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Qi H, Xie YY, Yang XJ, Xia J, Liu K, Zhang FX, Peng WJ, Wen FY, Li BX, Zhang BW, Yao XY, Li BY, Meng HD, Shi ZM, Wang Y, Zhang L. Susceptibility gene identification and risk evaluation model construction by transcriptome-wide association analysis for salt sensitivity of blood pressure. BMC Genomics 2024; 25:612. [PMID: 38890564 PMCID: PMC11184770 DOI: 10.1186/s12864-024-10409-9] [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: 03/08/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes. METHODS AND RESULTS A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P < 0.05 had the best predictive ability compared with other PRSs and PTRSs. The combinations of PRSs and PTRSs did not significantly increase the prediction accuracy of SSBP in the training and validation datasets. CONCLUSIONS Several known and novel susceptibility genes for SSBP were identified via multitissue TWAS analysis. The risk evaluation model constructed with the PRS of susceptibility genes showed better diagnostic performance than the transcript levels, which could be applied to screen for SSBP high-risk individuals.
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Affiliation(s)
- Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Capital Medical University, Beijing, 100088, China
| | - Yun-Yi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xiao-Jun Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Feng-Xu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Wen-Juan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Fu-Yuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bing-Xiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Wen Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xin-Yue Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Ya Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Hong-Dao Meng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zu-Min Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Yang Wang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China.
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2
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Chen DM, Dong R, Kachuri L, Hoffmann TJ, Jiang Y, Berndt SI, Shelley JP, Schaffer KR, Machiela MJ, Freedman ND, Huang WY, Li SA, Lilja H, Justice AC, Madduri RK, Rodriguez AA, Van Den Eeden SK, Chanock SJ, Haiman CA, Conti DV, Klein RJ, Mosley JD, Witte JS, Graff RE. Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer. HGG ADVANCES 2024; 5:100315. [PMID: 38845201 DOI: 10.1016/j.xhgg.2024.100315] [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: 01/17/2024] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 06/18/2024] Open
Abstract
Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.
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Affiliation(s)
- Dorothy M Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ruocheng Dong
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA
| | - Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - John P Shelley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Kerry R Schaffer
- Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Neal D Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Shengchao A Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Hans Lilja
- Departments of Pathology and Laboratory Medicine, Surgery, Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Translational Medicine, Lund University, 21428 Malmö, Sweden
| | | | | | | | | | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20814, USA
| | - Christopher A Haiman
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - David V Conti
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Robert J Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jonathan D Mosley
- Departments of Internal Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Epidemiology and Population Health, Stanford University, Stanford, CA 94305, USA; Departments of Biomedical Data Science and Genetics (by courtesy), Stanford University, Stanford, CA 94305, USA.
| | - Rebecca E Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94158, USA.
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Tan Q, Xu X, Zhou H, Jia J, Jia Y, Tu H, Zhou D, Wu X. A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors. Mol Psychiatry 2024:10.1038/s41380-024-02605-6. [PMID: 38816585 DOI: 10.1038/s41380-024-02605-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 04/30/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024]
Abstract
Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.
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Affiliation(s)
- Qilong Tan
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Xiaohang Xu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Hanyi Zhou
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Junlin Jia
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Yubing Jia
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
| | - Huakang Tu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
- National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dan Zhou
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Xifeng Wu
- Department of Big Data in Health Science School of Public Health, Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China.
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, 310058, China.
- School of Medicine and Health Science, George Washington University, Washington, DC, USA.
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4
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Song W, Shi Y, Lin GN. Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits. eLife 2024; 12:RP92574. [PMID: 38639992 PMCID: PMC11031082 DOI: 10.7554/elife.92574] [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] [Indexed: 04/20/2024] Open
Abstract
We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.
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Affiliation(s)
- Weichen Song
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong UniversityShanghaiChina
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X12 Institutes), Qingdao UniversityQingdaoChina
| | - Guan Ning Lin
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, School of Bioengineering, Shanghai Jiao Tong UniversityShanghaiChina
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5
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Valentim WL, Tylee DS, Polimanti R. A perspective on translating genomic discoveries into targets for brain-machine interface and deep brain stimulation devices. WIREs Mech Dis 2024; 16:e1635. [PMID: 38059513 PMCID: PMC11163995 DOI: 10.1002/wsbm.1635] [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: 03/20/2023] [Revised: 10/22/2023] [Accepted: 11/17/2023] [Indexed: 12/08/2023]
Abstract
Mental illnesses have a huge impact on individuals, families, and society, so there is a growing need for more efficient treatments. In this context, brain-computer interface (BCI) technology has the potential to revolutionize the options for neuropsychiatric therapies. However, the development of BCI-based therapies faces enormous challenges, such as power dissipation constraints, lack of credible feedback mechanisms, uncertainty of which brain areas and frequencies to target, and even which patients to treat. Some of these setbacks are due to the large gap in our understanding of brain function. In recent years, large-scale genomic analyses uncovered an unprecedented amount of information regarding the biology of the altered brain function observed across the psychopathology spectrum. We believe findings from genetic studies can be useful to refine BCI technology to develop novel treatment options for mental illnesses. Here, we assess the latest advancements in both fields, the possibilities that can be generated from their intersection, and the challenges that these research areas will need to address to ensure that translational efforts can lead to effective and reliable interventions. Specifically, starting from highlighting the overlap between mechanisms uncovered by large-scale genetic studies and the current targets of deep brain stimulation treatments, we describe the steps that could help to translate genomic discoveries into BCI targets. Because these two research areas have not been previously presented together, the present article can provide a novel perspective for scientists with different research backgrounds. This article is categorized under: Neurological Diseases > Genetics/Genomics/Epigenetics Neurological Diseases > Biomedical Engineering.
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Affiliation(s)
- Wander L. Valentim
- Faculty of Medicine, Federal University of Minas Gerais, Belo Horizonte, Brazil
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
| | - Daniel S. Tylee
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT
- VA CT Healthcare Center, West Haven, CT, USA
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6
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Jung H, Jung HU, Baek EJ, Kwon SY, Kang JO, Lim JE, Oh B. Integration of risk factor polygenic risk score with disease polygenic risk score for disease prediction. Commun Biol 2024; 7:180. [PMID: 38351177 PMCID: PMC10864389 DOI: 10.1038/s42003-024-05874-7] [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: 09/19/2023] [Accepted: 01/30/2024] [Indexed: 02/16/2024] Open
Abstract
Polygenic risk score (PRS) is useful for capturing an individual's genetic susceptibility. However, previous studies have not fully exploited the potential of the risk factor PRS (RFPRS) for disease prediction. We explored the potential of integrating disease-related RFPRSs with disease PRS to enhance disease prediction performance. We constructed 112 RFPRSs and analyzed the association of RFPRSs with diseases to identify disease-related RFPRSs in 700 diseases, using the UK Biobank dataset. We uncovered 6157 statistically significant associations between 247 diseases and 109 RFPRSs. We estimated the disease PRSs of 70 diseases that exhibited statistically significant heritability, to generate RFDiseasemetaPRS-a combined PRS integrating RFPRSs and disease PRS-and compare the prediction performance metrics between RFDiseasemetaPRS and disease PRS. RFDiseasemetaPRS showed better performance for Nagelkerke's pseudo-R2, odds ratio (OR) per 1 SD, net reclassification improvement (NRI) values and difference of R2 considered by variance of R2 in 31 out of 70 diseases. Additionally, we assessed risk classification between two models by examining OR between the top 10% and remaining 90% individuals for the 31 diseases; RFDiseasemetaPRS exhibited better R2, NRI and OR than disease PRS. These findings highlight the importance of utilizing RFDiseasemetaPRS, which can provide personalized healthcare and tailored prevention strategies.
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Affiliation(s)
- Hyein Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Hae-Un Jung
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | | | - Shin Young Kwon
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea
| | - Ji-One Kang
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea
| | - Ji Eun Lim
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea.
| | - Bermseok Oh
- Department of Biomedical Science, Graduate School, Kyung Hee University, Seoul, Republic of Korea.
- Mendel Inc, Seoul, Republic of Korea.
- Department of Biochemistry and Molecular Biology, School of Medicine, Kyung Hee University, Seoul, Republic of Korea.
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Nasti A, Okumura M, Takeshita Y, Ho TTB, Sakai Y, Sato TA, Nomura C, Goto H, Nakano Y, Urabe T, Nakamura S, Tamura T, Matsubara K, Takamura T, Kaneko S. The declining insulinogenic index correlates with inflammation and metabolic dysregulation in non-obese individuals assessed by blood gene expression. Diabetes Res Clin Pract 2024; 208:111090. [PMID: 38216088 DOI: 10.1016/j.diabres.2024.111090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 12/19/2023] [Accepted: 01/04/2024] [Indexed: 01/14/2024]
Abstract
AIMS Diabetes onset is difficult to predict. Since decreased insulinogenic index (IGI) is observed in prediabetes, and blood gene expression correlates with insulin secretion, candidate biomarkers can be identified. METHODS We collected blood from 96 participants (54 males, 42 females) in 2008 (age: 52.5 years) and 2016 for clinical and gene expression analyses. IGI was derived from values of insulin and glucose at fasting and at 30 min post-OGTT. Two subgroups were identified based on IGI variation: "Minor change in IGI" group with absolute value variation between -0.05 and +0.05, and "Decrease in IGI" group with a variation between -20 and -0.05. RESULTS Following the comparison of "Minor change in IGI" and "Decrease in IGI" groups at time 0 (2008), we identified 77 genes correlating with declining IGI, related to response to lipid, carbohydrate, and hormone metabolism, response to stress and DNA metabolic processes. Over the eight years, genes correlating to declining IGI were related to inflammation, metabolic and hormonal dysregulation. Individuals with minor change in IGI, instead, featured homeostatic and regenerative responses. CONCLUSIONS By blood gene expression analysis of non-obese individuals, we identified potential gene biomarkers correlating to declining IGI, associated to a pathophysiology of inflammation and metabolic dysregulation.
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Affiliation(s)
- Alessandro Nasti
- Information-Based Medicine Development, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan.
| | - Miki Okumura
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Yumie Takeshita
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Tuyen Thuy Bich Ho
- Information-Based Medicine Development, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan
| | - Yoshio Sakai
- Department of Gastroenterology, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan; Sakai Internal Medicine Clinic, Nonoichi, Ishikawa 921-8825, Japan
| | | | - Chiaki Nomura
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Hisanori Goto
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Yujiro Nakano
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Takeshi Urabe
- Department of Gastroenterology, Public Central Hospital of Matto Ishikawa, 3-8 Kuramitsu, Hakusan, Ishikawa 924-8588, Japan
| | | | - Takuro Tamura
- Research and Development Center for Precision Medicine, University of Tsukuba, Tsukuba 305-8550, Japan
| | | | - Toshinari Takamura
- Department of Endocrinology and Metabolism, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8640, Japan
| | - Shuichi Kaneko
- Information-Based Medicine Development, Kanazawa University, Graduate School of Medical Sciences, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan; Department of Gastroenterology, Kanazawa University Hospital, 13-1 Takara-machi, Kanazawa, Ishikawa 920-8641, Japan.
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8
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Hou Y, Dai H, Chen N, Zhao Z, Wang Q, Hou T, Zheng J, Wang T, Li M, Lin H, Wang S, Zheng R, Lu J, Xu Y, Chen Y, Liu R, Ning G, Wang W, Bi Y, Wang J, Xu M. Whole Blood-based Transcriptional Risk Score for Nonobese Type 2 Diabetes Predicts Dynamic Changes in Glucose Metabolism. J Clin Endocrinol Metab 2023; 109:114-124. [PMID: 37555255 PMCID: PMC10735316 DOI: 10.1210/clinem/dgad466] [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: 03/14/2023] [Revised: 08/02/2023] [Accepted: 08/04/2023] [Indexed: 08/10/2023]
Abstract
CONTEXT The performance of peripheral blood transcriptional markers in evaluating risk of type 2 diabetes (T2D) with normal body mass index (BMI) is unknown. OBJECTIVE We developed a whole blood-based transcriptional risk score (wb-TRS) for nonobese T2D and assessed its contributions on disease risk and dynamic changes in glucose metabolism. METHODS Using a community-based cohort with blood transcriptome data, we developed the wb-TRS in 1105 participants aged ≥40 years who maintained a normal BMI for up to 10 years, and we validated the wb-TRS in an external dataset. Potential biological significance was explored. RESULTS The wb-TRS included 144 gene transcripts. Compared to the lowest tertile, wb-TRS in tertile 3 was associated with 8.91-fold (95% CI, 3.53-22.5) higher risk and each 1-unit increment was associated with 2.63-fold (95% CI, 1.87-3.68) higher risk of nonobese T2D. Furthermore, baseline wb-TRS significantly associated with dynamic changes in average, daytime, nighttime, and 24-hour glucose, HbA1c values, and area under the curve of glucose measured by continuous glucose monitoring over 6 months of intervention. The wb-TRS improved the prediction performance for nonobese T2D, combined with fasting glucose, triglycerides, and demographic and anthropometric parameters. Multi-contrast gene set enrichment (Mitch) analysis implicated oxidative phosphorylation, mTORC1 signaling, and cholesterol metabolism involved in nonobese T2D pathogenesis. CONCLUSION A whole blood-based nonobese T2D-associated transcriptional risk score was validated to predict dynamic changes in glucose metabolism. These findings suggested several biological pathways involved in the pathogenesis of nonobese T2D.
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Affiliation(s)
- Yanan Hou
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Huajie Dai
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Na Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zhiyun Zhao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Qi Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tianzhichao Hou
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jie Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Tiange Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Mian Li
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hong Lin
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Shuangyuan Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Ruizhi Zheng
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jieli Lu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yu Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yuhong Chen
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Ruixin Liu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Guang Ning
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Weiqing Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yufang Bi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiqiu Wang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Min Xu
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
- Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
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9
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Chang Y, Zhou Y, Zhou J, Li W, Cao J, Jing Y, Zhang S, Shen Y, Lin Q, Fan X, Yang H, Dong X, Zhang S, Yi X, Shuai L, Shi L, Liu Z, Yang J, Ma X, Hao J, Chen K, Li MJ, Wang F, Huang D. Unraveling the causal genes and transcriptomic determinants of human telomere length. Nat Commun 2023; 14:8517. [PMID: 38129441 PMCID: PMC10739845 DOI: 10.1038/s41467-023-44355-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
Telomere length (TL) shortening is a pivotal indicator of biological aging and is associated with many human diseases. The genetic determinates of human TL have been widely investigated, however, most existing studies were conducted based on adult tissues which are heavily influenced by lifetime exposure. Based on the analyses of terminal restriction fragment (TRF) length of telomere, individual genotypes, and gene expressions on 166 healthy placental tissues, we systematically interrogate TL-modulated genes and their potential functions. We discover that the TL in the placenta is comparatively longer than in other adult tissues, but exhibiting an intra-tissue homogeneity. Trans-ancestral TL genome-wide association studies (GWASs) on 644,553 individuals identify 20 newly discovered genetic associations and provide increased polygenic determination of human TL. Next, we integrate the powerful TL GWAS with placental expression quantitative trait locus (eQTL) mapping to prioritize 23 likely causal genes, among which 4 are functionally validated, including MMUT, RRM1, KIAA1429, and YWHAZ. Finally, modeling transcriptomic signatures and TRF-based TL improve the prediction performance of human TL. This study deepens our understanding of causal genes and transcriptomic determinants of human TL, promoting the mechanistic research on fine-grained TL regulation.
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Affiliation(s)
- Ying Chang
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Yao Zhou
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Junrui Zhou
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Clinical Laboratory, Shanghai Children's Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Wen Li
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Jiasong Cao
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Yaqing Jing
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Shan Zhang
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Yongmei Shen
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Qimei Lin
- Tianjin Key Lab of Human Development and Reproductive Regulation, Tianjin Central Hospital of Obstetrics and Gynecology, Nankai University, Tianjin, China
| | - Xutong Fan
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Hongxi Yang
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xiaobao Dong
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Shijie Zhang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xianfu Yi
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Ling Shuai
- State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Tianjin Central Hospital of Gynecology Obstetrics/Tianjin Key Laboratory of Human Development and Reproductive Regulation, Nankai University, Tianjin, China
| | - Lei Shi
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Zhe Liu
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Jie Yang
- Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China
| | - Xin Ma
- Wuxi School of Medicine, Jiangnan University, Wuxi, China
| | - Jihui Hao
- Department of Pancreatic Cancer, Key Laboratory of Cancer Prevention and Therapy, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kexin Chen
- Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Mulin Jun Li
- Department of Bioinformatics, The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Key Laboratory of Immune Microenvironment and Disease (Ministry of Education), School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Department of Epidemiology and Biostatistics, Tianjin Key Laboratory of Molecular Cancer Epidemiology, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
| | - Feng Wang
- Department of Genetics and Tianjin Key Laboratory of Cellular and Molecular Immunology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Tianjin Medical University School of Stomatology, Tianjin Medical University, Tianjin, China.
- Department of Geriatrics, Tianjin Medical University General Hospital; Tianjin Geriatrics Institute, Tianjin, China.
| | - Dandan Huang
- Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, China.
- Wuxi School of Medicine, Jiangnan University, Wuxi, China.
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10
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Dahl A, Thompson M, An U, Krebs M, Appadurai V, Border R, Bacanu SA, Werge T, Flint J, Schork AJ, Sankararaman S, Kendler KS, Cai N. Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder. Nat Genet 2023; 55:2082-2093. [PMID: 37985818 PMCID: PMC10703686 DOI: 10.1038/s41588-023-01559-9] [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: 08/01/2022] [Accepted: 09/18/2023] [Indexed: 11/22/2023]
Abstract
Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its genetic architecture using a novel PRS-based pleiotropy metric. We further find that integration via summary statistics also enhances GWAS power and PRS predictions, but can introduce nonspecific genetic effects depending on input. Our work provides a simple and scalable approach to improve genetic studies in large biobanks by integrating shallow and deep phenotypes.
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Affiliation(s)
- Andrew Dahl
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA.
| | - Michael Thompson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Ulzee An
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Morten Krebs
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Vivek Appadurai
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
| | - Richard Border
- Department of Computer Science, 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
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Silviu-Alin Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Lundbeck Foundation GeoGenetics Centre, Natural History Museum of Denmark, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonathan Flint
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center-Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, Copenhagen, Denmark
- Neurogenomics Division, The Translational Genomics Research Institute (TGEN), Phoenix, AZ, USA
- Section for Geogenetics, GLOBE Institute, Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark
| | - Sriram Sankararaman
- Department of Computer Science, 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
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Kenneth S Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics and Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, Neuherberg, Germany.
- Computational Health Centre, Helmholtz Zentrum München, Neuherberg, Germany.
- School of Medicine, Technical University of Munich, Munich, Germany.
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11
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Seidlitz J, Mallard TT, Vogel JW, Lee YH, Warrier V, Ball G, Hansson O, Hernandez LM, Mandal AS, Wagstyl K, Lombardo MV, Courchesne E, Glessner JT, Satterthwaite TD, Bethlehem RAI, Bernstock JD, Tasaki S, Ng B, Gaiteri C, Smoller JW, Ge T, Gur RE, Gandal MJ, Alexander-Bloch AF. The molecular genetic landscape of human brain size variation. Cell Rep 2023; 42:113439. [PMID: 37963017 DOI: 10.1016/j.celrep.2023.113439] [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: 11/09/2022] [Revised: 06/13/2023] [Accepted: 10/27/2023] [Indexed: 11/16/2023] Open
Abstract
Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW. Genes associated with higher or lower BW showed distinct neurodevelopmental trajectories and spatial patterns that mapped onto functional and cellular axes of brain organization. Expression of BW genes was predictive of interspecies differences in brain size, and bioinformatic annotation revealed enrichment for neurogenesis and cell-cell communication. Genome-wide, transcriptome-wide, and phenome-wide association analyses linked BW gene sets to neuroimaging measurements of brain size and brain-related clinical traits. Cumulatively, these results represent a major step toward delineating the molecular pathways underlying human brain size variation in health and disease.
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Affiliation(s)
- Jakob Seidlitz
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Travis T Mallard
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Younga H Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA
| | - Varun Warrier
- Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK; Department of Psychology, University of Cambridge, Cambridge CB2 1TN, UK
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC 3052, Australia; Department of Paediatrics, University of Melbourne, Melbourne, Melbourne, VIC 3052, Australia
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö P663+Q9, Sweden; Memory Clinic, Skåne University Hospital, Malmö P663+Q9, Sweden
| | - Leanna M Hernandez
- Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90024, USA
| | - Ayan S Mandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Konrad Wagstyl
- Wellcome Centre for Human Neuroimaging, University College London, London WC1N 3AR, UK
| | - Michael V Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, 38068 Rovereto, Italy
| | - Eric Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA 92093, USA; Autism Center of Excellence, University of California, San Diego, San Diego, CA 92093, USA
| | - Joseph T Glessner
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Informatics and Neuroimaging Center, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | | | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard University, Boston, MA 02115, USA; Department of Neurosurgery, Boston Children's Hospital, Harvard University, Boston, MA 02115, USA; David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Shinya Tasaki
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Boston, MA 02142, USA; Department of Psychiatry, Harvard Medical School, Boston, MA 02142, USA; Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Raquel E Gur
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Gandal
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron F Alexander-Bloch
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
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12
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Kendall TJ, Jimenez-Ramos M, Turner F, Ramachandran P, Minnier J, McColgan MD, Alam M, Ellis H, Dunbar DR, Kohnen G, Konanahalli P, Oien KA, Bandiera L, Menolascina F, Juncker-Jensen A, Alexander D, Mayor C, Guha IN, Fallowfield JA. An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease. Nat Med 2023; 29:2939-2953. [PMID: 37903863 PMCID: PMC10667096 DOI: 10.1038/s41591-023-02602-2] [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: 04/12/2023] [Accepted: 09/20/2023] [Indexed: 11/01/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is the commonest cause of chronic liver disease worldwide and represents an unmet precision medicine challenge. We established a retrospective national cohort of 940 histologically defined patients (55.4% men, 44.6% women; median body mass index 31.3; 32% with type 2 diabetes) covering the complete MASLD severity spectrum, and created a secure, searchable, open resource (SteatoSITE). In 668 cases and 39 controls, we generated hepatic bulk RNA sequencing data and performed differential gene expression and pathway analysis, including exploration of gender-specific differences. A web-based gene browser was also developed. We integrated histopathological assessments, transcriptomic data and 5.67 million days of time-stamped longitudinal electronic health record data to define disease-stage-specific gene expression signatures, pathogenic hepatic cell subpopulations and master regulator networks associated with adverse outcomes in MASLD. We constructed a 15-gene transcriptional risk score to predict future hepatic decompensation events (area under the receiver operating characteristic curve 0.86, 0.81 and 0.83 for 1-, 3- and 5-year risk, respectively). Additionally, thyroid hormone receptor beta regulon activity was identified as a critical suppressor of disease progression. SteatoSITE supports rational biomarker and drug development and facilitates precision medicine approaches for patients with MASLD.
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Affiliation(s)
- Timothy J Kendall
- Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
- Edinburgh Pathology, University of Edinburgh, Edinburgh, UK
| | - Maria Jimenez-Ramos
- Institute for Regeneration and Repair, University of Edinburgh, Edinburgh, UK
| | - Frances Turner
- Edinburgh Genomics (Bioinformatics), University of Edinburgh, Edinburgh, UK
| | | | - Jessica Minnier
- OHSU-PSU School of Public Health, Oregon Health & Sciences University, Portland, OR, USA
- Knight Cancer Institute Biostatistics Shared Resource, Oregon Health & Sciences University, Portland, OR, USA
| | - Michael D McColgan
- Precision Medicine Scotland-Innovation Centre (PMS-IC), University of Glasgow, Glasgow, UK
| | - Masood Alam
- Precision Medicine Scotland-Innovation Centre (PMS-IC), University of Glasgow, Glasgow, UK
| | - Harriet Ellis
- Precision Medicine Scotland-Innovation Centre (PMS-IC), University of Glasgow, Glasgow, UK
| | - Donald R Dunbar
- Edinburgh Genomics (Bioinformatics), University of Edinburgh, Edinburgh, UK
| | - Gabriele Kohnen
- Pathology Department, Queen Elizabeth University Hospital, Glasgow, UK
| | | | - Karin A Oien
- Pathology Department, Queen Elizabeth University Hospital, Glasgow, UK
| | - Lucia Bandiera
- School of Engineering, Institute of Bioengineering, University of Edinburgh, Edinburgh, UK
- Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
| | - Filippo Menolascina
- School of Engineering, Institute of Bioengineering, University of Edinburgh, Edinburgh, UK
- Centre for Engineering Biology, University of Edinburgh, Edinburgh, UK
| | | | | | - Charlie Mayor
- NHS Greater Glasgow and Clyde Safe Haven, Glasgow, UK
| | - Indra Neil Guha
- National Institute of Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK
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13
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Li T, Ferraro N, Strober BJ, Aguet F, Kasela S, Arvanitis M, Ni B, Wiel L, Hershberg E, Ardlie K, Arking DE, Beer RL, Brody J, Blackwell TW, Clish C, Gabriel S, Gerszten R, Guo X, Gupta N, Johnson WC, Lappalainen T, Lin HJ, Liu Y, Nickerson DA, Papanicolaou G, Pritchard JK, Qasba P, Shojaie A, Smith J, Sotoodehnia N, Taylor KD, Tracy RP, Van Den Berg D, Wheeler MT, Rich SS, Rotter JI, Battle A, Montgomery SB. The functional impact of rare variation across the regulatory cascade. CELL GENOMICS 2023; 3:100401. [PMID: 37868038 PMCID: PMC10589633 DOI: 10.1016/j.xgen.2023.100401] [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: 10/07/2022] [Revised: 03/08/2023] [Accepted: 08/10/2023] [Indexed: 10/24/2023]
Abstract
Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.
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Affiliation(s)
- Taibo Li
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicole Ferraro
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Benjamin J. Strober
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Harvard School of Public Health, Epidemiology Department, Boston, MA, USA
| | | | - Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Marios Arvanitis
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Medicine, Division of Cardiology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Bohan Ni
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Laurens Wiel
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Dan E. Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rebecca L. Beer
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer Brody
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas W. Blackwell
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Robert Gerszten
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - W. Craig Johnson
- Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Henry J. Lin
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | | | - George Papanicolaou
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Pankaj Qasba
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Josh Smith
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Russell P. Tracy
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, USA
| | - David Van Den Berg
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew T. Wheeler
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Malone Center for Engineering of Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen B. Montgomery
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
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14
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Pividori M, Lu S, Li B, Su C, Johnson ME, Wei WQ, Feng Q, Namjou B, Kiryluk K, Kullo IJ, Luo Y, Sullivan BD, Voight BF, Skarke C, Ritchie MD, Grant SFA, Greene CS. Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms. Nat Commun 2023; 14:5562. [PMID: 37689782 PMCID: PMC10492839 DOI: 10.1038/s41467-023-41057-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 08/18/2023] [Indexed: 09/11/2023] Open
Abstract
Genes act in concert with each other in specific contexts to perform their functions. Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. It has been shown that this insight is critical for developing new therapies. Transcriptome-wide association studies have helped uncover the role of individual genes in disease-relevant mechanisms. However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. This representation is based on modules of genes with similar expression patterns across the same conditions. We observe that diseases are significantly associated with gene modules expressed in relevant cell types, and our approach is accurate in predicting known drug-disease pairs and inferring mechanisms of action. Furthermore, using a CRISPR screen to analyze lipid regulation, we find that functionally important players lack associations but are prioritized in trait-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies.
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Affiliation(s)
- Milton Pividori
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Binglan Li
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Matthew E Johnson
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Qiping Feng
- Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Division of Nephrology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, 10032, USA
| | | | - Yuan Luo
- Northwestern University, Chicago, IL, 60611, USA
| | - Blair D Sullivan
- Kahlert School of Computing, University of Utah, Salt Lake City, UT, 84112, USA
| | - Benjamin F Voight
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Carsten Skarke
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Struan F A Grant
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Casey S Greene
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, 80045, USA.
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15
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Brookes KJ. Evaluating the Classification Accuracy of Expression Quantitative Trait Loci Calculated Polygenic Risk Scores in Alzheimer's Disease. Int J Mol Sci 2023; 24:12799. [PMID: 37628980 PMCID: PMC10454324 DOI: 10.3390/ijms241612799] [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: 07/18/2023] [Revised: 08/09/2023] [Accepted: 08/12/2023] [Indexed: 08/27/2023] Open
Abstract
Polygenic risk scores (PRS) hold promise for the early identification of those at risk from neurodegenerative disorders such as Alzheimer's Disease (AD), allowing for intervention to occur prior to neuronal damage. The current selection of informative single nucleotide polymorphisms (SNPs) to generate the risk scores is based on the modelling of large genome-wide association data using significance thresholds. However, the biological relevance of these SNPs is largely unknown. This study, in contrast, aims to identify SNPs with biological relevance to AD and then assess them for their ability to accurately classify cases and controls. Samples selected from the Brains for Dementia Research (BDR) were used to produce gene expression data to identify potential expression quantitative trait loci (eQTLs) relevant to AD. These SNPs were then incorporated into a PRS model to classify AD and controls in the full BDR cohort. Models derived from these eQTLs demonstrate modest classification potential with an accuracy between 61% and 67%. Although the model accuracy is not as high as some values in the literature based on significance thresholds from genome-wide association studies, these models may reflect a more biologically relevant model, which may provide novel targets for therapeutic intervention.
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Affiliation(s)
- Keeley J Brookes
- Department of Biosciences, School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
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16
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Raben TG, Lello L, Widen E, Hsu SDH. Biobank-scale methods and projections for sparse polygenic prediction from machine learning. Sci Rep 2023; 13:11662. [PMID: 37468507 PMCID: PMC10356957 DOI: 10.1038/s41598-023-37580-5] [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: 03/24/2023] [Accepted: 06/23/2023] [Indexed: 07/21/2023] Open
Abstract
In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.
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Affiliation(s)
- Timothy G Raben
- Department of Physics and Astronomy, Michigan State University, Michigan, USA.
| | - Louis Lello
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Erik Widen
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Stephen D H Hsu
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
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17
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Long E, Wan P, Chen Q, Lu Z, Choi J. From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence. CELL GENOMICS 2023; 3:100320. [PMID: 37388909 PMCID: PMC10300605 DOI: 10.1016/j.xgen.2023.100320] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
While genome-wide association studies (GWAS) have discovered thousands of disease-associated loci, molecular mechanisms for a considerable fraction of the loci remain to be explored. The logical next steps for post-GWAS are interpreting these genetic associations to understand disease etiology (GWAS functional studies) and translating this knowledge into clinical benefits for the patients (GWAS translational studies). Although various datasets and approaches using functional genomics have been developed to facilitate these studies, significant challenges remain due to data heterogeneity, multiplicity, and high dimensionality. To address these challenges, artificial intelligence (AI) technology has demonstrated considerable promise in decoding complex functional datasets and providing novel biological insights into GWAS findings. This perspective first describes the landmark progress driven by AI in interpreting and translating GWAS findings and then outlines specific challenges followed by actionable recommendations related to data availability, model optimization, and interpretation, as well as ethical concerns.
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Affiliation(s)
- Erping Long
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peixing Wan
- Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
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18
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Vysotskiy M, Weiss LA. Combinations of genes at the 16p11.2 and 22q11.2 CNVs contribute to neurobehavioral traits. PLoS Genet 2023; 19:e1010780. [PMID: 37267418 DOI: 10.1371/journal.pgen.1010780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 05/09/2023] [Indexed: 06/04/2023] Open
Abstract
The 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with neurobehavioral traits including autism spectrum disorder (ASD), schizophrenia, bipolar disorder, obesity, and intellectual disability. Identifying specific genes contributing to each disorder and dissecting the architecture of CNV-trait association has been difficult, inspiring hypotheses of more complex models, such as multiple genes acting together. Using multi-tissue data from the GTEx consortium, we generated pairwise expression imputation models for CNV genes and then applied these elastic net models to GWAS for: ASD, bipolar disorder, schizophrenia, BMI (obesity), and IQ (intellectual disability). We compared the variance in these five traits explained by gene pairs with the variance explained by single genes and by traditional interaction models. We also modeled polygene region-wide effects using summed predicted expression ranks across many genes to create a regionwide score. We found that in all CNV-trait pairs except for bipolar disorder at 22q11.2, pairwise effects explain more variance than single genes. Pairwise model superiority was specific to the CNV region for all 16p11.2 traits and ASD at 22q11.2. We identified novel individual genes over-represented in top pairs that did not show single-gene signal. We also found that BMI and IQ have significant regionwide association with both CNV regions. Overall, we observe that genetic architecture differs by trait and region, but 9/10 CNV-trait combinations demonstrate evidence for multigene contribution, and for most of these, the importance of combinatorial models appears unique to CNV regions. Our results suggest that mechanistic insights for CNV pathology may require combinational models.
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Affiliation(s)
- Mikhail Vysotskiy
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California, United States of America
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, United States of America
| | - Lauren A Weiss
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, California, United States of America
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, California, United States of America
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19
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Chen DM, Dong R, Kachuri L, Hoffmann T, Jiang Y, Berndt SI, Shelley JP, Schaffer KR, Machiela MJ, Freedman ND, Huang WY, Li SA, Lilja H, Van Den Eeden SK, Chanock S, Haiman CA, Conti DV, Klein RJ, Mosley JD, Witte JS, Graff RE. Transcriptome-Wide Association Analysis Identifies Novel Candidate Susceptibility Genes for Prostate-Specific Antigen Levels in Men Without Prostate Cancer. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.04.23289526. [PMID: 37205487 PMCID: PMC10187439 DOI: 10.1101/2023.05.04.23289526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility to screen for prostate cancer (PCa). We thus conducted a transcriptome-wide association study (TWAS) of PSA levels using genome-wide summary statistics from 95,768 PCa-free men, the MetaXcan framework, and gene prediction models trained in Genotype-Tissue Expression (GTEx) project data. Tissue-specific analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10e-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61e-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses that combined associations across 45 tissues identified 155 genes that were statistically significantly (p < 0.05/22,249 = 2.25e-6) associated with PSA levels. Based on conditional analyses that assessed whether TWAS associations were attributable to a lead GWAS variant, we found 20 novel genes (11 single-tissue, 9 cross-tissue) that were associated with PSA levels in the TWAS. Of these novel genes, five showed evidence of colocalization (colocalization probability > 0.5): EXOSC9, CCNA2, HIST1H2BN, RP11-182L21.6, and RP11-327J17.2. Six of the 20 novel genes are not known to impact PCa risk. These findings yield new hypotheses for genetic factors underlying PSA levels that should be further explored toward improving our understanding of PSA biology.
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Affiliation(s)
- Dorothy M. Chen
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Ruocheng Dong
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, 94305, USA
| | - Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, 94305, USA
- Stanford Cancer Institute, Stanford University, Stanford, CA, 94305, USA
| | - Thomas Hoffmann
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Yu Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Sonja I. Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - John P. Shelley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Kerry R. Schaffer
- Division of Hematology and Oncology, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Mitchell J. Machiela
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Neal D. Freedman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Shengchao A. Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Hans Lilja
- Departments of Pathology and Laboratory Medicine, Surgery, Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, 10065, USA
- Department of Translational Medicine, Lund University, Malmö, 21428, Sweden
| | | | - Stephen Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, 20814, USA
| | - Christopher A. Haiman
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - David V. Conti
- Center for Genetic Epidemiology, Department of Population and Preventive Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90032, USA
- Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Robert J. Klein
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Jonathan D. Mosley
- Departments of Internal Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - John S. Witte
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
- Department of Epidemiology and Population Health, Stanford University, Stanford, CA, 94305, USA
- Departments of Biomedical Data Science and Genetics (by courtesy), Stanford University, Stanford, CA, 94305, USA
| | - Rebecca E. Graff
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, 94158, USA
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20
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Silveira PP, Meaney MJ. Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches. Neurobiol Dis 2023; 178:106008. [PMID: 36690304 DOI: 10.1016/j.nbd.2023.106008] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 12/30/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
We explore how functional genomics approaches that integrate datasets from human and non-human model systems can improve our understanding of the effect of gene-environment interplay on the risk for mental disorders. We start by briefly defining the G-E paradigm and its challenges and then discuss the different levels of regulation of gene expression and the corresponding data existing in humans (genome wide genotyping, transcriptomics, DNA methylation, chromatin modifications, chromosome conformational changes, non-coding RNAs, proteomics and metabolomics), discussing novel approaches to the application of these data in the study of the origins of mental health. Finally, we discuss the multilevel integration of diverse types of data. Advance in the use of functional genomics in the context of a G-E perspective improves the detection of vulnerabilities, informing the development of preventive and therapeutic interventions.
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Affiliation(s)
- Patrícia Pelufo Silveira
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada.
| | - Michael J Meaney
- Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University, Montreal, QC, Canada; Translational Neuroscience Program, Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (ASTAR), Singapore; Brain - Body Initiative, Agency for Science, Technology and Research (ASTAR), Singapore.
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21
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Burstein D, Hoffman G, Mathur D, Venkatesh S, Therrien K, Fanous AH, Bigdeli TB, Harvey PD, Roussos P, Voloudakis G. Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.17.23284670. [PMID: 36711948 PMCID: PMC9882426 DOI: 10.1101/2023.01.17.23284670] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
With the advent of healthcare-based genotyped biobanks, genome-wide association studies (GWAS) leverage larger sample sizes, incorporate patients with diverse ancestries and introduce noisier phenotypic definitions. Yet the extent and impact of phenotypic misclassification on large-scale datasets is not currently well understood due to a lack of statistical methods to estimate relevant parameters from empirical data. Here, we develop a statistical method and scalable software, PheMED, Phenotypic Measurement of Effective Dilution, to quantify phenotypic misclassification across GWAS using only summary statistics. We illustrate how the parameters estimated by PheMED relate to the negative and positive predictive value of the labeled phenotype, compared to ground truth, and how misclassification of the phenotype yields diluted effect-sizes of variant-phenotype associations. Furthermore, we apply our methodology to detect multiple instances of statistically significant dilution in real-world data. We demonstrate how effective dilution biases downstream GWAS replication and heritability analyses despite utilizing current best practices, and provide a dilution-aware meta-analysis approach that outperforms existing methods. Consequently, we anticipate that PheMED will be a valuable tool for researchers to address phenotypic data quality issues both within and across cohorts.
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Affiliation(s)
- David Burstein
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Gabriel Hoffman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Deepika Mathur
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sanan Venkatesh
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Karen Therrien
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Ayman H Fanous
- Department of Psychiatry, University of Arizona College of Medicine-Phoenix, Phoenix
- Carl T. Hayden Veterans Affairs Medical Center, Phoenix, Arizona
| | - Tim B Bigdeli
- VA New York Harbor Healthcare System, Brooklyn
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, New York
- Department of Epidemiology and Biostatistics, School of Public Health, SUNY Downstate Health Sciences University, Brooklyn, New York
| | - Philip D Harvey
- Bruce W. Carter Miami Veterans Affairs (VA) Medical Center, Miami, Florida
- University of Miami Miller School of Medicine, Miami, Florida
| | - Panos Roussos
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA
| | - Georgios Voloudakis
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
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22
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Bogdan R, Hatoum AS, Johnson EC, Agrawal A. The Genetically Informed Neurobiology of Addiction (GINA) model. Nat Rev Neurosci 2023; 24:40-57. [PMID: 36446900 PMCID: PMC10041646 DOI: 10.1038/s41583-022-00656-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/19/2022] [Indexed: 11/30/2022]
Abstract
Addictions are heritable and unfold dynamically across the lifespan. One prominent neurobiological theory proposes that substance-induced changes in neural circuitry promote the progression of addiction. Genome-wide association studies have begun to characterize the polygenic architecture undergirding addiction liability and revealed that genetic loci associated with risk can be divided into those associated with a general broad-spectrum liability to addiction and those associated with drug-specific addiction risk. In this Perspective, we integrate these genomic findings with our current understanding of the neurobiology of addiction to propose a new Genetically Informed Neurobiology of Addiction (GINA) model.
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Affiliation(s)
- Ryan Bogdan
- Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA.
| | - Alexander S Hatoum
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
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23
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Bhattacharya A, Hirbo JB, Zhou D, Zhou W, Zheng J, Kanai M, Pasaniuc B, Gamazon ER, Cox NJ. Best practices for multi-ancestry, meta-analytic transcriptome-wide association studies: Lessons from the Global Biobank Meta-analysis Initiative. CELL GENOMICS 2022; 2:100180. [PMID: 36341024 PMCID: PMC9631681 DOI: 10.1016/j.xgen.2022.100180] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The Global Biobank Meta-analysis Initiative (GBMI), through its diversity, provides a valuable opportunity to study population-wide and ancestry-specific genetic associations. However, with multiple ascertainment strategies and multi-ancestry study populations across biobanks, GBMI presents unique challenges in implementing statistical genetics methods. Transcriptome-wide association studies (TWASs) boost detection power for and provide biological context to genetic associations by integrating genetic variant-to-trait associations from genome-wide association studies (GWASs) with predictive models of gene expression. TWASs present unique challenges beyond GWASs, especially in a multi-biobank, meta-analytic setting. Here, we present the GBMI TWAS pipeline, outlining practical considerations for ancestry and tissue specificity, meta-analytic strategies, and open challenges at every step of the framework. We advise conducting ancestry-stratified TWASs using ancestry-specific expression models and meta-analyzing results using inverse-variance weighting, showing the least test statistic inflation. Our work provides a foundation for adding transcriptomic context to biobank-linked GWASs, allowing for ancestry-aware discovery to accelerate genomic medicine.
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Affiliation(s)
- Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA,Institute of Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA,Corresponding author
| | - Jibril B. Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dan Zhou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jie Zheng
- MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol BS8 2BN, UK
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA,Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita 565-0871, Japan
| | | | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, 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,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eric R. Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA,MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nancy J. Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
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24
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Kuo SIC, Poore HE, Barr PB, Chirico IS, Aliev F, Bucholz KK, Chan G, Kamarajan C, Kramer JR, McCutcheon VV, Plawecki MH, Dick DM. The role of parental genotype in the intergenerational transmission of externalizing behavior: Evidence for genetic nurturance. Dev Psychopathol 2022; 34:1-11. [PMID: 36200344 PMCID: PMC10076450 DOI: 10.1017/s0954579422000700] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The purpose of this study was to examine possible pathways by which genetic risk associated with externalizing is transmitted in families. We used molecular data to disentangle the genetic and environmental pathways contributing to adolescent externalizing behavior in a sample of 1,111 adolescents (50% female; 719 European and 392 African ancestry) and their parents from the Collaborative Study on the Genetics of Alcoholism. We found evidence for genetic nurture such that parental externalizing polygenic scores were associated with adolescent externalizing behavior, over and above the effect of adolescents' own externalizing polygenic scores. Mediation analysis indicated that parental externalizing psychopathology partly explained the effect of parental genotype on children's externalizing behavior. We also found evidence for evocative gene-environment correlation, whereby adolescent externalizing polygenic scores were associated with lower parent-child communication, less parent-child closeness, and lower parental knowledge, controlling for parental genotype. These effects were observed among participants of European ancestry but not African ancestry, likely due to the limited predictive power of polygenic scores across ancestral background. These results demonstrate that in addition to genetic transmission, genes influence offspring behavior through the influence of parental genotypes on their children's environmental experiences, and the role of children's genotypes in shaping parent-child relationships.
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Affiliation(s)
- Sally I-Chun Kuo
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Holly E Poore
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Peter B Barr
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
- VA New York Harbor Healthcare System, Brooklyn, NY, USA
| | | | - Fazil Aliev
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Grace Chan
- Department of Psychiatry, University of Connecticut School of Medicine, Farmington, CT, USA
| | - Chella Kamarajan
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - John R Kramer
- Department of Psychiatry, University of Iowa, Iowa City, IA, USA
| | - Vivia V McCutcheon
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA
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25
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Atkinson EG, Bianchi SB, Ye GY, Martínez-Magaña JJ, Tietz GE, Montalvo-Ortiz JL, Giusti-Rodriguez P, Palmer AA, Sanchez-Roige S. Cross-ancestry genomic research: time to close the gap. Neuropsychopharmacology 2022; 47:1737-1738. [PMID: 35739257 PMCID: PMC9372026 DOI: 10.1038/s41386-022-01365-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/27/2022] [Accepted: 06/10/2022] [Indexed: 12/23/2022]
Affiliation(s)
- Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Sevim B Bianchi
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gordon Y Ye
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - José Jaime Martínez-Magaña
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
| | - Grace E Tietz
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Janitza L Montalvo-Ortiz
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA CT Healthcare Center, West Haven, CT, USA
- US Department of Veterans Affairs National Center of Posttraumatic Stress Disorder, Clinical Neurosciences Division, West Haven, CT, USA
| | - Paola Giusti-Rodriguez
- Department of Psychiatry, University of Florida College of Medicine, Gainesville, FL, USA
| | - Abraham A Palmer
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sandra Sanchez-Roige
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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26
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Lu Z, Gopalan S, Yuan D, Conti DV, Pasaniuc B, Gusev A, Mancuso N. Multi-ancestry fine-mapping improves precision to identify causal genes in transcriptome-wide association studies. Am J Hum Genet 2022; 109:1388-1404. [PMID: 35931050 PMCID: PMC9388396 DOI: 10.1016/j.ajhg.2022.07.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
Transcriptome-wide association studies (TWASs) are a powerful approach to identify genes whose expression is associated with complex disease risk. However, non-causal genes can exhibit association signals due to confounding by linkage disequilibrium (LD) patterns and eQTL pleiotropy at genomic risk regions, which necessitates fine-mapping of TWAS signals. Here, we present MA-FOCUS, a multi-ancestry framework for the improved identification of genes underlying traits of interest. We demonstrate that by leveraging differences in ancestry-specific patterns of LD and eQTL signals, MA-FOCUS consistently outperforms single-ancestry fine-mapping approaches with equivalent total sample sizes across multiple metrics. We perform TWASs for 15 blood traits using genome-wide summary statistics (average nEA = 511 k, nAA = 13 k) and lymphoblastoid cell line eQTL data from cohorts of primarily European and African continental ancestries. We recapitulate evidence demonstrating shared genetic architectures for eQTL and blood traits between the two ancestry groups and observe that gene-level effects correlate 20% more strongly across ancestries than SNP-level effects. Lastly, we perform fine-mapping using MA-FOCUS and find evidence that genes at TWAS risk regions are more likely to be shared across ancestries than they are to be ancestry specific. Using multiple lines of evidence to validate our findings, we find that gene sets produced by MA-FOCUS are more enriched in hematopoietic categories than alternative approaches (p = 2.36 × 10-15). Our work demonstrates that including and appropriately accounting for genetic diversity can drive more profound insights into the genetic architecture of complex traits.
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Affiliation(s)
- Zeyun Lu
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA,Corresponding author
| | - Shyamalika Gopalan
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA,Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
| | - Dong Yuan
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - David V. Conti
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, 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,Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, 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
| | - Alexander Gusev
- Division of Population Sciences, Dana-Farber Cancer Institute & Harvard Medical School, Boston, MA, USA,Division of Genetics, Brigham & Women’s Hospital, Boston, MA, USA,Program in Medical and Population Genetics, The Broad Institute, Cambridge, MA, USA
| | - Nicholas Mancuso
- Biostatistics Division, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA,Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA,Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, USA,Corresponding author
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27
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Novel functional genomics approaches bridging neuroscience and psychiatry. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022. [PMID: 37519472 PMCID: PMC10382709 DOI: 10.1016/j.bpsgos.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The possibility of establishing a metric of individual genetic risk for a particular disease or trait has sparked the interest of the clinical and research communities, with many groups developing and validating genomic profiling methodologies for their potential application in clinical care. Current approaches for calculating genetic risk to specific psychiatric conditions consist of aggregating genome-wide association studies-derived estimates into polygenic risk scores, which broadly represent the number of inherited risk alleles for an individual. While the traditional approach for polygenic risk score calculation aggregates estimates of gene-disease associations, novel alternative approaches have started to consider functional molecular phenotypes that are closer to genetic variation and are less penalized by the multiple testing required in genome-wide association studies. Moving the focus from genotype-disease to genotype-gene regulation frameworks, these novel approaches incorporate prior knowledge regarding biological processes involved in disease and aggregate estimates for the association of genotypes and phenotypes using multi-omics data modalities. In this review, we discuss and list different functional genomics tools that can be used and integrated to inform researchers and clinicians for a better understanding and diagnosis of psychopathology. We suggest that these novel approaches can help generate biologically driven hypotheses for polygenic signals that can ultimately serve the clinical community as potential biomarkers of psychiatric disease susceptibility.
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28
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Espin-Garcia O, Baghel M, Brar N, Whittaker JL, Ali SA. Can genetics guide exercise prescriptions in osteoarthritis? FRONTIERS IN REHABILITATION SCIENCES 2022; 3:930421. [PMID: 36188938 PMCID: PMC9397982 DOI: 10.3389/fresc.2022.930421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 07/06/2022] [Indexed: 11/16/2022]
Abstract
Osteoarthritis (OA) is the most common form of arthritis and has a multifactorial etiology. Current management for OA focuses on minimizing pain and functional loss, typically involving pharmacological, physical, psychosocial, and mind-body interventions. However, there remain challenges in determining which patients will benefit most from which interventions. Although exercise-based interventions are recommended as first-line treatments and are known to be beneficial for managing both the disease and illness of OA, the optimal exercise “prescription” is unknown, due in part to our limited understanding of the precise mechanisms underlying its action. Here we present our perspective on the potential role of genetics in guiding exercise prescription for persons with OA. We describe key publications in the areas of exercise and OA, genetics and OA, and exercise and genetics, and point to a paucity of knowledge at the intersection of exercise, genetics, and OA. We suggest there is emerging evidence to support the use of genetics and epigenetics to explain the beneficial effects of exercise for OA. We identify missing links in the existing research relating to exercise, genetics, and OA, and highlight epigenetics as a promising mechanism through which environmental exposures such as exercise may impact OA outcomes. We anticipate future studies will improve our understanding of how genetic and epigenetic factors mediate exercise-based interventions to support implementation and ultimately improve OA patient care.
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Affiliation(s)
- Osvaldo Espin-Garcia
- Department of Biostatistics, Princess Margaret Cancer Centre and Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada
- Division of Biostatistics, Dalla Lana School of Public Health and Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
- *Correspondence: Osvaldo Espin-Garcia
| | - Madhu Baghel
- Bone and Joint Center, Department of Orthopaedic Surgery, Henry Ford Health, Detroit, MI, United States
| | - Navraj Brar
- Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, ON, Canada
| | - Jackie L. Whittaker
- Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada
- Arthritis Research Canada, Vancouver, BC, Canada
| | - Shabana Amanda Ali
- Bone and Joint Center, Department of Orthopaedic Surgery, Henry Ford Health, Detroit, MI, United States
- Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, United States
- Department of Physiology, College of Human Medicine, Michigan State University, East Lansing, MI, United States
- Shabana Amanda Ali
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29
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Hu X, Qiao D, Kim W, Moll M, Balte PP, Lange LA, Bartz TM, Kumar R, Li X, Yu B, Cade BE, Laurie CA, Sofer T, Ruczinski I, Nickerson DA, Muzny DM, Metcalf GA, Doddapaneni H, Gabriel S, Gupta N, Dugan-Perez S, Cupples LA, Loehr LR, Jain D, Rotter JI, Wilson JG, Psaty BM, Fornage M, Morrison AC, Vasan RS, Washko G, Rich SS, O'Connor GT, Bleecker E, Kaplan RC, Kalhan R, Redline S, Gharib SA, Meyers D, Ortega V, Dupuis J, London SJ, Lappalainen T, Oelsner EC, Silverman EK, Barr RG, Thornton TA, Wheeler HE, Cho MH, Im HK, Manichaikul A. Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program. Am J Hum Genet 2022; 109:857-870. [PMID: 35385699 PMCID: PMC9118106 DOI: 10.1016/j.ajhg.2022.03.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 03/04/2022] [Indexed: 12/17/2022] Open
Abstract
While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.
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Affiliation(s)
- Xiaowei Hu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Dandi Qiao
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Matthew Moll
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Pallavi P Balte
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA; Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA
| | - Rajesh Kumar
- Division of Allergy and Clinical Immunology, Ann and Robert H. Lurie Children's Hospital, Chicago, IL 60611, USA; Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xingnan Li
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Brian E Cade
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Cecelia A Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Tamar Sofer
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Donna M Muzny
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ginger A Metcalf
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Stacy Gabriel
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shannon Dugan-Perez
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Laura R Loehr
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA 02115, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA 98101, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Ramachandran S Vasan
- Boston University and the National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA 01702, USA; Department of Preventive Medicine and Epidemiology, School of Medicine and Public Health, Boston University, Boston, MA 02118, USA
| | - George Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - George T O'Connor
- Pulmonary Center, Boston University, School of Medicine, Boston, MA 02118, USA
| | - Eugene Bleecker
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY 10461, USA; Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Ravi Kalhan
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Sina A Gharib
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA 98109, USA
| | - Deborah Meyers
- Department of Medicine, University of Arizona, Tucson, AZ 85724, USA
| | - Victor Ortega
- Pulmonary and Critical Care, School of Medicine, Wake Forest University, Winston-Salem, NC 27157, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Stephanie J London
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Durham, NC 27709, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY 10013, USA; Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Elizabeth C Oelsner
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA
| | - Timothy A Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Heather E Wheeler
- Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA
| | | | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
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30
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Highland HM, Wojcik GL, Graff M, Nishimura KK, Hodonsky CJ, Baldassari AR, Cote AC, Cheng I, Gignoux CR, Tao R, Li Y, Boerwinkle E, Fornage M, Haessler J, Hindorff LA, Hu Y, Justice AE, Lin BM, Lin D, Stram DO, Haiman CA, Kooperberg C, Le Marchand L, Matise TC, Kenny EE, Carlson CS, Stahl EA, Avery CL, North KE, Ambite JL, Buyske S, Loos RJ, Peters U, Young KL, Bien SA, Huckins LM. Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. Am J Hum Genet 2022; 109:669-679. [PMID: 35263625 PMCID: PMC9069067 DOI: 10.1016/j.ajhg.2022.02.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/15/2022] [Indexed: 02/06/2023] Open
Abstract
One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB50k) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB50k, demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB50k analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.
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Affiliation(s)
- Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Mariaelisa Graff
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Katherine K Nishimura
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Chani J Hodonsky
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA; Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Antoine R Baldassari
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Alanna C Cote
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Yuqing Li
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Eric Boerwinkle
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Myriam Fornage
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center, Houston, TX 77030, USA; Brown Foundation Institute for Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center, Houston, TX 77030, USA
| | - Jeffrey Haessler
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Lucia A Hindorff
- Division of Genomic Medicine, NIH National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Yao Hu
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Anne E Justice
- Department of Population Health Sciences, Geisinger Health System, Danville, PA 17822, USA
| | - Bridget M Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Danyu Lin
- Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Daniel O Stram
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Christopher A Haiman
- Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Charles Kooperberg
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | | | - Tara C Matise
- Genetics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christopher S Carlson
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Eli A Stahl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Jose Luis Ambite
- Information Sciences Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Steven Buyske
- Statistics, Rutgers University, New Brunswick, NJ 08901-8554, USA
| | - Ruth J Loos
- Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ulrike Peters
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Kristin L Young
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Stephanie A Bien
- Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Laura M Huckins
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mental Illness Research, Education and Clinical Centers, James J. Peters Department of Veterans Affairs Medical Center, Bronx, NY 14068, USA.
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31
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Integrative transcriptomic, evolutionary, and causal inference framework for region-level analysis: Application to COVID-19. NPJ Genom Med 2022; 7:24. [PMID: 35318325 PMCID: PMC8940898 DOI: 10.1038/s41525-022-00296-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 02/15/2022] [Indexed: 11/09/2022] Open
Abstract
We developed an integrative transcriptomic, evolutionary, and causal inference framework for a deep region-level analysis, which integrates several published approaches and a new summary-statistics-based methodology. To illustrate the framework, we applied it to understanding the host genetics of COVID-19 severity. We identified putative causal genes, including SLC6A20, CXCR6, CCR9, and CCR5 in the locus on 3p21.31, quantifying their effect on mediating expression and on severe COVID-19. We confirmed that individuals who carry the introgressed archaic segment in the locus have a substantially higher risk of developing the severe disease phenotype, estimating its contribution to expression-mediated heritability using a new summary-statistics-based approach we developed here. Through a large-scale phenome-wide scan for the genes in the locus, several potential complications, including inflammatory, immunity, olfactory, and gustatory traits, were identified. Notably, the introgressed segment showed a much higher concentration of expression-mediated causal effect on severity (0.9–11.5 times) than the entire locus, explaining, on average, 15.7% of the causal effect. The region-level framework (implemented in publicly available software, SEGMENT-SCAN) has important implications for the elucidation of molecular mechanisms of disease and the rational design of potentially novel therapeutics.
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