More Like this

1. Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes

   https://doi.org/10.1093/bioinformatics/btx227  

   Duong, Dat ; Gai, Lisa ; Snir, Sagi ; Kang, Eun Yong ; Han, Buhm ; Sul, Jae Hoon ; Eskin, Eleazar ( July 2017 , Bioinformatics)

   There is recent interest in using gene expression data to contextualize findings from traditional genome-wide association studies (GWAS). Conditioned on a tissue, expression quantitative trait loci (eQTLs) are genetic variants associated with gene expression, and eGenes are genes whose expression levels are associated with genetic variants. eQTLs and eGenes provide great supporting evidence for GWAS hits and important insights into the regulatory pathways involved in many diseases. When a significant variant or a candidate gene identified by GWAS is also an eQTL or eGene, there is strong evidence to further study this variant or gene. Multi-tissue gene expression datasets like the Gene Tissue Expression (GTEx) data are used to find eQTLs and eGenes. Unfortunately, these datasets often have small sample sizes in some tissues. For this reason, there have been many meta-analysis methods designed to combine gene expression data across many tissues to increase power for finding eQTLs and eGenes. However, these existing techniques are not scalable to datasets containing many tissues, like the GTEx data. Furthermore, these methods ignore a biological insight that the same variant may be associated with the same gene across similar tissues.

   We introduce a meta-analysis model that addresses these problems in existing methods. We focus on the problem of finding eGenes in gene expression data from many tissues, and show that our model is better than other types of meta-analyses.

   Source code is at https://github.com/datduong/RECOV.

   Supplementary data are available at Bioinformatics online.

    

   more » « less
2. Supervised structural learning of semiparametric regression on high‐dimensional correlated covariates with applications to eQTL studies

   https://doi.org/10.1002/sim.9769  

   Liu, Wei ; Lin, Huazhen ; Liu, Li ; Ma, Yanyuan ; Wei, Ying ; Li, Yi ( May 2023 , Statistics in Medicine)

   Expression quantitative trait loci (eQTL) studies utilize regression models to explain the variance of gene expressions with genetic loci or single nucleotide polymorphisms (SNPs). However, regression models for eQTL are challenged by the presence of high dimensional non‐sparse and correlated SNPs with small effects, and nonlinear relationships between responses and SNPs. Principal component analyses are commonly conducted for dimension reduction without considering responses. Because of that, this non‐supervised learning method often does not work well when the focus is on discovery of the response‐covariate relationship. We propose a new supervised structural dimensional reduction method for semiparametric regression models with high dimensional and correlated covariates; we extract low‐dimensional latent features from a vast number of correlated SNPs while accounting for their relationships, possibly nonlinear, with gene expressions. Our model identifies important SNPs associated with gene expressions and estimates the association parameters via a likelihood‐based algorithm. A GTEx data application on a cancer related gene is presented with 18 novel eQTLs detected by our method. In addition, extensive simulations show that our method outperforms the other competing methods in bias, efficiency, and computational cost.

    

   more » « less
3. Estimation of cis-eQTL Effect Sizes Using a Log of Linear Model

   https://doi.org/10.1111/biom.12810  

   Palowitch, John ; Shabalin, Andrey ; Zhou, Yi-Hui ; Nobel, Andrew B. ; Wright, Fred A. ( October 2017 , Biometrics)

   The study of expression Quantitative Trait Loci (eQTL) is an important problem in genomics and biomedicine. While detection (testing) of eQTL associations has been widely studied, less work has been devoted to the estimation of eQTL effect size. To reduce false positives, detection methods frequently rely on linear modeling of rank-based normalized or log-transformed gene expression data. Unfortunately, these approaches do not correspond to the simplest model of eQTL action, and thus yield estimates of eQTL association that can be uninterpretable and inaccurate. In this article, we propose a new, log-of-linear model for eQTL action, termed ACME, that captures allelic contributions to cis-acting eQTLs in an additive fashion, yielding effect size estimates that correspond to a biologically coherent model of cis-eQTLs. We describe a non-linear least-squares algorithm to fit the model by maximum likelihood, and obtain corresponding p-values. We perform careful investigation of the model using a combination of simulated data and data from the Genotype Tissue Expression (GTEx) project. Our results reveal little evidence for dominance effects, a parsimonious result that accords with a simple biological model for allele-specific expression and supports use of the ACME model. We show that Type-I error is well-controlled under our approach in a realistic setting, so that rank-based normalizations are unnecessary. Furthermore, we show that such normalizations can be detrimental to power and estimation accuracy under the proposed model. We then show, through effect size analyses of whole-genome cis-eQTLs in the GTEx data, that using standard normalizations instead of ACME noticeably affects the ranking and sign of estimates.

    

   more » « less
4. Effective SNP ranking improves the performance of eQTL mapping

   https://doi.org/10.1002/gepi.22293  

   Jeng, X. Jessie ; Rhyne, Jacob ; Zhang, Teng ; Tzeng, Jung‐Ying ( March 2020 , Genetic Epidemiology)

   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 identifyingtrans‐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.

    

   more » « less
5. Integrative analysis of summary data from GWAS and eQTL studies implicates genes differentially expressed in Alzheimer’s disease

   https://doi.org/10.1186/s12864-022-08584-8  

   Lee, Brian ; Yao, Xiaohui ; Shen, Li ; for the Alzheimer’s Disease Neuroimaging Initiative ( June 2022 , BMC Genomics)

   Although genome-wide association studies (GWAS) have successfully located various genetic variants susceptible to Alzheimer’s Disease (AD), it is still unclear how specific variants interact with genes and tissues to elucidate pathologies associated with AD. Summary-data-based Mendelian Randomization (SMR) addresses this problem through an instrumental variable approach that integrates data from independent GWAS and expression quantitative trait locus (eQTL) studies in order to infer a causal effect of gene expression on a trait.

   Our study employed the SMR approach to integrate a set of meta-analytic cis-eQTL information from the Genotype-Tissue Expression (GTEx), CommonMind Consortium (CMC), and Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) consortiums with three sets of meta-analysis AD GWAS results.

   Our analysis identified twelve total gene probes (associated with twelve distinct genes) with a significant association with AD. Four of these genes survived a test of pleiotropy from linkage (the HEIDI test).Three of these genes – RP11-385F7.1, PRSS36, and AC012146.7 – have not yet been reported differentially expressed in the brain in the context of AD, and thus are the novel findings warranting further investigation.

    

   more » « less