A COVARIANCE-ENHANCED APPROACH TO MULTI-TISSUE JOINT EQTL MAPPING WITH APPLICATION TO TRANSCRIPTOME-WIDE ASSOCIATION STUDIES.
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ABSTRACT:
SUBMITTER: Molstad AJ
PROVIDER: S-EPMC8372768 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A transcriptome-wide association study (TWAS) attempts to identify disease associated genes by imputing gene expression into a genome-wide association study (GWAS) using an expression quantitative trait loci (eQTL) data set and then testing for associations with a trait of interest. Regulatory processes may be shared across related tissues and one natural extension of TWAS is harnessing cross-tissue correlation in gene expression to improve prediction accuracy. Here, we studied multi-tissue extensions of lasso regression and random forests (RF), joint lasso and RF-MTL (multi-task learning RF), respectively. We found that, on our chosen eQTL data set, multi-tissue methods were generally more accurate than their single-tissue counterparts, with RF-MTL performing the best. Simulations showed that these benefits generally translated into more associated genes identified, although highlighted that joint lasso had a tendency to erroneously identify genes in one tissue if there existed an eQTL signal for that gene in another. Applying the four methods to a type 1 diabetes GWAS, we found that multi-tissue methods found more unique associated genes for most of the tissues considered. We conclude that multi-tissue methods are competitive and, for some cell types, superior to single-tissue approaches and hold much promise for TWAS studies.
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Project description:BackgroundGene regulation is important for cells and tissues to function. It has been studied from two aspects at the genomic level, the identification of expression quantitative trait loci (eQTLs) and identification of long-range chromatin interactions. It is important to understand their relationship, such as whether eQTLs regulate their target genes through physical chromatin interaction. Although chromatin interactions have been widely believed to be one of the main mechanisms underlying eQTLs, most evidence came from studies of cell lines and yet no direct evidence exists for tissues.ResultsWe performed various joint analyses of eQTL and high-throughput chromatin conformation capture (Hi-C) data from 11 human primary tissue types and 2 human cell lines. We found that chromatin interaction frequency is positively associated with the number of genes that have eQTLs and that eQTLs and their target genes tend to fall into the same topologically associating domain (TAD). These results are consistent across all tissues and cell lines we evaluated. Moreover, in 6 out of 11 tissues (aorta, dorsolateral prefrontal cortex, hippocampus, pancreas, small bowel, and spleen), tissue-specific eQTLs are significantly enriched in tissue-specific frequently interacting regions (FIREs).ConclusionsOur data have demonstrated the close spatial proximity between eQTLs and their target genes among multiple human primary tissues.
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Project description:Here, we present a joint-tissue imputation (JTI) approach and a Mendelian randomization framework for causal inference, MR-JTI. JTI borrows information across transcriptomes of different tissues, leveraging shared genetic regulation, to improve prediction performance in a tissue-dependent manner. Notably, JTI includes the single-tissue imputation method PrediXcan as a special case and outperforms other single-tissue approaches (the Bayesian sparse linear mixed model and Dirichlet process regression). MR-JTI models variant-level heterogeneity (primarily due to horizontal pleiotropy, addressing a major challenge of transcriptome-wide association study interpretation) and performs causal inference with type I error control. We make explicit the connection between the genetic architecture of gene expression and of complex traits and the suitability of Mendelian randomization as a causal inference strategy for transcriptome-wide association studies. We provide a resource of imputation models generated from GTEx and PsychENCODE panels. Analysis of biobanks and meta-analysis data, and extensive simulations show substantially improved statistical power, replication and causal mapping rate for JTI relative to existing approaches.
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2012-04-30 | GSE15905 | GEO
Project description:Expression quantitative trait locus (eQTL) analysis has been widely used to understand how genetic variations affect gene expressions in the biological systems. Traditional eQTL is investigated in a pair-wise manner in which one SNP affects the expression of one gene. In this way, some associated markers found in GWAS have been related to disease mechanism by eQTL study. However, in real life, biological process is usually performed by a group of genes. Although some methods have been proposed to identify a group of SNPs that affect the mean of gene expressions in the network, the change of co-expression pattern has not been considered. So we propose a process and algorithm to identify the marker which affects the co-expression pattern of a pathway. Considering two genes may have different correlations under different isoforms which is hard to detect by the linear test, we also consider the nonlinear test.When we applied our method to yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 166 modules, with each module consisting of a group of genes and one eQTL where the eQTL regulate the co-expression patterns of the group of genes. We found that many of these modules have biological significance.We propose a network based covariance test to identify the SNP which affects the structure of a pathway. We also consider the nonlinear test as considering two genes may have different correlations under different isoforms which is hard to detect by linear test.
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Project description:The study of gene expression as an intermediate phenotype in associating genotype to disease is growing rapidly. The success of this approach depends heavily on the availability of accurate, high throughput platforms for assaying expression levels of genome-wide gene sets. We evaluated a new platform for gene expression, Phalanx Human OneArray (HOA), in 90 HapMap cell lines (CEU panel) for which Illumina BeadChip data were available (Stranger et al. 2007, PMID: 17873874). We found high reproducibility between technical HOA replicates (R = 0.99) and strong inter-platform correlation based on 2,458 sequence-matched probes (R = 0.87). We next performed an association analysis between gene expression levels and HapMap SNPs in the 60 unrelated CEU parents [SNP data downloaded directly from HapMap project (http://www.hapmap.org/)(HapMap phase II/Rel # 22)]. Among the 4356 genes (HOA array) showing variable expression, 122 (2.8%) had a significant cis association (p-value < 0.001) with at least one SNP. Of the 1751 genes showing variable expression on both platforms, 84 had a significant cis association with 36.9% of the genes (31/84) present in both data sets, 39.3 % (33/84) only in HOA, and 23.8% (20/84) only in Illumina. A subset of these 84 eQTLs overlap or are in strong linkage disequilibrium (LD) with reported complex disease/trait susceptibility loci. Combining the results of the two platforms provides a more comprehensive eQTL database (30% increase), which may help to accelerate our understanding of the molecular basis of quantitative traits and diseases. We quantified gene expression levels in the 90 HapMap cell lines (CEU panel) using Phalanx HOA for which Illumina BeadChip data were available (Stranger et al. 2007). We used the same set of RNA samples employed in Stranger et al 2007 (see GEO Series GSE6536, CEU panel; raw data were re-analyzed, ie, re-normalized, thereby generating new values) which allowed us to undertake a detailed comparison to the Illumina expression platform and profiled the 60 parent samples at Industrial Technology Research Institute (ITRI), Taiwan and the 30 children samples at Wellcome Trust Sanger Institute (WTSI), United Kingdom. We performed two in vitro transcription (IVT) reactions for each RNA sample and three technical replicates per IVT with exception of seven samples (15CP, 33CP, 43CP, 44CP, 48CP, 56CP, 60CP) that had two technical replicates for one of the two IVT reactions. *** This Series represents the Phalanx HOA data. *** File "info.pass.parent.txt" (a description of the relationship between an array and a sample and its replicates) is linked below as a supplementary file.
2012-04-29 | E-GEOD-15905 | biostudies-arrayexpress
Project description:Many expression quantitative trait loci (eQTL) studies have been conducted to investigate the biological effects of variants in gene regulation. However, these eQTL studies may suffer from low or moderate statistical power and overly conservative false-discovery rate. In practice, most algorithms for eQTL identification do not model the joint effects of multiple genetic variants with weak or moderate influence. Here we present a novel machine-learning algorithm, lasso least-squares kernel machine (LSKM-LASSO) that model the association between multiple genetic variants and phenotypic traits simultaneously with the existence of nongenetic and genetic confounding. With a more general and flexible framework for the estimation of genetic confounding, LSKM-LASSO is able to provide a more accurate evaluation of the joint effects of multiple genetic variants. Our simulations demonstrate that our approach outperforms three state-of-the-art alternatives in terms of eQTL identification and phenotype prediction. We then apply our method to genotype and gene expression data of 11 tissues obtained from the Genotype-Tissue Expression project. Our algorithm was able to identify more genes with eQTL than other algorithms. By incorporating a regularization term and combining it with least-squares kernel machine, LSKM-LASSO provides a powerful tool for eQTL mapping and phenotype prediction.
| S-EPMC7875251 | biostudies-literature
Project description:Linear discriminant analysis has been widely used to characterize or separate multiple classes via linear combinations of features. However, the high dimensionality of features from modern biological experiments defies traditional discriminant analysis techniques. Possible interfeature correlations present additional challenges and are often underused in modelling. In this paper, by incorporating possible interfeature correlations, we propose a covariance-enhanced discriminant analysis method that simultaneously and consistently selects informative features and identifies the corresponding discriminable classes. Under mild regularity conditions, we show that the method can achieve consistent parameter estimation and model selection, and can attain an asymptotically optimal misclassification rate. Extensive simulations have verified the utility of the method, which we apply to a renal transplantation trial.
| S-EPMC4912060 | biostudies-literature
Project description:MotivationIdentifying cis-acting genetic variants associated with gene expression levels-an analysis commonly referred to as expression quantitative trait loci (eQTLs) mapping-is an important first step toward understanding the genetic determinant of gene expression variation. Successful eQTL mapping requires effective control of confounding factors. A common method for confounding effects control in eQTL mapping studies is the probabilistic estimation of expression residual (PEER) analysis. PEER analysis extracts PEER factors to serve as surrogates for confounding factors, which is further included in the subsequent eQTL mapping analysis. However, it is computationally challenging to determine the optimal number of PEER factors used for eQTL mapping. In particular, the standard approach to determine the optimal number of PEER factors examines one number at a time and chooses a number that optimizes eQTLs discovery. Unfortunately, this standard approach involves multiple repetitive eQTL mapping procedures that are computationally expensive, restricting its use in large-scale eQTL mapping studies that being collected today.ResultsHere, we present a simple and computationally scalable alternative, Effect size Correlation for COnfounding determination (ECCO), to determine the optimal number of PEER factors used for eQTL mapping studies. Instead of performing repetitive eQTL mapping, ECCO jointly applies differential expression analysis and Mendelian randomization analysis, leading to substantial computational savings. In simulations and real data applications, we show that ECCO identifies a similar number of PEER factors required for eQTL mapping analysis as the standard approach but is two orders of magnitude faster. The computational scalability of ECCO allows for optimized eQTL discovery across 48 GTEx tissues for the first time, yielding an overall 5.89% power gain on the number of eQTL harboring genes (eGenes) discovered as compared to the previous GTEx recommendation that does not attempt to determine tissue-specific optimal number of PEER factors.Availabilityand implementationOur method is implemented in the ECCO software, which, along with its GTEx mapping results, is freely available at www.xzlab.org/software.html. All R scripts used in this study are also available at this site.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8058772 | biostudies-literature