Eucalyptus nitens (E. nitens) breeding population consisting of open-pollinated (OP) and controlled-pollinated (CP) families. We used 8221 trees from six progeny trials in this study. Of these, 868 progeny and 255 parents were genotyped with the E. nitens marker panel. Three traits; diameter at breast height (DBH), wood basic density (DEN), and kraft pulp yield (KPY) were analyzed. Two types of genomic relationship matrices based on identity-by-state (IBS) and identity-by-descent (IBD) were tested. Performance of the genomic best linear unbiased prediction (GBLUP) models with IBS and IBD matrices were compared with pedigree-based additive best linear unbiased prediction (ABLUP) models with and without the pedigree reconstruction. Similarly, the performance of the single-step GBLUP (ssGBLUP) with IBS and IBD matrices were compared with ABLUP models using all 8221 trees. Significant dominance effects were observed with the GBLUP-AD model for DBH. The predictive ability of DBH is higher with the GBLUP-AD model compared to other models. Similarly, the prediction accuracy of genotypic values is higher with GBLUP-AD compared to the GBLUP-A model. Among the two GBLUP models (IBS and IBD), no differences were observed in predictive abilities and prediction accuracies. While the estimates of predictive ability with additive effects were similar among all four models, prediction accuracies of ABLUP were lower than the GBLUP models. The prediction accuracy of ssGBLUP-IBD is higher than the other three models while the theoretical accuracy of ssGBLUP-IBS is consistently higher than the other three models across all three groups tested . Significant inbreeding depression was observed for DBH and KPY. While there is a linear relationship between inbreeding and DBH, the relationship between inbreeding and KPY is nonlinear and quadratic. These results indicate that the inbreeding depression of DBH is mainly due to directional dominance while in KPY it may be due to epistasis. Inbreeding depression may be the main source of the observed dominance effects in DBH. The significant dominance effect observed for DBH may be used to select complementary parents to improve the genetic merit of the progeny in E. nitens.Genomic selection (GS) is being increasingly adopted by the tree breeding community. Most of the GS studies in trees are focused on estimating additive genetic effects. Exploiting the dominance effects offers additional opportunities to improve genetic gain. To detect dominance effects, trait-relevant markers may be important compared to nonselected markers. Here, we used preselected markers to study the dominance effects in a  Eucalyptus nitens is a temperate plantation tree species grown predominantly in Southern Australia and Chile. E. nitens is the second most widely grown plantation hardwood species in Australia  has become routine in animal breeding, and it is increasingly being adopted by the tree breeding community  using preselected markers. Higher estimates of predictive ability were observed for growth traits when dominance effects were included in the prediction model.One of the methods used for the application of GS in tree breeding is the genomic best linear unbiased prediction (GBLUP) model. In GBLUP, the pedigree-based numerator relationship matrix (NRM) is replaced by the genomic relationship matrix (GRM) for estimating breeding values. MBVs estimated with the GBLUP will be more accurate as the GRM captures the realized relationships between the individuals within a family and relationships among individuals from different families leading to increased accuracy of the estimated MBVs  with the combined H-matrix. For this, they used the genotype data of a small number of parents from both the programs to develop a combined H-matrix. By using the H-matrix in ssGBLUP they were able to estimate breeding values for parents in environments where they were not progeny tested. However, the application of ssGBLUP in tree breeding needs to be approached cautiously. In animal breeding where ssGBLUP is being implemented, the pedigrees are deep and accurate pedigree records are generally maintained. In contrast, most of the tree breeding populations are shallow .Single-step genomic best linear unbiased prediction (ssGBLUP) models are increasingly being tested in forest trees , higher breeding value accuracies with lower bias were observed for growth and stem form using the ssGBLUP model with IBD compared to the IBS matrix  was used. Very few studies have used genomic relationship matrices based on identity-by-descent (IBD) to estimate genetic parameters. Models with an IBD matrix use marker information to track haplotype blocks within the known pedigree using linkage analysis. The accuracy of this method depends on the extent of the family structure. One of the advantages of this method is that it does not require dense markers due to the limited number of recombinations that occur between the generations ” method. Thus, the marker panels developed with the preselected markers consist of trait-relevant markers of different traits.In a previous project , we developed marker panels for several species of E. nitens. We have tested the ability of the preselected markers to identify the dominance effects of growth and wood traits. We have also compared the prediction accuracies of GBLUP and ssGBLUP models using IBS and IBD relationship matrices.In this study, we used preselected markers associated with various wood and growth traits to compare the predictive performance of the ABLUP, GBLUP, and ssGBLUP models in A third-generation breeding population consisting of 8221 trees from six progeny trials in Tasmania were used in this study . In totaFrom the total population of 8221 trees, 868 progeny from 172 families were genotyped. In addition, 547 parents of which 255 were the parents of the families used in this study were also genotyped. In total, 1415 trees were genotyped. Trees for genotyping were selected based on the criteria that they all have three traits measured including growth and wood quality traits. Families were selected based on the criteria that they have a minimum of 8 trees per family. The number of trees genotyped per family ranged from 1 to 15 with an average of 5 trees per family.3) and kraft pulp yield (KPY-%) were measured only in 868 genotyped trees. Wood density (DEN) was measured using a Resistograph tool (Diameter at breast height (DBH-cm) was measured across all the 8221 trees used in this study while wood density  as well as small biallelic INDELs. Thus, the E. nitens marker panel consists of preselected markers associated with growth and wood traits. Marker panels for genotyping were developed with the probes to capture short genomic regions spanning the selected candidate markers. As small genomic regions were sequenced, novel markers within the sequenced regions in addition to the targeted markers will also be genotyped with this method.In a previous project , markers with potential association with different traits of E. nitens marker panel. After filtering for markers with <90% call rate and minor allele frequency of <0.01, 3514 markers were selected for downstream analyses. On average each marker had 3% of missing data. Missing marker genotypes were imputed based on average genotype content.TGS was used to genotype the captured genomic regions at Gondwana Genomics Pty Ltd, Canberra. As specific regions were used for sequencing, high coverage (>75x) is generally obtained across the captured regions with this method. DNA from 868 progeny and 547 parents was used for genotyping with the We used the GRM from parental and progeny data to confirm or to identify the pedigree errors. Some of the documented relationships between the parents were corrected by the pedigree analysis with the markers. Pedigree errors among the progeny were corrected by identifying the right parents for some families. Pedigree errors were identified by comparing the genomic relationship coefficients between the members of a family and the documented parents. Individuals showing low relationship coefficients compared to the average relationship coefficients of a family and the documented parents were identified as errors. To identify the paternal parents of polycrosses, genomic relationship coefficients of a family were compared with those of the parents within the pollen mix groups used for generating polycrosses. Genomic relationship coefficients of 0.30–0.5 were used to confirm the parental and progeny relationships.F-test was performed. All these analyses were performed in R.Inbreeding was estimated using all the markers. Inbreeding was estimated with the “het” function of the “PLINK” 2.0 package  and spatial effects detected using a separable two-dimensional auto-regressive model  were used for developing the H-matrix. For H-matrix with IBD, the GRM from IBDLD was used. A-matrix from an error-corrected pedigree was used to develop the H-matrix.The combined H-matrix for ssGBLUP was developed using the following equation.The combined 11 and A22 represent relationships among individuals within the group “1” and the group “2,” respectively, A12 represents relationships among individuals between the group “1” and “2”, and A21 is the transpose of A12.For this, individuals were assigned to different groups based on available information; the group with the subscript “1” represent individuals that only had pedigree information and the group with the subscript “2” represent individuals that had only genomic information. AG and A22 the following adjustment was made to the G matrix.To have the same scale between The adjustment factors Where: Avg.diag is the average of diagonals and Avg.offdiag is the average of off-diagonal elements.The rescaled H-matrixThe weighted The additive relationship matrix and dominance matrix from the pedigree were developed using AGHmatrix software  to estimh2) was estimated as d2) was estimated as Narrow-sense heritability  which was then used to estimate the breeding values of the left-out fold (test samples) without the phenotypes. Predictive ability was estimated as the Pearson correlation between the predicted breeding values and the adjusted phenotypes of the test samples. Similarly, predictive accuracy was estimated as the Pearson correlation between the predicted breeding values from cross-validation with the estimated breeding values (EBVs) of test samples using marker and phenotype data of all the samples. This process is repeated ten times and the average of the 10 folds is used as the predictive ability and prediction accuracy of the model. When estimating prediction accuracies of a model, the predicted breeding values from cross-validation were correlated with the breeding values estimated with the data (marker and phenotype) of all the samples using that model.Predictive ability and accuracies are estimated separately for MBVs/molecular genetic values (MGVs) from GBLUP models and EBVs/estimated genetic values (EGVs) from ABLUP models. Breeding values estimated with GBLUP-IBS are indicated as MBVs, MBVs with GBLUP-IBD are indicated as MBV_IBD, genetic values with GBLUP-AD are indicated as MGVs and genetic values estimated with the ABLUP-AD model are indicated as EGVs.Predictive ability and accuracies of MBVs/MGVs are estimated by correlating MBVs/MGVs from cross-validation tests with the MBVs/MGVs estimated with all samples, respectively. Similarly, predictive ability and accuracies of EBVs/EGVs are estimated by correlating EBVs/EGVs from cross-validation tests with the EBVs/EGVs estimated with all samples, respectively.Analysis of variance (ANOVA) was performed to test the differences in predictive ability and prediction accuracy of different models. A Tukey’s multiple comparison test was performed to identify the significant differences between different models. A significance level of GRM was used to identify pedigree errors and to reconstruct pedigree by identifying paternal parents of OP and polycross families. Sixty out of 868 trees showed pedigree errors. In total 133 individuals from 19 families were assigned paternal parents thus converting them into full-sib families. Without the pedigree reconstruction, these families would have been treated as half-sib families. Pedigree corrected progeny were used in all the downstream analyses.The estimates of genetic parameters are lower with the increased utilization of genomic information . GeneticDominance effects estimated with the GBLUP-AD model is significant for only DBH among the three traits . For thiAmong the three traits, DEN had higher dominance effects with ABLUP-PR (pedigree reconstruction). Even though high dominance was observed with ABLUP (without the pedigree reconstruction), it was associated with high standard error . The modWe also estimated the genetic parameters with GBLUP using the IBD GRM. Only additive effects are estimated with the IBD matrix. For DBH, the genetic parameters estimated with IBD are similar to the IBS matrix. For DEN and KPY the additive effects and heritability estimates with IBD are slightly lower than the IBS matrix .F-test was performed. The partial F-test is not significant for DBH while it is significant for KPY  and additive and dominance model (A + D). Accuracy was estimated by correlating MBVs and MGVs estimated using all samples with MBVs/MGVs from cross-validation tests using additive (A) and additive and dominance (A + D) models. While there are significant differences between the models, the difference in the accuracy between the A and A + D models for MGVs is higher than MBVs . This inThe pair-wise relationship coefficients of the IBS matrix had higher dispersion than those of the IBD matrix . For unrIn addition to GBLUP, we also used the ssGBLUP model to estimate genetic parameters with all 8821 genotyped and nongenotyped trees. Only DBH was used in the ssGBLUP model as data of DEN and KPY was not available for nongenotyped trees. Genetic parameters were estimated with the two relationship matrices (IBS and IBD) in ssGBLUP. In addition to ssGBLUP, a pedigree-based ABLUP model was also fitted. The ssGBLUP models had better model fit compared to ABLUP models as indicated by the lower AIC values of the ssGBLUP models. Between the two ssGBLUP models, ssGBLUP-IBS had a better model fit . Higher Cross-validation tests were performed to test the performance of ssGBLUP and ABLUP models using all 8821 trees. To reduce the influence of several nongenotyped trees and to compare the results from GBLUP analysis, tests were performed within the 868 genotyped trees. Prediction accuracies of ssGBLUP-IBD are significantly higher than the other three models . PredictDBH data of all 8821 trees were used to compare the theoretical accuracies among the four models . Among all the four models, ssGBLUP-IBS had the highest accuracies among all three groups of trees , while the dominance variance observed at the marker depends on the r4 between the marker and causal variant. Thus, for detection of dominance effects markers that are in high LD with the causal variants are required  was observed for DEN with the ABLUP-PR model while the estimated dominance effect was low with the GBLUP-AD model  in an OP population of E. nitens leading to a recommendation of benchmarking MBV accuracies of GBLUP with pedigree corrected EBVs of ABLUP. Even though this may result in better accuracies compared to those estimated without the pedigree correction, these accuracies are still biased. Pedigree reconstruction can only capture higher-level relationships such as half-sibs or full-sibs of a family, but within family Mendelian segregation term and lower-level relationships or the hidden relationships between families are not captured. Under these conditions, we propose benchmarking MBVs/EBVs from cross-validation with the MBVs estimated using all the samples used in the study as GRM captures all kinds of relationships including the hidden relationships. The MBVs estimated with all samples used in a study will therefore be closer to the true breeding values. In a recent study, In several studies, the prediction accuracy of GBLUP is estimated by correlating MBVs with the EBVs of ABLUP. While this may be valid in animal breeding where deep pedigrees spanning several generations are used and the pedigree errors are minimal, this may not be useful in tree breeding with shallow pedigrees and several pedigree errors. Benchmarking the prediction accuracy of MBVs from GBLUP with less accurate EBVs will lead to biased estimates of the accuracy. This bias was highlighted by Klápště E. pellita . In their study, no improvement in theoretical accuracy was observed among the parents and nongenotyped samples with ssGBLUP models compared to the ABLUP model. The lack of improvement in the accuracy was attributed to the pedigree errors and undetected hidden relationships among the nongenotyped samples. Higher theoretical accuracy of parents and nongenotyped samples with ssGBLUP-IBS in this study may be due to higher and more accurate estimates of heritability estimated with the preselected markers.Among all the four models using 8821 trees, ssGBLUP-IBS had the highest heritability . While tTheoretical accuracies reflect how much an individual’s breeding value improves with more data , while tE. dunnii. Higher predictive ability was observed with IBD compared to the IBS matrix for DBH and stem straightness. They advocate the use of the IBD matrix in ssGBLUP analyses in OP tree evaluation. In this study, we compared the performance of the IBD and IBS matrices with GBLUP and ssGBLUP models. The predictive ability of IBS is higher than IBD with both GBLUP  of the progeny. Simulation studies have shown substantial improvement in selection response with mate allocation techniques based on genomic models with dominance effects. However, the extra response is only observed in the first generation of mating and thereafter it is not further improved but maintained  collected from several parents. Even when full-sib crosses are possible, no advantage is found with full-sib crosses compared to polycrosses (Detection of significant dominance effects has practical implications in tree breeding. In tree species that can be vegetatively propagated, whole genotypic values which include both additive and dominance effects can be used in selection to transfer them to the next generation (intained . Mate alIn this study, we observed significant dominance effects controlling DBH with preselected markers containing trait-relevant markers. Higher predictive abilities and higher total genotypic value accuracies were observed using prediction models with dominance effects. The performance of the models with IBS and IBD relationship matrices was similar within the genotyped samples using GBLUP. The prediction accuracy of IBD in ssGBLUP was higher than IBS. However, the theoretical accuracy of the ssGBLUP model with the IBS matrix is higher than the ssGBLUP model with the IBD matrix and ABLUP models. Improvement in theoretical accuracy was observed for nongenotyped samples and parents with the ssGBLUP-IBS model compared to other models. Significant inbreeding depression was observed for DBH and KPY. A linear relationship was observed between inbreeding and DBH while a nonlinear quadratic relationship was observed between inbreeding and KPY. This suggests that directional dominance contributes to the inbreeding depression of DBH while epistasis may be an important factor contributing to the inbreeding depression of KPY. The dominance effects observed for DBH in this study are mainly due to inbreeding depression. The significant dominance effect of DBH observed in this study may be used in mate allocation programs to select complementary parents to generate superior progeny.https://doi.org/10.25387/g3.15170883.The pedigree and trait data used in this study are in Supplementary Table S1. The marker genotype data used in this study are in Supplementary Table S2. Supplementary material is available at figshare:


Second, the genetic instrument for adult height, as reported by the GIANT consortium, was implemented, and summary-level MR of eGFRcreatinine and CKDcreatinine in a CKDGen genome-wide association study was performed , with multivariable MR being adjusted for the effects of genetic predisposition on body mass index. To replicate the findings, additional two-sample MR using the summary statistics of eGFRcystatinC and CKDcystatinC in UK Biobank was performed . In observational analysis, adult height was inversely associated with both eGFRcreatinine  and eGFRcystatinC  in a multivariable model adjusted for clinicodemographic, anthropometric, metabolic, and social factors. Moreover, multivariable summary-level MR showed that a taller genetically predicted adult height was causally linked to a lower log-eGFRcreatinine  and a higher risk of CKDcreatinine . Other pleiotropy-robust sensitivity MR analysis results supported the findings. In addition, similar results were obtained by two-sample MR of eGFRcystatinC  and CKDcystatinC  in UK Biobank. In conclusion, the results of this study suggest that a taller adult height is causally linked to worse kidney function in middle-aged to elderly individuals, independent of the effect of body mass index.As adult height is linked to various health outcomes, further investigation of its causal effects on kidney function later in life is warranted. This study involved a cross-sectional observational analysis and summary-level Mendelian randomization (MR) analysis. First, the observational association between height and estimated GFR determined by creatinine (eGFR Kidney disease is a major category of comorbidity, and the prevalence of chronic kidney disease (CKD) is increasing with the aging population and global obesity epidemic . ImpaireKidney function is commonly evaluated by estimated values, namely, the estimated glomerular filtration rate (eGFR), due to its greater availability than a directly measured GFR. Because of widespread serum creatinine testing and robustly developed eGFR equations , 4, eGFRHeight is an important factor related to health outcomes , as is bIn the abovementioned MR analysis, genetic instruments were used to examine the association between genetically predicted exposure and outcome traits . As one’Here, we present a cross-sectional observational study and an MR analysis focusing on causal effects of adult height on eGFR in middle-aged to elderly individuals. Along with our recent efforts to identify causative factors for kidney function through MR investigations –18, we hThe study was performed in accordance with the Declaration of Helsinki. The study was approved by the institutional review boards of Seoul National University Hospital (No. E-2005-006-1120) and the UK Biobank consortium (application No. 53799). Informed consent was waived as the study analyzed anonymous databases.The study incorporated three databases or their findings: 1) the previous GIANT consortium genome-wide association study (GWAS) meta-analysis for height, which was implemented to identify genetic instruments for height; 2) the previous CKDGen consortium GWAS for kidney function traits based on serum creatinine values, which was implemented as the outcome summary statistics for summary-level MR; and 3) UK Biobank data, which were used for cross-sectional observational analysis. The UK Biobank data was also used as another summary statistics for kidney function traits based on cystatin C levels for summary-level MR.The study mainly consisted of two parts . First, The abovementioned cross-sectional observational analysis was performed with UK Biobank data. In brief, UK Biobank is a population-scale prospective cohort including participants aged 40–69. The study recruited > 500,000 participants from 2006 to 2010 from 22 assessment centers in the United Kingdom, and the study is powered by its extensively phenotyped and genotyped information. Other details of the UK Biobank project can be found in the literature .For the cross-sectional observational study, we included 467,182 UK Biobank participants with adult height values, as measured by assessment staff during the inclusion visit, and both eGFR (creatinine) and eGFR (cystatin C), as calculated by the CKD-EPI equation, which includes information on biomarkers, age, sex, and ethnicity , 4. The First, the overall association between adult height and eGFR values was investigated by generalized additive models adjusted for age and BMI for each sex. Second, linear regression analysis was performed with the construction of stepwise multivariable models. The first model included basic factors, age, sex, and BMI. The second multivariable model included a list of body index variables, adding waist circumference, weight, and whole-body fat-free mass, as measured by a BC418 MA body composition analyzer . The third multivariable model included a wide range of metabolic parameters and social factors collected at inclusion visits, such as hypertension history, systolic BP, diastolic BP, dyslipidemia medication, LDL cholesterol, HDL cholesterol, triglycerides, diabetes mellitus, hemoglobin A1c, testosterone level, uric acid level, history of angina/heart attack/stroke, history of cancer, average moderate physical activity frequency, and smoking history. As information was missing in the dataset, the third fully adjusted model was repeatedly constructed in an imputed dataset established by multiple imputation with the changed equation method . The anaAs causal inference by observational analysis is limited, we further extended our findings to an MR investigation. In MR, genotype-determined inborn individuals are used as genetic instruments for an exposure trait of interest, and the association between genetically predicted exposure and an outcome trait is tested. Genetically proxied exposure can less likely be affected by confounders, and the direction of the association is toward outcome; thus, a significant finding in MR can support causal effects of the exposure trait on the outcome.A valid MR study requires three assumptions to be met to suggest causal inference . First, −8) for height by conditional and joint association analysis. Using the SNPs reported in the study has particular strength for an MR analysis as the samples included in the GWAS meta-analysis were completely independent from the UK Biobank data, allowing a two-sample MR analysis. Two-sample MR analysis has strength because the possibility of false-positive findings is minimal, supporting the robustness of a positive finding in MR, and such two-sample approach would not be possible if we used a different, recently reported GWAS results  for 567,460 individuals of European ancestry [As mentioned above, first, we implemented the recent CKDGen GWAS meta-analysis for log-transformed eGFR based on creatinine and CKD [creatinine-based eGFR < 60 mL/min/1.73 mancestry . These d2 or prevalent end-stage kidney disease history) using UK Biobank data, which is independent from the samples involved in the genetic instrument identification [2, and the first 10 principal components of the genetic information, and summary statistics for the SNPs included in the genetic instrument for adult height were obtained. The above outcome summary statistics were developed using PLINK 2.0 [Because creatinine values are likely to be affected by various body shape factors, we produced summary statistics for continuous eGFR based on the cystatin C level and CKD  for each sex are described in Overall, median eGFR values appeared to be higher in those who were taller than the median than in those shorter than the median . AdditioIn linear regression analysis , a talleAfter disregarding 27 SNPs that were palindromic with intermediate allele frequencies and 5 SNPs that did not overlap with the CKDGen results, 665 SNPs were utilized as the genetic instrument for eGFR, and 666 SNPs were used for CKD  Tables. These results were reproduced when we introduced the GWAS summary statistics from UK Biobank for eGFR (cystatin C) and CKD (cystatin C). Using 667 SNPs of the genetic instrument that were not palindromic (28 SNPs) or did not overlap (2 SNPs) in the UK Biobank dataset  Tables, In the current study, which involved MR analysis, we identified a causal linkage between taller height and lower eGFR or higher risk of CKD as defined by eGFR. The observational findings showed that adult height is inversely associated with estimated kidney function, even in the stringently adjusted model considering large number of covariates. The identified observational results are extended to causal inference by our MR analysis. The MR analysis results consistently indicated that a taller adult height causally reduces estimated kidney function in middle- to old-age individuals, independent of the effects of BMI.Two possible hypotheses can be considered to explain the study results. First, a simple explanation is that as eGFR is still an “estimated” metric of kidney function relying on equations and biomarkers, whereby a taller height may cause a higher serum creatinine or cystatin C value, independent of other factors, with a lower eGFR but not actually decreased kidney function . The sigThere are several limitations to this study. First, as measured GFR values were absent, directly proving the effects of height on actual kidney function was impossible. Despite efforts to reduce the effects of the difference between estimated and actual kidney function by including cystatin C values, further research is necessary to draw conclusions regarding the mechanisms of the causal effects of adult height on kidney function in middle-aged to elderly individuals. Second, the study was mainly based on individuals of European ancestry; thus, it is unclear whether the findings can be directly applied to other ethnicities. Third, there was some sample overlap between the CKDGen data and the samples of the GWAS used to identify genetic instruments, which may cause bias towards false-positive findings. However, the results from the two-sample MR, using the independent UK Biobank data, were similar to that from the CKDGen data, thus, the issue would not likely to change the findings of the current study. Fourth, the study only addressed adult height and estimated kidney function in those of middle to old age, and the link between height and kidney function in the pediatric population or young adults was beyond the scope of this study. Last, as MR analysis is weak in detecting nonlinear effects, it remains to be determined whether the findings can be applied to an extreme height range .In conclusion, taller adult height causally reduces eGFR, as determined by either creatinine or cystatin C values, in middle-aged to elderly individuals. Further study is necessary to elucidate the mechanism of this causal link, and whether a taller adult height causes glomerular overload leading to impaired kidney function later in life should be investigated.S1 FigIndividual SNP associations with outcome traits are presented. Outcomes were CKD and log-transformed eGFR determined based on serum creatinine-based eGFR values in CKDGen data and CKD and continuous eGFR determined based on serum cystatin C level in UK Biobank data.(TIF)Click here for additional data file.S2 FigMR results omitting each SNP at a time by the multiplicative random effect inverse variance weighted method are presented. Outcomes were CKD and log-transformed eGFR determined based on serum creatinine-based eGFR values in CKDGen data and CKD and continuous eGFR determined based on serum cystatin C level in UK Biobank data.(TIF)Click here for additional data file.S3 FigFunnel plots demonstrate square root precision on the y-axis and causal estimate on the x-axis. Asymmetry on each side of the overall causal estimates suggests the presence of a directional pleiotropic effect.(TIF)Click here for additional data file.S4 FigScatter plots showing the effect of a genetic variant on exposure on the x-axis and on outcome on the y-axis. Scatter plots are a helpful method to identify outliers and the overall distribution of SNP effects along with overall causal estimates.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S1 Methods(PDF)Click here for additional data file.


In the present study, the genetic architecture of the stress response, body weight, and disease resistance in European seabass were studied, providing fruitful results for further research. Initially, the above traits were recorded and genotyping on a large scale was performed in those fish. The recorded data and genotypes were combined and analyzed to find genomic areas affecting them as well as to estimate the heritability of those traits. Stress response traits and body weight were medium heritable, while genomic regions affecting them were detected. However, no genomic areas related to disease resistance were revealed. These findings improve our knowledge of the genetic structure of those traits and can be utilized in a breeding program for the genetic improvement of aquaculture broodstocks. Vibrio anguillarum was conducted in a subset (332) of the above fish to study disease resistance. Fish were genotyped with a 57k SNP array, and a Genome-Wide Association study (GWAS) was performed. Five SNPs were found to be statistically significant, three of which affect stress indicators and body weight (in a subgroup of the population), and a putative SNP affects growth performance, while no SNP associated with resistance to Vibrio was found. A moderate to high genomic heritability regarding stress indicators and body weight was estimated using the Restricted Maximum Likelihood (REML) process. Finally, the accuracy, along with the correlation between Estimated Breeding Values (EBVs) and Genomic Estimated Breeding Values (GEBVs), were calculated for all the traits.The majority of the genetic studies in aquaculture breeding programs focus on commercial traits such as body weight, morphology, and resistance against diseases. However, studying stress response in European seabass may contribute to the understanding of the genetic component of stress and its future use to select broodstock whose offspring may potentially be less affected by handling. A total of 865 European seabass offspring were used to measure body weight and stress response. Moreover, a disease challenge experiment with  The European seabass belongs to the Moronidae family, and apart from the wild populations naturally distributed in the North Atlantic, the Mediterranean, and the Black Sea, cultured populations are mainly farmed in Mediterranean countries. Between 1989, when the first farmed European seabass appeared, and in 2016 the production increased to 191,003 tones .The main commercial traits of interest in breeding programs are growth as well as morphology and disease resistance, while broodstock selection is performed only for a small number of generations . NeverthOncorhynchus mykiss) as well as in Atlantic salmon  and provided contradicting estimates on their heritability  for body weight and corrected phenotypes for stress response. While for repeated measurements of the stress response the variance of permanent environment was also included in the total phenotypic variance [h2 = aσ2/(aσ2 + eσ2+peσ2)] and repeatability was estimated by the sum of the additive genetic variance and the permanent environment variance divided by the total phenotypic variance [r = (aσ2 + peσ2)/(aσ2 + eσ2+peσ2)]. The calculation of the corrected phenotypes was performed using BLUPF90 [The estimation of heritability of all the traits (Equations (1) and (2)) and the genetic/phenotypic parameters for growth were performed using AIREMLF90 ,38. The  BLUPF90 , and the BLUPF90 ,38.Y corresponds to the vector of repeated measurements per stress indicator, b is the vector of the fixed effect (batch), Z is the indicator the matrix of the tested SNP, the SNP is the effect of the tested SNP, X and W are incidence matrices that related observation to fixed and permanent environmental effects. The υ is the additive genetic effect using the GRM, and it is described as ~N  (G is the GRM and aσ2 is the additive variance), and the pe is the permanent environment effect. Finally, e is the random residual.A univariate linear mixed model with repeated measurements GWAS for each stress indicator ) was performed in RepeatABEL/R software ,40. In tFurthermore, a univariate linear mixed model was performed per measurement per trait as well as on corrected phenotypes of the stress indicators (Equation (4)) using GEMMA software (version 0.94.1)  fitting Y corresponds to the vector of each measurement per stress indicator or the corrected phenotypes of each stress indicator, b is the fixed effect (batch), Z is the indicator of the matrix of the tested SNP, the SNP is the effect of the tested SNP and X is the incidence matrix that related observation to fixed effects. The υ is the additive genetic effect using the GRM, and it is described as ~N  (G is the GRM and aσ2 is the additive variance). Finally, e is the random residual.In Equation (4), the Y corresponds to the matrix of the body weight measurements in Equation (4). GWAS was performed using GEMMA software [A multitrait linear mixed model was used to predict the association between SNPs and growth performance. The four measurements of the body weight were used as dependent variables in the model (Equation (4)). Thus, the software .p-value less than 0.01 were considered as outliers and removed from the analysis. The Mahalanobis distance estimation was performed in R [In the multitrait GWAS, offspring were tested not to deviate from multivariate normality using the Mahalanobis distance, as described by Shim et al. . Offsprimed in R .Y corresponds to a vector containing each measurement of the body weight in Equation (4).Furthermore, a univariate linear mixed model per measurement was also performed using GEMMA software (version 0.94.1) , and thuVibrio anguillarum. Mortality was used as a dependent variable, described as binary data (Dead/Alive) in the model. GWAS was performed using GEMMA software [A univariate linear mixed model was used to identify the association between SNP and disease resistance against software . There wMAF is the Minor Allele Frequency of the SNP, the b is the effect size estimated, and the N is the sample size.The Bonferroni correction method was used to minimize the false-positive results in all of the above models , and it http://seabass.mpipz.mpg.de, accessed on 15 July 2021).The sequence of the significant SNP from GWAS were cross-referenced against the European seabass genome database to identify any linked genes that could be used as possible candidates  approach was used . For theFurthermore, the accuracy of the methods was estimated using five replicates of a fivefold cross-validation analysis, with the training set including 80% and the validation set including 20% of the population. In the validation set, all the offspring were selected randomly, and their phenotypes were masked to estimate accuracy . The pheFinally, the correlation between EBVs (using PRM) and GEBVs (using GRM) was calculated using the Spearman method for the body weight and stress indicators . The corThe descriptive statistics of all traits are illustrated per batch as well as jointly in Using the PRM, the heritability of cortisol, glucose, lactate, and lysozyme levels under a repeatability animal model was estimated at 0.45, 0.31, 0.61, and 0.63, respectively . The repEstimates of heritability of body weight in different ages during the experiment ranged from 0.54 to 0.75, using the PRM. Using the GRM, the heritability of weight per measurement ranged from 0.54 to 0.61 . The genNo significant association between SNPs and cortisol, glucose or lysozyme levels was detected using the repeated measurements GWAS analysis. However, one SNP associated with lactate levels in chromosome 19 (LG5) was found. The proportion of the phenotypic variance which was explained was 2.6% , Table 5Manhattan plots for univariate GWAS for each measurement of all the stress indicators and the univariate GWAS for the corrected phenotype for the stress indicators are illustrated in Analyzing batch 10, the QTL affecting lactate levels at the 3rd measurement located in chromosome 19 (LG5) was also detected to be significant in the present subgroup, with an approximately similar effect  when only batch 10 was analyzed . The proVibrio was found . The SNP (AX-172322981) affecting body weight was close to the fgf14 gene, based on Itoh and Ornitz [Fibroblast Growth Factor (FGF) family gene is involved in multiple biological procedures; however, fgf14 affects the neurological functions. Finally, none of those genes were related to the traits that are studied presently.The sequences of the significant SNP from GWAS were cross-referenced against the European seabass genome database, and the possible candidates of genes are illustrated in d Ornitz , who repFor stress indicators, the accuracy of EBVs was between 0.26 and 0.47 using the PRM, while for GEBVs ranged from 0.28 to 0.51 . CortisoIn terms of body weight, the accuracy of EBVs was between 0.42 and 0.54 for all growth stages using the PRM, while for GEBVs ranged from 0.40 to 0.60 . The las the PRM .Correlations between EBVs and GEBVs for biochemical and hormonal stress indicators ranged from 0.78 to 0.84 for all phenotypes. Finally, correlations between EBVs and GEBVs for body weight ranged from 0.85 to 0.91 for all measurements .Vibrio anguillarum injection. Furthermore, the heritability of the traits mentioned above was estimated, while for body weight, the genetic parameters were also estimated using PRM and GRM. Genomic selection and pedigree approaches were applied for all the traits. Finally, the correlation between breeding values as well as accuracy was calculated for stress indicators and growth.Broodstock and their offspring from two breeding seasons (batches) underwent genetic analysis using the 57k SNP Dlab-chip array  and GWASGenerally, cortisol levels showed a contradicted heritability among species because, in Atlantic salmon, they were low (0.07 and 0.05) ,12, whilIn our study, the heritability of cortisol and lactate was similar regardless of the use of the PRM and the GRM, indicating that genomic and pedigree-based relationships are providing similar estimates in a polygenic inheritance model. Our estimates of cortisol levels were similar to the estimates for the rainbow trout than those for the Atlantic salmon, while comparing them with the estimates for the European seabass, they are higher than the reported range (0.08–0.34) ,17,18,22However, the heritability of the glucose and lysozyme levels showed a different pattern to that of cortisol and lactate levels. The heritability of lysozyme levels was higher in the model using the GRM than the PRM, providing an increase of 0.12 in the estimate. Even though there was no significant QTL affecting it using GWAS with repeated measurements, one SNP associated with the first measurements of lysozyme levels was detected using univariate GWAS. The findings mentioned above could explain this increase. Chatziplis et al.  estimateFinally, the heritability of glucose levels showed a higher increase, jumping from 0.32 to 0.52 using the GRM. However, none of the performed GWAS showed any significant SNP-phenotype associations on the results, and the findings remained low, without any significant peaks in all the Manhattan plots. The heritability estimated by Chatziplis et al.  was simi2 = 0.36).The heritability of body weight in European seabass ranges from 0.38 to 0.63 in different growth stages (illustrated as 179 to 397 DPH or 29.5 to 741.9 gr) using pedigree relationship matrix ,18,19,20In this study, the heritability ranged from 0.75 to 0.54 (weight 1 to weight 4) using the PRM, while it was estimated as 0.61 to 0.57 using the GRM. Compared to the estimates in the literature, the heritability of weights 3 and 4 were in the reported range, but the heritability of weights 1 and 2 were higher than it. In our case, a drop of 0.14 in the heritability of weight 1, as well as a 0.1 decrease in weight 2, was noticed when the GRM was used. Weight 3 and 4 showed a similarity between the estimates, apart from an increase of 0.03 in the latter. However, Besson et al.  reportedFinally, body weight is affected by the stress response, and a negative genetic correlation between cortisol levels and body weight has been reported in different populations by Chatziplis et al.  and VandA few studies have reported stress response as a heritable trait, and QTL on linkage groups 1, 3, 14, and 23, using microsatellite markers, which explained a range of 0.15% to 10% of the proportion of phenotypic variation of stress response have been identified ,22. In cStarting with cortisol levels, the QTL reported by Chatziplis et al.  and MassQTL affecting the body weight has been reported on different LGs ,22,23 usFocusing on the increasing trailing in chr 23 (LG9), there is evidence of a genomic region that affects the growth performance because firstly, two SNP are above the threshold 0.1 (with Bonferroni correction), and secondly, there is also a trailing of SNP associations appearing in Manhattan plot . HoweverVibrio anguillarum have been found in other species, such as the rainbow trout, the Japanese flounder , and the half-smooth tongue sole (Cynoglossus semilaevis) [Vibrio injection at 0.32 ± 0.07, thus mortality appears to be a heritable trait. However, no QTL linked with microsatellite markers was detected in that study [Vibrio injection and SNPs (QTL affecting disease resistance against ilaevis) ,55,56,57ilaevis)  used a sat study  or in ouand SNPs . Nevertht-test was performed on accuracy between the two relationship matrices for glucose levels, the difference was not statistically significant (data not shown). Focusing on lysozyme levels, apart from the equal accuracy estimates which were detected, the highest correlation between EBVs and GEBVs was also found. On the other hand, cortisol levels showed higher prediction accuracy using GRM, but a lower correlation between EBVs and GEBVs was detected. Additionally, when the same square root of heritability  for the first two measurements, but for the later growth stages, the accuracy of prediction was slightly higher when using PRM compared to GRM. Based on the literature, the use of the GRM increases the accuracy of estimation; however, using this dataset, the findings did not confirm the pattern. When a pairwise In this study, there is some evidence of QTL affecting lysozyme and lactate levels in chr 17, 19, and 20. Lysozyme levels have been used as stress indicators for the Atlantic salmon and the rainbow trout ,12, and Focusing on the limitations of the GWAS and genomic selection, the main points are the small number of fish (862) participating in the analysis and the structure of the data . The use of the batch as the fixed effect was necessary because a significant difference between the average measurements of the trait was observed; moreover, there was population stratification between batch 10 and 13  was higher than the traditional pedigree approach for the body weight and stress indicators, except for the glucose levels.Five SNP were found statistically significant, three of which affecting stress indicators and body weight (into a subgroup of the population), and a putative SNP affecting growth performance, while no SNP associated with resistance to


Verbal memory was assessed by the Rey Auditory Verbal Learning Test, which included three measures of verbal memory: immediate recall, short-delay recall, and long-delay recall. We adopted a mixed-model approach to perform a joint GWAS of all participants, adjusting for ancestral background and familial relatedness. The inclusion of participants from all ancestries increased the power of the GWAS. Two novel genome-wide significant associations were found for short-delay and long-delay recall verbal memory. In particular, one locus (rs9896243) associated with long-delay recall was mapped to the NSF  gene, indicating the role of membrane fusion in adolescent verbal memory. Based on the GWAS in the European subset, we estimated the SNP-heritability to be 15% to 29% for the three verbal memory traits. We found that verbal memory was genetically correlated with schizophrenia, providing further evidence supporting verbal memory as an endophenotype for psychosis.Verbal memory impairment is one of the most prominent cognitive deficits in psychosis. However, few studies have investigated the genetic basis of verbal memory in a neurodevelopmental context, and most genome-wide association studies (GWASs) have been conducted in European-ancestry populations. We conducted a GWAS on verbal memory in a maximum of 11,017 participants aged 8.9 to 11.1 years in the Adolescent Brain Cognitive Development Study Psychotic disorders, including schizophrenia and bipolar disorder, are highly heritable (heritability estimated to be approximately 80%) ,2. Althod = −1.20 to −0.85 [d = −0.56 to −0.50 [d = −0.54 to −0.44 [d = −0.42 to −0.33 [Cognitive deficits of different severity are common in psychosis, and often manifest before symptom onset ,8. Thereto −0.85 ) and bipto −0.50 ). Milderto −0.44 ,15) and to −0.33 ,17). Thoto −0.33 ,19. TherPrevious GWASs have found over 200 loci associated with general cognitive ability, as well as its negative genetic correlation with schizophrenia ,22. ChroThe lack of diversity has long been an issue in GWASs, as 78% of published GWASs until 2019 were conducted only in European individuals . Unsurpr® in over 10,000 adolescents of diverse ancestries. We adopted a novel mixed-model approach using the GENESIS package to account for the diverse ancestries in the sample. We also estimated the heritability of the three verbal memory traits and their genetic correlations with other psychiatric traits, in order to investigate the shared genetic basis between verbal memory and psychosis.All in all, previous GWASs on verbal memory have mostly been conducted in European older adults, making them less relevant to psychosis as a neurodevelopmental condition. Therefore, the current study aims to investigate the genetic basis of verbal memory in adolescents as a neurodevelopmental endophenotype for psychosis. We conducted a GWAS on three verbal memory traits measured by the Adolescent Brain Cognitive Development (ABCD) Study® is a longitudinal study that recruited 11,878 participants aged about 9–11 years at baseline from 21 sites across the United States [® if they were not fluent in English, had a history of traumatic brain injury, or had a current diagnosis of schizophrenia, moderate/severe autism spectrum disorder, intellectual disability, or alcohol/substance use disorders [® can be found in a previous publication [The ABCD Studyd States ,40,41. Td States . Interesisorders ,43. For lication .®, participants were firstly verbally presented with a list of 15 words (List A) and immediately asked to recall as many words from the list as possible. The same procedure was repeated four times for the same list (List A) after the first trial. The total number of words correctly recalled in the five trials was recorded as the RAVLT immediate recall score. After learning List A, participants were presented with a different list  and asked to recall words from List B. Right after recalling List B, participants were asked to recall List A again, and the correct number of words recalled in List A was recorded as the RAVLT short-delay recall score. Participants were assessed by other cognitive tests in the following 30 min, and were asked to recall list A at the end of the assessment. The final number of words correctly recalled from List A was recorded as the RAVLT long-delay recall score.The Rey Auditory Verbal Learning Test (RAVLT) is a verbal memory test widely used in research and clinical practice . In the r2) < 0.3 or minor allele frequency (MAF) < 0.01. The final sample included 11,017 participants and 11,229,083 variants after quality control. Details of genotyping, imputation, and quality control can be found in the Participants were genotyped by the Rutgers University Cell and DNA Repository (RUCDR) using the Affymetrix NIDA SmokeScreen Array . All gen® included an ancestrally diverse sample with a combination of singletons, siblings, and twins, we used the GENESIS package in R-4.0.3 to conduct a linear mixed-model GWAS [Since the ABCD Studydel GWAS ,53. Basedel GWAS . We thendel GWAS  and the del GWAS . Finallydel GWAS . To perf−8. Additionally, we checked if the lead SNPs in loci that reached genome-wide significance for one verbal memory trait also reached the nominal significance level for the other two traits.A mixed-model genome-wide association test was conducted using the GENESIS package for the overall sample and the European subset . The RAVr2 [p < 5 × 10−8 independent from each other at r2 < 0.6. Lead SNPs of genomic loci were then defined as a subset of independent significant SNPs at r2 < 0.1. Lead SNPs with a distance closer than 250 kb were merged to one locus. SNPs with r2≥ 0.6 were considered to be in linkage disequilibrium (LD) with the lead SNPs and thus their good proxies.Summary statistics of the GWAS results of the three verbal memory traits in the whole sample were uploaded to the platform of Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA) . A referr2 . In FUMA−6. Promoter regions were defined as 250 bp upstream and 500 bp downstream from the transcriptional start site. SNPs were filtered by enhancers and genes were filtered by promoters based on annotations from the Roadmap Epigenomics Project brain tissue data [We used three strategies to map the lead SNPs and their good proxies to protein-coding genes. (1) Positional mapping: SNPs were mapped to genes if the SNPs are located within a 10 kb window around the gene boundaries. (2) Expression quantitative trait loci (eQTL) mapping: SNPs were mapped to genes if the SNPs have a significant impact on the gene expression. eQTLs were selected from GTEx v8 brain tissues and only significant SNP-gene pairs in GTEx (FDR < 0.05) were used . Mappingsue data .®, we used linkage disequilibrium score regression (LDSC) to estimate the SNP heritability  of the three verbal memory traits. We also used LDSC to test their genetic correlations (gr) with each other, as well as with three psychiatric traits  and educational attainment. LD scores calculated from the European participants in the 1000 Genomes Project (Phase 3) were used [https://www.med.unc.edu/pgc/; accessed on 2 November 2021) [https://www.thessgac.org/; accessed on 5 October 2021) [Based on the GWAS results of the European subset in the ABCD Studyere used . We usedct Phase  were useer 2021) ,62. The er 2021) . All GWA®, there were 5176 females (47%) and 5805 males (53%). The mean age of the participants was 9.9 years . Based on broadly defined ethnicity reported by parents, 5872 (53%) participants were reported as White, 2099 (19%) as Hispanic, 1684 (14%) as Black, and 1% as Asian. The remaining 11% included children of American Indian/Alaska Native, Native Hawaiian and other Pacific Islander, and mixed ethnicity. Participants scored on average 44.2 for RAVLT immediate recall (SD = 9.9), 9.7 for RAVLT short-delay recall (SD = 3.0), and 9.2 for RAVLT long-delay recall (SD = 3.2). The three verbal memory traits were highly correlated at the phenotype level: r = 0.72 between RAVLT immediate recall and short-delay recall (p < 0.001); r = 0.73 between RAVLT immediate recall and long-delay recall (p < 0.001); r = 0.79 between RAVLT short-delay recall and long-delay recall (p < 0.001). All three verbal memory traits were approximately normally distributed in the sample . For RAVLT long-delay recall, we found one genome-wide significant SNP (rs9896243) located in the intron of the NSF gene in chromosome 17 . The GWAS in the European subset included 5635 participants for RAVLT immediate recall, 5667 participants for RAVLT short-delay recall, and 5640 participants for RAVLT long-delay recall. No genome-wide significant associations were found for any of the three verbal memory traits . In contr2 = 0.61) results in a synonymous amino acid change in this gene according to HaploReg v4.1 [p = 1.76 × 10−6). Other variants in the locus have also been shown to affect the expression of the same gene in brain tissues. Additionally, the locus was mapped to seven other genes in the locus (17q21.31) based on brain eQTLs. Details of all mapped genes are summarised in For the gene mapping we considered the lead SNPs in the two novel loci, as well as their good proxies. In the locus associated with RAVLT short-delay recall (lead SNP: rs73984566), there was strong evidence for two genes, NABP1 and TMEFF2, based on chromatin interaction data in fetal cortex. The lead SNP (rs9896243) in the locus associated with RAVLT long-delay recall is located in the intron of the NSF gene, which was supported by positional mapping of the locus in FUMA. Additionally, a good proxy of the lead SNP  to be 0.15 (SE = 0.07) for RAVLT immediate recall, 0.29 (SE = 0.07) for RAVLT short-delay recall, and 0.21 (SE = 0.07) for RAVLT long-delay recall. The genetic correlations among the three verbal memory traits were very high: gr = 0.99 (SE = 0.12) between RAVLT immediate recall and short-delay recall (p < 0.001); gr = 0.91 (SE = 0.12) between RAVLT immediate recall and long-delay recall (p < 0.001); gr = 0.93 (SE = 0.06) between RAVLT short-delay recall and long-delay recall (p < 0.001). The results of their genetic correlations with other psychiatric traits are shown in gr = 0.31–0.53, p ≤ 0.001). Schizophrenia was found to be significantly and negatively correlated with both RAVLT immediate recall  and RAVLT short-delay recall , as well as RAVLT long-delay recall at a trend level . We also found a significant negative genetic correlation between RAVLT short-delay recall and major depressive disorder .Based on the GWAS in the European subset, LDSC estimated the SNP heritability  associated with RAVLT short-delay recall. Chromatin interaction data provided evidence for two genes associated with this locus, NABP1 and TMEFF2. NABP1 (Nucleic Acid Binding Protein 1) is involved in many ubiquitous DNA metabolic processes . The neuWe also found RAVLT short-delay recall to be associated with the 17q21.31 locus. The lead SNP, rs9896243, is located in the intronic region of the NSF  gene. eQTL data provided further evidence for the role of this gene. NSF is an important factor in synaptic neurotransmission that facilitates the recycling of SNARE  proteins, which mediate membrane fusion . Previou® was only composed of children and adolescents [Based on the results of the GWAS in the European subset, we estimated the SNP heritability to be 15%, 29%, and 21% for RAVLT immediate recall, short-delay recall, and long-delay recall. This supports the influence of common variants on verbal memory in adolescents. A previous family study using a similar measure  estimated the heritability to be 35%, 50%, and 73% for the three verbal memory traits, respectively, . The “milescents . There ilescents ,15,16,17We found significant and positive genetic correlations between all three verbal memory traits and educational attainment, which is expected given the close relationship between the two phenotypes. We also found negative genetic correlations between all three verbal memory traits and schizophrenia, although the association was trend-level for RAVLT long-delay recall. However, as the genetic correlations within the three traits were very high and only European participants were included in this analysis, we believe the trend-level association will reach statistical significance with bigger sample sizes. This finding is in line with previous studies that found deficits in verbal memory amongst the relatives of patients with psychosis, supporting the role of verbal memory as a psychosis endophenotype ,15,16,17®, our GWAS is still underpowered to detect SNPs with small effect sizes. Due to the lack of public data available for adolescent verbal memory in diverse populations, we were also not able to conduct replication analysis in an independent sample. Although promising, our findings are preliminary, and future studies with diverse adolescent samples are still needed to replicate our findings. Moreover, as the current methods for estimating SNP heritability and genetic correlations requires LD references from a certain population, we were only able to perform those analyses in the European subset due to the limited sample sizes of other ancestries in the ABCD Study®. Methodological advances are needed in the future to allow for the inclusion of more diverse samples in such analyses. Furthermore, potential sampling bias might exist in the current GWAS. It is possible that children and adolescents with better general cognitive ability were more likely to participate in the ABCD Study®, causing the sample to be biased towards this demographic. Nonetheless, we believe that the study sample was representative of the general population at least in RAVLT performance, as the mean scores were comparable to previous results obtained from children in similar age groups [The current study has limitations. Although benefiting from the large adolescent sample of the ABCD Studye groups ,83. Finae groups ,25, futu® and conducted a joint analysis of participants from all ancestries, which maximised the statistical power enabling genome-wide significant findings. Notably, the NSF gene was identified by both SNP-based association testing and gene mapping, indicating that synaptic neurotransmission and membrane fusion might play an important role in verbal memory during neurodevelopment. We also found negative genetic correlations between verbal memory and schizophrenia, suggesting their shared underlying mechanisms. Future research employing novel approaches, including multi-trait analysis of GWAS summary statistics and pathway-specific polygenic risk scores [In summary, this GWAS, one of the first in children, identified two compelling loci associated with verbal memory that now warrant mechanistic research. We benefited from the diversity within the ABCD Studyk scores ,85, will


Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1–6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F Maynard Smith and Haigh  demonstr2 resource population. The results deepen our understanding of the genetic architecture of growth and carcass traits in chickens.Growth traits play an important role in animal production and breeding programs. Growth traits are quantitative traits that are controlled by multiple genes, and it is difficult to make breakthrough progress with conventional breeding methods. Kemper, Visscher The animal experiments in this study were approved by the Institutional Animal Care and Use Committee (IACUC) of Henan Agricultural University under approval number 17-0118. All experiments strictly followed the guidelines of this committee.2 chicken resource population used in this study was established by the Henan Innovative Engineering Research Center of Poultry Germplasm Resource for genomic studies. The F2 resource population consisted of four cross-bred families (Anka-cocks mated with Gushi-hens) and three reciprocal families (Gushi-cocks mated with Anka-hens) as described previously , semi-evisceration weight (SEW), evisceration weight (EW), liver weight (LW1), heart weight (HW), gizzard weight (GW), spleen weight (SW), pancreas weight (PW), head weight (HW1), claw weight (CW1), double pinion weight (DPW), breast muscle weight (BMW), leg muscle weight (LMW), leg weight (LW), fat bandwidth (FBW), skin fat thickness (SFT), and abdominal fat weight (AFW). Moreover, the percentage traits were also calculated. BMR, LMR, LR, and AFR were the ratios of BMW, LMW, LW, and AFW to EW, and the other ratios for each carcass trait were calculated by dividing their weight by BW12, for example, CWR = CWe/BW12. The measurement methods have been previously detailed by Han et al. (P < 0.05) were ranked by normal scores with the Tukey method, and then transformed data were used in the following genetic analyses.Phenotypes, including growth traits and carcass traits, were measured for all 860 chickens. Growth traits consisted of body weight (BW) and body size. BW was recorded individually every 2 weeks from birth to 12 weeks . Body size was measured at 4, 8, and 12 weeks and included shank length (SL), shank girth (SG), chest depth (CD), chest width (CW), breast bone length (BBL), pectus angle (PA), body slanting length (BSL) and pelvis breadth (PB). Carcass traits were collected after 860 Fn et al.  and Li en et al. . The pheIndividual genomic DNA was isolated from blood samples by the Qiagen DNeasy Blood and Tissue Kit  according to the manufacturer’s instructions. The DNA quality and concentration satisfied the requirements for library construction of double-digest genotyping-by-sequencing (ddGBS)   was implemented using GCTA  in the GCTA program.y is the phenotypic values for each trait; W is a matrix of covariates (fixed effects) that control the population structure (top two PCs), sex and genotyping batch effect; in particular, BW0 is also considered as a covariate in all BW traits; α is a vector of corresponding coefficients including the intercept; β is the SNP effect and x is a vector of SNP genotypes; u is a vector of random effects with a covariance structure that follows a normal distribution as u ~ N , where K is a known genetic relationship matrix; and e is a vector of random errors.The following MLM was used:P value threshold was calculated using Bonferroni correction with an effective number of independent tests. We calculated the number of genome-wide independent markers using PLINK  > 5.72) based on the 26,352 independent markers. Manhattan and Q-Q plots were derived from the GWAS results using the CMplot package (https://github.com/YinLiLin/R-CMplot) within the R software (http://www.r-project.org/).The genome-wide significance http://circos.ca/) software package  program . For example, the pairwise Pearson’s r of BW8, BW10 and BW12 was >0.85. In addition, different body size measurements, including SL, CW, SG, BBL, and BSL, showed stronger correlative relationships at the same stage . However, a relatively weak correlation was observed in both the BW and partial body size traits  between the early- and late-growth stages. Furthermore, a strong correlation occurred between the CWe trait and parts of the CWe, such as the BMW, LMW, HW1, LW, CW1, EW, and SEW  > 5.72)  Fig. .Fig. 3Th10(P) = 7.35; intergenic) explained 2.31% of the phenotypic variance. The genomic control inflation factor (λ) calculated for all growth traits ranged from 0.97 to 1.00, which indicated acceptably low false positives  = 14.16; intron) could explain 10.14% of phenotypic variance. The genomic control inflation factor (λ) calculated for all growth traits ranged from 0.95 to 1.00, which is similar to the value found for growth traits  < 4, two genomic regions (Chr1:167.5–170.5 Mb and Chr4: 70.9–81.7 Mb) were obtained within a total range of 3 Mb.The genome-wide significant SNPs were mainly distributed throughout Chr1, Chr4, and Chr27 ; iii. SNP set III, high-LD SNPs with SNP set I and SNP set II (r2 > 0.7) with the removal of one of any pair of adjacent SNPs <100 bp apart. The SNPs obtained from the three datasets are listed in Table We found that significant SNPs on Chr1 had effects on multiple traits, and these SNPs were located in relatively large QTLs, which might affect BW together. Therefore, we screened the SNPs with the following criteria and obtained different SNP data sets. i. SNP set I, significant SNPs associated with BW at the age of different weeks; ii. SNP set II, significant SNPs associated with traits strongly correlated with BW  neighboring the significant locus also remained. Finally, a total of 277 unique putative candidate genes were used for enrichment analysis (Table MLNR) gene was enriched.Annotation of each significant locus revealed putative candidate genes. Moreover, the genes located within the high-LD region . Chicken growth traits are a class of complex traits with important economic impacts. Analyzing the genetic basis not only served as a key link for chicken breeding programs but also enhanced our understanding of the genetic pattern of QTLs.A large number of GWAS have shown that most comprehensive traits and diseases are controlled by multiple genes, and the effect of a single gene is relatively small. For example, a joint analysis of 183,727 people identified 180 height-related loci, which explained a total of only 12% of the phenotypic variations   at Chr1 functions in the regulation of vitamin D metabolism by binding to the vitamin D receptor, further affecting the development and maintenance of mineral ion homeostasis and skeletal integrity  gene located at Chr1 catalyzes the process of phosphorylation to activate AMP-activated protein kinase and then regulates food intake  line to identify QTLs on Chr1, -2, -4, -7, -8, and -13 that affected BW, and Chr4 had the largest single additive effect  gene located at Chr4 has been reported many times to be associated with chicken growth traits  gene located at Chr27 has been demonstrated to be the major gene for growth traits in ducks (Zhou et al. Park et al. found that Chr27 harbored a highly significant QTL for shank weight but no significant effect on growth (Hee-Bok et al. Growth is a complex trait and combined process that is influenced by food intake, appetite, energy composition, nutrient distribution, physical activity, metabolic rate, etc. Therefore, the genetic basis of the growth trait might be affected by many loci, each of which only explained a small variation of the trait. The results of selective-sweep mapping suggested that >100 loci might contribute to the selection response (Johansson et al. This study provided systematic phenotypic measurements and genetic mapping of growth and carcass traits in 734 chickens from a Gushi-Anka F2 population based on ddGBS sequencing data. A total of 470 significant SNPs for growth and carcass traits were detected and mapped on Chr1-6, -9, -10, -16, -18, -23, and -27, including new loci and genes. Remarkable effects of QTLs on Chr1, -4, and -27 were detected. The significant SNPs associated with multiple traits were all located on Chr1, -4, and -27. Haplotype analysis of the QTL on Chr1 showed a mosaic inheritance pattern. Significant SNPs and pathway enrichment revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for further study.Supplementary MaterialsSupplementary Table S3Supplementary Table S4Supplementary Table S5Supplementary Table S6Supplementary Table S7


Genome-wide association meta-analysis (GWAMA) is an effective approach to enlarge sample sizes and empower the discovery of novel associations between genotype and phenotype. Independent replication has been used as a gold-standard for validating genetic associations. However, as current GWAMA often seeks to aggregate all available datasets, it becomes impossible to find a large enough independent dataset to replicate new discoveries. Here we introduce a method, MAMBA , for assessing the “posterior-probability-of-replicability” for identified associations by leveraging the strength and consistency of association signals between contributing studies. We demonstrate using simulations that MAMBA is more powerful and robust than existing methods, and produces more accurate genetic effects estimates. We apply MAMBA to a large-scale meta-analysis of addiction phenotypes with 1.2 million individuals. In addition to accurately identifying replicable common variant associations, MAMBA also pinpoints novel replicable rare variant associations from imputation-based GWAMA and hence greatly expands the set of analyzable variants. In genome-wide association meta-analysis, it is often difficult to find an independent dataset of sufficient size to replicate associations. Here, the authors have developed MAMBA to calculate the probability of replicability based on consistency between datasets within the meta-analysis. These associated variants helped narrow down the list of potential causal genes, and provided numerous targets for biological follow-up and drug development8. For the years to come, it will be a central focus of disease biology to understand the functional and clinical consequences of GWAS loci.Genome wide association meta-analysis (GWAMA) is an effective approach to enlarge sample size and empower the discovery of genetic variants associated with complex traits. In the past decade, GWAMA identified numerous genetic variants that are associated with various complex traits, including cardiovascular diseases9. As GWAS discovery sample sizes increase, newly identified loci tend to have smaller effect sizes, or come from variants with rare minor allele frequency10, which makes finding a sufficiently powered replication dataset increasingly challenging. Moreover, after replication, studies often seek to jointly analyze the discovery and replication datasets to discover additional loci, which will be left unreplicated. As such, there is a compelling need to develop a principled statistical model-based approach to assess the replicability of genetic association studies when a suitable replication dataset is unavailable.A critical step preceding any functional follow-up is to confirm the validity of the identified association signals. Ascertainment bias, phenotyping or genotyping error, population structure, or cryptic relatedness can all cause false positive discoveries and mislead downstream functional studies that are costly to perform. To minimize false positive findings, replication is often conducted using an independent dataset. If the identified association remains significant, the signal is considered as replicated and likely valid. While replication is the gold standard for validating GWAS discovery, there is always a tension between the motivation of designating a suitably sized replication dataset, and aggregating all available cohorts in a discovery sample to maximize the power of genetic discovery. Just as in discovery samples, replication studies can also have type I or type II errors, so it is important that replication studies should be of sufficient sample size to convincingly distinguish the non-zero effect from the null effect11, random effect meta-analysis12, or their adaptations in GWAS14, do not specifically address the replicability problem. These methods may produce spurious meta-analysis results when some participating studies contain false positive signals. In practice, some ad hoc procedures may be applied to examine the validity of the results15, e.g. if the association signal is supported by a certain number of participating studies or if the heterogeneities of the genetic effect between genetically similar populations are small16, which can be hard to reproduce and generalize. Also, in order to protect against spurious associations, some overly conservative criteria may be applied in the quality control, e.g. studies may attempt to remove all low-frequency variants from imputation-based GWAS17, even though many of the imputed low-frequency variants may still be informative and causative. Some principled methods exist for assessing the replicability for biological experiments, including repfdr and SCREEN which were developed specifically for GWAMA20. These existing methods seek to leverage the strength and consistency of the signals between biological replicates to distinguish replicable and non-replicable signals. Yet there are several limitations to these approaches when applied to GWAMA. For one, they only rely on the statistical significance of the association but do not consider the estimated effect sizes, or the potential sample size differences between participating studies. Large datasets produce more significant p-values compared to smaller studies when the estimated association effect size (either genuine or spurious) is the same, so the significance of association in each cohort is not a reliable measure for replicability. Also, some of these methods (e.g. repfdr18) were developed for a few biological replicates and cannot scale well with meta-analyses with many participating studies.Classical approaches for meta-analysis, such as fixed effects17. In the published meta-analysis17, a stringent quality control was conducted and only variants with MAF > 0.1% were analyzed to ensure the quality of the results, yet it potentially left out well-imputed rare frequency variants with MAF < 0.1%. In this study, we reanalyze the common variants (with MAF > 1%) and low-frequency variants (0.1%<MAF < 1%) analyzed in the original study, as well as the rare imputed variants (with MAF < 0.1%) using MAMBA. Among the 556 published common and low-frequency variant signals, we identify only one with low PPR (<10%), while 529 have PPR greater than 99%. In our extended analysis of ~4300 rare imputed variants, we identify 2,807 variants with PPR greater than 99% with many being coding variants. These identified rare variant association signals pinpoint potential new loci with pleiotropic effects on lipids metabolisms, immunity, and substance use. MAMBA hence further expands the utility of imputation-based genetic studies to robustly study rare variants.We address the limitations of existing methods by developing a principled approach MAMBA  to assess the replicability of GWAMA association signals. Our approach models the genetic effects as a mixture of SNPs with real non-zero effects, normally-behaved null SNPs, and SNPs that have null effects but appear as spurious association signals in some participating studies due to artifacts in the data. MAMBA performs meta-analysis for genome-wide SNPs and calculates a posterior probability of replicability (PPR) that a given SNP has a non-zero replicable effect. Similar to other methods for assessing replicability, our method exploits cohort-level summary association statistics from multiple studies in GWAMA. It assigns a higher PPR to an association signal, if the SNP is significantly associated with the phenotype and its estimated effect sizes are consistent across multiple studies. Compared to other meta-analysis methods, MAMBA is much more robust to outlier studies. In the special case that fixed effects assumptions hold, and no heterogeneity or outliers are present, MAMBA is similar to a standard inverse-variance weighted meta-analysis (except that MAMBA imposes a prior on the distribution of effect sizes across SNPs), resulting in virtually no loss of power compared to the widely used fixed effect meta-analysis. We conduct extensive simulations to evaluate the performance of our approach in assessing the replicability of association signals in meta-analysis across a wide range of scenarios. We show that MAMBA can powerfully identify replicable association signals. It also improves the genetic effect estimates by borrowing information across genome-wide SNPs and applying shrinkage. We further demonstrate the value of the method by applying it to a GWAMA of several smoking and drinking addiction phenotypes from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), where summary statistics are aggregated from 35 individual study cohorts of European ancestry, and up to 1.2 million research participantsIn this work, summarily, we propose methods for assessing replicability from GWAMA, reanalyze an ultra-large-scale GWAMA of tobacco and alcohol use phenotype, and identify a number of interesting rare variant associations. The proposed methods and software will benefit future large-scale genetic studies using biobanks.p-value (p = 1 × 10−9). Just as in this example, an outlier from a contributing study may easily dominate the result in a fixed effect meta-analysis, even if a majority of the test statistics follow the null distribution. This insight motivated us to model the effect size estimates from participating studies as a mixture of outliers with inflated variance and normal well-behaved estimates.The MAMBA model was motivated by the observed patterns of outliers from multiple large-scale GWAMA on lipids levels and smoking drinking traits. As a motivating example, we plotted the contributed summary association statistics (i.e. the Z-score statistic) from each participating study Fig.  for a SNbj and its standard deviation sj from participating studies as input for a particular SNP j, i.e. Rj ~ Bernoulli (π), with Rj = 1 if the SNP has a real non-zero effect. When the SNP has null effect (i.e. Rj = 0), we further define an indicator variable Ojk ~ Bernoulli (λ) indicating whether the SNP is a spurious association with inflated variation (i.e. Ojk = 1) in study k, or it is a well-behaved null SNP (i.e. Ojk = 0). Conditional on the indicators, the distribution of the genetic effect bjk satisfiesMAMBA is a two-level mixture model that takes the genetic effect a is an inflation factor which captures the extent of inflation in the observed effect sizes for outlier summary statistics . As a special case, when no outliers exist, conditional on the mean value parameter μj, the MAMBA model reduces to that of a fixed effect meta-analysis21, i.e. Rj =1, Ojk = 1 is not considered in our model). To assess the replicability of GWAS loci, we choose the sentinel variant from each locus as input, which is pruned based upon linkage disequilibrium. We assume that the SNPs used in the model are independent, so the likelihood for all SNPs becomes the product of the likelihood of individual SNPs. When it is of interest to estimate the genetic effect sizes of all variants in a locus (and not merely the sentinel variants), we found that fitting the same model using correlated SNPs led to similar improvements as MAMBA in estimating genetic effect size. In this case, the model can be considered as a composite likelihood (MAMBA-est), which takes all SNPs in the identified loci as input. This allows for genome-wide estimates of genetic effect size, which can be used for many downstream analyses. If the primary goal of the analysis is assessing replicability, MAMBA is preferred to MAMBA-est due to its computational convenience. A more detailed comparison of MAMBA and MAMBA-est can be found in “Results”.Here H0:μj = 0 for each SNP. More model details can be found in the “Online Methods”.Using an expectation-maximization algorithm, we estimate the hyperparameters from the data, and calculate the PPR. MAMBA (and MAMBA-est) give improved estimates of the genetic effect by modeling the joint distribution of the effect sizes across different genetic variants. To facilitate the comparison with frequentist methods, we further developed a parametric bootstrap method to calculate p-values testing fixed effects inverse variance weighted meta-analysis (FE);12;random effects DerSimonian-Laird model (RE)13;Han and Eskin’s random effects model (RE2) that assumes no heterogeneity across studies under the null hypothesis14 that assumes for each SNP, a portion of the studies in the meta-analysis have null effects while the rest of the studies have fixed effects;Binary effects model (BE)19, a method which calculates the posterior probability that a SNP has non-zero effect in at least a given number of studies.SCREEN method for replicability analysisWe conducted extensive simulations to compare the performance of MAMBA with existing meta-analysis and replicability analysis methods. We assessed the models in terms of type I error control, power, and estimation of the underlying effect size. The models considered for comparison includeτ2. The effect size estimate variances As each method makes different assumptions regarding the distribution of the estimated effect sizes across studies, we considered 5 different data generation processes (DGP) to facilitate a comprehensive and fair comparison between different methods. Under each DGP, we simulated 60 million independent SNPs, and randomly picked 1% of SNPs to have true non-zero effect, which are normally distributed with mean zero and variance For the MAMBA DGP, we varied the severity of outlier test statistics, while for RE and RE2 DGP we varied the amount of effect heterogeneity across cohorts. For FE DGP we considered different magnitudes of fixed effects sizes. For BE DGP we randomly selected a fraction of the studies where the genetic effect of causal variants is non-zero. A complete and detailed breakdown of simulation scenarios considered can be found in the −6, 1 × 10−5, 1 × 10−3, and 0.05 for each method under different DGPs , all methods have controlled type I error for different significance thresholds, except for the RE method which tends to be conservative. When outliers are present in the dataset (DGP=MAMBA), all models except for MAMBA have inflated type-I error. The inflation of the type-I error rate becomes increasingly severe as the significance level becomes more stringent. For example, at α = 1 × 1024. The type I error inflation is due to the challenge of accurately estimating the heterogeneity in a set of meta-analysis studies. On the other hand, the MAMBA model produces better-calibrated p-values compared to the RE model even when the data is generated according to a RE model. For example, at α = 1 × 10−6, the RE method type-I error rate is 9.7 × 10−6, close to 10 times the significance threshold, while the type I errors for FE, RE2, and BE methods are all greater than 20 times the nominal threshold. The SCREEN model was not considered here, as it does not calculate meta-analysisp-values.Interestingly, the RE method does not have well-controlled type-I error even when the data are generated under a RE DGP. This is in fact consistent with previous investigations−6. The power comparison was based upon the recalibrated threshold  we used in the simulation studies. In general, one would expect some advantages for the RE2 method over alternatives in cases of more extreme effect heterogeneity. Finally, while the BE model has superior power when less than 90% of the studies are associated with the phenotype, the MAMBA and FE models are the most powerful methods when the genetic variant is associated with the phenotype in 90% or more of the studies. As the goal of the MAMBA model is to identify real and non-zero replicable associations where effects are present in all cohorts, the comparison result with BE is expected in cases where only a small proportion of studies are associated with the phenotype.We next compared each method in terms of power under different DGPs Fig. . As someIn assessing the accuracy of effect size estimation, we observed that the MAMBA model exhibits lower mean-squared error (MSE) between the estimated and true effect sizes compared to FE or RE methods regardless of the DGP Fig. . This isI2 heterogeneity statistic between 5–30%, the fraction of estimated outlier studies was large (~0.6), but the estimate of variance inflation α was moderate (between 1 and 1.6). As indicated by well-controlled Type I error, MAMBA appears to be flexible enough to adequately model a RE DGP. Under all DGP, the estimated proportion and variance of replicable non-zero effect SNPs were well estimated by the MAMBA model. We also ran additional simulation scenarios, considering cases where the MAMBA inflation factor was large and the proportion of outliers was small , and where the inflation factor is relatively modest . These scenarios are reflective of the models estimated for GSCAN addiction phenotypes. We found that MAMBA hyperparameter estimates remained unbiased with well-controlled Type-I error rates, with power and MSE of effect sizes improved compared to alternative methods , Smoking Initiation (SmkInit), Smoking Cessation (SmkCes), and Cigarettes Per Day (CigDay) phenotypes. Table We also used the GSCAN dataset to compare meta-analysis methods and their potential to assess replicability in GWAS. The GSCAN study consists of 35 contributing research studies and a combined sample size of up to 1.2 million participantsp-values < 1 × 10−5, we applied clumping25 and retained the SNP with the most significant p-value in each locus, and removed any SNPs within 500kB that have an LD coefficient of > 0.1 with the sentinel variant26. These retained SNPs were combined with non-significant  pruned variants with minor allele frequency (MAF) > 0.01 to fit the MAMBA model. The non-significant pruned variants are included in the dataset to ensure that the non-replicable mixture component of the MAMBA model is represented and can be accurately estimated. Their inclusion is for numerical considerations. We also applied MAMBA-est to all SNPs in identified loci. For both MAMBA and MAMBA-est, a separate model was estimated for each chromosome to allow the hyperparameters to vary across chromosomes. The average time for model convergence was less than 2 minutes for MAMBA models and less than 5 minutes for MAMBA-est , but similar results for the higher-ranking SNPs (which are more likely to have true non-zero effects)  Fig. . This dewer Fig. . On the SNP Fig. , suggestSNP Fig. .Table 2Kρ = 0.85, Spearman ρ = 0.875), and estimated P-values  between MAMBA and MAMBA-est.The MSE and Pearson correlation coefficient between discovery and replication cohort effect sizes were improved for all phenotypes and for practically every comparison considered, in particular for the genetic effect estimates for low and rare frequency variants with MAF < 1% , 87 SNPs had MAMBA PPR less than 0.1  . MAMBA thus can maximize the utility of the imputation-based GWAS, in particular for the discovery of associated lower frequency variants.Using MAMBA model outputs, we summarize the predicted number of outliers at each SNP and across GSCAN phenotype and MAF ranges Table . We obseThe promising results from simulation and real data analysis encouraged us to reanalyze the GSCAN data using all available studies including 23andMe. We leveraged MAMBA to determine replicable and non-replicable signals without imposing any preset filtering criteria.We first examined the replicability of the 556 reported hits (MAF > 0.1%) in the original GSCAN study, where we found 555 signals have PPR>99%. We identified rs79631993 to have low probability of replicability for the SmkInit phenotype . This SNP was highly significant as an outlier in one cohort, but became insignificant when meta-analyzed using the rest of the cohorts (Fixed Effects PVALUE=0.6).28 to determine potential effects of these variants on genes and transcript sites, and found 262 SNPs which may function as either stop-gain or missense mutations, or are intronic mutations with genome-wide significant p-values (PMAMBA < 5 × 10−8)  association signals which may be functionally important but were not identified in the original analyses. For GSCAN phenotypes, 4337 rare variants with MAF < 0.1% were analyzed, of which 2807 had PPR greater than 99%. We used the Ensembl Variant Effect Predictor29. We found that 39 of the 262 SNPs corresponded to genes with previously cited associations for another smoking–drinking trait, with 5 being associated with both smoking and drinking phenotypes36  . rs121908486 is a known pathogenic variant for the SLC7A9 gene, and is identified as replicable for both DrnkWk (PMAMBA < 7.6 × 10−7) and SmkCes (PMAMBA < 3 × 10−8) phenotypes. SLC7A9 is located within “amino acid transport across the plasma membrane” pathway which has also been associated with alcohol dependence37. A missense variant (rs28936679) in the AANAT gene is significantly associated with SmkCes (PMAMBA < 3 × 10−8), and moderately associated with SmkInit (PMAMBA < 1.39 × 10−7). AANAT is involved in melatonin synthesis and controlling night/day rhythm in melatonin production. Mediation of circadian rhythm-driven mechanisms and synthesis of melatonin through AANAT expression has been proposed as an influential mechanism for cocaine and potentially other drug addictions38.Among the 262 identified variants, a number of them are rare coding variants that point to genes with relevant mechanisms in addiction. The SNPs (rs121908486 and rs140272400) function as missense mutations, and reside in known lipids-associated genes  to protect against false positives. However, many of the low-frequency SNPs may still provide valuable association information. For future studies, we suggest using a more lenient filtering criteria in combination with PPR estimated by MAMBA to identify replicable associations, as current procedures for filtering variants may be overly conservative but can still fail to filter out spurious association signals.In real data analysis of addiction phenotypes, we found MAMBA outperforms conventional heuristic quality control procedures that are being used in GWAS studies, such as examining if a GWAS “peak” has a strand of neighboring variants in LD which are also significantly associated. As we showed in the results, some spurious association signals also have supporting neighbors, which would likely be missed by visual inspection but were correctly pinpointed by MAMBA. We also found that our method can reliably identify replicable low-frequency SNPs and improve the coverage of imputation-based GWAMA to lower frequency variants. In practice, imputation-based GWAS meta-analyses often remove all low-frequency variants , Rj ~ Bernoulli (π), Ojk ~ Bernoulli (λ)Together, the distribution for the summary statistics θ = , among which α is used to model the inflated effect sizes for “outlier” summary statistics.The hyperparameters of the model are denoted by j isHere, we assume that the contributed studies in a meta-analysis are non-overlapping and independent of each other, so the probability density function for a SNP As pruned SNPs are independent of each other, the joint likelihood satisfies:In fact, the likelihood in (4) can also be viewed as a composite likelihood when used to analyze genome-wide correlated SNPs and improve accuracy of genetic effect estimates (i.e. MAMBA-est).θ =  with an Expectation and Maximization (EM) algorithm (See We fit the joint model in (2) using an empirical Bayes approach, and estimate the hyperparameters j can be derived asThe posterior probability of a SNP having replicable effect (PPR) is estimated by from all L bootstrap datasets form an empirical distribution under the null hypothesis of no association. Let l. We can calculate the p-value for SNP j in the original dataset by p(Rj = 1|bj) is the PPR in the original dataset for SNP j, and m in the lth simulated dataset.The posterior probabilities of the simulated non-associated SNPs  is a binary trait that contrasts ever and never smokers. Ever smokers were defined as individuals who have smoked >99 cigarettes in their lifetime, which is consistent with the definition by the Centre for Disease ControlII.Cigarettes per day (CigDay) is a quantitative trait that measures the average number of cigarettes smoked per day by ever smokers;III.Smoking cessation (SmkCes) is a binary trait that contrasts former vs current smokers.IV.Drinks Per Week (DrnkWk) is a quantitative trait that measures the average number of drinks per day by regular drinkers.We evaluated the proposed methods using the meta-analysis dataset from the GSCAN consortiumAge of Initiation (AgeInit) was the only GSCAN consortium phenotype excluded from our analysis, as there were too few SNPs which surpassed genome-wide significance using fixed effects meta-analysis.−5).Step 0: We first perform fixed-effect GWAS meta-analysis to identify loci of interest with suggestive evidence of association  models, METASOFT software v2.0.1 was used53.Many analyses were conducted using R with packages including MatrixFurther information on research design is available in the Supplementary InformationReporting SummaryDescriptions of Additional Supplementary FilesSupplementary Data 1-16