CPEB3, ACVR1, MAST2 and CACNA1H) as most plausible pleiotropic genes for the traits under study. Three candidates  were associated with antagonistic pleiotropy, while ACVR1 with synergistic pleiotropic action. Current results provide a novel insight into the biological mechanism of the genetic trade-off between growth and reproduction, in broilers.The objective of the present study was to discover the genetic variants, functional candidate genes, biological processes and molecular functions underlying the negative genetic correlation observed between body weight (BW) and egg number (EN) traits in female broilers. To this end, first a bivariate genome-wide association and second stepwise conditional-joint analyses were performed using 2586 female broilers and 240 k autosomal SNPs. The aforementioned analyses resulted in a total number of 49 independent cross-phenotype (CP) significant SNPs with 35 independent markers showing antagonistic action i.e., positive effects on one trait and negative effects on the other trait. A number of 33 independent CP SNPs were located within 26 and 14 protein coding and long non-coding RNA genes, respectively. Furthermore, 26 independent markers were situated within 44 reported QTLs, most of them related to growth traits. Investigation of the functional role of protein coding genes via pathway and gene ontology analyses highlighted four candidates ( In general, the most important source for genetic correlations is usually pleiotropy4, however, genetic correlations may also arise from linkage disequilibrium (LD) among distinct loci5.Reproductive traits in livestock species often show an antagonistic relationship with growth traits that is manifested as negative genetic correlation between single members of trait complexes. In chicken, a typical example is the negative genetic correlation  associations are termed cross-phenotype (CP) associations9 and are not necessarily indicative of pleiotropic genetic variants.When searching for pleiotropic loci via GWAS, it is important to bear in mind that CP associations are based on statistical evidence regardless of the underlying cause11. In contrast to AP, synergistic pleiotropy13 (SP) occurs when a genetic variant simultaneously either increases or decreases performance in two different traits.On the contrary, pleiotropy occurs when a genetic locus truly affects more than one trait, simultaneously. When beneficial effects of a genetic factor on one trait are accompanied by negative effects on the other trait, antagonistic pleiotropy (AP) exists14. Specifically, in biological pleiotropy, a genetic variant or a gene affects multiple phenotypes since causal variants for different phenotypes can be colocalized in the same gene. In mediated pleiotropy, there is a causal relationship between two phenotypes so as a variant exerts an effect on one phenotype through the another one while spurious pleiotropy refers to a falsely association between marker and phenotypes due to bias, misclassification or linkage disequilibrium (LD). To overcome the challenge of spurious pleiotropy in the latter case, approaches such as LD pruning and conditional and joint analysis (cojo) can be applied to alleviate the high SNP interdependency arising from LD and to select the LD independent SNPs.Based on the mechanisms of action, pleiotropy can be distinguished in: biological , mediated  and spurious pleiotropy15 and for egg weights at different ages17. Nevertheless, no GWAS has, so far, been reported with the aim to discover genetic variants associated with body weight (BW) and egg number (EN) in chickens.In chicken, CP associations have already been reported by GWAS for various traits such as daily feed intake and efficiency14) we then applied conditional and joint analysis (cojo) of the SNP signals obtained from bivariate analysis to identify LD-independent CP SNPs. Finally, we investigated the functional role of the candidate genes underlying the independent CP SNPs in attempts to propose the most relevant pleiotropic genes implicated in the genetic control of traits under study.Driven from the scarcity of relevant reports, we have elaborated the present study with the aim to identify genetic variants and genes simultaneously affecting BW and EN in chickens. To this end, first we conducted a bivariate GWAS to identify SNP signals associated with both traits. As LD could generate spurious pleiotropic associations  were less than 1 indicating the absence of population structure or artifacts in the present data. Furthermore, the genomic genetic correlation (rg) between the two traits was estimated as high as − 0.183 ± 0.15 (results not shown). Figure 10 values) across the three GWAS. Specifically, a total number of 667 genome-wide significant SNPs  were detected by BW univariate analysis and these SNPs were dispersed across the 28 autosomes  on GGA12  presented the lowest p value (6.13E-32) after ‘cojo’ analysis. Table β) on the two traits obtained from bivariate analysis. In accordance with antagonistic pleiotropic action i.e. positive effects on one trait and negative effects on the other trait, the estimated effect sizes for the majority (35 out of 49) of the CP associations displayed opposing signs  is a weighted average of effect sizes of markers exhibiting antagonistic, synergistic and non pleiotropic action and only in the extreme case of rg = − 1 all the implicated markers would exhibit opposing effects on both traits.Note that the estimated genomic rZC3H18, one a synonymous variant of gene EXTL1 and 23 were intron variants of annotated genes .A total number of 40 positional candidate genes (of which 24 were annotated genes) were identified as lying within 33 independent SNPs while 16 SNPs were intergenic variants  to the marker.Figure NOL4 and ZC3H18) candidate genes and highlighted a total number of 120 enriched GO BPs . Nevertheless, only CPEB3, ACVR1, CACNA1H and MAST2 were found to participate in reproduction related processes. Specifically, CPEB3 was associated with three concurrent annotations related to reproduction   were associated with sexual reproduction (GO:0019953) while ACVR1 and MAST2 were further associated with cellular process involved in reproduction in multicellular organism (GO:0022412)  and was also member of two pathways: positive regulation of BMP signaling (GO:0030513) and TGF-beta signaling pathway (gga04350). Based on functional relevance derived from ontological and functional analyses, the four above genes  were nominated as most plausible pleiotropic candidate genes for BW and EN. The four above mentioned genes lied within respective numbers of SNPs i.e. CPEB3 , pcojo = 3.12E−07), ACVR1 (rs313879964 (GGA7), pcojo = 2.15E−22), MAST2 (rs317902708 (GGA8), pcojo = 3.88E−10) and CACNA1H , pcojo = 1.16E−11) (Table β) obtained from bivariate analysis for the respective markers are indicative of antagonistic pleiotropic action for the three genes i.e. CPEB3 , MAST2  and CACNA1H  and synergistic action for ACVR1 , catalytic activity (GO:0003824) and transferase activity (GO:0016740), CACNA1H of ion transmembrane transporter activity (GO:0015075), channel activity (GO:0015267) and transporter activity (GO:0005215) and finally CPEB3 of nucleic acid binding (GO:0003676) and translation regulator activity (GO:0045182) (results not shown).With regard to molecular function, the four candidate genes showed GO slim terms of binding (GO:0005488) and protein binding (GO:0005515), 3. As this genome-wide estimate only describes the cumulative CP effects of all implicated causal loci, in this study we further attempted to identify genetic variants with strong statistical associations for both traits, quantify their patterns of pleiotropic effects and explore the involved biological processes and/or pathways.In the present study, marker trait(s) association analyses along with in silico exploration of the biological role(s) of the implicated genes were jointly applied to refine our understanding of the genetic trade-off between BW and EN, in broilers. The genetic antagonism between the two traits was confirmed at the genome-wide level, as the estimate of the genomic genetic correlation between BW and EN was as high as − 0.18 herein, in concordance with previous findingsrs313298834) on GGA12 that has been associated with EN in female broilers18 and five more markers  associated with BW19 in broilers. We also noted a higher number of SNP signals passing the genome-wide FDR significance threshold (set up to 0.10 here) for BW when compared to EN. As the yield of GWAS critically depend on the underlying effect-size distribution of the implicated variants20, this is not a surprising finding and it may be attributed to the lower heritability estimate for EN (0.17) when contrasted to the respective estimate (0.30) for BW.Marker trait(s) association analysis verified previous results, by revealing one common SNP (6), a critical step in the present study was to identify only LD-independent SNPs via stepwise cojo analysis, as in other GWAS22. After overcoming the LD challenge, our results disclosed several genetic variants simultaneously affecting BW and EN with some of them pointing to most promising pleiotropic genes, as we discuss below.Bivariate analysis identified a vast number of genome-wide significant CP SNPs. To ensure that CP associations did not arise from LD between markers (spurious pleiotropyrs314529054 (GGA6). The marker is located within two reported QTLs related to ovary and body weight and lies within CPEB3 (cytoplasmic polyadenylation element binding protein 3) gene. Ontological and functional analysis suggested that the gene in question may be considered as a true pleiotropic gene, however, literature evidence on its functional relevance to the traits examined here is shortcoming. In mice, CPEB has been reported to control polyadenylation and translation during the dictyate stage of oocyte development and this regulation has also a profound influence on folliculogenesis23.The first SNP that served as a proxy to a plausible pleiotropic gene was rs313879964 (GGA7) pointed at a gene with a highly likely pleiotropic function. This specific marker lied within ACVR1  gene. This gene participates in several growth and reproduction related GO BPs and has well documented involvement in biological phenomena such as those examined here. As MEA showed, ACVR1 encodes for a bone morphogenetic protein (BMP) type I receptor of the transforming growth factor-beta (TGF-β) superfamily which plays a key role in cell growth while regulates several reproductive processes (such as follicular development and ovulation)24. In addition, ACVR1 regulates reproduction via the BMP and anti-Müllerian hormone (AMH) signaling25. In chickens, AMH is required for the urogenital development and germ cell migration26, is presented in early development of follicles and is expressed in small follicles27. So far, the chicken ACVR1 gene has been suggested as a positional candidate gene for body weight28, has a regulatory role during skeletal development in osteogenesis and chondrogenesis29 and is expressed within the chicken granulosa and thecal layers during ovarian follicle development30.In contrast to the previous marker, rs317902708 (GGA8) and rs317631529 (GGA14) pointed at two most promising pleiotropic genes i.e., MAST2 and CACNA1H, respectively. Specifically, MAST2 (microtubule associated serine/threonine kinase 2) has been previously detected by RNA-seq in visceral fat of broiler and layer females at the onset of sexual maturation31. Furthermore, human MAST2 gene has been reported to be involved in PI3K-AKT signaling pathway32 that regulates various cellular processes, such as proliferation, growth, apoptosis and cytoskeletal rearrangement33. On the other hand, CACNA1H  encodes for Cav3.2 channel that is a member of the voltage-gated calcium channel family. This gene participates in the T-type Ca2+ channels which contribute to signal transduction pathways regulating protein synthesis, development, proliferation and cell differentiation34 that are mainly expressed during embryonic development34. Particularly, these channels are involved in the early stages of muscle differentiation in humans35 and mice36. Female Cav3.2-/- null mutant mice presented decreased body weight37 and reduced litter size38. Moreover, Cav3.2 may have a role in reproduction since it facilitates the influx of Ca2+ in mouse oocytes and eggs to maintain Ca2+ homeostasis during oocyte maturation and post fertilization38. The murine Cacna1h gene is also upregulated in the proestrus of the Gonadotropin-releasing hormone (GnRH) neurons39. GnRH determines the pattern of secretion of follicle stimulating hormone (FSH) and luteinising hormone (LH) that regulate the endocrine function and gamete maturation of gonads40. So far, the chicken CACNA1H gene has only been associated with egg quality41 and body weight42.Two more markers, CPEB3, MAST2 and CACNA1H) were identified as ‘trade-off’ genes i.e. exhibiting antagonistic pleiotropy, while ACVR1 displayed synergistic pleiotropic action.Based on the approach followed herein, three candidates 44. They can also regulate numerous functions such as epigenetic modification, transcription and post-transcription while playing a key role in tissue development, muscle contraction/relaxation44 and myogenesis45. In chickens, lncRNAs have been reported to regulate muscle development, lipid metabolism, egg production and disease resistance46.Another interesting finding obtained herein was the presence of independent CP SNPs within long non-coding genes (lncRNAs: RNA transcripts greater than 200 bp in length). In nucleus, lncRNAs have been reported to function in-cis and in-trans whereby in-cis acting lncRNAs influence the expression of nearby genesTo conclude, present results provide a novel insight in the genetic mechanism underlying antagonistic interplay between growth and reproduction in broilers. Further follow-up studies (e.g. fine mapping and gene expression studies) are warranted to experimentally verify present findings.47 resulting in a total number of 544,927 autosomal SNPs. Quality control (QC) was performed first at a sample and second at a marker level. At a sample level, 406 animals were excluded due to call rate < 0.99 and autosomal heterozygosity outside the 1.5 IQR (inter-quartile range: 0.013). At the marker level, 305,660 SNPs autosomal SNPs were excluded due to: call rate < 0.95, minor allele frequency (MAF) < 0.05 and LD pruning (r2 > 0.99 within windows of 1 Mb inter-marker distances). Finally, a total of 2586 samples and 239,267 autosomal SNPs were retained for further analyses. All QC criteria were applied using the SNP & Variation Suite software (http://www.goldenhelix.com).Genotypic and phenotypic records were provided by Aviagen. The available data consisted of 2992 female broilers from a grand-grandparent (GGP) commercial line with phenotypic records on body weight (BW) at 35 days of age  and number of eggs (EN) per hen collected from 28 to 50 weeks of age . Animals were genotyped using the 600 k Affymetrix HD SNP arrayy is a n × 1 vector of phenotypic values of BW or EN for n = 2586 animals, W is a n × 53 matrix of covariates of fixed effects including hatch (36 classes) and mating group (17 classes), α is a c × 1 vector of the corresponding coefficients, x is a n × 1 vector of marker genotypes , β is the effect size of marker on BW or EN, u is a vector of random polygenic effects and e is a vector of random residuals. The random effects were assumed to be normally distributed with zero means and the following covariance structure:I is the nxn identity matrix, and G is the n x n genomic relationship matrix. Univariate analyses were performed using the factored spectrally transformed linear mixed model (FaST-LMM48) software (C++ Version 2.07) that was available at github (https://fastlmm.github.io/). Apart from SNP p values, FaST-LMM automatically computed the SNP q values using the false-discovery rate (FDR49) correction method.First, we performed univariate analyses to detect significant SNP associations for individual traits. The following univariate linear mixed model was applied:U ~ MNnx2 andY is a n × 2 matrix of 2 phenotypes for n = 2586 animals, W is a n × 53 matrix of covariates (fixed effects) including hatch (36 classes) and mating group (17 classes); A is a c × 53 matrix of the corresponding coefficients including the intercept; x is a n-vector of marker genotypes , β is 2-vector of marker effect sizes for the 2 phenotypes; U is a n × 2 matrix of random effects and E is a n × 2 matrix of random residuals. Furthermore, G is the n x n genomic relationship matrix (estimated as centered genomic matrix50), Vg is a 2 × 2 symmetric matrix of genetic (co)variance, I is a n × n identity matrix, Ve is a 2 × 2 symmetric positive definite matrix of residual variance component and MNn×2 denotes the n × 2 matrix normal distribution with mean 0, row covariance matrix V1 (n × n) and column covariance matrix V2 (2 × 2).A bivariate linear mixed model was then applied to identify significant CP SNP associations with both traits. Specifically, the following bivariate linear mixed model was used:0: β = 0, where β is a vector of the two marker effects, against the general alternative hypothesis H1: β ≠ 0. The Wald test statistic was used to infer the significant CP SNP associations. The genetic correlation (rg) was also estimated between the two traits. Bivariate analysis was performed using the GEMMA51 software (version 0.98.1).Association of each SNP with both traits was assessed by testing the null hypothesis that the marker effect sizes for both phenotypes are zero i.e. Hλ) was used to assess potential systematic bias due to population structure or the analytical approach52. If the λ value was greater than 1, it provided evidence for some systematic bias52. If the λ value was less than or equal to 1, no adjustment was needed53. λ was estimated using the SNP & Variation Suite software (http://www.goldenhelix.com).For each association analysis, the estimation of the genomic inflation factor (49 correction method was applied using R (http://www.r-project.org/). For all analyses, SNPs with FDR p values lower than 0.10 were considered as genome-wide significant.For univariate association analyses, FaST-LMM corrected SNP p values for multiple comparisons, so there was no need for an additional correction, however, for bivariate analysis the FDRcojo-slct’ option and the GCTA54 tool to select the independent SNPs. The cojo-GCTA analysis corrects β and p values of neighboring SNPs (in a sliding window of 10 Mb) based on the LD between the SNPs. This ensures that the SNP with the lowest p value is selected first for conditioning the effect on neighboring loci based on the LD between the neighboring SNPs and the selected SNP. Following LD-based correction of effect, all SNPs that remained significant under a p value threshold (4.2e−06) are run through the same process in a stepwise manner. A p value threshold as high as 4.2e−06  was used here to declare the independent significant SNPs. In short, cojo analysis identifies: (1) the number of independent SNP signals in a region and (2) association signals due to the joint effect of several SNPs. To identify the independent CP associations, we used as input in the cojo-GCTA analysis the summary-level statistics obtained by the bivariate analysis. Specifically, the b estimates along with their standard errors were used to estimate t values for the SNPs and t values were finally converted to p values using R code (http://www.r-project.org/).Results obtained from univariate and bivariate analyses were further subject to stepwise conditional and joint (cojo) analysis using the ‘https://www.ensembl.org/Tools/VEP55) tool was employed with the latest release .To predict the consequences of the independent CP significant SNPs on genes, transcripts, protein sequence and regulatory regions, the Variant Effect Predictor . The VEP tool was also used to search for positional candidate genes and for published QTLs including the independent CP significant SNPs. Note that both Ensembl and NCBI RefSeq transcript databases were used. With regard to published QTLs, VEP retrieves information via connections with Animal QTL database  and Online Mendelian Inheritance in Animals (OMIA) database for Gallus gallus.Physical positions of SNPs were also obtained by the VEP tool using the GRCg6a assembly (https://genecodis.genyo.es/57) was carried out. MEA removes the redundant terms and produces genes and annotations grouped in modules (or metagroups) which are functionally coherent and are ranked by their significance and relevance57. For MEA, we selected the species of Gallus gallus for the input genes and searched for Gene Ontology (GO) biological processes (BPs) as well as KEGG pathway significantly enriched concurrent annotations. Here, concurrent annotations with FDR p value lower than 0.05 were considered as significantly enriched.We conducted ontological and functional analysis of the positional candidate genes in efforts to elucidate their functional role and relevance to the traits under study. First, modular enrichment analysis (MEA) using GeneCodis 4.0 (https://go.princeton.edu/cgi-bin/LAGO58) was used to infer the GO BP terms of the candidate genes. Since there were unknown genes during exploration of chicken GO annotations, the human GO annotations were used here. Computation of p values was based on the hypergeometric distribution and a p value cut-off equal to 0.10 was set as denoting significantly enriched terms. Candidate genes associated with enriched GO BP and pathways relevant to growth and reproduction processes were considered as functionally relevant to the traits under study and were thus nominated as candidate pleiotropic genes. Finally, the GO term Mapper (https://go.princeton.edu/cgi-bin/GOTermMapper) was employed to infer the GO slim molecular function terms of the pleiotropic candidate genes using human genes as input.Second, the LAGO tool (All animals included in this study were not subjected to any invasive procedures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Figure S1.


Recent studies highlight the role of metabolites in immune diseases, but it remains unknown how much of this effect is driven by genetic and non-genetic host factors.We systematically investigate circulating metabolites in a cohort of 500 healthy subjects (500FG) in whom immune function and activity are deeply measured and whose genetics are profiled. Our data reveal that several major metabolic pathways, including the alanine/glutamate pathway and the arachidonic acid pathway, have a strong impact on cytokine production in response to ex vivo stimulation. We also examine the genetic regulation of metabolites associated with immune phenotypes through genome-wide association analysis and identify 29 significant loci, including eight novel independent loci. Of these, one locus (rs174584-FADS2) associated with arachidonic acid metabolism is causally associated with Crohn’s disease, suggesting it is a potential therapeutic target.This study provides a comprehensive map of the integration between the blood metabolome and immune phenotypes, reveals novel genetic factors that regulate blood metabolite concentrations, and proposes an integrative approach for identifying new disease treatment targets.The online version contains supplementary material available at 10.1186/s13059-021-02413-z. A growing body of evidence has revealed that metabolites have important regulatory roles in immune system function in both health , 2 and dIn the present study, we broadly interrogate the circulating blood metabolome and integrate 10,434 metabolite features with deep immunophenotyping from a population-based cohort  –13. We sTo get a comprehensive measure of the circulating blood metabolome, three different analytical approaches were used to profile metabolites: 1) a nuclear magnetic resonance (NMR) approach , 2) flow-injection TOF-M , and 3) an integrated measurement system of NMR, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS)  , 15. BM  a nuclea flow-inj an integt-test), but this was not the case for targeted features measured using the BM platform (P = 0.172)  < 0.05). Age had less influence on metabolite concentrations, with 25.1% of all metabolite features significantly associated with age, and 51.2% of these increasing with age. In total, gender contributed more variation than age to the circulating metabolites measured by GM and UM  and five categories of data including immunological modulators, immunoglobulins, platelets, cell counts, and gut microbiome, measured in the 500FG cohort , 18, 19.Moreover, there are 730 GM and 4 BM showing significant association with immune modulators , there are 11 genome-wide significant loci associated with 35 metabolic features from GM and 25 loci associated with 368 metabolic features from UM, respectively  explained 1.3–67.6% of the total variance in metabolites, with a median value of 8.1% based on multivariate linear regression analysis  were associated with at least one immune phenotype FDR < 0.05). We then explored the genetic determinants of them, by carrying out a genome-wide association analysis on ~ 4 million single nucleotide polymorphisms (SNPs) obtained by genotyping and imputation see the “” section. We thent-test). This suggests that the stronger relationship we observe between baseline metabolism and innate immune response, as compared to adaptive immune response, is independent of genetics.We have previously identified genetic regulation of cytokine production capacity upon stimulation in 500FG . MetabolVNN1 . These results suggest a potential genetic regulatory role on circulating metabolites through modulation of VNN1 expression levels. Interestingly, the VNN1 gene has been found to be involved in asthma corticosteroid treatment [S. aureus, and LPS  and stomach (P = 7.6 × 10−25) in the GTEx dataset [We next explored the function of the genetic variants within 29 genome-wide significant mQTLs using a permutation-based method see the “” section−8 and ten autoimmune diseases such as inflammatory bowel disease as well as other diseases such as Alzheimer’s disease and type 2 diabetes  [−9) and monocyte CD14+ proportions (P = 4.72 × 10−13) in the blood [We next applied a colocalization analysis  between Fig. −5) . It has he blood , and thehe blood .Fig. 5A2 < 0.01) as instruments, the results of four commonly used MR methods—weighted median estimator [−5, 3.11 × 10−6, 4.87 × 10−2, and 6.95 × 10−3, respectively; effect sizes = −0.06, −0.07, −0.07, and −0.06, respectively; Fig. Colocalization analysis  upon arastimator , inversestimator , and modstimator —consisteFADS1/FADS2 locus were associated to fatty acid metabolism, including the arachidonic acid pathway [FADS2 in blood in the GTEx [FADS2 expression levels  and measured the TNFα level after 24 h. Compared to the control group, TNFα production decreased in the FADS2-inhibited group after 24-h stimulation with Candida HK, which suggests that FADS2 has a promoting effect on immune response ), 5 × 10−8, 1 × 10−6, and 1 × 10−5) with GWAS catalog SNPs known to influence disease susceptibility . We also observed significant genetic overlap between mQTLs and auto-inflammatory traits (P = 3.54 × 10−31), blood-related phenotypes , heart rate (P = 1.19 × 10−44), and type 2 diabetes as represented by fasting glucose-related traits (P = 1.75 × 10−35). These enrichment results are also consistent at multiple mQTL thresholds . These genetic regulatory components shared between metabolites and diseases suggest that metabolism plays a functional role in complex phenotypes in humans.In addition to the arachidonic acid QTL that colocalized with Crohn’s disease, the  disease  , we calculated the cumulative cytokine variance explained by all baseline features. While the largest effect still came from genetic factors, metabolites had an additional contribution (0.048 in average) to the inter-individual variation in cytokine response  in prediction performance after adding metabolite data to the model, mostly coming from monocyte-derived cytokine production upon stimulation . This suggests that baseline metabolites have effects on cytokine production that are independent and in addition to genetic variation  than on T cell–derived cytokines , which implies that baseline metabolism is more involved in the innate immune response than in the adaptive response. Innate immune cells are wired to respond to the environment , and subVNN1 locus) with regulatory effects on circulating metabolite concentration. Our results can be accessed through an online browser (https://500 fg-hfgp.bbmri.nl) for future studies. We further highlight that deleterious effects arising from non-synonymous variants in exonic regions could be one of the mechanisms behind the genetic regulation of metabolite levels in the blood.Thus far, several GWAS studies have focused on metabolite measurements using a single analytical approach. In contrast, this study presents comprehensive measurements from three different platforms that map > 10,000 metabolic features covering glucose, lipid, amino acid, and lipoprotein metabolism (among others). We took advantage of the accurate annotation of targeted measurement (BM) in functional interpretation and of the high-throughput and unbiased measurement of untargeted approaches (GM and UM) in genetic factor identification. Even with the relatively limited sample size of our cohort, we were able to replicate 21 (out of 29) previously detected mQTLs and identify eight novel genomic loci  the association between the arachidonic acid pathway and immune phenotypes, and (3) the association between FADS2 and immune phenotypes . Furthermore, by integrating our data with other public data, we confirmed the association between FADS2 and Crohn’s disease and the association between arachidonic acid and Crohn’s disease. Since the gut is the more-relevant tissue compared to blood (where we measured arachidonic acid), we used a gut-specific organoid validation to provide further evidence supporting FADS2 as a key driver of Crohn’s disease and highlight how integration approaches can be used to infer novel disease-relevant markers using multi-omics data. Interestingly, 54 genes within the 29 mQTL loci we identified have been reported to be candidates for metabolic drug targets  in relevant immune diseases, and further validation of their potential as therapeutic targets is warranted.Our findings also uncovered the role of specific metabolites in the etiology of several immune-related diseases. For example, lower circulating arachidonic acid was found to be causally linked to Crohn’s disease. In agreement with previous studies in which arachidonic acid and  disease , 53, ourThis study analyzed a very complex set of phenotypes, and we must therefore acknowledge possible confounders and study limitations. Firstly, samples were collected in a standardized time-frame (morning) to limit possible bias, but were taken in different months of the year, which might have introduced unwanted variation caused by season. However, we observed no clear batch or month effect in the metabolic measurements. Secondly, non-fasting blood samples were drawn in the 500FG cohort, which meant that diet could have impacted blood metabolism , 63. HowThis study provides insights into how genetic differences impact metabolite levels, shape immune responses, and impact disease risk, information important for future biomedical and pharmaceutical targeting. In future studies, longitudinal measurements are needed to acquire more consistent and accurate circulating metabolite levels. In addition, single-cell RNA-sequencing technology could be used to study cell type–specific effects and uncover the interaction between genes and metabolites in immune-related diseases.Analysis was mainly performed in the 500FG cohort  which consists of 534 healthy individuals  of Caucasian origin. Their ages range from 18 to 75, with the majority  being 30 years old or younger. Volunteers with a mixed or other genetic background were excluded as were volunteers diagnosed with long-term diseases. Within this cohort, immune cell counts, cytokine production upon stimulations, platelets, globulins, and gut microbiome were measured. More detailed information can be found in previous publications , 18, 21.Serum metabolite levels were measured by three different technical platforms  in 500 healthy Dutch individuals. BM indicates samples measured on the Brainshake Metabolomics/Nightingale Health metabolic platform. These samples were processed following the automated standard protocol provided by Nightingale’s technology (Finland), and blood metabolites were quantified in absolute concentrations  and percentages using nuclear magnetic resonance (NMR) spectroscopy. On the UM platform , which mainly focuses on lipid metabolism, metabolites identified as m/z were measured in a large scale using a measurement system that integrates NMR, GC-MS, and LC-MS. Details can be found in the references , 15. GM Principal component analysis (PCA) was done with log10-transformed values. Sample values > 4 standard deviations from the mean value of PC1 and PC2 were considered as outliers, leading to the removal of one sample in the UM data.We checked the normal distribution of metabolite levels in the data from each platform using the Shapiro test. To achieve normality and consistency for QTL mapping, we log-transformed the metabolite data.−4 and MAF = 0.05 were used for SNP quality control. After removing 17 ethnic outliers identified by multidimensional scaling, genotype data was imputed taking Genome of the Netherlands as reference. For further description, see the reference [Genotype data from ~ 500 healthy Dutch individuals was measured using Illumina humanOmniExpress Exome-8v1.0 SNP chip Calling by Opticall 7.0  with defeference .Other baseline immune parameters, including 73 immune cell subpopulation frequencies, cytokine production response to 19 stimulations (91 different cytokine-stimulus pairs), modulators, immunoglobulins, and platelets, were measured in 500FG. Details can be found in the references , 18, 21.To measure gene expression data, RNA sequencing was performed on a subset of 89 samples from 500FG using Illumina Hiseq 2000 platform as previously described , 18, 21.Stool samples were collected 1 day prior to or on the day of blood collection. DNA of the gut microbiome was extracted and sequenced using the Illumina HiSeq 2000 platform. Taxonomic profiles were inferred with MetaPhlAn 2.2, and functional profiling was performed using HUMAnN2. This yielded 219 species and 639 MetaCyc pathways, as described in the reference .We intersected genotyped samples with samples from metabolite profile data and end up with 340 overlapping samples for BM QTL analysis, 397 for GM, and 458 for UM.Spearman correlation analysis was performed between metabolites and other types of data. Unsupervised hierarchical clustering using the “complete” approach based on “Euclidean” distance of Spearman correlation coefficients is shown as a heatmap created using the R package pheatmap.2. Details of the method can be found in a previous paper [To estimate the cytokine variance explained by metabolites and other immune parameters, we first filtered the features based on their Spearman correlation p-values, keeping only features passing specific thresholds  for further analysis. Potential confounder effects were then regressed out, and after removing collinearity, features were used in a multivariate linear model to estimate the proportion of variance explained indicated by total model-adjusted Rus paper .−12 was considered to be genome-wide significant. SNPs with linkage disequilibrium > 0.1 were identified as single genomic loci.mQTL mapping was done with the R Package Matrix-eQTL, taking age, gender, contraceptive usage, and cell population abundance as covariates in the linear model. A p-value < 4.8 × 102 was considered as the proportion of explained variance.To determine the accumulative effect of genetic factors on baseline metabolites, we applied a multivariate linear model to evaluate the metabolite variance explained by genetics after regressing out the contributions of age, gender, and contraceptive drug usage. Of the 1553 metabolites with suggestive mQTLs, 752 were measured in all the genotyped samples with no missing values. Total model-adjusted RAssociated variants were annotated using Annovar , webgestWe performed colocalization analysis  to look −8 as the threshold for arachidonic acid GWAS summary statistics, and only independent SNPs (r2 < 0.01) were kept for MR analysis using the R package TwoSampleMR [MR  is a staSampleMR . Four coSampleMR –51, wereTissues from a human colon were obtained from the UMC Utrecht with informed consent of the patient. The normal, non-transformed, mucosa was obtained from a patient with colon adenocarcinoma that was resected. The study was approved by the UMC Utrecht  ethical committee and was in accordance with the Declaration of Helsinki and according to Dutch law. This study is compliant with all relevant ethical regulations regarding research involving human participants. Human intestinal cells were isolated, processed, and cultured as described previously .FADS2 knockout organoids, gRNAs were selected using the Atum website and cloned in the Cas9-EGFP vector (addgene plasmid #48138) following the protocol described before [To generate d before . gRNAs uHuman colon organoids were transfected using these gRNAs cloned into the Cas9-EGFP vector, utilizing electroporation following a previously established protocol . One weeGWAS SNPs were collected from the National Human Genome Research Institute GWAS catalog grouped based on phenotype association  includin−5 were kept as genetic factors. Details can be found in a previous paper [Our objectives were to investigate whether metabolites can reveal predictive insights into cytokine production upon stimulation that is additive to the effects of genetics. We first correlated metabolites with cytokines and removed metabolite features not significantly correlated as metabolite predictors. SNPs with an association to a cytokine–stimulation pair with P < 5 × 10us paper .Prediction of cytokine levels was facilitated by training an Elastic Net model. A 10 × 2-fold cross-validation approach was used, where the data was first split randomly into training and test sets to validate the prediction. The training set was then split up once more for feature selection, and the procedure above was repeated 10 times. Prediction accuracy was evaluated by calculating Spearman correlations between the measured cytokine levels and the Elastic Net model predictions of the test sets. A t-test was then used to identify if there was a significant difference between the performance of the prediction model using SNPs only and that of the model using SNPs plus metabolites.R package ggplot2 was used to perform most visualizations, including Manhattan plots, bar charts, box plots, and violin plots. The package pheatmap was used to generate heatmaps. An online tool, Locus zoom , was useAdditional file 1. Table S1Additional file 2. Table S2Additional file 3. Table S3Additional file 4. Table S4Additional file 5. Fig. S1-8Additional file 6. Table S5Additional file 7. Table S6Additional file 8. Table S7Additional file 9. Table S8Additional file 10. Table S9Additional file 11. Table S10Additional file 12. Table S11Additional file 13. Table S12Additional file 14. Table S13Additional file 15. Table S14Additional file 16. Table S15Additional file 17. Table S16Additional file 18. Table S17Additional file 19. Table S18Additional file 20. Table S19Additional file 21. Table S20Additional file 22. Table S21Additional file 23. Table S22Additional file 24. Table S23Additional file 25. Review history.


Summary statistics from genome-wide association studies (GWAS) have facilitated the development of various summary data-based methods, which typically require a reference sample for linkage disequilibrium (LD) estimation. Analyses using these methods may be biased by errors in GWAS summary data or LD reference or heterogeneity between GWAS and LD reference. Here we propose a quality control method, DENTIST, that leverages LD among genetic variants to detect and eliminate errors in GWAS or LD reference and heterogeneity between the two. Through simulations, we demonstrate that DENTIST substantially reduces false-positive rate in detecting secondary signals in the summary-data-based conditional and joint association analysis, especially for imputed rare variants . We further show that DENTIST can improve other summary-data-based analyses such as fine-mapping analysis. Analyses of summary statistics from GWAS are subject to biases due to errors in the discovery GWAS or linkage disequilibrium reference data set or heterogeneity between data sets. Here, the authors propose a quality control method to be added to analysis of GWAS summary data that can reduce such biases. Summary statistics available from GWASs have facilitated the development of various summary-data-based methods3 such as those for fine-mapping11, imputing summary statistics at untyped variants13, estimating SNP-based heritability16, assessing causal or genetic relationship between traits19, prioritizing candidate causal genes for a trait23, and polygenetic risk prediction25. Most of the summary-data-based methods require linkage disequilibrium (LD) structure of the variants used, which are unavailable in the summary data but can be estimated from a reference cohort with individual-level genotypes, assuming a homogeneous LD structure between the GWAS and reference cohorts. Hence, summary-data-based analyses can be biased by not only errors in the GWAS and LD reference data sets but also differences between them for the following reasons. First, there are often errors in GWAS summary statistics resulting from data generation and analysis processes 27, some of which are not easy to detect, even if individual-level data are available. Second, there is often heterogeneity between data sets  because of differences in ancestry, and genotyping platform, imputation quality, and analysis pipeline. Although the recommended practice is to use an ancestry-matched reference cohort, samples with similar ancestries, such as populations of European ancestry, can still have discernable differences in LD structure28, and the effects of such differences on summary-data-based analyses are largely unexplored.Genome-wide association studies (GWASs) have been extraordinarily successful in uncovering genetic variants associated with complex human traits and diseasesdetecting errors in analyses of summary statistics). We show by simulation that DENTIST can effectively detect errors of several kinds after standard QC. We then demonstrate the utility of DENTIST as an additional QC step for multiple, frequently-used, summary data-based methods, including the conditional and joint analysis (COJO)6 of summary statistics, FINEMAP9, and LD score regression14.In this study, we propose a quality control (QC) method to identify errors in GWAS summary data and heterogeneity between summary data and LD reference by testing the difference between the observed z-score of each variant and its predicted value from the surrounding variants. The method has been implemented in a software tool named DENTIST (i (t in S2 (Methods).t in S2, and R is the LD correlation matrix calculated from a reference sample with t and i and variants t. Note that methods that leverage LD to predict GWAS test statistic of a variant  in the discovery GWAS or LD reference, and/or heterogeneity between them. If the difference between i is tagged by variants t. Following prior work15, we use the truncated singular value decomposition (SVD) method to compute a pseudo inverse of Details of the methodology can be found in the Methods section. In brief, we first use a sliding window approach to divide the variants into 2 Mb segments with a 500 kb overlap between two adjacent segments. Within each segment, we randomly partition variants into two subsets, S1 and S2, with an equal number of variants, and apply the statistic below to test the difference between the observed z-score of a variant PDENTIST < 5 × 10−8 from the variant group with PGWAS > 0.01, and apply the same proportion of variants removed to the variant group with PGWAS < 0.01 (note: the proportion of variants removed is capped at 0.5% in each group). This step is to create a more reliable set of variants for the next iteration. The problematic variants are prioritized and filtered out in the first few iterations so that the prediction of PDENTIST < 5 × 10−8 are removed in the final step without the constraints.One challenge for the DENTIST method is that errors can be present in both S1 and S2, and errors in S2 can inflate 30 (denoted by UK10K-WGS). A descriptive summary of the data sets used in this study can be found in Table P value < 10−6. We simulated a trait affected by 50 common, causal variants with effects drawn from q2, was 0.4%, on average). Prior to the simulations with errors, we performed a benchmark simulation  to show that the DENTIST test statistics were well calibrated, given the small fraction of variants removed by DENTIST in each simulation replicate and the small proportion of causal variants removed across simulation replicates, in the absence of errors and LD heterogeneity  using whole genome sequence (WGS) data on chromosome 22 of 3,642 unrelated individuals from the UK10K project after QCferror = 0.05, 0.1 or 0.15) of randomly selected individuals, and allelic error of each of the variants was introduced by swapping the effect allele by the other allele. We acknowledge that genotyping errors are unlikely to be a major concern for summary data-based analyses. The purpose of adding genotyping errors to the simulation was to quantify the ability of DENTIST to detect errors in general and to explore the possibility of using DENTIST as an additional QC step for GWAS analyses with individual data to detect genotyping errors not detected by the standard GWAS QC pipeline. The simulation was repeated 200 times with the causal and erroneous variants re-sampled in each simulation. We then ran DENTIST using UK10K-WGS or an independent sample (UKB-8K-1KGP) as the LD reference after standard QC of the discovery GWAS: removing variants with an HWE P value < 10−6 using the individual-level data or ∆AF > 0.1 where ∆AF is the difference in allele frequency (AF) between the summary data and reference sample. The independent sample UKB-8K-1KGP refers to a set of 8,000 unrelated individuals from the UK Biobank31 (UKB) with variants imputed from the 1000 Genomes Project (1KGP). The statistical power was measured by the proportion of erroneous variants in the data that can be detected by DENTIST. We also computed the fold enrichment in probability of an erroneous variant being detected by DENTIST compared to a random guess .We then simulated genotyping and allelic errors at 0.5% randomly selected variants respectively. Genotyping errors at each of these variants were simulated by altering the genotypes of a certain proportion (PGWAS < 5 × 10−8), the power of DENTIST increased to ~93% for the genotyping errors and 100% for the allelic errors (Supplementary Table ferror), e.g., the power in the ferror = 0.05 scenario was, on average, lower than that for ferror = 0.15 ; the results suggested that a choice of When using UKB-8K-1KGP as the reference, ~45% of the genotyping and ~95% of the allelic errors could be removed by the standard GWAS QC , in the presence of heterogeneity between the discovery GWAS and LD reference samples before and after DENTIST filtering. To mimic the reality that causal signals are often not perfectly captured by imputed variants, we simulated a phenotype affected by one or two sequence variants using WGS data  and performed association analyses using imputed data of the same individuals . More specifically, we first randomly selected one or two sequence variants with MAF ≥ 0.01  or 0.01 > MAF ≥ 0.001  from a locus as causal variant(s) to generate a phenotype . The proportion of phenotypic variance explained by the causal variant (denoted by q2) was set to 2% to achieve similar power to a scenario with q2 = 0.03% and n = 250,000  because the power of GWAS is determined by 35, and the Atherosclerosis Risk In Communities (ARIC) study36, with different degrees of ancestral differences with UK10K-1KGP . It should be noted that the false-positive COJO signals defined here are not false associations but falsely claimed as jointly associated  signals.COJO is a method that uses GWAS summary statistics and reference LD to run a conditional and joint multi-SNP regression analysisWhen using the discovery GWAS sample as the LD reference, the FPR of COJO was 0.1% for common-causal and 0.2% for rare-causal Fig. . When usThe power of COJO (without DENTIST) using in-sample LD from UK10K-1KGP or out-of-sample LD from the other references were similar: 77–80% for common-causal and 26–30% for rare-causal  varying from 10,000 to 150,000, non-overlapping in-sample references including UKBv3-8K , there was substantial inflation in the number of COJO signals compared to the benchmark  Table , with a 46 , Coronary Artery Disease (CAD), Type 2 Diabetes(T2D), Crohn’s Disease (CD), Major Depressive Disorder (MDD), Schizophrenia (SCZ), Ovarian Cancer (OC), Breast Cancer (BC), Height, and Body Mass Index (BMI) using GWAS summary data from prior studies46 Table , and the46 Table . The res46 Table  and 11.T9 is a method used for prioritizing causal variants from GWAS summary statistics. We assessed the effect of DENTIST QC on an FINEMAP analysis using the data simulated above with one, two or three common causal variants at a 1-Mb locus. The impact of the use of an out-of-sample LD reference on FINEMAP v1.4 was evaluated by the estimated number of causal variants before or after DENTIST QC, in comparison with the benchmark analysis using in-sample LD. We observed that FINEMAP using out-of-sample LD tended to overestimate the number of causal variants  is an approach which was originally developed to distinguish polygenicity from population stratification in GWAS summary data set by a weighted regression of GWAS χ2-statistic against the mean LD score across the LD score bins. We found that the GWAS mean χ2-statistic in the bin with the smallest mean LD score deviated from the value expected from a linear relationship between the LD score and χ2-statistic  in the summary data. In that case, the effect sizes of the SNP and another SNP in LD with it will be heavily overestimated in a joint or conditional analysis because the input effect sizes and LD pattern appear to suggest that the effects of the two SNPs mask each other in the marginal analysis. Even in the absence of errors in the summary data, the joint or conditional effects of two SNPs in LD can be estimated with substantial biases in the presence of large LD heterogeneity between the discovery and the reference. Compared to common variants, rare variants are less well imputed and thus are more likely to have LD heterogeneity between data sets, which may explain the remarkable improvement of COJO for rare variants before and after DENTIST QC.Given that a QC step can potentially remove true signals, we make sure that DENTIST is conservative in filtering variants. We show by simulation that in the absence of errors and data heterogeneity, the DENTIST test statistics were not inflated, and on average, only <0.05% variants were filtered out by DENTIST  needs to scale with GWAS sample size49. We have quantified the performance of DENTIST with respect to nref and shown that an nref of >5000 is recommended and the relationship between the performance of DENTIST and nref does not seem to depend on GWAS sample size , which makes it a complementary method to existing QC filters. We suggest that DENTIST-based QC should be applied after the standard QC as DENTIST is more powerful when the proportion of errors is smaller  to replace that in the discovery sample if individual-level genotypes of in the discovery GWAS are unavailable. In this case, Eq. The aim of the proposed method is to test the difference between the observed z-statistic of a variant and the predicted value from adjacent variants. To achieve this goal, we use a sliding window approach to divide genome into 2 Mb segments with a 500 kb overlap between one another and randomly partition the variants in a segment into two subsets, S1 and S2, with similar numbers of variants; we then use variants in S2 to predict those in S1 and vice versa. According to previous studies13, t, this model is the same as the conditional model in CAVIAR11. Hence, we can use the statistic below to test the difference between the observed and predicted z-scoresAs shown in previous studies13, the method is derived under the null hypothesis without imposing any specific assumption about the genetic architecture of the trait, although our simulations show that it performs well under the alternative hypothesis  to highly polygenic models  and found that the DENTIST test was still well calibrated  in the variant set to compute 15 to compute a pseudoinverse of k eigenvalues ranked by eigenvalues.w1, …, wk correspond to the largest k eigenvalues, and vectors v1, …, vk are the corresponding k eigenvectors. Given q = rank . We apply the same PDENTIST threshold to variants with PGWAS < 0.01 but capped the proportion of variants removed at the top x%. We impose an additional constraint that the proportion of variants removed is capped at the top 0.5% in either group. The default number of iterations is set to 10. In this iterative process, variants with large errors or LD heterogeneity between the discovery and LD reference samples are prioritized for removal in the first few iterations so that the prediction accuracy increases in the following iterations. After the iterations are completed, we remove all variants with PDENTIST < 5 × 10−8 in the final step without any of the constraints.A challenge in applying Eq. 35, Atherosclerosis Risk in Communities (ARIC) study36, UK10K29, and UK Biobank (UKB)31. The samples were genotyped using either WGS or SNP array technology (Table 50 using the Haplotype Reference Consortium (HRC)37 and UK10K reference panels51. We used different subsets of the imputed UKB data as the LD reference in this study, denoted with the prefix “UKBv3”, such as UKBv3-unrel , UKBv3-329K , UKBv3-20K  and UKBv3-8K . HRS, ARIC and UK10K cohorts were imputed to the 1KGP reference panel in prior studies33, and a subset of 8,000 unrelated individuals from UKB were imputed to the 1KGP reference panel in this study (referred to as UKB-8K-1KGP). The UK10K variants in common with those on an Illumina CoreExome array were used for 1KGP imputation33. The imputation dosage values were converted to best-guess genotypes in all the data sets except for UKBv3-all, in which the hard-called genotypes were converted from the imputation dosage values using PLINK v1.90b3.38—hard-call-threshold 0.1 (ref. 52). For all the data sets, standard QC was performed to remove variants with HWE test P value < 10−6, imputation INFO score <0.3, or MAF < 0.001. Since the hard-called genotypes had missing values, in UKBv3 and its subsets, we further removed variants with missingness rate > 0.05.This study is approved by the University of Queensland Human Research Ethics Committee  and the Westlake University Ethics Committee . A summary of the genotype data sets used in this study as well as their relevant information can be found in Table n = 328,577) except for 20,000 individuals randomly selected to create a non-overlapping reference sample . The height phenotype was pre-adjusted for sex and age. We conducted the association analysis using the simple linear regression model in the fastGWA53 module of GCTA v1.92.3 with the first 10 principle components (PCs) fitted as covariates.We performed a genome-wide association analysis for height using the genotype data of UKBv3-329K, i.e., all the unrelated individuals of European ancestry in the UKB (Further information on research design is available in the Nature Research Reporting Summary linked to this article.Supplementary informationPeer Review FileReporting summary


Danio rerio (zebrafish) are a powerful experimental model for genetic and developmental studies. Adaptation of zebrafish to study seizures was initially established using the common convulsant agent pentylenetetrazole (PTZ). Larval PTZ-exposed zebrafish exhibit clear behavioral convulsions and abnormal electrographic activity, reminiscent of interictal and ictal epileptiform discharge. By using this model, our laboratory developed simple locomotion-based and electrophysiological assays to monitor and quantify seizures in larval zebrafish. Zebrafish also offer multiple advantages for rapid genetic manipulation and high-throughput phenotype-based drug screening. Combining these seizure assays with genetically modified zebrafish that represent Dravet syndrome, a rare genetic epilepsy, ultimately contributed to a phenotype-based screen of over 3500 drugs. Several drugs identified in these zebrafish screens are currently in clinical or compassionate-use trials. The emergence of this ‘aquarium-to-bedside’ approach suggests that broader efforts to adapt and improve upon this zebrafish-centric strategy can drive a variety of exciting new discoveries. Summary: Screening programs using zebrafish could reshape traditional approaches to antiepileptic drug discovery. Here we describe steps to develop zebrafish models that represent human epilepsies, with an emphasis on a zebrafish model of Dravet syndrome. Relativexposure , we sougexposure , fit thiexposure . Early Pexposure . Eventuaexposure , providiexposure  and idens552didy/scn1lab larval zebrafish mutants mimic a haploinsufficient SCN1A loss-of-function mutation seen in most DS patients. Following identification by Herwig Baier's laboratory in a large-scale screen for saccade deficits , glutamate, dopamine, norepinephrine, serotonin and acetylcholine . A decaddeficits , we detelization , and explization  as well lization . Seizureher AEDs . Many ofebrafish . These u®) (®) (®) and lorcaserin (Belviq®) .With the establishment of this genetic zebrafish model, and in combination with optimized simple locomotion-based and electrophysiological recording assays, we initiated our first phenotype-based drug-screening program using a commercially available 312 compound drug library spiked with FDA-approved drugs . As FDA-®) , synthet®) (®) , structuBelviq®) . Intereseceptors . Althougeceptors . Moreovehttps://zebrafishproject.ucsf.edu/)  or, indeed, all genetic forms of epilepsy (as zebrafish orthologs for every human gene may not be available), zebrafish models could offer similar routes to drug discovery for personalized medicine. In line with this, we recently used CRISPR/Cas9 genome editing to generate 37 stable mutant zebrafish lines that represent a wide spectrum gene mutations important regarding human epilepsy (sf.edu/) . We envisf.edu/)  combinedsf.edu/) . More br


SERPINA1 that protects against CTS . Through various functional analyses, we conclude that at least 22 genes mediate CTS risk and highlight the role of 19 CTS variants in the biology of the extracellular matrix. We show that the genetic component to the risk is higher in bilateral/recurrent/persistent cases than nonrecurrent/nonpersistent cases. Anthropometric traits including height and BMI are genetically correlated with CTS, in addition to early hormonal-replacement therapy, osteoarthritis, and restlessness. Our findings suggest that the components of the extracellular matrix play a key role in the pathogenesis of CTS.Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy and has a largely unknown underlying biology. In a genome-wide association study of CTS , we found 53 sequence variants at 50 loci associated with the syndrome. The most significant association is with a missense variant (p.Glu366Lys) in  The underlying genetics of carpal tunnel syndrome is not well understood. Here, the authors perform a GWAS meta-analysis for carpal tunnel syndrome finding variants at 50 loci with connections to the extracellular matrix discovered through various functional analyses. Carpal tunnel syndrome (CTS) is the most common form of entrapment neuropathy, affecting around 3% of women and 2% of men at some stage in their lives2. It is caused by elevated pressure on the median nerve in the carpal tunnel3, a space-limited osteofibrous canal that, in addition to the median nerve, is bounded by the carpal bones, the transverse carpal ligament, and the flexor tendons4. The median nerve innervates the thenar muscles and the radial lumbricals, and CTS patients commonly experience severe pain and paresthesia, and less commonly weakness, in the median nerve distribution4.Entrapment neuropathies are common and debilitating. In these neuropathies, a peripheral nerve becomes compressed where it passes through narrow anatomical areas, due to swelling of surrounding tissues or anatomical abnormalities5. Other risk factors include age, female sex2, pregnancy6, obesity, diabetes, and heredity7.Although CTS has been widely studied, its pathophysiology is not fully understood. A combination of factors such as mechanical trauma (e.g. wrist fractures), fibrosis, and inflammation of tendons can lead to pressure on the median nerve within the carpal tunnel4. Several treatments are available for the management of CTS, ranging from lifestyle changes (e.g. reduction of certain work activities) to local corticosteroid injection, and in the most severe cases, surgical carpal tunnel decompression.The diagnosis of CTS is, together with medical history, obtained through medical assessment such as Phalen’s and Tinel’s test, and/or electrophysiological measurement9 and the heritability of CTS has been estimated to be 0.46 in women10. The largest reported genome-wide association study (GWAS) of CTS yielded 16 risk loci using data from 12,312 cases and 389,344 controls from the UK11. Here, we expand the previous study by combining 48,843 cases and 1,190,837 controls from Iceland (deCODE genetics), the UK (UK Biobank), Denmark , and Finland (Finngen) in a GWAS meta-analysis of CTS, and find 53 independent sequence variants at 50 loci, of which 32 variants have not been previously reported.The estimated familial occurrence of CTS is 17–39%12  at the corresponding locus in our results . The UK dataset was the largest and 10 of the 53 variants reached the genome-wide significance in that dataset. Of the 53 variants, 5 are low-frequency variants (EAF < 5%), including the missense variant (p.Glu366Lys) in SERPINA1, which has the largest effect on CTS . We constructed a polygenic risk score (PRS) based on effect estimates from the meta-analysis, excluding the UK dataset, and predicted into the UK dataset. The PRS explained 2% of the variance. SNP heritability estimated using linkage disequilibrium (LD) score regression14, was 0.19 (95% CI 0.11–0.27) in Iceland, 0.34 (95% CI 0.30–0.38) in the UK, and 0.16 (95% CI 0.11–0.20) in Denmark.Of the 53 variants, 32 represent novel associations with CTS while 21 have previously been associated with CTSi.e. mediating the effect of the variant on CTS), we annotated the 53 CTS sequence variants and correlated variants (r2 ≥ 0.8 and within ± 1MB). Of these, 35 were predicted to affect coding or splicing of a protein  and the radial nerve  to study the effect of the 53 CTS variants in these phenotypes. None of the variants associated with lesions of the ulnar or the radial nerve  using summary data from the meta-analysis, leaving each dataset out in turn. Using the CTS PRS, we estimated the effect size in five groups defined as (1) all CTS cases , (2) nonrecurrent or nonpersistent CTS cases , (3) bilateral, recurrent, or persistent CTS cases , (4) CTS cases that have undergone surgery , and (5) CTS cases that were diagnosed with CTS after surgery . The bilateral, recurrent, or persistent cases  C19. In the CTS study sample, 34% were males and 66% females (Supplementary Table P ≥ 0.05/53 = 9.4 × 10−4).CTS is more commonly reported among females than males and studies have shown that the carpal tunnel cross-sectional area relative to the size of the hand is smaller in femalesSERPINA1, present in all four datasets . SERPINA1 encodes α-1 antitrypsin and rs28929474-T has been reported to cause α-1 antitrypsin deficiency20. It has also been widely reported in the literature to associate with a broad range of traits26, including multiple plasma proteins. Our results show that the variant affects 36 plasma proteins (Supplementary Data r2 ≥ 0.8) with rs28929474 is a downstream variant, rs112635299-T  in 6Lys in SP ≤ 0.05/1319 = 3.8 × 10−5, Supplementary Data 27. We excluded the UK dataset from the meta-analysis as it overlaps with the data used in most of the summary statistics. Substantial genetic correlations have previously been reported between CTS and anthropometric traits including height and BMI11. Our collective data of genetic correlation  and BMI . Of the genes reported in the CTS meta-analysis, 22 have previously been associated with height and 12 with body fat distribution , other and unspecified osteoarthritis defined by ICD-10 code M19, which includes primary osteoarthritis of the wrists , and feeling restless .We performed genetic correlation analyses using the CTS meta-analysis and summary statistics from 1319 GWASs  and encode collagens, the most abundant proteins in the extracellular matrix where they give structural support to resident cells and account for 90% of bone matrix protein content29. COL11A1 has previously been implicated in CTS30 and COL11A1 and COL11A2 have been implicated in tendinopathies33. Mutations and abnormal expression of COL10A1 have been linked to abnormal chondrocyte hypertrophy and osteoarthritis in humans36, and Col10a1 knockout mice have a reduced thickness of growth plate and articular cartilage, altered bone content, and atypical distribution of matrix components within growth plate cartilage37. In our study, a variant in high LD (r2 = 0.98) with the CTS variant in COL10A1, associates with Collagen α-1(X) chain in plasma  family39. Three of the candidate causal genes highlighted in the study encode ADAMTS  and one encodes ADAMTS-like protein (ADAMTSL2), which resembles ADAMTS in structure but lacks proteolytic activity. ADAMTS have regulatory roles and are involved in the processing of procollagens to mature collagens and control the structure and function of the extracellular matrix40. Another collagen substrate and a highlighted gene, LOXL1, provides collagen fibers their tensile strength and structural integrity by oxidizing lysine residues, resulting in the covalent cross-linking and stabilization of these structural components39. The missense variant (p.Arg141Leu) in LOXL1 is among the top 1% of pathogenic variants (CADD score = 24.4). As ADAMTS process the COL substrates which are stabilized by LOX, we assume that these processes are inter-linked and speculate that distortion of one may affect the others and thus, increase the risk of CTS.Collagens are substrates for disintegrin and metalloproteases with thrombospondin motifs (ADAMTS)SERPINA1 and ITGB5. The missense variant (p.Glu366Lys) in SERPINA1 is the top signal in the meta-analysis and has a strong protective effect on CTS and also associates with the plasma levels of multiple proteins . The data in this study was approved by the NBC  following review by the Icelandic Data Protection Authority. All personal identifiers of the participants’ data were encrypted in accordance with the regulations of the Icelandic Data Protection Authority. The Icelandic CTS cases were obtained in collaboration with Icelandic physicians at Landspitali—National University Hospital in Reykjavik, the Registry of Primary Health Care Contacts, and the Registry of Contacts with Medical Specialists in Private Practice. The CTS cases were identified using International Classification of Diseases 10 (ICD-10) code G56.0, ICD-9 code 354.0, and Nomseco Classification of Surgical Procedures (NCSP) code ACC51 (decompression and freeing of nervus medianus) through the scrutiny of hospital records from 1985 to 2020. The cases with lesions of the ulnar and radial nerves were identified using ICD-10 code G56.2 and G56.3, respectively.42. The North West Research Ethics Committee reviewed and approved UK Biobank’s scientific protocol and operational procedures (REC Reference Number: 06/MRE08/65). This study was conducted using the UK Biobank resource under application number 24898. CTS cases were identified by searching for ICD-10 code G56.0 and Classification of Interventions and Procedures (OPCS) codes A651  and A692  in General Practice clinical event records (Field ID 42040) and UK hospital diagnoses (Field ID 41270 and 41271).The UK Biobank resource includes extensive phenotype and genotype data from ~500,000 participants in the age range of 40 to 69 from across the UK that have provided an informed consent43. The Danish Data Protection Agency (P-2019-99) and the National Committee on Health Research Ethics (NVK-1700407) approved the studies under which genetic data on DBDS participants were obtained. The DBDS data requested for this study was approved by the DBDS steering committee.The Copenhagen Hospital Biobank (CHB) is a research biobank, which contains left over samples from diagnostic procedures on hospitalized and outpatients in the Danish Capital Region hospitals. Under the “Genetics of pain and degenerative diseases” protocol, approved by the Danish Data Protection Agency (P-2019-51) and the National Committee on Health Research Ethics (NVK-18038012), we identified CTS cases by ICD-10 code G56.0 and NCSP code ACC51. The Danish Blood Donor Study (DBDS) Genomic Cohort is a nationwide study of ~110,000 blood donorsth, 2021 from a source available to consortium partners .The FinnGen database consists of samples collected from the Finnish biobanks and phenotype data collected at the national health registers. The Coordinating Ethics Committee of the Helsinki and Uusimaa Hospital District evaluated and approved the FinnGen research project. The project complies with existing legislation . The official data controller of the study is the University of Helsinki. Subjects were identified using ICD-10 code G56.0 and ICD-9 code 354.0. NCSP-FI codes (Finnish NCSP adaption) were not available. The summary statistics for CTS were imported on May 1144, British45, and Danish46 participants. All participants were genotypically verified as being of European descent.Genetic ancestry quality control was performed for the IcelandicIn total, we had 48,843 carpal tunnel cases  and 1,190,837 controls  in the meta-analysis.47. The genomes of 49,962 Icelanders were WGS using GAIIx, HiSeq, HiSeqX, and NovaSeq Illumina technology to a mean depth of at least 17.8×. Single nucleotide polymorphisms (SNPs) and insertions/deletions (indels) were identified and their genotypes were called using joint calling with Graphtyper48. In addition, over 166,000 Icelanders  have been genotyped using various Illumina SNP chips and their genotypes phased using long-range phasing49, which allows for improving genotype calls using haplotype sharing information. Subsequently, genealogic information was used to impute sequence variants into the chip-typed Icelanders, as well as their first- and second-degree relatives50 to increase the sample size and power for association analysis.The preparation of the Icelandic samples, genotyping, whole-genome sequencing (WGS), and imputation was performed at deCODE genetics51, and the Affymetrix UK Biobank Axiom array52 in the remaining participants. Samples were filtered on 98% variant yield and any duplicates removed. Over 32 million high-quality sequence variants and indels to a mean depth of at least 20× were identified using Graphtyper48. Quality-controlled chip genotype data were phased using Shapeit453. Variants where at least 50% of the samples had a GQ score > 0 were used to prepare a haplotype reference panel using in-house tools and the long-range phased chip data. The haplotype reference panel variants were then imputed into the chip genotyped samples using in-house tools and methods described above for the Icelandic data49.The UK Biobank samples were genotyped with a custom-made Affymetrix chip, UK BiLEVE Axiom in the first 50,000 individuals54. Samples and variants with less than 98% yield were excluded. A haplotype reference panel was prepared in the same manner as for the Icelandic and UK data49 by phasing whole-genome sequence genotypes of 15,576 individuals from Scandinavia, the Netherlands, and Ireland using the phased chip data. Graphtyper was used to call the genotypes which were subsequently imputed into the phased chip data. Whole genome sequencing, chip-typing, quality control, long-range phasing, and imputation from which the data for this analysis were generated was performed at deCODE genetics.Samples from 276,114 Danes from the CHB and DBDS were genotyped using Illumina Global Screening Array chips and long-range phased together with ~238,000 genotyped samples from North-western Europe using Eagle2-6), and minor allele count (<3) were excluded. High coverage (25–30×) WGS data were used to develop the Finnish population-specific SISu v3 imputation reference panel with Beagle 4.1. More than 16 million variants have been imputed (https://finngen.gitbook.io/documentation/methods/genotype-imputation).A custom-made FinnGen ThermoFisher Axiom array  was used to genotype ~177,000 FinnGen samples at Thermo Fisher genotyping service facility in San Diego. Genotype calls were made with AxiomGT1 algorithm. Individuals with ambiguous gender, high genotype missingness (>5%), excess heterozygosity (±4 SD), and non-Finnish ancestry were excluded. Variants with high missingness (>2%), low Hardy-Weinberg equilibrium (< 1 × 10https://r5.finngen.fi/) to test for association between sequence variants and CTS. For the additive model, the expected allele counts were used as a covariate. We used LD score regression to account for distribution inflation due to cryptic relatedness and population stratification14 and used the intercepts as correction factors (CF) in the Icelandic (CF = 1.19), UK (CF = 1.03), and Danish (CF = 1.03) datasets.We applied logistic regression using the Icelandic, UK, and Danish data and combined the results with imported association results from Finland , blood sample availability for the individual, and an indicator function for the overlap of the lifetime of the individual with the time span of phenotype collection. In the UK association analysis, we adjusted for sex, age, and the first 20 principal components55 based on effect estimates and standard errors in which each dataset was assumed to have a common OR but allowed to have different population frequencies for alleles and genotypes. The total number of variants included in the meta-analysis that had imputation information above 0.8 and MAF > 0.01% was 37,071,338 . We estimated the genome-wide significance threshold using a weighted Bonferroni adjustment that controls for the family-wise error rate12  and vice versa, the P-values were combined for all four datasets to identify the most likely causal variant at each locus and any secondary signals. Based on the number of variants tested, we chose a conservative P-value threshold of <5 × 10−8 for secondary signals.Conditional association analyses were performed on the GWASs from Iceland, the UK, and Denmark using true imputed genotypes of participants. Approximate conditional analyses (COJO), implemented in the GCTA-software57. LD estimated from almost 15,000 phased Icelandic samples, was used derive adjusted effect estimates by applying LDpred18. The effect estimates were used as weights. We generated PRS into the Icelandic, the UK, and Danish datasets and used leave-one-out meta-analyses, where the summary data from that particular dataset was omitted, to avoid any bias in PRS estimates. In addition, we meta-analyzed the results from the dataset-specific PRS analyses using a random-effects model, fitted via maximum likelihood estimation58. The unit of the effect for each PRS is in SD. Since the effects on CTS were comparable , we performed no further scaling.Polygenic risk score (PRS) was generated using a set of 611,000 high quality variants across the genome were used to avoid uncertainty due to imputation quality14 and precomputed LD scores based on about 1.1 million variants from European ancestry samples .We estimated the SNP heritability for the combined CTS GWASs from Iceland, the UK, and Denmark using LD score regressionP ≤ 3.8 × 10-5) from the UK Biobank27 with effective sample size over 5,000 were performed using LD score regression59, which suggests the minimal effective sample size of 5,000 for each trait to get unbiased estimates of genetic correlation and heritability. Since participants in the published GWAS studies are of Caucasian ancestry, we used pre-computed LD scores from a 1000 genome panel with r2 from HapMap3, excluding HLA region. The HLA region was excluded for its genetic complexity and association with a wide number of traits. The default parameters of the LD score regression were used to compute the genetic correlation and heritability estimates.Genetic correlation analyses between the CTS meta-analysis and 1,319 published GWAS traits  method to estimate the causal relationship between variables, a t-test to compute the r2 ≥ 0.8 and within ± 1MB) that are predicted to affect coding or splicing of a protein (variant effect predictor using Refseq gene set), mRNA expression  in multiple tissues from deCODE, GTEx , and other public datasets, and/or plasma protein levels (top protein quantitative trait loci [pQTL]).To highlight potential causal genes associating with CTS, we annotated the CTS associations or variants in high LD  was estimated using a generalized linear regression, assuming additive genetic effect and quantile normalized gene expression estimates, adjusting for measurements of sequencing artefacts, demographic variables, blood composition, and hidden covariates63.RNA sequencing was performed on whole blood from 13,175 Icelanders and on subcutaneous adipose tissue from 750 Icelanders, described in detail elsewhere® SOMAscan proteomics assay to test the association of the sequence variants with protein levels in plasma15. The assay scanned 4,907 aptamers that measure 4,719 proteins in samples from 35,559 Icelanders with genetic information available at deCODE genetics. Plasma protein levels were standardized and adjusted for year of birth, sex, and year of sample collection (2000–2019).We used the SomaLogic17. The genes were tested for over-representation in different gene sets, including Gene Ontology cellular components (MsigDB c5) and GWAS catalogue-reported genes.We performed gene-based enrichment analysis using the GENE2FUNC tool in FUMAFurther information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1-12Reporting Summary


Heliconius butterflies exhibit diverse warning color patterns and mimicry, thus providing a useful framework for investigating these questions. We studied intraspecific variation in de novo biosynthesized cyanogenic toxicity and its potential ecological and evolutionary sources in wild populations of Heliconius erato along environmental gradients, in common-garden broods and with feeding treatments. Our results demonstrate substantial intraspecific variation, including detectable variation among broods reared in a common garden. The latter estimate suggests considerable evolutionary potential in this trait, although predicting the response to selection is likely complicated due to the observed skewed distribution of toxicity values and the signatures of maternal contributions to the inheritance of toxicity. Larval diet contributed little to toxicity variation. Furthermore, toxicity profiles were similar along steep rainfall and altitudinal gradients, providing little evidence for these factors explaining variation in biosynthesized toxicity in natural populations. In contrast, there were striking differences in the chemical profiles of H. erato from geographically distant populations, implying potential local adaptation in the acquisition mechanisms and levels of defensive compounds. The results highlight the extensive variation and potential for adaptive evolution in defense traits for aposematic and mimetic species, which may contribute to the high diversity often found in these systems.Chemical defences against predators underlie the evolution of aposematic coloration and mimicry, which are classic examples of adaptive evolution. Surprisingly little is known about the roles of ecological and evolutionary processes maintaining defence variation, and how they may feedback to shape the evolutionary dynamics of species. Cyanogenic  Chemical defenses are a common means for animals to gain protection against predators. Defensive chemicals can be acquired from the larval or adult diet (sequestered) and/or biosynthesized de novo by the organism . ChemicaThese different mimicry relationships all imply predictable differences in toxicity among mimetic species and individuals , and yieExtensive variation in defensive toxins within species and across mimicry rings has been demonstrated repeatedly . Indeed,vs. ecological processes in the origins of the extensive variation observed in these traits. Overall, empirical data unraveling the patterns and origins of chemical defense variation are needed to help build a more complete understanding of the evolution and diversification of aposematic and mimetic systems.In those species that sequester and those that synthesize defensive compounds, there are also several potential adaptive explanations for the persistence of variation in nature . The selHeliconius butterflies. With their incredible diversity in wing coloration, they are a well-known example of aposematism and mimicry (Heliconius larvae feed exclusively on passion vines (Passiflora), with which they have radiated and speciated through a coevolutionary arms race  Can some of this variation be explained by environmental gradients or highly divergent habitats and mimicry environments across a wider geographic scale? (3) How much of the variation in biosynthesized chemical defenses is heritable and evolvable, and how much is governed by environmental (particularly dietary) variance? By examining our results in the context of the well-studied mimicry system of Heliconius butterflies, we discuss the potential wider implications of our findings for aposematic and mimetic systems.We studied ecological and evolutionary aspects of intraspecific variation in biosynthesized cyanogenic toxicity in e Amazon . The cole Amazon . Our fieHeliconius erato demophoon and its typical host plant P. biflora were collected in Panama along a steep rainfall gradient  from three populations  were separated by sex, weighed, and the body excluding wings and one half of the thorax was preserved in 1 ml 100% MeOH (methanolic preservation stabilizes cyanogenic glucosides (n = 42) were sampled in 1 ml 100% MeOH. We do not expect the sampled body part to influence estimates of cyanogenic glucoside compound concentrations, as these compounds are located in the hemolymph, and are thus expected to be evenly distributed throughout the body , Intermediate (n = 11) and Wet (n = 20) study areas  and Wet (n = 12) study areas for greenhouse cultivation.dy areas , and prePassiflora biflora were cultivated in greenhouses  in a soil mixture  . The quality of host plants in H. erato feeding treatments was manipulated with watering treatments, motivated by the importance of water availability for many cyanogenic plants , such that the surface soil was allowed to dry and the leaves slightly drooped between watering events. The dry treatment thus aimed to mimic conditions of drought stress.c plants . The P. H. erato populations descendent of the wild Dry population were established in greenhouse conditions (see Supplementary File 4 for details on rearing methods). Larvae were reared on std-type P. biflora. All butterflies were individually marked, and were allowed to fly, mate, and oviposit freely in 2 ×  2 × 2 m mesh cages with potted std-type P. biflora and a standardized diet of sugar solution supplemented with amino acids provided ad libitum. The butterfly cages were observed throughout the day for mating pairs, and mated females were moved to individual 2 ×  2 × 2 m mesh cages and allowed to oviposit on std-type P. biflora. Eggs of 14 females , weighed, and preserved in 100% MeOH .The effect of larval dietary cyanogen content and quality on adult butterfly biosynthesized toxicity was studied with feeding treatments. We expected larval dietary quality to affect butterfly toxicity due to potential variation in the acquisition of nitrogenous resources for cyanogen biosynthesis , possiblHeliconius larvae and adults, linamarin and lotaustralin, were analyzed from the butterfly samples using nuclear magnetic resonance (1H-NMR) with Bruker Avance III HD NMR spectrometer  (see 1H-NMR methods). The data were processed and analyzed using Bruker TopSpin software . The peaks of cyanogen compounds were identified based on comparison of sample 1H spectra with the 1H spectra of authentic reference samples of linamarin and lotaustralin. Note that we analyzed only the biosynthesized cyanogenic compounds , and Heyanogens , and theequester .All statistical analyses of the study were undertaken using R 3.4.4 . The valPassiflora biflora samples was analyzed using liquid chromatography-mass spectrometry (LC-MS/MS) with Agilent 1100 Series LC  hyphenated to a Bruker HCT-Ultra ion trap mass spectrometer  following the procedure of P. biflora were detected and quantified following The cyanogenic content of the P. biflora treatment types  with Dualex® Scientific+ leaf clip meter , were measured from the greenhouse-cultivated ip meter ; File S4r =0.897). Cyanogen concentration was not correlated with adult body mass in either sex . Adult mass also did not differ between the feeding treatments , and was not included as a variable in further analyses.The concentrations of the two tested biosynthesized cyanogens, linamarin and lotaustralin were strongly correlated with each other  as the proportion of the total phenotypic variance (VP) explained by the (broad-sense) genetic variance component (VG). When calculating heritabilities we included the variance due to sex in the total phenotypic variance, but we did not include the variance due to the treatment because this component of the variance is unlikely to represent natural variation (μ) as eμ = VG/µ2, where µ is the trait mean , which gives the expected percent change in the trait mean per generation under a unit strength of selection . The primary models  included the individual breeding value as a random effect, and the fixed effects of sex and feeding treatment , i.e., repeatability, with a linear mixed model approach. For this, we implemented the “rptR” package , with brm) as the difference in slope between a mother-offspring (mo) and a father-offspring (fo) regression, i.e., m = βmo − βfo , with the Intermediate population having higher concentrations than the two populations at the opposite extremes along the rainfall gradient  for total biosynthesized cyanogen concentration level (2) of 0.115 (H2 (linamarin) =0.139, H2  =0.067; R = 0.125, SE =0.059, CI = , P = 6.02e−05). Among-brood variance could originate from several sources including (1) additive genetic variance, (2) maternal environmental or genetic effects, and (3) shared environment, and below we explore these options further.The level of biosynthesized cyanogenic toxicity varied two-fold among the 20 common-garden reared broods  =1.89%, eμ =1.04%; First, we consider the estimated broad-sense genetic variance component and estimate evolutionary potential of toxicity, assuming the genetic component consists of largely additive genetic variance. The broad-sense evolvability of total cyanogen concentration was eG became negligible, illustrating that the two random terms explain essentially the same variance component . In turn, the midparent–offspring regression was nearly flat  =0.288, CI = ; m  =0.193, CI = ), which implies the existence of positive maternal effects in biosynthesized cyanogenic toxicity. However, uncertainty in these maternal effects remains notable, as illustrated by the wide confidence intervals.Our baseline estimates of repeatability, as well as broad-sense heritability and evolvability (Model 1) thus showed that there is moderate but detectable variation among broods in biosynthesized toxicity. We then estimated maternal and paternal contributions to this variation. When including maternal identity as a second random effect in the animal model (Model2), this maternal variance component explained most of the variance explained by the genetic variance component in Model1, whereas Vtrm/VP = 0.011; sex/VP = 0.011; res/VP = 0.874 for total cyanogens, F1,320 = 27.42, P < 0.001, effect size (Hedges’ g) =0.962 for total cyanogens), despite the two generations sharing common garden conditions.Finally, we investigated other sources, particularly environmental origins of variance. Larvae reared on different host plant types  did not H. erato. Wide variation in defensive toxins has often been found in chemically defended prey, yet the sources of this variation remains largely unclear , which could, given that it was largely composed of additive genetic effects, imply substantial evolutionary potential in biosynthesized chemical defenses of H. erato. Our broad-sense heritability estimate of biosynthesized cyanogenic toxicity (0.115 ± 0.086) is close to the average heritability value of physiological traits (0.12 ±0.05), based on a meta-analysis of a wide range of traits and taxa (Arctia plantaginis), in which no evidence for a genetic component to variation in the secretion amounts of de novo synthesized chemical defenses could be detected  of these tropical butterflies. For example, assuming Heliconius has six generations per year, and increased cyanogenic toxicity is selected for with selection as strong as selection on fitness as a trait  the maternal effect component fitted in our animal model seemed to explain the same component of variance as the broad-sense genetic component, as well as that (2) midparent–offspring and father-offspring regressions were non-significant while mother and offspring toxicity values were positively correlated. Given that mothers were raised in common garden conditions, and environmental variance is thus expected to be low, these effects likely have a genetic or epigenetic basis. At this point, it is unclear if the maternal effects that we document here are the result of maternally transmitted genes, epigenetic effects, direct transfer of toxins from mother to offspring in eggs , or othec toxins  and the t mating .m =0.25, which is comparable to the m =0.261 estimated here for cyanogenic toxicity, would over time exceed that of a trait with m = 0 by as much as 50% (assuming constant heritability)  . This efability) . The matability) . FurtherStudies of mimicry often focus on color pattern, i.e., the warning signal . HoweverHeliconius, the combination of a locally dynamic selective environment, flexible genetic mechanisms for generating warning signals  for chemically defended individuals (H. erato (Heliconius should measure the entire range of cyanogens (both sequestered and biosynthesized) and other chemical defense compounds potentially interacting to produce the unpalatability level perceived by relevant predators , whereas a smaller proportion had substantially higher levels, and the pattern was surprisingly similar in all populations. Such a pattern could be explained by age distributions  or steep environmental gradients in explaining toxicity profiles, despite a large component of unexplained variance in common-garden reared broods. Instead, the results suggest that adaptive processes originating from e.g., larval host species availability, mimicry environment, and individual-level energy optimization may play important roles in the variation of cyanogenic defenses. This is in line with our data indicating substantial broad-sense evolvability in cyanogenic toxicity, although more data will be needed to confirm the origins of the observed among-brood variance and shed light on how the indicated maternal effects will affect the overall selection response. Our study suggests that adaptive evolution could be an important force driving variation in defensive traits. Such adaptive processes could play a part in explaining the incredible diversity in aposematic and mimetic systems.The role of ecological and evolutionary processes in the origins and maintenance of the wide variation in chemical defenses, and the consequences for the evolution of aposematism and mimicry holds many unanswered questions. Our analysis of biosynthesized cyanogenic toxicity in 10.7717/peerj.11523/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.11523/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.11523/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.11523/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.11523/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.11523/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.11523/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.11523/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.11523/supp-9Supplemental Information 9Click here for additional data file.