Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification

Association of Genetically Predicted Skipping of COL4A4 Exon 27 with Hematuria and Albuminuria

Background:

Hematuria is an established sign of glomerular disease and can be associated with kidney failure, but there has been limited scientific study of this trait.

Methods:

Here, we combined genetic data from the UK Biobank with predicted gene expression and splicing from GTEx kidney cortex samples (n = 65) in a transcriptome-wide association study (TWAS) to identify additional potential biological mechanisms influencing hematuria.

Results:

The TWAS using kidney cortex identified significant associations for 5 genes in terms of expression and 3 significant splicing events. Notably, we identified an association between the skipping of COL4A4 exon 27, which is genetically predicted by intronic rs11898094 (minor allele frequency 13%), and hematuria. Association between this variant was also found with urinary albumin excretion. We found independent evidence supporting the same variant predicting this skipping event in glomeruli-derived mRNA transcriptomics data (n = 245) from NEPTUNE. The functional significance of loss of exon 27 was demonstrated using the split NanoLuc-based α3α4α5(IV) heterotrimer assay, in which type IV collagen heterotrimer formation was quantified by luminescence. The causal splicing variant for this skipping event is yet to be identified.

Conclusions:

In summary, by integrating multiple data types, we identify a potential splicing event associated with hematuria and albuminuria.

Partitioning and aggregating cross-tissue and tissue-specific genetic effects to identify gene-trait associations

TWAS have shown great promise in extending GWAS loci to a functional understanding of disease mechanisms. In an effort to fully unleash the TWAS and GWAS information, we propose MTWAS, a statistical framework that partitions and aggregates cross-tissue and tissue-specific genetic effects in identifying gene-trait associations. We introduce a non-parametric imputation strategy to augment the inaccessible tissues, accommodating complex interactions and non-linear expression data structures across various tissues. We further classify eQTLs into cross-tissue eQTLs and tissue-specific eQTLs via a stepwise procedure based on the extended Bayesian information criterion, which is consistent under high-dimensional settings. We show that MTWAS significantly improves the prediction accuracy across all 47 tissues of the GTEx dataset, compared with other single-tissue and multi-tissue methods, such as PrediXcan, TIGAR, and UTMOST. Applying MTWAS to the DICE and OneK1K datasets with bulk and single-cell RNA sequencing data on immune cell types showcases consistent improvements in prediction accuracy. MTWAS also identifies more predictable genes, and the improvement can be replicated with independent studies. We apply MTWAS to 84 UK Biobank GWAS studies, which provides insights into disease etiology.

Multi-tissue transcriptome-wide association studies identified 235 genes for intrinsic subtypes of breast cancer

BACKGROUND

Although genome-wide association studies (GWAS) of breast cancer (BC) identified common variants which differ between intrinsic subtypes, genes through which these variants act to impact BC risk have not been fully established. Transcriptome-wide association studies (TWAS) have identified genes associated with overall BC risk, but subtype-specific differences are largely unknown.

METHODS

We performed two multi-tissue TWAS for each BC intrinsic subtype, including an expression-based approach that collated TWAS signals from expression quantitative trait loci (eQTLs) across multiple tissues and a novel splicing-based approach that collated signals from splicing QTLs (sQTLs) across intron clusters and subsequently across tissues. We used summary statistics for five intrinsic subtypes including Luminal A-like, Luminal B-like, Luminal B/HER2-negative-like, HER2-enriched-like, and triple-negative BC, generated from 106 278 BC cases and 91 477 controls in the Breast Cancer Association Consortium.

RESULTS

Overall, we identified 235 genes in 88 loci that were associated with at least one of the five intrinsic subtypes. Most genes were subtype-specific, and many have not been reported in previous TWAS. We discovered common variants that modulate expression of CHEK2 confer increased risk to Luminal A-like BC, in contrast to the viewpoint that CHEK2 primarily harbors rare, penetrant mutations. Additionally, our splicing-based TWAS provided population-level support for MDM4 splice variants that increased the risk of triple-negative BC.

CONCLUSION

Our comprehensive, multi-tissue TWAS corroborated previous GWAS loci for overall BC risk and intrinsic subtypes, while underscoring how common variation that impacts expression and splicing of genes in multiple tissue types can be used to further elucidate the etiology of BC.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

Cis- and trans-eQTL TWASs of breast and ovarian cancer identify more than 100 susceptibility genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWASs) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have focused on the regulatory effects of risk-associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWASs of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole-genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents an in-depth look into the role of trans eQTLs in the complex molecular mechanisms underlying these diseases.

DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences

Background

Analyzing genomic sequences plays a crucial role in understanding biological diversity and classifying Bamboo species. Existing methods for genomic sequence analysis suffer from limitations such as complexity, low accuracy, and the need for constant reconfiguration in response to evolving genomic datasets.

Aim

This study addresses these limitations by introducing a novel Dual Heuristic Feature Selection-based Ensemble Classification Model (DHFS-ECM) for the precise identification of Bamboo species from genomic sequences.

Methods

The proposed DHFS-ECM method employs a Genetic Algorithm to perform dual heuristic feature selection. This process maximizes inter-class variance, leading to the selection of informative N-gram feature sets. Subsequently, intra-class variance levels are used to create optimal training and validation sets, ensuring comprehensive coverage of class-specific features. The selected features are then processed through an ensemble classification layer, combining multiple stratification models for species-specific categorization.

Results

Comparative analysis with state-of-the-art methods demonstrate that DHFS-ECM achieves remarkable improvements in accuracy (9.5%), precision (5.9%), recall (8.5%), and AUC performance (4.5%). Importantly, the model maintains its performance even with an increased number of species classes due to the continuous learning facilitated by the Dual Heuristic Genetic Algorithm Model.

Conclusion

DHFS-ECM offers several key advantages, including efficient feature extraction, reduced model complexity, enhanced interpretability, and increased robustness and accuracy through the ensemble classification layer. These attributes make DHFS-ECM a promising tool for real-time clinical applications and a valuable contribution to the field of genomic sequence analysis.

Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status

BACKGROUND

Although several transcriptome-wide association studies (TWASs) have been performed to identify genes associated with overall breast cancer (BC) risk, only a few TWAS have explored the differences in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER-) breast cancer. Additionally, these studies were based on gene expression prediction models trained primarily in breast tissue, and they did not account for alternative splicing of genes.

METHODS

In this study, we utilized two approaches to perform multi-tissue TWASs of breast cancer by ER subtype: (1) an expression-based TWAS that combined TWAS signals for each gene across multiple tissues and (2) a splicing-based TWAS that combined TWAS signals of all excised introns for each gene across tissues. To perform this TWAS, we utilized summary statistics for ER + BC from the Breast Cancer Association Consortium (BCAC) and for ER- BC from a meta-analysis of BCAC and the Consortium of Investigators of Modifiers of BRCA1 and BRCA2 (CIMBA).

RESULTS

In total, we identified 230 genes in 86 loci that were associated with ER + BC and 66 genes in 29 loci that were associated with ER- BC at a Bonferroni threshold of significance. Of these genes, 2 genes associated with ER + BC at the 1q21.1 locus were located at least 1 Mb from published GWAS hits. For several well-studied tumor suppressor genes such as TP53 and CHEK2 which have historically been thought to impact BC risk through rare, penetrant mutations, we discovered that common variants, which modulate gene expression, may additionally contribute to ER + or ER- etiology.

CONCLUSIONS

Our study comprehensively examined how differences in common variation contribute to molecular differences between ER + and ER- BC and introduces a novel, splicing-based framework that can be used in future TWAS studies.

Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.

postGWAS: A web server for deciphering the causality post the genome-wide association studies

While genome-wide association studies (GWAS) have unequivocally identified vast disease susceptibility variants, a majority of them are situated in non-coding regions and are in high linkage disequilibrium (LD). To pave the way of translating GWAS signals to clinical drug targets, it is essential to identify the underlying causal variants and further causal genes. To this end, a myriad of post-GWAS methods have been devised, each grounded in distinct principles including fine-mapping, co-localization, and transcriptome-wide association study (TWAS) techniques. Yet, no platform currently exists that seamlessly integrates these diverse post-GWAS methodologies. In this work, we present a user-friendly web server for post-GWAS analysis, that seamlessly integrates 9 distinct methods with 12 models, categorized by fine-mapping, colocalization, and TWAS. The server mainly helps users decipher the causality hindered by complex GWAS signals, including casual variants and casual genes, without the burden of computational skills and complex environment configuration, and provides a convenient platform for post-GWAS analysis, result visualization, facilitating the understanding and interpretation of the genome-wide association studies. The postGWAS server is available at http://g2g.biographml.com/.

Cis- and trans-eQTL TWAS of breast and ovarian cancer identify more than 100 risk associated genes in the BCAC and OCAC consortia

Transcriptome-wide association studies (TWAS) have investigated the role of genetically regulated transcriptional activity in the etiologies of breast and ovarian cancer. However, methods performed to date have only considered regulatory effects of risk associated SNPs thought to act in cis on a nearby target gene. With growing evidence for distal (trans) regulatory effects of variants on gene expression, we performed TWAS of breast and ovarian cancer using a Bayesian genome-wide TWAS method (BGW-TWAS) that considers effects of both cis- and trans-expression quantitative trait loci (eQTLs). We applied BGW-TWAS to whole genome and RNA sequencing data in breast and ovarian tissues from the Genotype-Tissue Expression project to train expression imputation models. We applied these models to large-scale GWAS summary statistic data from the Breast Cancer and Ovarian Cancer Association Consortia to identify genes associated with risk of overall breast cancer, non-mucinous epithelial ovarian cancer, and 10 cancer subtypes. We identified 101 genes significantly associated with risk with breast cancer phenotypes and 8 with ovarian phenotypes. These loci include established risk genes and several novel candidate risk loci, such as ACAP3, whose associations are predominantly driven by trans-eQTLs. We replicated several associations using summary statistics from an independent GWAS of these cancer phenotypes. We further used genotype and expression data in normal and tumor breast tissue from the Cancer Genome Atlas to examine the performance of our trained expression imputation models. This work represents a first look into the role of trans-eQTLs in the complex molecular mechanisms underlying these diseases.

Genome-wide association analysis of plasma lipidome identifies 495 genetic associations

The human plasma lipidome captures risk for cardiometabolic diseases. To discover new lipid-associated variants and understand the link between lipid species and cardiometabolic disorders, we perform univariate and multivariate genome-wide analyses of 179 lipid species in 7174 Finnish individuals. We fine-map the associated loci, prioritize genes, and examine their disease links in 377,277 FinnGen participants. We identify 495 genome-trait associations in 56 genetic loci including 8 novel loci, with a considerable boost provided by the multivariate analysis. For 26 loci, fine-mapping identifies variants with a high causal probability, including 14 coding variants indicating likely causal genes. A phenome-wide analysis across 953 disease endpoints reveals disease associations for 40 lipid loci. For 11 coronary artery disease risk variants, we detect strong associations with lipid species. Our study demonstrates the power of multivariate genetic analysis in correlated lipidomics data and reveals genetic links between diseases and lipid species beyond the standard lipids.

Multivariate adaptive shrinkage improves cross-population transcriptome prediction and 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 that 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 (TWASs) using different methods (elastic net, joint-tissue imputation [JTI], matrix expression quantitative trait loci [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 TWASs, we integrated publicly available multiethnic genome-wide association study (GWAS) summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study and Pan-ancestry genetic analysis of the UK Biobank (PanUKBB) 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 multiethnic TWASs, MASHR models yielded more discoveries that replicate in both PAGE and PanUKBB across all methods analyzed, including loci previously mapped in GWASs and loci previously not found in GWASs. Overall, our study demonstrates the importance of using methods that benefit from different populations' effect size estimates in order to improve TWASs for multiethnic or underrepresented populations.

Integrating eQTL and GWAS data characterises established and identifies novel migraine risk loci

Migraine-a painful, throbbing headache disorder-is the most common complex brain disorder, yet its molecular mechanisms remain unclear. Genome-wide association studies (GWAS) have proven successful in identifying migraine risk loci; however, much work remains to identify the causal variants and genes. In this paper, we compared three transcriptome-wide association study (TWAS) imputation models-MASHR, elastic net, and SMultiXcan-to characterise established genome-wide significant (GWS) migraine GWAS risk loci, and to identify putative novel migraine risk gene loci. We compared the standard TWAS approach of analysing 49 GTEx tissues with Bonferroni correction for testing all genes present across all tissues (Bonferroni), to TWAS in five tissues estimated to be relevant to migraine, and TWAS with Bonferroni correction that took into account the correlation between eQTLs within each tissue (Bonferroni-matSpD). Elastic net models performed in all 49 GTEx tissues using Bonferroni-matSpD characterised the highest number of established migraine GWAS risk loci (n = 20) with GWS TWAS genes having colocalisation (PP4 > 0.5) with an eQTL. SMultiXcan in all 49 GTEx tissues identified the highest number of putative novel migraine risk genes (n = 28) with GWS differential expression at 20 non-GWS GWAS loci. Nine of these putative novel migraine risk genes were later found to be at and in linkage disequilibrium with true (GWS) migraine risk loci in a recent, more powerful migraine GWAS. Across all TWAS approaches, a total of 62 putative novel migraine risk genes were identified at 32 independent genomic loci. Of these 32 loci, 21 were true risk loci in the recent, more powerful migraine GWAS. Our results provide important guidance on the selection, use, and utility of imputation-based TWAS approaches to characterise established GWAS risk loci and identify novel risk gene loci.

Combinations of genes at the 16p11.2 and 22q11.2 CNVs contribute to neurobehavioral traits

The 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with neurobehavioral traits including autism spectrum disorder (ASD), schizophrenia, bipolar disorder, obesity, and intellectual disability. Identifying specific genes contributing to each disorder and dissecting the architecture of CNV-trait association has been difficult, inspiring hypotheses of more complex models, such as multiple genes acting together. Using multi-tissue data from the GTEx consortium, we generated pairwise expression imputation models for CNV genes and then applied these elastic net models to GWAS for: ASD, bipolar disorder, schizophrenia, BMI (obesity), and IQ (intellectual disability). We compared the variance in these five traits explained by gene pairs with the variance explained by single genes and by traditional interaction models. We also modeled polygene region-wide effects using summed predicted expression ranks across many genes to create a regionwide score. We found that in all CNV-trait pairs except for bipolar disorder at 22q11.2, pairwise effects explain more variance than single genes. Pairwise model superiority was specific to the CNV region for all 16p11.2 traits and ASD at 22q11.2. We identified novel individual genes over-represented in top pairs that did not show single-gene signal. We also found that BMI and IQ have significant regionwide association with both CNV regions. Overall, we observe that genetic architecture differs by trait and region, but 9/10 CNV-trait combinations demonstrate evidence for multigene contribution, and for most of these, the importance of combinatorial models appears unique to CNV regions. Our results suggest that mechanistic insights for CNV pathology may require combinational models.

A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes

Genome-wide association studies (GWASs) have identified more than 200 genomic loci for breast cancer risk, but specific causal genes in most of these loci have not been identified. In fact, transcriptome-wide association studies (TWASs) of breast cancer performed using gene expression prediction models trained in breast tissue have yet to clearly identify most target genes. To identify candidate genes, we performed a GWAS analysis in a breast cancer dataset from UK Biobank (UKB) and combined the results with the GWAS results of the Breast Cancer Association Consortium (BCAC) by a meta-analysis. Using the summary statistics from the meta-analysis, we performed a joint TWAS analysis that combined TWAS signals from multiple tissues. We used expression prediction models trained in 11 tissues that are potentially relevant to breast cancer from the Genotype-Tissue Expression (GTEx) data. In the GWAS analysis, we identified eight loci distinct from those reported previously. In the TWAS analysis, we identified 309 genes at 108 genomic loci to be significantly associated with breast cancer at the Bonferroni threshold. Of these, 17 genes were located in eight regions that were at least 1 Mb away from published GWAS hits. The remaining TWAS-significant genes were located in 100 known genomic loci from previous GWASs of breast cancer. We found that 21 genes located in known GWAS loci remained statistically significant after conditioning on previous GWAS index variants. Our study provides insights into breast cancer genetics through mapping candidate target genes in a large proportion of known GWAS loci and discovering multiple new loci.

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.

Heuristic Analysis of Genomic Sequence Processing Models for High Efficiency Prediction: A Statistical Perspective

Genome sequences indicate a wide variety of characteristics, which include species and sub-species type, genotype, diseases, growth indicators, yield quality, etc. To analyze and study the characteristics of the genome sequences across different species, various deep learning models have been proposed by researchers, such as Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Multilayer Perceptrons (MLPs), etc., which vary in terms of evaluation performance, area of application and species that are processed. Due to a wide differentiation between the algorithmic implementations, it becomes difficult for research programmers to select the best possible genome processing model for their application. In order to facilitate this selection, the paper reviews a wide variety of such models and compares their performance in terms of accuracy, area of application, computational complexity, processing delay, precision and recall. Thus, in the present review, various deep learning and machine learning models have been presented that possess different accuracies for different applications. For multiple genomic data, Repeated Incremental Pruning to Produce Error Reduction with Support Vector Machine (Ripper SVM) outputs 99.7% of accuracy, and for cancer genomic data, it exhibits 99.27% of accuracy using the CNN Bayesian method. Whereas for Covid genome analysis, Bidirectional Long Short-Term Memory with CNN (BiLSTM CNN) exhibits the highest accuracy of 99.95%. A similar analysis of precision and recall of different models has been reviewed. Finally, this paper concludes with some interesting observations related to the genomic processing models and recommends applications for their efficient use.

COLONOMICS - integrative omics data of one hundred paired normal-tumoral samples from colon cancer patients

Colonomics is a multi-omics dataset that includes 250 samples: 50 samples from healthy colon mucosa donors and 100 paired samples from colon cancer patients (tumor/adjacent). From these samples, Colonomics project includes data from genotyping, DNA methylation, gene expression, whole exome sequencing and micro-RNAs (miRNAs) expression. It also includes data from copy number variation (CNV) from tumoral samples. In addition, clinical data from all these samples is available. The aims of the project were to explore and integrate these datasets to describe colon cancer at molecular level and to compare normal and tumoral tissues. Also, to improve screening by finding biomarkers for the diagnosis and prognosis of colon cancer. This project has its own website including four browsers allowing users to explore Colonomics datasets. Since generated data could be reuse for the scientific community for exploratory or validation purposes, here we describe omics datasets included in the Colonomics project as well as results from multi-omics layers integration.

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.4837327.

Polygenic transcriptome risk scores (PTRS) can improve portability of polygenic risk scores across ancestries

BACKGROUND

Polygenic risk scores (PRS) are valuable 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 leading to poor performance in populations of non-European ancestry.

RESULTS

We introduce the polygenic transcriptome risk score (PTRS), which is based on predicted transcript levels (rather than SNPs), and explore the portability of PTRS across populations using UK Biobank data.

CONCLUSIONS

We show that PTRS has a significantly higher portability (Wilcoxon p=0.013) in the African-descent samples where the loss of performance is most acute with better performance than PRS when used in combination.

Evaluation of Sex-Aware PrediXcan Models for Predicting Gene Expression

Gene-based methods such as PrediXcan use expression quantitative trait loci to build tissue-specific gene expression models when only genetic data is available. There are known sex differences in tissue-specific gene expression and in the genetic architecture of gene expression, but such differences have not been incorporated into predicted gene expression models to date. We built sex-aware PrediXcan models using whole blood transcriptomic data from the Genotype-Tissue Expression (GTEx) project (195 females and 371 males) and evaluated their performance in an independent dataset. Specifically, PrediXcan models were built following the method described in Gamazon et al. 2015, but we included both whole-sample and sex-specific models. Validation was evaluated leveraging lymphoblast RNA sequencing data from the EUR cohort of the 1000 Genomes Project (178 females and 171 males). Correlations (R2) between observed and predicted expression were evaluated in 5,283 autosomal genes to determine performance of models. In sum, we successfully predicted 1,149 genes in males and 623 in females, while 3,511 genes appeared to be not sex-specific. Of the sex-specific genes, 15% (189 genes in males and 73 genes in females) exhibited higher R2 in sex-specific models compared to whole-sample models, although the overall gain in predictive power was generally minimal and well within measurement error. Nevertheless, two female-specific genes and six male-specific genes showed significantly better prediction when using the sex-specific weights versus the whole-sample weights; furthermore, several of these genes play a role in mitochondrial metabolism, which is known to be influenced by sex hormones. Taken together, these results support previous reports of the small contribution of genetic architecture to sex-specific expression. Still, sex-aware PrediXcan models were able to provide robust sex-specific prediction signals. Future studies exploring the contribution of the X chromosome and tissue specificity on sex-specific genetically regulated expression will clarify the utility of this method.

Transcriptome-Wide Association Study for Inflammatory Bowel Disease Reveals Novel Candidate Susceptibility Genes in Specific Colon Subsites and Tissue Categories

BACKGROUND AND AIMS

Genome-wide association studies [GWAS] for inflammatory bowel disease [IBD] have identified 240 risk variants. However, the benefit of understanding the genetic architecture of IBD remains to be exploited. Transcriptome-wide association studies [TWAS] associate gene expression with genetic susceptibility to disease, providing functional insight into risk loci. In this study, we integrate relevant datasets for IBD and perform a TWAS to nominate novel genes implicated in IBD genetic susceptibility.

METHODS

We applied elastic net regression to generate gene expression prediction models for the University of Barcelona and University of Virginia RNA sequencing project [BarcUVa-Seq] and correlated expression and disease association research [CEDAR] datasets. Together with Genotype-Tissue Expression project [GTEx] data, and GWAS results from about 60 000 individuals, we employed Summary-PrediXcan and Summary-MultiXcan for single and joint analyses of TWAS results, respectively.

RESULTS

BarcUVa-Seq TWAS revealed 39 novel genes whose expression in the colon is associated with IBD genetic susceptibility. They included expression markers for specific colon cell types. TWAS meta-analysis including all tissues/cell types provided 186 novel candidate susceptibility genes. Additionally, we identified 78 novel susceptibility genes whose expression is associated with IBD exclusively in immune (N = 19), epithelial (N = 25), mesenchymal (N = 22) and neural (N = 12) tissue categories. Associated genes were involved in relevant molecular pathways, including pathways related to known IBD therapeutics, such as tumour necrosis factor signalling.

CONCLUSION

These findings provide insight into tissue-specific molecular processes underlying IBD genetic susceptibility. Associated genes could be candidate targets for new therapeutics and should be prioritized in functional studies.

Multitrait transcriptome-wide association study (TWAS) tests

Multitrait tests can improve power to detect associations between individual single-nucleotide polymorphisms (SNPs) and several related traits. Here, we develop methods for multi-SNP transcriptome-wide association (TWAS) tests to test the association between predicted gene expression levels and multiple phenotypes. We show that the correlation in TWAS test statistics for multiple phenotypes has the same form as multitrait statistics for the single-SNP setting. Thus, established methods for combining single-SNP test statistics across multiple traits can be extended directly to the TWAS setting. We performed an extensive evaluation across eight multitrait methods in simulations that varied gene-phenotype effect sizes in addition to the underlying covariance structure among the phenotypes. We found that all multitrait TWAS tests have well-calibrated Type I error (except ASSET, which can have a slightly elevated or depressed Type I error rate). Our results show that multitrait TWAS can improve statistical power compared with multiple single-trait TWAS followed by Bonferroni correction. To illustrate our approach to real data, we conducted a multitrait TWAS of four circulating lipid traits from the Global Lipids Genetics Consortium. We found that our multitrait Wald TWAS approach identified 506 genes associated with lipid levels compared with 87 identified through Bonferroni-corrected single-trait TWAS. Overall, we find that our proposed multitrait TWAS framework outperforms single-trait approaches to identify new genetic associations, especially for functionally correlated phenotypes and phenotypes with overlapping genome-wide association studies samples, leading to insights into the genetic architecture of multiple phenotypes.

Transcriptome prediction performance across machine learning models and diverse ancestries

Transcriptome prediction methods such as PrediXcan and FUSION have become popular in complex trait mapping. Most transcriptome prediction models have been trained in European populations using methods that make parametric linear assumptions like the elastic net (EN). To potentially further optimize imputation performance of gene expression across global populations, we built transcriptome prediction models using both linear and non-linear machine learning (ML) algorithms and evaluated their performance in comparison to EN. We trained models using genotype and blood monocyte transcriptome data from the Multi-Ethnic Study of Atherosclerosis (MESA) comprising individuals of African, Hispanic, and European ancestries and tested them using genotype and whole-blood transcriptome data from the Modeling the Epidemiology Transition Study (METS) comprising individuals of African ancestries. We show that the prediction performance is highest when the training and the testing population share similar ancestries regardless of the prediction algorithm used. While EN generally outperformed random forest (RF), support vector regression (SVR), and K nearest neighbor (KNN), we found that RF outperformed EN for some genes, particularly between disparate ancestries, suggesting potential robustness and reduced variability of RF imputation performance across global populations. When applied to a high-density lipoprotein (HDL) phenotype, we show including RF prediction models in PrediXcan revealed potential gene associations missed by EN models. Therefore, by integrating other ML modeling into PrediXcan and diversifying our training populations to include more global ancestries, we may uncover new genes associated with complex traits.

Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics

Background

Clinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown.

Methods

Our analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE.

Results

We found there existed a significant positive genetic correlation (r_g = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway.

Conclusion

This study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.

Exploiting the GTEx resources to decipher the mechanisms at GWAS loci

The resources generated by the GTEx consortium offer unprecedented opportunities to advance our understanding of the biology of human diseases. Here, we present an in-depth examination of the phenotypic consequences of transcriptome regulation and a blueprint for the functional interpretation of genome-wide association study-discovered loci. Across a broad set of complex traits and diseases, we demonstrate widespread dose-dependent effects of RNA expression and splicing. We develop a data-driven framework to benchmark methods that prioritize causal genes and find no single approach outperforms the combination of multiple approaches. Using colocalization and association approaches that take into account the observed allelic heterogeneity of gene expression, we propose potential target genes for 47% (2519 out of 5385) of the GWAS loci examined.

Fine-mapping and QTL tissue-sharing information improves the reliability of causal gene identification

The integration of transcriptomic studies and genome-wide association studies (GWAS) 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) can 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 receiver operating characteristic curves. All prediction models are made publicly available at predictdb.org.