More Like this

1. 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)

   Abstract 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
2. Efficient and effective control of confounding in eQTL mapping studies through joint differential expression and Mendelian randomization analyses

   https://doi.org/10.1093/bioinformatics/btaa715  

   Fan, Yue; Zhu, Huanhuan; Song, Yanyi; Peng, Qinke; Zhou, Xiang (February 2021, Bioinformatics)

   Robinson, Peter (Ed.)

   Abstract 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. 

   more » « less
3. Learning gene networks under SNP perturbation using SNP and allele-specific expression data

   https://doi.org/10.1101/2023.10.23.563661  

   Yoon, Jun Ho; Kim, Seyoung (October 2023, bioRxiv)

   Abstract Allele-specific expression quantification from RNA-seq reads provides opportunities to study the control of gene regulatory networks bycis-acting andtrans-acting genetic variants. Many existing methods performed a single-gene and single-SNP association analysis to identify expression quantitative trait loci (eQTLs), and placed the eQTLs against known gene networks for functional interpretation. Instead, we view eQTL data as a capture of the effects of perturbation of gene regulatory system by a large number of genetic variants and reconstruct a gene network perturbed by eQTLs. We introduce a statistical framework called CiTruss for simultaneously learning a gene network andcis-acting andtrans-acting eQTLs that perturb this network, given population allele-specific expression and SNP data. CiTruss uses a multi-level conditional Gaussian graphical model to modeltrans-acting eQTLs perturbing the expression of both alleles in gene network at the top level andcis-acting eQTLs perturbing the expression of each allele at the bottom level. We derive a transformation of this model that allows efficient learning for large-scale human data. Our analysis of the GTEx and LG×SM advanced intercross line mouse data for multiple tissue types with CiTruss provides new insights into genetics of gene regulation. CiTruss revealed that gene networks consist of local subnetworks over proximally located genes and global subnetworks over genes scattered across genome, and that several aspects of gene regulation by eQTLs such as the impact of genetic diversity, pleiotropy, tissue-specific gene regulation, and local and long-range linkage disequilibrium among eQTLs can be explained through these local and global subnetworks. 

   more » « less
4. Decategorified Heegaard Floer theory and actions of both E and F

   https://doi.org/10.4171/QT/204  

   Manion, Andrew (January 2024, Quantum Topology)

   We define larger variants of the vector spaces one obtains by decategorifying bordered (sutured) Heegaard Floer invariants of surfaces. We also define bimodule structures on these larger spaces that are similar to, but more elaborate than, the bimodule structures that arise from decategorifying the higher actions in bordered Heegaard Floer theory introduced by Rouquier and the author. In particular, these new bimodule structures involve actions of both odd generatorsEandFof\mathfrak{gl}(1|1), whereas the previous ones only involved actions ofE. Over\mathbb{F}_2, we show that the new bimodules satisfy the necessary gluing properties to give a 1+1 open-closed TQFT valued in graded algebras and bimodules up to isomorphism; in particular, unlike in previous related work, we have a gluing theorem when gluing surfaces along circles as well as intervals. Over the integers, we show that a similar construction gives two partially defined open-closed TQFTs with two different domains of definition depending on how parities are chosen for the bimodules. We formulate conjectures relating these open-closed TQFTs with the\mathfrak{psl}(1|1)Chern–Simons TQFT recently studied by Mikhaylov and Geer–Young. 

   more » « less
5. Telescoping bimodal latent Dirichlet allocation to identify expression QTLs across tissues

   https://doi.org/10.26508/lsa.202101297  

   Gewirtz, Ariel DH; Townes, F William; Engelhardt, Barbara E (August 2022, Life Science Alliance)

   Expression quantitative trait loci (eQTLs), or single-nucleotide polymorphisms that affect average gene expression levels, provide important insights into context-specific gene regulation. Classic eQTL analyses use one-to-one association tests, which test gene–variant pairs individually and ignore correlations induced by gene regulatory networks and linkage disequilibrium. Probabilistic topic models, such as latent Dirichlet allocation, estimate latent topics for a collection of count observations. Prior multimodal frameworks that bridge genotype and expression data assume matched sample numbers between modalities. However, many data sets have a nested structure where one individual has several associated gene expression samples and a single germline genotype vector. Here, we build a telescoping bimodal latent Dirichlet allocation (TBLDA) framework to learn shared topics across gene expression and genotype data that allows multiple RNA sequencing samples to correspond to a single individual’s genotype. By using raw count data, our model avoids possible adulteration via normalization procedures. Ancestral structure is captured in a genotype-specific latent space, effectively removing it from shared components. Using GTEx v8 expression data across 10 tissues and genotype data, we show that the estimated topics capture meaningful and robust biological signal in both modalities and identify associations within and across tissue types. We identify 4,645 cis-eQTLs and 995 trans-eQTLs by conducting eQTL mapping between the most informative features in each topic. Our TBLDA model is able to identify associations using raw sequencing count data when the samples in two separate data modalities are matched one-to-many, as is often the case in biological data. Our code is freely available at https://github.com/gewirtz/TBLDA . 

   more » « less