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1. Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease

   https://doi.org/10.7554/eLife.48476  

   D'Antonio, Matteo; Reyna, Joaquin; Jakubosky, David; Donovan, Margaret KR; Bonder, Marc-Jan; Matsui, Hiroko; Stegle, Oliver; Nariai, Naoki; D'Antonio-Chronowska, Agnieszka; Frazer, Kelly A (November 2019, eLife)

   The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes. 

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2. The transcriptome landscape of developing barley seeds

   https://doi.org/10.1093/plcell/koae095  

   Kovacik, Martin; Nowicka, Anna; Zwyrtková, Jana; Strejčková, Beáta; Vardanega, Isaia; Esteban, Eddi; Pasha, Asher; Kaduchová, Kateřina; Krautsova, Maryna; Červenková, Marie; et al (April 2024, The Plant Cell)

   Abstract Cereal grains are an important source of food and feed. To provide comprehensive spatiotemporal information about biological processes in developing seeds of cultivated barley (Hordeum vulgare L. subsp. vulgare), we performed a transcriptomic study of the embryo, endosperm, and seed maternal tissues collected from grains 4–32 days after pollination. Weighted gene co-expression network and motif enrichment analyses identified specific groups of genes and transcription factors (TFs) potentially regulating barley seed tissue development. We defined a set of tissue-specific marker genes and families of TFs for functional studies of the pathways controlling barley grain development. Assessing selected groups of chromatin regulators revealed that epigenetic processes are highly dynamic and likely play a major role during barley endosperm development. The repressive H3K27me3 modification is globally reduced in endosperm tissues and at specific genes related to development and storage compounds. Altogether, this atlas uncovers the complexity of developmentally regulated gene expression in developing barley grains. 

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3. A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

   https://doi.org/10.1093/nar/gkaa767  

   Shi, Xingjie; Chai, Xiaoran; Yang, Yi; Cheng, Qing; Jiao, Yuling; Chen, Haoyue; Huang, Jian; Yang, Can; Liu, Jin (September 2020, Nucleic Acids Research)

   Abstract Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWASs in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. Unfortunately, most existing multi-tissue methods focus on prioritization of candidate genes, and cannot directly infer the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWASs, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make full use of widely available GWASs summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and the false-positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWASs data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues. 

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4. Transcriptional profiling of human cartilage endplate cells identifies novel genes and cell clusters underlying degenerated and non-degenerated phenotypes

   https://doi.org/10.1186/s13075-023-03220-6  

   Kuchynsky, Kyle; Stevens, Patrick; Hite, Amy; Xie, William; Diop, Khady; Tang, Shirley; Pietrzak, Maciej; Khan, Safdar; Walter, Benjamin; Purmessur, Devina (December 2024, Arthritis Research & Therapy)

   Abstract BackgroundLow back pain is a leading cause of disability worldwide and is frequently attributed to intervertebral disc (IVD) degeneration. Though the contributions of the adjacent cartilage endplates (CEP) to IVD degeneration are well documented, the phenotype and functions of the resident CEP cells are critically understudied. To better characterize CEP cell phenotype and possible mechanisms of CEP degeneration, bulk and single-cell RNA sequencing of non-degenerated and degenerated CEP cells were performed. MethodsHuman lumbar CEP cells from degenerated (Thompson grade ≥ 4) and non-degenerated (Thompson grade ≤ 2) discs were expanded for bulk (N=4 non-degenerated,N=4 degenerated) and single-cell (N=1 non-degenerated,N=1 degenerated) RNA sequencing. Genes identified from bulk RNA sequencing were categorized by function and their expression in non-degenerated and degenerated CEP cells were compared. A PubMed literature review was also performed to determine which genes were previously identified and studied in the CEP, IVD, and other cartilaginous tissues. For single-cell RNA sequencing, different cell clusters were resolved using unsupervised clustering and functional annotation. Differential gene expression analysis and Gene Ontology, respectively, were used to compare gene expression and functional enrichment between cell clusters, as well as between non-degenerated and degenerated CEP samples. ResultsBulk RNA sequencing revealed 38 genes were significantly upregulated and 15 genes were significantly downregulated in degenerated CEP cells relative to non-degenerated cells (|fold change| ≥ 1.5). Of these, only 2 genes were previously studied in CEP cells, and 31 were previously studied in the IVD and other cartilaginous tissues. Single-cell RNA sequencing revealed 11 unique cell clusters, including multiple chondrocyte and progenitor subpopulations with distinct gene expression and functional profiles. Analysis of genes in the bulk RNA sequencing dataset showed that progenitor cell clusters from both samples were enriched in “non-degenerated” genes but not “degenerated” genes. For both bulk- and single-cell analyses, gene expression and pathway enrichment analyses highlighted several pathways that may regulate CEP degeneration, including transcriptional regulation, translational regulation, intracellular transport, and mitochondrial dysfunction. ConclusionsThis thorough analysis using RNA sequencing methods highlighted numerous differences between non-degenerated and degenerated CEP cells, the phenotypic heterogeneity of CEP cells, and several pathways of interest that may be relevant in CEP degeneration. 

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5. 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. 

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