Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits

Domain: biology
Published: 2026-03-13
Editorial outcome: major_revision

Abstract
Type 1 diabetes, particularly with childhood onset, is associated with altered neurocognitive traits, yet the underlying biological mechanisms are unclear. Here, we integrate genome-wide association results with single-cell epigenomic profiles and show that type 1 diabetes heritability is enriched in accessible chromatin of human brain-resident cells, most notably microglia, across neurodevelopment into adulthood. Bonferroni-corrected cross-trait genetic correlation analyses reveal negative correlations of type 1 diabetes with intelligence, executive function, and bipolar disorder, and a positive correlation with myasthenia gravis. Conjunctional false discovery rate analysis identifies pleiotropic loci jointly influencing type 1 diabetes and neurocognitive traits, including the 17q21.31 neurogenomic hub. Mendelian randomization further demonstrates protective effects of educational attainment, intelligence, Alzheimer’s disease, and bipolar disorder on type 1 diabetes risk, whereas liability to multiple sclerosis and myasthenia gravis increases type 1 diabetes risk. In the reverse direction, liability to type 1 diabetes is associated with increased risk of myasthenia gravis. We identify several gene expression regulatory variants in brain and immune cells that jointly influence type 1 diabetes and neurocognitive traits, some of which show concordant differential expression in disease-affected versus control tissue. Together, these findings highlight pleiotropic genetic and neuroimmune mechanisms that link type 1 diabetes with cognition and neuropsychiatric disease risk.

Introduction
Type 1 diabetes (T1D) is characterized by T cell-mediated destruction of insulin-producing pancreatic beta cells, necessitating lifelong dependence on insulin therapy to control glycemia. Despite advances in management, people with T1D lose more than a decade of life expectancy [1,2] and over two decades of healthy life [3] compared with the general population.

Cognitive deficits—which are antecedents and characteristic symptoms of neuropsychiatric disorders—are consistently more prevalent in people with T1D than in the general population [4]. Childhood-onset T1D is particularly associated with cognitive deficits, including reduced working memory, executive function, and performance IQ [5-8]. Children with T1D often underachieve educational goals compared to their peers [9-11], with even poorer outcomes in those with co-occurring psychiatric disorders [9]. Neuroimaging studies support these findings, showing altered neuroanatomical features such as reduced gray and white matter volumes in children with T1D [12-14]. Adults with T1D also exhibit accelerated cognitive decline [15,16] and smaller total brain volumes [16]. Epidemiological studies further demonstrate higher rates of psychiatric disorders in people with T1D compared to the general population [11,17-19].

Because hyperglycemia is the defining pathology of T1D, research has emphasized its impact on brain development and function, identifying correlations between poor glycemic control and cognitive or psychiatric comorbidities [8,20-23]. This view has reinforced the idea that neurocognitive deficits in T1D arise primarily as complications of dysglycemia [8,14,24-27].

T1D [29] and cognitive or neuropsychiatric disorders [30] are substantially heritable, with estimates reaching ~50% and ~80%, respectively. Despite this, whether shared genetic mechanisms contribute to their co-occurrence has not been systematically investigated. Observational studies are prone to confounding by sociodemographic factors and reverse causality. For example, impaired executive function may reduce adherence to glycemic control and exacerbate disease outcomes. Moreover, because T1D is frequently diagnosed during childhood or adolescence, which is a critical window for neurodevelopment [24-27,30]. Thus, clarifying whether shared genetic mechanisms jointly influence T1D and neurodevelopmental outcomes is essential.

In this study, we systematically investigate the shared genetic and cellular architecture between T1D and neurocognitive traits. By integrating genome-wide association data with single-cell epigenomic annotations, we demonstrate that T1D heritability is significantly enriched in accessible chromatin regions of brain-resident cells, particularly microglia. We further identify pleiotropic loci, genes, and genetically regulated expression patterns that converge on neuroimmune pathways, pointing to a shared genetic basis linking T1D with neurocognitive outcomes. Notably, these include the 17q21.31 locus, previously implicated in neurodevelopment and psychiatric disorders, which emerges as a key genomic hub of T1D–neurocognitive convergence.

Results
The heritability of T1D is enriched in brain-resident cells across development.
Genetic variants associated with complex traits and diseases often localize to regulatory regions where they act in a cell type–specific manner. To test whether T1D risk variants are enriched in regulatory landscapes of brain-resident cells, we applied stratified linkage disequilibrium score regression (S-LDSC) [31] to integrate fine-mapped T1D GWAS signals (18,942 T1D cases, 520,580 control individuals) [32] with single-nucleus ATAC-sequencing (snATAC-seq) profiles of human cortex across developmental stages from prenatal stage to adulthood [33]. Enrichment was defined as the proportion of T1D SNP-heritability explained by a given chromatin annotation divided by the proportion of SNPs in that annotation; values above 1 indicate more heritability than expected by chance [31,34], with significance assessed using one-sided S-LDSC P-values. This analysis revealed temporally dynamic and cell type–specific enrichment of T1D heritability, with the strongest and most consistent signal enrichment in microglia (prenatal enrichment = 5.04, z = 1.69, one-sided P = 0.045; adult enrichment = 10.80, z = 2.11, P = 0.017; Figure 1a). Astrocytes also showed nominal enrichment in the prenatal stage, whereas other glial and neuronal lineages showed little signal.

To assess specificity, we performed the same S-LDSC analysis using GWAS for height, a highly heritable polygenic trait without known immune or neural involvement [35]. There was nominal enrichment in only vascular cells during early prenatal (z = 1.74, P = 0.041) and late postnatal (z = 1.81, P = 0.035) stages and no enrichment in any other brain-resident cell type across all developmental stages (Supplementary Figure 1a-e).

Single-nucleus ATAC-seq annotations used for S-LDSC aggregate peaks across many nuclei within each cell-type cluster and may under-detect transient, rare, or highly context-specific chromatin accessibility states present in only a small subset of cells. To complement these cluster-level analyses, we next applied SCAVENGE (Single-Cell Analysis of Variant Enrichment through Network Propagation of Genomic Annotations), a network-propagation framework that infers trait-relevance scores (TRS) at single-cell resolution [36]. SCAVENGE confirmed persistent and robust enrichment of T1D heritability in microglia across all developmental stages (median TRS > 3.0–6.5) and additionally revealed highly significant, context-specific enrichment in inhibitory neurons (median TRS > 3) during the early postnatal and adult periods (Figure 1b–e). Smaller but notable stage-specific enrichments were also observed in excitatory neurons (early postnatal) and oligodendrocyte lineage cells (adulthood). These enrichments do not imply that brain-resident cells replace the established pathogenic role of peripheral immune cells in T1D [32]; instead, they suggest that T1D-associated variants also act through regulatory programs within the brain, consistent with pleiotropic genetic architecture.

To provide proper context, we performed parallel SCAVENGE analyses for Alzheimer’s disease (AD) and bipolar disorder (BPD) GWAS variants, two traits with known brain-cell architectures [37], as positive controls, and height as a negative control. For AD, enrichment was strongest and most specific in microglia as expected, with secondary signals in vascular cells and astrocytes (Figure 1f). For BPD, enrichment localized primarily to neurons in adulthood and to microglia at earlier developmental stages (Figure 1g), reflecting established disease biology. In contrast, height showed strong enrichment in vascular cells (TRS > 3.0) but no significant signal in microglia, neurons, or other brain cell types (Supplementary Figure 1b).

Overall, these complementary analyses indicate that, in addition to the well-established enrichment in peripheral immune cells [32], T1D genetic liability also impacts regulatory programs in brain-resident cell types, with the strongest and most consistent signal enrichment observed in microglia, alongside developmental stage–specific signals in other neural and glial populations that may be incompletely captured by current single-cell genomic annotations.

The genetic architecture of T1D overlaps with neurocognitive traits.
Given the enrichment of T1D heritability in brain-resident cells, we used cross-trait LDSC to assess genome-wide genetic correlation between T1D and each of 21 phenotypes with measurable neurocognitive components (Supplementary Table 1). These traits span four neurocognitive-related domains, including (i) core cognitive and learning traits (e.g., intelligence, executive function, educational attainment), (ii) psychiatric disorders (e.g., bipolar disorder and schizophrenia), (iii) neurological conditions (e.g., Alzheimer’s disease, Parkinson’s disease, migraine, sleep phenotypes), and (iv) neuroautoimmune disorders (e.g., multiple sclerosis, myasthenia gravis). For all T1D-neurocognitive trait pairs, the bivariate LDSC intercepts were close to 1.0, indicating minimal residual covariance and that sample overlap is unlikely to meaningfully bias the genetic correlation estimates.

T1D showed Bonferroni-corrected significant negative genetic correlations with intelligence (rg = –0.09, SE = 0.02, P = 7.8×10⁻⁵), executive function (rg = –0.08, SE = 0.03, P = 0.0013), and bipolar disorder (rg = –0.11, SE = 0.03, P = 6.4×10⁻⁵), and a positive correlation with myasthenia gravis (rg = 0.37, SE = 0.08, P = 3×10⁻⁶). Nominally significant (P < 0.05 but not meeting Bonferroni threshold P < 0.00238) included negative correlations with educational attainment and autism spectrum disorder and positive correlations with migraine, insomnia, and multiple sclerosis (Figure 2a; Supplementary Data 1).

To further dissect the genetic overlap between T1D and neurocognitive traits, we applied MiXeR, which estimates polygenicity and quantifies the number of causal variants shared between traits. Unlike LDSC, which summarizes genome-wide correlation as an average effect direction, MiXeR captures pleiotropy irrespective of direction. MiXeR revealed extensive variant sharing between T1D and neurocognitive traits (Figure 2b). MiXeR showed that T1D is moderately polygenic (~22,000 variants) and shares hundreds of causal variants with most of the examined neurocognitive traits. The strongest overlap was with myasthenia gravis, consistent with their shared autoimmune basis, but notable overlap was also observed with intelligence and educational attainment. MiXeR analyses revealed extensive sharing of causal variants between T1D and neurocognitive traits irrespective of effect direction. In several cases, substantial polygenic overlap co-occurred with weak or inverse genome-wide genetic correlations, indicating that shared variants may exert heterogeneous or opposing effects that are averaged out in LDSC-based correlation estimates.

Taken together, the LDSC and MiXeR analyses provide complementary perspectives, whereby the LDSC highlights the direction of genome-wide associations, while MiXeR underscores the pervasiveness of shared causal variants irrespective of direction. These support a model in which common brain–immune pathways exert pleiotropic influences across autoimmune, cognitive, and psychiatric outcomes.

Conjunctional false discovery analysis identifies loci jointly influencing T1D and neurocognitive traits.
To resolve locus-level overlap, we applied conjunctional false discovery rate (conjFDR) analysis, which leverages cross-trait enrichment to identify pleiotropic loci beyond those identified using conventional genome-wide significance thresholds [38,39]. Across trait pairs, the number of loci jointly associated with T1D and neurocognitive traits ranged from fewer than 10 to more than 100, with particularly extensive overlap for neuroautoimmune disorders such as multiple sclerosis (109 loci) and myasthenia gravis (69 loci) (Figure 3a-h; Supplementary Data 2). Substantial sharing was also evident for cognitive traits, including intelligence (63 loci), educational attainment (45 loci), and executive function (21 loci), and for psychiatric and neurodegenerative conditions such as Alzheimer’s disease (29 loci), bipolar disorder (13 loci), and Parkinson’s disease (9 loci). Importantly, conjFDR pleiotropic locus counts did not show a robust or consistent relationship with GWAS power across LDSC-derived metrics (mean χ² and SNP-heritability Z-score; Supplementary Figure 2; Supplementary Table 2). Traits spanning a wide range of effective GWAS power exhibited both high and low degrees of pleiotropic overlap with T1D, supporting the interpretation that conjFDR locus counts primarily reflect trait-specific shared genetic architecture rather than GWAS power.

Among the shared loci, the 17q21.31 region emerged as a pleiotropic hotspot, jointly associated with T1D and multiple neurocognitive traits. This structurally polymorphic region contains MAPT, KANSL1, CRHR1, and LRRC37A, genes implicated in neurodevelopment, neurodegeneration, and immune regulation, highlighting 17q21.31 as a candidate neuroimmune hub. Notably, this locus has been associated previously with cognitive phenotypes and various neuropsychiatric or neurodevelopmental disorders [40-43].

To assess biological context, we examined tissue-specific enrichment of pleiotropic loci. We focused on three representative pairs spanning cognitive, neuroautoimmune, and psychiatric axes using educational attainment, multiple sclerosis, and bipolar disorder, respectively (Figure 4). These neurocognitive traits also showed substantial LDSC genetic correlation and or MiXeR overlap with T1D. Shared loci between T1D and educational attainment were enriched in brain regions including the cerebellum, frontal cortex, and anterior cingulate cortex. Loci jointly influencing T1D and multiple sclerosis were enriched in immune-related tissues such as EBV-transformed lymphocytes, spleen, whole blood, and small intestine. Shared loci between T1D and bipolar disorder showed broad enrichment across all examined brain regions. Notably, enrichment in the pituitary was unique to bipolar disorder and stronger than for other traits, suggesting a potential neuroendocrine component to the shared genetic architecture.

These results indicate that pleiotropic loci linking T1D with neurocognitive traits are distributed across biologically distinct and coherent regulatory contexts. Loci shared between T1D and cognitive or psychiatric traits show preferential enrichment in brain tissues, whereas loci shared between T1D and neuroautoimmune traits are enriched in immune tissues, thereby underscoring a dual brain–immune architecture of T1D pleiotropy.

Bidirectional Mendelian randomization identifies directional and reciprocal associations between T1D and neurocognitive traits
To investigate potential causal relationships (i.e., vertical pleiotropy) between T1D and neurocognitive traits, we conducted bidirectional two-sample Mendelian randomization (MR) analyses. The inverse-variance weighted (IVW) method was used as the primary estimator, with weighted median and MR-Egger models applied as sensitivity analyses to assess robustness and residual pleiotropy. Bonferroni correction was applied across all IVW tests.

When modeling genetic liability to T1D as the exposure (Figure 5a; Supplementary Data 3), we observed a Bonferroni-significant association with increased risk of myasthenia gravis (OR = 1.20, 95% CI: 1.12–1.28, P = 7.0 × 10⁻⁸). Several additional associations reached nominal significance (P < 0.05) but did not survive Bonferroni correction (P ≥ 2.38 × 10⁻3), including positive associations with migraine and ischemic stroke, and inverse associations with schizophrenia, bipolar disorder, Parkinson’s disease, and amyotrophic lateral sclerosis.

In the reverse direction, where genetic liability to neurocognitive traits served as the exposure (Figure 5b; Supplementary Data 3), higher liability to multiple sclerosis (OR = 1.18, 95% CI: 1.09–1.27, P = 2.3 × 10⁻⁵) and myasthenia gravis (OR = 1.22, 95% CI: 1.12–1.33, P = 5.6 × 10⁻⁶) was associated with increased T1D risk. In contrast, higher genetic liability to educational attainment (OR = 0.67, 95% CI: 0.53–0.85, P = 8.2 × 10⁻⁴), intelligence (OR = 0.81, 95% CI: 0.71–0.93, P = 2.0 × 10⁻³), Alzheimer’s disease (OR = 0.80, 95% CI: 0.73–0.88, P = 4.2 × 10⁻⁶), and bipolar disorder (OR = 0.82, 95% CI: 0.76–0.89, P = 1.2 × 10⁻⁶) was associated with reduced T1D risk. Genetic liability to obsessive-compulsive disorder, short sleep duration, attention-deficit/hyperactivity disorder, and Parkinson’s disease showed nominal associations with T1D risk but did not remain significant after Bonferroni correction.

Collectively, these analyses reveal both directional and reciprocal genetic associations between T1D and neurocognitive traits, with particularly strong evidence supporting a protective effect of cognitive traits on T1D risk.

Shared gene expression regulatory variation in brain and immune cells links T1D and neurocognitive traits
We integrated GWAS signals for T1D and neurocognitive traits with expression quantitative trait loci (eQTLs) from bulk brain tissues, single-nucleus brain cell types, and peripheral immune populations using SMR/HEIDI (summary-data Mendelian randomization with heterogeneity in dependent instruments) to identify gene-expression regulatory mechanisms mediating shared genetic liability between T1D and neurocognitive traits. These analyses revealed pleiotropic eQTLs spanning chromosomes 1, 5, 6, 12, 16, and 17 in brain and peripheral immune compartments that jointly influence T1D risk and neurocognitive traits (Figure 6; Supplementary Data 4).

Higher AP4B1 expression on the 1q25.3 locus in monocytes was associated with reduced risk of both T1D and myasthenia gravis, consistent with its established roles in vesicular trafficking and immune regulation. On 5q11.2, increased ANKRD55 expression in CD4⁺ T cells conferred elevated risk of both T1D and multiple sclerosis, aligning with previous studies implicating this locus in autoimmune susceptibility. A similar pattern was observed at 6p22.2, where increased CENPW expression in stimulated CD8⁺ T cells associated with lower T1D risk and reduced cognitive performance (INT, EDU, EXF).

A richer pleiotropic architecture was observed at 12q13.2, involving RPS26, ERBB3, and SUOX, which exhibited coordinated but directionally distinct effects. Increased RPS26 expression in brain regions (substantia nigra, hypothalamus) and dendritic cells was associated with higher T1D risk and higher EDU/INT/EXF. By contrast, increased ERBB3 and SUOX expression in T-cell subsets and in the nucleus accumbens for SUOX, was associated with reduced T1D risk and lower cognitive performance traits. A second regulatory triad was identified at 16p11.2, where increased NPIPB7 and SULT1EA1 expression conferred elevated risk of both T1D and cognitive traits, whereas increased TUFM expression showed the opposite pattern, jointly reducing risk of T1D and EDU/INT/EXF. These directionally coherent effects highlight locus-specific bidirectional pleiotropy in a region known to regulate immunometabolic and neurodevelopmental processes.

The most extensive cluster of pleiotropic signals was observed at 17q21.31, a well-established neurodevelopmental and neuropsychiatric locus [40-43]. Multiple transcripts, including CRHR1, KANSL1, ARIL17A/B, PLEKKHM1, LRRC37A2, MAPT-AS1, and others, displayed shared regulatory associations across microglia, inhibitory and excitatory neurons, astrocytes, endothelia cells, and peripheral immune subsets. These transcripts exhibited highly consistent cross-trait patterns in different brain regions and cell types, influencing T1D, EDU/INT/EXF, bipolar disorder, PD, autism spectrum disorder, and migraine. Together, these results reinforce 17q21.31 as a major neuroimmune regulatory hub linking autoimmunity, neuropsychiatry, and cognition.

To explore whether genetically implicated regulatory effects from the SMR/HEIDI analyses correspond to transcriptional changes in human disease-relevant context, we examined single-nucleus RNA-seq and proteomic profiles from the dorsolateral prefrontal cortex of patients with Parkinson’s disease (PD) and control individuals. This analysis was motivated by three considerations: (i) several prior MR studies [44,45] and ours (Figure 6; Supplementary Data 4) revealed asymmetric bidirectional associations between PD and T1D; (ii) our conjFDR identified 17q21.31 as jointly associated with T1D and multiple neurocognitive traits, with the most significant association being with PD at this locus, and our SMR/HEIDI analyses further indicated shared regulatory architecture at this locus between T1D and PD along with other neurocognitive traits; and (iii) we had available matched snRNA-seq and proteomic datasets from PD cortex [46], enabling direct in vivo evaluation of genetically inferred effects.

Consistent with the SMR/HEIDI predictions, multiple transcripts at 17q21.31, together with altered protein abundance of RPS26 at 12q13.2, showed differential expression between PD and control donors in directions concordant with their genetically inferred effects on T1D liability (Supplementary Figure 3). These findings provide orthogonal evidence that the regulatory programs linking T1D to neurocognitive traits are perturbed in disease-affected human cortex.

Overall, these results demonstrate that pleiotropic gene-expression regulation across 1q25.3, 5q11.2, 6p22.2, 12q13.2, 16p11.2, and 17q21.31 constitutes a mechanistic axis through which shared genetic liability to T1D and neurocognitive traits is mediated across brain and immune cell types.

Methods
Study datasets and quality control
We used genome-wide association studies (GWAS) summary datasets and expression quantitative trait loci (eQTL) resources derived from individuals of predominantly European ancestry to minimize population heterogeneity and align with European linkage disequilibrium (LD) reference panels. The primary GWAS for T1D comprised 520,580 individuals (18,942 cases). Additional GWAS included cognitive traits (intelligence, educational attainment, executive function), psychiatric disorders (autism spectrum disorder, bipolar disorder, schizophrenia, obsessive-compulsive disorder, ADHD, major depression, PTSD, and neuroticism), neurological and neurodegenerative diseases (Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, multiple sclerosis, myasthenia gravis, migraine, ischemic stroke), and sleep-related phenotypes (insomnia and sleep duration).

For regulatory analyses, we incorporated multiple eQTL datasets: bulk brain tissue from GTEx v8 (N = 838), single-cell brain eQTLs spanning eight major cell types (N =192), single-cell blood eQTLs (N = 982), and immune-cell eQTLs across 18 purified cell populations (N = 200). These datasets enabled assessment of genetic regulation across both central nervous system and immune compartments.

Across all analyses, we applied stringent quality control procedures. SNPs were restricted to autosomal variants with minor allele frequency (MAF) >1% and imputation INFO >0.9. Variants with ambiguous strands were removed, and the extended major histocompatibility complex region (chr6:25–34 Mb) was excluded from analyses requiring LD independence due to complex long-range LD. Allele harmonization was performed across all datasets. LD estimation was based on the European reference panel from the 1000 Genomes Project Phase 3, ensuring ancestry-matched analyses.

This research complies with all the ethical regulations related to the secondary analysis of data collected by various cohorts, each of which obtained ethical approval and informed consent. All datasets used for analyses are summarized in Supplementary Table 1.

Stratified LD Score Regression (S-LDSC)
We applied stratified LD score regression (S-LDSC) [31] to estimate whether T1D SNP-heritability was enriched in accessible chromatin regions of specific cortical cell types across human neurodevelopment. Single-nucleus ATAC-seq (snATAC-seq) peak annotations were obtained from human cortex spanning prenatal, early postnatal (0–4 years), late postnatal (4–20 years), and adulthood (>20 years) developmental stages [33]. Peaks were converted into binary annotation files for each cell type and developmental stage.

S-LDSC analyses were performed using S-LDSC v1.0.1 with the baseline-LD model v2.2 and European reference LD scores from the 1000 Genomes Project Phase 3. Heritability enrichment was quantified as the proportion of SNP-heritability explained by each annotation divided by the proportion of SNPs overlapping that annotation. Statistical uncertainty was assessed using standard errors provided by the S-LDSC framework, and nominal p-values are reported to summarize evidence for enrichment. Results are interpreted as patterns of heritability enrichment across annotations.

SCAVENGE analysis
SCAVENGE (Single Cell Analysis of Variant Enrichment through Network propagation of Genomic data) is a computational algorithm that uses network propagation to map causal variants to their relevant cellular context at single-cell resolution [36]. SCAVENGE mitigates sparsity in single-cell epigenomic data by leveraging network propagation: a small subset of “seed” cells enriched for trait-relevant variants are identified, and trait information is propagated across a nearest-neighbor cell graph to generate TRS for all cells. Following published procedures [36,37], we implemented SCAVENGE analysis to integrate fine-mapped genetic variants for T1D (n = 520,580 samples) [32] with single-nucleus ATAC-seq (snATAC-seq) profiles of human cortex across distinct developmental stages—prenatal, early postnatal (0–4 years), late postnatal (4–20 years), and adulthood (>20 years) [33]. This analysis yielded trait relevance scores (TRS) that quantify the contribution of each cell type and stage to T1D genetic predisposition.

To initiate propagation, fine-mapped T1D variants were intersected with accessible chromatin peaks to identify seed cells. Trait information from seed cells was propagated across a cell-to-cell similarity network, generating TRS for all nuclei. Cells with TRS > 2 were classified as enriched, consistent with the SCAVENGE framework [36]. To validate enrichment at the population level, TRS were aggregated across annotated cell types and developmental stages, and statistical significance was assessed using hypergeometric testing with false discovery rate (FDR) correction [37].

For benchmarking and cross-trait contextualization, the same workflow was applied using genome-wide significant loci from Alzheimer’s disease [58], bipolar disorder [59] and height [60] GWAS, enabling direct comparisons of developmental and cell-type-specific enrichment patterns across disorders. All SCAVENGE analyses were performed in R using the SCAVENGE software (v.1.0.2).

Genome-wide genetic correlation analysis by cross-trait LDSC
We applied cross-trait linkage disequilibrium score regression (LDSC) to estimate genome-wide genetic correlations [61] between T1D and each neurocognitive trait. Narrow-sense SNP-heritability and pairwise genetic correlations were computed from GWAS summary statistics for T1D and each neurocognitive phenotype. As previously described [61], LDSC estimates genetic correlation as the proportion of shared genetic variance between traits attributable to common SNP effects across the genome. Analyses were performed using LDSC v1.0.1 with pre-computed LD scores from European populations of the 1000 Genomes Project Phase 3. SNPs with minor allele frequency (MAF) > 0.05 were retained, while variants in the major histocompatibility complex (MHC, chr6:25–34 Mb) region were excluded due to extensive long-range LD.

We corrected for multiple testing using the Bonferroni method, applying a significance threshold of P < 0.00238 (0.05/21 trait pairs). We also applied the Benjamini–Hochberg false-discovery rate (BH–FDR) procedure across all tests, considering q < 0.05 statistically significant, while associations with P < 0.05 but q ≥ 0.05 are reported as suggestive. Both FDR-adjusted q-values and Bonferroni significance indicators are reported in Supplementary Data 1.

Quantification of polygenic overlap by MiXeR analysis
We applied MiXeR [62] to quantify polygenic overlap between T1D and neurocognitive traits, providing a more nuanced measure of shared genetic architecture beyond global genetic correlation. MiXeR implements Gaussian causal mixture models to estimate both the number of causal variants unique to each trait and the number of variants with pleiotropic effects.

Univariate modeling. We first performed univariate MiXeR analysis (MiXeR v1.3) for T1D and each neurocognitive trait to estimate trait polygenicity (the number of SNPs accounting for 90% of SNP-heritability, h²SNP) and discoverability (the average effect size of trait-influencing variants). Variants with negligible effect sizes were excluded to ensure robust estimates.

Bivariate modeling. We then fit bivariate models to each T1D–neurocognitive trait pair to estimate the number of shared and unique causal variants. Model fit and convergence were evaluated using likelihood profiles and residual inspection. Dice coefficients were computed to summarize polygenic overlap on a 0–100% scale [63] and estimates of genome-wide genetic correlation were obtained using cross-trait LDSC for comparison.

Visualization. Results were summarized in Venn diagrams showing the number of unique and shared variants per trait pair. Conditional quantile–quantile (Q–Q) plots were generated to assess enrichment of SNP associations across traits[66]. Q–Q plots display the empirical cumulative distribution of P values in one phenotype, stratified by significance thresholds in the second phenotype (e.g., P ≤ 0.1, P ≤ 0.01, P ≤ 0.001), with leftward deflection from the null line indicating cross-trait enrichment.

Quality control. Analyses were restricted to autosomal SNPs with minor allele frequency (MAF) > 0.01 and imputation INFO score > 0.9, after removing strand‐ambiguous and multi-allelic variants. The MHC region was excluded due to long-range linkage disequilibrium (LD). To ensure consistent LD structure across datasets, we used the European subset of the 1000 Genomes Project Phase 3 reference panel (n = 503) for LD estimation. All summary statistics were harmonized to the same genome build (GRCh37/hg19) with allele alignment performed against the reference panel.

The MiXeR analyses were conducted in R (v4.x) using the MiXeR v1.3 package with default settings unless otherwise specified.

Shared locus and variant discovery using conjunctional FDR (conjFDR)
We applied conjunctional false discovery rate (conjFDR) analysis [38,39] to identify shared genetic loci influencing T1D and each neurocognitive phenotype. ConjFDR leverages cross-trait GWAS summary statistics to increase power for detecting pleiotropic variants while accounting for polygenic overlap.

As a first step, conditional quantile–quantile (Q–Q) plots were generated to evaluate enrichment of T1D-associated variants as a function of their significance in each neurocognitive phenotype (and vice versa). Evidence of leftward deflection in conditional Q–Q plots indicates polygenic overlap between the traits. Conditional FDR (condFDR) values were then computed for each SNP, representing the FDR of T1D associations given their significance in the neurocognitive phenotype (and vice versa). The conjFDR statistic was defined as the maximum of the two condFDR values per SNP, providing a conservative estimate of the probability that a variant is null for either or both traits. SNPs with conjFDR < 0.05 were considered jointly associated with T1D and the neurocognitive phenotype. Lead variants were annotated as the SNP with the lowest conjFDR value within each LD-independent locus, defined as r² < 0.1 within a ±250 kb window. LD was estimated using European reference data from the 1000 Genomes Project Phase 3. To minimize spurious associations, we used GWAS summary statistics pre-corrected for relevant covariates and population stratification. Analyses were restricted to autosomal SNPs, and only a single representative signal was retained in the extended major histocompatibility complex (MHC) region (chr6:26–34 Mb, hg19) due to complex LD.

All conjFDR analyses were conducted using the pleioFDR R package [64,65] following established workflows.

Tissue enrichment analysis of shared loci between T1D and neurocognitive traits
We performed tissue enrichment analysis of loci jointly associated with T1D and neurocognitive traits as identified by conjFDR. SNPs with conjFDR < 0.05 were submitted to the Functional Mapping and Annotation (FUMA) platform (v1.5.2) [66] to define LD-independent loci. Independent significant SNPs were identified using the European 1000 Genomes Project Phase 3 reference panel [67], applying thresholds of r² < 0.6 for clumping and a maximum merging distance of ≤250 kb. Lead SNPs were defined as variants with r² < 0.1 within each locus, with the lead SNP taken as the variant with the lowest conjFDR value.

Novelty of lead SNPs was assessed by examining whether they achieved genome-wide significance (P < 5 × 10⁻⁸) in the original GWAS for T1D or the neurocognitive trait. Genes were assigned to loci by proximity, defined as the gene with the closest transcription start site to the lead SNP. Gene-level analyses were conducted using MAGMA within the FUMA framework, testing for tissue-specific expression and gene-set enrichment based on GTEx v8 transcriptomic data. Gene expression was quantified in transcripts per million (TPM). Multiple-testing correction was applied using FDR < 0.05. To further prioritize functional candidate genes, we integrated locus-to-gene assignments from Open Targets Genetics [68], focusing on lead SNPs outside of the extended MHC region due to its complex LD.

Mendelian randomization analyses
We performed bidirectional two-sample Mendelian randomization (MR) to estimate causal relationships between T1D and neurocognitive phenotypes. MR uses genetic variants strongly associated with an exposure as instrumental variables (IVs) to test for potential causal effects of the exposure on the outcome.

Instrument selection and harmonization. SNPs associated with each exposure at genome-wide significance (P < 5 × 10⁻⁸; F statistic ≥ 10) were selected as IVs and pruned using LD clumping at r² < 0.001. Exposure and outcome summary statistics were harmonized by aligning alleles to the forward strand; ambiguous SNPs with non-inferable strands were excluded.

Primary MR analyses. SNP-specific causal effects were estimated using the Wald ratio and meta-analyzed across variants with the inverse-variance weighted (IVW) method under a multiplicative random-effects model [69]. IVW was used as the primary MR test, under the assumption that all IVs are valid and horizontal pleiotropy is absent.

Sensitivity analyses. To evaluate robustness to horizontal pleiotropy, we performed MR using the weighted median (WM) and MR-Egger regression methods [69-72]. The weighted median estimator provides consistent causal effect estimates if at least 50% of the weight comes from valid IVs, even if the remaining IVs are invalid. MR-Egger allows for directional pleiotropy by including an intercept term in the regression of outcome coefficients on exposure coefficients; the intercept test was used to evaluate the presence of unbalanced pleiotropy [71-73]. Under the null of no pleiotropy, the MR-Egger intercept approaches zero, and MR-Egger converges to IVW [69,74].

Multiple testing correction. We corrected for multiple testing using the Bonferroni method, applying a significance threshold of P < 0.00238 (0.05/21 trait pairs). We also applied the Benjamini–Hochberg false-discovery rate (BH–FDR) procedure across all tests, considering q < 0.05 statistically significant, while associations with P < 0.05 but q ≥ 0.05 are reported as suggestive. Both FDR-adjusted q-values and Bonferroni significance indicators are reported in Supplementary Data 3. Weighted median and MR-Egger estimators were treated as sensitivity analyses and were not subjected to additional multiplicity correction.

Reporting. For each exposure–outcome pair, we report the IVW causal effect estimate as an odds ratio (OR) with 95% confidence intervals (CI), together with the raw p-value, BH–FDR q-value, and Bonferroni significance flag, representing the change in outcome per one standard deviation (SD) increase in the exposure.

All analyses were conducted in R (v4.3.2; R Foundation for Statistical Computing, Vienna, Austria) using the TwoSampleMR package. Methods and reporting adhered to the STROBE-MR best-practice guidelines [75].

Summary-based Mendelian randomization and heterogeneity in dependent instruments (SMR/HEIDI) analysis
SMR extends the concept of MR, allowing testing of the hypothesis that genetically determined expression levels of a gene are associated with a phenotype. A key assumption of this approach is that the same underlying causal variant determines both gene expression and the disease/trait. SMR cannot distinguish between vertical pleiotropy—the situation in which variant influences phenotype via gene expression, and horizontal pleiotropy, the situation in which variant influences phenotype and gene expression but influences the phenotype at least partly independently of gene expression. To distinguish pleiotropy from linkage, the heterogeneity in dependent instruments (HEIDI) test was developed [55]. which exploits the observation that if gene expression and disease/trait are in vertical pleiotropy with the same causal variant, the causal effect is identical for any variant in linkage disequilibrium (LD) with the causal variant. Thus, greater heterogeneity among effect size statistics calculated for all significant cis-eQTLs implies a greater likelihood that linkage, rather than causality/vertical pleiotropy, explains the observed causal association. The heterogeneity statistic, the ‘HEIDI’ statistic, tests the hypothesis HEIDI = 0. This provides a formal test of heterogeneity, with P-values < 0.05 suggestive of linkage, rather than pleiotropy, as the underlying biological model [55]. Thus, the SMR & HEIDI approach is widely used to test if a transcript and phenotype are associated because of a shared causal variant (i.e., pleiotropy). We used SMR/HEIDI to integrate GWAS association signals for T1D and each neurocognitive trait with eQTL derived from bulk brain regions [76], and single-nucleus brain cells [77] and single-cell blood [78] and immune cells [79] to identify genes whose expression levels are jointly associated with T1D and neurocognitive traits. We performed SMR using the SMR software tool (SMR v1.0.2) in the command line using default options [55], i.e., cis-eQTLs selected based on minimum P = 5 × 10−8, eQTLs included for the HEIDI test based on minimum P = 1.57 × 10−3, eQTLs included for the HEIDI test if R2 with the top cis-eQTL was between 0.05 and 0.9, minimum number of SNPs included in the HEIDI test = 3, maximum number of SNPs included in the HEIDI test = 20 and physical window around probe within which the top cis-eQTL was selected = 2 MB. P-values were adjusted in R (v3.6.1) to control the false discovery rate at ɑ = 0.05 using the Benjamini–Hochberg procedure. Associations with PHEIDI <0.05—cutoff [80]—were considered likely due to linkage. Probes were excluded if any of the transcript or the top eQTL resided within the super-extended major histocompatibility complex (hg19 6:25 000 000–35 000 000) given the complex linkage disequilibrium structures within this region. Linkage disequilibrium estimation was performed using reference genomes obtained from the 1000 genomes samples of European ancestry [81]).

Transcriptomic and proteomic analysis for differential expression of SMR/HEIDI-identified genes in Parkinson’s disease-affected and control brain tissue
To investigate whether genetically predicted expression of genes in the brain associated with T1D manifests in human disease contexts, we analyzed our previously published single-nucleus transcriptomic and proteomic data from brain tissue of individuals with Parkinson’s disease (PD; n = 6) and matched control individuals (n = 6) [46]. This focus was motivated by suggestive Mendelian randomization (MR) associations between T1D and PD—particularly the nominal evidence for increased T1D risk with PD liability—together with conjFDR and SMR/HEIDI analyses identifying pleiotropic expression quantitative trait loci (eQTLs) shared between the two traits. We interrogated our previously published single-nucleus RNA-sequencing (snRNA-seq) and label-free quantitative proteomics datasets generated from the prefrontal cortex of PD patients and control subjects [46]. Genes were selected for transcriptomic and proteomic interrogation if their genetically predicted expression in bulk brain tissue (cortex, frontal cortex BA9, substantia nigra) or microglia was significantly associated with T1D risk based on SMR/HEIDI analysis.

Transcript-level differential expression analyses were conducted using the DESeq2 framework, incorporating covariate adjustment for age, sex, and postmortem interval, and following the analytical pipeline detailed in our previous publication. Genes with an adjusted P < 0.05 were considered differentially expressed.

In parallel, we assessed differential protein abundance of SMR-identified genes using label-free mass spectrometry-based proteomics data from the same cohort. Protein-level comparisons were performed using moderated t-tests with Benjamini–Hochberg false discovery rate correction. Given the modest sample size and resulting limited statistical power, we adopted a relaxed significance threshold of P < 0.1 to capture a broader set of biologically relevant signals.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overview\nMotivated by prior demonstrating phenotypic neurocognitive and psychiatric associations, this manuscript provides a series\nof analyses to evaluate how T1D genetically relates to traits and disorders in these classes of phenotypes at multiple\nbiological levels. The results are comprehensive and give insight into some of the genetic mechanisms that may link T1D to\ntraits like bipolar disorder and educational attainment. The code is also very well documented on a dedicated and public\nproject GitHub page. My primary concern is that the results currently include many lists of findings, but I think this needs to\nbe more effectively distilled down to the main takeaways by doing things like moving nominal results, small effects at global\nlevels, and follow-up results on small global effects to the online supplement. In addition, I think results at more granular\nlevels need to be more carefully interpreted relative to highly polygenic and pleiotropic signal for any human complex trait,\nincluding T1D. As currently written, I think the reader would both struggle to take away the most important take home points\nand, for non-genetics readers, would be tempted to overinterpret single loci (e.g., 17q21.31) or pathways (e.g., microglia) as\nnecessarily mediating the majority of the T1D and neurocognitive genetic overlap.\nMajor Comments.\n1. I strongly recommend cutting the nominal associations from Results and Discussion in favor of simply referring interested\nreaders to supplementary materials to evaluate those outside of the main text. In support of this, some of the nominal MR\neffects are quite small (e.g., OR = .98) but with small confidence intervals (.96-1), indicating the effect is likely small enough\nin the population to be near meaningless or entirely accounted for by some genetically correlated variable.\n2. I would additionally recommend only focusing follow-up results (MR, SMR/HEIDI, MAGMA, etc.) on traits that showed\nsignificant associations with T1D in LDSC and/or MiXeR in the main text. I understand the more granular levels of biological\nanalysis (e.g., eQTLs) can reveal results that might otherwise be masked by genome-wide LDSC estimates when there are\nlocal same and opposite direction genetic associations across two traits. However, as bivariate MiXeR’s estimate of\npolygenic overlap is designed to circumvent this exact issue, I would argue that pruning to significant traits for either method\nserves to distill results down for the reader to those that are most likely to be relevant in the population and clinically. My\nperspective is that given high polygenicity and pleiotropy, most traits likely have at least one shared causal variant, locus,\nor pathway with T1D.\n3. The title of the paper is about examining T1D and neurocognitive traits. The Results section also reads that the selected\ntraits reflect: “…21 cognitive, psychiatric, neurological, and sleep-related traits.” With that said, the inclusion of other immune-\nmediated conditions, namely myasthenia gravis and multiple sclerosis, seems like it falls outside of scope as currently\nwritten (though I recognize the Discussion refers to them as neuroimmune conditions, though this still feels like these traits\nshould be further distinguished from more strictly non-immune outcomes like bipolar disorder). I could imagine that the\nauthors want to retain these traits to help make point about dual brain-immune architecture, but in that case they should be\nclearly distinguished throughout the manuscript as containing a core immune component that would be expected (as the\nresults show) to produce stronger associations with T1D.\n4. I think the ending results section on Parkinson’s disease should be moved to the online supplement. The analyses appear\nwell-conducted (though are admittedly a bit beyond my expertise), but are not well-justified given the other results in the\nmanuscript. For example, there is no significant genome-wide genetic correlation or polygenic overlap identified from\nMiXeR, and the MR associations are nominal. This tells a story of T1D and PD as not being overly genetically associated.\nThis proteomics and prefrontal cortex ending results section I believe ignores both the highly polygenic nature of these\ndisorders and to what degree these specific linking genes would be unlikely to explain meaningful variation (or covariation)\nin the population. Meaningful variation is admittedly a very subjective threshold, and I don’t mean to impose my own\nsubjectivities onto this work, but I think several linking genes against a backdrop of thousands of genetic variants (e.g.,\n~22,000 for T1D, as estimated from MiXeR in this submission) is unlikely to meet that threshold for most scientists.\n5. There is a lot of focus on 17q21.31 in the Results and Discussion. For example, the authors write in the Results:\n“Gene regulation—anchored at 17q21.31 and extending to loci on chromosomes 1, 5, 6, 12, and 16—thus emerges as a key\nmechanistic axis linking autoimmunity, neurodevelopment, and cognition.”\nI agree that results show that this is a highly pleiotropic region, but like my point above about the polygenicity of these traits,\nI’m not convinced that 17q21.31 is a key mechanistic axis as I would anticipate that only a very small amount of the genetic\ncovariation is mediated by this individual locus. The authors may disagree, but this should be made clearer, or language\nshould be toned down with respect to how critical 17q21.31 is viewed for understanding covariation between T1D and\nneurocognitive traits.\n6. The authors write the following as a Limitation about MR findings and sample overlap:\n“Although Mendelian randomization supports directional inference, sample overlap and residual pleiotropy cannot be fully\nexcluded. Our use of UK Biobank data may introduce shared participants across some GWAS datasets, potentially biasing\ncausal estimates.”\nHowever, existing MR methods (e.g., MRBEE; Lorincz-Comi et al., 2024) can deal with sample overlap. Moreover, the level of\nsample overlap could be more directly quantified by cross-referencing supplementary tables of contributing cohorts and\ncase sample sizes or sampling covariation directly indexed using the bivariate LDSC intercept. As written, this is overly\nvague, and with some MR findings that have OR near the null of 1, correcting for sample overlap could potentially change\nqualitative conclusions or results highlighted.\nLorincz-Comi, N., Yang, Y., Li, G., & Zhu, X. (2024). MRBEE: a bias-corrected multivariable Mendelian randomization\nmethod. Human Genetics and Genomics Advances, 5(3).\nMinor Comments:\n1. Why does the second sentence begin with a “however”? A dysglycemia based interpretation for the neurocognitive profile\nof T1D does not seem counter to the idea that T1D and psychiatric disorders are highly heritable:\n“This view has reinforced the idea that T1D-associated neurocognitive deficits arise primarily as complications of\ndysglycemia. However, both T1D [29] and cognitive or neuropsychiatric disorders [30] are highly heritable—up to ~50% and\n80%, respectively.”\n2. The S-LDSC results are well-written, but is it true that it’s inherent to the S-LDSC method that it aggregates across nuclei?\nAlthough the functional annotations used to estimate enrichment here aggregate across nuclei, these annotations ultimately\njust require lists of genes or genetic variants identified according to some shared biological characteristic, which can include\nsingle-cell data. The relevant excerpt from the text is:\n“Because S-LDSC aggregates across nuclei and may under-detect transient or sparse signals in heterogeneous single-cell\ndata,…”\n3. Are these results sufficient to say microglia is the primary mediator in the brain? This is just what we can measure and\nwhat was included in the model, but there are many aspects of brain function and structure that you could imagine are not\ncurrently being captured in these models.\n“Together, these complementary analyses converge on microglia as the primary mediators of T1D genetic liability in the\nbrain, with additional stage-specific contributions from other neural and glial populations.”\n4. I think this sentence is written as if negative genetic correlation (i.e., “overlaps persisted despite”) would attenuate\nestimates from MiXeR, but that’s not the case.\n“Importantly, these overlaps persisted despite negative genetic correlations, indicating that many of the same variants\ninfluence both T1D and cognition but with opposite effect directions.”\n5. On page 6 the authors discuss the number of loci identified from conjFDR. Can the authors provide something like a\nscatterplot of power of the univariate GWAS (e.g., indexed by mean chi-square) predicting number of loci discovered? My\nargument for including this reflects a curiosity around whether multiple sclerosis and myasthenia gravis are outliers with\nrespect to pleiotropic loci discovered relative to power for discovery. Without this context, I worry that the reader would be\ninclined to make potentially inaccurate conclusions about the relative ordering of number of overlapping loci for the external\ntraits primarily (or solely) reflecting the level of shared genetic signal (as opposed to also including power differences across\nthe external trait GWAS).\n6. On page 6 the authors write the following, but I think this should be modified in two ways: (i) this gives a false sense that\nregions like the frontal cortex and amygdala are particularly relevant when in fact Fig 3k shows that every included brain\nregion is enriched for bipolar and (ii) enrichment for the pituitary is unique to bipolar and could be highlighted:\n“Shared loci between T1D and bipolar disorder (13 loci) were enriched in cortical and subcortical brain regions including\nfrontal cortex, hippocampus, putamen, and amygdala.”\n7. For the claim about the duality of the T1D architecture (the authors write on page 6: “underscoring the dual brain–immune\narchitecture of T1D pleiotropy”), I wonder if the authors could quantify this more explicitly by, for example, correlating the\nMAGMA results for bipolar and multiple sclerosis. That is, if these are truly divergent processes, I would expect a correlation\ncloser to 0 than across these two sets of enrichment findings.\n8. For this MR finding, I think ADHD is misinterpreted as being associated with increased T1D in the last phrase as the OR\nis < 1:\n“When modeling neurocognitive traits as exposures (Fig. 4b; Supplementary Table 4), higher liability to multiple sclerosis\n(OR = 1.18, 95% CI: 1.09–1.27, P = 2.3×10⁻⁵, q = 2×10⁻⁴), myasthenia gravis (OR = 1.22, 95% CI: 1.12–1.33, P = 5.6×10⁻⁶, q\n= 4×10⁻⁵), obsessive–compulsive disorder (OR = 1.07, 95% CI: 1.02–1.12, P = 3.7×10⁻³, q = 0.016), short sleep duration\n(OR = 1.26, 95% CI: 1.05–1.50, P = 0.011, q = 0.036), and ADHD (OR = 0.90, 95% CI: 0.83–0.98, P = 0.016, q = 0.048) was\nassociated with increased T1D risk.”\n9. Some of the sentences in the SMR/HEIDI section (example below) blend discussing effects as directional or merely\n“linked”. Direction of association should be clarified throughout this section as, to me, it’s currently unclear:\n“In contrast, LRRC37A4P and MAPT-AS1 expression correlated with elevated T1D risk and higher INT/EXF, with\nLRRC37A4P also linked to ASD, EDU, MG, and PD.”\n10. Does ASD diverges here indicate that eQTL effects were a blend of same direction and opposite direction effects for\nASD and T1D? This could be made more explicit.\n“While LDSC highlighted negative genome-wide correlations between T1D and cognition alongside paradoxical positive\ncorrelation between ASD and cognition, eQTL integration clarified this architecture: T1D and cognition typically move in the\nsame direction, whereas ASD diverges.”\n11. The authors note several times throughout the Introduction and Discussion that the cognitive associations are\nparticularly pronounced for childhood onset T1D. My understanding is that the T1D GWAS used includes both child and\nadult onset. Given the prominence of this point, examining age at onset stratified T1D GWAS and these associations could\nbe noted as a future direction -and- percentage of individuals with child onset T1D that were incorporated into the T1D\nGWAS could be noted in the Method section. For the latter point, I could imagine this data is not available for all participants,\nbut my hope would be that the reader could get some sense of proportion of child vs adult onset for the subset of cohorts that\nmay have provided this information in the original T1D GWAS.\n12. The 95% confidence intervals on bipolar and schizophrenia in Fig 4a look asymmetrical, which should be highlighted in\nthe figure note if this is not a mistake.\n(Remarks on code availability)\nThe code is well-documented on a dedicated project page on GitHub.", "reviewer_2": "Interesting comparison of T1D and positive and negative overlaps with neuro diseases.\nCan they compare their results with a previous study: https://pubmed.ncbi.nlm.nih.gov/37118290/\nThey discuss well their positive findings, but I wonder if they could expand the interpretation of contradictory findings,\nparticularly the negative association with mental health outcomes, ie SCZ and BD.\nWe know that SCZ has substantial negative association with cognition so maybe their findings are suggesting distinct\nmechanisms on how SCZ and TD1 may affect cognition? They suggest microglia in TD1, which in SCZ GWAS is not\nstrongly enriched. In fact, SCZ GWAS implicates more excitation/inhibition mechanisms than immune (although limitations\nexist etc)\nThe authors briefly mention E/I in their T1D analyses, but it was not clear to me what exactly the findings were e.g.,\n\"... further highlighted context-specific signals in excitatory and inhibitory neurons (early postnatal) \"\n- could they expand more on these findings? I think would offer a clue for the SCZ interpretation\n(maybe..)\nI also wonder about their Treg findings, see:\n\"On chromosome 12, SUOX expression in nucleus accumbens and regulatory T cells was linked to reduced T1D and lower\nEDU/INT/EXF\"\nThe findings for both Nac and tregs are very interesting, given the Nac role on negative/depressive symptom. How strong\nare these findings and what does SUOX does on Tregs (they don't seem to discuss that) - if SUOX is important for Treg\nfunction, would this suggest a direct impact of Tregs in cognition but reduced risk of T1D? How do they interpret these\nfindings in light of RCT aiming to restore treg function in TD1 and cognition in Alzheimer's?\nThey conclude:\n\"Finally, it suggests that cognitive and psychiatric features in T1D may not be mere complications of dysglycemia but instead\nreflect a shared genetic liability that predates clinical onset. \"\nI think they need to be more specific when they say \"psychiatric features\" in view of negative associations they found for\nSCZ/BD ..\nCan they include a paragraph in the Discussion about therapeutic implications, possible targets?\n(Remarks on code availability)\nSupportive of publication.", "reviewer_3": "Review for Saarah & Syeda et al.\nTo better understand the contribution of T1D on neuropsychiatric health, Saarah, Syeda et al. applied several genetic\ndiscovery analyses to publicly available European GWAS studies to identify brain-relevant celltypes and genes that may\nmediate shared genetic architecture of T1D and several neuropsychiatric traits. Cell type enrichment analyses, stratified LD\nscore regression with open chromatin regions called from pseudobulk brain scATAC-seq and SCAVENGE network affinity\npropagation nominated microglia as a putatively relevant cell type for this association. Cross-trait genetic correlations,\nMendelian Randomization (MR) were used to identify associations between traits, with several associations found. Further\nanalyses prioritized the well-established 17q21.31 (the MAPT locus) as a hotspot for T1D and neuropsychiatric disease\nliability. While limited MR analyses have been done, the cross-functional and cell-type enrichment analysis has not been\nconducted in the brain to my knowledge. Taken together this study, although I have some reservations related to statistics\nand interpretation of results.\nMajor Comments:\n• The enrichment analyses for T1D are only conducted in brain derived cell types, so it difficult to determine whether\nobserved enrichments are due to the cell types per se or through variants located in shared ATAC-seq peaks with peripheral\nimmune lineage cell types. It has been well established through S-LDSC regression and related tissue-enrichment analyses\nthat T1D variants are enriched in regulatory elements in peripheral immune cells, particularly the T cell lineage\n(PMID:34012112, 31548716; 9626097; 34127860, among others), and are key cells involved in loss of beta cell. The\nobservation for the borderline significant S-LDSC enrichment in microglia (apparently unadjusted P = 0.041-0.045) could be\ndue to similarities in open chromatin regions between microglia with peripheral immune cells, rather than microglia having a\ndriver role in T1D biology. As such, this observation should be substantiated by (1) comparing the overlap of variants\ncontributing to enrichment to peripheral immune cell types from peripheral blood scATAC-seq and (2) checking enrichment\nspecific of both celltypes {PMID: 29632380}. Including negative control traits (non-brain or immune related) in the\nSCAVENGE analysis would be useful to interpret the results. Similarly microglia appear to be poorly represented in the\neQTL analyses.\n• The 17q21.31 MAPT locus is well known it’s broad pleiotropic effect across traits, classically\nneuropsychiatric, but also traits like bone mineral density. The locus contains a common inversion with a breakpoint that overlaps MAPT’s promoter,\nthat leads to lower MAPT expression (PMID: 15654335; 14991810). The inversion also correlates with large blocks of\nvariants in strong LD, likely violating assumptions of the SMR/HEIDI analysis for similar reasons as the excluded MHC\nregion. Can the authors justify inclusion of this locus to their analysis in the SMR/HEIDI framework?\n• The comparisons of the SMR results on T1D to genes RNA-seq and proteomics from Parkingson’s disease presented in\npages 8-10 (Figure 6) are difficult to justify and interpret. Given that the MR between T1D and PD is only nominally\nsignificant, using the RNA/proteomics dataset from non-T1D patients does not support a downstream mechanistic claim\nabout specific genes. Was the overlap between T1D nominated and PD patient differential genes greater than expected by\nchance?\n• FDR for several analyses was conducted on a limited number of tests. The Benjamini-Hochberg procedure depends on the\nnumber of tests to accurately estimate the false-discovery rate, which can be unstable when few tests are performed.\nSignificance with Bonferroni correction should be reported.\nMinor Comments:\n• The figure text size is too small to be read.\n• Several labels on Figure 4 are misaligned.\n• What do the colors bars in Figure 3i-k represent? The legend mentions the dashed line indicates nominal significance.\n(Remarks on code availability)\nThe provided github provides example scripts for the analyses run. For example the LDSC contains the example of munging\nand running the cross-trait LDSC correlation between intellengence and T1D. The scripts are either provided as R/Rmd or\nbash scripts to be submitted to a SLURM scheduler. The code that I examined appeared correct.\nAs the paper does not propose novel software I did not download or test the code."}, "author_response": "POINT-BY-POINT RESPONSE TO REVIEWER COMMENTS\nWe thank the reviewers for their thorough evaluation and for the insightful, constructive critiques\nof our manuscript, “Shared genetic and neuroimmune architecture links type 1 diabetes\nwith neurocognitive traits.” We have carefully considered all comments and substantially\nstrengthened the manuscript through additional analyses, clarifications, and focused revisions.\nIn this resubmission, we performed new negative-control analyses by integrating height GWAS\n(a non-immune, non-neurocognitive trait) with the single-cell epigenomic datasets to confirm\nthat the T1D enrichments are not driven by global annotation biases. We now implement\nrigorous Bonferroni correction instead of BH-FDR for multiple testing across LDSC and MR\nanalyses, with all nominal findings moved to the Supplementary Materials to ensure\nconservative and transparent statistical reporting. We have also expanded and refined key\nsections, including cell type–specific enrichments, cross-disease comparisons, mechanistic\ninterpretation of pleiotropic loci, and neuroimmune pathways, to more appropriately address\nreviewer feedback. A concise paragraph discussing potential therapeutic implications has been\nadded to the Discussion, and we clarified methodological considerations, including the\nlimitations of causal inference and the implications of potential sample overlap.\nIn the revised manuscript, the framing of neurocognitive traits has been clarified, including\nexplicit categorization of cognitive, psychiatric, neurological, and neuroautoimmune phenotypes.\nAll revisions have been incorporated in the updated text.\nIn this point-by-point response document, reviewer comments are reproduced in black and our\nresponses provided in blue.\nWe are resubmitting both a clean version of the manuscript and a tracked-changes version with\nall edits highlighted in yellow.\nWe believe these revisions substantially strengthen the scientific rigor, clarity, and\ninterpretability of the manuscript, and we hope they fully address the reviewers’ comments.\n. [Response text continues for all reviewers...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors were responsive and I think the manuscript generally reads much better. I have two remaining comments.\n1. The authors updated the MiXeR results to write that:\n“Importantly, the MiXeR-estimated overlaps demonstrate that substantial numbers of shared causal variants influence both\nT1D and neurocognitive traits, including variants with opposite effect directions, relationships that are not captured by\ngenome-wide genetic correlation alone.”\nHowever, it’s not clear from Figure 2 that MiXeR reveals higher levels of polygenic overlap than what you get from LDSC.\nThat is, the authors write that MiXeR reveals that there are shared variants with opposite and same direction effects (which\nwould result in larger estimates from MiXeR relative to LDSC), but the Venn Diagrams from MiXeR seem consistent with\nresults from LDSC, which does not support this claim. If there is evidence of pleiotropic variants with same and opposite (i.e.,\nantagonistic) effects across T1D and neurocognitive traits, this needs to be more directly linked to the pattern of results\nshown in Figure 2 and the difference in estimates of genetic correlation and polygenic overlap from LDSC and MiXeR,\nrespectively.\n2. The authors wrote in their response to reviewers that:\n“To assess whether GWAS power influenced the number of conjFDR-discovered loci, we examined sample sizes and other\npower indicators across all external GWAS (Supplementary Table 1). We found no relationship between GWAS power and\nthe number of pleiotropic loci; notably, traits with the smallest case counts, such as myasthenia gravis and multiple sclerosis,\nshowed more shared loci with T1D than many of the other neurocognitive traits with substantially larger GWAS sample\nsizes. To prevent misinterpretation, we added a clarification in the Results stating that locus counts primarily reflect trait-\nspecific genetic architecture rather than power differences. Because power metrics did not correspond to pleiotropic locus\ncounts, a scatterplot would not add interpretive value and was therefore not included.\nA few things stand out as potentially inaccurate here. First, Supplementary Table 1 seems to only list GWAS sample size, so\nit’s not clear what the “other power indicators” the reviewers are referring to. Second, the authors state in the revised\nmanuscript: “Importantly, the number of conjFDR pleiotropic loci did not correspond to GWAS sample size or effective power\n(Suppl Table 1)…”. However, it’s not clear how this was evaluated as no statistical test or result is reported here or in\nSupplementary Table 1 (and Supplementary table 1 does not lend itself to the reader being able to evaluate this claim for\nthemselves). Finally, sample size is not necessarily a good indicator of power in a GWAS. Although myasthenia gravis has a\nmuch smaller case count, that is potentially offset by the fact that it is more heritable and has a more biologically defined\nphenotype that will reduce measurement error and could make power more commensurate with some of the other traits with\nhigher case counts. I would ask that the authors: (i) include mean chi-square from the GWAS of neurocognitive traits (and\npotentially the heritability Z-statistic from LDSC) as a more direct measure of GWAS power in Suppl. Table 1, and (ii) re-\nevaluate using these more direct power indicators whether the number of conjFDR findings is related to GWAS power of the\nexternal traits.\n(Remarks on code availability)\nThe code is open-source and well-documented.", "reviewer_2": "Replies and revised manunscript are improved.\nReferencing should be double checked, e.g. in one of the Replies the revised text references # 39, about T1D enriched in\nperipheral immune cells but reference 39 is about the human neocortex.\n(Remarks on code availability)", "reviewer_3": "Thank you for the points addressed in my last review. For one outstanding point in this revision, it appears that the\nmodification to the cross-trait LDSC analysis methods, text for multiple testing was inserted to both the S-LDSC and\n“Genome-wide genetic correlation analysis by cross-trait LDSC” sections. My understanding is the S-LDSC results were not\nadjusted for multiple hypothesis testing, so the text should only be included in the Genome-wide genetic correlation analysis\nby cross-trait LDSC section. If this is mistaken, then the language should be adjusted to be appropriate for S-LDSC, ie\nreplacing correlation with enrichment.\n(Remarks on code availability)"}, "author_response": "POINT-BY-POINT RESPONSE TO REVIEWER COMMENTS\nWe thank the reviewers for their thorough evaluation and constructive feedback on our manuscript, “Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits.” We have carefully considered the reviewer’s concerns and implemented targeted clarifications and corrections to ensure complete accuracy and transparency of the supplementary materials and GWAS power analyses. [Response text continues...]"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors write that they now include mean chi-square in an added supplementary table 2 as an added metric of power,\nbut this is not included. In the first revision, they had indicated additional power metrics were added to Supplementary Table\n1, and this was also not done. This is very concerning as a trend where the author’s are saying edits have been made that\nwere not implemented.\nAs requested, the authors now include SNP-based heritability Z-statistics in Supplementary Table 2, but there are major\nissues with the estimates reported. This includes the fact that these Z-statistics do not reflect the ratio of the estimate over the\nstandard error and half of those Z-statistics are listed as negative, which is not possible for well-powered traits like\neducational attainment.\n(Remarks on code availability)\nThe code is well documented."}, "author_response": "POINT-BY-POINT RESPONSE TO REVIEWER COMMENTS\nWe thank the reviewer for their careful evaluation and constructive feedback on our manuscript, “Shared genetic and neuroimmune architecture links type 1 diabetes with neurocognitive traits.” We have carefully considered the reviewer’s concerns and implemented targeted clarifications and corrections to ensure complete accuracy and transparency of the supplementary materials and GWAS power analyses. [Response text continues...]"}, {"round": 4, "reviewer_comments": {"reviewer_1": "My concerns have been addressed.\n(Remarks on code availability)\nAvailable code is well-documented."}, "author_response": ""}]

Decision letter