The plant material consisted of 170 landraces and 184 modern cultivars from the Mediterranean basin. The collection was phenotyped for agronomic and VI traits derived from multispectral images over 3 and 2 years, respectively. The GWAS identified 2,579 marker-trait associations (MTAs). The quantitative trait loci (QTL) overview index statistic detected 11 QTL hotspots involving more than one trait in at least 2 years. A CG analysis detected 12 CGs upregulated under abiotic stress in six QTL hotspots and 46 downregulated CGs in 10 QTL hotspots. The current study highlights the utility of VI to identify chromosome regions that contribute to yield and drought tolerance under rainfed Mediterranean conditions.Understanding the genetic basis of agronomic traits is essential for wheat breeding programs to develop new cultivars with enhanced grain yield under climate change conditions. The use of high-throughput phenotyping (HTP) technologies for the assessment of agronomic performance through drought-adaptive traits opens new possibilities in plant breeding. HTP together with a genome-wide association study (GWAS) mapping approach can be a useful method to dissect the genetic control of complex traits in wheat to enhance grain yield under drought stress. This study aimed to identify molecular markers associated with agronomic and remotely sensed vegetation index (VI)-related traits under rainfed conditions in bread wheat and to use an  Triticum aestivum L.) is the most common cultivated crop worldwide. It is grown on 216 million hectares of land, and its global production of 765 million tons of grain provides 19% of calories and 21% of the protein in the human diet . To cover the expected food demand of a world population that will increase up to 60% by 2050, wheat production needs to be increased by 1.7% per year  based on the use of multispectral images acquired from unmanned aerial vehicles (UAVs) has increasingly improved the assessment of agronomic traits  or association mapping (AM) is essential in breeding programs. GWAS is based on linkage disequilibrium , and plant features related to drought resistance assessed by HTP, to define the most important QTL hotspots for such traits and to perform Triticum aestivum L.) genotypes from the MED6WHEAT IRTA panel described by Rufo et al.  at a ratio of 1:4 between checks and tested genotypes and in 3.6 m2 plots with eight rows spaced 0.15 m apart. The sowing density was adjusted to 250 germinable seeds m−2, and the sowing dates were December 2, 2015; November 21, 2016; and November 15, 2017, whereas harvesting dates were July 7, 2016; July 5, 2017; and July 5, 2018. Weeds and diseases were controlled following standard practices at the site.The field trials were conducted at Gimenells, Lleida , northeastern Spain, under rainfed conditions for three consecutive seasons: 2016, 2017, and 2018. Average minimum and maximum monthly temperatures and rainfall were calculated from daily data recorded for a weather station close to the experimental fields. Climatic data  for the last 15 years corresponding to the weather station in Gimenells, Lleida (Spain) were downloaded from −1), number of spikes per m2 (NSm2), number of grains per m2 (NGm2), thousand kernel weight , aboveground biomass at physiological maturity , harvest index (HI), plant height  and early vigor . The HI was calculated as the ratio between grain and plant weights in a 1-m long row sample. The PH was measured at maturity for three main stems per plot and was measured from the soil to the top of the spike, excluding the awns. Early vigor was calculated by integrating the green area (GA) values obtained by ground-based red, green, and blue (RGB) images taken every 14 days as described by Casadesús and Villegas  according to the protocol described by Rufo et al. : grain yVillegas  from emeVillegas . The GSsin situ spectral measurements in ground calibration targets . Spectral measurements were conducted with a Jaz spectrometer . Jaz has a wavelength response from 200 to 1,100 nm and an optical resolution of 0.3–10.0 nm. The calibration of the spectrometer measurements was taken using a reference panel . Geometrical correction was conducted by using ground control points (GCPs) and measuring the position in each with a handheld global positioning system (GPS) . All images were mosaicked using AgisoftPhotoscan Professional version 1.6.2  software and geometrically and radiometrically corrected with QGIS 3.2.0 . Then, six spectral vegetation indices (VIs) were carefully selected based on their significance in relation to certain plant physiology features in wheat (_A) and (2) postanthesis (PA) at the milk and dough developmental stages (VI_PA).Image acquisition was conducted with a multispectral camera (Parrot Sequoia)  installed onboard an UAV . Images were acquired during 2 years, on April 21, 2017 and May 19, 2017 and on April 17, 2018 and May 18, 2018. Flights were always conducted at ~12:00 solar time under sunny conditions and with a wind speed below 12 m/s. The UAV flew at a height of 40 m above ground level (agl) and with a flight plan of 80/60 frontal and side overlap. The multispectral camera has four spectral bands located at wavelengths of 550 ± 40 nm (green), 660 ± 40 nm (red), 735 ± 10 nm (red edge), and 790 ± 40 (near infrared). The camera yields a resolution of 1,280 × 960 pixels. All images were radiometrically corrected through an external incident light sensor that measured the irradiance levels of light at the same bands as the sensor, as well as with in wheat . In addiin wheat  and Gomein wheat . All VIsThe panel was genotyped with 13,177 SNP markers using the Illumina Infinium 15K Wheat SNP Array at Trait Genetics GmbH , and 11,196 markers were ordered according to the SNP map developed by Wang et al. . To reduPhenotypic data were fitted to a linear mixed model considering the check cultivars as the fixed effect, and the row and column number and accessions as random in the model for each environment following the MIXED procedure of the SAS-STAT statistical package .where β is an unknown vector of fixed-effects parameters with known design matrix x, γ is an unknown vector of random-effects parameters with known design matrix z, and ε is an unknown random error vector whose elements are no longer required to be independent and homogeneous. Restricted maximum likelihood (REML) was used to estimate the variance components and to produce the best linear unbiased predictors (BLUPs) for agronomic traits and VIs .2) was estimated following Knapp et al.  when all genotypes belong to the same group. In addition, the (A) date was incorporated as a cofactor in the analysis, as reported in previous studies . A false discovery rate (FDR) threshold , using 3,696 markers according to the results of the linkage disequilibrium (LD) decay  data were retrieved from the high-confidence gene annotation at The experimental site has a typical Mediterranean climate characterized by an irregular pattern of yearly rainfall distribution, low temperatures in winter that rise sharply in spring, and high temperatures continuing until the end of the crop cycle. A summary of the genetic variation is shown in 2 (65.0%), harvest index (HI) (62.6%), plant height (PH) (61.4%), and GS87 (59.0%). Finally, the highest percentage explained by the interaction between year and SP was found fo rbiomass, NSm2 and thousand kernel weight (TKW), reaching 86.2, 84.9, and 71.0%, respectively. Significant differences were found between SPs for all traits. The year and the year × SP interactions were also significant for all traits, except for HI.The results of ANOVAs for the agronomic traits measured during the three growing seasons are shown in 2, NGm2, and TKW reached intermediate values between those obtained in the two subsequent years. The shortest duration of the preanthesis period and the longest grain filling duration (GFD) were also observed in 2016. On the other hand, the lowest yield, NSm2 and NGm2, the heaviest grains and the shortest GFD were observed in 2017. The GA reached the highest value in 2016, which was characterized as the wettest year during the period from January–March, i.e., the stem elongation stage, when the trait was measured. In contrast, 2017 was the driest year in the same period, which showed the lowest value for GA. In 2017, PH showed maximum values but biomass showed the lowest values at maturity. Finally, in 2018, biomass, the number of spikes, and grains per unit area showed high values, and the cycle until A was the longest.The mean values of phenotypic traits for each year and SP are shown in Significant differences in agronomic traits between SPs highlighted the division of the whole set into landraces and modern cultivars . Modern The mean values of the VIs and LAI (estimated by MTVI2) at A and at PA for 2017 and 2018 and the different SPs are shown in 2 (r = 0.90) and PH (r = −0.71). Interestingly, when analyzed VIs-related traits against agronomic traits, highly significant coefficients (r > 0.61) were found between GS65 and RDVI, MTVI2, GNDVI, LAI, NDVI_PA, and MSAVI_PA, and between GA and MSAVI, GNDVI, LAI, MTVI2, and RDVI_A. In addition, NGm2 and PH showed a moderate significant correlation with GNDVI_PA and NDVI_A, respectively (r = 0.46).Correlation coefficients between agronomic traits, VIs, and LAI were calculated , showingqi) coefficients  for each set separately and for the combined set. The highest R2 values were found in modern set regressions for GNDVI_A, GNDVI_PA, NDVI_PA, and GS65. Among the components of yield, thousand kernel weight (TKW) showed the highest R2 values in landrace set regressions, while in modern set regressions, NGm2 showed the highest R2 values.To quantify the relation between trait variation and population structure, multiple linear regressions were carried out between population structure  (62%) and included the two landraces allocated to Cluster B mentioned above. Moreover, Subcluster B2 included 91% of the cultivars from SP4 . Most of the modern Balkan cultivars (SP5) were grouped in Subcluster B1. Subcluster B1 was characterized by higher TCARIOSAVI_PA and GA, whereas Subcluster B2 was characterized by higher NSm2, GNDVI_A and the rest of the VIs assessed in PA.Each of these two clusters was separated into two subclusters, A1 and A2 for landraces and B1 and B2 for modern cultivars. Subcluster A1 was represented mainly by south Mediterranean landraces (77%), including those from the east and west regions, whereas Subcluster A2 contained most of the north Mediterranean landraces (62%). East Mediterranean landraces were in a single cluster within A1, whereas west Mediterranean landraces were distributed in other clusters within A1. Differences among subclusters, A1 and A2 were due to higher NSm10P > 4.8, a common threshold of –log10P > 3, as reported in the literature  for all traits per year and for the mean values across years is reported in The number of MTAs per chromosome for all years and for the mean values across years ranged from 9 on chromosome 4D to 354 on chromosome 1B . Genome 2 with the mean across years. MTAs for TKW were found mainly during 2016 (96%), and those for PH were found mainly during 2017 (68%).A total of 815 MTAs were identified for seven agronomic traits . Yield s10P > 3 were identified for 15 VI traits  as the threshold, whereas using a high threshold (3.5), a total of 41 peaks were detected . These 4https://wheat-urgi.versailles.inra.fr/Tools/JBrowse. A total of 1,342 gene models were detected, and to classify this information, Gene Ontology (GO) for 1,025 of the gene models (76%) was downloaded from https://wheat-urgi.versailles.inra.fr/Seq-Repository/Annotations  drought and heat stress time-course in seedlings, (2) spikes with water stress, and (3) seedlings treated with polyethylene glycol (PEG) to simulate drought, and DEGs were analyzed in four tissues  during different developmental phases . A total of 12 CGs that were upregulated under abiotic stress were found in six QTL hotspots and 46 were found downregulated in 10 QTL hotspots  under three abiotic stress conditions as reported in hotspots .Among the different upregulated DEGs, a defensin in hotspot QTL1A.1 showed the highest expression under abiotic stress conditions and was expressed in most of the tissues and all the developmental phases; it also showed the highest expression levels for each of the phases. All DEGs reported expression in the spikes with a range from 0.02 tpm for cytochrome b in QTL1B.2 to 3.48 tpm for defensin in QTL1A.1. Only four DEGs were expressed in the roots and five in the leaves/shoots and grain. Only zinc finger protein-like 1 in QTL2A.2 was expressed in all four plant tissues, showing the highest expression in roots. Regarding the developmental phase, no expression was reported in any stage for two DEGs, cytochrome b in QTL1B.2 and enoyl-[acyl-carrier-protein] reductase in QTL3D.1. The reproductive phase had the highest number of DEGs (nine out of 10 showing expression), whereas 6 were expressed in the seedlings and only four were expressed during the vegetative phase.Among the downregulated DEGs, the hotspot QTL1B.2 showed the highest number of downregulated DEGS (16), whereas QTL2A.2 did not show any of them. Three DEGs showed expression in all tissues, whereas any of them were downregulated in all of tissues. About six DEGs were expressed only in roots under non-stressed conditions, three in leaves/shoots, seven in spikes, and nine in grains, whereas DEGs non-expressed under abiotic stress in only one tissue corresponded to one in roots and six in grains. According to the developmental phase, 12 DEGs were expressed in all of them, whereas any DEG was downregulated in all of them. One DEG was expressed only in the seedling stage at normal conditions, whereas two were expressed only in the vegetative and 22 in the reproductive stages.The current study was conducted under typical Mediterranean environmental conditions, with a pattern of increasing temperatures during the spring and an irregular distribution of rainfall across years. A genome-wide association study (GWAS) panel of 354 bread wheat genotypes, including Mediterranean landraces and modern cultivars, was grown for 3 years under these conditions in northeastern Spain. Given that the decrease in the genetic diversity of wheat occurred during the second half of the twentieth century, associated with the introduction of high-yielding semidwarf cultivars  on the phenotypic expression of the agronomic traits, whereas year showed the largest effect for most of the VIs, followed by the year × SP interaction, with the SP effect being the lowest. The variability in agronomic traits was mostly caused by the different agronomic performances of wheat landraces and modern cultivars, as reported in previous studies , thus showing a shorter grain filling duration (GFD) in 2017. The high yields recorded, considering the rainfed conditions of the field trials, could be attributed to the high soil fertility (~3% of organic matter) and the superficial subsoil water layer at this site  as reported previously by Royo et al. . Among lAll traits related to high-throughput phenotyping (HTP) showed significant differences between years before and after anthesis (A), showing higher values for most of the VIs in 2018 than in the previous year. These higher values agree with the rainfall recorded for both years, which was significantly lower in 2017 than in 2018, mostly during the grain filling period. Furthermore, the difference in the mean values between growth stages was much higher in 2017. This result could be explained by the water scarcity particularly affecting the PA stage, which results in an important loss of chlorophyll content during the grain filling period; therefore, VIs using bands mostly placed in the near-infrared (NIR) and green regions showed lower values  at A, and these values were higher in the landraces. As reported in previous studies in durum wheat  and marker–trait associations (MTAs) associated with traits of interest to carry out marker-assisted selection (MAS) approaches and the introgression of alleles of commercial interest in adapted phenotypes.The highest number of MTAs related to agronomic traits was found in 2016, while 96% of MTAs related to VIs, GA, and the LAI were identified in 2017. It has been reported that under contrasting conditions, the G × E interaction could affect the identification of stable associations among different environments  was found to show the highest expression under drought stress. According to Kumar et al. , althougz et al. , such as2, and GNDVI_PA during the breeding process.Whereas differentiation among landraces were found for agronomic and VIs , in modern cultivars SPs differentiation were mainly due to GS65, GNDVI, and NDVI_PA. The use of a statistical approach as the QTL overview index for the definition of QTL hotspots resulted successful for the identification of consensus genome regions including most of the stable marker trait associations across years. The results of this study will be useful for our wheat breeding program by the selection of the appropriate genotypes carrying favorable alleles for the differential traits that will be useful for designing new crosses.The use of local landraces in breeding programs is considered a valuable approach to broadening the genetic variability of crops lost during the breeding process and improving traits of commercial importance. The results reported in the present study evidenced the selection for grain yield, HI, NG min silico approaches allowed gene mining in QTL hotspots, thus facilitating CG identification.Using The original contributions presented in the study are included in the article/JS: conceptualization and supervision. RR, AL, ML, JB, and JS: methodology. RR, JB, and JS: formal analysis. RR, AL, and JS: data curation. RR: writing—original draft preparation. RR, ML, JB, and JS: writing—review and editing. ML and JS: project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.This study was funded by the projects AGL2015-65351-R and PID2019-109089RB-C31 from the Spanish Ministry of Science and Innovation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Spodoptera frugiperda (J.E. Smith)) is likely to worsen the food insecurity situation in Africa. In the present study, a set of 424 maize lines were screened for their responses to FAW under artificial infestation to dissect the genetic basis of resistance. All lines were evaluated for two seasons under screen houses and genotyped with the DArTseq platform. Foliar damage was rated on a scale of 1 (highly resistant) to 9 (highly susceptible) and scored at 7, 14, and 21 days after artificial infestation. Analyses of variance revealed significant genotypic and genotype by environment interaction variances for all traits. Heritability estimates for leaf damage scores were moderately high and ranged from 0.38 to 0.58. Grain yield was negatively correlated with a high magnitude to foliar damage scores, ear rot, and ear damage traits. The genome-wide association study (GWAS) revealed 56 significant marker–trait associations and the predicted functions of the putative candidate genes varied from a defense response to several genes of unknown function. Overall, the study revealed that native genetic resistance to FAW is quantitative in nature and is controlled by many loci with minor effects.The recent invasion, rapid spread, and widescale destruction of the maize crop by the fall armyworm (FAW;  Maize (Zea mays L.) is a staple food for over 300 million people on the African continent . To achiFAW foliar feeding, especially on the furl and whorl leaves, destroys the plant growing points and retards maize growth and development. Yield reduction has been attributed to both FAW stem tunneling, which disrupts water and nutrient uptake, as well as the extensive leaf feeding damage which causes a direct loss of photosynthates . FAW-damTransgenic Bt crops provide an effective FAW control but impose a high selection pressure which results in the emergence of resistant FAW biotypes ,14, whilHost plant resistance is conferred by the plant’s biochemical constituents , structuIn the African context, FAW resistance breeding is less well studied . The utiA set of 424 DH lines developed by CIMMYT and 11 elite lines were evaluated for their responses to FAW under artificial infestation. All lines were screened for two seasons in screen houses at the Kiboko experimental station  in Kenya in 2020 and 2021. To control random variation, the experiment utilized an α lattice design with two replications. Test plots consisted of single rows 3 m long and 0.75 m apart. Each year by location combination was considered as a separate test environment. Standard agronomic management practices were implemented up to physiological maturity when the ears were harvested.Mass FAW rearing was conducted at the insectary within the Kenya Agricultural and Livestock Research Organization, Katumani Agricultural Experimental Station in Machakos county, Kenya using a nutritionally adequate CIMMYT artificial diet  under amAspergillus flavus; as a result, rots develop on individual kernels or part of the ear and result in rotten ears. Grain yield was obtained from the shelled grain weight adjusted to 15% and converted to tons per hectare. Ear damage was rated on a scale of 1–9, where 1 = no visible damage to the ear, 2 = damage to a few kernels ( < 5) or less than 5% damage to an ear, 3 = damage to a few kernels (6–15) or less than 10% damage to an ear, 4 = damage to (16–30) kernels or less than 15% damage to an ear, 5 = damage to (31–50) kernels or less than 25% damage to an ear, 6 = damage to (51–75) kernels or more than 35% but less than 50% damage to an ear, 7 = damage to (76–100) kernels or more than 50% but less than 60% damage to an ear, 8 = damage to >100 kernels or more than 60% but less than 100% damage to an ear, 9 = almost 100% damage to an ear. Considering each year by location as a separate test environment, phenotypic data were taken in two environments with two replications per environment.All plots were infested on the same day to ensure the uniformity of infestation. The level of leaf feeding damage for each plant per plot was rated 7, 14, and 21 days after artificial infestation using a visual rating scale of 1–9 . On thisith replicate, jth incomplete block within the ith replicate, kth genotype, Cov is the covariate, and ith replication, jth incomplete block, and the kth genotype. Blocks were considered as random effects while the replicates and genotypes were considered as fixed effects. Data from each trait met all the assumptions of the applied statistical model, i.e., normally distributed, constant variance, and independent ; as a reith environment and the environment by genotype interaction, respectively. The study treated replication as a fixed effect and all other treatments as random effects. On an entry-mean basis, the broad-sense heritability was estimated using the genotypic to phenotypic variance ratio from the derived variance components. Furthermore, to determine the genotypic effects of the investigated lines for each and across environments, a best linear unbiased estimation (BLUE) and best linear unbiased prediction (BLUP) were obtained.New terms were added to the model when performing trait analysis cross environments. The new linear model used was as follows;For GWAS, BLUPs were used. On the other hand, BLUEs were used for GS analyses. Comparisons of variability between entries were made using the least squared differences (LSD) at a 5% significance level. The performance analytics R package  was usedMaize leaf tissue samples were collected from eight young, healthy seedlings raised under screen house conditions at the V3 stage (3–4 weeks old). A composite sample of tissues collected from each line was stored at −80 °C and later freeze dried for 72 h. High quality genomic DNA was isolated from freeze-dried tissues using the standard CIMMYT laboratory protocol . The Div2 = 0.1 was taken, beyond which LD could be due to linkage.Principal component analysis (PCA) was implemented using 7950 markers that were distributed across the ten maize chromosomes. A two-dimensional plot of the first two principal components was generated using the multi-locus random SNP effect mixed linear model ‘mrMLM.GUI’ R package version 4.0.2 . Marker-p-values across all evaluated SNPs were inspected using the diagnostic quantile–quantile plots (Q–Q plots) generated in the R environment using the ‘qqman’ R package [p-values from the model fitted in GWAS were plotted against their expected values under the null hypothesis of no association. To summarize GWAS results per chromosome, Manhattan scatter plots were generated by plotting the genomic positions of the SNPs against their negative log base 10 of the p-values obtained from the GWAS model, and an F-test was conducted for the null hypothesis on the Y-axis. The source sequences of the significantly associated SNPs were used to perform BLAST searches against the B73 maize reference genome version 3 with a view to identify candidate genes located within a 10 kb window. The inferred biological functions of the candidate genes were retrieved from the maize genome database [Kinship and population structure were incorporated as covariates in the association analyses. The R package ‘FarmCPU-fixed and random model Circulating Probability Unification’ with GAPIT (Genome Association and Prediction Integrated Tool) was used for GWAS analysis . To eval package . On the database  and publBLUEs across environments for each trait were used in the GS analysis. The ridge-regression BLUP (RR-BLUP)  with a fThe frequency distribution of the BLUEs for foliar damage scores at 7, 14, and 21 days after artificial infestation, ear damage, ear rot, and grain yield exhibited approximately near normal distributions . Evaluatp < 0.01) genotypic and genotype by environment interaction variances for all traits. Heritability (h2) estimates for foliar damage scores were moderate with 0.58, 0.45, and 0.38 at 7, 14, and 21 days after artificial FAW infestation, respectively. Grain yield recorded the highest (0.62) heritability estimate when compared to the rest of the traits. Ear damage, ear rot, and grain yield showed moderately high coefficients of variation estimates, which suggested that the selection of FAW-resistant lines based on these traits could be effective. Pairwise Pearson correlation analysis revealed significant but negative correlations between grain yield and the rest of the traits  was used to partition total variance into genotypic, phenotypic, and variance due to genotype by environment interaction . Across-e traits . Foliar GWAS statistical power and resolution is reduced if the phenotypes of interest are significantly correlated with relatedness or population structure . PCA bas2) between pairs of SNPs while the x-axis values represent the physical distance in Mega base pairs (Mbp). The red line is the moving average of the 10 adjacent markers. Each dot represents a pair of distances between two markers on the window and their squared correlation coefficient. Across the ten chromosomes, the magnitude of r2 dropped sharply as the genetic distance between SNP markers increased. At LD cut off points of r2 = 0.1 , the average physical distance was 10 Mbp.p = 10–8) with foliar damage scores across all infestation durations were found. For ear damage scores, 13 SNPs distributed on chromosomes 1, 2, 3, 4, 5, 6, and 10 were detected. There were 13 SNPs significantly associated with ear rot resistance distributed across all chromosomes except chromosome 8. SNP DT5_86480332 was commonly detected for both ear damage and ear rot scores. Grain yield under artificial FAW infestation exhibited large peaks of association signals on chromosomes 4 and 10  located on chromosome 8 contributed the strongest estimated effect size (6.50) for the expression of the leaf feeding damage resistance trait. A set of putative candidate genes associated with significant SNPs were identified and their inferred biological functions varied from a defense response to a carbohydrate metabolic process  and DT2_192225273 (AA/CC) and boxplots B with DT5_86480332(AA/TT) and DT6_108383751). Two SNPs DT1_34838367 (CC/TT) and DT2_192225273 (AA/CC) in a combination of favorable alleles resulted in the score of 2.0 and 4.9 for ear damage and foliar damage, respectively (DT5_86480332 (AA/TT) and DT6_108383751, a decrease in ear damage score and foliar damage score and an increase in grain yield were also observed . FAW-damaged ears are predisposed to fungal attacks, rots, and mycotoxin contamination which adversely impact grain quantity and quality [The magnitude and the direction of the correlations for several FAW resistance indicator traits should be examined to understand the desirability of their relationships with grain yield, which is the most desirable agronomic trait with economic importance. Results from the correlation analysis indicated that grain yield was negatively correlated with a high magnitude to foliar damage scores, ear rot, and ear damage traits. The correlation between ear rot and ear damage was positive, strong, and highly significant . The high level of homozygosity in the maize lines used in the current study may have rendered recombination ineffective in breaking down LD. Varying estimates of LD decay have been reported in maize, such as 27.31 kb [The rate of linkage disequilibrium decay provides useful information required to implement meaningful association mapping study . In the 27.31 kb  and 14.927.31 kb . These s27.31 kb . This prGWAS revealed 56 significant marker–trait associations . ChromosThe shift in allele frequency proportions has been attributed to the natural or artificial selection of variants conferring a selective advantage . The resDT8_165429441), strongly associated (p = 10−5) with leaf damage scores 14 days after infestation, was found within a genomic region containing the GRMZM2G016802 gene that participates in the defense response by restricting injury occurrence and enhancing recovery after injury [DT1_34838367 and DT2_192225273 and boxplots B with DT5_86480332 and DT6_108383751) clearly support the effective role in improving the level of FAW resistance (Larger peaks on the Manhattan plots were suggestive of a strong association between the surrounding genomic region and the corresponding phenotypic trait and warrants further validation. Grain yield under artificial FAW infestation, for instance, exhibited peaks of association signals on chromosomes 4 and 10 which could be due to strong selection pressure during the breeding and domestication process . While br injury . The allsistance . These cFoliar damage in maize triggers a complex cascade of biological pathways mediated by various genetic factors, effector molecules, and signaling components and which leads to the accumulation of secondary metabolites with anti-feedant effects . SNP (DTGS facilitates the rapid selection of superior genotypes through ease in genotyping, which captures the maximum favorable alleles. The potential of GS models in identifying lines with favorable alleles in maize for different traits has been studied by different groups ,69,80. TTo investigate the genetic basis of FAW resistance, we employed a single panel consisting of 423 tropical maize lines for GWAS and genomic prediction. The phenotypic correlations of the FAW resistance traits investigated indicated that this panel can be used to select better-performing lines under FAW infestation. GWAS identified 56 SNPs associated with FAW resistance traits. The genomic regions identified can be used for selection efforts to enhance FAW resistance. Furthermore, the findings showed that including GS in maize breeding can successfully support phenotypic selection to improve maize native genetic resistance. Future work should, therefore, focus on validating the identified SNPs to enhance the efficacy of maize breeding in SSA.


The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo  and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype–environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions. Hybrid breeding programs are usually based on pureline methods, including the development of inbreeding lines by self-pollination or double-haploids, followed by progeny evaluation across heterotic pools  the genetic design and the genotypes used to form the training population; (2) the presence of a population structure; (3) the importance of non-additive effects controlling the desired characteristic; (4) the source of molecular markers used; and (5) the genotype × environment (G × E) interaction over contrasting environments. Therefore, this review aims to describe the most important outcomes in this field and report our research experience in a small-scale low budget breeding program under tropical growing conditions.Here we focused our review efforts on the GP of maize hybrids, particularly in the single-crosses of FHere, we highlighted the most important outcomes obtained by the Allogamous Breeding Laboratory of the University of São Paulo  and some other groups in testing GP for predicting maize hybrids. We present our review as a roadmap for small-scale and low-budget breeding programs due to the fact that most of our research is focused on optimizing GP in order to find the best training sets (TS), to select the best genotyping pipelines, and to choose the best multi-environment structures to predict scenarios of genotype × environment interaction. Our roadmap began with the efforts for germplasm characterization, which involves both molecular and phenotypic characterization. Before this step, it is necessary to develop the inbred lines during successive cycles of self-crossing. For most breeding programs, this step may involve the use of double haploid technology. After seed replication, field trials must be well-conducted, following certain management practices, which may evolve, for example, the use of optimum vs. nitrogen-limited conditions. A good statistical analysis and phenotype correction are important steps that impact further genomic analysis  controlling the desired trait are also outlined. Finally, we present how the source of the molecular markers, environment, and the modeling of genotype × environment interaction can affect the accuracy of GP. We also point out that the use of dominance effects in GP is crucial to deliver accurate predictions of maize hybrids. Results of 7 years of research in our public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. In the end, we revised some fields of work and the lessons we learned from both our experience and the results from other groups.The very first step on our scientific road was to carry out germplasm characterization on the newly acquired inbred lines  markers displayed a significant difference in the allelic frequencies (Fst > 0.2) between groups H_NAE and L_NAE. Pearson's correlation between NAE and the favorable alleles in this set of SNPs was 0.69. These SNPs can be useful for the marker-assisted selection (MAS) for low-N tolerance in maize breeding programs. The results of this study can assist maize breeders when identifying genotypes to be used in the development of low-N tolerance cultivars.Using this information, we have chosen 49 lines to compose the genitor bank of our breeding programs. We carried out the first complete diallel, which outlined the heterotic groups and the first GP training population, both used in the studies described below.Due to the considerable diversity, we then tried to identify whether the population structure within the dataset should be considered , one strategy is to incorporate autovectors and admixture coefficients as covariates (fixed effects) in genomic models . The useTropical and subtropical maize genotypes are not as organized as temperate ones, which mean that more than two heterotic pools can be used in crosses. Equivalently, a diverse population of inbred lines can be crossed with testers representing different genetic origins. Thus, although only the effect of alleles and their interactions make up the genetic structures of hybrids, it is essential to find the structure patterns and understand how this information affects the predictions. In this sense, we investigated the effect of population structure in the GPs of simple crossbreeding considering two scenarios: (1) applying the traditional GBLUP and four methods of adjusting population structure in the whole group and (2) using homogeneous (A-GBLUP), within-group analysis (W-GBLUP), multi-group analysis (MG-GBLUP), and inter-group analysis (AC-GBLUP) in stratified groups  populations but not for single hybrids. These results provided further knowledge about our germplasm and reassuring ways to perform GP.Post-hoc but relevant information about creating a training population is included in our realm of projects. We realized that the literature concerning GP in maize was quite vast, yet there was a significant shortage of studies on the best genetic design to build the training population.via NCII, and (ii) to identify the possibility of reducing the number of crosses and genitors to compose these TSs  aiming to reduce the number of individuals for training genomic prediction without reducing accuracy. For this purpose, we used the algorithm proposed by Akdemir et al.  with preOur results suggest that TC is the worst genetic design to be used as a TS to predict simple maize crosses that must be obtained through FD or NCII. On the other hand, NCII is the best TS for the prediction of hybrids taken from FD. In addition, combinations from FD or NCII can be well predicted using OTS, thus reducing the total number of crosses to be made. However, the number of parents and crosses per parent in the ST should be maximized.Due to the scarcity of resources in the initial phases, we addressed the possibility of incorporating public databases in the composition of our training populations , ear height , and the SNP markers data. A total of 29 training populations (TPs) were defined and divided into four scenarios to determine the best strategy to apply public databases to predict lines.The best predictions were achieved with the strategy of the TP composed by candidates selected with an optimization algorithm from all the public database and private lines, even at the smallest TP sizes evaluated (81 and 281 TP sizes). On the other hand, the lowest predictive abilities were achieved using only the Tropical USP database as training and validation populations (VP), due to its lack of genetic variability and reduced population size, hindering prediction. The results of all four scenarios of TP formation showed that the predictive ability increased with the increase of TP size, the relationship rate between TP and VP, and genetic variability.  genotyping and phenotyping of the germplasm program, composing 10% of TP, along with external individuals selected by optimization procedures (90% of TP), (b) out of 10%, established as the VP (new progeny with no phenotyping data), (c) validation and prediction of GEBV. In the second year, (a) genotyping and phenotyping of individuals from the germplasm program (10% of the TP), (b) once again, new individuals are to make up the VP (10% of TP), while the remaining individuals from the germplasm program are to be a part of TP, with the TP composed by 70% of external individuals selected from optimization procedures and 30% of internal individuals genotyped previously, (c) validation and prediction of GEBV. As genotyping will be performed annually, after 6 years, the TP would be composed exclusively of individuals from the program. In the sixth year, the best performer could optimize the training population with internal individuals, maintaining a good prediction ability index. This procedure optimizes the technical, operational, and financial balance, considering the resources available over time and each harvest.Nowadays, SNPs are the most widely used molecular markers in genomic studies, as they are abundant and evenly distributed in the genome. In addition, genotyping platforms that provide many markers have quickly, accurately, and cost-effectively allowed for the use of molecular tools, including GP. High-performance genotyping platforms, such as SNP-array and next-generation sequencing (NGS) provide thousands of markers for hundreds of samples, making them very suitable  using the most common reference genome, line B73 (GBS-B73), (ii) using a simulated reference genome built with GBS data, considering all inbred lines (GBS-Mock-All), and (iii) using a simulated reference genome built with GBS data from a single line, our heterotic pool tester L56 (GBS-Mock-L56). For this purpose, we used the USP data set mentioned above (see section above “Training populations using public databases”). To build the simulated genome, we used a pipeline developed by Melo et al.  affect the GP in our germplasm Sabadin, . We alsoo et al. , which cThe density and distribution of SNP markers varied according to the genotyping platform chosen. In our study, the SNP markers discovered by SNP-array and GBS-B73 had the same reference genome, which allowed us to compare them regarding marker distribution on chromosomes and detect coincident SNP as well. Despite the difference in the number of SNP markers , both platforms had similar distributions along the genome. However, only 300 SNP markers coincided, suggesting that they detected polymorphisms in different regions. Although this is an important result, these differences were only consistent for some GP models.r = 0.88), revealing that these approaches estimate the additive genomic relationship between hybrids in a similar way. However, for the Gd matrices, lower correlations were observed among all SNP data sets, which show that the polymorphism captured by these platforms estimated the dominance effects differently. GBS-Mock-L56 displayed low correlations with other SNP data sets and had a low performance for all downstream analyses, proving that it is an erroneous alternative to sample polymorphism within the population, since only polymorphisms between L56 and other individuals were identified. This information is crucial when the aim is to predict the genetic values of hybrids, although the architecture of the feature can influence the performance of GP models.The GBLUP model is based on the genomic relationship between genotypes to estimate the genetic values of non-phenotyped individuals. Therefore, assessing the genomic relationship is more important than the polymorphism resolution, which was confirmed when we evaluated the additive genomic relationship matrix (Ga) and the genomic dominance matrix (Gd). For the Ga matrices, high correlations were observed between the SNP-array, GBS-B73, and GBS-Mock-All SNP data sets  but below that when obtained with the additive model.This situation can change greatly when we consider the effects of dominance to estimate genetic values. In our study, except for GBS-Mock-L56, small differences in predictive capabilities were observed among SNP data sets, when we performed the GBLUP additive-dominance model. Furthermore, the differences were more remarkable for GY, supporting the fact that the inclusion of the dominance effects of GP models is more relevant for complex traits. The coefficients of determination between GEBV estimates remained high  offer very similar predictive abilities when using only additive effects in GP models. However, when we add dominance effects, their performance may change, especially when estimating hybrid performance. Dominance effects are critical to hybrid GP, and therefore, the choice of a genotyping platform may affect the estimates of genetic values. However, the differences appear to be small and acceptable in some cases. Furthermore, the use of a reference genome historically unrelated to the evaluated germplasm does not seem to be a decisive factor for GP since it can sample the haplotype variability among genotypes within the population. Another highlight uses a simulated reference genome to discover SNP since it does not depend on an external genome to detect polymorphisms. This strategy may be a valid alternative when conducting GP studies with reliable estimates, especially for orphan crops, where a reference genome is not yet available. Somehow, sampling polymorphisms consistently, using all genotypes within the population, is recommended to build the simulated genome.a priori GWAS in lines to predict its hybrid offspring has been explored by Galli et al. ; (iii) GP alone produced the best results.In previous GWAS . The results showed that non-additive effects play a crucial role in expressing quantitative characters under stress conditions  and non-linear kernels could improve prediction models, including G × E  for two different sets in Brazil. Bandeira e Sousa et al.  tested tSince 2017, some studies have pointed that the use of additive (A) plus non-additive effects  might drastically improve the accuracy of GP for maize hybrids  and additive-dominance (AD) main effects , along with the interactions (MDs models) including reaction-norm for A and D effects to predict GY  in a less computationally expensive way than GB. The GB kernel was the worst kernel method for exploring D effects to predict GY in maize hybrids. For all prediction scenarios , we observed that accuracy gains could only be achieved for GB-based models when including some envirotyping data as the main effect (W) or as reaction-norm (G × W = A × W + D × W). The non-linear kernels were also more efficient at using the phenotypic records in training models for CV1, CV2, and mostly for CV0. For CV0, the combination of DK and more straightforward reaction-norm models (including only A + D + W effects) achieved almost the same accuracy as more complex structures (A + D + W + A × W + D × W). This suggests that to predict future scenarios using actual TSs, the use of enviromic sources combined with additive and dominance genomic data, both modeled with non-linear kernels, is the best way to achieve higher mathematical accuracy biologically that better represents novel G × E conditions.Combined with phenotypic and genotypic data, the use of envirotypic data sources can leverage the molecular breeding strategies addressing the prediction of tested and untested environments, such as climate change scenarios , and the inclusion of envirotyping data boosted the prediction up to r = 0.504 (enviromic by development stage) and r = 0.485 .Costa-Neto et al.  presenteAn alternative approach for the use of environmental relatedness kernels is the adoption of single-covariate regressions . Furthermore, it uses algebra resources resulting in a significant gain in processing speed, especially for large data sets  common in tropical soils and related to maize nitrogen fixation. The GWAS outcomes helped understand how heterosis is important for improving the quality of crop systems by increasing the nitrogen use efficiency (NUE) of maize. Another promising approach is presented by Millet et al. . Therefore, GP has proven to be useful to enlarge the spectrum of individuals evaluated in silico for phenotyping traits, such as PH .Most of the applications described in the last section consider datasets with at least four environments and almost one thousand entries , which represent the reality for at least a small-scale breeding program. As discussed in the previous sections, with the increase in the availability of data, the computational demand and the power of cutting-edge testing hypotheses in maize breeding also increase (Rogers et al., https://data.mendeley.com/research-data/?page=0&search=%22Roberto%20Fritsche%20Neto%22.Publicly available datasets were analyzed in this study. This data can be found here: RF-N conceived and designed all studies. GG, FA, FS, DL, PM, LB, GC-N, and IG generated the dataset and performed the data analysis. RF-N wrote the manuscript. GG, KB, GC-N, and JC revised the text, which all the authors finally edited. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The handling editor declared a past co-authorship with one of the authors JC.


Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use an ontology-guided ML system to map single nucleotide variants (SNVs) focusing on 6 classic phenotypic traits in natural yeast populations. The 29 identified loci are largely novel and account for ~17% of the phenotypic variance, versus <3% for standard genetic analysis. Representative results show that sensitivity to hydroxyurea is linked to SNVs in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. This work demonstrates a knowledge-based approach to amplifying and interpreting signals in population genetic studies. Genome-wide association studies (GWAS) have identified many important loci for common diseases and other traits. However, the loci identified by these studies are almost always many steps away from an understanding of underlying biological mechanisms. Here we develop an approach using hierarchical biological knowledge to identify genes and pathways responsible for phenotypic traits. Variants identified by the new method could explain a substantially greater fraction of heritability than previously reported. Moreover, we identified mechanistic pathways by which each causal variant affects cellular function. For example, we find that sensitivity to hydroxyurea is tied to genetic variants in two alternative purine biosynthesis pathways, and that sensitivity to copper arises through failure to detoxify reactive oxygen species in fatty acid metabolism. The new approach is a potentially transformative concept for understanding the genetic drivers of phenotypic variance, with potential applications in understanding traits in biomedicine and agriculture. In recent decades, genome-wide association studies (GWAS) in humans have identified almost 19,000 associations between genetic loci and phenotypic traits . Of the A second major challenge for both GWAS and PRS is that these approaches do not provide clear insight into molecular mechanisms. Statistical associations between a genetic locus and a phenotypic trait have been typically difficult to translate to an understanding of which genetic variant(s) at that locus are causal, whether these variants affect the expression or activity of gene(s), and how these gene alterations give rise to variation in biological functions within cells and tissues. One way to identify potential links to candidate genes is to integrate orthogonal datasets, such as expression quantitative trait loci (eQTL), chromatin structure, or epigenetic marks –10. As mvia their effects on core biological functions.To address these challenges, a growing field of approaches has begun to look for causal and mechanistic links not in individual genes, but among groups of genes that are functionally related by pathways or gene networks –18. CallRelated to these approaches, we recently developed a supervised machine learning system for predicting the phenotypic outcome of genetic mutations using the method of “ontotypes” . In the Saccharomyces cerevisiae and, in parallel, phenotyped these isolates across 36 growth conditions [Here, we explore whether a similar ontotype methodology can be applied to decode genome-wide association studies. As a test-bed for exploration, we consider a recent GWAS that sequenced over 1000 isolates of nditions . The ininditions . In manyTo achieve greater coverage and mechanistic insight in how genetic variation leads to phenotypic variation in yeast, we now analyze the same collection of genetic isolates with a knowledge-based ontotypes approach. This analysis identifies a constellation of biological systems driving genotype-phenotype predictions, including the discovery that genetic variants in glycine cleavage play major roles in the response to genotoxic stress by hydroxyurea (HU). In nearly all cases, the genetic variants and associated biological systems identified by ontotype analysis have not been previously discovered by standard gene association tests.S. cerevisiae isolates [We explored mapping of causal variants from GWAS using genotype-phenotype data previously gathered in approximately 1000 natural isolates . Specifi2 values . With both models generating similar results, we chose to use the more conservative, established approach and focused our efforts on the results from the mixed random forest models. R2 scores for six traits – copper sulfate, benomyl, caffeine, sodium chloride (NaCl), HU and galactose – were notably higher than the others with values near or greater than 0.4 (well-predicted traits).One challenging aspect of population genetics is accounting for population structure. Here, we first sought to compare random forest models, which were used successfully in an earlier study using ontotypes , with “mues Figs  and S1A,2 scores to those of models using ontotypes as inputs but trained using neural networks or various linear prediction methods: ridge regression, lasso regression or best linear unbiased prediction (BLUP), a linear mixed model [2 values from the neural network models were similar to both the mixed random forest models and random forest models . Notably, ontotype models identified fewer, more focused sets of systems than other approaches (We then compared the systems (GO terms) identified by the ontotype-based mixed random forest models to those identified by the SNV- or gene-based mixed random forest models  of 0.0084 , 64 terms for benomyl  and 47 terms for 40 mM caffeine . For the other well-predicted traits, we found 42 terms for 50 mM caffeine , 59 terms for HU  and 30 terms for galactose . These findings suggest that our ontotype models can detect genetic associations based on the convergent effects of many SNVs, even when the marginal effects of each of these SNVs may not be significant.We also attempted to use two other approaches – GSA-SNP2  and GowiFET4 . Across all phenotypes, the distribution of R2 values from ontotype-based models using a data-derived hierarchy was similar to that of ontotype-based models using GO terms  associated with 14 growth conditions . For manHere we have seen that ontotype models can reveal the genes and systems underlying classic yeast growth phenotypes, and that in certain cases, these models provide more explanatory power than standard GWAS approaches. To transform an ontology into a multi-scale model capable of genotype-phenotype inference, we used an intuitive and parameter-free approach that simply determines mutational burden at the systems level. These ontotypes are then used as input features to train machine learning models for predicting phenotypes. The resulting ontotype-based models are readily interpretable, having recovered known mechanisms for certain phenotypes while suggesting novel functional roles in others.The intuition behind a systems-based genetic approach, including ours here and others –23, is tYeast are equipped with a variety of defense mechanisms against toxic doses of copper such as chelators, exporters and scavengers of the oxygen free radicals generated by copper-mediated reactions . Our GO-PEX1 and PEX6. The proteins encoded by these two genes form a hexameric complex consisting of three subunits of each. This complex plays a key role in the import of many proteins into peroxisomes [PEX1 (again) and FAA2. FAA2 is a peroxisome-localized enzyme involved in fatty acid activation and import [Our complimentary study of copper sensitivity using a data-driven ontology also identified a nested set of systems related to peroxisomes as playing a key role. Peroxisomes are cytoplasmic organelles surrounded by a single lipid bilayer. Although some proteins are delivered to the peroxisome by the ER, many others, including the ROS detoxification enzyme catalase A, are imported directly from the cytoplasm. In yeast, peroxisomes are also the sole site of fatty acid beta-oxidation and consequently a source of reactive oxygen species that can damage nucleic acids, proteins and lipids. One of the top data-derived systems consisted of two genes: oxisomes ,43. Anotd import . The prid import . Our resde novo purine synthesis through direct incorporation into the purine backbone for cancer proliferation [HU inhibits ribonucleotide reductase leading to reduced levels of dNTPs , inhibition of DNA synthesis and suppression of cellular proliferation. Depleted levels of intracellular dNTPs also lead to defective repair of single-strand DNA breaks. Our study of HU response identified as important features “glycine metabolic process” and its subsystems “glycine catabolic process” and “glycine decarboxylation via glycine cleavage.” The glycine cleavage system catabolizes glycine into 5,10-methylenetetrahydrofolate , an essential precursor for DNA synthesis . Glycineferation . Our finMPC1, MPC2 and MPC3. The protein products encoded by these genes form the mitochondrial pyruvate carrier (MPC), which transports pyruvate from the cytoplasm into the mitochondria where it can enter the tricarboxylic acid cycle [We also identified a pair of HU-response systems encompassing the genes id cycle ,48. Receid cycle . Taking One shortcoming of the current approach is that it only takes into account non-synonymous mutations, thereby ignoring all linked regulatory variation. In addition, the assignment of non-synonymous SNVs to genes does not take into account linkage disequilibrium. This could lead to erroneous gene scores as nearby SNVs may be inherited on the same haplotypes as the non-synonymous SNVs used in the model. Ultimately, the contribution of other variants such as SNVs in non-coding regions and copy number variations should be evaluated in phenotypic prediction. Another potential improvement would be a refined approach to scoring genes. Rather than the binary “wild type” or “mutated” states used here, a revised quantitative score might use information about known or predicted severity of variants.2 values as did fully connected neural networks. In two previous studies though, we found that neural networks performed better than other machine learning approaches, even in one case when sophisticated feature engineering was performed [Testing different machine learning approaches could also be a fruitful avenue for future studies. Here we found that ontotype-based random forest models (mixed or otherwise) resulted in similar Rerformed ,51. Collerformed . As one erformed ,51. One In summary, our ontotype methods for decoding GWAS have revealed new biological insights into the underlying mechanism of a number of phenotypes in yeast. It is notable that a simple mutation counting approach, using either GO or a data-driven ontology, could be used to generate predictive models with mechanistic insights, especially as these models have been trained on far fewer features than a typical GWAS model. As ontologies such as GO are popular and widespread in other organisms, the methods described here could be readily applied to tackle clinical or agricultural traits.http://1002genomes.u-strasbg.fr/files/). The matrix used for the GWAS analysis (1011GWASMatrix.tar.gz) included files in BED (.bed), BIM (.bim), and FAM (.fam) formats. After piling up these formats by PLINK [SNV data is available from the 1002 Yeast Genomes Project website  [We next sought to assign labels to the terms in the data-derived ontology. We used the ontology alignment algorithm  present yu/ddot)  with an P < 0.05) using SNV P values from the original yeast GWAS [P values from a GWAS. For the Gowinda analysis, the following command was used: java -Xmx4g -jar <path-to-gowinda>/Gowinda.jar—snp-file total_snps.txt—candidate-snp-file cand_snps.txt—gene-set-file goassociations_cg.txt—annotation-file annotation.gtf—simulations 100000—min-significance 1—gene-definition gene—threads 8—output-file results_gene_gene.txt—mode gene—min-genes 1.GSA-SNP2  and Gowiast GWAS . The nethttps://github.com/PMBio/limix. For predictions using neural networks, sigmoid activation functions and 3 hidden layers were used. The number of nodes per hidden layer was as follows: 1000, 400 and 100. This method is part of the keras software package and is available at https://github.com/keras-team/keras. For 5-fold cross-validation, the original sample was randomly partitioned into 5 equally sized subsamples. Of the 5 subsamples, a single subsample was held out as the validation data for testing the regression model. The remaining 4 subsamples were used as training data. Then, the cross-validation process was repeated 5 times with each of the 5 subsamples used exactly once as the validation data. The R2 score for each model is the average R2 score across the 5 validation tests.Random forests from the R-source ranger package (version 0.11.2)  were useP values. Features with P < 0.01 across all 5 folds had a positive effect on predictive performance. The feature importance scores and P values for the well-predicted traits from both the random forest models and mixed random forest models are provided in P value < 0.01 and used this value to calculate an empirical FDR.The importance of any input feature – ontotype, gene or SNV – in random forest-based models was generated using permutation tests as implemented in the ranger R package. The distribution of the importance under the null hypothesis of no association to the response is created by 100 replications of permuting the response, growing a random forest and computing the variable importance . One is P < 0.01) from either gene-based or SNV-based models [GOstats (ver 2.48.0) and GSEABase (ver 1.44.0) were used to perform GO enrichment analysis on statistically important features , can be estimated by calculating the proportion of the variance explained by SNVs: h2  = σ2g / (σ2g+σ2e), where σ2g and σ2e are estimates of additive genetic and epistatic variances for the trait, respectively. These two values are estimated using the additive and epistatic relationship matrices calculated by the sommer R package [The proportion of phenotypic variance according to additive genetic variation, known as narrow-sense heritability (h package . These rz-score) used in this study have been provided in the figure legend or in the respective results or Materials and Methods section. Data are presented as points or boxplots. For all boxplots, the upper and lower hinges correspond to the first and third quartiles. The whiskers extend from the hinge to the largest and smallest values, no further than 1.5 x interquartile range from the hinge. Values beyond this range are shown as individual points. The center line indicates the median.Statistical analyses were performed using R version 3.5.1. Detailed information regarding statistical tests  Odds ratios (B) and (C) statistical significance based on Fisher’s exact test comparing the overlay between the top 30 most important features from mixed random forest models and random forest models. (D) Scatter plot of R2 scores across 36 phenotypes in each model. NN, neural net; Mixed RF, Mixed random forest. Five-fold cross-validation was examined using ontotypes as input features.(A) Scatter plot of R(EPS)Click here for additional data file.S2 Fig2 values from five-fold cross-validation are averaged. Three types of input features are compared: ontotypes (A), genes (B) and SNVs (C).Related to Figs (EPS)Click here for additional data file.S3 FigRelated to (EPS)Click here for additional data file.S4 FigRelated to (EPS)Click here for additional data file.S5 Fig2 score across 36 traits (y axis), shown for two alternative genetic analysis methods (x axis). R2 scores from five-fold validation were averaged. (B) Scatterplot showing R2 scores obtained for data-derived systems versus literature-derived systems in GO. Blue line is the regression line with 95% confidence intervals shown with the pale blue shadow. (C) R2 scores for individual phenotypic traits when using data-derived systems.Related to (EPS)Click here for additional data file.S6 FigRelated to Figs (EPS)Click here for additional data file.S7 FigRelated to Figs (EPS)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.


Zea mais L.), a key crop that provides nutrition values and a renewable source of bioenergy worldwide. Genome-wide association studies (GWAS) can be used to identify causative genetic variants that influence the major physiological measures of senescence, which is used by plants as a defense mechanism against abiotic and biotic stresses affecting its performance. We measured four physiological and two agronomic traits that affect senescence. Six hundred seventy-two recombinant inbred lines (RILs) were evaluated in two consecutive years. Thirty-six candidate genes were identified by genome-wide association study (GWAS), and 11 of them were supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport, and sink activity. We identified a candidate gene, Zm00001d043586, significantly associated with chlorophyll, and independently studied its transcription expression in an independent panel. Our results showed that Zm00001d043586 affects chlorophyl rate degradation, a key determinant of senescence, at late plant development stages. These results contribute to better understand the genetic relationship of the important trait senescence with physiology related parameters in maize and provide new putative molecular markers that can be used in marker assisted selection for line development.Senescence is an important trait in maize ( Zea mais L.  have been identified using traditional linkage mapping, multiparent advanced generation intercrosses (MAGIC) populations and association mapping in diverse panels. MAGIC’s advantage compared to biparental populations is the possibility of simultaneous studies of more than two alleles, usually eight alleles . TraditiMAGIC populations have been used in QTL identification analysis in a wide range of crops including rice , wheat , fava beArabidopsis, a gene that encodes a cysteine protease influenced by auxin, cytokinin, and sugars. SAG12 homologous in barley (Hordeum vulgare), the HvS40 gene, has been used as a molecular marker of age-mediated leaf senescence because its expression inversely correlates with the photosynthetic efficiency decline in barley  and two months later (_2): chlorophyll index (Chlo) calculates the total chlorophyll content of a leaf, minimum chlorophyll fluorescence (F0) measures the light re-emitted by leaf chlorophyll molecules in the absence of photosynthetic light, and maximum quantum yield of photosystem II (Fv/Fm) that is a measure of the intrinsic (or maximum) efficiency of PSII. The trait measurements were taken from three plants of each plot using a chlorophyll content meter  and chlorophyll fluorometer , respectively. The chlorophyll fluorescence was measured on leaves by the saturation pulse method, a method in which leaves are adapted in dark conditions for 20 min prior to the measurement. In addition, agronomic traits were evaluated: visual score  was recorded according to a subjective visual scale of 1–5 , grain yield (t.ha–1), and flowering in days to silking.In 2014 and 2015, a subset of the 672 RILs and the 8 founder inbred lines were evaluated at Pontevedra, Spain. The experiments were arranged in a modified augmented design with 16 blocks and 50 lines within each block, 42 RILs, and 8 testers. Each experimental plot was composed of 17 plants and a total of 2.45 mMinimal level of fluorescence, F0, indicates the minimum energy required to excite chlorophyll and reactor centers to activate electron transport via PSII . The risGenomic DNA was isolated from 672 RILs along with their 8 parental lines. Coleoptiles were collected and used for genomic DNA extraction using DNasy Plant Minikit  according to the manufacturer’s instructions. Quality and quantity of DNA was checked in a Nanodrop 2000 spectrophotometer  and on agarose gels. The complete set, progenitor, and the 672 RILs were genotyped by sequencing (GBS)  at the BAnalysis of variance were performed in a two-way approach in SAS software 9.4 [version 9.4 ], by environment and the two environments together to account for genotype by environment interaction. As detailed in the field experimental design section, RILs were grown in an augmented design. To calculate the variance attributable to each factor, we modeled each phenotype considering inbred lines as fixed effects, and environments and blocks as random effects. The model used wasgi is the fixed effect of the ith maize line, kβ is the random effect of kth block, tj is the randomized effect of the jth environment, gtij is the effect of the ijth genotype by environment, and ijkε are residuals. Models were fitted in SAS 9.4 [version 9.4 ] using mixed model procedure (PROC MIXED). Estimations of genotypic effects were recorded as the best linear unbiased estimator (BLUEs), and based on the combined analysis, heritabilities (h2) were estimated for each trait according to g) and phenotypic correlations (rp) were calculated as described in where μ is the overall mean, Genome-wide association analysis (GWAS) was performed in Tassel 5.2.40  followiny is the vector of the phenotypes (BLUEs of the RILs), β is a vector of fixed effects, including the SNP marker tested, γ is a vector of random additive effects (inbred lines), X and Z represent design matrices that relate and with β and γ, respectively, and e is a random residuals vector. The variances of the random lines were modeled as:where K is the matrix n × n of kinship coefficients, and where variance . Estimatvariance . To test2) was determined in the regions containing each of the SNPs significantly associated with each phenotypic trait. For each significant SNP, linkage blocks were established using the Haploview software (The linkage disequilibrium (LD) measure  using EdgeR package (version: 3.28.1)  . The gen 3.28.1) , inbred p < 0.05). All traits showed a decrease between silking stage (_1) and two months after silking (_2). For visual scores, senescence showed a 2.41-fold change decrease; for chlorophyll index (Chlo), a 5.25-fold decrease; for minimum fluorescence (F0), a 1.18-fold decrease; and for the PII quenching ratio, a decline of 2.15-fold  and 2 months (_2) after silking among RILs and founders (.15-fold .p < 0.05) for all traits. For visual assessment, founder lines had a score above 3 units at silking stage, whereas the scoring 2 months later was significantly lower at ∼1  had a large phenotypic variability in both scoring times. At silking evaluation, for visual scoring, inbreds showed a phenotypic range between 2 and 5; Chlo range was between 6.8 and 74.6; F87 units .0_1 had the highest value at 0.81 followed by Chlo_1 at 0.75 and Fv/Fm _1 that had a 0.58 value. Heritability values were slightly lower for these traits when measured 2 months after silking at 0.63, 0.49, and 0.42, for Chlo_2, Fv/Fm _2, and F0_2, respectively. However, visual aspect evaluated at silking point had the lowest heritability at 0.27, and increased substantially after 2 months with a 0.69 value for visual_2  showed positive relationships r > 0.62 within five out of eight pairwise comparisons. In addition, minimum chlorophyll fluorescence (F0_2) evaluated 2 months after silking was significantly correlated with six out of eight of the other traits compared, with correlation coefficients at r > 0.23 for the trait F0_1 to a correlation r at 1.00 for visual_1  using the RILs population previously genotyped utilizing a GBS technology and assessed individual phenotypes at both time point, silking stage (_1) and 2 months after silking (_2) stage, to identify loci associated with all the traits. Each candidate SNP associated to the trait was then studied in Haploview to account for LD and to identify the haplotype interval linked to the trait of interest.For visual senescence, GWAS analysis detected eight significant SNPs associated at silking stage on chromosome (chr) 2, chr3, chr4, and chr8 . The perp = 3.86E–6) was located at 201,538,092 bp explaining 5.3% of the phenotypic variation  explaining 5.4% of the phenotypic variance. Quantitative trait loci analysis of PSII quenching evaluated 2 months after silking detected two SNPs significantly associated, one SNP located on chr3 and another one on chr6  explaining 6.8% of the phenotypic variance. Fluorescence measured at 2 months after silking, we reported 20 putative markers significantly associated with this trait: 18 SNPs located on chr3, 1 marker on chr 5, and another 1 on chr8 (p = 2.02E–6) explaining 6.3% of the phenotypic variation (For fluorescence (F0), QTL analysis detected 43 SNPs significantly associated to chlorophyl fluorescence at silking stage evaluation. Among these putative QTL markers, 35 SNPs were found on chr1, 2 SNPs were detected on chr2, 3 SNPs on chr3, and 1 putative marker on chr5, ch6, and chr7, respectively . On aver on chr8 . On aver on chr8 . The QTLariation .v/Fm_2) , there were seven candidates on chr3. For chlorophyl, there were three candidate genes at silking stage (Chlo_1) located on chr1 and five candidate genes on chr3 that were associated with the trait measured 2 months after silking (Chlo_2). Finally, for PSII quenching, we observed two candidate genes associated with this trait at late plant maturity (Fv/Fm_2) .Arabidopsis homologous; this gene encodes for a protein involved in senescence regulation of the S40 family. To evaluate how plant aging affects the gene expression of Zm00001d043586, located on chromosome 3 at position 201,538,092 bp, we compared the transcript expression obtained from RNA-seq at different time points after silking on seven different genotypes. Time sequence samples from each genotype were measured at silking (d0), 15 days after silking (d15), 30 days after silking (d30), 45 days after silking (d45), 65 days after silking (d65), and 90 days after silking (d90) depending on senescence earliness as described in p = 1.91E–14). Mid-early phenotypes such as PHT10 started to be significantly different at d30 with a logFC at 4.45 (2.22E–03). Mid-late phenotype, PA8637, showed gene expression difference to silking time at d45 with a logFC 4.76 (p = 3.25E–02). Finally, late senescence phenotype, PHW79, had a significant gene expression at 60 days after silking (d60) with a logFC 6.45 (p = 1.33E–04). Second, we studied the transcription differences between pairwise time sequence for each genotype (The comparative genomics search resulted in a candidate gene associated with Chlo_2 (Zm00001d043586) that was annotated as an genotype . Remarkagenotype .0 measures the minimum chlorophyll fluorescence, and Fv/Fm that represents the maximum quantum yield of photosystem II. This strategy resulted in the detection of QTLs linked with novel markers, suggesting that physiological screenings should be further investigated in the future.This research aimed to understand the genetic regions influencing senescence traits and the temporal changes between two key phenotypic stages silking (_1) and 2 months after silking (_2). We studied senescence in key physiological traits associated with plant aging: Chlo, calculates the total chlorophyll content of a leaf, F0_2, Fv/Fm_2, and visual_2. For example, in maize, 0_1 and Fv/Fm_1, indicating that the light exposure at early stage and its efficiency may not be sufficient at early stages of flowering time. However, that was not the case at later development, 2 months after flowering, with a high positive correlation at 0.85, suggesting that the source-sink apparatus may be more efficient toward the grain filling.Visual phenotypic correlations were low, indicating that selections based on visual scores may not be effective. Pairwise genetic correlation between all traits identified high positive (≥0.60) correlations between Chlo_1, Fv/Fm_1, and visual_1, and chlo_2, F0_1 had the highest value at 0.81, followed by Chlo_1 and Chlo_2, at 0.75 and 0.62, respectively. This results are in agreement with those obtained by Heritability results showed that FOur research provided novel QTLs that have not been previously reported. These molecular markers were primarily found with a flint European origin, likely because it was the first time that senescence physiological traits were studied using this genetic background. We found QTLs associated with Chlo located on chromosomes 1, 3, 4, 5, 6, 7, 8, and 10, both at silking and 2 months after silking. Similarly, Arabidopsis, playing a role as a convergence point between jasmonic acid and auxin-mediated signaling during jasmonic acid-induced foliar senescence (We found two genes associated with chlorophyll index (Chlo), represented by the SNPs on chromosome 1 significantly associated with Chlo_1  located within the candidate gene Zm00001d034073 in the version 2 of the B73 genome and corresponding to the gene Zm00001eb057820 in the version 5. This gene is homologous to the WRKY DNA-BINDING PROTEIN 57 gene (AT1G69310) in nescence . AlthougArabidopsis AT1G29640.1, also known as AtS40-1 (Hordeum vulgare) produced by the nucleus gene HvS40 was associated with the degeneration of chloroplasts occurring during naturally occurred senescence  were located within the candidate gene Zm00001d043586. The homologous gene in  AtS40-1 , has bee AtS40-1  because  AtS40-1 . For exanescence . In our nescence . Howevernescence ; howevernescence . Thus, onescence . Our resnescence , and thinescence . Thus, wThis study provides a systematic phenological characterization of a maize MAGIC population on two temporal time points conditions. We were able to identify not previously detected QTL loci for all the traits studied related to the onset of senescence at flowering time and also at late developmental stage, 2 months later. By using a multifounder maize population, we were able to capture much of the genetic variation present in elite cultivars and in particular the flint European genetic background. Thus, fine-scaling genomic regions of the developmental and physiological patterns can be exploited for senescence line development. We showed that the Zm00001d043586 gene was significantly associated with the chlorophyll rate, and it was of particular importance at later plant development due to its rate of gene expression response. This research supports the use of MAGIC population for QTL mapping that can assist in pyramidal selection using complementary alleles for crop enhancement.https://www.ncbi.nlm.nih.gov/bioproject/, PRJNA746402.The datasets presented in this study can be found in online repositories. The name of the repository and accession number can be found below: National Center for Biotechnology Information (NCBI) BioProject, BO: conceptualization. MC and BO: data collection. EM, JJ, RM, MC, and BO: data analysis. EM and MC: writing original draft. EM, RM, BO, JJ, and MC: writing—review and editing. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Enrolled individuals were free from cardiovascular disease, diabetes and were not under lipid-lowering medication. Genome-wide genotyping was conducted using the Axiom_PMRA.r3 array, and imputation was performed using the TOPMED multi-ancestry sample panel as reference. Total PCSK9 plasma concentrations were determined using the Quantikine SPC900 ELISA kit. We observed two genome-wide significant loci and seven loci that reached the pre-defined value of p threshold of 1×10−6. Significant variants were near KCNA5 and KCNA1, and LINC00353. Genetic variation at the PCSK9 locus was able to explain approximately 4% of the overall interindividual variations in PCSK9 levels. Colocalization analysis using eQTL data suggested RWDD3, ATXN7L1, KCNA1, and FAM177A1 to be potential mediators of some of the observed associations. Our results suggest that PCSK9 levels may be modulated by trans genetic variation outside of the PCSK9 gene and this may have clinical implications. Understanding both environmental and genetic predictors of PCSK9 levels may help identify new targets for cardiovascular disease treatment and contribute to a better assessment of the benefits of long-term PCSK9 inhibition.Pharmacological inhibition of PCSK9 (proprotein convertase subtilisin/kexin type 9) is an established therapeutic option to treat hypercholesterolemia, and plasma PCSK9 levels have been implicated in cardiovascular disease incidence. A number of genetic variants within the PCSK9 gene locus have been shown to modulate PCSK9 levels, but these only explain a very small percentage of the overall PCSK9 interindividual variation. Here we present data on the genetic association structure between PCSK9 levels and genom-wide genetic variation in a healthy sample from the general population. We performed a genome-wide association study of plasma PCSK9 levels in a sample of Brazilian individuals enrolled in the Estudo Longitudinal de Saude do Adulto cohort ( PCSK9 have been shown to cause familial hypercholesterolemia and increased cardiovascular risk (Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key modulator of LDL receptor (LDLR) degradation and, consequently, LDL-cholesterol (LDL-C) serum levels. Gain-of-function mutations in lar risk . On the lar risk . Furtherlar risk . As a relar risk .Understanding the factors that modulate interindividual variability of PCSK9 plasma levels is important for the better understanding of individual responses to treatment as well as the identification of new targets for cardiovascular disease treatment. The use of unbiased genetic approaches has the potential to contribute to increase our understanding of the two.We conducted a genome-wide association study (GWAS) in healthy individuals from the general population aiming at the identification of genetic variation associated to plasma PCSK9 levels.The study sample belongs to the Estudo Longitudinal de Saude do Adulto, NCT02320461 (ELSA-Brasil). For the present analysis, we included 810 participants that have both PCSK9 plasma levels and genome-wide genotype information.The ELSA-Brasil study design and cohort profile have been published elsewhere . BrieflyParticipants enrolled in the Sao Paulo site  without diabetes , without cardiovascular, renal or hepatic diseases , and who did not report prescription of lipid-lowering agents, were eligible for a PCSK9 ancillary and exploratory study. From the 1,751 randomly selected participants fulfilling the inclusion criteria for subsequent PCSK9 plasma concentration measurements , we usedA 12-h fasting blood sample was drawn in the morning soon after arrival at the research clinic, following standardized procedures for sample collection and processing. A standardized 75g oral glucose tolerance test was performed in all participants without known diabetes utilizing an anhydrous glucose solution. For measurement of fasting and post-load glucose, we used the hexokinase method ; for fasting and post-load insulin, an immunoenzymatic assay, and for HbA1c, high-pressure liquid chromatography. Total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), and triglycerides (TG) were measured with enzymatic colorimetric assays (ADVIA Chemistry). LDL-C was calculated using the Friedewald equation. When TG were≥400mg/dl, LDL-C was measured directly with an enzymatic colorimetric assay (ADVIA Chemistry).Total PCSK9 plasma concentrations were determined using the Quantikine SPC900 ELISA kit . A total of 11,524,071 SNPs were used for genome-wide analysis, 11,289,274 for autosomal, and 234,797 for X-chromosomal analyses.Imputation was performed using the Haplotype Reference Consortium Michigan Imputation Server using the TOPMED reference haplotype panel as reference. After imputation data were exported in the standard PLINK format, downstream QC procedures and statistical analysis were conducted using the latest PLINKFor colocalization analysis, we defined a window spanning 500 Kb center at the most associated variant in all regions classified as having a suggestive association signal. Information on all variants within this region was used for colocalization testing. We used the LocusFocusPCSK9 levels were log-transformed for all analyses. Baseline categorical parameters are presented using frequencies (proportions), and continuous parameters are presented using mean±SD. Before GWAS, we adjusted a linear model for log (PCSK9) adjusting for age. The residuals of this model were used for GWAS as a continuous variable. Confounding effects for age, sex, smoking and BMI were later tested for all genome-wide and suggestive GWA hits.p<5×10−8. Associations with p<1×10−6 were considered as suggestive and presented as a list of top SNPs.Genome-wide association analyses were conducted using plink. We conducted two analyses – one without any further adjustment and one adjusting for the first four principal components. The threshold for genome-wide significance was set to k=3 and defined three different subgroups with higher European, African and Native-American ancestries. Meta-analysis used a fixed-effect model and was calculated using plink –meta-analysis routine.Due to the high level of admixture and complex genetic population structure present in the Brazilian population, we conducted two different sensitivity analyses taking into consideration self-referred race and a particular individual position in a PCA plot generated using the 2 first principal components. Briefly, for the self-referred race sensitivity analysis, association summary statistics were generated in each of three established subgroups: whites, blacks and browns (“pardos” in Portuguese). For the PCA-defined subgroup analysis, we used k-means clustering with Local association plots were created using LocusZoom . Local lMediation analysis was conducted for selected loci. To select mark for a genetic risk score for plasma PCSK9 levels, we determined independently associated variants at the PCSK9 genomic locus (cis-pQTL) through fitting a multiple linear regression model using 20 nominally associated markers at this locus and a stepwise variable selection procedure. Genetic risk score was derived as the sum of weighted genotypes by their final regression coefficients.p=0.0006), TG (p=0.003), and LDL-C . A regional association plot of the locus is shown in is KCNA5 and significant eQTLs have been observed in the region for AKAP3, DYRK4, KCNA5, KCNA1, NDUFA9, and GALTN8. The region has been described as associated with serum uric acid levels in a previous GWAS. All summary statistics from the main analysis can be found at The strongest associations with PCSK9 plasma levels were observed on chromosome 12p13.32, top lead SNP rs116367042 , chr1p21.3 (nearest gene RWDD3), chr1q32.1 (nearest gene MAPKAPK2), chr7q22.3 (nearest gene ATXN7L1), chr9q31.3 (nearest gene MUSK), chr14q13.2 (nearest gene KIAA0391), and chr18q12.2 (nearest gene KIAA1328).Using a pre-defined suggestive significance threshold of 1×10l 7 loci . Those lUSP24 gene. Interestingly, previous studies in Europeans, African and other admixed samples have also described stronger associations for total cholesterol and LDL levels at this same region.Previous GWAS and candidate-gene association studies have observed significant associations between PCSK9, LDL-C, and TC levels and genetic variants at the PCSK9 locus. Here we extend these observations using a multi-ethnic sample . Of noteLinkage disequilibrium of the PCSK9 locus was resolved in four main haplotype blocks . Fifty-sp=5.186e-08) was only able to explain 5.2% of the overall variation in PCSK9 plasma levels in our sample, genetic information being the variable with the highest effect size in our model.Using information from all 20 tagged markers and a stepwise regression approach, we were able to derive a PCSK9 instrumental variable made of four independently associated markers at the PCSK9 locus  precluded efforts in trying to understand the relevance of the described genetic associations in these variables. These aspects should be better defined in further studies.This study has some potential limitations. First and foremost, we have not been able to find a suitable replication sample for our GWAS results. The observed genome-wide significant loci still need to be replicated in an independent sample to be, in effect, taken as drivers of PCSK9 plasma levels . In additrans genetic variation outside of the PCSK9 gene. Understanding both environmental and genetic predictors of PCSK9 levels may help identify new targets for cardiovascular disease treatment and contribute to better assessment of the benefits of long-term PCSK9 inhibition.In conclusion, we describe new genome-wide significant loci associated with PCSK9 plasma levels in a sample from a healthy population. Our results suggest that PCSK9 levels may be modulated by All summary statistics from the main analysis are publicly available as Supplementary Material to this manuscript . The datThe studies involving human participants were reviewed and approved by Research Ethics Committees and by the National Research Ethics Committee (CONEP). The patients/participants provided their written informed consent to participate in this study.IB conceptualized and designed the study, researched data, and wrote the manuscript. KP, IL, RS, GL, MB, AG, IS, JM, and JK acquired data, assisted with the data analysis, and edited the manuscript. SR-M performed experiments and analysed and interpreted the results. PL and AP supervised the work, performed statistical analyses, and wrote the manuscript. All authors reviewed the manuscript.RS is recipient of a scholarship from Conselho Nacional de Pesquisa e Desenvolvimento Tecnológico, Brazil (CNPq) #303734/2018-3. MB is recipient of a scholarship from Conselho Nacional de Pesquisa e Desenvolvimento Tecnológico, Brazil (CNPq) #310255/2018-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The ELSA-Brasil baseline study was supported by the Brazilian Ministry of Health (Science and Technology Department) and the Brazilian Ministry of Science and Technology .RS has received honoraria related to consulting, research and/or speaker activities from: Abbott, Ache, Amgen, Astra Zeneca, Esperion, EMS, Kowa, Libbs, Novo-Nordisk, Merck, MSD, Pfizer, PTC pharmaceuticals and Sanofi/Regeneron. MB has received honoraria related to consulting, research and/or speaker activities from: Boston Scientific, EMS, GE HealthCare, Novo-Nordisk, and Sanofi/Regeneron.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The reviewer MR declared a past co-authorship with one of the authors RS to the handling editor.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.