Drought is most likely the most significant abiotic stress affecting wheat yield. The discovery of drought-tolerant genotypes is a promising strategy for dealing with the world’s rapidly diminishing water resources and growing population. A genome-wide association study (GWAS) was conducted on 298 Iranian bread wheat landraces and cultivars to investigate the genetic basis of yield, yield components, and drought tolerance indices in two cropping seasons (2018–2019 and 2019–2020) under rainfed and well-watered environments.r2 = 0.32). However, the genomes D, A, and B have the highest LD, respectively. The single-locus mixed linear model (MLM) and multi-locus random-SNP-effect mixed linear model (mrMLM) identified 1711 and 1254 significant marker-trait association (MTAs) (-log10 P > 3) for all traits, respectively. A total of 874 common quantitative trait nucleotides (QTNs) were simultaneously discovered by both MLM and mrMLM methods. Gene ontology revealed that 11, 18, 6, and 11 MTAs were found in protein-coding regions (PCRs) for spike weight (SW), thousand kernel weight (TKW), grain number per spike (GN), and grain yield (GY), respectively.A heatmap display of hierarchical clustering divided cultivars and landraces into four categories, with high-yielding and drought-tolerant genotypes clustering in the same group. The results of the principal component analysis (PCA) demonstrated that selecting genotypes based on the mean productivity (MP), geometric mean productivity (GMP), harmonic mean (HM), and stress tolerance index (STI) can help achieve high-yield genotypes in the environment. Genome B had the highest number of significant marker pairs in linkage disequilibrium (LD) for both landraces  and cultivars . Similar to cultivars, marker pairs on chromosome 4A represented the strongest LD (The results identified rich regions of quantitative trait loci (QTL) on Ch. 4A and 5A suggest that these chromosomes are important for drought tolerance and could be used in wheat breeding programs. Furthermore, the findings indicated that landraces studied in Iranian bread wheat germplasm possess valuable alleles, that are responsive to water-limited conditions. This GWAS experiment is one of the few types of research conducted on drought tolerance that can be exploited in the genome-mediated development of novel varieties of wheat.The online version contains supplementary material available at 10.1186/s12870-023-04416-3. Triticum aestivum L.) [Providing 25% of the total proteins and calories in the human diet and food supplies worldwide relies on wheat (ivum L.) –3. Wheativum L.) . Global ivum L.) . The Fooivum L.) . Droughtivum L.) . Iran isivum L.) .Drought events adversely affect crop productivity by disrupting a wide range of biochemical/physiological functions on a global scale. Water deficit stress is the most severe environmental stress, limiting plant growth and development worldwide , 9. DrouDrought tolerance indices (DTIs) have been widely used for identifying compatible genotypes, by evaluating their performance under non-stressed and stressed environments , 16. In Wheat breeding programs need to use innovative technologies to explore the genetic basis of complicated quantitative traits . The genOur study used a collection of agronomic traits and drought tolerance indices between Iranian wheat varieties and landraces to identify significant SNP loci associated with drought-tolerance traits through a GWAS, using the MLM and mrMLM. The study further aimed to explore candidate genes for drought-resistance traits in bread wheat and to better understand the molecular mechanisms of the drought adaptation of bread wheat in order to facilitate the cultivation of drought-tolerant varieties.The box plots of wheat landraces and cultivars are displayed in Fig. The traits GY, GN, TKW, and SW in the normal environment exhibited the highest significant, positive association with the drought tolerance index MP. However, in the rain-fed environment, the mentioned traits had the strongest significant, positive connection with the drought tolerance index HM Fig. .Fig. 2CoWW, GYRF, and DTIs , 73 (20 cultivars and 53 landraces), 127 (38 cultivars and 89 landraces), and 50 (5 cultivars and 45 landraces), genotypes were found in the first, second, third, and fourth groups, respectively , and 49 genotypes with a medium-to-TKW (21 cultivars and 28 landraces), respectively. Moreover, the third and fourth groups contained 93 genotypes with a medium-to-low TKW (68 landraces and 25 cultivars), and 86 genotypes (22 cultivars and 64 landraces) with a low TKW , 74 (43 landraces and 31 cultivars), 121 (92 landraces and 29 cultivars), and 50 genotypes (45 landraces and 5 cultivars), respectively . Our purpose in grouping genotypes was to identify genotypes with common characteristics in terms of thousand kernel weight, grains number per spike, spike weight, and grain yield. Heatmap and clustering wheat accessions classified genotypes into four groups based on GYTIs Fig. a. Group WW, GYRF, MP, GMP, STI, and HM had direct associations with PC1. The PC2 had a positive correlation with the SSI, TOL, and ATI indices, whereas the DI index had a negative correlation with the PC2. Genotypes-based PCA revealed that several genotypes  had the highest GY in both rain-fed and well-watered environments  showed that 65.2% and 34% of the variance were explained by the first and second components, respectively. The attributes GYAmong the 566,439,207 reads identified in eight Ion Proton runs, 458,363,607 (about 81%) were high-quality barcoded ones. Eventually, 133,039 unique SNPs were identified after removing duplicate reads. A total of 43,525 SNPs were detected across all 21 wheat chromosomes after imputation and discarding those with > 20% missing values, > 10% heterozygosity, and < 5% minor allele frequency. In addition, a set of 43,525 imputed SNPs was obtained using the W7984 reference genome, and these SNPs were used to estimate genetic diversity. Overall 15,951, 21,864, and 5,710 SNPs were mapped to the A, B, and D genomes, respectively, accounting 36.7%, 50.2%, and 13.1% of the total SNPs, respectively  and subpopulations (ΔK) was plotted against each other to determine the appropriate number of subpopulations. Three subpopulations were observed as the strongest ΔK value at K = 3. Three subpopulations of 298 accessions were created using the structure software, S-I, S-II, and S-III Fig. a. S_I cor2 = 0.211 with varieties, out of which 700,991 (37.72%) had significant linkages at P < 0.001. The majority of significant marker pairs were located at a distance of < 10 cM, based on our observations. An analysis of landraces found 1,867,575 markers with r2 = 0.182, of which 847,725 (45.39%) displayed significant linkages at P < 0.001. Chromosome 4A marker pairs also demonstrated the strongest LD (r2 = 0.368). Most marker pairs with statistical significance were located at distances of < 10 cM. Genomes B and D had the most and least marker pairs , respectively  are highlighted in the Manhattan circle plot  were identified for GY and related stress tolerance indices [–log10 ely Fig. a. The nuely Fig. b,c,d. SNWW, GYRF, DTIs, PCA1, and PCA2 in genomes A, B, and D, respectively. Further, GYWW, GYRF, TOL, MP, GMP, STI, ATI, SSI, DI, HM, PCA1, and PCA2 had 48, 22, 97, 15, 20, 21, 85, 84, 47, 17, 20, and 101 significant markers, respectively. The number of important SNPs based on GN, TKW, SW, and associated stress tolerance indices was 367, 371, and 396, respectively  are highlighted in the Manhattan circle plot , MLM and mrMLM models identified a total of 67 and 24 MTAs for yield attributes and DTIs, respectively. There were a total of 26, 36, and 5 MTAs based on MLM, as well as 4, 20, and zero MTAs based on mrMLM, respectively, for genomes A, B, and D  and pleiotropic impact were investigated thoroughly. Based on GO for DTIs for GN, TKW, and SW traits, 11, 6, 18, and 11 markers containing overlapping genes were identified, which are contained in important molecular and biological processes. Several biological and molecular processes were attributed to some of the discovered MTAs, including defense response, glycolytic process, lipid biosynthetic process, lipid metabolic process, fatty acid biosynthetic process, and response to wounding. The other processes were carbohydrate metabolic process, protein binding, ATP binding, nucleic acid binding, DNA binding, zinc ion binding, oxidoreductase activity, sulfotransferase activity, lipid binding, RNA binding and DNA binding , grain weight/spike (R2 = 21.4% in 2004 and 15.8% in 2005), and spike number (R2 = 15.6% in 2004 and 5.4% in 2005). The QTL for GY located on chromosomes 6A, 6B, and 6D totally accounted for 27.2% and 31.7% of the total variation in this trait in 2004 and 2005, respectively.The results of our study are in line with those of some studies made on the bread wheat of Iran, including Salarpour et al. , SalarpoThe flanking sequences of 43,525 SNPs were compared to RefSeq v2.0 and aligned accordingly. Surprisingly, the majority of marker pairs were found in the protein-coding areas, which regulate transcription. Other factors contributing to drought tolerance include DNA binding, transcription factor activity, and transmembrane transport. Some pathways were discovered using the rice reference genome, including ascorbate and aldarate metabolism, oxidative phosphorylation, biosynthesis of amino acids, fatty acid elongation, and metabolic pathways. In the metabolism of ascorbate and aldarate sucrose synthase, and sucrose-phosphate synthase are both genes that are involved in a metabolic pathway that is associated with DS tolerance . DroughtMTAs are key elements for detecting genomic areas linked to agronomic traits in wheat under drought stress. The identified markers might be used to clone and fine map underlying genes, as well as perform gene introgression and marker-based selection in wheat under normal and drought conditions. The discovery of QTL-rich regions on Ch. 4A and 5A supports the theory that this chromosome is important for drought tolerance and should be utilized for wheat breeding. Furthermore, a large number of SNP correlations were discovered at the genome level on the B genome, which has been related to drought resistance. Further, the use of association mapping based on several drought tolerance indices can be highly effective in finding the most essential markers for drought tolerance as well as discovering linked gene networks.The research was conducted at the Agricultural and Natural Resources Campus of Tehran University. Figure 2) spaced 50 cm. The plant density was 300 plants/m2, and the sowing and harvesting dates in both years were November 1 and June 30. The threshold for irrigation implementation was determined based on 40 mm evaporation from an evaporation pan for well-watered crops. We used a crop coefficient of determination (KC) as well as a reference crop evapotranspiration equation, ET0 = Epan*Kpan, where Epan is the evaporation depth below the pan surface (40 mm), and Kpan denotes the pan coefficient (0.8) for each month, to determine evapotranspiration (ETC = KC * ET0) [2 (the cultivation area for 298 genotypes in two repeats) by the water discharge (10.8 m3/h). Water requirement (m3/ha) was estimated by multiplying the depth of ET0 (mm) by 10. Wheat is grown under the rain-fed regime and only receives rainfall as a source of water. Table P) and stress (YS) conditions, and the total average trait  for normal (This study followed 298 wheat genotypes collected from various regions and climates of Iran (Supplementary C * ET0) . In thisIn accordance with Alipour et al. , GBS libSTRUCTURE (version 2.3.4) was used to analyze the population structure of the landraces and cultivars of Iranian wheat . This stP) > 3 and –log10 (P) > 5 thresholds were used for statistically significant MTAs [Both MLM (mixed linear models) and mrMLM (multi-locus random-SNP-effect MLM) were applied to provide an unbiased estimation of marker effects. Using the MLM approach, it was possible to accurately associate marker traits between accessions and various MLM models for controlling both population structure (Q) and diffused associations (K) between accessions with the GAPIT package. In RStudio, GWAS was performed with the MLM and mrMLM using the GAPIT package . The MLMant MTAs . Confideant MTAs . A Manhaant MTAs .http://www.gramene.org/) gene annotation assessments. The identification of putative candidate genes was evaluated according to two parameters: a) being located in the vicinity of the peak marker, and b) having known functions and involvement in the studied traits in wheat . Moreover, the significant SNPs were utilized in the enrichment analysis of gene ontology via KOBAS version 2.0 for testing in the KEGG. Finally, gene pathways were identified through the rice reference genome) Finally, gene pathways were identified through the rice reference genome ([www.kegg.jp/kegg/kegg1.html).The sequences surrounding all significantly associated SNPs were obtained by aligning them with IWGSC RefSeq v2.0 of the wheat 90 K SNP database used for Gramene ( genome (–82; www.Descriptive statistics and correlation coefficients were obtained by R 4.1 using the ggplot2, dplyr, ggpubr, and psych packages to reveal the distribution of wheat traits. An analysis of heatmaps was performed in RStudio to classify wheat genotypes. Eventually, the evaluation and dispersion of wheat traits and genotypes across the biplot diagram were analyzed using PCA and the factoextra packages in RStudio.Additional file 1: Supplementary Table 1. A summary of LD observed among marker pairs and the number of significant marker pairs per genome and chromosome. Supplementary Table 2. Climatic data in the studied environments. Supplementary Table 3. Overview on the landraces and cultivars of Iranian wheat studied. Supplementary Fig 1. The KEGG pathway of oxidative phosphorylation. Supplementary Fig 2. The KEGG pathway of fatty acid elongation. Supplementary Fig 3. The KEGG pathway of metabolic pathways.Additional file 2.


Metabolomics measures an intermediate stage between genotype and phenotype, and may therefore be useful for breeding. Our objectives were to investigate genetic parameters and accuracies of predicted breeding values for malting quality (MQ) traits when integrating both genomic and metabolomic information. In total, 2430 plots of 562 malting spring barley lines from three years and two locations were included. Five MQ traits were measured in wort produced from each plot. Metabolomic features used were 24,018 nuclear magnetic resonance intensities measured on each wort sample. Methods for statistical analyses were genomic best linear unbiased prediction (GBLUP) and metabolomic-genomic best linear unbiased prediction (MGBLUP). Accuracies of predicted breeding values were compared using two cross-validation strategies: leave-one-year-out (LOYO) and leave-one-line-out (LOLO), and the increase in accuracy from the successive inclusion of first, metabolomic data on the lines in the validation population (VP), and second, both metabolomic data and phenotypes on the lines in the VP, was investigated using the linear regression (LR) method.For all traits, we saw that the metabolome-mediated heritability was substantial. Cross-validation results showed that, in general, prediction accuracies from MGBLUP and GBLUP were similar when phenotypes and metabolomic data were recorded on the same plots. Results from the LR method showed that for all traits, except one, accuracy of MGBLUP increased when including metabolomic data on the lines of the VP, and further increased when including also phenotypes. However, in general the increase in accuracy of MGBLUP when including both metabolomic data and phenotypes on lines of the VP was similar to the increase in accuracy of GBLUP when including phenotypes on the lines of the VP. Therefore, we found that, when metabolomic data were included on the lines of the VP, accuracies substantially increased for lines without phenotypic records, but they did not increase much when phenotypes were already known.MGBLUP is a useful approach to combine phenotypic, genomic and metabolomic data for predicting breeding values for MQ traits. We believe that our results have significant implications for practical breeding of barley and potentially many other species.The online version contains supplementary material available at 10.1186/s12711-023-00835-w. The aim of plant breeding activities is to maximise selection gain per unit of time . To reacVarious statistical models/methods have been proposed in GS, among which a widely used method is genomic best linear unbiased prediction (GBLUP) , 10. BesSimilar to dense genome-wide DNA-markers, ‘omics’ data such as transcriptomic and metabolomic data are becoming available in increasingly larger quantities and at decreasing cost. Such ‘omics’ data consist of measurements of effects that are intermediate between the genotype and phenotype of interest for breeding. In recent years, the use of multi-omics data for the prediction of trait phenotypes has been investigated by several studies –15. In tHordeum vulgare L.) is the most common source of malt that is used in brewing alcoholic beverages [Barley  was compared with the baseline genomic model (GBLUP) to estimate variance components (VC) and predict breeding values. The accuracies of predicted breeding values were evaluated with two cross-validation strategies: leave-one-year-out and leave-one-line-out schemes. In addition, the increase in accuracy from the successive inclusion of, first metabolomic data on the validation lines, and second both metabolomic and phenotype data on the validation lines, was assessed using the LR method .All data, including phenotypic, genomic, and metabolomic data, used are available in a public accessible repository .2). Each trial was designed as a randomised complete block comprising 20 to 45 lines with three replicates of each line [1H NMR spectra, the intensities were integrated over small chemical shift intervals, expressed in parts per million (ppm) in the frequency range from 0.70 ppm to 9.00 ppm.In this study, a dataset on 2430 plots for 562 spring barley malting lines was included. These barley lines and the measurements of MQ traits are part of the current genomic breeding program from Nordic Seed A/S, and represent a subset of the plots analysed in Guo et al. , i.e. coach line . Each trach line , excludiA widely used approach for genetic evaluation is BLUP, which was derived by CR Henderson in 1950 , 12 to pIn this study, GBLUP from Guo et al.  was applIn this study, MGBLUP was used to allow the integration of both metabolomic and genomic data for prediction of breeding values. The model and methods closely follow the development by Christensen et al. . First, The model developed by Christensen et al.  is a baswhere mentclass2pt{minimn et al. ), which mentclass2pt{minim1 and MGBLUP2 as shown below.In Christensen et al. , it was The first step is:mentclass2pt{minimmentclass2pt{minimo et al. , where f1, and the effects are defined similarly to those in Eq.  in the models described above. The VC in all three models were estimated by restricted maximum likelihood (REML) using the DMU software package . The relFor GBLUP, 2, i.e. here For MGBLUP, the calculation is n et al. , the hern et al. , the genIt is common in plant breeding  to proviCross-validation was carried out to compare the accuracies of predicted breeding values using GBLUP and MGBLUP. Two different cross-validation schemes, in which the whole population was divided into a training population (TP) and a validation population (VP), were investigated in this study based on two different hypotheses regarding the factors that influence prediction accuracy. The two schemes were leave-one-year-out (LOYO) and leave-one-line-out (LOLO). In LOYO, one out of three years was left out so that the accuracy of predicting one year from the other two years could be investigated. This scheme is similar to the prediction of lines from a new breeding cycle. In LOLO, one out of 562 lines was left out so that the accuracy of predicting one line from all the other lines could be investigated. This scheme is similar to the prediction of lines for which no MQ phenotypes have been measured but the other lines from the same breeding cycle have been phenotyped for MQ.In each round of the cross-validation, according to the setup for TP size, a certain number of plots were selected, and for the remaining plots both phenotypes and MF were masked . The phenotypes of the masked samples were predicted based on the TP together with the genomic information. As a measure of the accuracy of prediction, we report the correlation between phenotypes for each plot corrected for the location × year × trial fixed effect and the random batch effect based on GBLUP and the predicted breeding values from each model, and as an assessment of the dispersion bias we report the regression coefficient of these corrected phenotypes on predicted breeding values for each model. Differences in correlations between GBLUP and MGBLUP were assessed using a Hotelling-Williams t-test , 30 at aThe reader should note here that we do not present cross-validation results in the scenario in which only phenotypes in the VP were masked (i.e. metabolomic data in the VP were not masked), although such predicted breeding values are very relevant from a practical view. The reason is that, in this scenario, the correlation between corrected phenotypes and predicted breeding values would be influenced by environmental effects in a similar way to the situation with a bivariate model and records for the secondary trait being available for the lines in the VP , but also very large for FS, WC and WV. Consequently, very large proportions of the heritabilities are mediated by the metabolome. These large proportions of metabolome-mediated heritability are also consistent with one of our previous studies, which found significant genetic correlations between MF and MQ traits . Taken t1, and the variance of the phenotype is needed. In Additional file 1, with matrix 1 is very large, we consider that these very large total phenotypic variances from MGBLUP are not entirely implausible. However, we still do not have a complete understanding of this phenomenon.For MGBLUP, both the metabolomic variance and the total phenotypic variance are large for all the traits. Such large metabolomic variances were also reported by Guo et al. . For all1 and MGBLUP2, no longer applies, and also that more parameters have to be estimated). However, this deficiency refers to the joint model for the phenotype and MF in Eqs.   2) can emponents . This womponents . Models Predictive performances of GBLUP and MGBLUP were investigated by cross-validation using the LOYO and LOLO schemes. In the LOLO scheme, the aim was to predict breeding values of a barley line using the phenotypes of all the other lines, and hence the size of the TP was larger than in the LOYO scheme. In addition, lines are generally much more related within year than across years, which causes a stronger genetic relationship between TP and VP in the LOLO than in the LOYO scheme. Therefore, it was expected that a higher prediction accuracy could be obtained from LOLO than from LOYO, and the results obtained are consistent with this expectation. The results also show that prediction accuracies from the LOLO scheme for GBLUP and MGBLUP are similar, with nearly no dispersion bias for either GBLUP or MGBLUP. Hence, incorporating MF for the phenotyped plots, does not increase the accuracy of lines without phenotypes and MF. By combining these results with the results from the LR method, which shows that the ratios for MGBLUPgmp and MGBLUPg are the same as those for GBLUPgmp and MGBLUPg, we also see that incorporating MF for the phenotyped plots does not increase the accuracy of lines with phenotypes. The predictions from the LOLO scheme have less practical importance than those from the LOYO scheme, because, in practical breeding, we usually want to predict one year ahead.The LOYO scheme mimics a practical breeding system, since a new set of lines should be developed every year. Here, the prediction accuracies of GBLUP and MGBLUP are similar, except for BG and WV, which have higher prediction accuracies in MGBLUP than GBLUP, with the increase in accuracy being largest for BG. These results are consistent with BG being the trait with the largest proportion of estimated metabolomic variance, and also with the proportion of estimated metabolomic variance being large for WV. Regarding dispersion biases, there were only small differences between GBLUP and MGBLUP, but we observed very strong dispersion biases, in particular, for WV, but also for BG (primarily with GBLUP) and WC. Apparently, prediction across years does not work well for these traits and this dataset. An explanation may be that with only two years in the TP, the representation of the variation between performances of lines under different environmental conditions is insufficient for these two years. This interpretation is supported by the results in Table For MGBLUP and LOYO, a very important question for practical breeding is to assess the accuracy of predicted breeding values of the lines without phenotypic records  based on metabolomic samples , but as explained in the Methods section this cannot be assessed using cross-validation. An increase in accuracy from the inclusion of metabolomic data on lines in the VP can be assessed from the results of the ratios of accuracies by the LR method as follows. First, the accuracy from MGBLUPg can be computed by dividing the correlation between the predicted breeding values and corrected phenotypes in Table If we focus on the differences between the results for the MQ traits, we see that EY was the trait with the lowest proportion of variance explained by MF, and also the trait for which inclusion of metabolomics data on the lines in the VP without recorded phenotypes did not increase the accuracy of its predicted breeding value; and BG was the trait with the largest proportion of variance explained by metabolomics, and also the trait for which the inclusion of metabolomic data on the plots with phenotypes increased the accuracy of its predicted breeding value. The actual reasons for these differences in patterns between phenotypes are not known to us. However, we note that EY is a measure of the total concentration of fermentable and non-fermentable sugars, and since the dominant metabolites in the NMR spectra are sugars, then the normalisation of the NMR spectra could result in the removal of important differences related to EY. We also note that the BG trait is actually a metabolite in itself, which is represented in the NMR spectra in several MF.If we focus on the practical relevance of these results, we note that NMR was recorded on malt, and therefore only available after the plots were harvested and the samples malted. Using these MF for the prediction of lines for which the MQ traits are not recorded could be economically beneficial since the cost to record MQ traits could be avoided, but they do not represent a situation where the selection decision can be done in the early generations of a breeding cycle to decrease generation time and increase selection intensity. To do this, we need to be able to carry out non-destructive sampling, e.g. by sampling leaf cuts. Investigating the performance of MGBLUP or similar models in a situation where MF were observed on growing plants would be very relevant.In recent years, the integration of multi-omics data has received much interest for predicting the phenotypes of traits –15. In oIn conclusion, we have demonstrated that MGBLUP is a useful approach for combining phenotypic, genomic and metabolomic data for the prediction of the breeding values of MQ traits in barley. We believe that the results from the current study have significant implications for practical breeding of barley and potentially many other species.Additional file 1: Text S1. Predictive correlation with records on a secondary trait in the validation population. Derivations show that in this situation, predictive correlation is not an appropriate measure of the accuracy of predicted breeding values.Additional file 2: Table S1. Variance components from GBLUP and MBLUP1 for malting quality traits. Table S2. Relative variance components from GBLUP, MGBLUP1 and MGBLUP for malting quality traits. Table S3. Regression coefficients of predicted breeding values from whole data on predicted breeding values from partial data, for malting quality traits using GBLUP and MGBLUP (LOLO scheme). Table S4. Regression coefficients of predicted breeding values from whole data on predicted breeding values from partial data, for malting quality traits using GBLUP and MGBLUP (LOYO scheme).Additional file 3: Figure S1. Proportion of total phenotypic variance explained by each component in malting quality traits.Additional file 4: Text S2. Deviation between empirical variance and total phenotypic variance. Derivations show that the variance of empirical variance is much larger for MGBLUP than for GBLUP.


Although the accumulation of whole-genome sequencing (WGS) data has accelerated the identification of mutations underlying complex traits, its impact on the accuracy of genomic predictions is limited. Reliable genotyping data and pre-selected beneficial loci can be used to improve prediction accuracy. Previously, we reported a low-coverage sequencing genotyping method that yielded 11.3 million highly accurate single-nucleotide polymorphisms (SNPs) in pigs. Here, we introduce a method termed selective linkage disequilibrium pruning (SLDP), which refines the set of SNPs that show a large gain during prediction of complex traits using whole-genome SNP data.2). The parameters for each trait were optimized in the training population by five fold cross-validation and then tested in the validation population. Similar to previous GWAS prior-based methods, the performance of SLDP was mainly affected by the genetic architecture of the traits analyzed. Specifically, SLDP performed better for traits controlled by major quantitative trait loci (QTL) or a small number of quantitative trait nucleotides (QTN). Compared with two commercial SNP chips, genotyping-by-sequencing data, and an unselected whole-genome SNP panel, the SLDP strategy led to significant improvements in prediction accuracy, which ranged from 0.84 to 3.22% for real traits controlled by major or moderate QTL and from 1.23 to 11.47% for simulated traits controlled by a small number of QTN.We used the SLDP method to identify and select markers among millions of SNPs based on genome-wide association study (GWAS) prior information. We evaluated the performance of SLDP with respect to three real traits and six simulated traits with varying genetic architectures using two representative models (genomic best linear unbiased prediction and BayesR) on samples from 3579 Duroc boars. SLDP was determined by testing 180 combinations of two core parameters (GWAS P-value thresholds and linkage disequilibrium rThe SLDP marker selection method can be incorporated into mainstream prediction models to yield accuracy improvements for traits with a relatively simple genetic architecture, however, it has no significant advantage for traits not controlled by major QTL. The main factors that affect its performance are the genetic architecture of traits and the reliability of GWAS prior information. Our findings can facilitate the application of WGS-based genomic selection.The online version contains supplementary material available at 10.1186/s12711-023-00843-w. The genomic prediction (GP) method proposed by Meuwissen et al.  is commoGenomic best linear unbiased prediction (GBLUP) is a commonly used prediction model that is based on a linear mixed model, which assumes that the effects of all the genetic markers contribute to phenotypic variation, and follow the same normal distribution , 10. In From the perspective of genetic markers, the degree of linkage disequilibrium (LD) between markers and the causal variant has a major impact on prediction accuracy. With the decline in sequencing costs and the emergence of low-coverage sequencing (LCS) strategies, it is now possible to use whole-genome sequencing (WGS) data for GP. The higher marker density in WGS data could increase the LD between markers and quantitative trait loci (QTL), in addition to enhancing the opportunity to directly capture the causal variant, which may consequently increase the overall accuracy of GP, as reported by several simulation studies –21. HowePreviously, we reported LCS genotyping analyses that can yield millions of highly accurate SNPs in pigs . In the Sus scrofa 11.1 chromosome (SSC)7 and BF is a transitional quantitative trait affected by several major loci and many minor gene interactions [In total, 3579 Duroc boars born between 2011 and 2016 were sampled from one nucleus farm provided by the Guangdong Wen’s Foodstuff Group , which included some boars from previous studies , 30, 31.ractions , 32, 33.A previous study showed that using the same reference individuals for pre-selecting variants through a GWAS as the GP training population can decrease prediction accuracy and result in biased predicted breeding values . ConsideThe genomes of all boars were sequenced on an Illumina (paired-end 150) or BGI (paired-end 100) platform, with an average sequencing depth of ~ 0.68 × for all individuals. SNP calling for these individuals was performed following the processing procedures for LCS data as previously described . The BasSNPs on the commercial chip 80k (CC 80k), commercial chip 50k (CC 50k), and GBS panel were extracted from the LCS clean panel for marker detection simulation to evaluate the performance of different genotyping technologies in GP. Details on the GBS markers are reported in . Some SN2: 0.15, 0.30, or 0.50). The lower QTN condition (100) represented simulation scenarios where traits are influenced by a few major genes, while the higher QTN condition  represented scenarios of traits controlled by an infinite number of loci. We used the R package SIMER (https://github.com/xiaolei-lab/SIMER) for the simulation analyses. QTN were randomly selected from all SNPs and their effects were sampled from the same standard normal distribution using the SIMER software with default settings. Residual terms were sampled from another normal distribution with the variance parameter adjusted to the heritability level.To evaluate the performance of SLDP under known genetic architectures, we conducted phenotypic simulations based on real LCS 10.1 M genotypes. In the data simulation, only additive genetic and residual effects were considered. We simulated six traits, each with different numbers of quantitative trait nucleotides (QTN)  and heritability levels  , was as Implementing a strict threshold may result in false negatives, while a relaxed threshold may introduce false positives. To capture more genetic effects and explore the optimal parameter combination for each trait , we established a relatively conservative P-value threshold gradient of 0.0001 to 0.01 to select critical markers. We defined genome regions of 50 kb on either side of each associated SNP as QTL-covered regions.2 < 0.50) with all the actual QTN. Detected QTN or markers that have a higher LD (r2 ≥ 0.50) with QTN were classified as informative SNPs. The estimated false-positive rate was calculated by dividing the number of false-positive sites by the number of detected sites.For the simulated traits, markers in high LD with QTN are likely to emerge as significant. Hence false-positive SNPs were defined as non-QTN SNPs that have a low LD  with any significant SNPs. Then, we applied the same sliding window-based LD pruning method as used in LDP; however, all “important SNPs” were retained after SLDP. We tested 180 parameter combinations  for each real and simulated trait. For the simulated traits, both GWAS and real QTN locations were used in SLDP . In SLDP_QTN scenarios, the “important SNPs” were defined as real QTN or SNPs in high LD (r2 > 0.90) with any real QTN. Figure Selective linkage disequilibrium pruning considers prior information and amplifies the weights of candidate QTL regions in the prediction model. Figure –16), since it showed a discontinuous distribution. We conducted a principal component analysis (PCA) using the LCS_LD0.90 SNP set to determine the population structure of all the populations and samples. Additional file Table Table Sus scrofa 11.1 chromosome (SSC)7 QTL region. We further analyzed the genomic regions covered by significant (P < 0.0001) SNPs for each trait. Genomic regions of 50 kb on either side of each associated SNP were considered as QTL-covered regions; the 343 SNPs for AGE spanned a 7.10-Mb genomic region, the 12,624 SNPs for BF spanned a 21.30-Mb genomic region, and the SNP number for TTN i.e., 12,343 SNPs similar to that for BF in TTN spanned only a 13.87-Mb genomic region. These results suggest that the QTL distribution varies between traits.Table 2g) than AGE and BF. Conversely, BF had more SNPs with a variance different from 0 and the highest proportion of SNPs in the second and third distributions yet the smallest number of SNPs in the fourth distribution  SNPs had a variance of 0 for all traits and were estimated to be in the first distribution. TTN had a higher percentage of SNPs with a variance of 0 (98.17%) and a larger proportion of SNPs in the fourth distribution (0.01 * σon Table . These rWe used two SNP chips and two sequencing-based methods (GBS and LCS) for GP in order to investigate the effects of marker density and SNP genotyping on prediction accuracy and bias. Marker density exhibited a several 100-fold increase from the SNP chip to LCS data Table . HoweverSNPs in the LCS panel were ranked according to their GWAS P-values for the three real traits in the discovery population . The top 10% and bottom 10% SNPs were selected for accuracy assessment in the validation population to explore the performance of phenotypically related and unrelated SNPs during GP. The prediction accuracy of the top 10% SNPs for each trait was significantly higher than that of the bottom 10% SNPs in both the GBLUP and BayesR models Fig. . This in2 threshold gradients  were further evaluated in the validation population. The distribution of MAF changed after LDP, with the proportion of the lower and higher MAF SNPs increasing, especially when using a lower r2 threshold . Figure To assess the performance of SLDP for evaluating different traits with a known genetic structure, we performed prediction using simulated data (see Methods). Figure In practice, the information on the genomic localization of all the real QTN cannot be accurately obtained; therefore, we performed SLDP based on the GWAS results where the parameters were optimized in the training population to match the real traits. For SLDP_GWAS, the accuracy also increased compared with that obtained with the other marker panels for the three traits controlled by fewer QTN. For example, the accuracy of SLDP_GWAS increased by 6.93, 7.70, and 11.47% relative to LCS in the GBLUP model and increased by 5.30, 2.45, and 1.23% relative to the LCS_LD1 set in the BayesR model at heritability levels of 0.15, 0.30, and 0.60, respectively. However, the accuracy of SLDP_GWAS was significantly lower than that of SLDP_QTN due to the presence of false positives and negatives obtained in the GWAS; that is, some noisy SNPs might have been detected, while some true QTN might have been missed. Regarding bias, both SLDP_GWAS and SLDP_QTN had no stable advantage for all traits.The results of the simulated GWAS based on six simulated traits in the discovery population are shown in Fig. With the advent of next-generation sequencing technologies and the continuous reduction in sequencing costs, it is now feasible to apply GP using WGS data. Nevertheless, maximizing the benefits of WGS in GP remains challenging, especially when dealing with various complex traits. In this study, we systematically evaluated the factors that influence prediction accuracy when using WGS data by examining real and simulated traits in a single pig population and developed a methodology (SLDP) from the perspective of marker selection. This methodology was then used to select SNPs from a WGS high-density marker set by GWAS prior information, which led to an increase in prediction accuracy for real traits with major QTL and for simulation traits controlled by a small number of QTN. Prediction accuracy improved with the SNPs detected after SLDP compared to that obtained using data from SNP chips or GBS, suggesting that the use of pre-selected WGS data can improve GP accuracy of major traits.The application of WGS data in GP remains contentious. Meuwissen and Goddard  reportedThe basic LD pruning method can remove collinear SNPs, as high LD can cause redundancy of genomic information between markers. Previous findings have suggested that LD pruning can improve prediction accuracy in WGS data . ConsideMoreover, using SLDP, the accuracy increased to a greater extent for TTN than for BF, as more significant and major QTL exist for TTN  compared with GWAS prior-based SLDP (SLDP_GWAS) since both false positives and negatives exist in GWAS data  for the discovery, training and validation populations.Additional file 3: Figure S3. Manhattan plot for AGE, BF and TTN in the discovery population, samples excluding the discovery population  and all 3549 samples.Additional file 4: Table S1. Number of retained SNPs after filtration based on different r2 thresholds.Additional file 5: Table S2. Number of variants used in each SLDP scenario for AGE.Additional file 6: Table S3. Number of variants used in each SLDP scenario for BF.Additional file 7: Table S4. Number of variants used in each SLDP scenario for TTN.Additional file 8: Figure S4. Histogram plot of the MAF frequency distribution for each data set.Additional file 9: Figure S5. Heat map of genomic prediction accuracy for the simulated traits using SNPs after SLDP with r2 (from 1 to 0.05) and P-value (from 0.0001 to 0.01) gradients in the training population by BayesR.Additional file 10: Table S5. Summary of the GWAS for six simulation scenarios based on different P-value thresholds in the discovery population.


Thematic collections (TCs), which are composed of genotypes with superior agronomic traits and reduced size, offer valuable opportunities for parental selection in plant breeding programs. Three TCs were created to focus on crucial attributes: root yield (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits . The genotypes were ranked using the best linear unbiased predictors (BLUP) method, and a truncated selection was implemented for each collection based on specific traits. The TCs exhibited minimal overlap, with each collection comprising 72 genotypes (CC_Disease), 63 genotypes , and 64 genotypes (CC_Yield), representing 4%, 3.5%, and 3.5% of the total individuals in the entire collection, respectively. The Shannon–Weaver Diversity Index values generally varied but remained below 10% when compared to the entire collection. Most TCs exhibited observed heterozygosity, genetic diversity, and the inbreeding coefficient that closely resembled those of the entire collection, effectively retaining 90.76%, 88.10%, and 88.99% of the alleles present in the entire collection . A PCA of molecular and agro-morphological data revealed well-distributed and dispersed genotypes, while a discriminant analysis of principal components (DAPC) displayed a high discrimination capacity among the accessions within each collection. The strategies employed in this study hold significant potential for advancing crop improvement efforts. Manihot esculenta Crantz) plays a vital role in tropical countries as a primary source of carbohydrates. Its significance in the global market has expanded due to its wide range of applications, including starch production for food industries, utilization in non-food industries, an ingredient in animal feed, and bioethanol production [Cassava . Accessing the genetic diversity stored in BAGs is crucial for crop improvement, particularly in identifying clones with a high breeding value and superior agronomic performance. Boukar et al.  emphasizThe concept of core collections was Initially introduced by  as a strOlea europaea L. [According to Pessoa-Filho et al. , large gopaea L. , soy [11opaea L. , and chiopaea L. . Implemeopaea L.  showcaseExploring the genetic variations present in germplasm banks is crucial for effectively addressing the challenges and future scenarios related to increased genetic vulnerability to biotic and abiotic stresses . It alsoTo enhance the efficient utilization of cassava germplasm resources, three distinct thematic collections were meticulously developed. These collections were crafted with a keen focus on the core attributes of root yield (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits . In the CC_Yield collection, we selected clones with lower BLUPs for plant architecture and higher BLUPs for other traits. The CC_Disease collection, on the other hand, focused on genotypes with lower BLUPs for mite severity, as well as for diseases affecting both the aerial and root parts of the plant. In the CC_Root_quality collection, genotypes were chosen for exhibiting lower BLUPs in terms of cyanide content and shorter cooking times.The initial selection process involved picking 70, 79, and 79 accessions for each respective thematic collection. Then, the selected accessions were evaluated for their redundancy according to identity-by-state (IBS), with the aim of eliminating duplicates among the individuals selected in each thematic collection. Molecular analysis revealed a substantial degree of relatedness (>0.95) among 108 pairs of accessions . This diRegarding the molecular data, 347 accessions were not genotyped. Therefore, the analysis of genetic parameters in the thematic collections was conducted using a subset of 164 genotyped individuals. Among these genotyped individuals, 60 belonged to the CC_Disease collection, 56 belonged to the CC_Yield collection, and 48 belonged to the CC_Root_quality collection.The selection of accessions for the different thematic collections was characterized by limited coincidence, as these traits were not highly correlated. In most cases, the thematic collections consisted of exclusive accessions. This finding was supported by the Kappa index, which ranged from 0.048 to 0.176 . The high2 values (>0.30), except for the traits frogskin severity, bacterial blight severity, rust severity, marketable and unmarketable fresh root yield, average number of roots per plant, leaf retention, and plant vigor at 1.5 months.The broad-sense heritability . As a result, this increase contributed to a heightened prevalence of clones with pulp textures characterized as friable and very friable. These textures are directly linked to an improved cooking capacity. While the average total carotenoid content displayed no apparent variation, the CC_Root_quality thematic collection experienced an upsurge in the frequency of clones featuring cream and yellow-colored pulp.−1 vs. 19.55 t.ha−1, respectively); marketable root yield ; unmarketable root yield ; total fresh root yield ; dry root yield ; leaf retention ; and dry matter content . Furthermore, the vigor of plants within the CC_Yield collection, assessed at both 1.5 and 12 months after planting, displayed higher values in the thematic collection compared to the entire collection. By contrast, characteristics with less variation among cassava accessions in the thematic collection, relative to the entire collection, were highlighted through box plots for cortex thickness, harvest index, and average root length (The thematic collection dedicated to yield attributes (CC_Yield) comprised accessions that held significant importance for the industrial cassava market. Just like the characteristics observed in the other thematic collections, there was a distinct shift in the distribution curves of traits, favoring attributes related to yield. This shift was particularly pronounced for attributes that displayed substantial variation across the entire collection. For instance, the following attributes varied: plant height (216.06 cm in the thematic collection versus 191.67 cm in the entire collection); shoot yield , as well as either compact or open growth patterns . OverallThe evaluation of genetic diversity in the developed thematic collections was performed using the Shannon–Weaver index (ISW) . In geneHo, Hs, and Fis) that were very similar to those of the entire collection, both in terms of means and range , applied to both phenotypic and molecular data. The genetic relationships between the evaluated accessions were examined to infer the phenotypic and genetic structure . The firAdditionally, a discriminant analysis of principal components (DAPC) was conducted to examine the distribution pattern of clones within the thematic collections compared to the entire collection . By seleCassava breeding programs rely on effectively harnessing the genetic variability preserved in germplasm banks to identify suitable parents for producing high-performing clones. However, the selection, conservation, and comprehensive characterization of parents pose significant challenges for many cassava breeding programs, given the vast number of accessions stored in germplasm banks and the extensive array of hybrids generated by breeding programs.To address this issue, the concept of core collections, which are representative subsets of accessions capturing the genetic diversity of a species, has been employed. However, the increasing size and number of collections in germplasm banks have necessitated the implementation of strategies to constrain collection sizes. Core collections, in some cases, may still include a large number of accessions, thereby perpetuating the challenge they were designed to alleviate. Ideally, core collections should consist of a manageable number of accessions with sufficient genetic variability to be effectively utilized in genetic breeding programs .Conversely, thematic collections, comprising distinctive accessions possessing specific manageable traits, have emerged as a proposed strategy within breeding programs to tackle challenges related to collection size and alignment with breeder preferences . In aligTo fulfill the objective of preserving accessions with shared specific characteristics, while also possessing divergent phenotypic traits and alleles, it is crucial to eliminate potential redundancies within thematic collections . UnderstDuplicate accessions within cassava germplasm banks are a common issue that can hinder progress in the species’ improvement ,20,21. RGlycine max), Gou et al. [In the context of soybean , where 90% of the alleles from the entire collection were retained in a selection of 130 accessions to form a central collection for wild germplasm, facilitating an ex situ conservation of the species [While thematic collections are primarily focused on gathering accessions with specific characteristics rather than maximizing phenotypic or genotypic diversity, we conducted a comparative analysis of various genetic parameters with the entire collection. Through the analysis of molecular markers, the thematic collections CC_Disease, CC_Root_quality, and CC_Disease captured 90.76%, 88.10%, and 88.99% of the alleles present in the entire collection, respectively, indicating a substantial representation of alleles. Similar outcomes have been observed in other species, such as wild rice . In the context of pest resistance, [Mononychellus tanajoa (Bondar)) in African germplasm, utilizing a diversity panel comprising 845 improved cassava genotypes.In addition to population improvement, these thematic collections provide valuable resources for conducting associative mapping studies in germplasm or biparental populations, aiming to identify quantitative trait loci (QTLs) of interest. Associative mapping offers an alternative to family-based linkage mapping by associating allelic variations of agronomically important traits with molecular markers in the collections of unrelated individuals ,38. The istance,  identifiThe establishment of thematic collections and the screening of accessions with desirable traits related to root quality, yield, and disease resistance in this study will greatly facilitate associative mapping studies and potentially pave the way for future genomic selection investigations. By simultaneously predicting the genetic effects of markers, it becomes possible to capture the effects of a large number of loci and explain a significant portion of the genetic variation underlying quantitative traits. This research represents an initial step in a broader initiative aimed at characterizing the genetic foundation and maximizing the utilization of available cassava germplasm. Consequently, our work holds significant potential to contribute to national and international breeding programs, particularly those focusing on enhancing productivity and disease resistance traits.Comprising carefully selected accessions tailored to each specific theme and deliberately designed to be compact in size, the thematic collections developed in this study successfully fulfill the initial objective of facilitating the management and utilization of the most valuable and accessible accessions within each germplasm set. Furthermore, concentrating efforts on these thematic collections will streamline the comprehensive characterization of all relevant descriptors for the cassava species, thereby minimizing redundancies among accessions and enhancing the likelihood of identifying rare and valuable alleles for species improvement. This focused approach maximizes efficiency and optimizes the chances of making significant advancements in cassava breeding and germplasm enhancement.The study encompassed a collection of 1665 cassava accessions sourced from the germplasm bank and the breeding program of Embrapa Mandioca e Fruticultura, located in Cruz das Almas, Bahia, Brazil . The accessions were from various regions of Brazil, and some were obtained through exchange with countries such as Colombia, Venezuela, Nigeria, Mexico, and Uganda. The collection contained local and improved varieties obtained through breeding techniques, including crossings, mass selection, and identification by producers or research institutions. To establish thematic core collections, specific descriptors were evaluated based on their relevance to yield attributes, disease resistance, and root quality, as outlined by Fukuda et al. :Plant height (Pl.H), measured from the ground to the plant’s meristem;Plant architecture (Pl.A), evaluated using a 1–5 rating scale: 1 = excellent architecture (no branching or erect stems), 2 = good architecture (branching above 1.60 m or low branching with at least 1.6 m of erect stems), 3 means moderate architecture (branching above 1.20 m or low branching with at least 1.2 m of erect stems), 4 = poor architecture (branching above 0.80 m or low branching with at least 0.80 m of erect stems), and 5 = very poor architecture (highly branched clones with less than 0.80 m of erect stems);Plant type (Pl.T), assessed using a 1–5 rating scale: 1 = compact, 2 = open plant, 3 = umbrella-like, and 4 stands for cylindrical;−1 as the total weight of above-ground parts, including leaves, petioles, and stems;Shoot yield (ShY), measured in t.ha−1 and characterized by roots with an absence of pest and disease symptoms and the standard size and shape of the genotype;Marketable fresh root yield (C.FRY), measured in t.ha−1 as the rest of C.FRY;Unmarketable fresh root yield (NC.FRY), measured in t.ha−1;Total fresh root yield (T.FRY), assessed as C.FRY + NC.FRY in t.haDry matter content (DMC), expressed as a percentage and measured using the hydrostatic balance method ;Starch content (StC), expressed as a percentage and measured by extracting starch from the roots ;−1 and obtained by multiplying T.FRY and DMC;Dry root yield (DRY), measured in t.haHarvest index (HI), assessed as T.FRY/(T.FRY + ShY);Cortex thickness (Ro.CT), measured in millimeters (mm);The presence of a peduncle (Ro.Pe), assessed on a scale where 0 = sessile, 3 = pedunculated, and 5 = a mixture of sessile and pedunculated roots;Root constriction (Ro.Co), assessed using a 1–3 rating scale: 1 = few or no constrictions, 2 = some constrictions, and 3 = many constrictions;Average root length (Ro.Le), measured in centimeters (cm) from the base to the tip;Average root diameter (Ro.Di), measured in millimeters (mm) at the central part of the roots;Easy root peeling (Ro.EP), evaluated at harvest time on a rating scale: 3 = easy peeling, 5 = medium peeling, and 7 = difficult peeling;Leaf retention (Pl.LR), assessed using a rating scale: 1 = less than 5% leaf retention, 2 = 6–15% leaf retention, 3 = 16–30% leaf retention, 4 = 31–50% leaf retention, and 5 = >50% of leaf retention;Plant vigor at 1.5 months (Pl.V1.5M), assessed using a rating scale: 1 = low vigor, 3 = intermediate vigor, and 5 = high vigor;Plant vigor at 12 months (Pl.V12M), similar to Pl.V1.5M;The average number of roots per plant (Ro.NP).Mite severity (P.MS), rated as follows: 0 = no damage; 1 = some white–yellow spots extending to the base of leaves on the apical shoot; 2 = moderate yellow spots on all leaves; 3 = abundant spots on middle-third leaves, slight apical shoot deformation; 4 = severe leaf deformation on apical shoot, whitish appearance, some defoliation, and stem with yellow spots; 5 = highly twisted apical shoots /severe stem spots; 6 = dry apical shoots, leafless, or dead;Colletotrichum sp.) (D.AntS), rated as follows: 1 = no symptoms; 2 = small or old cankers on the lower half of the plant; 3 = deep cankers on the upper half of the plant; 4 = deep cankers with sporulation, leaf distortion, wilting, and apex drying; 5 = apical death or plant death;Anthracnose severity (Xanthomonas axonopodis pv. manihotis) (D.CBB), scored as follows: 1 = no symptoms, 2 = only foliar symptoms (angular leaf spot), 3 = dark necrotic lesions on stems or petioles, 4 = severe leaf symptoms and/or necrotic lesions with gum exudation, 5 = complete leaf loss with apical death or plant death;Bacterial blight severity  (D.BrLS), scored as follows: 0 = no symptoms; 1 = mild symptoms on the lower third leaves; 2 = leaf spots on lower leaves and yellowing on a few affected leaves; 3 = leaf spots on lower and middle leaves, yellowing on most affected leaves; 4 = leaf spots across the entire plant, lower leaf shedding; 5 = complete plant defoliation;Brown spot severity  (D.BlLS), evaluated similar to D.BrLS;Blight leaf spot severity (Uromyces manihotis) (D.RS), scored as follows: 0 = no pustules or damage on stems and leaves, 1 = a few pustules without branch drying, 2 = many pustules with branch drying;Rust severity (Frogskin severity (virus and phytoplasma complex) (D.FS), assessed as 0 = no symptoms on roots; 1 = few roots with corky epidermis 2 = many roots with a corky epidermis, deep cracks, or absence of tuberous root production;Phytophthora sp., Pythium sp., Phytopythium sp.), dry Rot (Fusarium sp.), and black rot  (D.RRS), rated as follows: 0 = no root rot, 1 = roots up to 25% with root rot, 2 = roots with more than 26% having root rot.Root rot severity (Root cooking time (Ro.CoT), measured at intervals of 15, 20, 25, 30, and 40 min, according to ;Root Pulp Color (Ro.PC), categorized as follows: 1 = white, 2 = cream, 3 = yellow, 4 = pink, 5 = orange;Root skin color (Ro.SC), rated as follows: 1 = white, 2 = yellow, 3 = light brown, 4 = dark brown;Root cortex color (Ro.CC), scored as 1 = white or cream, 2 = yellow, 3 = pink, and 4 = purple;Cooked root friability (Ro.RF), which is friability evaluated after cooking, based on : 1 = verHydrogen cyanide content (Ro.HCN), assessed using the picrate method as detailed by ;Total carotenoid content (Ro.TCC): the concentration of total carotenoids in roots determined following the methodology outlined by .After conducting characterizations across multiple years of cultivation  was performed using the sommer package in R software version 4.2.2 [To account for the diverse environments and years of cultivation, the combination of the location and year was collectively referred to as “environment” in this study. A joint analysis was conducted, considering all environments for each quantitative descriptor. The analysis was based on the following model: on 4.2.2 .We established three thematic collections, taking into consideration yield traits (CC_Yield), pest and disease resistance (CC_Disease), and root quality traits . The assemblage of the CC_Yield collection involved a process wherein 12 specific clones were chosen based on their low BLUP value for plant architecture and, conversely, their high BLUPs for critical traits such as T.FRY, DRY, Pl.V12M, Pl.V1.5M, DMC, and Ro.NP. Concurrently, the formation of the CC_Disease collection revolved around identifying genotypes with lower BLUPs for mite severity (P.MS), while also exhibiting reduced shoot disease severity like D.AntS, D.CBB, D.BrLS, D.BlLS, D.RS, as well as the root disease D.FS. As for the CC_Root quality collection, the selection process involved genotypes with lower BLUPs for Ro.HCN and Ro.CoT (lower than 25 min). Subsequently, the chosen genotypes underwent an additional refinement stage, emphasizing attributes such as cream/yellow pulp color, with high dry matter and carotenoid content values, along with roots demonstrating a friable or highly friable texture post-cooking.The establishment of thematic collections, as well as the assessment of genetic parameters and kinship analysis, relied on the utilization of phenotypic data and single nucleotide polymorphism (SNP) markers. The SNP marker data, previously reported by , was useOnce the collections were established, the extent of kinship between the chosen accessions was gauged using the identity-by-state (IBS) technique on an individual level. This evaluation was executed by employing the snpgdsIBS function from the SNPRelate package . Pairs oTo determine the degree of coincidence between the genotypes across the three thematic collections, a binary code approach was employed. Each individual within the collections was assigned a code of 1 if selected or 0 if unselected. Coincidences between the collections were then calculated using the Kappa index , which pi for a given trait, and n is the number of the phenotypic classes of the trait. To maintain values between 0 and 1 (ranging from monomorphism to maximum phenotypic diversity), all H′ indices were normalized and divided by the maximum value. For qualitative traits, k denotes the number of classes or scores of the descriptor, while for quantitative traits, six classes were estimated based on the lower and upper limits defined by the total observed range in the entire collection for each trait  on both phenotypic and molecular data. Phenotypic data was analyzed using the AMR package , while mFurthermore, to discriminate between the cassava thematic collections and the entire collection, a discriminant analysis of the principal components (DAPC) available in the adegenet package for R version 4.2.2 , which cIn this study, three thematic collections were developed focusing on root yield (CC_Yield), resistance to pests and diseases (CC_Disease), and root quality traits . These collections represent approximately 4% of the entire collection. Within each thematic collection, a noticeable improvement in mean values was observed compared to the entire collection. However, it is important to note that there was still a variation of more than 10% in the index of selection within each thematic collection, which was expected, due to the specific nature of each theme. The construction of these collections relied on the ranking of promising genotypes, which proved to be an effective strategy in selecting a diverse set of accessions with the desired traits for each theme.The three thematic collections presented in this study are characterized by their non-redundancy, manageability, and efficiency in gathering promising accessions, with desired traits related to productivity, resistance to pests and diseases, and root quality. These collections provide a valuable resource for the targeted characterization and organization of selected accessions, thereby facilitating the management and handling of the entire germplasm. Moreover, the carefully selected accessions within these thematic collections serve as a foundation for choosing parental lines that can be utilized in population improvement and in the generation of new cultivars.


Vigna radiata L. Wilczek) is one of the important warm-season food legumes, contributing substantially to nutritional security and environmental sustainability. The genetic complexity of yield-associated agronomic traits in mungbean is not well understood. To dissect the genetic basis of phenological and agronomic traits, we evaluated 153 diverse mungbean genotypes for two phenological (days to heading and days to maturity) and eight agronomic traits  under two environmental conditions. A wide array of phenotypic variability was apparent among the studied genotypes for all the studied traits. The broad sense of heritability of traits ranged from 0.31 to 0.95 and 0.21 to 0.94 at the Delhi and Ludhiana locations, respectively. A total of 55,634 genome-wide single nucleotide polymorphisms (SNPs) were obtained by the genotyping-by-sequencing method, of which 15,926 SNPs were retained for genome-wide association studies (GWAS). GWAS with Bayesian information and linkage-disequilibrium iteratively nested keyway (BLINK) model identified 50 SNPs significantly associated with phenological and agronomic traits. In total, 12 SNPs were found to be significantly associated with phenological traits across environments, explaining 7%–18.5% of phenotypic variability, and 38 SNPs were significantly associated with agronomic traits, explaining 4.7%–27.6% of the phenotypic variability. The maximum number of SNPs (15) were located on chromosome 1, followed by seven SNPs each on chromosomes 2 and 8. The BLAST search identified 19 putative candidate genes that were involved in light signaling, nitrogen responses, phosphorus (P) transport and remobilization, photosynthesis, respiration, metabolic pathways, and regulating growth and development. Digital expression analysis of 19 genes revealed significantly higher expression of 12 genes, viz. VRADI01G08170, VRADI11G09170, VRADI02G00450, VRADI01G00700, VRADI07G14240, VRADI03G06030, VRADI02G14230, VRADI08G01540, VRADI09G02590, VRADI08G00110, VRADI02G14240, and VRADI02G00430 in the roots, cotyledons, seeds, leaves, shoot apical meristems, and flowers. The identified SNPs and putative candidate genes provide valuable genetic information for fostering genomic studies and marker-assisted breeding programs that improve yield and agronomic traits in mungbean.Mungbean ( Vigna radiata (L.) R. Wilczek var. radiata] is an ancient grain legume cultivated extensively in South, East, and South-East Asia. Mungbean is native to Central Asia and India; however, its cultivation is rapidly expanding in other parts of the world. The area under mungbean cultivation is stretched over about 7.5–8.0 million ha globally  for agronomic traits have been reported in various crops such as wheat , rice D, corn Y, common GWAS offers higher mapping resolution than biparental mapping and has become a viable alternative to conventional linkage mapping. GWAS identifies QTLs based on the historic recombination that the panel of diverse germplasm has undergone through the presence of linkage disequilibrium (LD) between markers and QTLs. Hence, the power of GWAS depends on the degree of LD between the SNPs and QTLs. The resolution of genes/QTLs mapped by GWAS depends on how quickly the LD decays over a given genetic distance. The LD extent is reported as 72–290 kb in cultivated mungbean  and 3–60A relatively smaller genome size of 493 to 579 Mbp  makes muLocus-specificity, relative abundance, co-dominant nature, and amenability to high-throughput genotyping make single nucleotide polymorphism (SNP) markers highly desirable for marker-assisted breeding . The SNPThe association mapping (AM) panel consisted of 153 mungbean germplasm accessions from various origins (h2), and analysis of variance were carried out using Microsoft Excel. h2 was estimated based on the ratio of genotypic to phenotypic variance  and agronomic traits . Phenological traits and leaf nitrogen status using SPAD were measured in the full-bloom stage; plant architectural traits (plant height and number of primary branches) were recorded manually at maturity; and yield-associated traits  were recorded manually postharvest. Descriptive statistics, broad-sense heritability  was determined using the Evanno method (http://taylor0.biology.ucla.edu/structureHarvester/). Genetic relationships among cultivated accessions were analyzed using principal component analysis (PCA) in the software package GAPIT v.3.0 (r2) using the TASSEL program v5.0  decreased to half of their maximum value.The phylogenetic tree was constructed with 10,000 bootstraps in TASSEL using a hierarchical distance-based neighbor-joining approach. The population structure of the panel was analyzed using a clustering method based on Bayesian statistics with STRUCTURE v2.3.4 , employio method  with StrIT v.3.0 . The pairam v5.0 . The LD Q), and kinship coefficient matrix (k) to decipher marker-trait associations. GWAS was conducted using the “BLINK” model controlling for genetic background using PCA in GAPIT v3.0 with default parameters (https://zzlab.net/GAPIT/gapit_help_document.pdf). The threshold p-value was set to <0.00001 to improve the overall accuracy of marker-trait associations. The SNP loci with the highest R2 and lowest p-values were identified as significantly associated with phenological and agronomic traits. Identified SNP loci were compared with mungbean reference genome “Vigna radiata assembly v1.0” using BLAST search with “J Browse” in the “legume information system” platform (https://legacy.legumeinfo.org/genomes/jbrowse/?data=Vr1.0).Phenological and agronomic data were integrated with genome-wide SNP genotyping information generated using the association mapping panel, ancestry coefficient  for DF50, DF100, SPAD, SPP, and YPP. Days to 50% flowering varied significantly, with an average of 37 days among early and late flowering genotypes compared across the locations (h2. This study showed maximum heritability for PN (0.95) followed by SPAD (0.94) at the Delhi location whereas DF50 had the highest heritability (0.94) at the Ludhiana location. The mean values for phenological traits  and yield-contributing traits, viz., PL, PN, SPP, YPP, and 100SW, were higher at the Ludhiana location compared to the Delhi location. The traits SPAD, PB, and PH were higher at the Delhi location. The frequency distribution pattern of evaluated traits showed a normal distribution, which indicates the traits are quantitative and complex in nature (In two environments (Delhi and Ludhiana), 153 diverse mungbean genotypes , PN , and SPP . Phenological traits were positively correlated among themselves  as well as with PB (p < 0.01) and negatively correlated with plant height in both locations, i.e., Delhi and Ludhiana. The DF100 was positively correlated with the traits PL (r = 0.16) and PN (r = 0.17). The 100SW had a positive correlation with SPAD (r = 0.20) and PH (r = 0.19) (p < 0.05). However, a negative correlation was observed between DF100 and PH  , PN , DF100 (r = 0.23), DM (r = 0.21), SPP , and DF50  was observed. The PN was significantly correlated with DF100 (r = 0.29) and PB , DF50 (r = 0.17), and DM . Phenological traits were positively correlated, as in the case of the Delhi location, but negatively correlated with PH (p < 0.001)  were identified, out of which 55,634 chromosome-based SNPs with polymorphism were selected. The SNPs discovered were distributed throughout the genome, covering intergenic, intragenic, regulatory, and scaffold variants to the tune of 27%, 23%, 31%, and 19%, respectively. The maximum number of SNPs  were mapped on chromosome 1, whereas chromosome 10 had a minimum number of SNPs (745) mapped. Chromosome 10 had only 3.5 SNPs per 0.1 Mb, being a low SNP density , and 75% of these reads on average were mapped to the r2) of the mungbean population decreased to half of their maximum value at approximately 68 kb physical distance . The variation in the second-order statistical rate of the ΔK values served as evidence for the number of hypothetical ancestral populations (K). The K value displayed a peak at K = 2, demonstrating the ideal number of subpopulations in the panel as two  = 4 as the cut-off, a total of 50 SNPs significantly associated with various phenological and agronomic traits were identified under Delhi, Ludhiana, and combined (C) BLUP environments  to be located on chromosome 1, followed by seven SNPs each on chromosomes 2 and 8. A total of six SNPs were distributed over chromosome 6, four SNPs over chromosome 11, and three SNPs over chromosome 3. Chromosomes 4 and 5 harbored a single SNP each. However, no SNP was reported on chromosomes 6 and 10 Figure 6Arabidopsis genome database. The BLAST search identified 19 genes with high similarity and more than 80% identity to Arabidopsis genes. These identified genes were involved in the plant light signaling, nitrogen responses, P transport and remobilization, photosynthesis, respiration, multiple metabolic pathways, gametophyte development in legumes, regulating growth and development, and signaling pathways, thereby influencing yield directly or indirectly (Arabidopsis orthologs of these genes are phytochrome kinase (At1G18810), plant regulator RWP-RK family protein (At4G35270), pentatricopeptide repeat (PPR) superfamily protein (At4G14850), cytochrome P450 superfamily protein (At5G05690), adenine nucleotide alpha hydrolases-like superfamily protein (At3G53990), calmodulin-binding family protein (At4G33050), subtilisin-like serine protease 2 (At4G34980), rhamnogalacturonatelyase family protein (At2G22620), carbonic anhydrase 1 (At3G01500), transcription factor bHLH25-like (At4G37850), malate dehydrogenase (At3G47520), phosphate-responsive 1 family protein (At1G35140), chaperone protein DnaJ-related (At5G06130), nuclear factor Y, subunit C3 (At1G54830), RING/FYVE/PHD zinc finger superfamily protein (At5G22260), receptor lectin kinase (At3G59700), NAD(P)H dehydrogenase (At4G35760), cleavage and polyadenylation specificity factor 73 kDa subunit-II (At2G01730), and SNARE-associated Golgi protein family (At4G14950). These genes might play a vital role as potential candidate genes in regulating phenological and yield-associated agronomic traits in mungbean.Putative candidate genes for phenological and agronomic traits in mungbean were delineated by performing a BLAST search against the directly Table 2.VRADI01G08170 (At1G18810), VRADI11G09170 (At2G22620), VRADI02G00450 (At1G35140), VRADI01G00700 (At3G01500), VRADI07G14240 (At3G47520), VRADI03G06030 (At3G53990), VRADI02G14230 (At4G14850), VRADI08G01540 (At4G33050), VRADI09G02590 (At4G34980), VRADI08G00110 (At4G35760), VRADI02G14240 (At5G05690), and VRADI02G00430 (At5G06130) have high expression in different plant parts, including roots, cotyledons, seeds, leaves, shoot apical meristems, and flowers are depicted in The digital expression pattern of 19 genes revealed that 12 genes, namely Vigna species for phenological (days to first flowering and days to first pod maturity) and other agronomic traits . Traits with high heritability  may be successfully deployed in breeding programs.Evaluation and characterization of yield-contributing traits are prerequisites in any crop improvement program. Evaluation and characterization lead to the identification of superior genotype(s) and equip breeders with the most vital input for successful crop breeding. Nearly 1,500 indigenous and exotic collections were characterized and evaluated for 27 agro-morphological traits in mungbean. The study revealed that the cultivated mungbean gene pool exhibits a higher degree of variation for economically important traits like seed weight, flowering period, pod length, number of seeds per pod, and seed size . SimilarCorrelation studies among the best combinations of attributes significantly contributing to yield give insight into traits that are inherited together. High trait correlations may be due to the direct or indirect contribution of attributes towards one or more traits or because of pleiotropic effects. Positive correlation among traits of interest makes crop improvement easy and effective. Recognizing the importance of correlation among yield-contributing traits, many researchers have studied the correlation among yield traits in different crops . In the The power of the GWAS approach depends on the degree of LD between SNP and QTL, which is determined by the genetic distance between them. In addition, the resolution of a gene/QTL mapped by GWAS and the density of marker coverage needed for GWAS depend on how rapidly the LD decays over a particular distance . If LD dGWAS makes use of ancient recombination events that occurred in a group of genotypes, mainly in landraces, to identify either a causative gene for the trait of interest or to unravel the genetic architecture of complex traits by finding DNA markers, usually SNPs, underlying particular traits at relatively high resolution. The completion of the mungbean reference genome sequence by VRADI01G08170 encodes a phytochrome kinase enzyme that acts in plant light signaling and thereby regulates the days to flowering  and eight agronomic traits, viz., SPAD, PH, PB, PL, PN, SPP, 100SW, and YPP. lowering . Leaf nilowering . In the  legumes . The genuisition . Similarsoybeans . As a reVRADI02G14230 plays a key role in photosynthesis and respiration , VRADI11G09170 (At2G22620), VRADI02G00450 (At1G35140), VRADI01G00700 (At3G01500), VRADI07G14240 (At3G47520), VRADI03G06030 (At3G53990), VRADI02G14230 (At4G14850), VRADI08G01540 (At4G33050), VRADI09G02590 (At4G34980), VRADI08G00110 (At4G35760), VRADI02G14240 (At5G05690), and VRADI02G00430 (At5G06130) in the roots, cotyledons, seeds, leaves, shoot apical meristems, cotyledons, dry seeds, and flowers. The candidate genes identified in the present study may be involved in regulating phenological and yield-associated agronomic traits in mungbean. So far, only a few studies have been carried out for agronomic traits employing GWAS in mungbean. Hence, it is difficult to pinpoint precisely which candidate gene(s) significantly regulates agronomic traits in mungbean, which calls up for further in-depth study. The SNP information linked with distinct candidate genes found in GWAS analysis needs further validation, either in diverse populations or by using laboratory tests such as overexpression and knockout of candidate genes. Furthermore, true causal genes can be effectively deployed for developing superior cultivars in mungbean through MAS.Additionally, digital expression analysis of 19 genes revealed significantly higher expression of 12 genes, viz. https://www.ncbi.nlm.nih.gov/sra/PRJNA609409.]. Phenotyping data used in this study is available in Original SNP datasets are available in a publicly accessible repository: The original contributions presented in the study are publicly available. This data can be found here: [PM: conceptualization, investigation, data curation, and writing original draft. MA: methodology, software, and investigation. GM: conceptualization, resources, and writing—review and editing. SG: formal analysis and writing—review and editing. ND: investigation and formal analysis. AS: formal analysis. RB: methodology. SK: conceptualization and writing—review editing. RN: writing—review editing and fund acquisition. HD: conceptualization, methodology, writing—review editing, supervision, project administration, and fund acquisition. All authors contributed to the article and approved the submitted version.


This is achieved by first computing the posterior inclusion probabilities (PIPs) from an eQTL-based SuSiE model with the expression level of the candidate gene as the phenotype. These calculated PIPs are then utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. By leveraging eQTL information, SuSiE2 enhances the power of detecting causal SNPs while reducing false positives and the average size of credible sets by prioritizing functional variants within the candidate region. The advantages of SuSiE2 over SuSiE are demonstrated by simulations and an application to a single-cell epigenomic study for Alzheimer’s disease. We also demonstrate that eQTL information can be used by SuSiE2 to compensate for the power loss because of an inaccurate LD matrix.Genome-wide association studies (GWASs) have achieved remarkable success in associating thousands of genetic variants with complex traits. However, the presence of linkage disequilibrium (LD) makes it challenging to identify the causal variants. To address this critical gap from association to causation, many fine mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. In this manuscript, we introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine mapping, built on the sum of single-effects (SuSiE) regression model. Our new method, SuSiE 2, connects two sum of single-effects (SuSiE) models, one for eQTL analysis and one for genetic fine mapping. The posterior inclusion probabilities from an eQTL-based SuSiE model are utilized as prior inclusion probabilities for risk variants in another SuSiE model for the trait of interest. Through simulations and a real data analysis focused on Alzheimer’s disease, we demonstrate that SuSiE2 improves fine mapping results by simultaneously increasing statistical power, controlling the type I error rate, and reducing the average size of credible sets.Genome-wide association studies (GWASs) have proven powerful in detecting genetic variants associated with complex traits. However, there are challenges in distinguishing the causal variants from other variants strongly correlated with them. To better identify causal SNPs, many fine mapping methods have been proposed to assign well-calibrated probabilities of causality to candidate variants. We introduce a statistical framework that incorporates expression quantitative trait locus (eQTL) information to fine mapping, which can improve the accuracy and efficiency of association studies by prioritizing functional variants within the risk genes before evaluating the causation. Our new fine mapping framework, SuSiE Over the past decades, genome-wide association studies (GWASs) have achieved remarkable success in detecting thousands of genetic variants that are associated with complex traits . While GGenetic fine-mapping aims at inferring the causal genetic variants responsible for complex traits in a candidate region through disentangling LD patterns. Many fine mapping methods have been devised to assign well-calibrated probabilities of causality to candidate variants, taking into account the underlying LD pattern. For instance, some methods in the early stage estimate the probability of causality for each SNP under the assumption that each risk locus only harbors one causal variant , 5. To aIn recent years, expression quantitative trait locus (eQTL) studies have revealed an abundance of quantitative trait loci (QTLs) for gene expression . Integra2, begins by prioritizing risk variants using estimated PIPs from an eQTL-based SuSiE model with expression levels of risk genes serving as the phenotype. These PIPs are then utilized as prior inclusion probabilities in a standard SuSiE model for the trait of interest. Through simulations conducted on UKBiobank samples, we demonstrate that compared with SuSiE, SuSiE2 improves the power of detecting causal SNPs while reducing false positives regardless of using the in-sample LD matrix or an external reference panel. For real data analysis, SuSiE2 identifies more Alzheimer’s disease (AD) associated SNPs predicted from single-cell epigenomic data.In this study, we propose a new method of incorporating eQTL information to improve fine mapping results based on the SuSiE framework. Our new method, named SuSiEConsider a toy example of a multiple regression model between a standardized To quantify the uncertainty in which variables should be selected, BVSR methods  introducAlthough n model)  introducIn a multiple-regression model, a level With this Reporting as many credible sets as the data support, each with as few variables as possible.Prioritizing each candidate variable within a credible set with a posterior probability for this variable to be an effect variable.With the goal of identifying the genetic variants that causally affect some traits of interest, genetic fine mapping can be framed as a variable-selection problem. To pick the causal variant(s) in the presence of strong LD, one attractive approach is to use BVSR to assign a posterior probability distribution to risk variants. However, traditional BVSR methods still suffer from computational challenges and complicated posterior distributions , 20, sucUnder the SER model 1, there exists only one non-zero element in the coefficient vector Here, Suppose The SER model offers a simple inference strategy when there exists exactly one effect variable. However, the situation will be more complicated when there are multiple non-zero signals. To detect multiple effect variables while preserving the simplicity of the SER model, the sum of single-effects regression model (SuSiE) is developed by introducing multiple single-effect vectors and combining them with an additive structure:lgorithm  in S1 AlDifferent from existing BVSR models, SuSiE introduces a new model structure which naturally leads to an intuitive and fast algorithm for model fitting. Compared with traditional BVSR methods, SuSiE enjoys at least two key advantages:SuSiE provides a posterior summary which can be interpreted easily by introducing the concept of ”credible sets”.O(npK). The running time of SuSiE, CAVIAR, and FINEMAP with the in-sample LD matrix and summary statistics has been compared in simulations [SuSiE improves the computational efficiency, with a computational complexity ulations , where SIn the remaining parts of the method section, we will introduce a new framework to incorporate eQTL information into fine mapping based on SuSiE.Under the existence of strong LD, SuSiE assesses the uncertainty in variable selection by generating groups of variables, with each group aiming at capturing one effect variable. However, choosing the true causal variable from the credible set is still a difficult problem. One possible way to infer the effect variable more accurately is to integrate eQTL information into fine mapping, as SNPs associated with complex traits are significantly more likely to be eQTLs . Conside2, connects two SuSiE models for eQTL study and genetic fine mapping, respectively. For the first model, we use the gene expression level as the response variable and conduct a regression analysis on the risk region. This eQTL-based SuSiE model can be rewritten as 8:This new framework of eQTL-based fine mapping study, named SuSiESuppose from model 9 we detect single effects, with the corresponding PIPs denoted as 2 framework based on individual-level genotype data. We note that SuSiE has been extended to work with summary statistics [In the method section above, we describe the SuSiE model and the SuSiEatistics , which m2 can indeed increase efficiency and accuracy through simulation studies and a real data study on Alzheimer’s Disease (AD). Compared with the original SuSiE, SuSiE2 can improve the results of fine mapping in the following aspects while controlling type I error rate at an appropriate level:We demonstrate that integrating eQTL with fine mapping via SuSiE2 can improve the power of including causal variants in at least one credible set.SuSiE2 can decrease the average size for credible sets.SuSiEith SNP, ith gene expression level and trait of interest, respectively. The effect sizes of the causal SNPs were assumed to follow normal distributions with zero means and variances chosen to ensure The study population in our simulations consists of 10,000 randomly selected Europeans from the UKBB dataset, with each sample genotyped at 20,000 SNPs on chromosome 1. Assuming a total of 2 method that only used eQTL information from one risk locus , and SuSiE2 that used eQTL information from all the 5 or 10 risk loci  with the following three criteria:The heritability for the trait of interest was selected from {0.1,0.2,0.3,0.4,0.5}, and the total number of causal SNPs was fixed at 30. These causal SNPs were equally distributed across Power: the proportion of true effect variables included in at least one credible set.Type I error rate: the proportion of non-causal variables included in at least one credible set.Average size: the average size of credible sets detected.2 methods always improved the power of detecting causal SNPs and also reduced the average size of credible sets, and the improvement of SuSiE2_all was more significant compared with SuSiE2_partial. All three fine mapping approaches controlled the type I error rate at a low level with the 95% credible sets, but SuSiE2_all always had fewer false positives. The credible sets we obtained from SuSiE were designed to contain at least one effect variable with 95% probability. However, the target type I error rate here was the proportion of non-causal variables incorrectly detected, which can be largely influenced by the average size and total number of credible sets. This explains why the type I error rate seems to be way lower than the nominal level. When the number of risk loci increased from 5 to 10, the performance of SuSiE2 regarding all the criteria improved more significantly. With 10 risk loci, SuSiE2_all improved the power of detecting causal SNPs by 10% while reducing false positives by 50% when utilizing the in-sample LD matrix. Besides, we observed a 40% reduction in the average size of credible sets obtained by SuSiE2_all compared with the original SuSiE. It is worth mentioning that although not as good as SuSiE2_all, SuSiE2_partial achieved better performance compared with the original SuSiE, which indicated that considering only a small proportion of eQTL information can still help improve the results of fine mapping.We first compared the results with summary statistics and the in-sample LD matrix, with the results summarized in 2 improved the performance of fine mapping for all situations. When the number of risk loci was 10, SuSiE2_all achieved better performance with a 50% increase in power and a 30% reduction in the proportion of false positives. This suggests that eQTL information can compensate for the power lost because of inaccurate LD information.We also checked the performance of three fine mapping methods when using the LD matrix calculated from an external reference panel of 5,000 Europeans from the UKBB study, with the results summarized in 2 to a real dataset on Alzheimer’s disease. The summary statistics we used were from a recent meta-analysis of individuals from 13 cohorts, with a total of 1,126,563 individuals  included [In this section, we applied SuSiEincluded . This meWe obtained the gene expression levels for AD risk loci from the ROSMAP dataset , which cTo evaluate our method, we treated the predicted functional SNPs for Alzheimer’s diseases from a single-cell epigenomic analysis  as the vOur real data analysis was conducted with the following steps:We extracted all the common SNPs within 100kb upstream and downstream of each likely mediator as a target set.The LD matrix was calculated for each target set with a reference panel based on Europeans from the UKBB dataset.We fitted the eQTL-based SuSiE model with the ROSMAP dataset and calculated the PIP for each candidate SNP in the target set.2 results.PIPs from step 3 were treated as prior distributions and integrated into the fine mapping study based on summary statistics from the meta-analysis to get SuSiE2 and the original SuSiE that did not take advantage of the eQTL information. We only considered 20 mediator-risk loci pairs in the common part of the ROSMAP dataset, reference panel, and the meta-analysis dataset. We compared the AD mediators identified by SuSiE and SuSiE2, with the results summarized in 2 successfully identified nine out of 20 mediators, while SuSiE only captured five of them. In the coding region, there were in total seven causal SNPs, SuSiE identified two of them, while SuSiE2 detected three of them. In the non-coding region, the number of AD mediators identified by SuSiE was three, while the number of mediators identified by SuSiE2 was six. We also evaluated the properties of generated credible sets (CSs) by two methods, summarized in 2 also reduced the 75% quantile of the size of credible sets from 13 to 11, which suggests that SuSiE2 may avoid producing extremely large credible sets.Two fine mapping methods we considered were SuSiE2, as shown in 2 can identify more AD mediators by increasing the estimated PIPs of them, and all the mediators identified by SuSiE were also captured by SuSiE2. Besides, the points of many causal SNPs were distributed around the y = x line, which suggests that the SuSiE regression model may not be very sensitive to the choice of prior probabilities. The numerical results of PIPs estimated by SuSiE and SuSiE2 for every AD mediator are summarized in We also calculated the PIP for each mediator by SuSiE and SuSiE2 enhanced the PIPs for causal mediators, we display the examples of two risk loci in 2. The PIPs estimated from the eQTL study are used as the prior information by SuSiE or SuSiE2. For the PICALM locus (2 to capture this mediator in a credible set. However, the original SuSiE failed to include this variant in any credible sets. For the C14orf93 locus (2 failed to find any signal in the risk locus. The estimated PIPs by SuSiE were stable at a very low level, with the largest PIP smaller than 0.05. In contrast, with the prior information provided by the eQTL study, the signals for some candidate SNPs in this region were enhanced with the strongest PIP larger than 0.15. Besides, the PIPs for the remaining SNPs estimated by SuSiE2 were reduced towards zero, which indicated that SuSiE2 performed better in separating causal SNPs from non-causal variants.To illustrate that SuSiELM locus , a sligh93 locus , both Su2 achieved a better performance in separating causal SNPs from non-causal SNPs.In conclusion, the real data analysis results on the AD dataset also suggest that incorporating eQTL information in the SuSiE model increased the statistical power of identifying the true variants while reducing the average size of credible sets. Besides, SuSiE2 compared with the original SuSiE in improving the statistical power was more apparent when we used an external reference panel. The real data application in AD also suggests that SuSiE2 performed better than other methods in identifying the true AD mediators by prioritizing risk variants based on eQTL information before conducting the association study.Statistical fine mapping has been an important tool in detecting the true causal SNPs for complex traits of interest. Most widely used fine mapping methods are based on the Bayesian framework, and assigning a proper prior distribution to risk variants can improve both the accuracy and efficiency of fine mapping. As an important indicator of association with gene expression level, eQTL information can be incorporated into fine mapping by either conducting a colocalization study or fine mapping with annotations. In this manuscript, we proposed a new framework for integrating eQTL with fine mapping via the SuSiE model. Through the simulation study, we showed that this new framework can increase statistical power while reducing the average size of credible sets. The advantage of SuSiE2 may be improved so that we do not have to run SuSiE two times. In other words, we may accomplish the eQTL-adjusted SuSiE within one framework. Second, although our simulation suggests that SuSiE is generally robust to overstating of the total number of causal effects K in the IBSS algorithm [K in real data applications. A larger K sometimes leads to the finding of new credible sets. Based on our experience, we recommend increasing the parameter K starting from 1 and stopping this process when we fail to find new credible sets. Further investigation of the mechanisms underlying this phenomenon is needed to find the best way to select the parameter and make use of the prior information. Third, as eQTLs may be context and cell-type specific, we may jointly consider eQTLs across multiple conditions and also include other molecular QTL information to more comprehensively capture different mechanisms contributing to diseases.A number of issues remain to be addressed in the future. The first one is that the formulation of SuSiElgorithm , SuSiE w2, a statistical framework that incorporates eQTL information to fine mapping. By prioritizing variants within the candidate region with eQTL information, SuSiE2 improves the performance of fine mapping by simultaneously increasing statistical power, reducing false positives, and decreasing the average size of credible sets compared with the original SuSiE. We also demonstrate through simulations that eQTL information can compensate for the power loss because of inaccurate LD information. In the real data application, SuSiE2 confirms four more functional SNPs associated with AD predicted from single-cell epigenomic data compared with SuSiE. Evaluations of AD risk genes like PICALM and C14orf93 indicate that SuSiE2 enhances the PIPs for causal mediators and achieves superior performance in distinguishing causal SNPs from non-causal variants.In this manuscript, we have introduced SuSiESupplement 1