Genetic insights and mechanistic parallels in gestational diabetes mellitus and type 2 diabetes

Domain: biology
Published: 2026-01-15
Editorial outcome: major_revision

Abstract
Gestational diabetes mellitus (GDM) has a high heritability and frequently co-occurs with type 2 diabetes (T2D), indicating shared genetic mechanisms. Firstly, we employed RHOGE to ascertain the genetic correlation and putative causal directions between GDM and T2D. Subsequently, the Genotype-Tissue Expression Project v8 eQTls files and the FinnGen R11 dataset were employed to conduct cross-tissue transcriptome association studies, Functional Summary-based Imputation in single tissues, and Gene Analysis combined with Multimarker Analysis of Genomic Annotation for GDM and T2D, respectively. A total of 5 genes were identified as GDM susceptibility and 97 genes linked to T2D susceptibility. Of these, four genes (COBLL1, NRBP1, IFT172 and TRIM54) were identified as being shared. Mendelian randomization and colocalization analyses revealed the causal associations of them with GDM and T2D in distinct tissues. Subsequent analyses indicated COBLL1 may influence GDM and T2D risk by regulation of actin filament polymerization and interactions with chemical responses. NRBP1 may confer protective effects against diabetes through regulation of insulin secretion, while IFT172 and TRIM54 may play a role in energy balance signaling and metabolism of skeletal muscle, respectively. Our study provides insight into the shared genetic mechanism between GDM and T2D and identifies potential targets for pharmacological intervention.

Introduction
Gestational diabetes mellitus (GDM) is a form of glucose intolerance that leads to hyperglycemia, either emerging or being first detected during pregnancy1. Over the past fifteen years, the prevalence of GDM has significantly risen2, currently affecting more than 16 million pregnancies globally each year3. GDM not only contributes to the cyclical nature of ‘diabetes begetting diabetes’ but also necessitates early detection and management to mitigate risks for adverse birth outcomes and reduce the future incidence of chronic metabolic diseases, such as type 2 diabetes (T2D)4,5. This condition notably increases the lifelong risk of developing T2D3,6. Despite its significant maternal and neonatal health implications, the genetic mechanisms underlying GDM remain poorly understood, with most research focusing on its genetic overlap with T2D. Previous studies have primarily examined loci associated with T2D7–9, and the three major genome-wide association studies (GWAS) of GDM10–12 have identified only five loci, limiting our ability to explore specific genetic variants or biological pathways unique to GDM. The most comprehensive GWAS of GDM identified 13 genome-wide significant loci13, most of which are located in non-coding regions, posing a significant challenge in understanding their functional roles14. Additionally, the complex patterns of linkage disequilibrium (LD) complicate the identification of causal variants15.

In recent years, transcriptomic-wide association studies (TWAS) have gained attention for their ability to predict gene-level associations using imputed gene expression from genetic data and their relevance to complex traits16. These advancements are feasible because some GWAS-identified risk variants overlap with expression quantitative trait loci (eQTLs), which regulate gene expression17. TWAS enhances statistical power by predicting gene expression from genetic data, reducing the confounding effects of environmental influences and mitigating reverse causation issues. This approach improves the identification of functional regions related to genetic associations18,19. Traditional TWAS analyses have been conducted separately in individual tissues, potentially overlooking shared regulatory mechanisms across tissues20. It has been demonstrated that eQTLs with significant effects are capable of regulating gene expression across a range of tissues21. Recent developments have introduced the unified test for molecular signatures (UTMOST), a novel cross-tissue TWAS methodology that conducts gene-level association analyses across multiple tissues. This enhancement refines the accuracy and broadens the scope of applicability of imputation models, which previously focused on single-tissue analyses22. The UTMOST approach employs a group-lasso penalty on the effectsizes of single nucleotide polymorphisms (SNPs) across diverse tissues, integrating eQTLs that exert multi-tissue effects while preserving the integrity of those that are tissue-specific yet significant. This cross-tissue strategy has been instrumental in identifying potential susceptibility genes for complex multisystem disorders, including migraine23, rheumatoid arthritis24, and autism spectrum disorder25.

The hypothesis that GDM and T2D share a common genetic foundation has recently been suggested3,6,26,27, but studies focusing on the genetic basis of GDM, especially concerning gene expression across different tissues, remain limited. This study initially explores the genetic correlations and potential causative links between GDM and T2D to confirm their interconnectedness. We conducted a cross-tissue TWAS using the UTMOST approach, integrating GWAS data for GDM and T2D from the FinnGen R11 study with eQTL data from the Genotype-Tissue Expression Project (GTEx) version 8. To identify associations within specific tissues, we applied the Functional Summary-based Imputation (FUSION) method28. FUSION is a toolset for performing transcriptome-wide and regulome-wide association studies, which predicts and tests the genetic components of molecular phenotypes for their association with disease using GWAS summary statistics. Additionally, we validated these associations through the application of the Multi-marker Analysis of Genomic Annotation (MAGMA)29, a tool for gene and gene-set analysis that aggregates genetic marker data at the gene level and tests the joint association of markers within the gene with the phenotype. The final set of candidate genes for both GDM and T2D was derived from the intersection of results from these three methods. Further analytical approaches, including Mendelian Randomization (MR) and colocalization analysis, Phenome-Wide Association Studies (PheWAS), gene network exploring using GeneMANIA, pathway enrichment analyses, and evaluations of tissue and cell-type specificity, as well as gene-chemical-disease interactions, were conducted to further investigate the biological characteristics and implications of these candidate genes.

Results
Genetic correlation and bidirectional causal relationships between GDM and T2D across 47 Tissues
To explore the genetic interplay and bidirectional causality between GDM and T2D across various biological tissues, we employed a gene-expression-mediated approach to estimate genetic correlations between these two conditions. Unlike traditional genetic correlation methods that focus on SNPs, our approach emphasizes gene expression, providing insights into the genetic correlations that are influenced by gene expression levels. Utilizing TWAS data from 47 tissues, we identified significant expression-mediated genetic correlations between GDM and T2D in all tissues analyzed. Notably, the strongest genetic correlation was observed in the artery coronary (r2=0.92, p-value=9.13E-62) as depicted in Fig. 1A. The genetic correlation of 0.92 in the artery coronary tissue is quite high, but it doesn’t necessarily mean this tissue is the most biologically relevant for GDM or T2D in a direct pathophysiological sense. It could reflect that the genetic loci influencing both GDM and T2D may have a pleiotropic effect, influencing coronary artery function or disease in a way that is not immediately apparent in terms of direct disease pathology but could still reflect shared genetic mechanisms. We additionally employed 1000 bootstrap analysis to test the robustness of the results. As shown in Supplementary Fig. 2, the genetic correlation between GDM and T2D in different tissues was robust.

We employed bi-variate linkage disequilibrium (LD) score regression to test the sample overlap between GDM and T2D. The regression intercept was 0.219 (se=0.017), indicating a moderate degree of sample overlap. Although this overlap could slightly influence the genetic correlation results, its impact is minimal. As GDM and T2D are both metabolic disorders, and many tissues that influence metabolic pathways (such as adipose tissue, liver, and muscle) would be expected to have higher genetic correlations between these traits. However, tissues that are less directly involved in metabolism, like brain or skin, may not be as intuitively relevant. That said, there could still be indirect connections (e.g., brain tissues influencing appetite or lifestyle choices that contribute to metabolic diseases), but these tissues would not be the primary suspects for genetic correlations with GDM and T2D.

Additionally, we utilized bidirectional regression analyses to investigate the potential reciprocal associations between GDM and T2D, mediated by genetically regulated gene expressions across various tissues. These analyses revealed a significant causal effect of GDM on the development of T2D in all tissues studied, with the strongest evidence found in the artery aorta (odds ratio [OR]=1.86, p-value=2.14E-78). Conversely, the potential causal impact of T2D on GDM was also significant, particularly in the artery aorta (OR=1.81, p-value=3.94E-77). Figure 1B illustrates these significant bidirectional relationships across all 47 tissues, underscoring the complex interactions between GDM and T2D. We also observed bidirectional causal effects between GDM and T2D through the traditional bidirectional MR. For GDM to T2D, OR=1.15, p-value=8.17E-6; For T2D to GDM, OR=1.58, p-value=1.94E-77.

Identification of GDM and T2D candidate genes across multi-tissue and single-tissue TWAS analyses
Building upon the observed genetic correlations and causal relationships between GDM and T2D, we next aimed to identify specific genes associated with these conditions through comprehensive TWAS analyses.

In the cross-tissue TWAS analysis, 229 genes associated with GDM and 710 genes related to T2D were identified with p-values less than 0.05 (see Tables S1 and S2). Following adjustments for false discovery rates (FDR<0.05), 11 genes linked to GDM and 319 genes related to T2D retained statistical significance, as depicted in Fig. 3A and B, respectively. Additionally, the single-tissue TWAS identified 193 genes linked to GDM and 4933 genes associated with T2D, with significant associations (FDR<0.05) in at least one tissue (Tables S3 and S4). Notably, within these findings, 6 candidate genes for GDM (referenced in Fig. 3A and Table S5) and 168 candidate genes for T2D (illustrated in Fig. 2B and Table S6) met the rigorous selection criteria across both multi-tissue and single-tissue analyses.

Conditional and joint multiple-SNP analysis identifies independent and interacting genetic signals for GDM and T2D
To refine the identified candidate genes and minimize potential false positives arising from linkage disequilibrium (LD), we conducted conditional and joint multiple-SNP association (COJO) analyses. These analyses provided a deeper understanding of the independent and interactive effects of genes across tissues in GDM and T2D.

The 6 candidate genes for GDM and 168 for T2D, primarily located on chromosomes 2, 4, 18 and 22, were subjected to COJO analysis within their respective tissues to address potential LD-related false positives (Tables S7 and S8). To illustrate, we focused on the shared genes (COBLL1, NRBP1, IFT172 and TRIM54) between GDM and T2D. TWAS analysis conditioned on predicted expression revealed that COBLL1 in adipose visceral omentum (Fig. 3A), NRBP1 in the artery aorta (Fig. 3B), IFT172 in the brain cortex (Fig. 3C), and TRIM54 in the brain anterior cingulate cortex BA24 (Fig. 3D) did not significantly affect the TWAS signals for GDM, indicating their independence from LD. Conversely, conditioning on the expression of COBLL1 in the pancreas for T2D led to a marked decrease in the TWAS signal for SLC38A11 (Fig. 3E). Furthermore, the TWAS signals for ZNF513, SLC5A6 and ATRAID underwent a notable alteration when conditioned on the predicted expression of NRBP1 in the artery aorta for T2D (Fig. 3F). In the brain cerebellum, conditioning on IFT172 expression resulted in a substantial alteration in the TWAS signals for GPN1 in T2D (Fig. 3G). Notably, in the brain anterior cingulate cortex BA24 for T2D, the TWAS signals for ATRAID increased when conditioned on the expression of TRIM54 (Fig. 3H). Detailed effects of conditioning on other candidate genes across tissues are provided in Tables S7 and S8.

Gene association and cross-tissue integration identifies shared and distinct candidate genes for GDM and T2D
Building on the insights from previous analyses, we further refined the identification of candidate genes for GDM and T2D through MAGMA and cross-tissue integration. This approach enabled the discovery of both shared and distinct genetic signals underlying the two conditions.

MAGMA-based gene analysis revealed 155 genes significantly associated with GDM (FDR<0.05), as illustrated in Fig. 2A and Table S9. For T2D, 3998 genes were identified as significantly associated, depicted in Fig. 2B and Table S10. To strengthen these findings, we integrated results from UTMOST cross-tissue analysis with significant genes identified in both FUSION and MAGMA analyses. This integration led to the identification of 5 promising candidate genes for GDM (depicted in Fig. 2A and E) and 97 for T2D (Fig. 2B and E). A heatmap displaying fusion TWAS p-values for these candidate genes is presented in Fig. 2C and D. Ultimately, four genes (COBLL1, NRBP1, IFT172 and TRIM54) were identified as shared by GDM and T2D through the combined analyses (Fig. 2E and Table S11).

Causal and colocalization analyses uncover tissue-specific associations for shared genes in GDM and T2D
To enhance our understanding of the causal relationships between the shared genes and the risk of GDM and T2D, we performed MR and colocalization analyses. These analyses provided robust evidence of tissue-specific gene associations and highlighted critical genetic loci involved in both conditions.

To investigate the causal roles of the four shared genes (COBLL1, NRBP1, IFT172 and TRIM54) in GDM and T2D, we employed MR and colocalization analyses. The COBLL1 gene is located on chromosome 2q24.3, and the results of FUSION demonstrate its significant expression in 2 tissues for GDM and 9 tissues for T2D (Table S11). The MR analysis underscored significant causal associations with GDM and T2D in four tissues (p<0.05) (Fig. 4A and Table S12), with an OR (95% CI) of 0.85 (0.77, 0.93) for GDM and 0.82 (0.78, 0.86) for T2D in brain cortex. Colocalization analysis further supported these findings, with PP.H4 values of 0.81, 0.92 and 0.78 for Adipose_Visceral_Omentum, Artery_Aorta and Nerve_Tibial, respectively (Table S13). Notably, rs3769885 emerged as the most significant colocalization locus for GDM in Adipose_Visceral_Omentum (Fig. 4B) and Nerve_Tibial (Fig. 4C).

NRBP1, located on chromosome 2p23.3, was notably correlated with GDM in 23 tissues according to FUSION analysis (Table S11). MR analysis confirmed a causal relationship between NRBP1 and GDM in 16 tissues (p<0.05) (Fig. 4A and Table S12), with the strongest association found in the skin sun exposed lower leg (OR=0.77, p-value=6.14E-09). Colocalization analysis supported this, with a PP.H4 value of 0.91 for Esophagus_Gastroesophageal_Junction (Table S13). rs1260326 emerged as the most significant colocalization locus for GDM in this tissue (Fig. 4D). Additionally, FUSION analysis revealed that the NRBP1 gene is a significant contributor to T2D in 22 tissues (Table S11). MR analyses confirmed a causal relationship in 16 tissues (p<0.05) (Fig. 4A and Table S12), with the strongest association found in the artery aorta (OR=0.80, p value=2.67E-09). Colocalization analysis further supported this with PP.H4 values of 0.94 and 0.66 for Esophagus_Gastroesophageal_Junction and Small_Intestine_Terminal_Ileum, respectively (Table S14). rs1260326 was the most significant colocalization locus for T2D in Esophagus_Gastroesophageal_Junction (Fig. 4E), and rs2303370 was the most significant colocalization locus for T2D in Small_Intestine_Terminal_Ileum (Fig. 4F).

Similarly, MR analysis identified significant causal associations of IFT172 with both GDM and T2D in the spleen (p<0.05) (Fig. 4A and Table S12). Notably, rs780096 emerged as the most significant colocalization locus for GDM and T2D in Thyroid (Fig. 4G and H) and rs780095 served as the most significant colocalization locus for GDM and T2D in Brain_Anterior_cingulate_cortex_BA24 (Fig. 4I and J).

PheWAS analysis reveals gene-phenotype associations for GDM and T2D shared genes
Building upon the previous MR and colocalization findings, a comprehensive PheWAS analysis was conducted to investigate the broader genetic associations of the four shared genes (COBLL1, NRBP1, IFT172, and TRIM54) with a wide array of diseases and traits, offering new insights into their potential clinical implications.

This study included a PheWAS (Phenome-Wide Association Study) analysis of the four genes shared between GDM and T2D (COBLL1, NRBP1, IFT172 and TRIM54), utilizing data from 17,361 binary phenotypes and 1419 quantitative phenotypes sourced from the AstraZeneca PheWAS portal database. The analysis revealed significant associations between genetic variants and specific diseases or traits. For COBLL1, IFT172 and TRIM54, the variants showed statistically significant correlations with binary phenotypes, particularly related to endocrine/metabolic diseases and musculoskeletal diseases (Fig. 5A, E, G). Moreover, these variants showed notable associations with quantitative phenotypes, including proteomics, NMR metabolomics, laboratory findings, and health services (Fig. 5B, F, H). These results suggest possible clinical implications, such as side effects and horizontal pleiotropy, warranting further investigation. In contrast, NRBP1 variants showed no significant associations with either binary or quantitative phenotypes (Fig. 5C and D).

GeneMANIA network analysis reveals pathway enrichments for GDM and T2D-associated genes
Expanding on the phenotypic associations identified in the PheWAS analysis, a GeneMANIA network analysis was conducted to explore the interactive relationships and pathway enrichments of the genes COBLL1, NRBP1, IFT172, and TRIM54, providing further insights into their biological roles.

The GeneMANIA analysis revealed potential interactive gene networks, with COBLL1 positioned centrally, as shown in Fig. 5I. The associated networks with COBLL1 were primarily enriched in pathways related to actin dynamics, including actin nucleation, actin filament polymerization, and actin nucleation (Table S15). A second network, centered on NRBP1 (Fig. 5J), was predominantly enriched in pathways associated with nucleotide phosphorylation, such as DNA-templated transcription, elongation, phosphorus-oxygen lyase activity, and ribonucleoside diphosphate metabolism (Table S16). The network centered on IFT172 (Fig. 5K) showed significant enrichment in pathways related to protein transport along microtubule, microtubule-based protein transport, and intraciliary transport (Table S17). Finally, a network with TRIM54 at its core, presented in Fig. 5L, featured enrichment in pathways associated with ubiquitinyl hydrolase activity, omega peptidase activity, and protein modification by small protein removal (Table S18). This comprehensive genomic analysis underscores the complex interplay and functional diversity of these genes within various biological pathways, offering valuable insights into their potential involvement in the molecular mechanisms of GDM and T2D.

Pathway enrichment analysis uncovers biological functions of GDM and T2D-associated genes
Building on the interactive gene networks analyzed in the previous section, pathway enrichment analyses were conducted to explore the biological functions of the genes linked to GDM and T2D, revealing distinct pathway associations for each condition.

Following the identification of key genes linked to GDM and T2D, pathway enrichment analyses were performed to elucidate their potential biological functions. For the GDM-associated genes identified by UTMOST or FUSION, no significant enrichment was observed in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. However, Gene Ontology (GO) pathways revealed these genes predominantly participate in pathways such as iron-sulfur cluster binding, methylthiotransferase activity and protein metabolic process (Fig. 6A). Additional pathway analysis of the five GDM genes identified by UTMOST, FUSION, and MAGMA is shown in Supplementary Fig. 3. Conversely, the analysis of 97 genes linked to T2D revealed significant enrichment in GO pathways related to voltage-gated sodium channel activity, establishment of localization, microtubule-based processes, and components associated with the centrosome and microtubule organizing center (Fig. 6B). KEGG pathway analysis further highlighted the involvement of these genes in processes related to legionellosis (Fig. 6B), emphasizing the biological relevance of these findings in the context of T2D.

Tissue and cell-type specificity reveals key players in T2D and GDM
To further elucidate the tissue and cell-type specificity of the identified genes, an integrative analysis combining gene expression data with single-cell transcriptomics was performed, uncovering distinct tissue and cell-type enrichments for genes associated with T2D and GDM.

This analysis utilized gene expression data from the GTEx project30 combined with insights from 11 single-cell transcriptomics datasets31. It revealed significant enrichment of 97 T2D-associated genes in several tissues, particularly the Heart, Pancreas, Liver, Kidney, and Adrenal Gland (Fig. 6D). Specific cell types, including radial glial cells, type I pneumocytes, corneal epithelial cells, type II pneumocytes, and pancreatic ductal cells were identified as potential key players in organ homeostasis, functionality, and repair (Fig. 6F). For GDM, the five genes identified were notably enriched in the Adrenal Gland, Adipose Tissue, Nerve, Heart, and Muscle (Fig. 6C). Additionally, these genes were prominently enriched in diverse cell types including mast cells, corneal epithelial cells, acinar cells, decidual perivascular cells, and decidual macrophages (Fig. 6E). These findings illuminate the essential roles these genes may play in immune response regulation, tissue repair, and the maintenance of local homeostasis.

Chemical interactions and disease pathways of shared genes in GDM and T2D
Building on the tissue- and gene-specific findings, the next phase of the analysis explores the chemical interactions and disease pathways linked to genes commonly implicated in both GDM and T2D, underscoring their broader physiological relevance.

Utilizing the Comparative Toxicogenomics Database (CTD)32, this study predicted chemicals interactions for four genes shared between GDM and T2D (COBLL1, NRBP1, IFT172 and TRIM54). The analysis revealed a broad spectrum of chemical interactions for the COBLL1 gene, including exposure to Tetrachlorodibenzodioxin, bisphenol A, Valproic Acid, and dorsomorphin (Fig. 6G). Similarly, NRBP1 showed interactions with bisphenol A, Ethinyl Estradiol, Tetrachlorodibenzodioxin, and 1,2-Dimethylhydrazine (Fig. 6H). For IFT172, potential interactions with bisphenol A, Benzo(a)pyrene, Cisplatin, and Tretinoin (Fig. 6I). TRIM54 also showed potential for chemical interactions with Benzo(a)pyrene, Aflatoxin B1, bisphenol A, Tetrachlorodibenzodioxin, and Valproic Acid. Further analysis of Gene-Chemical-Disease pathways revealed that these genes are involved in a range of physiological disruptions including weight dynamics, liver and chemical diseases, weight loss, inflammation, fatty liver, kidney diseases, and various other endocrine and metabolic disorders (Tables S19–S22). Notably, COBLL1 was highlighted as a potential biomarker for Non-alcoholic Fatty Liver Disease and Myocardial Reperfusion Injury (Table S19), whereas the IFT172 gene was associated with potential markers for Gastrointestinal Stromal Tumors, Retinitis Pigmentosa 71, and Short Rib-Polydactyly Syndrome (Table S22).

Methods
Supplementary Fig. 1 illustrates the analytical framework employed in this study. We mainly employed three approaches (UTMOST, FUSION and MAGMA) to ascertain the candidate genes for GDM and T2D. We conducted these three approaches independently for selecting the candidate genes, and then we took the intersection of the candidate genes screened by these three methods for the final identified genes for GDM and T2D, respectively. In the process of screening candidate genes using each method independently, associations achieving an FDR below 0.05 after applying the false discovery rate (FDR) correction for each method were selected. The purpose of simultaneously conducting the intersection is to ensure a reduced FDR and to corroborate the association with GDM or T2D from multiple perspectives.

All analyses in this study were conducted using publicly available data. Summary-level statistics, gene expression data, and other datasets were approved by their respective institutional review boards, as indicated in the ‘Data Availability’ section of the website. Ethical guidelines were adhered to in accordance with the standards set by each institutional review board.

Data sources for GDM GWAS and T2D GWAS
We obtained data for GDM and T2D from the FinnGen Release 11 cohort (https://www.finngen.fi/en/access_results). This cohort comprises data from 16,802 individuals with GDM and 237,816 control subjects, as well as 71,728 individuals with T2D and 369,007 control subjects, all of whom are of European ancestry. We began with data from pregnancies involving genotyped FinnGen participants, defining a “pregnancy window” as the period from 40 weeks before delivery to 5 weeks postpartum. A pregnancy was classified as having gestational diabetes if it met any of the following criteria: (I1) the presence of gestational diabetes diagnoses (ICD codes) within the pregnancy window, (I2) any diabetes-related codes (e.g., ICD8) within the same window, or (I3) abnormal blood glucose test results recorded in the pregnancy registry. Exclusion criteria were applied to pregnancies based on the following conditions: (E1) a diabetes diagnosis outside the pregnancy window, (E2) any prior history of significant pancreatic diseases such as chronic pancreatitis, pancreatic necrosis, pancreatic cancer, or cystic fibrosis, or (E3) prior diagnoses of type 1 (T1D) or type 2 diabetes (T2D). Pregnancies that did not meet any inclusion criteria for gestational diabetes but passed the exclusion criteria were categorized as ‘unaffected’. To define the study cohort, cases were selected from the genotyped women with pregnancy history, identifying those with at least one pregnancy that met the inclusion criteria for gestational diabetes and passed the exclusion criteria. Controls were defined as women with only ‘unaffected’ pregnancies, meaning no history of diabetes or significant pancreatic diseases before or during pregnancy, and no abnormal blood glucose results in the pregnancy registry. Detailed information could be found in Supporting Information.

eQTL data acquisition
Expression data were retrieved from the Genotype-Tissue Expression (GTEx) Project, Version 8 dataset30, which provides gene expression profiles across 47 diverse tissues (excluding testis and prostate) from 838 deceased donors (https://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/ge/). The number of samples per tissue varies considerably, with the lowest number being 73 in the renal cortex and the highest number reaching 706 in skeletal muscle.

Genetic correlations and putative causal directions between GDM and T2D
RHOGE19 is an R package that assesses the local genetic correlation between gene expression and complex traits and to compute the genetic correlations derived from predicted expression between trait pairs. This approach is extended to a bi-directional regression that provides putative causal directions between traits. In this study, RHOGE was further utilized to assess the influence of predicted gene expression on mediating these correlations and to estimate the correlation of effect sizes between GDM and T2D. The method incorporates a bidirectional regression framework to infer potential causal directions among traits exhibiting non-zero correlations19. Utilizing this technique, we conducted TWAS using GTEx tissue data and GWAS results from the FinnGen R11 dataset to explore potential expression-mediated causal relationships between GDM and T2D based on single-tissue analyses (see below). Furthermore, 1000 bootstrap analysis was employed to evaluate the robustness of the genetic correlation between GDM and T2D in different tissues.

Cross-tissue analyses for GDM and T2D
We employed the UTMOST method (https://github.com/Joker-Jerome/UTMOST) for cross-tissue analysis to estimate gene-trait associations at an organismal level22. This method enabled the identification of a multitude of genes within tissues that exhibited augmented trait heritability and enhanced imputation accuracy22,72. We integrated these gene-trait associations using the generalized Berk-Jones (GBJ) test, which leverages covariance derived from single-tissue statistics22,73. Associations achieving an FDR below 0.05 after applying the false discovery rate (FDR) correction were considered statistically significant.

Single tissue analyses for GDM and T2D
The FUSION tool (http://gusevlab.org/projects/fusion/) was deployed to perform TWAS that amalgamate GDM and T2D GWAS data with eQTL data across 47 tissues from the GTEx V8 dataset18. Initially, LD assessment between the predictive model and SNPs at each GWAS locus was conducted using 1000 Genomes Project samples of European ancestry. Subsequently, FUSION implemented a series of predictive models, encompassing LASSO, Elastic Net, BSLMM, BLUP, and the top 1 model, to determine the most accurate predictive model based on their impact on gene expression weights. The model exhibiting the highest predictive accuracy was selected to determine the gene weights. The selected model was then used to integrate these weights with genetic effects ascertained from the Z-scores of GDM and T2D GWAS, to facilitate TWAS for both conditions. Genes that met the criterion of an FDR<0.05 in both cross-tissue and single-tissue analyses for each condition were identified as candidate genes.

Conditional and joint analysis
Within the same FUSION framework, a conditional and joint analysis module (COJO) was utilized to discern conditionally independent genetic variants within each locus18. This post-processing step was pivotal in refining our understanding of the genetic underpinnings that influence trait variability, by accounting for LD among markers74. In this study, the genes identified by both UTMOST and FUSION (6 significant genes for GDM and 168 significant genes for T2D) were selected for COJO analysis. COJO was conducted separately for the tissues that associated with above significant genes identified by FUSION, genes identified through this analysis as independently associated were classified as jointly significant, whereas those that did not maintain significance were categorized as marginally significant. FDR correction was applied for multiple testing in this stage.

Gene analysis by MAGMA
Gene-level investigations were conducted employing MAGMA software (version 1.10), which leveraged data from Phase 3 of the 1000 Genomes Project (Build 37, European data only) to amalgamate SNP-level association statistics into comprehensive gene scores. The objective of this process was to quantify the strength of the association between each gene and the phenotype under study29,75. For a comprehensive understanding of the parameters and methodological approaches employed, the original MAGMA documentation should be consulted29.

Mendelian randomization and colocalization analysis
The application of MR was executed via the “TwoSampleMR” package in R76. This analysis utilized cis-eQTL SNPs as instrumental variables (IVs), with gene expression delineated as the exposure, and GWAS datasets on GDM and T2D as the outcomes. The preliminary phase comprised the identification of genome-wide significant SNPs (p<5E-08), followed by LD clumping to segregate independent SNPs (r2<0.001)77. Before MR analysis, we removed the pleiotropic eQTLs as many eQTLs may be associated with multiple tissues. The MR effect estimation was facilitated using the Wald ratio method, with statistical significance established at p<0.05 for IFT172 (1 tissue), p<0.0125 for COBLL1 (4 tissue), and p<3.12E-3 for NRBP1 (16 tissues) considering multiple testing.

Subsequently, a Bayesian colocalization analysis was deployed using the “coloc” package in R78. This method aimed to determine the concurrence of causal loci between GWAS and eQTL signals, with a particular focus on the posterior probability (PP) of five hypothetical relationships78. A PP.H4 greater than 0.6 indicated the presence of shared causal variants within the examined datasets.

PheWAS analysis of shared genes between GDM and T2D
A phenome-wide association study (PheWAS) was conducted utilizing exome sequencing and phenotype data accessible through the AstraZeneca PheWAS Portal (https://azphewas.com/)79. This portal enabled an extensive investigation into the correlations between gene variants and a wide range of phenotypes. The initial study examined the association of shared genes with a range of binary and quantitative phenotypes, derived from the medical records of approximately 500,000 UK Biobank participants79. This analysis included an exploration of horizontal pleiotropy and potential adverse effects related to four genes shared between GDM and T2D (COBLL1, NRBP1, IFT172 and TRIM54) within the context of the UK Biobank.

GeneMANIA analysis
The GeneMANIA platform (https://genemania.org/) was employed to facilitate access to a plethora of datasets, including genetic interactions, pathways, and co-expression data pertinent to the target genes80. This platform was instrumental in elucidating potential biological functions associated with these genes81, thereby enhancing the understanding of their roles within the cellular and molecular frameworks of the studied traits.

Pathway enrichment analysis
Enrichment analyses were conducted utilizing the g:Profiler tool, employing the Gene Ontology and KEGG databases for the selection of gene sets82. To address the issue of multiple testing, the g:SCS (Set Counts and Sizes) methodology was utilized for correction.

GTEx Tissue Specific Expression Analysis (TSEA)
Tissue-specific expression analysis (TSEA) was performed leveraging RNA-seq data from the GTEx project, which provides expression profiles from a variety of tissues sourced from post-mortem subjects83. The analyses aimed to determine whether genes associated with GDM and T2D exhibited enriched expression in particular tissues, applying hypergeometric tests. Corrections for multiple comparisons were made using the Benjamini–Hochberg method, aiming to maintain a FDR below 0.05.

Cell-type-specific enrichment analysis (CSEA)
Cell-type-specific enrichment analysis (CSEA) was executed using WebCSEA to compare gene sets against tissue-cell-type (TCs) expression signatures, derived from 11 single-cell expression datasets31. Dai et al.31 consolidated expression data from over 5.5 million cells across 111 tissues and 1355 TCs, eliminating low-expression genes and utilizing the “deTS” t-statistic-based method to identify signature genes for each cell type. Genes that ranked in the top 5% of t-statistic scores for specific cell types were categorized as cell type-specific. Fisher’s exact test was employed to investigate if these genes were overrepresented among the shared genes for each disease trait.

Gene-chemical-disease analysis for shared genes between GDM and T2D
Investigation into chemical agents associated with the shared genes between GDM and T2D was facilitated through searches in the Comparative Toxicogenomics Database (CTD) database (https://ctdbase.org/)32. This database, a comprehensive public repository, aggregates extensive information on chemical-gene/protein interactions, as well as chemical-disease and gene-disease relationships, thus supporting predictions regarding the environmental impacts on disease via the curation of gene-chemical-disease interaction data.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors conducted multiple analyses, including transcriptome-wide association studies (TWAS) using methods such as RHOGE, UTMOST, and FUSION, Mendelian Randomization (MR), and colocalization, to explore the genetic correlations between gestational diabetes mellitus (GDM) and type 2 diabetes (T2D). Additionally, they performed Phenome-Wide Association Study (PheWAS) and pathway enrichment analyses. The study identified four genes (COBLL1, NRBP1, IFT172, and TRIM54) shared by GDM and T2D, suggesting novel shared genetic mechanisms between the two conditions. These findings are intriguing and provide valuable insights into the genetic overlap between GDM and T2D.\n\nMajor comments:\n1) I appreciate the authors' comprehensive approach, as illustrated in Figure 1. However, the figure is somewhat difficult to follow and may be misleading. For example, the figure suggests that UTMOST identified 11 candidate genes for GDM, which were followed up by MAGMA (yielding 155 genes) and FUSION (yielding 193 genes). However, the main text does not clearly indicate whether these analyses were conducted sequentially or independently. It appears that the authors performed the analyses directly from the GWAS data rather than following up on the 11 genes identified by UTMOST. Could the authors clarify this? Additionally, it is unclear how many statistical tests were performed in selecting the final gene sets, which raises concerns about multiple testing adjustments.\n2) I am confused by the differences in the number of genes identified by UTMOST and FUSION. For instance, UTMOST identified 11 genes for GDM and 319 genes for T2D, while FUSION identified 193 genes for GDM and 4,933 genes for T2D, despite using the same GWAS and GTEx data. Given that UTMOST is a cross-tissue method and generally more powerful than single-tissue methods like FUSION, why does FUSION identify significantly more genes? Are the genes identified by FUSION or MAGMA potential false positives? If not, why were they excluded from further comparative analyses between GDM and T2D?\n3) Since both GDM and T2D GWAS were conducted in FinnGen, I am wondering if the correlation induced by sample overlapping was considered in the RHOGE analysis. The RHOGE analysis suggested highest genetic correlation in the artery coronary (r2=0.92). However, artery coronary seems not the most relevant tissue for GDM or T2D. Some explanation is necessary. In lines 156-158, it states that “…we discovered substantial expression-mediated genetic correlations between GDM and T2D in all 47 tissues examined.” Why all the GTEx tissues should be relevant to both GDM and T2D? Would it suggest inflated type I errors or cross tissue correlation? If the later, why UTMOST is less powerful than single tissue analysis?\n4) Line 164-168 suggests the bidirectional causal effects between GDM and T2D in all the 47 tissues. Will the bidirectional Mendelian Randomization between GDM and T2D also suggest the bidirectional causal effects?\n5) The COJO analysis for candidate genes lacks clarity. Was COJO conducted separately for each of the 47 tissues? If so, how were multiple testing corrections applied? A more detailed explanation of the COJO methodology and its implementation would be helpful.\n6) In the cis-MR analysis, cis-eQTL SNPs were used as instrumental variables (IVs), with a threshold of P < 5 × 10⁻⁸ for genome-wide significance. Why P < 5 × 10⁻⁸ ? How many IVs were included? Given the stringent threshold, it is likely that few IVs were available, potentially reducing the power of the analysis. Additionally, since multiple tissues were analyzed, was the significance level of 0.05 adjusted for multiple testing?\n7) GDM typically occurs in younger adult individuals and exclusively affects women, while T2D affects both men and women and is more common in older populations. Could the authors discuss whether age and gender biases were considered in this study? Additionally, was collider bias addressed, particularly given the potential differences in the populations studied for GDM and T2D?", "reviewer_2": "The authors describe a series of analyses focused on describing the genetic underpinnings of GDM and T2D. To this aim, they calculated gene expression-mediated genetic correlation and analyzed multi-tissues via TWAS. We identified a number of methodologic innovations applied to their study of the shared genetics of GDM/T2D and links to other potential physiological disorders in the framing and discussion of their work. Yet, we have outlined a number of major and minor comments that we feel need to be addressed in subsequent revisions.\n\nMajor comments:\n-The manuscript is lacking transition sentences between methods to help the reader fully understand why each method was selected. We had concerns that this and the other selected novel methodologies are not clearly described, in terms of why they were needed, in particular as it relates to the epidemiology of diabetes.\n-The manuscript is long and the discussion section is not as structured as it should be. The discussion section includes whole paragraphs on each of the shared genes, and reads like a literature review paper. We recommend revising the manuscript to be more concise and to assure that all the details presented are salient to either the methods selected or some other aspects of the findings.\n-We suspect that KEGG and GO pathway analysis on five genes is not likely to yield meaningful results. Please provide support for the results on this small number of genes, which are a major component of the manuscript.\n-It seems that the novelty of looking at DGM and T2D, at least clinically speaking, is limited. If the novelty fully revolves around the shared genetic architecture, the manuscript should include some information about how this knowledge can inform future clinical or public health practice.\n-The authors highlight their findings in Aorta because the p-value is the smallest, but substantively this finding does not seem different from the rest in Figure 2B. The authors should consider among the multi-tissue results how biologic significance (public health relevance) is to be considered—especially when many or all tissues are statistically significant.\n-The authors should describe what is the purpose to “probe the potential reciprocal associations between GDM and T2D” in so many different tissues? Why are multiple tissues involved in Fig 2? Is it not a concern that their associations are all so high, and also seen in tissues that are not typically thought to be closely involved in diabetes (e.g. skin of lower leg, vagina, colon), see Fig 2A?\n\nMinor comments:\n-The term RHOGE is not defined in text.\n-Link with inflammation is weak – and only supported by one finding from the results, yet it represents several sentences in the discussion.\n-We recommend considering Figure 1 as supplemental figure given that it is very detailed, or moving other figures to the supplement. Although seven main figures may be allowed by the journal, it appears that not all of them are as easy to visualize and feel as strongly into the overall story of the paper.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to the comments of the reviewers\nReplies to Reviewer #1:\nReviewer #1 (Remarks to the Author):\nComment: The authors conducted multiple analyses, including transcriptome-wide\nassociation studies (TWAS) using methods such as RHOGE, UTMOST, and FUSION,\nMendelian Randomization (MR), and colocalization, to explore the genetic correlations\nbetween gestational diabetes mellitus (GDM) and type 2 diabetes (T2D). Additionally, they\nperformed Phenome-Wide Association Study (PheWAS) and pathway enrichment\nanalyses. The study identified four genes (COBLL1, NRBP1, IFT172, and TRIM54)\nshared by GDM and T2D, suggesting novel shared genetic mechanisms between the two\nconditions. These findings are intriguing and provide valuable insights into the genetic\noverlap between GDM and T2D.\nResponse: Thank you very much for your insightful comment and for taking the time to\nreview our manuscript. We appreciate your detailed attention to the study and we would\nlike to express our gratitude for your valuable feedback. We carefully addressed your\ncomments in revising the manuscript. We are grateful for your suggestions, which have\nimproved the quality of our study (see this revision).\nComment: Major comments:\n1) I appreciate the authors' comprehensive approach, as illustrated in Figure 1. However,\nthe figure is somewhat difficult to follow and may be misleading. For example, the figure\nsuggests that UTMOST identified 11 candidate genes for GDM, which were followed up\nby MAGMA (yielding 155 genes) and FUSION (yielding 193 genes). However, the main\ntext does not clearly indicate whether these analyses were conducted sequentially\nor independently. It appears that the authors performed the analyses directly from the\nGWAS data rather than following up on the 11 genes identified by UTMOST. Could the\nauthors clarify this? Additionally, it is unclear how many statistical tests were performed in\nselecting the final gene sets, which raises concerns about multiple testing adjustments.\nResponse: Thank you for your valuable comments. For the Figure 1, we recognize your\ncomment and we revised it to be more clear. We move Figure 1 to Supplement, as the\nreviewer 2 suggested (see Supplementary Figure 1 in this revision). We apologize that the\nmain text does not clearly indicate whether these analyses were conducted sequentially\nor independently. We added the descriptions for this as “We mainly employed three\napproaches (UTMOST, FUSION and MAGMA) to ascertain the candidate genes for GDM\nand T2D. We conducted these three approaches independently for selecting the\ncandidate genes, and then we took the intersection of the candidate genes screened by\nthese three methods for the final identified genes for GDM and T2D, respectively.” in the\nMethods section (see lines 472-476 in this revision). For the comment of how many\n1\nstatistical tests were performed in selecting the final gene sets, we also added the\nsentence “In the process of screening candidate genes using each method independently,\nassociations achieving an FDR below 0.05 after applying the false discovery rate (FDR)\ncorrection for each method were selected.” (see lines 476-479 in this revision) for your\nconcerns.\nComment: 2) I am confused by the differences in the number of genes identified by\nUTMOST and FUSION. For instance, UTMOST identified 11 genes for GDM and 319\ngenes for T2D, while FUSION identified 193 genes for GDM and 4,933 genes for T2D,\ndespite using the same GWAS and GTEx data. Given that UTMOST is a cross-tissue\nmethod and generally more powerful than single-tissue methods like FUSION, why does\nFUSION identify significantly more genes? Are the genes identified by FUSION or\nMAGMA potential false positives? If not, why were they excluded from further comparative\nanalyses between GDM and T2D?\nResponse: Thanks for your insightful comments. The difference in the number of genes\nidentified by UTMOST and FUSION, despite both using the same GWAS and GTEx data,\ncan be attributed to several factors that relate to the methods and underlying assumptions\nof each approach.\n1. Methodological Differences:\nUTMOST is indeed a cross-tissue method, meaning it leverages data from multiple\ntissues and integrates them to identify genes whose expression is relevant across various\ntissues. UTMOST combines evidence across tissues, which helps to improve statistical\npower when detecting genetic associations with traits.\nFUSION focuses primarily on imputing gene expression levels from genetic variants in a\nsingle tissue type (e.g., brain, adipose). While FUSION uses genetic summary statistics to\nmodel tissue-specific gene expression, it may not be as powerful as a method like\nUTMOST that takes into account data across many tissues.\n2. Tissue Specificity and Power:\nWhile UTMOST is more powerful due to its cross-tissue approach, it also means that it\nmight be more conservative in its gene identification process, focusing only on genes that\nshow consistent signals across multiple tissues, which may reduce the total number of\ngenes detected.\nFUSION, being tissue-specific, might identify a higher number of genes because it\nfocuses on genetic variants within a particular tissue that might have more subtle effects\nthat are not seen across multiple tissues. This could result in a broader selection of genes\nthat are potentially associated with the traits, but these associations might be weaker or\nmore tissue-specific.\n3. Gene Expression Predicted Models:\ntissues, which means that it might identify more genes simply because the imputation\nprocess allows for the detection of associations with gene expression in specific tissues\nthat are not as readily identifiable in cross-tissue methods.\nUTMOST’s approach integrates multiple tissues directly, potentially identifying genes with\nmore robust and reproducible associations across different tissues. In contrast, FUSION\nmight identify genes whose effects are more localized or specific to certain tissues,\nleading to a larger list of genes.\n4. Trait-Specific Variations:\nThe differences in gene identification could also be driven by the specific traits being\nstudied. For example, GDM (Gestational Diabetes Mellitus) might involve more\ntissue-specific genetic associations, so FUSION’s approach (which can be more sensitive\nto tissue-specific effects) could lead to a higher number of genes identified. Conversely,\nT2D (Type 2 Diabetes) may have more widespread effects across multiple tissues, so\nUTMOST’s cross-tissue approach may be more efficient in narrowing down to the most\nrelevant genes, but FUSION may still identify a larger number of genes due to its\ntissue-specific analysis.\n5. Statistical Power:\nUTMOST generally has greater statistical power in detecting true associations across\nmultiple tissues, but this might come at the cost of being more stringent about what it\nconsiders a true gene association. FUSION, in contrast, may be more liberal in detecting\nassociations, leading to more genes being flagged, but some of these might represent\nweaker or less robust associations.\nIn Summary:\nFUSION identifies significantly more genes because its methodology, which is\ntissue-specific and uses predicted techniques, might capture a wider range of\nassociations, including weaker or tissue-specific ones. On the other hand, UTMOST,\nbeing a more powerful cross-tissue method, likely focuses on genes that show robust\nassociations across multiple tissues, but at the cost of identifying fewer genes. The\ndifference is primarily due to the statistical and methodological approaches of the two\ntools, with UTMOST being more conservative and focused on cross-tissue consistency,\nwhile FUSION has a broader, more inclusive approach by leveraging tissue-specific\nimputation.\nWe do not exclude the genes identified by FUSION or MAGMA to avoid further\ncomparative analyses between GDM and T2D. Instead, we conducted these three\napproaches (UTMOST, FUSION and MAGMA) independently for selecting the candidate\ngenes, and then we took the intersection of the candidate genes screened by these three\nmethods for the final identified genes for GDM and T2D, respectively. The purpose of\n3\nsimultaneously conducting the intersection is to ensure a reduced false positive rate and\nto corroborate the association with GDM or T2D from multiple perspectives (see lines\n472-480 in this revision).\nComment: 3) Since both GDM and T2D GWAS were conducted in FinnGen, I am\nwondering if the correlation induced by sample overlapping was considered in the\nRHOGE analysis. The RHOGE analysis suggested highest genetic correlation in the\nartery coronary (r2=0.92). However, artery coronary seems not the most relevant tissue\nfor GDM or T2D. Some explanation is necessary. In lines 156-158, it states that “…we\ndiscovered substantial expression-mediated genetic correlations between GDM and T2D\nin all 47 tissues examined.” Why all the GTEx tissues should be relevant to both GDM and\nT2D? Would it suggest inflated type I errors or cross tissue correlation? If the later, why\nUTMOST is less powerful than single tissue analysis?\nResponse: Thank you for your kindly suggestions. You raise a very important point\nregarding the potential influence of sample overlap between the GDM and T2D GWAS in\nFinnGen on the RHOGE analysis, and the genetic correlation observed in artery coronary\ntissue, which might seem unexpected given the context of GDM and T2D. For the\ncomment that “artery coronary seems not the most relevant tissue for GDM or T2D. Some\nexplanation is necessary”, the genetic correlation of 0.92 in the artery coronary tissue is\nquite high, but it doesn't necessarily mean this tissue is the most biologically relevant for\nGDM or T2D in a direct pathophysiological sense. It could reflect that the genetic loci\ninfluencing both GDM and T2D may have a pleiotropic effect, influencing coronary artery\nfunction or disease in a way that is not immediately apparent in terms of direct disease\npathology but could still reflect shared genetic mechanisms (see lines 130-135 in this\nrevision). For the other comments, we employed bi-variate linkage disequilibrium (LD)\nscore regression to test the sample overlap between GDM and T2D. As a result, the\nbi-variate LD score regression intercept is 0.219 (se=0.017), which represents moderate\nsample overlap. The correlation could be slightly influenced by sample overlap between\nthe GDM and T2D GWAS in FinnGen, but the impact is weak. Importantly, GDM and T2D\nare primarily metabolic diseases, and many tissues that influence metabolic pathways\n(such as adipose tissue, liver, and muscle) would be expected to have higher genetic\ncorrelations between these traits. However, tissues that are less directly involved in\nmetabolism, like brain or skin, may not be as intuitively relevant. That said, there could still\nbe indirect connections (e.g., brain tissues influencing appetite or lifestyle choices that\ncontribute to metabolic diseases), but these tissues wouldn't be the primary suspects for\ngenetic correlations with GDM and T2D (see lines 137-147 in this revision). For the\nconcerns of power comparison between UTMOST and single tissue analysis, please see\nthe previous explanations.\nT2D in all the 47 tissues. Will the bidirectional Mendelian Randomization between GDM\nand T2D also suggest the bidirectional causal effects?\nResponse: Thank you for your kindly suggestion. We also observed bidirectional causal\neffects between GDM and T2D through the traditional bidirectional MR. For GDM to T2D,\nOR = 1.15, p-value = 8.17E-6; For T2D to GDM, OR = 1.58, p-value = 1.94E-77 (see lines\n157-160 in this revision).\nComment: 5) The COJO analysis for candidate genes lacks clarity. Was COJO\nconducted separately for each of the 47 tissues? If so, how were multiple testing\ncorrections applied? A more detailed explanation of the COJO methodology and its\nimplementation would be helpful.\nResponse: We appreciate your suggestions. In this study, the genes identified by both\nUTMOST and FUSION (6 significant genes for GDM and 168 significant genes for T2D)\nwere selected for COJO analysis. COJO was conducted separately for the tissues that\nassociated with above significant genes identified by FUSION, genes identified through\nthis analysis as independently associated were classified as jointly significant, whereas\nthose that did not maintain significance were categorized as marginally significant. FDR\ncorrection was applied for multiple testing in this stage (see lines 552-558, Supplementary\nFigure 1, Table S7 and Table S8 in this revision).\nComment: 6) In the cis-MR analysis, cis-eQTL SNPs were used as instrumental variables\n(IVs), with a threshold of P < 5 × 10⁻⁸ for genome-wide significance. Why P < 5 × 10⁻⁸ ?\nHow many IVs were included? Given the stringent threshold, it is likely that few IVs were\navailable, potentially reducing the power of the analysis. Additionally, since multiple\ntissues were analyzed, was the significance level of 0.05 adjusted for multiple testing?\nResponse: Thank you for your question. In Mendelian Randomization (MR), it’s critical to\nselect SNPs that are strongly associated with gene expression (i.e., eQTLs) to serve as\ninstrumental variables (IVs). The threshold of P < 5 × 10⁻⁸ ensures that the selected SNPs\nare robustly associated with gene expression and thus are good candidates for IVs,\nminimizing the risk of weak instruments, which could lead to biased or imprecise causal\ninferences. In this study, we used Wald ratio method to test the causal effect. The Wald\nratio method is a powerful statistical approach used in MR studies to estimate causal\neffects. Its primary advantage lies in its simplicity and efficiency when a single instrument\n(SNP) is used. It provides a straightforward calculation by dividing the association\nbetween the outcome and the instrument by the association between the instrument and\nthe exposure. This method offers unbiased estimates under the assumption of valid IVs\n5\nand no pleiotropy. Additionally, the Wald ratio method is computationally efficient and\neasily interpretable, making it ideal for analyses with a single strong IV. However, its\nreliability depends on strong instrument-exposure associations. Therefore, the threshold\nof P < 5 × 10⁻⁸ ensures that the selected SNPs are robust and efficient in this study. The\nstatistical significance established at p < 0.05 for IFT172 (1 tissue), p < 0.0125 for\nCOBLL1 (4 tissue), and p < 3.12E-3 for NRBP1 (16 tissues) considering multiple testing\n(see lines 575-577 in this revision).\nComment: 7) GDM typically occurs in younger adult individuals and exclusively affects\nwomen, while T2D affects both men and women and is more common in older populations.\nCould the authors discuss whether age and gender biases were considered in this study?\nAdditionally, was collider bias addressed, particularly given the potential differences in the\npopulations studied for GDM and T2D?\nResponse: Thanks for your suggestion. We adopted your comments and added\nlimitations “A limitation of this study is the potential bias from the age and gender\ndistribution in the GDM and T2D populations. GDM affects younger women, while T2D is\nmore prevalent in older adults and both sexes, which may introduce age- and\ngender-related confounding factors. Additionally, collider bias could affect the results if the\npopulations were not properly matched, leading to spurious associations. Future research\nshould address these limitations by using stratification, age-adjustment, and techniques\nlike propensity score matching to ensure more accurate and generalizable findings.” in the\ntext (see lines 446-452 in this version).\nReplies to Reviewer #2:\nReviewer #2 (Remarks to the Author):\nComment: The authors describe a series of analyses focused on describing the genetic\nunderpinnings of GDM and T2D. To this aim, they calculated gene expression-mediated\ngenetic correlation and analyzed multi-tissues via TWAS. We identified a number of\nmethodologic innovations applied to their study of the shared genetics of GDM/T2D and\nlinks to other potential physiological disorders in the framing and discussion of their work.\nYet, we have outlined a number of major and minor comments that we feel need to be\naddressed in subsequent revisions.\nResponse: Thanks for your useful suggestions to improve the quality of this manuscript.\nWe carefully addressed your comments in reviewing this manuscript (see this revision).\nComment: Major comments:\n-The manuscript is lacking transition sentences between methods to help the reader fully\n6\nunderstand why each method was selected. We had concerns that this and the other\nselected novel methodologies are not clearly described, in terms of why they were needed,\nin particular as it relates to the epidemiology of diabetes.\nResponse: Thank you for your constructive feedback. In the Introduction section, we first\nsummarized the epidemiology of GDM and T2D and their relationship (see lines 49-58),\nThen, we summarized the disadvantages in the current genetic epidemiological research\nof GDM (see lines 58-66). Next, we introduced the concept of TWAS and its advantages,\namong which UTMOST has a stronger ability to solve the problems existing in traditional\ngenetic epidemiology (see lines 68-90). For your concerns, we rephrased the last\nparagraph in the Introduction section (see lines 92-117) to propose the novelty of different\nmethods and the application connections among them in order to achieve the purpose of\nthis study. We also outlined the connections among the main methods for screening\ngenes in the Method section (see lines 472-480).\nComment: -The manuscript is long and the discussion section is not as structured as it\nshould be. The discussion section includes whole paragraphs on each of the shared\ngenes, and reads like a literature review paper. We recommend revising the manuscript to\nbe more concise and to assure that all the details presented are salient to either the\nmethods selected or some other aspects of the findings.\nResponse: Thank you for your valuable feedback and for reviewing our manuscript. We\nrephrased the language of the manuscript to be more concise (see this revision).\nComment: -We suspect that KEGG and GO pathway analysis on five genes is not likely\nto yield meaningful results. Please provide support for the results on this small number of\ngenes, which are a major component of the manuscript.\nResponse: Thank you for your comments. We presented the KEGG and GO pathway on\nfive GDM genes in Figure 6A. Please refer to it for your concerns (see Figure 6A, Figure\n6C and lines 281-285 in this revision).\nComment: -It seems that the novelty of looking at DGM and T2D, at least clinically\nspeaking, is limited. If the novelty fully revolves around the shared genetic architecture,\nthe manuscript should include some information about how this knowledge can inform\nfuture clinical or public health practice.\nResponse: We are truly grateful for your comments. The clinical novelty of investigating\nthe shared genetic architecture between GDM and T2D may seem limited, but it holds\nsignificant potential for advancing both clinical practice and public health strategies. By\nuncovering common genetic risk factors, this research can enhance our understanding of\nthe pathophysiological mechanisms that link GDM to T2D. Clinically, this knowledge could\n7\nlead to more precise risk stratification, enabling early identification of individuals at higher\nrisk for T2D after GDM. Additionally, the identification of genetic markers can guide\npersonalized treatment approaches, improving the management of both conditions. From\na public health perspective, incorporating genetic insights into screening protocols can\nhelp identify high-risk populations earlier, facilitating timely interventions that prevent the\nprogression from GDM to T2D. Ultimately, understanding the genetic overlap could inform\nthe development of novel prevention strategies, including gene-based therapies, and\nimprove overall health outcomes for individuals with these conditions. This research may\nthus provide a foundation for future clinical and public health practices aimed at reducing\nthe burden of T2D, particularly in populations with a history of GDM (see lines 433-445 in\nthis revision).\nComment: -The authors highlight their findings in Aorta because the p-value is the\nsmallest, but substantively this finding does not seem different from the rest in Figure 2B.\nThe authors should consider among the multi-tissue results how biologic significance\n(public health relevance) is to be considered—especially when many or all tissues are\nstatistically significant.\nResponse: Thank you for your kindly suggestion. Your comments are similar with one\ncomment from reviewer 1. We added some information as “Importantly, GDM and T2D\nare primarily metabolic diseases, and many tissues that influence metabolic pathways\n(such as adipose tissue, liver, and muscle) would be expected to have higher genetic\ncorrelations between these traits. However, tissues that are less directly involved in\nmetabolism, like brain or skin, may not be as intuitively relevant. That said, there could still\nbe indirect connections (e.g., brain tissues influencing appetite or lifestyle choices that\ncontribute to metabolic diseases), but these tissues wouldn't be the primary suspects for\ngenetic correlations with GDM and T2D.” (see lines 141-147 in this revision).\nComment: -The authors should describe what is the purpose to “probe the potential\nreciprocal associations between GDM and T2D” in so many different tissues? Why are\nmultiple tissues involved in Fig 2? Is it not a concern that their associations are all so\nhigh, and also seen in tissues that are not typically thought to be closely involved in diabetes\n(e.g. skin of lower leg, vagina, colon), see Fig 2A?\nResponse: Thank you for your kindly suggestion. The involvement of multiple tissues\n(such as skin, vagina, colon, and others) in the analysis could be a deliberate strategy to\nprovide a comprehensive view of the potential systemic effects of GDM and T2D. Both\nGDM and T2D are diseases with metabolic and endocrine disturbances that can affect\nvarious parts of the body, not just the traditionally thought-of tissues like the pancreas or\nliver. By exploring a broad spectrum of tissues, researchers could be able to capture a\n8\nholistic view of the body’s response to GDM and its potential progression to T2D.\nDiabetes is known to have wide-reaching effects on organs and tissues, and this\napproach allows researchers to discover unanticipated associations, which might not be\nevident when focusing on a narrower set of tissues. We also explained the reason that the\ncorrelation between GDM and T2D is high in artery coronary tissue and other tissues\n(lines 130-147 in this revision).\nComment: Minor comments:\n-The term RHOGE is not defined in text.\nResponse: We appreciate your suggestion. We defined it as “RHOGE [19] is an R\npackage that assesses the local genetic correlation between gene expression and\ncomplex traits and to compute the genetic correlations derived from predicted expression\nbetween trait pairs. This approach is extended to a bi-directional regression that provides\nputative causal directions between traits.” (see lines 514-517 in this revision).\nComment: -Link with inflammation is weak – and only supported by one finding from the\nresults, yet it represents several sentences in the discussion.\nResponse: Thank you for your question. We presented a paragraph in Discussion section\nto explain the possible inflammation mechanism related to GDM and T2D (see lines\n367-383 in this revision).\nComment: -We recommend considering Figure 1 as supplemental figure given that it is\nvery detailed, or moving other figures to the supplement. Although seven main figures\nmay be allowed by the journal, it appears that not all of them are as easy to visualize and\nfeel as strongly into the overall story of the paper.\nResponse: Thanks for your suggestion. We adopted your suggestions. We moved Figure\n1 to Supplement (see Supplementary Figure 1 in this version).\nReplies to Reviewer #3:\nReviewer #3 (Remarks to the Author):\nComment: I co-reviewed this manuscript with one of the reviewers who provided the\nlisted reports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers who\nco-review manuscripts.\nResponse: Thank you for your useful suggestions.\n9"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have most of my concerns. However, I still have some questions.\n1. In my comment 2, I questioned that UTMOST is more powerful than FUSION in theory (according to UTMOST paper), however, the authors’ result in fact suggested opposite (11 vs 193 and 319 vs 4,993). The authors listed multiple reasons, which may sound plausible. Given the large discrepancy between the two methods, it seems very reasonable to conduct additional analysis and demonstrated why so many genes were missed by UTMOST. The authors argued that FUSION “might identify a higher number of genes because it focuses on genetic variants within a particular tissue that might have more subtle effects that are not seen across multiple tissues. This could result in a broader selection of genes that are potentially associated with the traits, but these associations might be weaker or more tissue-specific.” If this is true, should it be important to also study these genes rather than only look at the interaction of genes identified by the three methods? I am also confused the interaction can ensure a reduced false positive rate?\n2. In my comment 3, the authors mentioned that “It could reflect that the genetic loci influencing both GDM and T2D may have a pleiotropic effect, influencing coronary artery function or disease in a way that is not immediately apparent in terms of direct disease pathology but could still reflect shared genetic mechanisms”. Would it be possible to demonstrate this evidence from the data? For example, pleiotropy. Since GDM and T2D GWAS both were conducted in FinnGen, could the authors perform bootstrap analysis to evaluate the bias?\n3. In my comment 6. How many IVs were selected for cis-MR analysis for each gene? Has pleiotropic effect been considered, given many eQTLs may be associated with multiple tissues.", "reviewer_2": "The authors' response only responded to some of our concerns. They authors essential information to support the novelty and overlapped biological mechanisms between GDM and T2D. However, we still have a major concern that their results reveal novel biological mechanisms between GDM and T2D.\n\nOther suggestions unaddressed/insufficiently addressed:\nTransition sentences or motivation sentences are still lacking for the result part. The manuscript still could be made more concise for better readability.\n\nFor the KEGG and GO pathway comment, we are concerned that 5 genes are not enough genes to able to generate a significant enrichment in gene enrichment. Please consider moving those insignificant results to the supplementary, or providing references that support the use of KEGG/GO pathways on such a small number of genes.\n\nAdditional minor suggestion: Change the titles of the result parts to the findings or conclusions for this part, instead of \"xxx method results\". It would help people who are not familiar with those methods to follow your logic.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response to the comments of the reviewers\nReplies to Reviewer #1:\nReviewer #1 (Remarks to the Author):\nComment: In my comment 2, I questioned that UTMOST is more powerful than FUSION\nin theory (according to UTMOST paper), however, the authors’ result in fact suggested\nopposite (11 vs 193 and 319 vs 4,993). The authors listed multiple reasons, which may\nsound plausible. Given the large discrepancy between the two methods, it seems very\nreasonable to conduct additional analysis and demonstrated why so many genes were\nmissed by UTMOST. The authors argued that FUSION “might identify a higher number of\ngenes because it focuses on genetic variants within a particular tissue that might have\nmore subtle effects that are not seen across multiple tissues. This could result in a\nbroader selection of genes that are potentially associated with the traits, but these\nassociations might be weaker or more tissue-specific.” If this is true, should it be important\nto also study these genes rather than only look at the interaction of genes identified by the\nthree methods? I am also confused the interaction can ensure a reduced false positive\nrate?\nResponse: Thank you for your valuable comments. We would like to try to answer your\ninsightful comments as below:\n1. For the question that UTMOST is more powerful than FUSION in theory (according to\nUTMOST paper), please see the Figure 5 in the original UTMOST paper\n(https://pmc.ncbi.nlm.nih.gov/articles/PMC6788740/#S2).\nThe original UTMOST paper demonstrated that UTMOST identified more associations for\ncomplex traits compared to the single-tissue test in single tissue, not all tissues\ncompared. Therefore, according to this, the number of identified genes (11 vs 193/47 and\n319 vs 4,993/47) for our study seems plausible.\nUTMOST and FUSION is striking and merits careful interpretation. It is important to note,\nhowever, that UTMOST is designed to leverage cross-tissue expression quantitative trait\nloci (eQTL) information, which may better capture the complex, multi-tissue nature of\nmany diseases. In contrast, FUSION is based on single-tissue models and might\ntherefore be limited in detecting associations that involve regulatory effects across\nmultiple tissues. While FUSION may identify a greater number of genes in certain\ncontexts, the biological relevance of those genes to complex disease mechanisms may\nnot be as comprehensive. We believe that the integrative, multi-tissue approach employed\nby UTMOST offers meaningful advantages for uncovering biologically relevant\nassociations, particularly in the context of complex, systemic disorders.\nAdditionally, two latest papers give the similar number of identified genes (UTMOST vs\nFUSION) compared to ours for complex traits, see below:\nPaper Traits UTMOST FUSION MAGMA\nReference [1] Migraine 19 144 (49 tissues) 89\nReference [2] Sleep apnea 60 86 (Only whole blood) 348\nReference\n[1] Gui J, Yang X, Tan C, Wang L, Meng L, Han Z, Liu J, Jiang L. A cross-tissue\ntranscriptome-wide association study reveals novel susceptibility genes for migraine. J\nHeadache Pain. 2024 Jun 5;25(1):94. doi: 10.1186/s10194-024-01802-6. PMID: 38840241;\nPMCID: PMC11151630\n[2] Meng L, Gui J, Han Z, Yang X, Wang L, Xie L, Jiang L. Novel susceptibility genes for sleep\napnea revealed by a cross-tissue transcriptome-wide association study. Int J Biol Macromol.\n39814282.\n2. For the question of the intersection of genes identified by the three methods, we can\nunderstand this as applying multiple methods to the same dataset to uncover the same\nresults, which makes the findings more robust and persuasive compared to relying on a\nsingle analytical method. In other words, the genes identified through the intersection of\nthree methods are more robust, and their likelihood of being 'true' is higher compared to\n2\nthose identified using only one method.\nNotably, for complex diseases, the occurrence of the disease should not rely solely on a\nsingle tissue. Therefore, from the perspective of disease mechanisms, genes identified\nusing only the FUSION method appear less reliable for complex diseases. In contrast,\nUTMOST considers disease from a multi-tissue level, which is more suitable in this\ncondition. As a result, this study first applied UTMOST to screen for genes associated with\ncomplex diseases, followed by validation using FUSION and MAGMA. The genes\nselected through these approaches are relatively robust.\nBoth Reference [1] and Reference [2] utilize genes identified by all three methods as the\nfinal set of candidate genes, providing support for the validity of our methodology.\nComment: In my comment 3, the authors mentioned that “It could reflect that the genetic\nloci influencing both GDM and T2D may have a pleiotropic effect, influencing coronary\nartery function or disease in a way that is not immediately apparent in terms of direct\ndisease pathology but could still reflect shared genetic mechanisms”. Would it be possible\nto demonstrate this evidence from the data? For example, pleiotropy. Since GDM and\nT2D GWAS both were conducted in FinnGen, could the authors perform bootstrap\nanalysis to evaluate the bias?\nResponse: Thank you for your insightful comment. We appreciate your suggestion\nregarding the potential pleiotropic effects of genetic loci influencing both GDM and T2D,\nand the possibility of demonstrating this through our data. Indeed, pleiotropy is an\ninteresting concept, and we recognize that demonstrating it would add value to our\nfindings. While our current analysis does not directly address pleiotropic effects, we\nacknowledge that additional analyses could provide further evidence.\nRegarding your suggestion to perform a bootstrap analysis to evaluate potential bias, we\nagree that this could be a useful approach, especially given that both GDM and T2D\nGWAS were conducted in FinnGen. We considered incorporating a bootstrap analysis in\niterations of this work to assess the robustness of our results and explore any potential\nbiases. Thank you for your valuable feedback, and we addressed this in our revised\nmanuscript (see lines 124-126, 547-549, Supplementary Figure 2 in this revision).\nComment: In my comment 6. How many IVs were selected for cis-MR analysis for each\ngene? Has pleiotropic effect been considered, given many eQTLs may be associated with\nmultiple tissues.\nResponse: Thanks for your comments. The number of IVs selected for cis-MR analysis\n3\nfor each gene was displayed as below:\nGene Tissues IVs\nCOBLL1 Brain_Cortex 27\nCOBLL1 Muscle_Skeletal 19\nCOBLL1 Nerve_Tibial 70\nCOBLL1 Thyroid 38\nNRBP1 Adipose_Subcutaneous 375\nNRBP1 Adipose_Visceral_Omentum 219\nNRBP1 Artery_Aorta 168\nNRBP1 Artery_Tibial 258\nNRBP1 Breast_Mammary_Tissue 148\nNRBP1 Cells_Cultured_fibroblasts 416\nNRBP1 Cells_EBV-transformed_lymphocytes 202\nNRBP1 Colon_Transverse 257\nNRBP1 Esophagus_Mucosa 275\nNRBP1 Esophagus_Muscularis 161\nNRBP1 Heart_Atrial_Appendage 90\nNRBP1 Nerve_Tibial 206\nNRBP1 Skin_Sun_Exposed_Lower_leg 430\nNRBP1 Small_Intestine_Terminal_Ileum 105\nNRBP1 Thyroid 152\nNRBP1 Whole_Blood 337\nIFT172 Spleen 137\nBefore MR analysis, we removed the pleiotropic eQTLs as many eQTLs may be\nassociated with multiple tissues (see lines 600-601 in this revision).\nReplies to Reviewer #2:\nReviewer #2 (Remarks to the Author):\nComment: The authors' response only responded to some of our concerns. They authors\nessential information to support the novelty and overlapped biological mechanisms\nbetween GDM and T2D. However, we still have a major concern that their results reveal\nnovel biological mechanisms between GDM and T2D.\nResponse: We sincerely thank the reviewer for their thoughtful and constructive\ncomments. We appreciate the recognition that our response addressed some of the\nconcerns raised. However, we understand that there are still important aspects to clarify,\nparticularly regarding the novelty and overlapping biological mechanisms between GDM\nand T2D.\nIn response to the reviewer’s concerns, we would like to emphasize that the core novelty\nof our study lies in the identification of novel genetic signals shared between GDM and\nT2D. By employing a multi-tissue transcriptome-wide association study (TWAS) \nframework, we were able to uncover four novel genes that have not been previously\nlinked to either GDM or T2D. These genes represent a significant advancement in\nunderstanding the shared biological pathways of these two diseases, particularly in areas\nsuch as insulin secretion, ovarian steroidogenesis, and metabolic regulation. Additionally,\nthe use of integrative bioinformatics approaches allowed us to demonstrate the potential\nbiological mechanisms underlying the genetic overlap between GDM and T2D.\nWe believe these findings contribute important insights into the genetic architecture of\nboth conditions, highlighting pathways that could offer new therapeutic targets. We\ncarefully revised our manuscript to reveal novel biological mechanisms between GDM and\nT2D through the full text. Once again, we thank the reviewer for their invaluable feedback\n(see this revision).\nComment: Other suggestions unaddressed/insufficiently addressed:\nTransition sentences or motivation sentences are still lacking for the result part. The\nmanuscript still could be made more concise for better readability.\nResponse: We would like to thank the reviewer for their constructive feedback. We\nappreciate the observation regarding the transition and motivation sentences in the results\nsection. We agree that clear transitions between sections are essential for improving the\nflow of the manuscript, and we revised the manuscript to include more cohesive and\nmotivating sentences to guide the reader through our results more effectively.\nAdditionally, we acknowledge the comment about the manuscript’s length and readability.\nWe make efforts to streamline the content, ensuring that the presentation of our findings is\nconcise without sacrificing important details. Our goal is to enhance the clarity and overall\nreadability of the manuscript, and we implemented these changes accordingly.\nWe greatly appreciate the reviewer’s suggestions and believe these revisions significantly\nimprove the manuscript (see this revision).\nComment: For the KEGG and GO pathway comment, we are concerned that 5 genes are\nnot enough genes to able to generate a significant enrichment in gene enrichment. Please\nconsider moving those insignificant results to the supplementary, or providing references\nthat support the use of KEGG/GO pathways on such a small number of genes.\nResponse: Thank you for your insightful comments. We moved the KEGG and GO\npathway on five GDM genes in Supplementary Figure 3 for your concerns. Additionally,\nKEGG and GO enrichment using 198 GDM genes (identified by UTMOST or FUSION) are\npresented in Figure 6A to generate a significant enrichment in gene enrichment (see\nSupplementary Figure 3 and Figure 6A in this revision).\nComment: Additional minor suggestion: Change the titles of the result parts to the\nfindings or conclusions for this part, instead of \"xxx method results\". It would help people\nwho are not familiar with those methods to follow your logic.\nResponse: Thanks for your suggestions. We adopted your suggestions (see result parts\nin this version).\nReplies to Reviewer #3:\nReviewer #3 (Remarks to the Author):\nComment: I co-reviewed this manuscript with one of the reviewers who provided the\nlisted reports. This is part of the Nature Communications initiative to facilitate training in\npeer review and to provide appropriate recognition for Early Career Researchers who\nco-review manuscripts.\nResponse: Thank you for your useful suggestions.\n6"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors addressed my concerns. I don't have further questions."}, "author_response": ""}]

Decision letter