Leveraging genomic and transcriptomic data of diverse ancestry to uncover mechanisms of psychiatric risk in the adult and developing brain

Domain: biology
Published: 2025-12-29
Editorial outcome: major_revision

Abstract
We explore strategies to harness ancestral diversity in PsychENCODE Consortium Genotype-Expression (GEx) reference panels (adult and developing brain) and Psychiatric Genomics Consortium GWAS data to improve genetically regulated expression (GReX) models and their use for Transcriptome-wide association study (TWAS) discoveries, uncovering previously unknown aspects of psychiatric functional genomics. We trained multiple GReX models on rigorously constructed GEx panel subsets, generated by downsampling, segregating, and/or mixing samples of Admixed African and European ancestries, and based on disease status. Ancestry-specific GReX genes were enriched in pathways involving mitochondrial functions, organelle structure, and metabolism. These models were integrated with ancestry-specific GWASs to conduct bipolar disorder, major depressive disorder, post traumatic stress disorder, and schizophrenia TWAS. TWAS signals obtained by applying AA- and EUR-specific GReX models to an ancestry-specific GWAS were largely concordant, and mismatched-TWAS (e.g. AA-GReX applied to EUR-GWAS) revealed biologically meaningful signals missed by matched-TWAS. Shared signals across the four disorders were more prominent in the developing brain, involving genes such as H4C13, ZSCAN12P1, and FLOT1, and pathways related to megakaryocyte and muscle development, and neurotransmitter regulation. Overall, we demonstrate concordance in shared TWAS signals across GReX models and provide insight into GReX-specific detectable genes and pathways.

Introduction
Psychiatric disorders, including bipolar disorder (BIP), major depressive disorder (MDD), post-traumatic stress disorder (PTSD), and schizophrenia (SCZ), are complex traits involving multiple genes and biological pathways. While Genome-Wide Association Studies (GWAS) have been instrumental in identifying genetic associations for these disorders1–6, challenges remain to optimize the discovery of biological targets from these large datasets7. GWAS hits often have small effect sizes and are difficult to interpret biologically, as many associations are located in noncoding regions of the genome. These associations may be pleiotropic or non-causative, resulting from linkage disequilibrium (LD). Transcriptome-Wide Association Studies (TWAS) address some of these challenges by effectively translating GWAS signals into meaningful molecular mechanisms at a tissue-specific level8,9 through a two-step process. First, a Genotype-Expression reference panel (GEx panel) is used to train Genetically Regulated gene eXpression (GReX) models, which predict gene expression based on genotype information. Second, SNP GWAS summary statistics are combined with SNP (single nucleotide polymorphism) weights derived from the GReX models to identify GTAs (Gene Trait Associations). This approach provides critical insights into the molecular underpinnings of psychiatric disorders, bridging genetic associations with biological functions10–12.

An underlying assumption of TWAS is that the relationships between SNPs and gene expression in the GEx panel are similar to those in the GWAS cohort. Furthermore, eQTL (expression quantitative trait loci) SNPs identified in a GEx panel that is integrated with GWAS data may be in LD with the actual causal SNP but may not directly regulate gene expression itself. If the genetic architecture of the GWAS cohort matches that of the GEx panel, these associations can still be informative. Hence, matching the ancestry of the GEx panel with that of the GWAS and performing a meta-analysis could be the most effective approach to enhance TWAS signals. However, most existing GEx panels and GWAS data are predominantly of European ancestry (EUR). With the emergence of datasets from diverse ancestries, the key question is how to leverage these resources to increase the power of TWAS results and to identify comprehensive signals across different ancestral populations. While some TWAS methods already exist to integrate cross-ancestry datasets13–15, often through adjustments for LD or Bayesian approaches, our focus lies in understanding genomic region-specific regulatory differences and biological differences across ancestrally distinct GEx panels. We want to explore how these differences, along with variations in a disease’s genetic architecture, can be leveraged to improve TWAS signals.

Our study addresses these questions in the context of integrating adult cortex (dorsolateral prefrontal cortex [DLPFC]) and developing brain (fetal) GEx panels with GWAS data for four psychiatric disorders, focusing on Admixed African (AA) and European (EUR) ancestries (Fig. 1A). We hypothesize that most of the SNPs have concordant effects on gene expression across ancestries, as suggested by emerging studies16–19. For the DLPFC GEx panel obtained from PsychENCODE20 cohorts spanning multiple psychiatric diagnosis (Supplementary Fig. 1a; “Methods”), we considered three ancestry groupings (AA, EUR, and AAEUR, where AAEUR models are bi-ancestry models constructed by combining AA and EUR subjects; Methods, Fig. 1B and Supplementary Fig. 1b–d) and two disease-status groupings (neurotypical controls [Ct] and all available samples including neurotypical controls and cases [All]). In total, we trained eight DLPFC GReX models of varying training sizes (Supplementary Figs. 1E and Supplementary Data 1), including AA-specific (GReX and GReX ), EUR-specific (GReX , GReX and GReX ), and bi-ancestry (GReX , GReX and GReX ). Here, the number appended to the GReX model’s name denotes the training sample size. The EURCt165 training set was downsampled from EURCt477 to match the demographic composition of AACt165, enabling fair comparison across ancestries. Further details are available in the Supplementary Notes. Building on this framework, ancestry-specific predicted genes were identified, and their enrichment at the pathway level was compared. Model performances were evaluated separately on AA and EUR samples in an external validation dataset for each GReX model. In addition, two ancestry-specific fetal GReX models were trained to identify and compare ancestry-specific genes and their pathway enrichments. We integrated these 10 GReX models (8 DLPFC and 2 fetal) with eight (4 × 2) ancestry-specific GWAS datasets of 4 psychiatric disorders (PTSD, MDD, BIP, and SCZ) across 2 ancestries (EUR and AA). Meta-analyses of TWAS were performed within each disorder using TWAS results of ancestry-specific GWAS obtained by applying various combinations of GReX pairs. Ancestry-specific TWAS signals were then compared at the gene and pathway levels. We found that shared signals across ancestries were concordant, while unshared signals were predominantly driven by minor allele frequency (MAF) differences. Pathway analyses of unshared ancestry-specific signals uncovered trait-specific archetypical pathways, emphasizing the biological relevance of these complementary signals.

Results
Performance of GReX models and ancestry specific predicted genes
GReX models are built using elastic net following the PrediXcan pipeline8,9 (“Methods”). Genes with average cross-validation R-squared (R²) more than 0.01 and p-value < 0.05 are included in downstream analysis (Supplementary Data 2). Across the 8 GReX models, the number of predicted genes positively correlated with training sample size (Fig. 1C and Supplementary Data 2). However, larger sample sizes yielded lower cross-validation R² (Fig. 1D), reflecting inclusion of genes with lower heritability. Of 17,406 distinct predicted genes, 2098 were predicted by all models and showed increasing mean R² with sample size (Fig. 1E and Supplementary Fig. 2). GReX predicts 4557 genes, while GReX predicts 4926 genes—8% more. The 2248 genes predicted by both models had correlated R² (Pearson’s r = 0.53, Fig. 2A). To assess whether cis-SNP regulation of overlapping genes was consistent across ancestries, we compared assigned SNP weights between the two models. About 12% of the predictor SNPs were shared across the two models, following non-random patterns shaped by MAFs distributions within each training dataset (Supplementary Notes). Despite modest overlap, SNP weights among 8,465 shared predictors were highly correlated (Pearson’s r = 0.6) with 97% agreement in direction (Fig. 2B). This concordance is notable given that models were trained on non-overlapping ancestral populations and GReX assigns weights iteratively to causal and tagged SNPs. Alongside concordance, discordant R² performances were observed (R² > 0.4 in one model and < 0.15 in the other), identifying 30 such genes in GReX and 10 in GReX (Fig. 2A). Examples included immune response-related genes (AA: BTN2A2 and LYPD8; EUR: CAMLG) and mitochondrial function-related genes (AA: SCO1; EUR: NDUFAF4). UVSSA showed higher R² in EUR (0.43 vs. 0.03 in AA), which could reflect its role in UV-induced DNA repair and possible stronger environmental selection in lighter-pigmented populations, highlighting ancestry-specific specialization of gene regulation detectable across tissues.

We consider the overlap of GReX and GReX predicted genes with those predicted in our higher-order GReX models (trained on a bigger sample size) as a proxy for reliability. We observed that 93% of the genes predicted by both were also predicted in all higher-order models, suggesting that the underlying genetic architecture and available signal in both population training datasets were sufficient to support consistent gene prediction (Supplementary Data 3). In contrast, 40% of GReX -specific and 30% of GReX -spefic genes were retained in all higher-order GReX models (Fig. 2C, Supplementary Fig. 2). Notably, 27% of GReX -specific genes were predicted only in higher-order AA models, and 38% of GReX -specific genes were predicted only in higher-order EUR models, highlighting ancestry-specific predictive power. We further connected these GReX -specific and GReX -specific genes, which were exclusively predicted in their respective higher-order ancestry models (Methods, Supplementary Data 3), to functional annotations. Shared pathways across GReX -specifc and GReX -specific genes included DNA metabolic processes, membrane organization, and lytic vacuole function (Fig. 2D and Supplementary Figs. 3, 4). Unshared GReX -specific pathways were enriched in oxidative stress, phagocytic vesicle formation, and mitotic cell cycle processes, while unshared GReX -specific pathways were enriched in processes related to the perinuclear region of the cytoplasm, calcium ion binding, Golgi membrane function, and viral responses. Suggesting the enrichment of several model-specific specialized pathways, particularly those involving organelle dynamics.

External validation of GReX models
Next, we validated our models using an independent DLPFC GEx panel (AA = 50, EUR = 246, Methods21,). As a performance measure, we used mean and Pearsons’ skewness of differences in the correlation of the predicted gene expression and observed gene expression (ρobs ) between each pair of GReX models (Fig. 2E, Methods). Across GReX models, overall performance differences were modest (mean distribution of correlation differences < 0.01), see Supplementary Notes for more details. Matched ancestry predictions—where the training and test cohorts share the same ancestry (AA on AA, EUR on EUR)—consistently provided an advantage, even when the mismatched model was trained on a larger cohort (e.g., EURCt477 vs. AACt165). Within matched ancestry settings, expanding training to include both cases and Ct (All) increased mean performance, although skewness in correlation differences indicated that subsets of genes were still better predicted by the smaller Ct models. Bi-ancestry models improved mean performance when compared to smaller training size GReX (165), though subsets of genes continued to be better predicted by ancestry-specific models; at larger training sizes (477), the ancestry-specific models outperformed the bi-ancestry models overall.

Effect size correlations of TWAS-based GTA results of the 8 GReX models across traits
We use JEPEGMiX software22 to perform TWAS analysis (Methods). Many combinations of TWAS analysis were performed by integrating our eight GReX models with single-ancestry (AA and EUR) GWAS of four psychiatric diseases: SCZ2,6, BIP1,2, PTSD4,5, and MDD3 (Methods22,) (Fig. 1A). This included three types of TWAS: (i) matched ancestry TWAS, with EUR GReX applied to EUR GWAS [GReX (GWAS )] and AA GWAS applied to AA GWAS [GReX (GWAS )]; (ii) mismatched ancestry TWAS, with EUR GReX applied to AA GWAS [GReX (GWAS )] and AA GReX applied to EUR GWAS [GReX (GWAS )] iii) bi-ancestry TWAS, with GReX applied to AA GWAS [GReX (GWAS )] and EUR GWAS [GReX (GWAS )] (Fig. 1A and Supplementary Data 4,5, Methods). It is important to note that the four traits had substantial differences in GWAS sample sizes, both across disorders and across ancestries within each disorder, which may influence the power and robustness of the TWAS results (Supplementary Fig. 5A, B). First, to assess whether the TWAS signals are primarily driven by biological signals from the underlying disease GWAS or by variation introduced through the choice of GReX model, we jointly clustered all TWAS results generated using single-ancestry GWAS based on correlations of their GTA z-scores. This analysis revealed a clear hierarchical clustering pattern, with the top layer corresponding to the ancestry of the underlying GWAS, followed by disorder-specific GWAS, and then by the applied 8 GReX models (“Methods”, Fig. 3A). In both ancestry-associated GWAS clusters, SCZ grouped with BIP and PTSD with MDD, consistent with known genetic correlations7 and confirmed in our study’s GWAS data (Supplementary Fig. 5C, “Methods”).

Notably, TWAS results generated using different GReX models applied to the same GWAS always clustered together, with sub-clusters reflecting the ancestry background of the GReX models (Fig. 3A and Supplementary Fig. 6), a detailed description can be found in Supplementary Notes. In addition, for all four traits, correlation of GTAs produced by different ancestry models on EUR GWAS [GReX (GWAS ) ~ GReX (GWAS )] are higher than those produced on AA GWAS [GReX (GWAS ) ~ GReX (GWAS )] (Fig. 3C). To determine whether the lower GTA correlations produced by ancestry-specific models using AA GWAS is not driven by the small sample GWAS AA sample sizes, we utilized an external PTSD cohort which is predominantly AA cohort (~96% people are of AA ancestry, Grady Trauma Project [GTP] cohort23,24). In this cohort, we could calculate GTAs using both raw genotype and GWAS summary statistics. We observed lower GTA correlations when using AA subjects vs. the EUR subjects (0.53 vs. 0.63 using raw genotype, 0.43 vs. 0.55 using summary statistics; Supplementary Fig. 8).

Enrichment p-value correlations of TWAS pathway results based on the 8 GReX models within traits
Our pathway analysis focused on the three domains of Gene Ontology: BP (biological processes), MF (molecular functions) and CC (cellular components)25. TWAS-pathway enrichment analysis of all the TWAS associated with single-ancestry GWAS mentioned above were performed using JEPEGMiX2-P22 (Methods), which accounts for LD among genes within each tested pathway as part of the integration of TWAS GTA statistics (Methods). Within each disorder, pathway enrichment p-value based clustering of TWAS results primarily grouped analyses by the ancestry of the underlying GWAS (Fig. 3C), consistent with the structure observed in GTA-based clustering (Fig. 3A). Within each GWAS-specific cluster, GReX models consistently clustered together (Supplementary Fig. 9). Unlike in GTA clustering, GReX -based pathway enrichments consistently clustered with the GReX models. Also, ancestry-based patterns of pathway signals in the higher-order EUR and AAEUR GReX models were less pronounced (Supplementary Fig. 9). As with GTA signals, pathway signal correlations between GReX and GReX were lower in AA GWAS (Fig. 3D; Supplementary Fig. 10A vs. S10B; Supplementary Fig. 10C vs. S10D). Interestingly, the comparison between GReX (EUR) and GReX (EUR) showed greater enrichment in biological pathways in SCZ and BIP, whereas greater agreement on non-enrichment BP was observed in PTSD and MDD (Fig. 3D).

Ancestry-specific differences in significant TWAS-based GTAs, their underlying factors, and putative causal associations
Risk genes in a TWAS analysis are identified as those with FDR-adjusted p < 0.05 (FDR-GTAs) (Methods). To assess the benefits of ancestry-specific GReX models, we compared the FDR-GTAs of the same size GReX model pair Ct165. Among the FDR-GTAs of EUR GWAS, a very small proportion was overlapped across the two Ct165 models within each disorder, with no overlap observed in BIP (BIP: 0%; MDD: 2, 8%; PTSD: 2, 12%; SCZ: 12, 16%) (Fig. 4A–D). The largest proportion of AACt165-specific FDR-GTAs was observed for PTSD (MDD: 5, 20%; BIP: 6, 40%; SCZ: 28, 40%; PTSD: 10, 66%) (Fig. 4A–D). Some of these GTAs were present in at least one of the 6 higher-order GReX model (40% MDD, 40% SCZ, 50% BIP and 50% PTSD) (Supplementary Figs. 11, 12). Sometimes, the same genomic locus can contain unshared FDR-GTAs across the two models, while other times, a genomic locus is unique to a specific ancestry model, see Supplementary Notes. We then sought to understand the factors underlying the presence of the unshared FDR-GTAs. We focused on SCZ, as it is the most heritable disorder with the strongest signal among the 4 disorders we study here. We first hypothesized that SNP resolution differences were the main driving force of the unshared FDR-GTAs. Indeed, we found that MAF of the predictor SNPs of the unshared FDR-GTAs were lower in the ancestry where the GTAs were not FDR-significant (70% of GReX -specific and 67% of GReX -specific; Methods, Fig. 4E, F). In several cases among the remaining ancestry-specific FDR-GTAs (e.g., FURIN, RBM6), the LD blocks26 in the ancestry where the gene was an FDR-GTA was smaller than the corresponding LD block in the other ancestry (Supplementary Fig. 13). We provide more details on LD and MAF impacts on TWAS results in Supplementary Notes. To further assess whether GReX (GWAS ) TWAS provides a meaningful signal, we compared the proportion of putative causal genes in the FDR-GTAs identified by GReX and GReX . Putative causal genes were identified using cTWAS27, which jointly fine-maps both GTAs and local SNPs together (Method). We found that 18% of AACt165-specific and 13% of EURCt165-specific genes were identified as putative causal genes, for more details see Supplementary Notes (Supplementary Figs. 14, 15).

In contrast to the TWAS with single ancestry EUR GWAS, which identified multiple FDR-GTAs, the TWAS associated with AA GWAS (conducted on cohorts significantly smaller than the EUR GWAS, Fig. 5A) did not identify FDR-GTAs, except for a single BIP FDR-GTA in the GReX (GWAS ) analysis (AL117339.4) (Fig. 4A).

Meta analysis of TWAS results from AA and EUR GWAS
We performed five types of meta-TWAS analyses within each trait to integrate results from an AA and a EUR GWAS-associated TWAS, enhancing power and reducing noise (Fig. 1A and Supplementary Data 6, 7). GReX and GReX pairs included: Ct165 (GReX and GReX ) – representing neurotypical control GReX models trained on identical sample sizes; CtMax (GReX and GReX ) - representing GReX models with the maximum number of neurotypical controls; and AllMax (GReX and GReX ) - representing GReX models trained on both cases and Ct. For example, Ct165 refers to meta-analysis of GReX (GWAS ) and GReX (GWAS ), while Ct165 refers to meta-analysis of GReX (GWAS ) and GReX (GWAS ). Meta TWAS-pathway results for all combinations were obtained using Fisher’s method (Methods) (Supplementary Data 8, 9).

Within each trait, metaAA and metaMis, as well as metaEUR and metaAnc, corresponding to the same GReX pair, showed very high correlations (Supplementary Fig. 17), influenced by the imbalanced GWAS sample sizes (Fig. 5A). Hence, among the four meta-analyses of the Ct165 pairs, >95% of FDR-GTAs from Ct165 and Ct165 were shared with Ct165 and Ct165 , respectively (Fig. 5A–D). Given this concordance, we focus only on the metaAA and metaEUR results. While some metaAA-specific and metaEUR-specific FDR-GTAs were observed, most overlapped with corresponding single-ancestry TWAS FDR-GTAs (Supplementary Fig. 18). There was low overlap of FDR-GTAs across the meta-analyses within each phenotype, except for BIP (12% in PTSD, 15% in SCZ, 4% in MDD and 0% in BIP; Fig. 5A–D).

Overlap of TWAS-based Pathways generated using eight GReX models within trait
In the Ct165 pair analysis, collectively, the FDR-pathways (FDR < 0.05) identified by GReX (GWAS ) and GReX (GWAS ) totaled 466 in SCZ, 106 in PTSD, 36 in MDD, and 5 in BIP, whereas AA GWAS did not yield any FDR-pathways (Supplementary Fig. 19–22 and Supplementary Data 8). GReX identified the largest proportion of unique pathways in SCZ (42%) and PTSD (41%), with SCZ also showing the highest sharing between GReX and GReX models (17%). Results of single ancestry GWAS were then meta analyzed as described in the above section. Read Supplementary Notes for detailed pathway overlap description28. To explore functional relationships, shared and unshared pathways were clustered based on their constituent genes and summarized using simplified enrichment (29, “Methods”), with cluster-wide word clouds providing a visual overview.

In the PTSD, Ct165 -specific FDR pathways were primarily linked to amino acid transport (glutamine, azole, histidine and alanine) (Fig. 6A), and transporter activities (symporter, antiporter, and transmembrane). Enrichment was driven by solute carrier (SLC) family genes, including neurotransmitter transporters such as glutamate (SLC1A6 and SLC1A7), GABA (SLC6A13), and their precursor glutamine (SLC38A3), reflecting diverse substrate specificities relevant to neurotransmitter cycling and amino acid homeostasis (Supplementary Fig. 27)30. In contrast, Ct165 -specific FDR pathways included negative voltage-gated calcium channel activity; secretion and regulation of endocrine and steroid hormones (corticosteroids, glucocorticoids, and corticotropin); organismal and behavioral responses to environmental and physiological stimuli (stress, hormones, and ethanol exposure); and epigenetic modification of histones H4K4 and H4K16 (Fig. 6B). Many of these enrichments were driven by CRHR1, the CRH receptor and a prototypical driver of the stress response pathway (Supplementary Fig. 27). Ct165 -specific FDR-Pathways involved transport processes related to GABA and glutamate neurotransmitter transporters, known to regulate hypothalamic activation, and Ct165 -specific FDR-Pathways involved stress-related signaling pathways. Together, these pathways capture distinct but interconnected components of hypothalamo-pituitary-adrenocotical (HPA) axis regulation31. In addition, positive regulation of adenylate cyclase activity was enriched in Ct165 , while cAMP-dependent protein kinase activity was specifically enriched in Ct165 . These pathways represent sequential steps in the cAMP signaling cascade, suggesting that the two models highlight distinct but connected components of the same biological process.

In SCZ, TWAS pathway analysis revealed extensive enrichment of immune-related pathways, consistent with prior evidence linking immune dysregulation to SCZ32,33. Many were shared between both metaAA and metaEUR , but we observed prominence of innate response more in Ct165 and adaptive and inflammatory response in metaEUR . Both models showed shared enrichment in lipid metabolism (including ceramide and glycosphingolipid), peptidase regulation and glycosidase activity, all processes linked to schizophrenia34,35. (Fig. 6C, D). The metaAA -specific FDR-Pathways included developmental processes, such as dentate gyrus and adenohypophysis development and neuroepithelial cell differentiation. Developmental abnormalities of the hippocampus, including dentate disruption, have been implicated in schizophrenia36. Some enriched cellular components were related to neurotransmission (dendritic tree and dopaminergic synapse), and intracellular trafficking (vesicle-associated compartments, including transport vesicles, endocytic vesicles, and their membranes) (Fig. 6C, D). In contrast, the metaEUR -specific pathways were associated with calcium channel activity, stress hormone signaling, ear auditory receptor, neurotrophic production and binding, epigenetic modification, collagen metabolism and the viral lifecycle (Fig. 6C). Auditory receptor pathways are notable given sensory processing deficits in SCZ, especially within the auditory cortex, which may contribute to hallucinations37.

The MDD TWAS pathway analysis showed limited metaAA -specific enrichments. The one notable Ct165 -specific pathway group was related to procollagen-specific proline dioxygenase activity, which may be particularly relevant, as proline metabolism has been associated with depression and anxiety severity38,39 (Fig. 6E). metaEUR -specific enrichments were also limited and comprised of immune related signaling, primarily involving TGFβ production, glycosylation processes and low-density lipoprotein (LDL) receptor activity (Fig. 6F). LDL receptors influence the central nervous system in a multifaceted manner, contributing to processes such as synaptic plasticity and neuromuscular junction development40. There was a limited signal observed for the Ct165 models in BIP.

Gain and loss of using All and bi-ancestry models
To assess the benefits of GReX and GReX models, we compared FDR-GTA results across models for the same underlying GWAS (Supplementary Figs. 11, 12, 28). We observed that the number of FDR-GTAs increased as the training sample size increased (pearson r > 0.8 between the number of FDR-GTAs and the training sample size of the corresponding GReX models within each set of TWAS analyses derived from the same GWAS), although both shared and unshared genes were present in the TWAS results (Supplementary Fig. 28B, C). This aligns with our observation that, although the number of predicted genes in a GReX model increases with training data size, both overlapping and non-overlapping genes are observed across models, including those trained with smaller sample sizes (Supplementary Fig. 2). To evaluate the benefit of Ct versus All models, we compared EURCt477 vs. EUR-All1032 (a 50% increase in sample size) and AACt165 vs. AAAll276 (a 40% increase) (Supplementary Fig. 28B). We noted that the gain in GReX -specific genes was much higher—57% for BIP, 56% for MDD, 49% for PTSD, and 48% for SCZ—compared to GReX , where the gains were 31, 32, 44, and 36%, respectively. The greater gain observed in AAAll276 may be attributable to the smaller absolute control sample size (n=165) compared to that of the EUR group (n=477).

Similarly, to evaluate Ct vs. bi-ancestry Ct model performance, we compared the FDR-GTAs from AACt165 and EURCt477 with those from AAEURCt330 and AAEURCt664 (Supplementary Fig. 28C). Since AAEURCt330 includes a comparable proportion of AA and EUR subjects (50% AA), whereas AAEURCt664 has a lower proportion of AA subjects (25% AA), we aimed to determine whether AACt165-specific signals were lost more in the lower AA proportion model. Although, except for SCZ (17 genes, 10% vs. 14 genes, 8.9% of overall findings), the number of AACt165-specific signals remained similar. Overall, AAEURCt664-specific FDR-GTAs included 34 in SCZ (20%), 12 in BIP (21%), 12 in MDD (23%), and 9 in PTSD (23%), but this came at the cost of losing ancestry-specific signals (e.g., 28% in SCZ, 41% in BIP, 30% in MDD, and 20% in PTSD for EUR-specific FDR-GTAs; 10% in SCZ, 7% in BIP, 10% in MDD, and 23% in PTSD for AA-specific FDR-GTAs). For example, some key signals, such as FURIN for SCZ, were absent in bi-ancestry TWAS results (Supplementary Fig. 29). We considered whether using a matched ancestry background GWAS would improve the situation, so we applied the bi-ancestry model on the bi-ancestry GWAS, but FURIN remained missing in both. As for the TWAS analysis associated with single-ancestry AA GWAS, we had very limited FDR-GTAs (PTSD: SUN3 in GReX (GWAS ); BIP: ADH5 in GReX (GWAS ) and ZNF37A in GReX (GWAS )).

TWAS pathway signals detected only by bi-ancestry and All models included spliceosome-related components across PTSD and SCZ — such as tri-snRNP, U2/U12, and snoRNA-associated complexes (Supplementary Figs. 30–32). Spliceosome machinery alterations have been implicated in PTSD41. In SCZ, bi-ancestry models also revealed blood–brain barrier–related pathways, an increasingly recognized feature of the disorder42. Across PTSD, MDD, and BIP, higher-order models further uncovered more synaptic, neuronal and related pathways (Supplementary Notes).

Fetal GReX models across ancestries: Training and TWAS results
To evaluate whether integrating developing human brain GEx panel with GWAS could also benefit from mismatched ancestry combinations, we trained 2 fetal brain tissue models: fetalAA (n=135; AA dataset) and fetalEUR (n=280; EUR dataset) (“Methods”43,). Due to sample size differences, conclusions are not directly comparable to the DLPFC Ct165 pair, but fetalAA-specific signals (from the smaller model) can be interpreted as a gain. fetalAA predicted 1538 genes and fetalEUR predicted 3519 genes with 1,051 common between the two (Fig. 7A; Supplementary Data 10). Correlation of R2 between the common genes was 0.59, and SNP-weights concordance was 95%, similar to DLPFC. Pathways of unshared genes were enriched in mitochondrial processes, cilia and vesicle transport, DNA repair and metabolism (Fig. 7B and Supplementary Data 11). Correlation of R² across fetal models and the DLPFC CtMax pair (EURCt477 and AACt165) revealed stronger within-tissue correlations, while cross-tissue correlations were higher when ancestry was matched compared to unmatched (Fig. 7C–E). A total of 2736 genes were predicted by both tissues, with 1569 being fetal-specific and 8954 being DLPFC-specific (Fig. 7F). These gene sets demonstrated varying degrees of pathway coverage (Supplementary Fig. 33). Adult brain tissue covered nearly all broad categories, whereas fetal tissue showed limited coverage for many categories, including disease, immune processes, and cellular responses to stimuli. However, as expected, fetal genes exhibited significant cell type enrichment in several fetal tissues, compared to adult DLPFC tissue (Supplementary Fig. 34).

We obtained TWAS of single-ancestry GWAS by integrating the fetal models with ancestry-specific GWASs: GReX (GWAS ), GReX (GWAS ), GReX (GWAS ) and GReX (GWAS ). meta TWAS were obtained by meta-analyzing combinations of these TWAS as done for DLPFC GReX pairs. TWAS correlation based on GTA z-score were clustered by GWAS (Fig. 7G, H). GReX -specific GTAs were 14% (4) in PTSD, 14% (21) SCZ, 1% (4) in MDD and 1% (4) in BIP (Supplementary Fig. 35; Supplementary Data 12–15). FDR-GTAs of TWAS of AA GWAS had no signal. GReX -specific pathways were 23% (13) PTSD, 7% (35) SCZ, 7% (14) BIP and 19% (10) MDD (Supplementary Figs. 36, 37 and Supplementary Data 16, 17).

In PTSD, fetalAA-specific enrichments included the membranes of transport, endocytic, and coated vesicles, and protein and histone methyltransferase complex (Supplementary Fig. 38). fetalEUR-specific pathways were enriched for synaptic and neurotransmitter-related functions (GABAergic synapse, dopaminergic synapse, dopaminergic synaptic transmission, neurotransmitter uptake, and glutamate receptor binding), megakaryocyte differentiation, skeletal muscle development, heterotypic adhesion, and cell fusion.

In SCZ, fetalAA-specific FDR-pathways included lipid transport, endocytic and vesicle membrane processes (Supplementary Fig. 38). fetalEUR-specific FDR-significant pathways showed a wide variety of enrichments. Neural function–related pathways included neurotransmitter regulation and uptake, neuropeptide hormone binding, glutamatergic, GABAergic, and dopaminergic synapse, and neuron and axon development. Other developmental pathways included skeletal muscle development, megakaryocyte and myeloid cell differentiation. Additional enrichments were observed in immune pathways, chromosome organization, protein regulation and quality control, cytoskeletal organization, ER function, lipid and sugar metabolism, and cell adhesion.

For MDD, fetalAA-specific FDR-pathways were enriched in sensory perception and olfaction functions (Supplementary Fig. 38), which have been associated with depression44. fetalEUR-specific enrichments included megakaryocyte and myeloid cell differentiation, regulation of hemopoiesis, chromatin and epigenetic regulation, protein localization, viral process modulation, nucleotide-sugar modification.

For BIP, most FDR-significant pathways were shared across fetalAA and fetalEUR models, including neuropeptide hormone activity, lipid transport and localization, and immune signaling. Exceptions included EURCt165-specific pathways related to neurotransmitter synaptic activity (including dopaminergic, GABAergic, and glutamatergic), megakaryocyte differentiation, and myotube and muscle tissue development.

TWAS-based GTAs and Pathways across DLPFC and fetal brain tissue within trait
We used the TWAS results of our largest neurotypical models, CtMax pair for DLPFC, to compare with the TWAS results of the fetal pair (Methods, Figs. 8, 9). Overall, higher overlaps across fetal ancestry-specific pairs compared to DLPFC pairs were found (Supplementary Notes; Fig. 8A–D, Supplementary Figs. 40, 41). Overall, the proportion of GTAs shared across tissues was 8% in PTSD, 7% in SCZ, 5% in MDD, and 2% in BIP. In PTSD, both tissues showed enrichment for chromatin modification and ER processes, with fetal tissue highlighting neurotransmitter and developmental programs, while DLPFC emphasized amino acid transport and stress hormone regulation (Supplementary Fig. 42). In SCZ, shared pathways included lipid metabolism, protein regulation, immune response, and synaptic and axon functions; fetal analyses were more enriched for neuronal signaling and developmental differentiation, whereas DLPFC highlighted sensory perception, cytoskeletal organization, and stress-related processes (Supplementary Fig. 43). We also notice that enrichment of overlapping pathways could arise from different contributing genes. For example, interferon beta regulation pathways involved BTN3A2 and HLA-DPA/B in DLPFC versus FLOT1, NMB, and IRF7 in fetal tissue. (Supplementary Fig. 44). In MDD, both fetal and DLPFC tissues shared pathways in nucleotide sugar biosynthesis and serine metabolism (Supplementary Fig. 45). Fetal analyses emphasized developmental and regulatory programs, including kinase activity, sensory and olfactory signaling, and chromatin/telomere organization, while DLPFC-specific pathways were linked to synaptic structure, axodendritic transport, Golgi function, and DNA methyltransferase activity. In BIP, fetal analyses showed broad neuropeptide and synaptic, chromatin and telomere, lipid metabolism, and stress/repair signals, while DLPFC signals were limited (Supplementary Fig. 46).

TWAS-based GTAs and Pathways across traits
We next examined FDR-GTAs shared across disorders and between developmental (fetal) and adult (DLPFC) brain models. Across all comparisons, we identified 101 GTAs that were shared either across tissues or across disorders (Fig. 10A, B). Of these, 2 were present in all four disorders, 13 in three disorders, and 61 in two disorders. All GTAs present in more than two disorders localized to three genomic regions — 17q21.31, 6p21, and 3p21 — and the majority were detected in fetal TWAS (100% of four-disorder GTAs and 76% of three-disorder GTAs). The two fetal-specific FDR-GTAs, H4C13 and ZSCAN12P1, that were prioritized across all four disorders, were found to have their cis SNPs in LD with open chromatin regions in a bulk fetal frontal cortex study45. Other fetal-specific FDR-GTAs prioritized in 3 disorders included AC006254.1, DND1P1, FLOT1, GNL3, NT5DC2, OR2B8P, and ZSCAN23. DLPFC-specific prioritized genes shared across 3 disorders included BTN3A2, CRHR1 and TRIM26 (Fig. 10B).

Many developmental pathways related to megakaryocyte biology and bone marrow differentiation were enriched in fetal TWAS across all disorders, driven primarily by H4C13. Multiple fetal FDR-significant pathways were shared by BIP, PTSD, and SCZ, including neurotransmitter-specific synaptic pathways, skeletal muscle developmental pathways, myoblast fusion, heterotypic cell–cell adhesion, and raft assembly (Supplementary Figs. 47, 48). Across both DLPFC and fetal TWAS pathway results, and consistent with patterns seen within individual disorders, we found that pathways shared across disorders could involve different contributing genes (Supplementary Fig. 49). Across disorder pairs, examples of shared pathways included vesicle membrane pathways (PTSD and SCZ) and olfactory receptor activity (SCZ and MDD). The largest overlap was observed between BIP and SCZ (Supplementary Fig. 47), which shared multiple enriched pathways, including immune pathways, lipid transport, and protein sumoylation. Despite the enrichment of many immune pathways across all disorders, relatively few specific pathways were shared universally. Similarly, pathways from generic functional classes—such as protein localization to chromatin, DNA packaging, telomere organization, and centromeric core domain organization—were present across all disorders.

Methods
DLPFC Data
Our DLPFC data processing and imputation work is largely based on our previous publication31.

Datasets. The reference genotype-expression sample for building a DLPFC GReX model was obtained from PEC (PsychENCODE)63. We used the data of subjects (age at death ≥ 18 years) with matched genotype and bulk DLPFC gene expression data derived from 1425 unique donors63. The data came from five studies (CMC, CMC-HBCC, LIBD, BrainGVEX, UCLA-ASD). It was downloaded from Synapse (node: syn8466658). The resulting dataset contained sequencing data from both rRNA-depletion and poly-A-selection libraries and contained both stranded and non-stranded samples.

Genotype. PEC genotypes were generated and imputed as previously reported20. Samples were genotyped using various Illumina platforms, including the Illumina 1M, Illumina HumanHap650 (h650), Illumina Infinium HumanOmniExpressExome v1.1b, Illumina Omni5, and Illumina h650 arrays. Raw genotype data were first converted to PLINK format and subjected to rigorous quality control (QC). Sample-level QC included filtering based on call rate below 95%, heterozygosity outliers, and sex discrepancies. Marker-level QC excluded SNPs with zero alternate alleles, call rates below 95%, Hardy-Weinberg equilibrium p-values < 1 × 10−6. Genotype was imputed using Minimac with the Haplotype Reference Consortium (HRC) panel. Post-Imputation, SNPs were filtered to retain only high-quality variants with an imputation quality score R2 > 0.3. Sub-studies imputed genotype files were merged using BCFtools (v1.9). Multi-allelic SNPs were removed, and genotype dosages were computed on a continuous scale from 0 (homozygous reference) to 2 (homozygous alternate), with 1 representing heterozygous genotypes.

Genotype based ancestry. We calculate ancestry proportions for each subject by projecting their genotype data onto principal components derived from known continental level ancestry markers. For this, we used the global ancestry pipeline (https://github.com/nievergeltlab/global_ancestry), which applies SNP weights64 to a set of 10,000 ancestry informative markers. These markers were evaluated against a reference panel comprising 2911 individuals from 71 diverse populations and six continental groups (k = 6)48. Subjects with a European ancestry proportion > 0.9 were labeled as EUR (n = 1032), while those with an African ancestry proportion > 0.6 were labeled as AA (n = 276) subjects (Supplementary Fig. 1).

RNA-seq. PEC tissue preparation, RNA extraction, cDNA library preparation, and sequencing procedures were reported in ref. 65. All RNA-seq samples were reprocessed using a uniform pipeline to avoid confounding effects of differential data preparation. PEC RNA-seq data were queried in bam format. We adopted the GTEx RNA-seq processing pipeline66. In brief, duplicates were marked with Picard (v.2.22.8) and RNA-seqC (v.2.3.5) was used to quantify gene expression. Samples were removed if they contained fewer than 10 million reads, more than 1.1% mismatch rate, or more than 30% intergenic reads.

Genetically-regulated gene expression (GReX) model
We adopted the PrediXcan database construction methodology to make our prediction model output compatible with the PrediXcan software family. We used elastic-net regularized regression, as it accounts for sparse eQTL architecture and robustly outperforms other available approaches8,67,68. In line with previous models, protein-coding genes, pseudogenes, and lncRNA were included in our database.

Processing of data for each GReX training dataset. Within each GReX training dataset: (a) RNA Seq data was normalized. RPKM Normalized RNA-seq counts were inverse quantile normalized. (b) SNPs with less than 0.01 MAF were dropped. (c) Five genetic principal components (PCs) were calculated. (d) PEER factors were calculated to account for unknown sources of technical variation between and within the different studies of the main dataset69. The number of PEER factors was chosen based on the training sample size: 30 for > 150 and ≤ 250, 45 for > 250 and ≤ 350 and 60 for > 350. Along with genetic PCs and peer factors, sex, age at death, library preparation (rRNA depletion vs polyA), sub-study, PMI, RIN, and sequencing platform were included as covariates for model construction.

Elastic-net regularized regression model. We applied elastic-net regularized regression to estimate SNP effect sizes within 1 Mb of each gene, following the metaXcan framework8. Model performance was estimated using nested cross-validation (5 outer folds for unbiased evaluation, with 10-fold inner cross-validation for lambda selection) and reported as the average square of the Pearson correlation (R²). Final SNP–gene weights were obtained by retraining models on the entire dataset using 10-fold cross-validation for lambda selection. Models with R2 > 0.01 and P < 0.05 were retained for downstream analyses, as in ref. 8.

Enrichment of GReX-specific genes. We perform gene set enrichment using metascape70 via their web service.

External validation data. The validation DLPFC gene expression and genotype data used were derived from 245 EUR (including 73 controls, 25 BIP, 85 MDD and 63 PTSD) and 50 AA (including 25 controls, 15 MDD, 7 PTSD, and 3 BIP) samples, part of a prior multi-region RNA-seq study (n = 32571). EUR and AA ancestry were assigned using the same method as described above. These brain samples were collected by the Lieber Institute of Brain Development (LIBD) as part of their brain collection72,73 that was acquired by the National PTSD Brain Bank74. For the validation analysis, we regressed out age, sex, disease status and RIN from normalized gene expression data and scaled the residuals.

External validation performance metrics. metaXcan (https://github.com/hakyimlab/MetaXcan) imputation pipeline with default setting was used to predict gene expression. To quantify observed performance ρobs (spearman correlation), predicted expression from the genotype of the validation dataset is correlated with observed expression. To compare performance between a pair of GReX model, the difference between ρobs is calculated for genes predicted in both models. mean, and skewness is calculated on this difference of ρobs :

TWAS (Transcriptome Wide Association Studies)
GWAS. We used the EUR and AA GWAS summary statistics for each trait as follows
• SCZ: EUR from PGC36 (n = 52017 cases / 127906 total) and AA from MVP2 (n = 5785 cases / 14122 total).
• PTSD: EUR from PGC34 (n = 137136 cases / 1222882 total) and AA from PGC34 (n = 11560 cases / 51034 total).
• MDD: EUR from PGC2+MVP3 (n = 254566 cases / 750414 total) and AA from MVP3 (n = 25843 cases / 59600 total).
• BIP: EUR from PGC31 (n = 41917 cases / 413466 total) and AA from MVP2 (n = 3027 cases / 12597 total).

Genetic Correlation. The genetic correlation across four GWAS datasets was calculated using the LDSC (Linkage Disequilibrium Score Regression) tool. Initially, GWAS summary statistics were reformatted and filtered to include HAPMAP3 SNPs. Predefined LD scores and weights provided by the tool were then applied to the reformatted summary statistics files to calculate genetic correlation.

Gene trait associations (GTAs). JEPEGMIX2-P software22 with default settings was used to conduct TWAS using our GReX models. JEPEGMIX2-P was applied on GWAS summary statistics to estimate GTAs. This method was preferable since it relied on a covariance matrix based on 33K samples compared to other TWAS methods, which use less than 3k samples75. To qualify GTAs as statistically significant (FDR-GTAs) in our TWAS analysis, we use FDR correction with a threshold of 0.05 instead of the Bonferroni correction. This choice is supported by the observation that JEPEGMiX delivers more stringent associations than metaXcan.

meta TWAS. meta TWAS for GTAs was performed using METAL76. Genes with the same effect size direction were kept. Determining signals passing multiple testing correction was based on FDR, i.e., an FDR-adjusted P < 0.05. We performed 5 types of meta-analyses of AA-based, EUR-based and AAEUR-based GTAs: (i) metaEUR that meta-analyzes GTAs produced using GReX applied on EUR GWAS and on AA GWAS [GReX (GWAS ) + GReX (GWAS )], (ii) metaAA that meta-analyzes GTAs produced using GReX applied on EUR GWAS and on AA GWAS [GReX (GWAS ) + GReX (GWAS )], (iii) meta ancestry-specific (metaAnc) that meta-analyzes GTAs produced using GReX applied on AA GWAS with GReX applied on EUR GWAS [GReX (GWAS ) + GReX (GWAS )], (iv) meta mismatch (metaMis) that meta-analyzes GTAs produced using GReX applied on EUR GWAS with GReX applied on AA GWAS [GReX (GWAS ) + GReX (GWAS )] and (v) metaAAEUR that meta-analyzes GTAs produced using GReX applied on AA GWAS and on EUR GWAS [GReX (GWAS ) + GReX (GWAS )].

TWAS-Pathway analysis. With JEPEGMIX2-P, we were able to conduct TWAS pathway analysis. JEPEGMIX2-P combines GTA z-score statistics in a pathway with Mahalanobis χ2 statistics, while adjusting for LD for the participating genes. In this case, we calculated disease association for GO-BP/MP/CC pathways. Determining signals passing multiple testing correction was based on FDR, i.e., an FDR-adjusted P < 0.05.

meta Pathway. Since JEPEGMiX2-P provides chisquare statistics pathways, we use fishercomb function from metaRNASeq package77 to perform meta-analysis. Since JEPEGMiX2-P provides chi-square statistics for each pathway, we use fishercomb function from the metaRNASeq package77 to perform meta-analysis.

TWAS pathway correlations. We performed pathway correlations using enrichment p-values, as we do not have an effect size.

Clustering of TWAS pathways. TWAS GO pathway analysis results were visualized by clustering selected pathways based on semantic similarity using the simplifyEnrichment package29. Within each ontology (BP, CC, and MF), pathways were analyzed using the GO_similarity function, where semantic similarities were calculated based on gene annotations to GO terms with the Wang similarity measure78. Clusters were then refined using the simplifyGO function, which applied the Louvain graph community method to form distinct pathway groups. Summaries of the biological functions within each cluster were visualized as word clouds attached to the similarity heatmap, offering a direct illustration of the common biological functions involved in each cluster. Within each ontology (BP, CC, and MF), pathways were analyzed using the GO_similarity function, where semantic similarities were calculated based on gene annotations to GO terms with the Wang similarity measure78. Clusters were then refined using the simplifyGO function, which applied the Louvain graph community method to form distinct pathway groups. Summaries of the biological functions within each cluster were visualized as word clouds attached to the similarity heatmap, offering a direct illustration of the common biological functions involved in each cluster.

cTWAS. The putative causal GTAs were obtained using cTWAS27, for GReX and GReX models applied on EUR SCZ GWAS summary statistics. Analyses were run using the default parameter settings of cTWAS, except that the minimum number of variables in a region was set to 10 (min_group_size = 10), and a posterior inclusion probability (PIP) threshold of > 0.5 was applied to identify putative causal genes. Linkage disequilibrium (LD) information was obtained from the pre-computed reference LD matrices of European samples from the UK Biobank available on the cTWAS website.

Rank-rank hypogeometeric overlap (RRHO)
RRHO2 for GTAs. RRHO plots for GTA results were generated using RRHO2 software79. For these analyses, we ranked genes based on p-values and the sign of the direction of effect of the GTAs generated by JEPEGMiX software.

RRHO for pathways. RRHO plots for pathways results were generated using RRHO software80. As JEPEGMIX pathway enrichments are based on chi-square statistics and hence lacks directionality of enrichment, pathway rankings were based solely on p-values.

LD Block calculations. We use the LD Matrix option from the LDlink26 online tool. Americans of African Ancestry in SW USA (ASW) population is used as a proxy for our AA population. All European population: Utah Residents from North and West Europe (CEU), Toscani in Italia (TSI), Finnish in Finland (FIN), British in England and Scotland (GBR) and Iberian population in Spain (IBS) was used to represent our EUR population.

Grady Trauma Project (GTP)
Target population. The target population for model testing in PTSD was drawn from the Grady Trauma Project (GTP), a cross-sectional cohort of trauma-exposed civilians23,24. GTP was launched over 15 years ago and exclusively recruits participants at the Grady Memorial Hospital, Atlanta, Georgia. The dataset is especially enriched for racially marginalized (>85% Black) and low-resourced adults. Genetic data was available for N = 7206. Global ancestry proportion was estimated using the global ancestry pipeline describe above81. EUR and AA subjects were identified in two ways: using criteria used in the generation of GTP PTSD GWAS summary statistics81 to enable comparison between the raw TWAS and summary-statitics based TWAS results. (a) EUR: (eur > 90%; N = 198), and (b) ancestrally Admixed African (N = 5284) based on (i) subjects with afr ≥ 5% African ancestry, < 90% European ancestry, < 5% East Asian, Native American, Oceanian, and Central-South Asian ancestry and (ii) subjects with ≥ 50% African ancestry, < 5% Native American, Oceanian, and < 1% Asian ancestry. Definition consistent with the ones used in this paper for the DLPFC cohort, EUR (eur > 0.9; N = 198) and AA (0.6 > afr; N = 5904).

Gene-trait associations (GTA). We performed transcriptome-wide association analysis (TWAS) at two levels for each psychiatric disorder examined i.e., (i) at the GWAS summary-statistics level, herein referred to as summary-based TWAS as described above, and (ii) at the genotype-level, herein referred to as raw TWAS. For raw TWAS, we first estimated GReX using the ancestry-specific models requiring genotype information as input, followed by phenotype-GReX association. GReX was estimated using metaXcan (https://github.com/hakyimlab/MetaXcan). Association was also implemented with PrediXcanAssociation.py. PTSD case-control status was estimated previously81. Covariates were comparable to the original GWAS, which included age, sex, and the first five genetic principal components.

Fetal Brain Tissue model
Dataset. The developing human brain data was obtained and processed as described here43. Briefly, this study combines five individual studies the Walker study82 contributed brain cortical samples (14-21 post-conception weeks [PCW]) generated using Ribo-Zero with 60M reads per sample RNA-seq and Illumina HumanOmni2.5 genotyping array. The O’Brien56 study included brain samples (12–19 PCW) sequenced with Ribo-Zero with 100M reads per sample RNA-Seq data and Illumina Infinium OmniExpress-24 BeadChip genotyping array. The Werling study83 comprised of DLPFC samples (6–37 PCW) sequenced with Ribo-Zero 37M reads per sample RNA-seq and 30X whole-genome sequencing. The LIBD study84 contributed DLPFC samples (14–20 PCE) sequenced with Ribo-Zero 150M reads per sample RNA-seq and Illumina 1M DuoV2 genotyping array. The HDBR study included bulk brain samples (4–20 PCW) sequenced with PolyA 50M reads and Illumina HumanOmni5-4v1-B genotyping array. Ten duplicate samples between the HDBR and O-Brien studies were removed, and the dataset was further filtered to retain only DNA-RNA matched and high-quality samples.

Genotype. Genotype Data from individual studies were uniformly processed and filtered for MAF < 0.01, HWE (--hwe 1 × 10−6), variants per sample missingness (--geno 0.05) and samples per variant missingness (--mind 0.10). QC-ed genotype data were imputed on the Michigan Imputation Server (Minimac4, Eagle v2.4 for phasing) using the TOPMed Freeze 5 reference panel, retaining variants with imputation R2 > 0.3. Post-imputation, studies were merged by intersecting variants, removing multi-allelic sites. Variants were filtered for MAF < 0.01, HWE (p < 1 × 10−6), and variants per sample missingness (--geno 0.05). LD-pruned (plink --indep-pairwise 50 50 0.2) data were merged with 1000 Genomes. PCA and k-nearest-neighbors (k = 50) were run to infer data ancestry. 135 AA and 280 EUR samples were used to build fetalAA and fetalEUR GReX models, respectively.

GReX Modeling. We used the imputed genotype data restricted to HapMap3 variants only and expression panel corrected by age, sex, 5 genotype PCs, and hidden component with priors (HPC). Using the TWAS-FUSION framework, five-fold cross-validation of three models of expression prediction (best cis-eQTL, elastic-net, SuSiE) were calculated and evaluated for accuracy. Although only elastic net models were retained with p-value < .05 and R2 > 0.01 for downstream analysis.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript presents a large study that integrates ancestry-informed eQTL models, constructed from both adult DLPFC tissues and fetal brain tissues, with large GWAS data on four psychiatric disorders through TWAS analysis. The study focuses on two ancestries: European (EUR) and Admixed African (AA), and found both shared and ancestry-specific genetic effects. Bi-ancestral models improved signal detection compared to single-ancestry models. Mismatched TWAS revealed novel GTAs not detected in ancestry-matched TWAS. This is an impressive undertaking with the potential to yield valuable insights. Since most large-scale omics data to date are still primarily based on populations of European descent, the rich genomic diversity of other ancestries has not been fully leveraged in the context of psychiatric disorders. While this study is both important and timely, I have some reservations however and would appreciate additional information and clarification on the following points.\nMajor comments\n(1) A large number of comparisons were made including various combinations of ancestries, eQTL models, ancestry-specific GWAS for multiple diseases, and both adult and developing brain tissues. Given the wide range of parameters, the results are difficult to interpretate and may, at times, lack meaningful conclusions, especially in light of certain inconsistencies.\n(2) The classification of AA appears inconsistent between the training DLPFC samples and the GTP cohort, as described in the Methods section. The definition of AA in the validation sample and fetal samples is unclear from the text.\n(3) In the GReX modeling, TWAS-FUSION was used for processing fetal brain tissues, while the PrediXcan method was applied to DLPFC tissue. What is the justification for using different methods? As a result, it’s unclear how much of the observed differences are due to tissue variations versus the use of different methodologies.\n(4) The adult brain tissue used is from DLPFC. Is the fetal brain tissue from the whole brain? If so, what is the justification for comparing a specific region of the adult brain with the whole fetal brain?\n(5) Meta-GWAS results appear to be dominated by ancestry-specific GWAS and diseases, regardless of the ancestry GReX models used (as indicated by the clustering shown in Figure 3B). This raises the question of whether the ancestry GReX models actually have any significant impact. However, this may simply be due to the highly imbalanced GWAS sample sizes between ancestries, which makes the interpretation unclear. Since larger sample sizes are correlated with a higher predicted number of genes, what then is the significance of ancestry? It seems the conclusion may be to include as many samples as possible. On the other hand, the findings from GReX -AA model could potentially be false positives (especially without further validation).\n(6) What are the biological significances of the ancestry-specific findings and for each psychiatric disease? The discussion section appears too general in addressing these respects.\n(7) The frequent use of acronyms makes the paper, particularly the results section, difficult to follow. Please consider using more readable sentences. Additionally, some figures are not clearly explained.\nMinor comments\nPage 4, last paragraph: p-value > 0.5. Is it <0.5?\nPage 5, 2nd paragraph: Ancestry-control specific pathways. What do these implicate?\nPage 5, 3rd paragraph: Does the independent DLPFC GEx panel for validation include only control samples or only cases or both? Training samples “Ct” include control only and “All” include both controls and cases. It’s not clear what the comparisons are.\nPage 5, 3rd paragraph middle section: EUR performance numbers were given in parathesis when mentioning AA models, while AA numbers given for EUR models. The whole sentence is confusing.\nPage 5, 3rd paragraph last 4 lines: Are the better performances from “All” samples due to the inclusion of cases or due to much larger sample sizes? Not sure if it can be distinguished here.\nPage 6, 2nd paragraph line 4: fig. SA?\nPage 6, 2nd paragraph: given that TWAS clustering is dominated by GWAS (first by ancestry and then by disease) rather than being influenced by GReX models, what’s the significance of applying GReX ancestry-specific or ancestry-mixed models?\nPage 6, 4th paragraph: The better performance of the EUR GWAS with any ancestry model may simply be a result of the much larger sample sizes in the EUR GWAS. Since the AA PTSD cohort (GTP cohort) is first introduced here, more details are needed, including sample sizes. What does 96% AA mean in this context, given that AA was classified quite loosely in the Methods section for this cohort? Given the way AA was classified (which may include a large proportion of EUR ancestry), it’s unclear whether the lower GTA correlation using AA is due to much lower sample sizes, mismatched ancestries, or both.\nFigure 4A-D: Gene names should be horizontal to be contained within each band.\nFigure E,F: What do bar colors represent?\nPage 7, 2nd paragraph, line 8: What is higher-order GRexX model?\nPage 7, 2nd paragraph, lines 11-13: The cytoband (6p21.3), where HLA region is mentioned in Figure 4D, is difficult to distinguish due to the green colors being indistinguishable\nPage 7, 2nd paragraph, lines 18-19: The sentence is confusing (not FDR, what does it mean?)\nFigure 5. If legends are the same for A-D, no need to repeat 4 times. If not, they should be. What does “intersection” here mean? FDR-GTA with meta-GWAS?\nFigure 6 and others. The TWAS labels need to be more explanatory instead of just using codes, as there is sufficient space. In Figure 6A, 6B, there are two legends with the same BP clusters (by name) but in different colors. Why is this? What do these colors represent?\nFigure 7F: x-axis label?\nFigure 7H: Is the heatmap correct? It appears that R2 values for every two neighboring pairs are identical. Could you clarify why this is the case?\nFigure 8: A-D: The Vann diagrams are nice, but what is the significance of comparing different tissues and different ancestry models? While some overlaps are visible, what are the interpretations?\nPage 8 lines 1-2: Could the unique genes captured by the GReX -AA model be false positives (given the lack of validation)? Is there evidence suggesting these genes are relevant?\nPage 8 2nd paragraph, last 4 lines: What is the conclusion and interpretation (or recommendation) here?\nPage 10 1st paragraph: What is the significance of comparing adult DLPFC with the fetal whole brain? How should the results be interpreted, given the numerous variables involved?\nPage 10 referring Figure S22. The topology appears to be quite similar across fetal-specific, DLPFC-specific, and shared. While there are color differences, what do these color represent?\nPage 11 Discussion section lines 4-6: Please be more specific. Which ancestry-specific pathways indicate environmental selection pressures?\nPage 11 last two lines to Page 12 1st paragraph: When integrating mis-matched ancestry models, how LD was handled? Is it possible that novel findings are false positives? Not sure if it’s a valid argument.\nPage 12 1st paragraph: Please be more specific. Which trait-specific pathways are referred here?\nPage 30: What is PEC? Please spell out all acronyms when first introduced.\nPage 30 next-to-the-last line: What is db, a file type?\nPage 31 section 2.1: Spell out acronyms when first used.\nPage 31 section 2.3: The writing is a bit confusing in this paragraph. What is the reference dataset? The relation between 5 subsets and 10-fold cross-validation is not clearly stated.\nPage 31 section 2.4: Were brain samples from control or from PTSD patients?\nPage 31 section 2.4: How were ancestry classification of EUR and AA determined for validation sample as if they’re different from the training sample, it may affect the results.\nPage 31 section 2.4: How were the residuals scaled?\nPage 33 section 4.1: In target population, to classify AA, criteria (i) and (ii) are very different. While criteria (ii) require at least 50% African ancestry, (i) requires African ancestry > 5%, European ancestry < 90%, which can hardly be called Admixed African, as most samples likely have very high % of European ancestries. Criteria for AA are also different (in terms of % African ancestry) between target sample and reference samples. Why is it the case? Sample size doesn’t seem to be the reason.\nPage 33 section 4.2: The number of PCs used in analyses are not consistent, with some analysis (DLPFC data) including 4 PCs, others using 5. Why?\nPage 33 section 5.1: In addition to the reference, a brief description should be provided.\nPage 33 section 5.2: How were fetal AA and EUR samples classified?\nReference 55 needed to be double-checked.\nIn addition, could you explain how mismatched ancestry results are being validated and how you confirm those are not spurious due to divergent LD structures? Would additional calculations help confirm that these signals are truly novel and not artifacts?\nIt would also be helpful in the discussion section to provide guidance on when researchers should use ancestry-specific vs. bi/multi-ancestry models, as well as suggestions for future analyses. Specifically, is it better to use larger single-ancestry reference panels or to focus on improved multi-ancestry integration methods?", "reviewer_2": "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.", "reviewer_3": "This study integrated European (EUR) and admixed African (AA) genotype-expression (GEx) panels with ancestry-specific GWAS data to dissect the genetic architecture of four psychiatric disorders. The authors demonstrated the benefits of leveraging diverse ancestral backgrounds in TWAS analysis, highlighting concordant regulatory mechanisms across ancestries while identifying ancestry-specific genes and pathways. Overall, the study demonstrates the benefits of leveraging diverse ancestral backgrounds in TWAS analysis, providing insights into which genes and pathways are better captured by ancestry-specific panels.\nI have some concerns and comments about this manuscript.\n1. Fetal brain models are compared to adult DLPFC but lack mechanistic explanations for fetal or DLPFC-specific findings. Authors should link these differences to neurodevelopmental trajectories or disease onset timelines.\n2. The authors should provide detailed methodological protocols for global ancestry proportion inference rather than merely citing prior studies.\n3. Among the 2,248 genes jointly predicted by both EURCt165 and AACt165 models, authors observed high concordance in SNP weights (Pearson’s r = 0.6) and directionality of effects (97% agreement). While this suggests potential regulatory conservation across ancestries, it is critical to address whether this concordance reflects true biological consistency or shared genetic component due to historical admixture events in the AA population.\n4. Lack of biological interpretation of pathways: While pathway enrichments are reported, the biological relevance of ancestry-specific pathways (e.g., oxidative stress in AA, viral responses in EUR) is not sufficiently explained. Connecting these findings to previous disease mechanisms or functional studies would add significance to the results.\n5. In this manuscript, the authors use PrediXcan to train GreX models, according previous reports, The MR-JTI method can improve the accuracy of the prediction model, and the method also considers the effect of horizontal pleiotropy and potential confounding factors, so why not use MR-JTI for model training？\n6. The 8 models predicted a total of 17,406 distinct genes, EURCt165 predicted 4,557 genes, AACt165 predicted 4,926 genes, while 2,098 genes were predicted by all of these models. Besides, 40% of AACt165-specific genes and 30% of EURCt165-spefic genes were predicted in all higher-order GReX models, it suggests that genes predicted by lower-order GReX models are not consist with those genes predicted by higher-order GReX models. Does this mean that the predicted model is less accurate when the sample size is small?\n7. As shown in Figure2D, in the same disease state, AA-specific and EUR-specific genes are enriched in different pathways, there are only a few consistent pathways, how to explain this result?\n8. Just as the author mentioned, some TWAS methods already exist to integrate cross-ancestry datasets，does the method mentioned in this manuscript offers a significant increase in statistical power compared to previous methods? Whether the authors have compared the statistical power of the different methods?\n9. It seems a large proportion of TWAS signals in Fig.9 were from the MHC region, considering the complex LD structure of MHC region, the authors should take this into account when performing analyses.\n10. Given there is no clear evidence to show that MDD and PTSD are originated from abnormal neurodevelopmental processes, why selected these two disorders in this study ? Selecting relevant or appropriate tissue types is important.\nAdditional Minor Comments:\n1. The abstract should be streamlined to highlight novel discoveries.\n2. Check for consistency in terminology (e.g., \"bi-ancestry models \" vs. \"bi-ancestral models \").\n3. Fig.9 needs to be improved, it is too crowded and fuzzy.", "reviewer_4": "This study comprehensively investigates genetically regulated gene expression across two ancestries, four severe mental illnesses, and multiple GReX/meta‐analysis configurations to identify optimal approaches for uncovering biologically relevant insights. It makes unique contributions to the field of cross‐ancestry TWAS and offers guidance on leveraging emerging ancestrally diverse gene expression reference panels derived from specialty cohorts.\nAdditional strengths include a meticulous study design and control configuration that incorporate:\n- Matching sample sizes (via down‐sampling for European cohorts).\n- Inclusion of fetal brain samples from gene expression sources.\n- Comparison with models trained exclusively on neurotypical controls.\n- Investigation across four diseases, enhancing both the study’s broad applicability and the generalizability of its findings.\nThe following comments do not detract from the value of the study but reflect best practices and proposed revisions aimed at increasing the clarity and accuracy of the analyses. Minimal new analyses are proposed.\nOVERALL\n1. Limit figures to protein‐coding genes to enhance interpretability; additional transcripts may be provided in the supplementary files/tables.\n2. Given the high polymorphism, gene density, and complex LD patterns in the MHC region, exclude it from analyses performed with current methods. If not excluded, include a clear disclaimer in the discussion acknowledging this known limitation of current TWAS approaches.\nRESULTS\n1. Effect size correlations of TWAS results generated using eight GReX models across traits:\n1.1. Note that S-PrediXcan introduced adjustments in version 0.8 (see\nhttps://www.biorxiv.org/content/10.1101/2023.10.17.562831v2 and\nhttps://github.com/hakyimlab/MetaXcan/releases/tag/v0.8.0) to address inflation issues. Please specify which version was\nused for calculating the non-centrality parameter (NCP)\n1.2. Which figure is this: (see Fig. SA).\n1.3. In the Materials and Methods section, provide detailed information on how the NCP was estimated or calculated.\nAlthough “lambda” is an acceptable notation, to avoid confusion with lambda GC--given the target readership--it is preferable\nto use the abbreviation “NCP” or a similar term.\n2. Overlap of TWAS-based GTAs generated using eight GReX models within trait:\n2.1. Replace vague terms such as “general correlation” with quantitative measures (e.g., regression analysis or p-values) to\nprovide clarity.\n2.2. Avoid non-specific statements like “there was some overlap” and instead specify the degree of overlap as a percentage\nin parentheses.\n3. Overlap of TWAS-based Pathways generated using eight GReX models within trait:\n3.1. Please include an RRHO (Rank-Rank Hypergeometric Overlap) plot for both pathway and TWAS results.\nAdditionally, the Results section would benefit from high-level summarization at key points. Keep in mind that focusing\nsolely on the overlap of FDR-significant hits may depend on statistical power; actual concordance might be higher than what\nis reflected by the FDR-significant GTAs or pathway overlaps.\nMETHODS\n1.2. In the revised manuscript, include key details on genotype preparation from the cited study that are pertinent to this work\n(e.g., the reference panel used for imputation). Briefly describe the variant-level filtering performed prior to training the GReX\nmodels for European (EUR), African American (AA), and AA-European (AAEUR) cohorts.\n1.4. Clarify the statement “more than 1.1%” by specifying the context or metric to which it refers.\n1.5. Confirm whether the “library preparation covariate” refers to the assay/kit used, and clarify if the genetic principal\ncomponents were extracted from the entire mega-analysis.\n2. Although the database includes \"protein-coding genes, pseudogenes, and lncRNA”, for the final figures it is more\nstreamlined to display only protein-coding genes to appeal to a broader audience.\n2.1. Clarify whether the RNA-seq data (already residualized in section 1.5) was re-residualized.\n2.3.\na) It is unclear where the Spearman correlation (rho) is derived from in the text. Refer to the function\nnested_cv_elastic_net_perf() in the PredictDB Tutorial (https://github.com/hakyimlab/PredictDB-\nTutorial/blob/master/code/gtex_v7_nested_cv_elnet.R), noting that R’s default method for both cor() and stats::cor.test() is\nPearson.\nb) If Spearman correlation was indeed used, note that Fisher’s z-score transformation is not applicable.\nc) The description of cross-validation folds is confusing; if you used n_folds=10 and n_trait_test_folds=5, these represent the\ndefault settings.\nIn summary, unless a custom implementation was used, simply state that the standard PredictDB pipeline (PredictDB\nTutorial; https://github.com/hakyimlab/PredictDB-Tutorial) with default settings was applied.\n2.5. Specify which correlation measure is reported (e.g., Spearman).\n3.3.\na) The original authors now refer to MetaXcan as S-PrediXcan, and their latest version includes corrections for polygenicity.\nIt remains unclear whether the new S-PrediXcan or JEPEGMiX is more stringent.\nb) A justification for the use of FDR correction is not necessary, as both Bonferroni and FDR methods are acceptable in the\nliterature.\n4 reads G*au*dy Trauma Project\n4.1 the (i) description of AA is confusing \"subjects with afr ≥5% African ancestry\" isn't 5% too relaxed?\n5.2 Specify ancestry assignment approach. If different approaches were used, please comment on whether these methods\nare comparable.\nFIGURES\nOverall: Many panels lack sample size (n) information.\nFigure 1B: PsychENCODE not Psych*o*ENCODE. There are also instances in the results with this error.\nFigure 2B: This plot may be misleading, as a large number of SNP weights may not shared between the AACt165 and\nEURCt165 models. In the caption, specify the number of shared SNP weights (e.g., “1,000 SNPs (1%) serve as predictors in\nboth models”) since a high correlation may be observed even with a small number of shared SNPs.\nFigure 2E: Use “rho” for Spearman correlations and “r” for Pearson correlations.\nFigure 3A: caption: the x-axis and y-axis descriptions and color indicators (blue and red) appear to be inverted\nFigure 3B: Provide insights into why PTSD clusters closer to SCZ/BIP in the EUR dataset but clusters closer to PTSD in the\nAFR dataset.\nFigure 3C: The figure is illegible even when zoomed in.\nFigure 4:\na) Remove transcripts from the (extended) MHC region.\nb) Include only protein-coding genes.\nc) Analysis of the 17q21.31 inversion region (Figure 4c) is particularly challenging in cross-ancestry studies; the current\napproach may not adequately address this without sensitivity analyses. It is acceptable to exclude these regions and\nacknowledge this limitation.\nFigures 4E and 4F are a nice addition to the study.\nFigure 6: Some figures appear to have been stitched together and have two sets of legends. In addition, Fig. 6A is\nmisaligned.\nFigure 9: Correct the caption to metaAAfeta*l* (remove extraneous characters).\nBoth Figures 9B and 9D read PTSD.\nDISCUSSION\nThe first half of the discussion effectively summarizes the key findings and their impact.\n- SNP weights concordance analysis: mention among x% of overlapping SNP weights.\n- Separate the use of disease-controls and bi-ancestral models in the text; currently, \"Increasing sample size by including\ndisease subjects or creating bi-ancestral models enhances overall signal detection and uncovers nuanced biologically\nmeaningful associations\". These are two different topics that shouldn’t be pooled together.\n- The data presented in this study suggest that “higher order” ancestry-specific models outperform control (Ct) models in\nprediction performance on independent test datasets, likely due to larger training sample sizes. This implies that training\nsample size is the dominant factor driving performance, and the potential benefits of including control brains in real-world\napplications remain unclear (beyond theoretical scenarios where, for example, 80% of brains are from MDD patients).\nStating this conclusion more forcefully would challenge the unfounded belief held by some investigators that neurotypical\ncontrol TWAS studies are either optimal or indispensable.\n- Regarding bi-ancestral models, the authors have not provided a definitive recommendation. Based on their findings, the\nmodest increase in power does not appear to justify the potential drawbacks. Instead of using bi-ancestral models directly,\nperforming ancestry-specific analyses followed by multi-ancestry fine-mapping (e.g., with MA-FOCUS) seems a more\npractical approach. However, if the authors believe that their data support the routine use of bi-ancestral models, they should\nclarify and elaborate further.\n- Acknowledge additional study limitations: fetal brain samples were pooled from various developmental stages (although\nclustering results support their use in this study), and due to the small sample size of AA samples, tract-based approaches\ncould not be explored (despite the high level of admixture in AA).\nSUPPLEMENTARY\n- Standardize the naming of supplementary figures (e.g., consistently use the “Figure SX” format)."}, "author_response": "[response to the reviewer is provided in this blue color ]\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe manuscript presents a large study that integrates ancestry-informed eQTL models, constructed\nfrom both adult DLPFC tissues and fetal brain tissues, with large GWAS data on four psychiatric\ndisorders through TWAS analysis. The study focuses on two ancestries: European (EUR) and Admixed\nAfrican (AA), and found both shared and ancestry-specific genetic effects. Bi-ancestral models\nimproved signal detection compared to single-ancestry models. Mismatched TWAS revealed novel\nGTAs not detected in ancestry-matched TWAS. This is an impressive undertaking with the potential to\nyield valuable insights. Since most large-scale omics data to date are still primarily based on\npopulations of European descent, the rich genomic diversity of other ancestries has not been fully\nleveraged in the context of psychiatric disorders. While this study is both important and timely, I have\nsome reservations however and would appreciate additional information and clarification on the\nfollowing points.\nWe are happy that the reviewer considers our study important, timely, and large-scale, and that they\nvalue its contribution to advancing the use of diverse ancestry data in TWAS analyses. We also\nappreciate their valuable remarks. Below, we address each point in detail and have made corresponding\nchanges to the manuscript. We hope that these revisions, made in response to the reviewer’s remarks,\nhave further enhanced the clarity and comprehensiveness of the manuscript.\nMajor comments\n(1) A large number of comparisons were made including various combinations of ancestries, eQTL\nmodels, ancestry-specific GWAS for multiple diseases, and both adult and developing brain tissues.\nGiven the wide range of parameters, the results are difficult to interpretate and may, at times, lack\nmeaningful conclusions, especially in light of certain inconsistencies.\nResponse: We have made the below changes throughout the manuscript\n1. We have now organized the results using more sections by breaking down results further. For\nexample, we have introduced separate sections describing advantages of GReX (“eQTL” models):\ni) ancestry specific gains; ii) All-specific gains compared to Ct models and biancestry-specific\ngains compared to Ct models.\n2. We have streamlined the result sections to highlight main findings and have moved extra details\nto supplementary material for readers who are interested in extra details. For example, we have\nimproved the pathway descriptions and have tried to put enriched TWAS pathways in broader\ncategories to compare them across brain tissue types, for better understanding of developmental\nvs adult enrichments.\n3. Existing figures have been improved for better legibility and clarity.\n4. Extra figures have been added to help understand shared and unshared results in the various\ncomparisons made. For example, we have now added, fig. S18, Venn diagram to provide overlaps\nof the AA GWAS, EUR GWAS and meta GWAS results. We have now added, fig. S28B-C, bar plots\nto explain shared and unshared signals comparing All vs Ct and Mix vs Ct GReX models.\n(2) - The classification of AA appears inconsistent between the training DLPFC samples and the GTP\ncohort, as described in the Methods section.\nPage 6, 4th paragraph: The better performance of the EUR GWAS with any ancestry model may simply\nbe a result of the much larger sample sizes in the EUR GWAS. Since the AA PTSD cohort (GTP cohort)\nis first introduced here, more details are needed, including sample sizes. What does 96% AA mean in\nthis context, given that AA was classified quite loosely in the Methods section for this cohort? Given\nthe way AA was classified (which may include a large proportion of EUR ancestry), it’s unclear whether\nthe lower GTA correlation using AA is due to much lower sample sizes, mismatched ancestries, or\nboth.\nPage 33 section 4.1: In target population, to classify AA, criteria (i) and (ii) are very different. While\ncriteria (ii) require at least 50% African ancestry, (i) requires African ancestry > 5%, European ancestry\n< 90%, which can hardly be called Admixed African, as most samples likely have very high % of\nEuropean ancestries. Criteria for AA are also different (in terms of % African ancestry) between target\nsample and reference samples. Why is it the case? Sample size doesn’t seem to be the reason.\nResponse:\n- A great point made by the reviewer to enhance accuracy! In our initial analysis, we defined AA\nand EUR ancestry in the GTP cohort based on the criteria outlined with the PGC-PTSD GWAS\n(Freeze2; Nievergelt et al., 2019) to be able to use the published summary\nNievergelt Jajoo60 statistics and then enable direct comparison between summary-\nstatistics-based TWAS results with the raw genotype-based TWAS\nresults. We have now also applied the ancestry definitions we used for\n107 5177 727\n(1.8%) (86.1%) (12.1%) the DLPFC samples to the GTP cohort (i.e., AA people are defined as those\nmore than 60% African ancestry.). Using this approach AA group was\nslightly different (n = 5904, previously 5284, overlap between then was\n5177). Accordingly, we performed raw TWAS analyses using both\nancestry definitions and now present results from both approaches. The results from both\ndefinitions remained comparable.\n-The definition of AA in the validation sample and fetal samples is unclear from the text.\nResponse:\n- validation data: Thank you for bringing this up, we had used self-reported ancestry. But it is\nimportant to use genotype-based ancestry definitions, so we have now applied the same\ndefinition as used for our training datasets. Method, result section, and Fig. 2E, have been\nupdated to reflect these changes. The downstream conclusions remained the same.\n- fetal data: ancestries were assigned using genotype type data albeit with a different method:\n“LD-pruned (plink --indep-pairwise 50 5 0.2) data were merged with 1000 Genomes. PCA and k-\nnearest-neighbors (k=50) were run to infer data ancestry. “ This definition is different from the\none applied on our DLPFC datasets. fetal data was processed, and models were trained in\nGandal’s lab. Since, rules to obtain access to fetal data are much stricter, we couldn’t get IRB\napplication approved to replicate DLPFC training data definition. But we show that at-least in\nDLPFC training data, the two definitions mostly agree (we also explain this in response to\nReviewer 4 Method 5.2).\n- Page 33 section 5.2: How were fetal AA and EUR samples classified?\n- Response: Information added.\n(3) In the GReX modeling, TWAS-FUSION was used for processing fetal brain tissues, while the\nPrediXcan method was applied to DLPFC tissue. What is the justification for using different methods?\nAs a result, it’s unclear how much of the observed differences are due to tissue variations versus the\nuse of different methodologies.\nResponse:\n- We completely agree with the reviewer and apologize for the confusion created by our methods\ndescription. The fetal brain models were indeed trained using TWAS-FUSION. However, for the\npurposes of TWAS analysis, we only used the elastic net models that are analogous to the\nPrediXcan elastic net models. As a result, the observed TWAS comparisons should primarily\nreflect tissue and ancestry variation rather than methodological differences in model training.\n- We would also like to note here that while re-examining our fetal model results, we noticed that\nwe had made a small error on when applying the standard filtering based on R2>0.01 and p-\nvalue< 0.05. This meant for fetalAA we now have 1538 GReX (previously we had 1696) and for\nfetalEUR we now have 3519 (previously we had 3410). We have rectified this error by rerunning\nfetal TWAS pipelines and have updated the concerning figures (Fig. 7-10, fig. S33-47). We would\nlike to report that this did not have any major impact on the downstream results.\n(4) - The adult brain tissue used is from DLPFC. Is the fetal brain tissue from the whole brain? If so, what\nis the justification for comparing a specific region of the adult brain with the whole fetal brain?\n- Page 10 1st paragraph: What is the significance of comparing adult DLPFC with the fetal whole\nbrain? How should the results be interpreted, given the numerous variables involved?\nResponse: Indeed, being mindful of this point is crucial while interpreting DLPFC vs fetal brain TWAS\nresults and we have now mentioned this in the respective discussion section. It is true that fetal brain\ntissue samples are profiled on the entire brain as it is\nharder to just acquire brain region specific RNA profiles.\nAnd while we understand that brain region specificity is\nlost in the fetal brain GReX models, it is presently the best\nresource available to infer developing brain signals.\nNonetheless, it is worth noting that ciseQTL signals\nacross adult brain regions are highly correlated and are\nclustered together when analyzed together with non-\nbrain tissues based on ciseQTL signals (see figure on the\nleft from (Consortium 2020); brain regions are\nhighlighted by a square box). Further, region specific\nexpression in fetal brain were noted to have similar\npatterns as like in adult brain regions (Braun, Danan-\nGotthold et al. 2023).\nThis could suggest similar correlations of ciseQTLs across\nbrain regions within fetal brain, o r the least similar separation of brain signals vs other tissues (Braun,\nDanan-Gotthold et al. 2023). Hence, we think that our fetal brain GReX models could be proxied to infer\nthings at the least to infer developing brain specific signals, which can be compared to DLPFC adult\nbrain signals as a proxy to adult brain signals.\n“…, absence of these signals in fetal analyses may reflect greater tissue heterogeneity in fetal samples\nused, which spans across many brain regions across a broad range of gestational age spanning though\nall three trimesters, compared to cortex brain restricted to adult brain. “\n(5) Meta-GWAS results appear to be dominated by ancestry-specific GWAS and diseases, regardless of\nthe ancestry GReX models used (as indicated by the clustering shown in Figure 3B). This raises the\nquestion of whether the ancestry GReX models actually have any significant impact. However, this\nmay simply be due to the highly imbalanced GWAS sample sizes between ancestries, which makes the\ninterpretation unclear.\nResponse: We thank the reviewer for the thoughtful comment which raises two points:\n1. Clustering based on GWAS: It is in-fact true that TWAS results are driven by the underlying GWAS,\nwhich we think is an encouraging sign as it confirms that biological signals concerning disease\nspecificity are not lost when applying different GReX models. Although, within a GWAS cluster\nwe do identify GReX specific clusters, emphasizing differences in gene prioritization by\ninformation variability across the GReX models. To highlight these:\na. we have now improved labels in Fig 3B for better readability and have also added fig. S9\nfor individual disease heatmap to observe these patterns more clearly.\nb. We have also improved the result section to highlight these patterns.\n“First, to assess whether the TWAS signals are primarily driven by biological signals from the\nunderlying disease GWAS or by variation introduced through the choice of GReX model, we jointly\nclustered all TWAS results generated using single-ancestry GWAS based on correlations of their\nGTA z-scores. This analysis revealed a clear hierarchical clustering pattern, with the top layer\ncorresponding to the ancestry of the underlying GWAS, followed by disorder-specific GWAS, and\nthen by the applied 8 GReX models (Methods, Fig. 3A). Within each of the two ancestry-associated\nGWAS clusters, SCZ clustered with BIP, and PTSD clustered with MDD, reflecting the known genetic\ncorrelation among the trait pairs (Derks, Thorp et al. 2022), and also observed on the GWAS data\nused in our study (fig. S5C, Methods).\nNotably, TWAS results generated using different GReX models applied to the same GWAS always\nclustered together, with subclusters reflecting the ancestry background of the GReX models (fig.\nS6). Except for MDD, in clusters corresponding to the AA GWAS for each disorder, AA, EUR, and\nAAEUR GReX models each appeared together in their own subcluster. In the clusters based on EUR\nGWAS, AA GReX models were consistently grouped together, whereas EUR and AAEUR models\nfrequently intermixed. The correlations between the two AA GReX models were generally higher\nthan, or comparable to, their correlations with higher-order GReX models (EUR or bi-ancestry) (fig.\nS6-7). Notably, higher-order GReX models exhibited stronger correlations with each other, even\nwhen EUR- and AAEUR clustering patterns were present (Fig. 3A, fig. S6).”\n2. Meta-TWAS results are indeed driven mostly by the combination of GReX model and EUR GWAS\n(fig. S17), as EUR GWAS has more power due to much higher underlying cohort size, we have\nmodified the text below in the manuscript for to clarify.\n“Clustering based on meta z-scores reveals trait-specific clusters (fig. S17). Within each trait,\nmetaAA and metaMis, as well as metaEUR and metaAnc, corresponding to the same GReX pair,\nshowed very high correlations with each other. These correlations are influenced by the imbalanced\nGWAS sample sizes (Fig. 5A). As a result among the four meta-analyses of the Ct165 pairs, over\n95% of FDR-GTAs from Ct165 and Ct165 were shared with Ct165 and\nmetaMix metaAnc metaEUR\nCt165 , respectively (Fig. 5A-D). Given this high degree of concordance, we henceforth focus\nmetaAA\nonly on the metaAA and metaEUR results.”\n(6) Since larger sample sizes are correlated with a higher predicted number of genes, what then is the\nsignificance of ancestry? It seems the conclusion may be to include as many samples as possible.\nResponse: This is a great question! We observe in external validation that even though higher order\n(by sample size) GReX models could have better overall prediction but there remains a cluster of genes\nthat are still better predicted by ancestry specific GReX (Fig. 2E). We expected and observed similar\nadvantageous ancestry-specific TWAS signals (fig. S28). We have also now improved and streamlined\nour results to highlight such gained and lost GReX specific TWAS signals by introducing a new result\nsection Gain and loss of using All and bi-ancestry models and by adding fig. S28.\n“For example, some key signals, such as FURIN for SCZ, were absent in bi-ancestry TWAS results (fig. S29).\nWe considered whether using a matched ancestry background GWAS would improve the situation, so we\napplied the bi-ancestry model on the bi-ancestry GWAS, but FURIN remained missing in both.”\n(7) - On the other hand, the findings from GReX -AA model could potentially be false positives\n(especially without further validation).\n- Page 11 last two lines to Page 12 1st paragraph: When integrating mis-matched ancestry models,\nhow LD was handled? Is it possible that novel findings are false positives? Not sure if it’s a valid\nargument.\n- Page 8 lines 1-2: Could the unique genes captured by the GReX -AA model be false positives (given\nthe lack of validation)? Is there evidence suggesting these genes are relevant?\nResponse:\nIn TWAS literature, a downstream step to TWAS findings generally involves fine-mapping of causal gene\nsets and a careful evaluation of the prioritized genes and pathways in relation to the disease. So, one\nway of validating GReX (GWAS ) TWAS results would be to see: a) if the proportion of putative causal\nAA EUR\ngenes identified are comparable to the ones identified in GReX (GWAS ) TWAS results, b) if\nEUR EUR\nprioritized genes and pathways have biological significance in relation to the disease.\nWe have now added an additional fine-mapping step using\nAn example of the impact of LD di3erences between A and B ancestry on TWAS results\nassuming SNP1 regulates gene expression and SNP2 is a causal disease variant. Just for a causal-TWAS (cTWAS) (Zhao, Crouse et al. 2024). Since\nthe sake of the argument we assume perfect LD, i.e. when in LD SNP1 and SNP2 have\ncorrelation 1. Here, GReXA is GReX model trained on samples of A ancestry, GReXB is\nwe are training GReX in one ancestry and using it on GWAS\nGReX models trained on samples of B ancestry. GWASA is GWAS summary statistics\nobtained using samples of A ancestry.\nof another ancestry, it requires some careful consideration\nwhen performing fine-mapping. Also, note that since we\nGReXB GReXA GWASA GReXA(GWASA ) GReXB(GWASA )\nuse elastic net, if two SNPs are identical in values then\nNot in LD in\nboth A and B equal weights are assigned to them in the elastic net\nW1=w; W2=0 W1=w; W2=0 Z1=Z; Z2~0 Similar signal Similar signal training model. This means that if a causal SNP regulating\nNot in LD in B\nand LD in A\nW1=w; W2=0 W1=w/2; W2=w/2 Z1≠0; Z2≠0 Diluted signal potentiated signal gene expression is in LD with another SNP, elastic net\ntraining model would assign weight to both to both SNPs\nLD in A and B\nW1=w/2; W2=w/2 W1=w/2; W2=w/2 Z1≠0; Z2≠0 Similar signal Similar signal based on the LD-correlation levels. See the figure on the\nLD in B and left, as an example of different combinations of\nnot LD in A\nW1=w/2; W2=w/2 W1=w; W2=0 Z1=Z; Z2~0 potentiated signal (cT D W ilu A t S e c d a s n ig h n e a l l p!)\npossibilities of LD differences and how the TWAS signal\nwould be impacted. We use cTWAS because it considers\nGReX and as well as nearby SNPs together for fine-mapping, providing an opportunity for SNP to be\nprioritized in case GReX imputations carry wrong signals. We calculated LD using a reference panel\nmatched to the ancestry of the target GWAS. With these careful considerations we identify similar\nproportion of putative causal genes in both TWAS results [GReX (GWAS ) vs. GReX (GWAS )].\nAA EUR EUR EUR\nAlso, now we explain in more detail about the GTAs that were picked by one ancestry model and not\nthe others. These differences were largely attributable to either MAF differences or LD differences.\n“Indeed we found that MAF of the predictor SNPs of the unshared FDR-GTAs were lower in the ancestry\nwhere the GTAs were not FDR-significant (70% of GReXAACt165-specific and 67% of GReXEURCt165-\nspecific; Methods, Fig. 4E, F). In several cases among the remaining ancestry specific FDR-GTAs (e.g.\nFURIN, RBM6), the LD blocks (Machiela and Chanock 2015) in the ancestry where the gene was an FDR-\nGTA was smaller than the corresponding LD block in the other ancestry (fig. S13). We provide more details\non LD and MAF impacts on TWAS results here (Jajoo 2025). To further assess whether\nGReX (GWAS ) TWAS provides meaningful signal, we compared the proportion of putative causal\nAACt165 EUR\ngenes in the FDR-GTAs identified by GReX and GReX . Putative causal genes were identified\nAACt165 EURCt165\nusing cTWAS (Zhao, Crouse et al. 2024), which jointly fine-maps both GTAs and local SNPs together\n(Method). We found that 18% of AACt165-specific and 13% of EURCt165-specific genes were identified as\nputative causal genes, more details are described here (Jajoo 2025) (fig. S14-15). “\nWe have now also explained our results better to provide biological relevance to AA-specific signals\nthroughout the results sections.\n(6) What are the biological significances of the ancestry-specific findings and for each psychiatric\ndisease? The discussion section appears too general in addressing these respects.\nPage 5, 2nd paragraph: Ancestry-control specific pathways. What do these implicate?\nResponse:\nBased on our observation, ancestry-specific findings are mostly driven by MAF and LD differences. So,\nwe believe that most ancestry-specific signals are simply complementary and help understand the\nmolecular mechanisms involved in a disease better. We have now added examples in result sections to\nexplain more biological relevance of the ancestry specific TWAS signals and how different aspects of\nsimilar biological processes are captured cross ancestries. Although, along with this interpretation, we\ndo agree that it is very much possible that MAF difference of SNPs involved in prioritized genes across\nancestry could also imply subtypes of disease or symptoms that vary in prevalence across ancestries.\nWe have now added this in our discussion.\n“Biological relevance of the enriched TWAS-pathways strongly supports our approach of\nintegrating ancestry-specific GReX models with both matched and mismatched ancestry-specific GWAS.\nFor example, ancestry-mismatched signals from GReX (GWAS ), absent in matched TWAS signals from\nAA EUR\nGReX (GWAS ), included many biologically relevant pathways, e.g., neurotransmitter transporters in\nEUR EUR\nPTSD, sensory and olfactory processes in MDD, and dopaminergic synapse activity in SCZ. We also observe\nancestry-specific unshared TWAS signals with common biological themes across ancestries. For instance,\nin PTSD, both AA- and EUR-specific results pointed to HPA axis–related biology: AA-specific signals\ninvolving neurotransmitter transporter pathways that can influence HPA-axis regulation at the\nhypothalamus level, and EUR-specific signals involving stress-response pathways reflecting downstream\nglucocorticoid signaling. Likewise, in SCZ, immune-related pathways were present in both ancestries, with\nAA-specific results enriched for innate immune responses and EUR-specific results enriched for more\nadaptive immune processes. Additionally, aligning with our GReX model ancestry-specific gene pathway\nenrichments, certain pathway groups consistently appeared in ancestry-specific TWAS results. For\nexample, vesicle transport–related pathways were more prominent in AA, whereas hormone- and\nenvironmental-response pathways were more pronounced in EUR. These unshared signals largely reflect\ndifferences in MAF and LD, where LD patterns can amplify or dilute causal variant signals, and MAF\ndifferences dictate whether a variant is informative or uninformative for model training, arising from true\npopulation variation or from sampling error due to small training sample sizes. Additionally, our\nobservation on the concordance of GReX and TWAS suggests that, for a given SNP, the SNP–gene and\nSNP–disease relationships may follow the same biological mechanism across ancestries. However,\ndifferences in SNP prevalence can alter the gene expression profiles across the populations and potentially\ncontribute to ancestry-specific disease subtypes. As the biological mechanisms become clearer, these\ninsights will help refine SNP–disease relationships in a way that supports personalized medicine and drug\nrepurposing strategies guided by an individual’s genetic profile, built on a more comprehensive\nunderstanding of disease mechanisms across populations.”\n(7) The frequent use of acronyms makes the paper, particularly the results section, difficult to follow.\nPlease consider using more readable sentences. Additionally, some figures are not clearly explained.\nWe realized that acronyms used had many discrepancies. We have now introduced some of our\nacronyms better and have used them in a consistent manner. Further, we have worked on figure\nvisualizations and figure captions.\nMinor comments\nPage 4, last paragraph: p-value > 0.5. Is it <0.5?\nYes. Fixed!\nPage 5, 3rd paragraph: Does the independent DLPFC GEx panel for validation include only control\nsamples or only cases or both? Training samples “Ct” include control only and “All” include both\ncontrols and cases. It’s not clear what the comparisons are.\nResponse:\nWe appreciate the detail noticed here. GEx panel used for validation have both cases and controls. We\nhave now regressed out disease effect from the observed gene expression. The method is updated to\nreflect this change.\n“The validation DLPFC gene expression and genotype data used were derived from 245 EUR (including 73\ncontrols, 25 BIP, 85 MDD and 63 PTSD) and 50 AA (including 25 controls, 15 MDD, 7 PTSD, and 3 BIP)\nsamples part of a prior multi-region RNA-seq study (n=325; (Jaffe, Tao et al. 2022)). These brain samples\nwere collected by the Lieber Institute of Brain Development (LIBD) as part of their PTSD brain collection\n(Deep-Soboslay, Martin et al. 2004, Mighdoll, Deep-Soboslay et al. 2018) that was acquired by the\nNational PTSD Brain Bank (Friedman, Huber et al. 2017). For the validation analysis, we regressed out\nage, sex, disease status and RIN from normalized gene expression data and scaled the residuals.”\nPage 5, 3rd paragraph middle section: EUR performance numbers were given in parathesis when\nmentioning AA models, while AA numbers given for EUR models. The whole sentence is confusing.\nResponse:\nWe have refined the sentence with consistent notations.\n“In AA samples, GReX outperformed GReX models, as observed in the mean and Pearson’s\nAACt165 EUR\nskewness of the distribution of correlation differences (𝜌+,- - 𝜌+,- ): 0.008 (mean) and 0.02\n!\"#$%%&'()* !\"#$./\"\n(skewness) when compared with GReX , 0.002 and 0.04 when compared with GReX and 0\nEURCt165 EURCt477\nand 0.07 when compared with GReX .”\nEURAll1032\nPage 5, 3rd paragraph last 4 lines: Are the better performances from “All” samples due to the inclusion\nof cases or due to much larger sample sizes? Not sure if it can be distinguished here.\nResponse:\nWe agree with the reviewer that it is indeed not straightforward to build mechanistic model to\ndistinguish the two types of GReX models. Although, we do want to highlight the empirical observation\nwe have made, which is that the overall gains among GReXpairs, EURDown165 vs EURCt477, AACt165\nvs AAAll277 and EURCt477 vs EURAll1032, are training sample size dependent. But to address reviewer’s\nconcern we emphasize more that our observations are empirical in nature.\n“Our empirical observations from DLPFC (adult brain) GReX models show that while increasing sample\nsize generally improves the number of predicted genes, FDR-GTAs, and pathway enrichments, the rate of\ngain diminishes with higher-order models (larger sample size). For example, the AACt165 model (165\nindividuals) benefits substantially from higher-order models,….”\nPage 6, 2nd paragraph line 4: fig. SA?\nFixed!\nPage 6, 2nd paragraph: given that TWAS clustering is dominated by GWAS (first by ancestry and then\nby disease) rather than being influenced by GReX models, what’s the significance of applying GReX\nancestry-specific or ancestry-mixed models?\nResponse:\nA subcluster within a cluster of TWAS results corresponding to the same GWAS are impacted by the\nchoice of GReX. So, the significance of using these models is that for instance, ancestry-specific models\nprovide complementary signals. And mixed models gain some power due to sample size, but also lose\nsome signals.\nTo address this comment and as well as Major comment (1), we have now improved presentation of\nGReX clustering, improved the description to bring in clarity and as well as streamlined downstream\nresult sections to explain the gains and losses when using AA vs EUR vs AAEUR models. For more details\nwe refer to our Major comments (1) response.\nFigure 4A-D: Gene names should be horizontal to be contained within each band.\nResponse: Fixed!\nFigure E,F: What do bar colors represent?\nResponse: Legend added!\nPage 7, 2nd paragraph, line 8: What is higher-order GRexX model?\nResponse: Both ancestry specific and mix models trained on larger training sample size\nPage 7, 2nd paragraph, lines 11-13: The cytoband (6p21.3), where HLA region is mentioned in Figure\n4D, is difficult to distinguish due to the green colors being indistinguishable\nResponse: We have now assigned shades of colors per chromosome. And while some similar color\ncould be assigned across chromosomes but since gene names occur in sequential order of\nchromosome, it is now easier to differentiate genes at-least at the neighboring cytoband level.\nPage 7, 2nd paragraph, lines 18-19: The sentence is confusing (not FDR, what does it mean?)\nResponse: Fixed!\nFigure 5. If legends are the same for A-D, no need to repeat 4 times. If not, they should be. What does\n“intersection” here mean? FDR-GTA with meta-GWAS?\nResponse: We reworded it. We mean metaTWAS FDR GTAs.\nFigure 6 and others. The TWAS labels need to be more explanatory instead of just using codes, as\nthere is sufficient space. In Figure 6A, 6B, there are two legends with the same BP clusters (by name)\nbut in different colors. Why is this? What do these colors represent?\nResponse: We have improved the colors now. They basically represent GO domains.\nFigure 7F: x-axis label?\nResponse: Fixed.\nFigure 7H: Is the heatmap correct? It appears that R2 values for every two neighboring pairs are\nidentical. Could you clarify why this is the case?\nResponse: Yes because metaAA vs metaMix and metaEUR vs metaAnc are very much similar.\nFigure 8: A-D: The Vann diagrams are nice, but what is the significance of comparing different tissues\nand different ancestry models? While some overlaps are visible, what are the interpretations?\nResponse: We have streamlined our results and discussions, excerpts from discussion.\n“Comparison of TWAS signals in fetal brain and DLPFC revealed both shared and distinct pathway\nenrichments across disorders. Notably, fetal brain analyses showed enrichment for peripheral\ndevelopmental processes—including skeletal muscle differentiation and megakaryocyte-related\npathways—across all four disorders. These findings may reflect neurovascular-related signals or shared\ndevelopmental gene programs between vascular and neural cells, both of which could potentially influence\nneural development, angiogenesis, and blood–brain barrier formation (Stoufflet, Tielens et al. 2023).\nImmune-related pathways were also broadly enriched across disorders and tissues, underscoring the\ncomplex and ongoing interplay between neural and peripheral signals from early development through\nadulthood. For example, myokines released from skeletal muscle or platelet-derived factors from\nmegakaryocytes may contribute to this cross-talk, potentially shaping brain plasticity and disease\nvulnerability throughout the lifespan (Stoufflet, Tielens et al. 2023). More overlapped findings emerged\nwith fetal TWAS compared to adult TWAS, suggesting a shared neuronal developmental component across\nmultiple psychiatric disorders. Divergence in disease onset (Solmi, Radua et al. 2022) and\nmanifestation may arise when genetic risk, particularly in its impact on the postnatal brain, interacts with\nlife stressors occurring at different stages, such as birth hypoxia in schizophrenia, childhood trauma in\nPTSD, and adult life stressors in depression. …”\nPage 8 2nd paragraph, last 4 lines: What is the conclusion and interpretation (or recommendation)\nhere? It would also be helpful in the discussion section to provide guidance on when researchers\nshould use ancestry-specific vs. bi/multi-ancestry models, as well as suggestions for future analyses.\nSpecifically, is it better to use larger single-ancestry reference panels or to focus on improved multi-\nancestry integration methods?\nResponse: We have now streamlined our results on bi-ancestry and as well as All models. And have\nalso added a discussion paragraph.\n“Further increases in sample size could be achieved by merging ancestry-specific datasets to\ngenerate multi-ancestry models. We have shown that such models can yield unique genes and pathways;\nfor example, spliceosome complex–related pathways, were captured only in higher-order models. Such\nnuanced findings might be relevant or noise, as little is known about the impact of altered splicing\nmachinery on psychiatric diseases. In general, genes with low heritability and located in recombination\ncold-spots could benefit from such models. However, many signals, including important ones such as\nFURIN in the SCZ TWAS, were lost—signals that were otherwise captured in AA-specific or EUR-specific\nmodels. Notably, FURIN is a EUR-specific SCZ FDR-GTA with smaller LD in EUR compared to AA. When\ntraining bi-ancestry GReX models, it is important to account for the complexities introduced by differing\nLD structures and MAF distributions. These differences can dilute causal SNP effects by distributing weights\nacross tagged variants, introduce collinearity in genetic principal components and predictor SNPs, and\ngive rise to multimodal gene expression patterns that may be distorted during preprocessing. Further,\nwhen feasible, providing multiple versions trained on different sample groupings could greatly benefit the\nresearch community.”\nPage 10 referring Figure S22. The topology appears to be quite similar across fetal-specific, DLPFC-\nspecific, and shared. While there are color differences, what do these color represent?\nResponse: Indeed, the topology of all the three pathway figures are the same, because it represents\nthe hierarchical visualization of REACTOME pathways. It is the colors that change across the three sets,\nand it represents enrichment of the pathways. The darker the region the higher is the enrichment.\nPage 11 Discussion section lines 4-6: Please be more specific. Which ancestry-specific pathways\nindicate environmental selection pressures?\nResponse: The opening paragraph of the Discussion was intended as a high-level summary of the\nfindings that are dissected in detail in the following paragraphs (e.g., ancestry-specific predicted genes\nand pathways, GReX-specific TWAS results, etc.). To address the reviewer’s request, we have gone back\nto the Results to update results.\ne.g. “One prominent gene is UVSSA, with a higher R² in EUR (0.03 in AA and 0.43 in EUR), which is\ninvolved in DNA damage repair caused by ultraviolet radiation, likely under stronger selection in lighter-\npigmented populations at higher latitudes, emphasizing an ancestry-specific specialization of gene\nregulation that can be detected across the body (can be possibly detected in skin and in the brain).”\nPage 12 1st paragraph: Please be more specific. Which trait-specific pathways are referred here?\nResponse: The first paragraph of the Discussion is intended as a high-level summary and therefore\ndoes not provide trait-by-trait pathway details, which are instead elaborated in the Results and in\nsubsequent Discussion paragraphs. To address this point, we have refined the Results section to more\nclearly highlight the trait-specific pathways, so that the subsequent summary in the Discussion is better\nanchored to the detailed descriptions provided earlier.\nPage 30: What is PEC? Please spell out all acronyms when first introduced.\nResponse: Fixed!\nPage 30 next-to-the-last line: What is db, a file type?\nResponse: It is a file type, used by metaXcan, and is updated.\nPage 31 section 2.1: Spell out acronyms when first used.\nPage 31 section 2.3: The writing is a bit confusing in this paragraph. What is the reference dataset?\nThe relation between 5 subsets and 10-fold cross-validation is not clearly stated.\nResponse: We agree that the original sentence was ambiguously phrased. What we intended to convey\nis that we used a nested cross-validation approach to obtain unbiased performance estimates, with an\nouter loop of 5 folds and an inner loop of 10 folds for hyperparameter tuning. The final model\nparameters were selected using 10-fold cross-validation and the model was then retrained on the entire\ndataset. As also noted by Reviewer 4, this procedure follows the standard PrediXcan pipeline, and we\nare not introducing methodological novelty in this step. We have revised the description accordingly\nand now cite the PrediXcan reference for further methodological details.\n“We applied elastic-net regularized regression to estimate SNP effect sizes within 1 Mb of each gene,\nfollowing the PrediXcan framework (Gamazon, Wheeler et al. 2015). Model performance was estimated\nusing nested cross-validation (5 outer folds for unbiased evaluation, with 10-fold inner cross-validation\nfor lambda selection) and reported as the average square of the Pearson correlation (R²). Final SNP–gene\nweights were obtained by retraining models on the entire dataset using 10-fold cross-validation for\nlambda selection. Models with R2 > 0.01 and P < 0.05 were retained for downstream analyses, as in [8].”\nPage 31 section 2.4: Were brain samples from control or from PTSD patients?\nResponse: From both, and we have updated the method to reflect that.\n“The validation DLPFC gene expression and genotype data used were derived from 254 EUR (including 73\ncontrol) and 60 AA (including 25 control) samples part of a prior multi-region RNA-seq study (n=325;\n(Jaffe, Tao et al. 2022)). These brain samples were collected by the Lieber Institute of Brain Development\n(LIBD) as part of their PTSD brain collection (Deep-Soboslay, Martin et al. 2004, Mighdoll, Deep-Soboslay\net al. 2018) that was acquired by the National PTSD Brain Bank (Friedman, Huber et al. 2017). For the\nvalidation analysis, we regressed out age, sex, dx and RIN from normalized gene expression data and\nscaled the residuals. “\nPage 31 section 2.4: How were ancestry classification of EUR and AA determined for validation sample\nas if they’re different from the training sample, it may affect the results.\nResponse: Thank you for pointing this. We now use eur> .9 and aa > .6 to define EUR and AA\nancestry, results have been modified and they have not changed major conclusions.\nPage 31 section 2.4: How were the residuals scaled?\nResponse: Using scale function in R.\nPage 33 section 4.2: The number of PCs used in analyses are not consistent, with some analysis\n(DLPFC data) including 4 PCs, others using 5. Why?\nResponse: Typo is fixed, we used 5 PC always.\nPage 33 section 5.1: In addition to the reference, a brief description should be provided.\nResponse: Added!\nReference 55 needed to be double-checked.\nResponse: Fixed\nIn addition, could you explain how mismatched ancestry results are being validated and how you\nconfirm those are not spurious due to divergent LD structures? Would additional calculations help\nconfirm that these signals are truly novel and not artifacts?\nResponse: We refer to response we made to major comment (5).\nReviewer #2 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of\nthe Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nResponse: Thank you we appreciate your efforts on co-reviewing with Reviewer 3.\nReviewer #3 (Remarks to the Author):\nThis study integrated European (EUR) and admixed African (AA) genotype-expression (GEx) panels\nwith ancestry-specific GWAS data to dissect the genetic architecture of four psychiatric disorders. The\nauthors demonstrated the benefits of leveraging diverse ancestral backgrounds in TWAS analysis,\nhighlighting concordant regulatory mechanisms across ancestries while identifying ancestry-specific\ngenes and pathways. Overall, the study demonstrates the benefits of leveraging diverse ancestral\nbackgrounds in TWAS analysis, providing insights into which genes and pathways are better captured\nby ancestry-specific panels.\nResponse: We would like to thank the reviewer for highlighting the novelty of our work and our\nfindings. Below we address the concerns of the reviewer and have updated the manuscript to reflect\nthe changes to highlight developing brain signals vs adult brain and their potential significance and\nprovided an approach to identify putative causal downstream genes for our TWAS results.\nI have some concerns and comments about this manuscript.\n1. Fetal brain models are compared to adult DLPFC but lack mechanistic explanations for fetal or\nDLPFC-specific findings. Authors should link these differences to neurodevelopmental trajectories or\ndisease onset timelines.\nResponse: Thank you for this feedback. We have now reorganized our section that compares fetal brain\nresults with adult DLPFC results for clarity and have also connected our findings to existing literature\nto understand their potential functional implications. However, downstream interpretation of fetal\nfindings remain challenging, as the functional roles of many implicated genes are not yet fully\ncharacterized. We have also added below two paragraphs in the discussion covering this.\n“Comparison of TWAS signals in fetal brain and DLPFC revealed both shared and distinct pathway\nenrichments across disorders. Notably, fetal brain analyses showed enrichment for peripheral\ndevelopmental processes—including skeletal muscle differentiation and megakaryocyte-related\npathways—across all four disorders. These findings may reflect neurovascular-related signals or shared\ndevelopmental gene programs between vascular and neural cells, both of which could potentially influence\nneural development, angiogenesis, and blood–brain barrier formation (Stoufflet, Tielens et al. 2023).\nImmune-related pathways were also broadly enriched across disorders and tissues, underscoring the\ncomplex and ongoing interplay between neural and peripheral signals from early development through\nadulthood. For example, myokines released from skeletal muscle or platelet-derived factors from\nmegakaryocytes may contribute to this cross-talk, potentially shaping brain plasticity and disease\nvulnerability throughout the lifespan (Stoufflet, Tielens et al. 2023). More overlapped findings emerged\nwithin fetal TWAS compared to adult TWAS, suggesting a shared neuronal developmental component\nacross multiple psychiatric disorders. Divergence in disease onset (Solmi, Radua et al. 2022) and\nmanifestation may arise when genetic risk, particularly in its impact on the postnatal brain, interacts with\nlife stressors occurring at different stages, such as birth hypoxia in schizophrenia, childhood trauma in\nPTSD, and adult life stressors in depression.\nMany of the FDR-GTAs, particularly in the fetal analysis, are only partially understood, and their\nfunctional roles in brain development and impact on psychiatric disease remain known. Several notable\nexamples from our results, including FLOT1, H4C13, and ZSCAN12P1, illustrate this complexity. FLOT1,\nabundant in the brain and associated with lipid raft scaffolding, participates in diverse processes such as\nimmune signaling, neurotransmitter transport, and serotonin-related brain function. It has been\nimplicated in earlier fetal studies of SCZ and BIP (O'Brien, Hannon et al. 2018), yet its role in\nneurodevelopment remains unknow (Schubert, Thalamuthu et al. 2021, Howes and Shatalina 2022). In\nour study, FLOT1 was identified in PTSD, SCZ, and BIP, suggesting possible involvement in shared features\nsuch as psychosis, which are less common in MDD. H4C13 was among the top contributors in a meta-\npsychiatric pathway enriched across disorders and overlaps with open chromatin regions in fetal brain,\nsuggesting a regulatory role during neurodevelopment, though its precise functions remain undefined.\nZSCAN12P1 has been implicated as one of the top genes explaining genetic correlations between immune\ndisorders and psychiatric disease (Breunig, Lee et al. 2025), yet little is known about its biological role.\nThese examples underscore the need for experimental dissection of molecular mechanisms in brain\ndevelopment, with particular attention to stage-specific processes—especially in the fetal brain—where\nearly perturbations may set the stage for later disease risk.”\n10. Given there is no clear evidence to show that MDD and PTSD are originated from abnormal\nneurodevelopmental processes, why selected these two disorders in this study ? Selecting relevant or\nappropriate tissue types is important.\nResponse:\nThank you for bringing this up. This is an important and ongoing area of investigation, as the literature\nsuggests that neurodevelopmental processes may play differential roles across the spectrum of\npsychiatric disorders (Ansorge, Hen et al. 2007, Michelini, Carlisi et al. 2024). For example, maternal\nstress has been shown to increase risk of PTSD and MDD in offspring (Yehuda, Halligan et al. 2001,\nYehuda, Bell et al. 2008), potentially through gene–environment (G×E) interactions and epigenetic\nregulation occurring around conception, during fetal development, and in early postnatal life (Klengel\nand Binder 2015, Lautarescu, Craig et al. 2020). In our study, TWAS analyses of MDD and PTSD revealed\nseveral fetal-specific signals that were not observed in adult DLPFC TWAS analyses, suggesting a unique\ncontribution of early developmental stages. These signals encompassed both shared pathways across\ndisorders (e.g., megakaryocyte and skeletal muscle developmental processes) and disorder-specific\npathways (e.g., neurotransmitter-specialized processes in BIP, PTSD, and SCZ; sensory perception and\nolfaction in MDD; vesicle transport in PTSD and SCZ). Importantly, fetal brain data contributed more\nstrongly to cross-disorder shared signals than adult brain data, suggesting that shared early\ndevelopmental risk factors may interact with environmental exposures to alter which disorder ultimately\nmanifests. Supporting hypothesized G×E mechanisms during neurodevelopmental stages in shaping\npsychiatric disease risk, making it even more difficult to decode neurodevelopmental stage–related risk\nin PTSD and MDD in the literature.\n2. The authors should provide detailed methodological protocols for global ancestry proportion\ninference rather than merely citing prior studies.\nResponse: It has been updated in the method.\n“Global ancestry proportions were estimated using the standardized global ancestry pipeline\n(https://github.com/nievergeltlab/global_ancestry), which applies SNPweights (Chen, Pollack et al. 2013)\nto a set of 10,000 ancestry informative markers. These markers were evaluated against a reference panel\ncomprising 2,911 individuals from 71 diverse populations and six continental groups (k = 6) [48]. Subjects\nwith a European ancestry proportion > 0.9 were labeled as EUR (n = 1,032), while those with an African\nancestry proportion > 0.6 were labeled as AA (n = 276) subjects (fig. S1). “\n3. Among the 2,248 genes jointly predicted by both EURCt165 and AACt165 models, authors\nobserved high concordance in SNP weights (Pearson’s r = 0.6) and directionality of effects (97%\nagreement). While this suggests potential regulatory conservation across ancestries, it is critical to\naddress whether this concordance reflects true biological consistency or shared genetic component\ndue to historical admixture events in the AA population.\nResponse: We acknowledge the limitation of the admix nature of our AA training datasets used in our\nstudy and note this in the Discussion. However, evidence from other studies (Mogil, Andaleon et al.\n2018, Kachuri, Mak et al. 2023, Chen, Liu et al. 2024, Taylor, Chhetri et al. 2024) indicates that eQTL effect\nsizes show substantial concordance across diverse ancestries, including those from non-admixed\npopulations as well.\nIn our analysis, we included global genetic principal components as covariates, thereby regressing out\noverall ancestral background effects and ensuring that models are trained primarily on local genetic\nvariation. For a given gene and its cis region (SNPs used for predictions are restricted to cis region), in\nthe AA training dataset, about 40% of individuals are projected to have local European ancestry,\nmeaning most of the prediction signal is still driven by African ancestral background. Furthermore, as\nshown in (Gay, Gloudemans et al. 2020), local ancestry contributes less to variation in gene expression\nthan SNP-level genetic variation itself, suggesting that our findings are unlikely to be driven solely by\nadmixture structure.\n4. Lack of biological interpretation of pathways: While pathway enrichments are reported, the\nbiological relevance of ancestry-specific pathways (e.g., oxidative stress in AA, viral responses in EUR)\nis not sufficiently explained. Connecting these findings to previous disease mechanisms or functional\nstudies would add significance to the results.\nResponse: We appreciate this feedback. Based on this feedback we have now elaborated on many of\nthe enriched pathways in the results section, as to how they are relevant to the concerning disorder and\nhave cited relevant existing literature. Below are few examples\n“The SCZ metaAA -specific FDR-Pathways included several developmental stage-related processes,\nCt165\nsuch as dentate gyrus development, adenohypophysis development and neuroepithelial cell\ndifferentiation. Notably, developmental abnormalities of the hippocampus—including structural\ndisruptions in the dentate region—have been implicated in SCZ, consistent with the involvement of these\npathways (Wegrzyn, Juckel et al. 2022). …. \nIn contrast, the metaEUR -specific pathways were associated with calcium channel activity, stress\nCt165\nhormone signaling, ear auditory receptor, neurotrophic production and binding, epigenetic modification,\ncollagen metabolism and the viral life cycle (Fig. 6C). Auditory receptor pathways are particularly\nintriguing given that sensory processing deficits in SCZ, especially within the auditory cortex, are well-\ndocumented and may contribute to the manifestation of hallucinations (Javitt and Sweet 2015).\n”\n5. In this manuscript, the authors use PrediXcan to train GreX models, according to previous reports,\nThe MR-JTI method can improve the accuracy of the prediction model, and the method also considers\nthe effect of horizontal pleiotropy and potential confounding factors, so why not use MR-JTI for\nmodel training？\nResponse: Thank you for raising this point. We agree that JTI can be useful particularly when building\nGReX models from similar tissues, as it allows borrowing of information across related contexts. For\nexample, if we were constructing multiple brain region–specific fetal GReX models, leveraging JTI could\nimprove performance compared to single-tissue modeling. But here due to limited fetal samples, we\nare only building one GReX model across heterogenous collection of fetal brain tissues. Between fetal\nand adult brain tissue, gene expression programs differ substantially due to developmental stage–\nspecific gene activation and silencing, making it preferable to obtain individual tissue-specific signals.\nIn terms of causal gene prioritization our power is limited due to our training datasets sizes\n(specially the AA) and that some of the psychiatric disorders studied (MDD and PTSD) are not highly\nheritable. To address this, we now perform a hybrid fine-mapping of prioritized genes and cis-SNPs\ntogether using cTWAS (Zhao, Crouse et al. 2024) to show that matched and mismatched SCZ TWAS\nFDR-GTAs carry similar proportion of putative causal genes. We also explain in response to reviewer 1\nMajor comment (5), how this can be particularly helpful in the context of mismatched LD patterns of\nGReX and GWAS. Finally, we recognize that approaches such as MR-JTI, can be valuable for\ndifferentiating causal gene associations, and we highlight this in the revised discussion.\n6. The 8 models predicted a total of 17,406 distinct genes, EURCt165 predicted 4,557 genes, AACt165\npredicted 4,926 genes, while 2,098 genes were predicted by all of these models. Besides, 40% of\nAACt165-specific genes and 30% of EURCt165-spefic genes were predicted in all higher-order GReX\nmodels, it suggests that genes predicted by lower-order GReX models are not consistent with those\ngenes predicted by higher-order GReX models. Does this mean that the predicted model is less\naccurate when the sample size is small?\nResponse: 40% and 30% genes are the one predicted in both AA and EUR ancestry-specific higher-\norder models. We also have genes, predicted in matched higher-order ancestry specific models (26%\nin AACt165 and 38% in EURCt165). Overall, it is only 12% in AACt165 and 13% in EURCt165, were not\npredicted in any higher-order model (larger sample size). We have now clarified it in the manuscript\nand as well as in the figures.\n7. As shown in Figure2D, in the same disease state, AA-specific and EUR-specific genes are enriched in\ndifferent pathways, there are only a few consistent pathways, how to explain this result?\nResponse: Thank you for this comment. We have now added two paragraphs in the discussion to\nexplain this:\nOne paragraph is interpretation related:\n“… These unshared signals largely reflect differences in MAF and LD, where LD patterns can amplify\nor dilute causal variant signals, and MAF differences dictate whether a variant is informative or\nuninformative for model training, arising from true population variation or from sampling error due to\nsmall training sample sizes. Additionally, our observation on the concordance of GReX and TWAS suggests\nthat, for a given SNP, the SNP–gene and SNP–disease relationships may follow the same biological\nmechanism across ancestries; however, differences in variant prevalence can shift the prevalence of the\ncorresponding TWAS signal in the population and potentially contribute to ancestry-specific disease\nsubtypes. As the biological mechanisms become clearer, these insights will help refine SNP–disease\nrelationships in a way that supports personalized medicine and drug repurposing strategies guided by an\nindividual’s genetic profile, built on a more comprehensive understanding of disease mechanisms across\npopulations.”\nThe other paragraph is limitation related:\n“….Further added complexity is while comparing TWAS pathway analyses is that the set of genes\ntested within each pathway can vary across results due to differences in underlying GReX model coverage.\nThis introduces variability in enrichment signals, both from differences in the genes included and the\ncorresponding LD adjustments. As a result, while enriched pathways may differ across analyses of the\nsame GWAS using different GReX models, some convergence is expected—and indeed observed—at the\nlevel of shared functional hierarchies and biologically related processes. Looking ahead, novel pathways\nwithout established disease links represent promising avenues for future investigation, particularly\nthrough integrated analysis of the specific genes driving these enrichments. “\n8. Just as the author mentioned, some TWAS methods already exist to integrate cross-ancestry datasets.\nDoes the method mentioned in this manuscript offers a significant increase in statistical power\ncompared to previous methods? Whether the authors have compared the statistical power of the\ndifferent methods?\nResponse: While many TWAS approaches for cross-ancestry analysis already exist, they rely on\nassumptions about the relationship of genetic and gene regulation variation across rather than directly\nexamining these differences. The goal of our study is not to propose a new statistical framework, but\nrather investigate how mismatched ancestry analyses are advantageous, which genes and pathways are\nmost likely to benefit, and what underlying genetic architectural features (e.g., MAF and LD) can explain\nancestry-based model performs differences. We have modified our discussion a bit to highlight this.\n“we use a straightforward approach to integrate mismatched GEx panels with GWAS to better understand\nand disentangle patterns of ancestry-driven gene regulation, since more complex methods might obscure\nancestry specificity and the underlying driving forces. The knowledge gained from our study could guide\nthe use of more sophisticated methods. For instance, METRO, which assumes a homogeneous SNP-to-\ngene effect across ancestries, performs TWAS by integrating weighted GReX across ancestries with a\nGWAS. Meanwhile, MATS could be valuable for rare cases of heterogeneous SNP-to-gene effects across\nancestries. TESLA enables meta-analyses across ancestry-specific GWAS by projecting signals based on\ntheir direction in the chosen GEx panel.”\n9. It seems a large proportion of TWAS signals in Fig.9 were from the MHC region, considering the\ncomplex LD structure of MHC region, the authors should take this into account when performing\nanalyses.\nResponse: We have now split the upset plot to separate the signals from known polymorphic regions\n(Fig.10A and Fig. 10B). And have added this to the discussion\n“..although the goal of TWAS is to prioritize genes at the tissue level for a given disorder, signals at complex\nloci—particularly chr6p21 (highly polymorphic region) and chr17q21.31 (inversion polymorphism)—may\nnot always reflect accurate causal relationships across populations. Excluding these loci is also not a viable\nsolution, as they harbor well-established functional genes (e.g., C4A in schizophrenia, implicated in\nsynaptic pruning via microglia; CRHR1 in PTSD, a key mediator of the stress response). Importantly,\nbecause GTAs represent independent signals, any locus-specific biases would only influence GTAs within\nthat region, and retaining these loci does not affect genome-wide TWAS results. However, pathway\nanalyses that include genes from these regions could be influenced by such complexities and should be\ninterpreted with caution.”\nAdditional Minor Comments:\n1. The abstract should be streamlined to highlight novel discoveries.\nResponse:\n“Integrations of large-scale genomic data with brain-based transcriptomics have revealed many important\nmolecular mechanisms in psychiatric disorders, yet many aspects remain unknown. An underexplored\ndimension is how ancestry-related variation in genetic architecture and gene regulation can be utilized to\nuncover additional genomic signals. To address this, we explored strategies that harness the ancestral\ndiversity of individuals in adult and developing brain Genotype-Expression (GEx) reference panels of the\nPsychENCODE Project and psychiatric GWAS of the Psychiatric Genomics Consortium to enhance\ndetection of GReX (Genetically Regulated gene expression) and transcriptome-wide association study\n(TWAS) signals. To investigate alternative GEx-level choices, we trained GReX models using rigorously\nconstructed subsets of the human postmortem dorsolateral prefrontal cortex GEx panel, generated through\ndownsampling, segregating, and mixing samples of Admixed African (AA) and European (EUR) ancestries,\nwhile considering disease status in the subset design. TWAS-based gene trait associations (GTAs) were\nobtained by integrating these GReX models with ancestry-specific GWASs of bipolar disorder (BIP), major\ndepressive disorder (MDD), post-traumatic stress disorder (PTSD) and schizophrenia (SCZ). Genes with\nancestry-specific GReX were enriched in specialized pathways involving mitochondrial functions, organelle\nstructure, and metabolism, while genes with GReX in both ancestries demonstrated high concordance\n(>95%) in predictor SNP weight directions. Applying GReX from either ancestry to a single ancestry GWAS\nproduced GTAs with concordant effects sizes while also revealing unique FDR significant gene- and\npathway-level signals that would otherwise be missed in matched-ancestry TWAS. This included TWAS\nsignals such as glutamine signaling in PTSD, olfactory function and proline-peptide DNA activity in MDD,\nand dopaminergic pathways in SCZ. Shared signals across the four disorders were more prominent in the\ndeveloping brain, involving genes such as H4C13, ZSCAN12P1, and FLOT1, and pathways related to\nmegakaryocyte and muscle development, and neurotransmitter regulation. In conclusion, we demonstrate\nthe benefits of leveraging diverse ancestral backgrounds in TWAS analysis, provide insights into which\ngenes and pathways are better captured by ancestry-specific panels, and advocate for genomic region-\nspecific TWAS integration strategies over a uniform genome-wide approach to uncover molecular\nmechanisms.”\n2. Check for consistency in terminology (e.g., \"bi-ancestry models \" vs. \"bi-ancestral models \").\nResponse: Bi-ancestry is now used across the manuscript.\n3. Fig.9 needs to be improved, it is too crowded and fuzzy.\nResponse: Manhattan plots labeling has been improved, and their sizes have been increased. The\nupset plot has been moved to a new panel and is divided into two upset plots (Fig 10).\nReviewer #4 (Remarks to the Author):\nThis study comprehensively investigates genetically regulated gene expression across two ancestries,\nfour severe mental illnesses, and multiple GReX/meta-analysis configurations to identify optimal\napproaches for uncovering biologically relevant insights. It makes unique contributions to the field of\ncross-ancestry TWAS and offers guidance on leveraging emerging ancestrally diverse gene expression\nreference panels derived from specialty cohorts.\nAdditional strengths include a meticulous study design and control configuration that incorporate:\n- Matching sample sizes (via down-sampling for European cohorts).\n- Inclusion of fetal brain samples from gene expression sources.\n- Comparison with models trained exclusively on neurotypical controls.\n- Investigation across four diseases, enhancing both the study’s broad applicability and the\ngeneralizability of its findings.\nThe following comments do not detract from the value of the study but reflect best practices and\nproposed revisions aimed at increasing the clarity and accuracy of the analyses. Minimal new analyses\nare proposed.\nResponse: First, we would like to thank the reviewer for their thorough evaluation of our work and for\nhighlighting the valuable aspects and meticulous nature of this study. The reviewer has provided\nvaluable feedback on the clarity of the method description and as well as presentation of results to\nfacilitate broader implications of our findings. We also appreciate the structured format of the feedback,\nwhich made it efficient for us to address them. We have made our best effort to address all the\ncomments below and have incorporated the necessary changes into the manuscript. We hope that\nthese revisions have enhanced the accuracy of the methodological descriptions and refined the focus\nand clarity of our results.\nOVERALL\n1. Limit figures to protein-coding genes to enhance interpretability; additional transcripts may be\nprovided in the supplementary files/tables.\nResponse: We agree that it would make results more streamlined. Although, for instance the beauty of\nperforming TWAS is that it helps us understand more about the functional aspects of non-coding SNPs.\nWe believe that non-coding genes could also hold importance. But to emphasize the protein-coding\ngenes, we have now highlighted them with italicized and bold font, in the main figures. We have also\nimproved the legibility of the texts in the figures.\n2. Given the high polymorphism, gene density, and complex LD patterns in the MHC region, exclude it\nfrom analyses performed with current methods. If not excluded, include a clear disclaimer in the\ndiscussion acknowledging this known limitation of current TWAS approaches.\nResponse: This comment is copied again along with related comments in Figure section below and\nhave been addressed there.\nRESULTS\n1. Effect size correlations of TWAS results generated using eight GReX models across traits:\n1.1. Note that S-PrediXcan introduced adjustments in version 0.8\n(see https://www.biorxiv.org/content/10.1101/2023.10.17.562831v2 and https://github.com/hakyimlab\n/MetaXcan/releases/tag/v0.8.0) to address inflation issues. Please specify which version was used for\ncalculating the non-centrality parameter (NCP)\nResponse: We thank the reviewer for pointing out the new feature of S-PrediXcan that addresses\ninflation issues. Using this feature require a flag and an additional parameter that is to be calculated\nwhile training the for GReX models. Since, we were using default setting, this feature was not explored.\nAs suggested by the reviewer in comments 3.3a and 3.3b, we have now removed this part from the\nmanuscript as it doesn’t contribute to any of our results and/or conclusions.\n1.2. Which figure is this: (see Fig. SA).\nResponse: Following suggestions in comments 3.3a and 3.3b it has been now removed from\nmanuscript.\n1.3. In the Materials and Methods section, provide detailed information on how the NCP was\nestimated or calculated. Although “lambda” is an acceptable notation, to avoid confusion with lambda\nGC--given the target readership--it is preferable to use the abbreviation “NCP” or a similar term.\nResponse: Following suggestions in reviewer comments’ 3.3a and 3.3b it has been now removed from\nmanuscript.\n2. Overlap of TWAS-based GTAs generated using eight GReX models within trait:\n2.1. Replace vague terms such as “general correlation” with quantitative measures (e.g., regression\nanalysis or p-values) to provide clarity.\nResponse: Updated “We observed that the number of FDR-GTAs increased as the GreX model training\nsample size increased (pearson r > .8 between the number of FDR-GTAs and the training sample size of\nthe corresponding GReX models within each set of TWAS analyses derived from the same GWAS), although\nboth shared and unshared genes were present in the TWAS results (Fig. S28).”\n2.2. Avoid non-specific statements like “there was some overlap” and instead specify the degree of\noverlap as a percentage in parentheses.\nResponse: Updated: “There was low overlap of FDR-GTAs across the four meta-analyses within each\nphenotype, except for BIP (12% in PTSD, 15% in SCZ, 4% in MDD and 0% in BIP; Fig. 5A-D).”\n3. Overlap of TWAS-based Pathways generated using eight GReX models within trait:\n3.1. Please include an RRHO (Rank-Rank Hypergeometric Overlap) plot for both pathway and TWAS\nresults. Additionally, the Results section would benefit from high-level summarization at key points.\nKeep in mind that focusing solely on the overlap of FDR-significant hits may depend on statistical\npower; actual concordance might be higher than what is reflected by the FDR-significant GTAs or\npathway overlaps.\nResponse: This was a very thoughtful advice, and we appreciate the reviewer for suggesting. We\nfound RRHO plot generated using pathway enrichment p-values (since direction of enrichment is not\ncomputable) very informative, and now they are part of main Fig. 3 panel, and a new section has been\ncreated for pathway RRHO plots.\n“Enrichment p-value correlations of TWAS pathway results based on the 8 GReX models within\ntrait\nOur pathway analysis focused on the three domains of Gene Ontology: BP (biological processes),\nMF (molecular functions) and CC (cellular components) [26]. TWAS-pathway enrichment analysis of all the\nTWAS associated with single-ancestry GWAS mentioned above were performed using JEPEGMiX-P [23]\n(Methods), which accounts for LD among genes within each tested pathway as part of the integration of\nTWAS GTA statistics (Methods). Within each disorder, pathway enrichment p-value based clustering of\nTWAS results primarily grouped analyses by the ancestry of the underlying GWAS (Fig. 3C), consistent\nwith the structure observed in GTA-based clustering (Fig. 3A). Within each GWAS-specific cluster, GReX\nmodels consistently clustered together (fig. S9). Unlike in GTA clustering, GReX -based pathway\nAA\nenrichments consistently clustered with the GReX models. Also, ancestry-based patterns of pathway\nAAEURCt330\nsignals in the higher-order EUR and AAEUR GReX models were less pronounced (fig. S9). As with GTA\nsignals, pathway signal correlations between GReX and GReX were lower in AA GWAS (Fig.\nAACt165 EURCt165\n3D; fig S10 A vs. S10B; fig S10C vs. S10D). Interestingly, the comparison between GReX (EUR) and\nAACt165\nGReX (EUR) showed greater agreement in the enriched biological pathways in SCZ and BIP, whereas\nEURCt165\ngreater agreement on non-enriched BP was observed in PTSD and MDD (Fig. 3D). “\nMETHODS\n1.2. In the revised manuscript, include key details on genotype preparation from the cited study that\nare pertinent to this work (e.g., the reference panel used for imputation). Briefly describe the variant-\nlevel filtering performed prior to training the GReX models for European (EUR), African American (AA),\nand AA-European (AAEUR) cohorts.\nResponse: Below has been added.\n“PEC genotypes were generated and imputed as previously reported [20]. Samples were genotyped using\nvarious Illumina platforms, including the Illumina 1M, Illumina HumanHap650 (h650), Illumina Infinium\nHumanOmniExpressExome v1.1b, Illumina Omni5, and Illumina h650 arrays. Raw genotype data were\nfirst converted to PLINK format and subjected to rigorous quality control (QC). Sample-level QC included\nfiltering based on call rate below 95%, heterozygosity outliers, and sex discrepancies. Marker-level QC\nexcluded SNPs with zero alternate alleles, call rates below 95%, Hardy-Weinberg equilibrium p-values <\n1x 10-6. Genotype was imputed using Minimac with the Haplotype Reference Consortium (HRC) panel.\nPost-Imputation, SNPs were filtered to retain only high-quality variants with imputation quality score R2\n> 0.3. Sub-studies imputed genotype files were merged using BCFtools (v1.9). Multi-allelic SNPs were\nremoved, and genotype dosages were computed on continuous scale from 0 (homozygous reference) to 2\n(homozygous alternate), with 1 representing heterozygous genotypes.“\n1.4. Clarify the statement “more than 1.1%” by specifying the context or metric to which it refers.\nResponse: Typo is fixed.\n“Samples were removed if they contained fewer than 10 million reads, more than 1.1% mismatch rate, or\nmore than 30% intergenic reads.”\n1.5. Confirm whether the “library preparation covariate” refers to the assay/kit used, and clarify if the\ngenetic principal components were extracted from the entire mega-analysis.\nResponse: By library preparation, we meant rRNAdepletion and polyA. Also, genetic PCs were\ncalculated within each GReX training dataset. We have not moved this section under section 2 for more\nclarity. Please see response to comment 2.1.\n2. Although the database includes \"protein-coding genes, pseudogenes, and lncRNA”, for the final\nfigures it is more streamlined to display only protein-coding genes to appeal to a broader audience.\nResponse: We have now italicized the protein-coding genes and have made the figures more legible.\n2.1. Clarify whether the RNA-seq data (already residualized in section 1.5) was re-residualized.\nResponse: RNA-seq counts were processed for each GReX dataset separately. We have now moved the\ndetails together to clarify that.\n“Processing of data for each GReX training dataset\nWithing each GReX training dataset: a) RNA Seq data was normalized. RPKM Normalized RNA-seq counts\nwere inverse quantile normalized. b) SNPs with less than .01 MAF were dropped. c) four genetic principal\ncomponents (PCs) were calculated d) PEER factors were calculated to account for unknown sources of\ntechnical variation between and within the different studies of the main dataset. The number of PEER\nfactors was chosen based on the training sample size: 30 for >150 and <=250, 45 for >250 and <= 350\nand 60 for >350. Along with genetic PCs and peer factors, sex, age at death, library preparation\n(rRNAdepletion vs polyA), sub-study, PMI, RIN, and sequencing platform were included as covariates for\nmodel construction. “\n2.3.a) It is unclear where the Spearman correlation (rho) is derived from in the text.\nRefer to the function nested_cv_elastic_net_perf() in the PredictDB Tutorial\n(https://github.com/hakyimlab/PredictDB-Tutorial/blob/master/code/gtex_v7_nested_cv_elnet.R),\nnoting that R’s default method for both cor() and stats::cor.test() is Pearson.\nb) If Spearman correlation was indeed used, note that Fisher’s z-score transformation is not\napplicable.\nc) The description of cross-validation folds is confusing; if you used n_folds=10 and\nn_trait_test_folds=5, these represent the default settings.\nIn summary, unless a custom implementation was used, simply state that the standard PredictDB\npipeline (PredictDB Tutorial; https://github.com/hakyimlab/PredictDB-Tutorial) with default settings\nwas applied.\nResponse: Indeed, we are using all default settings, including Pearson for correlation metric. Following\nreviewer advice, we have now made the description clear and compact, and we refer to PrediXcan for\nmore details.\n“We applied elastic-net regularized regression to estimate SNP effect sizes within 1 Mb of each gene,\nfollowing the PrediXcan framework (Gamazon, Wheeler et al. 2015). Model performance was estimated\nusing nested cross-validation (5 outer folds for unbiased evaluation, with 10-fold inner cross-validation\nfor lambda selection) and reported as the average square of the Pearson correlation (R²). Final SNP–gene\nweights were obtained by retraining models on the entire dataset using 10-fold cross-validation for\nlambda selection. Models with R2 > 0.01 and P < 0.05 were retained for downstream analyses, as in [8].\n“\n2.5. Specify which correlation measure is reported (e.g., Spearman).\nResponse: Updated to Spearman!\n3.3. a) The original authors now refer to MetaXcan as S-PrediXcan, and their latest version includes\ncorrections for polygenicity.\nIt remains unclear whether the new S-PrediXcan or JEPEGMiX is more stringent.\nb) A justification for the use of FDR correction is not necessary, as both Bonferroni and FDR methods\nare acceptable in the literature.\nResponse: Thank you following your advice we have removed the justification and just use FDR. Also\nplease see our response to RESULTS 1.1.\n4 reads G*au*dy Trauma Project\n4.1 the (i) description of AA is confusing \"subjects with afr ≥5% African ancestry\" isn't 5% too relaxed?\nResponse: This is a very valid point. In our initial analysis, we defined AA and EUR ancestry in the Grady\nTrauma Project (GTP) cohort based on the criteria used in the generation of GTP PTSD GWAS summary\nstatistics (as included in Nievergelt Nat Coms 2019) provided by PGC-PTSD to enable direct comparison\nbetween the raw TWAS and summary-statistics-based TWAS results. We have now also incorporated\nthe ancestry definitions we used for the DLPFC samples in the GTP cohort. Using this approach, AA\ngroup was slightly different (n = 5904, previously 5284, overlap between then was 5177). Accordingly,\nwe performed raw TWAS analyses using both ancestry definitions and now present results from both\napproaches, which were comparable.\n5.2 Specify ancestry assignment approach. If different approaches were used, please comment on\nwhether these methods are comparable.\nResponse: We have now added the method used for ancestry assignment. “LD-pruned (plink --indep-\npairwise 50 5 0.2) data were merged with 1000 Genomes. PCA and k-nearest-neighbors (k=50) were run\nto infer data ancestry.”\nEven though the fetal study used a genotype-\nbased assignment, this approach is still distinct\nfrom the ancestry assignment method we applied\nin the DLPFC datasets. For the fetal data, processing\nand model training were performed by the first\nauthor of the original publication from the Gandal\nlab. Due to stricter data sharing requirements and\nthe high confidentiality of the cohort, IRB approval\ncould not be obtained. While we do not have\naccess to the fetal dataset itself, we assessed the comparability of definitions within the DLPFC data. As\nshown in the confusion matrix, the 1000G based assignment (used in fetal data) and our ancestry PC\nbased assignment (used in DLPFC data) were largely concordant, supporting that the two methods yield\nconsistent classifications.\nFIGURES\nOverall: Many panels lack sample size (n) information.\nResponse: We have now added N when we can (e.g. figure 2 legends as pointed below) and have also\nchanged figures to better repserent numbers (e.g. Figure 3 intersection size is added to correlation\nplot).\nFigure 1B: PsychENCODE not Psych*o*ENCODE. There are also instances in the results with this error.\nResponse: Fixed!\nFigure 2B: This plot may be misleading, as a large number of SNP weights may not shared between\nthe AACt165 and EURCt165 models. In the caption, specify the number of shared SNP weights (e.g.,\n“1,000 SNPs (1%) serve as predictors in both models”) since a high correlation may be observed even\nwith a small number of shared SNPs.\nResponse:\nThis is a great point. We have updated the figure caption, and as well as the description in the results.\nFurther we provide more details on predictor SNPs in supplementary text.\n“Firstly, we found that about 12% of the predictor SNPs were shared across the two models, and that these\nwere non-random predictors explained by MAF differences, we provide more details here [27]. Despite a\nmodest overlap we found a high correlation in SNP weights among the 8,465 shared predictor SNPs\nbetween the two models (Pearson’s r = 0.6), with 97% agreement in the direction of effect (Fig. 2B). “\nFigure 2E: Use “rho” for Spearman correlations and “r” for Pearson correlations.\nResponse: Updated!\nFigure 3A: caption: the x-axis and y-axis descriptions and color indicators (blue and red) appear to be\ninverted\nResponse: Fixed!\nFigure 3B: Provide insights into why PTSD clusters closer to SCZ/BIP in the EUR dataset but clusters\ncloser to PTSD in the AFR dataset.\nResponse: Two things have changed. One, we had made error in plotting, which occurred due to fixing\nthe labels in heatmap. Second, we have now updated PTSD AA GWAS to the latest one from (Nievergelt,\nMaihofer et al. 2024), which has further improved the correlation between MDD and PTSD in AA dataset.\nFigure 3C: The figure is illegible even when zoomed in.\nResponse:\nFixed!\n[Added reviewer OVERALL comment 2 here, to respond it with Figure 4 comments together]\n2. Given the high polymorphism, gene density, and complex LD patterns in the MHC region, exclude it\nfrom analyses performed with current methods. If not excluded, include a clear disclaimer in the\ndiscussion acknowledging this known limitation of current TWAS approaches.\nFigure 4:\na) Remove transcripts from the (extended) MHC region.\nb) Include only protein-coding genes.\nc) Analysis of the 17q21.31 inversion region (Figure 4c) is particularly challenging in cross-ancestry\nstudies; the current approach may not adequately address this without sensitivity analyses. It is\nacceptable to exclude these regions and acknowledge this limitation.\nResponse: This is a great point. We have now added the following limitation in the discussion.\n“Although TWAS aims to prioritize genes at the tissue level for a given disorder, signals from complex\nloci—particularly chr6p21 (a highly polymorphic region) and chr17q21.31 (characterized by a EUR-\nenriched inversion polymorphism)—may not reliably reflect causal relationships across populations.\nExcluding these loci is also not a viable solution, as they harbor well-established functional genes (e.g.,\nC4A in schizophrenia, implicated in synaptic pruning via microglia; CRHR1 in PTSD, a key mediator of the\nstress response). Importantly, because GTAs represent independent signals, any locus-specific biases would\nonly influence GTAs within that region, and retaining these loci does not affect genome-wide TWAS results.\nHowever, pathway analyses that include genes from these regions could be influenced by such\ncomplexities and should be interpreted with caution.”\nFigures 4E and 4F are a nice addition to the study.\nResponse: Appreciate your comment!\nFigure 6: Some figures appear to have been stitched together and have two sets of legends. In\naddition, Fig. 6A is misaligned.\nResponse: Fixed!\nFigure 9: Correct the caption to metaAAfeta*l* (remove extraneous characters).\nBoth Figures 9B and 9D read PTSD.\nResponse: Fixed!\nDISCUSSION\nThe first half of the discussion effectively summarizes the key findings and their impact.\n- SNP weights concordance analysis: mention among x% of overlapping SNP weights.\nResponse: Updated\n“In this study, we demonstrated that differences between EUR and AA ancestry-specific GReX models\nin adult and developing brain tissues were largely attributable to MAFs. Nonetheless, the modest set of\nshared predictors exhibited concordant SNP weights, indicating consistency in genetic regulation across\nancestries.”\n- Separate the use of disease-controls and bi-ancestral models in the text; currently, \"Increasing\nsample size by including disease subjects or creating bi-ancestral models enhances overall signal\ndetection and uncovers nuanced biologically meaningful associations\". These are two different topics\nthat shouldn’t be pooled together.\nResponse: We agree with the reviewer’s advice to separate the two aspects of increasing training\nsample size through including disease samples and through merging ancestry-specific samples. We\nhave organized discussion but have also made changes in result section for clarity.\n“When deciding whether to include cases alongside neurotypical controls, researchers should weigh total\nsample size against the extent to which disease samples may exhibit gene expression changes driven by\nfactors such as pathophysiology or medication effects. This could lead to findings influenced reverse\ncausation (Gamazon, Wheeler et al. 2015). If including cases, researchers could benefit by developing\nstrategies to mitigate non-linear disease impacts on gene expression, whether affecting individual genes\nor coordinated sets of genes within pathways. Further increases in sample size could be achieved by\nmerging ancestry-specific datasets to generate multi-ancestry models. We have shown that such models\ncan yield unique genes and pathways; for example, spliceosome complex–related pathways, were captured\nonly in higher-order models. Such nuanced findings might be relevant or noise, as little is known about\nthe impact of altered splicing machinery on psychiatric diseases. In general, genes with low heritability\nand located in recombination cold-spots could benefit from such models. However, many signals,\nincluding important ones such as FURIN in the SCZ TWAS, were lost—signals that were otherwise captured\nin AA-specific or EUR-specific models. Notably, FURIN is a EUR-specific SCZ FDR-GTA with smaller LD in\nEUR compared to AA. When training bi-ancestry GReX models, it is important to account for the\ncomplexities introduced by differing LD structures and MAF distributions. These differences can dilute\ncausal SNP effects by distributing weights across tagged variants, introduce collinearity in genetic principal\ncomponents and predictor SNPs, and give rise to multimodal gene expression patterns that can be\ndistorted during RNA preprocessing. Further, when feasible, providing multiple versions trained on\ndifferent sample groupings could greatly benefit the research community.“\n- The data presented in this study suggest that “higher order” ancestry-specific models outperform\ncontrol (Ct) models in prediction performance on independent test datasets, likely due to larger\ntraining sample sizes. This implies that training sample size is the dominant factor driving\nperformance, and the potential benefits of including control brains in real-world applications remain\nunclear (beyond theoretical scenarios where, for example, 80% of brains are from MDD patients).\nStating this conclusion more forcefully would challenge the unfounded belief held by some\ninvestigators that neurotypical control TWAS studies are either optimal or indispensable.\nResponse: Discussion section has been modified, please see above the modified paragraph.\n- Regarding bi-ancestral models, the authors have not provided a definitive recommendation. Based\non their findings, the modest increase in power does not appear to justify the potential drawbacks.\nInstead of using bi-ancestral models directly, performing ancestry-specific analyses followed by multi-\nancestry fine-mapping (e.g., with MA-FOCUS) seems a more practical approach. However, if the\nauthors believe that their data support the routine use of bi-ancestral models, they should clarify and\nelaborate further.\nResponse: Discussion section has been modified, please see above the modified paragraph.\n- Acknowledge additional study limitations: fetal brain samples were pooled from various\ndevelopmental stages (although clustering results support their use in this study), and due to the\nsmall sample size of AA samples, tract-based approaches could not be explored (despite the high\nlevel of admixture in AA).\nResponse: “Despite observing many relevant neurodevelopmental signals, absence of these signals in\nfetal analyses may reflect greater tissue heterogeneity in fetal samples used, which spans across many\nbrain regions across a broad range of gestational age spanning though all three trimesters, compared to\ncortex brain restricted to adult brain.”\n“Further, using local ancestry based GWAS summary statistics and GReX modeling could enhance the\nresults (Atkinson, Maihofer et al. 2021).”\nSUPPLEMENTARY\n- Standardize the naming of supplementary figures (e.g., consistently use the “Figure SX” format).\nFixed!\nReferences\nAnsorge, M. S., R. Hen and J. A. Gingrich (2007). \"Neurodevelopmental origins of depressive\ndisorders.\" Curr Opin Pharmacol 7(1): 8-17.\nAtkinson, E. G., A. X. Maihofer, M. Kanai, A. R. Martin, K. J. Karczewski, M. L. Santoro, J. C. Ulirsch, Y.\nKamatani, Y. Okada, H. K. Finucane, K. C. Koenen, C. M. Nievergelt, M. J. Daly and B. M. Neale (2021).\n\"Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost\npower.\" Nature Genetics 53(2): 195-204.\nBraun, E., M. Danan-Gotthold, L. E. Borm, K. W. Lee, E. Vinsland, P. Lonnerberg, L. Hu, X. Li, X. He, Z.\nAndrusivova, J. Lundeberg, R. A. Barker, E. Arenas, E. Sundstrom and S. Linnarsson (2023).\n\"Comprehensive cell atlas of the first-trimester developing human brain.\" Science 382(6667):\neadf1226.\nBreunig, S., Y. H. Lee, E. W. Karlson, A. Krishnan, J. M. Lawrence, L. S. Schaffer and A. D. Grotzinger\n(2025). \"Examining the genetic links between clusters of immune-mediated diseases and psychiatric\ndisorders.\" Translational Psychiatry 15(1): 252.\nChen, C. Y., S. Pollack, D. J. Hunter, J. N. Hirschhorn, P. Kraft and A. L. Price (2013). \"Improved ancestry\ninference using weights from external reference panels.\" Bioinformatics 29(11): 1399-1406.\nChen, Y., S. Liu, Z. Ren, F. Wang, Q. Liang, Y. Jiang, R. Dai, F. Duan, C. Han, Z. Ning, Y. Xia, M. Li, K. Yuan,\nW. Qiu, X. X. Yan, J. Dai, R. F. Kopp, J. Huang, S. Xu, B. Tang, L. Wu, E. R. Gamazon, T. Bigdeli, E.\nGershon, H. Huang, C. Ma, C. Liu and C. Chen (2024). \"Cross-ancestry analysis of brain QTLs enhances\ninterpretation of schizophrenia genome-wide association studies.\" Am J Hum Genet.\nConsortium, G. T. (2020). \"The GTEx Consortium atlas of genetic regulatory effects across human\ntissues.\" Science 369(6509): 1318-1330.\nDeep-Soboslay, A., C. E. Martin and J. E. Kleinman (2004). \"The development of a posttraumatic stress\ndisorder brain collection.\" Psychiatry 67(4): 416-418.\nDerks, E. M., J. G. Thorp and Z. F. Gerring (2022). \"Ten challenges for clinical translation in psychiatric\ngenetics.\" Nat Genet 54(10): 1457-1465.\nFriedman, M. J., B. R. Huber, C. B. Brady, R. J. Ursano, D. M. Benedek, N. W. Kowall, A. C. McKee and G.\nTraumatic Stress Brain Research (2017). \"VA's National PTSD Brain Bank: a National Resource for\nResearch.\" Curr Psychiatry Rep 19(10): 73.\nGamazon, E. R., H. E. Wheeler, K. P. Shah, S. V. Mozaffari, K. Aquino-Michaels, R. J. Carroll, A. E. Eyler, J.\nC. Denny, G. T. Consortium, D. L. Nicolae, N. J. Cox and H. K. Im (2015). \"A gene-based association\nmethod for mapping traits using reference transcriptome data.\" Nat Genet 47(9): 1091-1098.\nGay, N. R., M. Gloudemans, M. L. Antonio, N. S. Abell, B. Balliu, Y. Park, A. R. Martin, S. Musharoff, A. S.\nRao, F. Aguet, A. N. Barbeira, R. Bonazzola, F. Hormozdiari, K. G. Ardlie, C. D. Brown, H. K. Im, T.\nLappalainen, X. Wen, S. B. Montgomery and G. T. Consortium (2020). \"Impact of admixture and\nancestry on eQTL analysis and GWAS colocalization in GTEx.\" Genome Biology 21(1): 233.\nHowes, O. D. and E. Shatalina (2022). \"Integrating the Neurodevelopmental and Dopamine\nHypotheses of Schizophrenia and the Role of Cortical Excitation-Inhibition Balance.\" Biol Psychiatry\n92(6): 501-513.\nJaffe, A. E., R. Tao, S. C. Page, K. R. Maynard, E. A. Pattie, C. V. Nguyen, A. Deep-Soboslay, R. Bharadwaj,\nK. A. Young, M. J. Friedman, D. E. Williamson, G. Traumatic Stress Brain Research, J. H. Shin, T. M. Hyde,\nK. Martinowich and J. E. Kleinman (2022). \"Decoding Shared Versus Divergent Transcriptomic\nSignatures Across Cortico-Amygdala Circuitry in PTSD and Depressive Disorders.\" Am J Psychiatry\n179(9): 673-686.\nJajoo, A. (2025). \"Supplementary Material\n.\"\nJavitt, D. C. and R. A. Sweet (2015). \"Auditory dysfunction in schizophrenia: integrating clinical and\nbasic features.\" Nat Rev Neurosci 16(9): 535-550.\nKachuri, L., A. C. Y. Mak, D. Hu, C. Eng, S. Huntsman, J. R. Elhawary, N. Gupta, S. Gabriel, S. Xiao, K. L.\nKeys, A. Oni-Orisan, J. R. Rodriguez-Santana, M. A. LeNoir, L. N. Borrell, N. A. Zaitlen, L. K. Williams, C.\nR. Gignoux, E. G. Burchard and E. Ziv (2023). \"Gene expression in African Americans, Puerto Ricans and\nMexican Americans reveals ancestry-specific patterns of genetic architecture.\" Nat Genet 55(6): 952-\n963.\nKlengel, T. and E. B. Binder (2015). \"Epigenetics of Stress-Related Psychiatric Disorders and Gene x\nEnvironment Interactions.\" Neuron 86(6): 1343-1357.\nLautarescu, A., M. C. Craig and V. Glover (2020). \"Prenatal stress: Effects on fetal and child brain\ndevelopment.\" Int Rev Neurobiol 150: 17-40.\nMachiela, M. J. and S. J. Chanock (2015). \"LDlink: a web-based application for exploring population-\nspecific haplotype structure and linking correlated alleles of possible functional variants.\"\nBioinformatics 31(21): 3555-3557.\nMichelini, G., C. O. Carlisi, N. R. Eaton, J. T. Elison, J. D. Haltigan, R. Kotov, R. F. Krueger, R. D. Latzman,\nJ. J. Li, H. F. Levin-Aspenson, G. A. Salum, S. C. South, K. Stanton, I. D. Waldman and S. Wilson (2024).\n\"Where do neurodevelopmental conditions fit in transdiagnostic psychiatric frameworks?\nIncorporating a new neurodevelopmental spectrum.\" World Psychiatry 23(3): 333-357.\nMighdoll, M. I., A. Deep-Soboslay, R. A. Bharadwaj, J. A. Cotoia, D. M. Benedek, T. M. Hyde and J. E.\nKleinman (2018). \"Implementation and clinical characteristics of a posttraumatic stress disorder brain\ncollection.\" J Neurosci Res 96(1): 16-20.\nMogil, L. S., A. Andaleon, A. Badalamenti, S. P. Dickinson, X. Guo, J. I. Rotter, W. C. Johnson, H. K. Im, Y.\nLiu and H. E. Wheeler (2018). \"Genetic architecture of gene expression traits across diverse\npopulations.\" PLoS Genet 14(8): e1007586.\nNievergelt, C. M., A. X. Maihofer, E. G. Atkinson, C. Y. Chen, K. W. Choi, J. R. I. Coleman, N. P.\nDaskalakis, L. E. Duncan, R. Polimanti, C. Aaronson, A. B. Amstadter, S. B. Andersen, O. A. Andreassen,\nP. A. Arbisi, A. E. Ashley-Koch, S. B. Austin, E. Avdibegovic, D. Babic, S. A. Bacanu, D. G. Baker, A.\nBatzler, J. C. Beckham, S. Belangero, C. Benjet, C. Bergner, L. M. Bierer, J. M. Biernacka, L. J. Bierut, J. I.\nBisson, M. P. Boks, E. A. Bolger, A. Brandolino, G. Breen, R. A. Bressan, R. A. Bryant, A. C. Bustamante, J.\nBybjerg-Grauholm, M. Baekvad-Hansen, A. D. Borglum, S. Borte, L. Cahn, J. R. Calabrese, J. M. Caldas-\nde-Almeida, C. Chatzinakos, S. Cheema, S. A. P. Clouston, L. Colodro-Conde, B. J. Coombes, C. S. Cruz-\nFuentes, A. M. Dale, S. Dalvie, L. K. Davis, J. Deckert, D. L. Delahanty, M. F. Dennis, F. Desarnaud, C. P.\nDiPietro, S. G. Disner, A. R. Docherty, K. Domschke, G. Dyb, A. D. Kulenovic, H. J. Edenberg, A. Evans, C.\nFabbri, N. Fani, L. A. Farrer, A. Feder, N. C. Feeny, J. D. Flory, D. Forbes, C. E. Franz, S. Galea, M. E.\nGarrett, B. Gelaye, J. Gelernter, E. Geuze, C. F. Gillespie, S. B. Goleva, S. D. Gordon, A. Goci, L. R. Grasser,\nC. Guindalini, M. Haas, S. Hagenaars, M. A. Hauser, A. C. Heath, S. M. J. Hemmings, V. Hesselbrock, I. B.\nHickie, K. Hogan, D. M. Hougaard, H. Huang, L. M. Huckins, K. Hveem, M. Jakovljevic, A. Javanbakht, G.\nD. Jenkins, J. Johnson, I. Jones, T. Jovanovic, K. I. Karstoft, M. L. Kaufman, J. L. Kennedy, R. C. Kessler, A.\nKhan, N. A. Kimbrel, A. P. King, N. Koen, R. Kotov, H. R. Kranzler, K. Krebs, W. S. Kremen, P. F. Kuan, B.\nR. Lawford, L. A. M. Lebois, K. Lehto, D. F. Levey, C. Lewis, I. Liberzon, S. D. Linnstaedt, M. W. Logue, A.\nLori, Y. Lu, B. J. Luft, M. K. Lupton, J. J. Luykx, I. Makotkine, J. L. Maples-Keller, S. Marchese, C. Marmar,\nN. G. Martin, G. A. Martinez-Levy, K. McAloney, A. McFarlane, K. A. McLaughlin, S. A. McLean, S. E.\nMedland, D. Mehta, J. Meyers, V. Michopoulos, E. A. Mikita, L. Milani, W. Milberg, M. W. Miller, R. A.\nMorey, C. P. Morris, O. Mors, P. B. Mortensen, M. S. Mufford, E. C. Nelson, M. Nordentoft, S. B.\nNorman, N. R. Nugent, M. O'Donnell, H. K. Orcutt, P. M. Pan, M. S. Panizzon, G. A. Pathak, E. S. Peters,\nA. L. Peterson, M. Peverill, R. H. Pietrzak, M. A. Polusny, B. Porjesz, A. Powers, X. J. Qin, A.\nRatanatharathorn, V. B. Risbrough, A. L. Roberts, A. O. Rothbaum, B. O. Rothbaum, P. Roy-Byrne, K. J.\nRuggiero, A. Rung, H. Runz, B. P. F. Rutten, S. S. de Viteri, G. A. Salum, L. Sampson, S. E. Sanchez, M.\nSantoro, C. Seah, S. Seedat, J. S. Seng, A. Shabalin, C. M. Sheerin, D. Silove, A. K. Smith, J. W. Smoller, S.\nR. Sponheim, D. J. Stein, S. Stensland, J. S. Stevens, J. A. Sumner, M. H. Teicher, W. K. Thompson, A. K.\nTiwari, E. Trapido, M. Uddin, R. J. Ursano, U. Valdimarsdottir, M. Van Hooff, E. Vermetten, C. H. Vinkers,\nJ. Voisey, Y. Wang, Z. Wang, M. Waszczuk, H. Weber, F. R. Wendt, T. Werge, M. A. Williams, D. E.\nWilliamson, B. S. Winsvold, S. Winternitz, C. Wolf, E. J. Wolf, Y. Xia, Y. Xiong, R. Yehuda, K. A. Young, R.\nM. Young, C. C. Zai, G. C. Zai, M. Zervas, H. Zhao, L. A. Zoellner, J. A. Zwart, T. deRoon-Cassini, S. J. H.\nvan Rooij, L. L. van den Heuvel, A. Study, T. Estonian Biobank Research, I. FinnGen, H. A.-I. Psychiatry,\nM. B. Stein, K. J. Ressler and K. C. Koenen (2024). \"Genome-wide association analyses identify 95 risk\nloci and provide insights into the neurobiology of post-traumatic stress disorder.\" Nat Genet 56(5):\n792-808.\nO'Brien, H. E., E. Hannon, M. J. Hill, C. C. Toste, M. J. Robertson, J. E. Morgan, G. McLaughlin, C. M.\nLewis, L. C. Schalkwyk, L. S. Hall, A. F. Pardiñas, M. J. Owen, M. C. O'Donovan, J. Mill and N. J. Bray\n(2018). \"Expression quantitative trait loci in the developing human brain and their enrichment in\nneuropsychiatric disorders.\" Genome Biol 19(1): 194.\nSchubert, K. O., A. Thalamuthu, A. T. Amare, J. Frank, F. Streit, M. Adl, N. Akula, K. Akiyama, R. Ardau, B.\nArias, J. M. Aubry, L. Backlund, A. K. Bhattacharjee, F. Bellivier, A. Benabarre, S. Bengesser, J. M.\nBiernacka, A. Birner, C. Marie-Claire, M. Cearns, P. Cervantes, H. C. Chen, C. Chillotti, S. Cichon, S. R.\nClark, C. Cruceanu, P. M. Czerski, N. Dalkner, A. Dayer, F. Degenhardt, M. Del Zompo, J. R. DePaulo, B.\nEtain, P. Falkai, A. J. Forstner, L. Frisen, M. A. Frye, J. M. Fullerton, S. Gard, J. S. Garnham, F. S. Goes, M.\nGrigoroiu-Serbanescu, P. Grof, R. Hashimoto, J. Hauser, U. Heilbronner, S. Herms, P. Hoffmann, L. Hou,\nY. H. Hsu, S. Jamain, E. Jimenez, J. P. Kahn, L. Kassem, P. H. Kuo, T. Kato, J. Kelsoe, S. Kittel-Schneider, E.\nFerensztajn-Rochowiak, B. Konig, I. Kusumi, G. Laje, M. Landen, C. Lavebratt, M. Leboyer, S. G.\nLeckband, M. Maj, C. Major Depressive Disorder Working Group of the Psychiatric Genomics, M.\nManchia, L. Martinsson, M. J. McCarthy, S. McElroy, F. Colom, M. Mitjans, F. M. Mondimore, P.\nMonteleone, C. M. Nievergelt, M. M. Nothen, T. Novak, C. O'Donovan, N. Ozaki, U. Osby, S. Papiol, A.\nPfennig, C. Pisanu, J. B. Potash, A. Reif, E. Reininghaus, G. A. Rouleau, J. K. Rybakowski, M. Schalling, P.\nR. Schofield, B. W. Schweizer, G. Severino, T. Shekhtman, P. D. Shilling, K. Shimoda, C. Simhandl, C. M.\nSlaney, A. Squassina, T. Stamm, P. Stopkova, F. Tekola-Ayele, A. Tortorella, G. Turecki, J. Veeh, E. Vieta,\nS. H. Witt, G. Roberts, P. P. Zandi, M. Alda, M. Bauer, F. J. McMahon, P. B. Mitchell, T. G. Schulze, M.\nRietschel and B. T. Baune (2021). \"Combining schizophrenia and depression polygenic risk scores\nimproves the genetic prediction of lithium response in bipolar disorder patients.\" Transl Psychiatry\n11(1): 606.\nSolmi, M., J. Radua, M. Olivola, E. Croce, L. Soardo, G. Salazar de Pablo, J. Il Shin, J. B. Kirkbride, P.\nJones, J. H. Kim, J. Y. Kim, A. F. Carvalho, M. V. Seeman, C. U. Correll and P. Fusar-Poli (2022). \"Age at\nonset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies.\" Mol\nPsychiatry 27(1): 281-295.\nStoufflet, J., S. Tielens and L. Nguyen (2023). \"Shaping the cerebral cortex by cellular crosstalk.\" Cell\n186(13): 2733-2747.\nTaylor, D. J., S. B. Chhetri, M. G. Tassia, A. Biddanda, S. M. Yan, G. L. Wojcik, A. Battle and R. C. McCoy\n(2024). \"Sources of gene expression variation in a globally diverse human cohort.\" Nature 632(8023):\n122-130.\nWegrzyn, D., G. Juckel and A. Faissner (2022). \"Structural and Functional Deviations of the\nHippocampus in Schizophrenia and Schizophrenia Animal Models.\" Int J Mol Sci 23(10).\nYehuda, R., A. Bell, L. M. Bierer and J. Schmeidler (2008). \"Maternal, not paternal, PTSD is related to\nincreased risk for PTSD in offspring of Holocaust survivors.\" J Psychiatr Res 42(13): 1104-1111.\nYehuda, R., S. L. Halligan and L. M. Bierer (2001). \"Relationship of parental trauma exposure and PTSD\nto PTSD, depressive and anxiety disorders in offspring.\" J Psychiatr Res 35(5): 261-270.\nZhao, S., W. Crouse, S. Qian, K. Luo, M. Stephens and X. He (2024). \"Adjusting for genetic confounders\nin transcriptome-wide association studies improves discovery of risk genes of complex traits.\" Nat\nGenet 56(2): 336-347."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my concerns adequately and comprehensively in the revision. I have just a few minor\ncomments.\nPage 11, the last line: the number of genes predicated from fetaAA and fetaEUR look the same as before, unlike what’s\nstated in the response.\nPage 36, section 2.1 c) It still states 4 genetic PCs used, in contradiction to the response.\nPage 11, fetalAA (n=135), fetalEUR(n=280). Sample sizes changed from 165 to 135 and 277 to 280. But in the text the\nsamples are still referred to as fetalAA165 throughout and fetalEUR277.\nPage 37, section 3.4: “metaTWAS for GTAs was performed using METAL” appeared twice.", "reviewer_2": "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.", "reviewer_3": "The authors addressed my comments and concerns. I have no further comments."}, "author_response": ""}]

Decision letter