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Mao D, Liu C, Wang L, Ai-Ouran R, Deisseroth C, Pasupuleti S, Kim SY, Li L, Rosenfeld JA, Meng L, Burrage LC, Wangler MF, Yamamoto S, Santana M, Perez V, Shukla P, Eng CM, Lee B, Yuan B, Xia F, Bellen HJ, Liu P, Liu Z. AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders. NEJM AI 2024; 1:10.1056/aioa2300009. [PMID: 38962029 PMCID: PMC11221788 DOI: 10.1056/aioa2300009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
Abstract
BACKGROUND Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. METHODS AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts. RESULTS AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network. CONCLUSIONS AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).
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Affiliation(s)
- Dongxue Mao
- Department of Pediatrics, Baylor College of Medicine, Houston
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
| | - Chaozhong Liu
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
- Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston
| | - Linhua Wang
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
- Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston
| | - Rami Ai-Ouran
- Department of Pediatrics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
- Department of Data Science and AI, Al Hussein Technical University, Amman, Jordan
| | - Cole Deisseroth
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
| | - Sasidhar Pasupuleti
- Department of Pediatrics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
| | - Seon Young Kim
- Department of Pediatrics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
| | - Lucian Li
- Department of Pediatrics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
| | - Linyan Meng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Baylor Genetics, Houston7
| | - Lindsay C Burrage
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
| | - Michael F Wangler
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
| | - Shinya Yamamoto
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
| | | | | | | | - Christine M Eng
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Baylor Genetics, Houston7
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
| | - Bo Yuan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Human Genome Sequencing Center, Baylor College of Medicine, Houston
| | - Fan Xia
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Baylor Genetics, Houston7
| | - Hugo J Bellen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
- Department of Neuroscience, Baylor College of Medicine, Houston
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston
- Baylor Genetics, Houston7
| | - Zhandong Liu
- Department of Pediatrics, Baylor College of Medicine, Houston
- Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston
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Zucca S, Nicora G, De Paoli F, Carta MG, Bellazzi R, Magni P, Rizzo E, Limongelli I. An AI-based approach driven by genotypes and phenotypes to uplift the diagnostic yield of genetic diseases. Hum Genet 2024:10.1007/s00439-023-02638-x. [PMID: 38520562 DOI: 10.1007/s00439-023-02638-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 12/27/2023] [Indexed: 03/25/2024]
Abstract
Identifying disease-causing variants in Rare Disease patients' genome is a challenging problem. To accomplish this task, we describe a machine learning framework, that we called "Suggested Diagnosis", whose aim is to prioritize genetic variants in an exome/genome based on the probability of being disease-causing. To do so, our method leverages standard guidelines for germline variant interpretation as defined by the American College of Human Genomics (ACMG) and the Association for Molecular Pathology (AMP), inheritance information, phenotypic similarity, and variant quality. Starting from (1) the VCF file containing proband's variants, (2) the list of proband's phenotypes encoded in Human Phenotype Ontology terms, and optionally (3) the information about family members (if available), the "Suggested Diagnosis" ranks all the variants according to their machine learning prediction. This method significantly reduces the number of variants that need to be evaluated by geneticists by pinpointing causative variants in the very first positions of the prioritized list. Most importantly, our approach proved to be among the top performers within the CAGI6 Rare Genome Project Challenge, where it was able to rank the true causative variant among the first positions and, uniquely among all the challenge participants, increased the diagnostic yield of 12.5% by solving 2 undiagnosed cases.
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Affiliation(s)
- S Zucca
- enGenome Srl, 27100, Pavia, Italy
| | - G Nicora
- enGenome Srl, 27100, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - M G Carta
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - R Bellazzi
- enGenome Srl, 27100, Pavia, Italy
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - P Magni
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.
- University of Pavia, 27100, Pavia, Italy.
| | - E Rizzo
- enGenome Srl, 27100, Pavia, Italy
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Kim HH, Kim DW, Woo J, Lee K. Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders. Hum Genomics 2024; 18:28. [PMID: 38509596 PMCID: PMC10956189 DOI: 10.1186/s40246-024-00595-8] [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: 06/15/2023] [Accepted: 03/03/2024] [Indexed: 03/22/2024] Open
Abstract
BACKGROUND In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions. RESULTS We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data. CONCLUSIONS 3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.
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Affiliation(s)
- Ho Heon Kim
- Research and Development Center, 3billion, 14th floor, 416 Teheran-ro, Gangnam-gu, Seoul, 06193, Republic of Korea
| | - Dong-Wook Kim
- Research and Development Center, 3billion, 14th floor, 416 Teheran-ro, Gangnam-gu, Seoul, 06193, Republic of Korea
| | - Junwoo Woo
- Research and Development Center, 3billion, 14th floor, 416 Teheran-ro, Gangnam-gu, Seoul, 06193, Republic of Korea
| | - Kyoungyeul Lee
- Research and Development Center, 3billion, 14th floor, 416 Teheran-ro, Gangnam-gu, Seoul, 06193, Republic of Korea.
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4
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Yuan X, Su J, Wang J, Dai B, Sun Y, Zhang K, Li Y, Chuan J, Tang C, Yu Y, Gong Q. Refined preferences of prioritizers improve intelligent diagnosis for Mendelian diseases. Sci Rep 2024; 14:2845. [PMID: 38310124 PMCID: PMC10838329 DOI: 10.1038/s41598-024-53461-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 01/31/2024] [Indexed: 02/05/2024] Open
Abstract
Phenotype-guided gene prioritizers have proved a highly efficient approach to identifying causal genes for Mendelian diseases. In our previous study, we preliminarily evaluated the performance of ten prioritizers. However, all the selected software was run based on default settings and singleton mode. With a large-scale family dataset from Deciphering Developmental Disorders (DDD) project (N = 305) and an in-house trio cohort (N = 152), the four optimal performers in our prior study including Exomiser, PhenIX, AMELIE, and LIRCIAL were further assessed through parameter optimization and/or the utilization of trio mode. The in-depth assessment revealed high diagnostic yields of the four prioritizers with refined preferences, each alone or together: (1) 83.3-91.8% of the causal genes were presented among the first ten candidates in the final ranking lists of the four tools; (2) Over 97.7% of the causal genes were successfully captured within the top 50 by either of the four software. Exomiser did best in directly hitting the target (ranking the causal gene at the very top) while LIRICAL displayed a predominant overall detection capability. Besides, cases affected by low-penetrance and high-frequency pathogenic variants were found misjudged during the automated prioritization process. The discovery of the limitations shed light on the specific directions of future enhancement for causal-gene ranking tools.
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Affiliation(s)
- Xiao Yuan
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Jieqiong Su
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Jing Wang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Bing Dai
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yanfang Sun
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Keke Zhang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yinghua Li
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, Guangdong, China
| | - Jun Chuan
- Genetalks Biotech. Co., Ltd., Changsha, Hunan, China
| | - Chunyan Tang
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China
| | - Yan Yu
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China.
| | - Qiang Gong
- Changsha Kingmed Center for Clinical Laboratory, Lutian Road 28, Changsha, 410000, Hunan, China.
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Glessner JT, Ningappa MB, Ngo KA, Zahid M, So J, Higgs BW, Sleiman PMA, Narayanan T, Ranganathan S, March M, Prasadan K, Vaccaro C, Reyes-Mugica M, Velazquez J, Salgado CM, Ebrahimkhani MR, Schmitt L, Rajasundaram D, Paul M, Pellegrino R, Gittes GK, Li D, Wang X, Billings J, Squires R, Ashokkumar C, Sharif K, Kelly D, Dhawan A, Horslen S, Lo CW, Shin D, Subramaniam S, Hakonarson H, Sindhi R. Biliary atresia is associated with polygenic susceptibility in ciliogenesis and planar polarity effector genes. J Hepatol 2023; 79:1385-1395. [PMID: 37572794 PMCID: PMC10729795 DOI: 10.1016/j.jhep.2023.07.039] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 07/07/2023] [Accepted: 07/18/2023] [Indexed: 08/14/2023]
Abstract
BACKGROUND & AIMS Biliary atresia (BA) is poorly understood and leads to liver transplantation (LT), with the requirement for and associated risks of lifelong immunosuppression, in most children. We performed a genome-wide association study (GWAS) to determine the genetic basis of BA. METHODS We performed a GWAS in 811 European BA cases treated with LT in US, Canadian and UK centers, and 4,654 genetically matched controls. Whole-genome sequencing of 100 cases evaluated synthetic association with rare variants. Functional studies included whole liver transcriptome analysis of 64 BA cases and perturbations in experimental models. RESULTS A GWAS of common single nucleotide polymorphisms (SNPs), i.e. allele frequencies >1%, identified intronic SNPs rs6446628 in AFAP1 with genome-wide significance (p = 3.93E-8) and rs34599046 in TUSC3 at sub-threshold genome-wide significance (p = 1.34E-7), both supported by credible peaks of neighboring SNPs. Like other previously reported BA-associated genes, AFAP1 and TUSC3 are ciliogenesis and planar polarity effectors (CPLANE). In gene-set-based GWAS, BA was associated with 6,005 SNPs in 102 CPLANE genes (p = 5.84E-15). Compared with non-CPLANE genes, more CPLANE genes harbored rare variants (allele frequency <1%) that were assigned Human Phenotype Ontology terms related to hepatobiliary anomalies by predictive algorithms, 87% vs. 40%, p <0.0001. Rare variants were present in multiple genes distinct from those with BA-associated common variants in most BA cases. AFAP1 and TUSC3 knockdown blocked ciliogenesis in mouse tracheal cells. Inhibition of ciliogenesis caused biliary dysgenesis in zebrafish. AFAP1 and TUSC3 were expressed in fetal liver organoids, as well as fetal and BA livers, but not in normal or disease-control livers. Integrative analysis of BA-associated variants and liver transcripts revealed abnormal vasculogenesis and epithelial tube formation, explaining portal vein anomalies that co-exist with BA. CONCLUSIONS BA is associated with polygenic susceptibility in CPLANE genes. Rare variants contribute to polygenic risk in vulnerable pathways via unique genes. IMPACT AND IMPLICATIONS Liver transplantation is needed to cure most children born with biliary atresia, a poorly understood rare disease. Transplant immunosuppression increases the likelihood of life-threatening infections and cancers. To improve care by preventing this disease and its progression to transplantation, we examined its genetic basis. We find that this disease is associated with both common and rare mutations in highly specialized genes which maintain normal communication and movement of cells, and their organization into bile ducts and blood vessels during early development of the human embryo. Because defects in these genes also cause other birth defects, our findings could lead to preventive strategies to lower the incidence of biliary atresia and potentially other birth defects.
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Affiliation(s)
- Joseph T Glessner
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mylarappa B Ningappa
- Hillman Center for Pediatric Transplantation, UPMC-Children's Hospital of Pittsburgh, and Thomas E Starzl Transplant Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kim A Ngo
- Department of Bioengineering, University of California, San Diego, San Diego, La Jolla, CA, USA
| | - Maliha Zahid
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Juhoon So
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Brandon W Higgs
- Hillman Center for Pediatric Transplantation, UPMC-Children's Hospital of Pittsburgh, and Thomas E Starzl Transplant Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Patrick M A Sleiman
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tejaswini Narayanan
- Department of Bioengineering, University of California, San Diego, San Diego, La Jolla, CA, USA
| | - Sarangarajan Ranganathan
- Division of Pathology and Laboratory Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Michael March
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Krishna Prasadan
- Rangos Research Center Animal Imaging Core, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Courtney Vaccaro
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Miguel Reyes-Mugica
- Division of Pediatric Pathology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Jeremy Velazquez
- Department of Pathology, School of Medicine, Pittsburgh Liver Research Center, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Claudia M Salgado
- Division of Pediatric Pathology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Mo R Ebrahimkhani
- Department of Pathology, School of Medicine, Pittsburgh Liver Research Center, McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lori Schmitt
- Histology Core Laboratory Manager, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Dhivyaa Rajasundaram
- Department of Pediatrics, Division of Health Informatics, Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Morgan Paul
- Hillman Center for Pediatric Transplantation, UPMC-Children's Hospital of Pittsburgh, and Thomas E Starzl Transplant Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Renata Pellegrino
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - George K Gittes
- Surgeon-in-Chief Emeritus, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Dong Li
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Xiang Wang
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan Billings
- Center for Applied Genomics (CAG), Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert Squires
- Pediatric Gastroenterology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Chethan Ashokkumar
- Hillman Center for Pediatric Transplantation, UPMC-Children's Hospital of Pittsburgh, and Thomas E Starzl Transplant Institute, University of Pittsburgh, Pittsburgh, PA, USA
| | - Khalid Sharif
- Paediatric Liver Unit Including Intestinal Transplantation, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Deirdre Kelly
- Paediatric Liver Unit Including Intestinal Transplantation, Birmingham Women's and Children's NHS Foundation Trust, Birmingham, UK
| | - Anil Dhawan
- Paediatric Liver GI and Nutrition Center and MowatLabs, NHS Foundation Trust, King's College Hospital, London, UK
| | - Simon Horslen
- Pediatric Gastroenterology, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Cecilia W Lo
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Donghun Shin
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shankar Subramaniam
- Department of Bioengineering, University of California, San Diego, San Diego, La Jolla, CA, USA; Department of Computer Science and Engineering, and Nanoengineering, University of California, San Diego, San Diego, La Jolla, CA, USA.
| | - Hakon Hakonarson
- Divisions of Human Genetics and Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
| | - Rakesh Sindhi
- Hillman Center for Pediatric Transplantation, UPMC-Children's Hospital of Pittsburgh, and Thomas E Starzl Transplant Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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Suarez-Pajes E, Tosco-Herrera E, Ramirez-Falcon M, Gonzalez-Barbuzano S, Hernandez-Beeftink T, Guillen-Guio B, Villar J, Flores C. Genetic Determinants of the Acute Respiratory Distress Syndrome. J Clin Med 2023; 12:3713. [PMID: 37297908 PMCID: PMC10253474 DOI: 10.3390/jcm12113713] [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: 04/17/2023] [Revised: 05/18/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition that arises from multiple causes, including sepsis, pneumonia, trauma, and severe coronavirus disease 2019 (COVID-19). Given the heterogeneity of causes and the lack of specific therapeutic options, it is crucial to understand the genetic and molecular mechanisms that underlie this condition. The identification of genetic risks and pharmacogenetic loci, which are involved in determining drug responses, could help enhance early patient diagnosis, assist in risk stratification of patients, and reveal novel targets for pharmacological interventions, including possibilities for drug repositioning. Here, we highlight the basis and importance of the most common genetic approaches to understanding the pathogenesis of ARDS and its critical triggers. We summarize the findings of screening common genetic variation via genome-wide association studies and analyses based on other approaches, such as polygenic risk scores, multi-trait analyses, or Mendelian randomization studies. We also provide an overview of results from rare genetic variation studies using Next-Generation Sequencing techniques and their links with inborn errors of immunity. Lastly, we discuss the genetic overlap between severe COVID-19 and ARDS by other causes.
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Affiliation(s)
- Eva Suarez-Pajes
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Eva Tosco-Herrera
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Melody Ramirez-Falcon
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Silvia Gonzalez-Barbuzano
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
| | - Tamara Hernandez-Beeftink
- Department of Population Health Sciences, University of Leicester, Leicester LE1 7RH, UK
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester LE1 7RH, UK
| | - Beatriz Guillen-Guio
- Department of Population Health Sciences, University of Leicester, Leicester LE1 7RH, UK
- NIHR Leicester Biomedical Research Centre, University of Leicester, Leicester LE1 7RH, UK
| | - Jesús Villar
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Research Unit, Hospital Universitario de Gran Canaria Dr. Negrín, 35019 Las Palmas de Gran Canaria, Spain
| | - Carlos Flores
- Research Unit, Hospital Universitario Nuestra Señora de Candelaria, 38010 Santa Cruz de Tenerife, Spain
- CIBER de Enfermedades Respiratorias (CIBERES), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Genomics Division, Instituto Tecnológico y de Energías Renovables (ITER), 38600 Santa Cruz de Tenerife, Spain
- Faculty of Health Sciences, University of Fernando Pessoa Canarias, 35450 Las Palmas de Gran Canaria, Spain
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