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Sanghvi MM, Young WJ, Naderi H, Burns R, Ramírez J, Bell CG, Munroe PB. Using Genomics to Develop Personalized Cardiovascular Treatments. Arterioscler Thromb Vasc Biol 2025; 45:866-881. [PMID: 40244646 DOI: 10.1161/atvbaha.125.319221] [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: 02/04/2025] [Accepted: 04/07/2025] [Indexed: 04/18/2025]
Abstract
Advances in genomic technologies have significantly enhanced our understanding of both monogenic and polygenic etiologies of cardiovascular disease. In this review, we explore how the utilization of genomic information is bringing personalized medicine approaches to the forefront of cardiovascular disease management. We describe how genomic data can resolve diagnostic uncertainty, support cascade screening, and inform treatment strategies. We discuss how genome-wide association studies have identified thousands of genetic variants associated with polygenic cardiovascular diseases, and how integrating these insights into polygenic risk scores can enhance personalized risk prediction beyond traditional clinical algorithms. We detail how pharmacogenomics approaches leverage genotype information to guide drug selection and mitigate adverse events. Finally, we present the paradigm-shifting approach of gene therapy, which holds the promise of being a curative intervention for cardiovascular conditions.
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Affiliation(s)
- Mihir M Sanghvi
- William Harvey Research Institute (M.M.S., W.J.Y., H.N., R.B., J.R., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- NIHR Barts Biomedical Research Centre (M.M.S., W.J.Y., H.N., R.B., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom (M.M.S., W.J.Y., H.N.)
| | - William J Young
- William Harvey Research Institute (M.M.S., W.J.Y., H.N., R.B., J.R., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- NIHR Barts Biomedical Research Centre (M.M.S., W.J.Y., H.N., R.B., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom (M.M.S., W.J.Y., H.N.)
| | - Hafiz Naderi
- William Harvey Research Institute (M.M.S., W.J.Y., H.N., R.B., J.R., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- NIHR Barts Biomedical Research Centre (M.M.S., W.J.Y., H.N., R.B., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom (M.M.S., W.J.Y., H.N.)
| | - Richard Burns
- William Harvey Research Institute (M.M.S., W.J.Y., H.N., R.B., J.R., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- NIHR Barts Biomedical Research Centre (M.M.S., W.J.Y., H.N., R.B., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
| | - Julia Ramírez
- William Harvey Research Institute (M.M.S., W.J.Y., H.N., R.B., J.R., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- Aragon Institute of Engineering Research, University of Zaragoza, Spain (J.R.)
- Centro de Investigación Biomédica en Red, Biomedicina, Bioingeniería y Nanomedicina, Zaragoza, Spain (J.R.)
| | - Christopher G Bell
- William Harvey Research Institute (M.M.S., W.J.Y., H.N., R.B., J.R., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- NIHR Barts Biomedical Research Centre (M.M.S., W.J.Y., H.N., R.B., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
| | - Patricia B Munroe
- William Harvey Research Institute (M.M.S., W.J.Y., H.N., R.B., J.R., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
- NIHR Barts Biomedical Research Centre (M.M.S., W.J.Y., H.N., R.B., C.G.B., P.B.M.), Queen Mary University of London, United Kingdom
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Zeng T, Wang J, Liu Z, Wang X, Zhang H, Ai X, Deng X, Wu K. Identification of Candidate Genes and eQTLs Related to Porcine Reproductive Function. Animals (Basel) 2025; 15:1038. [PMID: 40218432 PMCID: PMC11987867 DOI: 10.3390/ani15071038] [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: 01/30/2025] [Revised: 03/31/2025] [Accepted: 04/01/2025] [Indexed: 04/14/2025] Open
Abstract
Expression quantitative trait locus (eQTL) mapping is an effective tool for identifying genetic variations that regulate gene expression. An increasing number of studies suggested that SNPs associated with complex traits in farm animals are considered as expression quantitative trait loci. Identifying eQTLs associated with gene expression levels in the endometrium helps to unravel the regulatory mechanisms of genes related to reproductive functions in this tissue and provides molecular markers for the genetic improvement of high-fertility sow breeding. In this study, 218 RNA-seq data from pig endometrial tissue were used for eQTL analysis to identify genetic variants regulating gene expression. Additionally, weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes involved in reproductive functions. The eQTL analysis identified 34,876 significant cis-eQTLs regulating the expression of 5632 genes (FDR ≤ 0.05), and 90 hub genes were identified by WGCNA analysis. By integrating eQTL and WGCNA results, 14 candidate genes and 16 fine-mapped cis-eQTLs were identified, including FRK, ARMC3, SLC35F3, TMEM72, FFAR4, SOWAHA, PSPH, FMO5, HPN, FUT2, RAP1GAP, C6orf52, SEL1L3, and CLGN, which were involved in the physiological processes of reproduction in sows through hormone regulation, cell adhesion, and amino acid and lipid metabolism. These eQTLs regulate the high expression of candidate genes in the endometrium, thereby affecting reproductive-related physiological functions. These findings enhance our understanding of the genetic basis of reproductive traits and provide valuable genetic markers for marker-assisted selection (MAS), which can be applied to improve sow fecundity and optimize breeding strategies for high reproductive performance.
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Affiliation(s)
- Tong Zeng
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.Z.); (J.W.); (Z.L.); (H.Z.); (X.A.)
| | - Ji Wang
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.Z.); (J.W.); (Z.L.); (H.Z.); (X.A.)
| | - Zhexi Liu
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.Z.); (J.W.); (Z.L.); (H.Z.); (X.A.)
- Frontier Technology Research Institute of China Agricultural University in Shenzhen, Shenzhen 518119, China
| | - Xiaofeng Wang
- Beijing Municipal General Station for Animal Husbandry & Veterinary Service, Beijing 100107, China;
| | - Han Zhang
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.Z.); (J.W.); (Z.L.); (H.Z.); (X.A.)
| | - Xiaohua Ai
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.Z.); (J.W.); (Z.L.); (H.Z.); (X.A.)
| | - Xuemei Deng
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.Z.); (J.W.); (Z.L.); (H.Z.); (X.A.)
| | - Keliang Wu
- National Engineering Laboratory for Animal Breeding, Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China; (T.Z.); (J.W.); (Z.L.); (H.Z.); (X.A.)
- Sichuan Advanced Agricultural & Industrial Institute, China Agricultural University, Chengdu 611430, China
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Seah C, Sidamon-Eristoff AE, Huckins LM, Brennand KJ. Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment. J Clin Invest 2025; 135:e185102. [PMID: 40026250 PMCID: PMC11870735 DOI: 10.1172/jci185102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2025] Open
Abstract
Exposure to traumatic stress is common in the general population. Variation in the brain's molecular encoding of stress potentially contributes to the heterogeneous clinical outcomes in response to traumatic experiences. For instance, only a minority of those exposed to trauma will develop post-traumatic stress disorder (PTSD). Risk for PTSD is at least partially heritable, with a growing number of genetic factors identified through GWAS. A major limitation of genetic studies is that they capture only the genetic component of risk, whereas PTSD by definition requires an environmental traumatic exposure. Furthermore, the extent, timing, and type of trauma affects susceptibility. Here, we discuss the molecular mechanisms of PTSD risk together with gene × environment interactions, with a focus on how either might inform genetic screening for individuals at high risk for disease, reveal biological mechanisms that might one day yield novel therapeutics, and impact best clinical practices even today. To close, we discuss the interaction of trauma with sex, gender, and race, with a focus on the implications for treatment. Altogether, we suggest that predicting, preventing, and treating PTSD will require integrating both genotypic and environmental information.
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Affiliation(s)
- Carina Seah
- Department of Genetics and Genomics and
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anne Elizabeth Sidamon-Eristoff
- Department of Psychiatry, Division of Molecular Psychiatry
- Interdepartmental Neuroscience Program, Wu Tsai Institute, and
- MD-PhD Program, Yale University School of Medicine, New Haven, Connecticut, USA
| | | | - Kristen J. Brennand
- Department of Genetics and Genomics and
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Psychiatry, Division of Molecular Psychiatry
- Interdepartmental Neuroscience Program, Wu Tsai Institute, and
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Halder SK, Melkani GC. The Interplay of Genetic Predisposition, Circadian Misalignment, and Metabolic Regulation in Obesity. Curr Obes Rep 2025; 14:21. [PMID: 40024983 PMCID: PMC11872776 DOI: 10.1007/s13679-025-00613-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/11/2025] [Indexed: 03/04/2025]
Abstract
PURPOSE OF REVIEW This review explores the complex interplay between genetic predispositions to obesity, circadian rhythms, metabolic regulation, and sleep. It highlights how genetic factors underlying obesity exacerbate metabolic dysfunction through circadian misalignment and examines promising interventions to mitigate these effects. RECENT FINDINGS Genome-wide association Studies (GWAS) have identified numerous Single Nucleotide Polymorphisms (SNPs) associated with obesity traits, attributing 40-75% heritability to body mass index (BMI). These findings illuminate critical links between genetic obesity, circadian clocks, and metabolic processes. SNPs in clock-related genes influence metabolic pathways, with disruptions in circadian rhythms-driven by poor sleep hygiene or erratic eating patterns-amplifying metabolic dysfunction. Circadian clocks, synchronized with the 24-h light-dark cycle, regulate key metabolic activities, including glucose metabolism, lipid storage, and energy utilization. Genetic mutations or external disruptions, such as irregular sleep or eating habits, can destabilize circadian rhythms, promoting weight gain and metabolic disorders. Circadian misalignment in individuals with genetic predispositions to obesity disrupts the release of key metabolic hormones, such as leptin and insulin, impairing hunger regulation and fat storage. Interventions like time-restricted feeding (TRF) and structured physical activity offer promising strategies to restore circadian harmony, improve metabolic health, and mitigate obesity-related risks.
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Affiliation(s)
- Sajal Kumar Halder
- Department of Pathology, Division of Molecular and Cellular Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, 35294, USA
| | - Girish C Melkani
- Department of Pathology, Division of Molecular and Cellular Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
- UAB Nathan Shock Center, Birmingham, AL, 35294, USA.
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Douville NJ, Bastarache L, He J, Wu KHH, Vanderwerff B, Bertucci-Richter E, Hornsby WE, Lewis A, Jewell ES, Kheterpal S, Shah N, Mathis M, Engoren MC, Douville CB, Surakka I, Willer C, Kertai MD. Polygenic Score for the Prediction of Postoperative Nausea and Vomiting: A Retrospective Derivation and Validation Cohort Study. Anesthesiology 2025; 142:52-71. [PMID: 39250560 PMCID: PMC11620327 DOI: 10.1097/aln.0000000000005214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 08/07/2024] [Indexed: 09/11/2024]
Abstract
BACKGROUND Postoperative nausea and vomiting (PONV) is a key driver of unplanned admission and patient satisfaction after surgery. Because traditional risk factors do not completely explain variability in risk, this study hypothesized that genetics may contribute to the overall risk for this complication. The objective of this research is to perform a genome-wide association study of PONV, derive a polygenic risk score for PONV, assess associations between the risk score and PONV in a validation cohort, and compare any genetic contributions to known clinical risks for PONV. METHODS Surgeries with integrated genetic and perioperative data performed under general anesthesia at Michigan Medicine (Ann Arbor, Michigan) and Vanderbilt University Medical Center (Nashville, Tennessee) were studied. PONV was defined as nausea or emesis occurring and documented in the postanesthesia care unit. In the discovery phase, genome-wide association studies were performed on each genetic cohort, and the results were meta-analyzed. Next, the polygenic phase assessed whether a polygenic score, derived from genome-wide association study in a derivation cohort from Vanderbilt University Medical Center, improved prediction within a validation cohort from Michigan Medicine, as quantified by discrimination (c-statistic) and net reclassification index. RESULTS Of 64,523 total patients, 5,703 developed PONV (8.8%). The study identified 46 genetic variants exceeding the threshold of P < 1 × 10-5, occurring with minor allele frequency greater than 1%, and demonstrating concordant effects in both cohorts. Standardized polygenic score was associated with PONV in a basic model, controlling for age and sex (adjusted odds ratio, 1.027 per SD increase in overall genetic risk; 95% CI, 1.001 to 1.053; P = 0.044), a model based on known clinical risks (adjusted odds ratio, 1.029; 95% CI, 1.003 to 1.055; P = 0.030), and a full clinical regression, controlling for 21 demographic, surgical, and anesthetic factors, (adjusted odds ratio, 1.029; 95% CI, 1.002 to 1.056; P = 0.033). The addition of polygenic score improved overall discrimination in models based on known clinical risk factors (c-statistic, 0.616 compared to 0.613; P = 0.028) and improved net reclassification of 4.6% of cases. CONCLUSIONS Standardized polygenic risk was associated with PONV in all three of the study's models, but the genetic influence was smaller than exerted by clinical risk factors. Specifically, a patient with a polygenic risk score greater than 1 SD above the mean has 2 to 3% greater odds of developing PONV when compared to the baseline population, which is at least an order of magnitude smaller than the increase associated with having prior PONV or motion sickness (55%), having a history of migraines (17%), or being female (83%) and is not clinically significant. Furthermore, the use of a polygenic risk score does not meaningfully improve discrimination compared to clinical risk factors and is not clinically useful. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Nicholas J. Douville
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan; and Institute of Healthcare Policy and Innovation and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jing He
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | | | | | | | - Adam Lewis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Sachin Kheterpal
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan
| | - Nirav Shah
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan
| | - Michael Mathis
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan; and Institute of Healthcare Policy and Innovation and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan
| | - Milo C. Engoren
- Department of Anesthesiology, Michigan Medicine, Ann Arbor, Michigan
| | | | - Ida Surakka
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan
| | | | - Miklos D. Kertai
- Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, Tennessee
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Xie X, Zhang Q, Liu YG. Rice GWAS-to-Gene uncovers the polygenic basis of traits. SCIENCE CHINA. LIFE SCIENCES 2024; 67:2783-2785. [PMID: 39279008 DOI: 10.1007/s11427-024-2716-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Accepted: 08/27/2024] [Indexed: 09/18/2024]
Affiliation(s)
- Xianrong Xie
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, College of Agriculture, College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China
| | - Qunjie Zhang
- College of Agriculture, South China Agricultural University, Guangzhou, 510642, China
| | - Yao-Guang Liu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangdong Laboratory for Lingnan Modern Agriculture, College of Agriculture, College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China.
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Subirana-Granés M, Hoffman J, Zhang H, Akirtava C, Nandi S, Fotso K, Pividori M. Genetic studies through the lens of gene networks. ARXIV 2024:arXiv:2410.23425v1. [PMID: 39575117 PMCID: PMC11581109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located in non-coding regions, making the link to biological function elusive. While traditional approaches, such as transcriptome-wide association studies (TWAS), have advanced our understanding by linking genetic variants to gene expression, they often overlook gene-gene interactions. Here, we review current approaches to integrate different molecular data, leveraging machine learning methods to identify gene modules based on co-expression and functional relationships. These integrative approaches, like PhenoPLIER, combine TWAS and drug-induced transcriptional profiles to effectively capture biologically meaningful gene networks. This integration provides a context-specific understanding of disease processes while highlighting both core and peripheral genes. These insights pave the way for novel therapeutic targets and enhance the interpretability of genetic studies in personalized medicine.
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Affiliation(s)
- Marc Subirana-Granés
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jill Hoffman
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Haoyu Zhang
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Christina Akirtava
- Department of Biochemistry and Molecular Genetics, RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Sutanu Nandi
- Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Kevin Fotso
- Office of Information Technology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Milton Pividori
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO 80045, USA; Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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Gilbertson EN, Brand CM, McArthur E, Rinker DC, Kuang S, Pollard KS, Capra JA. Machine Learning Reveals the Diversity of Human 3D Chromatin Contact Patterns. Mol Biol Evol 2024; 41:msae209. [PMID: 39404010 PMCID: PMC11523124 DOI: 10.1093/molbev/msae209] [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: 12/24/2023] [Revised: 06/26/2024] [Accepted: 08/08/2024] [Indexed: 10/23/2024] Open
Abstract
Understanding variation in chromatin contact patterns across diverse humans is critical for interpreting noncoding variants and their effects on gene expression and phenotypes. However, experimental determination of chromatin contact patterns across large samples is prohibitively expensive. To overcome this challenge, we develop and validate a machine learning method to quantify the variation in 3D chromatin contacts at 2 kilobase resolution from genome sequence alone. We apply this approach to thousands of human genomes from the 1000 Genomes Project and the inferred hominin ancestral genome. While patterns of 3D contact divergence genome wide are qualitatively similar to patterns of sequence divergence, we find substantial differences in 3D divergence and sequence divergence in local 1 megabase genomic windows. In particular, we identify 392 windows with significantly greater 3D divergence than expected from sequence. Moreover, for 31% of genomic windows, a single individual has a rare divergent 3D contact map pattern. Using in silico mutagenesis, we find that most single nucleotide sequence changes do not result in changes to 3D chromatin contacts. However, in windows with substantial 3D divergence just one or a few variants can lead to divergent 3D chromatin contacts without the individuals carrying those variants having high sequence divergence. In summary, inferring 3D chromatin contact maps across human populations reveals variable contact patterns. We anticipate that these genetically diverse maps of 3D chromatin contact will provide a reference for future work on the function and evolution of 3D chromatin contact variation across human populations.
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Affiliation(s)
- Erin N Gilbertson
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
| | - Colin M Brand
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Evonne McArthur
- Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - David C Rinker
- Department of Biological Sciences, Vanderbilt University, Nashville, TN, USA
| | - Shuzhen Kuang
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
| | - Katherine S Pollard
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA
- Chan Zuckerberg Biohub SF, San Francisco, CA, USA
| | - John A Capra
- Biomedical Informatics Graduate Program, University of California San Francisco, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
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Bruner WS, Grant SFA. Translation of genome-wide association study: from genomic signals to biological insights. Front Genet 2024; 15:1375481. [PMID: 39421299 PMCID: PMC11484060 DOI: 10.3389/fgene.2024.1375481] [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: 01/23/2024] [Accepted: 09/24/2024] [Indexed: 10/19/2024] Open
Abstract
Since the turn of the 21st century, genome-wide association study (GWAS) have successfully identified genetic signals associated with a myriad of common complex traits and diseases. As we transition from establishing robust genetic associations with diverse phenotypes, the central challenge is now focused on characterizing the underlying functional mechanisms driving these signals. Previous GWAS efforts have revealed multiple variants, each conferring relatively subtle susceptibility, collectively contributing to the pathogenesis of various common diseases. Such variants can further exhibit associations with multiple other traits and differ across ancestries, plus disentangling causal variants from non-causal due to linkage disequilibrium complexities can lead to challenges in drawing direct biological conclusions. Combined with cellular context considerations, such challenges can reduce the capacity to definitively elucidate the biological significance of GWAS signals, limiting the potential to define mechanistic insights. This review will detail current and anticipated approaches for functional interpretation of GWAS signals, both in terms of characterizing the underlying causal variants and the corresponding effector genes.
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Affiliation(s)
- Winter S. Bruner
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Struan F. A. Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Division of Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA, United States
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10
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Wolujewicz P, Aguiar-Pulido V, Thareja G, Suhre K, Elemento O, Finnell RH, Ross ME. Integrative computational analyses implicate regulatory genomic elements contributing to spina bifida. GENETICS IN MEDICINE OPEN 2024; 2:101894. [PMID: 39669613 PMCID: PMC11613821 DOI: 10.1016/j.gimo.2024.101894] [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] [Received: 02/23/2024] [Revised: 09/11/2024] [Accepted: 09/12/2024] [Indexed: 12/14/2024]
Abstract
Purpose Spina bifida (SB) arises from complex genetic interactions that converge to interfere with neural tube closure. Understanding the precise patterns conferring SB risk requires a deep exploration of the genomic networks and molecular pathways that govern neurulation. This study aims to delineate genome-wide regulatory signatures underlying SB pathophysiology. Methods An untargeted, genome-wide approach was used to interrogate regulatory regions for rare single-nucleotide and copy-number variants (rSNVs and rCNVs, respectively) predicted to affect gene expression, comparing results from SB patients with healthy controls. Qualifying variants were subjected to a deep learning prioritization framework to identify the most functionally relevant variants, as well as the likely target genes affected by these rare regulatory variants. Results This ensemble of computational tools identified rSNVs in specific transcription factor binding sites (TFBSs) that distinguish SB cases from controls. rSNV enrichment was found in specific TFBSs, especially CCCTC-binding factor binding sites. These TFBSs were subjected to a deep learning prioritization framework to identify the most functionally relevant variants, as well as the likely target genes affected by these rSNVs. The functional pathways or modules implicated by these regulated genes serve protein transport, cilia assembly, and central nervous system development. Moreover, the detected rare copy-number variants in SB cases are positioned to disrupt gene regulatory networks and alter 3-dimensional genomic architectures, including brain-specific enhancers and topologically associated domain boundaries of relevant cell types. Conclusion Our study provides a resource for identifying and interpreting genomic regulatory DNA variant contributions to human SB genetic predisposition.
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Affiliation(s)
- Paul Wolujewicz
- Center for Neurogenetics, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
| | - Vanessa Aguiar-Pulido
- Center for Neurogenetics, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
- Department of Computer Science, University of Miami, Coral Gables, FL
| | | | - Karsten Suhre
- Center for Neurogenetics, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
- Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Olivier Elemento
- Center for Neurogenetics, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY
| | - Richard H. Finnell
- Center for Precision Environmental Health, Departments of Molecular and Cellular Biology, Molecular and Human Genetics and Medicine, Baylor College of Medicine, Houston, TX
| | - M. Elizabeth Ross
- Center for Neurogenetics, Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY
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Brlek P, Bulić L, Bračić M, Projić P, Škaro V, Shah N, Shah P, Primorac D. Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives. Cells 2024; 13:504. [PMID: 38534348 PMCID: PMC10969765 DOI: 10.3390/cells13060504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.
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Affiliation(s)
- Petar Brlek
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia; (P.B.)
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Luka Bulić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia; (P.B.)
| | - Matea Bračić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia; (P.B.)
| | - Petar Projić
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
| | | | - Nidhi Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Parth Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Dragan Primorac
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia; (P.B.)
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Split, 21000 Split, Croatia
- Eberly College of Science, The Pennsylvania State University, State College, PA 16802, USA
- The Henry C. Lee College of Criminal Justice and Forensic Sciences, University of New Haven, West Haven, CT 06516, USA
- REGIOMED Kliniken, 96450 Coburg, Germany
- Medical School, University of Rijeka, 51000 Rijeka, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Mostar, 88000 Mostar, Bosnia and Herzegovina
- National Forensic Sciences University, Gujarat 382007, India
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12
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Han D, Li Y, Wang L, Liang X, Miao Y, Li W, Wang S, Wang Z. Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data. Brief Bioinform 2024; 25:bbae110. [PMID: 38517697 PMCID: PMC10959158 DOI: 10.1093/bib/bbae110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 03/24/2024] Open
Abstract
Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.
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Affiliation(s)
- Dongmei Han
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Yurun Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Linxiao Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Xuan Liang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Yuanyuan Miao
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Wenran Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
| | - Zhen Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, 200031, China
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13
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Zhu T, Zhou X, You Y, Wang L, He Z, Chen D. cisDynet: An integrated platform for modeling gene-regulatory dynamics and networks. IMETA 2023; 2:e152. [PMID: 38868212 PMCID: PMC10989917 DOI: 10.1002/imt2.152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Accepted: 11/06/2023] [Indexed: 06/14/2024]
Abstract
Chromatin accessibility sequencing has been widely used for uncovering genetic regulatory mechanisms and inferring gene regulatory networks. However, effectively integrating large-scale chromatin accessibility datasets has posed a significant challenge. This is due to the lack of a comprehensive end-to-end solution, as many existing tools primarily emphasize data preprocessing and overlook downstream analyses. To bridge this gap, we have introduced cisDynet, a holistic solution that combines streamlined data preprocessing using Snakemake and R functions with advanced downstream analysis capabilities. cisDynet excels in conventional data analyses, encompassing peak statistics, peak annotation, differential analysis, motif enrichment analysis, and more. Additionally, it allows to perform sophisticated data exploration, such as tissue-specific peak identification, time course data modeling, integration of RNA-seq data to establish peak-to-gene associations, constructing regulatory networks, and conducting enrichment analysis of genome-wide association study (GWAS) variants. As a proof of concept, we applied cisDynet to reanalyze comprehensive ATAC-seq datasets across various tissues from the Encyclopedia of DNA Elements (ENCODE) project. The analysis successfully delineated tissue-specific open chromatin regions (OCRs), established connections between OCRs and target genes, and effectively linked these discoveries with 1861 GWAS variants. Furthermore, cisDynet was instrumental in dissecting the time course open chromatin data of mouse embryonic development, revealing the dynamic behavior of OCRs over developmental stages and identifying key transcription factors governing differentiation trajectories. In summary, cisDynet offers researchers a user-friendly solution that minimizes the need for extensive coding, ensures the reproducibility of results, and greatly simplifies the exploration of epigenomic data.
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Affiliation(s)
- Tao Zhu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life SciencesNanjing UniversityNanjingChina
| | - Xinkai Zhou
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life SciencesNanjing UniversityNanjingChina
| | - Yuxin You
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life SciencesNanjing UniversityNanjingChina
| | - Lin Wang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life SciencesNanjing UniversityNanjingChina
| | - Zhaohui He
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life SciencesNanjing UniversityNanjingChina
| | - Dijun Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life SciencesNanjing UniversityNanjingChina
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14
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Alemany S, Soler-Artigas M, Cabana-Domínguez J, Fakhreddine D, Llonga N, Vilar-Ribó L, Rodríguez-Urrutia A, Palacio J, González-Castro AM, Lobo B, Alonso-Cotoner C, Simrén M, Santos J, Ramos-Quiroga JA, Ribasés M. Genome-wide multi-trait analysis of irritable bowel syndrome and related mental conditions identifies 38 new independent variants. J Transl Med 2023; 21:272. [PMID: 37085903 PMCID: PMC10120121 DOI: 10.1186/s12967-023-04107-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/05/2023] [Indexed: 04/23/2023] Open
Abstract
BACKGROUND Irritable bowel syndrome (IBS) is a chronic disorder of gut-brain interaction frequently accompanied by mental conditions, including depression and anxiety. Despite showing substantial heritability and being partly determined by a genetic component, the genetic underpinnings explaining the high rates of comorbidity remain largely unclear and there are no conclusive data on the temporal relationship between them. Exploring the overlapping genetic architecture between IBS and mental conditions may help to identify novel genetic loci and biological mechanisms underlying IBS and causal relationships between them. METHODS We quantified the genetic overlap between IBS, neuroticism, depression and anxiety, conducted a multi-trait genome-wide association study (GWAS) considering these traits and investigated causal relationships between them by using the largest GWAS to date. RESULTS IBS showed to be a highly polygenic disorder with extensive genetic sharing with mental conditions. Multi-trait analysis of IBS and neuroticism, depression and anxiety identified 42 genome-wide significant variants for IBS, of which 38 are novel. Fine-mapping risk loci highlighted 289 genes enriched in genes upregulated during early embryonic brain development and gene-sets related with psychiatric, digestive and autoimmune disorders. IBS-associated genes were enriched for target genes of anti-inflammatory and antirheumatic drugs, anesthetics and opioid dependence pharmacological treatment. Mendelian-randomization analysis accounting for correlated pleiotropy identified bidirectional causal effects between IBS and neuroticism and depression and causal effects of the genetic liability of IBS on anxiety. CONCLUSIONS These findings provide evidence of the polygenic architecture of IBS, identify novel genome-wide significant variants for IBS and extend previous knowledge on the genetic overlap and relationship between gastrointestinal and mental disorders.
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Affiliation(s)
- Silvia Alemany
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre On Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
| | - María Soler-Artigas
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre On Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Judit Cabana-Domínguez
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre On Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Dana Fakhreddine
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Natalia Llonga
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Laura Vilar-Ribó
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre On Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Amanda Rodríguez-Urrutia
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre On Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Judit Palacio
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
| | - Ana María González-Castro
- Laboratory of Neuro-Immuno-Gastroenterology, Digestive System Research Unit, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Barcelona, Spain
| | - Beatriz Lobo
- Laboratory of Neuro-Immuno-Gastroenterology, Digestive System Research Unit, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Barcelona, Spain
- Department of Gastroenterology, Vall d'Hebron Barcelona Hospital Campus, Vall d'Hebron Hospital Universitari, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain
- Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Carmen Alonso-Cotoner
- Laboratory of Neuro-Immuno-Gastroenterology, Digestive System Research Unit, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Barcelona, Spain
- Department of Gastroenterology, Vall d'Hebron Barcelona Hospital Campus, Vall d'Hebron Hospital Universitari, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain
- Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERHED), Instituto de Salud Carlos III, Madrid, Spain
| | - Magnus Simrén
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Centre for Functional GI and Motility Disorders, University of North Carolina, Chapel Hill, NC, USA
| | - Javier Santos
- Laboratory of Neuro-Immuno-Gastroenterology, Digestive System Research Unit, Vall d'Hebron Institut de Recerca (VHIR), Vall d'Hebron Hospital Universitari, Barcelona, Spain
- Department of Gastroenterology, Vall d'Hebron Barcelona Hospital Campus, Vall d'Hebron Hospital Universitari, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain
- Department of Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERHED), Instituto de Salud Carlos III, Madrid, Spain
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain
- Biomedical Network Research Centre On Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry Mental Health and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron, 119-129, 08035, Barcelona, Spain.
- Department of Mental Health, Hospital Universitari Vall d'Hebron, Barcelona, Spain.
- Biomedical Network Research Centre On Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain.
- Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
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Cooper YA, Guo Q, Geschwind DH. Multiplexed functional genomic assays to decipher the noncoding genome. Hum Mol Genet 2022; 31:R84-R96. [PMID: 36057282 PMCID: PMC9585676 DOI: 10.1093/hmg/ddac194] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/14/2022] Open
Abstract
Linkage disequilibrium and the incomplete regulatory annotation of the noncoding genome complicates the identification of functional noncoding genetic variants and their causal association with disease. Current computational methods for variant prioritization have limited predictive value, necessitating the application of highly parallelized experimental assays to efficiently identify functional noncoding variation. Here, we summarize two distinct approaches, massively parallel reporter assays and CRISPR-based pooled screens and describe their flexible implementation to characterize human noncoding genetic variation at unprecedented scale. Each approach provides unique advantages and limitations, highlighting the importance of multimodal methodological integration. These multiplexed assays of variant effects are undoubtedly poised to play a key role in the experimental characterization of noncoding genetic risk, informing our understanding of the underlying mechanisms of disease-associated loci and the development of more robust predictive classification algorithms.
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Affiliation(s)
- Yonatan A Cooper
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Medical Scientist Training Program, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Qiuyu Guo
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
- Program in Neurogenetics, Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, University of California Los Angeles, Los Angeles, CA, USA
- Institute of Precision Health, University of California Los Angeles, Los Angeles, CA, USA
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