1
|
Yu Z, Coorens THH, Uddin MM, Ardlie KG, Lennon N, Natarajan P. Genetic variation across and within individuals. Nat Rev Genet 2024; 25:548-562. [PMID: 38548833 DOI: 10.1038/s41576-024-00709-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/09/2024] [Indexed: 04/12/2024]
Abstract
Germline variation and somatic mutation are intricately connected and together shape human traits and disease risks. Germline variants are present from conception, but they vary between individuals and accumulate over generations. By contrast, somatic mutations accumulate throughout life in a mosaic manner within an individual due to intrinsic and extrinsic sources of mutations and selection pressures acting on cells. Recent advancements, such as improved detection methods and increased resources for association studies, have drastically expanded our ability to investigate germline and somatic genetic variation and compare underlying mutational processes. A better understanding of the similarities and differences in the types, rates and patterns of germline and somatic variants, as well as their interplay, will help elucidate the mechanisms underlying their distinct yet interlinked roles in human health and biology.
Collapse
Affiliation(s)
- Zhi Yu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Md Mesbah Uddin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
2
|
Lu J, Wang Z. Mendelian Randomization Provides No Evidence for the Bidirectional Relationship between Type 2 Diabetes and Venous Thromboembolism in East Asians and African Americans. Semin Thromb Hemost 2024. [PMID: 39029518 DOI: 10.1055/s-0044-1788568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/21/2024]
Affiliation(s)
- Jiawen Lu
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Zhenqian Wang
- Department of Health Technology and Informatics, Faculty of Health and Social Sciences, The Hong Kong Polytechnic University, Hong Kong, China
- School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, China
| |
Collapse
|
3
|
Leask MP, Crișan TO, Ji A, Matsuo H, Köttgen A, Merriman TR. The pathogenesis of gout: molecular insights from genetic, epigenomic and transcriptomic studies. Nat Rev Rheumatol 2024:10.1038/s41584-024-01137-1. [PMID: 38992217 DOI: 10.1038/s41584-024-01137-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/11/2024] [Indexed: 07/13/2024]
Abstract
The pathogenesis of gout involves a series of steps beginning with hyperuricaemia, followed by the deposition of monosodium urate crystal in articular structures and culminating in an innate immune response, mediated by the NLRP3 inflammasome, to the deposited crystals. Large genome-wide association studies (GWAS) of serum urate levels initially identified the genetic variants with the strongest effects, mapping mainly to genes that encode urate transporters in the kidney and gut. Other GWAS highlighted the importance of uncommon genetic variants. More recently, genetic and epigenetic genome-wide studies have revealed new pathways in the inflammatory process of gout, including genetic associations with epigenomic modifiers. Epigenome-wide association studies are also implicating epigenomic remodelling in gout, which perhaps regulates the responsiveness of the innate immune system to monosodium urate crystals. Notably, genes implicated in gout GWAS do not include those encoding components of the NLRP3 inflammasome itself, but instead include genes encoding molecules involved in its regulation. Knowledge of the molecular mechanisms underlying gout has advanced through the translation of genetic associations into specific molecular mechanisms. Notable examples include ABCG2, HNF4A, PDZK1, MAF and IL37. Current genetic studies are dominated by participants of European ancestry; however, studies focusing on other population groups are discovering informative population-specific variants associated with gout.
Collapse
Affiliation(s)
- Megan P Leask
- Department of Physiology, University of Otago, Dunedin, Aotearoa, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Tania O Crișan
- Department of Medical Genetics, "Iuliu Haţieganu" University of Medicine and Pharmacy, Cluj-Napoca, Romania
| | - Aichang Ji
- Affiliated Hospital of Qingdao University, Qingdao University, Qingdao, China
| | - Hirotaka Matsuo
- Department of Integrative Physiology and Bio-Nano Medicine, National Defense Medical College, Saitama, Japan
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Tony R Merriman
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.
- Department of Microbiology and Immunology, University of Otago, Dunedin, Aotearoa, New Zealand.
| |
Collapse
|
4
|
Floris M, Moschella A, Alcalay M, Montella A, Tirelli M, Fontana L, Idda ML, Guarnieri P, Capasso M, Mammì C, Nicoletti P, Miozzo M. Pharmacogenetics in Italy: current landscape and future prospects. Hum Genomics 2024; 18:78. [PMID: 38987819 PMCID: PMC11234611 DOI: 10.1186/s40246-024-00612-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 04/30/2024] [Indexed: 07/12/2024] Open
Abstract
Pharmacogenetics investigates sequence of genes that affect drug response, enabling personalized medication. This approach reduces drug-induced adverse reactions and improves clinical effectiveness, making it a crucial consideration for personalized medical care. Numerous guidelines, drawn by global consortia and scientific organizations, codify genotype-driven administration for over 120 active substances. As the scientific community acknowledges the benefits of genotype-tailored therapy over traditionally agnostic drug administration, the push for its implementation into Italian healthcare system is gaining momentum. This evolution is influenced by several factors, including the improved access to patient genotypes, the sequencing costs decrease, the growing of large-scale genetic studies, the rising popularity of direct-to-consumer pharmacogenetic tests, and the continuous improvement of pharmacogenetic guidelines. Since EMA (European Medicines Agency) and AIFA (Italian Medicines Agency) provide genotype information on drug leaflet without clear and explicit clinical indications for gene testing, the regulation of pharmacogenetic testing is a pressing matter in Italy. In this manuscript, we have reviewed how to overcome the obstacles in implementing pharmacogenetic testing in the clinical practice of the Italian healthcare system. Our particular emphasis has been on germline testing, given the absence of well-defined national directives in contrast to somatic pharmacogenetics.
Collapse
Affiliation(s)
- Matteo Floris
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy.
| | - Antonino Moschella
- Unit of Medical Genetics, Grande Ospedale Metropolitano Bianchi-Melacrino-Morelli, Reggio Calabria, Italy
| | - Myriam Alcalay
- Department of Experimental Oncology, European Institute of Oncology IRCCS, Milano, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milano, Italy
| | - Annalaura Montella
- CEINGE Biotecnologie Avanzate, Napoli, Italy
- Department of Molecular Medicine and Medical Biotechnology (DMMBM), Università degli Studi di Napoli "Federico II", Napoli, Italy
| | - Matilde Tirelli
- CEINGE Biotecnologie Avanzate, Napoli, Italy
- Department of Molecular Medicine and Medical Biotechnology (DMMBM), Università degli Studi di Napoli "Federico II", Napoli, Italy
| | - Laura Fontana
- Medical Genetics Unit, Department of Health Sciences, ASST Santi Paolo e Carlo, Università degli Studi di Milano, Milan, Italy
| | - Maria Laura Idda
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | | | - Mario Capasso
- CEINGE Biotecnologie Avanzate, Napoli, Italy
- Department of Molecular Medicine and Medical Biotechnology (DMMBM), Università degli Studi di Napoli "Federico II", Napoli, Italy
| | - Corrado Mammì
- Unit of Medical Genetics, Grande Ospedale Metropolitano Bianchi-Melacrino-Morelli, Reggio Calabria, Italy
| | - Paola Nicoletti
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Monica Miozzo
- Medical Genetics Unit, Department of Health Sciences, ASST Santi Paolo e Carlo, Università degli Studi di Milano, Milan, Italy.
| |
Collapse
|
5
|
Carey CE, Shafee R, Wedow R, Elliott A, Palmer DS, Compitello J, Kanai M, Abbott L, Schultz P, Karczewski KJ, Bryant SC, Cusick CM, Churchhouse C, Howrigan DP, King D, Davey Smith G, Neale BM, Walters RK, Robinson EB. Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation. Nat Hum Behav 2024:10.1038/s41562-024-01909-5. [PMID: 38965376 DOI: 10.1038/s41562-024-01909-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 05/14/2024] [Indexed: 07/06/2024]
Abstract
Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.
Collapse
Affiliation(s)
- Caitlin E Carey
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
| | - Rebecca Shafee
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Section on Developmental Neurogenomics, National Institute of Mental Health, Bethesda, MD, USA
| | - Robbee Wedow
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Sociology, Purdue University, West Lafayette, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
- AnalytiXIN, Indianapolis, IN, USA
- Center on Aging and the Life Course, Purdue University, West Lafayette, IN, USA
- Department of Statistics, Purdue University, West Lafayette, IN, USA
| | - Amanda Elliott
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Duncan S Palmer
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Nuffield Department of Population Health, Medical Sciences Division University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - John Compitello
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Masahiro Kanai
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Liam Abbott
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick Schultz
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Konrad J Karczewski
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Samuel C Bryant
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Caroline M Cusick
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Claire Churchhouse
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel P Howrigan
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel King
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - George Davey Smith
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Benjamin M Neale
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Raymond K Walters
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Elise B Robinson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
6
|
Wang H, Xiao F, Gao Z, Guo L, Yang L, Li G, Kong Q. Methylation entropy landscape of Chinese long-lived individuals reveals lower epigenetic noise related to human healthy aging. Aging Cell 2024; 23:e14163. [PMID: 38566438 PMCID: PMC11258444 DOI: 10.1111/acel.14163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/12/2024] [Accepted: 03/15/2024] [Indexed: 04/04/2024] Open
Abstract
The transition from ordered to noisy is a significant epigenetic signature of aging and age-related disease. As a paradigm of healthy human aging and longevity, long-lived individuals (LLI, >90 years old) may possess characteristic strategies in coping with the disordered epigenetic regulation. In this study, we constructed high-resolution blood epigenetic noise landscapes for this cohort by a methylation entropy (ME) method using whole genome bisulfite sequencing (WGBS). Although a universal increase in global ME occurred with chronological age in general control samples, this trend was suppressed in LLIs. Importantly, we identified 38,923 genomic regions with LLI-specific lower ME (LLI-specific lower entropy regions, for short, LLI-specific LERs). These regions were overrepresented in promoters, which likely function in transcriptional noise suppression. Genes associated with LLI-specific LERs have a considerable impact on SNP-based heritability of some aging-related disorders (e.g., asthma and stroke). Furthermore, neutrophil was identified as the primary cell type sustaining LLI-specific LERs. Our results highlight the stability of epigenetic order in promoters of genes involved with aging and age-related disorders within LLI epigenomes. This unique epigenetic feature reveals a previously unknown role of epigenetic order maintenance in specific genomic regions of LLIs, which helps open a new avenue on the epigenetic regulation mechanism in human healthy aging and longevity.
Collapse
Affiliation(s)
- Hao‐Tian Wang
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Fu‐Hui Xiao
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Zong‐Liang Gao
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Li‐Yun Guo
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Li‐Qin Yang
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Gong‐Hua Li
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
| | - Qing‐Peng Kong
- Key Laboratory of Genetic Evolution & Animal Models (Chinese Academy of Sciences), Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging StudyKIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of SciencesKunmingChina
- CAS Center for Excellence in Animal Evolution and GeneticsChinese Academy of SciencesKunmingChina
| |
Collapse
|
7
|
Zhou J, Zhang Y, Yang T, Zhang K, Li A, Li M, Peng X, Chen M. Causal relationships between lung cancer and sepsis: a genetic correlation and multivariate mendelian randomization analysis. Front Genet 2024; 15:1381303. [PMID: 39005629 PMCID: PMC11239446 DOI: 10.3389/fgene.2024.1381303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 06/10/2024] [Indexed: 07/16/2024] Open
Abstract
Background Former research has emphasized a correlation between lung cancer (LC) and sepsis, but the causative link remains unclear. Method This study used univariate Mendelian Randomization (MR) to explore the causal relationship between LC, its subtypes, and sepsis. Linkage Disequilibrium Score (LDSC) regression was used to calculate genetic correlations. Multivariate MR was applied to investigate the role of seven confounding factors. The primary method utilized was inverse-variance-weighted (IVW), supplemented by sensitivity analyses to assess directionality, heterogeneity, and result robustness. Results LDSC analysis revealed a significant genetic correlation between LC and sepsis (genetic correlation = 0.325, p = 0.014). Following false discovery rate (FDR) correction, strong evidence suggested that genetically predicted LC (OR = 1.172, 95% CI 1.083-1.269, p = 8.29 × 10-5, P fdr = 2.49 × 10-4), squamous cell lung carcinoma (OR = 1.098, 95% CI 1.021-1.181, p = 0.012, P fdr = 0.012), and lung adenocarcinoma (OR = 1.098, 95% CI 1.024-1.178, p = 0.009, P fdr = 0.012) are linked to an increased incidence of sepsis. Suggestive evidence was also found for small cell lung carcinoma (Wald ratio: OR = 1.156, 95% CI 1.047-1.277, p = 0.004) in relation to sepsis. The multivariate MR suggested that the partial impact of all LC subtypes on sepsis might be mediated through body mass index. Reverse analysis did not find a causal relationship (p > 0.05 and P fdr > 0.05). Conclusion The study suggests a causative link between LC and increased sepsis risk, underscoring the need for integrated sepsis management in LC patients.
Collapse
Affiliation(s)
- Jiejun Zhou
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Youqian Zhang
- Health Science Center, Yangtze University, Jingzhou, Hubei, China
| | - Tian Yang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Kun Zhang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Anqi Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Meng Li
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Xiaojing Peng
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Mingwei Chen
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| |
Collapse
|
8
|
Koenig Z, Yohannes MT, Nkambule LL, Zhao X, Goodrich JK, Kim HA, Wilson MW, Tiao G, Hao SP, Sahakian N, Chao KR, Walker MA, Lyu Y, Rehm HL, Neale BM, Talkowski ME, Daly MJ, Brand H, Karczewski KJ, Atkinson EG, Martin AR. A harmonized public resource of deeply sequenced diverse human genomes. Genome Res 2024; 34:796-809. [PMID: 38749656 PMCID: PMC11216312 DOI: 10.1101/gr.278378.123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 05/07/2024] [Indexed: 05/18/2024]
Abstract
Underrepresented populations are often excluded from genomic studies owing in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high-quality set of 4094 whole genomes from 80 populations in the HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also show substantial added value from this data set compared with the prior versions of the component resources, typically combined via liftOver and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared with previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality-control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.
Collapse
Affiliation(s)
- Zan Koenig
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Mary T Yohannes
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Lethukuthula L Nkambule
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Xuefang Zhao
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Julia K Goodrich
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Heesu Ally Kim
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Michael W Wilson
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Grace Tiao
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Stephanie P Hao
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Nareh Sahakian
- Broad Genomics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, 02141, USA
| | - Katherine R Chao
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Mark A Walker
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Data Sciences Platform, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Yunfei Lyu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02115, USA
| | - Heidi L Rehm
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Benjamin M Neale
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Michael E Talkowski
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Mark J Daly
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
- Institute for Molecular Medicine Finland, 00290 Helsinki, Finland
| | - Harrison Brand
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, USA
| | - Konrad J Karczewski
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Elizabeth G Atkinson
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA
| | - Alicia R Martin
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA;
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| |
Collapse
|
9
|
Milbourn H, McCartney D, Richmond A, Campbell A, Flaig R, Robertson S, Fawns-Ritchie C, Hayward C, Marioni RE, McIntosh AM, Porteous DJ, Whalley HC, Sudlow C. Generation Scotland: an update on Scotland's longitudinal family health study. BMJ Open 2024; 14:e084719. [PMID: 38908846 DOI: 10.1136/bmjopen-2024-084719] [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] [Indexed: 06/24/2024] Open
Abstract
PURPOSE Generation Scotland (GS) is a large family-based cohort study established as a longitudinal resource for research into the genetic, lifestyle and environmental determinants of physical and mental health. It comprises extensive genetic, sociodemographic and clinical data from volunteers in Scotland. PARTICIPANTS A total of 24 084 adult participants, including 5501 families, were recruited between 2006 and 2011. Within the cohort, 59% (approximately 14 209) are women, with an average age at recruitment of 49 years. Participants completed a health questionnaire and attended an in-person clinic visit, where detailed baseline data were collected on lifestyle information, cognitive function, personality traits and mental and physical health. Genotype array data are available for 20 026 (83%) participants, and blood-based DNA methylation (DNAm) data for 18 869 (78%) participants. Linkage to routine National Health Service datasets has been possible for 93% (n=22 402) of the cohort, creating a longitudinal resource that includes primary care, hospital attendance, prescription and mortality records. Multimodal brain imaging is available in 1069 individuals. FINDINGS TO DATE GS has been widely used by researchers across the world to study the genetic and environmental basis of common complex diseases. Over 350 peer-reviewed papers have been published using GS data, contributing to research areas such as ageing, cancer, cardiovascular disease and mental health. Recontact studies have built on the GS cohort to collect additional prospective data to study chronic pain, major depressive disorder and COVID-19. FUTURE PLANS To create a larger, richer, longitudinal resource, 'Next Generation Scotland' launched in May 2022 to expand the existing cohort by a target of 20 000 additional volunteers, now including anyone aged 12+ years. New participants complete online consent and questionnaires and provide postal saliva samples, from which genotype and salivary DNAm array data will be generated. The latest cohort information and how to access data can be found on the GS website (www.generationscotland.org).
Collapse
Affiliation(s)
- Hannah Milbourn
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Centre for Medical Informatics, Institute of Population Health Sciences and Informatics, The University of Edinburgh Usher, Edinburgh, UK
| | - Daniel McCartney
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Anne Richmond
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Robin Flaig
- Centre for Medical Informatics, Institute of Population Health Sciences and Informatics, The University of Edinburgh Usher, Edinburgh, UK
| | - Sarah Robertson
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Centre for Medical Informatics, Institute of Population Health Sciences and Informatics, The University of Edinburgh Usher, Edinburgh, UK
| | - Chloe Fawns-Ritchie
- Division of Psychology, School of Humanities, Social Sciences and Law, University of Dundee, Dundee, UK
- Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Caroline Hayward
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Andrew M McIntosh
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Division of Psychiatry, The University of Edinburgh, Edinburgh, UK
- Institute of Population Health Sciences and Informatics, The University of Edinburgh Usher, Edinburgh, UK
| | - Cathie Sudlow
- Institute of Population Health Sciences and Informatics, The University of Edinburgh Usher, Edinburgh, UK
| |
Collapse
|
10
|
Salvatore M, Kundu R, Shi X, Friese CR, Lee S, Fritsche LG, Mondul AM, Hanauer D, Pearce CL, Mukherjee B. To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice. J Am Med Inform Assoc 2024; 31:1479-1492. [PMID: 38742457 PMCID: PMC11187425 DOI: 10.1093/jamia/ocae098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/14/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
OBJECTIVES To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. MATERIALS AND METHODS We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results. RESULTS For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. DISCUSSION Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis. CONCLUSION EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.
Collapse
Affiliation(s)
- Maxwell Salvatore
- Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
| | - Ritoban Kundu
- Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
| | - Christopher R Friese
- Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Center for Improving Patient and Population Health, School of Nursing, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Department of Health Management and Policy, University of Michigan, Ann Arbor, MI 48109-2029, United States
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Graduate School of Data Science, Seoul National University, Gwanak-gu, Seoul, Republic of Korea
| | - Lars G Fritsche
- Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States
| | - David Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI 48109-2054, United States
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Rogel Cancer Center, Michigan Medicine, University of Michigan, Ann Arbor, MI 48109-2029, United States
| | - Bhramar Mukherjee
- Department of Epidemiology, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Center for Precision Health Data Science, Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109-2029, United States
| |
Collapse
|
11
|
Lin WD, Liao WL, Chen WC, Liu TY, Chen YC, Tsai FJ. Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population. BMC Genomics 2024; 25:607. [PMID: 38886662 PMCID: PMC11184693 DOI: 10.1186/s12864-024-10526-5] [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: 12/09/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) describes a group of progressive lung diseases causing breathing difficulties. While COPD development typically involves a complex interplay between genetic and environmental factors, genetics play a role in disease susceptibility. This study used genome-wide association studies (GWAS) and polygenic risk score (PRS) to elucidate the genetic basis for COPD in Taiwanese patients. RESULTS GWAS was performed on a Taiwanese COPD case-control cohort with a sample size of 5,442 cases and 17,681 controls. Additionally, the PRS was calculated and assessed in our target groups. GWAS results indicate that although there were no single nucleotide polymorphisms (SNPs) of genome-wide significance, prominent COPD susceptibility loci on or nearby genes such as WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 (C9orf135) were identified, which had not been reported in previous studies. Thirteen susceptibility loci, such as CHRNA4, AFAP1, and DTWD1, previously reported in other populations were replicated and confirmed to be associated with COPD in Taiwanese populations. The PRS was determined in the target groups using the summary statistics from our base group, yielding an effective association with COPD (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.02-1.17, p = 0.011). Furthermore, replication a previous lung function trait PRS model in our target group, showed a significant association of COPD susceptibility with PRS of Forced Expiratory Volume in one second (FEV1)/Forced Vital Capacity (FCV) (OR 0.89, 95% CI 0.83-0.95, p = 0.001). CONCLUSIONS Novel COPD-related genes were identified in the studied Taiwanese population. The PRS model, based on COPD or lung function traits, enables disease risk estimation and enhances prediction before suffering. These results offer new perspectives on the genetics of COPD and serve as a basis for future research.
Collapse
Affiliation(s)
- Wei-De Lin
- Department of Medical Research, China Medical University Hospital, Taichung, 404327, Taiwan
- School of Post Baccalaureate Chinese Medicine, China Medical University, Taichung, 404333, Taiwan
| | - Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 404333, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, 404327, Taiwan
| | - Wei-Cheng Chen
- Department of Internal Medicine, Pulmonary and Critical Care Medicine, China Medical University Hospital, Taichung, 404333, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 404327, Taiwan
| | - Ting-Yuan Liu
- Department of Medical Research, Million-Person Precision Medicine Initiative, China Medical University Hospital, Taichung, 404327, Taiwan
| | - Yu-Chia Chen
- Department of Medical Research, Million-Person Precision Medicine Initiative, China Medical University Hospital, Taichung, 404327, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung, 404327, Taiwan.
- School of Chinese Medicine, China Medical University, Taichung, 404333, Taiwan.
- Division of Genetics and Metabolism, China Medical University Children's Hospital, Taichung, 404327, Taiwan.
- Department of Medical Genetics, China Medical University Hospital, Taichung, 404327, Taiwan.
- Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, 413305, Taiwan.
- Department of Medical Research, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 404327, Taiwan.
| |
Collapse
|
12
|
Chen Z, Gao N, Wang X, Chen X, Zeng Y, Li C, Yang X, Cai Q, Wang X. Shared genetic aetiology of respiratory diseases: a genome-wide multitraits association analysis. BMJ Open Respir Res 2024; 11:e002148. [PMID: 38834332 PMCID: PMC11163672 DOI: 10.1136/bmjresp-2023-002148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
OBJECTIVE This study aims to explore the common genetic basis between respiratory diseases and to identify shared molecular and biological mechanisms. METHODS This genome-wide pleiotropic association study uses multiple statistical methods to systematically analyse the shared genetic basis between five respiratory diseases (asthma, chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis, lung cancer and snoring) using the largest publicly available genome wide association studies summary statistics. The missions of this study are to evaluate global and local genetic correlations, to identify pleiotropic loci, to elucidate biological pathways at the multiomics level and to explore causal relationships between respiratory diseases. Data were collected from 27 November 2022 to 30 March 2023 and analysed from 14 April 2023 to 13 July 2023. MAIN OUTCOMES AND MEASURES The primary outcomes are shared genetic loci, pleiotropic genes, biological pathways and estimates of genetic correlations and causal effects. RESULTS Significant genetic correlations were found for 10 paired traits in 5 respiratory diseases. Cross-Phenotype Association identified 12 400 significant potential pleiotropic single-nucleotide polymorphism at 156 independent pleiotropic loci. In addition, multitrait colocalisation analysis identified 15 colocalised loci and a subset of colocalised traits. Gene-based analyses identified 432 potential pleiotropic genes and were further validated at the transcriptome and protein levels. Both pathway enrichment and single-cell enrichment analyses supported the role of the immune system in respiratory diseases. Additionally, five pairs of respiratory diseases have a causal relationship. CONCLUSIONS AND RELEVANCE This study reveals the common genetic basis and pleiotropic genes among respiratory diseases. It provides strong evidence for further therapeutic strategies and risk prediction for the phenomenon of respiratory disease comorbidity.
Collapse
Affiliation(s)
- Zhe Chen
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University Department of Thoracic Surgery, Changsha, Hunan, China
| | - Ning Gao
- Department of Cardiovascular Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xuanye Wang
- Department of Oncology, Xi'an Jiaotong University Second Affiliated Hospital Department of Oncology, Xi'an, Shaanxi, China
| | - Xiangming Chen
- Department of Orthopaedics, Xiangya Hospital Central South University, Changsha, Hunan, China
| | - YaQi Zeng
- Department of Psychiatry, Brain Hospital of Hunan Province, Changsha, Hunan, China
| | - Cong Li
- Department of Radiology, The Second Xiangya Hospital of Central South University Department of Radiology, Changsha, Hunan, China
| | - Xiahong Yang
- Department of Anesthesiology, The Second Xiangya Hospital of Central South University Department of Anesthesiology, Changsha, Hunan, China
| | - Qidong Cai
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University Department of Thoracic Surgery, Changsha, Hunan, China
| | - Xiang Wang
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University Department of Thoracic Surgery, Changsha, Hunan, China
| |
Collapse
|
13
|
Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
Collapse
Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
| |
Collapse
|
14
|
Zhang Y, Wen Q, Li L. Commentary: Genetic Association of Circulating Adipokines with Risk of Idiopathic Pulmonary Fibrosis: A Two‑Sample Mendelian Randomization Study. Lung 2024; 202:357-359. [PMID: 38625406 DOI: 10.1007/s00408-024-00687-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Accepted: 03/21/2024] [Indexed: 04/17/2024]
Affiliation(s)
- Youqian Zhang
- Department of Oncology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Jingzhou, 434000, Hubei, China
| | - Qiong Wen
- Department of Oncology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Jingzhou, 434000, Hubei, China.
| | - Li Li
- Department of Oncology, The First Affiliated Hospital of Yangtze University, No. 55, Jianghan North Road, Jingzhou, 434000, Hubei, China.
| |
Collapse
|
15
|
Gerring ZF, Thorp JG, Treur JL, Verweij KJH, Derks EM. The genetic landscape of substance use disorders. Mol Psychiatry 2024:10.1038/s41380-024-02547-z. [PMID: 38811691 DOI: 10.1038/s41380-024-02547-z] [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: 07/17/2023] [Revised: 03/21/2024] [Accepted: 03/28/2024] [Indexed: 05/31/2024]
Abstract
Substance use disorders represent a significant public health concern with considerable socioeconomic implications worldwide. Twin and family-based studies have long established a heritable component underlying these disorders. In recent years, genome-wide association studies of large, broadly phenotyped samples have identified regions of the genome that harbour genetic risk variants associated with substance use disorders. These regions have enabled the discovery of putative causal genes and improved our understanding of genetic relationships among substance use disorders and other traits. Furthermore, the integration of these data with clinical information has yielded promising insights into how individuals respond to medications, allowing for the development of personalized treatment approaches based on an individual's genetic profile. This review article provides an overview of recent advances in the genetics of substance use disorders and demonstrates how genetic data may be used to reduce the burden of disease and improve public health outcomes.
Collapse
Affiliation(s)
- Zachary F Gerring
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jackson G Thorp
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jorien L Treur
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Karin J H Verweij
- Department of Psychiatry, Amsterdam UMC, location University of Amsterdam, Amsterdam, the Netherlands
| | - Eske M Derks
- Translational Neurogenomics Laboratory, Mental Health and Neuroscience, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.
| |
Collapse
|
16
|
Ni Y, Zhang D, Tang W, Xiang L, Cheng X, Zhang Y, Feng Y. Body mass index, smoking behavior, and depression mediated the effects of schizophrenia on chronic obstructive pulmonary disease: trans-ethnic Mendelian-randomization analysis. Front Psychiatry 2024; 15:1405107. [PMID: 38846919 PMCID: PMC11155452 DOI: 10.3389/fpsyt.2024.1405107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 05/09/2024] [Indexed: 06/09/2024] Open
Abstract
Background Previous studies have highlighted the association between schizophrenia (SCZ) and chronic obstructive pulmonary disease (COPD), yet the causal relationship remains unestablished. Methods Under the genome-wide significance threshold (P<5×10-8), data from individuals of European (EUR) and East Asian (EAS) ancestries with SCZ were selected for analysis. Univariable Mendelian randomization (MR) explored the causal relationship between SCZ and COPD. Linkage disequilibrium score (LDSC) regression was used to calculate genetic correlation, while multivariable and mediation MR further investigated the roles of six confounding factors and their mediating effects. The primary method utilized was inverse-variance weighted (IVW), complemented by a series of sensitivity analyses and false discovery rate (FDR) correction. Results LDSC analysis revealed a significant genetic correlation between SCZ and COPD within EUR ancestry (rg = 0.141, P = 6.16×10-7), with no such correlation found in EAS ancestry. IVW indicated a significant causal relationship between SCZ and COPD in EUR ancestry (OR = 1.042, 95% CI 1.013-1.071, P = 0.003, PFDR = 0.015). Additionally, replication datasets provide evidence of consistent causal associations(P < 0.05 & PFDR < 0.05). Multivariable and mediation MR analyses identified body mass index (BMI)(Mediation effect: 50.57%, P = 0.02), age of smoking initiation (Mediation effect: 27.42%, P = 0.02), and major depressive disorder (MDD) (Mediation effect: 60.45%, P = 6.98×10-5) as partial mediators of this causal relationship. No causal associations were observed in EAS (OR = 0.971, 95% CI 0.875-1.073, P = 0.571, PFDR = 0.761) ancestry. No causal associations were found in the reverse analysis across the four ancestries (P > 0.05 & PFDR > 0.05). Conclusions This study confirmed a causal relationship between SCZ and the risk of COPD in EUR ancestry, with BMI, smoking, and MDD serving as key mediators. Future research on a larger scale is necessary to validate the generalizability of these findings across other ancestries.
Collapse
Affiliation(s)
- Yao Ni
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - DaWei Zhang
- Department of Clinical Laboratory, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
- Department of Clinical Laboratory, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wenlong Tang
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Liming Xiang
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Xiaoding Cheng
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| | - Youqian Zhang
- Health Science Center, Yangtze University, Jingzhou, Hubei, China
| | - Yanyan Feng
- Department of Dermatovenereology, Chengdu Second People’s Hospital, Chengdu, Sichuan, China
| |
Collapse
|
17
|
Liao S, Wang Y, Zhou J, Liu Y, He S, Zhang L, Liu M, Wen D, Sun P, Lu G, Wang Q, Ouyang Y, Song Y. Associations between chronic obstructive pulmonary disease and ten common cancers: novel insights from Mendelian randomization analyses. BMC Cancer 2024; 24:601. [PMID: 38760826 PMCID: PMC11100175 DOI: 10.1186/s12885-024-12381-9] [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: 11/19/2023] [Accepted: 05/14/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a significant global health issue, suspected to elevate the risk for various cancers. This study sought to discern whether COPD serves as a risk marker or a causative factor for prevalent cancers. METHODS We employed univariable MR (UVMR) analyses to investigate the causal relationship between COPD and the top ten common cancers. Sensitivity analyses were performed to validate the main findings. Multivariable MR (MVMR) and two-step MR analyses were also conducted. False-discovery-rate (FDR) was used to correct multiple testing bias. RESULTS The UVMR analysis demonstrated notable associations between COPD and lung cancer (odds ratio [OR] = 1.42, 95%CI 1.15-1.77, FDR = 6.37 × 10-3). This relationship extends to lung cancer subtypes such as squamous cell carcinoma (LUSC), adenocarcinoma (LUAD), and small cell lung cancer (SCLC). A tentative link was also identified between COPD and bladder cancer (OR = 1.53, 95%CI 1.03-2.28, FDR = 0.125). No significant associations were found between COPD and other types of cancer. The MVMR analysis that adjusted for smoking, alcohol drinking, and body mass index did not identify any significant causal relationships between COPD and either lung or bladder cancer. However, the two-step MR analysis indicates that COPD mediated 19.2% (95% CI 12.7-26.1%), 36.1% (24.9-33.2%), 35.9% (25.7-34.9%), and 35.5% (26.2-34.8%) of the association between smoking and overall lung cancer, as well as LUAD, LUSC, and SCLC, respectively. CONCLUSIONS COPD appears to act more as a risk marker than a direct cause of prevalent cancers. Importantly, it partially mediates the connection between smoking and lung cancer, underscoring its role in lung cancer prevention strategies.
Collapse
Affiliation(s)
- Shixia Liao
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Yanwen Wang
- West China School of Medicine, Sichuan University, Chengdu, 610041, China
| | - Jian Zhou
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Yuting Liu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Shuangfei He
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Lanying Zhang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Maomao Liu
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Dongmei Wen
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Pengpeng Sun
- Department of Osteopathy, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China
| | - Guangbing Lu
- Department of Respiration, Meishan Hospital of Traditional Chinese Medicine in Sichuan Province, Meishan, 620010, China
| | - Qi Wang
- China-Canada Medical and Health Science Association, Toronto, L3R 1A3, Canada
| | - Yao Ouyang
- Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China.
| | - Yongxiang Song
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical University, Guizhou, 563003, China.
| |
Collapse
|
18
|
Su Y, Zhang Y, Xu J. Genetic variations in anti-diabetic drug targets and COPD risk: evidence from mendelian randomization. BMC Pulm Med 2024; 24:240. [PMID: 38750544 PMCID: PMC11094874 DOI: 10.1186/s12890-024-02959-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 03/09/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Previous research has emphasized the potential benefits of anti-diabetic medications in inhibiting the exacerbation of Chronic Obstructive Pulmonary Disease (COPD), yet the role of anti-diabetic drugs on COPD risk remains uncertain. METHODS This study employed a Mendelian randomization (MR) approach to evaluate the causal association of genetic variations related to six classes of anti-diabetic drug targets with COPD. The primary outcome for COPD was obtained from the Global Biobank Meta-analysis Initiative (GBMI) consortium, encompassing a meta-analysis of 12 cohorts with 81,568 cases and 1,310,798 controls. Summary-level data for HbA1c was derived from the UK Biobank, involving 344,182 individuals. Positive control analysis was conducted for Type 2 Diabetes Mellitus (T2DM) to validate the choice of instrumental variables. The study applied Summary-data-based MR (SMR) and two-sample MR for effect estimation and further adopted colocalization analysis to verify evidence of genetic variations. RESULTS SMR analysis revealed that elevated KCNJ11 gene expression levels in blood correlated with reduced COPD risk (OR = 0.87, 95% CI = 0.79-0.95; p = 0.002), whereas an increase in DPP4 expression corresponded with an increased COPD incidence (OR = 1.18, 95% CI = 1.03-1.35; p = 0.022). Additionally, the primary method within MR analysis demonstrated a positive correlation between PPARG-mediated HbA1c and both FEV1 (OR = 1.07, 95% CI = 1.02-1.13; P = 0.013) and FEV1/FVC (OR = 1.08, 95% CI = 1.01-1.14; P = 0.007), and a negative association between SLC5A2-mediated HbA1c and FEV1/FVC (OR = 0.86, 95% CI = 0.74-1.00; P = 0.045). No colocalization evidence with outcome phenotypes was detected (all PP.H4 < 0.7). CONCLUSION This study provides suggestive evidence for anti-diabetic medications' role in improving COPD and lung function. Further updated MR analyses are warranted in the future, following the acquisition of more extensive and comprehensive data, to validate our results.
Collapse
Affiliation(s)
- Yue Su
- Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Youqian Zhang
- Yangtze University, Jingzhou, Hubei Province, 434000, China
| | - Jinfu Xu
- Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, No. 507 Zhengmin Road, Shanghai, 200433, China.
| |
Collapse
|
19
|
Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
Collapse
Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| |
Collapse
|
20
|
Kemper KE, Sidorenko J, Wang H, Hayes BJ, Wray NR, Yengo L, Keller MC, Goddard M, Visscher PM. Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank. Nat Commun 2024; 15:3776. [PMID: 38710707 PMCID: PMC11074304 DOI: 10.1038/s41467-024-47802-7] [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: 05/11/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024] Open
Abstract
The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2-5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10-3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer's risk APOE- E 4 allele (rs429358) was significantly associated with weight loss ( β = -0.047 kg per yr, s.e. 0.007, P = 2.2 × 10-11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10-4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.
Collapse
Affiliation(s)
- Kathryn E Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
| | - Julia Sidorenko
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Huanwei Wang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Michael Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia
- Biosciences Research Division, Agriculture Victoria, Bundoora, VIC, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
| |
Collapse
|
21
|
Abou-Karam R, Cheng F, Gady S, Fahed AC. The Role of Genetics in Advancing Cardiometabolic Drug Development. Curr Atheroscler Rep 2024; 26:153-162. [PMID: 38451435 DOI: 10.1007/s11883-024-01195-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2024] [Indexed: 03/08/2024]
Abstract
PURPOSE OF REVIEW The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. RECENT FINDINGS Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.
Collapse
Affiliation(s)
- Roukoz Abou-Karam
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fangzhou Cheng
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Shoshana Gady
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Akl C Fahed
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, 185 Cambridge Street|CPZN 3.128, Boston, MA, 02114, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
22
|
Pan T, Bai L, Zhu D, Wei Y, Zhao Q, Feng F, Wang Z, Xu Y, Zhou X. The causal relationship between genetically predicted blood metabolites and idiopathic pulmonary fibrosis: A bidirectional two-sample Mendelian randomization study. PLoS One 2024; 19:e0300423. [PMID: 38626141 PMCID: PMC11020755 DOI: 10.1371/journal.pone.0300423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/28/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND Numerous metabolomic studies have confirmed the pivotal role of metabolic abnormalities in the development of idiopathic pulmonary fibrosis (IPF). Nevertheless, there is a lack of evidence on the causal relationship between circulating metabolites and the risk of IPF. METHODS The potential causality between 486 blood metabolites and IPF was determined through a bidirectional two-sample Mendelian randomization (TSMR) analysis. A genome-wide association study (GWAS) involving 7,824 participants was performed to analyze metabolite data, and a GWAS meta-analysis involving 6,257 IPF cases and 947,616 control European subjects was conducted to analyze IPF data. The TSMR analysis was performed primarily with the inverse variance weighted model, supplemented by weighted mode, MR-Egger regression, and weighted median estimators. A battery of sensitivity analyses was performed, including horizontal pleiotropy assessment, heterogeneity test, Steiger test, and leave-one-out analysis. Furthermore, replication analysis and meta-analysis were conducted with another GWAS dataset of IPF containing 4,125 IPF cases and 20,464 control subjects. Mediation analyses were used to identify the mediating role of confounders in the effect of metabolites on IPF. RESULTS There were four metabolites associated with the elevated risk of IPF, namely glucose (odds ratio [OR] = 2.49, 95% confidence interval [95%CI] = 1.13-5.49, P = 0.024), urea (OR = 6.24, 95% CI = 1.77-22.02, P = 0.004), guanosine (OR = 1.57, 95%CI = 1.07-2.30, P = 0.021), and ADpSGEGDFXAEGGGVR (OR = 1.70, 95%CI = 1.00-2.88, P = 0.0496). Of note, the effect of guanosine on IPF was found to be mediated by gastroesophageal reflux disease. Reverse Mendelian randomization analysis displayed that IPF might slightly elevate guanosine levels in the blood. CONCLUSION Conclusively, hyperglycemia may confer a promoting effect on IPF, highlighting that attention should be paid to the relationship between diabetes and IPF, not solely to the diagnosis of diabetes. Additionally, urea, guanosine, and ADpSGEGDFXAEGGGVR also facilitate the development of IPF. This study may provide a reference for analyzing the potential mechanism of IPF and carry implications for the prevention and treatment of IPF.
Collapse
Affiliation(s)
- Tingyu Pan
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Le Bai
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Dongwei Zhu
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Yun Wei
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Qi Zhao
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Fanchao Feng
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Zhichao Wang
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Yong Xu
- School of Chinese Medicine, School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Xianmei Zhou
- Department of Pulmonary and Critical Care Medicine, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| |
Collapse
|
23
|
Davis CN, Khan Y, Toikumo S, Jinwala Z, Boomsma DI, Levey DF, Gelernter J, Kember RL, Kranzler HR. A Multivariate Genome-Wide Association Study Reveals Neural Correlates and Common Biological Mechanisms of Psychopathology Spectra. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.06.24305166. [PMID: 38645045 PMCID: PMC11030494 DOI: 10.1101/2024.04.06.24305166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
There is considerable comorbidity across externalizing and internalizing behavior dimensions of psychopathology. We applied genomic structural equation modeling (gSEM) to genome-wide association study (GWAS) summary statistics to evaluate the factor structure of externalizing and internalizing psychopathology across 16 traits and disorders among European-ancestry individuals (n's = 16,400 to 1,074,629). We conducted GWAS on factors derived from well-fitting models. Downstream analyses served to identify biological mechanisms, explore drug repurposing targets, estimate genetic overlap between the externalizing and internalizing spectra, and evaluate causal effects of psychopathology liability on physical health. Both a correlated factors model, comprising two factors of externalizing and internalizing risk, and a higher-order single-factor model of genetic effects contributing to both spectra demonstrated acceptable fit. GWAS identified 409 lead single nucleotide polymorphisms (SNPs) associated with externalizing and 85 lead SNPs associated with internalizing, while the second-order GWAS identified 256 lead SNPs contributing to broad psychopathology risk. In bivariate causal mixture models, nearly all externalizing and internalizing causal variants overlapped, despite a genetic correlation of only 0.37 (SE = 0.02) between them. Externalizing genes showed cell-type specific expression in GABAergic, cortical, and hippocampal neurons, and internalizing genes were associated with reduced subcallosal cortical volume, providing insight into the neurobiological underpinnings of psychopathology. Genetic liability for externalizing, internalizing, and broad psychopathology exerted causal effects on pain, general health, cardiovascular diseases, and chronic illnesses. These findings underscore the complex genetic architecture of psychopathology, identify potential biological pathways for the externalizing and internalizing spectra, and highlight the physical health burden of psychiatric comorbidity.
Collapse
Affiliation(s)
- Christal N. Davis
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Yousef Khan
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Zeal Jinwala
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Dorret I. Boomsma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, The Netherlands and Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Daniel F. Levey
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- VA Connecticut Healthcare Center, West Haven, CT, USA
| | - Joel Gelernter
- VA Connecticut Healthcare Center, West Haven, CT, USA
- Departments of Psychiatry, Genetics, and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Rachel L. Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Henry R. Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Center for Studies of Addiction, Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| |
Collapse
|
24
|
Davis C, Khan Y, Toikumo S, Jinwala Z, Boomsma D, Levey D, Gelernter J, Kember R, Kranzler H. A Multivariate Genome-Wide Association Study Reveals Neural Correlates and Common Biological Mechanisms of Psychopathology Spectra. RESEARCH SQUARE 2024:rs.3.rs-4228593. [PMID: 38659902 PMCID: PMC11042423 DOI: 10.21203/rs.3.rs-4228593/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
There is considerable comorbidity across externalizing and internalizing behavior dimensions of psychopathology. We applied genomic structural equation modeling (gSEM) to genome-wide association study (GWAS) summary statistics to evaluate the factor structure of externalizing and internalizing psychopathology across 16 traits and disorders among European-ancestry individuals (n's = 16,400 to 1,074,629). We conducted GWAS on factors derived from well-fitting models. Downstream analyses served to identify biological mechanisms, explore drug repurposing targets, estimate genetic overlap between the externalizing and internalizing spectra, and evaluate causal effects of psychopathology liability on physical health. Both a correlated factors model, comprising two factors of externalizing and internalizing risk, and a higher-order single-factor model of genetic effects contributing to both spectra demonstrated acceptable t. GWAS identified 409 lead single nucleotide polymorphisms (SNPs) associated with externalizing and 85 lead SNPs associated with internalizing, while the second-order GWAS identified 256 lead SNPs contributing to broad psychopathology risk. In bivariate causal mixture models, nearly all externalizing and internalizing causal variants overlapped, despite a genetic correlation of only 0.37 (SE = 0.02) between them. Externalizing genes showed cell-type specific expression in GABAergic, cortical, and hippocampal neurons, and internalizing genes were associated with reduced subcallosal cortical volume, providing insight into the neurobiological underpinnings of psychopathology. Genetic liability for externalizing, internalizing, and broad psychopathology exerted causal effects on pain, general health, cardiovascular diseases, and chronic illnesses. These findings underscore the complex genetic architecture of psychopathology, identify potential biological pathways for the externalizing and internalizing spectra, and highlight the physical health burden of psychiatric comorbidity.
Collapse
Affiliation(s)
| | - Yousef Khan
- University of Pennsylvania Perelman School of Medicine
| | | | - Zeal Jinwala
- University of Pennsylvania Perelman School of Medicine
| | - D Boomsma
- Vrije Universiteit Amsterdam, The Netherlands
| | | | | | | | | |
Collapse
|
25
|
Espinosa Dice AL, Lawn RB, Ratanatharathorn A, Roberts AL, Denckla CA, Kim AH, de la Rosa PA, Zhu Y, VanderWeele TJ, Koenen KC. Childhood maltreatment and health in the UK Biobank: triangulation of outcome-wide and polygenic risk score analyses. BMC Med 2024; 22:135. [PMID: 38523269 PMCID: PMC10962116 DOI: 10.1186/s12916-024-03360-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 03/15/2024] [Indexed: 03/26/2024] Open
Abstract
BACKGROUND Childhood maltreatment is common globally and impacts morbidity, mortality, and well-being. Our understanding of its impact is constrained by key substantive and methodological limitations of extant research, including understudied physical health outcomes and bias due to unmeasured confounding. We address these limitations through a large-scale outcome-wide triangulation study. METHODS We performed two outcome-wide analyses (OWAs) in the UK Biobank. First, we examined the relationship between self-reported maltreatment exposure (number of maltreatment types, via Childhood Trauma Screener) and 414 outcomes in a sub-sample of 157,316 individuals using generalized linear models ("observational OWA"). Outcomes covered a broad range of health themes including health behaviors, cardiovascular disease, digestive health, socioeconomic status, and pain. Second, we examined the relationship between a polygenic risk score for maltreatment and 298 outcomes in a non-overlapping sample of 243,006 individuals ("genetic OWA"). We triangulated results across OWAs based on differing sources of bias. RESULTS Overall, 23.8% of the analytic sample for the observational OWA reported at least one maltreatment type. Of 298 outcomes examined in both OWAs, 25% were significant in both OWAs and concordant in the direction of association. Most of these were considered robust in the observational OWA according to sensitivity analyses and included outcomes such as marital separation (OR from observational OWA, ORo = 1.25 (95% CI: 1.21, 1.29); OR from genetic OWA, ORg = 1.06 (1.03, 1.08)), major diet changes due to illness (ORo = 1.27 (1.24, 1.29); ORg = 1.01 (1.00, 1.03)), certain intestinal diseases (ORo = 1.14 (1.10, 1.18); ORg = 1.03 (1.01, 1.06)), hearing difficulty with background noise (ORo = 1.11 (1.11, 1.12); ORg = 1.01 (1.00, 1.01)), knee arthrosis (ORo = 1.13 (1.09, 1.18); ORg = 1.03 (1.01, 1.05)), frequent sleeplessness (ORo = 1.21 (1.20, 1.23); ORg = 1.02 (1.01, 1.03)), and low household income (ORo = 1.28 (1.26, 1.31); ORg = 1.02 (1.01, 1.03)). Approximately 62% of results were significant in the observational OWA but not the genetic OWA, including numerous cardiovascular outcomes. Only 6 outcomes were significant in the genetic OWA and null in the observational OWA; these included diastolic blood pressure and glaucoma. No outcomes were statistically significant in opposite directions in the two analyses, and 11% were not significant in either OWA. CONCLUSIONS Our findings underscore the far-reaching negative effects of childhood maltreatment in later life and the utility of an outcome-wide triangulation design with sensitivity analyses for improving causal inference.
Collapse
Affiliation(s)
- Ana Lucia Espinosa Dice
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Rebecca B Lawn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrew Ratanatharathorn
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York City, NY, USA
| | - Andrea L Roberts
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Christy A Denckla
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Ariel H Kim
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Pedro A de la Rosa
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Institute for Culture and Society, University of Navarra, Pamplona, Spain
| | - Yiwen Zhu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Tyler J VanderWeele
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Psychiatric Neurodevelopmental Genetics Unit, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| |
Collapse
|
26
|
Li Y, Xue F, Li B, Yang Y, Fan Z, Shu J, Yang X, Wang X, Lin J, Copana C, Zhao B. Analyzing bivariate cross-trait genetic architecture in GWAS summary statistics with the BIGA cloud computing platform. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.28.538585. [PMID: 38559152 PMCID: PMC10979906 DOI: 10.1101/2023.04.28.538585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
As large-scale biobanks provide increasing access to deep phenotyping and genomic data, genome-wide association studies (GWAS) are rapidly uncovering the genetic architecture behind various complex traits and diseases. GWAS publications typically make their summary-level data (GWAS summary statistics) publicly available, enabling further exploration of genetic overlaps between phenotypes gathered from different studies and cohorts. However, systematically analyzing high-dimensional GWAS summary statistics for thousands of phenotypes can be both logistically challenging and computationally demanding. In this paper, we introduce BIGA (https://bigagwas.org/), a website that aims to offer unified data analysis pipelines and processed data resources for cross-trait genetic architecture analyses using GWAS summary statistics. We have developed a framework to implement statistical genetics tools on a cloud computing platform, combined with extensive curated GWAS data resources. Through BIGA, users can upload data, submit jobs, and share results, providing the research community with a convenient tool for consolidating GWAS data and generating new insights.
Collapse
Affiliation(s)
- Yujue Li
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Fei Xue
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxuan Li
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Yilin Yang
- Department of Computer and Information Science and Electrical and Systems Engineering, School of Engineering & Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Juan Shu
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiaochen Yang
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Xiyao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Jinjie Lin
- Yale School of Management, Yale University, New Haven, CT 06511, USA
| | - Carlos Copana
- Department of Statistics, Purdue University, West Lafayette, IN 47907, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Applied Mathematics and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
| |
Collapse
|
27
|
Jumonville G, Hong D, Khan A, DeWan A, Leal SM, Weng C, Petukhova L. Digital biobanks are underutilized in dermatology and create opportunities to reduce the burden of skin disease. Br J Dermatol 2024; 190:566-568. [PMID: 37936310 PMCID: PMC10941321 DOI: 10.1093/bjd/ljad439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/29/2023] [Accepted: 12/17/2023] [Indexed: 11/09/2023]
Abstract
Digital biobanks that integrate genetic data with health data captured by digital sources are used routinely to discover genes, biomarkers, gene–environment interactions and pharmacogenetic relationships across many clinical areas. There remain many opportunities in dermatology to further use biobank data to increase our knowledge about the genetic architecture of skin disease, to resolve disease mechanisms that can be modulated by medical interventions and to discover genetically derived disease relationships that inform on drug repurposing and adverse events. Such knowledge promises to reduce the global burden of skin disease and facilitates the development of tailored medical care.
Collapse
Affiliation(s)
| | | | | | - Andrew DeWan
- Department of Chronic Disease Epidemiology, Center for Perinatal, Pediatric and Environmental Epidemiology, Yale University, New Haven, CT,USA
| | - Suzanne M Leal
- Center for Statistical Genetics (Gertrude H. Sergievsky Center, Taub Institute for Alzheimer’s Disease and the Aging Brain)
- Department of Neurology, Columbia University, NY, USA
| | | | - Lynn Petukhova
- Department of Epidemiology (Mailman School of Public Health)
- Dermatology (all in the Vagelos College of Physicians & Surgeons)
| |
Collapse
|
28
|
Bastaki K, Velayutham D, Irfan A, Adnan M, Mohammed S, Mbarek H, Qoronfleh MW, Jithesh PV. Forging the path to precision medicine in Qatar: a public health perspective on pharmacogenomics initiatives. Front Public Health 2024; 12:1364221. [PMID: 38550311 PMCID: PMC10977610 DOI: 10.3389/fpubh.2024.1364221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2024] [Accepted: 02/20/2024] [Indexed: 04/02/2024] Open
Abstract
Pharmacogenomics (PGx) is an important component of precision medicine that promises tailored treatment approaches based on an individual's genetic information. Exploring the initiatives in research that help to integrate PGx test into clinical setting, identifying the potential barriers and challenges as well as planning the future directions, are all important for fruitful PGx implementation in any population. Qatar serves as an exemplar case study for the Middle East, having a small native population compared to a diverse immigrant population, advanced healthcare system, national genome program, and several educational initiatives on PGx and precision medicine. This paper attempts to outline the current state of PGx research and implementation in Qatar within the global context, emphasizing ongoing initiatives and educational efforts. The inclusion of PGx in university curricula and healthcare provider training, alongside precision medicine conferences, showcase Qatar's commitment to advancing this field. However, challenges persist, including the requirement for population specific implementation strategies, complex genetic data interpretation, lack of standardization, and limited awareness. The review suggests policy development for future directions in continued research investment, conducting clinical trials for the feasibility of PGx implementation, ethical considerations, technological advancements, and global collaborations to overcome these barriers.
Collapse
Affiliation(s)
- Kholoud Bastaki
- Clinical and Pharmacy Practice Department, College of Pharmacy, QU Health, Qatar University, Doha, Qatar
| | - Dinesh Velayutham
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| | - Areeba Irfan
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| | - Mohd Adnan
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| | - Sawsan Mohammed
- College of Medicine, Pre-Clinical Education Department, QU Health, Qatar University, Doha, Qatar
| | | | - M. Waild Qoronfleh
- Q3 Research Institute (QRI), Research & Policy Division, Ann Arbor, MI, United States
| | - Puthen Veettil Jithesh
- College of Health & Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
| |
Collapse
|
29
|
Lo Faro V, Bhattacharya A, Zhou W, Zhou D, Wang Y, Läll K, Kanai M, Lopera-Maya E, Straub P, Pawar P, Tao R, Zhong X, Namba S, Sanna S, Nolte IM, Okada Y, Ingold N, MacGregor S, Snieder H, Surakka I, Shortt J, Gignoux C, Rafaels N, Crooks K, Verma A, Verma SS, Guare L, Rader DJ, Willer C, Martin AR, Brantley MA, Gamazon ER, Jansonius NM, Joos K, Cox NJ, Hirbo J. Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation. Cell Rep Med 2024; 5:101430. [PMID: 38382466 PMCID: PMC10897632 DOI: 10.1016/j.xcrm.2024.101430] [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: 01/05/2022] [Revised: 03/28/2023] [Accepted: 01/25/2024] [Indexed: 02/23/2024]
Abstract
Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.
Collapse
Affiliation(s)
- Valeria Lo Faro
- Department of Ophthalmology, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands; Department of Clinical Genetics, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands; Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, UCLA, Los Angeles, CA, 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
| | - Dan Zhou
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ying Wang
- 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
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - 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 Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Esteban Lopera-Maya
- University of Groningen, UMCG, Department of Genetics, Groningen, the Netherlands
| | - Peter Straub
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Priyanka Pawar
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ran Tao
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Serena Sanna
- University of Groningen, UMCG, Department of Genetics, Groningen, the Netherlands; Institute for Genetics and Biomedical Research (IRGB), National Research Council (CNR), Cagliari, Italy
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan; Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka, Japan; Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka, Japan; Center for Infectious Disease Education and Research (CiDER), Osaka University, Osaka, Japan
| | - Nathan Ingold
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Queensland University of Technology, Brisbane, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia
| | - Stuart MacGregor
- Statistical Genetics, QIMR Berghofer Medical Research Institute, Queensland University of Technology, Brisbane, QLD, Australia
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ida Surakka
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Jonathan Shortt
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Chris Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Kristy Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Anurag Verma
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Shefali S Verma
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, USA; Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J Rader
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA; Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA; Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cristen Willer
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway; Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Alicia R Martin
- 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
| | - Milam A Brantley
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric R Gamazon
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nomdo M Jansonius
- Department of Ophthalmology, Amsterdam University Medical Center (AMC), Amsterdam, the Netherlands
| | - Karen Joos
- Vanderbilt Eye Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nancy J Cox
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jibril Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
| |
Collapse
|
30
|
Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [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: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
Collapse
Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| |
Collapse
|
31
|
Salvatore M, Kundu R, Shi X, Friese CR, Lee S, Fritsche LG, Mondul AM, Hanauer D, Pearce CL, Mukherjee B. To weight or not to weight? Studying the effect of selection bias in three large EHR-linked biobanks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.12.24302710. [PMID: 38405832 PMCID: PMC10888982 DOI: 10.1101/2024.02.12.24302710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Objective To explore the role of selection bias adjustment by weighting electronic health record (EHR)-linked biobank data for commonly performed analyses. Materials and methods We mapped diagnosis (ICD code) data to standardized phecodes from three EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n=244,071), Michigan Genomics Initiative (MGI; n=81,243), and UK Biobank (UKB; n=401,167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to be more representative of the US adult population. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted four common descriptive and analytic tasks comparing unweighted and weighted results. Results For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB's estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted PheWAS for colorectal cancer, the strongest associations remained unaltered and there was large overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. Discussion Weighting had limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation more. Results from untargeted association analyses should be followed by weighted analysis when effect size estimation is of interest for specific signals. Conclusion EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.
Collapse
Affiliation(s)
- Maxwell Salvatore
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
| | - Ritoban Kundu
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Xu Shi
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Christopher R Friese
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
- Center for Improving Patient and Population Health, School of Nursing, University of Michigan, Ann Arbor, MI, USA
- Department of Health Management and Policy, University of Michigan, Ann Arbor, MI, USA
| | - Seunggeun Lee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea
| | - Lars G Fritsche
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Alison M Mondul
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - David Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Celeste Leigh Pearce
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Bhramar Mukherjee
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
- Center for Precision Health Data Science, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| |
Collapse
|
32
|
Jiang G, Liu W, Wang X, Wang Z, Song C, Chen R, He Z, Li H, Zheng M, Mao W. The causality between systemic inflammatory regulators and chronic respiratory diseases: A bidirectional Mendelian-randomization study. Cytokine 2024; 174:156470. [PMID: 38071841 DOI: 10.1016/j.cyto.2023.156470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 10/29/2023] [Accepted: 12/04/2023] [Indexed: 01/01/2024]
Abstract
INTRODUCTION Accumulative evidence suggests the associations between systemic inflammatory regulators and chronic respiratory diseases (CRDs). However, the intrinsic causation remains implicit. Therefore, this study aimed to examine causative associations by mendelian randomization (MR) and to identify valuable active factors. METHODS Based on data from the GWAS database, we performed MR analyses of 41 serum cytokines from 8,293 Finnish and European descent cohorts from GBMI and UKBB for five major CRDs. We mainly applied inverse variance weighted regression, supplemented by MR-Egger regression, weighted median, maximum likelihood, weighted mode, and simple mode algorithms. Moreover, sensitivity analyses were conducted using Cochrane's Q test, MR-Egger intercept, MR-PRESSO Global test and MR-Steiger filtering. Eventually, the consistency of MR results was assessed by leave-one-out. RESULTS Our results suggest that 12 genetically predicted systemic inflammatory regulators probably participate in the progression of CRDs, including four risk factors (IL-1RA, IL-4, MIP-1A, PDGF-BB) and one protective factor (IL-6) in IPF, two protective factors (SCF, SDF-1A) in COPD, and two protective factors (SCF, SDF-1A) in asthma, two protective factors (GROA, IL-2RA) were also included in asthma, whereas only one factor (HGF) was protective against bronchiectasis. Additionally, two protective factors (FGF-BASIC, G-CSF) were identified in sarcoidosis. Sensitivity analyses showed no horizontal pleiotropy and significant heterogeneity. Finally, based on the findings of inverse MR analysis, no inverse causal association was uncovered, confirming the robustness of results. CONCLUSION Our study unearths potential associations between systemic inflammatory modulators and common CRDs, providing new insights for inflammation-mediated CRD prevention and therapeutic approaches.
Collapse
Affiliation(s)
- Guanyu Jiang
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China
| | - Weici Liu
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China
| | - Xiaokun Wang
- Department of Orthopedics, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Zifeng Wang
- Department of Orthopedics, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China
| | - Chenghu Song
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China
| | - Ruo Chen
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China
| | - Zhao He
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China
| | - Huixing Li
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China
| | - Mingfeng Zheng
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China.
| | - Wenjun Mao
- Department of Thoracic Surgery, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, Jiangsu, China.
| |
Collapse
|
33
|
He Y, Martin AR. We need more-diverse biobanks to improve behavioural genetics. Nat Hum Behav 2024; 8:197-200. [PMID: 38158402 DOI: 10.1038/s41562-023-01795-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Affiliation(s)
- Yixuan He
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia R Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
| |
Collapse
|
34
|
Lauschke VM, Zhou Y, Ingelman-Sundberg M. Pharmacogenomics Beyond Single Common Genetic Variants: The Way Forward. Annu Rev Pharmacol Toxicol 2024; 64:33-51. [PMID: 37506333 DOI: 10.1146/annurev-pharmtox-051921-091209] [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] [Indexed: 07/30/2023]
Abstract
Interindividual variability in genes encoding drug-metabolizing enzymes, transporters, receptors, and human leukocyte antigens has a major impact on a patient's response to drugs with regard to efficacy and safety. Enabled by both technological and conceptual advances, the field of pharmacogenomics is developing rapidly. Major progress in omics profiling methods has enabled novel genotypic and phenotypic characterization of patients and biobanks. These developments are paralleled by advances in machine learning, which have allowed us to parse the immense wealth of data and establish novel genetic markers and polygenic models for drug selection and dosing. Pharmacogenomics has recently become more widespread in clinical practice to personalize treatment and to develop new drugs tailored to specific patient populations. In this review, we provide an overview of the latest developments in the field and discuss the way forward, including how to address the missing heritability, develop novel polygenic models, and further improve the clinical implementation of pharmacogenomics.
Collapse
Affiliation(s)
- Volker M Lauschke
- Dr. Margarete Fischer-Bosch Institute for Clinical Pharmacology, Stuttgart, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden;
- Tübingen University, Tübingen, Germany
| | - Yitian Zhou
- Dr. Margarete Fischer-Bosch Institute for Clinical Pharmacology, Stuttgart, Germany
- Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden;
- Tübingen University, Tübingen, Germany
| | | |
Collapse
|
35
|
Verma SS, Gudiseva HV, Chavali VRM, Salowe RJ, Bradford Y, Guare L, Lucas A, Collins DW, Vrathasha V, Nair RM, Rathi S, Zhao B, He J, Lee R, Zenebe-Gete S, Bowman AS, McHugh CP, Zody MC, Pistilli M, Khachatryan N, Daniel E, Murphy W, Henderer J, Kinzy TG, Iyengar SK, Peachey NS, Taylor KD, Guo X, Chen YDI, Zangwill L, Girkin C, Ayyagari R, Liebmann J, Chuka-Okosa CM, Williams SE, Akafo S, Budenz DL, Olawoye OO, Ramsay M, Ashaye A, Akpa OM, Aung T, Wiggs JL, Ross AG, Cui QN, Addis V, Lehman A, Miller-Ellis E, Sankar PS, Williams SM, Ying GS, Cooke Bailey J, Rotter JI, Weinreb R, Khor CC, Hauser MA, Ritchie MD, O'Brien JM. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. Cell 2024; 187:464-480.e10. [PMID: 38242088 DOI: 10.1016/j.cell.2023.12.006] [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: 04/05/2023] [Revised: 07/24/2023] [Accepted: 12/04/2023] [Indexed: 01/21/2024]
Abstract
Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.
Collapse
Affiliation(s)
- Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harini V Gudiseva
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Venkata R M Chavali
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca J Salowe
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David W Collins
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vrathasha Vrathasha
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rohini M Nair
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sonika Rathi
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Jie He
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Roy Lee
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Selam Zenebe-Gete
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anita S Bowman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Maxwell Pistilli
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Naira Khachatryan
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ebenezer Daniel
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jeffrey Henderer
- Department of Ophthalmology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Tyler G Kinzy
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Neal S Peachey
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Linda Zangwill
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Christopher Girkin
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Radha Ayyagari
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Jeffrey Liebmann
- Department of Ophthalmology, Columbia University Medical Center, Columbia University, New York, NY, USA
| | | | - Susan E Williams
- Division of Ophthalmology, Department of Neurosciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stephen Akafo
- Unit of Ophthalmology, Department of Surgery, University of Ghana Medical School, Accra, Ghana
| | - Donald L Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | | | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Adeyinka Ashaye
- Department of Ophthalmology, University of Ibadan, Ibadan, Nigeria
| | - Onoja M Akpa
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Tin Aung
- Singapore Eye Research Institute, Singapore, Singapore
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ahmara G Ross
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Qi N Cui
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Victoria Addis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Lehman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eydie Miller-Ellis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prithvi S Sankar
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gui-Shuang Ying
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica Cooke Bailey
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Department of Pharmacology and Toxicology, Center for Health Disparities, Brody School of Medicine. East Carolina University, Greenville, NC, 27834, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Robert Weinreb
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joan M O'Brien
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. joan.o'
| |
Collapse
|
36
|
Pozdeyev N, Dighe M, Barrio M, Raeburn C, Smith H, Fisher M, Chavan S, Rafaels N, Shortt JA, Lin M, Leu MG, Clark T, Marshall C, Haugen BR, Subramanian D, Crooks K, Gignoux C, Cohen T. Thyroid Cancer Polygenic Risk Score Improves Classification of Thyroid Nodules as Benign or Malignant. J Clin Endocrinol Metab 2024; 109:402-412. [PMID: 37683082 DOI: 10.1210/clinem/dgad530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 08/29/2023] [Accepted: 09/05/2023] [Indexed: 09/10/2023]
Abstract
CONTEXT Thyroid nodule ultrasound-based risk stratification schemas rely on the presence of high-risk sonographic features. However, some malignant thyroid nodules have benign appearance on thyroid ultrasound. New methods for thyroid nodule risk assessment are needed. OBJECTIVE We investigated polygenic risk score (PRS) accounting for inherited thyroid cancer risk combined with ultrasound-based analysis for improved thyroid nodule risk assessment. METHODS The convolutional neural network classifier was trained on thyroid ultrasound still images and cine clips from 621 thyroid nodules. Phenome-wide association study (PheWAS) and PRS PheWAS were used to optimize PRS for distinguishing benign and malignant nodules. PRS was evaluated in 73 346 participants in the Colorado Center for Personalized Medicine Biobank. RESULTS When the deep learning model output was combined with thyroid cancer PRS and genetic ancestry estimates, the area under the receiver operating characteristic curve (AUROC) of the benign vs malignant thyroid nodule classifier increased from 0.83 to 0.89 (DeLong, P value = .007). The combined deep learning and genetic classifier achieved a clinically relevant sensitivity of 0.95, 95% CI [0.88-0.99], specificity of 0.63 [0.55-0.70], and positive and negative predictive values of 0.47 [0.41-0.58] and 0.97 [0.92-0.99], respectively. AUROC improvement was consistent in European ancestry-stratified analysis (0.83 and 0.87 for deep learning and deep learning combined with PRS classifiers, respectively). Elevated PRS was associated with a greater risk of thyroid cancer structural disease recurrence (ordinal logistic regression, P value = .002). CONCLUSION Augmenting ultrasound-based risk assessment with PRS improves diagnostic accuracy.
Collapse
Affiliation(s)
- Nikita Pozdeyev
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Division of Endocrinology Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Manjiri Dighe
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Martin Barrio
- Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Raeburn
- Division of GI, Trauma, and Endocrine Surgery, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Harry Smith
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Matthew Fisher
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Sameer Chavan
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nicholas Rafaels
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Jonathan A Shortt
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Meng Lin
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael G Leu
- Information Technology Services, UW Medicine, Seattle, WA 98195, USA
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
- Division of Hospital Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA
| | - Toshimasa Clark
- Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Carrie Marshall
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Bryan R Haugen
- Division of Endocrinology Metabolism and Diabetes, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- University of Colorado Cancer Center, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | | | - Kristy Crooks
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Gignoux
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Trevor Cohen
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA 98195, USA
| |
Collapse
|
37
|
Zhu J, Zhou D, Yu M, Li Y. Appraising the causal role of smoking in idiopathic pulmonary fibrosis: a Mendelian randomization study. Thorax 2024; 79:179-181. [PMID: 37217291 DOI: 10.1136/thorax-2023-220012] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Smoking has been considered a risk factor for idiopathic pulmonary fibrosis (IPF) in observational studies. To assess whether smoking plays a causal role in IPF, we performed a Mendelian randomization study using genetic association data of 10 382 cases with IPF and 968 080 controls. We found that genetic predisposition to smoking initiation (based on 378 variants) and lifetime smoking (based on 126 variants) were associated with a higher risk of IPF. Our study suggests a potential causal effect of smoking on increasing IPF risk from a genetic perspective.
Collapse
Affiliation(s)
- Jiahao Zhu
- Department of Epidemiology and Health Statistics, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dan Zhou
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Min Yu
- Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang, China
| | - Yingjun Li
- Department of Epidemiology and Health Statistics, School of Public Health, Hangzhou Medical College, Hangzhou, Zhejiang, China
| |
Collapse
|
38
|
Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
Collapse
Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational 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 Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| |
Collapse
|
39
|
Kępińska AP, Johnson JS, Huckins LM. Open Science Practices in Psychiatric Genetics: A Primer. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:110-119. [PMID: 38298792 PMCID: PMC10829621 DOI: 10.1016/j.bpsgos.2023.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/11/2023] [Indexed: 02/02/2024] Open
Abstract
Open science ensures that research is transparently reported and freely accessible for all to assess and collaboratively build on. Psychiatric genetics has led among the health sciences in implementing some open science practices in common study designs, such as replication as part of genome-wide association studies. However, thorough open science implementation guidelines are limited and largely not specific to data, privacy, and research conduct challenges in psychiatric genetics. Here, we present a primer of open science practices, including selection of a research topic with patients/nonacademic collaborators, equitable authorship and citation practices, design of replicable, reproducible studies, preregistrations, open data, and privacy issues. We provide tips for informative figures and inclusive, precise reporting. We discuss considerations in working with nonacademic collaborators and distributing research through preprints, blogs, social media, and accessible lecture materials. Finally, we provide extra resources to support every step of the research process.
Collapse
Affiliation(s)
- Adrianna P. Kępińska
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Jessica S. Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
- Psychiatry Department, The University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina
| | - Laura M. Huckins
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Psychiatry, Yale University, New Haven, Connecticut
| |
Collapse
|
40
|
He Y, Qian DC, Diao JA, Cho MH, Silverman EK, Gusev A, Manrai AK, Martin AR, Patel CJ. Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors. Nat Commun 2023; 14:8297. [PMID: 38097585 PMCID: PMC10721891 DOI: 10.1038/s41467-023-44047-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 11/28/2023] [Indexed: 12/17/2023] Open
Abstract
Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We perform a longitudinal analysis of COPD in the UK Biobank to derive and validate the Socioeconomic and Environmental Risk Score which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. The Socioeconomic and Environmental Risk Score is more predictive of COPD than smoking status and pack-years. Individuals in the highest decile of the risk score have a greater risk for incident COPD compared to the remaining population. Never smokers in the highest decile of exposure risk are more likely to develop COPD than previous and current smokers in the lowest decile. In general, the prediction accuracy of the Social and Environmental Risk Score is lower in non-European populations. While smoking status is often considered in screening COPD, our finding highlights the importance of other non-smoking environmental and socioeconomic variables.
Collapse
Affiliation(s)
- Yixuan He
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - David C Qian
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, USA
| | - James A Diao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Edwin K Silverman
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander Gusev
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Arjun K Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Chirag J Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
41
|
Chen CY, Chen TT, Feng YCA, Yu M, Lin SC, Longchamps RJ, Wang SH, Hsu YH, Yang HI, Kuo PH, Daly MJ, Chen WJ, Huang H, Ge T, Lin YF. Analysis across Taiwan Biobank, Biobank Japan, and UK Biobank identifies hundreds of novel loci for 36 quantitative traits. CELL GENOMICS 2023; 3:100436. [PMID: 38116116 PMCID: PMC10726425 DOI: 10.1016/j.xgen.2023.100436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/21/2021] [Accepted: 10/09/2023] [Indexed: 12/21/2023]
Abstract
Genome-wide association studies (GWASs) have identified tens of thousands of genetic loci associated with human complex traits. However, the majority of GWASs were conducted in individuals of European ancestries. Failure to capture global genetic diversity has limited genomic discovery and has impeded equitable delivery of genomic knowledge to diverse populations. Here we report findings from 102,900 individuals across 36 human quantitative traits in the Taiwan Biobank (TWB), a major biobank effort that broadens the population diversity of genetic studies in East Asia. We identified 968 novel genetic loci, pinpointed novel causal variants through statistical fine-mapping, compared the genetic architecture across TWB, Biobank Japan, and UK Biobank, and evaluated the utility of cross-phenotype, cross-population polygenic risk scores in disease risk prediction. These results demonstrated the potential to advance discovery through diversifying GWAS populations and provided insights into the common genetic basis of human complex traits in East Asia.
Collapse
Affiliation(s)
- Chia-Yen Chen
- Biogen, Cambridge, MA 02142, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Tzu-Ting Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
| | - Yen-Chen Anne Feng
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
- Institute of Health Data Analytics and Statistics, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
| | - Mingrui Yu
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shu-Chin Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
| | - Ryan J. Longchamps
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Shi-Heng Wang
- National Center for Geriatrics and Welfare Research, National Health Research Institutes, Miaoli 35053, Taiwan
- Department of Public Health, College of Public Health, China Medical University, Taichung 40678, Taiwan
| | - Yi-Hsiang Hsu
- Marcus Institute for Aging Research and Harvard Medical School, Boston, MA 02131, USA
- Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
- Harvard School of Public Health, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hwai-I. Yang
- Genomics Research Center, Academia Sinica, Taipei 115201, Taiwan
- Institute of Clinical Medicine, National Yang-Ming University, Taipei 112304, Taiwan
- Doctoral Program of Clinical and Experimental Medicine, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
- Biomedical Translation Research Center, Academia Sinica, Taipei 115021, Taiwan
| | - Po-Hsiu Kuo
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, Taipei 106319, Taiwan
| | - Mark J. Daly
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, Finland
| | - Wei J. Chen
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
- Department of Public Health & Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100025, Taiwan
- Department of Psychiatry, College of Medicine and National Taiwan University Hospital, Taipei 106319, Taiwan
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02114, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli 35053, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
| |
Collapse
|
42
|
Alpeeva EV, Sharova NP, Sharov KS, Vorotelyak EA. Russian Biodiversity Collections: A Professional Opinion Survey. Animals (Basel) 2023; 13:3777. [PMID: 38136814 PMCID: PMC10740833 DOI: 10.3390/ani13243777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/15/2023] [Accepted: 11/22/2023] [Indexed: 12/24/2023] Open
Abstract
Biodiversity collections are important vehicles for protecting endangered wildlife in situations of adverse anthropogenic influence. In Russia, there are currently a number of institution- and museum-based biological collections, but there are no nation-wide centres of biodiversity collections. In this paper, we report on the results of our survey of 324 bioconservation, big-data, and ecology specialists from different regions of Russia in regard to the necessity to create several large national biodiversity centres of wildlife protection. The survey revealed specific goals that have to be fulfilled during the development of these centres for the protection and restoration of endangered wildlife species. The top three problems/tasks (topics) are the following: (1) the necessity to create large national centres for different types of specimens; (2) the full sequencing and creation of different "omic" (genomic, proteomic, transcriptomic, etc.) databases; (3) full digitisation of a biodiversity collection/centre. These goals may constitute a guideline for the future of biodiversity collections in Russia that would be targeted at protecting and restoring endangered species. With the due network service level, the translation of the website into English, and permission from the regulator (Ministry of Science and Higher Education of Russian Federation), it can also become an international project.
Collapse
Affiliation(s)
| | | | - Konstantin S. Sharov
- Koltzov Institute of Developmental Biology of Russian Academy of Sciences, 26 Vavilov Street, 119334 Moscow, Russia; (E.V.A.); (N.P.S.)
| | | |
Collapse
|
43
|
Zhou H, Kember RL, Deak JD, Xu H, Toikumo S, Yuan K, Lind PA, Farajzadeh L, Wang L, Hatoum AS, Johnson J, Lee H, Mallard TT, Xu J, Johnston KJA, Johnson EC, Nielsen TT, Galimberti M, Dao C, Levey DF, Overstreet C, Byrne EM, Gillespie NA, Gordon S, Hickie IB, Whitfield JB, Xu K, Zhao H, Huckins LM, Davis LK, Sanchez-Roige S, Madden PAF, Heath AC, Medland SE, Martin NG, Ge T, Smoller JW, Hougaard DM, Børglum AD, Demontis D, Krystal JH, Gaziano JM, Edenberg HJ, Agrawal A, Justice AC, Stein MB, Kranzler HR, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nat Med 2023; 29:3184-3192. [PMID: 38062264 PMCID: PMC10719093 DOI: 10.1038/s41591-023-02653-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 10/18/2023] [Indexed: 12/17/2023]
Abstract
Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses. Cross-ancestry fine mapping improved the identification of likely causal variants. Prioritizing genes through gene expression and chromatin interaction in brain tissues identified multiple genes associated with PAU. We identified existing medications for potential pharmacological studies by a computational drug repurposing analysis. Cross-ancestry polygenic risk scores showed better performance of association in independent samples than single-ancestry polygenic risk scores. Genetic correlations between PAU and other traits were observed in multiple ancestries, with other substance use traits having the highest correlations. This study advances our knowledge of the genetic etiology of PAU, and these findings may bring possible clinical applicability of genetics insights-together with neuroscience, biology and data science-closer.
Collapse
Affiliation(s)
- Hang Zhou
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
| | - Rachel L Kember
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joseph D Deak
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sylvanus Toikumo
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kai Yuan
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Penelope A Lind
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Leila Farajzadeh
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Lu Wang
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Alexander S Hatoum
- Department of Psychological and Brain Sciences, Washington University in St. Louis, Saint Louis, MO, USA
| | - Jessica Johnson
- Pamela Sklar Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyunjoon Lee
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Travis T Mallard
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jiayi Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | | | - Emma C Johnson
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Trine Tollerup Nielsen
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Marco Galimberti
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cecilia Dao
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Daniel F Levey
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Cassie Overstreet
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Enda M Byrne
- Child Health Research Centre, The University of Queensland, Brisbane, Queensland, Australia
| | - Nathan A Gillespie
- Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, USA
| | - Scott Gordon
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ian B Hickie
- Brain and Mind Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - John B Whitfield
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Ke Xu
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | - Laura M Huckins
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Lea K Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sandra Sanchez-Roige
- Department of Medicine, Division of Medical Genetics, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Andrew C Heath
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Sarah E Medland
- Psychiatric Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
| | - Nicholas G Martin
- Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Tian Ge
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Jordan W Smoller
- Stanley Center for Psychiatric Research, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Anders D Børglum
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Ditte Demontis
- Department of Biomedicine - Human Genetics, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
- Center for Genomics and Personalized Medicine, Aarhus, Denmark
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - John H Krystal
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
- National Center for PTSD, US Department of Veterans Affairs, West Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Psychiatry and Behavioral Health Services, Yale-New Haven Hospital, New Haven, CT, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology and Research Information Center (MAVERIC), Boston Veterans Affairs Healthcare System, Boston, MA, USA
- Department of Medicine, Divisions of Aging and Preventative Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Howard J Edenberg
- Department of Biochemistry and Molecular Biology, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arpana Agrawal
- Department of Psychiatry, Washington University School of Medicine, Saint Louis, MO, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Center for Interdisciplinary Research on AIDS, Yale School of Public Health, New Haven, CT, USA
| | - Murray B Stein
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Psychiatry Service, VA San Diego Healthcare System, San Diego, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Henry R Kranzler
- Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Joel Gelernter
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA.
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA.
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA.
| |
Collapse
|
44
|
Maldonado BL, Piqué DG, Kaplan RC, Claw KG, Gignoux CR. Genetic risk prediction in Hispanics/Latinos: milestones, challenges, and social-ethical considerations. J Community Genet 2023; 14:543-553. [PMID: 37962783 PMCID: PMC10725387 DOI: 10.1007/s12687-023-00686-4] [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: 12/01/2022] [Accepted: 10/18/2023] [Indexed: 11/15/2023] Open
Abstract
Genome-wide association studies (GWAS) have allowed the identification of disease-associated variants, which can be leveraged to build polygenic scores (PGSs). Even though PGSs can be a valuable tool in personalized medicine, their predictive power is limited in populations of non-European ancestry, particularly in admixed populations. Recent efforts have focused on increasing racial and ethnic diversity in GWAS, thus, addressing some of the limitations of genetic risk prediction in these populations. Even with these efforts, few studies focus exclusively on Hispanics/Latinos. Additionally, Hispanic/Latino populations are often considered a single population despite varying admixture proportions between and within ethnic groups, diverse genetic heterogeneity, and demographic history. Combined with highly heterogeneous environmental and socioeconomic exposures, this diversity can reduce the transferability of genetic risk prediction models. Given the recent increase of genomic studies that include Hispanics/Latinos, we review the milestones and efforts that focus on genetic risk prediction, summarize the potential for improving PGS transferability, and highlight the challenges yet to be addressed. Additionally, we summarize social-ethical considerations and provide ideas to promote genetic risk prediction models that can be implemented equitably.
Collapse
Affiliation(s)
- Betzaida L Maldonado
- Human Medical Genetics & Genomics Graduate Program, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA.
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA.
- Department of Biomedical Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA.
| | - Daniel G Piqué
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Section of Genetics and Metabolism, Department of Pediatrics, Children's Hospital Colorado, Aurora, CO, USA
| | - Robert C Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Katrina G Claw
- Human Medical Genetics & Genomics Graduate Program, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Department of Biomedical Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
| | - Christopher R Gignoux
- Human Medical Genetics & Genomics Graduate Program, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Colorado Center for Personalized Medicine, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
- Department of Biomedical Informatics, University of Colorado-Anschutz Medical Campus, Aurora, CO, USA
| |
Collapse
|
45
|
Zhu F, Zhang P, Liu Y, Bao C, Qian D, Ma C, Li H, Yu T. Mendelian randomization suggests a causal relationship between gut dysbiosis and thyroid cancer. Front Cell Infect Microbiol 2023; 13:1298443. [PMID: 38106470 PMCID: PMC10722196 DOI: 10.3389/fcimb.2023.1298443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 11/06/2023] [Indexed: 12/19/2023] Open
Abstract
Background Alterations in gut microbiota composition and function have been linked to the development and progression of thyroid cancer (TC). However, the exact nature of the causal relationship between them remains uncertain. Methods A bidirectional two-sample Mendelian randomization (TSMR) analysis was conducted to assess the causal connection between gut microbiota (18,340 individuals) and TC (6,699 cases combined with 1,613,655 controls) using data from a genome-wide association study (GWAS). The primary analysis used the inverse-variance weighted (IVW) method to estimate the causal effect, with supplementary approaches including the weighted median, weighted mode, simple mode, and MR-Egger. Heterogeneity and pleiotropy were assessed using the Cochrane Q test, MR-Egger intercept test, and MR-PRESSO global test. A reverse TSMR analysis was performed to explore reverse causality. Results This study identified seven microbial taxa with significant associations with TC. Specifically, the genus Butyrivibrio (OR: 1.127, 95% CI: 1.008-1.260, p = 0.036), Fusicatenibacter (OR: 1.313, 95% CI: 1.066-1.618, p = 0.011), Oscillospira (OR: 1.240, 95% CI: 1.001-1.536, p = 0.049), Ruminococcus2 (OR: 1.408, 95% CI: 1.158-1.711, p < 0.001), Terrisporobacter (OR: 1.241, 95% CI: 1.018-1.513, p = 0.032) were identified as risk factors for TC, while The genus Olsenella (OR: 0.882, 95% CI: 0.787-0.989, p = 0.031) and Ruminococcaceae UCG004 (OR: 0.719, 95% CI: 0.566-0.914, p = 0.007) were associated with reduced TC risk. The reverse MR analysis found no evidence of reverse causality and suggested that TC may lead to increased levels of the genus Holdemanella (β: 0.053, 95% CI: 0.012~0.094, p = 0.011) and decreased levels of the order Bacillales (β: -0.075, 95% CI: -0.143~-0.006, p = 0.033). No significant bias, heterogeneity, or pleiotropy was detected in this study. Conclusion This study suggests a potential causal relationship between gut microbiota and TC, providing new insights into the role of gut microbiota in TC. Further research is needed to explore the underlying biological mechanisms.
Collapse
Affiliation(s)
- Feng Zhu
- Department of Gastroenterology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
- Department of Gastroenterology, The First People’s Hospital of Kunshan, Suzhou, China
| | - Pengpeng Zhang
- Department of Lung Cancer Surgery, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Thoracic Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| | - Ying Liu
- Department of General Surgery, Affiliated Hospital of Nanjing University of TCM, Jiangsu Province Hospital of TCM, Nanjing, China
| | - Chongchan Bao
- Department of Breast and Thyroid Surgery, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Dong Qian
- Department of General Surgery, Affiliated Hospital of Nanjing University of TCM, Jiangsu Province Hospital of TCM, Nanjing, China
| | - Chaoqun Ma
- Department of General Surgery, Affiliated Hospital of Nanjing University of TCM, Jiangsu Province Hospital of TCM, Nanjing, China
| | - Hua Li
- Department of General Surgery, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Ting Yu
- Department of Gastroenterology, The First Affiliated Hospital with Nanjing Medical University, Nanjing, China
| |
Collapse
|
46
|
Hou T, Wang Q, Dai H, Hou Y, Zheng J, Wang T, Lin H, Wang S, Li M, Zhao Z, Chen Y, Xu Y, Lu J, Liu R, Ning G, Wang W, Xu M, Bi Y. Interactive Association Between Gut Microbiota and Thyroid Cancer. Endocrinology 2023; 165:bqad184. [PMID: 38051644 DOI: 10.1210/endocr/bqad184] [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: 07/13/2023] [Revised: 11/17/2023] [Accepted: 11/30/2023] [Indexed: 12/07/2023]
Abstract
CONTEXT The association between the gut microbiota and thyroid cancer remains controversial. OBJECTIVE We aimed to systematically investigate the interactive causal relationships between the abundance and metabolism pathways of gut microbiota and thyroid cancer. METHODS We leveraged genome-wide association studies for the abundance of 211 microbiota taxa from the MiBioGen study (N = 18 340), 205 microbiota metabolism pathways from the Dutch Microbiome Project (N = 7738), and thyroid cancer from the Global Biobank Meta-analysis Initiative (N cases = 6699 and N participants = 1 620 354). We performed a bidirectional Mendelian randomization (MR) to investigate the causality from microbiota taxa and metabolism pathways to thyroid cancer and vice versa. We performed a systematic review of previous observational studies and compared MR results with observational findings. RESULTS Eight taxa and 12 metabolism pathways had causal effects on thyroid cancer, where RuminococcaceaeUCG004 genus (P = .001), Streptococcaceae family (P = .016), Olsenella genus (P = .029), ketogluconate metabolism pathway (P = .003), pentose phosphate pathway (P = .016), and L-arginine degradation II in the AST pathway (P = .0007) were supported by sensitivity analyses. Conversely, thyroid cancer had causal effects on 3 taxa and 2 metabolism pathways, where the Holdemanella genus (P = .015) was supported by sensitivity analyses. The Proteobacteria phylum, Streptococcaceae family, Ruminococcus2 genus, and Holdemanella genus were significantly associated with thyroid cancer in both the systematic review and MR, whereas the other 121 significant taxa in observational results were not supported by MR. DISCUSSIONS These findings implicated the potential role of host-microbiota crosstalk in thyroid cancer, while the discrepancy among observational studies calls for further investigations.
Collapse
Affiliation(s)
- 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| | - 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
| |
Collapse
|
47
|
D’Urso S, Hwang LD. New Insights into Polygenic Score-Lifestyle Interactions for Cardiometabolic Risk Factors from Genome-Wide Interaction Analyses. Nutrients 2023; 15:4815. [PMID: 38004209 PMCID: PMC10675788 DOI: 10.3390/nu15224815] [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: 10/11/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
The relationship between lifestyles and cardiometabolic outcomes varies between individuals. In 382,275 UK Biobank Europeans, we investigate how lifestyles interact with polygenic scores (PGS) of cardiometabolic risk factors. We identify six interactions (PGS for body mass index with meat diet, physical activity, sedentary behaviour and insomnia; PGS for high-density lipoprotein cholesterol with sedentary behaviour; PGS for triglycerides with meat diet) in multivariable linear regression models including an interaction term and show stronger associations between lifestyles and cardiometabolic risk factors among individuals with high PGSs than those with low PGSs. Genome-wide interaction analyses pinpoint three genetic variants (FTO rs72805613 for BMI; CETP rs56228609 for high-density lipoprotein cholesterol; TRIB2 rs4336630 for triglycerides; PInteraction < 5 × 10-8). The associations between lifestyles and cardiometabolic risk factors differ between individuals grouped by the genotype of these variants, with the degree of differences being similar to that between individuals with high and low values for the corresponding PGSs. This study demonstrates that associations between lifestyles and cardiometabolic risk factors can differ between individuals based upon their genetic profiles. It further suggests that genetic variants with interaction effects contribute more to such differences compared to those without interaction effects, which has potential implications for developing PGSs for personalised intervention.
Collapse
Affiliation(s)
| | - Liang-Dar Hwang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4067, Australia;
| |
Collapse
|
48
|
Allayee H, Farber CR, Seldin MM, Williams EG, James DE, Lusis AJ. Systems genetics approaches for understanding complex traits with relevance for human disease. eLife 2023; 12:e91004. [PMID: 37962168 PMCID: PMC10645424 DOI: 10.7554/elife.91004] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 10/16/2023] [Indexed: 11/15/2023] Open
Abstract
Quantitative traits are often complex because of the contribution of many loci, with further complexity added by environmental factors. In medical research, systems genetics is a powerful approach for the study of complex traits, as it integrates intermediate phenotypes, such as RNA, protein, and metabolite levels, to understand molecular and physiological phenotypes linking discrete DNA sequence variation to complex clinical and physiological traits. The primary purpose of this review is to describe some of the resources and tools of systems genetics in humans and rodent models, so that researchers in many areas of biology and medicine can make use of the data.
Collapse
Affiliation(s)
- Hooman Allayee
- Departments of Population & Public Health Sciences, University of Southern CaliforniaLos AngelesUnited States
- Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern CaliforniaLos AngelesUnited States
| | - Charles R Farber
- Center for Public Health Genomics, University of Virginia School of MedicineCharlottesvilleUnited States
- Departments of Biochemistry & Molecular Genetics, University of Virginia School of MedicineCharlottesvilleUnited States
- Public Health Sciences, University of Virginia School of MedicineCharlottesvilleUnited States
| | - Marcus M Seldin
- Department of Biological Chemistry, University of California, IrvineIrvineUnited States
| | - Evan Graehl Williams
- Luxembourg Centre for Systems Biomedicine, University of LuxembourgLuxembourgLuxembourg
| | - David E James
- School of Life and Environmental Sciences, University of SydneyCamperdownAustralia
- Faculty of Medicine and Health, University of SydneyCamperdownAustralia
- Charles Perkins Centre, University of SydneyCamperdownAustralia
| | - Aldons J Lusis
- Departments of Human Genetics, University of California, Los AngelesLos AngelesUnited States
- Medicine, University of California, Los AngelesLos AngelesUnited States
- Microbiology, Immunology, & Molecular Genetics, David Geffen School of Medicine of UCLALos AngelesUnited States
| |
Collapse
|
49
|
Shuey MM, Stead WW, Aka I, Barnado AL, Bastarache JA, Brokamp E, Campbell M, Carroll RJ, Goldstein JA, Lewis A, Malow BA, Mosley JD, Osterman T, Padovani-Claudio DA, Ramirez A, Roden DM, Schuler BA, Siew E, Sucre J, Thomsen I, Tinker RJ, Van Driest S, Walsh C, Warner JL, Wells QS, Wheless L, Bastarache L. Next-generation phenotyping: introducing phecodeX for enhanced discovery research in medical phenomics. Bioinformatics 2023; 39:btad655. [PMID: 37930895 PMCID: PMC10627409 DOI: 10.1093/bioinformatics/btad655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 09/13/2023] [Indexed: 11/08/2023] Open
Abstract
MOTIVATION Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION phecodeX is available at https://github.com/PheWAS/phecodeX.
Collapse
Affiliation(s)
- Megan M Shuey
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - William W Stead
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Ida Aka
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - April L Barnado
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Julie A Bastarache
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Elly Brokamp
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Meredith Campbell
- Department of Pediatrics, Virginia Commonwealth University, Richmond, VA 23219, United States
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jeffrey A Goldstein
- Department of Pathology, Northwestern Feinberg School of Medicine, Chicago, IL 60611, United States
| | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Beth A Malow
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Travis Osterman
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Dolly A Padovani-Claudio
- Department of Ophthalmology, Vanderbilt University Medical Center, Nashville, TN 37232, United States
| | - Andrea Ramirez
- All of Us Research Program, National Institutes of Health, Bethesda, MD 20892, United States
| | - Dan M Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Bryce A Schuler
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Edward Siew
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jennifer Sucre
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Isaac Thomsen
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Rory J Tinker
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Sara Van Driest
- All of Us Research Program, National Institutes of Health, Bethesda, MD 20892, United States
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Colin Walsh
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Jeremy L Warner
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Lee Wheless
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States
| |
Collapse
|
50
|
Fatumo S, Sathan D, Samtal C, Isewon I, Tamuhla T, Soremekun C, Jafali J, Panji S, Tiffin N, Fakim YJ. Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions. Genome Med 2023; 15:87. [PMID: 37904243 PMCID: PMC10614359 DOI: 10.1186/s13073-023-01245-9] [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: 03/04/2023] [Accepted: 10/12/2023] [Indexed: 11/01/2023] Open
Abstract
Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa.
Collapse
Affiliation(s)
- Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda.
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
- Department of Non-Communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
| | - Dassen Sathan
- H3Africa Bioinformatics Network (H3ABioNet) Node, University of Mauritius, Reduit, Mauritius
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-Food and Health, Faculty of Sciences Dhar El Mahraz-Sidi Mohammed Ben Abdellah University, 30000, Fez, Morocco
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, P. M. B. 1023, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Km 10 Idiroko Road, P.M.B. 1023, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE), Covenant University, P.M.B. 1023, Ota, Ogun State, Nigeria
| | - Tsaone Tamuhla
- Division of Computational Biology, Integrative Biomedical Sciences Department, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Chisom Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- Department of Immunology and Molecular Biology, College of Health Science, Makerere University, Kampala, Uganda
| | - James Jafali
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi
- Clinical Infection, Microbiology & Immunology, The University of Liverpool, Liverpool, UK
| | - Sumir Panji
- Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | - Nicki Tiffin
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | | |
Collapse
|