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Wang G, Zhang H, Shao M, Tian M, Feng H, Li Q, Cao C. Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases. Comput Struct Biotechnol J 2024; 23:2478-2486. [PMID: 38952424 PMCID: PMC11215961 DOI: 10.1016/j.csbj.2024.05.050] [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: 02/01/2024] [Revised: 05/31/2024] [Accepted: 05/31/2024] [Indexed: 07/03/2024] Open
Abstract
Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.
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Affiliation(s)
- Guishen Wang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Hangchen Zhang
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Mengting Shao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Min Tian
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
| | - Hui Feng
- College of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China
| | - Qiaoling Li
- Department of Cardiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Chen Cao
- Key Laboratory for Bio-Electromagnetic Environment and Advanced Medical Theranostics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
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2
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Li C, Chen K, Fang Q, Shi S, Nan J, He J, Yin Y, Li X, Li J, Hou L, Hu X, Kellis M, Han X, Xiong X. Crosstalk between epitranscriptomic and epigenomic modifications and its implication in human diseases. CELL GENOMICS 2024; 4:100605. [PMID: 38981476 DOI: 10.1016/j.xgen.2024.100605] [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: 01/10/2024] [Revised: 04/17/2024] [Accepted: 06/14/2024] [Indexed: 07/11/2024]
Abstract
Crosstalk between N6-methyladenosine (m6A) and epigenomes is crucial for gene regulation, but its regulatory directionality and disease significance remain unclear. Here, we utilize quantitative trait loci (QTLs) as genetic instruments to delineate directional maps of crosstalk between m6A and two epigenomic traits, DNA methylation (DNAme) and H3K27ac. We identify 47 m6A-to-H3K27ac and 4,733 m6A-to-DNAme and, in the reverse direction, 106 H3K27ac-to-m6A and 61,775 DNAme-to-m6A regulatory loci, with differential genomic location preference observed for different regulatory directions. Integrating these maps with complex diseases, we prioritize 20 genome-wide association study (GWAS) loci for neuroticism, depression, and narcolepsy in brain; 1,767 variants for asthma and expiratory flow traits in lung; and 249 for coronary artery disease, blood pressure, and pulse rate in muscle. This study establishes disease regulatory paths, such as rs3768410-DNAme-m6A-asthma and rs56104944-m6A-DNAme-hypertension, uncovering locus-specific crosstalk between m6A and epigenomic layers and offering insights into regulatory circuits underlying human diseases.
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Affiliation(s)
- Chengyu Li
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311121, China; State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China
| | - Kexuan Chen
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311121, China; State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China
| | - Qianchen Fang
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311121, China; State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China
| | - Shaohui Shi
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311121, China; State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China
| | - Jiuhong Nan
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311121, China; State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China
| | - Jialin He
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311121, China; State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China
| | - Yafei Yin
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Xiaoyu Li
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Jingyun Li
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China
| | - Lei Hou
- Department of Medicine, Biomedical Genetics Section, Boston University, Boston, MA 02118, USA
| | - Xinyang Hu
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310058, China
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Xikun Han
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Xushen Xiong
- The Second Affiliated Hospital & Liangzhu Laboratory, Zhejiang University School of Medicine, Hangzhou 311121, China; State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 311121, China.
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3
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Cao X, Su Y, Zhao T, Zhang Y, Cheng B, Xie K, Yu M, Allan A, Klee H, Chen K, Guan X, Zhang Y, Zhang B. Multi-omics analysis unravels chemical roadmap and genetic basis for peach fruit aroma improvement. Cell Rep 2024; 43:114623. [PMID: 39146179 DOI: 10.1016/j.celrep.2024.114623] [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: 05/03/2024] [Revised: 07/15/2024] [Accepted: 07/29/2024] [Indexed: 08/17/2024] Open
Abstract
Selection of fruits with enhanced health benefits and superior flavor is an important aspect of peach breeding. Understanding the genetic interplay between appearance and flavor chemicals remains a major challenge. We identify the most important volatiles contributing to consumer preferences for peach, thus establishing priorities for improving flavor quality. We quantify volatiles of a peach population consisting of 184 accessions and demonstrate major reductions in the important flavor volatiles linalool and Z-3-hexenyl acetate in red-fleshed accessions. We identify 474 functional gene regulatory networks (GRNs), among which GRN05 plays a crucial role in controlling both red flesh and volatile content through the NAM/ATAF1/2/CUC (NAC) transcription factor PpBL. Overexpressing PpBL results in reduced expression of PpNAC1, a positive regulator for Z-3-hexenyl acetate and linalool synthesis. Additionally, we identify haplotypes for three tandem PpAATs that are significantly correlated with reduced gene expression and ester content. We develop genetic resources for improvement of fruit quality.
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Affiliation(s)
- Xiangmei Cao
- Laboratory of Fruit Quality Biology/Zhejiang Key Laboratory of Horticultural Crop Quality Improvement, Zhejiang University, Zijingang Campus, Hangzhou 310058, China
| | - Yike Su
- Laboratory of Fruit Quality Biology/Zhejiang Key Laboratory of Horticultural Crop Quality Improvement, Zhejiang University, Zijingang Campus, Hangzhou 310058, China
| | - Ting Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Yuanyuan Zhang
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences/Jiangsu Key Laboratory of Horticultural Crop Genetic Improvement, Nanjing, Jiangsu 210014, China
| | - Bo Cheng
- Laboratory of Fruit Quality Biology/Zhejiang Key Laboratory of Horticultural Crop Quality Improvement, Zhejiang University, Zijingang Campus, Hangzhou 310058, China
| | - Kaili Xie
- Laboratory of Fruit Quality Biology/Zhejiang Key Laboratory of Horticultural Crop Quality Improvement, Zhejiang University, Zijingang Campus, Hangzhou 310058, China
| | - Mingliang Yu
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences/Jiangsu Key Laboratory of Horticultural Crop Genetic Improvement, Nanjing, Jiangsu 210014, China
| | - Andrew Allan
- The New Zealand Institute for Plant & Food Research Limited (Plant & Food Research) Mt Albert, Auckland Mail Centre, Private Bag 92169, Auckland 1142, New Zealand
| | - Harry Klee
- Laboratory of Fruit Quality Biology/Zhejiang Key Laboratory of Horticultural Crop Quality Improvement, Zhejiang University, Zijingang Campus, Hangzhou 310058, China
| | - Kunsong Chen
- Laboratory of Fruit Quality Biology/Zhejiang Key Laboratory of Horticultural Crop Quality Improvement, Zhejiang University, Zijingang Campus, Hangzhou 310058, China
| | - Xueying Guan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Yuyan Zhang
- Institute of Pomology, Jiangsu Academy of Agricultural Sciences/Jiangsu Key Laboratory of Horticultural Crop Genetic Improvement, Nanjing, Jiangsu 210014, China.
| | - Bo Zhang
- Laboratory of Fruit Quality Biology/Zhejiang Key Laboratory of Horticultural Crop Quality Improvement, Zhejiang University, Zijingang Campus, Hangzhou 310058, China; Hainan Institute of Zhejiang University, Sanya, Hainan 572000, China.
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Zhang L, Huang T, He H, Xu F, Yang C, Lu L, Tian G, Wang L, Mi J. Unraveling the molecular mechanisms of Ace2-mediated post-COVID-19 cognitive dysfunction through systems genetics approach. Exp Neurol 2024:114921. [PMID: 39142369 DOI: 10.1016/j.expneurol.2024.114921] [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: 04/03/2024] [Revised: 08/03/2024] [Accepted: 08/10/2024] [Indexed: 08/16/2024]
Abstract
The dysregulation of Angiotensin-converting enzyme 2 (ACE2) in central nervous system is believed associates with COVID-19 induced cognitive dysfunction. However, the detailed mechanism remains largely unknown. In this study, we performed a comprehensive system genetics analysis on hippocampal ACE2 based on BXD mice panel. Expression quantitative trait loci (eQTLs) mapping showed that Ace2 was strongly trans-regulated, and the elevation of Ace2 expression level was significantly correlated with impaired cognitive functions. Further Gene co-expression analysis showed that Ace2 may be correlated with the membrane proteins in Calcium signaling pathway. Further, qRT-PCR confirmed that SARS-CoV-2 spike S1 protein upregulated ACE2 expression together with eight membrane proteins in Calcium Signaling pathway. Moreover, such elevation can be attenuated by recombinant ACE2. Collectively, our findings revealed a potential mechanism of Ace2 in cognitive dysfunction, which could be beneficial for COVID-19-induced cognitive dysfunction prevention and potential treatment.
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Affiliation(s)
- Liyuan Zhang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai 264003, China
| | - Tingting Huang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai 264003, China
| | - Hongjie He
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai 264003, China
| | - Fuyi Xu
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai 264003, China
| | - Chunhua Yang
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai 264003, China
| | - Lu Lu
- University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - Geng Tian
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai 264003, China
| | - Lei Wang
- Harbin Medical University, Harbin 150086, Heilongjiang Province, China.
| | - Jia Mi
- Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Binzhou Medical University, Shandong, Yantai 264003, China.
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5
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Farhangi S, Gòdia M, Derks MFL, Harlizius B, Dibbits B, González-Prendes R, Crooijmans RPMA, Madsen O, Groenen MAM. Expression genome-wide association study identifies key regulatory variants enriched with metabolic and immune functions in four porcine tissues. BMC Genomics 2024; 25:684. [PMID: 38992576 PMCID: PMC11238464 DOI: 10.1186/s12864-024-10583-w] [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: 02/02/2024] [Accepted: 07/01/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Integration of high throughput DNA genotyping and RNA-sequencing data enables the discovery of genomic regions that regulate gene expression, known as expression quantitative trait loci (eQTL). In pigs, efforts to date have been mainly focused on purebred lines for traits with commercial relevance as such growth and meat quality. However, little is known on genetic variants and mechanisms associated with the robustness of an animal, thus its overall health status. Here, the liver, lung, spleen, and muscle transcriptomes of 100 three-way crossbred female finishers were studied, with the aim of identifying novel eQTL regulatory regions and transcription factors (TFs) associated with regulation of porcine metabolism and health-related traits. RESULTS An expression genome-wide association study with 535,896 genotypes and the expression of 12,680 genes in liver, 13,310 genes in lung, 12,650 genes in spleen, and 12,595 genes in muscle resulted in 4,293, 10,630, 4,533, and 6,871 eQTL regions for each of these tissues, respectively. Although only a small fraction of the eQTLs were annotated as cis-eQTLs, these presented a higher number of polymorphisms per region and significantly stronger associations with their target gene compared to trans-eQTLs. Between 20 and 115 eQTL hotspots were identified across the four tissues. Interestingly, these were all enriched for immune-related biological processes. In spleen, two TFs were identified: ERF and ZNF45, with key roles in regulation of gene expression. CONCLUSIONS This study provides a comprehensive analysis with more than 26,000 eQTL regions identified that are now publicly available. The genomic regions and their variants were mostly associated with tissue-specific regulatory roles. However, some shared regions provide new insights into the complex regulation of genes and their interactions that are involved with important traits related to metabolism and immunity.
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Affiliation(s)
- Samin Farhangi
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands.
| | - Martijn F L Derks
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Topigs Norsvin Research Center, 's-Hertogenbosch, The Netherlands
| | | | - Bert Dibbits
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Rayner González-Prendes
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Ausnutria BV, Zwolle, The Netherlands
| | | | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
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Weldy CS, Li Q, Monteiro JP, Guo H, Galls D, Gu W, Cheng PP, Ramste M, Li D, Palmisano BT, Sharma D, Worssam MD, Zhao Q, Bhate A, Kundu RK, Nguyen T, Li JB, Quertermous T. Smooth muscle expression of RNA editing enzyme ADAR1 controls vascular integrity and progression of atherosclerosis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.602569. [PMID: 39026721 PMCID: PMC11257488 DOI: 10.1101/2024.07.08.602569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Mapping the genomic architecture of complex disease has been predicated on the understanding that genetic variants influence disease risk through modifying gene expression. However, recent discoveries have revealed that a significant burden of disease heritability in common autoinflammatory disorders and coronary artery disease is mediated through genetic variation modifying post-transcriptional modification of RNA through adenosine-to-inosine (A-to-I) RNA editing. This common RNA modification is catalyzed by ADAR enzymes, where ADAR1 edits specific immunogenic double stranded RNA (dsRNA) to prevent activation of the double strand RNA (dsRNA) sensor MDA5 ( IFIH1 ) and stimulation of an interferon stimulated gene (ISG) response. Multiple lines of human genetic data indicate impaired RNA editing and increased dsRNA sensing to be an important mechanism of coronary artery disease (CAD) risk. Here, we provide a crucial link between observations in human genetics and mechanistic cell biology leading to progression of CAD. Through analysis of human atherosclerotic plaque, we implicate the vascular smooth muscle cell (SMC) to have a unique requirement for RNA editing, and that ISG induction occurs in SMC phenotypic modulation, implicating MDA5 activation. Through culture of human coronary artery SMCs, generation of a conditional SMC specific Adar1 deletion mouse model on a pro-atherosclerosis background, and with incorporation of single cell RNA sequencing cellular profiling, we further show that Adar1 controls SMC phenotypic state, is required to maintain vascular integrity, and controls progression of atherosclerosis and vascular calcification. Through this work, we describe a fundamental mechanism of CAD, where cell type and context specific RNA editing and sensing of dsRNA mediates disease progression, bridging our understanding of human genetics and disease causality.
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Wang Q, Antone J, Alsop E, Reiman R, Funk C, Bendl J, Dudley JT, Liang WS, Karr TL, Roussos P, Bennett DA, De Jager PL, Serrano GE, Beach TG, Van Keuren-Jensen K, Mastroeni D, Reiman EM, Readhead BP. Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease. Nat Commun 2024; 15:5815. [PMID: 38987616 PMCID: PMC11237088 DOI: 10.1038/s41467-024-49790-0] [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: 10/27/2023] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
The emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer's disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.
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Affiliation(s)
- Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Jerry Antone
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Eric Alsop
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Rebecca Reiman
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Jaroslav Bendl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Joel T Dudley
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Winnie S Liang
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Timothy L Karr
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Geidy E Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Thomas G Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | | | - Diego Mastroeni
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, 85006, USA
| | - Benjamin P Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA.
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Renganaath K, Albert FW. Trans-eQTL hotspots shape complex traits by modulating cellular states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.14.567054. [PMID: 38014174 PMCID: PMC10680915 DOI: 10.1101/2023.11.14.567054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits, but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.
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Affiliation(s)
- Kaushik Renganaath
- Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, MN 55455, USA
| | - Frank W Albert
- Department of Genetics, Cell Biology, & Development, University of Minnesota, Minneapolis, MN 55455, USA
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9
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Giel AS, Bigge J, Schumacher J, Maj C, Dasmeh P. Analysis of Evolutionary Conservation, Expression Level, and Genetic Association at a Genome-wide Scale Reveals Heterogeneity Across Polygenic Phenotypes. Mol Biol Evol 2024; 41:msae115. [PMID: 38865495 PMCID: PMC11247350 DOI: 10.1093/molbev/msae115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 03/22/2024] [Accepted: 05/03/2024] [Indexed: 06/14/2024] Open
Abstract
Understanding the expression level and evolutionary rate of associated genes with human polygenic diseases provides crucial insights into their disease-contributing roles. In this work, we leveraged genome-wide association studies (GWASs) to investigate the relationship between the genetic association and both the evolutionary rate (dN/dS) and expression level of human genes associated with the two polygenic diseases of schizophrenia and coronary artery disease. Our findings highlight a distinct variation in these relationships between the two diseases. Genes associated with both diseases exhibit a significantly greater variance in evolutionary rate compared to those implicated in monogenic diseases. Expanding our analyses to 4,756 complex traits in the GWAS atlas database, we unraveled distinct trait categories with a unique interplay among the evolutionary rate, expression level, and genetic association of human genes. In most polygenic traits, highly expressed genes were more associated with the polygenic phenotypes compared to lowly expressed genes. About 69% of polygenic traits displayed a negative correlation between genetic association and evolutionary rate, while approximately 30% of these traits showed a positive correlation between genetic association and evolutionary rate. Our results demonstrate the presence of a spectrum among complex traits, shaped by natural selection. Notably, at opposite ends of this spectrum, we find metabolic traits being more likely influenced by purifying selection, and immunological traits that are more likely shaped by positive selection. We further established the polygenic evolution portal (evopolygen.de) as a resource for investigating relationships and generating hypotheses in the field of human polygenic trait evolution.
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Affiliation(s)
- Ann-Sophie Giel
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | - Jessica Bigge
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | | | - Carlo Maj
- Centre for Human Genetics, Marburg University, Marburg, Germany
| | - Pouria Dasmeh
- Centre for Human Genetics, Marburg University, Marburg, Germany
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Institute for Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
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10
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Ahrens CW, Murray K, Mazanec RA, Ferguson S, Jones A, Tissue DT, Byrne M, Borevitz JO, Rymer PD. Genomic determinants, architecture, and constraints in drought-related traits in Corymbia calophylla. BMC Genomics 2024; 25:640. [PMID: 38937661 PMCID: PMC11209971 DOI: 10.1186/s12864-024-10531-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 06/14/2024] [Indexed: 06/29/2024] Open
Abstract
BACKGROUND Drought adaptation is critical to many tree species persisting under climate change, however our knowledge of the genetic basis for trees to adapt to drought is limited. This knowledge gap impedes our fundamental understanding of drought response and application to forest production and conservation. To improve our understanding of the genomic determinants, architecture, and trait constraints, we assembled a reference genome and detected ~ 6.5 M variants in 432 phenotyped individuals for the foundational tree Corymbia calophylla. RESULTS We found 273 genomic variants determining traits with moderate heritability (h2SNP = 0.26-0.64). Significant variants were predominantly in gene regulatory elements distributed among several haplotype blocks across all chromosomes. Furthermore, traits were constrained by frequent epistatic and pleiotropic interactions. CONCLUSIONS Our results on the genetic basis for drought traits in Corymbia calophylla have several implications for the ability to adapt to climate change: (1) drought related traits are controlled by complex genomic architectures with large haplotypes, epistatic, and pleiotropic interactions; (2) the most significant variants determining drought related traits occurred in regulatory regions; and (3) models incorporating epistatic interactions increase trait predictions. Our findings indicate that despite moderate heritability drought traits are likely constrained by complex genomic architecture potentially limiting trees response to climate change.
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Affiliation(s)
- Collin W Ahrens
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, 2753, Australia.
- Cesar Australia, Brunswick, VIC, 3058, Australia.
| | - Kevin Murray
- Research School of Biology, Australian National University, Canberra, ACT, 2600, Australia
| | - Richard A Mazanec
- Biodiversity and Conservation Science, Western Australian Department of Biodiversity, Conservation and Attractions, Kensington, WA, 6151, Australia
| | - Scott Ferguson
- Research School of Biology, Australian National University, Canberra, ACT, 2600, Australia
| | - Ashley Jones
- Research School of Biology, Australian National University, Canberra, ACT, 2600, Australia
| | - David T Tissue
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, 2753, Australia
| | - Margaret Byrne
- Biodiversity and Conservation Science, Western Australian Department of Biodiversity, Conservation and Attractions, Kensington, WA, 6151, Australia
| | - Justin O Borevitz
- Research School of Biology, Australian National University, Canberra, ACT, 2600, Australia
| | - Paul D Rymer
- Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, 2753, Australia
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11
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Huang H, Ji F, Hu C, Huang J, Liu F, Han Z, Liu L, Cao M, Fu G. Identifying Novel Proteins for Chronic Pain: Integration of Human Brain Proteomes and Genome-wide Association Data. THE JOURNAL OF PAIN 2024:104610. [PMID: 38909833 DOI: 10.1016/j.jpain.2024.104610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 06/11/2024] [Accepted: 06/13/2024] [Indexed: 06/25/2024]
Abstract
Numerous genome-wide association studies have identified risk genes for chronic pain, yet the mechanisms by which genetic variants modify susceptibility have remained elusive. We sought to identify key genes modulating chronic pain risk by regulating brain protein expression. We integrated brain proteomic data with the largest genome-wide dataset for multisite chronic pain (N = 387,649) in a proteome-wide association study (PWAS) using discovery and confirmatory proteomic datasets (N = 376 and 152) from the dorsolateral prefrontal cortex. Leveraging summary data-based Mendelian randomization and Bayesian colocalization analysis, we pinpointed potential causal genes, while a transcriptome-wide association study integrating 452 human brain transcriptomes investigated whether cis-effects on protein abundance extended to the transcriptome. Single-cell RNA-sequencing data and single-nucleus transcriptomic data revealed cell-type-specific expression patterns for identified causal genes in the dorsolateral prefrontal cortex and dorsal root ganglia (DRG), complemented by RNA microarray analysis of expression profiles in other pain-related brain regions. Of the 22 genes cis-regulating protein abundance identified by the discovery PWAS, 18 (82%) were deemed causal by summary data-based Mendelian randomization or Bayesian colocalization analysis analyses, with 7 of these 18 genes (39%) replicating in the confirmatory PWAS, including guanosine diphosphate-mannose pyrophosphorylase B, which also associated at the transcriptome level. Several causal genes exhibited selective expression in excitatory and inhibitory neurons, oligodendrocytes, and astrocytes, while most identified genes were expressed across additional pain-related brain regions. This integrative proteogenomic approach identified 18 high-confidence causal genes for chronic pain, regulated by cis-effects on brain protein levels, suggesting promising avenues for treatment research and indicating a contributory role for the DRG. PERSPECTIVE: The current post genome-wide association study analyses identified 18 high-confidence causal genes regulating chronic pain risk via cis-modulation of brain protein abundance, suggesting promising avenues for future chronic pain therapies. Additionally, the significant expression of these genes in the DRG indicated a potential contributory role, warranting further investigation.
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Affiliation(s)
- Haoquan Huang
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; Medical Research Center of Shenshan Medical Center, Sun Yat-Sen Memorial Hospital, Shanwei, China
| | - Fengtao Ji
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Chuwen Hu
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jingxuan Huang
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Fan Liu
- Medical Research Center of Shenshan Medical Center, Sun Yat-Sen Memorial Hospital, Shanwei, China
| | - Zhixiao Han
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Ling Liu
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Minghui Cao
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; Medical Research Center of Shenshan Medical Center, Sun Yat-Sen Memorial Hospital, Shanwei, China
| | - Ganglan Fu
- Department of Anesthesiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China; Medical Research Center of Shenshan Medical Center, Sun Yat-Sen Memorial Hospital, Shanwei, China.
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12
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Xiong Y, Wang T, Wang W, Zhang Y, Zhang F, Yuan J, Qin F, Wang X. Plasma proteome analysis implicates novel proteins as potential therapeutic targets for chronic kidney disease: A proteome-wide association study. Heliyon 2024; 10:e31704. [PMID: 38828357 PMCID: PMC11140797 DOI: 10.1016/j.heliyon.2024.e31704] [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: 01/13/2024] [Revised: 05/13/2024] [Accepted: 05/21/2024] [Indexed: 06/05/2024] Open
Abstract
Chronic kidney disease (CKD) is prevalent globally with limited therapeutic drugs available. To systemically identify novel proteins involved in the pathogenesis of CKD and possible therapeutic targets, we integrated human plasma proteomes with the genome-wide association studies (GWASs) of CKD, estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN) to perform proteome-wide association study (PWAS), Mendelian Randomization and Bayesian colocalization analyses. The single-cell RNA sequencing data of healthy human and mouse kidneys were analyzed to explore the cell-type specificity of identified genes. Functional enrichment analysis was conducted to investigate the involved signaling pathways. The PWAS identified 22 plasma proteins significantly associated with CKD. Of them, the significant associations of three proteins (INHBC, LMAN2, and SNUPN) were replicated in the GWASs of eGFR, and BUN. Mendelian Randomization analyses showed that INHBC and SNUPN were causally associated with CKD, eGFR, and BUN. The Bayesian colocalization analysis identified shared causal variants for INHBC in CKD, eGFR, and BUN (all PP4 > 0.75). The single-cell RNA sequencing revealed that the INHBC gene was sparsely scattered within the kidney cells. This proteomic study revealed that INHBC, LMAN2, and SNUPN may be involved in the pathogenesis of CKD, which represent novel therapeutic targets and warrant further exploration in future research.
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Affiliation(s)
- Yang Xiong
- Department of Urology and Andrology Laboratory, West China Hospital, Sichuan University, Sichuan, 610041, China
| | - Tianhong Wang
- Department of Anesthesiology, West China Hospital, Sichuan University, Sichuan, 610041, China
| | - Wei Wang
- Department of Urology and Andrology Laboratory, West China Hospital, Sichuan University, Sichuan, 610041, China
| | - Yangchang Zhang
- Department of Public Health, Capital Medical University, Beijing, 100000, China
| | - Fuxun Zhang
- Department of Urology, Tangdu Hospital, The Air Force Medical University, Xi'an, Shaanxi, 710000, China
| | - Jiuhong Yuan
- Department of Urology and Andrology Laboratory, West China Hospital, Sichuan University, Sichuan, 610041, China
| | - Feng Qin
- Department of Urology and Andrology Laboratory, West China Hospital, Sichuan University, Sichuan, 610041, China
| | - Xianding Wang
- Department of Urology and Andrology Laboratory, West China Hospital, Sichuan University, Sichuan, 610041, China
- Kidney Transplant Center, Transplant Center, West China Hospital, Sichuan University, Sichuan, 610041, China
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13
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Curion F, Theis FJ. Machine learning integrative approaches to advance computational immunology. Genome Med 2024; 16:80. [PMID: 38862979 PMCID: PMC11165829 DOI: 10.1186/s13073-024-01350-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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14
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Lindemann-Perez E, Perez JC. Candida albicans natural diversity: a resource to dissect fungal commensalism and pathogenesis. Curr Opin Microbiol 2024; 80:102493. [PMID: 38833793 DOI: 10.1016/j.mib.2024.102493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/02/2024] [Accepted: 05/22/2024] [Indexed: 06/06/2024]
Abstract
Candida albicans is a ubiquitous fungus of humans. It is not only a component of the oral and intestinal microbiota of most healthy adults but also a major cause of mucosal disorders and life-threatening disseminated infections. Until recently, research on the biology and pathogenesis of the fungus was largely based on a single clinical isolate. We review investigations that have started to dissect a diverse set of C. albicans strains. Using different approaches to leverage the species' phenotypic and/or genetic diversity, these studies illuminate the wide range of interactions between fungus and host. While connecting genetic variants to phenotypes of interest remains challenging, research on C. albicans' natural diversity is central to understand fungal commensalism and pathogenesis.
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Affiliation(s)
- Elena Lindemann-Perez
- Department of Microbiology and Molecular Genetics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, USA
| | - J Christian Perez
- Department of Microbiology and Molecular Genetics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, USA.
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15
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Caudal É, Loegler V, Dutreux F, Vakirlis N, Teyssonnière É, Caradec C, Friedrich A, Hou J, Schacherer J. Pan-transcriptome reveals a large accessory genome contribution to gene expression variation in yeast. Nat Genet 2024; 56:1278-1287. [PMID: 38778243 PMCID: PMC11176082 DOI: 10.1038/s41588-024-01769-9] [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: 02/14/2023] [Accepted: 04/24/2024] [Indexed: 05/25/2024]
Abstract
Gene expression is an essential step in the translation of genotypes into phenotypes. However, little is known about the transcriptome architecture and the underlying genetic effects at the species level. Here we generated and analyzed the pan-transcriptome of ~1,000 yeast natural isolates across 4,977 core and 1,468 accessory genes. We found that the accessory genome is an underappreciated driver of transcriptome divergence. Global gene expression patterns combined with population structure showed that variation in heritable expression mainly lies within subpopulation-specific signatures, for which accessory genes are overrepresented. Genome-wide association analyses consistently highlighted that accessory genes are associated with proportionally more variants with larger effect sizes, illustrating the critical role of the accessory genome on the transcriptional landscape within and between populations.
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Affiliation(s)
- Élodie Caudal
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Victor Loegler
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Fabien Dutreux
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | | | | | - Claudia Caradec
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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16
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Kopell BH, Kaji DA, Liharska LE, Vornholt E, Valentine A, Lund A, Hashemi A, Thompson RC, Lohrenz T, Johnson JS, Bussola N, Cheng E, Park YJ, Shah P, Ma W, Searfoss R, Qasim S, Miller GM, Chand NM, Aristel A, Humphrey J, Wilkins L, Ziafat K, Silk H, Linares LM, Sullivan B, Feng C, Batten SR, Bang D, Barbosa LS, Twomey T, White JP, Vannucci M, Hadj-Amar B, Cohen V, Kota P, Moya E, Rieder MK, Figee M, Nadkarni GN, Breen MS, Kishida KT, Scarpa J, Ruderfer DM, Narain NR, Wang P, Kiebish MA, Schadt EE, Saez I, Montague PR, Beckmann ND, Charney AW. Multiomic foundations of human prefrontal cortex tissue function. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.17.24307537. [PMID: 38798344 PMCID: PMC11118644 DOI: 10.1101/2024.05.17.24307537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The prefrontal cortex (PFC) is a region of the brain that in humans is involved in the production of higher-order functions such as cognition, emotion, perception, and behavior. Neurotransmission in the PFC produces higher-order functions by integrating information from other areas of the brain. At the foundation of neurotransmission, and by extension at the foundation of higher-order brain functions, are an untold number of coordinated molecular processes involving the DNA sequence variants in the genome, RNA transcripts in the transcriptome, and proteins in the proteome. These "multiomic" foundations are poorly understood in humans, perhaps in part because most modern studies that characterize the molecular state of the human PFC use tissue obtained when neurotransmission and higher-order brain functions have ceased (i.e., the postmortem state). Here, analyses are presented on data generated for the Living Brain Project (LBP) to investigate whether PFC tissue from individuals with intact higher-order brain function has characteristic multiomic foundations. Two complementary strategies were employed towards this end. The first strategy was to identify in PFC samples obtained from living study participants a signature of RNA transcript expression associated with neurotransmission measured intracranially at the time of PFC sampling, in some cases while participants performed a task engaging higher-order brain functions. The second strategy was to perform multiomic comparisons between PFC samples obtained from individuals with intact higher-order brain function at the time of sampling (i.e., living study participants) and PFC samples obtained in the postmortem state. RNA transcript expression within multiple PFC cell types was associated with fluctuations of dopaminergic, serotonergic, and/or noradrenergic neurotransmission in the substantia nigra measured while participants played a computer game that engaged higher-order brain functions. A subset of these associations - termed the "transcriptional program associated with neurotransmission" (TPAWN) - were reproduced in analyses of brain RNA transcript expression and intracranial neurotransmission data obtained from a second LBP cohort and from a cohort in an independent study. RNA transcripts involved in TPAWN were found to be (1) enriched for RNA transcripts associated with measures of neurotransmission in rodent and cell models, (2) enriched for RNA transcripts encoded by evolutionarily constrained genes, (3) depleted of RNA transcripts regulated by common DNA sequence variants, and (4) enriched for RNA transcripts implicated in higher-order brain functions by human population genetic studies. In PFC excitatory neurons of living study participants, higher expression of the genes in TPAWN tracked with higher expression of RNA transcripts that in rodent PFC samples are markers of a class of excitatory neurons that connect the PFC to deep brain structures. TPAWN was further reproduced by RNA transcript expression patterns differentiating living PFC samples from postmortem PFC samples, and significant differences between living and postmortem PFC samples were additionally observed with respect to (1) the expression of most primary RNA transcripts, mature RNA transcripts, and proteins, (2) the splicing of most primary RNA transcripts into mature RNA transcripts, (3) the patterns of co-expression between RNA transcripts and proteins, and (4) the effects of some DNA sequence variants on RNA transcript and protein expression. Taken together, this report highlights that studies of brain tissue obtained in a safe and ethical manner from large cohorts of living individuals can help advance understanding of the multiomic foundations of brain function.
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17
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Teyssonnière EM, Trébulle P, Muenzner J, Loegler V, Ludwig D, Amari F, Mülleder M, Friedrich A, Hou J, Ralser M, Schacherer J. Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast. Proc Natl Acad Sci U S A 2024; 121:e2319211121. [PMID: 38696467 PMCID: PMC11087752 DOI: 10.1073/pnas.2319211121] [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: 11/02/2023] [Accepted: 03/25/2024] [Indexed: 05/04/2024] Open
Abstract
Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein coexpression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.
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Affiliation(s)
- Elie Marcel Teyssonnière
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Pauline Trébulle
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
| | - Julia Muenzner
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Victor Loegler
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Daniela Ludwig
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Fatma Amari
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
| | - Anne Friedrich
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Jing Hou
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
| | - Markus Ralser
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, OxfordOX3 7BN, United Kingdom
- Department of Biochemistry, Charitéplatz 1, Charité – Universitätsmedizin Berlin, Berlin10117, Germany
- Max Planck Institute for Molecular Genetics, Berlin14195, Germany
| | - Joseph Schacherer
- UMR 7156 Génétique Moléculaire, Génomique et Microbiologie, Université de Strasbourg, CNRS, Strasbourg67000, France
- Institut Universitaire de France, Paris75000, France
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18
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De-Kayne R, Perry BW, McGowan KL, Landers J, Arias-Rodriguez L, Greenway R, Rodríguez Peña CM, Tobler M, Kelley JL. Evolutionary Rate Shifts in Coding and Regulatory Regions Underpin Repeated Adaptation to Sulfidic Streams in Poeciliid Fishes. Genome Biol Evol 2024; 16:evae087. [PMID: 38788745 PMCID: PMC11126329 DOI: 10.1093/gbe/evae087] [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] [Accepted: 04/13/2024] [Indexed: 05/26/2024] Open
Abstract
Adaptation to extreme environments often involves the evolution of dramatic physiological changes. To better understand how organisms evolve these complex phenotypic changes, the repeatability and predictability of evolution, and possible constraints on adapting to an extreme environment, it is important to understand how adaptive variation has evolved. Poeciliid fishes represent a particularly fruitful study system for investigations of adaptation to extreme environments due to their repeated colonization of toxic hydrogen sulfide-rich springs across multiple species within the clade. Previous investigations have highlighted changes in the physiology and gene expression in specific species that are thought to facilitate adaptation to hydrogen sulfide-rich springs. However, the presence of adaptive nucleotide variation in coding and regulatory regions and the degree to which convergent evolution has shaped the genomic regions underpinning sulfide tolerance across taxa are unknown. By sampling across seven independent lineages in which nonsulfidic lineages have colonized and adapted to sulfide springs, we reveal signatures of shared evolutionary rate shifts across the genome. We found evidence of genes, promoters, and putative enhancer regions associated with both increased and decreased convergent evolutionary rate shifts in hydrogen sulfide-adapted lineages. Our analysis highlights convergent evolutionary rate shifts in sulfidic lineages associated with the modulation of endogenous hydrogen sulfide production and hydrogen sulfide detoxification. We also found that regions with shifted evolutionary rates in sulfide spring fishes more often exhibited convergent shifts in either the coding region or the regulatory sequence of a given gene, rather than both.
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Affiliation(s)
- Rishi De-Kayne
- Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA 95060, USA
| | - Blair W Perry
- School of Biological Sciences, Washington State University, Pullman, WA 99164, USA
| | - Kerry L McGowan
- School of Biological Sciences, Washington State University, Pullman, WA 99164, USA
| | - Jake Landers
- School of Biological Sciences, Washington State University, Pullman, WA 99164, USA
| | - Lenin Arias-Rodriguez
- División Académica de Ciencias Biológicas, Universidad Juárez Autónoma de Tabasco (UJAT), Villahermosa, México
| | - Ryan Greenway
- Division of Biology, Kansas State University, Manhattan, KS 66506, USA
| | - Carlos M Rodríguez Peña
- Instituto de Investigaciones Botánicas y Zoológicas, Universidad Autónoma de Santo Domingo, Santo Domingo 10105, Dominican Republic
| | - Michael Tobler
- Department of Biology, University of Missouri–St. Louis, St. Louis, MO 63131, USA
- Whitney R. Harris World Ecology Center, University of Missouri–St. Louis, St. Louis, MO 63121, USA
- WildCare Institute, Saint Louis Zoo, St. Louis, MO 63110, USA
| | - Joanna L Kelley
- Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA 95060, USA
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19
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Lee DJ, Moon JS, Song DK, Lee YS, Kim DS, Cho NJ, Gil HW, Lee EY, Park S. Genome-wide association study and fine-mapping on Korean biobank to discover renal trait-associated variants. Kidney Res Clin Pract 2024; 43:299-312. [PMID: 37919891 PMCID: PMC11181046 DOI: 10.23876/j.krcp.23.079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 06/15/2023] [Accepted: 06/20/2023] [Indexed: 11/04/2023] Open
Abstract
BACKGROUND Chronic kidney disease is a significant health burden worldwide, with increasing incidence. Although several genome- wide association studies (GWAS) have investigated single nucleotide polymorphisms (SNP) associated with kidney trait, most studies were focused on European ancestry. METHODS We utilized clinical and genetic information collected from the Korean Genome and Epidemiology Study (KoGES). RESULTS More than five million SNPs from 58,406 participants were analyzed. After meta-GWAS, 1,360 loci associated with estimated glomerular filtration rate (eGFR) at a genome-wide significant level (p = 5 × 10-8) were identified. Among them, 399 loci were validated with at least one other biomarker (blood urea nitrogen [BUN] or eGFRcysC) and 149 loci were validated using both markers. Among them, 18 SNPs (nine known ones and nine novel ones) with 20 putative genes were found. The aggregated effect of genes estimated by MAGMA gene analysis showed that these significant genes were enriched in kidney-associated pathways, with the kidney and liver being the most enriched tissues. CONCLUSION In this study, we conducted GWAS for more than 50,000 Korean individuals and identified several variants associated with kidney traits, including eGFR, BUN, and eGFRcysC. We also investigated functions of relevant genes using computational methods to define putative causal variants.
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Affiliation(s)
- Dong-Jin Lee
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Jong-Seok Moon
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, Republic of Korea
| | - Dae Kwon Song
- Department of Biology, College of Natural Sciences, Soonchunhyang University, Asan, Republic of Korea
- Support Center (Core-Facility) for Bio-Bigdata Analysis and Utilization of Biological Resources, Soonchunhyang University, Asan, Republic of Korea
| | - Yong Seok Lee
- Department of Biology, College of Natural Sciences, Soonchunhyang University, Asan, Republic of Korea
- Support Center (Core-Facility) for Bio-Bigdata Analysis and Utilization of Biological Resources, Soonchunhyang University, Asan, Republic of Korea
| | - Dong-Sub Kim
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Nam-Jun Cho
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Hyo-Wook Gil
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
| | - Eun Young Lee
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
- Institute of Tissue Regeneration, Soonchunhyang University College of Medicine, Cheonan, Republic of Korea
| | - Samel Park
- Department of Internal Medicine, Soonchunhyang University Cheonan Hospital, Cheonan, Republic of Korea
- Department of Integrated Biomedical Science, Soonchunhyang Institute of Medi-bio Science (SIMS), Soonchunhyang University, Cheonan, Republic of Korea
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20
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Cai W, Hu J, Zhang Y, Guo Z, Zhou Z, Hou S. Cis-eQTLs in seven duck tissues identify novel candidate genes for growth and carcass traits. BMC Genomics 2024; 25:429. [PMID: 38689208 PMCID: PMC11061949 DOI: 10.1186/s12864-024-10338-7] [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: 10/29/2023] [Accepted: 04/23/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Expression quantitative trait loci (eQTL) studies aim to understand the influence of genetic variants on gene expression. The colocalization of eQTL mapping and GWAS strategy could help identify essential candidate genes and causal DNA variants vital to complex traits in human and many farm animals. However, eQTL mapping has not been conducted in ducks. It is desirable to know whether eQTLs within GWAS signals contributed to duck economic traits. RESULTS In this study, we conducted an eQTL analysis using publicly available RNA sequencing data from 820 samples, focusing on liver, muscle, blood, adipose, ovary, spleen, and lung tissues. We identified 113,374 cis-eQTLs for 12,266 genes, a substantial fraction 39.1% of which were discovered in at least two tissues. The cis-eQTLs of blood were less conserved across tissues, while cis-eQTLs from any tissue exhibit a strong sharing pattern to liver tissue. Colocalization between cis-eQTLs and genome-wide association studies (GWAS) of 50 traits uncovered new associations between gene expression and potential loci influencing growth and carcass traits. SRSF4, GSS, and IGF2BP1 in liver, NDUFC2 in muscle, ELF3 in adipose, and RUNDC1 in blood could serve as the candidate genes for duck growth and carcass traits. CONCLUSIONS Our findings highlight substantial differences in genetic regulation of gene expression across duck primary tissues, shedding light on potential mechanisms through which candidate genes may impact growth and carcass traits. Furthermore, this availability of eQTL data offers a valuable resource for deciphering further genetic association signals that may arise from ongoing extensive endeavors aimed at enhancing duck production traits.
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Affiliation(s)
- Wentao Cai
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Jian Hu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Yunsheng Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Zhanbao Guo
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Zhengkui Zhou
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China
| | - Shuisheng Hou
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, China.
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21
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Gu CC, Matter A, Turner A, Aggarwal P, Yang W, Sun X, Hunt SC, Lewis CE, Arnett DK, Anson B, Kattman S, Broeckel U. Transcriptional Variabilities in Human hiPSC-derived Cardiomyocytes: All Genes Are Not Equal and Their Robustness May Foretell Donor's Disease Susceptibility. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.18.584138. [PMID: 38659937 PMCID: PMC11042381 DOI: 10.1101/2024.04.18.584138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Human induced pluripotent stem cells (hiPSCs) are frequently used to study disease-associated variations. We characterized transcriptional variability from a hiPSC-derived cardiomyocyte (hiPSC-CM) study of left ventricular hypertrophy (LVH) using donor samples from the HyperGEN study. Multiple hiPSC-CM differentiations over reprogramming events (iPSC generation) across 7 donors were used to assess variabilities from reprogramming, differentiation, and donor LVH status. Variability arising from pathological alterations was assessed using a cardiac stimulant applied to the hiPSC-CMs to trigger hypertrophic responses. We found that for most genes (73.3%~85.5%), technical variability was smaller than biological variability. Further, we identified and characterized lists of "noise" genes showing greater technical variability and "signal" genes showing greater biological variability. Together, they support a "genetic robustness" hypothesis of disease-modeling whereby cellular response to relevant stimuli in hiPSC-derived somatic cells from diseased donors tends to show more transcriptional variability. Our findings suggest that hiPSC-CMs can provide a valid model for cardiac hypertrophy and distinguish between technical and disease-relevant transcriptional changes.
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22
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Lu Z, Wang X, Carr M, Kim A, Gazal S, Mohammadi P, Wu L, Gusev A, Pirruccello J, Kachuri L, Mancuso N. Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.15.24305836. [PMID: 38699369 PMCID: PMC11065034 DOI: 10.1101/2024.04.15.24305836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.
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Affiliation(s)
- Zeyun Lu
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Xinran Wang
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew Carr
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Artem Kim
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Steven Gazal
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children’s Research Institute, Seattle, WA, USA
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaiʻi Cancer Center, University of Hawaiʻi at Mānoa, Honolulu, HI, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - James Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA
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Jeong R, Bulyk ML. Chromatin accessibility variation provides insights into missing regulation underlying immune-mediated diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.12.589213. [PMID: 38659802 PMCID: PMC11042205 DOI: 10.1101/2024.04.12.589213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Most genetic loci associated with complex traits and diseases through genome-wide association studies (GWAS) are noncoding, suggesting that the causal variants likely have gene regulatory effects. However, only a small number of loci have been linked to expression quantitative trait loci (eQTLs) detected currently. To better understand the potential reasons for many trait-associated loci lacking eQTL colocalization, we investigated whether chromatin accessibility QTLs (caQTLs) in lymphoblastoid cell lines (LCLs) explain immune-mediated disease associations that eQTLs in LCLs did not. The power to detect caQTLs was greater than that of eQTLs and was less affected by the distance from the transcription start site of the associated gene. Meta-analyzing LCL eQTL data to increase the sample size to over a thousand led to additional loci with eQTL colocalization, demonstrating that insufficient statistical power is still likely to be a factor. Moreover, further eQTL colocalization loci were uncovered by surveying eQTLs of other immune cell types. Altogether, insufficient power and context-specificity of eQTLs both contribute to the 'missing regulation.'
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Affiliation(s)
- Raehoon Jeong
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
| | - Martha L. Bulyk
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Bioinformatics and Integrative Genomics Graduate Program, Harvard University, Cambridge, MA 02138, USA
- Department of Pathology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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24
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Rentzsch P, Kollotzek A, Mohammadi P, Lappalainen T. Recalibrating differential gene expression by genetic dosage variance prioritizes functionally relevant genes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.10.588830. [PMID: 38645217 PMCID: PMC11030425 DOI: 10.1101/2024.04.10.588830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Differential expression (DE) analysis is a widely used method for identifying genes that are functionally relevant for an observed phenotype or biological response. However, typical DE analysis includes selection of genes based on a threshold of fold change in expression under the implicit assumption that all genes are equally sensitive to dosage changes of their transcripts. This tends to favor highly variable genes over more constrained genes where even small changes in expression may be biologically relevant. To address this limitation, we have developed a method to recalibrate each gene's differential expression fold change based on genetic expression variance observed in the human population. The newly established metric ranks statistically differentially expressed genes not by nominal change of expression, but by relative change in comparison to natural dosage variation for each gene. We apply our method to RNA sequencing datasets from rare disease and in-vitro stimulus response experiments. Compared to the standard approach, our method adjusts the bias in discovery towards highly variable genes, and enriches for pathways and biological processes related to metabolic and regulatory activity, indicating a prioritization of functionally relevant driver genes. With that, our method provides a novel view on DE and contributes towards bridging the existing gap between statistical and biological significance. We believe that this approach will simplify the identification of disease causing genes and enhance the discovery of therapeutic targets.
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Affiliation(s)
- Philipp Rentzsch
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Aaron Kollotzek
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
| | - Pejman Mohammadi
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA; Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA; Department of Genome Science, University of Washington, Seattle, WA, USA
| | - Tuuli Lappalainen
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Solna, Sweden
- New York Genome Center, New York, NY, USA
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25
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Delgado D, Gillard M, Tong L, Demanelis K, Oliva M, Gleason KJ, Chernoff M, Chen L, Paner GP, Vander Griend D, Pierce BL. The Impact of Inherited Genetic Variation on DNA Methylation in Prostate Cancer and Benign Tissues of African American and European American Men. Cancer Epidemiol Biomarkers Prev 2024; 33:557-566. [PMID: 38294689 PMCID: PMC10990789 DOI: 10.1158/1055-9965.epi-23-0849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/29/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND American men of African ancestry (AA) have higher prostate cancer incidence and mortality rates compared with American men of European ancestry (EA). Differences in genetic susceptibility mechanisms may contribute to this disparity. METHODS To gain insights into the regulatory mechanisms of prostate cancer susceptibility variants, we tested the association between SNPs and DNA methylation (DNAm) at nearby CpG sites across the genome in benign and cancer prostate tissue from 74 AA and 74 EA men. Genome-wide SNP data (from benign tissue) and DNAm were generated using Illumina arrays. RESULTS Among AA men, we identified 6,298 and 2,641 cis-methylation QTLs (meQTL; FDR of 0.05) in benign and tumor tissue, respectively, with 6,960 and 1,700 detected in EA men. We leveraged genome-wide association study (GWAS) summary statistics to identify previously reported prostate cancer GWAS signals likely to share a common causal variant with a detected meQTL. We identified nine GWAS-meQTL pairs with strong evidence of colocalization (four in EA benign, three in EA tumor, two in AA benign, and three in AA tumor). Among these colocalized GWAS-meQTL pairs, we identified colocalizing expression quantitative trait loci (eQTL) impacting four eGenes with known roles in tumorigenesis. CONCLUSIONS These findings highlight epigenetic regulatory mechanisms by which prostate cancer-risk SNPs can modify local DNAm and/or gene expression in prostate tissue. IMPACT Overall, our findings showed general consistency in the meQTL landscape of AA and EA men, but meQTLs often differ by tissue type (normal vs. cancer). Ancestry-based linkage disequilibrium differences and lack of AA representation in GWAS decrease statistical power to detect colocalization for some regions.
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Affiliation(s)
- Dayana Delgado
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637
| | - Marc Gillard
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637
| | - Lin Tong
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637
| | - Kathryn Demanelis
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA 15261
- UPMC Hillman Cancer Center, Pittsburgh, PA 15232
| | - Meritxell Oliva
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637
- Genomics Research Center, AbbVie, North Chicago, IL 60064
| | | | - Meytal Chernoff
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637
- Interdisciplinary Scientist Training Program, University of Chicago, Chicago, IL, USA
- University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - Lin Chen
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637
| | - Gladell P. Paner
- Department of Pathology, University of Chicago, Chicago, IL 60637
| | - Donald Vander Griend
- Department of Pathology, University of Illinois at Chicago, Chicago, IL 60607
- The University of Illinois Cancer Center, Chicago, IL
| | - Brandon L. Pierce
- Department of Public Health Sciences, University of Chicago, Chicago, IL 60637
- Department of Human Genetics, University of Chicago, Chicago, IL 60615
- Comprehensive Cancer Center, University of Chicago, Chicago, IL 60637
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26
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Tsouris A, Brach G, Friedrich A, Hou J, Schacherer J. Diallel panel reveals a significant impact of low-frequency genetic variants on gene expression variation in yeast. Mol Syst Biol 2024; 20:362-373. [PMID: 38355920 PMCID: PMC10987670 DOI: 10.1038/s44320-024-00021-0] [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: 09/15/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Unraveling the genetic sources of gene expression variation is essential to better understand the origins of phenotypic diversity in natural populations. Genome-wide association studies identified thousands of variants involved in gene expression variation, however, variants detected only explain part of the heritability. In fact, variants such as low-frequency and structural variants (SVs) are poorly captured in association studies. To assess the impact of these variants on gene expression variation, we explored a half-diallel panel composed of 323 hybrids originated from pairwise crosses of 26 natural Saccharomyces cerevisiae isolates. Using short- and long-read sequencing strategies, we established an exhaustive catalog of single nucleotide polymorphisms (SNPs) and SVs for this panel. Combining this dataset with the transcriptomes of all hybrids, we comprehensively mapped SNPs and SVs associated with gene expression variation. While SVs impact gene expression variation, SNPs exhibit a higher effect size with an overrepresentation of low-frequency variants compared to common ones. These results reinforce the importance of dissecting the heritability of complex traits with a comprehensive catalog of genetic variants at the population level.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Anne Friedrich
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR 7156, Strasbourg, France.
- Institut Universitaire de France (IUF), Paris, France.
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27
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Kadagandla S, Kapoor A. Identification of candidate causal cis -regulatory variants underlying electrocardiographic QT interval GWAS loci. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.13.584880. [PMID: 38585875 PMCID: PMC10996567 DOI: 10.1101/2024.03.13.584880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Identifying causal variants among tens or hundreds of associated variants at each locus mapped by genome-wide association studies (GWAS) of complex traits is a challenge. As vast majority of GWAS variants are noncoding, sequence variation at cis -regulatory elements affecting transcriptional expression of specific genes is a widely accepted molecular hypothesis. Following this cis -regulatory hypothesis and combining it with the observation that nucleosome-free open chromatin is a universal hallmark of all types of cis -regulatory elements, we aimed to identify candidate causal regulatory variants underlying electrocardiographic QT interval GWAS loci. At a dozen loci, selected for higher effect sizes and a better understanding of the likely causal gene, we identified and included all common variants in high linkage disequilibrium with the GWAS variants as candidate variants. Using ENCODE DNase-seq and ATAC-seq from multiple human adult cardiac left ventricle tissue samples, we generated genome-wide maps of open chromatin regions marking putative regulatory elements. QT interval associated candidate variants were filtered for overlap with cardiac left ventricle open chromatin regions to identify candidate causal cis -regulatory variants, which were further assessed for colocalizing with a known cardiac GTEx expression quantitative trait locus variant as additional evidence for their causal role. Together, these efforts have generated a comprehensive set of candidate causal variants that are expected to be enriched for cis -regulatory potential and thereby, explaining the observed genetic associations.
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28
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Penner-Goeke S, Binder EB. Linking environmental factors and gene regulation. eLife 2024; 13:e96710. [PMID: 38497535 PMCID: PMC10948141 DOI: 10.7554/elife.96710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
A technique called mSTARR-seq sheds light on how DNA methylation may shape responses to external stimuli by altering the activity of sequences that control gene expression.
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Affiliation(s)
- Signe Penner-Goeke
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
| | - Elisabeth B Binder
- Department of Genes and Environment, Max Planck Institute of Psychiatry, Munich, Germany
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29
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Daher G, Santos-Bezerra DP, Cavaleiro AM, Pelaes TS, Admoni SN, Perez RV, Machado CG, do Amaral FG, Cipolla-Neto J, Correa-Giannella ML. Rs4862705 in the melatonin receptor 1A gene is associated with renal function decline in type 1 diabetes individuals. Front Endocrinol (Lausanne) 2024; 15:1331012. [PMID: 38549765 PMCID: PMC10972958 DOI: 10.3389/fendo.2024.1331012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/21/2024] [Indexed: 04/02/2024] Open
Abstract
Aim The pathogenesis of chronic diabetes complications has oxidative stress as one of the major elements, and single-nucleotide polymorphisms (SNPs) in genes belonging to antioxidant pathways modulate susceptibility to these complications. Considering that melatonin is a powerful antioxidant compound, our aim was to explore, in a longitudinal cohort study of type 1 diabetes (T1D) individuals, the association of microvascular complications and SNPs in the gene encoding melatonin receptor 1A (MTNR1A). Methods Eight SNPs in MTNR1A were genotyped in 489 T1D individuals. Besides cross-sectional analyses of SNPs with each one of the microvascular complications (distal polyneuropathy, cardiovascular autonomic neuropathy, retinopathy, and diabetic kidney disease), a longitudinal analysis evaluated the associations of SNPs with renal function decline in 411 individuals followed up for a median of 8 years. In a subgroup of participants, the association of complications with urinary 6-sulfatoxymelatonin (aMT6s) concentration was investigated. Results The group of individuals with a renal function decline ≥ 5 mL min-1 1.73 m-2 year-1 presented a higher frequency of the A allele of rs4862705 in comparison with nondecliners, even after adjustment for confounding variables (OR = 1.84, 95% CI = 1.20-2.82; p = 0.0046). No other significant associations were found. Conclusions This is the first study showing an association between a variant in a gene belonging to the melatonin system and renal function decline in the diabetic setting.
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Affiliation(s)
- Gustavo Daher
- Laboratório de Carboidratos e Radioimunoensaios (LIM-18), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Daniele Pereira Santos-Bezerra
- Laboratório de Carboidratos e Radioimunoensaios (LIM-18), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
- Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Ana Mercedes Cavaleiro
- Laboratório de Carboidratos e Radioimunoensaios (LIM-18), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Tatiana Souza Pelaes
- Laboratório de Carboidratos e Radioimunoensaios (LIM-18), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Sharon Nina Admoni
- Laboratório de Carboidratos e Radioimunoensaios (LIM-18), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Ricardo Vessoni Perez
- Laboratório de Carboidratos e Radioimunoensaios (LIM-18), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Cleide Guimarães Machado
- Divisão de Oftalmologia, Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Fernanda Gaspar do Amaral
- Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
- Department of Physiology, Federal University of São Paulo (UNIFESP), São Paulo, Brazil
| | - José Cipolla-Neto
- Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Maria Lúcia Correa-Giannella
- Laboratório de Carboidratos e Radioimunoensaios (LIM-18), Hospital das Clínicas HCFMUSP, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
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Schneider H, Krizanac AM, Falker-Gieske C, Heise J, Tetens J, Thaller G, Bennewitz J. Genomic dissection of the correlation between milk yield and various health traits using functional and evolutionary information about imputed sequence variants of 34,497 German Holstein cows. BMC Genomics 2024; 25:265. [PMID: 38461236 DOI: 10.1186/s12864-024-10115-6] [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: 08/17/2023] [Accepted: 02/13/2024] [Indexed: 03/11/2024] Open
Abstract
BACKGROUND Over the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM. RESULTS In general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits` genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets` high effect. CONCLUSION Our results confirm the connection of milk production and health traits in dairy cattle via the animals` metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits` connection in more detail.
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Affiliation(s)
- Helen Schneider
- Institute of Animal Science, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Ana-Marija Krizanac
- Department of Animal Sciences, University of Göttingen, 37077, Göttingen, Germany
| | | | - Johannes Heise
- Vereinigte Informationssysteme Tierhaltung w.V. (VIT), 27283, Verden, Germany
| | - Jens Tetens
- Department of Animal Sciences, University of Göttingen, 37077, Göttingen, Germany
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts University of Kiel, 24098, Kiel, Germany
| | - Jörn Bennewitz
- Institute of Animal Science, University of Hohenheim, 70599, Stuttgart, Germany
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31
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O'Brien CL, Summers KM, Martin NM, Carter-Cusack D, Yang Y, Barua R, Dixit OVA, Hume DA, Pavli P. The relationship between extreme inter-individual variation in macrophage gene expression and genetic susceptibility to inflammatory bowel disease. Hum Genet 2024; 143:233-261. [PMID: 38421405 PMCID: PMC11043138 DOI: 10.1007/s00439-024-02642-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/14/2024] [Indexed: 03/02/2024]
Abstract
The differentiation of resident intestinal macrophages from blood monocytes depends upon signals from the macrophage colony-stimulating factor receptor (CSF1R). Analysis of genome-wide association studies (GWAS) indicates that dysregulation of macrophage differentiation and response to microorganisms contributes to susceptibility to chronic inflammatory bowel disease (IBD). Here, we analyzed transcriptomic variation in monocyte-derived macrophages (MDM) from affected and unaffected sib pairs/trios from 22 IBD families and 6 healthy controls. Transcriptional network analysis of the data revealed no overall or inter-sib distinction between affected and unaffected individuals in basal gene expression or the temporal response to lipopolysaccharide (LPS). However, the basal or LPS-inducible expression of individual genes varied independently by as much as 100-fold between subjects. Extreme independent variation in the expression of pairs of HLA-associated transcripts (HLA-B/C, HLA-A/F and HLA-DRB1/DRB5) in macrophages was associated with HLA genotype. Correlation analysis indicated the downstream impacts of variation in the immediate early response to LPS. For example, variation in early expression of IL1B was significantly associated with local SNV genotype and with subsequent peak expression of target genes including IL23A, CXCL1, CXCL3, CXCL8 and NLRP3. Similarly, variation in early IFNB1 expression was correlated with subsequent expression of IFN target genes. Our results support the view that gene-specific dysregulation in macrophage adaptation to the intestinal milieu is associated with genetic susceptibility to IBD.
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Affiliation(s)
- Claire L O'Brien
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Kim M Summers
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Natalia M Martin
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Dylan Carter-Cusack
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Yuanhao Yang
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia
| | - Rasel Barua
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia
| | - Ojas V A Dixit
- Centre for Research in Therapeutics Solutions, Faculty of Science and Technology, University of Canberra, Canberra, ACT, Australia
| | - David A Hume
- Mater Research Institute-University of Queensland, Translational Research Institute, Brisbane, QLD, Australia.
| | - Paul Pavli
- Inflammatory Bowel Disease Research Group, Canberra Hospital, Canberra, ACT, Australia.
- School of Medicine and Psychology, College of Health and Medicine, Australian National University, Canberra, ACT, Australia.
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Lappalainen T, Li YI, Ramachandran S, Gusev A. Genetic and molecular architecture of complex traits. Cell 2024; 187:1059-1075. [PMID: 38428388 DOI: 10.1016/j.cell.2024.01.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 12/20/2023] [Accepted: 01/16/2024] [Indexed: 03/03/2024]
Abstract
Human genetics has emerged as one of the most dynamic areas of biology, with a broadening societal impact. In this review, we discuss recent achievements, ongoing efforts, and future challenges in the field. Advances in technology, statistical methods, and the growing scale of research efforts have all provided many insights into the processes that have given rise to the current patterns of genetic variation. Vast maps of genetic associations with human traits and diseases have allowed characterization of their genetic architecture. Finally, studies of molecular and cellular effects of genetic variants have provided insights into biological processes underlying disease. Many outstanding questions remain, but the field is well poised for groundbreaking discoveries as it increases the use of genetic data to understand both the history of our species and its applications to improve human health.
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Affiliation(s)
- Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
| | - Yang I Li
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA; Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Sohini Ramachandran
- Ecology, Evolution and Organismal Biology, Center for Computational Molecular Biology, and the Data Science Institute, Brown University, Providence, RI 029129, USA
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
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Zou D, Cai Y, Jin M, Zhang M, Liu Y, Chen S, Yang S, Zhang H, Zhu X, Huang C, Zhu Y, Miao X, Wei Y, Yang X, Tian J. A genetic variant in the immune-related gene ERAP1 affects colorectal cancer prognosis. Chin Med J (Engl) 2024; 137:431-440. [PMID: 37690994 PMCID: PMC10876254 DOI: 10.1097/cm9.0000000000002845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Indexed: 09/12/2023] Open
Abstract
BACKGROUND Findings on the association of genetic factors and colorectal cancer (CRC) survival are limited and inconsistent, and revealing the mechanism underlying their prognostic roles is of great importance. This study aimed to explore the relationship between functional genetic variations and the prognosis of CRC and further reveal the possible mechanism. METHODS We first systematically performed expression quantitative trait locus (eQTL) analysis using The Cancer Genome Atlas (TCGA) dataset. Then, the Kaplan-Meier analysis was used to filter out the survival-related eQTL target genes of CRC patients in two public datasets (TCGA and GSE39582 dataset from the Gene Expression Omnibus database). The seven most potentially functional eQTL single nucleotide polymorphisms (SNPs) associated with six survival-related eQTL target genes were genotyped in 907 Chinese CRC patients with clinical prognosis data. The regulatory mechanism of the survival-related SNP was further confirmed by functional experiments. RESULTS The rs71630754 regulating the expression of endoplasmic reticulum aminopeptidase 1 ( ERAP1 ) was significantly associated with the prognosis of CRC (additive model, hazard ratio [HR]: 1.43, 95% confidence interval [CI]: 1.08-1.88, P = 0.012). The results of dual-luciferase reporter assay and electrophoretic mobility shift assay showed that the A allele of the rs71630754 could increase the binding of transcription factor 3 (TCF3) and subsequently reduce the expression of ERAP1 . The results of bioinformatic analysis showed that lower expression of ERAP1 could affect the tumor immune microenvironment and was significantly associated with severe survival outcomes. CONCLUSION The rs71630754 could influence the prognosis of CRC patients by regulating the expression of the immune-related gene ERAP1 . TRIAL REGISTRATION No. NCT00454519 ( https://clinicaltrials.gov/ ).
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Affiliation(s)
- Danyi Zou
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430022, China
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Yimin Cai
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Meng Jin
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Ming Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Yizhuo Liu
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Shuoni Chen
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Shuhui Yang
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Heng Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Xu Zhu
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Chaoqun Huang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430071, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, Hubei 430030, China
- Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Yongchang Wei
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Xiaojun Yang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, Hubei 430071, China
| | - Jianbo Tian
- Department of Epidemiology and Biostatistics, School of Public Health, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, Hubei 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
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Zhang Z, Liu X, Zhang S, Song Z, Lu K, Yang W. A review and analysis of key biomarkers in Alzheimer's disease. Front Neurosci 2024; 18:1358998. [PMID: 38445255 PMCID: PMC10912539 DOI: 10.3389/fnins.2024.1358998] [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: 12/20/2023] [Accepted: 02/02/2024] [Indexed: 03/07/2024] Open
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein-protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.
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Affiliation(s)
- Zhihao Zhang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Xiangtao Liu
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Suixia Zhang
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- State Key Laboratory of Pathogenesis, Prevention, Treatment of Central Asian High Incidence Diseases, First Affiliated Hospital of Xinjiang Medical University, Ürümqi, China
| | - Zhixin Song
- College of Medical Engineering and Technology, Xinjiang Medical University, Ürümqi, China
| | - Ke Lu
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
| | - Wenzhong Yang
- School of Computer Science and Technology, Xinjiang University, Ürümqi, China
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35
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Diniz WJS, Afonso J, Kertz NC, Dyce PW, Banerjee P. Mapping Expression Quantitative Trait Loci Targeting Candidate Genes for Pregnancy in Beef Cows. Biomolecules 2024; 14:150. [PMID: 38397387 PMCID: PMC10886872 DOI: 10.3390/biom14020150] [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: 12/09/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/25/2024] Open
Abstract
Despite collective efforts to understand the complex regulation of reproductive traits, no causative genes and/or mutations have been reported yet. By integrating genomics and transcriptomics data, potential regulatory mechanisms may be unveiled, providing opportunities to dissect the genetic factors governing fertility. Herein, we identified regulatory variants from RNA-Seq data associated with gene expression regulation in the uterine luminal epithelial cells of beef cows. We identified 4676 cis and 7682 trans eQTLs (expression quantitative trait loci) affecting the expression of 1120 and 2503 genes, respectively (FDR < 0.05). These variants affected the expression of transcription factor coding genes (71 cis and 193 trans eQTLs) and genes previously reported as differentially expressed between pregnant and nonpregnant cows. Functional over-representation analysis highlighted pathways related to metabolism, immune response, and hormone signaling (estrogen and GnRH) affected by eQTL-regulated genes (p-value ≤ 0.01). Furthermore, eQTLs were enriched in QTL regions for 13 reproduction-related traits from the CattleQTLdb (FDR ≤ 0.05). Our study provides novel insights into the genetic basis of reproductive processes in cattle. The underlying causal mechanisms modulating the expression of uterine genes warrant further investigation.
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Affiliation(s)
- Wellison J. S. Diniz
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
| | - Juliana Afonso
- Embrapa Pecuária Sudeste, Rodovia Washington Luiz, Km 234, s/n, Fazenda Canchim, São Carlos 13560-970, SP, Brazil;
| | - Nicholas C. Kertz
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
| | - Paul W. Dyce
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
| | - Priyanka Banerjee
- Department of Animal Sciences, Auburn University, Auburn, AL 36849, USA; (N.C.K.); (P.W.D.); (P.B.)
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36
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Han D, Li Y, Wang L, Liang X, Miao Y, Li W, Wang S, Wang Z. Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data. Brief Bioinform 2024; 25:bbae110. [PMID: 38517697 PMCID: PMC10959158 DOI: 10.1093/bib/bbae110] [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: 11/08/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/24/2024] Open
Abstract
Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.
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Affiliation(s)
- Dongmei Han
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Yurun Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Linxiao Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Xuan Liang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Yuanyuan Miao
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Wenran Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Zhen Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
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Mei H, Simino J, Li L, Jiang F, Bis JC, Davies G, Hill WD, Xia C, Gudnason V, Yang Q, Lahti J, Smith JA, Kirin M, De Jager P, Armstrong NJ, Ghanbari M, Kolcic I, Moran C, Teumer A, Sargurupremraj M, Mahmud S, Fornage M, Zhao W, Satizabal CL, Polasek O, Räikkönen K, Liewald DC, Homuth G, Callisaya M, Mather KA, Windham BG, Zemunik T, Palotie A, Pattie A, van der Auwera S, Thalamuthu A, Knopman DS, Rudan I, Starr JM, Wittfeld K, Kochan NA, Griswold ME, Vitart V, Brodaty H, Gottesman R, Cox SR, Psaty BM, Boerwinkle E, Chasman DI, Grodstein F, Sachdev PS, Srikanth V, Hayward C, Wilson JF, Eriksson JG, Kardia SLR, Grabe HJ, Bennett DA, Ikram MA, Deary IJ, van Duijn CM, Launer L, Fitzpatrick AL, Seshadri S, Bressler J, Debette S, Mosley TH. Multi-omics and pathway analyses of genome-wide associations implicate regulation and immunity in verbal declarative memory performance. Alzheimers Res Ther 2024; 16:14. [PMID: 38245754 PMCID: PMC10799499 DOI: 10.1186/s13195-023-01376-6] [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: 03/03/2023] [Accepted: 12/26/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia. METHODS We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes. RESULTS The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues. CONCLUSIONS VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.
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Affiliation(s)
- Hao Mei
- Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA.
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA.
| | - Jeannette Simino
- Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA.
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA.
| | - Lianna Li
- Department of Biology, Tougaloo College, Jackson, MS, USA
| | - Fan Jiang
- Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Joshua C Bis
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Gail Davies
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - W David Hill
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Charley Xia
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
| | - Jari Lahti
- Turku Institute for Advanced Research, University of Turku, Turku, Finland
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Mirna Kirin
- Work completed while at The University of Edinburgh, Edinburgh, UK
| | - Philip De Jager
- Taub Institute for Research On Alzheimer's Disease and the Aging Brain, Columbia Irving University Medical Center, New York, NY, USA
- Center for Translational and Computational Neuro-Immunology, Columbia University Medical Center, New York, NY, USA
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | | | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus Medical Center University Medical Center, Rotterdam, The Netherlands
| | - Ivana Kolcic
- School of Medicine, University of Split, Split, Croatia
| | - Christopher Moran
- Department of Geriatric Medicine, Frankston Hospital, Peninsula Health, Melbourne, Australia
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Murali Sargurupremraj
- Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, University of Bordeaux, Bordeaux, France
| | - Shamsed Mahmud
- Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center, Jackson, MS, USA
| | - Myriam Fornage
- The Brown Foundation Institute of Molecular Medicine for the Prevention of Human Diseases, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Claudia L Satizabal
- The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Ozren Polasek
- School of Medicine, University of Split, Split, Croatia
- Algebra University College, Ilica 242, Zagreb, Croatia
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - David C Liewald
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Georg Homuth
- Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Michele Callisaya
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | - B Gwen Windham
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Medicine, Division of Geriatrics, School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Aarno Palotie
- Department of Medicine, Department of Neurology and Department of Psychiatry, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alison Pattie
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Sandra van der Auwera
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Neuroscience Research Australia, Sydney, Australia
| | | | - Igor Rudan
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - John M Starr
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Rostock, Germany
| | - Nicole A Kochan
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
| | - Michael E Griswold
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Medicine, School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Veronique Vitart
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Henry Brodaty
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW, Australia
| | - Rebecca Gottesman
- Stroke, Cognition, and Neuroepidemiology (SCAN) Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Simon R Cox
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Bruce M Psaty
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Daniel I Chasman
- Harvard Medical School, Boston, MA, USA
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Francine Grodstein
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, School of Psychiatry, University of New South Wales, Sydney, Australia
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney, Australia
| | - Velandai Srikanth
- Department of Geriatric Medicine, Frankston Hospital, Peninsula Health, Melbourne, Australia
- Peninsula Clinical School, Central Clinical School, Monash University, Melbourne, Australia
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Caroline Hayward
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - James F Wilson
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Johan G Eriksson
- Department of General Practice and Primary Health Care, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Public Health Solutions, Chronic Disease Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
- Folkhälsan Research Centre, Helsinki, Finland
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/ Greifswald, Rostock, Germany
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center University Medical Center, Rotterdam, The Netherlands
| | - Ian J Deary
- Department of Psychology, Lothian Birth Cohorts Group, University of Edinburgh, 7 George Square, Edinburgh, EH8 9JZ, UK
| | - Cornelia M van Duijn
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Lenore Launer
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, Bethesda, MD, USA
| | - Annette L Fitzpatrick
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Family Medicine, University of Washington, Seattle, WA, USA
| | - Sudha Seshadri
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Jan Bressler
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Stephanie Debette
- Inserm, Bordeaux Population Health Research Center, Team VINTAGE, UMR 1219, University of Bordeaux, Bordeaux, France
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Department of Neurology, CHU de Bordeaux, Bordeaux, France
| | - Thomas H Mosley
- Gertrude C. Ford Memory Impairment and Neurodegenerative Dementia (MIND) Center, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Medicine, School of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Neurology, University of Mississippi Medical Center, Jackson, MS, USA
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Ehsan N, Kotis BM, Castel SE, Song EJ, Mancuso N, Mohammadi P. Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data. Nat Commun 2024; 15:522. [PMID: 38225224 PMCID: PMC10789818 DOI: 10.1038/s41467-024-44710-8] [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: 01/31/2022] [Accepted: 12/30/2023] [Indexed: 01/17/2024] Open
Abstract
Expression Quantitative Trait Loci (eQTLs) are critical to understanding the mechanisms underlying disease-associated genomic loci. Nearly all protein-coding genes in the human genome have been associated with one or more eQTLs. Here we introduce a multi-variant generalization of allelic Fold Change (aFC), aFC-n, to enable quantification of the cis-regulatory effects in multi-eQTL genes under the assumption that all eQTLs are known and conditionally independent. Applying aFC-n to 458,465 eQTLs in the Genotype-Tissue Expression (GTEx) project data, we demonstrate significant improvements in accuracy over the original model in estimating the eQTL effect sizes and in predicting genetically regulated gene expression over the current tools. We characterize some of the empirical properties of the eQTL data and use this framework to assess the current state of eQTL data in terms of characterizing cis-regulatory landscape in individual genomes. Notably, we show that 77.4% of the genes with an allelic imbalance in a sample show 0.5 log2 fold or more of residual imbalance after accounting for the eQTL data underlining the remaining gap in characterizing regulatory landscape in individual genomes. We further contrast this gap across tissue types, and ancestry backgrounds to identify its correlates and guide future studies.
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Affiliation(s)
- Nava Ehsan
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Bence M Kotis
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Stephane E Castel
- Department of Systems Biology, Columbia University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Eric J Song
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern, California, CA, USA
| | - Pejman Mohammadi
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA, USA.
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, Seattle, WA, USA.
- Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA.
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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Tsouris A, Brach G, Schacherer J, Hou J. Non-additive genetic components contribute significantly to population-wide gene expression variation. CELL GENOMICS 2024; 4:100459. [PMID: 38190102 PMCID: PMC10794783 DOI: 10.1016/j.xgen.2023.100459] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/19/2023] [Accepted: 11/09/2023] [Indexed: 01/09/2024]
Abstract
Gene expression variation, an essential step between genotype and phenotype, is collectively controlled by local (cis) and distant (trans) regulatory changes. Nevertheless, how these regulatory elements differentially influence gene expression variation remains unclear. Here, we bridge this gap by analyzing the transcriptomes of a large diallel panel consisting of 323 unique hybrids originating from genetically divergent Saccharomyces cerevisiae isolates. Our analysis across 5,087 transcript abundance traits showed that non-additive components account for 36% of the gene expression variance on average. By comparing allele-specific read counts in parent-hybrid trios, we found that trans-regulatory changes underlie the majority of gene expression variation in the population. Remarkably, most cis-regulatory variations are also exaggerated or attenuated by additional trans effects. Overall, we showed that the transcriptome is globally buffered at the genetic level mainly due to trans-regulatory variation in the population.
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Affiliation(s)
- Andreas Tsouris
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Gauthier Brach
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France
| | - Joseph Schacherer
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France; Institut Universitaire de France (IUF), Paris, France.
| | - Jing Hou
- Université de Strasbourg, CNRS, GMGM UMR, 7156 Strasbourg, France.
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40
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Pereira V, Kuzmin E. Trans-regulatory variant network contributes to missing heritability. CELL GENOMICS 2024; 4:100470. [PMID: 38216282 PMCID: PMC10794830 DOI: 10.1016/j.xgen.2023.100470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 11/30/2023] [Accepted: 11/30/2023] [Indexed: 01/14/2024]
Abstract
In a recent Cell Genomics article, Tsouris et al.1 analyze the transcriptomes of a large diallel panel of hybrids from Saccharomyces cerevisiae natural isolates to study cis- and trans-regulatory changes underlying gene expression variation. Vanessa Pereira and Elena Kuzmin discuss the authors' findings and the wider context in missing heritability research in this preview.
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Affiliation(s)
- Vanessa Pereira
- Department of Biology, Concordia University, Montreal, Canada; Centre for Applied Synthetic Biology, Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada
| | - Elena Kuzmin
- Department of Biology, Concordia University, Montreal, Canada; Centre for Applied Synthetic Biology, Centre for Structural and Functional Genomics, Concordia University, Montreal, Canada; Department of Human Genetics, Rosalind & Morris Goodman Cancer Institute, McGill University, Montreal, Canada.
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41
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Zeng H, Zhang W, Lin Q, Gao Y, Teng J, Xu Z, Cai X, Zhong Z, Wu J, Liu Y, Diao S, Wei C, Gong W, Pan X, Li Z, Huang X, Chen X, Du J. PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs. Nucleic Acids Res 2024; 52:D980-D989. [PMID: 37956339 PMCID: PMC10767803 DOI: 10.1093/nar/gkad1080] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 10/13/2023] [Accepted: 10/27/2023] [Indexed: 11/15/2023] Open
Abstract
To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.
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Affiliation(s)
- Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Wenjing Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zhanming Zhong
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Yuqiang Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Wentao Gong
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Zedong Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xiaoyu Huang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Xifan Chen
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
| | - Jinshi Du
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University, Guangzhou 510642, China
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Chen C, Liu Y, Luo M, Yang J, Chen Y, Wang R, Zhou J, Zang Y, Diao L, Han L. PancanQTLv2.0: a comprehensive resource for expression quantitative trait loci across human cancers. Nucleic Acids Res 2024; 52:D1400-D1406. [PMID: 37870463 PMCID: PMC10767806 DOI: 10.1093/nar/gkad916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 09/29/2023] [Accepted: 10/06/2023] [Indexed: 10/24/2023] Open
Abstract
Expression quantitative trait locus (eQTL) analysis is a powerful tool used to investigate genetic variations in complex diseases, including cancer. We previously developed a comprehensive database, PancanQTL, to characterize cancer eQTLs using The Cancer Genome Atlas (TCGA) dataset, and linked eQTLs with patient survival and GWAS risk variants. Here, we present an updated version, PancanQTLv2.0 (https://hanlaboratory.com/PancanQTLv2/), with advancements in fine-mapping causal variants for eQTLs, updating eQTLs overlapping with GWAS linkage disequilibrium regions and identifying eQTLs associated with drug response and immune infiltration. Through fine-mapping analysis, we identified 58 747 fine-mapped eQTLs credible sets, providing mechanic insights of gene regulation in cancer. We further integrated the latest GWAS Catalog and identified a total of 84 592 135 linkage associations between eQTLs and the existing GWAS loci, which represents a remarkable ∼50-fold increase compared to the previous version. Additionally, PancanQTLv2.0 uncovered 659516 associations between eQTLs and drug response and identified 146948 associations between eQTLs and immune cell abundance, providing potentially clinical utility of eQTLs in cancer therapy. PancanQTLv2.0 expanded the resources available for investigating gene expression regulation in human cancers, leading to advancements in cancer research and precision oncology.
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Affiliation(s)
- Chengxuan Chen
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
| | - Yuan Liu
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
| | - Mei Luo
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Jingwen Yang
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Yamei Chen
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Runhao Wang
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Joseph Zhou
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
| | - Yong Zang
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
| | - Lixia Diao
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Leng Han
- Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN 46202, USA
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX 77030, USA
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Paramasivam G, Sanmugam A, Palem VV, Sevanan M, Sairam AB, Nachiappan N, Youn B, Lee JS, Nallal M, Park KH. Nanomaterials for detection of biomolecules and delivering therapeutic agents in theragnosis: A review. Int J Biol Macromol 2024; 254:127904. [PMID: 37939770 DOI: 10.1016/j.ijbiomac.2023.127904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 10/30/2023] [Accepted: 11/03/2023] [Indexed: 11/10/2023]
Abstract
Nanomaterials are emerging facts used to deliver therapeutic agents in living systems. Nanotechnology is used as a compliment by implementing different kinds of nanotechnological applications such as nano-porous structures, functionalized nanomaterials, quantum dots, carbon nanomaterials, and polymeric nanostructures. The applications are in the initial stage, which led to achieving several diagnoses and therapy in clinical practice. This review conveys the importance of nanomaterials in post-genomic employment, which includes the design of immunosensors, immune assays, and drug delivery. In this view, genomics is a molecular tool containing large databases that are useful in choosing an apt molecular inhibitor such as drug, ligand and antibody target in the drug delivery process. This study identifies the expression of genes and proteins in analysis and classification of diseases. Experimentally, the study analyses the design of a disease model. In particular, drug delivery is a boon area to treat cancer. The identified drugs enter different phase trails (Trails I, II, and III). The genomic information conveys more essential entities to the phase I trials and helps to move further for other trails such as trails-II and III. In such cases, the biomarkers play a crucial role by monitoring the unique pathological process. Genetic engineering with recombinant DNA techniques can be employed to develop genetically engineered disease models. Delivering drugs in a specific area is one of the challenging issues achieved using nanoparticles. Therefore, genomics is considered as a vast molecular tool to identify drugs in personalized medicine for cancer therapy.
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Affiliation(s)
- Gokul Paramasivam
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical & Technical Sciences (SIMATS), Saveetha Nagar, Thandalam, Chennai 602105, Tamil Nadu, India.
| | - Anandhavelu Sanmugam
- Department of Applied Chemistry, Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur 602117, Tamil Nadu, India
| | - Vishnu Vardhan Palem
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical & Technical Sciences (SIMATS), Saveetha Nagar, Thandalam, Chennai 602105, Tamil Nadu, India
| | - Murugan Sevanan
- Department of Biotechnology, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore 641114, Tamil Nadu, India
| | - Ananda Babu Sairam
- Department of Applied Chemistry, Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur 602117, Tamil Nadu, India
| | - Nachiappan Nachiappan
- Department of Applied Chemistry, Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur 602117, Tamil Nadu, India
| | - BuHyun Youn
- Department of Biological Sciences, Pusan National University, Busan 46241, Republic of Korea
| | - Jung Sub Lee
- Department of Orthopaedic Surgery, Biomedical Research Institute, Pusan National University Hospital, Busan 46241, Republic of Korea; School of Medicine, Pusan National University, Busan 46241, Republic of Korea
| | - Muthuchamy Nallal
- Department of Chemistry, Pusan National University, Busan 46241, Republic of Korea.
| | - Kang Hyun Park
- Department of Chemistry, Pusan National University, Busan 46241, Republic of Korea.
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44
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Teng J, Gao Y, Yin H, Bai Z, Liu S, Zeng H, Bai L, Cai Z, Zhao B, Li X, Xu Z, Lin Q, Pan Z, Yang W, Yu X, Guan D, Hou Y, Keel BN, Rohrer GA, Lindholm-Perry AK, Oliver WT, Ballester M, Crespo-Piazuelo D, Quintanilla R, Canela-Xandri O, Rawlik K, Xia C, Yao Y, Zhao Q, Yao W, Yang L, Li H, Zhang H, Liao W, Chen T, Karlskov-Mortensen P, Fredholm M, Amills M, Clop A, Giuffra E, Wu J, Cai X, Diao S, Pan X, Wei C, Li J, Cheng H, Wang S, Su G, Sahana G, Lund MS, Dekkers JCM, Kramer L, Tuggle CK, Corbett R, Groenen MAM, Madsen O, Gòdia M, Rocha D, Charles M, Li CJ, Pausch H, Hu X, Frantz L, Luo Y, Lin L, Zhou Z, Zhang Z, Chen Z, Cui L, Xiang R, Shen X, Li P, Huang R, Tang G, Li M, Zhao Y, Yi G, Tang Z, Jiang J, Zhao F, Yuan X, Liu X, Chen Y, Xu X, Zhao S, Zhao P, Haley C, Zhou H, Wang Q, Pan Y, Ding X, Ma L, Li J, Navarro P, Zhang Q, Li B, Tenesa A, Li K, Liu GE, Zhang Z, Fang L. A compendium of genetic regulatory effects across pig tissues. Nat Genet 2024; 56:112-123. [PMID: 38177344 PMCID: PMC10786720 DOI: 10.1038/s41588-023-01585-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 10/13/2023] [Indexed: 01/06/2024]
Abstract
The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.
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Affiliation(s)
- Jinyan Teng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Yahui Gao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Hongwei Yin
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonghao Bai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Shuli Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
- School of Life Sciences, Westlake University, Hangzhou, China
| | - Haonan Zeng
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Lijing Bai
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zexi Cai
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Bingru Zhao
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Xiujin Li
- Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China
| | - Zhiting Xu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Qing Lin
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Zhangyuan Pan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenjing Yang
- College of Animal Science and Technology, China Agricultural University, Beijing, China
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Xiaoshan Yu
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Dailu Guan
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Yali Hou
- Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | - Brittney N Keel
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Gary A Rohrer
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | | | - William T Oliver
- ARS, USDA, U.S. Meat Animal Research Center, Clay Center, NE, USA
| | - Maria Ballester
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Programme, Institut de Recerca i Tecnologia Agroalimentàries (IRTA), Torre Marimon, Caldes de Montbui, Spain
| | - Oriol Canela-Xandri
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Konrad Rawlik
- Baillie Gifford Pandemic Science Hub, University of Edinburgh, Edinburgh, UK
| | - Charley Xia
- Lothian Birth Cohort studies, University of Edinburgh, Edinburgh, UK
- Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - Yuelin Yao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- School of Informatics, The University of Edinburgh, Edinburgh, UK
| | - Qianyi Zhao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenye Yao
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Liu Yang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Houcheng Li
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Huicong Zhang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Wang Liao
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Tianshuo Chen
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Peter Karlskov-Mortensen
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Merete Fredholm
- Animal Genetics, Bioinformatics and Breeding, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marcel Amills
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Alex Clop
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
- Consejo Superior de Investigaciones Científicas, Barcelona, Spain
| | - Elisabetta Giuffra
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Jun Wu
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaodian Cai
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Shuqi Diao
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiangchun Pan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Chen Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Jinghui Li
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Hao Cheng
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Sheng Wang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Goutam Sahana
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Mogens Sandø Lund
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Jack C M Dekkers
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Luke Kramer
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | | | - Ryan Corbett
- Department of Animal Science, Iowa State University, Ames, IA, USA
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Ole Madsen
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
| | - Marta Gòdia
- Animal Breeding and Genomics, Wageningen University and Research, Wageningen, The Netherlands
- Department of Animal Genetics, Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus de la Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Dominique Rocha
- Paris-Saclay University, INRAE, AgroParisTech, GABI, Jouy-en-Josas, France
| | - Mathieu Charles
- Paris-Saclay University, INRAE, AgroParisTech, GABI, SIGENAE, Jouy-en-Josas, France
| | - Cong-Jun Li
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA
| | - Hubert Pausch
- Animal Genomics, ETH Zurich, Universitaetstrasse 2, Zurich, Switzerland
| | - Xiaoxiang Hu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Laurent Frantz
- Palaeogenomics Group, Department of Veterinary Sciences, Ludwig Maximilian University, Munich, Germany
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Yonglun Luo
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
- Lars Bolund Institute of Regenerative Medicine, Qingdao-Europe Advanced Institute for Life Sciences, BGI-Research, Qingdao, China
| | - Lin Lin
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Zhongyin Zhou
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Zitao Chen
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Leilei Cui
- School of Life Sciences, Nanchang University, Nanchang, China
- Human Aging Research Institute and School of Life Science, Nanchang University, and Jiangxi Key Laboratory of Human Aging, Jiangxi, China
- UCL Genetics Institute, University College London, London, UK
| | - Ruidong Xiang
- Faculty of Veterinary and Agricultural Science, The University of Melbourne, Parkville, Victoria, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, Victoria, Australia
| | - Xia Shen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine, Fudan University, Guangzhou, China
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Pinghua Li
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Ruihua Huang
- Institute of Swine Science, Nanjing Agricultural University, Nanjing, China
| | - Guoqing Tang
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Mingzhou Li
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, China
| | - Yunxiang Zhao
- College of Animal Science and Technology, Guangxi University, Nanning, China
| | - Guoqiang Yi
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Zhonglin Tang
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Jicai Jiang
- Department of Animal Science, North Carolina State University, Raleigh, NC, USA
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaolong Yuan
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Xiaohong Liu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yaosheng Chen
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Xuewen Xu
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education and College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Pengju Zhao
- Hainan Institute, Zhejiang University, Yongyou Industry Park, Yazhou Bay Sci-Tech City, Sanya, China
| | - Chris Haley
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, Davis, CA, USA
| | - Qishan Wang
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Sciences, Zhejiang University, Hangzhou, China
| | - Xiangdong Ding
- College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Li Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA
| | - Jiaqi Li
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China
| | - Pau Navarro
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK
| | - Qin Zhang
- College of Animal Science and Technology, Shandong Agricultural University, Tai'an, China
| | - Bingjie Li
- Scotland's Rural College (SRUC), Roslin Institute Building, Midlothian, UK
| | - Albert Tenesa
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
- The Roslin Institute, Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Midlothian, UK.
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
| | - George E Liu
- Animal Genomics and Improvement Laboratory, Henry A. Wallace Beltsville Agricultural Research Center, Agricultural Research Service (ARS), U.S. Department of Agriculture (USDA), Beltsville, MD, USA.
| | - Zhe Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, National Engineering Research Center for Breeding Swine Industry, Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, College of Animal Science, South China Agricultural University (SCAU), Guangzhou, China.
| | - Lingzhao Fang
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
- MRC Human Genetics Unit at the Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK.
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45
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Kuderna LFK, Ulirsch JC, Rashid S, Ameen M, Sundaram L, Hickey G, Cox AJ, Gao H, Kumar A, Aguet F, Christmas MJ, Clawson H, Haeussler M, Janiak MC, Kuhlwilm M, Orkin JD, Bataillon T, Manu S, Valenzuela A, Bergman J, Rouselle M, Silva FE, Agueda L, Blanc J, Gut M, de Vries D, Goodhead I, Harris RA, Raveendran M, Jensen A, Chuma IS, Horvath JE, Hvilsom C, Juan D, Frandsen P, Schraiber JG, de Melo FR, Bertuol F, Byrne H, Sampaio I, Farias I, Valsecchi J, Messias M, da Silva MNF, Trivedi M, Rossi R, Hrbek T, Andriaholinirina N, Rabarivola CJ, Zaramody A, Jolly CJ, Phillips-Conroy J, Wilkerson G, Abee C, Simmons JH, Fernandez-Duque E, Kanthaswamy S, Shiferaw F, Wu D, Zhou L, Shao Y, Zhang G, Keyyu JD, Knauf S, Le MD, Lizano E, Merker S, Navarro A, Nadler T, Khor CC, Lee J, Tan P, Lim WK, Kitchener AC, Zinner D, Gut I, Melin AD, Guschanski K, Schierup MH, Beck RMD, Karakikes I, Wang KC, Umapathy G, Roos C, Boubli JP, Siepel A, Kundaje A, Paten B, Lindblad-Toh K, Rogers J, Marques Bonet T, Farh KKH. Identification of constrained sequence elements across 239 primate genomes. Nature 2024; 625:735-742. [PMID: 38030727 PMCID: PMC10808062 DOI: 10.1038/s41586-023-06798-8] [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: 02/09/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023]
Abstract
Noncoding DNA is central to our understanding of human gene regulation and complex diseases1,2, and measuring the evolutionary sequence constraint can establish the functional relevance of putative regulatory elements in the human genome3-9. Identifying the genomic elements that have become constrained specifically in primates has been hampered by the faster evolution of noncoding DNA compared to protein-coding DNA10, the relatively short timescales separating primate species11, and the previously limited availability of whole-genome sequences12. Here we construct a whole-genome alignment of 239 species, representing nearly half of all extant species in the primate order. Using this resource, we identified human regulatory elements that are under selective constraint across primates and other mammals at a 5% false discovery rate. We detected 111,318 DNase I hypersensitivity sites and 267,410 transcription factor binding sites that are constrained specifically in primates but not across other placental mammals and validate their cis-regulatory effects on gene expression. These regulatory elements are enriched for human genetic variants that affect gene expression and complex traits and diseases. Our results highlight the important role of recent evolution in regulatory sequence elements differentiating primates, including humans, from other placental mammals.
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Affiliation(s)
- Lukas F K Kuderna
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Jacob C Ulirsch
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Sabrina Rashid
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Mohamed Ameen
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Laksshman Sundaram
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Glenn Hickey
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Anthony J Cox
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Hong Gao
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Arvind Kumar
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Francois Aguet
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Matthew J Christmas
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Hiram Clawson
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
| | | | - Mareike C Janiak
- School of Science, Engineering and Environment, University of Salford, Salford, UK
| | - Martin Kuhlwilm
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria
| | - Joseph D Orkin
- Département d'Anthropologie, Université de Montréal, Montréal, Quebec, Canada
| | - Thomas Bataillon
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - Shivakumara Manu
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- Laboratory for the Conservation of Endangered Species, CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
| | - Alejandro Valenzuela
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Juraj Bergman
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Aarhus, Denmark
| | | | - Felipe Ennes Silva
- Research Group on Primate Biology and Conservation, Mamirauá Institute for Sustainable Development, Tefé, Brazil
- Evolutionary Biology and Ecology (EBE), Département de Biologie des Organismes, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Lidia Agueda
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
| | - Julie Blanc
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
| | - Marta Gut
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
| | - Dorien de Vries
- School of Science, Engineering and Environment, University of Salford, Salford, UK
| | - Ian Goodhead
- School of Science, Engineering and Environment, University of Salford, Salford, UK
| | - R Alan Harris
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Muthuswamy Raveendran
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Axel Jensen
- Department of Ecology and Genetics, Animal Ecology, Uppsala University, Uppsala, Sweden
| | | | - Julie E Horvath
- North Carolina Museum of Natural Sciences, Raleigh, NC, USA
- Department of Biological and Biomedical Sciences, North Carolina Central University, Durham, NC, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA
- Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David Juan
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Joshua G Schraiber
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | | | - Fabrício Bertuol
- Universidade Federal do Amazonas, Departamento de Genética, Laboratório de Evolução e Genética Animal (LEGAL), Manaus, Brazil
| | - Hazel Byrne
- Department of Anthropology, University of Utah, Salt Lake City, UT, USA
| | | | - Izeni Farias
- Universidade Federal do Amazonas, Departamento de Genética, Laboratório de Evolução e Genética Animal (LEGAL), Manaus, Brazil
| | - João Valsecchi
- Research Group on Terrestrial Vertebrate Ecology, Mamirauá Institute for Sustainable Development, Tefé, Brazil
- Rede de Pesquisa em Diversidade, Conservação e Uso da Fauna da Amazônia - RedeFauna, Manaus, Brazil
- Comunidad de Manejo de Fauna Silvestre en la Amazonía y en Latinoamérica-ComFauna, Iquitos, Peru
| | - Malu Messias
- Universidade Federal de Rondônia, Porto Velho, Brazil
| | | | - Mihir Trivedi
- Laboratory for the Conservation of Endangered Species, CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
| | - Rogerio Rossi
- Instituto de Biociências, Universidade Federal do Mato Grosso, Cuiabá, Brazil
| | - Tomas Hrbek
- Universidade Federal do Amazonas, Departamento de Genética, Laboratório de Evolução e Genética Animal (LEGAL), Manaus, Brazil
- Department of Biology, Trinity University, San Antonio, TX, USA
| | - Nicole Andriaholinirina
- Life Sciences and Environment, Technology and Environment of Mahajanga, University of Mahajanga, Mahajanga, Madagascar
| | - Clément J Rabarivola
- Life Sciences and Environment, Technology and Environment of Mahajanga, University of Mahajanga, Mahajanga, Madagascar
| | - Alphonse Zaramody
- Life Sciences and Environment, Technology and Environment of Mahajanga, University of Mahajanga, Mahajanga, Madagascar
| | - Clifford J Jolly
- Department of Anthropology, New York University, New York, NY, USA
| | - Jane Phillips-Conroy
- Department of Neuroscience, Washington University School of Medicine in St Louis, St Louis, MO, USA
| | - Gregory Wilkerson
- Keeling Center for Comparative Medicine and Research, MD Anderson Cancer Center, Bastrop, TX, USA
| | - Christian Abee
- Keeling Center for Comparative Medicine and Research, MD Anderson Cancer Center, Bastrop, TX, USA
| | - Joe H Simmons
- Keeling Center for Comparative Medicine and Research, MD Anderson Cancer Center, Bastrop, TX, USA
| | | | - Sree Kanthaswamy
- School of Interdisciplinary Forensics, Arizona State University, Phoenix, AZ, USA
- California National Primate Research Center, University of California, Davis, CA, USA
| | - Fekadu Shiferaw
- Guinea Worm Eradication Program, The Carter Center Ethiopia, Addis Ababa, Ethiopia
| | - Dongdong Wu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Long Zhou
- Center for Evolutionary and Organismal Biology, Zhejiang University School of Medicine, Hangzhou, China
| | - Yong Shao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Guojie Zhang
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
- Center for Evolutionary and Organismal Biology, Zhejiang University School of Medicine, Hangzhou, China
- Villum Centre for Biodiversity Genomics, Section for Ecology and Evolution, Department of Biology, University of Copenhagen, Copenhagen, Denmark
- Liangzhu Laboratory, Zhejiang University Medical Center, Hangzhou, China
- Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Julius D Keyyu
- Tanzania Wildlife Research Institute (TAWIRI), Arusha, Tanzania
| | - Sascha Knauf
- Institute of International Animal Health/One Health, Friedrich-Loeffler-Institut, Federal Research Institute for Animal Health, Greifswald-Insel Riems, Germany
- Professorship for International Animal Health/One Health, Faculty of Veterinary Medicine, Justus Liebig University, Giessen, Germany
| | - Minh D Le
- Department of Environmental Ecology, Faculty of Environmental Sciences, University of Science and Central Institute for Natural Resources and Environmental Studies, Vietnam National University, Hanoi, Vietnam
| | - Esther Lizano
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Stefan Merker
- Department of Zoology, State Museum of Natural History Stuttgart, Stuttgart, Germany
| | - Arcadi Navarro
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Tilo Nadler
- Cuc Phuong Commune, Nho Quan District, Vietnam
| | - Chiea Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | | | - Patrick Tan
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- SingHealth Duke-NUS Institute of Precision Medicine (PRISM), Singapore, Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore
| | - Weng Khong Lim
- SingHealth Duke-NUS Institute of Precision Medicine (PRISM), Singapore, Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, Singapore
- SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Singapore
| | - Andrew C Kitchener
- Department of Natural Sciences, National Museums Scotland, Edinburgh, UK
- School of Geosciences, Edinburgh, UK
| | - Dietmar Zinner
- Cognitive Ethology Laboratory, Germany Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
- Department of Primate Cognition, Georg-August-Universität Göttingen, Göttingen, Germany
- Leibniz ScienceCampus Primate Cognition, Göttingen, Germany
| | - Ivo Gut
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
| | - Amanda D Melin
- Department of Anthropology and Archaeology, University of Calgary, Calgary, Alberta, Canada
- Department of Medical Genetics, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Katerina Guschanski
- Department of Ecology and Genetics, Animal Ecology, Uppsala University, Uppsala, Sweden
- Institute of Ecology and Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Robin M D Beck
- School of Science, Engineering and Environment, University of Salford, Salford, UK
| | - Ioannis Karakikes
- Cardiovascular Institute, Stanford University, Stanford, CA, USA
- Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA
| | - Kevin C Wang
- Department of Cancer Biology, Stanford University, Stanford, CA, USA
- Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Govindhaswamy Umapathy
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
- Laboratory for the Conservation of Endangered Species, CSIR-Centre for Cellular and Molecular Biology, Hyderabad, India
| | - Christian Roos
- Gene Bank of Primates and Primate Genetics Laboratory, German Primate Center, Leibniz Institute for Primate Research, Göttingen, Germany
| | - Jean P Boubli
- School of Science, Engineering and Environment, University of Salford, Salford, UK
| | - Adam Siepel
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Anshul Kundaje
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Benedict Paten
- UC Santa Cruz Genomics Institute, University of California, Santa Cruz, CA, USA
| | - Kerstin Lindblad-Toh
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jeffrey Rogers
- Human Genome Sequencing Center and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Tomas Marques Bonet
- IBE, Institute of Evolutionary Biology (UPF-CSIC), Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain.
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain.
- Institut Català de Paleontologia Miquel Crusafont, Universitat Autònoma de Barcelona, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
- Universitat Pompeu Fabra, Barcelona, Spain.
| | - Kyle Kai-How Farh
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA.
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Xu LL, Zhou XJ, Zhang H. An Update on the Genetics of IgA Nephropathy. J Clin Med 2023; 13:123. [PMID: 38202130 PMCID: PMC10780034 DOI: 10.3390/jcm13010123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
Immunoglobulin A (IgA) nephropathy (IgAN), the most common form of glomerulonephritis, is one of the leading causes of end-stage kidney disease (ESKD). It is widely believed that genetic factors play a significant role in the development of IgAN. Previous studies of IgAN have provided important insights to unravel the genetic architecture of IgAN and its potential pathogenic mechanisms. The genome-wide association studies (GWASs) together have identified over 30 risk loci for IgAN, which emphasizes the importance of IgA production and regulation in the pathogenesis of IgAN. Follow-up fine-mapping studies help to elucidate the candidate causal variant and the potential pathogenic molecular pathway and provide new potential therapeutic targets. With the rapid development of next-generation sequencing technologies, linkage studies based on whole-genome sequencing (WGS)/whole-exome sequencing (WES) also identify rare variants associated with IgAN, accounting for some of the missing heritability. The complexity of pathogenesis and phenotypic variability may be better understood by integrating genetics, epigenetics, and environment. We have compiled a review summarizing the latest advancements in genetic studies on IgAN. We similarly summarized relevant studies examining the involvement of epigenetics in the pathogenesis of IgAN. Future directions and challenges in this field are also proposed.
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Affiliation(s)
- Lin-Lin Xu
- Renal Division, Peking University First Hospital, Beijing 100034, China; (L.-L.X.); (H.Z.)
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100034, China
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China; (L.-L.X.); (H.Z.)
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100034, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Beijing 100034, China; (L.-L.X.); (H.Z.)
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100034, China
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Boocock J, Alexander N, Tapia LA, Walter-McNeill L, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.07.570640. [PMID: 38106186 PMCID: PMC10723400 DOI: 10.1101/2023.12.07.570640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1, which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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Affiliation(s)
- James Boocock
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Noah Alexander
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leslie Alamo Tapia
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Laura Walter-McNeill
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Chetan Munugala
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Joshua S Bloom
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Leonid Kruglyak
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry, University of California, Los Angeles, Los Angeles, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
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48
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Matkarimov BT, Saparbaev MK. Chargaff's second parity rule lies at the origin of additive genetic interactions in quantitative traits to make omnigenic selection possible. PeerJ 2023; 11:e16671. [PMID: 38107580 PMCID: PMC10725672 DOI: 10.7717/peerj.16671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 12/19/2023] Open
Abstract
Background Francis Crick's central dogma provides a residue-by-residue mechanistic explanation of the flow of genetic information in living systems. However, this principle may not be sufficient for explaining how random mutations cause continuous variation of quantitative highly polygenic complex traits. Chargaff's second parity rule (CSPR), also referred to as intrastrand DNA symmetry, defined as near-exact equalities G ≈ C and A ≈ T within a single DNA strand, is a statistical property of cellular genomes. The phenomenon of intrastrand DNA symmetry was discovered more than 50 years ago; at present, it remains unclear what its biological role is, what the mechanisms are that force cellular genomes to comply strictly with CSPR, and why genomes of certain noncellular organisms have broken intrastrand DNA symmetry. The present work is aimed at studying a possible link between intrastrand DNA symmetry and the origin of genetic interactions in quantitative traits. Methods Computational analysis of single-nucleotide polymorphisms in human and mouse populations and of nucleotide composition biases at different codon positions in bacterial and human proteomes. Results The analysis of mutation spectra inferred from single-nucleotide polymorphisms observed in murine and human populations revealed near-exact equalities of numbers of reverse complementary mutations, indicating that random genetic variations obey CSPR. Furthermore, nucleotide compositions of coding sequences proved to be statistically interwoven via CSPR because pyrimidine bias at the 3rd codon position compensates purine bias at the 1st and 2nd positions. Conclusions According to Fisher's infinitesimal model, we propose that accumulation of reverse complementary mutations results in a continuous phenotypic variation due to small additive effects of statistically interwoven genetic variations. Therefore, additive genetic interactions can be inferred as a statistical entanglement of nucleotide compositions of separate genetic loci. CSPR challenges the neutral theory of molecular evolution-because all random mutations participate in variation of a trait-and provides an alternative solution to Haldane's dilemma by making a gene function diffuse. We propose that CSPR is symmetry of Fisher's infinitesimal model and that genetic information can be transferred in an implicit contactless manner.
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Affiliation(s)
- Bakhyt T. Matkarimov
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan
- L.N.Gumilev Eurasian National University, Astana, Kazakhstan
| | - Murat K. Saparbaev
- Groupe «Mechanisms of DNA Repair and Carcinogenesis», CNRS UMR9019, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif, France
- Al-Farabi Kazakh National University, Almaty, Kazakhstan
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49
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Flint J, Heffel MG, Chen Z, Mefford J, Marcus E, Chen PB, Ernst J, Luo C. Single-cell methylation analysis of brain tissue prioritizes mutations that alter transcription. CELL GENOMICS 2023; 3:100454. [PMID: 38116123 PMCID: PMC10726494 DOI: 10.1016/j.xgen.2023.100454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/08/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023]
Abstract
Relating genetic variants to behavior remains a fundamental challenge. To assess the utility of DNA methylation marks in discovering causative variants, we examined their relationship to genetic variation by generating single-nucleus methylomes from the hippocampus of eight inbred mouse strains. At CpG sequence densities under 40 CpG/Kb, cells compensate for loss of methylated sites by methylating additional sites to maintain methylation levels. At higher CpG sequence densities, the exact location of a methylated site becomes more important, suggesting that variants affecting methylation will have a greater effect when occurring in higher CpG densities than in lower. We found this to be true for a variant's effect on transcript abundance, indicating that candidate variants can be prioritized based on CpG sequence density. Our findings imply that DNA methylation influences the likelihood that mutations occur at specific sites in the genome, supporting the view that the distribution of mutations is not random.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Matthew G Heffel
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Zeyuan Chen
- Department of Computer Science, Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA
| | - Joel Mefford
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Emilie Marcus
- Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Patrick B Chen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Jason Ernst
- Department of Computer Science, Samueli School of Engineering, University of California Los Angeles, Los Angeles, CA, USA; Department of Biological Chemistry, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Chongyuan Luo
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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50
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Zeng Y, Jain R, Lam M, Ahmed M, Guo H, Xu W, Zhong Y, Wei GH, Xu W, He HH. DNA methylation modulated genetic variant effect on gene transcriptional regulation. Genome Biol 2023; 24:285. [PMID: 38066556 PMCID: PMC10709945 DOI: 10.1186/s13059-023-03130-5] [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: 04/21/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Expression quantitative trait locus (eQTL) analysis has emerged as an important tool in elucidating the link between genetic variants and gene expression, thereby bridging the gap between risk SNPs and associated diseases. We recently identified and validated a specific case where the methylation of a CpG site influences the relationship between the genetic variant and gene expression. RESULTS Here, to systematically evaluate this regulatory mechanism, we develop an extended eQTL mapping method, termed DNA methylation modulated eQTL (memo-eQTL). Applying this memo-eQTL mapping method to 128 normal prostate samples enables identification of 1063 memo-eQTLs, the majority of which are not recognized as conventional eQTLs in the same cohort. We observe that the methylation of the memo-eQTL CpG sites can either enhance or insulate the interaction between SNP and gene expression by altering CTCF-based chromatin 3D structure. CONCLUSIONS This study demonstrates the prevalence of memo-eQTLs paving the way to identify novel causal genes for traits or diseases associated with genetic variations.
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Affiliation(s)
- Yong Zeng
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
| | - Rahi Jain
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Magnus Lam
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Musaddeque Ahmed
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Haiyang Guo
- Department of Clinical Laboratory, the Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250033, Shandong, China
| | - Wenjie Xu
- MOE Key Laboratory of Metabolism and Molecular Medicine and Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Fudan University Shanghai Cancer Center, Shanghai Medical College of Fudan University, Shanghai, China
| | - Yuan Zhong
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Gong-Hong Wei
- MOE Key Laboratory of Metabolism and Molecular Medicine and Department of Biochemistry and Molecular Biology of School of Basic Medical Sciences, Fudan University Shanghai Cancer Center, Shanghai Medical College of Fudan University, Shanghai, China
- Biocenter Oulu & Faculty of Biochemistry and Molecular Medicine, University of Oulu, Oulu, Finland
| | - Wei Xu
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
| | - Housheng Hansen He
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Canada.
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