HEFT: eQTL analysis of many thousands of expressed genes while simultaneously controlling for hidden factors

FastQTLmapping: an ultra-fast and memory efficient package for mQTL-like analysis

BACKGROUND

FastQTLmapping addresses the need for an ultra-fast and memory-efficient solver capable of handling exhaustive multiple regression analysis with a vast number of dependent and explanatory variables, including covariates. This challenge is especially pronounced in methylation quantitative trait loci (mQTL)-like analysis, which typically involves high-dimensional genetic and epigenetic data.

RESULTS

FastQTLmapping is a precompiled C++ software solution accelerated by Intel MKL and GSL, freely available at https://github.com/Fun-Gene/fastQTLmapping . Compared to state-of-the-art methods (MatrixEQTL, FastQTL, and TensorQTL), fastQTLmapping demonstrated an order of magnitude speed improvement, coupled with a marked reduction in peak memory usage. In a large dataset consisting of 3500 individuals, 8 million SNPs, 0.8 million CpGs, and 20 covariates, fastQTLmapping completed the entire mQTL analysis in 4.5 h with only 13.1 GB peak memory usage.

CONCLUSIONS

FastQTLmapping effectively expedites comprehensive mQTL analyses by providing a robust and generic approach that accommodates large-scale genomic datasets with covariates. This solution has the potential to streamline mQTL-like studies and inform future method development for efficient computational genomics.

LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.

Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology

Significant innovations in next-generation sequencing techniques and bioinformatics tools have impacted our appreciation and understanding of RNA. Practical RNA sequencing (RNA-Seq) applications have evolved in conjunction with sequence technology and bioinformatic tools advances. In most projects, bulk RNA-Seq data is used to measure gene expression patterns, isoform expression, alternative splicing and single-nucleotide polymorphisms. However, RNA-Seq holds far more hidden biological information including details of copy number alteration, microbial contamination, transposable elements, cell type (deconvolution) and the presence of neoantigens. Recent novel and advanced bioinformatic algorithms developed the capacity to retrieve this information from bulk RNA-Seq data, thus broadening its scope. The focus of this review is to comprehend the emerging bulk RNA-Seq-based analyses, emphasizing less familiar and underused applications. In doing so, we highlight the power of bulk RNA-Seq in providing biological insights.

Expression of endocannabinoid system components in human airway epithelial cells: impact of sex and chronic respiratory disease status

Cannabis smoking is the dominant route of delivery, with the airway epithelium functioning as the site of first contact. The endocannabinoid system is responsible for mediating the physiological effects of inhaled phytocannabinoids. The expression of the endocannabinoid system in the airway epithelium and contribution to normal physiological responses remains to be defined. To begin to address this knowledge gap, a curated dataset of 1090 unique human bronchial brushing gene expression profiles was created. The dataset included 616 healthy subjects, 136 subjects with asthma, and 338 subjects with COPD. A 32-gene endocannabinoid signature was analysed across all samples with sex and disease-specific analyses performed. Immunohistochemistry and immunoblots were performed to probe in situ and in vitro protein expression. CB₁, CB₂, and TRPV1 protein signal is detectable in human airway epithelial cells in situ and in vitro, justifying examining the downstream endocannabinoid pathway. Sex status was associated with differential expression of 7 of 32 genes. In contrast, disease status was associated with differential expression of 21 of 32 genes in people with asthma and 26 of 32 genes in people with COPD. We confirm at the protein level that TRPV1, the most differentially expressed candidate in our analyses, was upregulated in airway epithelial cells from people with asthma relative to healthy subjects. Our data demonstrate that the endocannabinoid system is expressed in human airway epithelial cells with expression impacted by disease status and minimally by sex. The data suggest that cannabis consumers may have differential physiological responses in the respiratory mucosa.

Effective SNP ranking improves the performance of eQTL mapping

Genome-wide expression quantitative trait loci (eQTLs) mapping explores the relationship between gene expression and DNA variants, such as single-nucleotide polymorphism (SNPs), to understand genetic basis of human diseases. Due to the large number of genes and SNPs that need to be assessed, current methods for eQTL mapping often suffer from low detection power, especially for identifying trans-eQTLs. In this paper, we propose the idea of performing SNP ranking based on the higher criticism statistic, a summary statistic developed in large-scale signal detection. We illustrate how the HC-based SNP ranking can effectively prioritize eQTL signals over noise, greatly reduce the burden of joint modeling, and improve the power for eQTL mapping. Numerical results in simulation studies demonstrate the superior performance of our method compared to existing methods. The proposed method is also evaluated in HapMap eQTL data analysis and the results are compared to a database of known eQTLs.

A Multi-Omics Perspective of Quantitative Trait Loci in Precision Medicine

Quantitative trait loci (QTL) analysis is an important approach to investigate the effects of genetic variants identified through an increasing number of large-scale, multidimensional 'omics data sets. In this 'big data' era, the research community has identified a significant number of molecular QTLs (molQTLs) and increased our understanding of their effects. Herein, we review multiple categories of molQTLs, including those associated with transcriptome, post-transcriptional regulation, epigenetics, proteomics, metabolomics, and the microbiome. We summarize approaches to identify molQTLs and to infer their causal effects. We further discuss the integrative analysis of molQTLs through a multi-omics perspective. Our review highlights future opportunities to better understand the functional significance of genetic variants and to utilize the discovery of molQTLs in precision medicine.

Genetic correlations reveal the shared genetic architecture of transcription in human peripheral blood

Transcript co-expression is regulated by a combination of shared genetic and environmental factors. Here, we estimate the proportion of co-expression that is due to shared genetic variance. To do so, we estimated the genetic correlations between each pairwise combination of 2469 transcripts that are highly heritable and expressed in whole blood in 1748 unrelated individuals of European ancestry. We identify 556 pairs with a significant genetic correlation of which 77% are located on different chromosomes, and report 934 expression quantitative trait loci, identified in an independent cohort, with significant effects on both transcripts in a genetically correlated pair. We show significant enrichment for transcription factor control and physical proximity through chromatin interactions as possible mechanisms of shared genetic control. Finally, we construct networks of interconnected transcripts and identify their underlying biological functions. Using genetic correlations to investigate transcriptional co-regulation provides valuable insight into the nature of the underlying genetic architecture of gene regulation.

Covariance of gene expression pairs is due to a combination of shared genetic and environmental factors. Here the authors estimate the genetic correlation between highly heritable pairs and identify transcription factor control and chromatin interactions as possible mechanisms of correlation.

Nonconvex Penalty Based Low-Rank Representation and Sparse Regression for eQTL Mapping

This paper addresses the problem of accounting for confounding factors and expression quantitative trait loci (eQTL) mapping in the study of SNP-gene associations. The existing convex penalty based algorithm has limited capacity to keep main information of matrix in the process of reducing matrix rank. We present an algorithm, which use nonconvex penalty based low-rank representation to account for confounding factors and make use of sparse regression for eQTL mapping (NCLRS). The efficiency of the presented algorithm is evaluated by comparing the results of 18 synthetic datasets given by NCLRS and presented algorithm, respectively. The experimental results or biological dataset show that our approach is an effective tool to account for non-genetic effects than currently existing methods.

RRmix: A method for simultaneous batch effect correction and analysis of metabolomics data in the absence of internal standards

With the surge of interest in metabolism and the appreciation of its diverse roles in numerous biomedical contexts, the number of metabolomics studies using liquid chromatography coupled to mass spectrometry (LC-MS) approaches has increased dramatically in recent years. However, variation that occurs independently of biological signal and noise (i.e. batch effects) in metabolomics data can be substantial. Standard protocols for data normalization that allow for cross-study comparisons are lacking. Here, we investigate a number of algorithms for batch effect correction and differential abundance analysis, and compare their performance. We show that linear mixed effects models, which account for latent (i.e. not directly measurable) factors, produce satisfactory results in the presence of batch effects without the need for internal controls or prior knowledge about the nature and sources of unwanted variation in metabolomics data. We further introduce an algorithm-RRmix-within the family of latent factor models and illustrate its suitability for differential abundance analysis in the presence of strong batch effects. Together this analysis provides a framework for systematically standardizing metabolomics data.

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

Expression QTLs Mapping and Analysis: A Bayesian Perspective

The aim of expression Quantitative Trait Locus (eQTL) mapping is the identification of DNA sequence variants that explain variation in gene expression. Given the recent yield of trait-associated genetic variants identified by large-scale genome-wide association analyses (GWAS), eQTL mapping has become a useful tool to understand the functional context where these variants operate and eventually narrow down functional gene targets for disease. Despite its extensive application to complex (polygenic) traits and disease, the majority of eQTL studies still rely on univariate data modeling strategies, i.e., testing for association of all transcript-marker pairs. However these "one at-a-time" strategies are (1) unable to control the number of false-positives when an intricate Linkage Disequilibrium structure is present and (2) are often underpowered to detect the full spectrum of trans-acting regulatory effects. Here we present our viewpoint on the most recent advances on eQTL mapping approaches, with a focus on Bayesian methodology. We review the advantages of the Bayesian approach over frequentist methods and provide an empirical example of polygenic eQTL mapping to illustrate the different properties of frequentist and Bayesian methods. Finally, we discuss how multivariate eQTL mapping approaches have distinctive features with respect to detection of polygenic effects, accuracy, and interpretability of the results.

Four Susceptibility Loci for Gallstone Disease Identified in a Meta-analysis of Genome-Wide Association Studies

BACKGROUND & AIMS

A genome-wide association study (GWAS) of 280 cases identified the hepatic cholesterol transporter ABCG8 as a locus associated with risk for gallstone disease, but findings have not been reported from any other GWAS of this phenotype. We performed a large-scale, meta-analysis of GWASs of individuals of European ancestry with available prior genotype data, to identify additional genetic risk factors for gallstone disease.

METHODS

We obtained per-allele odds ratio (OR) and standard error estimates using age- and sex-adjusted logistic regression models within each of the 10 discovery studies (8720 cases and 55,152 controls). We performed an inverse variance weighted, fixed-effects meta-analysis of study-specific estimates to identify single-nucleotide polymorphisms that were associated independently with gallstone disease. Associations were replicated in 6489 cases and 62,797 controls.

RESULTS

We observed independent associations for 2 single-nucleotide polymorphisms at the ABCG8 locus: rs11887534 (OR, 1.69; 95% confidence interval [CI], 1.54-1.86; P = 2.44 × 10(-60)) and rs4245791 (OR, 1.27; P = 1.90 × 10(-34)). We also identified and/or replicated associations for rs9843304 in TM4SF4 (OR, 1.12; 95% CI, 1.08-1.16; P = 6.09 × 10(-11)), rs2547231 in SULT2A1 (encodes a sulfoconjugation enzyme that acts on hydroxysteroids and cholesterol-derived sterol bile acids) (OR, 1.17; 95% CI, 1.12-1.21; P = 2.24 × 10(-10)), rs1260326 in glucokinase regulatory protein (OR, 1.12; 95% CI, 1.07-1.17; P = 2.55 × 10(-10)), and rs6471717 near CYP7A1 (encodes an enzyme that catalyzes conversion of cholesterol to primary bile acids) (OR, 1.11; 95% CI, 1.08-1.15; P = 8.84 × 10(-9)). Among individuals of African American and Hispanic American ancestry, rs11887534 and rs4245791 were associated positively with gallstone disease risk, whereas the association for the rs1260326 variant was inverse.

CONCLUSIONS

In this large-scale GWAS of gallstone disease, we identified 4 loci in genes that have putative functions in cholesterol metabolism and transport, and sulfonylation of bile acids or hydroxysteroids.

Airway Epithelial Expression Quantitative Trait Loci Reveal Genes Underlying Asthma and Other Airway Diseases

Genome-wide association studies (GWASs) have identified loci that are robustly associated with asthma and related phenotypes; however, the molecular mechanisms underlying these associations need to be explored. The most relevant tissues to study the functional consequences of asthma are the airways. We used publically available data to derive expression quantitative trait loci (eQTLs) for human epithelial cells from small and large airways and applied the eQTLs in the interpretation of GWAS results of asthma and related phenotypes. For the small airways (n = 105), we discovered 660 eQTLs at a 10% false discovery rate (FDR), among which 315 eQTLs were not previously reported in a large-scale eQTL study of whole lung tissue. A large fraction of the identified eQTLs is supported by data from Encyclopedia of DNA Elements (ENCODE) showing that the eQTLs reside in regulatory elements (57.5 and 67.6% of cis- and trans-eQTLs, respectively). Published pulmonary GWAS hits were enriched as airway epithelial eQTLs (9.2-fold). Further, genes regulated by asthma GWAS loci in epithelium are significantly enriched in immune response pathways, such as IL-4 signaling (FDR, 5.2 × 10(-4)). The airway epithelial eQTLs described in this study are complementary to previously reported lung eQTLs and represent a powerful resource to link GWAS-associated variants to their regulatory function and thus elucidate the molecular mechanisms underlying asthma and airway-related conditions.

Chimeric EWSR1-FLI1 regulates the Ewing sarcoma susceptibility gene EGR2 via a GGAA microsatellite

Deciphering the ways in which somatic mutations and germline susceptibility variants cooperate to promote cancer is challenging. Ewing sarcoma is characterized by fusions between EWSR1 and members of the ETS gene family, usually EWSR1-FLI1, leading to the generation of oncogenic transcription factors that bind DNA at GGAA motifs. A recent genome-wide association study identified susceptibility variants near EGR2. Here we found that EGR2 knockdown inhibited proliferation, clonogenicity and spheroidal growth in vitro and induced regression of Ewing sarcoma xenografts. Targeted germline deep sequencing of the EGR2 locus in affected subjects and controls identified 291 Ewing-associated SNPs. At rs79965208, the A risk allele connected adjacent GGAA repeats by converting an interspaced GGAT motif into a GGAA motif, thereby increasing the number of consecutive GGAA motifs and thus the EWSR1-FLI1-dependent enhancer activity of this sequence, with epigenetic characteristics of an active regulatory element. EWSR1-FLI1 preferentially bound to the A risk allele, which increased global and allele-specific EGR2 expression. Collectively, our findings establish cooperation between a dominant oncogene and a susceptibility variant that regulates a major driver of Ewing sarcomagenesis.

Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

Differential expression of vitamin E and selenium-responsive genes by disease severity in chronic obstructive pulmonary disease

Antioxidant nutritional status is hypothesized to influence chronic obstructive pulmonary disease (COPD) susceptibility and progression. Although past studies relate antioxidants to gene expression, there are no data in patients with COPD. This study investigated the hypothesis that antioxidant status is compromised in patients with COPD, and antioxidant-responsive genes differentially express in a similar pattern. Lung tissue samples from patients with COPD were assayed for vitamin E and gene expression. Selenium and vitamin E were assayed in corresponding plasma samples. Discovery based genome-wide expression analysis compared moderate, severe, and very severe COPD (GOLD II-IV) patients to mild and at-risk/normal (GOLD 0-I). Hypotheses-driven analyses assessed differential gene expression by disease severity for vitamin E-responsive and selenium-responsive genes. GOLD II-IV COPD patients had 30% lower lung tissue vitamin E levels compared to GOLD 0-I participants (p = 0.0082). No statistically significant genome-wide differences in expression by disease severity were identified. Hypothesis-driven analyses of 109 genes found 16 genes differentially expressed (padjusted < 0.05) by disease severity including 6 selenium-responsive genes (range in fold-change -1.39 to 2.25), 6 vitamin E-responsive genes (fold-change -2.30 to 1.51), and 4 COPD-associated genes. Lung tissue vitamin E in patients with COPD was associated with disease severity and vitamin E-responsive genes were differentially expressed by disease severity. Although nutritional status is hypothesized to contribute to COPD risk, and is of therapeutic interest, evidence to date is mainly observational. The findings reported herein are novel, and support a role of vitamin E in COPD progression.

Vitamin D-responsive SGPP2 variants associated with lung cell expression and lung function

Background

Vitamin D is associated with lung health in epidemiologic studies, but mechanisms mediating observed associations are poorly understood. This study explores mechanisms for an effect of vitamin D in lung through an in vivo gene expression study, an expression quantitative trait loci (eQTL) analysis in lung tissue, and a population-based cohort study of sequence variants.

Methods

Microarray analysis investigated the association of gene expression in small airway epithelial cells with serum 25(OH)D in adult non-smokers. Sequence variants in candidate genes identified by the microarray were investigated in a lung tissue eQTL database, and also in relation to cross-sectional pulmonary function in the Health, Aging, and Body Composition (Health ABC) study, stratified by race, with replication in the Framingham Heart Study (FHS).

Results

13 candidate genes had significant differences in expression by serum 25(OH)D (nominal p < 0.05), and a genome-wide significant eQTL association was detected for SGPP2. In Health ABC, SGPP2 SNPs were associated with FEV₁ in both European- and African-Americans, and the gene-level association was replicated in European-American FHS participants. SNPs in 5 additional candidate genes (DAPK1, FSTL1, KAL1, KCNS3, and RSAD2) were associated with FEV₁ in Health ABC participants.

Conclusions

SGPP2, a sphingosine-1-phosphate phosphatase, is a novel vitamin D-responsive gene associated with lung function. The identified associations will need to be followed up in further studies.