Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies

Xiaowei Hu; Daniel S Araujo; Chachrit Khunsriraksakul; Lida Wang; Quan Sun; Jia Wen; Lingbo Zhou; Lynette Ekunwe; Leslie A Lange; Ethan M Lange; Stephen B Montgomery; Alexander P Reiner; Francois Aguet; Kristin G Ardlie; Tuuli Lappalainen; Christopher R Gignoux; Esteban G Burchard; Kent D Taylor; Xiuqing Guo; Jerome I Rotter; Stephen S Rich; Elaine Cornell; Peter Durda; Russell P Tracy; Yongmei Liu; W Craig Johnson; George P Papanicolaou; Minoli A Perera; Michael H Cho; Dajiang J Liu; Laura M Raffield; Yun Li; TOPMed Multi-Omics Working Group; Heather E Wheeler; Hae Kyung Im; Ani Manichaikul

doi:10.1016/j.ajhg.2026.03.008

. 2026 Apr 2;113(4):828–841. doi: 10.1016/j.ajhg.2026.03.008

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies

Xiaowei Hu ^1,^∗, Daniel S Araujo ^2,³, Chachrit Khunsriraksakul ⁴, Lida Wang ⁵, Quan Sun ^6,^7,^8,⁹, Jia Wen ¹⁰, Lingbo Zhou ⁶, Lynette Ekunwe ¹¹, Leslie A Lange ¹², Ethan M Lange ¹², Stephen B Montgomery ¹³, Alexander P Reiner ¹⁴, Francois Aguet ¹⁵, Kristin G Ardlie ¹⁵, Tuuli Lappalainen ¹⁶, Christopher R Gignoux ¹⁷, Esteban G Burchard ¹⁸, Kent D Taylor ¹⁹, Xiuqing Guo ¹⁹, Jerome I Rotter ¹⁹, Stephen S Rich ¹, Elaine Cornell ²⁰, Peter Durda ²⁰, Russell P Tracy ²⁰, Yongmei Liu ²¹, W Craig Johnson ²², George P Papanicolaou ²³, Minoli A Perera ²⁴, Michael H Cho ²⁵, Dajiang J Liu ⁴, Laura M Raffield ¹⁰, Yun Li ^6,¹⁰; TOPMed Multi-Omics Working Group, Heather E Wheeler ², Hae Kyung Im ²⁶, Ani Manichaikul ^1,^∗∗

¹Department of Genome Sciences, University of Virginia, Charlottesville, VA, USA

²Department of Biology, Loyola University Chicago, Chicago, IL, USA

³Department of Human Genetics, University of Chicago, Chicago, IL, USA

⁴Department of Bioinformatics and Genomics, Pennsylvania State University, Hershey, PA, USA

⁵Department of Public Health Sciences, Pennsylvania State University, Hershey, PA, USA

⁶Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

⁷Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

⁸Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

⁹Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

¹⁰Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

¹¹Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA

¹²Department of Biomedical Informatics, University of Colorado-Anschutz, Aurora, CO, USA

¹³Department of Genetics, Department of Pathology, Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

¹⁴Department of Epidemiology, University of Washington, Seattle, WA, USA

¹⁵The Broad Institute of MIT and Harvard, Cambridge, MA, USA

¹⁶New York Genome Center, New York, NY, USA

¹⁷Colorado Center for Personalized Medicine, Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

¹⁸Department of Medicine, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

¹⁹The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA

²⁰Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA

²¹Division of Cardiology, Department of Medicine, Duke University, Durham, NC, USA

²²Department of Biostatistics, University of Washington, Seattle, WA, USA

²³Epidemiology Branch, National Heart, Lung, and Blood Institute, Bethesda, MD, USA

²⁴Department of Pharmacology, Northwestern University, Chicago, IL, USA

²⁵Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

²⁶Section of Genetic Medicine, The University of Chicago, Chicago, IL, USA

^∗

Corresponding author xh6dx@virginia.edu

^∗∗

Corresponding author am3xa@virginia.edu

PMCID: PMC13078710 NIHMSID: NIHMS2161406 PMID: 41932314

Summary

Reliable reference transcriptome prediction models are key to accurate multi-ancestry transcriptome-wide association studies (TWASs). We propose three methods leveraging functionally informed variants (FIVs) for transcriptome prediction models to improve multi-ancestry TWASs. We trained models on 1,287 multi-ancestry participants from the Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). We validated models’ prediction accuracy on two external independent datasets, Geuvadis and Jackson Heart Study. To test robustness of our methods for TWASs, we integrated models with three multi-ancestry GWASs from blood cell, lipid, and pulmonary function traits, respectively. Our methods presented similar prediction accuracy while using a smaller and functionally informed set of variants compared to the benchmark method, elastic net (EN). Overall, our methods achieved higher power and accuracy (with average improved accuracy of 24% over EN) for TWASs. However, no single proposed method outperformed all GWAS traits. To further improve TWAS performance, we propose an omnibus approach that aggregates TWAS summary statistics from our methods. The omnibus approach yielded the highest number of Bonferroni-significant TWAS genes for all GWAS traits, and it further improved TWAS power and accuracy for blood cell traits. Additionally, the omnibus approach detected some trait-relevant important genes that the EN missed. Our study demonstrates the value of including FIVs in multi-ancestry transcriptome prediction models for improving TWAS performance. Further, the observed TWAS improvement depends on the GWAS trait’s relevance to the PBMCs used to build our transcriptome prediction models.

Keywords: transcriptome prediction models, transcriptome-wide association study, functional annotation, multi-ancestry, peripheral blood mononuclear cells, PBMCs, RNA-seq, omnibus approach

We proposed methods leveraging functionally informed variants for multi-ancestry transcriptome prediction and demonstrated improvements on multi-ancestry TWAS performance. Our proposed omnibus approach, which aggregates TWAS results, further improves TWAS power. The observed TWAS improvement depends on the GWAS trait’s relevance to peripheral blood mononuclear cells used to build our models.

Introduction

Although large-scale genome-wide association studies (GWASs) have identified numerous genetic variants associated with complex human diseases or traits, the inference from identified genetic variants to candidate genes remains challenging. Over 90% of GWAS variants are in non-protein-coding regions, and many candidate genes at GWAS loci are not the closest.¹^,² Transcriptome-wide association study (TWAS), a widely used approach that integrates GWAS with transcriptome data,³ has potential to identify candidate genes and has gained a broad range of applications to human genetics.¹^,⁴^,⁵^,⁶^,⁷ Three steps are involved in performing TWAS analysis. First, reference transcriptome prediction models are trained in genotype data and tissue- or cell type-specific transcriptome data. Second, genetically regulated gene expression (GReX) is imputed by integrating individual-level genotypes of GWAS with reference transcriptome prediction models. Last, the association test between imputed GReX and GWAS trait is performed to identify trait-associated genes. TWAS can also be used with GWAS summary statistics via tools like S-PrediXcan.³ Hence, reliable reference transcriptome prediction models are critical for accurate TWAS analysis.

Recently, multi-ancestry GWASs have been emerging, adding more biological insights into non-European ancestries and signals shared across ancestries.⁸^,⁹^,¹⁰ At present, reference transcriptome prediction models for TWAS have been constructed primarily based on individuals of European ancestry. However, studies have shown that the integration of GWAS with reference transcriptome prediction models from mismatched ancestries had suboptimal power.¹¹^,¹² Further, the interpretation of ancestry-mismatched TWAS results may be difficult, as causal variants underlying GWAS and expression quantitative trait locus (eQTL) hits may differ by ancestry. Reliable multi-ancestry transcriptome prediction models are needed for multi-ancestry TWAS.

The genomic variants overlapping with key annotations (e.g., fine-mapping, epigenetic processes, and 3D genomics-informed regions), defined as functionally informed variants (FIVs) in this study, are shown to be more likely to influence gene expression.¹³ Recent studies have demonstrated the power of incorporating FIVs into transcriptome prediction models to improve both prediction model accuracy and downstream TWAS performance.¹⁴^,¹⁵^,¹⁶ However, the existing transcriptome models with FIVs were built on individuals of European ancestry. There is a lack of ancestry diversity for transcriptome prediction models with FIVs.

Motivated by previous studies, we hypothesized that multi-ancestry transcriptome prediction models using FIV annotation could improve both prediction accuracy and multi-ancestry TWAS performance. Here, we investigated the prediction accuracy of multi-ancestry transcriptome prediction models with FIVs (hereafter referred to as FIV-based methods) and explored the benefits of methods on multi-ancestry TWASs. We constructed transcriptome prediction models on 1,287 multi-ancestry participants from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program Multi-Ethnic Study of Atherosclerosis (MESA) with RNA sequencing (RNA-seq) data from peripheral blood mononuclear cells (PBMCs). The developed transcriptome prediction models were integrated with three large-scale multi-ancestry GWASs of blood cell traits,⁸ lipid traits,⁹ and pulmonary function traits,¹⁰ respectively, to evaluate models’ performance for multi-ancestry TWASs. Further, inspired by the increased power of aggregated TWASs from different models or tissues in previous studies,¹⁶^,¹⁷^,¹⁸ we developed an omnibus approach for our FIV-based methods by aggregating their TWAS p values.

Material and methods

Overview of the approach

An overview of the study design is shown in Figure 1. We applied four different prediction methods to construct transcriptome prediction models based on multi-ancestry participants from the NHLBI TOPMed MESA. The prediction methods examined included three FIV-based methods: elastic net with fine-mapped variants (EN-FM), prediction using models informed by chromatin conformation and epigenomics (PUMICE),¹⁶ and pumice with fine-mapped variants (PUMICE-FM) as well as one method examined for the purpose of benchmarking, elastic net (EN), in the PrediXcan¹⁹ framework. We examined the prediction accuracy of transcriptome prediction models in the MESA training data as well as two external independent validation datasets from the Geuvadis²⁰ consortium and the Jackson Heart Study²¹ (JHS). Finally, we applied transcriptome prediction models to three large-scale multi-ancestry GWASs to evaluate each model’s performance for multi-ancestry TWASs. In addition to assessing TWAS performance for the four methods, we also evaluated an omnibus approach for TWASs that aggregates TWAS summary statistics from our FIV-based methods. In total, there were five sets of TWAS results for each GWAS trait, and we assessed both power and accuracy for each TWAS.

Study design

The two pie charts present ancestry distribution of MESA and Geuvadis participants. TOPMed, Trans-Omics for Precision Medicine; MESA, Multi-Ethnic Study of Atherosclerosis; PBMC, peripheral blood mononuclear cell; EN, elastic net; EN-FM, EN with fine-mapped variants; PUMICE, prediction using models informed by chromatin conformation and epigenomics; PUMICE-FM, PUMICE with fine-mapped variants; Omnibus, aggregated model of EN-FM, PUMICE, and PUMICE-FM; LCL, lymphoblastoid cell line; JHS, Jackson Heart Study; GWAS, genome-wide association study; NHW, non-Hispanic White; AFA, African American; HIS, Hispanic/Latino; CHN, Chinese; CEU, Utah residents with ancestry from northern and western Europe; FIN, Finnish in Finland; GBR, British in England and Scotland; TSI, Toscani in Italy; YRI, Yoruba in Ibadan, Nigeria.

Training data for multi-ancestry transcriptome prediction models

The TOPMed MESA 1,287 multi-ancestry participants with TOPMed Freeze 8 whole-genome sequencing (WGS) data and RNA-seq data from PBMCs were used to build transcriptome prediction models. The TOPMed MESA data are under controlled access in the database of Genotypes and Phenotypes (dbGaP) through study accession number phs001416.v4.p1. Data in dbGaP can be downloaded by controlled access with an approved application submitted through the dbGaP website (https://www.ncbi.nlm.nih.gov/gap).

MESA

MESA is a longitudinal study of subclinical cardiovascular disease and risk factors that predict progression to clinically overt cardiovascular disease or progression of subclinical disease.²² Between 2000 and 2002, MESA recruited 6,814 men and women 45–84 years of age from Forsyth County, North Carolina; New York, New York; Baltimore, Maryland; St. Paul, Minnesota; Chicago, Illinois; and Los Angeles, California. Exclusion criteria were clinical cardiovascular disease, weight exceeding 136 kg (300 lb), pregnancy, and impediment to long-term participation. Approximately 38% of the recruited participants are White, 28% African American, 22% Hispanic/Latino, and 12% Asian, predominantly of Chinese descent. All research was carried out in accordance with relevant guidelines and regulations. All participants provided informed consent, and the protocols of MESA were approved by the institutional review boards (IRBs) of collaborating institutions and the NHLBI. Research involving human research participants was performed in accordance with the Declaration of Helsinki.

RNA-sequencing

RNA-seq was performed for samples obtained from selected MESA participants at exam 1 (corresponding to 2000–2002) and exam 5 (2010–2011). RNA-seq was performed at the Broad Institute Genomics Platform as part of the TOPMed MESA Multi-Omics project based on the following criteria: (1) restrict to those already included in the TOPMed WGS effort,²³ (2) preserve the race/ancestry distribution of participants in the parent MESA cohort, and (3) maximize the amount of overlapping omics data (with the other omics included in the TOPMed MESA Multi-Omics pilot requiring availability of plasma samples for proteomics/metabolomics, RNA from monocytes or T cells for RNA-seq, and whole blood for DNA methylation profiling).

WGS

Genotypes were obtained from WGS data in TOPMed Freeze 8 samples. Approximately 30× WGS was performed at the Broad Institute of MIT and Harvard, and the TOPMed Informatics Research Center performed joint genotype calling using all samples in Freeze 8 on the GRCh38 assembly. Details regarding DNA sample extraction, WGS data sequencing methods, and quality control are available at https://topmed.nhlbi.nih.gov/topmed-whole-genome-sequencing-methods-freeze-8.

Preparation of RNA-seq data for transcriptome prediction modeling

Quality control of the WGS data and preparation of gene expression levels for prediction modeling were done as described previously.²⁴ The residuals of expression levels were generated from a linear regression model of rank-based inverse normalization of expression levels adjusted for age, sex, and the first 10 genotype and 10 expression principal components (PCs) for individuals with only one-time point data (exam 1 or 5). The number of PCs is the same as in the original study²⁴ that performed transcriptome prediction modeling with the same gene expression data. For individuals with two exams, the average expression data from two exams were used as described previously.²⁴ We used the residuals of expression levels as dependent variables later for transcriptome prediction model development. Based on participant self-reported race/ethnicity and comparison with the global proportion of each ancestry that was computed in a previous study of MESA participants,²⁵ 41%, 26%, 25%, and 8% of the participants were categorized as non-Hispanic White (NHW), African American (AFA), Hispanic/Latino (HIS), and Chinese (CHN), respectively, among the 1,287 TOPMed MESA participants (Figure 1).

Transcriptome prediction methods

We developed three prediction methods with FIVs to build transcriptome prediction models, and we used EN in the PrediXcan¹⁹ framework as a benchmark for comparison. For all four methods, we built transcriptome prediction models between residuals of expression levels and cis-SNPs within the 1 Mb upstream region of the transcription start site (TSS) and 1 Mb downstream region of the transcription end site (TES). The prediction performance was estimated using a nested cross-validation approach as described in earlier works.¹⁶^,¹⁹ As suggested in previous works,¹⁵^,¹⁶^,¹⁹^,²⁴ a model was considered significant and was kept if the average of cross-validated Pearson correlation coefficients were >0.1 and the estimated p value was <0.05.

EN

For each gene, EN includes all variants within the cis region for variable selection without incorporating variants’ priors informed by their biological functions. We used the R package glmnet²⁶ to implement EN modeling with a mixing parameter α = 0.5.

EN-FM

Motivated by the goal of using the most likely causal variants, we applied SuSiE²⁷ to 1,287 TOPMed MESA multi-ancestry participants to obtain fine-mapped variants for each gene by using MESA in-sample linkage disequilibrium (LD) and SuSiE’s default setup of parameters, including maximum number of non-zero effects in the SuSiE regression model L = 10 and minimum absolute correlation allowed in a credible set = 0.5. The genomic variants included in our fine-mapping analysis were cis SNPs within the 1Mb upstream region of the TSS and 1 Mb downstream region of the TES for each gene. Since fine-mapped variants with higher posterior inclusion probability (PIP) are the variants most likely to be causal for a gene expression trait, we used (1-PIP) as a penalty for each variant included in EN modeling, as described in Barbeira et al.¹⁵ As a result, the variants with higher PIP received smaller penalties and were upweighted in prediction modeling. The usage of (1-PIP) as a penalty is the main difference between EN-FM and EN modeling. We implemented EN-FM modeling in R/glmnet²⁶ with a mixing parameter α = 0.5.

PUMICE

PUMICE¹⁶ separates genomic variants into essential and non-essential predictors depending on whether they overlap with user-specified functional annotation. Additionally, PUMICE allows different choices for regions that harbor cis-regulatory variants as another tuning. We used epigenomic annotation tracks as functional annotation and 3D genomes as cis-regulatory regions. Given that neither epigenomic annotation nor 3D genomic data were available for PBMCs, we used the available data from Epstein-Barr virus (EBV)-transformed lymphocytes as a proxy. The epigenomic annotation data, including histone H3 lysine 27 acetylation (H3K27ac), histone H3 lysine 4 trimethylation (H3K4me3), DNase hypersensitive, and CCCTC-binding factor (CTCF), were obtained from the Encyclopedia of DNA Elements (ENCODE)²⁸^,²⁹ via accession number ENCFF028SGJ and under dataset ENCSR480YCS (https://www.encodeproject.org/annotations/ENCSR480YCS/). The 3D genomics data were obtained from GSE63525 ³⁰ (loop, topologically associating domain [TAD], and domain), and GSE86189 ³¹ (Promoter Capture Hi-C [pcHi-C]) for the lymphoblastoid cell line (GM12878). Both epigenomic annotation and 3D genomics data resources were built on GRCh37/hg19; we lifted them over to GRCh38/hg38 to align with the genomic build of TOPMed MESA genotypes using the the University of California Santa Cruz (UCSC) LiftOver tool.³² We followed code provided in the PUMICE Github (https://github.com/ckhunsr1/PUMICE) to run PUMICE with default settings.

PUMICE-FM

Prior investigation of the PUMICE method found that a 250 kb window surrounding the gene start and end site was the most frequent choice among the best-performing models.¹⁶ Accordingly, we applied the 250 kb window surrounding the TSS and TES as cis-regulatory region and used fine-mapping as functional annotation to separate genomic variants into essential and non-essential groups for PUMICE-FM. The method for fine-mapping is provided above in the section on EN-FM modeling.

Prediction accuracy validation analysis

To evaluate the prediction accuracy of all four transcriptome prediction methods, we used two independent external datasets from Geuvadis and JHS to validate prediction performance.

Geuvadis

The data included 449 multi-ancestry participants with WGS and RNA-seq from lymphoblastoid cell lines (LCLs) in the Geuvadis²⁰ consortium. Quality control of the WGS data and preparation of gene expression levels for prediction modeling have been described previously.²⁴ The residuals of expression levels were generated from a linear regression model of rank-based inverse normalization of expression levels adjusted for the first 10 genotype and 10 expression PCs. The number of PCs is the same as in the original study,²⁴ which performed transcriptome prediction modeling with the same gene expression data. Among 449 Geuvadis participants, 80% and 20% of the participants were categorized as Europeans (Utah residents with ancestry from northern and western Europe [CEU], Finnish in Finland [FIN], British in England and Scotland [GBR], and Toscani in Italy [TSI]) and Africans (Yoruba in Ibadan, Nigeria [YRI]), respectively (Figure 1). The Geuvadis data can be accessed at https://www.internationalgenome.org/data-portal/data-collection/geuvadis.

JHS

The JHS data included 1,012 AFA participants with TOPMed Freeze 8 WGS and RNA-seq from PBMCs.²¹ Quality-control of the WGS and RNA-seq data have been described previously.³³ Using a linear regression model for the outcome of rank-based inverse normalized expression levels with covariate adjustment for age, sex, the first 10 genotype PCs, and 70 probabilistic estimations of expression residuals (PEER) factors, we obtained residuals of expression levels for use in evaluation of prediction accuracy for the MESA models. The genotype PCs and PEER factors were calculated as described previously,³³ and we used the same number of PCs and PEER factors as suggested in the original study³³ that performed eQTL mapping. The JHS RNA-seq and WGS data are under controlled access in dbGaP through study accession number phs000286.v6.p2 and phs000964.v5.p1, respectively.

To validate prediction accuracy, we computed the Pearson correlation coefficient between the observed residuals of expression and the estimated GReX¹⁹ for each gene. The estimated GReX was calculated by multiplying the genotype dosage of each variant by its respective weight that was estimated from training models and then summing across all variants for each gene.

TWAS

To examine the performance of transcriptome prediction models in multi-ancestry TWAS, we applied S-PrediXcan³ to integrate multi-ancestry GWAS summary statistics with transcriptome prediction models to get TWAS results. We followed code provided in the S-PrediXcan Github (https://github.com/hakyimlab/MetaXcan/wiki/S-PrediXcan-Command-Line-Tutorial). The TOPMed MESA individual-level genotype data were used to compute the covariance matrix that is required by running the S-PrediXcan pipeline. We selected three large-scale multi-ancestry GWASs for investigation—(1) GWAS of eight blood cell traits,⁸ (2) GWAS of five lipid traits,⁹ and (3) GWAS of four pulmonary function traits¹⁰—to test the robustness of the transcriptome prediction methods for downstream TWAS analyses. Among the blood cell traits, we conducted TWASs for six white blood cell traits—basophil (BASO), eosinophil (EOS), lymphocyte (LYM), monocyte (MONO), neutrophil (NEU), and white blood cell count (WBC)—and two platelet traits—mean platelet volume (MPV) and platelet count (PLT). For lipid traits, we performed TWAS for five traits: high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), log-transformed triglycerides (logTGs), nonHDL-C, and total cholesterol (TC). For pulmonary function traits, we generated TWASs for four traits: forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC), ratio of FEV₁ to FVC (FEV₁/FVC), and peak expiratory flow rate (PEF). All GWAS summary statistics are available in the GWAS Catalog³⁴ (https://www.ebi.ac.uk/gwas/home) except multi-ancestry GWASs for blood cell traits. We obtained multi-ancestry GWAS summary statistics of blood cell traits via the lead contact of Chen et al.⁸ For the other traits, the GWAS Catalog study accession numbers for multi-ancestry GWAS are GCST90239649–GCST90239673 for lipid traits and GCST90244092–GCST90244095 for pulmonary function traits. The GWAS Catalog study accession numbers for European (EUR)-specific GWASs are GCST90002292–GCST90002357 for blood cell traits, GCST90239652–GCST90239676 for lipid traits, and GCST90292609–GCST90292612 for pulmonary function traits.

Curated genes for TWAS evaluation

For each GWAS trait, we prepared a curated list of known or candidate genes for evaluation of TWAS performance in identifying trait-relevant genes. The curated gene lists were identified from previous gene prioritization efforts for the same GWAS. For blood cell traits, curated genes were defined as those in Category 1 from the Variant Annotation Method Pointing to Interesting Regulatory Effects (VAMPIRE)³⁵; these genes were selected based on consistent evidence from epigenomic/sequence constraint features, expression/protein/splicing QTLs, and 3D genome conformation. For lipid and pulmonary function traits, we defined curated genes that were prioritized by the criteria that did not use transcriptome data information. More specifically, we defined curated genes as genes prioritized in Kanoni et al.³⁶ according to at least one of the following criteria: genes associated with Mendelian lipid disorders, human ortholog of mouse knockout genes associated with lipid phenotypes, genes with lipid-associated protein-altering variants, and genes nearest sentinel GWAS variants. For pulmonary function traits, we constructed the list of curated genes as those prioritized in Shrine et al.,¹⁰ implicated by at least one of the following criteria: genes associated with Mendelian respiratory diseases, human ortholog of mouse knockout genes associated with respiratory phenotypes, genes with significant rare (minor-allele frequency [MAF] < 1%) exonic associations (p < 5 × 10⁻⁶) near (±500 kb) sentinel GWAS variants using both single-variant and gene-based collapsing tests, and genes nearest sentinel GWAS variants. As stated previously,¹⁰^,³⁵^,³⁶ the annotation resources used to identify the curated genes were mainly from individuals of EUR ancestry.

Omnibus approach of FIV-based methods

Inspired by the increased power of aggregated TWASs from different models or tissues in the previous studies,¹⁶^,¹⁷^,¹⁸ we developed an omnibus approach for our FIV-based methods by aggregating TWAS p values from EN-FM, PUMICE, and PUMICE-FM using the aggregated Cauchy association test (ACAT).³⁷ If a gene had a corresponding prediction model from at least two of our FIV-based methods, then we applied the ACAT to the p values from each corresponding method to get aggregated p values. The corresponding Z scores were then derived from the aggregated p values. If a gene was only predicted by one of our FIV-based methods, we then used the available TWAS summary statistics for the gene’s omnibus TWAS results.

TWAS evaluation

TWAS results for each of the four proposed TWAS methods (EN-FM, PUMICE, PUMICE-FM, and omnibus) and the method for benchmarking (EN) were evaluated for each GWAS trait. We began by checking the quantile-quantile (QQ) plots with corresponding λ, a measure to quantify the inflation in the test statistics of TWASs. We then implemented TWAS inflation correction in the R package BACON³⁸ with 50,000 iterations and 10,000 burnin period when λ > 1.15. Next, as a proxy for evaluation of TWAS power, we compared median chi-square test statistics of all TWAS genes that were included in the curated gene lists. TWAS genes are genes with non-missing TWAS summary statistics (i.e., Z score and p value). The chi-square test statistic for a TWAS gene is computed as the square of the inflation-corrected TWAS’s Z score of the gene. We performed a Mann-Whitney U test to test whether the observed median chi-square test statistic from each proposed method was significantly greater than that from TWASs from EN prediction models. In addition to evaluating improved power at nominal significance level, we also applied a Bonferroni correction cutoff of 0.0125 (0.05/4) on four pairwise comparisons (EN-FM vs. EN, PUMICE vs. EN, PUMICE-FM vs. EN, and Omnibus vs. EN) for each trait to define a stringent significance.

Finally, we evaluated the accuracy of Bonferroni-significant TWAS genes that were included in curated gene lists using the F1 score. The F1 score, a harmonic mean of the precision and recall, measures the accuracy of significant TWAS genes by accounting for true positives, false positives, and false negatives. F1 score = 2 × precision × recall/(precision + recall). A higher F1 score indicates better performance. We treated curated genes as actual class and Bonferroni-significant TWAS genes as predicted class. Hence, each TWAS gene has a binary indicator (yes or no) for both actual and prediction classes. We then measured true positives, false positives, and false negatives to calculate the F1 score for each prediction method.

Comparison between multi-ancestry and single-ancestry TWASs

To further investigate the TWAS performance built on EUR-specific prediction models and EUR-specific GWAS to evaluate the benefits of our proposed multi-ancestry prediction models on TWAS over single-ancestry models, we conducted additional analyses by (1) building our proposed three prediction models (i.e., EN-FM, PUMICE, and PUMICE-FM) on the same MESA NHW participants as used in our MESA multi-ancestry models, hereafter referred to as EUR models; (2) integrating these EUR models with EUR-specific GWASs of blood cell traits,⁸ lipid traits,⁹ and pulmonary function traits¹⁰ to get their corresponding TWAS, hereafter referred to as EUR_EUR; (3) integrating the EUR models with three multi-ancestry GWASs to get their corresponding TWAS, hereafter referred to as EUR_Multi; (4) integrating our multi-ancestry models with EUR-specific GWASs to get their corresponding TWAS, hereafter referred to as Multi_EUR; and (5) applying the omnibus approach to EUR_EUR, EUR_Multi, and Multi_EUR TWASs to get their corresponding omnibus TWAS for each of 17 GWAS traits. By following the labeling rule, our original TWASs built on multi-ancestry prediction models and multi-ancestry GWASs are referred to as Multi_Multi.

Results

Similar prediction accuracy while using a smaller set of variants by FIV-based methods

After removing non-significant gene expression prediction models (Material and methods), there were similar numbers of genes remaining for EN (11,897), EN-FM (11,033), PUMICE (11,601), and PUMICE-FM (11,000) (Figure 2A). EN had the largest median model size (median number of SNPs in the models of 49), while EN-FM produced the smallest median model size (median number of SNPs in the models of 3), which was expected based upon fine-mapping of diverse ancestry participants (Figure 2A). To make a fair comparison of prediction accuracy evaluated by Pearson correlation coefficient between the observed residuals of expression and the estimated GReX across the four different methods, we considered the intersection of genes with prediction models available from all four methods for both discovery (MESA) and validation (Geuvadis and JHS) analyses. Based on the overlapped set of 8,659 genes across the four methods trained by MESA multi-ancestry participants, the median Pearson correlation coefficients are similar (Figure 2B) among the four methods, and most genes had similar prediction accuracy (Figure S1). Likewise, no noticeable difference in prediction accuracy among the four methods was observed in validation analysis evaluated on the overlapped set of 9,533 and 9,098 genes for Geuvadis multi-ancestry participants and JHS AFAs, respectively (Figure S2). However, the JHS had much higher prediction accuracy than Geuvadis (median Pearson correlation coefficient = 0.2 from JHS vs. 0.1 from Geuvadis) (Figure S2), which indicates the importance of cell type relevance in the replication of transcriptome prediction.

Transcriptome prediction model size and prediction accuracy

(A) Boxplots of transcriptome prediction model size of genes in MESA multi-ancestry training data. The solid line inside the box indicates the median, box edges represent 25^th and 75^th quartiles, whiskers extend to the minimum and maximum values within 1.5 times the interquartile range (IQR), individual points indicate data outside 1.5 times the IQR.

(B) Violin plots of prediction accuracy of 8,659 common genes across four methods of MESA multi-ancestry training data. The prediction accuracy is evaluated by Pearson correlation coefficient between the observed residuals of expression and the estimated genetically regulated expression component (GReX).The embedded boxplots indicate the median (horizontal line) and IQR (rectangular box). Whiskers extend to the minimum and maximum values within 1.5 times the IQR. The width of the violin plots represents the density of Pearson correlation coefficient at a specific value.

(C) Pie chart representing the distribution of the number of our proposed methods achieving higher prediction accuracy than EN, examined across 8,659 genes with prediction models for all methods. Zero indicates that EN had higher prediction accuracy.

(D) Stacked bar chart of the proportion of shared SNPs between EN and the proposed models. MESA, Multi-Ethnic Study of Atherosclerosis; EN, Elastic Net; EN-FM, Elastic Net with Fine-Mapped variants; PUMICE, Prediction Using Models Informed by Chromatin conformation and Epigenomics; PUMICE-FM, PUMICE with Fine-Mapped variants.

Although the four methods presented similar prediction accuracy, 84% of 8,659 common genes showed higher prediction accuracy in at least one of our FIV-based methods compared to EN (Figure 2C). More specifically, 43% of genes exhibited higher prediction accuracy in all three methods with FIVs (Figure 2C). Among our three FIV-based methods, EN-FM had the most overlap of SNPs used to predict the same gene with EN, while PUMICE had the least intersection with EN. We observed that 82% of 8,659 common genes from EN-FM had at least 80% shared SNPs with EN; however, the model size of EN-FM was much smaller than EN, which indicates the advantage of using fine-mapped variants for predicting gene expression (Figure 2D). On the other hand, we found that only 7% of common 8,659 genes from PUMICE shared at least 80% SNPs with EN, which indicates that the set of SNPs included in two methods was quite different (Figure 2D). Furthermore, we specifically examined the LD of SNPs included in models for genes in which the proposed FIV-based method produced the same prediction accuracy as EN (i.e., Pearson correlation coefficient, rounded to the nearest hundredth). There were 1,648, 1,804, and 1,762 such genes from EN-FM, PUMICE, and PUMICE-FM, respectively. Overall, most SNPs included in the three FIV-based methods were uncorrelated (i.e., LD ≤ 0.2) with SNPs included in the EN (Figure S3). However, the proportion of highly correlated (i.e., LD ≥ 0.8) SNPs increases as the proportion of shared SNPs increases; for example, 14% and 3% of PUMICE SNPs are highly correlated with EN SNPs in the scenario of genes that share 80%–100% and 0%–50% of model SNPs, respectively (Figure S3B). Moreover, we found that the SNPs included in our FIV-based methods were closer to both TSSs and TESs compared with SNPs included in EN (Figure S4).

Overall, our three FIV-based methods had fewer variants included in the prediction models while achieving similar gene expression prediction performance as the benchmark method, EN.

Overall higher TWAS power from FIV-based methods

To assess transcriptome prediction methods on multi-ancestry TWAS performance, we integrated three large-scale multi-ancestry GWASs with transcriptome prediction models to obtain trait-specific TWASs (Material and methods). We first checked genomic inflation of TWAS and found a genomic inflation measure, λ, ranging from 1.19 to 1.54 for blood cell traits (Figures S5–S12), from 1.28 to 1.47 for lipid traits (Figures S13–S17), and from 1.18 to 1.32 for pulmonary function traits (Figures S18–S21). The inflated TWAS could be attributed in part to large proportions of true positive results due to powerful GWAS and the polygenicity of GWAS traits, as pointed out in a previous study of TWAS inflation.³⁹ However, given the large levels of genomic inflation, we applied genomic control (GC) correction to results from each of the TWAS (Material and methods). The λ for GC-corrected TWAS ranged from 1.06 to 1.16 (Figures S5–S21). Our subsequent evaluation of TWAS results was based on GC-corrected TWAS. Overall, EN produced the largest number of TWAS genes, while EN-FM had the smallest list of genes combined across all traits (Table S1). To evaluate overall TWAS power, we overlapped all TWAS genes with curated genes and compared models’ median chi-square test statistic (Material and methods). The chi-square test statistic for a TWAS gene is computed as the square of GC-corrected TWAS’s Z score of the gene. The total number of curated genes for each GWAS trait is shown in Table S2, and a full list of curated genes is provided in Tables S3, S4, and S5 for blood cell, lipid, and pulmonary function traits, respectively. The number of TWAS genes overlapping with curated genes is shown in Table S6. In general, EN overlapped most with curated genes for blood cell and pulmonary function traits, while PUMICE had more overlapped curated genes for lipid traits (Table S6). Although EN identified more curated genes, the median chi-square test statistics from at least one of our FIV-based methods were higher than those from EN across all traits except BASO (Table S7). Further, our FIV-based methods had nominally significantly higher (p < 0.05) median chi-square values for three GWAS traits; however, EN did not significantly outperform in any traits. More specifically, PUMICE produced higher median chi-square values for PLT (p = 0.0395), LDL-C (p = 0.0419), and TC (p = 0.0264), and PUMICE-FM had significantly higher median chi-square values for PLT (p = 0.0220) at nominal significance level (Figure 3; Table S7).

Power of TWAS evaluated by distribution of chi-square values

The horizontal line inside the box indicates the median; box edges represent 25^th and 75^th quartiles. Whiskers extend to the minimum and maximum values within 1.5 times the IQR. Individual points indicate data outside 1.5 times the IQR. ^∗p < 0.05, ^∗∗p < 0.005 from Mann-Whitney U test without correcting for multiple testing. The genes that were included in the evaluation were an overlap of GC-corrected TWAS genes with curated genes for each trait. For visualization purposes, the chi-square values over 200 have been excluded from the plots but not excluded from statistical tests. The number of genes included in the statistical test for each method and each trait is provided in Table S6. There were 15, 24, and 21 genes excluded from the plot for PLT, LDL-C, and TC, respectively. The number shown at the top of each boxplot is the median chi-square value. Chi-square value, the square of GC-corrected TWAS’s Z score of a gene; GC, genomic control; TWAS, transcriptome-wide association study; p value, from Mann-Whitney U test to test whether the median chi-square value from the proposed method was significantly greater than that from EN TWAS; PLT, platelet count; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; Omnibus, aggregated model of EN-FM, PUMICE, and PUMICE-FM.

Higher accuracy of significant TWAS genes from FIV-based methods

To evaluate accuracy of statistically significant TWAS genes, we overlapped Bonferroni-significant TWAS genes with curated genes (hereafter referred to as Bonferroni-curated TWAS genes) and then calculated the F1 scores for Bonferroni-curated TWAS genes to assess their accuracy. The Bonferroni threshold was based on the rounded number of union of TWAS genes across the four methods and across the 17 GWAS traits; i.e., 0.05/13,000 = 3.85 × 10⁻⁶. The number of Bonferroni-significant TWAS genes and number of Bonferroni-curated TWAS genes are shown in Figures S22 and S23, respectively, for each GWAS trait. Our FIV-based methods not only produced the same or more Bonferroni-curated TWAS genes in 13 of 17 GWAS traits compared with EN (Figure S23) but also had higher F1 scores for all tested GWAS traits except BASO and WBC (Figure 4). The improvement of accuracy ranges from 5% to 15% for blood cell traits, from 4% to 18% for lipid traits, and from 12% to 133% for pulmonary function traits (Figure 4). Prominent improvements were observed for PEF (F1 = 0.14 from PUMICE vs. 0.06 from EN) and FVC (F1 = 0.07 from EN-FM vs. 0.03 from EN; Figure 4). In sensitivity analysis, we further calculated F1 scores using overlapped genes between false discovery rate (FDR)-significant TWAS genes (FDR < 0.05) and curated genes. Our FIV-based methods still outperform EN for most GWAS traits (Figure S24).

Accuracy of Bonferroni-significant TWAS genes evaluated by F1 score

(A) F1 scores of eight blood cell traits.

(B) F1 scores of five lipid traits.

(C) F1 scores of four pulmonary function traits.

The genes included in the evaluation were overlapping Bonferroni-significant TWAS genes with curated genes for each trait. F1 score = 2 × precision × recall/(precision + recall); BASO, basophil; EOS, eosinophil; LYM, lymphocyte; MONO, monocyte; NEU, neutrophil; WBC, white blood cell count; MPV, mean platelet volume; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; logTGs, log-transformed triglycerides; nonHDL-C, non-high-density lipoprotein cholesterol; FEV1, forced expiratory volume in 1 s; FVC, forced vital capacity; FEV1/FVC, ratio of FEV1 to FVC; PEF, peak expiratory flow rate. EN, Elastic Net; EN-FM, Elastic Net with Fine-Mapped variants; PUMICE, Prediction Using Models Informed by Chromatin conformation and Epigenomics; PUMICE-FM, PUMICE with Fine-Mapped variants; Omnibus, aggregated model of EN-FM, PUMICE, and PUMICE-FM.

Further improved TWAS power by the omnibus approach

Although our FIV-based methods leveraging FIVs showed higher accuracy than the benchmark EN method for TWAS, no single proposed method uniformly outperformed for all tested GWAS traits. Inspired by the increased power of aggregated TWAS from different models or tissues in the previous studies, we sought to aggregate TWAS summary statistics from three FIV-based methods using an omnibus approach (Material and methods). We followed the same evaluation procedure to assess omnibus TWAS. We began by evaluating genomic inflation and then applied GC correction to omnibus TWAS. The GC-corrected λ ranged from 1.08 to 1.19 for all GWAS traits (Table S8). Our subsequent evaluation of omnibus TWAS results was based on GC-corrected TWAS as well. The omnibus approach produced the largest number of TWAS genes (Table S1; ranging from 12,619 to 12,672) and produced the most overlapped genes with curated gene lists for all GWAS traits (Table S6) compared to EN. For overall TWAS power, the omnibus approach had the greatest median of Chi-square values for all GWAS traits except for BASO, LDL-C, FEV₁, and FVC (Table S7). Furthermore, the omnibus approach yielded nominally significantly higher (p < 0.05) TWAS power than EN in five of eight blood cell traits and in two of five lipid traits (Table S7). The most statistically significantly improved power was found for TC (p = 0.0012), which passed Bonferroni significance (i.e., p < 0.0125; Material and methods) (Figure 3; Table S7). However, the omnibus approach did not achieve significantly greater power than EN for any of the four pulmonary function traits (Table S7). In the evaluation of accuracy for Bonferroni-significant TWAS genes, although the omnibus approach only produced higher F1 scores for six of eight blood cell traits (Figure 4), the omnibus approach had the largest number of Bonferroni-significant TWAS genes for all GWAS traits (Figure S22) and produced the most Bonferroni-curated TWAS genes for all GWAS traits except for BASO (Figure S23).

Further, we compared Bonferroni-curated TWAS genes identified between EN and omnibus. The common Bonferroni-curated TWAS genes between EN and omnibus had the same TWAS effect direction for all GWAS traits (Tables S9, S10, and S11). We then summarized the number of Bonferroni-curated TWAS genes uniquely identified by either method in Table S12 and provide TWAS summary statistics of these genes in Tables S13, S14, and S15. The omnibus approach uniquely identified more Bonferroni-curated TWAS genes than EN for all GWAS traits except for BASO, with a count ratio (omnibus/EN) ranging from 1.6 (FEV₁/FVC) to 12 (EOS) (Table S12). Besides, the omnibus approach uniquely identified 2 and 7 Bonferroni-curated TWAS genes for FVC and PEF respectively, while the EN did not uniquely identify any genes for these two traits (Table S12). We further checked some important curated genes that were uniquely identified by the omnibus approach. We list three examples here for the demonstration of improvement from our omnibus approach. The first example is TRAF1 for LYM. TRAF1 (tumor necrosis factor receptor-associated factor 1) is a protein-coding gene and is located on chromosome 9. TRAF1 has been reported to be a protein playing a role in the survival, proliferation, and cytokine production of LYMs.⁴⁰ Additionally, the increased expression of TRAF1 has been shown to activate LYMs.⁴¹^,⁴² Our omnibus approach identified it as a Bonferroni-curated TWAS gene (LYM TWAS p = 1.15 × 10⁻¹³ and Z score = 7.42; Table S13). However, the EN method produced a much weaker signal for this gene (LYM TWAS p = 0.04 and Z score = 2.11; Table S13). The second example is APOA1 for HDL-C. APOA1 (apolipoprotein A1), located on chromosome 11, is a protein-coding gene. APOA1 is a vital HDL-C protein that makes up the majority of HDL-C and helps in the production of HDL particles.⁴³^,⁴⁴^,⁴⁵ This gene was a Bonferroni-significant TWAS gene from our omnibus approach (HDL-C TWAS p = 5.09 × 10⁻³⁶ and Z score = 12.53; Table S14); however, it was not included in the EN model. Likewise, TGFBR3, a protein-coding gene, was the most significant Bonferroni-curated gene uniquely identified by our omnibus approach for FEV₁/FVC ratio but was missed by the EN model (Table S15). TGFBR3 (transforming growth factor β receptor 3), located on chromosome 1, is one of TGF-β superfamily members. The TGF-β superfamily proteins play a key role in the regulation of extracellular matrix composition and alveolar epithelial cell and fibroblast function in the lung.⁴⁶ The potential key role of TGFBR3 in the pathogenesis of chronic obstructive pulmonary disease (COPD) susceptibility has been suggested in several studies.⁴⁶^,⁴⁷^,⁴⁸

Multi-ancestry prediction models improved TWAS performance over single-ancestry models

As the three multi-ancestry GWASs are dominated by EUR ancestry (i.e., about 75%, 80%, and 81% of GWAS participants are EUR for blood cell traits, lipid traits, and pulmonary function traits, respectively), we further investigated the TWAS performance built on EUR-specific prediction models and EUR-specific GWASs to evaluate the benefits of our proposed multi-ancestry prediction models over single-ancestry models. As we have demonstrated the improvement of TWAS from the omnibus approach, the evaluation of new TWASs focused on their omnibus results on the performance of power, accuracy, number of Bonferroni-significant TWAS genes, and number of Bonferroni-curated TWAS genes. We found that the multi-ancestry prediction models produced higher TWAS power than EUR models for blood cell traits on both multi-ancestry GWASs (i.e., Multi_Multi vs. EUR_Multi) and EUR-specific GWASs (i.e., Multi_EUR vs. EUR_EUR) except for BASO and WBC (Figures S25 and S26). More specifically, EOS and LYM had significantly higher power from Multi_EUR than EUR_EUR (nominal significance p = 0.02 for EOS and Bonferroni significance p = 7.77 × 10⁻⁴ for LYM) and significantly higher power from Multi_Multi than EUR_Multi (nominal significance p = 0.04 for EOS and Bonferroni significance p = 2.33 × 10⁻³ for LYM) (Figure S25). However, the multi-ancestry prediction models produced similar TWAS power with EUR models for lipid traits (Figure S27), and it had higher TWAS power only for PEF among four pulmonary function traits (Figure S28) on both multi-ancestry and EUR-specific GWASs. For the accuracy of Bonferroni-significant TWAS genes, the multi-ancestry prediction models produced similar or higher TWAS accuracy compared with EUR models for all GWAS traits except for BASO and FVC (Figure S29). The outperformance of multi-ancestry prediction models for all 17 traits except FVC was also observed for the counts of Bonferroni-significant and Bonferroni-curated TWAS genes (Figures S30 and S31).

Our investigation supports the overall advantage of using multi-ancestry prediction models over EUR-specific models for both multi-ancestry and EUR-specific GWASs of blood cell traits, lipid traits, and pulmonary function traits, which indicates the capability of our proposed multi-ancestry prediction models on reducing impact on TWAS discovery due to mild ancestry mismatch between the TWAS reference model and GWAS. Furthermore, we found that the multi-ancestry prediction models performed similarly for 14 of 17 GWAS traits for comparisons between multi-ancestry and EUR-specific GWASs (i.e., Multi_Multi vs. Multi_EUR) in the evaluation of TWAS power and accuracy. More specifically, both TWAS power and accuracy were higher from Multi_Multi TWAS for NEU, FEV₁, and PEF compared with their Multi_EUR TWASs; however, the Multi_EUR TWASs were higher in power and accuracy for BASO, WBC, and MPV (Figures S25–S29). These results indicate that using our proposed multi-ancestry prediction models on multi-ancestry GWASs yields performance similar or better than the EUR-specific GWASs for TWAS discovery of blood cell traits, lipid traits, and pulmonary function traits.

Discussion

We proposed three FIV-based methods that upweight genomic variants overlapped with key annotations (i.e., fine-mapping, epigenetic processes, and 3D genomics-informed regions) to improve both prediction accuracy of multi-ancestry transcriptome prediction models and power of multi-ancestry TWASs. Building on 1,287 multi-ancestry participants from the TOPMed MESA with TOPMed Freeze 8 WGS data and RNA-seq data from PBMCs, our proposed methods presented similar prediction accuracy in both MESA discovery analysis and validation analysis with two external datasets (Geuvadis and JHS) while using a smaller and functionally informed set of variants compared to the benchmark method, EN. Considering the imperfect match of both ancestry composition and cell type relevance between MESA discovery data and two external validation datasets, our validation results support the generalizability of our methods across conditions. The higher prediction accuracy observed in JHS compared to Geuvadis may support the importance of cell type relevance in the prediction accuracy of transcriptome models. Further, our FIV-based methods demonstrated improved multi-ancestry TWAS power and accuracy for a variety of GWAS traits. While the three multi-ancestry GWASs are dominated by individuals of EUR ancestry, TWAS using multi-ancestry models still demonstrated overall advantages in performance compared to TWAS using EUR-specific models. These findings support the potential of broad applicability of our proposed multi-ancestry prediction models for TWAS based on multi-ancestry GWAS. We hypothesize that the benefits of using our multi-ancestry gene expression prediction models for TWAS discovery could be attributed in part to the broader ancestral representation of these models, which may alleviate negative impacts of ancestry mismatch observed in prior studies that focused on single-ancestry prediction models.¹¹^,¹²^,²⁴ We also showed that the observed improvement in TWAS performance depends on the GWAS trait’s relevance to the tissue or cell type(s) used to build the models.

The smaller model size from our FIV-based methods is noticeable, especially for EN-FM. EN-FM has a median model size of 3; however, it achieved similar prediction accuracy as EN which has a much larger median model size of 49. This confirms the importance of including and upweighting FIVs in the prediction models. The accuracy of multi-ancestry fine-mapping in our study was affected by sample size, which may affect model performance of EN-FM and PUMICE-FM. As observed in the previous studies of multi-ancestry fine-mapping, differences in LD patterns and association effect sizes across ancestries may contribute to improved fine-mapping.⁸^,⁹^,⁴⁹ Although our transcriptome prediction models were built on diverse ancestry populations, the overall sample size (i.e., 1,287 multi-ancestry participants) is not large enough to produce powerful multi-ancestry eQTLs and thus affects the accuracy of fine-mapping. Additionally, the original PUMICE¹⁶ study concluded that PUMICE would achieve better performance from using tissue- or cell-matched functional annotation data. Due to the unavailability of epigenetic processes and 3D genomics data for PBMCs, we used available data from EBV-transformed LYMs as a proxy to construct the PUMICE model, which probably affected model performance.

To test the robustness of our FIV-based methods for TWAS, we integrated our models with three large-scale multi-ancestry GWASs from eight blood cell traits,⁸ five lipid traits,⁹ and four pulmonary function traits.¹⁰ Our FIV-based methods presented higher power and accuracy for multi-ancestry TWAS even though no single proposed method uniformly outperformed all tested GWAS traits. The improved power and accuracy from our FIV-based methods on multi-ancestry TWAS might be attributed to the inclusion of FIVs and the upweighting of these variants as well. EN assigns penalties to variants and estimates their weights mathematically without knowing the prior importance of variants. Some biologically important variants might be excluded or underweighted from the EN model due to their small coefficients, and the small coefficients might be caused by small MAF. While our FIV-based methods presented improved TWAS performance, the variation in performance across three TWASs was likely due to the GWAS trait’s relevance to PBMCs. Our FIV-based methods produced significantly higher TWAS power than EN for one blood cell trait and two lipid traits but not for any of four pulmonary function traits. However, our FIV-based methods had higher accuracy of Bonferroni-significant TWAS genes for all tested GWAS traits except for two blood cell traits, BASO and WBC.

Realizing that different transcriptome prediction methods might benefit genes differently due to gene’s underlying architecture (e.g., sparse vs. polygenic) and that no single method works uniformly well for all genes, we hypothesized that it could be beneficial to further improve TWAS by an omnibus approach that aggregates TWAS summary statistics from our three FIV-based methods. Our proposed omnibus approach produced significantly higher TWAS power for five of eight blood cell traits compared to the improvement in only one blood cell trait by a single FIV-based method. Further, among the curated genes, while EN uniquely identified some of them at Bonferroni significance, the omnibus approach identified many more. This result indicates the improved capability of our proposed omnibus approach to identify trait-relevant genes. As other studies demonstrated that aggregated TWASs from different methods or tissues increase power,¹⁶^,¹⁷^,¹⁸ the omnibus approach could be a trend for future TWAS methods that leverage multiple transcriptome prediction methods with different assumptions of underlying genetic architectures. While we observed overall improvements in TWAS performance based on the proposed multi-ancestry prediction methods, we should be aware that our current approach does not allow the identification of significant ancestry-specific TWAS genes, as our methods do not have ancestry-shared and ancestry-specific components to differentiate them. We believe that this topic will be an important topic for future TWAS research.

Some limitations of our study must be noted. First, the power of our developed transcriptome prediction models is limited due to the sample size of multi-ancestry participants and accuracy of FIVs. We believe that our model performance can be substantially improved by increasing the sample size and number of participants from different ancestry groups. With increased sample size, we believe that the accuracy of multi-ancestry fine-mapping would be improved as well, which would directly affect the performance of models with fine-mapped variants. The epigenetic and 3D genomics data used to construct PUMICE are not from PBMCs but from a proxy cell type, EBV-transformed LYMs, which would affect model performance. Second, our assessment of TWAS power and accuracy depends on the curated genes that were identified by previous gene prioritization efforts¹⁰^,³⁵^,³⁶ for the same published GWASs that we used to carry out TWAS analyses. We should caution that these curated genes may not be ground truth for GWAS traits. Further, the annotation resources used to identify the curated genes were mainly from individuals of EUR ancestry even though the GWAS efforts were carried out as multi-ancestry studies. Accordingly, we should be aware that those curated genes are only the approximations of the “true” causal genes for the corresponding GWAS traits. Third, PBMCs are not the most relevant cell type for all GWAS traits we examined in the study. Accordingly, we should be cautious in the interpretation of our multi-ancestry TWAS results for traits that have less relevance to PBMC transcriptomes. Future work developing multi-ancestry transcriptome models using disease-relevant tissues may prove useful in identifying biologically relevant genes from multi-ancestry TWASs. We should note that the proposed methods including FIVs in the multi-ancestry transcriptome prediction models can be extended to other tissues straightforwardly with both diverse ancestry transcriptomes and functional annotation data from disease-relevant tissues. Last, although our study shows improvements in TWAS performance by using multi-ancestry versus EUR-specific prediction models in the analysis of EUR-specific GWASs, these findings may arise from increased sample sizes, resolution, and variant representation afforded by the multi-ancestry prediction approach as well as the high representation of EUR ancestry in the MESA cohort. We should caution that ancestry mismatch between TWAS reference models and GWAS may yield reduced power for TWAS discoveries in some cases, as pointed out by several previous studies of multi-ancestry TWAS.¹¹^,¹²^,²⁴

In conclusion, our study demonstrates the value of including FIVs in the multi-ancestry transcriptome prediction models to improve multi-ancestry TWASs. Additionally, we show that the improvement of TWAS performance depends on the GWAS trait’s relevance to the tissue or cell type used to build the models. Our proposed methods leveraging FIVs for gene expression prediction in the context of TWAS can be extended naturally to other tissues and other omics data as well.

Data and code availability

Our developed multi-ancestry TOPMed MESA transcriptome prediction models and code are available at https://zenodo.org/records/18644222.

Acknowledgments

This work is supported by R01-HL153248 (to A.M. and X.H.) and R01-ES036042 (to D.J.L.). L.M.R. is funded by R01AG075884. Y.L. is funded by U01HG011720, R01HL146500, and R01HL165061. H.E.W. and D.S.A. are funded by R15HG009569. The authors acknowledge Research Computing at The University of Virginia (https://rc.virginia.edu) for providing computational resources and technical support, which contributed to the results reported within this manuscript. The authors gratefully acknowledge the cohorts and participants who provided biological samples and data for the Trans-Omics in Precision Medicine (TOPMed) program supported by the National Heart, Lung, and Blood Institute (NHLBI). Study-specific acknowledgments are given in the supplemental information. A full list of investigators for the NHLBI TOPMed Consortium is provided at https://topmed.nhlbi.nih.gov/topmed-banner-authorship.

Author contributions

X.H. and A.M. contributed to the conception of study design, analysis of data, and writing of the manuscript. D.S.A. and H.E.W. provided derived gene expression and genotype data of MESA and Geuvadis. C.K., L.W., and D.J.L. provided 3D genomic and epigenomic data of EBV-transformed lymphocytes. Q.S., J.W., L.E., L.Z., L.M.R., and Y.L. provided gene expression and genotype data of the Jackson Heart Study. Q.S., L.M.R., and Y.L. provided curated gene lists for blood cell traits. All authors contributed to critical review of the manuscript. All authors and the TOPMed Multi-Omics Working Group provided administrative, technical, or material support.

Declaration of interests

S.S.R. and L.M.R. are consultants to the NHLBI TOPMed Administrative Coordinating Center (Westat). M.H.C. has received grant funding from Bayer and consulting fees from Apogee Therapeutics and BMS.

Published: April 2, 2026

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.ajhg.2026.03.008.

Contributor Information

Xiaowei Hu, Email: xh6dx@virginia.edu.

Ani Manichaikul, Email: am3xa@virginia.edu.

Supplemental information

Document S1. Figures S1–S31 and supplemental methods

mmc1.pdf^{(3.2MB, pdf)}

Data S1. Tables S1–S15

mmc2.xlsx^{(385.3KB, xlsx)}

Document S2. Article plus supplemental information

mmc3.pdf^{(12.5MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S31 and supplemental methods

mmc1.pdf^{(3.2MB, pdf)}

Data S1. Tables S1–S15

mmc2.xlsx^{(385.3KB, xlsx)}

Document S2. Article plus supplemental information

mmc3.pdf^{(12.5MB, pdf)}

Data Availability Statement

Our developed multi-ancestry TOPMed MESA transcriptome prediction models and code are available at https://zenodo.org/records/18644222.

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PERMALINK

Multi-ancestry transcriptome prediction with functionally informed variants in TOPMed MESA improves performance of transcriptome-wide association studies

Xiaowei Hu

Daniel S Araujo

Chachrit Khunsriraksakul

Lida Wang

Quan Sun

Jia Wen

Lingbo Zhou

Lynette Ekunwe

Leslie A Lange

Ethan M Lange

Stephen B Montgomery

Alexander P Reiner

Francois Aguet

Kristin G Ardlie

Tuuli Lappalainen

Christopher R Gignoux

Esteban G Burchard

Kent D Taylor

Xiuqing Guo

Jerome I Rotter

Stephen S Rich

Elaine Cornell

Peter Durda

Russell P Tracy

Yongmei Liu

W Craig Johnson

George P Papanicolaou

Minoli A Perera

Michael H Cho

Dajiang J Liu

Laura M Raffield

Yun Li

Heather E Wheeler

Hae Kyung Im

Ani Manichaikul

Summary

Introduction

Material and methods

Overview of the approach

Figure 1.

Training data for multi-ancestry transcriptome prediction models

MESA

RNA-sequencing

WGS

Preparation of RNA-seq data for transcriptome prediction modeling

Transcriptome prediction methods

EN

EN-FM

PUMICE

PUMICE-FM

Prediction accuracy validation analysis

Geuvadis

JHS

TWAS

Curated genes for TWAS evaluation

Omnibus approach of FIV-based methods

TWAS evaluation

Comparison between multi-ancestry and single-ancestry TWASs

Results

Similar prediction accuracy while using a smaller set of variants by FIV-based methods

Figure 2.

Overall higher TWAS power from FIV-based methods

Figure 3.

Higher accuracy of significant TWAS genes from FIV-based methods

Figure 4.

Further improved TWAS power by the omnibus approach

Multi-ancestry prediction models improved TWAS performance over single-ancestry models

Discussion

Data and code availability

Acknowledgments

Author contributions

Declaration of interests

Footnotes

Contributor Information

Supplemental information

References

Associated Data

Supplementary Materials