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Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features–referred to as canonical variables (CVs)–within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

We simulated RNA-seq reads from a diploid transcriptome derived from the Drosophila melanogaster reference transcriptome, restricted to chromosomes 2, 3, 4, and X, simulating RNA transcripts from female flies. The simulation contained a total of 10 samples, with an average sequencing depth of 50 million paired-end reads per sample. The maternal reference transcripts included the protein-coding and non-coding RNA from Ensembl (release 100), and the paternal reference transcripts were created from maternal transcripts by adding single nucleotide variants. To create paternal alleles, we randomly selected 5 exons from each gene, and the mid-position nucleotide of the selected exons was substituted with its complement nucleotide. Genes overlapping simple tandem repeats of size 50 basepairs (bp) or larger were excluded from the simulation, leaving 14,821 genes. While the majority of genes (93.3%) were simulated to not have AI, two types of AI were simulated: ``concordant AI'', where all isoforms had concordant allelic fold change, and ``discordant AI'', where there were discordant allelic fold changes among the isoforms of the gene. In the discordant AI case, the RNA abundance was balanced across the two alleles when summed across all the isoforms of the gene. We randomly selected 1,000 genes using the following criterion: 1) the number of annotated isoforms in the selected gene was between three and six, 2) the gene had at least two isoforms sharing the same transcription start site (TSS), 3) the gene had at least two distinctive TSS. Half of the selected genes (500) were simulated to have concordant AI and the other half (500) were simulated to have discordant AI, and the remaining 13,821 genes were simulated to have allelic balance. Code for producing the data is provided at the following GitHub repo: mikelove/ase-sim File description: ase-sim_quant_dirs.tgz - Salmon quantification directories granges.rda - GRanges (Bioc) object containing simulation parameters for all features results_tsv.tgz - tables of results for all methods evaluated se_gene.rda, se_oracle.rda, se_tss.rda, se_txp.rda - SummarizedExperiment objects of Salmon quantification at various levels of resolution transcripts.fa.gz - cDNA sequence for the diploid transcriptome

Traditional predictive models for transcriptome-wide association studies (TWAS) consider only single nucleotide polymorphisms (SNPs) local to genes of interest and perform parameter shrinkage with a regularization process. These approaches ignore the effect of distal-SNPs or possible effects underlying the SNP-gene association. Here, we outline multi-omic strategies for transcriptome imputation from germline genetics for testing gene-trait associations by prioritizing distal-SNPs to the gene of interest. In one extension, we identify mediating biomarkers (CpG sites, microRNAs, and transcription factors) highly associated with gene expression and train predictive models for these mediators using their local SNPs. Imputed values for mediators are then incorporated into the final model as fixed effects with local SNPs to the gene included as regularized effects. In the second extension, we assess distal-eSNPs (SNPs in eQTLs) for their mediation effect through mediators local to these distal-eSNPs. Highly mediated distal-eSNPs are then included in the eventual transcriptomic prediction model. We show considerable gains in percent variance explained of gene expression and TWAS power to detect gene-trait associations using simulation analysis and real data applications with TCGA breast cancer data and in ROS/MAP brain tissue data. This integrative approach to transcriptome-wide imputation and association studies aids in understanding the complex interactions underlying genetic regulation within a tissue and identifying important risk genes for various traits and disorders.

BackgroundThe relationship between germline genetic variation and breast cancer survival is largely unknown, especially in understudied minority populations who often have poorer survival. Genome-wide association studies (GWAS) have interrogated breast cancer survival but often are underpowered due to subtype heterogeneity and clinical covariates and detect loci in non-coding regions that are difficult to interpret. Transcriptome-wide association studies (TWAS) show increased power in detecting functionally relevant loci by leveraging expression quantitative trait loci (eQTLs) from external reference panels in relevant tissues. However, ancestry- or race-specific reference panels may be needed to draw correct inference in ancestrally diverse cohorts. Such panels for breast cancer are lacking.ResultsWe provide a framework for TWAS for breast cancer in diverse populations, using data from the Carolina Breast Cancer Study (CBCS), a population-based cohort that oversampled black women. We perform eQTL analysis for 406 breast cancer-related genes to train race-stratified predictive models of tumor expression from germline genotypes. Using these models, we impute expression in independent data from CBCS and TCGA, accounting for sampling variability in assessing performance. These models are not applicable across race, and their predictive performance varies across tumor subtype. Within CBCS (N = 3,828), at a false discovery-adjusted significance of 0.10 and stratifying for race, we identify associations in black women nearAURKA,CAPN13,PIK3CA, andSERPINB5via TWAS that are underpowered in GWAS.ConclusionsWe show that carefully implemented and thoroughly validated TWAS is an efficient approach for understanding the genetics underpinning breast cancer outcomes in diverse populations.

Data sets in which measurements of two (or more) types are obtained from a common set of samples arise in many scientific applications. A common problem in the exploratory analysis of such data is to identify groups of features of different data types that are strongly associated. A bimodule is a pair (A, B) of feature sets from two data types such that the aggregate cross-correlation between the features in A and those in B is large. A bimodule (A, B) is stable if A coincides with the set of features that have significant aggregate correlation with the features in B, and vice-versa. In this paper we propose and investigate an iterative testing-based procedure (BSP) to identify stable bimodules in bi-view data. We carry out a thorough simulation study to assess the performance of BSP, and present an extended application to the problem of expression quantitative trait loci (eQTL) analysis using recent data from the GTEx project. In addition, we apply BSP to climatology data to identify regions in North America where annual temperature variation affects precipitation., Comment: 24 pages, 6 figures. R package: https://github.com/miheerdew/cbce

Additional file 1 Supplementary Material for “Alignment and mapping methodology influence transcript abundance estimation”. The file contains supplementary tables and figures for the manuscript.

Additional file 2 Review history.

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.