Obesity is a major risk factor for cardiovascular disease, stroke, and type 2 diabetes (T2D). Excessive accumulation of fat in the abdomen further increases T2D risk. Abdominal obesity is measured by calculating the ratio of waist-to-hip circumference adjusted for the body-mass index (WHRadjBMI), a trait with a significant genetic inheritance. Genetic loci associated with WHRadjBMI identified in genome-wide association studies are predicted to act through adipose tissues, but many of the exact molecular mechanisms underlying fat distribution and its consequences for T2D risk are poorly understood. Further, mechanisms that uncouple the genetic inheritance of abdominal obesity from T2D risk have not yet been described. Here we utilize multi-omic data to predict mechanisms of action at loci associated with discordant effects on abdominal obesity and T2D risk. We find six genetic signals in five loci associated with protection from T2D but also with increased abdominal obesity. We predict the tissues of action at these discordant loci and the likely effector Genes (eGenes) at three discordant loci, from which we predict significant involvement of adipose biology. We then evaluate the relationship between adipose gene expression of eGenes with adipogenesis, obesity, and diabetic physiological phenotypes. By integrating these analyses with prior literature, we propose models that resolve the discordant associations at two of the five loci. While experimental validation is required to validate predictions, these hypotheses provide potential mechanisms underlying T2D risk stratification within abdominal obesity. Metabolic syndrome (MetSyn) is a cluster of dysregulated metabolic conditions that tend to occur together to increase the risk for cardiometabolic disorders such as type 2 diabetes (T2D) . This clDespite the promise of these obesity-centered therapeutic strategies, there has also been a growing body of evidence describing a rare phenotype known as metabolically healthy obesity (MHO) . MHO desMost of the genetic mechanisms of MHO described have been associated with increased BMI without increased disease risk. For example, the missense variant rs373863828 in CREB3 regulatory factor has been shown to increase BMI without a corresponding increase in HOMA-IR and circulating TG, or a decrease in circulating adiponectin . Ob/ob mAs complex traits with both environmental and genetic risk factors, abdominal obesity and T2D have been the subject of multiple genome-wide association studies (GWAS). While GWAS have identified hundreds of genetic loci associated with abdominal obesity and T2D, moving from association to mechanism at a locus is not trivial. The use of colocalization analysis (COLOC), which identifies loci that contain shared genetic architecture for multiple traits of interest, can inform mechanistic hypotheses moving from association to function by integrating data from multiple studies . For exaTo advance the understanding of mechanisms linking body fat distribution to T2D risk, independent of overall obesity, we used COLOC and TACTICAL to predict the mechanisms of action at genetic loci associated with both T2D and WHR, both adjusted for the BMI. Using the most recent GWAS summary statistics, QTL summary statistics, tissue-specific gene expression data, and high-resolution epigenetic annotations, we predicted the shared genetic architecture of T2DadjBMI and WHRadjBMI at 79 genetic loci. Here, we present the identification of five loci that contained association signals with discordant effects on abdominal fat and T2D risk, meaning that the allele of the lead variant associated with protection from T2D was associated with increased abdominal fat accumulation. We predicted the eGenes and tissues of action at these five loci and explored the relationship between adipose eGene expression with cellular and physiological phenotypes. Here, we provide data-driven hypotheses about predicted candidate causal eGenes at GWAS loci with associations that recall metabolically healthy abdominal obesity.To identify genetic loci which contained pleiotropic association signals for both T2DadjBMI and WHRadjBMI, we performed colocalization analysis . This anWe next investigated the genetic and physiological consequences of discordant variants. We performed a phenome-wide association study (PheWAS) for anthropometric and glycemic traits with the most highly powered GWAS available . We usedWe then queried the variant effect predictor (VEP) to discover genetic variant annotations . VEP preTo investigate the role of eGenes in physiological phenotypes and cellular phenotypes, we evaluated the correlation of adipose tissue eGene expression and T2D-relevant phenotypes since these correlations can reveal biologically relevant functional relationships . To predTHADA-AS  to be the sole eGene. At the 5q21.1 locus, we predicted GIN1 (SAT), PAM (SAT & SKM), and PPIP5K2 (SAT) to be the eGenes. The association signal at rs6860588 was associated with a novel alternative splicing isoform of PAM in SAT, which skips the 14th exon. Using SMR, we predicted four eGenes at two discordant loci. At the 5q21.1 locus, we predicted that the genetic association signal represented by rs6860588 was also associated with the regulation of EIF3KP1 , PPIP5K2 (SAT), and GIN1 . At the discordant association signal in the 19q13.11 locus, we predicted that the genetic association signal represented by variant rs3786897 was also associated with the regulation of PEPD . As the colocalization transcripts GIN1 and PPIP5K2 were replicated with both methods (To predict effector genes (eGenes) regulated by discordant variants, we next predicted the colocalization of QTLs with the WHRadjBMI and T2DadjBMI GWAS. Colocalization of a GWAS association signal with a genetic regulatory association signal can be used to prioritize mechanisms underlying association. We obtained expression QTL (eQTL) and splicing QTL (sQTL) summary statistics from multiple cohorts and tissue groups . We extr methods , these r methods  for disc methods . Because methods , we reasPAM. We then computed the biweight midcorrelation of transcript counts or splice junction counts with 23 metabolic phenotypes. We found significant  correlations of adipose tissue gene expression of three genes with 13 phenotypes  and C-reactive protein (CRP). IL-1RA plays a protective role in resolving inflammation  on the discordant variants.We obtained the 99% credible set of SNPs from the results of Bayesian factor analysis implemented through Coloc.abf at each locus. We calculated the Z-scores for the association test of each genetic variant and the GWAS trait. If the Z-score associated with SNP had the opposite sign for association with WHRadjBMI and T2DadjBMI with respect to the same allele and the p-value for the association with both traits was less than 1e-05, we considered the variant discordant. We then identified in which loci the SNPs were located, and queried haploReg  LD data We queried the GWAS meta-analysis associations of glycemic and anthropometric traits for each lead discordant variant in the Type 2 Diabetes Knowledge Portal (T2DKP) . We addiWe obtained GWAS summary statistics for WC, hip circumference (HC), WHR, WHRadjBMI, T2D, and T2DadjBMI. We extracted summary statistics of variants within genetic loci containing a discordant association signal  and perfWe performed variable selection in multiple regression as implemented in the R package SuSiE . This meWe obtained eQTL data from the Genotype-Tissue Expression (GTEx) for 49 tissues , the StoWe used the functional GWAS (fGWAS)  command-We conducted TOA score analysis using the credible set of SNPs from each of the 79 colocalized loci. TACTICAL computes the TOA score with the SNP-level Bayesian probabilities, the SNP annotation maps, and the annotation enrichment scores. We used the Coloc.abf PPH4 scores for the SNP-level Bayesian probability, the fGWAS annotation enrichment scores, and the SNP annotation maps to compute the TOA score at all colocalized loci. We separated independent association signals in the same loci (LD <0.5) with HaploReg . With TAFor each eGene, we computed the biweight midcorrelation and its significance, as implemented by the Weighted Genetic Coexpression Network Analysis (WGCNA) package , betweenWe obtained Simpson-Golabi-Behmel syndrome preadipocyte adipogenesis time series gene expression data from GEO (accession number GSE76131) . We eval This study presents a valuable finding on five genome-wide association study loci displaying discordant effects on T2D and abdominal obesity. The evidence supporting the claims of the authors is solid, although further experiments are required to describe the mechanisms through which a genetic variant can confer increased abdominal obesity while offering protection from T2D risk. The work will be of interest to researchers working within the fields of variant-to-function analysis and endocrinology. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Tune H Pers as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by David James as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Marcel den Hoed (Reviewer #2).Thank you for submitting your article "Predicting mechanisms of action at genetic loci associated with discordant effects on type 2 diabetes and abdominal fat accumulation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) Please carefully address, through additional analyses, the comments related to whether the proposed effector genes are likely "healthy obesity genes", or whether there are at these loci either multiple causal genes per locus, or single causal genes acting on different traits via different tissues and mechanisms.2) Please carefully address the comments on WHRadjBMI being a trait that is complicated to interpret due to its composite structure, ideally by including analyses focusing on the two different fat depots that the WHR measure is based on.3) Please address, where appropriate through additional analyses, all remaining comments raised by the three referees.Reviewer #2 (Recommendations for the authors):I read your manuscript with great interest and think the findings are interesting. Nevertheless, I hope the points raised below can help make the manuscript even better, and help support some of the conclusions drawn.1. The trait that is WHRadjBMI has puzzled me for a while. Ratios can be problematic per se if two traits have a non-zero intercept (I am not sure if this is the case for WC and HC?), and adjusting for a heritable trait (BMI) may introduce collider bias, as was elegantly shown by Day et al. (https://pubmed.ncbi.nlm.nih.gov/26849114/). As a result, an association with WHRadjBMI may not reflect the same biological phenomenon for each locus. Hence, in addition to associations with WHRadjBMI and T2DadjBMI, it would be informative to also show summary statistics for associations of these loci with waist circumference, hip circumference, BMI and T2D in Figure 1.2. By running a co-localization analysis with sets of SNPs within 200kb of a genome-wide significant SNP in either GWAS, the number of truly co-localizing signals may be underestimated due to secondary association signals in either or both GWASs. It could be worthwhile to additionally run conditional analyses for each outcome to identify such loci and run the co-localization analysis for each association signal separately.3. The authors state that associations were considered discordant if the z-score had an opposite direction for the same allele. Presumably, the association with both traits also needed to be significant?4. In a best-case scenario, WHRadjBMI indeed reflects abdominal obesity, as intended. However, associations with WHRadjBMI then reflect the net association with adiposity across all individual abdominal fat depots. This does not tell us which lipid depot(s) drive(s) the association with WHRadjBMI. Could the authors explore associations with e.g., liver fat and pancreas fat as derived by MRI in UK biobank data for these six loci?5. Associations don't provide insights into causal pathways or directions of effect. Hence, I suggest refraining from using causal language, e.g., in line 75: "protecting against T2D but increasing abdominal fat accumulation". Even if a causal variant acts on both outcomes through a single causal gene and pathway (both are debatable at this stage), then there are still four possible courses of action. Similarly, in the discussion (line 237) the authors state that: "increased expression of THADA-AS results in increased abdominal fat accumulation but decreased T2D risk". No evidence was provided that justifies this conclusion based on an association.6. Even if there is only one causal gene, its effects on different outcomes may be mediated by multiple tissues and cell types . Since all six loci showing directionally discordant associations with WHRadjBMI and T2DadjBMI are located in non-coding regions, these loci likely have a regulatory role, e.g., as enhancers. Hence, the seemingly directionally discordant associations may well be mediated by more than one causal gene. I would argue that neither the "one gene, multiple pathways" nor the "multiple genes" scenarios reflect uncoupling.7. Co-localization of credible variants with sQTLs and eQTLs does not mean that such genes are causal for the traits of interest, as the association between the expression of a gene X and the outcome of interest can be confounded by the expression of the truly causal gene Y, with the expression of both genes X and Y influenced by the enhancer that is flagged by the causal variant. To get one step closer to such inference, it would be informative if the authors could show chromatin-chromatin interaction data for credible variants in all six discordant loci, across all relevant cell types.8. Gene prioritization is a fast-moving field, and a range of bioinformatics approaches – but no gold standard – are available to prioritize candidate genes. Results using proof-of-concept loci have not shown very convincing results for the predictive ability of eQTL associations to flag causal genes, even in the presence of co-localization. Hence, it would be informative to see which genes are prioritized in these loci by other available approaches – including but not limited to chromatin accessibility QTLs in relevant cell types – and if there is consensus across those approaches as to which gene(s) are likely causal.9. Even if there are loci that uncouple genetic effects on abdominal adiposity and risk of type-2 diabetes, the causal beneficial effect of weight loss is so large in comparison that it would no doubt remain advisable for most if not all individuals to lose weight to reduce the risk of T2D and its complications.10. Why was ATAC-sec data provided for only one of six discordant loci? In the discussion the authors state that tissues and mechanisms of action are predicted for six loci, but only one or two are described in the paper.Reviewer #3 (Recommendations for the authors):1. The rationale for the study is unclear. The authors introduce the study as an investigation into factors explaining individual differences in the relationship between obesity and cardiometabolic health, particularly the "metabolically healthy obesity" and "lean, insulin resistance" phenotypes. However, the analyses use outcome phenotypes that are adjusted for BMI (i.e. obesity): WHRadjBMI and T2DadjBMI. Thus, the results do not explain differences in obesity status and metabolic health, but rather tell about differences in body fat distribution and T2D risk, independent of obesity. Furthermore, a relatively low abdominal fat accumulation is one of the key characteristics of the "metabolically healthy obesity" phenotype associated with lower T2D risk, while the "lean, insulin resistance" phenotype associated with increased T2D risk, is characterized by relative increase in abdominal adiposity. Thus, the discordant association pattern of relatively higher abdominal adiposity but lower risk of T2D, investigated by the authors, does not exemplify either of these two phenotypes. Could the authors think of a biological or clinical example that could represent the discordant genetic association pattern they are aiming to characterize, in order to create a more plausible rationale for the present analyses?2. Overall, it is difficult to imagine a plausible biological explanation for a relationship where higher WHRadjBMI would mediate a lower risk of T2DadjBMI. It would be really interesting is such biology exists, but at the moment the paper does not give much help to the reader in interpreting what biological mechanism could underlie such a relationship. An alternative explanation for the findings is the possibility that the genetic effects are driven by independent effects of the same genes on biological pathways in two separate tissues (e.g. adipose tissue and pancreas), which would then generate the seemingly discordant genetic association pattern between WHRadjBMI and T2DadjBMI.3. A clear weakness in the study is the lack of any consideration to the subcomponents of the WHR phenotype: waist and hip circumference. As WHR is calculated as the ratio of waist circumference to hip circumference, WHR can increase if waist circumference increases or if hip circumference decreases, reflecting an increase in abdominal fat or a decrease in gluteofemoral fat, respectively. Waist and hip circumferences have very different implications for cardiometabolic health and T2D risk. Thus, is it critical to include an analysis on genetic effects on the two different fat depots, to understand the biological underpinnings of the findings. In addition, further GWAS results could be used to look into more detailed fat distribution phenotypes, such as the amount of visceral fat and subcutaneous fat in different body compartments.4. The results show that two of the independent discordant loci (represented by rs3776705 and rs459193), associated with lower risk of T2DadjBMI, show genome-wide significant associations with higher 2-hour glucose levels, and one of the loci (rs459193) also shows a genome-wide significant association with higher fasting glucose levels, despite their protective effect on T2D risk. The findings appear counter-intuitive, considering that T2D is diagnosed based on elevated fasting and 2-hour glucose values. Particularly for these two loci, it would be critical to make sure that the directions of effects and the reported reference alleles are validated in a further set of data, to make sure that the discordant effects are not due to mismatched alleles.5. The paper includes several overstated claims about the clinical impact of the findings and use of the findings for personalized risk stratification . The authors should revise the manuscript to avoid misleading the readers, by overstating the implications of their findings. Essential revisions:1) Please carefully address, through additional analyses, the comments related to whether the proposed effector genes are likely "healthy obesity genes", or whether there are at these loci either multiple causal genes per locus, or single causal genes acting on different traits via different tissues and mechanisms.We appreciate the suggestions for additional analysis to determine the proposed effector disease genes. To increase confidence in effector genes, we performed summary-based randomization (SMR) using T2DadjBMI and WHRadjBMI with QTLs. This provided an orthogonal method for colocalization that infers the causality of QTLs for GWAS associations. To control for false positives associated with MR methods, we implemented a false-discovery rate threshold of 5e-02 and performed a visual inspection of the association signal as visualized by LocusCompare. Implementing a requirement that a single variant that is associated with both the eQTL and trait of interest yielded 20 significant eQTL colocalizations, which have now been reported in Supplementary File 7. Additionally, we updated the table labels in the supplementary files to differentiate between colocalization results achieved using Coloc.abf and using SMR (Supplementary Files 6 & 7). We have updated our interpretation in the results to reflect the potential causal genes as either single effector genes, or multiple causal genes per locus acting on different traits .2) Please carefully address the comments on WHRadjBMI being a trait that is complicated to interpret due to its composite structure, ideally by including analyses focusing on the two different fat depots that the WHR measure is based on.We appreciate the comments on the choice of WHRadjBMI as a trait. To increase the interpretability of our analysis, we performed a multi-trait colocalization analysis for each of the loci containing a discordant association signal in the SNP set using the summary statistics for Waist Circumference (WC), Hip Circumference (HC), the ratio of Waist-to-Hip circumference (WHR), Body-Mass Index (BMI), WHR adjusted for BMI (WHRadjBMI), Type 2 Diabetes (T2D), and T2D adjusted for BMI (T2DadjBMI). In this recursive multi-trait colocalization analysis, traits are pairwise analyzed, and then summary statistics from traits are successively added and removed until the highest likelihood is achieved. This is repeated until all the colocalization of genetic association signals for each trait is tested and the maximal number of high-confidence colocalized groups of traits at each locus is achieved. This analysis was able to replicate colocalization at all six discordant loci. Further, at three of the six discordant loci the WHRadjBMI association signal was colocalized with the WC association signal. The colocalization analysis with the additional traits is now summarized in Supplementary File 3. The text has been updated to reflect these results (lines 123-127) and the methods (lines 353-358). In addition, we performed a comprehensive phenome-wide association study (PheWAS) of the individual abdominal fat depots underlying WHRadjBMI as well as the molecular glycemic phenotypes underlying T2DadjBMI . The text has been updated to reflect the results of this PheWAS (lines 108-144).3) Please address, where appropriate through additional analyses, all remaining comments raised by the three referees.We appreciate the comments raised by referees and feel they have significantly improved the quality of the manuscript. We aimed to respond to each comment raised by referees comprehensively and have included responses to all comments below.Reviewer #2 (Recommendations for the authors):I read your manuscript with great interest and think the findings are interesting. Nevertheless, I hope the points raised below can help make the manuscript even better, and help support some of the conclusions drawn.1. The trait that is WHRadjBMI has puzzled me for a while. Ratios can be problematic per se if two traits have a non-zero intercept (I am not sure if this is the case for WC and HC?), and adjusting for a heritable trait (BMI) may introduce collider bias, as was elegantly shown by Day et al. (https://pubmed.ncbi.nlm.nih.gov/26849114/). As a result, an association with WHRadjBMI may not reflect the same biological phenomenon for each locus. Hence, in addition to associations with WHRadjBMI and T2DadjBMI, it would be informative to also show summary statistics for associations of these loci with waist circumference, hip circumference, BMI and T2D in Figure 1.We appreciate the comments on the choice of WHRadjBMI as a trait. To increase the interpretability of our analysis, we performed a multi-trait colocalization analysis for each of the loci containing a discordant association signal in the SNP set using the summary statistics for Waist Circumference (WC), Hip Circumference (HC), the ratio of Waist-to-Hip circumference (WHR), Body-Mass Index (BMI), WHR adjusted for BMI (WHRadjBMI), Type 2 Diabetes (T2D), and T2D adjusted for BMI (T2DadjBMI). In this recursive multi-trait colocalization analysis, traits are pairwise analyzed, and then summary statistics from traits are successively added and removed until the highest likelihood is achieved. This is repeated until all the colocalization of genetic association signals for each trait is tested and the maximal number of high-confidence colocalized groups of traits at each locus is achieved. This analysis was able to replicate colocalization at all six discordant loci. Further, at three of the six discordant loci the WHRadjBMI association signal was colocalized with the WC association signal. The colocalization analysis with the additional traits is now summarized in Supplementary File 3. The text has been updated to reflect these results (lines 123-127) and the methods (lines 353-358). In addition, we performed a comprehensive phenome-wide association study (PheWAS) of the individual abdominal fat depots underlying WHRadjBMI as well as the molecular glycemic phenotypes underlying T2DadjBMI . The text has been updated to reflect the results of this PheWAS (lines 108-144). Further, the point is excellently taken about the collider bias, and this suggestion has increased the quality of our manuscript.2. By running a co-localization analysis with sets of SNPs within 200kb of a genome-wide significant SNP in either GWAS, the number of truly co-localizing signals may be underestimated due to secondary association signals in either or both GWASs. It could be worthwhile to additionally run conditional analyses for each outcome to identify such loci and run the co-localization analysis for each association signal separately.We apologize for the confusion, and the Methods section has been updated to reflect this. SNP windows of 200kb were constructed and then merged with nearby windows to form the loci that underwent colocalization analysis. This was to include all variants in linkage disequilibrium with the lead signal at the locus, as well as to include secondary association signals. To determine if the initial SNP window of 200kb significantly altered results, we altered the initial SNP window to 500kb and did not discover new loci containing discordant association signals, or find that previously identified loci were no longer significant. We amended the text in the Methods (line 327) to reflect this change.Additionally, we implemented the sum of single effects (SuSiE) model to relax the single causal variant assumption and parse the effects of potential multiple causal signals at a locus. In conjunction with colocalization analysis, this found a separate discordant association signal in the 5q21.2 locus that was even more strongly associated with genetic regulation than the primary signal identified in our earlier analysis . We report our results in Supplementary File 3 and throughout the Results section. We again thank the reviewers for this particularly helpful suggestion that has improved the quality of our manuscript.3. The authors state that associations were considered discordant if the z-score had an opposite direction for the same allele. Presumably, the association with both traits also needed to be significant?This is an excellent point, and our methods section has been updated to clarify this (lines 332 – 341). We additionally added all of the p-values for the association to Supplementary File 1 to further clarify the significance of association within the 99% credible set of SNPs. The association with both traits needed to reach at least a nominal, locus-wide significance of 5e-04. The signals for both WHRadjBMI and T2DadjBMI at four of the five discordant loci reached a genome-wide significance level of 5e-08. At the locus represented by variant rs74567345, the association p-value for WHRadjBMI was 1.599e-05. At the locus represented by the variant rs3786897, the association p-value for T2DadjBMI was 1.6e-07. Because these loci passed visual inspection as well as having significant Bayesian factors (>0.70) we considered them for further analysis. Additionally, while genome-wide significance is traditionally used to prioritize variant candidates for further investigation, work done in the laboratory of Brian W. Parks (PMID: 32197071) demonstrates that even variants with only nominal, local significance in GWAS can also have functional relevance to GWAS traits. We additionally used SuSiE to parse the colocalization of multiple causal signals in loci and found the association signal represented by variant rs6860588 that did reach genome-wide significance for association with both traits, as described in the Methods section and the response to reviewers above.4. In a best-case scenario, WHRadjBMI indeed reflects abdominal obesity, as intended. However, associations with WHRadjBMI then reflect the net association with adiposity across all individual abdominal fat depots. This does not tell us which lipid depot(s) drive(s) the association with WHRadjBMI. Could the authors explore associations with e.g., liver fat and pancreas fat as derived by MRI in UK biobank data for these six loci?We thank the reviewers for an excellent suggestion here. We followed this line of inquiry and reported the results in Figure 1C in response to the concerns raised above. This suggestion improved our results. The text has been updated to reflect these changes (lines 349-352).5. Associations don't provide insights into causal pathways or directions of effect. Hence, I suggest refraining from using causal language, e.g., in line 75: "protecting against T2D but increasing abdominal fat accumulation". Even if a causal variant acts on both outcomes through a single causal gene and pathway (both are debatable at this stage), then there are still four possible courses of action. Similarly, in the discussion (line 237) the authors state that: "increased expression of THADA-AS results in increased abdominal fat accumulation but decreased T2D risk". No evidence was provided that justifies this conclusion based on an association.We thank the reviewers for their comments on this topic in particular. We have changed the language we used in those specific lines and throughout the manuscript to reflect greater caution with causal language. We have moved to language that proposes a model that reconciles discordant effects and emphasized in the Discussion the need for experimental validation and further studies to conclusively show that discordant loci eGene expression or splicing are causal (lines 312-324).6. Even if there is only one causal gene, its effects on different outcomes may be mediated by multiple tissues and cell types . Since all six loci showing directionally discordant associations with WHRadjBMI and T2DadjBMI are located in non-coding regions, these loci likely have a regulatory role, e.g., as enhancers. Hence, the seemingly directionally discordant associations may well be mediated by more than one causal gene. I would argue that neither the "one gene, multiple pathways" nor the "multiple genes" scenarios reflect uncoupling.We thank the reviewer for this comment, and we added it to the Discussion section (lines 360-380) to reflect this uncertainty of interplay between variants, genes, and tissues. We also removed causal language throughout the manuscript for metabolically healthy obesity. Because of the directionally discordant associations, we do still believe the variants confer protection from diabetes but increased abdominal obesity and propose models to reconcile this.7. Co-localization of credible variants with sQTLs and eQTLs does not mean that such genes are causal for the traits of interest, as the association between the expression of a gene X and the outcome of interest can be confounded by the expression of the truly causal gene Y, with the expression of both genes X and Y influenced by the enhancer that is flagged by the causal variant. To get one step closer to such inference, it would be informative if the authors could show chromatin-chromatin interaction data for credible variants in all six discordant loci, across all relevant cell types.We thank the reviewers for this suggestion– we thought following these lines of inquiry would significantly improve the quality of our manuscript. We obtained the chromatin-chromatin interaction data from Hi-C experiments performed on adipocytes, skeletal myocytes, and pancreatic islets. We were unable to obtain interaction data from Hi-C experiments performed on hepatocytes. We then searched for the presence of variants in our credible set in the “bait” and “prey” regions of chromatin and were unable to discover the presence of discordant variants in the credible set within regions containing significant chromatin-chromatin interactions. While this revealed significant associations in the loci of interest, none of the discordant variants were in “bait” or “prey” regions in any cell line or tissue of interest. In summary, we concluded that chromatin-chromatin interactions are not a significant driver at the QTL colocalizations. We report the studies we obtained chromatin-chromatin interaction data from in Figure 2 – source data 2 and the results of our analysis in lines 156-157.8. Gene prioritization is a fast-moving field, and a range of bioinformatics approaches – but no gold standard – are available to prioritize candidate genes. Results using proof-of-concept loci have not shown very convincing results for the predictive ability of eQTL associations to flag causal genes, even in the presence of co-localization. Hence, it would be informative to see which genes are prioritized in these loci by other available approaches – including but not limited to chromatin accessibility QTLs in relevant cell types – and if there is consensus across those approaches as to which gene(s) are likely causal.We appreciate this comment from the reviewer. We used Summary-Based Mendelian randomization to prioritize eGenes with a direct association between expression levels and traits of interest. This orthogonal method to Coloc.abf provided us with more significant associations and replicated a limited set from Coloc.abf (Supplementary Files 6 and 7). We considered looking into chromatin accessibility QTLs (caQTLs) in relevant cell types as well, but we were only able to obtain liver caQTL data. Because we did not propose mechanisms of action in the liver for our discordant loci, we decided this would not represent a further prioritization or more association data to support or discredit our hypotheses and did not move forward with this. Nonetheless, we thank the reviewer for this suggestion as this did prompt us to look more critically at publicly available data to support and prioritize our candidate genes. We decided instead to look at time series preadipocyte differentiation data to see if our candidate eGenes played a role in adipogenesis . This revealed significant associations with dynamic expression across adipogenesis. Because adipogenesis is known to be a significant molecular regulator of adipose tissue distribution, this helped us to prioritize candidate genes and form hypotheses at the 2p21 and 5q21.2 loci.9. Even if there are loci that uncouple genetic effects on abdominal adiposity and risk of type-2 diabetes, the causal beneficial effect of weight loss is so large in comparison that it would no doubt remain advisable for most if not all individuals to lose weight to reduce the risk of T2D and its complications.We agree with the reviewer, and thank them for this comment. We have added clarifying language in the introduction (lines 53-106) and Discussion section (lines 277-291) to further reflect the value of our investigation in the proper context of basic science research into mechanisms and not clinical advice.10. Why was ATAC-sec data provided for only one of six discordant loci? In the discussion the authors state that tissues and mechanisms of action are predicted for six loci, but only one or two are described in the paper.We thank the reviewer for this suggestion, and have removed the ATAC-Seq results from our manuscript.Reviewer #3 (Recommendations for the authors):1. The rationale for the study is unclear. The authors introduce the study as an investigation into factors explaining individual differences in the relationship between obesity and cardiometabolic health, particularly the "metabolically healthy obesity" and "lean, insulin resistance" phenotypes. However, the analyses use outcome phenotypes that are adjusted for BMI (i.e. obesity): WHRadjBMI and T2DadjBMI. Thus, the results do not explain differences in obesity status and metabolic health, but rather tell about differences in body fat distribution and T2D risk, independent of obesity. Furthermore, a relatively low abdominal fat accumulation is one of the key characteristics of the "metabolically healthy obesity" phenotype associated with lower T2D risk, while the "lean, insulin resistance" phenotype associated with increased T2D risk, is characterized by relative increase in abdominal adiposity. Thus, the discordant association pattern of relatively higher abdominal adiposity but lower risk of T2D, investigated by the authors, does not exemplify either of these two phenotypes. Could the authors think of a biological or clinical example that could represent the discordant genetic association pattern they are aiming to characterize, in order to create a more plausible rationale for the present analyses?We thank the reviewer for their suggestion. We aimed to incorporate this into our Discussion section in lines 254-264 and 277-291 and reflect caution with strong claims. We additionally have amended our introduction to put our findings in the context of the field, describing progress made to describe MHO loci that act on overall obesity, but not on loci associated with abdominal obesity but with protection from type 2 diabetes (lines 76-83). Finally, we found literature evidence supporting our hypothesis at the 2p21 locus containing the gene encoding THADA, which we expand upon in the discussion (lines 292-305).2. Overall, it is difficult to imagine a plausible biological explanation for a relationship where higher WHRadjBMI would mediate a lower risk of T2DadjBMI. It would be really interesting is such biology exists, but at the moment the paper does not give much help to the reader in interpreting what biological mechanism could underlie such a relationship. An alternative explanation for the findings is the possibility that the genetic effects are driven by independent effects of the same genes on biological pathways in two separate tissues (e.g. adipose tissue and pancreas), which would then generate the seemingly discordant genetic association pattern between WHRadjBMI and T2DadjBMI.We agree with the reviewer, and have attempted to interpret our results and propose a biological explanation for two of our five loci where we were able to reconcile our results. The text has been amended to include this, and our Figure 4 and Figure 4 – supplementary figure 2 are focused on this goal. The text has been amended in lines 297-321 and 361-381. We hope this addresses the concerns about biological plausibility. We additionally include literature evidence supporting our hypothesis at the 2p21 locus containing the gene encoding THADA, which we expand upon in the discussion (lines 292-305).3. A clear weakness in the study is the lack of any consideration to the subcomponents of the WHR phenotype: waist and hip circumference. As WHR is calculated as the ratio of waist circumference to hip circumference, WHR can increase if waist circumference increases or if hip circumference decreases, reflecting an increase in abdominal fat or a decrease in gluteofemoral fat, respectively. Waist and hip circumferences have very different implications for cardiometabolic health and T2D risk. Thus, is it critical to include an analysis on genetic effects on the two different fat depots, to understand the biological underpinnings of the findings. In addition, further GWAS results could be used to look into more detailed fat distribution phenotypes, such as the amount of visceral fat and subcutaneous fat in different body compartments.We appreciate the comments on the choice of WHRadjBMI as a trait. To increase the interpretability of our analysis, we performed a multi-trait colocalization analysis for each of the loci containing a discordant association signal in the SNP set using the summary statistics for Waist Circumference (WC), Hip Circumference (HC), the ratio of Waist-to-Hip circumference (WHR), Body-Mass Index (BMI), WHR adjusted for BMI (WHRadjBMI), Type 2 Diabetes (T2D), and T2D adjusted for BMI (T2DadjBMI). In this recursive multi-trait colocalization analysis, traits are pairwise analyzed, and then summary statistics from traits are successively added and removed until the highest likelihood is achieved. This is repeated until all the colocalization of genetic association signals for each trait is tested and the maximal number of high-confidence colocalized groups of traits at each locus is achieved. This analysis was able to replicate colocalization at all six discordant loci. Further, at three of the six discordant loci the WHRadjBMI association signal was colocalized with the WC association signal. The colocalization analysis with the additional traits is now summarized in Supplementary File 3. The text has been updated to reflect these results (lines 123-127) and the methods (lines 353-358). In addition, we performed a comprehensive phenome-wide association study (PheWAS) of the individual abdominal fat depots underlying WHRadjBMI as well as the molecular glycemic phenotypes underlying T2DadjBMI . The text has been updated to reflect the results of this PheWAS (lines 108-144). Further, the point is excellently taken about the collider bias, and this suggestion has increased the quality of our manuscript. We appreciate the thoughtful suggestions.4. The results show that two of the independent discordant loci (represented by rs3776705 and rs459193), associated with lower risk of T2DadjBMI, show genome-wide significant associations with higher 2-hour glucose levels, and one of the loci (rs459193) also shows a genome-wide significant association with higher fasting glucose levels, despite their protective effect on T2D risk. The findings appear counter-intuitive, considering that T2D is diagnosed based on elevated fasting and 2-hour glucose values. Particularly for these two loci, it would be critical to make sure that the directions of effects and the reported reference alleles are validated in a further set of data, to make sure that the discordant effects are not due to mismatched alleles.We thank the reviewer for this suggestion. We found an error in the code that did flip the effect for fasting glucose for this locus and corrected it by pulling our PheWAS from the Type 2 Diabetes Knowledge Portal (T2DKP) directly instead of from individual manuscript summary statistics files. The effects now make sense in the context of protection from type 2 diabetes. We thank the reviewers for this suggestion and report our new results in Figure 1C.5. The paper includes several overstated claims about the clinical impact of the findings and use of the findings for personalized risk stratification . The authors should revise the manuscript to avoid misleading the readers, by overstating the implications of their findings.We thank the reviewer for this suggestion, and have changed the specific language in lines 77-78 and 259-263. We have additionally revised the manuscript throughout to use greater caution with the implications of our findings.


Ann Appl Stat 2019a;13:60–84; AoAS) proposed the composite test to provide adjusted P-values for single-mediator analyses.Mediation analysis is performed to evaluate the effects of a hypothetical causal mechanism that marks the progression from an exposure, through mediators, to an outcome. In the age of high-throughput technologies, it has become routine to assess numerous potential mechanisms at the genome or proteome scales. Alongside this, the necessity to address issues related to multiple testing has also arisen. In a sparse scenario where only a few genes or proteins are causally involved, conventional methods for assessing mediation effects lose statistical power because the composite null distribution behind this experiment cannot be attained. The power loss hence decreases the true mechanisms identified after multiple testing corrections. To fairly delineate a uniform distribution under the composite null, Huang (Genome-wide analyses of sparse mediation effects under composite null hypotheses. MACtest. The utility is demonstrated by analyzing the lung adenocarcinoma datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium. We further investigate the genes and networks whose expression may be regulated by smoking-induced epigenetic aberrations.Our contribution is to extend the method to multimediator analyses, which are commonly encountered in genomic studies and also flexible to various biological interests. Using the generalized Berk–Jones statistics with the composite test, we proposed a multivariate approach that favors dense and diverse mediation effects, a decorrelation approach that favors sparse and consistent effects, and a hybrid approach that captures the edges of both approaches. Our analysis suite has been implemented as an R package MACtest is available on https://github.com/roqe/MACtest.An R package  Section 3.1 describes numerous simulations to demonstrate that the composite test outperforms the other two tests when only a few mechanisms are causally involved. Finally, Section 3.2 details the application of our methods to The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and the Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma (CPTAC-LUAD) datasets to demonstrate the utility of our testing procedures.The following sections are organized as below, from Sections 2.1–2.3 we first introduce the composite test for single-mediator analyses proposed by S be the exposure, M be the mediator, Y be the outcome, and X be the covariates of an individual. Consider a genome-wide experiment g possible mechanisms to be verified, denoted as i-th causal graph  is defined as a directed graph a denotes a directed edge from x to y. For example, in the following application study, the experiment identifying the genes whose expression is affected by smoking-induced DNA methylation: S indicates smoking status, M indicates methylation level, Y indicates gene expression and g is the number of candidate genes in the genome-wide experiment. The mechanism for each gene is depicted by a causal graph with three directed edges: smoking status points to methylation level of one site, methylation level points to gene expression, and smoking status points to gene expression; the aim is to test all the candidate mechanisms in the experiment and identify those with mediation effects. We thus propose two models given a causal graph Here, we introduce the notation of single-mediator models in genome-scale analyses. Let n individuals with the same graphical structure  is the probability density function of the normal product distribution and can be calculated through numerical integration, ϵ is bounded and can be ignored when H0  denotes an undirected edge between x and y, We generalize the single-mediator model into the multimediator model as depicted in disjoint effect . The independence among estimators decorrelation approach and a multivariate approach for achieving the required independence.The main difference between the single-mediator and the multimediator models lies in the dependency between estimators in The main steps of the decorrelation approach include (i) applying decorrelation between mediators, (ii) using path-level statistics to assess the mediation effect under the composite null, and (iii) integrating path-level statistics into one graph-level statistics for decorrelated mediators, u using the eigendecomposition of D is a diagonal matrix, and the correlation between The vector of ija, transform ijb, and concatenate them into a vector of experiment level, namely ij-index indicates the j-th path in the i-th graph, and the k-index indicates the k-th path in the genome-wide experiment. Next, we can obtain a P-value for each path  since iT only considers the information within one graph. Here, a random variable S is defined to indicate the number of significant paths in the graph:BJ statistic integrating the independent paths in one causal graph is thus:Z, Z in magnitude, P-values of BJ statistics, namely P-values of the BJ statistics can be calculated using the method provided by After collecting the atistics . We ignoindependence at mechanism level as described in S to M into one statistic M to Y into another statistic P-value of mediation effect.In our model settings, mediators are correlated within a mechanism but are independent across mechanisms. In other words, the S, and their product represents the causal graph i is ignored from here. Let GBJ statistic,P-values of the GBJ statistics can be calculated using a generalized version of Moscovich-Eiger and Nadler’s method, provided by The first step is to provide graph-level statistics that integrate all paths and their dependency in causal graphs to achieve independence at the graph level. Two test statistics for ia and ib, respectively, to apply P-values of ia and ib also converge to standard normal distribution under the null; the proof is found in ia and ib into a vector of experiment level, namely P-value for each causal graph from S toward iY in the experiment by using We transform P-values of mediation effect, namely We use the P-value without estimating their correlation. Here, the i-th ACAT statistics for the mediation effect can be defined asP-value of ACAT is approximated by In the following simulation, we demonstrate that both approaches have their edges. The decorrelation approach prefers effects with a consistent direction (e.g. most of the relevant pathways are upregulated or downregulated), but the multivariate prefers effects with disturbance (e.g. some pathways are upregulated but some are downregulated). For those who have limited knowledge of their data, we also provide a hybrid approach that integrates both approaches without raising the burden of multiple comparisons. The aggregated Cauchy association test (ACAT)  statistiWe conclude the method section by highlighting the thinking behind the proposed approaches. P-values using a ready-made statistic; therefore, as long as the decorrelation and multivariate approaches are able to control type I error, the hybrid results also follow. Here, we only focus on the performance of the hybrid approach under the alternative. The proposed approaches can be applied to other P-value integration statistics or multivariate statistics as well, so we included higher criticism (HC)  statistic ; we then generated 30 potential mediators by Y by h mediators with a signal from the 30 M’s; the nonzero signals Sα and Mβ of the h mediators followed NormalWe performed comprehensive simulation analyses to demonstrate the strengths and limitations of the decorrelation approach and multivariate approach. The hybrid approach integrates two ism (HC)  and the st minP;  in the dtatistic , and thetatistic  in the mP-values of mediation effects introduced in α level in this genome-wide simulation; the composite null behind the mediator model led to conservative results in both normality-based and joint significant tests, and this observation is consistent with the previous results by P-values, so we are able to control the type I error up to the significance level no disjoint effects as null, discussion and proof are found in We demonstrated that the proposed approaches are sufficiently accurate for controlling the genome-wide type I error rate. We simulated three types of null scenarios, as described in We performed comprehensive simulations as described in m = 100 in P-values, Z-statistics to perform . In brief, we suggest the users use the hybrid approach if the underlying mechanisms are unknown. Otherwise, one can choose either or both approaches to capture the target genes with the desired properties. The decorrelation and multivariate approaches represent different types of biological changes, and as we can see in the data application, the genes identified from both approaches only have a few overlaps.We summarize the simulations section: (i) we are able to control the type I error in the proposed multimediator approaches, (ii) the composite test outperforms other tests of mediation effect in both levels of sparsity, (iii) the multivariate approach is more sensitive to diverse signals, the decorrelation approach to consistent signals, and the hybrid approach captures both edges, and (iv) GBJ is a fair choice among the multivariate statistics because its flexibility to tolerate mild correlation and one-side null , i-th gene expression as the outcome iY, and methylation levels related to the target gene as the mediators miRNA-protein dataset from CPTAC, in which we had sex, age, race, and smoking records of 99 individuals with at most 20% missing data. The dataset included 6955 proteins regulated by miRNA; the smoking status is denoted as S (46 nonsmokers versus 53 smokers), i-th protein expression as iY, and miRNA expression related to the target protein as We present two data applications using multiomic datasets from TCGA-LUAD , the metWe used the two datasets to demonstrate the scenarios of different experimental scales: a general genome-wide study (P-values provided by the proposed approaches with a cutoff of 0.1; the decorrelation and the multivariate approaches reported 739 and 602 genes, respectively. The union of the two approaches included 1212 genes; the small number of overlaps supported that the two approaches pursue different biological patterns. The hybrid approach identified 590 genes, from which 386 overlap the genes suggested by the decorrelation approach and 332 overlap the genes suggested by the multivariate approach. Then, we applied pathway analyses on the significant genes in order to confirm that the underlying pathways have a consistent biological interpretation with our prior knowledge of lung adenocarcinoma. We used the STRING (We applied the FDR adjustment on the nominal In this article, we developed the decorrelation and multivariate approaches for the purpose of using the composite test on the multimediator models. We performed extensive simulations to demonstrate the advantage of each approach: the decorrelation approach performs better when signals are consistent, and the multivariate approach provides better power when signals are diverse; compared with the joint significance and the normality-based tests, the composite test has the best power on mediation assessment in genome-wide analyses. To release the burden of multiple comparisons, we further provided a hybrid approach that captures the edges of both approaches. The utility of the proposed mediation analyses was demonstrated through their application in the analysis of data from the TCGA-LUAD and CPTAC-LUAD. Using the genes identified from the proposed statistics, we further analyzed the corresponding pathways, and our results exhibited good agreement with those of previous studies.btad544_Supplementary_DataClick here for additional data file.


Traditionally, heritability has been estimated using family-based methods such as twin studies. Advancements in molecular genomics have facilitated the development of methods that use large samples of (unrelated or related) genotyped individuals. Here, we provide an overview of common methods applied in genetic epidemiology to estimate heritability, i.e. the proportion of phenotypic variation explained by genetic variation. We provide a guide to key genetic concepts required to understand heritability estimation methods from family-based designs (twin and family studies), genomic designs based on unrelated individuals  and family-based genomic designs . We describe how heritability is estimated for each method and the assumptions underlying its estimation, and discuss the implications when these assumptions are not met. We further discuss the benefits and limitations of estimating heritability within samples of unrelated individuals compared with samples of related individuals. Overall, this article is intended to help the reader determine the circumstances when each method would be appropriate and why. Key MessagesA range of methods exists to estimate heritability—the genetic contributions to epidemiological phenotypes, each with strengths and limitations.Understanding these is important to correctly interpret results from genetic epidemiology studies and weigh (or counterweigh) evidence appropriately.Methods for estimating heritability should be carefully considered concerning the underlying assumptions, biases and suitability of the data.Common issues across methods developed to estimate heritability include bias due to population stratification, epistasis and assortative mating.Genomic and non-genomic methods using samples of related individuals can account for unobserved environmental confounding.Box 1: Defining heritability,IJE online). Estimates of heritability cannot necessarily be translated into a wider or more general population as it is specific to the population within the sample.Heritability: The proportion of phenotypic variation for a specific measurement that can be attributed to genetic variation. Narrow-sense heritability is solely an estimate of additive genetic effects—the summed effects of multiple genes contributing to a single phenotype.There is evidence that most phenotypes are heritable, with heritability typically being higher for biological proximal, e.g. eye colour, than social or behavioural traits, e.g. extraversion.,Many human phenotypes are influenced by a complex mix of genetic and environmental factors. Therefore, it is important to comprehensively account for genetic influence when discerning how phenotypic variation in the population arises. The broad role of genetics is commonly quantified using heritability—the proportion of phenotypic variation that genetic variation can statistically explain see .Box 1: Methods to estimate heritability from samples of related individuals capitalize on the (average) shared genetic variance between relatives, e.g. offspring share broadly half of their genotype with siblings. Path analysis has been widely implemented, assuming a linear variance model. This separates the phenotype of interest into components: the additive genetic (A), common familial environment (C) and the environmental contribution unique to the individual (E), termed the ACE model.IJE online). These SNPs are not detected in genome-wide association studies as they require much larger sample sizes for their effects to be reliably estimated.,It has been theorized that a further portion of heritability, as estimated by some genomic methods, is not captured as the contributing single-nucleotide polymorphisms (SNPs) are rareIJE online).IJE online). ‘Narrow-sense heritability’ refers to phenotypic variation explained by additive genetic variation only.,Many methods exist to estimate heritability, which require different testable and untestable assumptions. These estimators can be influenced by demographic, familial and genomic factors such as population stratification, indirect genetic effects, assortative mating, linkage disequilibrium (LD) and epistasis (see IJE online).Here, we describe methods for estimating heritability, their assumptions and their biases. We have selected approaches that are widely implemented in the literature and group them into three sections : (i) famFamily-based designs use samples of closely related individuals, e.g. siblings or parents and offspring. Family-based designs can estimate heritability even if molecular genetic data are unavailable.IJE online) is zero. However, by implementing an ADE model , estimated heritability may be partitioned into additive and dominance variance components. Notably, to the extent that MZ and DZ twins experience parental indirect genetic effects similarly, these effects will be included within the common environment (C) component. Twin studies may also be used to study gene–environment interactions.Twin studies examine phenotypic differences between monozygotic (MZ) twins, who are genetically identical at conception, and dizygotic (DZ) twins, who, on average, share 50% of their segregating genetic variation,,,,IJE online) for more information about the validity of the EEA.The shared environment makes an equal contribution to the phenotype of interest across both MZ and DZ twin pairs, termed the ‘equal environment assumption’ (EEA).‘Twins are generalizable to the general population’ regarding the phenotype of interest. The validity of this assumption has been demonstrated in multiple studies.,‘Random mating’ occurs within the population.IJE online).The ‘confounding role of environmental similarity’ on genetic factors and outcomes is limited (see IJE online) for the extended twin design.If all these assumptions hold, then comparing the phenotypic correlations of MZ and DZ twins can reliably estimate heritability.The mass characterization of the human genome has generated a rich, vast resource for genetics research.IJE online). The reference panel accounts for some LD structure within a given population, which means population stratification bias can be separated from genuine polygenicity. Summary statistics from multiple SNPs are regressed on the LD score of each SNP of interest, with the estimated intercept quantifying the bias. The LDSR slope estimates the variance of the phenotype explained by all SNPs (i.e. the heritability) used to estimate the LD scores.IJE online) test statistics to be differentiated from confounding bias. Software has been created for the implementation of LD score regression (see IJE online). SNPs may tag both individual large and multiple weak effects, in contrast to other genome-wide methods. However, LDSR estimates are less accurate where fewer SNPs are available. LDSR depends on several assumptions:Linkage disequilibrium score regression  is a regression-based method that can separate genetic and confounding effects and estimate SNP heritability.IJE online).The ‘variance explained per SNP is uncorrelated with the LD score’. Therefore, rare SNPs are assumed to have larger effect sizes than common SNPs.The ‘target sample of interest is well matched to the LD reference panel’.IJE online).IJE online). The following assumptions are required for GREML estimation:Genomic relatedness restricted maximum likelihood (GREML) estimates SNP heritability from measured genomic data. Notably, the method uses unrelated individuals who are assumed to vary randomly in their genetic similarity. The technique capitalizes on the logic that more genetically related individuals are more phenotypically similar in a population of unrelated people. Practically, this method is implemented by constructing a genetic relationship matrix (GRM) capturing the degree of relatedness between every pair of individuals at every SNP location (see IJE online) and inflates heritability estimates of specific phenotypes in the presence of indirect effects.‘All genetic effects are direct’ , even though transmitted variants can also influence phenotypes indirectly via the parents. If indirect genetic effects exist, these will be attributed to direct effects, as illustrated in ,,‘Individuals do not share environmental influences’. The restriction to unrelated individuals  limits confounding by common environmental effects alongside reducing contamination of non-additive genetic effects.‘GREML only captures direct, additive effects of common SNPs’ and not rare genetic effects or non-additive genetic effects. SNP-based methods may therefore be used to validate estimates derived from family-based methods requiring alternative assumptions. Thus, as with other methods using measured SNP data, estimates obtained via GREML can be considered to provide a lower bound of heritability.IJE online).There is ‘random mating’, meaning that GREML heritability estimates may be biased in the presence of assortative mating because of directional LD between SNPs , the complex of genes coding the proteins that regulate the human immune system, are particularly important for a phenotype].IJE online) for methods to overcome this limitation.In addition, GREML is highly sensitive to uneven LD, over- and under-estimating heritability in high and low LD areas, respectively. This is because the correlation between SNPs distorts their estimated contribution to heritability; see IJE online).An extension to GREML using unrelated individuals has been developed. In LD- and MAF-stratified GREML (GREML-LDMS), effects of less-common variants can be estimated through the use of LD and MAF data on imputed SNPs. For details on this method, see IJE online). Various methods have been developed to enable the implementation of genomic methods in large samples of related individuals.A shared limitation of the genomic methods applied to unrelated individuals is an inability to account for environmental confounding (see IJE online) can estimate narrow-sense heritability in samples of individuals who are not closely related.,Identity-by-descent (IBD) .Statistical methods using sibling regression to estimate heritability are dependent on the following assumptions:‘Estimates must also account for regions of LD’.‘Estimates of the percentage of shared IBD between siblings across the entire genome are proportional to the number of causal additive SNPs between siblings’ for the trait of interest.‘Siblings who have inherited an IBD segment from a common ancestor have identical genetic segments in that region’. Hence estimates include the rare variant effects in that region (except de novo mutation and other variant-introducing events).‘The additive genetic covariance between relatives is proportional to the proportion of the genome that is shared IBD’.There is ‘random mating’. However, sibling regression estimates may be inflated by assortative mating as the induced phenotypic correlation increases the genetic and phenotypic variation in the population, alongside relative pairwise phenotypic covariances. Thus, the common environmental effect between individuals could be overestimated and result in inflated heritability estimates unless assortative mating is accounted for.It is possible to implement GREML to estimate the heritability of binary traits, such as in case–control studies.,The trait has an ‘underlying normally distributed threshold liability model’. Note that this is a statistically untestable assumption. This has caused some controversy in interpreting heritability estimates for binary traits within the literature.The ‘estimate of the variance’ that may be ‘apportioned to the SNPs’ on the ‘binary scale’ may be ‘linearly transformed to a continuous liability scale’.,The authors note here that adjustment for ascertainment bias is necessary and any element of the data that may cause allele frequencies between the cases and controls to differ greater than that expected under the null hypothesis may produce spurious estimates as within-group relatedness (e.g. case–case members) will be greater than between groups (e.g. case–control members). Therefore, when implementing GREML with binary traits, stringent quality control of data is required.IJE online) for details on GREML-KIN, M-GCTA, Trio-GCTA and RDR.The following four methods—GREML-kinship (GREML-KIN), maternal-genome-wide complex trait analysis (M-GCTA), Trio-GCTA and relatedness disequilibrium regression (RDR)—are extensions to GREML using related individuals. GREML-KIN estimates heritability whilst controlling for the shared environment by including relatives. M-GCTA, Trio-GCTA and RDR are similar approaches as the phenotypic variance of the offspring is decomposed into direct and indirect genetic effects by including one or both parental genotypes. See We provide a summary of the advantages and disadvantages of the methods discussed in A range of methods is available to estimate heritability using family-based and genomic designs. Each approach has distinct advantages and limitations, such as controlling bias due to population stratification in related individuals or the wide availability of large-sample GWAS data. Triangulation of heritability estimates across different methods with different assumptions within samples is likely to provide the most robust evidence for the heritability of phenotypes, with consideration for any expected estimand differences.Ethics approval not needed for this study containing no participants.dyac224_Supplementary_DataClick here for additional data file.


This study was conducted to characterize semen traits (ejaculate volume (VOL), mass motility (MM), sperm livability (LS), percentage of abnormal sperms (AS), and sperm concentration (CONC)) of Egyptian buffalo bulls and evaluate the importance of some nongenetic factors (year (YC) and season (SC) of semen collection and age of bull genetically and environmentally at collection (ABC)) affecting the investigated traits. A total of 7761 normal semen ejaculates were collected from 26 bulls from 2009 to 2019. Single-trait and bivariate repeatability animal models using Bayesian methods were used to estimate variance components, heritability, repeatability, and genetic correlations among the investigated semen traits. YC and ABC exerted significant effects on most semen traits, whereas SC exerted no significant effect on all the investigated semen traits. Heritability estimates were 0.08, 0.52, 0.51, 0.04, and 0.49 for VOL, MM, LS, AS, and CONC, respectively. Repeatability estimates were 0.14, 0.82, 0.79, 0.06, and 0.78 for VOL, MM, LS, AS, and CONC, respectively. The genetic correlations between MM and each of LS and CONC were highly significant , and that between LS and CONC was also highly significant (0.92 ± 0.20). The high heritability estimates for MM, LS, and CONC combined with the favorable high significant genetic correlations between these traits indicated that direct selection for MM may be an effective method to enhance semen quality in Egyptian buffalo bulls and consequently improve fertility. In dairy cattle, fertility is considered one of the most essential economic characteristics . Using a graduated glass tube, the ejaculate volume was measured directly in milliliters to the nearest 0.1 ml. The percentage of spermatozoa wave motion in a drop of semen deposited on a glass slide was used to calculate semen mass motility (%). The abnormal sperm (%) were measured according to the procedure adopted by Blom (9 spermatozoa/mL) in each ejaculate was determined using a Neubauer hemocytometer. Percentage of livability, motility of spermatozoa, then sperm count was estimated by using a warm microscope stage in post-diluted, post-equilibrated and post-thawed semen adjusted at 37°C. Research microscope with high power magnification (×400) and warmed stage (37°C) was used to estimate the percentage of progressive sperm motility  at Sakha, Kafr El-Sheikh Governorate, belonging to the Animal Production Research Institute, Ministry of Agriculture, Dokki, Cairo, Egypt. According to NRC , the bul by Blom  and BarbAn analysis using the GLM procedure of SAS  was conducted to evaluate the significance of the nongenetic environmental effects of season of semen collection, year of semen collection, and age of the buffalo bull by fitting the following linear model:Yijkl=the observation of the examined trait ; μ = a trait-specific underpinning constant; Si = the fixed effect of the ith season of semen collection ; Tj = the fixed effect of the jth year of semen collection ; Ak = the fixed effect of the kth age of the bull at semen collection , and eijkl is the residual of the model.where A Bayesian approach was used to estimate variance components and population genetic parameters for the investigated semen traits using the THRGIBBS1F90 software  genetic effects was assumed to have a normal distribution with mean 0 and variance Aσ2a, where A is the numerator relationship matrix based on the pedigree, and σ2a is the additive genetic variance. The vector of the permanent environmental effects was assumed to be normally distributed with mean 0 and variance Icσ2pe, where Ic is the identity matrix with the number of buffalo bulls as its order, and σ2pe is the permanent environmental variance. Heritability (h2) was calculated as the ratio of genetic variation due to the additive genetic variance to the total phenotypic variance (r) was calculated by dividing the additive genetic variance plus permanent environmental variance on the total phenotypic variance  effects on MM, LS, and CONC but not on VOL and AS. However, ABC exerted significant (P = 0.0001) effects on VOL, MM, LS, and CONC but not on AS, considering that AS remained significantly unaffected by any one of the nongenetic examined factors. The season of semen collection exerted no significant effect on all the examined semen traits. It is possible that the relevant effect of year of collection on most semen characteristics is due to differences in nutrition, climate, and management procedures across years (Khattab et al., Table The estimates of variance components, heritability, and repeatability for semen traits are given in Table The phenotypic and genetic correlations between any two traits illustrate how a change in one character affects the other. Knowledge of genetic and phenotypic correlations is essential for generating selection indices and the selection for multiple traits. The majority of semen traits examined in this study had desirable genetic correlations with each other. Estimates of the genetic correlation for VOL, MM, LS, AS, and CONC traits ranged from 0.17 to 0.99. However, estimates of the phenotypic correlation between these traits ranged from 0.15 to 0.99 Table . HoweverIn conclusion, the year of semen collection and age of bull at semen collection exerted significant effects on semen traits of Egyptian buffalo bulls. However, the season of semen collection did not exert relevant impact on the examined semen traits. The high heritability estimates for MM, LS, and CONC, as well as the positive high significant genetic correlation between these traits, suggested that direct selection for MM could be an efficient method to increase semen quality in Egyptian buffalo bulls and, thus, improve fertility.


Pmeta < 5.0 × 10–8 and Psingle-trait < 1 × 10–4), all of which were LD-independent. Genes with a nominal gene-based association (Pgene ≤ 0.05) were significantly enriched (overlapping) across the migraine, headache, and glycemic traits. Mendelian randomisation analyses produced intriguing, but inconsistent, evidence for a causal relationship between migraine and headache with multiple glycemic traits; and consistent evidence suggesting increased fasting proinsulin levels may causally decrease the risk of headache. Our findings indicate that migraine, headache, and glycemic traits share a common genetic etiology and provide genetic insights into the molecular mechanisms contributing to their comorbid relationship.The co-occurrence of migraine and glycemic traits has long been reported in observational epidemiological studies, but it has remained unknown how they are linked genetically. We used large-scale GWAS summary statistics on migraine, headache, and nine glycemic traits in European populations to perform cross-trait analyses to estimate genetic correlation, identify shared genomic regions, loci, genes, and pathways, and test for causal relationships. Out of the nine glycemic traits, significant genetic correlation was observed for fasting insulin (FI) and glycated haemoglobin (HbA1c) with both migraine and headache, while 2-h glucose was genetically correlated only with migraine. Among 1703 linkage disequilibrium (LD) independent regions of the genome, we found pleiotropic regions between migraine and FI, fasting glucose (FG), and HbA1c, and pleiotropic regions between headache and glucose, FI, HbA1c, and fasting proinsulin. Cross-trait GWAS meta-analysis with glycemic traits, identified six novel genome-wide significant lead SNPs with migraine, and six novel lead SNPs with headache (The online version contains supplementary material available at 10.1007/s00439-023-02532-6. Migraine is a debilitating neurological disorder that affects about 15% of the global population and was first described as a ‘hypoglycemic headache’ in 1935  , and Type 1 diabetes (T1D) were obtained from the Pan-UKBB. We used phenocode 250.1 for “T1D” , biomarker 30,750 for “HbA1c” , and biomarker 30,740 for “glucose”  to identify and access the GWAS summary statistics from the Pan-UKBB.https://magicinvestigators.org/), comprising a total of 281,416 individuals with more than 70% European descent , fasting insulin (FI), and 2-h glucose after an oral glucose challenge (2-h glucose) from the meta-analyses of glucose and insulin-related traits consortium (MAGIC)  were calculated using the LD score regression (LDSC) software  were constrained to zero if they were not significantly different from zero. We examined nine glycemic traits in this study; however, these are correlated to a certain degree. Hence, we estimated the effective number of independent traits using matrix spectral decomposition (matSpD) (Nyholt rg matrix (Supplementary Table S4). MatSpD estimated that the nine glycemic traits were equivalent to 6.1849 independent traits. Therefore, to adjust for multiple testing, we considered a genetic correlation finding study-wide significant in the LDSC analysis at P < 8.26 × 10–3 (0.05/6.1849) and nominally significant when P < 0.05.SNP-based bivariate genetic correlations  Nyholt  of the pWe used pairwise GWAS analysis (GWAS-PW)  but were not genome-wide significant in the individual trait GWAS before meta-analysis (5 × 10–8 < Psingle-trait < 0.05) were considered potentially novel.To detect shared SNPs and loci, we conducted a cross-trait meta-analysis between migraine and glycemic traits, and between headache and glycemic traits. We used the fixed effect (FE) and the modified random effects (RE2) models within the METASOFT software . We began by identifying significant independent SNPs based on their cross-trait meta-analysis P-value (P < 5 × 10–8) and independence from one another (r2 < 0.6) within a 1 Mb frame  were classified as the lead SNP. The European ancestry 1000 Genomes Project reference panel within FUMA was used to calculate all LD information   with an original GWAS trait SNP were considered to be within a known locus and thus excluded  mapping to the same gene(s) did not overlap with any previously reported loci in the GWAS catalog  for the same traits used in the meta-analysis, we identified it as a novel locus. To identify novel SNPs with strong evidence for association to both migraine and a glycemic trait, we highlight the novel lead SNPs with a genome-wide significant association in the cross-trait meta-analysis (Pmeta < 5 × 10–8) and suggestive association in the individual migraine and glycemic trait GWAS (Psingle trait < 1 × 10–4).We used the FUMA web tool . The IVW model has the drawback that even a single invalid IV might cause the overall estimate to be biased  outlier procedure is used in the GSMR analysis to remove SNPs that have a pleiotropic effect  was deemed study-wide significant, and P < 0.05 was considered nominally significant.We used two-sample Mendelian randomisation (2SMR) approaches to test for a causal relationship  between the genetic liability to glycemic traits on migraine and headache, as well as the reverse of causal effects of genetic liability to migraine and headache on glycemic traits. Inverse-variance weighted (IVW) model was used as the primary method  model  was used to determine the locations and boundaries of the genes, and the 1000 Genomes Project European reference panel was utilised to estimate LD  regardless of gene size and LD pattern among SNPs , and then for each LD block, it conducted an eigenvalue analysis of the correlation matrix to calculate the effective number of independent SNPs  found in our gene-based analysis. We estimated the effective number of independent genes for each GWAS trait individually.Gene-based association results may be correlated across neighbouring genes due to LD between the most significant SNP (‘best SNP’) assigned to each gene. Thus, the genetic type I error calculator (GEC) . The number of genes overlapping both traits at each of the three P-value levels was first referred to as the raw number of overlapping genes. Then, we calculated the effective number of independent overlapping genes using independent gene-based analysis to determine whether the proportions of overlapping genes were more than expected by chance  method to combine gene-based association P-values across two traits. Recent research  was calculated that accounts for multiple testing  and headache  genetically correlated with FI. Likewise, there were significant genetic correlations between migraine  and headache  with HbA1c. Additionally, migraine  and headache  demonstrated a significant genetic correlation with fasting proinsulin, although this relationship was not significant after adjusting for multiple testing. Furthermore, 2-h glucose  and T1D  both produced evidence for genetic correlation with migraine, but not with a headache. There was no evidence of a genetic correlation between migraine and headache with glucose, FG, HOMA-B, and HOMA-IR  (Table h2) and rg analyses were robust to potential sample overlap, to aid in the interpretation of downstream analyses, we examined the LDSC genetic covariance intercept (gcov_int). For migraine, the gcov_int values were all relatively small, with only 2-h glucose (P = 0.016) and HOMA-IR (P = 0.044) significantly different from zero, and these were not significant after adjusting for multiple testing. Similarly, the gcov_int values for headache were small, with only glucose (P = 0.005), proinsulin (P = 0.021), and T1D (P = 0.003) being significantly different from zero, and only glucose and T1D significant after adjusting for multiple testing. The gcov_int results (Supplementary Table S3) suggest no substantial sample overlap between migraine and headache with each of the glycemic traits, and the cross-trait SNP meta-analyses were unlikely to be confounded by sample overlap.Our genetic correlation analyses reflect the relationships between migraine and headache with glycemic traits. We found migraine  and 17 (PPA3 > 0.5) genomic regions with a shared association between migraine and any of the four glycemic traits  (Supplementary Table S5). The findings from the GWAS-PW study of migraine and glycemic traits, and headache and glycemic traits are described in Table Pmeta < 5 × 10–8 and Psingle-trait < 0.05), respectively (Supplementary Tables S6 and S7). We highlight the lead SNPs having genome-wide significance in the cross-trait meta-analysis (Pmeta < 5 × 10–8) and suggestive association in the individual trait GWAS (Psingle-trait < 1 × 10–4). Seven of the 44 putative novel lead SNPs between migraine and glycemic traits met this criterion  being novel for migraine but not FG. The strongest signal was found on chromosome 4 in the MANBA region . The second strongest signal was found on chromosome 3, closest to the ADAMTS9 gene .The cross-trait meta-analysis combining migraine and glycemic traits and headache and glycemic traits found 44 (42 are novel for both traits) and 40 (33 are novel for both traits) potential novel independent lead SNPs (Psingle-trait < 1 × 10–4) with the single traits GWAS  estimates, the detected lead SNPs substantially affect both headache and the respective glycemic trait. These seven lead SNPs were mapped to the nearest protein-coding genes of MANBA, ELFN1, AP3B1, HSPA4, MANBA, NBEAL1, and GOLGA6A. Six of these lead SNPs or loci are novel for both headache and glycemic traits since they were not previously implicated in these traits, while one locus (MANBA) being novel for headache but not FG. ELFN1 gene represented the most significant locus . The second locus was assigned to the HSPA4 gene , and the third most significant signal was found near the MANBA gene . Interestingly, a novel locus mapped to the MANBA gene was common across the migraine, headache, and glycemic traits. This locus was also identified in the cross-trait GWAS meta-analysis between migraine and FG (lead SNP rs223482), headache and FG (lead SNP: rs223482), and headache and glucose (lead SNP rs223497); and SNPs rs223482 and rs223497 were found to be in high LD (r2 = 0.85). We also noticed that 10 of the 14 putative novel lead loci revealed in the cross-trait meta-analysis between migraine and headache with glycemic traits were in the genomic region having a posterior probability association (PPA3 > 0.5) in the GWAS-PW association analysis (Supplementary Tables S6 and S7). Supplementary Tables S6 and S7 show the results of cross-trait meta-analysis between migraine and glycemic traits (44 lead SNPs) and headache and glycemic traits (40 lead SNPs) with unadjusted P < 0.05 in their individual single-trait GWAS, respectively.In the cross-trait meta-analysis of headache and glycemic traits, seven out of the 40 putative novel lead SNPs were found (Supplementary Table S7) to be suggestively associated  and headache (17%) with FG at Pgene ≤ 0.05 was significantly higher [Pbinomial-test = 1.24 × 10–16 (migraine and FG) and Pbinomial-test = 4.74 × 10–14 (headache and FG)] than the expected proportion , providing additional evidence for highly significant genetic overlap between migraine and headache with FG (Supplementary Table S8 (Table II)). Using both stricter (P ≤ 0.01) and less strict (P ≤ 0.1) P value thresholds also produced evidence for significant gene-based genetic overlap between migraine and each of the glycemic traits, and between headache and each of the glycemic traits (Supplementary Table S8).Supplementary Table S8 displays the findings of binomial tests to assess whether the proportion of genes with small P < (0.05/effective number of independent genes in the gene-based test), 30 genes were overlapping migraine and more than one glycemic trait and 66 genes were overlapping headache and more than one glycemic trait  were common to both migraine and headache that overlapped more than one glycemic trait. Furthermore, five of the 30 genome-wide significant genes overlapping migraine and more than one glycemic trait were the nearest genes to a lead migraine SNP    overlapping headache and more than one glycemic trait was the nearest gene to a lead migraine SNP  and headache (PFCP < 3.59 × 10–6 and Psingle-trait < 0.05) with more than one glycemic trait, respectively. Among these overlapping genes, 181 were common to migraine and headache that overlapped more than one glycemic trait .At a gene-based genome-wide significant threshold of P = 0.01), although this association did not remain significant after adjusting for multiple testing (Table P = 4.48 × 10–5) on migraine (Table P = 0.203) was not significantly different from zero, indicating no significant directional or unbalanced pleiotropy. However, Cochran’s Q statistics for IVW (P = 3.76 × 10–12) and MR-Egger (P = 9.16 × 10–12) showed strong heterogeneity among SNPs. Even though MR-PRESSO found three outliers, the effect of removing them was minimal (data not shown). Although the IVW, MR-Egger, and MR-PRESSO analyses did not support these findings, they all agreed with the direction of the results  and T1D  increases migraine risk, whereas liability to increased glucose , HOMA-IR  and HbA1c  decreases migraine risk; however, these associations were not study-wide significant after adjusting for multiple testing, and primary and other sensitivity MR analyses did not support these findings , which was corroborated by all MR sensitivity analyses, although these results were not study-wide significant after adjusting for multiple testing. However, GSMR analysis  did produce significant evidence for a causal relationship between headache and proinsulin (Table P = 0.16) demonstrated that this was not due to horizontal pleiotropy, and there was no heterogeneity (Cochran’s QPivw = 0.09). In addition, no outlier was found by MR-PRESSO, and the global test  was not significant, indicating that there is no horizontal pleiotropy. Additionally, GSMR analysis revealed a significant association between genetic liability to T1D , HbA1c , and HOMA-IR  casually decreases the risk of headache ; however, this association was not significant after adjusting for multiple testing. The weighted median model corroborated this with similar effect sizes; however, the IVW, MR-Egger, and MR-PRESSO models did not provide evidence for a significant causal association of migraine on HbA1c. There was evidence for heterogeneity (Cochran’s QPivw = 1.08 × 10–43), although the MR-Egger intercept (P = 0.81) indicated that this was most likely not caused by horizontal pleiotropy. There was no significant evidence of migraine having a causal effect on any of the eight remaining glycemic traits. Furthermore, we found no evidence for a causal association between genetic liability to headache on any of the nine glycemic traits. Further details of the genetic instruments utilised for migraine, headache, and glycemic traits in 2SMR (Supplementary Tables S14–S17) and GSMR (Supplementary Tables S18-S19) analysis are presented in the Supplementary Tables.Reverse MR analyses were also performed to test for a causal effect of migraine and headache (exposure) on the nine glycemic traits (outcome) Tables  and 8. Sz-score was obtained for fasting proinsulin (z < 7), which may lead to an inaccurate heritability estimate, our LCV analyses found significant evidence for partial genetic causality between fasting proinsulin and both migraine  and headache  (Supplementary Table S20). No other glycemic traits showed significant evidence for genetic causality with migraine and headache in the LCV analyses. Supplementary Table S20 contains all LCV model results.Although a relatively small SNP-based heritability Pathway analyses of the genes associated with migraine and the glycemic traits, and headache and the glycemic traits found multiple pathways significantly enriched by the overlapping associated genes (Supplementary Tables S21 and S22). There were no significantly enriched pathways for genes overlapping migraine and HOMA-IR. However, genes associated with migraine and the remaining eight glycemic traits were enriched in pathways including ‘regulation of carbohydrate catabolic process,’ ‘response to axon injury,’ ‘chromatin assembly or disassembly,’ ‘signalling by Notch,’ ‘pre-notch transcription and translation,’ ‘pre-notch expression and processing,’ ‘oxidative stress induced senescence,’ ‘systemic lupus erythematosus,’ and ‘alcoholism’. In addition, genes associated with headache and glycemic traits were enriched in pathways including ‘magnesium ion binding,’ ‘chromatin assembly or disassembly,’ ‘antigen processing and presentation of exogenous peptide antigen,’ ‘nucleosome assembly,’ ‘type I diabetes mellitus,’ ‘autoimmune thyroid disease,’ and ‘systemic lupus erythematosus’ pathways. These pathways may play a role in the mechanisms underlying the co-occurrence of migraine, headache, and glycemic traits. Supplementary Tables S21 and S22 provide further details on these pathways, including the genes involved.This in-depth genome-wide cross-trait study explores the shared genetic basis underpinning migraine and headache with glycemic traits. We discovered a nominally significant genetic correlation between migraine and five glycemic traits , and between headache and three glycemic traits . When the genome was divided into independent genomic regions for the pairwise association (GWAS-PW) analysis, we found eight and twelve genomic regions containing significant pleiotropic effects between migraine and glycemic traits, and headache and glycemic traits, respectively. Using cross-trait meta-analysis, we found multiple shared loci between migraine and headache with glycemic traits, and MR analysis indicated a causal role for fasting proinsulin in preventing headache.In the LDSC analyses, 2-h glucose, FI and HbA1c produced a statistically significant genetic correlation with migraine, while FI and HbA1c produced a significant genetic correlation with a headache. These results offer new insight and are consistent with the known relationship between migraine and higher glucose and insulin levels in published observational epidemiological research  is a neuronal protein that plays a crucial function in neural development through its involvement in neuronal adhesion ; many of these loci have been previously reported to be the nearest genes to a lead migraine SNP , headache and FG (lead SNP: rs223482), and headache and glucose (lead SNP rs223497) in the cross-trait meta-analyses. We also identified the MANBA locus as a significant pleiotropic region (chr4: 103221459-105304491) in the GWAS-PW analyses for migraine and FG, headache and FG, and headache and glucose. A study of the MANBA expression in brain tissues suggests that the cerebellar cortex, medulla, and pons are the primary sites where it is expressed, in line with the idea that the cerebellar cortex plays a role in migraine pathophysiology  in this region (± 500 kb) have been strongly associated with essential hypertension  shared across migraine and glycemic traits and six novel loci shared across headache and glycemic traits, providing leads into the potential genes and biological pathways underlying their comorbidity Table . NotablyKCNK16, located near lead SNP rs9380862, was revealed as a significant shared locus associated with both migraine and glucose. KCNK16 encodes potassium two-pore domain channel subfamily K member 16 . This channel was shown to be expressed in human and animal pancreatic beta cells and is crucial for the cell’s electrical excitability and glucose-induced insulin secretion  was found to be associated with migraine. Additionally, KCNK16 and KCNK17 variants were associated with a higher risk of epilepsy as they altered channel currents and spike frequencies  protein family, which may also play a key role in the pathophysiological processes underlying the diseases of the central nervous system like ischemic stroke and spinal cord injury  domain. NBEAL1 was previously identified in a GWAS of migraine , located near lead SNP rs28728306, is abundantly expressed in hippocampal gamma-aminobutyric acid (GABA)-ergic interneurons. A cross-trait genetic study very recently found ELFN1 implicated in migraine, headache, and T2D  were associated with both migraine and headache. Interestingly, neuronal subunits express the protein encoded by the STAC3 gene, which has Src homology 3 and cysteine-rich domains    were associated with migraine, headache, and at least five glycemic traits (Supplementary Table S11). Genes like ADAMTSL4 encode a protease and play a vital role in various biological processes, such as inflammation and angiogenesis  have been associated with migraine, headache, and proinsulin. Intriguingly, AMBRA1 is involved in many cellular processes, including neuroprotection, apoptosis, autophagy, and neuronal development , is involved in activating beclin-1-regulated autophagy and favours the autophagosome core complex , cellular processes, oxidative stress-induced senescence, epigenetic mechanisms, catabolic and autoimmune pathways. According to earlier observational studies  at a genome-wide significant level in our MR analyses. Thus, further investigations are warranted using future, more powerful GWAS summary statistics (producing stronger genetic instruments) for HOMA-B, HOMA-IR, and T1D.The current study confirmed and provides an improved understanding of the relationship between migraine, headache, and glycemic traits by identifying genetic correlation, and shared loci and genes, and inferring causal association. These findings provide novel insight into the potential underlying biology influencing migraine, headache, and glycemic traits. The implicated genes, loci, and pathways, provide important targets for further functional investigations to uncover the precise molecular mechanisms that contribute to their comorbidity. Furthermore, findings from this study provide important motivation and avenues to develop novel treatment strategies for managing glycemic traits in migraine and headache patients, particularly increasing fasting proinsulin levels to protect against headache.Supplementary file1 (XLSX 1190 KB)Below is the link to the electronic supplementary material.