A total of 135 M2 families were sown in the field resulting in approximately 4,000 plants. Traits related to phenology , plant morphology of vegetative parts  and yield  were recorded and analyzed to evaluate genetic variability. An evaluation of the efficacy of low-cost TILLING  to discover mutations in the M2 generation was undertaken. Mutation screening focused on genes involved in resistance to two important diseases of chickpea; Ascochyta blight (AB) and Fusarium wilt (FW), as well as genes responsible for early flowering. Analysis of variance showed a highly significant difference among mutant families for all studied traits. The higher estimates of genetic parameters  were recorded for number of seeds per plant and yield. Total yield was highly significant and positively correlated with number of pods and seeds per plant. Path analysis revealed that the total number of seeds per plant had the highest positive direct effect followed by hundred seed weight parameter. One cluster from nine exhibited the highest mean values for total number of pods and seeds per plant as well as yield per plant. According to Dunnett’s test, 37 M2 families superior to the control were determined for five agronomical traits. Pilot experiments with low-cost TILLING show that the seed stock used for mutagenesis is homogeneous and that small mutations do not predominate at the dosage used.In order to increase genetic variability for chickpea improvement, the Kabuli genotype, variety Ghab4, was treated with 280 Grays of gamma rays . Field characterization began with the M Cicer arietinum L.) is the second largest consumed pulse crop of the world, is grown in over 50 countries, and traded across 140  selection in mutation breeding experiments has been reported in various crops tivation . Mutatiotivation . Inducedtivation . The effus crops . Therefor traits . This, h2) and genetic advance (GA) for various quantitative traits are useful in designing an effective breeding program , heritability  . It is a2 generation, comparisons between M2 families and the control variety (non-irradiated) should be carried out. The determination of M2 families that are significantly superior to the parent allows selection of improved genotypes. The present study was conducted to evaluate genetic variability induced by gamma rays. Its purpose was to determine the effective agronomic traits with the aim of improving selection in next generations, but also to make selection of genotypes that present an agronomic improvement at a very early generation. Genotypic screening with the use of low-cost TILLING was conducted on the same generation (M2), and a panel of genes of interest involved in the major chickpea diseases, Ascochyta blight (AB) and Fusarium wilt (FW), as well as genes involved in the early flowering pathway.In order to study the effect of irradiation on MKabuli chickpea variety Ghab 4, a selection from FLIP 93-93C developed by the International Center for Agricultural Research in the Dry Areas (ICARDA) was treated with gamma rays , at the Nuclear Research Centre of Algiers (CRNA). The optimum dose of LD50/RD50, corresponding to 280 Grays was applied for the production of high mutant frequency .1 generation, the dose of 280 Grays was applied on 9000 healthy and dry (12–13% moisture content) seeds. Untreated seeds were used as control. All seeds were sown in field trial at the experimental station of the Higher National Agronomic School (ENSA), Algiers. The soil characteristic in this field trials is loam clay having 0.07% of nitrogen, 3.6% of organic matter, 10.37 ppm available phosphorus and 7.6 pH. The total precipitation during the trial period from January to July for the two experimental years (2014 and 2015) was 415 mm and 333 mm, respectively. The seed sowing distance was 30 cm × 20 cm with a total area of 700 m2. All the M1 plants were harvested separately for raising M2 generation. Seeds collected from individual M1 plants were sown as M2 families  in randomized block design with three replications. This resulted in 4050 M2 plants.In order to produce the M2 mutant plants. Data was collected and analyzed to assess the extent of induced genetic variability.Observations of various quantitative traits, as indicated in Table 2) and genetic advance as percentage of mean (GA %) were calculated using the following equations:The analysis of variance for ten phenotypic characters was carried out using Genstat software version 12.1 . Genotypr is the number of replications.Where MSG is the mean square of genotypes, MSE is the mean square of error, and 2g is genotypic variance and σ2e is the mean squares of error.Where σ2e)Error variance , moderate (10–20%), and high (20% and above) following where σThe heritability percentage was categorized as low (0–30%), moderate (30–60%), and high (≥60%) in accordance with k) was assumed to be 5%; where k = 2.06, a constant. σp is the phenotypic standard deviation and H2 is the broad sense heritability.Was calculated using the method of Genetic advance as percentage of mean was categorized as low (0–10%), moderate (10–20%), and high (>20%) .r) were calculated among all the measured traits using IBM SPSS for Windows Version 20 (2011). Path coefficient analysis permits a critical examination of specific direct and indirect effects of characters and measures the relative importance of each of them in determining the ultimate goal yield. In the present study, we used yield as a dependent variable. This analysis was done using the IBM SPSS AMOS Windows Version 24 (2016) software.Pearson’s correlation coefficients  using the Mahalanobis’s distance matrix in Ward2 hierarchical agglomerative clustering method (To determine the genetic affinity of Mg method .In order to identify mutant families significantly superior over the parent (untreated control) the Dunnett’s and Fisher’s (LSD) tests were employed using IBM SPSS AMOS software program Windows Version 24 (2016) and Genstat software version 12.1 , respect2 putative mutants were chosen for optimization of the low-cost TILLING assays. Non-mutated c.v. Ghab 4 and three different Desi chickpea genotypes were selected as control samples. Seed from the maintained mutant lines will be shared upon request provided sufficient material exists.Eighty-one samples of chickpea MTwo different protocols were used for extraction of genomic DNA from chickpea leaves; the low-cost DNA extraction protocol developed by PBGL  and the Extracted DNA was quantified with the use of 1% agarose gel with lambda DNA standards and NanoDrop 1000 Spectrophotometer . Genomichttp://www.proweb.org/input/ and Primer3 and http://bioinfo.ut.ee/primer3-0.4.0/.Primers were designed with the use of NCBI’s database and Primer3 program. The web based programs Coddle 2 plants and parent, followed by enzymatic mismatch cleavage and agarose gel visualization of enzymatic mismatch products , 1.5 μL L primer (10 μM), 1.5 μL R primer (10 μM), 0.38 μL TaKaRa HS taq (5 U/μL). A volume of 7.5 μL DNA and 22.5 μL PCR mix was used.Different template DNA concentrations were tested  and a concentration of 1 ng was chosen for subsequent assays. The PCR following PCR mix was used : 82.5 μL HThe thermocycling conditions were: 95°C for 2 min; loop 1 for 8 cycles ; loop 2 for 45 cycles ; 72°C for 5 min; 99°C for 10 min; loop 3 for 70 cycles .2O, 4 μL CJE buffer and 2 μL CJE nuclease were mixed and incubated for 15 min at 45°C. Digestions were stopped by cooling the reaction to 8°C and adding 10 μl of 0.25 M EDTA (pH = 8.0). Ten microliters of final reaction were separated by gel electrophoresis on a 1.5% agarose gel stained with ethidium bromide.To evaluate performance of different primer combinations, 5 μl of the PCR products was separated on 1.5% agarose gel electrophoresis. Ten microliters of the PCR products were used for enzymatic mismatch cleavage using crude celery juice extract (CJE) containing the nuclease CELI. Different enzyme concentrations were tested, and the following digestion reaction conditions were used for subsequent assays: 10 μl PCR product, 14 μL H2).Sanger sequencing reactions were conducted to evaluate results obtained in enzymatic mismatch cleavage assays. Briefly, pre-sequencing amplification and verification of the PCR product was conducted to ensure sufficient production of a single amplicon. PCR reactions where then purified using one of two methods: enzymatic method  or by using the Qiagen kit (PCR purification kit). Samples were then quantified by fluorimetry (Qubit 2.0) and sent for sequencing to a commercial sequencing facility  was observed for total number of seeds/plant and seed yield  followed by total number of pods/plant (55.3%), number of primary branches (31%), hundred seed weight (24.1%), height of first pod (19.3%) and number of seeds/pod (18.4%). Whereas, the coefficient of variation for all other morphological characters was less than 15%. The least coefficient of variation was recorded for days to flowering (2.7%). Analyses of variance showed a very highly significant difference among the families for all the traits studied. The minimum and maximum values for each trait indicated a wide range of differences between genotypes for various characters  were recorded for number of seeds per plant and seed yield  followed by number of pods per plant (50.70%) and number of primary branches (24.48%). Nevertheless, the smallest GCV was for days to flowering (2.72%). Whereas, the traits classification order obtained is the same for PCV values, the higher PCV values (>20%) were number of seeds per plant and seed yield , followed by number of pods per plant (74.50%) and number of primary branches and hundred seed weight . The smallest PCV was for days to flowering (3.82%).The same result was obtained for the genetic advance expressed as a percentage of mean. Higher estimates of genetic advance (>30%) were recorded for number of seeds per plant and seed yield  followed by number of pods per plant (71.08%). The lowest value for the genetic advance was for days to flowering (3.99%). The highest value of heritability was recorded for days to maturity (61%). Moderate values between 39 and 54% were observed for all the other traits studied Table . Three qCorrelation coefficients among morphological traits and yield and its components showed that seed yield was highly significant and positively correlated with number of pods and seeds per plant Table . Positivp = 1.07) effect followed by hundred seed weight (p = 0.23). Plant height showed negligible positive direct effect on seeds yield per plant (p = 0.03). This trait had an important positive indirect effect (p = 0.55) through total number of seeds per plant. The number of seeds per pod and total number of pods per plant contributed with negative direct effects  on seeds yield. While, date to flowering, height of first pod and number of primary branches showed negligible negative direct effects on yield .Path diagrams showing effect relationships of seeds yield and its components are presented in Figure p = 1.04) followed by plant height (p = 0.55), number of seeds per pod (0.47), height of first pod (0.38), number of primary branches (p = 0.18) and hundred seed weight (p = 0.01). Whereas, date to flowering (p = -0.35), date to maturity (p = -0.40) and total number of seeds per plant (p = -0.13) showed negative indirect effect on seeds yield. The indirect effects on seeds yield of the majority of studied traits is mainly through total number of seeds per plant.Regarding the indirect effects on seed yield per plant, it was found that total pods per plant showed maximum positive indirect effect , number of primary branches and number of seeds/pod].Results of Dunnett’s test for the ten traits studied are listed in Table Considering the number of days to flowering and the number of days to maturity, 18 and 13 families, respectively, presented mean values higher than that of the control. Only one family showed mean values lower than control, while the others remained in the same class as the control. For the height of first pod, four mutant families showed means scored above the control. For number of primary branches and number of seeds per pod, one mutant family showed a superior performance in comparison to the control. While for the following characters: total number of pods per plant, total number of seeds per plant, seed yield and hundred seed weight, no significant positive differences were identified with Dennett’s test.Whereas the groupings of averages according to Fisher’s test (LSD) analysis for these traits showed significant differences. Considering the total number of pods per plant, nine families, presented mean values higher than the control. For the total number of seeds per plant and the yield, four families  and two families (GH105 and GH50), respectively, showed means above the control. Finally, 30 families presented hundred seed weight higher than that of the control. For the plant height according to Fisher’s test (LSD) seven families  presented mean values higher than that of the control.An important step in the development of low-cost TILLING is the production of genomic DNA that is of high molecular weight and of sufficient quantity for planned assays. We first sought to test molecular weight of genomic DNA produced using a home-made low-cost extraction protocol versus a commercial Qiagen kit. Agarose gel analysis shows that both methods produce a single high molecular weight band Figure . The conSequences of candidate genes were obtained from the NCBI database. Primer design was carried out using the CODDLE tool to choose optimal gene regions for TILLING. This tool incorporates the Primer3 software for primer design. Twelve primer pairs were designed Table  for the 2 putative mutant. Four different genomic DNA concentrations were evaluated. After amplification, PCR reactions were visualized on the 1% agarose gel  genomic DNA is mixed with each sample prior to PCR amplification. This way both homozygous and heterozygous mutations can be detected. TILLING assays were performed with the 10 selected primers and 81 M2 genomic DNAs mixed with wild type. Products of enzymatic digestion were visualized on 1.5% agarose gel.Low-cost TILLING assays utilize a crude enzyme mixture that recognizes and cleaves heteroduplex DNA formed by denaturation and annealing of PCR products of different sequence. As such, the assay will not detect homozygous mutations if screening individual MNo nucleotide variation was observed in these assays Figure . ObserveGenetic variability is a pre-requisite to the selection of superior genotypes in any crop. Thousands of years of human selection, however, has led to the loss of potentially important allelic variation. Strategies that aim to increase genetic variation within a crop species can dramatically improve the efficiency of the breeding process. Unlike wide-crosses, mutagenesis can be applied directly to the elite cultivar without unwanted linkage drag. In this study, we used gamma irradiation on Algerian chickpeas. The success of this approach relies on the application of an irradiation dosage that induces sufficient variation while still maintaining fertility. Variation can be measured phenotypically and genotypically. While genotypic analysis allows precise estimations of DNA variation, phenotypic analysis provides important data on the expression of useful traits and their heritability.2 generation of mung bean (Vigna radiata L.) and black gram (Vigna mungo L. Hepper) have been reported  for which the coefficient of variation was very high. This variation was not only genetic but also was influenced by the growing environment. reported . High ge2, and GA) noted in this study for seed yield, pods per plant and seeds per plant have also been reported by High heritability with moderate genetic gain observed in the mutant population indicates that the characters were governed by additive gene interactions. This is interesting to consider as traits important to breeding may be due to mutation of more than one locus. This has implications when considering reverse-genetic approaches such as TILLING or CRISPR. Further, higher estimates of genetic components  using 20 targets comprising genomic DNA and cDNA sequences. The frequency of mutations determined was one per 165 kb. This frequency is ∼1.6× higher than reported in Arabidopsis  are known to induce primarily single point mutations. The expectation is that SNP mutations can accumulate to higher levels that other mutation types because a high percentage of SNP mutations will have no negative effect on gene function. Reported mutation densities in diploids has ranged between 1 mutation per 150 kb to over one mutation per one million base pairs . In chicbidopsis .2 rice irradiated lines and the percentage of mutation sites per total sequence was 0.67.The spectrum of mutations induced by gamma irradiation is, however, more diverse. Recent advances in next generation sequencing are allowing a more comprehensive measurement of gamma-induced mutation events. For example, large insertion and deletion events predominate in gamma irradiated maize and poplar, while seed irradiation of rice resulted in an abundance of SNP and small indel variation . The lowIt is likely that fewer mutations will accumulate in combinations of genotypes and dosages where the induction of large indels predominates. This is because essential genes will be quickly knocked out leading to lethality. Based on current data, this makes the evaluation of gamma irradiated populations more challenging compared to chemically mutagenized populations. Certain combinations of dosage and genotype may produce a higher or lower frequency of small mutations. In this context, our observed density of <1 small mutation per million base pairs fits within observations for gamma irradiated crops. It remains possible that our population harbors more large variation that cannot be observed by low-cost TILLING. A low-coverage whole genome sequencing approach (LCWGS) can be applied to discover and catalog such variation in plants . As sequ15N-labeled fertilizer to evaluate their drought tolerance. The goal of this evaluation is to identify chickpea genotypes expressing high agronomic nitrogen and water use efficiency. The physiological traits analysis and measurements utilizing 15N and 13C, are in progress. Low-cost TILLING assays on the whole M2 population are also ongoing and should be finalized by the beginning of 2019. Further evaluations should shed light on the link between induced genotypic variation and the observed expressed phenotypes. This will provide useful information on gene function and also provide markers to facilitate breeding. Collaborations with interested researchers on this mutant population are highly encouraged, particularly on topics surrounding food technology.This work highlights the efficacy of employing both phenotypic and genotypic screening approaches. We conclude that new variation has been induced in chickpea by treatment of seed irradiation through phenotypic observations. This suggests that the population is suitable for breeding. We have also adapted low-cost mutation discovery assays for chickpea and show these are suitable for recovery of small nucleotide variations. We further conclude that larger induced mutations may be present in this population at sufficient frequency to warrant evaluation by whole genome sequencing. Characterization of the mutant population is ongoing. The promising mutants are currently being tested under controlled conditions (rainfed and irrigated) with the use of ML, WA-T, and AA conceived the study. WA-T conducted the experiments. WA-T and ML analyzed the data. WA-T and ML wrote the manuscript. JJ-C and BT contributed to data analysis and manuscript preparation of the genotyping sections of this manuscript. AA, LJ and contributed to writing. All authors read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


FTO and ALKBH5.The Genetic Analysis Workshop (GAW) presents an opportunity to collaboratively evaluate methodology relevant to current issues in genetic epidemiology. The GAW20 data combine real clinical trial data with fictitious epigenetic drug response endpoints. Considering the evidence suggesting that networks of interactions between many genes underlie complex phenotypes, we utilize differential methylation status to identify a relevant gene set for enrichment analysis and use this to infer potential biological function underlying drug response. We highlight the pertinence of considering the potential for widespread epistatic interactions in the absence of main effects, and present evidence of epistasis between single-nucleotide polymorphisms (SNPs) on the two RNA demethylases  The GAW is a forum for investigators to develop and critique new analytical methods for complex traits on a shared data set. The GAW20 data provide simulated replications based on the Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN) clinical trial, had participants been subject to treatment with a fictitious drug with a pharmacoepigenetic effect on triglyceride response . These dDespite evidence for multi-locus underpinnings of phenotype-genotype association, the multiple-testing burden associated with fitting interaction models is stringent due to the high dimensionality of genomic data . Strateg6-methyladenosine modification in the pharmacoepigenetic response on triglycerides, and consider how miRNA adenosine methylation rather than CpG methylation may impact the phenotype. Lacking direct data for miRNA adenosine methylation, we perform a targeted epistasis search between loci on the two RNA demethylases FTO and ALKBH5, and find evidence for statistical epistasis between one variant within each respective gene. Repeating the analysis with the real data revealed four significant interactions between variants across these genes. Overall, we present an example workflow in which integration of multiple sources of information can help uncover biological meaning in the absence of significant main effects.In this study, we hypothesize that CpG sites that are differentially methylated with respect to treatment are associated with the pharmacoepigenetic mechanism of the fictitious drug. Considering the evidence for multi-locus models of complex disease etiology, we hypothesize that the drug response is better evaluated by gene set enrichment analyses than by single locus models. By integrating results from gene ontology, drug-disease association, and microRNA (miRNA) target analyses we find evidence implicating the relevance of adenosine and miRNAs with known epigenetic regulation and roles in lipid metabolism. From this, we infer the potential importance of the Nn = 680) of the post-treatment methylation and triglyceride levels.The GOLDN data and companion simulations for GAW20 are previously described , 6. ReleWe define the phenotype of interest as the log ratio of the average post-treatment triglyceride level to the average pre-treatment triglyceride level. Due to the high correlations between triglyceride levels at pre-treatment time points 1 and 2 and post-treatment time points 3 and 4 , and presence of a value for at least one of time point 1 or 2 and 3 or 4 for each individual, we singly impute missing values via linear regression.t-tests for pre- and post-treatment methylation levels .Significantly differentially methylated CpG sites are identified via paired − 6).Linear models are fit to test the relationships between the phenotype and methylation status of the significant CpG sites identified above, characterized as a single predictor: the log ratio of post-treatment to pre-treatment methylation  [All CpG sites that pass the initial lt.org/) .FTO and ALKBH5 by calculating p-values for the likelihood ratio tests, comparing the linear models containing each FTO-ALKBH5 SNP-SNP pair, with and without their interaction term .We investigate potential epistasis between the two RNA demethylases − 7.We tested 463,995 CpG sites for differential methylation prior to versus post-treatment; 212,018 CpG sites passed the Bonferroni threshold of 1.08 × 10None of the 212,018 CpG sites that are significantly differentially methylated reached genome-wide significance for association with the phenotype . Some CpGs have more than one corresponding gene listed, and many genes have multiple CpGs, for a total gene list length of 4443. The top result from the drug association analysis is adenosine . The top result from the miRNA target analysis is miR-124a .Our gene set is constructed from all CpG sites with FTO and 5 SNPs on ALKBH5 for the 680 subjects. We only test for epistatic interactions between SNPs across the two genes (and do not test for interactions between SNPs on the same gene), for a total of 340 tested interactions and therefore a Bonferroni threshold of 0.00015. One pair of SNPs, rs2192872 from FTO and rs8068517 from ALKBH5, have a significant p-value for the likelihood ratio test comparing the models with and without the interaction (p = 2.01 × 10− 5).The GAW20 GWAS data with the simulated phenotype include complete observations for 68 SNPs on FTO and 5 SNPs on ALKBH5 (Bonferroni threshold of 0.000196). Four pairs of SNPs have significant p values for the likelihood ratio test comparing the models with and without the interaction  is a reversible, dynamic posttranscriptional modification that is regulated by miRNAs, and its demethylation has been shown to regulate adipogenesis [6A determine substrate specificity for the two RNA demethylases, FTO and ALKBH5 [6A mark and these demethylases, we were motivated to check for epistasis between SNPs on these genes. Although we did find four pairs of loci with statistically significant interactions, the small sample size means that some SNP-SNP genotypes have few observations, warranting investigation of this interaction in a larger study and further molecular clarification of the distinct and mutual roles of FTO and ALKBH5. Rather than attempting to explain complex phenotypes solely in terms of single locus main effects, we posit that interaction models better represent the underlying regulatory nature of the genome, and that the joint effect of perturbations to multiple interacting partners can help better explain complex phenotypes. This analysis of epistatic interactions between loci on two genes serves as an illustrative example of how interactions can be significant in the absence of significant main effects, and highlights the need for analyses that integrate multiple sources of data to narrow the search space for plausible interactions.Our gene set enrichment analyses were motivated by the goal of making inferences about the mechanism of action of the fictitious drug, assuming that differential methylation could reveal a set of genes associated with a drug that is functionally similar to the fictitious one in question. Upon attending GAW, it became apparent that interaction analysis of the simulated data was not appropriate given the nature of the simulation, and the interaction identified can therefore be considered a false positive. However, reimplementing the same analytic pipeline using the real data produced largely comparable results and identified four pairs of loci between tabolism –10. The tabolism –14 and ftabolism –19. N6-Mogenesis –25. Reced ALKBH5 –28. If m


During the last decade, there has been a huge development of Genome-Wide Association Studies (GWAS), and thousands of loci associated to complex traits have been detected. These efforts have led to the creation of public databases of GWAS results, making a huge source of information available on the genetic background of many diverse traits. Here we present GWEHS (Genome-Wide Effect size and Heritability Screener), an open-source online application to screen loci associated to human complex traits and diseases from the NHGRI-EBI GWAS Catalog. This application provides a way to explore the distribution of effect sizes of loci affecting these traits, as well as their contribution to heritability. Furthermore, it allows for making predictions on the change in the expected mean effect size, as well as in the heritability as new loci are found. The application enables inferences on whether the additive contribution of loci expected to be discovered in the future will be able to explain the estimates of familial heritability for the different traits. We illustrate the use of this tool, compare some of the results obtained with those from a previous meta-analysis, and discuss its uses and limitations. Genome-Wide Association Studies (GWAS) have become the standard tool to find new loci underlying the genetic basis of many complex traits and diseases , yet theEfforts to find all variants responsible for human traits have led to the creation of big consortiums ,14 as weThough not very extended, the number of open-source tools is also increasing in the last years, and this is also useful for the scientific community, who is becoming more aware of the problems derived from the lack of reproducibility in science ,22,23. Hhttps://gitlab.com/elcortegano/GWEHS) [http://gwehs.uvigo.es. Its status will always be indicated at the GitLab repository, together with all documentation files. The repository also contains instructions to run the server locally both using R or a Docker image. Below we briefly summarize the details of these scripts.The application presented here, relies on the SNP information published in hundreds of scientific publications, and that is made publicly accessible thanks to the NHGRI-EBI GWAS Catalog ,16. In ao/GWEHS) . The onlhttps://gitlab.com/elcortegano/GWEHS/wikis/home) [In this section we describe methods used to process the GWAS Catalog data. These steps are run on the server side before the deployment of the application by means of a ‘preprocessing.sh’ script (see the repository link above), and thus do not take part on the analyses run when using GWEHS. A friendly summary of the data processing can be found at the repository’s wiki page (is/home) .https://www.ebi.ac.uk/gwas/ [P-value (P), genomic position (chromosome number and position in base pairs), PubmedID (PMID), publication date, population sample information, and the disease or trait examined. In order to avoid ambiguity, we will refer here to the mapped gene by the term “locus”, as they sometimes include inter-genic regions (identified by a hyphen character in the gene name). However, the application will allow for more flexible definitions, as loci can also be filtered by setting a physical distance in mega bases . Loci names are truncated to the first one when there are several names separated by comma (due to overlapping transcripts). These names were usually the same, and coincident with the reported gene name as shown by the GWAS Catalog. All SNP entries with incomplete information, or with a –log(P) lower than 5  are removed. Population sample information is given in text form by means of two different fields: ‘Initial sample description’ and ‘Replication sample description’. From that, we estimate the population sample size as the sum of the sample’s sizes extracted from both descriptions.The GWAS Catalog database is publicly available for download at uk/gwas/ . For theAfter the first filtering step, all traits are examined for their associated number of loci and PMIDs. Traits with a number of loci lower than 30 are also excluded, as a minimum number of loci is desired to fit their effects to a distribution see  below. TAV, equals the narrow-sense heritability .Traits with loci effects measured as beta-coefficient (BETA) or odd ratios (OR) are treated separately. From the GWAS Catalog, it is not always clear when an effect belongs to one type or the other. As a proxy, we avoid ambiguity by regarding the minimum effect size by PMID and trait, and classifying the corresponding locus effect as OR if this value is equal to or higher than 1 , or BETA otherwise. For a few traits there is such a conflict as different PMIDs are classified with discordant effect types. For these traits, we selected the most representative effect type , and removed the other one. In any case, the effect type used will always be indicated in the box located together with the plot of distribution effect sizes (see examples below). At this point, the preprocessing is ended for all traits with BETA effects. Since these effects are measured in phenotypic standard deviations, their contribution to additive genetic variance, P) and disease prevalence (k), penetrance (f) can be calculated, assuming an additive effect of the risk ratios (RR) , following the expression fAA = k / (AAP + ½AaRRP + aaRRP), where the suffix ‘A’ refers to the wild-type allele, and lower-case ‘a’ to the risk allele. Risk ratios can be computed by means of the OR value and penetrance simply as RR = OR / (1 AA+ f (OR – 1)). These two expressions must be solved recursively until convergence. Then, from the values of penetrance and risk ratios, the liability mean is computed for each genotype (assuming Hardy-Weinberg equilibrium), and the corresponding variance in liability (the SNP-contributed h2) is obtained. This SNP-contributed h2 can be equaled to the classical expression 2α2q(1 − q) [q being the risk allele frequency), from which the effect size in phenotypic standard deviations  is obtained. Note that OR traits need a prevalence value, which will affect the magnitude of the effect size in phenotypic standard deviation units. We advise to insert the prevalence value as accurate as possible, although it has been reported that deviations from the true value of prevalence, when this is small, have little impact on this transformation process [−4. Note that effect sizes (α) are estimates of the average effect of allelic substitution [Regarding OR traits, their effects are transformed to BETA effects using the method developed by So et al. . In shorq(1 − q)  , but also by publication date, which is more easily curated and easy to read from the Catalog. Nevertheless, the most recent analyses are usually those with the highest sample sizes, so it is frequent that a consideration of studies by publication time or sample size are coincident, serving as an indicator of the statistic power and/or the power of the methods used in GWAS.The application has an exploratory tool and an inference tool. These are explained in the two subsections below. In general, the methodology followed is that used by López-Cortegano and Caballero , though P-value and/or physical distance between SNPs. If more than one trait is selected, they are analyzed as a whole, with the restriction that traits classified with different effect types (BETA and OR) cannot be mixed. When loci are filtered on the basis of their genomic position, a sliding window of custom size is defined so that only one locus will remain in it, that with the most significant associated SNP. A filtering can also be made by removing outliers from the data using Tukey’s classical method [P-value, genomic window or removal of outliers) is too restricted, the analysis cannot be done. The application will also inform on any errors that could arise from the used options in the screen corresponding to the distribution of effect sizes. All the methods described in this section are run from the main ‘gwehs.Rmd’ file  at execution time.Depending on the user’s input, one or more biological traits are selected and filtered regarding their l method . After tN is retained per locus. If two or more SNPs associated with a given locus share the same value of N (or publication date if this is selected as sorting criterion), that with the lowest P-value is kept. Thus, a given locus (GWAS named gene or intergenic sequence) is assigned the estimated effect and frequency of that particular SNP. The loci effects are fitted into a set of known parametric distributions  using the R package ‘fitdistrplus’ [Exploratory analyses are based on a set of unique loci which putatively constitute the genetic basis of the trait found so far in all available to date GWAS. For the dataset used here, only the SNP found in the PMID with the highest value of strplus’  and a mastrplus’ , though N as before, but now in a cumulative way, so that loci from studies with higher N  are added to those found in studies with lower N. To make the inference process dependent only on the discovery of new variants and not on the quality of the estimates at different PMIDs, the effects and frequencies of loci repeated between PMIDs are updated with those of the PMID with the highest N. For example, if locus L has an effect α and frequency q in the PMID with the highest N, then all entries of L in PMIDs with lower N will be assigned the same effect α and q, independently of the values present in the Catalog, as it is assumed that the estimation with the highest N is the most accurate one. If, alternatively, sorting is made by publication date rather than population sample size, the same procedure is followed but based on the criterion of date.The inference analyses are based on a dataset where loci are sorted by population sample size bY = aX) is used, but linear (Y = a + Xb) and four-parameter logistic regression Y = c + (d − c) / (1 + exp(b(lnX − lne))) models are also available, with Y and X being the dependent and independent variables respectively , and letters from ‘a’ to ‘e’ the regression parameters to be estimated. In all cases, their corresponding values of AIC are returned. We choose the exponential regression as default based on a previous meta-analyses where it gave the best fit [Three different regression models are considered in order to fit the change of mean loci effect size and trait heritability to the loci found with an increasing number of PMIDs. By default, a two-parameter exponential regression , in order to infer the expected heritability by sampling new candidate loci. For the distribution of effect sizes, the parametric distribution can be selected by the user, but a gaussian distribution is always assumed for MAF. Details of this process can be found in López-Cortegano and Caballero [Inferences on heritability are done by means of two different approaches. First, by using the regression methods described above directly on the heritability estimated at each discovery step . The second approach is a sampling-regression method that considers the change  in the parameters of the distribution of effect sizes, as well as the change in the distribution of minor allele frequencies . This cluster is based on their common genetic background, but note that we could also analyse them separately. On the left  for the previous analysis, including the original trait name they belong to, and their PMID, effect size, risk allele frequency and contribution to heritability . This taRegression analysis is used to fit the change of the mean effect size and heritability as new loci are being discovered with higher and higher sample sizes . By default, a two-parameter exponential regression is used, which usually gives the best fit according to previous results , though 2h are reached by a number of loci lower than those observed in 2h = 0.25, and that the full familial heritability cannot apparently be explained adding the effects of more and more variants .An alternative, and more reliable, view on the missing number of loci is given in To check the performance of the application, we re-evaluated some of the 16 traits analyzed by López-Cortegano and Caballero  using GWThe exponential regression is again the model that better explains the decrease in the mean effect size (40%) together with the logistic model (33%). As for the increase of the explained heritability, both the logistic and exponential regressions represent the best fit for a 40% of traits each. This balance turns in favor of the exponential regression if we only regard results where convergence to the model is achieved. We recommend using an exponential approach always when possible, as the logistic regression tends to give predictions that reach an upper limit of heritability immediately after the last observation . Using these models, about one third of the traits analyzed by López-Cortegano and Caballero  cannot hWe provide a new resource tool that will let researchers easily mine the GWAS Catalog and explore the genetic background and architecture of a large number of complex traits and diseases, as well as to make rough inferences on their missing heritability. This should give researchers the opportunity to check the state of knowledge of traits of interest, as well as to know how the different variants found by GWAS contribute to genetic variation for the trait or disease under study. This information is provided in an interactive and graphical environment, but can be conveniently downloaded to use in subsequent analyses. These analyses are mainly exploratory, however, and can be subject to some limitations (see below), therefore the results obtained should be taken with caution.5 SNP associations to more than 3,000 different publication studies, covering multiple and diverse complex traits and diseases, also in the number of hundreds. In this regard, GWEHS allows users to query updated results for a given number of human traits, including information on the most representative SNPs, the associated locus , as well as the associated effect, risk allele frequency, contribution to heritability and significance level. These results can be conveniently downloaded in a spreadsheet format for subsequent analyses (see example data in P ≤ 5·10−8) will be selected in order to avoid false positives, but also potentially reducing the number of selected variants.Up to date, the GWAS Catalog database contains results for more than 10The distribution of effect sizes has been a topic of extensive research, as it is fundamental to understand the genetic architecture of complex traits and diseases, e.g., if genetic variability and heritability are mainly explained by multiple low effect variants or a few variants of high effect sizes . GWEHS aAll analyses are of course limited to the accuracy of the corresponding estimates of effect sizes and frequencies. For example, analyses for OR traits are conditional to an estimate of prevalence, which is required to compute contributions to heritability. Although deviations from the true prevalence value have low impact for less prevalent diseases , we adviGWEHS provides a way to make inferences on the expected decrease of the mean locus effect size as well as the increase in the explained heritability see , allowinA new software is provided to explore the GWAS Catalog and make inferences on the heritability of human traits and diseases. Despite the limitations, we expect this application to be of interest for the human genetics and genomics community, and that this tool will serve also to illustrate the communicating potential of tools based on simple markdown documents. Since these documents can be embed as chunks of executable code and graphs  and interactive functions, they have the potential to become a strong standard to share results and to write scientific reports.


N = 2562–3468). We observed a genome-wide significant effect (p < 1 × 10−8) on rapid automatized naming of letters (RANlet) for variants on 18q12.2, within MIR924HG , and a suggestive association on 8q12.3 within NKAIN3 . rs17663182 (18q12.2) also showed genome-wide significant multivariate associations with RAN measures (p = 1.15 × 10−8) and with all the cognitive traits tested (p = 3.07 × 10−8), suggesting  pleiotropic effects of this variant. A polygenic risk score (PRS) analysis revealed significant genetic overlaps of some of the DD-related traits with educational attainment (EDUyears) and ADHD. Reading and spelling abilities were positively associated with EDUyears (p ~ [10−5–10−7]) and negatively associated with ADHD PRS (p ~ [10−8−10−17]). This corroborates a long-standing hypothesis on the partly shared genetic etiology of DD and ADHD, at the genome-wide level. Our findings suggest new candidate DD susceptibility genes and provide new insights into the genetics of dyslexia and its comorbities.Developmental dyslexia (DD) is one of the most prevalent learning disorders, with high impact on school and psychosocial development and high comorbidity with conditions like attention-deficit hyperactivity disorder (ADHD), depression, and anxiety. DD is characterized by deficits in different cognitive skills, including word reading, spelling, rapid naming, and phonology. To investigate the genetic basis of DD, we conducted a genome-wide association study (GWAS) of these skills within one of the largest studies available, including nine cohorts of reading-impaired and typically developing children of European ancestry ( It shows a prevalence of 5–12% among school-aged children, implying life-long learning difficulties for most of the affected individuals1. DD is characterized by a high rate of comorbidity with other neuropsychiatric conditions like attention-deficit hyperactivity disorder (ADHD), depression, and anxiety disorders2. Dyslexic individuals usually have severe and persistent problems in accurate and fluent reading and spelling, and in reading comprehension3. These problems are often associated with early deficits in neurocognitive skills, such as the ability to recognize and manipulate the phonemic constituents of speech , the ability to store such phonemes while reading , or the ability to fast map known visual symbols onto spoken word representations (known as naming speed)4. All these abilities show moderate-to-high heritability (40–80%)7 and significant genetic correlations with DD5. Hence, they represent cognitive indicators of dyslexia risk that are optimally suited for investigating the genetic mechanisms at its basis.Developmental dyslexia (DD) is a neurodevelopmental disorder affecting the ability of learning to read and to spell, in spite of adequate intelligence, educational opportunities, and in the absence of overt neurological and sensorial deficits9). Only a few of these genes have been implicated in DD, reading ability, and underlying cognitive skills in at least two independent studies or datasets1. These include DYX1C1 (15q21)10, KIAA0319 (6p22)14, DCDC2 (6p22)18, MRPL19/GCFC2 (2p12)19, ROBO1 (3p12)22, GRIN2B24, FOXP227 and CNTNAP229.In the last two decades, several studies investigating both DD and the underlying cognitive skills have been carried out to better understand the genetic and neurobiological basis of dyslexia. On the one hand, linkage and targeted association analyses have suggested different candidate DD susceptibility genes (reviewed in refs. p < 10−5)34, with only one recent study reporting a genome-wide significant association 35. The first GWAS for reading ability used DNA pooling of low vs. high reading ability groups in ~1500 7-year-old children, which were genotyped with a low-density single-nucleotide polymorphism (SNP) microarray 34. The SNPs showing the largest allele frequency differences between low- and high-ability groups were tested in an additional follow-up cohort of 4258 children, finally identifying 10 SNPs showing nominally significant associations with continuous variation in reading ability34. However, Luciano et al.36 later found no evidence of replication of these findings in an adolescent population sample of Australian twins and siblings (N = 1177). A later genome-wide linkage and association scan on ∼133,000 SNPs, in a sample of 718 subjects from 101 dyslexia-affected families, identified an association with dyslexia status at rs9313548, near FGF18 (5q35.1)33. More recently, three GWAS studies with different designs were carried out with the aim of identifying shared genetic contributions to reading and language abilities. Luciano et al.32 performed a GWAS on quantitative reading- and language-related traits in two population-based cohorts (N ∼ 6500), analyzing word reading, nonword repetition, and a composite score of reading and spelling abilities. They reported a suggestive association of rs2192161  with nonword repetition and of rs4807927  with both the word reading and the reading–spelling score. A case-control GWAS comparing dyslexic (N = 353), language impaired (LI) (N = 163), and comorbid cases (N = 174) to a population-based control dataset (N = 4117) identified nominally significant associations with comorbid DD-LI cases drawn from the same population cohort used by Luciano et al.32. The most significant associations were detected at rs12636438 and rs1679255, mapping to ZNF385D (3p24.3)30. Another GWAS analyzed the first principal component from various reading- and language-related traits (both with and without IQ adjustment) in three datasets comprising children with reading or language problems and their siblings (N = 1862), and reported suggestive associations at rs59197085, upstream of CCDC136/FLNC (7q32.1), and at rs5995177, within RBFOX2 (22q12.3)31. More recently, Truong et al.35 reported a genome-wide significant multivariate association of rs1555839 (10q23.31) with two skills predicting DD risk, namely rapid automatized naming (RAN) and rapid alternating stimulus (RAS), in a multisite case-control study of DD made up of individuals of non-European ancestry (N = 1263). This SNP, located upstream of the pseudogene RPL7P34, was also associated with measures of word reading and was replicated with RAN traits in an independent cohort from Colorado35, partially overlapping with the Colorado dataset analyzed by Gialluisi et al.31.On the other hand, most of the genome-wide association studies (GWAS) published so far have identified mainly suggestive associations with DD and related cognitive traits . We observed a genome-wide significant association at 18q12.2 and an association approaching genome-wide significance at 8q12.3, both with rapid automatized naming . These genetic effects extended beyond the RAN domain, to other DD-related skills. Also, we detected significant genetic overlaps of the traits analyzed with educational attainment and ADHD risk. Overall, our findings provide new insights in the genetic etiology of dyslexia and related cognitive traits.To help unravel the genetic basis of DD and related neurocognitive skills, we conducted a large international collaborative GWAS. We analyzed the cognitive traits word reading, spelling, decoding skills, phoneme awareness, verbal short-term memory, and naming speed, in nine cohorts of reading impaired and typically developing participants of European ancestry (maximum Table N = 374), Germany (N = 1061), Finland (N = 336), France (N = 165), Hungary (N = 243), The Netherlands (N = 311), and Switzerland (N = 67). Cases were defined as subjects showing more than 1.25 standard deviations (SD) below grade level on a standardized word reading test, while controls were defined as subjects with less than 0.85 SD below grade level on the same test38. In addition, we included two family-based datasets in the study. One of these, from Colorado, United States (USA), contained children showing a school history of reading difficulties as well as their siblings 39. The other one, from the United Kingdom (UK), consisted of subjects with a formal diagnosis of dyslexia and their unaffected siblings 40. Ethical approval was obtained for each cohort at the local level, and written informed consent was obtained for all the participants or their parents, as described elsewhere42.Unrelated DD cases and controls were recruited across seven different European countries, namely Austria  association study42, and part of the German dataset has been investigated in relation to mathematical abilities23 and to neurophysiological DD endophenotypes44, such datasets were never analyzed jointly in a GWAS of neurocognitive traits related to dyslexia. In the present study, samples from Austria, Germany, and Switzerland were merged into a single dataset , since they shared language, genetic ancestry, phenotypic measures, and selection criteria45. No other cohorts were approached for the present study, and all the cohorts contacted presented no refusal or lack of requirements for inclusion criteria.Although the family-based datasets have been previously investigated in GWAS studies45. Briefly, raw scores from psychometric tests were grade-normed (age-adjusted in Colorado) and then z-standardized to reduce skewness, with the exception of the DigSpan score, which was only z-normalized45. No phenotypic outliers were detected in any of the datasets analyzed (see Supplementary Methods for details).We focused on the core phenotypes of dyslexia, namely word reading (WRead), nonword reading (NWRead), and word spelling (WSpell), and on five neurocognitive measures underlying reading ability and dyslexia . These skills included phoneme awareness (PA), digit span , and rapid automatized naming of letters (RANlet), digits (RANdig), and pictures (RANpic). These traits showed moderate-to-high cross-trait correlations (see Table 46 and QCTOOL v1.4 (see URLs), as described in Supplementary Methods and elsewhere47. Within each dataset, SNPs were filtered out if they showed a variant call rate < 98%; a minor allele frequency (MAF) <5%, or a Hardy–Weinberg Equilibrium (HWE) test p-value <10−6. Moreover, samples showing a genotyping rate <98%, cryptic relatedness (in datasets of unrelated subjects), identity-by-descent (IBD) not corresponding to the available pedigree information (in sibling-based datasets), and mismatches between genetic and pedigree-based sex were discarded. Furthermore, genetic ancestry outliers—detected in a multidimensional scaling (MDS) analysis of pairwise genetic distance—and samples showing significant deviations in genome-wide heterozygosity were also filtered out 49. Imputation was performed using IMPUTE2 v2.3.250 in 5 Mb chunks with 500 kb buffers, filtering out variants that were monomorphic in the 1000 Genomes EUR (European) samples. Chunks with < 51 genotyped variants or concordance rates < 92% were fused with neighboring chunks and re-imputed. Finally, imputed variants (genotype probabilities) were filtered out for IMPUTE2 INFO metric < 0.8, MAF < 5% and HWE test p-values <10, using QCTOOL v1.4. We checked again for the absence of genetic ancestry and genome-wide heterozygosity outliers after imputation, which revealed substantial concordance with pre-imputation QC. Further details on the filters used in genotype QC are reported in Table For imputation, autosomal variants were aligned to the 1000 Genomes phase I v3 reference panel 51, using a genetic relationship matrix (GRM) of samples as a random effect while disabling normalization to unit variance for tested SNPs.After genotype QC and imputation, autosomal genotype probabilities were tested for association with the continuous traits available within each dataset. In the datasets containing only unrelated subjects—namely AGS, Finland, France, Hungary, and The Netherlands—association with genotype dosage was tested through linear regression in PLINK v1.9, using the first 10 genetic ancestry (MDS) components as covariates. In the sibling-based datasets (Colorado and UK), a generalized linear mixed-effects model association test was carried out through FastLMM v2.0752. Following the software guidelines, pooled analysis was conducted in two steps: a first run was carried out to compute genomic inflation factors, which were then used to correct meta-analysis statistics in a second run. The numbers of subjects involved in our pooled analysis were 3468 for WRead, 3399 for WSpell, 3409 for NWRead, 3093 for PA, 2591 for DigSpan, 2563 for RANlet and RANdig, and 2562 for RANpic .We also carried out a genome-wide multivariate genetic association analysis through TATESix Table . This anR2) and tested potential epistatic effects of the variants identified. Similarly, we looked for effects of these variants on the other cognitive traits tested in our study through a horizontal pleiotropy test, aimed at detecting effects that were independent on the one observed on RANlet. Also, we looked for independent genetic effects in the genomic regions where these variants lay (18q12.2 and 8q12.3). Finally, we tested them for association with structural neuroimaging measures, which may be potentially correlated with reading and language abilities, namely subcortical volumes (see below). These analyses are reported in details in the Supplementary Methods section.The most significant associations detected were further investigated to assess their robustness through a permutation-based test. Moreover, we computed their effect size . Of note, most of the candidate variants identified in these genes have been already tested in studies showing a variable degree of overlap with our cohorts (reviewed in refs. 9), hence they cannot be formally replicated within the scope of the current study. For this reason, we focused our replication effort on six candidate SNPs among these variants, for which a statistically significant association (p < 0.05 after correction for multiple testing) has been reported in the past in datasets other than ours, but was never formally replicated. These SNPs included rs6803202, rs4535189, rs331142 and rs12495133 in ROBO122, rs7782412 in FOXP227, and rs5796555 in GRIN2B24.First, we assessed all the variants mapping to nine candidate genes (up to 10 kb from the 5′- or 3′-UTR): 35. These included all those variants reported to be associated in previous GWAS papers, including genome-wide significant associations (p < 5 × 10−8), suggestive associations (p < 1 × 10−5), or variants reported as the most significant associations . Again, some of these variants were identified by studies partially overlapping with our datasets31, while for other SNPs tested the statistics from the original papers were not fully available or not always directly comparable, due to either different design of the study or to different traits analyzed35. Therefore, a direct comparison was possible only for few variants (see relevant Results section).We next tested all the variants showing the strongest associations with DD and related cognitive traits in previous GWAS56. First, genetic variants were assigned to protein-coding genes based on their position according to the NCBI 37.3 (hg19) build, extending gene boundaries by 10 kb from the 3′- and 5′-UTR. A total of 18,033 genes  included at least one variant that passed internal QC, and were thus tested in gene-based enrichment analysis. Gene-based statistics were computed using the single-variant association statistics calculated in the GWAS of each phenotype, using default settings. To account for linkage disequilibrium (LD) among the variants tested, we used genetic data from all the datasets pooled together. Given the number of genes  and of independent latent traits (5) tested, the Bonferroni-corrected genome-wide significance threshold for this analysis was set to α = 0.05 /  = 5.5 × 10−7.Gene-based association analyses for the phenotypic traits tested were performed using MAGMA v1.06collection C2, subcollection CP; see URLs). To correct enrichment statistics for testing of multiple pathways, we used an adaptive permutation procedure with default settings . Hence, for gene-set analysis we corrected the significance threshold only for the number of independent latent traits tested (α = 0.05/5 = 0.01).Using the results of the gene-based association analysis, we carried out a pathway-based enrichment test for each trait analyzed in the study, through a competitive gene-set analysis in MAGMA v1.06. We tested for enrichment 1329 canonical pathways  from the Molecular Signatures Database website . We used the eight-univariate GWAS carried out here as a target, namely WRead, WSpell, NWRead, PA, RANlet, RANdig, RANpic, and DigSpan. As training GWAS, we selected 12 different studies, involving seven subcortical volumes previously tested in a large GWAS 58; an educational attainment trait 59; and four neuropsychiatric disorders. These included ADHD 60; autism spectrum disorder 61; major depressive disorder 62; and schizophrenia 63, and were selected in light of their comorbidity with dyslexia reported by previous literature66. Similarly, the choice to test subcortical volumes was driven by the increasing evidence implicating subcortical structures in reading and language abilities (as reviewed in refs. 68).To assess the genetic overlap of common variants between the dyslexia-related skills tested here and other correlated phenotypes, we carried out a polygenic risk score (PRS) analysis using PRSice v1.25p-values ≤ 0.05 in each training GWAS, and in linkage equilibrium (r2 < 0.05) with the local top hit within a 300 kb window. Only SNPs which had been tested both in the training and in the target GWAS were tested. The number of SNPs meeting these criteria ranged from 11,017 for MDD vs. DigSpan and RAN traits, to 25,409 for SCZ vs. WRead, WSpell, NWRead, and PA. To verify the robustness of our results, we repeated the analysis at increasing association significance (PT) thresholds in the training GWAS .We performed a Summary–Summary Statistic Based Analysis using only SNPs with association p-values ≤ 0.05 in each training GWAS and computed Pearson’s correlation of effect sizes  with each of the target GWAS analyzed. The significance threshold for these analyses was corrected for multiple testing of five independent target GWAS  = 6.94 × 10−5.To have an indication on the concordance of shared genetic effects for each pair of traits, we selected variants with association p-values, along with significance thresholds adequately corrected for multiple testing (see Subjects and methods section).For each analysis presented below, we report the empirical p < 1 × 10−8), mapped to chromosome 18q12.2. The most significant association was observed for rs17663182  β (SE) = 0.35 (0.06)). All the SNPs significantly associated on 18q12 were located within the non-coding gene MIR924HG . An additional, independent association approaching genome-wide significance was observed with RANlet at rs16928927  β (SE) = −0.4 (0.07)) on 8q12.3. This SNP was located within the first intron of NKAIN3 , and a suggestive association at rs16928927 (p = 1.46 × 10−7). Similarly, a multivariate association test focused on the three RAN traits revealed a genome-wide significant association of rs17663182 (p = 1.15 × 10−8), while rs16928927 association only approached significance (p = 5.45 × 10−8). However, neither of these two SNPs showed significant effects independent from RANlet on any other trait . These variants showed consistent allelic trends Fig. , but expit Table . Similarit Table , as wellit Table . These Sit Table .Fig. 3FoDetailed results of the GWAS analyses for each univariate trait are reported in Supplementary Figure DYX1C1, DCDC2, KIAA0319, C2ORF3, MRPL19, ROBO1, GRIN2B, FOXP2, and CNTNAP2. We reported associations for all these variants in Table ROBO1 variant rs12495133 with WSpell  β (SE) = −0.06 (0.03)), with an allelic trend concordant with the original report22.In total, 12,785 variants were annotated to nine candidate genes previously implicated in dyslexia by at least two independent studies, namely p < 0.05) that were comparable with those reported by previous independent studies (Table CSE1L gene (20q13.13), with both word  β (SE) = −0.12 (0.04)) and nonword reading  β (SE) = −0.1 (0.04)). These associations showed the same direction of effect as in the original report34.Similarly, among variants associated with DD and related cognitive measures in previous GWAS efforts  with NWRead . Similarly, also in the gene-set analysis of 1329 canonical pathways from the MSigDB website, no pathway was significantly enriched  = 0.64 (0.16); see Table Gene-level analyses of single-variant association signals in MAGMA revealed no significant enrichment of genes after correcting for testing of 18,033 protein-coding genes and of five independent latent traits (α = 5.5 × 10p < 6.94 × 10−5) with ADHD risk, and with educational attainment (EDUyears). The ADHD PRS was negatively associated with WRead, WSpell, and NWRead , while EDUyears polygenic score was positively associated with WRead, WSpell, NWRead, DigSpan, and PA . These results were substantially confirmed at different PT thresholds  has been reported across different orthographies45 and over the life span70. Furthermore, RAN turned out to be a significant predictor of poor reading skills across orthographies38 and is also used in kindergarten to identify children at risk of dyslexia71. Good predictivity of reading performance has been reported for both alphanumeric and non-alphanumeric RAN measures, although correlations are usually stronger between alphanumeric RAN and reading fluency, compared with non-alphanumeric RAN . The correlations between alphanumeric (letters and numbers) RAN and reading skills are also significant through adulthood73. Importantly, RAN predicts later reading performance independently from reading experience or early differences in reading ability76, and from phoneme awareness. While RAN has been shown to be an important predictor for reading problems, it should be noted that, in line with multiple-deficit models of dyslexia77, RAN represents one of several predictors of DD risk and reading abilities (reviewed in ref. 78). More recently, RAN has been also associated with other learning disorders, like dyscalculia, and it has been hypothesized that RAN deficits in dyslexic children may be independent from those detected in dyscalculic children80.We identified a genome-wide significant effect on rapid automatized naming of letters, which showed a  pleiotropic influence on the whole RAN domain and, to a lesser extent, on reading abilities. Rapid naming reflects the automaticity of visual–verbal access necessary for efficient word decoding, and accounts for a significant proportion of variance in word reading ability, especially reading fluency, which is independent of the well-established language and phonological processes implicated in reading, like phoneme awarenessMIR924HG . Additional significant associations were detected in the same region for other variants, all in high LD with the lead SNP, which suggests that they identified the same genetic effect on RANlet. This observation was supported by the absence of strong independent genetic effects on RANlet within a 100 kb window surrounding the strongest signal at rs17663182. An extensive lookup of these 18q12.2 variants in common online gene expression databases—including the Genotype-Tissue Expression portal (GTEx)81, the Brain eQTL Almanac (Braineac)82, the Blood eQTL browser83, and the seeQTL database84 —revealed weak evidence of expression quantitative trait loci (eQTL) involving rs17663182 and neighboring associated SNPs. Braineac reports nominally significant eQTL effects  for these SNPs on MIR924HG expression in the occipital cortex, thalamus, and substantia nigra. In addition, HaploReg v4.1 indicated the presence of histone marks usually associated with transcriptional activity in the same region, such as H3K4me1, H3K27ac, and H3K9ac85. To the best of our knowledge, no regulatory role is known for MIR924HG, and MIR924 has not been functionally characterized so far. Nonetheless, the significant associations on 18q12.2 represent an interesting genetic effect for three main reasons:The most significant association signal with RANlet was observed for rs17663182, a variant located within 89. In a genome-wide linkage analysis of a German cohort partly overlapping with our AGS dataset, a linkage peak to a principal component of RAN scores was observed in a region encompassing the microsatellite marker D18S1102, located ~2.1 Mb downstream of rs1766318289. Similarly, a linkage signal was later reported for the same marker with a composite RAN score, in a Dutch sib-pair sample. However, this association was weaker after including parents of the sib-pairs in the analysis86. Early evidence for linkage in 18q12 has been reported with word reading and orthographic coding, in samples partially overlapping with our Colorado and UK datasets88. In line with these findings, rs17663182 showed associations with traits other than RANlet in our analysis, including RANdig, RANpic, WRead, and NWRead (further discussed below). It would be tempting to connect the linkage signals mentioned above with the SNP associations at rs17663182, but it is important to point out that this association likely represents only a small fraction of these linkage signals or even a distinct genetic effect, because linkage and association analyses tend to detect different effects90.First and foremost, evidence of genetic linkage to dyslexia-related cognitive traits has been reported for this region in previous studies, although not always reaching statistical significance91 allowed us to identify a series of interesting candidate target genes which MIR924 could regulate. These include candidate dyslexia susceptibility genes like MRPL19 and KIAA0319L, although these did not show the highest predicted binding scores to MIR924 .Second, a search for binding sites through the online database TargetScanHuman v7.1MIR924HG is expressed in a number of cancer cell lines, but consistently in samples representing iPS differentiation into neurons, according to the FANTOM5 miRNA promoter analysis92. This is interesting in the context that at least three dyslexia candidate genes  have been implicated in regulating neuronal migration and cilia functions in model systems9.Third, NKAIN3 (Na+/K+ transporting ATPase interacting 3), a gene which is widely and specifically expressed in the brain, especially in the fetal temporal lobe, in newborn and in adult hippocampal regions93.In the analysis of RANlet, we observed an additional association approaching genome-wide significance at rs16928927 (8q12.3). This intronic variant is located within Of note, both our lead SNPs showed associations with different cognitive measures analyzed in this study, especially with RAN traits. This multitrait association trend is particularly noticeable for rs17663182, which showed convincing evidence of influence within and even beyond the RAN domain, extending to reading abilities, as suggested by a genome-wide significant multivariate association with all the cognitive traits analyzed. However, a horizontal pleiotropy test on both variants did not reveal any significant effect specific to cognitive traits other than RANlet. This suggests that these variants likely exert their genetic influence on the common phenotypic variance underlying these traits, with different magnitude of effect on each measure, rather than on trait-specific phenotypic variance.58. Considering the sample size of this neuroimaging genetic analysis , we deem it unlikely that this lack of support is caused by a lack of power. However, this negative result does not rule out genetic effects of the variants detected here on other brain structures involved in reading networks, such as the inferior frontal gyrus and the temporal and parietal gyri. These potential associations should be tested in the future, as was previously done for other variants associated with reading-related traits95.Despite the biological appeal of the top association signals mentioned above, an imaging genetic assessment of these SNPs did not reveal any significant effect on variation in seven different subcortical volumes97. Third, different genetic backgrounds of the populations analyzed may be a factor when comparing or meta-analyzing different association studies. The haplotype structure in a specific region may differ between populations, and so may change the LD between the tag SNP (where the association is detected) and the genuine causal SNP (which determines the association). In the presence of substantial population stratification, this could even result in contrasting directions of effect for the same SNP in different studies99. Fourth, the inconsistent results from association studies may be due to different age ranges of the samples analyzed, e.g., when comparing an adult population with a datasets made up of children37. An alternative explanation may be that the original findings were type I errors, since false-positive results may easily occur in analyses of relatively small samples100. While this is a less likely explanation for those associated SNPs which have been functionally investigated, it may reasonably account for spurious associations, which are more likely to be affected by publication biases  and reporting biases .An assessment of candidate genes and SNPs implicated in dyslexia and related traits by other studies provided weak or (in most cases) no evidence of replication. Several possible factors may account for these apparently contrasting results. First, the heterogeneity of recruitment of the samples analyzed may lead to discrepant results across different studies: some genetic variants may have stronger effects in the lower tail of the reading and language skills distributions  and negligible effects in a broader range of variation . Second, the heterogeneity of assessment of the phenotypes may result in traits that ostensibly tap into the same cognitive domain but actually represent slightly different abilities. This applies not only to continuous DD-related measures, but also to the classification of dyslexia cases and controls, for which a consensus is far from being reached in the scientific community102. In a PRS analysis comparing educational attainment with reading efficiency and comprehension, the same EDUYears score used here59 accounted for 2.1% (at the age of 7) to 5.1% (at the age of 14) of the variance in such reading measures in a UK sample (N = 5825), and this association remained significant even after correcting for general cognitive ability and socioeconomic status101. More recently, Luciano et al.102 used the results of a previous GWAS on reading and language-related traits32 to test genetic correlations with several health, socioeconomic, and brain structure measures collected in adults from the UK . Polygenic scores increasing these traits—namely word reading, nonword repetition, and a reading–spelling score— were all positively associated with a binary index of educational attainment (college or university degree)102. In our paper, we replicate these findings by reporting that variants nominally associated with EDUyears explain almost 2% of the total variance in WRead (used here as a target trait), and extend the evidence of genetic overlap to other behavioral features—WSpell and NWRead—and to cognitive predictors of dyslexia risk like PA and DigSpan.Another interesting finding of our study is the significant genetic overlap that some of the traits analyzed showed with educational attainment (EDUyears) and ADHD. Educational attainment was already reported to share a significant proportion of genetic variance with word reading ability105, and has been later corroborated by molecular genetic studies. The existence of overlapping risk loci between DD and ADHD suggests that these regions could be the potential sites of liability underlying ADHD–DD comorbidity1. Candidate DD susceptibility genes like DYX1C1, DCDC2, and KIAA0319 have been associated with inattention and hyperactivity/impulsivity108, and candidate ADHD genes like DRD4 have been investigated for linkage and association with DD, with inconsistent results110. More recently, Mascheretti et al.111 found significant main and interactive associations upon hyperactivity/impulsivity involving DCDC2 and KIAA0319, while Sánchez-Móran et al.25 reported stronger associations of candidate KIAA0319 and FOXP2 variants with ADHD–DD comorbid cases, compared with simple dyslexic subjects. In line with this evidence, our findings provide further support to a partly shared genetic etiology of DD and ADHD at the genome-wide level.Our PRS analysis also revealed a shared genetic basis for ADHD risk and the core dyslexia features WRead, WSpell, and NWRead. This long-standing hypothesis was originally supported by behavioral genetics studies of twins18, the absence of a follow-up cohort to replicate the genome-wide significant associations detected, and the modest power to detect small effect sizes (see Supplementary Methods). These are counterbalanced by strengths of our study, which include the variety of continuous neurocognitive traits analyzed, covering all the most relevant dyslexia-related behavioral phenotypes, and the homogeneity of QC procedures among datasets, which are fundamental to improve statistical power. Indeed, most of our samples were collected in the context of a large international consortium for studying the neurobiological/genetic basis of dyslexia (Neurodys), whose main purpose is to homogenize traits and datasets to allow for comparable analyses across different countries42. Overall, this study represents an early step of one of the largest international collaborations aimed at clarifying the genetic basis of reading abilities and disabilities, which will hopefully contribute to shed a light on the neurobiology of dyslexia.Among the limitations of our study are a certain variability in the inclusion criteria and phenotypic assessment of some cohortshttp://www.genecards.org/. FANTOM5 Zenbu database: http://fantom.gsc.riken.jp/zenbu/. PLINK: https://www.cog-genomics.org/plink2. QCTOOL: http://www.well.ox.ac.uk/~gav/qctool/. MatSpD: http://gump.qimr.edu.au/general/daleN/matSpD/. MAGMA: http://ctg.cncr.nl/software/magma. MSigDB: http://software.broadinstitute.org/gsea/msigdb; PRSice: http://prsice.info/. Genotype-Tissue Expression portal (GTEx): http://www.gtexportal.org/home/. Brain eQTL Almanac (Braineac): http://www.braineac.org/. Blood eQTL: http://genenetwork.nl/bloodeqtlbrowser/. seeQTL: http://www.bios.unc.edu/research/genomic_software/seeQTL. HaploReg: http://archive.broadinstitute.org/mammals/haploreg/haploreg.php. TargetScan: http://www.targetscan.org/. LocusZoom: http://www.locuszoom.org/Human Integrated Protein Expression Database: Suppementary MethodsSupplementary Results: Manhattan and QQ plotsSupplementary Results: Characterization of top association signalsSupplementary Results: Most significant single variant associations (GWAS)Supplementary Results: Associations in candidate dyslexia susceptibility genesSupplementary Results: Associations of candidate SNPs from previous GWASSupplementary Results: most significant gene-based associationsSupplementary Results: most significant pathway-based associationsSupplementary Results: PRS analysis