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Cai X, Li X, Liang C, Zhang M, Dong Z, Yu W. The effect of metabolism-related lifestyle and clinical risk factors on digestive system cancers in East Asian populations: a two-sample Mendelian randomization analysis. Sci Rep 2024; 14:9474. [PMID: 38658636 PMCID: PMC11043381 DOI: 10.1038/s41598-024-60122-6] [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: 12/29/2023] [Accepted: 04/18/2024] [Indexed: 04/26/2024] Open
Abstract
Metabolic factors play a critical role in the development of digestive system cancers (DSCs), and East Asia has the highest incidence of malignant tumors in the digestive system. We performed a two-sample Mendelian randomization analysis to explore the associations between 19 metabolism-related lifestyle and clinical risk factors and DSCs, including esophageal, gastric, colorectal, hepatocellular, biliary tract, and pancreatic cancer. The causal association was explored for all combinations of each risk factor and each DSC. We gathered information on the instrumental variables (IVs) from various sources and retrieved outcome information from Biobank Japan (BBJ). The data were all from studies of east Asian populations. Finally, 17,572 DSCs cases and 195,745 controls were included. Our analysis found that genetically predicted alcohol drinking was a strong indicator of gastric cancer (odds ratio (OR) = 0.95; 95% confidence interval (CI): 0.93-0.98) and hepatocellular carcinoma (OR = 1.11; 95% CI: 1.05-1.18), whereas coffee consumption had a potential protective effect on hepatocellular carcinoma (OR = 0.69; 95% CI: 0.53-0.90). Triglyceride was potentially associated with a decreased risk of biliary tract cancer (OR = 0.53; 95% CI: 0.34-0.81), and uric acid was associated with pancreatic cancer risk (OR = 0.59; 95% CI: 0.37-0.96). Metabolic syndrome (MetS) was associated with esophageal and gastric cancer. Additionally, there was no evidence for a causal association between other risk factors, including body mass index, waist circumference, waist-to-hip ratio, educational levels, lipoprotein cholesterol, total cholesterol, glycine, creatinine, gout, and Graves' disease, and DSCs. The leave-one-out analysis revealed that the single nucleotide polymorphism (SNP) rs671 from the ALDH2 gene has a disproportionately high contribution to the causal association between alcohol drinking and gastric cancer and hepatocellular carcinoma, as well as the association between coffee consumption and hepatocellular carcinoma. The present study revealed multiple metabolism-related lifestyle and clinical risk factors and a valuable SNP rs671 for DSCs, highlighting the significance of metabolic factors in both the prevention and treatment of DSCs.
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Affiliation(s)
- Xianlei Cai
- Department of Gastrointestinal Surgery, Ningbo Medical Center Lihuili Hospital, The Lihuili Affiliated Hospital, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Xueying Li
- Department of Gastroenterology, First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, China
- Department of Gastroenterology, Ningbo First Hospital, Ningbo, 315000, Zhejiang, China
| | - Chao Liang
- Department of Gastrointestinal Surgery, Ningbo Medical Center Lihuili Hospital, The Lihuili Affiliated Hospital, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Miaozun Zhang
- Department of Gastrointestinal Surgery, Ningbo Medical Center Lihuili Hospital, The Lihuili Affiliated Hospital, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Zhebin Dong
- Department of Gastrointestinal Surgery, Ningbo Medical Center Lihuili Hospital, The Lihuili Affiliated Hospital, Ningbo University, Ningbo, 315000, Zhejiang, China
| | - Weiming Yu
- Department of Gastrointestinal Surgery, Ningbo Medical Center Lihuili Hospital, The Lihuili Affiliated Hospital, Ningbo University, Ningbo, 315000, Zhejiang, China.
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Boltz T, Schwarz T, Bot M, Hou K, Caggiano C, Lapinska S, Duan C, Boks MP, Kahn RS, Zaitlen N, Pasaniuc B, Ophoff R. Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders. Am J Hum Genet 2024; 111:323-337. [PMID: 38306997 PMCID: PMC10870131 DOI: 10.1016/j.ajhg.2023.12.018] [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: 06/05/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 02/04/2024] Open
Abstract
Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.
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Affiliation(s)
- Toni Boltz
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Tommer Schwarz
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Merel Bot
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Sandra Lapinska
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Chenda Duan
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Marco P Boks
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Rene S Kahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands; Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - Noah Zaitlen
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Neurology, University of California Los Angeles, Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Roel Ophoff
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA; Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA; Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA; Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands.
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Boltz T, Schwarz T, Bot M, Hou K, Caggiano C, Lapinska S, Duan C, Boks MP, Kahn RS, Zaitlen N, Pasaniuc B, Ophoff R. Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.24.542156. [PMID: 37293101 PMCID: PMC10245943 DOI: 10.1101/2023.05.24.542156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-wide association studies (GWAS) have uncovered susceptibility loci associated with psychiatric disorders like bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome with unknown causal mechanisms of the link between genetic variation and disease risk. Expression quantitative trait loci (eQTL) analysis of bulk tissue is a common approach to decipher underlying mechanisms, though this can obscure cell-type specific signals thus masking trait-relevant mechanisms. While single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell type proportions and cell type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-Seq from 1,730 samples derived from whole blood in a cohort ascertained for individuals with BP and SCZ this study estimated cell type proportions and their relation with disease status and medication. We found between 2,875 and 4,629 eGenes for each cell type, including 1,211 eGenes that are not found using bulk expression alone. We performed a colocalization test between cell type eQTLs and various traits and identified hundreds of associations between cell type eQTLs and GWAS loci that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on cell type expression regulation and found examples of genes that are differentially regulated dependent on lithium use. Our study suggests that computational methods can be applied to large bulk RNA-Seq datasets of non-brain tissue to identify disease-relevant, cell type specific biology of psychiatric disorders and psychiatric medication.
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Affiliation(s)
- Toni Boltz
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Tommer Schwarz
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Merel Bot
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Christa Caggiano
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Sandra Lapinska
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
| | - Chenda Duan
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Marco P Boks
- Department of Psychiatry, Brain Center University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Rene S Kahn
- Department of Psychiatry, Brain Center University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - Noah Zaitlen
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Roel Ophoff
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Bioinformatics Interdepartmental Program, University of California Los Angeles, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- Department of Psychiatry, Erasmus University Medical Center, Rotterdam, the Netherlands
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