GKnowMTest—Genomic Knowledge‐guided Multiplte Testing) to enable a prioritized pathway‐guided GWAS scan with a very large number of gene‐level annotations. We demonstrate that the proposed strategy improves overall power and maintains the Type 1 error globally. Our method works on genome‐wide summary level data and a user‐specified list of pathways . It automatically reweights the input p values by incorporating the pathway enrichments as “adaptively learned” from the data using a cross‐validation technique to avoid overfitting. We used whole‐genome simulations and some publicly available GWAS data sets to illustrate the application of our method. The GKnowMTest framework has been implemented as a user‐friendly open‐source R package.Many variants with low frequencies or with low to modest effects likely remain unidentified in genome‐wide association studies (GWAS) because of stringent genome‐wide thresholds for detection. To improve the power of detection, variant prioritization based on their functional annotations and epigenetic landmarks has been used successfully. Here, we propose a novel method of prioritization of a GWAS by exploiting gene‐level knowledge  and show that it further improves power. Often, disease associated variants are found near genes that are coinvolved in specific biological pathways relevant to disease process. Utilization of this knowledge to conduct a prioritized scan increases the power to detect loci that map to genes clustered in a few specific pathways. We have developed a computationally scalable framework based on penalized logistic regression (termed  To discover all such variants by standard GWAS (or meta‐anlaysis) alone would require huge sample sizes  have discovered thousands of single nucleotide polymorphisms (SNPs) robustly associated with various common complex diseases and traits  related to immunity. Due to accumulation of several small effects of multiple variants, such pathways can be identified as “relatively important” empirically based on a “peek” at the GWAS data. A SNP “A” on a gene that maps to k′ (<k) of these out of these k important pathways is a priori much more likely to be a true susceptibility SNP, compared to another SNP “B” that is in a gene desert or a SNP “C” that maps to genes in other pathways, and hence it is natural to upweight SNP “A” relative to SNPs “B” and “C” as a genome‐wide multiple‐testing strategy to improve overall power of discovery. In this strategy, multiple SNPs clustering in some common pathways boost each other to receive higher weight, whereas SNPs in isolated or insufficiently annotated genes automatically get down‐weighted.In the context of prioritizing SNPs in GWA studies, the primary focus has been on SNP‐level annotations such as those based on physical context of a SNP (genic/intergenic or exonic/intronic) and tissue specific functional annotations overlapping a SNP , or SNPs assoicated with multiple correlated traits . For this, we need a pathway‐guided GWAS strategy that can help discover more SNPs by improving the statistical power of detection. Some existing methods allow incorporation of pathways in GWAS analysis using Bayesian models to achieve prioritization and return posteriors or Bayes factors as output  and a user‐supplied list of pathways as input. It maps the SNPs onto genes  and then onto the pathways. The “prior” chance of each SNP to be “truly” associated, is estimated in a data‐driven manner based on the “degree of enrichment” of the pathway(s) that the SNP maps to. Once the priors are determined, the multiple testing penalty is allocated differentially so that SNPs with higher prior are penalized less and have greater chance of being discovered. Here, we used the p value weighting method  and at the same time weights can be allocated “optimally” in the sense of maximizing overall power of discovery. The method returns weighted p values of each SNP which can be treated as p values from a standard GWAS in the sense of global error and a standard Manhattan plot can be used to visualize results.The proposed method starts by accepting summary‐level data  to fit our model and estimate the prior probability of each SNP based on annotations. This approach only requires fitting penalized logistic regression for model fitting, making it fast and stable in terms of convergence even in presence of large number of annotations. Through genome‐wide simulations we demonstrate that our approach maintains correct global false‐positive rates and improves power. We demonstrate application of the method on multiple real GWAS datasets . The framework has been implemented in an R package “GKnowMTest” (Genomic Knowledge‐guided Multiple Testing) that is freely available from github. It will be submitted to the R/Bioconductor repository that conveniently hosts a rich collection of annotation packages and mirrors some publicly available pathway databases and ontologies.To achieve computational scalability, we use a novel technique termed PMLR (22.1Z‐scores) and (b) “Prioritized‐Testing” step where Type 1 error is allocated differentially to the annotation categories based on the enrichments. For each module we propose efficient and computationally scalable methods as described below.Our algorithm consists of two distinct modules for (a) “Enrichment‐Estimation” of annotation categories, where the degree of enrichment of each functional annotation is estimated from the genome‐wide data  defined based on knowledge external to the GWAS. Specifically, here we consider gene‐level priors in the form of user‐specified “gene sets” (pathways). SNPs are mapped to pathways based on their physical proximity to genes of each pathway (see section “Mapping SNPs to gene‐level annotations”). Thus each SNP is mapped either to a few pathways or remains unmapped. This provides an annotation matrix for the SNPs, Vjk=1 if the jth SNP maps to the kth annotation and Vjk=0 otherwise.Suppose we have summary‐level data in the form of Z‐scores of each SNP are marginally drawn from a two‐group mixture model of a null ) and an alternate distribution f1, with the “prior probability of being a true SNP” πj possibly varying across SNPs. We assume that this prior probability is determined by the annotations of the SNP through a logistic linear model. Thus, the Z‐score of the jth SNP is distributed as follows:γ denotes the log‐odds of a SNP to be associated if it does not belong to any of the annotations and η denotes the vector of log‐odds ratios contributed by each annotation. Logistic modeling is commonly used to accommodate large number of prior annotations , whereas in other contexts it indicates causality of a SNP in a multivariate model, which is meaningful in the context of identifying causal variants.We assume that the 2.1.2The product of marginal likelihoods of each SNP can be maximized to obtain reasonably good estimates (see “Supporting Information Methods”) of the parameters. It is given as product of the mixture density for each SNP that isf1 to be normal .As the first step, the prior information is ignored and marginal “Posterior Probability of Association” (mPPA) of each SNP is derived. This can be done in various ways such as by assuming Vi denotes the ith row of the annotation matrix V, corresponding to the ith equivalence class. To fit the parameters we minimize the function below for specific values of α and λ, that is,loglik denotes the log‐likelihood function based on the logistic model. For model fitting, see sections “Computation and Scalability” and “Choice of Tuning Parameter” below. Finally, the shrunk estimates of the SNP prior probabilities are given by the fitted values.To derive SNP prior probabilities, we use penalized logistic regression of the mPPA values on the annotations and a sparseness penalty as follows. When there are a large number of SNPs having identical set of annotations the SNPs can be merged into an equivalence class and represented as a single row. The average within‐class mPPA value is used as the response. This strategy can provide substantial computational savings (see “Supporting Information Methods”).2.1.3p values  can be used after the reweighting of p values.After prior probabilites are estimated, existing methods control FDR or posterior probabilities of SNPs or regions to be true  in each annotation group, we use a decision theoretic criterion similar to Roeder and Wasserman ((α=.05).Given prior probabilities of truths asserman . We conswi′ are the SNP‐level p value weights, the expected number of true positives isF1¯ and Φ¯ denote the null and alternative upper tail CDFs of the Z‐scores. This needs to be minimized under the gobal Type 1 error constraint w′¯=1 (see “Supporting Information Methods” for details). The weighted p values discussed above are not uniformly distributed under the null hypothesis. However, they can be treated as usual p values in the sense that the global (average) Type 1 error is maintained at the target level α.Assuming βi=log[πiˆ1−πiˆ]. We performed limited experiments with higher order polynomials, but did not find any significant improvement after cubic exponent. Invoking the constraint that w′¯=1, we get,, an unconstrained optimization problem. As an alternative distribution we used N here, but a nonparametric CDF estimate F1ˆ can be used instead.Here, we assume the SNP‐level weights in a class  to be a cubic function of the prior log‐odds of truth for SNPs in that class, that isEquation  can be m. This is similar to simple weights “proportional to odds‐ratio” considered previously . Any SNP mapping to any of the genes in a particular pathway was considered to map to that pathway/annotation (See “Supporting Inforamation Methods”). We used biological knowledge from three different databases. These were (a) the gene lists for 229 KEGG pathways  is chosen by performing 10‐fold Cross Validation. We divide the entire SNP set into training and test sets 10 times for cross‐validation (CV). For each CV replicate, we randomly assign half of the SNPs to the training set and the remaining to the test set. Assignment of SNPs is done in short segments  to account for LD structure. We fit our penalized logistic regression model using glmnet on the training data and calculate overall power  for the “Simple Weighting” method in the test data for different values of λ. We select the λ for which the maximum power is obtained on an average over 10 sets.The tuning parameter 2.1.6We have implemented our algorithm described above into a scalable pipeline within an R package “GKnowMTest.”locfdr . For the real data sets which we analyzed, LASSO, Ridge, and Elastic‐Net penalties returned very similar results (results not shown). Hence we used LASSO  throughout for our analyses and simulations.The sparse generalized linear model (GLM) in Equation  can be eoptim function in R. To assess the computation times of our Enrichment Estimation (PMLR) to a MLE based approach, we compared the running times with FGWAS software .We developed an in‐house algorithm for directly simulating a large number of (independent) causal loci retrospectively for a given number of cases and controls. The algorithm (implemented in R) works by simulating a latent variables for causal loci from multivariate normality with different means in cases and controls, and then thresholding the latent variables to get the genotypes (see “Supporting Information Methods”). In this way, 500 replicates of the 2.2.2p < 1e−05) of psoriasis from the GWAS catalog. The simulations were repeated 500 times to check the Type 1 errors for various levels by treating SNPs outside ±1 MB of the causal SNPs as null SNPs. We used the Z scores of the causal SNPs (but not neighboring SNPs) to assess power at α=10−3 or 10−5 or 10−7.Our simulations were modeled using psoriasis, in the sense that the MAFs and ORs of the causal SNPs and genomic locations were chosen to be close to that of 25 suggestive GWAS loci  and Cubic weights (CPW) as discussed in Secion 2, we checked if the Type 1 errors are maintained at different levels starting from 0.1 to stringent levels 1e−07.p values for different weighting schemes. It was observed that the Type 1 error for both the SPW and CPW schemes are correctly maintained within target levels. It should be noted that here we considered only global Type 1 error (not SNP specific) averaged over all null SNPs across the genome using 500 whole genome simulations  was consistently higher than unweighted analysis across levels of significance . The power of the two alternative weighting schemes is similar, although cubic weights (CPW) performed slightly better than simple weights (SPW) at lower levels. In terms of power for specific SNPs , we found that while some SNPs gain power, some are essentially unaffected while a few of them lose power. It is evident from the figure that the gain in power is much more both in terms of magnitude and number of causal SNPs compared to the loss of power. Isolated true SNPs (not connected to other true SNPs through supplied annotations) are expected to lose power. Thus supplying better annotations in pathway‐guided search can help in discovering additional loci missed by primary unbiased GWAS (see Section 3.1.3Tp denoting “number of true pathways” (selected for enrichment) and Tg denoting the “number of true genes” allocated to each such pathway. We found that the overall power of the study is considerably higher for the “moderately connected” list and highest for the “highly connected” pathway list. The increase of power with connectivity is maintained across all significance levels. Further, we did simulations with increasing connectivity among a randomly selected set of “null genes” (instead of “true genes” used for power simulations), and confirmed that there is no inflation of global Type 1 error . These results show that when the causal loci are more connected to each other in pathways, they contribute to the enrichment of the pathway and thus increase the overall power of detecting causal SNPs from weighted analysis.It is expected that when a causal gene is “more related” to other causal loci through pathways, it should have a higher power of being detected. The prioritization method works better when more causal SNPs cluster into a smaller number of pathways. To illustrate this, we generated three pathway lists with increasing degree of connectivity among “truly associated” genes  and type 2 diabetes. The type 2 diabetes data results are explained below. Results for the other diseases are described in “Supporting Information Results” section.3.1.4λ values  for the LASSO penalty.It is crucial to estimate the degree of penalization from the data, because if penalty is over‐specified or under‐specified it can lead to loss of power. To verify this, we estimated power of the causal SNPs for a sequence of λ values  and increases gradually with decreasing λ . This is because when λ is too large, the weights shrink towards zero, and the PMLR method becomes essentially similar to “unweighted analysis” which has lower power. However, for very small λ values overfitting can result in loss of power. The figure shows that weighted analysis with cross‐validation avoids over‐fitting and has the highest power overall across levels.Figure 3.23.2.1It is of interest to look at the computational speed‐up achieved using PMLR instead of full‐likelihood maximization generally used in other tools. We used FGWAS . For this, we considered KEGG pathways  values and glmnet for penalized logistic regression using LASSO penalty . A decreasing sequence of λ values was used to derive priors and hence p value weights .Using the summary results obtained from the analysis of psoriasis data  with various λ values for LASSO penalty. As expected, the weights have increasing variability around 1 as the degree of shrinkage (λ) decreases. For the largest λ value, the coefficients are all shrunk to zero and hence the weights are all 1 .Figure 3.2.3p < 5e−08) from the GWAS catalog  except toward the bottom of the table, containing SNPs for which there is no association signal in this data. Further the SNP rs20541 mapping to IL13 gene become genome‐wide significant using the MERGED annotation. The results for “MERGED” seem to be dominated by “GO.BP” which has relatively much larger number of annotations but there were exceptions such as rs492602 SNP in the FUT2 gene. Overall, the MERGED annotation set was the most powerful giving close to smallest p values in most cases. Thus, in situations where there are multiple annotations with different kinds of information, creating a pooled annotation set (such as “MERGED”) may be effective. We used the MERGED annotation set for all the reweighted GWAS analyses in this article. It should be noted that “MERGED” is just an example of a generic annotation set, such annotations sets can be defined in many different ways. However, a suitable set of annotations to be used for a specific reweighted GWAS analysis should be predetermined by the investigator rather than using a trial‐and‐error approach  and MERGED annotations separately and inspected 18 known psoriasis associated SNPs  consortium website. These data were based on the stage 1 meta‐analysis of 8,130 T2DM cases and 38,987 T2DM controls of from European population . We found one crossover locus of IL13 for psoriasis, two crossover loci BLK and ITGAM/ITGAX genes for SLE and four crossover loci near TFPI, CYP2A1, PECAM1, and CXCL12 for CAD data set. Unweighted and weighted 4In this article, we introduced a new efficient analytic pipeline to enable pathway‐guided search in a GWAS. Using this pipeline we illustrated that substantial power improvement can be achieved in GWAS by using knowledge of biologically meaningful categorization of genes, for example, pathways, ontology terms and so forth. The power gain is highlighted by the “novel loci”  detected in the pathway‐guided reanalyses of real GWAS data sets. Using, whole‐genome simulations, the “pathway‐guided GWAS” is shown to have correct global false‐positive rate, and improved power for well connected SNPs. As expected, there is loss of power for isolated SNPs, but we found such power loss to be less common and also much less in magnitude. Moreover, with better prior annotation strategies and as annotation databases become more comprehensive and accurate, most “isolated” true SNPs are likely to be connected with other causal genes, further reducing this concern. Isolated SNPs involving novel biological mechanisms (without known connections in annotation databases) would have higher power to be detected by primary (unbiased) GWAS analysis.p value weighting to control FWER. The primary advantage of PMLR over existing methods for enrichment estimation in GWAS is that it involves two simple steps, local FDR calculation followed by a  logistic regression. Thus it can be implemented using standard GLM and penalized GLM software in any statistical package. Also, compared to some existing prioritization algorithms requiring high‐dimensional MLE or posteriors, it is faster, scalable to large number of annotations and less likely to encounter numerical problems such as nonconvergence, making it suitable for routine use.Our pipeline consists of a new method called PMLR for adaptively estimating “enrichments”  followed by optimal A limitation of our method, which is not specific to the method proposed here, is that the local FDR approach ignores the association  structure of the SNPs in deriving the marginal PPAs. To our knowledge, all other existing prior‐incorporation methods assume independence across SNPs or SNP blocks. In the future, we plan to overcome this limitation by allowing for correlations among SNPs in the mPPA estimation step in a computationally efficient manner. A key benefit of our modular approach is that our pipeline can be easily adapted to be used in conjunction with other alternative methods for mPPA estimation and/or Type 1 error allocation methods in future.p value threshold approach. Second, we have developed a statistically powerful method simply from pathway knowledge (gene‐level knowledge), without the use of more detailed information such as SNP‐level annotations. Existing SNP prioritization methods make assumptions such as “one true SNP per region” to identify causal variants, so they are not applicable (without modifications) in our context, that is, discovery of “associated SNPs” using gene‐level priors. In the future, a careful study of SNP‐level annotations and pathways would be required to understand the best way of combining these two sources of information in a scalable manner.We have incorporated gene‐level prior knowledge only. Our method is not comparable to the existing prioritization methods. To our knowledge, no existing method incorporates gene‐level priors  using a conventional The list of pathways supplied to our method should generally not be restricted to “candidate pathways” thought to be important for the disease under study. An unbiased list of pathways or “gene sets” (possibly a large number) representing meaningful biological categories may be supplied. Depending on a disease context, important categories of genes  may be added to the pool of annotations to potentially improve power. However, restricting genes  can reduce power. Also, it should be recognized that reweighted analysis of a GWAS data set is as likely as the original GWAS to yield a false‐positive finding. The chance of making a false‐positive discovery is further enhanced if an investigator explores multiple annotation databases until a discovery is made. To guard against such false‐positives, we recommend that an investigator should predetermine the annotations to be used and also follow stringent criteria for replication and validation to confirm a novel finding.In conclusion, we have provided a framework and have developed a method that can enable routine use of pathway and other gene‐level annotations for prioritization in GWAS. Pathway‐guided GWAS can be effectively used in practice for secondary reweighted GWA scan to make additional discoveries beyond those revealed by the primary unbiased GWAS analysis. Our method allows flexibility to investigators to either use standard pathway or ontology databases or define their own gene sets. Being modular in nature, it allows for future extensions to more genomic data types and bioinformatic knowledge from multiple sources.Supplementary InformationClick here for additional data file.


Bemisia tabaci is one of the main pests of vegetable crops in the tropics, including in Indonesia. The vertical distribution pattern of B. tabaci on investigated vegetable plants can be used to establish a sample observation unit in monitoring activities of pest control management. This data showed the vertical distribution pattern of B. tabaci on vegetable plants gathered from two different locations, namely from the high land of Ciwidey District, Bandung City  and the low land of Sliyeg District, Indramayu City . The data of B. tabaci nymph population were then analyzed to determine vertical distribution patterns. Specifications TableValue of the data•Bemisia tabaci population on lowland and highland vegetable crops.The data obtained here contribute to our understanding of the vertical distribution pattern of the •These data will likely be useful for practitioners and farmers when planning pest control measures.•The data can be used as basic data for reference in determining the best plant sample unit (leaf position) for the future experiment1Bemisia tabaci in seven vegetable crops originating from two locations: eggplant, towel gourd, cucumber, and long beans from high land of Ciwidey; and beans, kidney beans, and tomatoes from the low land of Silyeg. The vertical distribution pattern data of B. tabaci is important to determine on which leaves  the nymphs are clumped. This information can be used as a basis for taking leaf samples, where the nymphs cluster for integrated pest control. Several parameters related to the distribution of B. tabaci in various vegetable crops were analyzed. Data on the distribution pattern, dispersion index value, and χ2 value of seven vegetable crops are presented in The data report the vertical distribution patterns of B. tabaci nymphs was evenly distributed , while B. tabaci nymphs were clumped on the towel gourd. On the middle leaves, the population of B. tabaci nymphs was random, clumped, and uniformly distributed. On the old leaves, the population of B. tabaci nymphs was mostly clumped or follow a negative binomial distribution. The distribution pattern of the B. tabaci nymph population was obtained from the dispersion index value and χ2 value. The calculated χ2 values were compared with χ2 tables at = 0.05 and 0.95 with n = 1 degrees of freedom. χ2 tables  < χ2 counts < χ2 tables  show a random distribution, χ2 counts < χ2 tables  shows the distribution was evenly distributed, and when χ2 count > χ2 table  the distribution was clumped. String beans and kidney beans were two commodities with high population density, but the nymph populations on shoots do not form aggregates, where the distribution followed the positive or Poisson distributed binomials. As for the middle and old leaves, the population spreads following a negative binomial distribution.On the young leaves, the population of B. tabaci nymphs for the seven vegetable crops are presented in Data on the grouping index value of B. tabaci nymphs on vegetable plants from Ciwidey and Indramayu is presented in B. tabaci nymphs on the young leaves than on middle and old leaves. Whereas in long bean and tomato, the number of B. tabaci nymphs in young, middle, and old leaves was not significant.The average vertical distribution data for 22.1B. tabaci, specifically eggplants, cucumbers, towel gourd, and long beans from the Sliyeg Subdistrict, Indramayu District, and kidney beans, string beans, and tomatoes from Ciwidey Subdistrict, Bandung District. Each plant was divided into three parts: young leaves, middle leaves, and old leaves. Four leaves were then taken per section. Samples were determined diagonally (consisting of five plots), plucked, put into plastic containers or jars that perforated for ventilation, and the total number of B. tabaci nymphs counted.Leaf samples were derived from plants that were attacked by 2.2B. tabaci nymphs was determined by counting all of the nymphs on a leaf sample. On each sample, all B. tabaci nymphs were counted.The number of 2.3I) was calculated, and if the distribution was clustered, the grouping index (k) was also calculated. The vertical distribution pattern was tested using the Chi-square (χ2) test for goodness of fit To determine the vertical distribution pattern of the population, the dispersion index , I = 1 shows a random distribution pattern (Poisson), and I < 1 shows a uniform distribution pattern .The distribution pattern can be determined from the value of FxStep 2: The frequency distribution r number of individual nymphs. The data are summarized in the following table.The frequency distribution was summarized as the number of individual nymphs per sample unit, that is the number of sample unit with 0, 1, 2, …, P(x) grouping index (k)Step 3: The negative binomial probabilities P(x) were used to calculate the probability of finding x individual nymphs in a leaf sample unit, where x = 0, 1, 2, …., r individual nymph. P(x) was calculated using the following equation:Negative binomial probabilities I analysis from the distribution pattern suitability test is clumped , then proceed with calculating the value of the grouping index (k).If the result of The k (rough) value is obtained from Eq. (1)The rough k value is substituted into n = number of samplesfo = number of samples with zero individualsExStep 4: The expected negative binomial frequencies, Ex represents the expected frequencies of x = 0, 1, 2, …., r individual nymphs as the following equation:N = number of samplesP(x) = chanceStep 5: Goodness-of-fit test statistic, χ2Fx in step 2) compare to expected frequencies (Ex in step 4). The chi-square χ2 was calculated using the following equation:2 = Chi-SquareThe chi-square χ2 test was used to ascertain how well the observed frequencies . Furthermore, to determine the age of leaves that have an effect, the ANOVA was supplemented with Duncan's multiple range test at 5% significance level.The numbers of The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


Oryza sativa. An abundance of phenotypic variation was present in the three stigma characteristics of these collections. We identified four significant SNPs associated with stigma length, 20 SNPs with style length (SYL), and 17 SNPs with the sum of stigma and style length, which were detected repeatedly in more than four environments. Of these SNPs, 28 were novel. We identified two causal gene loci for SYL, OsSYL3 and OsSYL2; OsSYL3 was co‐localized with the grain size gene GS3. The SYL of accessions carrying allele AAOsSYL3 was significantly longer than that of those carrying allele CCOsSYL3. We also demonstrated that the outcrossing rate of female parents carrying allele AAOsSYL2 increased by 5.71% compared with that of the isogenic line carrying allele CCOsSYL2 in an F1 hybrid seed production field. The allele frequencies of AAOsSYL3 and AAOsSYL2 decreased gradually with an increase in latitude in the Northern Hemisphere. Our results should facilitate the improvement in stigma characteristics of parents of hybrid rice.Stigma characteristics are important factors affecting the seed yield of hybrid rice per unit area. Natural variation of stigma characteristics has been reported in rice, but the genetic basis for this variation is largely unknown. We performed a genome‐wide association study on three stigma characteristics in six environments using 1.3 million single‐nucleotide polymorphism (SNPs) characterized in 353 diverse accessions of  1 hybrid seeds every year. The yield of F1 hybrid seeds is affected by stigma characteristics. The cloning of AAOsSYL2 filled a gap in the genetic study of style length and could be used for improving stigma characteristics to increase the yield of F1 seeds.The utilization of heterosis in rice is an effective strategy to enhance the grain yield. However, this entails the production of F The yield of F1 hybrid rice seeds is determined by the outcrossing seed setting rate for a given number of spikelets per unit area. The outcrossing seed setting rate in rice is mainly affected by the stigma exertion percentage, which is largely determined by stigma length (STL), style length (SYL), and the sum of stigma and style length (TSSL) , ) Figure .et al., Oryza ranges from 1.25 to 3.21 mm  of 2.9–20.0%; 9 of the 13 QTLs have an R2 > 10%. The additive effect of QTL qSSL‐3 on chromosome 3 is the largest and can increase TSSL by 9.2%  for controlling TSSL has also been identified  data, we performed a genome‐wide association study (GWAS) and identified significant SNP loci. Further, using the method of gene‐based association we identified a novel causative gene OsSYL2 for SYL. The function of AAOsSYL2 was validated by transgenic complementation testing and evaluation of F1 seed production in the paddy field. These results filled in the gap from gene cloning and functional analysis of stigma characteristics. The gene‐based association method used in this study resulted in the ability to access the causative gene/SNP for OsSYL2. The favorable alleles identified herein could facilitate improvement in stigma characteristics of the parents of hybrid rice.Hybrid rice has been cultivated commercially in China for 43 years. In the past 20 years, the average seed production yield has remained stagnant at 2.25 t haindica and japonica subspecies  Figure , the phees Table ; the meaes Table ; and thees Table , with CVes Table . These rs Figure . The ress Figure , indicatA total of 1.90 billion paired‐end reads of length 150 bp were obtained from 353 rice accessions resequenced using the Illumina resequencing platform, with an average coverage depth of 4.36‐fold for each accession. After mapping against the Nipponbare reference genome sequence, we identified 1 326 094 SNPs after excluding the sites with missing data for more than 18% of all the accessions. We observed 463 740 SNPs in the genic regions: 48 054 synonymous, 52 283 non‐synonymous, 270 622 intronic, 62 181 3′‐untranslated region (UTR), and 30 600 5′‐UTR SNPs.indica and japonica groups had high values of FST , including a total length of 1.6 Mb in japonica rice and 0.7 Mb in indica rice, indicating that they contain gene loci that may be involved in geographic adaptation  was higher than that of japonica rice (4.34 × 10−4). These results indicate the presence of rich genomic diversity at the SNP level among the 353 accessions.Based on the SNP data, the SNP density and nucleotide diversity (π) showed great variation along chromosomes Figure a,b. Somen Figure c. The FK value  in the whole rice population, the indica subgroup, and the japonica subgroup. The extent of LD was measured by the chromosomal distance at which r2 decreased to half its maximum value. The LD decay distances in the whole rice population, the indica subgroup, and the japonica subgroup were 177 kb (r2 = 0.26), 57 kb (r2 = 0.26), and 214 kb (r2 = 0.28), respectively g et al.  (167 kb)Using the mixed linear model with correction of kinship bias, we conducted GWAS between stigma characteristics and SNPs [minimum allele frequency (MAF) > 0.05] in the 353 rice accessions. In this population, 41 significantly associated SNP loci were detected in the 34 LD regions Table .R2 . Hereafter, the gene Os03g0407400 is referred to as OsSYL3. OsSYL3 was classified into two haplotypes based on three SNPs in its cDNA containing one SNP in an UTR, one in an intron and one missense SNP in the coding region   in the 16.69–16.87 Mb region, there were 15 candidate genes associated with the significant SNP loci Figure a,b. In tn Figure . This mi) Figure .AAOsSYL3 was higher than that of CCOsSYL3 in young panicles at differentiation stages 6, 7, and 8, but no significant difference was found at stage 5  with longer TSSL and SYL and three accessions  with shorter TSSL and SYL. The results showed that the expression of AAOsSYL3 in each of the three accessions with longer TSSL and SYL was significantly higher than that of CCOsSYL3 in each of the three accessions with shorter TSSL and SYL. By searching the China Rice Data Center (http://www.ricedata.cn/gene/) websites, we found that the gene locus Os03g0407400 was identical to Grain Size 3 (GS3) reported by Fan et al.  RT‐PCR results showed that the expression of  Figures  and S8. n et al. . The all10P ≥ 5.5); it was located within the gene locus Os02g0733900 (MSU ID LOC_Os02g50110). Hereafter, gene Os02g0733900 is referred to as OsSYL2. The full length of OsSYL2 is 602 bp, including one exon and no introns. Gene OsSYL2 encodes an 80‐amino‐acid protein. No putative conserved domains have been detected for OsSYL2. OsSYL2 was classified into two haplotypes based on three SNPs in its cDNA containing one missense SNP in the coding region and two SNPs in the UTR   Figure  and S8. R Figure . The misR Figure . The ave) Figure .AAOsSYL2 was higher than that of CCOsSYL2 in young panicles at differentiation stage 8, but no significant differences were found at stages 5, 6, and 7 , but found no SNP loci in the cis‐element regulatory region. We speculated that phenotypic variation between the accessions with the AA allele and those with the CC allele was caused by SNP loci in the coding sequence region. Thus, we conducted transformation of OsSYL2 gene to confirm our speculation.Based on the results of GWAS, no SNP loci found in the promoter region of AAOsSYL2 and empty vector into Nipponbare, respectively. Compared with the plants of Nipponbare genome, plants transformed with the allele AAOsSYL2 had a longer SYL whereas those transformed with the empty vector showed no phenotypic change  × purple rice accession and Nipponbare (AAOsSYL2) × purple rice accession. The potential of AAOsSYL2 for hybrid rice seed production was evaluated by calculating the percentage of purple seedlings in the germination experiment with the two F1 populations. After investigating the frequency of purple seedlings, we found that in the combination of Nipponbare (CCOsSYL2) × purple rice, the percentage of purple seedlings, which represents the outcrossing seed setting rate, was 9.47%  × purple rice, the outcrossing seed setting rate was 15.18%, an extra 5.71% compared with the former combination.To further evaluate the potential of the OsSYL2 and OsSYL3 loci in Oryzarufipogon and Oryza nivara, we sequenced eight accessions of O. rufipogon and four accessions of O. nivara at 5.64‐fold coverage. The information for 12 wild rice accessions is presented in Figure OsSYL2 and OsSYL3 loci were CC in both O. rufipogon and O. nivara, which was similar to OsSYL2 and OsSYL3 loci in japonica rice. We investigated the stigma characteristics of wild rice and found that STL was longer than SYL. The average SYL of japonica rice was 0.62 ± 0.12 mm, which was not significantly different from that of O. rufipogon (0.50 ± 0.08 mm) and O. nivara (0.53 ± 0.07 mm)  was found to be mainly distributed in accessions collected from low‐latitude regions, such as southern and central China, which is consistent with the grain length distribution of accessions collected in these areas  to 19.35%  Table . The reset al. . In the s Figure , which phttp://www.gramene.org/markers/) and the China Rice Data Center database (http://www.ricedata.cn/gene/), local LD regions harboring the 13 associated SNP sites were overlapped with the flanking regions of two QTLs (qSYL3 and qSYL6) and two genes (GL7 and GS3) reported previously  could regulate SYL. The allele AAOsSYL3 increased SYL and the allele CCOsSYL3 decreased SYL.We identified two GWAS signals significantly associated with SYL to nearly single‐gene resolution. Gene i et al.  reportedu et al.  confirmeOsSYL2 is a gene newly identified in this study. The full length of OsSYL2 is 602 bp, including one exon and no introns. Gene OsSYL2 encodes an 80‐amino‐acid protein. We have demonstrated that a base C‐to‐A non‐synonymous mutation at nt 126 in the coding sequence of OsSYL2 caused the long SYL phenotype by qRT‐PCR, the complementation test and evaluation of F1 seed production potential in the paddy field. By searching the STRING website, we found that 10 proteins interacted with OsSYL2 (http://string‐db.org/cgi/network.pl?taskId=jf8rY202mAHU)  interacted with the JAZ protein to inhibit GA, resulting in a shorter SYL. The OsSYL2AA protein, containing asparagine, interacted with JAZ protein weakly or not at all, allowing us to deduce that the inhibition of GA was weakened or relieved and SYL was lengthened. These results provide the basis for the further functional research into the OsSYL2 gene.Gene ) Figure . Of thesg et al.  reportedg et al. . TherefoAAOsSYL2 allele nor the AAOsSYL3 allele, which increase SYL, are found in wild rice. This suggests that the genetic factors driving TSSL length and increased outcrossing rate in wild species are different from those mapped here. We speculated that the AAOsSYL2 allele or the AAOsSYL3 allele were naturally occurring mutations and were selected for during rice domestication.In this study we found that neither the indica rice (at low latitude) was longer than that of temperate japonica rice (at high latitude). Collectively, we inferred that there may be selective hitchhiking between SYL and rough rice grain length because the latter became shorter from indica to temperate japonica rice, which is a consequence of artificial selection. Thus, the accessions with the two alleles, AAOsSYL2 and AAOsSYL3, can be used to increase SYL in the maintainer lines  of hybrid japonica rice by crossing and marker‐assisted selection breeding methods.We also found that SYL from javanica accessions. The 206 modern improved cultivars were from China (170), Vietnam (21), Japan (7), the Philippines (6), Indonesia (1), and Malaysia (1). The 13 javanica accessions were from Indonesia (9) and China (4). Information regarding the accessions, including the variety name, country of origin, latitude, and longitude, is listed in Table To reflect geographical distribution (different latitudes) and phenotypic differences in stigma characteristics, we selected the 353 rice accessions, including 134 landraces from China, 206 modern improved cultivars, and 13 The seeds of 353 germplasms were collected, stored, and supplied by the State Key Laboratory of Crop Genetics and Germplasm Enhancement at Nanjing Agricultural University. All 353 accessions were grown during the normal season (May to October) across six different environments, over 3 years (2014–2016) and two locations. The two locations were Nanjing  in Jiangsu province and Yuanyang  in Henan province. In each environment, the field trials were conducted with two replicates, using a completely randomized block design. Each plot contained 40 plants with five rows. The plants were spaced at 20 cm × 17 cm and managed in accordance with routine agricultural management practices.http://mshot.qianyan.biz/). The STL, SYL, and TSSL were measured with a micrometer , 5 μg of genomic DNA from each accession was used to construct paired‐end sequencing libraries, with insert sizes of approximately 350 bp. Paired‐end 150 bp reads were obtained using the Illumina HiSeq X10 platform, and the raw sequences were further processed by removing the adaptor contamination and low‐quality reads, yielding a total of 0.532 Tb of genomic sequence data, with an average of 5.48‐fold genomic coverage for each of the 191 accessions. All library construction, sequencing, and sequence cleaning was performed by Mega Genomics‐Beijing (http://www.megagenomics.cn/mobile.php/).For each of the 191 accessions to be sequenced, two blades from a single plant were collected at tillering stage (1 month after seedling transplanting) for extraction of genomic DNA using the standard cetyltrimethylammonium bromide protocol  an  werhttp://rapdb.dna.affrc.go.jp/) using Bowtie 2 software. The parameter used for read mapping was bowtie2‐x<bt2‐ids> {−1<m1>−2<m2>|‐U<r>}‐S [<hit>]. The reads used for further SNP calling must have a unique mapping position in the Nipponbare genome and a mapping score of more than 60. Finally, 95% of the total reads were mapped to the scaffolds of the Nipponbare genome; the 3% of reads which did not map to any location or mapped to multiple locations were removed. The mapping results were converted to the VCF format by the software SAMtools (version 0.1.18) . Those SNPs with a MAF lower than 5% in the population were removed.All paired‐end sequence reads were aligned against the Nipponbare genome sequence downloaded from the International Rice Genome Sequencing Project , 10 repeats were performed. We analyzed the results by the EVANNO method with STRUCTURE HARVESTER    of et al., −5 as the threshold. The Manhattan and quantile–quantile plots were generated by the qqman  and the genotypic data of 1 326 094 common SNPs (MAF > 0.05) with a mixed linear model (MLM) program by GAPIT (version 2.12) , total RNA was extracted from young panicles at development stages 5–8  to perform the first‐strand cDNA synthesis by reverse transcription from 1 µg of RNA. The 18S rRNA gene was used as an internal control. Real‐time quantitative RT‐PCR was conducted in a 96‐well thermocycler  using SYBR Green (Vazyme). We set the following cycling conditions: first, denaturation, at 95°C for 5 min; followed by an amplification and quantification program, 40 cycles of 95°C for 10 sec, 60°C for 30 sec, and 72°C for 60 sec with a single fluorescence measurement; and third, the melting curve (60–95°C with a heating rate of 0.1°C sec–1 and continuous fluorescence measurement); and finally, a cooling step to 40°C. Three independent replicates were performed. The sequences of the primers used for qRT‐PCR are listed in Table −ΔCt, where ΔCt = Cttarget gene−Ct18S rRNA.Using an ultrapure RNA kit HII vector. The primer sets used for PCR are listed in Table Agrobacterium tumefaciens (EHA105) and transferred into Nipponbare. The corresponding empty vector was also transformed into Nipponbare as a control. Twenty‐five independent T1 seedlings obtained were grown in a paddy field under natural conditions. The T2 seeds harvested from T1 plants at the maturity stage were grown in the paddy field in the next rice growing season (May to October). At the tillering stage, the three allele genotypes  on the Os02g0733900 locus were determined using the primers listed in Table AAOs02g0733900 and CCOs02g0733900 plants at flowering stage.The full‐length genomic DNA of OsSYL2 gene in F1 hybrid rice seed production we selected Nipponbare (short stigma), transgenic complementary line with AAOsSYL2 (long stigma), as the female parent, and a purple rice accession as the male parent. The female and male lines were grown in a 4:2 row ratio. The male line was planted on both sides in four rows each, and the female material was planted in the middle in two rows. During the flowering stage, artificial supplementary pollination was performed twice per day. After 30 days, the seeds of the female lines were harvested. The potential of OsSYL2 for hybrid rice seed production was evaluated by detecting the percentage of purple seedlings in the germination experiment in the F1 populations. Ten thousand ‘F1’ seeds were sampled from each F1 combination and sown in plastic plates (39 cm × 39 cm × 6 cm) to investigate the frequency of purple seedlings. Two replicates were conducted for each combination.To evaluate the potential of the OsSYL2 gene in hybrid rice seed production. XD and YY performed the data analysis. XD and YY wrote the manuscript draft, which was revised by YW and DH.DH and XD designed the experiments and managed the project. XD, YZ, XC, ZF, QL, JJ, DL, YL, BF, ZW, EL, XH, SZ, DS, HW, YL, and SC conducted field planting, phenotyping, and DNA sampling. XD and YZ prepared DNA samples, qRT‐PCR and transformation analysis, and performed the test evaluating the potential of the The authors declare no competing financial interests.Figure S1. Phenotypic distribution of stigma length, style length and the sum of stigma and style length traits in the germplasm collection in six environments.Figure S2. Genetic diversity across 12 chromosomes.Figure S3. Population structure analysis of 353 rice accessions and the decay of linkage disequilibrium.Figure S4. Neighbor‐joining tree with accession ID.Figure S5. Manhattan plots and quantile–quantile plots depicting the results of genome‐wide association study for the stigma length trait using a mixed line model in the 353 cultivated rice accessions in each environment.Figure S6. Manhattan plots and quantile–quantile plots depicting the results of genome‐wide association study for the style length trait using a mixed line model in the 353 cultivated rice accessions in each environment.Figure S7. Manhattan plots and quantile–quantile plots depicting the results of genome‐wide association study for the sum of stigma and style length trait using a mixed line model in the 353 cultivated rice accessions in each environment.Figure S8. The morphology of young panicles in different development stages.Figure S9. The gene allele frequency differences at the causal polymorphisms of OsSYL2 and OsSYL3 in five geographic groups.Figure S10. Protein networks interacting with OsSYL2. Network nodes represent proteins and edges represent protein–protein association.Click here for additional data file.Table S1. Names and origins of 353 rice accessions used for association mapping and the corresponding Q‐values calculated by STRUCTURE software.Click here for additional data file.Table S2. Basic statistics of the three stigma characteristics in each environment.Click here for additional data file.Table S3. The results of joint analysis of variance for the three stigma characteristics.Click here for additional data file.Table S4. The distribution of the significant association single‐nucleotide polymorphism loci detected in the rice population composed of 353 accessions in the six environments.Click here for additional data file.Table S5. The single‐nucleotide polymorphism information in the 16.69–16.87 Mb candidate region for style length and the sum of stigma and style length traits.Click here for additional data file.Table S6. Candidate gene annotation in the linkage disequilibrium region 16.69–16.87 Mb associated with style length and the sum of stigma and style length traits.Click here for additional data file.Table S7. The single‐nucleotide polymorphism information in the 30.45–30.65 Mb candidate region for style length trait.Click here for additional data file.Table S8. Candidate gene annotation in the linkage disequilibrium region 30.45–30.65 Mb associated with style length trait.Click here for additional data file.Table S9. Correlation coefficients between grain length trait and stigma characteristics.Click here for additional data file.Table S10. The results of significantly associated single‐nucleotide polymorphism loci detected in this study overlapped with the quantitative trait loci/genes of rice stigma characteristics reported previously.Click here for additional data file.Table S11. Primers used in this study.Click here for additional data file.


NAL1 and GNP1 genes regulating the rice GNP and grain yield have been cloned, their allelic diversity, functional differences in rice germplasms, and effects of their combination on GNP and grain yield remain unclear.The grain number per panicle (GNP), which is one of three grain yield components, is an important trait for the genetic improvement of rice. Although the Oryza sativa) and 8–10 wild rice (Oryza rufipogon) germplasms, 16 and 14 haplotypes were identified for NAL1 and GNP1, respectively. The NAL1 gene had the strongest effects on GNP in indica (xian) and japonica (geng) subpopulations. In contrast, GNP1 had no significant effects in the geng subpopulation and was rare in the xian background, in which the superior GNP1 allele (GNP1–6) was detected in only 4.0% of the 198 germplasms. Compared with the transgenic lines with GNP1 or NAL1, the transgenic lines with both genes had a higher GNP  and grain yield  across 3 years. The two genes combined in the introgression lines in Lemont background resulted in especially favorable effects on the GNP.Based on DNA sequences of these two genes in 198 cultivated rice (GNP1 and NAL1 exhibited obvious differentiation and their combinations can significantly increase the grain yield in geng rice cultivars. These observations provide insights into the molecular basis of the GNP and may be useful for rice breeding of high yield potential by pyramiding GNP1 and NAL1.Our results indicated that the  Oryza sativa L.) is one of the most important staple food crops worldwide. Therefore, research aimed at increasing rice yield is critical for ensuring food production and satisfying the demands of a rapidly growing global population. Grain number per panicle (GNP), seed setting rate, effective panicle number per plant (EPN) and grain weight are the most important components of rice grain yield. Moreover, the GNP is also an important agronomic characteristic related to the ideal plant architecture  , which affects leaf morphogenesis by regulating polar auxin transport, was first cloned from a loss-of-function narrow-leaf rice mutant by Qi et al. , the sink , and the EPN in diverse genetic backgrounds  is regulated by OsGA20ox1 . So, GNP1 also has pleiotropic effects on the development of multiple traits related to the source (e.g. leaf size) and the sink . The superior NAL1 allele from the low-yielding Lemont variety can further increase 3.2%–3.8% grain yield of high-yielding Teqing variety in different environments  affecting yield-related traits, including i et al. . PreviouGNP1 and NAL1 from 198 cultivated rice (Oryza sativa) and 8–10 wild rice (Oryza rufipogon) germplasms. The allelic frequencies and the associated differences in the GNP were compared between the xian and geng subpopulations. Furthermore, the potential utility of combining NAL1 and GNP1 to increase the rice grain yield was investigated by comparing the lines with either NAL1 or GNP1 with the lines carrying both NAL1 and GNP1. The results presented herein may help to clarify the comprehensive role of these two genes alone and in combination on the GNP and the potential rice grain yield. This study may also be useful for developing an efficient way to increase the GNP, thereby increasing the overall rice grain yield.In this study, we re-sequenced xian and 43 geng varieties as well as three Aus and six admixed varieties. There were significant differences (P < 0.05) in the GNP and FLW, but not in the PH, between the xian and geng subpopulations over 3 years , and PH  per kilobase (π = 2.48 × 10− 3) of the GNP1 locus was slightly fewer than 2.64 SNPs per kilobase (π = 2.64 × 10− 3) in O. rufipogon  was nearly 2-fold higher than that in O. rufipogon. A neutrality test revealed a lack of selection signals in both subpopulations   in the 198 analyzed accessions was re-sequenced. A total of 22 SNPs and four insertions/deletions (InDels) with a minimum allele frequency > 5% were considered for a haplotype analysis. Of these polymorphisms, 19 SNPs and three InDels were located in the promoter and 5′ UTR, including two 11-bp InDels in the promoter region. Additionally, one synonymous SNP and one nonsynonymous SNP leading to the replacement of an alanine by a valine were present in the coding region, with the remaining SNP and InDel located in the 3′ UTR  were constructed based on the SNPs and InDels. A phylogenetic network for GNP1 alleles was constructed based on the 22 SNPs and four InDels in O. sativa and O. rufipogon . Haplotypes GNP1–3 (represented by Lemont) and GNP1–4 were mainly present in accessions belonging to the geng subpopulation , whereas the other four haplotypes  were predominantly detected in xian varieties   defined by one InDel at position I_1530 in the promoter region  as well as a large genetic distance from GNP1–2  is identical to that in Teqing with the superior GNP1 haplotype described by Wu et al. (GNP1–1 to GNP1–6 because of the high frequency of these haplotypes in the rice germplasms. No differences in the GNP and PH were observed between the germplasms with the two major geng haplotypes (GNP1–3 and GNP1–4) in the geng subpopulation   were identified based on one nonsynonymous SNP  were identified , whereas the other three haplotypes  were mainly present in the geng germplasms   geng, 8 (40%) xian, and 2 (10%) Aus accessions.Sixteen haplotypes  were observed based on three nonsynonymous SNPs  located at 1662 bp upstream of the ATG  that was significantly associated with the GNP and PH  located in the third exon  leading to the replacement of an arginine by a histidine was significantly associated with the GNP and FLW  with the Lemont background (LT-ILs) and the Teqing background (TQ-ILs)  and NAL1 (RM317–RM255) intervals: GNP1WNAL1W , GNP1SNAL1W , GNP1WNAL1S , and GNP1SNAL1S . We observed that the GNP of the germplasms with GNP1SNAL1S, GNP1WNAL1S, or GNP1SNAL1W was higher than that of the germplasms with GNP1WNAL1W. Interestingly, the ILs carrying GNP1SNAL1S had a higher GNP than the lines with GNP1SNAL1W or GNP1WNAL1S  (Table GNP1SNAL1S had a better average grain yield per plant (GYP) than the germplasms with GNP1WNAL1S in Beijing and Sanya  because of the substantially higher GNP associated with GNP1SNAL1S. However, the germplasms with GNP1SNAL1S had a GYP that was similar to that of the germplasms with GNP1SNAL1W because of a slightly lower grain weight and EPN  with those of homozygous transgenic lines with GNP1 derived from the high-yielding xian cultivar Teqing , NAL1 derived from the geng cultivar Lemont , or both GNP1 and NAL1. Relative to the corresponding values for Nongken58, the GNP1-carrying transgenic plants had a significantly higher GNP as well as a substantially higher PH and flag leaf length (FLL)  and PH , but a significantly lower thousand grain weight (TGW)  across 3 years  (Table To further clarify the combined effects of LL) Fig. , Table 4EPN Fig. , Table 4NAL1 was the most important locus for the GNP in both the xian and geng genetic backgrounds. These results were consistent with the findings of earlier studies involving near isogenic lines or transgenic rice lines carrying various alleles in diverse backgrounds , which may differentially affect the development of multiple traits related to leaf type, plant type, and panicle type  to G allele . In 198 panels, accessions carrying NAL1-P3 protein haplotype had more GNP in 2014 and 2016, and larger FLW in 3 years compared with those carrying NAL1-P2 and the insertion of a retrotransposon. A sequence comparison indicated that NAL1–2 and NAL1–6 differed because of a single-base InDel (S_1672) in the second intron and four nucleotide variations in the 3′ UTR  significantly associated with the GNP in the 198 analyzed germplasms (Table NAL1–2 and the other two major xian haplotypes (NAL1–1 and NAL1–3) in the xian genetic background. A sequence comparison revealed that NAL1–2 and the other two haplotypes (NAL1–1 and NAL1–3) differed regarding the presence of previously reported nucleotide variations in the fifth exon resulting in two amino acid substitutions  , even though these haplotypes include the two multiple-base sequence variations in the promoter. Interestingly, there were significant differences in the GNP between GNP1–6 and GNP1–1 within the xian subpopulation, despite the absence of the two multiple-base sequence differences in the promoter region  based on the 3 K RGP database . These landraces include a defined allele comprising the geng-like DNA flanking NAL1 in the xian background, which was likely the result of naturally occurring crosses and the subsequent introgression during domestication after the emergence of the H-type allele in the geng subpopulation. A 5895-bp retrotransposon insertion in the second exon of NAL1 was detected only in haplotype NAL1–4 of geng, suggesting that germplasms with haplotype NAL1–4 might be from the same ancestor. The NAL1–4 haplotype was present in 18 germplasms widely distributed in nine countries on five continents  , may have evolved from a unique ancient O. rufipogon germplasm. Additionally, the GNP1 nucleotide diversity in the geng landraces (π = 0.00085) was 68% lower than that of O. rufipogon (π = 0.00264). In contrast, the GNP1 nucleotide diversity in the xian landraces (π = 0.00255) decreased by only 3%   that resulted in increased grain number (sink-related trait) and wider flag leaves (source-related trait) may be the most appropriate germplasms for the molecular breeding of super high-yielding rice varieties with ideal plant characteristics and a balanced sink and source relationship. The findings described herein may be relevant for introducing superior haplotypes of a single gene into a single variety with the ideal genotype.At the single-gene level, we identified favorable haplotypes for the two genes influencing the GNP. The NAL1 allele from Lemont in a Teqing genetic background was 3.2%–3.8% higher than that of Teqing in three environments  and GNP1–6 haplotypes were respectively detected in only 2% and 4% of the 198 germplasms  in the Lemont genetic background had a higher GNP than the lines with only one strong allele (GNP1WNAL1S or GNP1SNAL1W)  Table . In the 1W Table . MoreoveL1 Table . These rNAL1 transgenic plants, the lines carrying NAL1 and GNP1 had a significantly higher flag leaf area (source-related trait), GNP (sink-related trait), and EPN, which increased the GYP by an average of 8.3%–12.3%  may enable the breeding of super-high-yielding rice varieties with optimized plant characteristics and source–sink relationships.Compared with the .3% Fig. . Similar.0% Fig. . Our resNAL1 and GNP1, respectively. The NAL1 gene had the strongest effects on GNP in xian and geng subpopulations while GNP1 had no significant effects in the geng subpopulation and was rare in the xian background. The transgenic lines with both genes exhibited higher GNP and grain yield than the transgenic lines with GNP1 or NAL1. The two genes combined in the introgression lines in Lemont background had favorable effects on the GNP. These results should contribute to rice breeding for high yield through pyramiding GNP1 and NAL1.We identified 16 and 14 haplotypes for O. sativa germplasms varying regarding the GNP, FLW, and PH from 28 countries were obtained from the China National Crop Genebank of the Chinese Academy of Agricultural Science and the International Rice Research Institute. An additional 8–10 common wild rice (O. rufipogon) germplasms were obtained from the Guangxi Academy of Agricultural Sciences, Guangxi, China. Basic information for each germplasm is provided in Additional file A total of 198 2F8, 123 BC3F7, and 46 BC4F6) in the Lemont background (LT-ILs) and 252 lines  in the Teqing background (TQ-ILs) were generated from the consecutive backcrossing and several generations of selfing between the xian cultivar Teqing and the geng cultivar Lemont  backgrounds.Two sets of reciprocal ILs comprising 201 lines  and a standard PCR protocol.Fresh leaves were collected from the field-grown plants for a genomic DNA extraction with the cetyltrimethylammonium bromide method  and then sequenced, after which one allele sequence was randomly selected. To ensure accuracy, the sequencing was performed twice with the ABI 3730 system at the Beijing Genomics Institute, China. Sequence contigs were assembled with the SEQUENCHER 4.1.2 program . Details regarding the PCR primers and sequencing are listed in Additional file Because of the existence of heterozygous genotypes in GNP1 and NAL1 genome sequences, with a window size of 100 bp and a step size of 20 bp, in which pairwise insertions and deletions were removed with DNASP (version 5.0). The evolutionary history was inferred based on the neighbor-joining method of the MEGA5 program . We used a model-based Bayesian clustering analysis method implemented with the STRUCTURE program (version 2.3.4)  for each germplasm phenotype over 3 years were calculated with the R package rrBLUP (version 4.3) Endelman . The SNPActin gene was used as the internal control. Each sample was analyzed with three technical replicates.Yong panicles were collected from five plants per germplasm at the panicle initiation stage for a subsequent gene expression analysis. Total RNA was extracted from the young panicles with the TRIzol reagent (Invitrogen), after which 2 μg total RNA was used as the template for a cDNA synthesis with SuperScript III reverse transcriptase (Invitrogen) in a final volume of 20 μl. A real-time PCR assay was performed with gene-specific primers  was digested with restriction enzymes and then inserted into pCAMBIA1300 at the SbfI and PstI sites to produce the pCAMBIA1300-GNP1–2 recombinant plasmid. The 13.8-kb NAL1 fragment was removed from pCAMBIA1300-NAL1 by a digestion with BamHI and SbfI. The resulting NAL1 fragment (13.8-kb) was inserted into the BamHI and SbfI sites of pCAMBIA1300-GNP1–2 to generate the pCAMBIA1300-GNP1 + NAL1 recombinant plasmid. The three recombinant plasmids were separately introduced into Agrobacterium tumefaciens strain EHA105 cells for the subsequent transformation of a geng variety, Nongken58. The primer sets used for the PCR amplifications are listed in Additional file For the 2 area in each plot and then air dried before determining the GYP.The three transgenic lines and Nongken58 were planted with a randomized block design (with three replicates) at a spacing of 25 × 17 cm in Sanya , Hainan province, China during December 2015–April 2016, February–June, 2017, and December 2017–April 2018, respectively. At the heading stage, the FLL, FLW, and PH were measured for the main stem of each plant. At maturity, eight plants were sampled and dried in an oven at 70 °C for 5 days, after which the EPN, GNP, and TGW were calculated. Additionally, plants were harvested from a 2 mt-test. Duncan’s multiple range test was conducted to determine the significance of any differences (P < 0.05). The data analyses were performed with the SAS software. To dissect effect of genotype-by-environment interaction (GEI) on the tested traits, variance components were estimated using multiple-site analysis with all effects treated as random. Heritability across environments was computed using the estimated variance components as VG/(VG + VGEI/s + Ve/sr), where VG, VGEI, and Ve are the variances of genotype, GEI, and residual error, respectively, s is the number of environments, and r is the number of replicates. All analyses were conducted with the PBTools package (http://bbi.irri.org/products) developed by IRRI.Differences in the phenotypic values and in the gene expression levels between the haplotypes (containing more than seven accessions) were examined by a one-way ANOVA or Student’s Additional file 1: Figure S1. Comparison of grain number per panicle, flag leaf width and plant height between xian and geng rice (Oryza sativa L.) subpopulations in 198 accessions. (a–c) Cyan and orange colors indicate xian and geng, respectively. The *, **, *** denotes significance of Student’ s t test at P < 0.05, P < 0.01, and P < 0.001, respectively.Additional file 2: Figure S2. Population structure analyses of 198 accessions. (a) Scree plot from GAPIT showing the selection of PCs for association study. (b) PCA plot based on the screen plot in the rice diversity panel. (c) Scree plot from STRUCTURE showing the selection of Q. (d) Bayesian clustering of 198 accessions using STRUCTURE program.Additional file 3 : Table S1. Details of the accessions investigated and two grain number gene haplotypes.Additional file 4 : Table S2. Nucleotide diversity of GNP1 and NAL1 genes. S, segregation sites; π, average number of nucleotide differences per site between random two sequences; θ, Watterson estimator; D1, Tajima’s D; D2, Fu and Li’D; D3, Fu and Li’F.Additional file 5 : Figure S3. Sliding-windows analysis of GNP1 (a) and NAL1 (b) nucleotide diversity in O. sativa ssp. xian, O. sativa ssp. geng and O. rufipogon. The y-axis represents nucleotide diversity (π), and the genomic structure is shown at the bottom, where the black boxes indicate exons and the white boxes indicate introns and other noncoding regions.Additional file 6 : Figure S4. Protein diversity of GNP1 (a). Comparison of grain number per panicle, and plant height between two protein types in xian subpopulations in 198 panel (b–c). Cyan and orange colors indicate GNP1-P1 and GNP1-P2, respectively. The *, **, *** denotes significance of Student’ s t test at P < 0.05, P < 0.01, and P < 0.001, respectively.Additional file 7 : Figure S5. Expression level of NAL1 and GNP1 haplotypes in young panicle at the panicle initiation stage. (a) Expression level of GNP1–1 and GNP1–6 in xian subpopulation. (b) Expression level of NAL1–2, NAL1–4 and NAL1–6 in geng subpopulation. (c) Expression level of NAL1–1, NAL1–2 and NAL1–3 in xian subpopulation. Error bars indicate SD; Letters are ranked by Duncan’s test at P < 0.05. The * denotes significance of Student’ s t test at P < 0.05.Additional file 8 : Figure S6. Protein diversity of NAL1 (a). Comparison of grain number per panicle, and flag leaf width among three protein types in xian and geng subpopulations, respectively (b–e). Cyan, orange, and lightpink colors indicate NAL1-P1, NAL1-P2 and NAL1-P3, respectively. The *, **, *** denotes significance of Student’ s t test at P < 0.05, P < 0.01, and P < 0.001, respectively.Additional file 9 : Table S3. Variance components and heritabilty estimated by multiple-site analysis.Additional file 10 : Figure S7. Comparison of grain number per panicle, flag leaf width and effective panicle number between R-type and H-type alleles located in the third exon of NAL1 gene in 3 K panel in xian and geng subpopulation, respectively. ***, P < 0.001 (Student’ s t test).Additional file 11 : Table S4. Distribution of protein mutation sites diversity at amino acid position 233 of NAL1 gene. R, arginine; H, histidine.Additional file 12 : Figure S8. Geographic distributions of different haplotypes of NAL1 and GNP1 among the 28 areas sampled.Additional file 13 : Table S5. Details of the wild rice (Oryza rufipogon).Additional file 14 : Table S6. Sequencing primers used in this study.


Hypholoma species. Low titres and complex structure constrain the exploitation of this secondary metabolite. Here, we de novo sequenced the H. fasciculare genome to identify a candidate biosynthetic gene cluster for production of naematolin. Using Aspergillus oryzae as a heterologous host for gene expression, the activity of several sesquiterpene synthases were investigated, highlighting one atypical sesquiterpene synthase apparently capable of catalysing the 1,11 and subsequent 2,10 ring closures, which primes the synthesis of the distinctive structure of caryophyllene derivatives. Co-expression of the cyclase with an FAD oxidase adjacent within the gene cluster generated four oxidised caryophyllene-based sesquiterpenes: 5β,6α,8β-trihydroxycariolan, 5β,8β-dihydroxycariolan along with two previously unknown caryophyllene derivatives 2 and 3. This represents the first steps towards heterologous production of such basidiomycete-derived caryophyllene-based sesquiterpenes, opening a venue for potential novel antimicrobials via combinatorial biosynthesis.Naematolin is a biologically active sesquiterpene produced by The online version of this article (10.1007/s12033-019-00199-x) contains supplementary material, which is available to authorised users. With the evolving resistance to almost all existing antibiotics, there is an urgent need for new classes of antibiotics that are naturally produced .Lately, new classes of antibiotics have emerged based on terpene-based structures isolated from Basidiomycetes , but to Hypholoma produce structurally diverse terpenes, including sesquiterpenes + .Compound 2 Fig. c was isoc 0.15, CHCl3); IR νmax 3325, 2972, 1379, 1046 cm−1; δH  0.90 , 1.21 , 1.48 , 1.50 , 1.70 , 1.78 , 1.80 , 2.11 , 2.52 , 2.87 , 2.93 , 3.88 , 4.18 , 4.62 , 4.80 , 4.81 , 5.20 ; δC  25.6 (C-12), 26.0 (C-2), 29.6 (C-13), 33.3 (C-11), 36.3 (C-3), 37.7 (C-10), 42.9 (C-9), 48.3(C-7), 52.3 (C-1), 61.6 (C-14), 68.7 (C-6), 113.7 (C-15), 128.2 (C-5), 139.7 (C-4), 148.0 (C-8); HRESIMS m/z 259.1675 [M+Na]+ .Compound 3 Fig. b was alsA. oryzae were assayed by disc diffusion for antibiotic properties against a panel of microbes. Compounds 1–4 all showed some weak activity against B. subtilis, although not as much as naematolin and none had any effect on the other bacteria in the test panel , and if so, were there enough candidate tailoring genes also present at the same locus to deliver production of the mature compound. Our heterologous expression of Hfas94a and Hfas94b confirmed that both enzymes produce humulene as their major product as was predicted given that Hfas94a and 94b demonstrated high sequence similarity with O. olearius enzymes Omp-6 and Omp-7 that produce protoilludane, highlighting the promiscuous feature of sesquiterpene synthases [A. oryzae generated several products, the major most likely being a caryophyllene isomer, further demonstrating the limitation of bioinformatic predictions of gene function.Naematolin is a modified form of a caryophyllene isomer. To date, there are no reported caryophyllene synthases from basidiomycete fungi, precluding the direct identification of such genes in ynthases . This ruIn connection with conserved motifs, DDxxD and NSE are the most common ones of SQS enzymes apparently responsible for their catalytic activity; however, advanced biochemical investigations on these motifs suggested the presence of more specific catalytic motifs in such enzymes –26. SequA. oryzae, we then co transformed it with its adjacent FADox biosynthetic gene, allowing the production of two novel caryophyllene oxides (compounds 2 and 3), along with two previously known oxygenated caryophyllene isomers (compounds 1 and 4). The presence of more than one hydroxyl group in the produced caryophyllene isomers suggests that FADox is multifunctional [Following our successful expression of Hfas344 in nctional . Such munctional  and fournctional , respectHypholoma species. Its antiviral, antioxidant and antimicrobial activity is well documented. However, its development as potential drug has been hindered by its low titre and structure complexity. To scale up production and further modify its chemical structure for the development of novel bioactive molecules, its biosynthetic gene cluster was a prerequisite. Our start point was sequencing the whole genome of the native producer Hypholoma and predict all potential terpene synthases. This was followed by sequence analysis and heterologous expression of putative SQS with their related tailoring genes in A. oryzae, allowing the production of caryophyllene and four analogous derivatives of which two have shown novel chemical structure. Caryophyllene itself has many medical properties, including insecticidal, apoptosis stimulator, antileishmanial and antifungal activities [B. subtilis throughout this research.In this work, we aimed to assign the biosynthetic gene cluster of naematolin. Naematolin is a bioactive bicyclic sesquiterpene that naturally initiates from caryophyllene core in tivities –36, indiThis is the first report of caryophyllene synthase being identified from basidiomycetes and of a FAD tailoring gene involving in two oxidation reactions. Revealing such genes involved in sesquiterpene antimicrobials will therefore provide an important tool to enhance effects of nature-based drugs.Supplementary material 1 (DOCX 92244 kb)Below is the link to the electronic supplementary material.


Genomic prediction using multi-allelic haplotype models improved the prediction accuracy for all seven human phenotypes, the normality transformed high density lipoproteins, low density lipoproteins, total cholesterol, triglycerides, weight, and the original height and body mass index without normality transformation. Eight SNP sets with 40,941-380,705 SNPs were evaluated. The increase in prediction accuracy due to haplotypes was 1.86-8.12%. Haplotypes using fixed chromosome distances had the best prediction accuracy for four phenotypes, fixed number of SNPs for two phenotypes, and gene-based haplotypes for high density lipoproteins and height (tied for best). Haplotypes of coding genes were more accurate than haplotypes of all autosome genes that included both coding and noncoding genes for triglycerides and weight, and nearly the same as haplotypes of all autosome genes for the other phenotypes. Haplotypes of noncoding genes (mostly lncRNAs) only improved the prediction accuracy over the SNP models for high density lipoproteins, total cholesterol, and height. ChIP-seq haplotypes had better prediction accuracy than gene-based haplotypes for total cholesterol, body mass index and low density lipoproteins. The accuracy of ChIP-seq haplotypes was most striking for low density lipoproteins, where all four haplotype models with ChIP-seq haplotypes had similarly high prediction accuracy over the best prediction model with gene-based haplotypes. Haplotype epistasis was shown to be the reason for the increased accuracy due to haplotypes. Low density lipoproteins had the largest haplotype epistasis heritability that explained 14.70% of the phenotypic variance and was 31.27% of the SNP additive heritability, and the largest increase in prediction accuracy relative to the best SNP model (8.12%). Relative to the SNP additive heritability of the same regions, noncoding genes had the highest haplotype epistasis heritability, followed by coding genes and ChIP-seq for the seven phenotypes. SNP and haplotype heritability profiles showed that the integration of SNP and haplotype additive values compensated the weakness of haplotypes in estimating SNP heritabilities for four phenotypes, whereas models with haplotype additive values fully accounted for SNP additive values for three phenotypes. These results showed that haplotype analysis can be a method to utilize functional and structural genomic information to improve the accuracy of genomic prediction. Genomic selection using genome-wide single nucleotide polymorphism (SNP) markers has been widely used in livestock and crop species , and genTo understand the performance of haplotype genomic prediction, an empirical hypothesis postulates that a haplotype additive value is the sum of all SNP additive values and an epistasis value within the haplotype plus a potential haplotype loss . Under tApplying these methods and tools to the Framingham Heart Study data, we aimed at achieving the following objectives for seven human phenotypes. The first objective was to find the best haplotype prediction model for each phenotype by evaluating numerous haplotype models based on structural and functional genomic information. Structural genomic information included fixed chromosome distance and fixed number of SNPs per haplotype block. Functional genomic information included coding genes, noncoding genes, autosome genes that included both coding and noncoding genes, and ChIP-seq data. A major question to answer was whether functional genomic information was relevant to haplotype genomic prediction and whether functional genomic information alone had the best prediction model for any phenotype. For each type of structural and functional genomic information and for each haplotype block size, four haplotype models were evaluated: haplotype additive values only, haplotype and SNP additive values, haplotype additive and SNP dominance values, and haplotype and SNP additive values and SNP dominance values. The observed prediction accuracies of these haplotype models from ten-fold cross validations were compared with those of the best SNP model either with SNP additive values only or with SNP additive and dominance values. The second objective was to assess the contribution of haplotype epistasis to the phenotypic variance, and the relationship between haplotype epistasis heritability and the increase in prediction accuracy due to haplotypes for the best haplotype prediction models. The third objective was to evaluate the performance of different types of functional genomic information for haplotype genomic prediction. Finally, we evaluated the effects of different SNP densities on the accuracy of haplotype genomic prediction to provide indications whether medium SNP densities such as those used in animal genomic prediction might be suitable for haplotype genomic prediction, and to assess the performance of various SNP densities for gene-based haplotype analysis.1, and 486,356 SNPs had known chromosome positions on GRCh38.p13. From these 486,356 autosome SNPs, two high density sets of SNP data were analyzed for prediction accuracy of haplotype models: the 380K set of 380,705 SNP markers with minor allele frequencies (MAF) of 0.05, and the 320K set of 327,430 SNPs with MAF of 0.10. Most results in this article were derived from these two high-density SNP sets. The 320K SNP set was designed to serve two purposes, to reduce the number of rare haplotypes through the removal of more SNPs with low frequencies than in the 380K set, and to determine the effect of reduced SNP density on haplotype prediction accuracy. To investigate the minimal SNP density required for haplotype genomic prediction, three medium density SNP sets of 42K, 63K and 76K were selected from the 380K and three medium density SNP sets of 41K, 65K and 82K were selected from 320K SNP sets (The Framingham Heart Study (FHS) data (2019 version) had 7565 individuals with genotypes of the 500K SNP panel that had 488,146 autosome SNPs. The SNP coordinates were converted to those of GRCh38.p13 using human dbSNPSNP sets .2 , low density lipoproteins (LDL), total cholesterol (TC), triglycerides (TG), height (HT), weight (WT), and body mass index (BMI), with 3657-7564 observations . BMI was2 . TG was 2 , but the2 . Therefo2  implemen2   ranging from 5 to 1000 Kb per haplotype block, fixed numbers of SNPs per haplotype block ranging 2-500 SNPs per block initially, but only results of 2-100 SNPs will be reported because the 300-SNP and 500-SNP blocks had severely reduced accuracies. For the 380K SNP set with MAF of 0.05, the method of fixed chromosome distance had an increasing number of haplotypes per block as the block size increases, averaging 102.06 haplotypes per block for 50 Kb blocks to 8,703.03 per block for 1000 Kb blocks; an increasing average number of SNPs per blocks as block size increases, ranging from 7.92 SNPs for the 50 Kb blocks to 141.26 SNPs for the 1000 Kb blocks; and a decreasing number of blocks ranging from 47,701 blocks for 50 Kb blocks to 2,695 blocks for 1000 Kb blocks as block size increases . In cont2. Protein coding genes were classified as coding genes, and all other genes were classified as noncoding genes. Long noncoding RNAs (lncRNAs) were the majority (84-88%) of the noncoding genes with at least two SNPs per gene for haplotype analysis  filesanalysis . The ChIagle 5.1  with defHaplotypes using functional genomic information were derived from the 380K and 320K SNP sets. For the 380K SNP set with MAF of 0.05, the number of haplotype blocks was 18,080 for all autosome genes, 12,676 for coding genes, and 10,111 for noncoding genes. The average size of the gene blocks was 95.34 Kb for coding genes, 49.82 Kb for noncoding genes, and 90.13 Kb for all coding and noncoding genes of the autosomes. ChIP-seq had 21,474 blocks with average block size of 75.51 Kb . A 2-Kb Genomic best linear unbiased prediction (GBLUP) of genetic values and genomic restricted maximum likelihood (GREML) estimation of variance components and heritabilities were calculated using the GVCHAP program  that impZ = N × n incidence matrix allocating phenotypic observations to each individual = identity matrix for one observation per individual (N = n), N = number of observations, n = number of individuals, αo = m × 1 column vector of SNP additive effects, m = number of SNPs, Wα = n × m model matrix of αo, δo = m × 1 column vector of column vector for dominance effects of SNP genotypes, Wδ = n × m model matrix of δo, αh = nαh×1 column vector of haplotype additive effects, nαh = number of haplotype additive effects, Wαh = n×nαh model matrix of αh, b = c × 1 column vector of fixed effects, c = number of fixed effects, X = N×c model matrix of b, a = Wααo = n × 1 SNP genomic additive values, d = Wδδo = n × 1 SNP genomic dominance values, ah = Wαhαoh = n × 1 haplotype genomic additive values, and e = N × 1 column vector of random residuals. Fixed effects included sex and cholesterol treatment as classification variables, and age, glucose and BMIO as covariables for HDL, TC, LDL and TG; and sex as classification variable and age as covariable for HTO, WT and BMIO. The SNP coding in Wα and Wδ is the same as the quantitative genetics coding for SNPs  = Xb, and the second moments resulting from the reparameterized and equivalent model are:where for SNPs , and theartition . The firAg = α = SNP genomic additive relationship matrix; Dg = Agh = α = δ = αh = V = phenotypic variance-covariance matrix. The GVCHAP program  and heritability-based method (HBM). However, the VBM method may have numerical problems . Under t = SNP dominance heritability estimate from Model 5 with SNP additive and dominance values,  = SNP dominance heritability estimate from a haplotype model with SNP dominance values (Model 1 or 3), and  = haplotype additive heritability estimate from any model with haplotypes (Models 1–4). Depending on the prediction model, where Then, estimate of haplotype epistasis heritability of Eq. 6 for a specific haplotype prediction model can be expressed as one of the following formulae:The heritability estimates on the right-hand sides of Eqs. 7–16 are available from the GREML output files of GVCHAP . RelativThe method of estimating haplotype heritability described above is based on the theoretical result of invariance property of GBLUP and GREML to repeated SNPs  and the ah = n × 1 haplotype genomic additive values, a = n × 1 SNP genomic additive values, εh = n × 1 haplotype epistasis values, and τh = n × 1 haplotype loss of additive values based on empirical evidence that haplotype-only model was less accurate than SNP model in some cases. According to the invariance property that GBLUP and GREML are unaffected by duplicate SNPs (as) and haplotype additive values approximately predicts the summation of SNP additive values and haplotype epistasis effects as defined by Eq. 19, i.e.,where ate SNPs , the comEqs. 19 and 20 are the foundation for estimating haplotype epistasis heritability using Eqs. 13–16. With haplotype loss, estimates of haplotype epistasis heritability using Eqs. 13–16 underestimate the true epistasis heritability. Haplotype loss likely is due to the less accurate estimates of SNP effects by the haplotype model than by a SNP model, and likely is the reason why the combination of SNP and haplotype values in the prediction model can increase the prediction accuracy, as shown in this study. Without haplotype loss, haplotype additive values accurately estimate SNP additive values and the inclusion of SNP additive values in the prediction model is not helpful, as shown in this study.A heritability profile in this research is a Manhattan plot of heritability estimates of SNPs or haplotype blocks using the SNPEVG2 program . The herThe seven phenotypes including the original and normality transformed triglycerides all had improved prediction accuracy due to haplotypes in the prediction model. The increase in prediction accuracy due to haplotypes for the best haplotype models relative to the prediction accuracy of the best SNP model  was in the range of 1.86-8.12% . Figure For the three cholesterol phenotypes, HDL had the highest prediction accuracy (0.298), followed by TC (0.295) and LDL (0.253). However, LDL had the largest accuracy increase due to haplotypes, 8.12% accuracy increase over the best SNP model, followed by TC (3.15%) and HDL (2.76%). The best prediction model was Model 1 with SNP additive and dominance values and haplotype additive values using autosome gene boundaries as haplotype blocks for HDL, Model 4 with haplotype additive values using 12-SNP haplotype blocks for LDL, and Model 3 with SNP dominance values and haplotype additive values using 50-Kb haplotype blocks for TC .O had the most skewed distribution  and the highest total heritability  for HTO and WT, and Model 2 with SNP and haplotype additive values for BMIO, but the best method for haplotype blocking was 200-Kb blocks for HTO, 12-SNP blocks for WT and 100-Kb blocks for BMIO , showing that the inclusion of dominance values in the prediction model decreased the additive heritability for all phenotypes  due to dominance values over the SNP additive model  was in the range of 0.312 for TG to 0.773 for HTenotypes . Dominanve model . Haplotyve model . The lacve model . The facO, WT, and BMIO). In general, haplotype epistasis heritability estimates were in concordance with accuracy increases due to haplotypes except BMIO, and were strongly correlated with the prediction accuracy across the seven phenotypes  , and 15-O    was simiModel 5) , and hapModel 5)  was simiModel 5) . HoweverModel 5)  all idenprofiles  did not e values  was simie values , and hape values  was simie values . The twoe values  all idenprofiles  did not profiles  were difprofiles . The incO, BMIO, WT, HDL) with the A + D + H model (Model 1) as the best prediction model. The SNP heritability profiles from the A + D model (Model 5)   and thatModel 5)  were virModel 5)  and thatModel 5)  were alsstimates . The LPLstimates  but did stimates . This diAPOB, CELSR2 and BCAM genes had the highest SNP heritability estimates, and these genes also had the highest haplotype heritability estimates . The SNP heritability profile of TC  showed tstimates . Given tstimates  were fulstimates , and thestimates  were fulstimates .The comparison of SNP and haplotype heritability profiles showed that haplotype loss was in the form of less accurate estimates of SNP heritabilities than those from the corresponding SNP models, and the inclusion of SNP additive values in the prediction model compensated the haplotype loss. The exact reason for haplotype loss was unknown, but incorrect size of haplotype blocks for chromosome regions with high SNP heritabilities such as excessively large blocks  had the best prediction model using the 320K, and the other six phenotypes had the best prediction models using the 380K  is encouraging that results of this studies may apply to some other populations. For examples, the Results in this study showed haplotypes using structural and functional genomic information improved the accuracy of genomic prediction. Haplotypes using structural genomic information covering all autosomes had the best prediction models for most phenotypes, whereas haplotypes using autosome gene boundaries had the best prediction model for one phenotype and tied for the best for another phenotype even though the gene haplotypes only covered 50.78% of the autosomes. Haplotypes using coding gene boundaries covering 37.66% of the autosomes were nearly as accurate as haplotypes based on all autosome genes for two phenotypes and was more accurate than haplotypes based on all autosome genes for two phenotypes. Although ChIP-seq alone did not have the best prediction model for any trait, ChIP-seq had higher prediction accuracy than all autosome genes for one trait, and improved the prediction accuracy over the best SNP models when combined with SNP additive an dominance values for another five traits. These results showed that functionality of the genome is relevant to genomic prediction and that multi-allelic haplotype analysis can be a method to utilize both functional and structural genomic information for genomic prediction. Haplotype epistasis was the reason for the increased prediction accuracy of haplotype models over SNP models. Noncoding genes (mostly lncRNAs) had the highest haplotype epistasis heritability relative to the SNP heritability in the same regions, followed by coding genes and ChIP-seq. These results provided new understanding of the genetic mechanism underlying the accuracy of genomic prediction, and indicated the widespread presence of local epistasis within haplotype blocks affecting the seven human phenotypes, particularly low density lipoproteins that had the largest haplotype epistasis heritability and relative increase in prediction accuracy. The integration of SNP and haplotype additive values in the prediction model improved the prediction for phenotypes where haplotype additive heritability estimates did not identify chromosome locations with high SNP additive heritability estimates, but haplotype-only models were the best prediction models for three phenotypes, indicating that haplotype effects fully accounted for SNP effects or estimated SNP effects more accurately than the SNP models.The datasets presented in this study are available from dbGaP, and data access requires approval from the National Heart, Lung, and Blood Institute (NHLBI).YD conceived the study. ZL, CT, DP, and YD analyzed the data. LM processed an early version of the FHS data that were used for preliminary analysis. DP and LM contributed computing tools. YD, ZL, CT, and DP prepared the manuscript. All authors contributed to the article and approved the submitted version.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.