Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels  and three MAF levels  were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore–Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS. Triticum aestivum L.) is one of the major cultivated crops that is growing on approximately 200 million hectares worldwide and delivers one fifth of the total caloric demands of the global population [Wheat  for GY, 534 to 693 for SN, 39.38 to 49.82 g for TKW, 8.83 to 9.64 cm for SL, 184 to 199 days for HD, and 77.60 to 91.52 cm for PH  and yield-related traits, i.e., spike number per square meter (SN), thousand-kernel weight (TKW), spike length (SL), heading days (HD), and plant height (PH) of the 166 wheat accessions in different environments (locations in cropping seasons) are shown in m for PH . Overallectively . High heectively .r) between traits ranged from −0.45 to 0.39 (r = 0.39) and lowest negative correlation with PH  but positively correlated with PH (r = 0.27). TKW was negatively correlated with HD (r = −0.25).The Pearson’s correlation coefficient . In the second scenario, three subsets of markers were generated by removing markers with MAF levels under or equal to different thresholds  for each missing level. The number of SNPs decreased significantly after the application of missing rate and MAF QC . The disr2 was 0.065 in the whole genome, and the average LD decay distances for 10, 100, and 10,000 Mb were estimated to be 0.38, 0.28, and 0.14, respectively. The scatter plot between r2 and physical distance (Mb) showed that LD decreased with increasing physical distance  to 0.682 (TKW) and varied significantly among the six traits, five missing rate levels, and three MAF levels . OverallPrediction accuracy for the six traits and seven GS models with non-QC or QC to keep the missing rate levels <40% and MAF values >5% are shown in The effect of genotype imputation on prediction accuracy was evaluated, using the QC to keep missing rate levels <40% and the MAF values >5% as an example. Prediction accuracies of yield and yield-related traits for the seven GS models using imputed markers are shown in Manhattan and quantile-quantile (Q-Q) plots from GWAS were given in A better understanding of the factors that affect the prediction accuracy of GS is crucial to deploying GS within the conventional breeding scheme ,19. MissIn most cases, QC on SNPs improved the prediction accuracy, irrespective of the traits and GS models. But the significance of improvement varied with trait features, GS models, as well as QC combinations. The QC levels for missing rate and MAF are important factors that affect prediction accuracy, in accordance with previous studies ,24. The Our results also revealed that QC for missing rate and MAF affected genome coverage . The lowDifferent missing rate thresholds have been adopted for QC on SNPs in previous studies ,15,24,31For most traits, prediction accuracies were negatively affected by a high MAF threshold . There wThe choice of GS models depends on the maximum prediction ability and computation efficiency of a model across a wide range of traits and datasets. In this study, the prediction accuracy varied substantially among traits and GS models . There wThe QC to keep missing rate levels <40% and MAF values >5% was used as an example to investigate the effect of imputation and GWAS on GS, and which QC scenario prediction accuracy was the highest. Missing values were imputed based on the empirical distribution of genotypes obtained from observed values, because this method requires less computational burden. Poland et al.  reported10P = 1) was adopted in this study to identify more significant markers. The low threshold definitely increased the false positive of GWAS, but it is generally accepted that false positive does not have a significant effect on prediction accuracy. In this study, both TP and VP were used in GWAS, and GWAS and GS were conducted on the same population. In practice, the GS model constructed from TP would be used for other breeding populations only having genotypic data. More evidence is still needed before concluding that GWAS always improves the accuracy of GS.GWAS-derived markers improved prediction accuracy of GS, whether the imputation was conducted before GWAS or not. This was similar to the results by Lozada et al. , who rephttp://www.isbreeding.net/wheatGS/.The wheat population was comprised of 166 diverse accessions, namely, 143 accessions from the Yellow and Huai River Valley Facultative Wheat Zone of China and 23 varieties from five other countries . The names and origins of these accessions are presented in −1. SNs were counted for each plot and converted to spike number per square meter. TKW was measured by weighting 1000 random kernels from each plot after harvest. SLs were measured from the base of the rachis to the top spikelet, excluding awns. HD was recorded on 50% emergence of the spike; PH was measured as the distance between the soil surface and top of spike, excluding awns after physiological maturity. These traits were considered to represent a wide range of heritability and genetic architecture.Six yield and yield-related traits, namely, GY, SN, TKW, SL, HD, and PH, were evaluated at each location. GY was measured as the weight of grain harvested kg·hahttps://www.isbreeding.net/) [Five fresh leaves of each accession were sampled, and DNA extraction was carried out by the modified CTAB method . The genng.net/) . For QC ijky is the phenotypic value, μ is the overall mean, iG is the genotypic effect, jE is the environmental effect, ijGE is the genotype-by-environment interaction effect, k/jR is the kth replication effect in the jth environment, and ijkε is the residual effect. From the theoretical expectation of mean square (MS), genetic variance  for each line across multiyear trials, ANOVA, and phenotypic correlation analysis were conducted using QTL IciMapping V4.2 . BLUE vaHeritability at the plot level and mean level was calculated using Equation (3).r2 was calculated by the TASSEL software (freely available from https://tassel.bitbucket.io/) using the full matrix and sliding window options [https://brcwebportal.cos.ncsu.edu/powermarker/) [https://cran.r-project.org/web/packages/ggplot2/index.html) [LD between markers measured as  options  and plotmarker/) . Plot viex.html) .T-test was conducted to compare the significance of difference between QC and non-QC cases. The detailed descriptions of these seven models are described in the next subsection. Comparison of prediction accuracy among traits and GS models was also conducted under this QC scenario, i.e., missing rate levels <40% and MAF >5%.Seven GS models were implemented in the Intel FORTRAN program for estimating prediction accuracies (code written by L.Z. and J.W.), i.e., BLUP , GBLUP , RRBLUP y is a (n × 1) vector of phenotypic values; X is a (n × p) incidence matrix for fixed effects; β is a (p × 1) vector of fixed effects; Z is a (n × 1) incidence matrix for random effects; ε is a (n × 1) vector of independently random residual following distribution I being the identity matrix in the present case [p is the number of fixed effects, n is the number of genotypes, and m is the number of markers.The BLUP mixed model is described as follows:ent case . In addiy is the vector of phenotypic values, 1n is the vector of n ones, μ is the overall mean, Z is the design matrix for random effects, u is the random effect with G is a genomic relationship matrix between individuals estimated from genotypes, and ε is the vector of random residuals following distribution The standard GBLUP model is described as follows: ,46.y is the vector of phenotypic values; 1n is the vector of n ones; μ is the overall mean; Z is the design matrix for random effects; u is the random e1ffect with K is the additive relationship matrix, which is the density matrix in the present study; and ε is the vector of random residuals following distribution The rrBLUP model is described as follows: .y is the vector of phenotypic values, X is the design matrix for fixed effects, β is the vector of marker fixed effects, Z is the design matrix for random effects, u is the vector of random effects, ε is the vector of random residual following distribution I is the identity matrix. The estimator of β is λ can be expressed as 2 norm. The “β minimizing the expression inside the brackets.The model is fitted by:β is The model is the same as the model described in λ refers to the shrinkage and regularization parameter calculated by 1 norm.The LASSO was developed by Tibshirani . λ referThe RForest model is integrated with classification or regression trees that rely on bootstrap samples and splits original data into multiple subsets of non-overlapping sets . The preTo evaluate the effect of genotype imputation on prediction accuracy of GS, missing values in genotypic data were imputed using samples from the empirical distribution of marker genotypes . Imputedhttps://cran.r-project.org/web/packages/rrBLUP/index.html/) was used to perform GWAS before GS [10P was set at 1, in order not to miss any small-effect quantitative trait nucleotides. The QC to keep missing rate levels <40% and MAF values >5% was used as an example. Markers with unknown physical positions were excluded before GWAS. As a result, a total of 5201 markers were employed for GWAS. Two GWAS-derived GS scenarios were designed. In the first scenario, imputation for missing genotypes was conducted before GWAS; in the second scenario, non-imputed genotypic data was used to perform GWAS.To evaluate the effect of GWAS-based marker selection on prediction accuracy of GS, the “GWAS” function of R package rrBLUP V4.6  under different marker QC scenarios  for yield and yield-related traits. No single QC combination or GS model can yield better performance in prediction accuracy for all traits. In general, a combination of moderate missing rate levels (20% to 40%) and MAF (5%) yielded better prediction accuracy, regardless of the traits and GS models. Prediction accuracy of the six traits was affected by the heritability, genetic architecture, and GS models. GenInv, RidgeReg, and RForest models yielded higher prediction accuracy than other models across traits. The effect of genotype imputation and GWAS-based marker selection was also evaluated in this study. The results showed that imputation had marginal effect on GS but using GWAS-derived markers improved the prediction accuracy of GS.


Bubalus bubalis) is the best dairy animal contributing 68% to total milk production in Pakistan. In this study, we identified genome-wide single nucleotide polymorphisms (SNPs) to estimate various population genetic parameters such as diversity, pairwise population differentiation, linkage disequilibrium (LD) distribution and for genome-wide association study for milk yield and body weight traits in the Nili-Ravi dairy bulls that they may pass on to their daughters who are retained for milking purposes. The genotyping by sequencing approach revealed 13,039 reference genome-anchored SNPs with minor allele frequency of 0.05 among 167 buffalos. Population structure analysis revealed that the bulls were grouped into two clusters (K = 2), which indicates the presence of two different lineages in the Pakistani Nili-Ravi water buffalo population, and we showed the extent of admixture of these two lineages in our bull collection. LD analysis revealed 4169 significant SNP associations, with an average LD decay of 90 kb for these buffalo genome. Genome-wide association study involved a multi-locus mixed linear model for milk yield and body weight to identify genome-wide male effects. Our study further illustrates the utility of the genotyping by sequencing approach for identifying genomic regions to uncover additional demographic complexity and to improve the complex dairy traits of the Pakistani Nili-Ravi water buffalo population that would provide the lot of economic benefits to dairy industry.The domestic Nili-Ravi water buffalo ( Bubalus bubalis) is a well-used dairy animal in livestock in Pakistan because it contributes about 68% of the total milk production in Pakistan    and an r buffalo , which i buffalo . Recentl buffalo , Chinese buffalo , Tunisia buffalo , Korean  buffalo  and Colo buffalo .We performed GWAS using genome-wide SNPs for several novel candidate genes related to milk yield and body weight traits that are significant breeding goals for dairy industry. Gamma-secretase subunit APH-1B pseudogene/Rho guanine nucleotide exchange factor 26 gene (Gene ID: S1_158054912) present on chromosome 1 encodes a large protein that functions as a GDP-to-GTP exchange factor and is reported to be a significant candidate gene for the maternal effect on weaning weight and milk yield traits in cattle –76. In tUncharacterized LOC112586670/3-beta-hydroxysteroid-Delta(8) is one of the major enzymes that participates early in lactation in dairy cattle. An lncRNA (LOC112586670) fragment is a candidate gene associated with buffalo milk production , 83–86. In the bovine, fatty acid desaturase 2-like protein FADS2P1/olfactory receptor 5G3-like (Gene ID: S16_3831231) is a potential genetic marker for fatty acid composition in cattle milk –101. In We identified alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase 3 (Gene ID: S6_67621520) as a novel marker for body weight, and it also contributes to protein glycosylation for milk synthesis –113. U6 Several female fertility traits in dairy cattle showed reduced genetic correlations among milk production traits because of low or moderate heritabilities , 128. ThFST (P<0.001) differentiation for low and high bulls for milk yield and body weight to understand pattern of selection [FST values indicating positive selection at these regions. Flori et al. [FST values for GHR gene in milk yield of cattle and also identified 13 highly significant genomic regions with varied pairwise FST subjected to recent and/or strong positive selection. For body weight in our study, chromosomes 7 and 18 had high positive selection signals.We also performed pairwise population election . All sigi et al.  indicateDefining ROH in a complex pedigree can be a way to examine homozygosity levels across the genome that has resulted by inbreeding and selection . In thisOur study demonstrates the utility of the GBS approach for genetic analysis of complex dairy traits and for understanding the population structure of Pakistani Nili-Ravi buffalos. Our SNP analysis revealed that Nili-Ravi bulls are admixed with two lineages. Our GWAS for milk yield and body weight identified SNPs located in candidate genes previously identified in other dairy breeds. Results of this study suggested that male effects contribute strong genetic effects for milk yield. In future, favorable allele combinations from this study could be used to generate Nili-Ravi breeding bulls with high milk yield and body weight by selecting the bull with higher genetic merit is tantamount that will produce a daughter, who in-turn produces the greatest net revenue for dairy industry in terms of improved or superior commercial traits. Performance of their daughters will later be used to update these measures. Since these dairy bull data were publicly available, individual bull efficiency results can be disseminated to potential semen buyers.S1 FigEach individual is represented by a single vertical line divided into K colored segments, where K is the number of ancestral populations.(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.


Childhood epilepsies have a strong genetic contribution, but the disease trajectory for many genetic etiologies remains unknown. Electronic medical record (EMR) data potentially allow for the analysis of longitudinal clinical information but this has not yet been explored.We analyzed provider-entered neurological diagnoses made at 62,104 patient encounters from 658 individuals with known or presumed genetic epilepsies. To harmonize clinical terminology, we mapped clinical descriptors to Human Phenotype Ontology (HPO) terms and inferred higher-level phenotypic concepts. We then binned the resulting 286,085 HPO terms to 100 3-month time intervals and assessed gene–phenotype associations at each interval.SCN1A at 1.0 years, “Severe intellectual disability” with PURA at 9.75 years, and “Infantile spasms” and “Epileptic spasms” with STXBP1 at 0.5 years. The identified associations reflect known clinical features of these conditions, and manual chart review excluded provider bias.We analyzed a median follow-up of 6.9 years per patient and a cumulative 3251 patient years. Correcting for multiple testing, we identified significant associations between “Status epilepticus” with Some aspects of the longitudinal disease histories can be reconstructed through EMR data and reveal significant gene–phenotype associations, even within closely related conditions. Gene-specific EMR footprints may enable outcome studies and clinical decision support. The disease course in childhood epilepsies is dynamic over time,15 and longitudinal information on natural history and outcome is limited due to the rarity of each genetic cause. Furthermore, clinical characterization of rare genetic entities is often restricted to case series,19 which cannot distinguish clinical features associated with a specific gene from clinical features shared between related diseases.Genetic factors are increasingly implicated in childhood epilepsies, and with the advent of massive parallel sequencing technologies more than 200 novel genetic etiologies have been identified in the last decade.21 and several phenotyping algorithms are already validated to extract clinical features.24 However, the longitudinal aspect of EMR data that is relevant to assess disease histories over time largely has been unexplored. Maintaining the temporal relationship between clinical features is critical in disorders that follow prominent age-related patterns, such as the childhood epilepsies. More importantly, the overall quality of EMR data is unexplored, and clinical data entry into EMR systems is often considered a nuisance by providers. Therefore, accuracy and precision of clinical phenotypes in a system primary created for billing purposes may rightfully be questioned. Nevertheless, given the magnitude and availability of EMR data, even limited reliability would allow for conclusions about longitudinal disease histories that would otherwise require time-consuming manual chart review.The adoption of electronic medical records (EMR) provides a new opportunity to leverage clinical data for genomic research. Large national and international initiatives have started to link biorepositories with EMR data,Here, we mapped EMR data in individuals with childhood epilepsies who underwent genetic testing to Human Phenotype Ontology (HPO) terms. We analyzed data of 658 individuals followed for a median 6.9 years with a cumulative 3251 patient years, including 232 individuals with a definite genetic diagnosis. We assessed and identified significant gene–phenotype associations, demonstrating that EMR data can be used to identify gene-specific signatures even in clinically closely related disease entities such as the childhood epilepsies.Informed consent for participation in this study was obtained from subjects themselves or parents of all probands in agreement with the Declaration of Helsinki, and the study was completed per protocol with local approval by the Children’s Hospital of Philadelphia (CHOP) Institutional Review Board (IRB 15-12226).n = 100), exome sequencing (n = 109), or other testing modalities including single-nucleotide polymorphism (SNP) arrays (n = 9) or single-gene tests (n = 14). Genetic results were reviewed and, if necessary, reclassified according to the criteria of the American College of Medical Genetics and Genomics (ACMG).25The current analysis was performed on individuals enrolled in the Epilepsy Genetics Research Project (EGRP) at Children’s Hospital of Philadelphia, which has enrolled patients with known or presumed genetic epilepsies since 2014. Genetic etiologies in the EGRP cohort were assessed in a clinical and research setting, including gene panel sequencing  that can be accessed via the Clarity database (EPIC). Every provider contact is documented within the EMR, including clinic and emergency room visits, hospital admissions, telephone calls, refills, and visits for laboratory work and imaging. All documented contacts are referred to as “encounters” within the EMR. At every encounter, the medical personnel is responsible for updating a current list of all active medical diagnoses, which is termed the “problem list.” Additionally, at a subset of encounters including inpatient and outpatient visits, providers are required to assign “encounter diagnoses,” which are the medical problems associated with or addressed in that encounter. Problem lists and encounter diagnoses within the Clarity database are encoded in Intelligent Medical Objects terms  that are mapped to International Classification of Diseases, Ninth/Tenth Revision (ICD9/10) codes. In contrast to ICD9/10 codes, IMO provides an intuitive language interface that includes common clinical terminology such as “Absence seizure” or “Generalized epilepsy” rather than ICD codes. For our study, we extracted encounter diagnoses and problem lists for all individuals enrolled in the study for every encounter documented in the EMR. All information included in our study was derived from routine clinical care; encounters for research purposes only were not included. From each encounter, we extracted the problem lists, encounter diagnoses, and age of the patient at the encounter. We included IMO problems and diagnosis terms based on a selection of ICD10 codes related to neurological diagnoses  and merged IMO terms on the diagnosis and problem list.26 This custom dictionary was created by a team of providers and researchers who reviewed each of the 1479 IMO terms associated with neurology-related ICD9/ICD10 codes mentioned above. We used the Clinical Text Analysis and Knowledge Extraction System (cTAKES) natural language processing algorithm27 to generate a preliminary dictionary, which was subsequently reviewed and adapted manually. To avoid false annotations, we limited the annotation of epilepsy syndromes to high-level phenotypic terms . For example, for the association of SCN1A and “Status epilepticus” (HP:0002133), the most significant association was at 1 year with −log10(pgxp_max) = 6.74. Each phenotype at each time point was analyzed independently and no information was used from past or future time points. Correction for multiple testing was subsequently performed using the Benjamini–Hochberg method with a false discovery rate (FDR) of 0.05. All statistical tests were performed using the R Statistical Framework, including the ggplot2 package.Frequencies for each HPO term were determined using the number of individuals with available data at each time point, including both the initially assigned HPO terms and the propagated higher-level terms. For each causative genetic etiology in the cohort, frequencies for each HPO term at each time point were assessed and compared with the frequency of each HPO term in the remainder of the cohort. The significance of the association was determined using a two-sided Fisher’s exact test. Subsequently, for each gene–phenotype combination the 3-month interval with the most significant association was identified (pn = 268) were the most common genetic etiologies, followed by focal epilepsies (n = 156) and genetic generalized epilepsies (n = 97). In our cohort, 102 distinct genetic etiologies were identified, including 36 causative genes identified in two or more individuals , STXBP1 (n = 22), SCN2A (n = 12), KCNQ2 (n = 8), and KCNT1 (n = 6). The median age of seizure onset was 1.34 years (range 0 to 18 years). We restricted the analysis to neurology-related diagnoses and problems coded by clinicians during patient care encounters, comprising 62,104 data points  terms in the HPO, generating a total of 528 discrete HPO terms. In total, the mapping to defined HPO terms, inclusion of higher-level terms, and binning to 100 discrete time intervals resulted in 286,085 HPO terms.28 This mapping allowed us to identify common higher-level terms between individuals at each time point and to determine the true frequency of each phenotypic term in the cohort, as more specific terms resulted in an inclusion of all higher-level phenotypic terms, even though these higher-level terms may not have been directly mapped  represent a distinct seizure type that manifests in infancy but may continue throughout early childhood in genetic epilepsies, which is reflected in the mapping of this term based on EMR data  typically occur between 6 months and 6 years and did, in fact, map to the corresponding time intervals Fig. .29 Likewata Fig. .30 Furthp = 1.84e−7) with SCN1A at 1.0 years, “Severe intellectual disability”  with PURA at 9.75 years, and “Infantile spasms”  and “Epileptic spasms”  with STXBP1 at 0.5 years. These gene–phenotype associations replicate the known natural history for SCN1A,36PURA,37 and STXBP1,16 and demonstrate that our EMR data mapping and harmonization approach correctly identifies known gene–phenotype associations in our cohort that were previously reported. We manually reviewed patient charts for SCN1A at 1.0 years, PURA at 9.75 years, and STXBP1 at 0.5 years and found that that phenotypic terms were accurate and not biased by individual providers.We next analyzed the association of the 528 HPO terms with the 36 genetic etiologies identified in two or more individuals included in our study Table . When liSCN1A, “Severe intellectual disability” (HP:0010864) with PURA, and “Infantile spasms” (HP:0012469) with STXBP1. We excluded the association of “Epileptic spasms” (HP:0011097) with STXBP1 as this phenotypic term is the direct parent term of “Infantile spasms” (HP:0012469) and therefore added no additional information. The association of the significance over time demonstrates that the association peak for all three associations occurs at discrete time points. To better understand features leading to the observed patterns, we examined the frequencies of the specific phenotypes in individuals with and without the genetic etiology  with STXBP1, the phenotypic features show a high frequency in the gene-positive group for SCN1A at 1.0 years and STXBP1 at 0.5 years. The PURA signal is only based on three individuals between 10 and 15 years, all individuals assigned the phenotypic term “Severe intellectual disability” (HP:0010864). The frequency of this phenotypic term increased in the overall cohort over time, but only at the age of 9.75 years is this discrepancy large enough to generate the most extreme p value (p = 2.96e−6). The phenotypic associations with PURA further highlight that we are only able to make assertions about phenotypic associations within the range of EMR usage. EMR usage for all three PURA-related disorders only overlapped between 4.75 years and 10 years, indicating that important phenotypic associations outside this age window were largely inaccessible to us and were likely missed.We found two patterns. For “Status epilepticus” (HP:0002133) with In our study, we assessed whether data derived from EMR might aid in identifying longitudinal phenotypic patterns for genetic epilepsies. Within the limitations of this study, we demonstrate that heterogeneous EMR can be harmonized and mapped through a common framework such as the HPO. Using this tool, we discovered time-dependent associations between genetic etiologies and phenotypes that recapitulate essential aspects of the natural history of these conditions. Our study may therefore provide a general framework to reconstruct age-dependent phenotypes in genetic epilepsies and neurodevelopmental disorders from EMR data. While this approach can only capture a subset of the phenotypic depth, EMR data are ubiquitously available, and such a framework can assist in supplementing growing genetic data sets with longitudinal phenotypes.The age dependence of clinical features in our study demonstrates two important properties of longitudinal phenotypes. First, even though our study included 658 individuals, no more than 266 individuals contribute to each time point Fig. . We expeSTXBP1. In addition, this most significant association was limited to a relative narrow time interval around 6–9 months. Our framework therefore allows us to identify significant gene–phenotype associations in conditions with a broad phenotypic range, using real-world data derived from an ongoing collection of clinical data captured in the EMR. We acknowledge that many other genetic etiologies included in our study are known to be associated with infantile spasms. However, these etiologies were too rare in our cohort or the age when infantile spasms typically emerge was outside the period of EMR usage for individuals with other genetic etiologies. While our study was primarily focused on identifying phenotype association with single genes, we observed that further associations can be captured when genes are grouped , present in 12% of our cohort, only shows a significant association with 39 Despite these conceptual limitations, our study provides a general model that outlines the three critical components of any framework to capture longitudinal phenotypic data: data extraction, mapping/harmonization techniques, and strategies for temporal binning. Addressing these limitations can be conceptualized as improving one of these components within this framework.The data extraction and mapping algorithms applied in our study have several limitations, including the restriction to diagnosis and problem lists related to neurology-related ICD9/10 codes and our inability to assess negated phenotypes.There are additional notable limitations of an automated EMR extraction approach compared with a traditional retrospective chart review. These limitations must be weighed against the potential benefit of the automated extraction approach, which allows for significantly larger sample sizes due to the prohibitive cost of manual chart review. For example, the encounter diagnoses and problem lists are entered by various health-care providers, which may introduce bias, including a potential lack of detail in the coded features. This limitation becomes particularly relevant when trying to assess the absence of significant associations. We therefore do not claim that our approach generates a detailed representation of the overall phenotypic landscape but identifies features that emerge despite the inherent limitations of EMR data.A further limitation is our focus on a single health-care network. While this allowed us to include homogeneous data and perform manual chart review, we cannot claim that our methods are immediately generalizable to other data sets of combined genomic and EMR data. However, our study presents a first attempt to show that EMR data can reconstruct some aspects of the disease history in genetic epilepsies. This may be further tested in future EMR/exome data sets once these resources become available.KCNQ2 when decreasing bin width  for our analysis, which was the most recent version at the time of initial data analysis. This HPO version does not yet fully reflect the latest seizure classifications of the International League Against Epilepsy (ILAE).SCN1A-, STXBP1-, and PURA-related disorders have time-dependent associations with distinct clinical features that stand out from the wider group of known or presumed genetic epilepsies. Identifying disease trajectories using large-scale phenotypic data may become a critical component for clinical decision support and learning health-care systems, particularly in rare genetic neurological disorders where available clinical information is limited.In summary, our study demonstrates that EMR data can be used to elucidate aspects of the longitudinal disease histories in epilepsies and neurodevelopmental disorders. This is accomplished in our study through harmonization of clinical terminology through the HPO framework and binning into discrete time intervals. Using this method, we show that several genetic etiologies including Supplementary DataSupplementary Table 2Supplementary Table 3Supplementary Table 4Supplementary Table 5


Fifty elite maize inbred lines were selected based on their response to MLN under artificial inoculation. Crosses were made in a half diallel mating design to produce 307 F1 hybrids. All hybrids were evaluated in MLN quarantine facility in Naivasha, Kenya for two seasons under artificial inoculation. All 50 inbreds were genotyped with genotyping-by-sequencing (GBS) SNPs. The phenotypic variation was significant for all traits and the heritability was moderate to high. We observed that hybrids were superior to the mean performance of the parents for disease severity (−14.57%) and area under disease progress curve (AUDPC) (14.9%). Correlations were significant and moderate between line per se and GCA; and mean of parental value with hybrid performance for both disease severity and AUDPC value. Very low and negative correlation was observed between parental lines marker based genetic distance and heterosis. Nevertheless, the correlation of GCA effects was very high with hybrid performance which can suggests as a good predictor of MLN resistance. Genomic prediction of hybrid performance for MLN is high for both traits. We therefore conclude that there is potential for prediction of hybrid performance for MLN. Overall, the estimated quantitative genetic parameters suggest that through targeted approach, it is possible to develop outstanding lines and hybrids for MLN resistance.Prior knowledge on heterosis and quantitative genetic parameters on maize lethal necrosis (MLN) can help the breeders to develop numerous resistant or tolerant hybrids with optimum resources. Our objectives were to (1) estimate the quantitative genetic parameters for MLN disease severity, (2) investigate the efficiency of the prediction of hybrid performance based on parental  Tombusviridae group and any member virus from the Potyviridae group mostly Sugarcane mosaic virus (SCMV) is one of the most important maize diseases in sub Saharan Africa (SSA)  × 100, where Pmax is the better resistant parent. Pairwise Pearson’s correlation coefficients (r) were calculated for HP with MPV, r(MPV:HP), and the GCA effects with the lines per se performance, r(GCA:per se). Further, we also tested the Pearson’s correlation of HP with the sum of GCA effects of both the parents, r(GCA: HP). The correlation between the Roger’s genetic distance between the parents and their MPH was estimated, r(GD:MPH). Correlation between Roger’s genetic distance and F1 HP was also calculated, r(GD:F1HP). All analyses were performed using the R software (http://www.R-project.org/).For each combination of parental lines, mid-parent value (MPV), relative mid-parent heterosis (MPH), and relative better parent heterosis (BPH) were calculated using hybrid performance (HP) as follows: MPV = (P1 + P2) / 2, MPH = [(HP − MPV) / MPV] × 100, and BPH = [(HP − Pn by p matrix M describing the marker states of the n individuals at loci p, the additive model is defined as:Parametric prediction method G-BLUP accounting for the GCA was used to predict the performance of the single-crosses for MLN DS and AUDPC values. BLUEs across environments were used for the prediction. For the GP of the hybrid’s performance, we used a two-stage approach , where, Y is n by 1 the vector of phenotypic data, mu a fixed effect, beta a vector of marker effects and e a vector of errors. Note that the term GBLUP usually refers to a reformulated version using g = M * beta. In which g is assumed to be normally distributed with mean zero and variance covariance G that is the marker-derived relationship matrix obtained as VanRaden [Here, VanRaden . It must


Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients’ data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases. Thus far, pleiotropy analysis using individual-level Electronic Health Records data has been limited to data from one site. Here, the authors introduce Sum-Share, a method designed to efficiently and losslessly integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. However, not all of the disease-causing loci are strongly associated with the phenotype, and thus a better understanding of the genetic etiology underlying complex diseases requires complementary approaches4. For most complex diseases, the current paradigm is that genes do not act in isolation. A growing body of genetic research suggests genetic pleiotropy, where one genetic locus or gene influences many phenotypes, is ubiquitous in complex traits7. Compared with the individual genetic variant association, analysis of pleiotropy can not only provide additional insights into shared genetic mechanisms among seemingly unrelated phenotypes8, but these connections could also be used as therapeutic targets for drug repositioning. In addition, leveraging information from pleiotropy has been shown to boost the statistical power to detect genetic associations and the predictive power of genetic risk factors10.Personalized prevention and treatment of diseases require a comprehensive understanding of the underlying genetic etiology. So far, numerous population-level genetic studies have been carried out to understand the associations between genetics and diseases. Notably, genome-wide association studies (GWAS) have systematically identified thousands of genetic loci associated with various human traits and diseases11. EHRs uniquely offer a comprehensive set of patients’ disease diagnoses as well as clinical measurements, which, in combination with genetic data, enable investigation of the genotype and phenotype relationships of multiple traits6. Indeed, many studies have utilized EHRs with linked genetic data to carry out phenome-wide association studies (PheWAS) to examine pleiotropy14. In PheWAS studies, association analysis of one or more single nucleotide polymorphisms (SNPs) can be used to identify pleiotropic effects on multiple phenotypes. While PheWAS is conceptually straightforward, it does not model multiple phenotypes together in the model. As a result, PheWAS may have a reduced power to detect pleiotropy due to both computational cost and multiple testing penalties. To alleviate the multiple testing burden, several methods have been developed to jointly model multiple phenotypes to detect pleiotropy. However, many of the joint-model methods suffer from limitations such as requiring phenotypes to be continuous. They also carry a high computational cost and may lack proper covariate adjustments15.Electronic Health Record (EHR) data have rapidly become a promising data source for conducting genetic research due to the growing availability of EHR linked genetic data16 and the UK BioBank17. Typically, an EHR system covers a specific service region and thus is representative of the patient population of the region. Therefore, data in each EHR system is limited in size and is also influenced by disease prevalence and demographic composition18. As a result, research findings using data from one EHR may not be generalizable to the whole population or reproducible across different EHR systems. Thus, it would be advantageous to integrate data from multiple EHRs to obtain generalizable results for a larger population and to improve power by maximizing sample size. When patients’ individual-level data are freely shareable, genetic and clinical data from multiple EHRs can be combined to perform a gold standard pleiotropy analysis. However, due to identifiability and privacy concerns, patients’ genetic and clinical information is often heavily protected and rarely shared across different EHRs. A potential solution is to utilize summary statistics to transfer information across datasets. As an example, the use of GWAS summary statistics has allowed low-cost and privacy-preserving alternative access to individuals’ genetic data. Summary statistics have been used successfully to perform single variant association tests, gene-based tests, fine-mapping, analysis of pleiotropic effects19 as well as meta-analyses22 without accessing patient-level data. However, analyses using summary statistics may introduce potential bias due to differences in study populations24. On the contrary, lossless integration, where the analysis of multiple datasets produces identical results compared with that of the combined individual-level data, could avoid this type of bias. Thus, methods that enable lossless privacy-preserving information sharing across EHRs are critically needed.Recently, EHRs with linked genetic data have become increasingly available under initiatives such as the Electronic Medical Records and Genomics (eMERGE)(SUMmary Statistics from multiple electronic HeAlth Records for plEiotropy) to detect pleiotropy. This method allows for flexible covariate adjustment for each phenotype, is computationally more efficient than traditional methods, and leads to mathematically identical results as compared to analyses of pooled patient-level data from different sites. Importantly, Sum-Share only relies on summary statistics from different sites.In this study, we developed Sum-Share Using simulations, we show that Sum-Share is computationally efficient and achieved better statistical power than PheWAS in detecting pleiotropic effects. We apply Sum-Share to seven EHRs in eMERGE phase 3 data to detect pleiotropic effects between five cardiovascular-related phenotypes . The integrated analysis identifies 1734 SNPs that showed significant pleiotropic associations compared with just 1 SNP when using EHR data from an individual site. To further evaluate our results, we re-analyze the significant SNPs using PheWAS in the UK BioBank data. This analysis identifies known genes as well as discovers new genes associated with cardiovascular diseases.Figure To our knowledge, there are no methods that can losslessly perform pleiotropic tests using data from multiple EHRs without pooling individual-level data. Thus, our goal here is to demonstrate that Sum-Share improves statistical power as compared to standard PheWAS analysis under various simulation settings. To perform PheWAS, ten simulated EHR datasets were pooled together and standard PheWAS analysis was performed. The data pooling for PheWAS was achieved through meta-analysis or mega-analysis. In meta-analysis (PheWAS-meta), the summary statistics of each dataset were combined using the inverse-variance-weighting method. In contrast, mega-analysis (PheWAS- mega) pools the individual-level data and the analysis was performed on the larger pooled data. Sum-Share, as illustrated in Fig. P-values obtained from Sum-Share showed perfect correlation with p-values using the pooled data for all SNPs’ categories  into common MAF ≈ 0.3), low frequency (MAF ≈ 0.05) and rare (MAF ≈ 0.01) groups. , low freGenetic-linked EHR data from eight geographically distinct sites were used to detect pleiotropic effects among five common cardiovascular-related diseases . Patients’ individual-level data from the EHRs can be combined through the eMERGE network. However, for this analysis, we only analyzed the summary-level data from the EHRs to demonstrate the method’s performance. The patients’ disease status was determined by the counts of the respective ICD-9 codes. Figure p-values were Bonferroni adjusted to account for multiple testing. As for comparisons, the analysis was carried out using data from each individual site as well as combined data from two of the largest sites  or all eight sites. For site-specific analyses, Sum-Share did not need to aggregate information from other sites, thus no distributed analysis was carried out. The combined eight sites analysis resulted in 1734 significantly associated SNPs, and the two-site analysis yielded 171 significant pleiotropic compared to only one SNP across the site-specific analyses . In total, ~6.1 million SNPs were analyzed and their p < 8.19 × 10−9) were displayed in Fig. 25 (Supplementary Data The 1734 significantly associated SNPs (p < 5.89 × 10−6) with at least one of the phenotypes   outcomes and covariates. Thus, it cannot adjust for continuous covariates such as principal components in the pleiotropic analysis. We compared the power of PheWAS with continuous covariates adjustment versus Sum-Share with categorical covariates (discretized continuous covariates). Sum-Share still has significantly higher power than PheWAS . SNPs were imputed using the Haplotype Reference Consortium 1.1 reference under genome build 37, which resulted in 39 million total genetic variants42. SNP genotypes were filtered and processed using the standard pipeline43 so that the genotype and sample call rate were ≥99%, imputation score > 0.4, Hardy-Weinberg equilibrium p-value > 0.00001, and the MAF of the SNPs were ≥0.05. To reduce the effect of population structures, only unrelated individuals of European ancestry were used. For related individuals , one of each pair was removed. In total, 59,136 individuals and 6,106,952 SNPs were analyzed.In this study, genotype data with linked EHR data was obtained from the eMERGE network17. Individuals were genotyped on two related types of genotype arrays (UK BiLEVE Axiom Array or UK Biobank Axiom Array) across 106 batches and imputed using the merged UK10K and 1000 Genomes phase 3 reference panels44. The UK Biobank data were obtained under application # 32133.The UK Biobank publicly released phase 2 of deep genetic and phenotypic data on ~500,000 individuals across the United KingdomFor sample quality control, first, individuals with SNPs missing at a rate >5% and high heterozygosity were removed due to poor quality. Second, one person within each pair of related individuals was removed. The relatedness threshold was set at second-degree relatives, which corresponds to the identity by descent π-hat value ≥0.25. Third, individuals who had mismatched self-reported and genetic-inferred sex were not included in the study. Genetic variants with imputation info score <0.3 and MAF <0.01 were excluded. For genotype data, we extracted the 1734 significant SNPs identified by Sum-Share. Out of 1734 SNPs, 1698 SNPs were also genotyped by the UK BioBank and passed the above quality control.Because eMERGE’s phenotype data were derived from ICD-9 codes and UK BioBank contains mostly ICD-10 codes, we manually curated the corresponding ICD-10 codes for the five cardiovascular phenotypes. ICD-10 codes were E66.9 for obesity, E03.9 for hypothyroidism, E11.9 for type 2 diabetes, E78.0 for hypercholesterolemia, and E78.5 for hyperlipidemia.X) with multiple phenotypes . For each phenotype we assumeβ is the corresponding log odds ratio of the SNP. Denote α =  and β = . To simultaneously model multiple phenotypes in multiple EHRs, Sum-Share used an adapted composite likelihood approach. More specifically, it assumed K clinical sites, and the sample size in the k-th site was nk. For the i-th subject in the k-th site, H0:β = 0, against Hα:β ≠ 0. Denote q phenotypes, yik of the combined dataset across all sites, S is the score function which is obtained by taking the first derivative of the likelihood function in (1), and V is the estimated variance of S. Through some derivation, we haveH0:β = 0), the test statistic follows a χ2 distribution with q degrees of freedom asymptotically. Therefore, the p-value of the test can be calculated asχ2 distribution with q degrees of freedom.Sum-Share jointly studies the association between one SNP .T distributivity is provided in Box The pseudo-code for obtaining the test statistic Pseudo-code  of  the  Sum-Share  algorithmBox 1.  In sitek = 1,…KdonkCalculate and share endfork = 1,…KdoObtain the overall mean Sk and Vk using (4)Calculate and share endCalculates T by (2) and obtain the p-value by (3).Obtain the test statistic p-value from the SNP-phenotype association was Bonferroni adjusted by the number of phenotypes to obtain an adjusted p-value. The minimum adjusted p-value of all phenotypes was used to determine the power of PheWAS6.In PheWAS analysis, a logistic regression is applied between an SNP and each phenotype to determine its association. Each Various pleiotropic models were simulated to compare Sum-Share with PheWAS. As Sum-Share is able to integrate summary-level information from multiple EHRs, ten datasets under each pleiotropic model were simulated to mimic ten EHRs. Two types of data integration were performed for PheWAS: meta-analysis and mega-analysis. For meta-analysis, PheWAS analysis was performed on each dataset and the resulting association coefficients were integrated using inverse-variance-weighting. In mega-analysis, individual-level data from the ten datasets were pooled together to create a combined dataset, which was used for a PheWAS analysis. Similarly, Sum-Share was used to analyze these datasets distributively, through the integration of summary statistics from the ten datasets, or in a pooled analysis, which used the combined individual-level data. The procedure was repeated 1000 times. Power was calculated as the percentage of times a method identified significant pleiotropic associations out of 1000 repetitions.n patients and q phenotypes, Y is a n × q matrix of phenotypes, X is an n × 1 vector of SNP ∈{0,1,2} and following the Hardy-Weinberg equilibrium, β is a 1 × q coefficient vector, and β0 is the n × q intercept matrix of constant numbers. The SNP was simulated either as common, with MAF = 0.3, or low frequency, with MAF = 0.05. Ten phenotypes were simulated with binary disease status yi ∈ {0,1}q for each individual i. For each EHR, we simulated n = 100 patients, thus there were a total of 1000 patients in ten EHRs.Pleiotropic effects were generated from the following logistic model.An SNP was simulated to exhibit different pleiotropic patterns with the ten phenotypes including: same direction, opposite direction, and sparse effects.0 = −0.5.Under this model, an SNP was simulated to be associated with the ten phenotypes under the same effect, i.e., 0 = −0.5.Under this model, an SNP was simulated to be associated with the first five phenotypes under one effect and the other five phenotypes under the opposite direction of effect, i.e., β0 = −0.5. Intuitively, the SNP was strongly associated with the first phenotype, but gradually decreased its association with the other phenotypes.Under the sparsity model, not all phenotypes were associated with the SNP. A decaying model was used to simulate the genotype and phenotype associations. The model was The above models assumed independence between the phenotypes. However, phenotypes could also be correlated while exhibiting pleiotropic associations. Thus, another set of simulation data was created that had the same pleiotropic effects, namely, same direction, opposite direction, and sparsity, at the same time the phenotypes were also made to be correlated. The correlation matrix for the phenotypes is shown in Fig. 45.The rmvnorm function in the mvtnorm package was used to generate binary phenotypes from the correlation matrixn = 2000, 6000, and 12,000 were simulated as previously outlined . Sum-Share was used to analyze the three data separately to obtain the power in each dataset. Then, Sum-Share was used to integrate the datasets to evaluate the power in the integrated dataset.To evaluate the impact of sample size on the power of Sum-Share, three datasets with sample size n = 2000) datasets were simulated as in Section ‘Power comparison with PheWAS in multiple EHRs’, same direction of effects. The MAFs of the SNP in the three datasets are: 0.05, 0.1, and 0.4. Sum-Share was used to integrate the three datasets and its power was evaluated. The power of Sum-Share using the integrated data was then compared to three additional datasets (n = 6000) that have the same underlying genetic model, but homogeneous in MAF = 0.05, 0.1, or 0.4. In summary, Sum-Share was used to compare the power using the integrated datasets  and three individual datasets .When integrating multiple genetic datasets, it is possible that the same SNP could have different allele frequencies across datasets, e.g., common in one dataset and rare in another. By integrating multiple datasets, an SNP will have an averaged allele frequency that is reflective of all data. The impact of the averaged allele frequency on power is evaluated as follows. Three equally sized , we randomly selected sets of SNPs and phenotypes from six EHR sites , hypothyroidism (ICD-9 244.9), Type 2 diabetes (ICD-9 250.00), Hypercholesterolemia (ICD-9 272.0), and Hyperlipidemia (ICD-9 272.4).−16, p-values smaller than this threshold were reported as 2.22 × 10−16. This conversion did not affect the final results.For each SNP, its association with the five phenotypes was evaluated using Sum-Share, adjusting for gender and age. For controls, age was determined as the age of patients’ last visit recorded in the EHR. For cases, each patient and disease diagnosis were associated with an age, which was calculated as the median age of the ICD-9 code assignments of a particular disease. The age was discretized into three nearly equal-sized bins: low (age < 50), medium (50 < age < 75), or high (age > 75). The discretization was necessary to preserve data privacy and to reduce computational cost. Without discretization, patients’ information could be exposed if, for example, only one patient at the age of 90 had a certain disease. Because the minimum p-value output by R was 2.22 × 10p-value threshold 0.05/6106952 = 8.19 × 10−9. Significant SNPs were re-analyzed in the UK BioBank using PheWAS to identify any SNPs that can also be discovered using the standard approach. Similarly, the SNPs were also analyzed using the Sum-Share method. All of the significant SNPs as well as the subset of significant SNPs that were identified in the UK BioBank were annotated using the Ensembl Variant Effect Predictor to identify the corresponding genes25.SNPs were deemed significant if they passed the Bonferroni adjusted Further information on research design is available in the Description of Additional Supplementary FilesSupplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting Summary


Polypoid crop breeders can balance resources between density and sequencing depth, dosage information and fewer highly informative SNPs recommended, non-additive models and QTL advantages on prediction dependent on trait architecture.F1 population of hexaploid sweetpotato and a diversity panel of 380 tetraploid potato, genotyped using different platforms to answer the following questions: (i) do polyploid crop breeders need to invest more for additional sequencing depth? (ii) how many markers are required to make selection decisions? (iii) does considering non-additive genetic effects improve predictive ability (PA)? (iv) does considering dosage or quantitative trait loci (QTL) offer significant improvement to PA? Our results show that only a small number of highly informative single nucleotide polymorphisms  are adequate for prediction in the type of populations we analyzed. We also show that considering dosage information and models considering only additive effects had the best PA for most traits, while the comparative advantage of considering non-additive genetic effects and including known QTL in the predictive model depended on trait architecture. We conclude that genomic selection can help accelerate the rate of genetic gains in potato and sweetpotato. However, application of genomic selection should be considered as part of optimizing the entire breeding program. Additionally, since the predictions in the current study are based on single populations, further studies on the effects of haplotype structure and inheritance on PA should be studied in actual multi-generation breeding populations.The autopolyploid nature of potato and sweetpotato ensures a wide range of meiotic configurations and linkage phases leading to complex gene-action and pose problems in genotype data quality and genomic selection analyses. We used a 315-progeny biparental The online version of this article (10.1007/s00122-020-03673-2) contains supplementary material, which is available to authorized users. Phenotyping under recurrent selection has been an important approach for variety development in plant breeding, with substantial success to date. However, this process may take a long time for most crops, particularly for clonally propagated crops  via QTL mapping and genome-wide association studies (GWAS), but they have had little practical application in the actual development of new cultivars through plant breeding to date, especially for complex quantitative traits, since identifying the causal genes underlying QTL needed to make their application practical is costly  of hexaploid sweetpotato, both developed by the International Potato Center (CIP) and genotyped using different platforms, to address the following questions: (i) do polyploid crop breeders need to invest more resources for additional sequencing depth? (ii) how many genetic markers are required to make selection decisions? (iii) does the consideration of non-additive genetic effects add value to predictive ability (PA) to enhance genetic gains either for population improvement or product development in polyploid crops? (iv) given the multiple alleles at loci with diverse meiotic configurations and linkage phases, does considering dosage, haplotypic or QTL effects offer significant improvement to PA to enhance genetic advances? We also discuss other factors that need to be considered while adopting GS as a decision support tool in an optimized breeding program.Public sector breeding programs like those conducted in centers which are part of the Consultative Group on International Agricultural Research (CGIAR), and in the individual National Agricultural Research Systems (NARS) existing in many countries, are currently undergoing breeding program optimization efforts in order to keep up with the challenges of climate change and population increase  was developed by crossing a US-bred high β-carotene variety, ‘Beauregard,’ with an adapted, locally preferred, starchy, low β-carotene landrace variety, ‘Tanzania,’ at CIP—Peru. These two parents differ in additional traits of interest, and the population will henceforth be referred to as the BT population. The population was evaluated in six environments of Peru, for various quality-related and yield-related traits, between 2016 and 2017. The design was an 80 × 4 α-lattice with two–three replications, depending on location. The information about these trials is further described in the publications by Gemenet et al. , L = 5 environments, allowing for heterogeneity of genetic variances and covariances across environments. The best fitting model was chosen by Akaike’s information criterion (AIC) and differed slightly for the different traits. For DM, Starch and FC_U, a factor analytic model  in Peru, China and Ethiopia. Henceforth, we shall refer to this population as the TON panel. The evaluation of the panel was carried out in diverse agro-ecological zones, and in subsets of genotypes subject to participating NARS’ partner capacity and/or ability to produce enough mini-tubers for experimentation. The experimental sites, experimental designs and the number of genotypes evaluated per experiment are summarized in Table Unlike in sweetpotato where phenotype and genotype data were balanced across experiments, (292 + Parents for DArTSeq and 315 + parents for GBSpoly), the potato experiments were unbalanced in terms of experimental genotypes. For the purposes of this study, we only selected the locations with the highest training population per trait. Consequently, we used AYP from Kunming , WMT from Kunming , LB from Oxapampa , LB from Yunnan , PVY from Lima , TTW averaged across three treatments of 2016 in Ica  and TTW in 2016 from Heilongjiang , all having number of genotypes indicated in Table The full 315-progeny of the BT (sweetpotato) population was genotyped together with the parents using an optimized protocol for hexaploid sweetpotato, ‘GBSpoly’ at North Carolina State University (NCSU). Additionally, a subsample of 292-progeny and the two parents of the BT population were genotyped by DArTSeq™ in Australia, under the collaboration between the Integrated Genotyping Service and Support (IGSS) platform at the Biosciences east and central Africa (BecA) hub in Nairobi, Kenya and DArT. The 380 genotypes of the TON population (potato) were genotyped by GBS at Cornell University.PstI-MseI method was selected. DNA samples were processed in digestion/ligation reactions principally as per Kilian et al. (PstI-compatible adaptor with two different adaptors corresponding to two different restriction enzyme (RE) overhangs. The PstI-compatible adapter was designed to include Illumina flowcell attachment sequence, primer sequence and ‘staggered,’ varying length barcode region, similar to the sequence reported by Elshire et al. (MseI-compatible overhang sequence. Only ‘mixed fragments’ (PstI-MseI) were effectively amplified in 30 rounds of PCR using the following reaction conditions: (i) 94 °C for 1 min, (ii) 30 cycles of: 94 °C for 20 s, 58 °C for 30 s, 72 °C for 45 s and (iii) 72 °C for 7 min. After PCR, equimolar amounts of amplification products from each sample of the 96-well microtiter plate were bulked and applied to c-Bot (Illumina) bridge PCR followed by sequencing on Illumina Hiseq 2000. The sequencing (single read) was run for 77 cycles. Sequences generated from each lane were processed using proprietary DArT analytical pipelines. In the primary pipeline, the FastQ files were first processed to filter away poor-quality sequences, applying more stringent selection criteria to the barcode region compared to the rest of the sequence. This was to ensure reliability in the assignments of the sequences to specific samples carried in the ‘barcode split’ step. Approximately 2,000,000 sequences per barcode/sample were identified and used in marker calling. Finally, identical sequences were collapsed into ‘fastqcoll files.’ The fastqcoll files were ‘groomed’ using DArT PL’s proprietary algorithm which corrects low-quality base from singleton tag into a correct base using collapsed tags with multiple members as a template. The ‘groomed’ fastqcoll files were used in the secondary pipeline for DArT PL’s proprietary SNP and SilicoDArT (presence/absence of restriction fragments in representation) calling algorithms (DArTsoft14). For SNP calling, all tags from all libraries included in the DArTsoft14 analysis were clustered using DArT PL’s C ++ algorithm at the threshold distance of 3, followed by parsing of the clusters into separate SNP loci using a range of technical parameters, especially the balance of read counts for the allelic pairs. Additional selection criteria were added to the algorithm based on analysis of approximately 1000 controlled cross populations. Testing a range of tag counts parameters facilitated selection of true allelic variants from paralogous sequences. In addition, multiple samples were processed from DNA to allelic calls as technical replicates and scoring consistency was used as the main selection criteria for high-quality/low error rate markers. Calling quality was assured by high average read depth per locus (> 30X). The SNPs were coded as 0 = AA, 1 = BB, 2 = AB and ‘-’= Missing. The sequences were not aligned to a reference genome because by the time of genotyping, the diploid references  CutSmart buffer was used to make up a total volume of 30 µl. Purification of the digested samples was done using AMPure XP magnetic beads from ThermoFisher™ and quantified with PicoGreen assay. Barcodes were designed to account for substitution and indel errors and had an 8-bp buffer sequence to ensure that the barcode lay within high-quality base call regions of the sequence reads. Additional double digests on 64-plex pooled samples, purification and size selection steps were carried out as described by Wadl et al.  and l et al.  before pEcoT221 restriction enzyme, and 48-plex libraries were prepared for sequencing, using customized GBS protocols at Cornell. The resultant FastQ files were quality controlled and variant calling done using GATK HaplotypeCaller option  via anther culture by the potato genome sequencing consortium (PGSC). Version PGSC_DM_v4.03 of the reference genome was used in alignment. The barcodes were removed using stacks, and the ends were trimmed using trim-galore, followed by mapping to the reference using BWA. Resultant SAM files were processed using samtools and variants called using GATK Haplotype caller, targeting biallelic SNPs only. The SNPs were coded according to the dosage of the alternative allele as 0 = AAAA, 1 = AAAB, 2 = AABB, 3 = ABBB and 4 = BBBB. The SNP filtering was done using bcftools allowing only for those SNPs with MAF of ≥ 3%, call rate of ≥ 70%, average genotype quality (GQ) ≥ 30 and minimum read depth (DP) ≥ 16  to estimate the predictive ability of the models using both simple/oligo traits  and complex traits (storage root or tuber yield and yield component traits in both), as defined in Table h2) for each trait as:L = 5 for sweetpotato following the accelerated breeding scheme currently implemented . Differences in PA among models per trait were tested using a simple one-way analysis of variance with models as factor. The correlation coefficients per replication were Fisher Z-transformed and means compared on these Z values using a one-way ANOVA with models as factor. The average PA was then obtained by back transforming the average of the Z values. Quantitative-genetic parameters were tested for the additive model with or without dosage by obtaining the additive genetic variation a et al.  as:5\docF1 population, we used the original GBSpoly data, using different filtering criteria to end up with different number of markers. We used three criteria (i) total number of SNPs filtered at 10% MAF and ≥ 90% call rate, (ii) total number of SNPs filtered at 30% MAF and ≥ 90% call rate  and (iii) a random sample of 15,000 SNPs from the total number of SNPs and filtered at 30% MAF and ≥ 90% call rate. In potato, the total number of SNPs was filtered using two criteria: (i) 30% MAF and ≥ 90% call rate, (ii) 40% MAF and ≥ 90% call rate. Predictions were carried out for all traits measured using these criteria. To separate the effects of allele frequency from the effects of number of markers on PA, we also used the original GBSpoly data in sweetpotato, filtered at constant MAF and randomly sampled different number of markers, which we used to compare PA in one quality-related simple trait  and one yield-related complex trait . We used 10, 000, 5000, 1000 and 500 SNPs, all filtered to MAF ≥ 5%. The model considering only additive effects (Add_6x_GBSpoly) was used in comparing the effect of allele frequency and number of markers in sweetpotato, while all three models were tested between the two filtering criteria in potato.For the sweetpotato By taking advantage of the fully phased integrated linkage map from BT . The call rates and polymorphic information content (PIC) are shown in Fig. Cornell GBS in potato resulted in 295,401 biallelic SNPs at the variant calling step that were then hard-filtered to 3262 high confidence SNPs by setting MAF ≥ 3%, call rate ≥ 70% and average read depth (DP) ≥ 16 (Online Resource 4). The 3262 SNP profiles are shown in Fig. For GBSpoly in sweetpotato called according to Wadl et al. , the empThe comparison of models with(out) QTL and use of markers per se or haplotypes was carried out only in sweetpotato, using 30,684 SNPs from the same genotyping platform and data set, filtered and processed as described by Mollinari et al. , which wIn sweetpotato, the diploidized additive model (Add_2x_DArTSeq) using data from DArTSeq performed equally well or sometimes better than the additive model using high confidence dosage data from GBSpoly (Add_6x_GBSpoly), depending on trait architecture, for simpler quality-related traits Fig. . DM had Our results in potato indicated that an increased number of markers by more than double did not have an effect of equal magnitude on PA  Q-BLUP based on relationship matrices from QTL-peak haplotypes, (ii) Q + G-BLUP fitting two terms based on QTL-peak haplotypes and the rest of the markers in the linkage map, (iii) G-BLUP, predictions using markers spaced every 2 cM in the genetic map without considering QTL. The PA results are shown in Fig. L = 5 years in sweetpotato and L = 8 in potato, which are the cycle lengths of current breeding schemes at CIP , we evaluated quantitative-genetic parameters for this model in comparison with the additive model without dosage for both crops (Add_2x_DArTseq for sweetpotato and Pseudo_6x_for potato). Narrow-sense heritability Having a reliable, cost-efficient genotyping platform that ensures faster data turn-around to breeding programs on time to impact selection and advancement decisions is a must for routine application of genomic selection in plant breeding programs. Here we have compared results based on data from three GBS-based platforms, two of which provide data at the commercial diploid sequencing depth level (DArTSeq and GBSCornell). About 100 × read depth was required to confidently call all the five heterozygous dosage classes of sweetpotato, against 25-30x required for the diploids. These results agree with studies in potato where Uitdewilligen et al.  reportedOur results in both potato and sweetpotato show that additive-effects-only models, whether diploidized or with dosage, were comparatively better in PA than the models considering non-additive effects for all simple traits. This comparative advantage, however, lessened with more complex traits, where non-additive effects and inclusion of dosage information became slightly more relevant, although in most cases the additive-effects-only model with dosage still remained the best in terms of PA. This finding makes sense in quantitative genetic terms as the more the number of genes affecting a trait, the more the expected interaction among loci. In sweetpotato for example, issues of ‘missing’ heritability have been established for yield-related traits using the current BT population in multiple environments, where only a few QTL with very small effects were reported even though a very dense, well phased hexaploid genetic map was used , marker density, environmental variances and covariance among traits Supplementary material 2 (TIFF 85 kb)Supplementary material 3 (CSV 10709 kb)Supplementary material 4 (CSV 2507 kb)Supplementary material 5 (PDF 306 kb)Supplementary material 6 (CSV 22165 kb)Supplementary material 7 (PDF 211 kb)Supplementary material 8 (CSV 32 kb)Supplementary material 9 (CSV 28 kb)Supplementary material 10 (PDF 7 kb)Below is the link to the electronic supplementary material.


The QTL-allele system underlying two spectral reflectance physiological traits, NDVI  and CHL (chlorophyll index), related to plant growth and yield was studied in the Chinese soybean germplasm population (CSGP), which consisted of 341 wild accessions (WA), farmer landraces (LR), and released cultivars (RC). Samples were evaluated in the Photosynthetic System II imaging platform at Nanjing Agricultural University. The NDVI and CHL data were obtained from hyperspectral reflectance images in a randomized incomplete block design experiment with two replicates. The NDVI and CHL ranged from 0.05–0.18 and 1.20–4.78, had averages of 0.11 and 3.57, and had heritabilities of 78.3% and 69.2%, respectively; the values of NDVI and CHL were both significantly higher in LR and RC than in WA. Using the RTM-GWAS (restricted two-stage multi-locus genome-wide association study) method, 38 and 32 QTLs with 89 and 82 alleles and 2–4 and 2–6 alleles per locus were identified for NDVI and CHL, respectively, which explained 48.36% and 51.35% of the phenotypic variation for NDVI and CHL, respectively. The QTL-allele matrices were established and separated into WA, LR, and RC submatrices. From WA to LR + RC, 4 alleles and 2 new loci emerged, and 1 allele was excluded for NDVI, whereas 6 alleles emerged, and no alleles were excluded, in LR + RC for CHL. Recombination was the major motivation of evolutionary differences. For NDVI and CHL, 39 and 32 candidate genes were annotated and assigned to GO groups, respectively, indicating a complex gene network. The NDVI and CHL were upstream traits that were relatively conservative in their genetic changes compared with those of downstream agronomic traits. High-throughput phenotyping integrated with RTM-GWAS provides an efficient procedure for studying the population genetics of traits. With the rapid development of high-throughput genome sequencing technology, high-quality genotype data can be obtained quickly and cheaply, enabling the detection of quantitative trait loci (QTL) at a high level of resolution through genome-wide association studies (GWAS) . PreviouMultispectral and hyperspectral reflectance images have been widely used in high-throughput phenotyping platforms; the spectral index has been found to be closely related to the growth and development of crops . In studChlorophyll is the primary component involved in plant photosynthesis and is closely related to biomass accumulation and yield formation, making it critically important for crop improvement. The rapid and non-destructive estimation of chlorophyll content facilitates genetic studies of chlorophyll. Chlorophyll content can be predicted using different wavelength spectra; for example, there is a strong correlation between the reflectance ratio of the near-infrared band to the 700-nm band and chlorophyll content . The hypNDVI and CHL are both spectral reflectance physiological traits related to plant growth and yield. To evaluate the usefulness of these traits in breeding programs, knowledge of their variability and genetic basis in germplasms is essential. Previously, the measurement of these two physiological traits was tedious and often not possible using traditional instruments. Now, multispectral and hyperspectral images have greatly facilitated the measurement of these traits. The greenhouse high-throughput phenotyping platform (GHTPP) in the Plant Phenomics Research Center (PPRC) at Nanjing Agricultural University (NJAU) has been used by several studies. Phenotype data from the high-throughput phenotyping platforms of previous studies have primarily been used for the prediction of agronomic traits, such as plant yield . However1.The aims of this study were the following: (i) characterize variation in two spectral reflectance physiological traits, NDVI and CHL, in the Chinese soybean germplasm population (CSGP), including wild accessions (WA), cultivated farmer landraces (LR), and released modern cultivars (RC), using the facilities and equipment of the GHTPP at the PPRC, NJAU, to compare wild and cultivated soybeans; (ii) explore genetic variation in QTL-alleles through association mapping using the novel RTM-GWAS procedure and evolutionary changes from WA to LR and RC; (iii) predict the genetic potentials of the germplasm population through recombination among the accessions; and (iv) predict the candidate genes as well as the gene constitutions of NDVI and CHL in the CSGP based on information in SoyBaseA total of 341 soybean accessions of the CSGP, including 76 WAs, 83 LRs, and 182 RCs, were sampled in this study. A randomized incomplete block design experiment with two replicates was conducted for high-throughput phenotyping. Because of the space limitations of the high-throughput phenotyping platform, the accessions were randomly divided into two groups. Two replicates of the phenotyping experiment were performed for the first group of 172 accessions on September 9, 2019 and October 13, 2019, and for the second group of 169 accessions on November 20, 2019 and May 1, 2020. For each test, approximately 4∼5 viable seeds were selected from each accession and were planted in a plastic pot (Φ23 × H17 cm). The experimental soil was a 3:1 mixture of vermiculite and nutrient soil; one best soybean seedling remained in each pot on the seventh day after sowing. The temperature in the greenhouse was maintained between 25–33°C, and light was provided for 16 hours (06:00 to 22:00).https://www.phenovation.com/) with a CCD (charge-coupled device) camera, spectral LEDs (light-emitting diodes) for actinic treatment, an illuminated area of 70 cm × 70 cm, and a spectral range of 350–1000 nm. The spectral reflectance images were captured at six different wavelengths. From these images, the NDVI and CHL of the plant canopy for individual pots were estimated. According to CropReporter, the NDVI was calculated as (RNIR−Rred)/(RNIR + Rred), and the CHL was calculated as RNIR, Rred, and R700 are the spectral reflectance in the near-infrared band, the visible red band, and the 700-nm wavelength band, respectively.The greenhouse high-throughput phenotyping platform at the PPRC, NJAU, was used for phenotyping. The platform consisted of a planting area, irrigation area, and PSII (Photosynthetic System II) imaging room. An automatic and high-throughput transfer system was used to transfer plants from the planting/growing area to the imaging room. The PSII imaging room was equipped with a camera system  to control system operation, CropReporter  to control the camera system, and the image analysis software Data Analysis  to process the data. The two traits were unitless, as they represent relative values of reflectance. All of the NDVI and CHL values were obtained directly from the platform system.The automatic phenotype measurements began on the sixth day after sowing (DAS6). Each pot with a plant was transferred from the planting area into the PSII imaging room to measure NDVI and CHL. Each pot was then returned to the planting/growing area. This phenotyping process was automatically executed every 3 days, and a total of nine measurements  were taken throughout the experimental period for each accession type.The 341 soybean accessions were genotyped with RAD-seq (restriction site-associated DNA sequencing) in previous studies . A totalThe RTM-GWAS (restricted two-stage multi-locus genome-wide association study) procedure  was usedyi = μ + gi + εi, where yi is the observed corrected phenotype of the i-th accession, μ is the population mean, gi is the genotypic effect of the i-th accession, and εi is the random error following a normal distribution with a mean of zero and variance of σ2. The analysis of variance of the corrected phenotype data was performed using the PROC GLM in SAS/STAT 9.4 , and variance components were estimated using PROC VARCOMP with the REML method. The trait heritability estimate was calculated as r is the number of replicates. This h2 is heritability in narrow sense because The experiment consisted of an incomplete block design. The plot values were adjusted using the block means according to the equal block mean assumption because the material set in each block was randomly selected; therefore, the entire experiment was treated as a completely randomized design with two replicates. The linear model for the adjusted dataset was There were nine measurements  for each trait (NDVI or CHL) each accession. The trait heritability value was used to assess the goodness of the trait expression, and the measurement with a highest heritability value was chosen to represent the trait. Therefore, the variance components and heritability values were estimated based on analysis of variance for all the nine measurements, and the measurement with the highest trait heritability value was used for genome-wide association study.y = 1μ + Wa + Xb + e, where y is the phenotype, μ is the population mean, W is the eigenvector matrix representing the population structure, X is the design matrix of locus genotype, a and b are vectors of corresponding effects. At the first stage, X includes only a single SNPLDB marker for pre-selection. At the second stage, X includes multiple SNPLDB markers for multi-locus modeling. In the present study, at the first stage under the single locus model, 1,296 and 2,147 SNPLDBs were pre-selected from the 11,716 SNPLDBs for the second stage of stepwise regression association analysis under the multi-locus model for NDVI and CHL, respectively. As the RTM-GWAS was based on the multi-locus model having built-in control for the experiment-wise error rate, a normal significance level of 0.05 was used for QTL detection in this study.The RTM-GWAS method was used for QTL-allele detection . Brieflyin silico , the candidate genes for NDVI and CHL were annotated from the identified QTLs. Next, the annotated candidate genes were subjected to gene ontology (GO) analysis using the Williams 82 genome version 1 (Wm82.a1.v1.1) as the reference genome. The candidate genes were searched within the interval (with a 50-kb flanking expansion) of the associated loci. In order to have a preliminary validation of the annotated candidate genes, the RNA Seq-Atlas project data set in SoyBase (see footnote 1) was downloaded and analyzed to assess the expression level of the annotated genes for NDVI and CHL.Phenotype measurements for each accession were taken nine times on different days during growth ; the trait heritability at each measurement ranged between 16.0–78.3% and 25.4–69.2% for NDVI and CHL, respectively. The NDVI at DAS21 and CHL at DAS24 had the highest heritabilities and were thus examined in subsequent analyses.The frequency distribution showed that the NDVI ranged from 0.05 to 0.18 with an average of 0.11 . The entBoth NDVI and CHL, two physiological traits related to photosynthesis and growth, significantly differed between wild  and cultivated  soybeans, suggesting that cultivated soybeans have experienced significant improvements in photosynthesis- and growth-related traits following their domestication . Thus, tThe analysis of variance revealed significant differences among accessions for both NDVI and CHL, indicating that there was significant genetic variation for the two spectral reflectance traits . The traAccording to the RTM-GWAS procedure involving the use of 1,296 and 2,147 multi-allelic SNPLDB markers preselected at the first stage for the second stage multi-locus multi-allele association analysis, a total of 38 and 32 SNPLDBs, each with 2–4 and 2–6 alleles were determined to be significantly associated with NDVI and CHL, respectively , 4.R2 ≥ 1%) explained 34.77% of the PV and 21 small-contribution loci (R2 < 1%) explained 13.62% of the PV (The 38 NDVI-associated loci explained 48.39% of the phenotypic variation (PV), among which 17 large-contribution loci  matrix , which rA and LR .Similar results were obtained for CHL. There were 76 alleles on 32 loci in WA; 76 wild alleles on 32 loci were passed to LR, with the emergence of 4 new alleles on 4 loci for a total of 80 alleles on 32 loci; no alleles were excluded . From LRqNdvi-01-5, qNdvi-02-1, qNdvi-03-1, and qNdvi-15-2); qNdvi-01-5 and qNdvi-03-1 were also newly formed in LR and RC, respectively. One allele in qNdvi-05-4 was excluded in LR, and 1 allele in qNdvi-05-3 was excluded in RC. For CHL, there were 6 newly emerged alleles on 5 loci , and 1 allele on qChl-01-1 was excluded in RC. The allele frequencies of the newly emerged alleles in LR + RC ranged between 3.40–13.58%, and the new alleles were not dominant over older alleles.All of the emerged and excluded alleles and their associated QTLs are listed in Thus, genetic changes were limited during the evolution from WA to LR and RC, as the three subpopulations shared a large number of common alleles. Among the 89 wild alleles of 38 NDVI-associated loci, 80 alleles were shared among the three subpopulations and among the 76 wild alleles of 32 CHL-associated loci, and 75 wild alleles were shared among the three subpopulations . Here, tIn summary, these two spectral reflectance physiological traits (NDVI and CHL) were genetically conservative. Inheritance played a major role in determining the genetic motivation, For NDVI, 4 alleles on 4 loci emerged; for CHL, 6 alleles on 5 loci emerged. Two new loci emerged for NDVI, but none emerged for CHL. For NDVI, only 1 allele on 1 locus was excluded, whereas no alleles were excluded for CHL. The transition from WA to LR and from LR to RC took approximately 5,000 and 100 years, respectively; despite this long history, large genetic changes have not occurred, especially during the transition from WA to LR. This genetic stability indicates that the two physiological traits NDVI and CHL are highly conservative compared with other agronomic traits. However, the same number of genetic changes occurred during the transition from WA to LR  and the transition from LR to RC (100 years), indicating that the enhanced artificial breeding in the transition from LR to RC accelerated the rate of genetic change.To assess the recombination potential for NDVI and CHL in the CSGP, a total of 57,970 possible single crosses among the 341 accessions were simulated based on the QTL-allele matrix; possible crosses between the accessions for each subpopulation were also simulated. The 99th percentile of 2,000 progenies of each cross was used to represent the recombination potential . For NDVFor CHL, the recombination potentials within WA, LR, and RC were also not large , but the predicted value was larger in LR and RC (the superior populations) than in WA. Among the three between-subpopulation crosses, the highest recombination potential was observed for WA × LR ; for the crosses at the entire population level, the CHL was 5.41, which was the same as that observed for WA × LR . Thus, cFrom the detected QTLs, a total of 39 candidate genes were annotated on 20 NDVI-associated loci, and 32 candidate genes on 22 CHL-associated loci . Only 9 Glyma05g23230 and Glyma14g06630 showed especially high expression level. For CHL, there were 28 out of 32 annotated genes were expressed in 14 soybean tissues, and Glyma08g10960 showed high expression level in young leaf and pod shell, indicating these identified candidate genes are possibly functional.The validation of these candidate genes are left for further studies, however, a preliminary verification was carried out using the transcriptome data set of RNA Seq-Atlas project in SoyBase (see footnote 1). The gene expression results  showed tThis study presented a genetic analysis of two spectral reflectance traits, NDVI and CHL, using a high-throughput phenotyping platform. Both NDVI and CHL showed significant genetic variation in CSGP, indicating that the spectral reflectance phenotyping data can not only be used for predicting agronomic traits but also for dissecting their underlying genetic basis. In this study, 89 alleles on 38 loci for NDVI and 82 alleles on 32 loci for CHL were detected, and the RTM-GWAS method was used to characterize their allele effects. High-throughput phenotyping integrated with RTM-GWAS was an efficient method for identifying the QTL-allele systems for NDVI and CHL. However, only 48.36% and 51.35% of the PV for NDVI and CHL were explained by the detected loci, which is low compared with other agronomic traits, such as 100-seed weight  and 21 and 13 small-contribution (R2 < 1%) QTLs for NDVI and CHL, respectively. The PV in RTM-GWAS is relatively lower than that in single-locus model such as the mixed linear model method . In singThe fact of many QTLs each with a smaller PV is a characteristic of a quantitative trait controlled by a large number of QTLs. Or as we understand, the total PV of a quantitative trait in fact is a projection of a large number of genes/QTLs with different but interrelated biological functions onto the trait plane in a specific population. The statistically estimated genetic effect or PV of a QTL is relative to and largely depends on the genetic background of the population. A same QTL may exhibit varying effects in different populations with different genetic background. For example, the PV of a QTL in a simple genetic background such as near-isogenic lines is much greater than that in a germplasm population. Therefore, the small-contribution QTL in a study may exhibit large effects in other populations with simple genetic background. In fact, the purpose of the RTM-GWAS method is to achieve a relatively thorough detection of whole-genome QTLs and their multiple alleles or the QTL-allele system rather than a few individual large PV QTLs. Thus for the genetic improvement of quantitative traits in plant breeding, background control and foreground control are both important. It is likely that breakthroughs can be achieved through increase of positive alleles and decrease of negative alleles among multiple loci, rather than through recombination between/among a few loci like in the qualitative trait situation.Both phenotypic and genotypic analysis showed that the two spectral reflectance traits were genetically conservative in comparison to the agronomic traits, such as seed yield, 100 seed weight, days to flowering . BecauseHowever, some potential for improvement in NDVI and CHL was observed from WA to LR + RC, although the improvement was small . The genThe previous studies on high-throughput phenotypes in crops usually focused on predicting yield-related agronomic traits, such as plant height, biomass and seed yield . For exahttps://github.com/njau-sri/leiwang2020ndvichl.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: JG designed the study. LW and FL performed the experiments. JH, LW, and FL performed data analysis. XH, WW, GX, JL, and GZ participated in the experiments and data collection. JH, LW, and JG drafted the manuscript. All authors reviewed and approved the manuscript.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.