Biology subjects

Wheeler, H. E.

Publications and source records attributed to Wheeler, H. E..

12 recordsLinked to original sources

bioRxiv2023

Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations

Transcriptome prediction models built with data from European-descent individuals are less accurate when applied to different populations because of differences in linkage disequilibrium patterns and allele frequencies. We hypothesized methods that leverage shared regulatory effects across different conditions, in this case, across different populations may improve cross-population transcriptome prediction. To test this hypothesis, we made transcriptome prediction models for use in transcriptome-wide association studies (TWAS) using different methods (Elastic Net, Joint-Tissue Imputation (JTI), Matrix eQTL, Multivariate Adaptive Shrinkage in R (MASHR), and Transcriptome-Integrated Genetic Association Resource (TIGAR)) and tested their out-of-sample transcriptome prediction accuracy in population-matched and cross-population scenarios. Additionally, to evaluate model applicability in TWAS, we integrated publicly available multi-ethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology Study (PAGE) and Pan-UK Biobank with our developed transcriptome prediction models. In regard to transcriptome prediction accuracy, MASHR models performed better or the same as other methods in both population-matched and cross-population transcriptome predictions. Furthermore, in multi-ethnic TWAS, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWAS and new loci previously not found in GWAS. Overall, our study demonstrates the importance of using methods that benefit from different populations effect size estimates in order to improve TWAS for multi-ethnic or underrepresented populations.

genomics↗

bioRxiv2021

Protein prediction for trait mapping in diverse populations

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n=183), Chinese (n=71), European (n=416), and Hispanic/Latino (n=301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ~50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods at https://doi.org/10.5281/zenodo.4837328. Author summaryGene regulation is a critical mechanism underlying complex traits. Transcriptome-wide association studies (TWAS) have helped elucidate potential mechanisms because each association connects a gene rather than a variant to the complex trait. Like genome-wide association studies (GWAS), most TWAS are still conducted exclusively in populations of European ancestry, which misses the opportunity to test the full spectrum of human genetic variation for associations with complex traits. Here, move beyond the transcriptome and because protein measurement assays are growing to allow interrogation of the proteome, we use data from TOPMed MESA to develop genetic predictors of protein abundance in diverse ancestry populations. We compare model-building strategies with the goal of providing the best resource for protein association discovery with available data. We demonstrate how these prediction models can be used to perform proteome-wide association studies (PWAS) in diverse populations. We show the most protein-trait associations were discovered, colocalized, and replicated in independent cohorts using proteome prediction model training populations with similar ancestries to individuals in the GWAS. We shared our protein prediction models and performance statistics publicly to facilitate future proteome mapping studies in diverse populations.

genomics↗

bioRxiv2020

Polygenic transcriptome risk scores improve portability of polygenic risk scores across ancestries

Polygenic risk scores (PRS) are on course to translate the results of genome-wide association studies (GWAS) into clinical practice. To date, most GWAS have been based on individuals of European-ancestry, meaning that the utility of PRS for non-European populations is limited because SNP effects and LD patterns may not be conserved across populations. We hypothesized that cross population prediction at the level of genes rather than SNPs would be more effective, since the effect of genes on traits is likely to be more highly conserved. Therefore, we developed a framework to convert effect sizes at SNPs into effect sizes for genetically predicted transcript abundance, which we used for prediction in non-European populations. We compared this approach, which we call polygenic transcriptome risk scores (PTRS), to PRS, using data from 17 quantitative traits that were measured in multiple ancestries (European, African, East Asian, and South Asian) by UK Biobank. On average, PTRS using whole blood predicted transcriptome had lower absolute prediction accuracy than PRS, as we expected since not all regulatory processes were captured by a single tissue. However, as hypothesized, we found that in the African target set, the portability (prediction accuracy relative to the European reference set) was significantly higher for PTRS than PRS (p=0.03) with additional gain when transcriptomic prediction models ancestry matched the target population (p=0.021). Taken together, our results suggest that using PTRS can improve prediction in underrepresented populations and that increasing the diversity of transcriptomic data may be an effective way to improve portability of GWAS results between populations and help reduce health disparities.

genetics↗

bioRxiv2020

Multi-ethnic transcriptome-wide association study of prostate cancer

The genetic risk for prostate cancer has been governed by a few rare variants with high penetrance and over 150 commonly occurring variants with lower impact on risk; however, most of these variants have been identified in studies containing exclusively European individuals. People of non-European ancestries make up less than 15% of prostate cancer GWAS subjects. Across the globe, incidence of prostate cancer varies with population due to environmental and genetic factors. The discrepancy between disease incidence and representation in genetics highlights the need for more studies of the genetic risk for prostate cancer across diverse populations. To better understand the genetic risk for prostate cancer across diverse populations, we performed PrediXcan and GWAS in a cohort of 4,769 self-identified African American (2,463 cases and 2,306 controls), 2,199 Japanese American (1,106 cases and 1,093 controls), and 2,147 Latin American (1,081 cases and 1,066 controls) individuals from the Multiethnic Genome-wide Scan of Prostate Cancer. We used prediction models from 46 tissues in GTEx version 8 and five models from monocyte transcriptomes in the Multi-Ethnic Study of Artherosclerosis. Across the three populations, we predicted 19 gene-tissue pairs, including five unique genes, to be significantly (lfsr < 0.05) associated with prostate cancer. One of these genes, NKX3-1, replicated in a larger European cohort. At the SNP level, 110 SNPs met genome-wide significance in the African American cohort while 123 SNPs met significance in the Japanese American cohort. Fine mapping revealed three significant independent loci in the African American cohort and two significant independent loci in the Japanese American cohort. These identified loci confirm findings from previous GWAS of prostate cancer in diverse cohorts while PrediXcan-identified genes suggest potential new directions for prostate cancer research in populations across the globe.View Full Text

genetics↗

bioRxiv2020

Fine-mapping and QTL tissue-sharing information improve causal gene identification and transcriptome prediction performance

The integration of transcriptomic studies and GWAS (genome-wide association studies) via imputed expression has seen extensive application in recent years, enabling the functional characterization and causal gene prioritization of GWAS loci. However, the techniques for imputing transcriptomic traits from DNA variation remain underdeveloped. Furthermore, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium between distinct causal variants. Therefore, the best prediction performance models may not necessarily lead to more reliable causal gene discovery. With the goal of improving discoveries without increasing false positives, we develop and compare multiple transcriptomic imputation approaches using the most recent GTEx release of expression and splicing data on 17,382 RNA-sequencing samples from 948 post-mortem donors in 54 tissues. We find that informing prediction models with posterior causal probability from fine-mapping (dap-g) and borrowing information across tissues (mashr) lead to better performance in terms of number and proportion of significant associations that are colocalized and the proportion of silver standard genes identified as indicated by precision-recall and ROC (Receiver Operating Characteristic) curves. All prediction models are made publicly available at predictdb.org. Author summaryIntegrating molecular biology information with genome-wide association studies (GWAS) sheds light on the mechanisms tying genetic variation to complex traits. However, associations found when linking eQTL studies to complex traits through methods like PrediXcan can lead to false positives due to linkage disequilibrium of distinct causal variants. By integrating fine-mapping information into the models, and leveraging the widespread tissue-sharing of eQTLs, we improve the proportion of likely causal genes among significant gene-trait associations, as well as the prediction of "ground truth" genes.

genomics↗

bioRxiv2019

Transcriptome association studies of neuropsychiatric traits in African Americans implicate PRMT7 in schizophrenia

In the past fifteen years, genome-wide association studies (GWAS) have provided novel insight into the genetic architecture of various complex traits; however, this insight has been primarily focused on populations of European descent. This emphasis on European populations has led to individuals of recent African descent being grossly underrepresented in the study of genetics. With African Americans making up less than two percent of participants in neuropsychiatric GWAS, this discrepancy is magnified in diseases such as schizophrenia and bipolar disorder. In this study, we performed GWAS and the gene-based association method PrediXcan for schizophrenia (n=2,256) and bipolar disorder (n=1,019) in African American cohorts. In our PrediXcan analyses, we identified PRMT7 (P = 5.5 x 10-6, local false sign rate = 0.12) as significantly associated with schizophrenia following an adaptive shrinkage multiple testing adjustment. This association with schizophrenia was confirmed in the much larger, predominantly European, Psychiatric Genomics Consortium. In addition to the PRMT7 association with schizophrenia, we identified rs10168049 (P = 1.0 x 10-6) as a potential candidate locus for bipolar disorder with highly divergent allele frequencies across populations, highlighting the need for diversity in genetic studies.

genomics↗

bioRxiv2019

Quantifying genetic regulatory variation in human populations improves transcriptome analysis in rare disease patients

Transcriptome data holds substantial promise for better interpretation of rare genetic variants in basic research and clinical settings. Here, we introduce ANalysis of Expression VAriation (ANEVA) to quantify genetic variation in gene dosage from allelic expression (AE) data in a population. Application to GTEx data showed that this variance estimate is robust across datasets and is correlated with selective constraint in a gene. We next used ANEVA variance estimates in a Dosage Outlier Test (ANEVA-DOT) to identify genes in an individual that are affected by a rare regulatory variant with an unusually strong effect. Applying ANEVA-DOT to AE data form 70 Mendelian muscular disease patients showed high accuracy in detecting genes with pathogenic variants in previously resolved cases, and lead to one confirmed and several potential new diagnoses in cases previously unresolved. Using our reference estimates from GTEx data, ANEVA-DOT can be readily incorporated in rare disease diagnostic pipelines to better utilize RNA-seq data.\n\nOne Sentence SummaryNew statistical framework for modelling allelic expression characterizes genetic regulatory variation in populations and informs diagnosis in rare disease patients

genomics↗

bioRxiv2018

Genetically regulated gene expression underlies lipid traits in Hispanic cohorts

Plasma lipid levels are risk factors for cardiovascular disease, a leading cause of death worldwide. While many studies have been conducted in genetic variation underlying lipid levels, they mainly comprise individuals of European ancestry and thus their transferability to non-European populations is unclear. We performed genome-wide (GWAS) and imputed transcriptome-wide association studies of four lipid traits in the Hispanic Community Health Study/Study of Latinos cohort (HCHS/SoL, n = 11,103), replicated top hits in the Multi-Ethnic Study of Atherosclerosis (MESA, n = 3,855), and compared the results to the larger, predominantly European ancestry meta-analysis by the Global Lipids Genetics Consortium (GLGC, n = 196,475). In our GWAS, we found significant SNP associations in regions within or near known lipid genes, but in our admixture mapping analysis, we did not find significant associations between local ancestry and lipid phenotypes. In the imputed transcriptome-wide association study in multiple tissues and in different ethnicities, we found 59 significant gene-tissue-phenotype associations (P < 3.61x10-8) with 14 unique significant genes, many of which occurred across multiple phenotypes, tissues, and ethnicities and replicated in MESA (45/59) and in GLGC (44/59). These include well-studied lipid genes such as SORT1, CETP, and PSRC1, as well as genes that have been implicated in cardiovascular phenotypes, such as CCL22 and ICAM1. The majority (40/59) of significant associations colocalized with expression quantitative trait loci (eQTLs), indicating a possible mechanism of gene regulation in lipid level variation. To fully characterize the genetic architecture of lipid traits in diverse populations, larger studies in non-European ancestry populations are needed.

genetics↗

bioRxiv2018

Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits

Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available GTEx tissues and find 2356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.

genetics↗

bioRxiv2018

Integrating Predicted Transcriptome From Multiple Tissues Improves Association Detection

Integration of genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) studies is needed to improve our understanding of the biological mechanisms underlying GWAS hits, and our ability to identify therapeutic targets. Gene-level association test methods such as PrediXcan can prioritize candidate targets. However, limited eQTL sample sizes and absence of relevant developmental and disease context restricts our ability to detect associations. Here we propose an efficient statistical method that leverages the substantial sharing of eQTLs across tissues and contexts to improve our ability to identify potential target genes: MulTiXcan. MulTiXcan integrates evidence across multiple panels while taking into account their correlation. We apply our method to a broad set of complex traits available from the UK Biobank and show that we can detect a larger set of significantly associated genes than using each panel separately. To improve applicability, we developed an extension to work on summary statistics: S-MulTiXcan, which we show yields highly concordant results with the individual level version. Results from our analysis as well as software and necessary resources to apply our method are publicly available.

genomics↗

bioRxiv2018

Genetic architecture of gene expression traits across diverse populations

For many complex traits, gene regulation is likely to play a crucial mechanistic role. How the genetic architectures of complex traits vary between populations and subsequent effects on genetic prediction are not well understood, in part due to the historical paucity of GWAS in populations of non-European ancestry. We used data from the MESA (Multi-Ethnic Study of Atherosclerosis) cohort to characterize the genetic architecture of gene expression within and between diverse populations. Genotype and monocyte gene expression were available in individuals with African American (AFA, n=233), Hispanic (HIS, n=352), and European (CAU, n=578) ancestry. We performed expression quantitative trait loci (eQTL) mapping in each population and show genetic correlation of gene expression depends on shared ancestry proportions. Using elastic net modeling with cross validation to optimize genotypic predictors of gene expression in each population, we show the genetic architecture of gene expression for most predictable genes is sparse. We found the best predicted gene, TACSTD2, was the same across populations with R2 > 0.86 in each population. However, we identified a subset of genes that are well-predicted in one population, but poorly predicted in another. We show these differences in predictive performance are due to allele frequency differences between populations. Using genotype weights trained in MESA to predict gene expression in independent populations showed that a training set with ancestry similar to the test set is better at predicting gene expression in test populations, demonstrating an urgent need for diverse population sampling in genomics. Our predictive models and performance statistics in diverse cohorts are made publicly available for use in transcriptome mapping methods at .\n\nAuthor summaryMost genome-wide association studies (GWAS) have been conducted in populations of European ancestry leading to a disparity in understanding the genetics of complex traits between populations. For many complex traits, gene regulation is critical, given the consistent enrichment of regulatory variants among trait-associated variants. However, it is still unknown how the effects of these key variants differ across populations. We used data from MESA to study the underlying genetic architecture of gene expression by optimizing gene expression prediction within and across diverse populations. The populations with genotype and gene expression data available are from individuals with African American (AFA, n=233), Hispanic (HIS, n=352), and European (CAU, n=578) ancestry. After calculating the prediction performance, we found that there are many genes that were well predicted in one population are poorly predicted in another. We further show that a training set with ancestry similar to the test set resulted in better gene expression predictions, demonstrating the need to incorporate diverse populations in genomic studies. Our gene expression prediction models and performance statistics are publicly available to facilitate future transcriptome mapping studies in diverse populations.

genomics↗

bioRxiv2017

Integrative cross tissue analysis of gene expression identifies novel type 2 diabetes genes

To understand the mechanistic underpinnings of type 2 diabetes (T2D) loci mapped through GWAS, we performed a tissue-specific gene association study in a cohort of over 100K individuals (ncases {approx} 26K,ncontrols {approx} 84K) across 44 human tissues using MetaXcan, a summary statistics extension of PrediXcan. We found that 90 genes significantly (FDR < 0.05) associated with T2D, of which 24 are previously reported T2D genes, 29 are novel in established T2D loci, and 37 are novel genes in novel loci. Of these, 13 reported genes, 15 novel genes in known loci, and 6 genes in novel loci replicated (FDRrep < 0.05) in an independent study (ncases {approx} 10K, ncontrols {approx} 62K). We also found enrichment of significant associations in expected tissues such as liver, pancreas, adipose, and muscle but also in tibial nerve, fibroblasts, and breast. Finally, we found that monogenic diabetes genes are enriched in T2D genes from our analysis suggesting that moderate alterations in monogenic (severe) diabetes genes may promote milder and later onset type 2 diabetes.

genetics↗