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Luo H, Zhang P, Zhang W, Zheng Y, Hao D, Shi Y, Niu Y, Song T, Li Y, Zhao S, Chen H, Xu T, He S. Recent positive selection signatures reveal phenotypic evolution in the Han Chinese population. Sci Bull (Beijing) 2023; 68:2391-2404. [PMID: 37661541 DOI: 10.1016/j.scib.2023.08.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/08/2023] [Accepted: 08/10/2023] [Indexed: 09/05/2023]
Abstract
Characterizing natural selection signatures and relationships with phenotype spectra is important for understanding human evolution and both biological and pathological mechanisms. Here, we identified 24 genetic loci under recent selection by analyzing rare singletons in 3946 high-depth whole-genome sequencing data of Han Chinese. The loci include immune-related gene regions (MHC cluster, IGH cluster, STING1, and PSG), alcohol metabolism-related gene regions (ADH1B, ALDH2, and ALDH3B2), and the olfactory perception gene OR4C16, in which the MHC cluster, ADH1B, and ALDH2 were also identified by TOPMed and WestLake Biobank. Among the signals, the IGH cluster is particularly interesting, in which the favored allele of variant 14_105737776_C_T (rs117518546, IgG1-G396R) promotes immune response, but also increases the risk of an autoimmune disease systemic lupus erythematosus (SLE). It is also surprising that our newly discovered ALDH3B2 evolved in the opposite direction to ALDH2 for alcohol metabolism. Besides monogenic traits, we found that multiple complex traits experienced polygenic adaptation. Particularly, multi-methods consistently revealed that lower blood pressure was favored in natural selection. Finally, we built a database named RePoS (recent positive selection, http://bigdata.ibp.ac.cn/RePoS/) to integrate and display multi-population selection signals. Our study extended our understanding of natural evolution and phenotype adaptation in Han Chinese as well as other populations.
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Affiliation(s)
- Huaxia Luo
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Department of Pediatrics, Peking University First Hospital, Beijing 100034, China
| | - Peng Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Wanyu Zhang
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yu Zheng
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Di Hao
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yirong Shi
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiwei Niu
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yanyan Li
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Shilei Zhao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; China National Center for Bioinformation, Beijing 100101, China
| | - Hua Chen
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China; China National Center for Bioinformation, Beijing 100101, China.
| | - Tao Xu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Shandong First Medical University & Shandong Academy of Medical Sciences, Taian 271016, China.
| | - Shunmin He
- Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
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52
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Lin C, Liu W, Jiang W, Zhao H. Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression. Biol Methods Protoc 2023; 8:bpad024. [PMID: 37901453 PMCID: PMC10599978 DOI: 10.1093/biomethods/bpad024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/08/2023] [Accepted: 10/16/2023] [Indexed: 10/31/2023] Open
Abstract
Genetic association signals have been mostly found in noncoding regions through genome-wide association studies (GWAS), suggesting the roles of gene expression regulation in human diseases and traits. However, there has been limited success in colocalizing expression quantitative trait locus (eQTL) with disease-associated variants. Mediated expression score regression (MESC) is a recently proposed method to quantify the proportion of trait heritability mediated by genetically regulated gene expressions (GReX). Applications of MESC to GWAS results have yielded low estimation of mediated heritability for many traits. As MESC relies on stringent independence assumptions between cis-eQTL effects, gene effects, and nonmediated SNP effects, it may fail to characterize the true relationships between those effect sizes, which leads to biased results. Here, we consider the robustness of MESC to investigate whether the low fraction of mediated heritability inferred by MESC reflects biological reality for complex traits or is an underestimation caused by model misspecifications. Our results suggest that MESC may lead to biased estimates of mediated heritability with misspecification of gene annotations leading to underestimation, whereas misspecification of SNP annotations may lead to overestimation. Furthermore, errors in eQTL effect estimates may lead to underestimation of mediated heritability.
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Affiliation(s)
- Chen Lin
- Department of Biostatistics, Yale University, New Haven, CT 06510, United States
| | - Wei Liu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
| | - Wei Jiang
- Department of Biostatistics, Yale University, New Haven, CT 06510, United States
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06510, United States
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, CT 06510, United States
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53
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Fair B, Najar CBA, Zhao J, Lozano S, Reilly A, Mossian G, Staley JP, Wang J, Li YI. Global impact of aberrant splicing on human gene expression levels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.13.557588. [PMID: 37745605 PMCID: PMC10515962 DOI: 10.1101/2023.09.13.557588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Alternative splicing (AS) is pervasive in human genes, yet the specific function of most AS events remains unknown. It is widely assumed that the primary function of AS is to diversify the proteome, however AS can also influence gene expression levels by producing transcripts rapidly degraded by nonsense-mediated decay (NMD). Currently, there are no precise estimates for how often the coupling of AS and NMD (AS-NMD) impacts gene expression levels because rapidly degraded NMD transcripts are challenging to capture. To better understand the impact of AS on gene expression levels, we analyzed population-scale genomic data in lymphoblastoid cell lines across eight molecular assays that capture gene regulation before, during, and after transcription and cytoplasmic decay. Sequencing nascent mRNA transcripts revealed frequent aberrant splicing of human introns, which results in remarkably high levels of mRNA transcripts subject to NMD. We estimate that ~15% of all protein-coding transcripts are degraded by NMD, and this estimate increases to nearly half of all transcripts for lowly-expressed genes with many introns. Leveraging genetic variation across cell lines, we find that GWAS trait-associated loci explained by AS are similarly likely to associate with NMD-induced expression level differences as with differences in protein isoform usage. Additionally, we used the splice-switching drug risdiplam to perturb AS at hundreds of genes, finding that ~3/4 of the splicing perturbations induce NMD. Thus, we conclude that AS-NMD substantially impacts the expression levels of most human genes. Our work further suggests that much of the molecular impact of AS is mediated by changes in protein expression levels rather than diversification of the proteome.
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Affiliation(s)
- Benjamin Fair
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Carlos Buen Abad Najar
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Junxing Zhao
- Department of Medicinal Chemistry, University of Kansas, Lawrence, KS 66047, USA
| | - Stephanie Lozano
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
- Present address: Center for Neuroscience, University of California Davis, Davis, CA 95618, USA
| | - Austin Reilly
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Gabriela Mossian
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Jonathan P Staley
- Department of Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL 60637, USA
| | - Jingxin Wang
- Department of Medicinal Chemistry, University of Kansas, Lawrence, KS 66047, USA
| | - Yang I Li
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
- Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
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54
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Johansen N, Somasundaram S, Travaglini KJ, Yanny AM, Shumyatcher M, Casper T, Cobbs C, Dee N, Ellenbogen R, Ferreira M, Goldy J, Guzman J, Gwinn R, Hirschstein D, Jorstad NL, Keene CD, Ko A, Levi BP, Ojemann JG, Pham T, Shapovalova N, Silbergeld D, Sulc J, Torkelson A, Tung H, Smith K, Lein ES, Bakken TE, Hodge RD, Miller JA. Interindividual variation in human cortical cell type abundance and expression. Science 2023; 382:eadf2359. [PMID: 37824649 DOI: 10.1126/science.adf2359] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 07/30/2023] [Indexed: 10/14/2023]
Abstract
Single-cell transcriptomic studies have identified a conserved set of neocortical cell types from small postmortem cohorts. We extended these efforts by assessing cell type variation across 75 adult individuals undergoing epilepsy and tumor surgeries. Nearly all nuclei map to one of 125 robust cell types identified in the middle temporal gyrus. However, we found interindividual variance in abundances and gene expression signatures, particularly in deep-layer glutamatergic neurons and microglia. A minority of donor variance is explainable by age, sex, ancestry, disease state, and cell state. Genomic variation was associated with expression of 150 to 250 genes for most cell types. This characterization of cellular variation provides a baseline for cell typing in health and disease.
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Affiliation(s)
| | | | | | | | | | - Tamara Casper
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Charles Cobbs
- Swedish Neuroscience Institute, Seattle,WA 98122, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Richard Ellenbogen
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Manuel Ferreira
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Junitta Guzman
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ryder Gwinn
- Swedish Neuroscience Institute, Seattle,WA 98122, USA
| | | | | | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98104, USA
| | - Andrew Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Boaz P Levi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jeffrey G Ojemann
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Daniel Silbergeld
- Department of Neurological Surgery, University of Washington, Seattle, WA 98104, USA
| | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Amy Torkelson
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Kimberly Smith
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
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55
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Mai J, Lu M, Gao Q, Zeng J, Xiao J. Transcriptome-wide association studies: recent advances in methods, applications and available databases. Commun Biol 2023; 6:899. [PMID: 37658226 PMCID: PMC10474133 DOI: 10.1038/s42003-023-05279-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Genome-wide association study has identified fruitful variants impacting heritable traits. Nevertheless, identifying critical genes underlying those significant variants has been a great task. Transcriptome-wide association study (TWAS) is an instrumental post-analysis to detect significant gene-trait associations focusing on modeling transcription-level regulations, which has made numerous progresses in recent years. Leveraging from expression quantitative loci (eQTL) regulation information, TWAS has advantages in detecting functioning genes regulated by disease-associated variants, thus providing insight into mechanisms of diseases and other phenotypes. Considering its vast potential, this review article comprehensively summarizes TWAS, including the methodology, applications and available resources.
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Affiliation(s)
- Jialin Mai
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Mingming Lu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyao Zeng
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Jingfa Xiao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
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56
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Aygün N, Krupa O, Mory J, Le B, Valone J, Liang D, Love MI, Stein JL. Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.30.555019. [PMID: 37693528 PMCID: PMC10491258 DOI: 10.1101/2023.08.30.555019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk post-mortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA-editing and alternative polyadenylation (APA), within a cell-type-specific population of human neural progenitors and neurons. More RNA-editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting genetically mediated post-transcriptional regulation during brain development lead to differences in brain function.
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Affiliation(s)
- Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica Mory
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Brandon Le
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jordan Valone
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael I. Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lead contact
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Parisien M, Buxbaum C, Granovsky Y, Yarnitsky D, Diatchenko L. Prospective Blood Transcriptomics Study in a Motor Vehicle Collision Cohort Identified a Protective Function of the SAMD15 Gene Against Chronic Pain. THE JOURNAL OF PAIN 2023; 24:1604-1616. [PMID: 37116672 DOI: 10.1016/j.jpain.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 04/05/2023] [Accepted: 04/20/2023] [Indexed: 04/30/2023]
Abstract
Traumatic brain injuries following motor vehicle collisions (MVCs) are ubiquitous. Surprisingly, there are no correlates between concussion impact force and long-term pain outcomes. To study the molecular underpinnings of chronic pain after MVC, we assembled a prospective cohort of 36 subjects that experienced MVC and suffered documented mild traumatic brain injuries. For each participant, a first blood sample was drawn within 72 hours of the collision, then a second one at the 6-month mark. Pain was also assessed at the second blood draw to determine if pain became chronic or resolved. Blood samples enabled transcriptomics analyses for immune cells. At the transcriptome-wide level, we found that Sterile Alpha Motif Domain Containing 15 (SAMD15) mRNA was significantly upregulated with time in subjects who resolved their pain whereas unregulated in those with persistent pain. Using several large publicly available datasets, such as the UK Biobank and the GTeX portal, we then linked elevated SAMD15 gene expression, elevated neutrophils cell counts, and decreased risk for chronic pain to increased dosage of the T allele at SNP rs4903580, situated within SAMD15's gene locus. The causality between the components of our model was established and supported by Mendelian randomization. Overall, our results support the role of SAMD15 as a potential gene effector for neutrophil-dependent chronic pain development. PERSPECTIVE: This article highlights the potential protective role of the SAMD15 gene against chronic pain following a mild traumatic brain injury. The expression of the gene is associated with a SNP rs4903580, which is itself associated with neutrophils counts as well as chronic pain in large genetic studies.
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Affiliation(s)
- Marc Parisien
- Faculty of Dental Medicine and Oral Health Sciences, Department of Anesthesia, Faculty of Medicine and Health Sciences, Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
| | - Chen Buxbaum
- Department of Neurology, Rambam Health Care Campus, and Clinical Neurophysiology Lab, Faculty of Medicine, Technion, Haifa, Israel
| | - Yelena Granovsky
- Department of Neurology, Rambam Health Care Campus, and Clinical Neurophysiology Lab, Faculty of Medicine, Technion, Haifa, Israel
| | - David Yarnitsky
- Department of Neurology, Rambam Health Care Campus, and Clinical Neurophysiology Lab, Faculty of Medicine, Technion, Haifa, Israel
| | - Luda Diatchenko
- Faculty of Dental Medicine and Oral Health Sciences, Department of Anesthesia, Faculty of Medicine and Health Sciences, Alan Edwards Centre for Research on Pain, McGill University, Montreal, Canada
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Xu C, Song LY, Zhou Y, Ma DN, Ding QS, Guo ZJ, Li J, Song SW, Zhang LD, Zheng HL. Integration of eQTL and GWAS analysis uncovers a genetic regulation of natural ionomic variation in Arabidopsis. PLANT CELL REPORTS 2023; 42:1473-1485. [PMID: 37516984 DOI: 10.1007/s00299-023-03042-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 06/12/2023] [Indexed: 08/01/2023]
Abstract
KEY MESSAGE This study provided important insights into the genetic architecture of variations in A. thaliana leaf ionome in a cell-type-specific manner. The functional interpretation of traits associated variants by expression quantitative trait loci (eQTL) analysis is usually performed in bulk tissue samples. While the regulation of gene expression is context-dependent, such as cell-type-specific manner. In this study, we estimated cell-type abundances from 728 bulk tissue samples using single-cell RNA-sequencing dataset, and performed cis-eQTL mapping to identify cell-type-interaction eQTL (cis-eQTLs(ci)) in A. thaliana. Also, we performed Genome-wide association studies (GWAS) analyses for 999 accessions to identify the genetic basis of variations in A. thaliana leaf ionome. As a result, a total of 5,664 unique eQTL genes and 15,038 unique cis-eQTLs(ci) were significant. The majority (62.83%) of cis-eQTLs(ci) were cell-type-specific eQTLs. Using colocalization, we uncovered one interested gene AT2G25590 in Phloem cell, encoding a kind of plant Tudor-like protein with possible chromatin-associated functions, which colocalized with the most significant cis-eQTL(ci) of a Mo-related locus (Chr2:10,908,806:A:C; P = 3.27 × 10-27). Furthermore, we prioritized eight target genes associated with AT2G25590, which were previously reported in regulating the concentration of Mo element in A. thaliana. This study revealed the genetic regulation of ionomic variations and provided a foundation for further studies on molecular mechanisms of genetic variants controlling the A. thaliana ionome.
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Affiliation(s)
- Chaoqun Xu
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
| | - Ling-Yu Song
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
| | - Ying Zhou
- School of Medicine, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, 361102, China
| | - Dong-Na Ma
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, School of Food Science and Technology, Jiangnan University, Wuxi, 214122, Jiangsu, China
| | - Qian-Su Ding
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
| | - Ze-Jun Guo
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
| | - Jing Li
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
| | - Shi-Wei Song
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
| | - Lu-Dan Zhang
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China
| | - Hai-Lei Zheng
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, 361104, China.
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59
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Carrillo-Perez F, Pizurica M, Ozawa MG, Vogel H, West RB, Kong CS, Herrera LJ, Shen J, Gevaert O. Synthetic whole-slide image tile generation with gene expression profile-infused deep generative models. CELL REPORTS METHODS 2023; 3:100534. [PMID: 37671024 PMCID: PMC10475789 DOI: 10.1016/j.crmeth.2023.100534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/10/2023] [Accepted: 06/22/2023] [Indexed: 09/07/2023]
Abstract
In this work, we propose an approach to generate whole-slide image (WSI) tiles by using deep generative models infused with matched gene expression profiles. First, we train a variational autoencoder (VAE) that learns a latent, lower-dimensional representation of multi-tissue gene expression profiles. Then, we use this representation to infuse generative adversarial networks (GANs) that generate lung and brain cortex tissue tiles, resulting in a new model that we call RNA-GAN. Tiles generated by RNA-GAN were preferred by expert pathologists compared with tiles generated using traditional GANs, and in addition, RNA-GAN needs fewer training epochs to generate high-quality tiles. Finally, RNA-GAN was able to generalize to gene expression profiles outside of the training set, showing imputation capabilities. A web-based quiz is available for users to play a game distinguishing real and synthetic tiles: https://rna-gan.stanford.edu/, and the code for RNA-GAN is available here: https://github.com/gevaertlab/RNA-GAN.
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Affiliation(s)
- Francisco Carrillo-Perez
- Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, School of Medicine, 1265 Welch Road, Stanford, CA 94305-547, USA
- Computer Engineering, Automatics and Robotics Department, University of Granada, C. Periodista Daniel Saucedo Aranda, s/n, Granada, 18014 Granada, Spain
| | - Marija Pizurica
- Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, School of Medicine, 1265 Welch Road, Stanford, CA 94305-547, USA
- Internet Technology and Data Science Lab (IDLab), Ghent University, Technologiepark-Zwijnaarde 126, Gent, 9052 Gent, Belgium
| | - Michael G. Ozawa
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Dr, Palo Alto, CA 94304, USA
| | - Hannes Vogel
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Dr, Palo Alto, CA 94304, USA
| | - Robert B. West
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Dr, Palo Alto, CA 94304, USA
| | - Christina S. Kong
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Dr, Palo Alto, CA 94304, USA
| | - Luis Javier Herrera
- Computer Engineering, Automatics and Robotics Department, University of Granada, C. Periodista Daniel Saucedo Aranda, s/n, Granada, 18014 Granada, Spain
| | - Jeanne Shen
- Department of Pathology, Stanford University School of Medicine, 300 Pasteur Dr, Palo Alto, CA 94304, USA
| | - Olivier Gevaert
- Stanford Center for Biomedical Informatics Research (BMIR), Stanford University, School of Medicine, 1265 Welch Road, Stanford, CA 94305-547, USA
- Department of Biomedical Data Science, Stanford University, School of Medicine, Medical School Office Building (MSOB), 1265 Welch Road, Stanford, CA 94305-547, USA
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60
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Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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61
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Luo R, Yan J, Oh JW, Xi W, Shigaki D, Wong W, Cho HS, Murphy D, Cutler R, Rosen BP, Pulecio J, Yang D, Glenn RA, Chen T, Li QV, Vierbuchen T, Sidoli S, Apostolou E, Huangfu D, Beer MA. Dynamic network-guided CRISPRi screen identifies CTCF-loop-constrained nonlinear enhancer gene regulatory activity during cell state transitions. Nat Genet 2023; 55:1336-1346. [PMID: 37488417 PMCID: PMC11012226 DOI: 10.1038/s41588-023-01450-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 06/20/2023] [Indexed: 07/26/2023]
Abstract
Comprehensive enhancer discovery is challenging because most enhancers, especially those contributing to complex diseases, have weak effects on gene expression. Our gene regulatory network modeling identified that nonlinear enhancer gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Using human embryonic stem cell definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. We discovered a comprehensive set of enhancers for each of the core endoderm-specifying transcription factors. Many enhancers had strong effects mid-transition but weak effects post-transition, consistent with the nonlinear temporal responses to enhancer perturbation predicted by the modeling. Integrating three-dimensional genomic information, we were able to develop a CTCF-loop-constrained Interaction Activity model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and systematic enhancer discovery in both normal and pathological cell state transitions.
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Affiliation(s)
- Renhe Luo
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Jielin Yan
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Jin Woo Oh
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Wang Xi
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Dustin Shigaki
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Wilfred Wong
- Computational & Systems Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Hyein S Cho
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Dylan Murphy
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
- Department of Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Ronald Cutler
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Bess P Rosen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Julian Pulecio
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Dapeng Yang
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Rachel A Glenn
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Weill Cornell Graduate School of Medical Sciences, Weill Cornell Medicine, New York City, NY, USA
| | - Tingxu Chen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Qing V Li
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
- Louis V. Gerstner Jr. Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, New York City, NY, USA
| | - Thomas Vierbuchen
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA
| | - Simone Sidoli
- Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Effie Apostolou
- Department of Medicine, Weill Cornell Medicine, New York City, NY, USA
| | - Danwei Huangfu
- Developmental Biology Program, Sloan Kettering Institute, New York City, NY, USA.
| | - Michael A Beer
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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62
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Gaulton KJ, Preissl S, Ren B. Interpreting non-coding disease-associated human variants using single-cell epigenomics. Nat Rev Genet 2023; 24:516-534. [PMID: 37161089 PMCID: PMC10629587 DOI: 10.1038/s41576-023-00598-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 05/11/2023]
Abstract
Genome-wide association studies (GWAS) have linked hundreds of thousands of sequence variants in the human genome to common traits and diseases. However, translating this knowledge into a mechanistic understanding of disease-relevant biology remains challenging, largely because such variants are predominantly in non-protein-coding sequences that still lack functional annotation at cell-type resolution. Recent advances in single-cell epigenomics assays have enabled the generation of cell type-, subtype- and state-resolved maps of the epigenome in heterogeneous human tissues. These maps have facilitated cell type-specific annotation of candidate cis-regulatory elements and their gene targets in the human genome, enhancing our ability to interpret the genetic basis of common traits and diseases.
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Affiliation(s)
- Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego School of Medicine, La Jolla, CA, USA.
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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63
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Chung A, Reilly MP, Bauer RC. ADAMTS7: a Novel Therapeutic Target in Atherosclerosis. Curr Atheroscler Rep 2023; 25:447-455. [PMID: 37354304 DOI: 10.1007/s11883-023-01115-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/01/2023] [Indexed: 06/26/2023]
Abstract
PURPOSE OF REVIEW Genome-wide association studies have repeatedly linked the metalloproteinase ADAMTS7 to coronary artery disease. Here we aim to highlight recent findings surrounding the human genetics of ADAMTS7, novel mouse models that investigate ADAMTS7 function, and potential substrates of ADAMTS7 cleavage. RECENT FINDINGS Recent genome-wide association studies in coronary artery disease have replicated the GWAS signal for ADAMTS7 and shown that the signal holds true even across different ethnic groups. However, the direction of effect in humans remains unclear. A recent novel mouse model revealed that the proatherogenicity of ADAMTS7 is derived from its catalytic functions, while at the translational level, vaccinating mice against ADAMTS7 reduced atherosclerosis. Finally, in vitro proteomics approaches have identified extracellular matrix proteins as candidate substrates that may be causal for the proatherogenicity of ADAMTS7. ADAMTS7 represents an enticing target for therapeutic intervention. The recent studies highlighted here have replicated prior findings, confirming the genetic link between ADAMTS7 and atherosclerosis, while providing further evidence in mice that ADAMTS7 is a targetable proatherogenic enzyme.
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Affiliation(s)
- Allen Chung
- Cardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University, New York, NY, USA
| | - Muredach P Reilly
- Cardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University, New York, NY, USA
- Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA
| | - Robert C Bauer
- Cardiometabolic Genomics Program, Division of Cardiology, Department of Medicine, Columbia University, New York, NY, USA.
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64
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Yu L, Xu L, Chu H, Peng J, Sacharidou A, Hsieh HH, Weinstock A, Khan S, Ma L, Durán JGB, McDonald J, Nelson ER, Park S, McDonnell DP, Moore KJ, Huang LJS, Fisher EA, Mineo C, Huang L, Shaul PW. Macrophage-to-endothelial cell crosstalk by the cholesterol metabolite 27HC promotes atherosclerosis in male mice. Nat Commun 2023; 14:4101. [PMID: 37491347 PMCID: PMC10368733 DOI: 10.1038/s41467-023-39586-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 06/20/2023] [Indexed: 07/27/2023] Open
Abstract
Hypercholesterolemia and vascular inflammation are key interconnected contributors to the pathogenesis of atherosclerosis. How hypercholesterolemia initiates vascular inflammation is poorly understood. Here we show in male mice that hypercholesterolemia-driven endothelial activation, monocyte recruitment and atherosclerotic lesion formation are promoted by a crosstalk between macrophages and endothelial cells mediated by the cholesterol metabolite 27-hydroxycholesterol (27HC). The pro-atherogenic actions of macrophage-derived 27HC require endothelial estrogen receptor alpha (ERα) and disassociation of the cytoplasmic scaffolding protein septin 11 from ERα, leading to extranuclear ERα- and septin 11-dependent activation of NF-κB. Furthermore, pharmacologic inhibition of cyp27a1, which generates 27HC, affords atheroprotection by reducing endothelial activation and monocyte recruitment. These findings demonstrate cell-to-cell communication by 27HC, and identify a major causal linkage between the hypercholesterolemia and vascular inflammation that partner to promote atherosclerosis. Interventions interrupting this linkage may provide the means to blunt vascular inflammation without impairing host defense to combat the risk of atherosclerotic cardiovascular disease that remains despite lipid-lowering therapies.
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Affiliation(s)
- Liming Yu
- Center for Pulmonary and Vascular Biology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lin Xu
- Quantitative Biomedical Research Center and Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Haiyan Chu
- Center for Pulmonary and Vascular Biology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Jun Peng
- Center for Pulmonary and Vascular Biology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Anastasia Sacharidou
- Center for Pulmonary and Vascular Biology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Hsi-Hsien Hsieh
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Ada Weinstock
- Department of Medicine, New York University School of Medicine, New York, NY, 10016, USA
- Department of Medicine, University of Chicago School of Medicine, Chicago, IL, 60637, USA
| | - Sohaib Khan
- University of Cincinnati Cancer Center, Cincinnati, OH, 45267, USA
| | - Liqian Ma
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | | | - Jeffrey McDonald
- Department of Molecular Genetics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Erik R Nelson
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
| | - Sunghee Park
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Donald P McDonnell
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC, 27710, USA
| | - Kathryn J Moore
- Department of Medicine, New York University School of Medicine, New York, NY, 10016, USA
| | - Lily Jun-Shen Huang
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Edward A Fisher
- Department of Medicine, New York University School of Medicine, New York, NY, 10016, USA
| | - Chieko Mineo
- Center for Pulmonary and Vascular Biology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Department of Cell Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Linzhang Huang
- State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, 200433, China.
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Fudan University, Shanghai, 200433, China.
- Institute of Metabolism and Integrative Biology, Fudan University, Shanghai, 200433, China.
| | - Philip W Shaul
- Center for Pulmonary and Vascular Biology, Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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65
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Przytycki PF. Uncovering the genetic circuits that drive diseases. NATURE COMPUTATIONAL SCIENCE 2023; 3:584-585. [PMID: 38177750 DOI: 10.1038/s43588-023-00475-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Affiliation(s)
- Pawel F Przytycki
- Faculty of Computing & Data Sciences, Boston University, Boston, MA, USA.
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66
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Song L, Sun X, Qi T, Yang J. Mixed model-based deconvolution of cell-state abundances (MeDuSA) along a one-dimensional trajectory. NATURE COMPUTATIONAL SCIENCE 2023; 3:630-643. [PMID: 38177744 PMCID: PMC10766563 DOI: 10.1038/s43588-023-00487-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 06/13/2023] [Indexed: 01/06/2024]
Abstract
Deconvoluting cell-state abundances from bulk RNA-sequencing data can add considerable value to existing data, but achieving fine-resolution and high-accuracy deconvolution remains a challenge. Here we introduce MeDuSA, a mixed model-based method that leverages single-cell RNA-sequencing data as a reference to estimate cell-state abundances along a one-dimensional trajectory in bulk RNA-sequencing data. The advantage of MeDuSA lies primarily in estimating cell abundance in each state while fitting the remaining cells of the same type individually as random effects. Extensive simulations and real-data benchmark analyses demonstrate that MeDuSA greatly improves the estimation accuracy over existing methods for one-dimensional trajectories. Applying MeDuSA to cohort-level RNA-sequencing datasets reveals associations of cell-state abundances with disease or treatment conditions and cell-state-dependent genetic control of transcription. Our study provides a high-accuracy and fine-resolution method for cell-state deconvolution along a one-dimensional trajectory and demonstrates its utility in characterizing the dynamics of cell states in various biological processes.
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Affiliation(s)
- Liyang Song
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Xiwei Sun
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Ting Qi
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China.
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang, China.
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67
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Xiao Y, Wang J, Li J, Zhang P, Li J, Zhou Y, Zhou Q, Chen M, Sheng X, Liu Z, Han X, Guo G. An analytical framework for decoding cell type-specific genetic variation of gene regulation. Nat Commun 2023; 14:3884. [PMID: 37391400 PMCID: PMC10313894 DOI: 10.1038/s41467-023-39538-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 06/16/2023] [Indexed: 07/02/2023] Open
Abstract
A deeper understanding of genetic regulation and functional mechanisms underlying genetic associations with complex traits and diseases is impeded by cellular heterogeneity and linkage disequilibrium. To address these limits, we introduce Huatuo, a framework to decode genetic variation of gene regulation at cell type and single-nucleotide resolutions by integrating deep-learning-based variant predictions with population-based association analyses. We apply Huatuo to generate a comprehensive cell type-specific genetic variation landscape across human tissues and further evaluate their potential roles in complex diseases and traits. Finally, we show that Huatuo's inferences permit prioritizations of driver cell types associated with complex traits and diseases and allow for systematic insights into the mechanisms of phenotype-causal genetic variation.
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Affiliation(s)
- Yanyu Xiao
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, 311121, China
| | - Jingjing Wang
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China.
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, 311121, China.
| | - Jiaqi Li
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China
| | - Peijing Zhang
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, 311121, China
| | - Jingyu Li
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China
| | - Yincong Zhou
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310003, China
| | - Qing Zhou
- Life Sciences Institute, Zhejiang University, Hang Zhou, Zhejiang, 310058, China
| | - Ming Chen
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310003, China
| | - Xin Sheng
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, 311121, China
| | - Zhihong Liu
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, 311121, China
| | - Xiaoping Han
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China.
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang, 310058, China.
| | - Guoji Guo
- Center for Stem Cell and Regenerative Medicine, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China.
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, Zhejiang, 311121, China.
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang, 310058, China.
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 314400, China.
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68
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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Craig Johnson W, Berg DVD, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Graham Barr R, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546528. [PMID: 37425716 PMCID: PMC10326995 DOI: 10.1101/2023.06.26.546528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA
- Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital LMU Munich, Munich, Germany
- Computational Health Center, Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Brielin C. Brown
- New York Genome Center, New York, NY, USA
- Data Science Institute, Columbia University, New York, NY, USA
| | | | - Russell P. Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D. Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D. Smith
- Northwest Genomic Center, University of Washington, Seattle, WA, USA
| | - Thomas W. Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S. Rich
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R. Graham Barr
- Epidemiology and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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69
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Advani J, Corso-Diaz X, Kwicklis M, van Asten F, Ratnapriya R, Mehta P, Hamel A, Mahrotra S, Segrè A, Kiel C, Strunz T, Weber B, Chew E, Hernandez D, Montezuma S, Ferrington D, Swaroop A. QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration. RESEARCH SQUARE 2023:rs.3.rs-3011096. [PMID: 37398472 PMCID: PMC10312909 DOI: 10.21203/rs.3.rs-3011096/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
DNA methylation (DNAm) provides a crucial epigenetic mark linking genetic variations to environmental influence. We analyzed array-based DNAm profiles of 160 human retinas with co-measured RNA-seq and > 8 million genetic variants, uncovering sites of genetic regulation in cis (37,453 mQTLs and 12,505 eQTLs) and 13,747 eQTMs (DNAm loci affecting gene expression), with over one-third specific to the retina. mQTLs and eQTMs show non-random distribution and enrichment of biological processes related to synapse, mitochondria, and catabolism. Summary data-based Mendelian randomization and colocalization analyses identify 87 target genes where methylation and gene-expression changes likely mediate the genotype effect on age-related macular degeneration (AMD). Integrated pathway analysis reveals epigenetic regulation of immune response and metabolism including the glutathione pathway and glycolysis. Our study thus defines key roles of genetic variations driving methylation changes, prioritizes epigenetic control of gene expression, and suggests frameworks for regulation of AMD pathology by genotype-environment interaction in retina.
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Affiliation(s)
| | | | | | | | | | - Puja Mehta
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Andrew Hamel
- Department of Ophthalmology, Massachusetts Eye and Ear
| | | | | | | | | | | | - Emily Chew
- National Eye Institute/National Institutes of Health
| | | | | | | | - Anand Swaroop
- National Eye Institute, National Institutes of Health
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Benaglio P, Newsome J, Han JY, Chiou J, Aylward A, Corban S, Miller M, Okino ML, Kaur J, Preissl S, Gorkin DU, Gaulton KJ. Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex immune trait variants using single nucleus ATAC-seq in peripheral blood. PLoS Genet 2023; 19:e1010759. [PMID: 37289818 DOI: 10.1371/journal.pgen.1010759] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 04/25/2023] [Indexed: 06/10/2023] Open
Abstract
Gene regulation is highly cell type-specific and understanding the function of non-coding genetic variants associated with complex traits requires molecular phenotyping at cell type resolution. In this study we performed single nucleus ATAC-seq (snATAC-seq) and genotyping in peripheral blood mononuclear cells from 13 individuals. Clustering chromatin accessibility profiles of 96,002 total nuclei identified 17 immune cell types and sub-types. We mapped chromatin accessibility QTLs (caQTLs) in each immune cell type and sub-type using individuals of European ancestry which identified 6,901 caQTLs at FDR < .10 and 4,220 caQTLs at FDR < .05, including those obscured from assays of bulk tissue such as with divergent effects on different cell types. For 3,941 caQTLs we further annotated putative target genes of variant activity using single cell co-accessibility, and caQTL variants were significantly correlated with the accessibility level of linked gene promoters. We fine-mapped loci associated with 16 complex immune traits and identified immune cell caQTLs at 622 candidate causal variants, including those with cell type-specific effects. At the 6q15 locus associated with type 1 diabetes, in line with previous reports, variant rs72928038 was a naïve CD4+ T cell caQTL linked to BACH2 and we validated the allelic effects of this variant on regulatory activity in Jurkat T cells. These results highlight the utility of snATAC-seq for mapping genetic effects on accessible chromatin in specific cell types.
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Affiliation(s)
- Paola Benaglio
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Jacklyn Newsome
- Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, California, United States of America
| | - Jee Yun Han
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Joshua Chiou
- Biomedical Sciences Graduate Program. University of California San Diego, San Diego, California, United States of America
| | - Anthony Aylward
- Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, California, United States of America
| | - Sierra Corban
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Michael Miller
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Mei-Lin Okino
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Jaspreet Kaur
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - David U Gorkin
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
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71
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López Rodríguez M, Arasu UT, Kaikkonen MU. Exploring the genetic basis of coronary artery disease using functional genomics. Atherosclerosis 2023; 374:87-98. [PMID: 36801133 DOI: 10.1016/j.atherosclerosis.2023.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 02/05/2023]
Abstract
Genome-wide Association Studies (GWAS) have identified more than 300 loci associated with coronary artery disease (CAD), defining the genetic risk map of the disease. However, the translation of the association signals into biological-pathophysiological mechanisms constitute a major challenge. Through a group of examples of studies focused on CAD, we discuss the rationale, basic principles and outcomes of the main methodologies implemented to prioritize and characterize causal variants and their target genes. Additionally, we highlight the strategies as well as the current methods that integrate association and functional genomics data to dissect the cellular specificity underlying the complexity of disease mechanisms. Despite the limitations of existing approaches, the increasing knowledge generated through functional studies helps interpret GWAS maps and opens novel avenues for the clinical usability of association data.
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Affiliation(s)
- Maykel López Rodríguez
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland; Department of Pathology and Laboratory Medicine, University of California, UCLA, Los Angeles, USA.
| | - Uma Thanigai Arasu
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland
| | - Minna U Kaikkonen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, 70211, Finland.
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72
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Kachuri L, Mak ACY, Hu D, Eng C, Huntsman S, Elhawary JR, Gupta N, Gabriel S, Xiao S, Keys KL, Oni-Orisan A, Rodríguez-Santana JR, LeNoir MA, Borrell LN, Zaitlen NA, Williams LK, Gignoux CR, Burchard EG, Ziv E. Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture. Nat Genet 2023; 55:952-963. [PMID: 37231098 PMCID: PMC10260401 DOI: 10.1038/s41588-023-01377-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 03/21/2023] [Indexed: 05/27/2023]
Abstract
We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Donglei Hu
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Scott Huntsman
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Jennifer R Elhawary
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Shujie Xiao
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
| | - Kevin L Keys
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Berkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA
| | - Akinyemi Oni-Orisan
- Department of Clinical Pharmacy, University of California, San Francisco, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Luisa N Borrell
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York, New York, NY, USA
| | - Noah A Zaitlen
- Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - L Keoki Williams
- Center for Individualized and Genomic Medicine Research, Henry Ford Health System, Detroit, MI, USA
- Department of Internal Medicine, Henry Ford Health System, Detroit, MI, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Esteban González Burchard
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Elad Ziv
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA.
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73
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Luo J, Wu X, Cheng Y, Chen G, Wang J, Song X. Expression quantitative trait locus studies in the era of single-cell omics. Front Genet 2023; 14:1182579. [PMID: 37284065 PMCID: PMC10239882 DOI: 10.3389/fgene.2023.1182579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 04/26/2023] [Indexed: 06/08/2023] Open
Abstract
Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.
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Affiliation(s)
- Jie Luo
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xinyi Wu
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Yuan Cheng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Guang Chen
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Jian Wang
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xijiao Song
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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74
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Pagadala M, Sears TJ, Wu VH, Pérez-Guijarro E, Kim H, Castro A, Talwar JV, Gonzalez-Colin C, Cao S, Schmiedel BJ, Goudarzi S, Kirani D, Au J, Zhang T, Landi T, Salem RM, Morris GP, Harismendy O, Patel SP, Alexandrov LB, Mesirov JP, Zanetti M, Day CP, Fan CC, Thompson WK, Merlino G, Gutkind JS, Vijayanand P, Carter H. Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response. Nat Commun 2023; 14:2744. [PMID: 37173324 PMCID: PMC10182072 DOI: 10.1038/s41467-023-38271-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.
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Affiliation(s)
- Meghana Pagadala
- Biomedical Sciences Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Timothy J Sears
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Victoria H Wu
- Department of Pharmacology, UCSD Moores Cancer Center, La Jolla, CA, 92093, USA
| | - Eva Pérez-Guijarro
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Hyo Kim
- Undergraduate Bioengineering Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Andrea Castro
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - James V Talwar
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Steven Cao
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | | | | | - Divya Kirani
- Undergraduate Biology and Bioinformatics Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jessica Au
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Rany M Salem
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | - Gerald P Morris
- Department of Pathology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Olivier Harismendy
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego School of Medicine, La Jolla, CA, 92093, USA
| | - Sandip Pravin Patel
- Center for Personalized Cancer Therapy, Division of Hematology and Oncology, UC San Diego Moores Cancer Center, San Diego, CA, 92037, USA
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jill P Mesirov
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Maurizio Zanetti
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA
- The Laboratory of Immunology and Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Wesley K Thompson
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | - Glenn Merlino
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - J Silvio Gutkind
- Department of Pharmacology, UCSD Moores Cancer Center, La Jolla, CA, 92093, USA
| | | | - Hannah Carter
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA, 92093, USA.
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Tan WX, Sim X, Khoo CM, Teo AKK. Prioritization of genes associated with type 2 diabetes mellitus for functional studies. Nat Rev Endocrinol 2023:10.1038/s41574-023-00836-1. [PMID: 37169822 DOI: 10.1038/s41574-023-00836-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/28/2023] [Indexed: 05/13/2023]
Abstract
Existing therapies for type 2 diabetes mellitus (T2DM) show limited efficacy or have adverse effects. Numerous genetic variants associated with T2DM have been identified, but progress in translating these findings into potential drug targets has been limited. Here, we describe the tools and platforms available to identify effector genes from T2DM-associated coding and non-coding variants and prioritize them for functional studies. We discuss QSER1 and SLC12A8 as examples of genes that have been identified as possible T2DM candidate genes using these tools and platforms. We suggest further approaches, including the use of sequencing data with increased sample size and ethnic diversity, single-cell omics data for analyses, glycaemic trait associations to predict gene function and, potentially, human induced pluripotent stem cell 'village' cultures, to strengthen current gene functionalization workflows. Effective prioritization of T2DM-associated genes for experimental validation could expedite our understanding of the genetic mechanisms responsible for T2DM to facilitate the use of precision medicine in its treatment.
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Affiliation(s)
- Wei Xuan Tan
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Chin Meng Khoo
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Adrian K K Teo
- Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
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76
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Marrella MA, Biase FH. Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing. J Anim Sci Biotechnol 2023; 14:62. [PMID: 37143150 PMCID: PMC10161580 DOI: 10.1186/s40104-023-00861-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 03/05/2023] [Indexed: 05/06/2023] Open
Abstract
BACKGROUND A gap currently exists between genetic variants and the underlying cell and tissue biology of a trait, and expression quantitative trait loci (eQTL) studies provide important information to help close that gap. However, two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution. Multiple pipelines have been suggested to address this. For instance, the most recent analysis of the human and farm Genotype-Tissue Expression (GTEx) project proposes using trimmed means of M-values (TMM) to normalize the data followed by an inverse normal transformation. RESULTS In this study, we reasoned that eQTL analysis could be carried out using the same framework used for differential gene expression (DGE), which uses a negative binomial model, a statistical test feasible for count data. Using the GTEx framework, we identified 35 significant eQTLs (P < 5 × 10-8) following the ANOVA model and 39 significant eQTLs (P < 5 × 10-8) following the additive model. Using a differential gene expression framework, we identified 930 and six significant eQTLs (P < 5 × 10-8) following an analytical framework equivalent to the ANOVA and additive model, respectively. When we compared the two approaches, there was no overlap of significant eQTLs between the two frameworks. Because we defined specific contrasts, we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles. Yet, these were not identified by the GTEx framework. CONCLUSIONS Our results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed. Our proposed approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.
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Affiliation(s)
- Mackenzie A Marrella
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Fernando H Biase
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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Li S, Schmid KT, de Vries DH, Korshevniuk M, Losert C, Oelen R, van Blokland IV, Groot HE, Swertz MA, van der Harst P, Westra HJ, van der Wijst MGP, Heinig M, Franke L. Identification of genetic variants that impact gene co-expression relationships using large-scale single-cell data. Genome Biol 2023; 24:80. [PMID: 37072791 PMCID: PMC10111756 DOI: 10.1186/s13059-023-02897-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 03/16/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies show how genetic variants affect downstream gene expression. Single-cell data allows reconstruction of personalized co-expression networks and therefore the identification of SNPs altering co-expression patterns (co-expression QTLs, co-eQTLs) and the affected upstream regulatory processes using a limited number of individuals. RESULTS We conduct a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets using a novel filtering strategy followed by a permutation-based multiple testing approach. Before the analysis, we evaluate the co-expression patterns required for co-eQTL identification using different external resources. We identify a robust set of cell-type-specific co-eQTLs for 72 independent SNPs affecting 946 gene pairs. These co-eQTLs are replicated in a large bulk cohort and provide novel insights into how disease-associated variants alter regulatory networks. One co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases, affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell activation and autoimmune disease. Among these genes, we identify enrichment for targets of five T-cell-activation-related transcription factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases. CONCLUSION Our co-eQTL results highlight the importance of studying context-specific gene regulation to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy and technical guidelines will facilitate future co-eQTL identification, further elucidating unknown disease mechanisms.
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Affiliation(s)
- Shuang Li
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Katharina T Schmid
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany
| | - Dylan H de Vries
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Maryna Korshevniuk
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Corinna Losert
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany
| | - Roy Oelen
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | - Irene V van Blokland
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Hilde E Groot
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Morris A Swertz
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
- Genomics Coordination Center, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Harm-Jan Westra
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Matthias Heinig
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Computer Science, School of Computation, Information and Technology, Technical University Munich, Munich, Germany.
- Munich Heart Alliance, DZHK (German Center for Cardiovascular Research), Munich, Germany.
| | - Lude Franke
- Genetics Department, University Medical Center Groningen, Groningen, the Netherlands.
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Carlberg C, Raczyk M, Zawrotna N. Vitamin D: A master example of nutrigenomics. Redox Biol 2023; 62:102695. [PMID: 37043983 PMCID: PMC10119805 DOI: 10.1016/j.redox.2023.102695] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023] Open
Abstract
Nutrigenomics attempts to characterize and integrate the relation between dietary molecules and gene expression on a genome-wide level. One of the biologically active nutritional compounds is vitamin D3, which activates via its metabolite 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3) the nuclear receptor VDR (vitamin D receptor). Vitamin D3 can be synthesized endogenously in our skin, but since we spend long times indoors and often live at higher latitudes where for many winter months UV-B radiation is too low, it became a true vitamin. The ligand-inducible transcription factor VDR is expressed in the majority of human tissues and cell types, where it modulates the epigenome at thousands of genomic sites. In a tissue-specific fashion this results in the up- and downregulation of primary vitamin D target genes, some of which are involved in attenuating oxidative stress. Vitamin D affects a wide range of physiological functions including the control of metabolism, bone formation and immunity. In this review, we will discuss how the epigenome- and transcriptome-wide effects of 1,25(OH)2D3 and its receptor VDR serve as a master example in nutrigenomics. In this context, we will outline the basis of a mechanistic understanding for personalized nutrition with vitamin D3.
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79
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Li J, Hou H, Sun J, Ding Z, Xu Y, Li G. Systematic pan-cancer analysis identifies transmembrane protein 158 as a potential therapeutic, prognostic and immunological biomarker. Funct Integr Genomics 2023; 23:105. [PMID: 36977915 DOI: 10.1007/s10142-023-01032-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/12/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023]
Abstract
The purpose of this study was to investigate the expression significance, predictive value, immunologic function, and biological role of transmembrane protein 158 (TMEM158) in the development of pan-cancer. To achieve this, we utilized data from multiple databases, including TCGA, GTEx, GEPIA, and TIMER, to collect gene transcriptome, patient prognosis, and tumor immune data. We evaluated the association of TMEM158 with patient prognosis, tumor mutational burden (TMB), and microsatellite instability (MSI) in pan-cancer samples. We performed immune checkpoint gene co-expression analysis and gene set enrichment analysis (GSEA) to better understand the immunologic function of TMEM158. Our findings revealed that TMEM158 was significantly differentially expressed between most types of cancer tissues and their adjacent normal tissues and was associated with prognosis. Moreover, TMEM158 was significantly correlated with TMB, MSI, and tumor immune cell infiltration in multiple cancers. Co-expression analysis of immune checkpoint genes showed that TMEM158 was related to the expression of several common immune checkpoint genes, especially CTLA4 and LAG3. Gene enrichment analysis further revealed that TMEM158 was involved in multiple immune-related biological pathways in pan-cancer. Overall, this systematic pan-cancer analysis suggests that TMEM158 is generally highly expressed in various cancer tissues and is closely related to patient prognosis and survival across multiple cancer types. TMEM158 may serve as a significant predictor of cancer prognosis and modulate immune responses to various types of cancer.
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Affiliation(s)
- Jiayi Li
- School of Management, Shandong University, Jinan, 250100, Shandong, China
- School of Graduate, Hanyang University, Seoul, 04763, South Korea
| | - Haiguang Hou
- Department of Anatomy, School of Basic Medical Sciences, Shandong University, Jinan, 250012, Shandong, China
| | - Jinhao Sun
- Department of Anatomy, School of Basic Medical Sciences, Shandong University, Jinan, 250012, Shandong, China
| | - Zhaoxi Ding
- Department of Anatomy, School of Basic Medical Sciences, Shandong University, Jinan, 250012, Shandong, China
| | - Yingkun Xu
- Department of Breast and Thyroid Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Guibao Li
- Department of Anatomy, School of Basic Medical Sciences, Shandong University, Jinan, 250012, Shandong, China.
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80
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Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532468. [PMID: 36993743 PMCID: PMC10054947 DOI: 10.1101/2023.03.13.532468] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Ming Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xuan Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Department of Psychiatry, Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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81
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Luo R, Yan J, Oh JW, Xi W, Shigaki D, Wong W, Cho H, Murphy D, Cutler R, Rosen BP, Pulecio J, Yang D, Glenn R, Chen T, Li QV, Vierbuchen T, Sidoli S, Apostolou E, Huangfu D, Beer MA. Dynamic network-guided CRISPRi screen reveals CTCF loop-constrained nonlinear enhancer-gene regulatory activity in cell state transitions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531569. [PMID: 36945628 PMCID: PMC10028945 DOI: 10.1101/2023.03.07.531569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Comprehensive enhancer discovery is challenging because most enhancers, especially those affected in complex diseases, have weak effects on gene expression. Our network modeling revealed that nonlinear enhancer-gene regulation during cell state transitions can be leveraged to improve the sensitivity of enhancer discovery. Utilizing hESC definitive endoderm differentiation as a dynamic transition system, we conducted a mid-transition CRISPRi-based enhancer screen. The screen discovered a comprehensive set of enhancers (4 to 9 per locus) for each of the core endoderm lineage-specifying transcription factors, and many enhancers had strong effects mid-transition but weak effects post-transition. Through integrating enhancer activity measurements and three-dimensional enhancer-promoter interaction information, we were able to develop a CTCF loop-constrained Interaction Activity (CIA) model that can better predict functional enhancers compared to models that rely on Hi-C-based enhancer-promoter contact frequency. Our study provides generalizable strategies for sensitive and more comprehensive enhancer discovery in both normal and pathological cell state transitions.
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82
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Cilleros-Portet A, Lesseur C, Marí S, Cosin-Tomas M, Lozano M, Irizar A, Burt A, García-Santisteban I, Martín DG, Escaramís G, Hernangomez-Laderas A, Soler-Blasco R, Breeze CE, Gonzalez-Garcia BP, Santa-Marina L, Chen J, Llop S, Fernández MF, Vrijhed M, Ibarluzea J, Guxens M, Marsit C, Bustamante M, Bilbao JR, Fernandez-Jimenez N. Potentially causal associations between placental DNA methylation and schizophrenia and other neuropsychiatric disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.03.07.23286905. [PMID: 36945560 PMCID: PMC10029044 DOI: 10.1101/2023.03.07.23286905] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Increasing evidence supports the role of placenta in neurodevelopment and potentially, in the later onset of neuropsychiatric disorders. Recently, methylation quantitative trait loci (mQTL) and interaction QTL (iQTL) maps have proven useful to understand SNP-genome wide association study (GWAS) relationships, otherwise missed by conventional expression QTLs. In this context, we propose that part of the genetic predisposition to complex neuropsychiatric disorders acts through placental DNA methylation (DNAm). We constructed the first public placental cis-mQTL database including nearly eight million mQTLs calculated in 368 fetal placenta DNA samples from the INMA project, ran cell type- and gestational age-imQTL models and combined those data with the summary statistics of the largest GWAS on 10 neuropsychiatric disorders using Summary-based Mendelian Randomization (SMR) and colocalization. Finally, we evaluated the influence of the DNAm sites identified on placental gene expression in the RICHS cohort. We found that placental cis-mQTLs are highly enriched in placenta-specific active chromatin regions, and useful to map the etiology of neuropsychiatric disorders at prenatal stages. Specifically, part of the genetic burden for schizophrenia, bipolar disorder and major depressive disorder confers risk through placental DNAm. The potential causality of several of the observed associations is reinforced by secondary association signals identified in conditional analyses, regional pleiotropic methylation signals associated to the same disorder, and cell type-imQTLs, additionally associated to the expression levels of relevant immune genes in placenta. In conclusion, the genetic risk of several neuropsychiatric disorders could operate, at least in part, through DNAm and associated gene expression in placenta.
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Affiliation(s)
- Ariadna Cilleros-Portet
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Corina Lesseur
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sergi Marí
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Marta Cosin-Tomas
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Manuel Lozano
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de Valéncia, Valencia, Spain
- Preventive Medicine and Public Health, Food Sciences, Toxicology and Forensic Medicine Department, Universitat de València, Valencia, Spain
| | - Amaia Irizar
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of the Basque Country (UPV/EHU), Leioa, Spain
- Biodonostia Health Research Institute, 20013, San Sebastian, Spain
| | - Amber Burt
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Iraia García-Santisteban
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Diego Garrido Martín
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona (UB), 08028 Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, 08003 Barcelona, Spain
| | - Geòrgia Escaramís
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Departament de Biomedicina, Facultat de Medicina i Ciències de la Salut, Institut de Neurociències, Universitat de Barcelona, Casanova 143, Barcelona, Spain
| | - Alba Hernangomez-Laderas
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Raquel Soler-Blasco
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de Valéncia, Valencia, Spain
- Department of Nursing, Universitat de València, Valencia, Spain
| | - Charles E. Breeze
- UCL Cancer Institute, University College London, 72 Huntley St, London WC1E 6DD, United Kingdom
| | - Bárbara P. Gonzalez-Garcia
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Loreto Santa-Marina
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Biodonostia Health Research Institute, 20013, San Sebastian, Spain
- Department of Health of the Basque Government, Subdirectorate of Public Health of Gipuzkoa, Avenida Navarra 4, 20013, San Sebastian, Spain
| | - Jia Chen
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sabrina Llop
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Epidemiology and Environmental Health Joint Research Unit, FISABIO-Universitat Jaume I-Universitat de Valéncia, Valencia, Spain
| | - Mariana F. Fernández
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Biomedical Research Center (CIBM) & Department of Radiology and Physical Medicine, School of Medicine University of Granada, 18016 Granada, Spain; Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), 18012 Granada, Spain
| | - Martine Vrijhed
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Jesús Ibarluzea
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Biodonostia Health Research Institute, 20013, San Sebastian, Spain
- Department of Health of the Basque Government, Subdirectorate of Public Health of Gipuzkoa, Avenida Navarra 4, 20013, San Sebastian, Spain
| | - Mònica Guxens
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Centre, Rotterdam, The Netherlands
| | - Carmen Marsit
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Mariona Bustamante
- ISGlobal, Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), Instituto de Salud Carlos III, 28029, Madrid, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Jose Ramon Bilbao
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
| | - Nora Fernandez-Jimenez
- Department of Genetics, Physical Anthropology and Animal Physiology, Biocruces-Bizkaia Health Research Institute and University of the Basque Country (UPV/EHU), Leioa, Spain
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83
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D'Antonio M, Nguyen JP, Arthur TD, Matsui H, D'Antonio-Chronowska A, Frazer KA. Fine mapping spatiotemporal mechanisms of genetic variants underlying cardiac traits and disease. Nat Commun 2023; 14:1132. [PMID: 36854752 PMCID: PMC9975214 DOI: 10.1038/s41467-023-36638-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 02/10/2023] [Indexed: 03/02/2023] Open
Abstract
The causal variants and genes underlying thousands of cardiac GWAS signals have yet to be identified. Here, we leverage spatiotemporal information on 966 RNA-seq cardiac samples and perform an expression quantitative trait locus (eQTL) analysis detecting eQTLs considering both eGenes and eIsoforms. We identify 2,578 eQTLs associated with a specific developmental stage-, tissue- and/or cell type. Colocalization between eQTL and GWAS signals of five cardiac traits identified variants with high posterior probabilities for being causal in 210 GWAS loci. Pulse pressure GWAS loci are enriched for colocalization with fetal- and smooth muscle- eQTLs; pulse rate with adult- and cardiac muscle- eQTLs; and atrial fibrillation with cardiac muscle- eQTLs. Fine mapping identifies 79 credible sets with five or fewer SNPs, of which 15 were associated with spatiotemporal eQTLs. Our study shows that many cardiac GWAS variants impact traits and disease in a developmental stage-, tissue- and/or cell type-specific fashion.
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Affiliation(s)
- Matteo D'Antonio
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Timothy D Arthur
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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84
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Zhang J, Zhao H. eQTL Studies: from Bulk Tissues to Single Cells. ARXIV 2023:arXiv:2302.11662v1. [PMID: 36866231 PMCID: PMC9980190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
An expression quantitative trait locus (eQTL) is a chromosomal region where genetic variants are associated with the expression levels of certain genes that can be both nearby or distant. The identifications of eQTLs for different tissues, cell types, and contexts have led to better understanding of the dynamic regulations of gene expressions and implications of functional genes and variants for complex traits and diseases. Although most eQTL studies to date have been performed on data collected from bulk tissues, recent studies have demonstrated the importance of cell-type-specific and context-dependent gene regulations in biological processes and disease mechanisms. In this review, we discuss statistical methods that have been developed to enable the detections of cell-type-specific and context-dependent eQTLs from bulk tissues, purified cell types, and single cells. We also discuss the limitations of the current methods and future research opportunities.
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Affiliation(s)
- Jingfei Zhang
- Information Systems and Operations Management, Emory University
| | - Hongyu Zhao
- Department of Biostatistics, Yale University
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85
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Ma S, Wang C, Khan A, Liu L, Dalgleish J, Kiryluk K, He Z, Ionita-Laza I. BIGKnock: fine-mapping gene-based associations via knockoff analysis of biobank-scale data. Genome Biol 2023; 24:24. [PMID: 36782330 PMCID: PMC9926792 DOI: 10.1186/s13059-023-02864-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 01/23/2023] [Indexed: 02/15/2023] Open
Abstract
We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability. We further illustrate its ability to pinpoint potential causal genes at [Formula: see text] of the associated loci.
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Affiliation(s)
- Shiyang Ma
- Department of Biostatistics, Columbia University, New York, NY, USA
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Chen Wang
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Linxi Liu
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - James Dalgleish
- Department of Biostatistics, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Zihuai He
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
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86
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Stikker BS, Hendriks RW, Stadhouders R. Decoding the genetic and epigenetic basis of asthma. Allergy 2023; 78:940-956. [PMID: 36727912 DOI: 10.1111/all.15666] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 01/17/2023] [Accepted: 01/30/2023] [Indexed: 02/03/2023]
Abstract
Asthma is a complex and heterogeneous chronic inflammatory disease of the airways. Alongside environmental factors, asthma susceptibility is strongly influenced by genetics. Given its high prevalence and our incomplete understanding of the mechanisms underlying disease susceptibility, asthma is frequently studied in genome-wide association studies (GWAS), which have identified thousands of genetic variants associated with asthma development. Virtually all these genetic variants reside in non-coding genomic regions, which has obscured the functional impact of asthma-associated variants and their translation into disease-relevant mechanisms. Recent advances in genomics technology and epigenetics now offer methods to link genetic variants to gene regulatory elements embedded within non-coding regions, which have started to unravel the molecular mechanisms underlying the complex (epi)genetics of asthma. Here, we provide an integrated overview of (epi)genetic variants associated with asthma, focusing on efforts to link these disease associations to biological insight into asthma pathophysiology using state-of-the-art genomics methodology. Finally, we provide a perspective as to how decoding the genetic and epigenetic basis of asthma has the potential to transform clinical management of asthma and to predict the risk of asthma development.
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Affiliation(s)
- Bernard S Stikker
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Rudi W Hendriks
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - Ralph Stadhouders
- Department of Pulmonary Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.,Department of Cell Biology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
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87
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García-Pérez R, Ramirez JM, Ripoll-Cladellas A, Chazarra-Gil R, Oliveros W, Soldatkina O, Bosio M, Rognon PJ, Capella-Gutierrez S, Calvo M, Reverter F, Guigó R, Aguet F, Ferreira PG, Ardlie KG, Melé M. The landscape of expression and alternative splicing variation across human traits. CELL GENOMICS 2023; 3:100244. [PMID: 36777183 PMCID: PMC9903719 DOI: 10.1016/j.xgen.2022.100244] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/08/2022] [Accepted: 12/07/2022] [Indexed: 12/31/2022]
Abstract
Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.
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Affiliation(s)
- Raquel García-Pérez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Jose Miguel Ramirez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Aida Ripoll-Cladellas
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Ruben Chazarra-Gil
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Winona Oliveros
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Oleksandra Soldatkina
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Mattia Bosio
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Paul Joris Rognon
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
- Department of Economics and Business, Universitat Pompeu Fabra, Barcelona, Catalonia 08005, Spain
- Department of Statistics and Operations Research, Universitat Politècnica de Catalunya, Barcelona, Catalonia 08034, Spain
| | - Salvador Capella-Gutierrez
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
| | - Miquel Calvo
- Statistics Section, Faculty of Biology, Universitat de Barcelona (UB), Barcelona, Catalonia 08028, Spain
| | - Ferran Reverter
- Statistics Section, Faculty of Biology, Universitat de Barcelona (UB), Barcelona, Catalonia 08028, Spain
| | - Roderic Guigó
- Bioinformatics and Genomics, Center for Genomic Regulation, Barcelona, Catalonia 08003, Spain
| | | | - Pedro G. Ferreira
- Department of Computer Science, Faculty of Sciences, University of Porto, Rua do Campo Alegre, 4169-007 Porto, Portugal
- Laboratory of Artificial Intelligence and Decision Support, INESC TEC, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto, Institute for Research and Innovation in Health (i3s), R. Alfredo Allen 208, 4200-135 Porto, Portugal
| | | | - Marta Melé
- Department of Life Sciences, Barcelona Supercomputing Center (BCN-CNS), Barcelona, Catalonia 08034, Spain
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88
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Sharma N, Banerjee P, Sood A, Midha V, Thelma BK, Senapati S. Celiac disease-associated loci show considerable genetic overlap with neuropsychiatric diseases but with limited transethnic applicability. J Genet 2023. [DOI: 10.1007/s12041-022-01413-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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89
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Humphrey J, Venkatesh S, Hasan R, Herb JT, de Paiva Lopes K, Küçükali F, Byrska-Bishop M, Evani US, Narzisi G, Fagegaltier D, Sleegers K, Phatnani H, Knowles DA, Fratta P, Raj T. Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes. Nat Neurosci 2023; 26:150-162. [PMID: 36482247 DOI: 10.1038/s41593-022-01205-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/13/2022] [Indexed: 12/13/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. In this study, we investigated gene expression changes in ALS via RNA sequencing in 380 postmortem samples from cervical, thoracic and lumbar spinal cord segments from 154 individuals with ALS and 49 control individuals. We observed an increase in microglia and astrocyte gene expression, accompanied by a decrease in oligodendrocyte gene expression. By creating a gene co-expression network in the ALS samples, we identified several activated microglia modules that negatively correlate with retrospective disease duration. We mapped molecular quantitative trait loci and found several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how common genetic variants associated with splicing of C9orf72 act as proxies for the well-known repeat expansion, and we use the same mechanism to suggest ATXN3 as a putative risk gene.
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Affiliation(s)
- Jack Humphrey
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Sanan Venkatesh
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rahat Hasan
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jake T Herb
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Katia de Paiva Lopes
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | | | | | - Delphine Fagegaltier
- New York Genome Center, New York, NY, USA
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - David A Knowles
- New York Genome Center, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Pietro Fratta
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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90
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Chen Y, Zhang H, Sun X. Improving the performance of single-cell RNA-seq data mining based on relative expression orderings. Brief Bioinform 2022; 24:6931720. [PMID: 36528803 PMCID: PMC9851298 DOI: 10.1093/bib/bbac556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/10/2022] [Accepted: 11/16/2022] [Indexed: 12/23/2022] Open
Abstract
The advent of single-cell RNA-sequencing (scRNA-seq) provides an unprecedented opportunity to explore gene expression profiles at the single-cell level. However, gene expression values vary over time and under different conditions even within the same cell. There is an urgent need for more stable and reliable feature variables at the single-cell level to depict cell heterogeneity. Thus, we construct a new feature matrix called the delta rank matrix (DRM) from scRNA-seq data by integrating an a priori gene interaction network, which transforms the unreliable gene expression value into a stable gene interaction/edge value on a single-cell basis. This is the first time that a gene-level feature has been transformed into an interaction/edge-level for scRNA-seq data analysis based on relative expression orderings. Experiments on various scRNA-seq datasets have demonstrated that DRM performs better than the original gene expression matrix in cell clustering, cell identification and pseudo-trajectory reconstruction. More importantly, the DRM really achieves the fusion of gene expressions and gene interactions and provides a method of measuring gene interactions at the single-cell level. Thus, the DRM can be used to find changes in gene interactions among different cell types, which may open up a new way to analyze scRNA-seq data from an interaction perspective. In addition, DRM provides a new method to construct a cell-specific network for each single cell instead of a group of cells as in traditional network construction methods. DRM's exceptional performance is due to its extraction of rich gene-association information on biological systems and stable characterization of cells.
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Affiliation(s)
- Yuanyuan Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China,College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Hao Zhang
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiao Sun
- Corresponding author: Xiao Sun, State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China. Tel: +8613951989906; E-mail:
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91
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Voskuhl R, Itoh Y. The X factor in neurodegeneration. J Exp Med 2022; 219:e20211488. [PMID: 36331399 PMCID: PMC9641640 DOI: 10.1084/jem.20211488] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/22/2022] [Accepted: 10/12/2022] [Indexed: 07/25/2023] Open
Abstract
Given the aging population, it is important to better understand neurodegeneration in aging healthy people and to address the increasing incidence of neurodegenerative diseases. It is imperative to apply novel strategies to identify neuroprotective therapeutics. The study of sex differences in neurodegeneration can reveal new candidate treatment targets tailored for women and men. Sex chromosome effects on neurodegeneration remain understudied and represent a promising frontier for discovery. Here, we will review sex differences in neurodegeneration, focusing on the study of sex chromosome effects in the context of declining levels of sex hormones during aging.
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Affiliation(s)
- Rhonda Voskuhl
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Yuichiro Itoh
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
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92
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Long E, Yin J, Funderburk KM, Xu M, Feng J, Kane A, Zhang T, Myers T, Golden A, Thakur R, Kong H, Jessop L, Kim EY, Jones K, Chari R, Machiela MJ, Yu K, Iles MM, Landi MT, Law MH, Chanock SJ, Brown KM, Choi J. Massively parallel reporter assays and variant scoring identified functional variants and target genes for melanoma loci and highlighted cell-type specificity. Am J Hum Genet 2022; 109:2210-2229. [PMID: 36423637 PMCID: PMC9748337 DOI: 10.1016/j.ajhg.2022.11.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 11/02/2022] [Indexed: 11/24/2022] Open
Abstract
The most recent genome-wide association study (GWAS) of cutaneous melanoma identified 54 risk-associated loci, but functional variants and their target genes for most have not been established. Here, we performed massively parallel reporter assays (MPRAs) by using malignant melanoma and normal melanocyte cells and further integrated multi-layer annotation to systematically prioritize functional variants and susceptibility genes from these GWAS loci. Of 1,992 risk-associated variants tested in MPRAs, we identified 285 from 42 loci (78% of the known loci) displaying significant allelic transcriptional activities in either cell type (FDR < 1%). We further characterized MPRA-significant variants by motif prediction, epigenomic annotation, and statistical/functional fine-mapping to create integrative variant scores, which prioritized one to six plausible candidate variants per locus for the 42 loci and nominated a single variant for 43% of these loci. Overlaying the MPRA-significant variants with genome-wide significant expression or methylation quantitative trait loci (eQTLs or meQTLs, respectively) from melanocytes or melanomas identified candidate susceptibility genes for 60% of variants (172 of 285 variants). CRISPRi of top-scoring variants validated their cis-regulatory effect on the eQTL target genes, MAFF (22q13.1) and GPRC5A (12p13.1). Finally, we identified 36 melanoma-specific and 45 melanocyte-specific MPRA-significant variants, a subset of which are linked to cell-type-specific target genes. Analyses of transcription factor availability in MPRA datasets and variant-transcription-factor interaction in eQTL datasets highlighted the roles of transcription factors in cell-type-specific variant functionality. In conclusion, MPRAs along with variant scoring effectively prioritized plausible candidates for most melanoma GWAS loci and highlighted cellular contexts where the susceptibility variants are functional.
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Affiliation(s)
- Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jinhu Yin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Karen M. Funderburk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Mai Xu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - James Feng
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Alexander Kane
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Timothy Myers
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Alyxandra Golden
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Rohit Thakur
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Hyunkyung Kong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Lea Jessop
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Eun Young Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Kristine Jones
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Raj Chari
- Genome Modification Core, Frederick National Lab for Cancer Research, National Cancer Institute, Frederick, MD, USA
| | - Mitchell J. Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Kai Yu
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | | | - Mark M. Iles
- Leeds Institute for Data Analytics, School of Medicine, University of Leeds, Leeds LS2 9NL, UK
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Matthew H. Law
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia,Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia,School of Biomedical Sciences, University of Queensland, Brisbane, QLD, Australia
| | - Stephen J. Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Kevin M. Brown
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA,Corresponding author
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93
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Bonaguro L, Schulte-Schrepping J, Carraro C, Sun LL, Reiz B, Gemünd I, Saglam A, Rahmouni S, Georges M, Arts P, Hoischen A, Joosten LA, van de Veerdonk FL, Netea MG, Händler K, Mukherjee S, Ulas T, Schultze JL, Aschenbrenner AC. Human variation in population-wide gene expression data predicts gene perturbation phenotype. iScience 2022; 25:105328. [PMID: 36310583 PMCID: PMC9614568 DOI: 10.1016/j.isci.2022.105328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 07/13/2022] [Accepted: 10/07/2022] [Indexed: 11/24/2022] Open
Abstract
Population-scale datasets of healthy individuals capture genetic and environmental factors influencing gene expression. The expression variance of a gene of interest (GOI) can be exploited to set up a quasi loss- or gain-of-function "in population" experiment. We describe here an approach, huva (human variation), taking advantage of population-scale multi-layered data to infer gene function and relationships between phenotypes and expression. Within a reference dataset, huva derives two experimental groups with LOW or HIGH expression of the GOI, enabling the subsequent comparison of their transcriptional profile and functional parameters. We demonstrate that this approach robustly identifies the phenotypic relevance of a GOI allowing the stratification of genes according to biological functions, and we generalize this concept to almost 16,000 genes in the human transcriptome. Additionally, we describe how huva predicts monocytes to be the major cell type in the pathophysiology of STAT1 mutations, evidence validated in a clinical cohort.
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Affiliation(s)
- Lorenzo Bonaguro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Jonas Schulte-Schrepping
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Caterina Carraro
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, 35131 Padova, Italy
| | - Laura L. Sun
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | | | - Ioanna Gemünd
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Department of Microbiology and Immunology, the University of Melbourne, at the Peter Doherty Institute for Infection and Immunity, Parkville, 3010 VIC, Australia
| | - Adem Saglam
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
| | - Souad Rahmouni
- Unit of Animal Genomics, GIGA-Institute, University of Liège, 4000 Liège, Belgium
| | - Michel Georges
- Unit of Animal Genomics, GIGA-Institute, University of Liège, 4000 Liège, Belgium
| | - Peer Arts
- Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Genetics and Molecular Pathology, Centre for Cancer Biology, SA Pathology and the University of South Australia, Adelaide, 5000 SA, Australia
| | - Alexander Hoischen
- Department of Human Genetics and Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
| | - Leo A.B. Joosten
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Department of Medical Genetics, “Iuliu Hatieganu” University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Frank L. van de Veerdonk
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
| | - Mihai G. Netea
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
- Immunology and Metabolism, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
| | - Kristian Händler
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Sach Mukherjee
- Statistics and Machine Learning, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Thomas Ulas
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Joachim L. Schultze
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), PRECISE Platform for Genomics and Epigenomics at DZNE and University of Bonn, 53127 Bonn, Germany
| | - Anna C. Aschenbrenner
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), 53127 Bonn, Germany
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53113 Bonn, Germany
- Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525 Nijmegen, the Netherlands
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94
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Borie R, Cardwell J, Konigsberg IR, Moore CM, Zhang W, Sasse SK, Gally F, Dobrinskikh E, Walts A, Powers J, Brancato J, Rojas M, Wolters PJ, Brown KK, Blackwell TS, Nakanishi T, Richards JB, Gerber AN, Fingerlin TE, Sachs N, Pulit SL, Zappala Z, Schwartz DA, Yang IV. Colocalization of Gene Expression and DNA Methylation with Genetic Risk Variants Supports Functional Roles of MUC5B and DSP in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2022; 206:1259-1270. [PMID: 35816432 PMCID: PMC9746850 DOI: 10.1164/rccm.202110-2308oc] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 07/05/2022] [Indexed: 11/16/2022] Open
Abstract
Rationale: Common genetic variants have been associated with idiopathic pulmonary fibrosis (IPF). Objectives: To determine functional relevance of the 10 IPF-associated common genetic variants we previously identified. Methods: We performed expression quantitative trait loci (eQTL) and methylation quantitative trait loci (mQTL) mapping, followed by co-localization of eQTL and mQTL with genetic association signals and functional validation by luciferase reporter assays. Illumina multi-ethnic genotyping arrays, mRNA sequencing, and Illumina 850k methylation arrays were performed on lung tissue of participants with IPF (234 RNA and 345 DNA samples) and non-diseased controls (188 RNA and 202 DNA samples). Measurements and Main Results: Focusing on genetic variants within 10 IPF-associated genetic loci, we identified 27 eQTLs in controls and 24 eQTLs in cases (false-discovery-rate-adjusted P < 0.05). Among these signals, we identified associations of lead variants rs35705950 with expression of MUC5B and rs2076295 with expression of DSP in both cases and controls. mQTL analysis identified CpGs in gene bodies of MUC5B (cg17589883) and DSP (cg08964675) associated with the lead variants in these two loci. We also demonstrated strong co-localization of eQTL/mQTL and genetic signal in MUC5B (rs35705950) and DSP (rs2076295). Functional validation of the mQTL in MUC5B using luciferase reporter assays demonstrates that the CpG resides within a putative internal repressor element. Conclusions: We have established a relationship of the common IPF genetic risk variants rs35705950 and rs2076295 with respective changes in MUC5B and DSP expression and methylation. These results provide additional evidence that both MUC5B and DSP are involved in the etiology of IPF.
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Affiliation(s)
| | | | | | - Camille M. Moore
- Department of Biostatistics and Bioinformatics and
- Center for Genes, Environment, and Health
| | | | | | - Fabienne Gally
- Department of Medicine
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | | | | | | | | | - Mauricio Rojas
- Department of Internal Medicine, Ohio State College of Medicine, The Ohio State University, Columbus, Ohio
| | - Paul J. Wolters
- Department of Medicine, University of California, San Francisco, California
| | | | - Timothy S. Blackwell
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Tomoko Nakanishi
- Department of Human Genetics, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Canada
| | - J. Brent Richards
- Department of Human Genetics, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Canada
| | - Anthony N. Gerber
- Department of Medicine
- Department of Medicine, and
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Tasha E. Fingerlin
- Department of Biostatistics and Bioinformatics and
- Center for Genes, Environment, and Health
- Department of Immunology and Genomic Medicine, National Jewish Health, Denver, Colorado
| | - Norman Sachs
- Cell Biology, Vertex Pharmaceuticals, San Diego, California; and
| | - Sara L. Pulit
- Computational Genomics, Vertex Pharmaceuticals, Boston, Massachusetts
| | - Zachary Zappala
- Computational Genomics, Vertex Pharmaceuticals, Boston, Massachusetts
| | - David A. Schwartz
- Department of Medicine
- Department of Microbiology and Immunology, University of Colorado Anschutz Medical Campus; Aurora, Colorado
| | - Ivana V. Yang
- Department of Medicine
- Department of Epidemiology, Colorado School of Public Health, Aurora, Colorado
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95
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Tissue dissociation for single-cell and single-nuclei RNA sequencing for low amounts of input material. Front Zool 2022; 19:27. [DOI: 10.1186/s12983-022-00472-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/27/2022] [Indexed: 11/15/2022] Open
Abstract
Abstract
Background
Recent technological advances opened the opportunity to simultaneously study gene expression for thousands of individual cells on a genome-wide scale. The experimental accessibility of such single-cell RNA sequencing (scRNAseq) approaches allowed gaining insights into the cell type composition of heterogeneous tissue samples of animal model systems and emerging models alike. A major prerequisite for a successful application of the method is the dissociation of complex tissues into individual cells, which often requires large amounts of input material and harsh mechanical, chemical and temperature conditions. However, the availability of tissue material may be limited for small animals, specific organs, certain developmental stages or if samples need to be acquired from collected specimens. Therefore, we evaluated different dissociation protocols to obtain single cells from small tissue samples of Drosophila melanogaster eye-antennal imaginal discs.
Results
We show that a combination of mechanical and chemical dissociation resulted in sufficient high-quality cells. As an alternative, we tested protocols for the isolation of single nuclei, which turned out to be highly efficient for fresh and frozen tissue samples. Eventually, we performed scRNAseq and single-nuclei RNA sequencing (snRNAseq) to show that the best protocols for both methods successfully identified relevant cell types. At the same time, snRNAseq resulted in less artificial gene expression that is caused by rather harsh dissociation conditions needed to obtain single cells for scRNAseq. A direct comparison of scRNAseq and snRNAseq data revealed that both datasets share biologically relevant genes among the most variable genes, and we showed differences in the relative contribution of the two approaches to identified cell types.
Conclusion
We present two dissociation protocols that allow isolating single cells and single nuclei, respectively, from low input material. Both protocols resulted in extraction of high-quality RNA for subsequent scRNAseq or snRNAseq applications. If tissue availability is limited, we recommend the snRNAseq procedure of fresh or frozen tissue samples as it is perfectly suited to obtain thorough insights into cellular diversity of complex tissue.
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Putscher E, Hecker M, Fitzner B, Boxberger N, Schwartz M, Koczan D, Lorenz P, Zettl UK. Genetic risk variants for multiple sclerosis are linked to differences in alternative pre-mRNA splicing. Front Immunol 2022; 13:931831. [PMID: 36405756 PMCID: PMC9670805 DOI: 10.3389/fimmu.2022.931831] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 10/12/2022] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND Multiple sclerosis (MS) is a chronic immune-mediated disease of the central nervous system to which a genetic predisposition contributes. Over 200 genetic regions have been associated with increased disease risk, but the disease-causing variants and their functional impact at the molecular level are mostly poorly defined. We hypothesized that single-nucleotide polymorphisms (SNPs) have an impact on pre-mRNA splicing in MS. METHODS Our study focused on 10 bioinformatically prioritized SNP-gene pairs, in which the SNP has a high potential to alter alternative splicing events (ASEs). We tested for differential gene expression and differential alternative splicing in B cells from MS patients and healthy controls. We further examined the impact of the SNP genotypes on ASEs and on splice isoform expression levels. Novel genotype-dependent effects on splicing were verified with splicing reporter minigene assays. RESULTS We were able to confirm previously described findings regarding the relation of MS-associated SNPs with the ASEs of the pre-mRNAs from GSDMB and SP140. We also observed an increased IL7R exon 6 skipping when comparing relapsing and progressive MS patients to healthy subjects. Moreover, we found evidence that the MS risk alleles of the SNPs rs3851808 (EFCAB13), rs1131123 (HLA-C), rs10783847 (TSFM), and rs2014886 (TSFM) may contribute to a differential splicing pattern. Of particular interest is the genotype-dependent exon skipping of TSFM due to the SNP rs2014886. The minor allele T creates a donor splice site, resulting in the expression of the exon 3 and 4 of a short TSFM transcript isoform, whereas in the presence of the MS risk allele C, this donor site is absent, and thus the short transcript isoform is not expressed. CONCLUSION In summary, we found that genetic variants from MS risk loci affect pre-mRNA splicing. Our findings substantiate the role of ASEs with respect to the genetics of MS. Further studies on how disease-causing genetic variants may modify the interactions between splicing regulatory sequence elements and RNA-binding proteins can help to deepen our understanding of the genetic susceptibility to MS.
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Affiliation(s)
- Elena Putscher
- Rostock University Medical Center, Department of Neurology, Division of Neuroimmunology, Rostock, Germany
| | - Michael Hecker
- Rostock University Medical Center, Department of Neurology, Division of Neuroimmunology, Rostock, Germany
| | - Brit Fitzner
- Rostock University Medical Center, Department of Neurology, Division of Neuroimmunology, Rostock, Germany
| | - Nina Boxberger
- Rostock University Medical Center, Department of Neurology, Division of Neuroimmunology, Rostock, Germany
| | - Margit Schwartz
- Rostock University Medical Center, Department of Neurology, Division of Neuroimmunology, Rostock, Germany
| | - Dirk Koczan
- Rostock University Medical Center, Institute of Immunology, Rostock, Germany
| | - Peter Lorenz
- Rostock University Medical Center, Institute of Immunology, Rostock, Germany
| | - Uwe Klaus Zettl
- Rostock University Medical Center, Department of Neurology, Division of Neuroimmunology, Rostock, Germany
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97
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Lu M, Zhang Y, Yang F, Mai J, Gao Q, Xu X, Kang H, Hou L, Shang Y, Qain Q, Liu J, Jiang M, Zhang H, Bu C, Wang J, Zhang Z, Zhang Z, Zeng J, Li J, Xiao J. TWAS Atlas: a curated knowledgebase of transcriptome-wide association studies. Nucleic Acids Res 2022; 51:D1179-D1187. [PMID: 36243959 PMCID: PMC9825460 DOI: 10.1093/nar/gkac821] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/08/2022] [Accepted: 09/14/2022] [Indexed: 01/30/2023] Open
Abstract
Transcriptome-wide association studies (TWASs), as a practical and prevalent approach for detecting the associations between genetically regulated genes and traits, are now leading to a better understanding of the complex mechanisms of genetic variants in regulating various diseases and traits. Despite the ever-increasing TWAS outputs, there is still a lack of databases curating massive public TWAS information and knowledge. To fill this gap, here we present TWAS Atlas (https://ngdc.cncb.ac.cn/twas/), an integrated knowledgebase of TWAS findings manually curated from extensive literature. In the current implementation, TWAS Atlas collects 401,266 high-quality human gene-trait associations from 200 publications, covering 22,247 genes and 257 traits across 135 tissue types. In particular, an interactive knowledge graph of the collected gene-trait associations is constructed together with single nucleotide polymorphism (SNP)-gene associations to build up comprehensive regulatory networks at multi-omics levels. In addition, TWAS Atlas, as a user-friendly web interface, efficiently enables users to browse, search and download all association information, relevant research metadata and annotation information of interest. Taken together, TWAS Atlas is of great value for promoting the utility and availability of TWAS results in explaining the complex genetic basis as well as providing new insights for human health and disease research.
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Affiliation(s)
| | | | | | | | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaowei Xu
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Hongyu Kang
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Li Hou
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Yunfei Shang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiheng Qain
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Liu
- North China University of Science and Technology Affiliated Hospital, Tangshan 063000, China
| | - Meiye Jiang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Congfan Bu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jinyue Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhewen Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zaichao Zhang
- Department of Biology, The University of Western Ontario, London, OntarioN6A 5B7, Canada
| | - Jingyao Zeng
- Correspondence may also be addressed to Jingyao Zeng.
| | - Jiao Li
- Correspondence may also be addressed to Jiao Li.
| | - Jingfa Xiao
- To whom correspondence should be addressed. Tel: +86 10 8409 7443; Fax: +86 10 8409 7720;
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98
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Thompson M, Gordon MG, Lu A, Tandon A, Halperin E, Gusev A, Ye CJ, Balliu B, Zaitlen N. Multi-context genetic modeling of transcriptional regulation resolves novel disease loci. Nat Commun 2022; 13:5704. [PMID: 36171194 PMCID: PMC9519579 DOI: 10.1038/s41467-022-33212-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 09/07/2022] [Indexed: 12/01/2022] Open
Abstract
A majority of the variants identified in genome-wide association studies fall in non-coding regions of the genome, indicating their mechanism of impact is mediated via gene expression. Leveraging this hypothesis, transcriptome-wide association studies (TWAS) have assisted in both the interpretation and discovery of additional genes associated with complex traits. However, existing methods for conducting TWAS do not take full advantage of the intra-individual correlation inherently present in multi-context expression studies and do not properly adjust for multiple testing across contexts. We introduce CONTENT-a computationally efficient method with proper cross-context false discovery correction that leverages correlation structure across contexts to improve power and generate context-specific and context-shared components of expression. We apply CONTENT to bulk multi-tissue and single-cell RNA-seq data sets and show that CONTENT leads to a 42% (bulk) and 110% (single cell) increase in the number of genetically predicted genes relative to previous approaches. We find the context-specific component of expression comprises 30% of heritability in tissue-level bulk data and 75% in single-cell data, consistent with cell-type heterogeneity in bulk tissue. In the context of TWAS, CONTENT increases the number of locus-phenotype associations discovered by over 51% relative to previous methods across 22 complex traits.
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Affiliation(s)
- Mike Thompson
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
| | - Mary Grace Gordon
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew Lu
- UCLA-Caltech Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Anchit Tandon
- Department of Mathematics, Indian Institute of Technology Delhi, Hauz Khas, Delhi, India
| | - Eran Halperin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California Los Angeles, Los Angeles, CA, USA
- Department of Anesthesiology and Perioperative Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, US
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, US
| | - Chun Jimmie Ye
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Brunilda Balliu
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Noah Zaitlen
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA.
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99
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Jiang Y, Harigaya Y, Zhang Z, Zhang H, Zang C, Zhang NR. Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions. Cell Syst 2022; 13:737-751.e4. [PMID: 36055233 PMCID: PMC9509445 DOI: 10.1016/j.cels.2022.08.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/23/2022] [Accepted: 08/11/2022] [Indexed: 01/26/2023]
Abstract
The epigenetic control of gene expression is highly cell-type and context specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the target gene expression through its binding to a cis-regulatory region. We propose a nonparametric approach, TRIPOD, to detect and characterize the three-way relationships between a TF, its target gene, and the accessibility of the TF's binding site using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate the cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data and identify regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on the SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks and super-enhancer annotations. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yuchao Jiang
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Genetics, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC 27599, USA.
| | - Yuriko Harigaya
- Curriculum in Bioinformatics and Computational Biology, School of Medicine, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Zhaojun Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hongpan Zhang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Chongzhi Zang
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Nancy R Zhang
- Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA.
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100
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Atla G, Bonàs-Guarch S, Cuenca-Ardura M, Beucher A, Crouch DJM, Garcia-Hurtado J, Moran I, Irimia M, Prasad RB, Gloyn AL, Marselli L, Suleiman M, Berney T, de Koning EJP, Kerr-Conte J, Pattou F, Todd JA, Piemonti L, Ferrer J. Genetic regulation of RNA splicing in human pancreatic islets. Genome Biol 2022; 23:196. [PMID: 36109769 PMCID: PMC9479353 DOI: 10.1186/s13059-022-02757-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 08/23/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Non-coding genetic variants that influence gene transcription in pancreatic islets play a major role in the susceptibility to type 2 diabetes (T2D), and likely also contribute to type 1 diabetes (T1D) risk. For many loci, however, the mechanisms through which non-coding variants influence diabetes susceptibility are unknown. RESULTS We examine splicing QTLs (sQTLs) in pancreatic islets from 399 human donors and observe that common genetic variation has a widespread influence on the splicing of genes with established roles in islet biology and diabetes. In parallel, we profile expression QTLs (eQTLs) and use transcriptome-wide association as well as genetic co-localization studies to assign islet sQTLs or eQTLs to T2D and T1D susceptibility signals, many of which lack candidate effector genes. This analysis reveals biologically plausible mechanisms, including the association of T2D with an sQTL that creates a nonsense isoform in ERO1B, a regulator of ER-stress and proinsulin biosynthesis. The expanded list of T2D risk effector genes reveals overrepresented pathways, including regulators of G-protein-mediated cAMP production. The analysis of sQTLs also reveals candidate effector genes for T1D susceptibility such as DCLRE1B, a senescence regulator, and lncRNA MEG3. CONCLUSIONS These data expose widespread effects of common genetic variants on RNA splicing in pancreatic islets. The results support a role for splicing variation in diabetes susceptibility, and offer a new set of genetic targets with potential therapeutic benefit.
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Affiliation(s)
- Goutham Atla
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Silvia Bonàs-Guarch
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain.
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
| | - Mirabai Cuenca-Ardura
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
| | - Anthony Beucher
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Daniel J M Crouch
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Javier Garcia-Hurtado
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain
| | - Ignasi Moran
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Present Address: Life Sciences Department, Barcelona Supercomputing Center (BSC), 08034, Barcelona, Spain
| | - Manuel Irimia
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Rashmi B Prasad
- Lund University Diabetes Centre, Clinical Research Center, Malmö, Sweden
- Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA, USA
| | - Lorella Marselli
- Department of Clinical and Experimental Medicine, AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Mara Suleiman
- Department of Clinical and Experimental Medicine, AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Thierry Berney
- Cell Isolation and Transplantation Center, University of Geneva, Geneva, Switzerland
| | - Eelco J P de Koning
- Department of Medicine, Leiden University Medical Center, Leiden, the Netherlands
- Hubrecht Institute/KNAW, Utrecht, the Netherlands
| | - Julie Kerr-Conte
- University of Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Lille (CHU Lille), Institute Pasteur Lille, U1190 -European Genomic Institute for Diabetes (EGID), F59000, Lille, France
| | - Francois Pattou
- University of Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Lille (CHU Lille), Institute Pasteur Lille, U1190 -European Genomic Institute for Diabetes (EGID), F59000, Lille, France
| | - John A Todd
- JDRF/Wellcome Diabetes and Inflammation Laboratory, Wellcome Centre for Human Genetics, Nuffield Department of Medicine, NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Lorenzo Piemonti
- Diabetes Research Institute, IRCCS Ospedale San Raffaele and Università Vita-Salute San Raffaele, Milan, Italy
| | - Jorge Ferrer
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain.
- Centro de Investigación Biomédica en red Diabetes y enfermedades metabólicas asociadas (CIBERDEM), Barcelona, Spain.
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
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