guidance for pyramiding desirable alleles associated with CC and SG in molecular design of genotypes with high photosynthetic efficiency.Gene function can be manipulated by alterations in expression level and protein sequence, and polymorphisms causing protein-coding differences are most likely to be important functional SNPs associated with target traitsOsSG1 might be a major gene accounting for variation in CC, and also control of SG. In GWAS of indica, strong signals of six indices around OsSG1 suggested that there could be a pleiotropic gene regulating CC, SG and ACC in a single LD block. KEGG pathway analysis showed that OsSG1 was involved in glutathione metabolism, together with four known genes RNRS1, RNRS2, OsAPX2 and RNRL1 for CC or SG. In a previous study16, mutants of RNRS1 and RNRL1 produced chlorotic leaves in a growth stage-dependent manner under field conditions, and yeast two-hybrid analysis showed that the interacting activities were RNRL1:RNRS1 > RNRL1:rnrs1 > rnrl1:RNRS1 > rnrl1:rnrs1, which correlated with the degree of chlorosis for each genotype16. The activity of RNRL1 homolog RNRS2 could supplement RNRS1 activity in chloroplast biogenesis in developing leaves16. OsAPX2 mutants had significantly lower CC than wild-type plants and over-expression increased CC to a level higher than in wild-type plants51. Since these genes involved the regulatory mechanism of CC or SG, we suggest that further investigation of the glutathione metabolic network could help in genetic dissection of CC and SG.One important finding in our study was that indica and japonica, and further subpopulation differentiation. Previous studies and this study show that CC and SG in japonica are significantly higher than in indica25. However, the pathway of physiological change during domestication of distinct subpopulations remains unclear. Genetic analysis using well-characterized domestication loci indicated that japonica and indica were close to wild rice subpopulations Or-III and Or-I, respectively. Japonica was first domesticated from Or-III in southern China (Fig. japonica (Table japonica, indica and wild rice, higher CC and SG could have enabled japonica to adapt to higher latitudes with longer days and lower light intensities (Fig. NOL and SSG4 genes for chlorophyll content, the NOL-1 and SSG-1 haplotypes for higher CC levels were detected only in japonica, implying that they were new mutations acquired during domestication of japonica. All haplotypes of CHR729 and OsFRDL1 were detected in wild rice, and CHR729-2 and OsFRDL1-1 were prevalent haplotypes in japonica whereas CHR729-1, OsFRDL1-2 and OsFRDL1-3 predominated in indica. Our results suggest that during domestication of japonica, the planting areas gradually extended from low altitudes to high altitudes along with the changes in light intensity and daylength. During this adaptation new natural mutations for higher CC and SG were preserved, and gradually accumulated along with natural elite variation from wild rice.Asian cultivated rice is well known for its rich within-species diversity with two major subspecies, ina Fig. . Our phyies Fig. . Howeveries Fig. . Among tSupplementary Table S1 Correlation coefficients of six indices in the entire association panelSupplementary Table S2 Differences between indica and japonica accessions for 6 indices of chlorophyll content and stay-greenSupplementary Table S3 Significant association signal for SFH in full populaiton, indica and japonicaSupplementary Table S4 Expression of 20 genes within candidate region on chromosome 7Supplementary Table S5 Nucleotide diversity and Tajima's D test for 152 known genes and OsST1.Supplementary Figures