Although evolution is known to tune physiological traits to the local environment, we lack knowledge of the specific links between molecular and evolutionary mechanisms that shape this process in nature. Here, we investigate the evolution of stomatal conductance and WUE in an Arabidopsis population that colonized an island with a montane cloud scrubland ecosystem characterized by seasonal drought and fog-based precipitation. We find that stomatal conductance increases and WUE decreases in the colonizing population relative to its closest outgroup population from temperate North Africa. Genome-wide association mapping reveals a polygenic basis of trait variation, with a substantial contribution from a nonsynonymous single-nucleotide polymorphism in MAP KINASE 12 (MPK12 G53R), which explains 35% of the phenotypic variance in WUE in the island population. We reconstruct the spatially explicit evolutionary history of MPK12 53R on the island and find that this allele increased in frequency in the population due to positive selection as Arabidopsis expanded into the harsher regions of the island. Overall, these findings show how adaptation shaped quantitative eco-physiological traits in a new precipitation regime defined by low rainfall and high humidity.Energy production and metabolism are intimately linked to ecological and environmental constraints across the tree of life. In plants, which depend on sunlight to produce energy, the link between primary metabolism and the environment is especially strong. By governing CO Matching physiological traits to the environment is crucial for survival and reproductive success across diverse life forms. Under directional selection, distributions of traits in a population are expected to shift toward their new optima due to differential fitness over evolutionary time , resultiIn annual plants, flowering later can provide more time for the accumulation of resources, resulting in a potential fitness benefit . HoweverWhile precipitation is often considered to be synonymous with rainfall, in many regions, plants rely heavily on “horizontal” precipitation in the form of clouds or fog. These include the Lomas of Peru, fog deserts of Namibia, coastal western North American redwood forests and scrublands, and the seasonal montane cloud forests and scrublands of tropical Africa, Australia, and South America . Such ecArabidopsis thaliana is the major molecular model plant as well as an important eco-evolutionary model  north-facing slopes, where vegetation is bathed in moisture derived from humid trade winds  in the A and B, A. thaliana sites  and stomatal conductance (gas-exchange capacity) in “well-watered” (WW) and moderate water-deficit (WD) conditions in 152 lines from the CVI of Santo Antão and 24 representative Moroccan outgroup lines , and WUE was reduced in the Santo Antão population relative to the Moroccan outgroup population in both conditions . As expected, WUE was strongly negatively correlated with stomatal conductance across Santo Antão lines  population than in the Moroccan population in both watering conditions , which is commonly referred to as “chip heritability” . The estA, supplementary fig. S5A, supplementary fig. S6A, supplementary fig. S7A, R2 = 0.12, P < 2.2 × 10−16 for the average traits, R2 = 0.28, P < 2.2 × 10−16 for the drought response of traits, R2 = 0.16, P < 2.2 × 10−16 for WW, and R2 = 0.55, P < 2.2 × 10−16 for WD) between both traits across Santo Antão lines. Overall, we found that the genetic architecture was moderately complex for WUE and stomatal conductance and that a significant fraction of the genetic basis for the traits is shared between traits based on their genetic correlations.Next, we investigated the genetic architecture of the traits using a Bayesian sparse linear-mixed model (LMM) that allows for a mixture of large and infinitesimal genetic effects . We founfig. 313C measurements, we detected a single Bonferroni significant peak on the end of chromosome 2 (B) as well as in WW and WD conditions  gene, which was previously implicated in WUE in Cvi-0×Ler-0 RIL and NIL mapping populations (MPK12 region in the drought response of WUE (B). This variant explained 35% of the variation in the average WUE, 10% for drought response, 33% for WW, and 29% for WD in the Santo Antão population.To identify specific loci underlying variation in the average traits, drought response of traits, and both conditions, we used a LMM approach that controls for population structure by including a relatedness matrix in the model . For themosome 2 B as wellulations . Furthere of WUE B. This vMPK12 across the genome in the drought response of WUE. One of the highest peaks on chromosome 5  contains PBL27 (AT5G18610), which encodes a receptor-like cytoplasmic kinase that is required to phosphorylate the SLOW ANION CHANNEL-ASSOCIATED HOMOLOG 3 (SLAH3) for antifungal immunity and chitin-induced stomatal closure. It has been shown that this signal transduction is independent of ABA-induced SLAH3 activation , a cofactor for multiple plant enzymes: abscisic acid (ABA), auxin, and nitrate  K232X variant, a major determinant of flowering time in A. thaliana  gene, which catalyzes the efflux of calcium from the cytoplasm. The cuticle mutant eca2 that has an altered phenotype in cutin and wax showed a plant defense response to different biotic stresses, including biotrophic and necrotrophic pathogens and herbivory insects , CNX1 (8–2%), and WRKY57 (4–2%), unchanged for ECA2 (6%), and the PVE increased for FRI (4–6%) with MPK12 G53R as a covariate. Overall, these results support a moderately polygenic architecture for the drought response of WUE.We identified several potentially interesting associations in addition to tivation . Another nitrate . Anotherased ABA . A peak thaliana . Lovell  insects . Since sB, supplementary fig. S7B, MPK12 region. Here, the proportion of the genetic variance explained by MPK12 G53R was 10% in the average stomatal conductance, 7% in the WW condition, and 12% in the WD condition. Plants from the natural population carrying the derived MPK12 53R allele had lower WUE and higher stomatal conductance than those carrying the ancestral G53 allele  and neighbor-joining tree using LD-pruned genome-wide SNP variation . The third PC further distinguishes lines within Espongeiro. Conversely, the two geographically separated subpopulations, Pico and Espongeiro, appear to be closely related despite their large geographic distance, suggesting recent spread and ongoing migration. These results are consistent with subpopulation split times inferred previously  and 2.8 kya . Overall, our results are consistent with a model where the strong effect MPK12 53R variant arose early relative to other variants that impact WUE.We next investigated the evolutionary history of the WUE trait in the Santo Antão natural population. We estimated the ages of loci associated with average WUE and found that the derived MPK12 G53R variant segregates at intermediate frequency (43%) in Santo Antão and exhibits structure across subpopulations  and lower in the moister eastern Pico region (29%).The t ∼3 kya . The comMPK12 53R variant across the island, we examined the genealogical relationships between populations and individuals for the genomic region linked to this variant. The maximum likelihood topology for the 50-SNP window centered on the MPK12 locus matched the major genome-wide topology . The deepest branches of the derived MPK12 53R haplotype are found in the Ribeira and Espongeiro subpopulations, suggesting this allele first arose and rose to high frequency there. Clustering of individuals within the genealogical tree and the frequency distribution across the island suggest that MPK12 53R spread through multiple migrants into the Pico subpopulation in the past few hundred years (D). However, the MPK12 53R allele frequency has remained low in the moister Pico region. The allele frequency difference across populations suggested that MPK12 53R may be favored in the warmer, more exposed Ribeira/Espongeiro region, where rapid growth to escape drought would be most important.To better understand the origin and historical spread of the ed years D. HoweveMPK12 53R allele was adaptive in Santo Antão. When an allele is driven quickly to high frequency in a population due to a partial selective sweep, a signature of an extended haplotype with reduced linked variation is expected . To determine whether this locus is an outlier for haplotype homozygosity relative to the genome as a whole, we calculated the integrated haplotype score .We next asked whether there was evidence the expected . Consistexpected  for the 3 allele A and B. ore iHS;  across tiHS results. Since the overall genetic variation in Santo Antão is low, the number of genes with iHS signals is also limited. Therefore, we did not expect to have high power in a GO enrichment analysis. Still, we found a marginally significant enrichment for several biological processes. GO analysis revealed enrichment in genes regulating stomatal closure, abscission, osmotic stress, salicylic acid–mediated signaling, transcription elongation from RNA polymerase II promoter, regulation of DNA-templated transcription elongation, transition to flowering, auxin-activated signaling pathway, cellular response to an organic substance, and signal transduction . Enrichment of the transition to the flowering category was also driven by three genes: CLAVATA 2 (CLV2), EMBRYONIC FLOWER 1 (EMF1), and ARF2. Overall, these results suggest that both flowering time and water balance may have been important selection pressures in the CVI population.We next used gene ontology (GO) enrichment to assess evidence of selection on traits based on the 10[P-value] = 1.52; supplementary fig. S13A, 10[P-value] = 1.37; supplementary fig. S13B, fig. 5C, MPK12 53R against the previously inferred trajectory of historical population size (eN) using whole-genome trees . RDA links genomic variation to environmental predictors while accounting for geographic population structure by including geographic distance as a model covariate. We found a significant association between climate and genomic variation overall  and applied a stepwise model-building algorithm (ordistep) to determine which bioclimatic variables  from plants we propagated in a growth chamber set to simulate humidity, air and soil temperature, soil chemistry and precipitation, photoperiod, and light availability of an Espongeiro site in Santo Antão  (MPK12 53R variant increases fitness independently from FRI 232X under CVI (Espongeiro) conditions.Finally, we asked whether the population genetic evidence we found for positive selection translated to a reproductive advantage in an experimental setting. To determine whether to Antão . We obse0.00519)  online, A. thaliana population that colonized a novel precipitation regime. We found that average stomatal conductance increased and WUE decreased in the humid CVI population relative to the North African outgroup. We found that trait architecture was polygenic, with an important contribution from a nonsynonymous variant (G53R) in mitogen-activated protein kinase 12 (MPK12), which explained 35% of the trait variance in WUE in the Santo Antão island population. We found evidence that the derived MPK12 53R variant is evolving under positive selection based on its association with temperature across the island . Finally, we found that the derived MPK12 53R variant conferred higher fitness than the ancestral MPK12 G53 variant in plants grown in CVI conditions , a key phytohormone involved in abiotic stress responses , in particular in those that carry the derived  limited . Further limited , 2011. TMPK12 explained a substantial fraction of the trait variation. Understanding how adaptation has occurred in specific cases can inform models and predictions of how populations might generally adapt to novel environments. Although we have information about functional loci and variants from QTL mapping studies in a range of species, it has only rarely been possible to connect results from QTL studies back to the ecology of the relevant natural population. This study serves as an example of how it was possible to reconstruct the evolutionary history of a functional variant as it arose and spread across the landscape. Further, studies such as this one can inform models that aim to predict how species adapt as the environment changes or expand their ranges into more severe climates.Although the traits studied here are polygenic, our findings revealed that one variant in https://github.com/HancockLab/CVI) for SNP discovery and variant calling. The variant call format (VCF) file  as previously described (n = 152) and Moroccan (n = 24) A. thaliana lines (2 s light intensity). For each trait, two independent replicate experiments were performed. In each experiment, two replicates per genotype and two watering conditions were used. The first was a WW condition in which pots were provided with 60% of the maximum soil water content  not limiting for vegetative rosette growth. The second condition was a “water-deficit condition” (WD) in which pots were provided with 25% SWC (1.4 g H2O/g dry soil). Plants were propagated on peat moss plugs, then selected for homogeneous germination and transferred onto the Phenoscope table 8 days later, that is, 8 days after sowing (DAS). On the Phenoscope, SWC reached 60% for control-treated plants at 12 DAS and 25% for moderate-drought-treated plants at 16 DAS. At 32 DAS, the whole rosette of two replicates for each genotype per treatment was collected, ground, and analyzed for carbon isotope discrimination (δ13C) as an estimate of WUE. Isotope discrimination analysis was conducted at the CEPLAS Plant Metabolism and Metabolomics Laboratory, Heinrich Heine University Düsseldorf (HHU) as described previously . According to the manual guide, the Porometer device was calibrated before measurements with a 100% humidity filter paper as a reference. It was challenging to measure the rosette leaves directly due to their reduced size and the small area of the SC-1 Porometer leaf clamp. Therefore, a fully developed leaf per line and per treatment of each genotype was examined immediately after detachment. The measurements were performed across several days (29–32 DAS) around mid-day.wilcox.test in the stat_compare_means function . To correct for over-dispersion of the seed number, we used a negative binomial GLM using the “glm.nb” function in the “MASS” v.7.3-54 package in R.We obtained the individual data for the total seed number (as a proxy of fitness) from . Since nWe estimated heritability for traits in this study based on the proportion of the phenotypic variance explained by all genotyped SNPs, which is commonly referred to as “chip heritability” . To perfAccording to N is the sample size.where To infer the genetic architecture of the traits, we used a polygenic GWA Bayesian sparse linear-mixed model (BSLMM) implemented in GEMMA , which mcor.test function. We evaluated the significance of correlations with the t-test implemented in the cor.test function.We then investigated the genetic correlations between traits using the multivariate model in GEMMA . Accordi2r) >0.1 across windows of 50 Kb with a step size of 10 bp. Then, we removed variants with missing data by setting the parameter −geno to 0.In a preprocessing step before population structure analysis, we used PLINK v1.9 to prune our SNP sets for linkage disequilibrium by removing any variables with correlation coefficients , and we obtained the maximum likelihood trees in sliding windows of 50 SNPs using the script “phyml_sliding_windows.py” . Then, we ran Twisst on the complete set of inferred trees for the five Santo Antão subpopulations  to calculate the exact weighting of each local window. We used the Cova de Paúl subpopulation as an outgroup in this step. To plot the topologies, we used R v3.3.4 . We used bcftools v1.9 . For the recombination map, we corrected a published map based on crosses , we used the script TreeViewSample.sh, with 10*N steps (N is the number of haplotypes) and 1,000 burn-in iterations.We used RELATE v1.1.4  to inferols v1.9  to filteved from  for the  crosses  for the  crosses . For coa crosses  for the https://worldclim.org/data/worldclim21.html), at a resolution of 30 s (∼1 km2). We also obtained site-specific data for the accumulated rainfall amount during the growing season and aridity index from CHELSA , in which X represents a set of environmental variables and Y represents a dependent matrix of genotypic data. It links genomic variation to environmental predictors while accounting for geographic population structure by including geographic distance as a model covariate. Genotype data from a set of genome-wide LD-pruned SNPs  and environmental data (ordistep” function in the R package “vegan” v. 2.5-7 (Radj2) and removes the nonsignificant variables one at a time using permutation tests.We used the RDA approach implemented in the R package “v. 2.5-7  to investal data  online wv. 2.5-7  with 1,0EHH  and pXP-EHH = −log10(1–2|Φ[XP-EHH]-0.5|), where Φ(x) represents the Gaussian cumulative distribution function. We used the default parameters for all analyses.To detect signatures of positive selection in the Santo Antão population, we used three haplotype-based methods: the EHH , the intEHH iHS; , and croon 2.0.2  in R. FoiHS scores, we conducted GO enrichment analysis. For this, we used the top 1% of SNP variants  identified through the genome-wide iHS scores across the genomes of the Santo Antão population. Gene names were extracted based on the SNP position using the TAIR10 GFF3 gene annotation file through SNPEff  we used CLUES (To infer a selection coefficient based on the reconstructed historical frequency trajectory for the derived ed CLUES . CLUES ued CLUES  online.msad031_Supplementary_DataClick here for additional data file.


Genetic improvement for nitrogen use efficiency (NUE) can play a very crucial role in sustainable agriculture. Root traits have hardly been explored in major wheat breeding programs, more so in spring germplasm, largely because of the difficulty in their scoring. A total of 175 advanced/improved Indian spring wheat genotypes were screened for root traits and nitrogen uptake and nitrogen utilization at varying nitrogen levels in hydroponic conditions to dissect the complex NUE trait into its component traits and to study the extent of variability that exists for those traits in Indian germplasm. Analysis of genetic variance showed a considerable amount of genetic variability for nitrogen uptake efficiency (NUpE), nitrogen utilization efficiency (NUtE), and most of the root and shoot traits. Improved spring wheat breeding lines were found to have very large variability for maximum root length (MRL) and root dry weights (RDW) with strong genetic advance. In contrast to high nitrogen (HN), a low nitrogen (LN) environment was more effective in differentiating wheat genotypes for NUE and its component traits. Shoot dry weight (SDW), RDW, MRL, and NUpE were found to have a strong association with NUE. Further study revealed the role of root surface area (RSA) and total root length (TRL) in RDW formation as well as in nitrogen uptake and therefore can be targeted for selection to further the genetic gain for grain yield under high input or sustainable agriculture under limited inputs. NUE is the product of N uptake efficiency (NUpE) and N utilization efficiency (NUtE). Theoretically, NUE can be improved by increasing either NUtE or NUpE, or both. A NUE of around 33% has been estimated for cereal grain crops around the globe [iculture  , maximum root length (MRL), root dry weight (RDW), shoot dry weight (SDW) and shoot nitrogen (N) percent were collected from8-week-old seedlings. Furthermore, the root–shoot ratio (R:S) was computed by dividing the root dry weight (RDW) by the shoot dry weight (SDW); and the total dry weight (TDW) was calculated by adding SDW and RDW. The NUE and its component traits  and NUtE (nitrogen utilization efficiency)) were calculated as per the procedure described in ,16 Moll Based on the mean values for different traits, a selection of 19 genotypes  was madeThe plants were kept in hydroponic conditions for up to four weeks to collect comprehensive data on the root parameters in the selected genotypes. Roots of more than 4-week-old seedlings generally become intermingled in hydroponics systems, making it difficult to access the exact data on root traits. Except for the maximum root length (MRL), all other root traits such as total root length (TRL), root surface area (RSA), root volume (RV), average root diameter (AD), and the number of tips (N tips) were calculated by scanning the roots with a root scanner and a winRHIZO pro image analyzer  for each plant . MRL is 2), and genetic advance (GA) as well as path analyses. The same program was used to conduct cluster analysis using Euclidean distance and average linkage. R program was used to determine the Karl Pearson correlation coefficient and principal component analysis. MS-Excel 2007 was used to create the graphical chart shown.The analysis of variance (ANOVA) along with interaction effects for 175 genotypes under HN and LN was carried out for the hydroponic experiment with GenStat release 14.1 software. Statistical software IndoStat version 9.2 was used to calculate various variability parameters viz., the phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), heritability (HGenetic variability for traits influencing NUE under hydroponic conditions.p < 0.001) main effects due to genotypes in both high and low nitrogen environments for all of the traits studied (p < 0.001) for all of the traits studied. The variability parameter viz., mean performance, range, the genotypic coefficient of variation, phenotypic coefficient of variation, broad-sense heritability, genetic advance, and genetic advance percentage of the mean for various ten characters were analyzed and are presented in The analysis of variance showed significant  had the highest RSA and PBW 621-50 (20 cm2) had the lowest, whereas, under LN, HD2824/VL796 (153 cm2) had the highest and PBW343/PICCI LOCAL/RL6080 had the lowest (30 cm2). Furthermore, as shown in The experiment was conducted twice, and the results are presented as a pooled mean and range for different root traits .The overunder LN . This trCorrelation analysis was used to determine the relationship between and among different root and shoot traits. RDW exhibited a positive and significant correlation with RSA (0.603), RV (0.519), TRL (0.741), and SDW (0.637) in subset genotypes under HN . Though Under LN, RDW had a positive and significant correlation with RSA (0.526), RV (0.480), AD (0.457), TRL (0.549), and SDW (0.514) . SimilarThe direct and indirect effects of various traits on RDW under HN and LN were analyzed and presented in Under HN, TRL (1.722), followed by RSA (0.642), had the maximum direct effect on RDW. Other component traits contributed largely through an indirect route. RV made a significant contribution to RDW mostly through RSA and TRL. Similarly, under LN, TRL (0.515) and SDW (0.472) both contributed directly to RDW. Other factors had a negligible positive relationship with RDW.The correlation coefficient analysis in the subset genotypes  showed tTo find the correlation between the hydroponic and soil data, an experiment with a PVC pipe filled with soil was conducted . The resThe use of nitrogenous fertilizer has increased at a much faster rate than grain yield in most cereal crops. The increasing concern about environmental pollution caused by unused N fertilizer has forced global policy planners to incentivize the development of nitrogen use efficient varieties. The existence of genetic variability for NUE and its subsequent exploitation, even in major crops such as wheat, for the development of N-efficient varieties has been largely ignored because of the cheaper cost and abundant supply of N sources. NUE is a complex trait dependent upon a number of component traits such as nitrogen-capturing root traits, optimum portioning of captured N in different organs, and efficient utilization for higher yield realization. Thus, genetic improvement for NUE requires thorough study and an understanding of the various contributing trait.The analysis of variance showed significant differences among the genotypes, and environments with a strong genotype X environment interaction for all of the traits under study. Since the material under study comprises the advance breeding lines of a breeding program mainly focused on yield enhancement and better adaptation, along with some released varieties with no history of direct selection for N capturing root traits or other physiological processes relevant for N utilization, an abundance of variability for various traits related to NUE in the material is natural. However, the indirect effect of selection for better physiology for grain yield cannot be ruled out as N is an important element involved in every path relevant for yield formation. A strong genotype X environment (high and low N) interaction suggests a separate breeding program for each environment. The existence of sufficient genetic variation for traits relevant to NUEis also reported in several other cereal crops ,23,24,255 and F6) for most of the root traits with sufficiently high heritability under hydroponics can be effectively integrated with most wheat breeding programs. High heritability for various traits relevant to NUE has also earlier been reported in maize by [The large range for the majority of traits under study provides enough scope for genetic manipulation. The effectiveness of selection for difficult-to-score root traits and a derivative trait such as NUE can be improved through hydroponic screening as it minimizes the environmental impact, strongly indicated by the narrow difference between the GCV and PCV values in the presented study. Our results corroborate earlier findings ,27,28,29maize by  Presterlmaize by  Laperchemaize by  Sathish maize by  Naveen amaize by     accommodcumented  (Deng etcumented  (Palta, cumented  (HawkesfVrn1. Studies in rice [In the present investigation, we found a significant positive correlation of NUE with NUpE, RDW, PH, and MRL under both HN and LN environments. Furthermore, the association between NUE and NUpE was much stronger under the HN than LN conditions. Time series analysis of released varieties in Mexico  (Ortiz-M in rice  (Muurine in rice  (Sinebo Larger and deep-rooted systems generally have better foraging capabilities, even from the deeper soil profile, and thereby the root: shoot ratio has always been considered a criterion for improving drought and nutrient use efficiency  (SrividhAs root biomass was found to play an important role in N foraging and uptake, the important question before us was to find the root traits responsible for higher root biomass. Since these root traits are very difficult to score, even under hydroponic conditions, we selected a subset of genotypes based on their root biomass. Moreover, these traits are highly influenced by variable environments imposed by heterogeneous soil  (Lynch, The germplasm under study shows the existence of sufficient genetic variability for the shoot and root traits relevant to NUE.A low nitrogen environment is more effective in discriminating the genotypes for root traits compared to a HN environment.RDW was found to be highly associated with better NUE under both nitrogen-rich and nitrogen-poor environments.Under N poor environment, the maximum root length can be directly selected to improve the NUE whereas N uptake can be improved by directly selecting the root biomass under an N-rich environment.Selection for root biomass is not in conflict with above-ground biomass, which is highly relevant for higher yield realization in the future.


Traditional genome-wide association study focuses on testing one-to-one relationship between genetic variants and complex human diseases or traits. While its success in the past decade, this one-to-one paradigm lacks efficiency because it does not utilize the information of intrinsic genetic structure and pleiotropic effects. Due to privacy reasons, only summary statistics of current genome-wide association study data are publicly available. Existing summary statistics-based association tests do not consider covariates for regression model, while adjusting for covariates including population stratification factors is a routine issue.In this work, we first derive the correlation coefficients between summary Wald statistics obtained from linear regression model with covariates. Then, a new test is proposed by integrating three-level information including the intrinsic genetic structure, pleiotropy, and the potential information combinations. Extensive simulations demonstrate that the proposed test outperforms three other existing methods under most of the considered scenarios. Real data analysis of polyunsaturated fatty acids further shows that the proposed test can identify more genes than the compared existing methods.https://github.com/bschilder/ThreeWayTest.Code is available at  Genome-wide association study (GWAS) has become a powerful tool for analyzing the association between genetic variants and complex human diseases and traits, such as hypertension , type 2 Traditionally, GWAS tests the “one-to-one” association between phenotypes and SNPs. That is, it performs univariate regression analysis on one trait and one SNP while adapting the nominal n status . Recent n status . For exan status , ankylosn status , and pson status . Second,n status . Third, n status , C-alphan status , ACAT  can be calculated asthe P-values, The proposed test is a combination of three A parameter bootstrap procedure:Step 1. Estimate the correlation coefficient matrix of Step 2. Generate B independent and identical observations from ith observation with Step 3. For Step 4. Use the cumulants estimation proposed by 2 distribution 2 distribution Step 5. Calculate the P-values 2 test. Other P-values can be generated by first calculating Step 4., we can calculate the P-values bywhere Step 6. We conduct simulation study to investigate the performances of the proposed TWT by comparing its type I error rates and powers with three other methods including metaCCA , MGAS V, and MATq rows and m columns, where elements of jth row and kth column are m, q, and m and q, we consider two scenarios of M Wald test statistics M Wald test statistics and calculate the empirical type I error rates and empirical powers based on Generally speaking, we produce individual data and calculate Wald test statistics fication . Then usP-value methods, which generates powerful results when there is only one non-zero element. However, the power of MGAS decreases quickly with the increment of the correlation coefficient between phenotypes. Under scenario From the figures, we can conclude that our method either produces the most powerful results or has power similar to that of the most powerful method while all other three methods lost power drastically under some scenarios. TWT generates powerful results when the pleiotropic effect exist, i.e. the non-zero elements in https://grasp.nhlbi.nih.gov/.We analyzed the summary statistics data from the polyunsaturated fatty acids study conducted by The original data were collected based on SNPs and 495 343 SNPs were included. To perform the multiple-to-multiple test, these SNPs were located in different genes with P-values >.05 (Population stratification was previously adjusted for in the calculation of original summary statistics, Thus, the calculation of correlation matrix between Wald test statistics belongs to case two as described in our correlation estimation section. Following the estimation method mentioned in correlation estimation section, we use 1000 Genomes Project data to extract top two principal coordinates with selected SNPs used to calculate population stratification  and usedues >.05 .P-values <Bonferroni correction was used to evaluate the significance. Since 12 984 genes were included, P-values of these 12 genes can be found in P-values smaller than P-values are replaced as P-values.From the figure, we can see that 12 genes are only detected significantly by our proposed method TWT. P-values of this gene for metaCCA, MGAS, MAT, and TWT are To further verify our findings, we point out gene PPP5C. The ty acids  and is aty acids . Other gty acids  and typety acids , which aHere, we mainly used proximity to annotate SNPs with target genes. However, linking SNPs with their closest genes may not be the best way in some situations, since regulatory SNPs do not necessarily regulate the closest genes . Thus, mAfter the data pre-processing procedure similar as mentioned above, 14 477 genes were included for further analysis. Thus, the significance level was set to ty acids .Summary statistics have become a popular method of data sharing in GWAS since individual level data are not available due to privacy and legal restriction. Although one-to-one analysis has been successful in the past decade, there is still missing heritability of complex diseases . TherefoM summary Wald test statistics, and providing a procedure to estimate the correlation coefficient between two summary Wald test statistics while adjusting for covariates. Since summary statistics are calculated and collected under different studies, the calculation of these summary statistics may also be different from study to study. Thus, a generalized estimation method for the correlation coefficient needs to be derived. We also considered other situations of summary statistics, which are commonly encountered in GWAS data. Meta-analysis summary statistics are commonly used when multiple cohorts exist in studies (To mine the information hidden in the summary data, we proposed a new multiple-to-multiple test statistic called TWT by integrating the intrinsic genetic structure, pleiotropy, and potential information combinations. Besides this test, this work also makes two other useful contributions including the derivation of multivariate normal distribution for the vectorized form of  studies . From Suq and m are both large, we point out that it might take a long time to run with existing methods including TWT. The most time-consuming step is the calculation of null distribution of 2 distribution to approximate the null distribution of P-values arbitrarily small that satisfy GWAS stringent significance level. However, we still need to generate random samples to estimate the parameter of generalized χ2 distribution. Although using smaller sample size to conduct estimation of the parameters will save much time, to ensure the accuracy of our final P-values, we use large sample size. Large sample generation and calculation is the main reason that our proposed methods were computationally slow under high dimension situations. Thus, it is valuable to build test statistics specially designed for large q and m, such as the sliding windows strategy. Also, inflated type I error was observed in some scenarios, such as 2 distribution to approximate P-values without complex bootstrap procedures, it may have inflated type I error rate because of the approximation error. Generally speaking, the inflation is related to the correlation structure of the P-values and significance level (P-values P-values are smallest, we can determine which effect is most likely to exist. For example, we observe that P-values, this indicates that multiple SNPs may associate with one particular phenotype. To find this phenotype, we can further decompose P-values each representing one phenotype, we can further find which P-value is the smallest, then, we can identify which phenotype is most likely to have association with multiple SNPs. Further studies are envisioned to investigate these areas.Unlike other methods directly generalized traditional method, TWT is specially designed for multiple-to-multiple situations, which can fully use the information that is ignored by other methods. Simulations also show that our proposed method outperforms other methods when the dimensions of traits and variants are high and the signals are sparse, which are commonly encountered in GWAS data. Multidimensional analysis has recently become popular not only in the area of GWAS but also in gene expression data. The corresponding study is called gene set enrichment analysis . This ince level . Thus, ice level  and becoce level . Althougbtad182_Supplementary_DataClick here for additional data file.


Vitis spp., rigorous screening of genotypes among the produced polyploids to identify those exhibiting desired traits remains a major bottleneck. In this perspective, we propose the integration of the genomic selection approach with omics data to predict genotypes with desirable traits among the vast unique individuals generated through polyploidization. This integrated approach can be a powerful tool for accelerating the breeding of grapevines to develop novel and improved grapevine varieties.Grapevines are economically important woody perennial crops widely cultivated for their fruits that are used for making wine, grape juice, raisins, and table grapes. However, grapevine production is constantly facing challenges due to climate change and the prevalence of pests and diseases, causing yield reduction, lower fruit quality, and financial losses. To ease the burden, continuous crop improvement to develop superior grape genotypes with desirable traits is imperative. Polyploidization has emerged as a promising tool to generate genotypes with novel genetic combinations that can confer desirable traits such as enhanced organ size, improved fruit quality, and increased resistance to both biotic and abiotic stresses. While previous studies have shown high polyploid induction rates in  These are extensively cultivated for their fruits, which are used in wine production, along with for grape juice, raisins, and table grapes. The wine industry has a substantial influence on the global economy. Additionally, grapes also contain beneficial compounds, such as resveratrol and flavonoids, which have been shown to have antioxidant, anti-inflammatory, and anti-cancer properties and help prevent chronic diseases  of the produced polyploids can be an interesting option, while predicting the desirable genotypes following the artificial polyploidization. In general, GS involves using genomic information to predict the breeding value of plants and selecting the best individuals with desired traits of interest for further breeding . In the 2Vitis through various methods and anti-mitotic agents. In vivo methods, which involve treating the entire plant or a part of a plant, have been attempted for polyploidization in Vitis species, but have remained less effective compared to in vitro methods. For example, in a study by 2O to cuttings of 41B Chasselas and Fercal (Vitis vinifera L.) rootstocks did not result in the production of any polyploid individuals through in vivo methods. 2O to treat a total of 1200 plants belonging to two grapevine cultivars, yielding only one tetraploid genotype for each cultivar. These results suggest that in vivo methods for polyploidization in Vitis species are not an effective approach in obtaining high frequency polyploids.Polyploidization has been used as a tool for crop improvement for many years. To date, this technique has been successfully used in many species to obtain traits such as increased fruit size, enhanced disease resistance, and tolerance to a variety of stresses . Recent in vitro methods offers a promising avenue for inducing polyploids in many Vitis species. in vitro treatment of colchicine at a concentration of 0.2% was most effective for producing tetraploid plantlets in Vitis, with a tetraploid rate of 25%. Vitis through colchicine treatment, with a polyploid induction rate of 37.78%, when pre-embryogenic calli were used. Additionally, Vitis. Although, oryzalin was found to be more effective than colchicine in polyploid induction efficiency in Vitis spp., ranging between 1.66-10.5% compared to 3.2-5%  has emerged . MAS offAs compared to MAS, a more promising approach for polyploid screening generated from polyploidization could be GS, which uses genomic information to predict the performance of plants, enabling breeders to select desirable traits more efficiently and accurately . GS has 3In general, GS includes all the genomics-driven strategies to select the best individuals from a testing population (TE) for breeding. The TE and the training populations (TR) are the key components of any genomic selection process. While TE refers to individuals with only genomic data, TR includes the group of individuals for whom both genomic and phenotypic data are available. In the current context, the individuals generated via polyploidization following genotyping will serve as the TE. Previously, traditional techniques such as PCR-based techniques and single nucleotide polymorphism (SNP) genotyping arrays have been used . HoweverOmics data such as genomics, transcriptomics, and metabolomics can provide valuable as well as novel insights on how to improve the precision of genomic relationship estimation in polyploids. Since the availability of the grapevine genome , omics-based studies have been extensively used to study polyploidy and heterozygosity in grapevines . For exaVitis vinifera training cultivars and assessed a total of 127 traits. Additionally, they also combined several other traits, making a total of 152. Despite using an extensive dataset, the study achieved high prediction accuracy for 50% of the response variables. Once phenotypic and genotypic data have been obtained from the TR population, they can be employed to construct prediction models, using phenotype as the response and genotype as the predictor. To date, several parametric models such as genomic best linear unbiased prediction (GBLUP), Bayesian regression-based methods (like BayesA), sparse linear mixed model methods (like BayesB), and Bayesian least absolute shrinkage and selection operator (BLASSO) methods (like BayesC) have been developed for GS. These models address different challenges and offer unique advantages. For a comprehensive understanding of these statistical models, one can refer to in vitro polyploidization.The genotyping of the TR population is followed by its phenotyping. To ensure accurate phenotyping, it’s important to carefully design experiments and select appropriate traits to measure. Selection of traits and prioritization should be relevant to the goals of crop improvement, such as yield, disease resistance, drought tolerance, or nutritional quality. The previous GS studies on grapevines assessed various traits related to agronomical characteristics, drought tolerance, and yield components. These studies aimed to enhance understanding of these traits’ genetic architecture and identify molecular markers associated with their variation . Althoug4Artificial polyploidization in grapevines presents a bottleneck in plant breeding, but the use of GS for genotype screening following polyploidization offers a promising approach to address this issue. Although, for successful prediction of elite polyploid genotypes, it is crucial to consider the potential shortcomings to avoid or address them. Genotyping the produced grape polyploids via omics-based GS can introduce unique challenges, such as integration of the multi-omics data along with their proper management and interpretation and functional annotation and biological relevance of omics markers. Multi-omics datasets, which are often produced using diverse technologies and platforms, lead to data heterogeneity, besides producing a high-dimensional and complex data landscape. Hence, despite being informative and precise, the volume and complexity of the data make its management difficult, which might require dedicated tools and algorithms which are competent of handling the dimensionality and complexity of multi-omics data. In addition to these, multi-omics datasets usually contain a large number of variables and features, which complicates the downstream analyses. Besides data complexity, we also recommend focusing on reference genome availability and trait-marker associations. Alongside reference genome availability, detection of the biologically significant links between omics markers and complex traits in polyploids can also be intricate.Synthetic polyploids are known to have better adaptability to a wide range of environments compared to their diploid counterparts. However, it is important to assess the Genotype × Environment (G×E) interactions, as they can greatly influence the predictive potential of GS . AdditioRB and MS drafted the manuscript. RB designed and created the figure. EF-C, LS and RS critically reviewed the manuscript and acquired funding. RB and MS revised the manuscript. All authors contributed to the article and approved the submitted version.


Epilepsy is a highly heritable disorder affecting over 50 million people worldwide, of which about one-third are resistant to current treatments. Here we report a multi-ancestry genome-wide association study including 29,944 cases, stratified into three broad categories and seven subtypes of epilepsy, and 52,538 controls. We identify 26 genome-wide significant loci, 19 of which are specific to genetic generalized epilepsy (GGE). We implicate 29 likely causal genes underlying these 26 loci. SNP-based heritability analyses show that common variants explain between 39.6% and 90% of genetic risk for GGE and its subtypes. Subtype analysis revealed markedly different genetic architectures between focal and generalized epilepsies. Gene-set analyses of GGE signals implicate synaptic processes in both excitatory and inhibitory neurons in the brain. Prioritized candidate genes overlap with monogenic epilepsy genes and with targets of current antiseizure medications. Finally, we leverage our results to identify alternate drugs with predicted efficacy if repurposed for epilepsy treatment. Genome-wide association meta-analyses identify 26 risk loci for epilepsy, including 19 loci specific to genetic generalized epilepsy. Prioritized candidate genes implicate synaptic processes and overlap with targets of antiseizure medications. It is estimated that over 50 million people worldwide have active epilepsy, with an annual cumulative incidence of 68 per 100,000 persons2.The epilepsies are a heterogeneous group of neurological disorders, characterized by an enduring predisposition to generate unprovoked seizures3, in contrast to other common neurodevelopmental disorders, where phenotypic subtypes are more difficult to define. Differences in the genetic architecture of clinical subtypes of epilepsy are also emerging, to complement the clinical partitioning7. The rare but severe epileptic encephalopathies are usually nonfamilial and are largely caused by single de novo dominant variants, often involving genes encoding ion channels or proteins of the synaptic machinery8. Both common and rare variants have been shown to contribute to the milder and more common focal and generalized epilepsies. This is particularly true for generalized epilepsy, which is primarily constituted by genetic generalized epilepsy (GGE)10. Nevertheless, previous genetic studies of common epilepsies have explained only a limited proportion of this common genetic variant, or single-nucleotide polymorphism (SNP)-based, heritability—9.2% for focal and 32.1% for GGE10.Similar to other common neurodevelopmental disorders, epilepsies have substantial genetic risk contributions from both common and rare genetic variations. Analysis of the epilepsies benefits from deep phenotyping, which allows clinical subtypes to be distinguished11. Diet, surgery and neuromodulation represent additional treatment options that can be effective in small subgroups of patients12. Accurate classification of clinical presentations is an important guiding factor in epilepsy treatment.Epilepsy is typically treated using antiseizure medications (ASMs). However, despite the availability of over 25 licensed ASMs worldwide, a third of people with epilepsy experience continuing seizures4. Results suggest markedly different genetic architectures between focal and generalized forms of epilepsy. Combining these results with those from less-stringently phenotyped biobank and deCODE genetics epilepsy cases did not substantially increase signal, despite almost doubling the sample size to 51,678 cases and 1,076,527 controls. Our findings shed light on the enigmatic biology of generalized epilepsy and the importance of accurate syndromic phenotyping and may facilitate drug repurposing for new therapeutic approaches.Here we report the third epilepsy genome-wide association study (GWAS) meta-analysis by the International League against Epilepsy (ILAE) Consortium on complex epilepsies, comprising a total of 29,944 deeply phenotyped cases recruited from tertiary referral centers and 52,538 controls, approximately doubling the previous sample size4 with unpublished data from the Epi25 collaborative10 and four additional cohorts  and ‘GGE’ . We further conducted meta-analyses in individuals of European ancestry of the well-defined GGE subtypes of juvenile myoclonic epilepsy , childhood absence epilepsy , juvenile absence epilepsy  and generalized tonic-clonic seizures alone , as well as the FE subtypes of FE with hippocampal sclerosis , FE with other lesions  and lesion-negative FE . The same controls  were shared across the different subphenotypes. We ran a variety of follow-up analyses to identify potential sex-specific signals and obtain biological insights and opportunities for drug repurposing. Sample size prevented the inclusion of other ethnicities in the subtype analyses.We performed a GWAS meta-analysis by combining the previously published effort from our consortium4, the 2q24.3 locus was composed of two independently significant signals , located at 2p16.1, constitutes three independently significant signals. Similarly, the new locus 12q13.13 was composed of two independently significant signals rs7580482, synonymous), located in SCN1A. Seventy-four percent of ‘all epilepsy’ SNPs and 64% of GGE SNPs were located in open chromatin regions, as indicated by a minimum chromatin state of 1–7 (ref. 14). Further annotation by Combined Annotation-Dependent Depletion (CADD) scores predicted that 11 ‘all epilepsy’ and 50 GGE SNPs were deleterious (CADD score > 12.37) (ref. 15). LDAK heritability analyses showed significant enrichment of signal in ‘super-enhancers’ , of which one was new (8q23.1) and the other two (4p12 and 16p11.2) previously reported4. Our analysis of CAE  consolidated an established genome-wide significant signal at 2p16.1, which was also observed in the GGE and all epilepsy GWAS. We did not find any genome-wide significant loci for JAE (n = 671), GTCSA (n = 499), ‘nonlesional FE’ , ‘FE with HS’  or ‘FE with other lesions’ .To assess potential syndrome-specific loci, we performed GWAS on seven well-defined FE and GGE subtypes , might be due to some form of bias 13. The attenuation ratio was lowest for GGE (0.11), which includes the vast majority of significant loci  intercepts were lower (Supplementary Table 18 (P = 0.04) and targets of ASMs .Using FUMA18 , the ‘al18 . Thirty-19. This analysis yielded 39 significant genic associations—six with ‘all epilepsy’ and 37 with GGE , after correction for 16,371 tested genes  to assess whether epilepsy was associated with differential gene expression in the brain 20,21. ThRMI1 was associated with GGE in both TWAS (P = 4.0 × 10−10) and SMR (P = 5.2 × 10−8), as well as with ‘all epilepsy’ . RMI1 has a crucial role in genomic stability23 and has not been previously associated with epilepsy or any other Mendelian trait .Of note, expression of We used a combination of ten different criteria to identify the most likely implicated gene within each of the 26 associated loci from the meta-analysis . This reVRK2 or FANCL24. Our gene prioritization analysis suggests the transcription factor BCL11A as the culprit gene, located 2.5 Mb upstream of the lead SNPs at this locus. Two of three lead SNPs are in enhancer regions (as assessed by chromatin states in brain tissue) that are linked to the BCL11A promoter via 3D chromatin interactions  database did not reveal a significant association of our lead SNPs with BCL11A expression.The strongest association signal for GGE was found at 2p16.1, consistent with our previous results where we implicated 26) and ran association across epilepsy, focal and GGE phenotypes, as well as the seven subphenotypes (HLA-B (position 31432494), which had a P value of 3.8 × 10−7 for GGE.The highly polymorphic HLA region has been associated with various neuropsychiatric and autoimmune neurological disorders. Therefore, we imputed HLA alleles and amino acid residues using CookHLA v1.0.1 : 15.5–19.9%) for all epilepsy, 16.0% (14.0–18.0%) for FE and 39.6% (34.3–44.6%) for GGE. Heritabilities were notably higher for all individual GGE subtypes, ranging from 49.6% (14.0–85.3%) for GTCSA to 90.0% (63.3–116.6%) for JAE 29 confirmed that most of the heritability signal is shared among the four GGE syndromes, with some subtype-specific signals  consortium to assess whether our GGE-associated genes were enriched for expression in specific tissues and cell types .Cell-type specificity analyses of GGE data using various single-cell RNA-sequencing reference datasets  revealed30. These analyses showed that GGE was associated with intracellular signal transduction  and excitability in the synapse . None of the other 16 synaptic functions showed any association ; however, the lead SNPs of these two signals show low allelic correlation . Interestingly, the direction of effect of this signal is opposite in females and males. This sex difference is further corroborated by significant sex heterogeneity (P = 1.54 × 10−8) and sex-differentiated GWAS (P = 5.6 × 10−9) (ref. 35). Sex-related differences in transcription levels in human heart have previously been reported for KCNIP2 (ref. 36). We did not find any sex-divergent signals for ‘all’ or FE. These analyses were limited by a reduction in sample size and prone to random fluctuation.There are known sex-related patterns in the epidemiology of epilepsy. Although females have a marginally lower incidence of epilepsy than males, GGE is known to occur more frequently in femalesrG > 0.9), and none of these correlations were significantly different from 1 . These results suggest that, with the exception of the female-specific 10q24.32 signal, the overall genetic basis of common epilepsy appears largely similar between males and females.We used LDSC to assess the genetic correlation between male-only and female-only GWAS. The male and female GWAS of ‘all epilepsy,’ FE and GGE were strongly genetically correlated  for the same SNP, or SNPs in strong LD with our lead SNPs  were found with febrile seizures, stroke, headache, ADHD, type 2 diabetes and intelligence  and higher than drugs used to treat any other human disease , consistent with clinical experience40. Furthermore, the predicted order of efficacy for GGE of individual ASMs matched their observed order in the largest head-to-head randomized controlled clinical trials for generalized epilepsy41, an observation unlikely to occur by chance (P < 1 × 10−6).For GGE, broad-spectrum ASMs were predicted to be more effective than narrow-spectrum ASMs  codes; NXPH2) emerged for FE  or the ‘GGE’ GWAS (n = 2). One locus (2q12.1) was new. The combined biobank and deCODE genetics meta-analysis for GGE identified five new loci, but four loci from our primary GWAS fell below the threshold of significance  as well as the Parkinson’s disease drug safinamide and the nonsteroidal anti-inflammatory drug celecoxib. Both safinamide and celecoxib have evidence of antiseizure activity44. SCN8A, which encodes a voltage-gated sodium channel, is an established epileptic encephalopathy gene and is associated here with common epilepsies. Nav1.6 (encoded by SCN8A) is targeted by commonly used sodium channel-blocking drugs, the most efficacious ASMs for people with monogenic SCN8A-related epilepsies, that are often caused by gain-of-function pathogenic variants45. Additional drugs targeting Nav1.6 include safinamide and quinidine. RYR2 encodes a ryanodine receptor, is an established cardiac disorder gene, has recently been implicated in epilepsy47 and is targeted by caffeine as well simvastatin, atorvastatin and carvedilol. The acetylcholine receptor gene CHRM3 has been previously associated with epilepsy48 and is targeted by drugs including solifenacin, used to treat urinary incontinence.The 26 associated loci included some notable monogenic epilepsy genes. These include the calcium channel gene 49  loci with high effect sizes, have a higher degree of polygenicity and/or have a lower contribution of common heritable risk variation. Our attempt to mitigate this heterogeneity by performing subtype analysis contrasted with the results from GGE, suggesting different genetic architectures, consistent with the experience from studies of common51. Population biobanks are also probably ascertaining milder epilepsies that are responsive to treatment, contrasting with the enrichment for refractory epilepsies at tertiary referral centers.This work highlights the challenges of working with epilepsy cohorts ascertained through large biobanking initiatives. Accurate classification of epilepsy requires a combination of clinical features, electrophysiology and neuroimaging. Such details were absent from the biobanks we worked with. Rather, phenotypes were generally limited to ICD codes, which are prone to misclassification53.Moreover, a proportion of adults with epilepsy have an acquired brain lesion, such as stroke, tumors or head trauma. Biobanks typically provide self-reported clinical information and codes from primary care and inpatient hospital care episodes, but not neurological specialist outpatient records that would indicate whether previous brain insults were considered relevant to epilepsy. As a result, the inclusion of the biobank data appeared to introduce more heterogeneity. This contrasts with genetic mapping of other polygenic diseases like type 2 diabetes and migraine, which are relatively easy and reliable to diagnose and classify, resulting in a great increase in GWAS loci when including data from the same biobanks as included in our study54 and multiple sclerosis55, whereas migraine does not appear to have brain cell specificity53. We further refine this signal by showing the involvement of synapse biology, primarily intracellular signal transduction and synapse excitability. These findings suggest an important role of synaptic processes in excitatory and inhibitory neurons throughout the brain, which could be a potential therapeutic target. Indeed, synaptic vesicle transport is a known target of the ASMs levetiracetam and brivaracetam56.We found enrichment of GGE variants in brain-expressed genes, involving excitatory and inhibitory neurons, but not any other brain cell type. This contrasts with other neurological diseases. For example, microglia are involved in Alzheimer’s disease57 and ALS58. The genes prioritized in our GWAS signals also overlapped with known targets of current ASMs4, and we have provided a list of other drugs that directly target these genes. Moreover, using a systems-based approach39, we highlight drugs that are predicted to be efficacious when repurposed for epilepsy, based on their ability to perturb function and abundance in gene expression. Insights from GWAS of epilepsy have the potential to accelerate the development of new treatments via the identification of promising drug repurposing candidates for clinical trials59. We anticipate that follow-up studies of the highlighted drugs in this study could show clinical efficacy in epilepsy treatment.We confirmed that our GWAS-identified genes had substantial overlap with monogenic epilepsy genes. A similar convergence of common and rare variant associations has been observed for other neurological neuropsychiatric conditions including schizophreniaIn summary, these new data reveal markedly different genetic architectures between the milder and more common focal and generalized epilepsies, provide new biological insights to disease etiology and highlight drugs with predicted efficacy when repurposed for epilepsy treatment.Local institutional review boards approved study protocols at each contributing site. All study participants provided written, informed consent for the use of their data in genetic studies of epilepsy. For minors, written informed consent was obtained from their parents or legal guardian.4. Here we included five new cohorts  and violation of Hardy–Weinberg equilibrium (P < 10−10). In addition, the Epi25 cohort was split by ancestry, based on principal component analysis. Individuals were removed if their heterozygous/homozygous ratio was >4 s.d. from the mean. We also removed one from each pair of related samples (determined by identity-by-descent >0.2) and removed individuals with ambiguous or nonmatching genetically imputed sex. Furthermore, 3,180 duplicates between the Epi25 cohort and the previously published genome-wide mega-analysis4 were identified based on genotype and were removed from the Epi25 cohort. Of the 3,180 duplicates, 1,226 were GGE and 1,402 FE. Before imputation, cohorts were cross-referenced to the Haplotype Reference Consortium (HRC) panel to ensure SNPs matched in terms of strand, position and ref/alt allele assignment. Additionally, SNPs were removed if they were absent in the HRC panel, if they had a >20% allele frequency difference with the HRC panel or if any AT/GC SNPs had MAFs >40%, using tools available from https://www.well.ox.ac.uk/~wrayner/tools/. Data from Janssen Pharmaceuticals, Austrian GenEpa, Swiss GenEpa, Norwegian GenEpa and BPCCC were then imputed using the Wellcome Sanger Institutes’ imputation server (https://imputation.sanger.ac.uk/), using EAGLE v2.4.1 (ref. 60) for phasing, and the Positional Burrows–Wheeler Transform algorithm61 v3.1 for imputation. The HRC reference panel r1.1 was used as a reference for imputation  (ref. 62). Similarly, data from the Epi25 cohort were imputed using the Michigan Imputation server (https://imputationserver.sph.umich.edu/). We used the HRC r1.1 as the reference panel for individuals of European and Asian ancestry and the 1000 Genomes Phase 3 v5  for individuals of African ancestry. Default imputation parameters were used. Due to data sharing restrictions and with the Epi25 cohort data located in the USA and the other cohorts located in the European Union, we were unable to merge the data or use the same imputation server. Postimputation QC was largely similar among all cohorts. The Epi25 cohort used an in-house pipeline, where imputed dosages were used for genome-wide association analyses, filtering on imputation INFO > 0.3, MAF < 1%, genotype coverage <0.98 and Hardy–Weinberg violations (P < 10−5). For all other cohorts, the same procedures as our previous study4 were used—imputed datasets were converted to hard-coded PLINK format, requiring a more stringent imputation filtering of INFO > 0.9 . Furthermore, we removed SNPs with MAF < 5%, genotype coverage <0.98 and Hardy–Weinberg violations (P < 10−5)(ref. 4). We removed SNPs <5% MAF in the Janssen Pharmaceuticals, Austrian GenEpa, Swiss GenEpa, Norwegian GenEpa and BPCCC cohorts for QC reasons, and note there will be a corresponding loss in study power for lower frequency SNPs in the ‘focal’ and ‘all epilepsy’ epilepsy analysis.Study participants were genotyped on SNP arrays . SAIGE was performed in two steps. First, we fit the null logistic mixed model to estimate the variance component and other model parameters. For this step, SNPs were filtered on-call rate >0.98 and MAF > 5%, and SNPs were pruned to obtain approximate independent markers (window size of 100 SNPs and r2 > 0.3). Second, we tested for the association between each genetic variant and phenotypes by applying SPA to the score test statistics. Next, we performed P value-based fixed-effects meta-analyses with METAL v2020-05-05 (ref. 64) for each of the main phenotypes , as well as the subphenotypes, weighted by effective samples sizes (neff = 4/(1/ncases + 1/ncontrols)) to account for case–control imbalance. We performed multi-ancestry and European-only meta-analyses for the main phenotypes, and restricted the subphenotype analyses to Europeans only, due to limited sample size in other ancestries. We included all SNPs  that were present in at least the previous mega-analysis and the Epi25 dataset, which together account for 88% of the total sample size. We calculated genomic inflation factors (λ), mean χ2 and LD-score regression intercepts to assess potential inflation of the test statistic. Because λ is known to scale with sample size, we also calculated λ1000, which is λ corrected for an equivalent sample size of 1,000 cases and 1,000 controls65. We limited these analyses to participants of European ancestry because LD-structure depends on ethnicity and Europeans constituted 92% of cases. For forest plots of genome-wide significant hits, Beta/SE was estimated from METAL z scores using a previously published formula22. For P–M plots, m values were generated using the default settings of the tool Metasoft v2.0.0 (ref. 66).GWAS of the Janssen Pharmaceuticals, Swiss GenEpa, Norwegian GenEpa and Austrian GenEpa cohorts was performed as a mega-analysis, as described previously67, Biobank Japan69 and FinnGen release R6 (ref. 70)) and from deCODE genetics71 (Iceland). The Biobank Japan, FinnGen and deCODE genetics epilepsy cases were further assigned into either ‘focal’ or ‘generalized’ epilepsy, whereas the UK Biobank samples were not subdivided based on seizure localization, as the relevant clinical details were unavailable to facilitate an accurate subdivision (see Supplementary Table Summary statistics for epilepsy GWAS were obtained from three population biobanks (UK Biobank64), weighted by effective sample size (neff = 4/(1/ncases + 1/ncontrols)) to account for case–control imbalance.Fixed-effects meta-analyses were conducted using METAL v2020-05-05  code 111. Exclusion criteria were ICD-9 3452/3453 and ICD-8 34520. GGE was determined with ICD-10 G40.3, ICD-9 345(0-3) and ICD-8 34519. Exclusion criteria were ICD-8 34511. FE was determined with ICD-10 G40.0, G40.1, G40.2, ICD-9 345(45) and ICD-8 3453.Epilepsy was determined with ICD-10 G40 and ICD-9 345 excluding 3452/3453. GGE with ICD-10 G40.3/G40.4/G40.6/G40.7 or ICD-9 3450/3451/3456, and FE with ICD-10 G40.0/G40.1/G40.2 or ICD-9 3454/3455.Cases were classified into ‘Broad_Epilepsy,’ being any form of epilepsy; ‘Idiopathic_Epilepsy,’ being epilepsy with onset under 40 years and no known cause or ‘Idiopathic_Focal_Epilepsy’ and ‘Idiopathic_Generalized_Epilepsy,’ where focal and generalized syndromes could be ascertained.64. To account for case–control imbalance, the effective sample size for each cohort was calculated as neff = 4/(1/ncases + 1/ncontrols)). GWAS Manhattan plots were generated using the qqman package73 in R v3.6.0. Genome-wide significant loci were mapped onto genes using the FUMA web platform18.Control data were population-matched samples with no history of epilepsy. GWAS fixed-effects meta-analyses were conducted using METALWe performed three meta-analyses. As a primary analysis, we meta-analyzed all nonbiobank samples, then we meta-analyzed only biobank/deCODE genetics samples and finally, we performed a combined meta-analysis of biobank/deCODE genetics and nonbiobank samples.74 is a meta-analysis-based pleiotropy detection approach that identifies common or shared genetic effects between two or more related, but distinct traits. We used ASSET v2.2.0 with a genome-wide significance level of α = 5 × 10−8. We applied ASSET to the subset of European-ancestry samples, comprising 6,952  GGE cases and 14,939  FE cases from the Epi25 and our consortium as well as 42,434 partially overlapping controls from both consortia. Note that ASSET accounts for sample overlap in the analysis. Effect sizes, standard errors and the effective sample sizes estimated were from the main meta-analysis.ASSET76, we included a specific analysis of the HLA. HLA types and amino acid residues were imputed using CookHLA software v1.0.1 (ref. 26), with the 1000 Genomes Phase 3 used as a reference panel77. Samples were grouped by genetic ancestry for imputation.Given the prior association of the HLA with autoimmune epilepsy78). The following three phenotypes were analyzed: ‘all epilepsy’, FE and GGE. Samples from the ILAE and Epi25 datasets were analyzed separately, and the association results were meta-analyzed across datasets and ancestries using PLINK v1.9 (ref. 79).Following imputation, association analysis was conducted using the HLA Analysis Toolkit (HATK) v1.2  with the lead SNP in the locus.We annotated all genome-wide significant SNPs and tagged SNPs within the loci from our multi-ancestry meta-analyses. ANNOVAR v2017-07-17 was used to retrieve the location and function of each SNP17) was used (with default settings) to increase the effective sample size from our European ancestry GGE subphenotype analysis by pairing it with the strongly correlated overall GGE GWAS with a larger sample size. MTAG accounts for sample overlap between traits and uses the fact that estimations of effect size and standard error of a primary GWAS, in this case GGE subtypes, can be improved by matching them to a genetically correlated secondary GWAS, in this case GGE17. Similarly, we applied MTAG to combine FE with GGE.MTAG v1.0.8 (ref. 18) with the same parameters as published previously4. We defined genome-wide significant loci as the region encompassing all SNPs with P < 10−4 that were in LD (r2 > 0.2) with the lead SNP . We used a combination of positional mapping (within 250 kb from the locus), eQTL mapping (SNPs with FDR corrected eQTL P < 0.05 in blood or brain tissue) and 3D Chromatin Interaction Mapping (FDR P < 10−6 in brain tissue).To map genome-wide significant loci from our multi-ancestry meta-analyses to specific genes, we used FUMA v1.3.8  eQTL data from the PsychENCODE consortium, which includes dorsolateral prefrontal cortex tissue from 1,695 individuals21.TWAS was performed with FUSION v3, with default settings22. Although TWAS and SMR have similar aims, the differences in methods and reference datasets result in complementary information. As opposed to the FUSION TWAS method, which uses multi-SNP imputation of gene expression, SMR uses Mendelian randomization to test whether the effect size of an SNP on epilepsy is mediated by the expression of specific genes. We performed SMR analyses with default settings, using European-only GWAS and the MetaBrain expression data as reference, a new eQTL dataset including 2,970 human brain samples83.SMR v1.03 is an additional method to assess the association between epilepsy and expression of specific genes84) to assess the heterogeneity of effect sizes between sexes and sex-differentiated associations35. Sex-differentiated analyses are meta-analyses between female-only and male-only GWAS, allowing for different effect sizes between the sexes, while sex-heterogeneity tests the difference in effect size for each SNP between female-only and male-only GWAS35.We performed a GWAS, as described above, for all epilepsy  and GGE  separately for participants of either sex, after which we performed fixed-effects meta-analyses with METAL to merge the different cohorts. We performed meta-analyses between the male and female GWAS with GWAMA v2.2.2 86). Next, we assessed whether the gene was more strongly expressed in brain tissues than nonbrain tissues, by comparing the average expression in all brain tissues with all nonbrain tissues.Brain expression: for each mapped gene, we calculated the mean expression in all brain and nonbrain tissues based on data from the GTEx project v8  and a list of drug targets from a recent publication .Knockout mouse: we assessed whether a knockout of the gene in a mouse model results in a nervous system (phenotype ID: MP:0003631) or a neurological/behavior phenotype (MP:0005386) in the Mouse Genome Informatics database . The gene with the highest score was chosen as the most likely implicated gene  with BCL11A expression.Most eQTL databases, like PsychENCODE and MetaBrain, restrict eQTL analyses to 1 Mb distance between genes and SNPs. To specifically assess the hypothesis of long-distance regulation of 92. We used default settings in LDAK and precalculated LD weights from 2,000 European (white British) reference samples under the BLD–LDAK SumHer model92. SNP-based heritabilities were converted to liability scale heritability estimates, using the following formula: h2l = h2o × K2(1 − K)2/p(1 − p) × Z2, where K is the disease prevalence, p is the proportion of cases in the sample and Z is the standard normal density at the liability threshold. To decrease downward bias, we performed these calculations based on the effective sample sizes , after which p = 0.5 can be assumed93, with the same population prevalences as our previous study  underlying epilepsy risk was calculated by a causal mixture model (MiXeR) v1.2.0 (ref. 38). MiXeR uses a likelihood-based framework to estimate the amount of causal SNPs underlying a trait, without the need to pinpoint which specific SNPs are involved. Furthermore, MiXeR allows for power calculations to assess the required sample size to explain a certain proportion of SNP-based heritability by genome-wide significant SNPs.We calculated SNP-based heritability on the European-only GWAS using LDAK v5.2, as it was recently shown to give more accurate heritability estimates for complex traits, when compared to other methods including LDSC29. In the first stage, the empirical genetic covariance matrix and sampling covariance matrix are estimated using an extension of multivariable LDSC. This matrix is extended to include SNP effects for the multivariate GWAS SEM. In the second stage, an SEM is specified, and its parameters are estimated such that the discrepancies in the model covariance matrix and the empirical covariance matrix are minimized. The Genomic SEM models are specified such that the SNP effect, defined by multiple traits, occurs at a level of a latent factor (Fg), and the model fit is assessed using model chi-square, Akaike information criterion and standardized root mean square. However, this method also provides evidence of heterogeneity between the phenotypes via the QSNP statistics, which show the extent to which the univariate regression effects of SNPs for each phenotype are explained by a common genetic factor. QSNP is a chi-square distributed statistic that can test whether SNPs act entirely through a common factor.Genomic SEM entails two stages of estimation94. The Zhong dataset (GSE104276) contains RNA-sequencing data from 2,309 human brain cells at different stages of development95. We performed FDR correction across datasets to assess which cell types were significantly associated with GGE. As a sensitivity analysis, we performed stratified LDSC with default settings using the cell-specific gene expression weights from the PsychENCODE consortium to compare GABAergic with glutamatergic neuron enrichment96.We used MAGMA v1.08 (as implemented in FUMA) to perform tissue and cell-type enrichment based on our multi-ancestry meta-analyses. First, we assessed whether our GGE GWAS was enriched for specific tissues from the GTEx database. Similarly, we assessed the enrichment of genes expressed in the brain at 11 general developmental stages, using data from the BrainSpan consortium. Next, we assessed whether GGE was associated with specific cell types, by cross-referencing two single-cell RNA-sequencing databases of human developmental and adult brain samples. The PsychENCODE database contains RNA-sequencing data from 4,249 human brain cells from developmental stages and 27,412 human adult brain cellsP < 5 × 10−8 for SNPs at all 26 genome-wide significant loci.Using the FUMA web application, we searched the GWAS catalog for previously reported associations with 90, we decided to use LDSC as it is the more established method of the two for genetic correlations and used by almost all genetic correlation atlases and databases98.Genetic correlations between ‘all’, FE and GGE and 18 other traits were computed with LDSC v1.01, using default settings. For these analyses, we used our European-only GWAS. Traits highlighted by the GWAS catalog analysis and/or those with established epilepsy comorbidity were prioritized and pursued provided recent summary statistics were available for public download  v1.2.0 to quantify the polygenic overlap between GGE with the same 18 traits as assessed with LDSC. Bivariate MiXeR analyses estimate the total amount of causal SNPs underlying each trait, after which it assesses how many of these SNPs are shared between two traits. Notably, the number of overlapping SNPs is calculated regardless of the direction of effect. This makes it different from overall genetic correlation analyses such as LDSC, where overlapping SNPs with mixed directions of effect can cancel each other out, resulting in low genetic correlation. We used the same publicly available summary statistics as used for LDSC  imputed gene expression data from the FUSION analyses, as described above, and (2) gene-based P values from MAGMA (see above), with default settings. We predicted the relative efficacy of 1,343 drugs in total (Supplementary Data 39. We determined the statistical significance of drug identification and prioritization results by comparing the results to those from a null distribution generated by performing 106 random permutations of the scores assigned to drugs.We used a recently developed method that uses the GWAS for a disease to predict the relative efficacy of drugs for the diseaseFurther information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-023-01485-w.Supplementary InformationSupplementary Tables 1–16, Supplementary Figs. 1–26, Supplementary Note, source code for Fig. 2 and Supplementary References.Reporting SummaryPeer Review FileSupplementary Data 1Genome-wide significant SNPs across epilepsy types. Functional annotation of the 2,355 genome-wide significant SNPs across the 22 GGE loci and 612 SNPs from all epilepsy loci.Supplementary Data 2Genes mapped for the meta-analysis results. Gene mapping of the ‘all epilepsy’ meta-analysis and the GGE analysis using FUMA.Supplementary Data 3GWGAS results. Analysis of gene-based association score based on the aggregate of all SNPs inside each gene using MAGMA.Supplementary Data 4TWAS results. Analysis of differential gene expression in brain for epilepsy using FUSION TWAS.Supplementary Data 5SMR results. Analysis of potentially causal relationship between brain expression and epilepsy using SMR.Supplementary Data 6Scores of biological prioritization criteria for each mapped gene, of each genome-wide significant locus.Supplementary Data 7Gene-set analyses. Analysis of biological processes in association with GGE using MAGMA.Supplementary Data 8Median ranks and AUROCs of all drug groups.Supplementary Data 9Prediction of the relative efficacy of drugs for epilepsy. Prediction of the relative efficacy of drugs for epilepsy.Supplementary Data 10Gene lists used in gene prioritization.