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Nuttle X, Burt ND, Currall B, Moysés-Oliveira M, Mohajeri K, Bhavsar R, Lucente D, Yadav R, Tai DJC, Gusella JF, Talkowski ME. Parallelized engineering of mutational models using piggyBac transposon delivery of CRISPR libraries. CELL REPORTS METHODS 2024; 4:100672. [PMID: 38091988 PMCID: PMC10831954 DOI: 10.1016/j.crmeth.2023.100672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/14/2023] [Accepted: 11/21/2023] [Indexed: 01/25/2024]
Abstract
New technologies and large-cohort studies have enabled novel variant discovery and association at unprecedented scale, yet functional characterization of these variants remains paramount to deciphering disease mechanisms. Approaches that facilitate parallelized genome editing of cells of interest or induced pluripotent stem cells (iPSCs) have become critical tools toward this goal. Here, we developed an approach that incorporates libraries of CRISPR-Cas9 guide RNAs (gRNAs) together with inducible Cas9 into a piggyBac (PB) transposon system to engineer dozens to hundreds of genomic variants in parallel against isogenic cellular backgrounds. This method empowers loss-of-function (LoF) studies through the introduction of insertions or deletions (indels) and copy-number variants (CNVs), though generating specific nucleotide changes is possible with prime editing. The ability to rapidly establish high-quality mutational models at scale will facilitate the development of isogenic cellular collections and catalyze comparative functional genomic studies investigating the roles of hundreds of genes and mutations in development and disease.
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Affiliation(s)
- Xander Nuttle
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
| | - Nicholas D Burt
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Benjamin Currall
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Mariana Moysés-Oliveira
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Kiana Mohajeri
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; PhD program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Riya Bhavsar
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Diane Lucente
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Rachita Yadav
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Derek J C Tai
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - James F Gusella
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA; Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA
| | - Michael E Talkowski
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Department of Neurology, Harvard Medical School, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA; Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA.
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2
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Xu LL, Zhou XJ, Zhang H. An Update on the Genetics of IgA Nephropathy. J Clin Med 2023; 13:123. [PMID: 38202130 PMCID: PMC10780034 DOI: 10.3390/jcm13010123] [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: 11/09/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/12/2024] Open
Abstract
Immunoglobulin A (IgA) nephropathy (IgAN), the most common form of glomerulonephritis, is one of the leading causes of end-stage kidney disease (ESKD). It is widely believed that genetic factors play a significant role in the development of IgAN. Previous studies of IgAN have provided important insights to unravel the genetic architecture of IgAN and its potential pathogenic mechanisms. The genome-wide association studies (GWASs) together have identified over 30 risk loci for IgAN, which emphasizes the importance of IgA production and regulation in the pathogenesis of IgAN. Follow-up fine-mapping studies help to elucidate the candidate causal variant and the potential pathogenic molecular pathway and provide new potential therapeutic targets. With the rapid development of next-generation sequencing technologies, linkage studies based on whole-genome sequencing (WGS)/whole-exome sequencing (WES) also identify rare variants associated with IgAN, accounting for some of the missing heritability. The complexity of pathogenesis and phenotypic variability may be better understood by integrating genetics, epigenetics, and environment. We have compiled a review summarizing the latest advancements in genetic studies on IgAN. We similarly summarized relevant studies examining the involvement of epigenetics in the pathogenesis of IgAN. Future directions and challenges in this field are also proposed.
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Affiliation(s)
- Lin-Lin Xu
- Renal Division, Peking University First Hospital, Beijing 100034, China; (L.-L.X.); (H.Z.)
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100034, China
| | - Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China; (L.-L.X.); (H.Z.)
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100034, China
| | - Hong Zhang
- Renal Division, Peking University First Hospital, Beijing 100034, China; (L.-L.X.); (H.Z.)
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment, Peking University, Ministry of Education, Beijing 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100034, China
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3
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Birnbaum R. Rediscovering tandem repeat variation in schizophrenia: challenges and opportunities. Transl Psychiatry 2023; 13:402. [PMID: 38123544 PMCID: PMC10733427 DOI: 10.1038/s41398-023-02689-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023] Open
Abstract
Tandem repeats (TRs) are prevalent throughout the genome, constituting at least 3% of the genome, and often highly polymorphic. The high mutation rate of TRs, which can be orders of magnitude higher than single-nucleotide polymorphisms and indels, indicates that they are likely to make significant contributions to phenotypic variation, yet their contribution to schizophrenia has been largely ignored by recent genome-wide association studies (GWAS). Tandem repeat expansions are already known causative factors for over 50 disorders, while common tandem repeat variation is increasingly being identified as significantly associated with complex disease and gene regulation. The current review summarizes key background concepts of tandem repeat variation as pertains to disease risk, elucidating their potential for schizophrenia association. An overview of next-generation sequencing-based methods that may be applied for TR genome-wide identification is provided, and some key methodological challenges in TR analyses are delineated.
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Affiliation(s)
- Rebecca Birnbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomics Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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4
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Konopka G, Bhaduri A. Functional genomics and systems biology in human neuroscience. Nature 2023; 623:274-282. [PMID: 37938705 DOI: 10.1038/s41586-023-06686-1] [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: 02/23/2023] [Accepted: 09/27/2023] [Indexed: 11/09/2023]
Abstract
Neuroscience research has entered a phase of key discoveries in the realm of neurogenomics owing to strong financial and intellectual support for resource building and tool development. The previous challenge of tissue heterogeneity has been met with the application of techniques that can profile individual cells at scale. Moreover, the ability to perturb genes, gene regulatory elements and neuronal activity in a cell-type-specific manner has been integrated with gene expression studies to uncover the functional underpinnings of the genome at a systems level. Although these insights have necessarily been grounded in model systems, we now have the opportunity to apply these approaches in humans and in human tissue, thanks to advances in human genetics, brain imaging and tissue collection. We acknowledge that there will probably always be limits to the extent to which we can apply the genomic tools developed in model systems to human neuroscience; however, as we describe in this Perspective, the neuroscience field is now primed with an optimal foundation for tackling this ambitious challenge. The application of systems-level network analyses to these datasets will facilitate a deeper appreciation of human neurogenomics that cannot otherwise be achieved from directly observable phenomena.
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Affiliation(s)
- Genevieve Konopka
- Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, USA.
- Peter O'Donnell Jr Brain Institute, UT Southwestern Medical Center, Dallas, TX, USA.
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, CA, USA.
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5
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Taylor JJ, Lin C, Talmasov D, Ferguson MA, Schaper FLWVJ, Jiang J, Goodkind M, Grafman J, Etkin A, Siddiqi SH, Fox MD. A transdiagnostic network for psychiatric illness derived from atrophy and lesions. Nat Hum Behav 2023; 7:420-429. [PMID: 36635585 PMCID: PMC10236501 DOI: 10.1038/s41562-022-01501-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 11/23/2022] [Indexed: 01/13/2023]
Abstract
Psychiatric disorders share neurobiology and frequently co-occur. This neurobiological and clinical overlap highlights opportunities for transdiagnostic treatments. In this study, we used coordinate and lesion network mapping to test for a shared brain network across psychiatric disorders. In our meta-analysis of 193 studies, atrophy coordinates across six psychiatric disorders mapped to a common brain network defined by positive connectivity to anterior cingulate and insula, and by negative connectivity to posterior parietal and lateral occipital cortex. This network was robust to leave-one-diagnosis-out cross-validation and specific to atrophy coordinates from psychiatric versus neurodegenerative disorders (72 studies). In 194 patients with penetrating head trauma, lesion damage to this network correlated with the number of post-lesion psychiatric diagnoses. Neurosurgical ablation targets for psychiatric illness (four targets) also aligned with the network. This convergent brain network for psychiatric illness may partially explain high rates of psychiatric comorbidity and could highlight neuromodulation targets for patients with more than one psychiatric disorder.
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Affiliation(s)
- Joseph J Taylor
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Christopher Lin
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Talmasov
- Departments of Neurology and Psychiatry, Columbia University Medical Center, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Michael A Ferguson
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Center for the Study of World Religions, Harvard Divinity School, Cambridge, MA, USA
| | - Frederic L W V J Schaper
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jing Jiang
- Stead Family Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, IA, USA
- Iowa Neuroscience Institute, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | - Madeleine Goodkind
- Departments of Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
- New Mexico Veterans Affairs Healthcare System, Albuquerque, NM, USA
| | - Jordan Grafman
- Departments of Physical Medicine and Rehabilitation, Neurology, & Psychiatry, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Shirley Ryan Ability Lab, Chicago, IL, USA
| | - Amit Etkin
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Wu Tsai Neurosciences Institute at Stanford, Stanford University School of Medicine, Stanford, CA, USA
- Alto Neuroscience, Los Altos, CA, USA
| | - Shan H Siddiqi
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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6
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Kato H, Kimura H, Kushima I, Takahashi N, Aleksic B, Ozaki N. The genetic architecture of schizophrenia: review of large-scale genetic studies. J Hum Genet 2023; 68:175-182. [PMID: 35821406 DOI: 10.1038/s10038-022-01059-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/12/2022] [Accepted: 06/20/2022] [Indexed: 11/09/2022]
Abstract
Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.
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Affiliation(s)
- Hidekazu Kato
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hiroki Kimura
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan
| | - Nagahide Takahashi
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Branko Aleksic
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan.,Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan
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7
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Koesterich J, An JY, Inoue F, Sohota A, Ahituv N, Sanders SJ, Kreimer A. Characterization of De Novo Promoter Variants in Autism Spectrum Disorder with Massively Parallel Reporter Assays. Int J Mol Sci 2023; 24:3509. [PMID: 36834916 PMCID: PMC9959321 DOI: 10.3390/ijms24043509] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/13/2023] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common, complex, and highly heritable condition with contributions from both common and rare genetic variations. While disruptive, rare variants in protein-coding regions clearly contribute to symptoms, the role of rare non-coding remains unclear. Variants in these regions, including promoters, can alter downstream RNA and protein quantity; however, the functional impacts of specific variants observed in ASD cohorts remain largely uncharacterized. Here, we analyzed 3600 de novo mutations in promoter regions previously identified by whole-genome sequencing of autistic probands and neurotypical siblings to test the hypothesis that mutations in cases have a greater functional impact than those in controls. We leveraged massively parallel reporter assays (MPRAs) to detect transcriptional consequences of these variants in neural progenitor cells and identified 165 functionally high confidence de novo variants (HcDNVs). While these HcDNVs are enriched for markers of active transcription, disruption to transcription factor binding sites, and open chromatin, we did not identify differences in functional impact based on ASD diagnostic status.
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Affiliation(s)
- Justin Koesterich
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ 08854, USA
- Department of Cell and Developmental Biology, Rutgers University, Piscataway, NJ 08854, USA
| | - Joon-Yong An
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neuroscience, University of California, San Francisco, CA 94143, USA
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
- BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul 02841, Republic of Korea
| | - Fumitaka Inoue
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, CA 94158, USA
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto 606-8501, Japan
| | - Ajuni Sohota
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California, San Francisco, CA 94158, USA
| | - Stephan J. Sanders
- Department of Psychiatry and Behavioral Sciences, Weill Institute for Neuroscience, University of California, San Francisco, CA 94143, USA
- Institute for Human Genetics, University of California, San Francisco, CA 94158, USA
- Institute for Developmental and Regenerative Medicine, Old Road Campus, Roosevelt Dr, Headington, Oxford OX3 7TY, UK
| | - Anat Kreimer
- Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ 08854, USA
- Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
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8
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Wang C, Dai J, Qin N, Fan J, Ma H, Chen C, An M, Zhang J, Yan C, Gu Y, Xie Y, He Y, Jiang Y, Zhu M, Song C, Jiang T, Liu J, Zhou J, Wang N, Hua T, Liang S, Wang L, Xu J, Yin R, Chen L, Xu L, Jin G, Lin D, Hu Z, Shen H. Analyses of rare predisposing variants of lung cancer in 6,004 whole genomes in Chinese. Cancer Cell 2022; 40:1223-1239.e6. [PMID: 36113475 DOI: 10.1016/j.ccell.2022.08.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 07/08/2022] [Accepted: 08/15/2022] [Indexed: 12/24/2022]
Abstract
We present the largest whole-genome sequencing (WGS) study of non-small cell lung cancer (NSCLC) to date among 6,004 individuals of Chinese ancestry, coupled with 23,049 individuals genotyped by SNP array. We construct a high-quality haplotype reference panel for imputation and identify 20 common and low-frequency loci (minor allele frequency [MAF] ≥ 0.5%), including five loci that have never been reported before. For rare loss-of-function (LoF) variants (MAF < 0.5%), we identify BRCA2 and 18 other cancer predisposition genes that affect 5.29% of individuals with NSCLC, and 98.91% (181 of 183) of LoF variants have not been linked previously to NSCLC risk. Promoter variants of BRCA2 also have a substantial effect on NSCLC risk, and their prevalence is comparable with BRCA2 LoF variants. The associations are validated in an independent case-control study including 4,410 individuals and a prospective cohort study including 23,826 individuals. Our findings not only provide a high-quality reference panel for future array-based association studies but depict the whole picture of rare pathogenic variants for NSCLC.
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Affiliation(s)
- Cheng Wang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Juncheng Dai
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Na Qin
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Jingyi Fan
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Hongxia Ma
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine (Suzhou Centre), Gusu School, Nanjing Medical University, Suzhou 215002, Jiangsu, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Congcong Chen
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Mingxing An
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Jing Zhang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Caiwang Yan
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yayun Gu
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yuan Xie
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yuanlin He
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Yue Jiang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Meng Zhu
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Ci Song
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Tao Jiang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Jia Liu
- Department of Health Promotion & Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi 214145, Jiangsu, China
| | - Jun Zhou
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Nanxi Wang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Tingting Hua
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Shuang Liang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Lu Wang
- Department of Health Promotion & Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi 214145, Jiangsu, China
| | - Jing Xu
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Rong Yin
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Department of Thoracic Surgery Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210029, Jiangsu, China
| | - Liang Chen
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Lin Xu
- Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Department of Thoracic Surgery Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing 210029, Jiangsu, China
| | - Guangfu Jin
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China
| | - Dongxin Lin
- Department of Etiology and Carcinogenesis, National Cancer Center and Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Zhibin Hu
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine (Suzhou Centre), Gusu School, Nanjing Medical University, Suzhou 215002, Jiangsu, China.
| | - Hongbing Shen
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, Jiangsu, China; Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, China; State Key Laboratory of Reproductive Medicine (Suzhou Centre), Gusu School, Nanjing Medical University, Suzhou 215002, Jiangsu, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China.
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9
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Derks EM, Thorp JG, Gerring ZF. Ten challenges for clinical translation in psychiatric genetics. Nat Genet 2022; 54:1457-1465. [PMID: 36138228 DOI: 10.1038/s41588-022-01174-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 07/27/2022] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies have identified hundreds of robust genetic associations underlying psychiatric disorders and provided important biological insights into disease onset and progression. There is optimism that genetic findings will pave the way to precision psychiatry by facilitating the development of more effective treatments and the identification of groups of patients that these treatments should be targeted toward. However, there are several challenges that must be addressed before genetic findings can be translated into the clinic. In this Perspective, we highlight ten challenges for the field of psychiatric genetics, focused on the robust and generalizable detection of genetic risk factors, improved definition and assessment of psychopathology and achieving better clinical indicators. We discuss recent advancements in the field that will improve the explanatory and predictive power of genetic data and ultimately contribute to improving the management and treatment of patients with a psychiatric disorder.
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Affiliation(s)
- Eske M Derks
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
| | - Jackson G Thorp
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.,Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia
| | - Zachary F Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
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10
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Akingbuwa WA, Hammerschlag AR, Bartels M, Nivard MG, Middeldorp CM. Ultra-rare and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia. Mol Psychiatry 2022; 27:3699-3707. [PMID: 35665764 PMCID: PMC9708595 DOI: 10.1038/s41380-022-01621-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 04/21/2022] [Accepted: 05/11/2022] [Indexed: 02/08/2023]
Abstract
Both common and rare genetic variants (minor allele frequency >1% and <0.1% respectively) have been implicated in the aetiology of schizophrenia. In this study, we integrate single-cell gene expression data with publicly available Genome-Wide Association Study (GWAS) and exome sequenced data in order to investigate in parallel, the enrichment of common and (ultra-)rare variants related to schizophrenia in several functionally relevant gene-sets. Four types of gene-sets were constructed 1) protein-truncating variant (PTV)-intolerant (PI) genes 2) genes expressed in brain cell types and neurons ascertained from mouse and human brain tissue 3) genes defined by synaptic function and location and 4) intersection genes, i.e., PI genes that are expressed in the human and mouse brain cell gene-sets. We show that common as well as ultra-rare schizophrenia-associated variants are overrepresented in PI genes, in excitatory neurons from the prefrontal cortex and hippocampus, medium spiny neurons, and genes enriched for synaptic processes. We also observed stronger enrichment in the intersection genes. Our findings suggest that across the allele frequency spectrum, genes and genetic variants likely to be under stringent selection, and those expressed in particular brain cell types, are involved in the same biological pathways influencing the risk for schizophrenia.
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Affiliation(s)
- Wonuola A Akingbuwa
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centres, Amsterdam, the Netherlands.
| | - Anke R Hammerschlag
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centres, Amsterdam, the Netherlands
- Child Health Research Centre, the University of Queensland, Brisbane, QLD, Australia
| | - Meike Bartels
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam University Medical Centres, Amsterdam, the Netherlands
| | - Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Christel M Middeldorp
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Child Health Research Centre, the University of Queensland, Brisbane, QLD, Australia
- Child and Youth Mental Health Service, Children's Health Queensland Hospital and Health Services, Brisbane, QLD, Australia
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11
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Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders. Pharmaceutics 2022; 14:pharmaceutics14071464. [PMID: 35890359 PMCID: PMC9319329 DOI: 10.3390/pharmaceutics14071464] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 06/30/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Abstract
Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.
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12
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Nehme R, Pietiläinen O, Artomov M, Tegtmeyer M, Valakh V, Lehtonen L, Bell C, Singh T, Trehan A, Sherwood J, Manning D, Peirent E, Malik R, Guss EJ, Hawes D, Beccard A, Bara AM, Hazelbaker DZ, Zuccaro E, Genovese G, Loboda AA, Neumann A, Lilliehook C, Kuismin O, Hamalainen E, Kurki M, Hultman CM, Kähler AK, Paulo JA, Ganna A, Madison J, Cohen B, McPhie D, Adolfsson R, Perlis R, Dolmetsch R, Farhi S, McCarroll S, Hyman S, Neale B, Barrett LE, Harper W, Palotie A, Daly M, Eggan K. The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia. Nat Commun 2022; 13:3690. [PMID: 35760976 PMCID: PMC9237031 DOI: 10.1038/s41467-022-31436-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 06/08/2022] [Indexed: 12/30/2022] Open
Abstract
It is unclear how the 22q11.2 deletion predisposes to psychiatric disease. To study this, we generated induced pluripotent stem cells from deletion carriers and controls and utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Here, we show that upon differentiation into neural progenitor cells, the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including autism. In excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common and rare variants. To understand how the deletion contributed to these changes, we defined the minimal protein-protein interaction network that best explains gene expression alterations. We found that many genes in 22q11.2 interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways. Our findings suggest that the 22q11.2 deletion impacts genes that may converge with psychiatric risk loci to influence disease manifestation in each deletion carrier.
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Affiliation(s)
- Ralda Nehme
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA.
| | - Olli Pietiläinen
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA.
| | - Mykyta Artomov
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Matthew Tegtmeyer
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Vera Valakh
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Leevi Lehtonen
- Institute for Molecular Medicine Finland, University of Helsinki, FI-00014, Helsinki, Finland
| | - Christina Bell
- Department of Cell Biology, Blavatnik Institute of Harvard Medical School, Boston, MA, USA
| | - Tarjinder Singh
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Aditi Trehan
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - John Sherwood
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Danielle Manning
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Emily Peirent
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Rhea Malik
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Ellen J Guss
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Derek Hawes
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Amanda Beccard
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Anne M Bara
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Dane Z Hazelbaker
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Emanuela Zuccaro
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Giulio Genovese
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Alexander A Loboda
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- ITMO University, St. Petersburg, Russia
- Almazov National Medical Research Centre, Saint-Petersburg, Russia
| | - Anna Neumann
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Christina Lilliehook
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Outi Kuismin
- Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- PEDEGO Research Unit, University of Oulu, FI-90014, Oulu, Finland
- Medical Research Center, Oulu University Hospital, FI-90014, Oulu, Finland
- Department of Clinical Genetics, Oulu University Hospital, 90220, Oulu, Finland
| | - Eija Hamalainen
- Institute for Molecular Medicine Finland, University of Helsinki, FI-00014, Helsinki, Finland
| | - Mitja Kurki
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Institute for Molecular Medicine Finland, University of Helsinki, FI-00014, Helsinki, Finland
- Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Christina M Hultman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Anna K Kähler
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, SE-171 77, Stockholm, Sweden
| | - Joao A Paulo
- Department of Cell Biology, Blavatnik Institute of Harvard Medical School, Boston, MA, USA
| | - Andrea Ganna
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Jon Madison
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Bruce Cohen
- Department of Psychiatry, McLean Hospital, Belmont, MA, 02478, USA
| | - Donna McPhie
- Department of Psychiatry, McLean Hospital, Belmont, MA, 02478, USA
| | - Rolf Adolfsson
- Umea University, Faculty of Medicine, Department of Clinical Sciences, Psychiatry, 901 85, Umea, Sweden
| | - Roy Perlis
- Psychiatry Dept., Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Ricardo Dolmetsch
- Novartis Institutes for Biomedical Research, Novartis, Cambridge, MA, 02139, USA
| | - Samouil Farhi
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Steven McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Steven Hyman
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Ben Neale
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
| | - Lindy E Barrett
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Wade Harper
- Department of Cell Biology, Blavatnik Institute of Harvard Medical School, Boston, MA, USA
| | - Aarno Palotie
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Institute for Molecular Medicine Finland, University of Helsinki, FI-00014, Helsinki, Finland
- Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Mark Daly
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Institute for Molecular Medicine Finland, University of Helsinki, FI-00014, Helsinki, Finland
- Psychiatric & Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Kevin Eggan
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, 02142, USA.
- Department of Stem Cell and Regenerative Biology, and the Harvard Institute for Stem Cell Biology, Harvard University, Cambridge, MA, 02138, USA.
- BioMarin Pharmaceutical, San Rafael, CA, 94901, USA.
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13
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Merino D, Fernandez A, Gérard AO, Ben Othman N, Rocher F, Askenazy F, Verstuyft C, Drici MD, Thümmler S. Adverse Drug Reactions of Olanzapine, Clozapine and Loxapine in Children and Youth: A Systematic Pharmacogenetic Review. Pharmaceuticals (Basel) 2022; 15:ph15060749. [PMID: 35745668 PMCID: PMC9230864 DOI: 10.3390/ph15060749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/08/2022] [Accepted: 06/10/2022] [Indexed: 01/27/2023] Open
Abstract
Children and youth treated with antipsychotic drugs (APs) are particularly vulnerable to adverse drug reactions (ADRs) and prone to poor treatment response. In particular, interindividual variations in drug exposure can result from differential metabolism of APs by cytochromes, subject to genetic polymorphism. CYP1A2 is pivotal in the metabolism of the APs olanzapine, clozapine, and loxapine, whose safety profile warrants caution. We aimed to shed some light on the pharmacogenetic profiles possibly associated with these drugs’ ADRs and loss of efficacy in children and youth. We conducted a systematic review relying on four databases, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 recommendations and checklist, with a quality assessment. Our research yielded 32 publications. The most frequent ADRs were weight gain and metabolic syndrome (18; 56.3%), followed by lack of therapeutic effect (8; 25%) and neurological ADRs (7; 21.8%). The overall mean quality score was 11.3/24 (±2.7). In 11 studies (34.3%), genotyping focused on the study of cytochromes. Findings regarding possible associations were sometimes conflicting. Nonetheless, cases of major clinical improvement were fostered by genotyping. Yet, CYP1A2 remains poorly investigated. Further studies are required to improve the assessment of the risk–benefit balance of prescription for children and youth treated with olanzapine, clozapine, and/or loxapine.
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Affiliation(s)
- Diane Merino
- Department of Child and Adolescent Psychiatry, Children’s Hospitals of Nice CHU-Lenval, 06200 Nice, France; (D.M.); (A.F.); (F.A.)
- CoBTek Laboratory, Université Côte d’Azur, 06100 Nice, France
- Department of Pharmacology and Pharmacovigilance Center, University Hospital of Nice, 06000 Nice, France; (A.O.G.); (N.B.O.); (F.R.); (M.-D.D.)
| | - Arnaud Fernandez
- Department of Child and Adolescent Psychiatry, Children’s Hospitals of Nice CHU-Lenval, 06200 Nice, France; (D.M.); (A.F.); (F.A.)
- CoBTek Laboratory, Université Côte d’Azur, 06100 Nice, France
| | - Alexandre O. Gérard
- Department of Pharmacology and Pharmacovigilance Center, University Hospital of Nice, 06000 Nice, France; (A.O.G.); (N.B.O.); (F.R.); (M.-D.D.)
| | - Nouha Ben Othman
- Department of Pharmacology and Pharmacovigilance Center, University Hospital of Nice, 06000 Nice, France; (A.O.G.); (N.B.O.); (F.R.); (M.-D.D.)
| | - Fanny Rocher
- Department of Pharmacology and Pharmacovigilance Center, University Hospital of Nice, 06000 Nice, France; (A.O.G.); (N.B.O.); (F.R.); (M.-D.D.)
| | - Florence Askenazy
- Department of Child and Adolescent Psychiatry, Children’s Hospitals of Nice CHU-Lenval, 06200 Nice, France; (D.M.); (A.F.); (F.A.)
- CoBTek Laboratory, Université Côte d’Azur, 06100 Nice, France
| | - Céline Verstuyft
- Service de Génétique Moléculaire, Pharmacogénétique et Hormonologie, Hôpital Bicêtre, Groupe Hospitalier Paris Saclay, AP–HP, 94270 Le Kremlin-Bicêtre, France;
- CESP/UMR-S1178, Inserm, Université Paris-Sud, 92290 Paris, France
| | - Milou-Daniel Drici
- Department of Pharmacology and Pharmacovigilance Center, University Hospital of Nice, 06000 Nice, France; (A.O.G.); (N.B.O.); (F.R.); (M.-D.D.)
| | - Susanne Thümmler
- Department of Child and Adolescent Psychiatry, Children’s Hospitals of Nice CHU-Lenval, 06200 Nice, France; (D.M.); (A.F.); (F.A.)
- CoBTek Laboratory, Université Côte d’Azur, 06100 Nice, France
- Correspondence:
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14
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Toyama M, Takasaki Y, Branko A, Kimura H, Kato H, Nawa Y, Kushima I, Ishizuka K, Shimamura T, Ogi T, Ozaki N. Exome sequencing of Japanese schizophrenia multiplex families supports the involvement of calcium ion channels. PLoS One 2022; 17:e0268321. [PMID: 35536790 PMCID: PMC9089874 DOI: 10.1371/journal.pone.0268321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Accepted: 04/27/2022] [Indexed: 11/18/2022] Open
Abstract
Background Most sequencing studies of schizophrenia (SCZ) have focused on de novo genetic variants due to interpretability. However, investigating shared rare variants among patients in the same multiplex family is also important. Relatively large-scale analyses of SCZ multiplex families have been done in Caucasian populations, but whether detected variants are also pathogenic in the Japanese population is unclear because of ethnic differences in rare variants. Materials and methods We performed whole-exome sequencing (WES) of 14 Japanese SCZ multiplex families. After quality control and filtering, we identified rare variants shared among affected persons within the same family. A gene ontology (GO) analysis was performed to identify gene categories possibly affected by these candidate variants. Results We found 530 variants in 486 genes as potential candidate variants from the 14 SCZ multiplex families examined. The GO analysis demonstrated significant enrichment in calcium channel activity. Conclusion This study provides supporting evidence that calcium ion channel activity is involved in SCZ. WES of multiplex families is a potential means of identifying disease-associated rare variants for SCZ.
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Affiliation(s)
- Miho Toyama
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yuto Takasaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Aleksic Branko
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Hiroki Kimura
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- * E-mail:
| | - Hidekazu Kato
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yoshihiro Nawa
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Itaru Kushima
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Kanako Ishizuka
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Teppei Shimamura
- Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Tomoo Ogi
- Department of Genetics, Research Institute of Environmental Medicine, Nagoya University, Nagoya, Japan
- Department of Human Genetics and Molecular Biology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
- Institute for Glyco-core Research (iGCORE), Nagoya University, Nagoya, Japan
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15
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Tran S, Prober DA. Validation of Candidate Sleep Disorder Risk Genes Using Zebrafish. Front Mol Neurosci 2022; 15:873520. [PMID: 35465097 PMCID: PMC9021570 DOI: 10.3389/fnmol.2022.873520] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/14/2022] [Indexed: 12/31/2022] Open
Abstract
Sleep disorders and chronic sleep disturbances are common and are associated with cardio-metabolic diseases and neuropsychiatric disorders. Several genetic pathways and neuronal mechanisms that regulate sleep have been described in animal models, but the genes underlying human sleep variation and sleep disorders are largely unknown. Identifying these genes is essential in order to develop effective therapies for sleep disorders and their associated comorbidities. To address this unmet health problem, genome-wide association studies (GWAS) have identified numerous genetic variants associated with human sleep traits and sleep disorders. However, in most cases, it is unclear which gene is responsible for a sleep phenotype that is associated with a genetic variant. As a result, it is necessary to experimentally validate candidate genes identified by GWAS using an animal model. Rodents are ill-suited for this endeavor due to their poor amenability to high-throughput sleep assays and the high costs associated with generating, maintaining, and testing large numbers of mutant lines. Zebrafish (Danio rerio), an alternative vertebrate model for studying sleep, allows for the rapid and cost-effective generation of mutant lines using the CRISPR/Cas9 system. Numerous zebrafish mutant lines can then be tested in parallel using high-throughput behavioral assays to identify genes whose loss affects sleep. This process identifies a gene associated with each GWAS hit that is likely responsible for the human sleep phenotype. This strategy is a powerful complement to GWAS approaches and holds great promise to identify the genetic basis for common human sleep disorders.
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16
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Lago SG, Bahn S. The druggable schizophrenia genome: from repurposing opportunities to unexplored drug targets. NPJ Genom Med 2022; 7:25. [PMID: 35338153 PMCID: PMC8956592 DOI: 10.1038/s41525-022-00290-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 02/04/2022] [Indexed: 12/04/2022] Open
Abstract
There have been no new drugs for the treatment of schizophrenia in several decades and treatment resistance represents a major unmet clinical need. The drugs that exist are based on serendipitous clinical observations rather than an evidence-based understanding of disease pathophysiology. In the present review, we address these bottlenecks by integrating common, rare, and expression-related schizophrenia risk genes with knowledge of the druggability of the human genome as a whole. We highlight novel drug repurposing opportunities, clinical trial candidates which are supported by genetic evidence, and unexplored therapeutic opportunities in the lesser-known regions of the schizophrenia genome. By identifying translational gaps and opportunities across the schizophrenia disease space, we discuss a framework for translating increasingly well-powered genetic association studies into personalized treatments for schizophrenia and initiating the vital task of characterizing clinically relevant drug targets in underexplored regions of the human genome.
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Affiliation(s)
- Santiago G Lago
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
| | - Sabine Bahn
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
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17
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Abstract
BACKGROUND To date, besides genome-wide association studies, a variety of other genetic analyses (e.g. polygenic risk scores, whole-exome sequencing and whole-genome sequencing) have been conducted, and a large amount of data has been gathered for investigating the involvement of common, rare and very rare types of DNA sequence variants in bipolar disorder. Also, non-invasive neuroimaging methods can be used to quantify changes in brain structure and function in patients with bipolar disorder. AIMS To provide a comprehensive assessment of genetic findings associated with bipolar disorder, based on the evaluation of different genomic approaches and neuroimaging studies. METHOD We conducted a PubMed search of all relevant literatures from the beginning to the present, by querying related search strings. RESULTS ANK3, CACNA1C, SYNE1, ODZ4 and TRANK1 are five genes that have been replicated as key gene candidates in bipolar disorder pathophysiology, through the investigated studies. The percentage of phenotypic variance explained by the identified variants is small (approximately 4.7%). Bipolar disorder polygenic risk scores are associated with other psychiatric phenotypes. The ENIGMA-BD studies show a replicable pattern of lower cortical thickness, altered white matter integrity and smaller subcortical volumes in bipolar disorder. CONCLUSIONS The low amount of explained phenotypic variance highlights the need for further large-scale investigations, especially among non-European populations, to achieve a more complete understanding of the genetic architecture of bipolar disorder and the missing heritability. Combining neuroimaging data with genetic data in large-scale studies might help researchers acquire a better knowledge of the engaged brain regions in bipolar disorder.
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Affiliation(s)
- Mojtaba Oraki Kohshour
- Institute of Psychiatric Phenomics and Genomics, University Hospital LMU Munich, Germany; and Department of Immunology, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Iran
| | - Sergi Papiol
- Institute of Psychiatric Phenomics and Genomics, University Hospital LMU Munich, Germany; and Department of Psychiatry and Psychotherapy, University Hospital LMU Munich, Germany
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, USA
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics, University Hospital LMU Munich, Germany; and Department of Psychiatry and Behavioral Sciences, SUNY Upstate Medical University, USA
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18
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Giangrande EJ, Weber RS, Turkheimer E. What Do We Know About the Genetic Architecture of Psychopathology? Annu Rev Clin Psychol 2022; 18:19-42. [DOI: 10.1146/annurev-clinpsy-081219-091234] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In the second half of the twentieth century, twin and family studies established beyond a reasonable doubt that all forms of psychopathology are substantially heritable and highly polygenic. These conclusions were simultaneously an important theoretical advance and a difficult methodological obstacle, as it became clear that heritability is universal and undifferentiated across forms of psychopathology, and the radical polygenicity of genetic effects limits the biological insight provided by genetically informed studies at the phenotypic level. The paradigm-shifting revolution brought on by the Human Genome Project has recapitulated the great methodological promise and the profound theoretical difficulties of the twin study era. We review these issues using the rubric of genetic architecture, which we define as a search for specific genetic insight that adds to the general conclusion that psychopathology is heritable and polygenic. Although significant problems remain, we see many promising avenues for progress. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- Evan J. Giangrande
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
| | - Ramona S. Weber
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
| | - Eric Turkheimer
- Department of Psychology, University of Virginia, Charlottesville, Virginia, USA
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19
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Vickers A, Tewary M, Laddach A, Poletti M, Salameti V, Fraternali F, Danovi D, Watt FM. Plating human iPSC lines on micropatterned substrates reveals role for ITGB1 nsSNV in endoderm formation. Stem Cell Reports 2021; 16:2628-2641. [PMID: 34678211 PMCID: PMC8581167 DOI: 10.1016/j.stemcr.2021.09.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 09/22/2021] [Accepted: 09/23/2021] [Indexed: 12/03/2022] Open
Abstract
Quantitative analysis of human induced pluripotent stem cell (iPSC) lines from healthy donors is a powerful tool for uncovering the relationship between genetic variants and cellular behavior. We previously identified rare, deleterious non-synonymous single nucleotide variants (nsSNVs) in cell adhesion genes that are associated with outlier iPSC phenotypes in the pluripotent state. Here, we generated micropatterned colonies of iPSCs to test whether nsSNVs influence patterning of radially ordered germ layers. Using a custom-built image analysis pipeline, we quantified the differentiation phenotypes of 13 iPSC lines that harbor nsSNVs in genes related to cell adhesion or germ layer development. All iPSC lines differentiated into the three germ layers; however, there was donor-specific variation in germ layer patterning. We identified one line that presented an outlier phenotype of expanded endodermal differentiation, which was associated with a nsSNV in ITGB1. Our study establishes a platform for investigating the impact of nsSNVs on differentiation.
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Affiliation(s)
- Alice Vickers
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Mukul Tewary
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Anna Laddach
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Great Maze Pond, London SE1 9RT, UK; Development and Homeostasis of the Nervous System Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Martina Poletti
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK; Quadram Institute, Norwich Research Park, Norwich NR4 7UZ, UK
| | - Vasiliki Salameti
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK
| | - Franca Fraternali
- Randall Centre for Cell and Molecular Biophysics, King's College London, New Hunt's House, Great Maze Pond, London SE1 9RT, UK
| | - Davide Danovi
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK; bit.bio, Babraham Research Campus, The Dorothy Hodgkin Building, Cambridge CB22 3FH, UK
| | - Fiona M Watt
- Centre for Stem Cells and Regenerative Medicine, King's College London, Guy's Hospital, Floor 28, Tower Wing, Great Maze Pond, London SE1 9RT, UK.
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20
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Franks PW, Melén E, Friedman M, Sundström J, Kockum I, Klareskog L, Almqvist C, Bergen SE, Czene K, Hägg S, Hall P, Johnell K, Malarstig A, Catrina A, Hagström H, Benson M, Gustav Smith J, Gomez MF, Orho-Melander M, Jacobsson B, Halfvarson J, Repsilber D, Oresic M, Jern C, Melin B, Ohlsson C, Fall T, Rönnblom L, Wadelius M, Nordmark G, Johansson Å, Rosenquist R, Sullivan PF. Technological readiness and implementation of genomic-driven precision medicine for complex diseases. J Intern Med 2021; 290:602-620. [PMID: 34213793 DOI: 10.1111/joim.13330] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 03/21/2021] [Accepted: 04/12/2021] [Indexed: 12/20/2022]
Abstract
The fields of human genetics and genomics have generated considerable knowledge about the mechanistic basis of many diseases. Genomic approaches to diagnosis, prognostication, prevention and treatment - genomic-driven precision medicine (GDPM) - may help optimize medical practice. Here, we provide a comprehensive review of GDPM of complex diseases across major medical specialties. We focus on technological readiness: how rapidly a test can be implemented into health care. Although these areas of medicine are diverse, key similarities exist across almost all areas. Many medical areas have, within their standards of care, at least one GDPM test for a genetic variant of strong effect that aids the identification/diagnosis of a more homogeneous subset within a larger disease group or identifies a subset with different therapeutic requirements. However, for almost all complex diseases, the majority of patients do not carry established single-gene mutations with large effects. Thus, research is underway that seeks to determine the polygenic basis of many complex diseases. Nevertheless, most complex diseases are caused by the interplay of genetic, behavioural and environmental risk factors, which will likely necessitate models for prediction and diagnosis that incorporate genetic and non-genetic data.
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Affiliation(s)
- P W Franks
- From the, Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Malmö, Sweden.,Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - E Melén
- Department of Clinical Science and Education Södersjukhuset, Karolinska Institutet, Stockholm, Sweden
| | - M Friedman
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - J Sundström
- Department of Cardiology, Akademiska Sjukhuset, Uppsala, Sweden.,George Institute for Global Health, Camperdown, NSW, Australia.,Medical Sciences, Uppsala University, Uppsala, Sweden
| | - I Kockum
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.,Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - L Klareskog
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.,Department of Rheumatology, Karolinska Institutet, Stockholm, Sweden
| | - C Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - S E Bergen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - K Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - S Hägg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - P Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Oncology, Södersjukhuset, Stockholm, Sweden
| | - K Johnell
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - A Malarstig
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Pfizer, Worldwide Research and Development, Stockholm, Sweden
| | - A Catrina
- Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - H Hagström
- Department of Medicine, Karolinska Institutet, Stockholm, Sweden.,Division of Hepatology, Department of Upper GI, Karolinska University Hospital, Stockholm, Sweden
| | - M Benson
- Department of Pediatrics, Linkopings Universitet, Linkoping, Sweden.,Division of Ear, Nose and Throat Diseases, Department of Clinical Sciences, Intervention and Technology (CLINTEC), Karolinska Institutet, Stockholm, Sweden
| | - J Gustav Smith
- Department of Cardiology and Wallenberg Center for Molecular Medicine, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden.,Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - M F Gomez
- From the, Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Malmö, Sweden
| | - M Orho-Melander
- From the, Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Malmö, Sweden
| | - B Jacobsson
- Division of Health Data and Digitalisation, Norwegian Institute of Public Health, Genetics and Bioinformatics, Oslo, Norway.,Department of Obstetrics and Gynecology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Obstetrics and Gynecology, Institute of Clinical Sciences, University of Gothenburg, Gothenburg, Sweden
| | - J Halfvarson
- School of Medical Sciences, Örebro University, Örebro, Sweden
| | - D Repsilber
- Functional Bioinformatics, Örebro University, Örebro, Sweden
| | - M Oresic
- School of Medical Sciences, Örebro University, Örebro, Sweden.,Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, FI, Finland
| | - C Jern
- Department of Clinical Genetics and Genomics, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.,Department of Laboratory Medicine, Institute of Biomedicine, University of Gothenburg, Gothenburg, Sweden
| | - B Melin
- Department of Radiation Sciences, Oncology, Umeå Universitet, Umeå, Sweden
| | - C Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Osteoporosis Centre, CBAR, University of Gothenburg, Gothenburg, Sweden.,Department of Drug Treatment, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - T Fall
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
| | - L Rönnblom
- Department of Medical Sciences, Rheumatology & Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - M Wadelius
- Department of Medical Sciences, Clinical Pharmacogenomics & Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - G Nordmark
- Department of Medical Sciences, Rheumatology & Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Å Johansson
- Institute for Immunology, Genetics, and Pathology, Uppsala University, Uppsala, Sweden
| | - R Rosenquist
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - P F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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21
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Iwaki H, Leonard HL, Makarious MB, Bookman M, Landin B, Vismer D, Casey B, Gibbs JR, Hernandez DG, Blauwendraat C, Vitale D, Song Y, Kumar D, Dalgard CL, Sadeghi M, Dong X, Misquitta L, Scholz SW, Scherzer CR, Nalls MA, Biswas S, Singleton AB. Accelerating Medicines Partnership: Parkinson's Disease. Genetic Resource. Mov Disord 2021; 36:1795-1804. [PMID: 33960523 PMCID: PMC8453903 DOI: 10.1002/mds.28549] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 01/20/2021] [Accepted: 02/11/2021] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Whole-genome sequencing data are available from several large studies across a variety of diseases and traits. However, massive storage and computation resources are required to use these data, and to achieve sufficient power for discoveries, harmonization of multiple cohorts is critical. OBJECTIVES The Accelerating Medicines Partnership Parkinson's Disease program has developed a research platform for Parkinson's disease (PD) that integrates the storage and analysis of whole-genome sequencing data, RNA expression data, and clinical data, harmonized across multiple cohort studies. METHODS The version 1 release contains whole-genome sequencing data derived from 3941 participants from 4 cohorts. Samples underwent joint genotyping by the TOPMed Freeze 9 Variant Calling Pipeline. We performed descriptive analyses of these whole-genome sequencing data using the Accelerating Medicines Partnership Parkinson's Disease platform. RESULTS The clinical diagnosis of participants in version 1 release includes 2005 idiopathic PD patients, 963 healthy controls, 64 prodromal subjects, 62 clinically diagnosed PD subjects without evidence of dopamine deficit, and 705 participants of genetically enriched cohorts carrying PD risk-associated GBA variants or LRRK2 variants, of whom 304 were affected. We did not observe significant enrichment of pathogenic variants in the idiopathic PD group, but the polygenic risk score was higher in PD both in nongenetically enriched cohorts and genetically enriched cohorts. The population analysis showed a correlation between genetically enriched cohorts and Ashkenazi Jewish ancestry. CONCLUSIONS We describe the genetic component of the Accelerating Medicines Partnership Parkinson's Disease platform, a solution to democratize data access and analysis for the PD research community. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Hirotaka Iwaki
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Hampton L. Leonard
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Mary B. Makarious
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | | | | | - Bradford Casey
- The Michael J. Fox Foundation for Parkinson's ResearchNew YorkNew YorkUSA
| | - J. Raphael Gibbs
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Dena G. Hernandez
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | - Daniel Vitale
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | - Yeajin Song
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | - Clifton L. Dalgard
- Department of Anatomy, Physiology & GeneticsUniformed Services University of the Health SciencesBethesdaMarylandUSA
- The American Genome CenterUniformed Services University of the Health SciencesBethesdaMarylandUSA
| | - Mahdiar Sadeghi
- SanofiFraminghamMassachusettsUSA
- Northeastern UniversityBostonMassachusettsUSA
| | - Xianjun Dong
- Harvard Medical SchoolBrigham and Women's HospitalBostonMassachusettsUSA
| | | | - Sonja W. Scholz
- National Institute of Neurological Disorders and StrokeBethesdaMarylandUSA
- Department of NeurologyJohns Hopkins UniversityBaltimoreMarylandUSA
| | | | - Mike A. Nalls
- Data Tecnica InternationalGlen EchoMarylandUSA
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
| | | | - Andrew B. Singleton
- Center for Alzheimer's and Related DementiasNational Institute on AgingBethesdaMarylandUSA
- Laboratory of NeurogeneticsNational Institute on AgingBethesdaMarylandUSA
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22
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Choi L, An JY. Genetic architecture of autism spectrum disorder: Lessons from large-scale genomic studies. Neurosci Biobehav Rev 2021; 128:244-257. [PMID: 34166716 DOI: 10.1016/j.neubiorev.2021.06.028] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 06/17/2021] [Accepted: 06/17/2021] [Indexed: 12/20/2022]
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a strong genetic component. Recently developed genomic technologies, including microarray and next-generation sequencing (NGS), have enabled researchers to genetic analyses aimed at identifying genetic variations associated with ASD and to elucidate the genetic architecture of the disorder. Large-scale microarray, exome sequencing analyses, and robust statistical methods have resulted in successful gene discovery and identification of high-confidence ASD genes from among de novo and inherited variants. Efforts have been made to understand the genetic architecture of ASD using whole-genome sequencing and genome-wide association studies aimed at identifying noncoding mutations and common variants associated with ASD. In addition, the development of systems biology approaches has resulted in the integration of genetic findings with functional genomic datasets, thereby providing a unique insight into the functional convergence of ASD risk genes and their neurobiology. In this review, we summarize the latest findings of ASD genetic studies involving large cohorts and discuss their implications in ASD neurobiology and in clinical practice.
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Affiliation(s)
- Leejee Choi
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul, 02841, Republic of Korea; Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea
| | - Joon-Yong An
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul, 02841, Republic of Korea; Department of Integrated Biomedical and Life Science, Korea University, Seoul, 02841, Republic of Korea; Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, 02841, Republic of Korea; BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, 02841, Republic of Korea.
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23
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Werling DM, Pochareddy S, Choi J, An JY, Sheppard B, Peng M, Li Z, Dastmalchi C, Santpere G, Sousa AMM, Tebbenkamp ATN, Kaur N, Gulden FO, Breen MS, Liang L, Gilson MC, Zhao X, Dong S, Klei L, Cicek AE, Buxbaum JD, Adle-Biassette H, Thomas JL, Aldinger KA, O'Day DR, Glass IA, Zaitlen NA, Talkowski ME, Roeder K, State MW, Devlin B, Sanders SJ, Sestan N. Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex. Cell Rep 2021; 31:107489. [PMID: 32268104 PMCID: PMC7295160 DOI: 10.1016/j.celrep.2020.03.053] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 11/06/2019] [Accepted: 03/16/2020] [Indexed: 02/08/2023] Open
Abstract
Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common variants that alter gene expression (expression quantitative trait loci [eQTLs]) constantly across development or predominantly during prenatal or postnatal stages. Both "constant" and "temporal-predominant" eQTLs are enriched for loci associated with neuropsychiatric traits and disorders and colocalize with specific variants. Expression levels of more than 12,000 genes rise or fall in a concerted late-fetal transition, with the transitional genes enriched for cell-type-specific genes and neuropsychiatric risk loci, underscoring the importance of cataloging developmental trajectories in understanding cortical physiology and pathology.
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Affiliation(s)
- Donna M Werling
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Sirisha Pochareddy
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Jinmyung Choi
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Joon-Yong An
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Integrated Biomedical and Life Science, Korea University, Seoul 02841, Republic of Korea; School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul 02841, Republic of Korea
| | - Brooke Sheppard
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Minshi Peng
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Zhen Li
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Neurosciences, University of California, San Diego, San Diego, CA 92093, USA
| | - Claudia Dastmalchi
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Gabriel Santpere
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Neurogenomics Group, Research Programme on Biomedical Informatics, Hospital del Mar Medical Research Institute, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - André M M Sousa
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Andrew T N Tebbenkamp
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Navjot Kaur
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Forrest O Gulden
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Michael S Breen
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lindsay Liang
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Michael C Gilson
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Xuefang Zhao
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA 02142, USA
| | - Shan Dong
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - A Ercument Cicek
- Department of Computer Engineering, Bilkent University, Ankara 06800, Turkey; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Joseph D Buxbaum
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Homa Adle-Biassette
- Department of Pathology, Lariboisière Hospital, APHP, Biobank BB-0033-00064, and Université de Paris, 75006 Paris, France
| | - Jean-Leon Thomas
- Department of Neurology, Yale University School of Medicine, New Haven, CT 06511, USA; UMRS1127, Sorbonne Université, Institut du Cerveau et de la Moelle Épinière, 75013 Paris, France
| | - Kimberly A Aldinger
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, WA 98101, USA; Brotman Baty Institute for Precision Medicine, Seattle, WA 98195, USA
| | - Diana R O'Day
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - Ian A Glass
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - Noah A Zaitlen
- Department of Medicine, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Michael E Talkowski
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Harvard Medical School, Boston, MA 02115, USA; Program in Medical and Population Genetics and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA 02142, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Matthew W State
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Stephan J Sanders
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Comparative Medicine, Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, CT 06510, USA; Program in Cellular Neuroscience, Neurodegeneration, and Repair and Yale Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA.
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24
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Park JH, Lim SW, Myung W, Park I, Jang HJ, Kim S, Lee MS, Chang HS, Yum D, Suh YL, Kim JW, Kim DK. Whole-genome sequencing reveals KRTAP1-1 as a novel genetic variant associated with antidepressant treatment outcomes. Sci Rep 2021; 11:4552. [PMID: 33633223 PMCID: PMC7907209 DOI: 10.1038/s41598-021-83887-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/08/2021] [Indexed: 12/30/2022] Open
Abstract
Achieving remission following initial antidepressant therapy in patients with major depressive disorder (MDD) is an important clinical result. Making predictions based on genetic markers holds promise for improving the remission rate. However, genetic variants found in previous genetic studies do not provide robust evidence to aid pharmacogenetic decision-making in clinical settings. Thus, the objective of this study was to perform whole-genome sequencing (WGS) using genomic DNA to identify genetic variants associated with the treatment outcomes of selective serotonin reuptake inhibitors (SSRIs). We performed WGS on 100 patients with MDD who were treated with escitalopram (discovery set: 36 remitted and 64 non-remitted). The findings were applied to an additional 553 patients with MDD who were treated with SSRIs (replication set: 185 remitted and 368 non-remitted). A novel loss-of-function variant (rs3213755) in keratin-associated protein 1-1 (KRTAP1-1) was identified in this study. This rs3213755 variant was significantly associated with remission following antidepressant treatment (p = 0.0184, OR 3.09, 95% confidence interval [CI] 1.22-7.80 in the discovery set; p = 0.00269, OR 1.75, 95% CI 1.22-2.53 in the replication set). Moreover, the expression level of KRTAP1-1 in surgically resected human temporal lobe samples was significantly associated with the rs3213755 genotype. WGS studies on a larger sample size in various ethnic groups are needed to investigate genetic markers useful in the pharmacogenetic prediction of remission following antidepressant treatment.
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Affiliation(s)
- Jong-Ho Park
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea.,Clinical Genomics Center, Samsung Medical Center, Seoul, Korea
| | - Shinn-Won Lim
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Woojae Myung
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Inho Park
- Precision Medicine Center, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hyeok-Jae Jang
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea
| | - Seonwoo Kim
- Statistics and Data Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea
| | - Min-Soo Lee
- Department of Psychiatry, College of Medicine, Korea University, Seoul, Korea
| | - Hun Soo Chang
- Soonchunhyang Medical Institute, College of Medicine, Soonchunhyang University, Asan, Korea
| | - DongHo Yum
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Yeon-Lim Suh
- Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jong-Won Kim
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Korea. .,Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Korea.
| | - Doh Kwan Kim
- Department of Psychiatry, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 135-710, Korea.
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25
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Toraman B, Bilginer SÇ, Hesapçıoğlu ST, Göker Z, Soykam HO, Ergüner B, Dinçer T, Yıldız G, Ünsal S, Kasap BK, Kandil S, Kalay E. Finding underlying genetic mechanisms of two patients with autism spectrum disorder carrying familial apparently balanced chromosomal translocations. J Gene Med 2021; 23:e3322. [PMID: 33591602 DOI: 10.1002/jgm.3322] [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: 11/11/2020] [Revised: 01/26/2021] [Accepted: 02/14/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Genetic etiologies of autism spectrum disorders (ASD) are complex, and the genetic factors identified so far are very diverse. In complex genetic diseases such as ASD, de novo or inherited chromosomal abnormalities are valuable findings for researchers with respect to identifying the underlying genetic risk factors. With gene mapping studies on these chromosomal abnormalities, dozens of genes have been associated with ASD and other neurodevelopmental genetic diseases. In the present study, we aimed to idenitfy the causative genetic factors in patients with ASD who have an apparently balanced chromosomal translocation in their karyotypes. METHODS For mapping the broken genes as a result of chromosomal translocations, we performed whole genome DNA sequencing. Chromosomal breakpoints and large DNA copy number variations (CNV) were determined after genome alignment. Identified CNVs and single nucleotide variations (SNV) were evaluated with VCF-BED intersect and Gemini tools, respectively. A targeted resequencing approach was performed on the JMJD1C gene in all of the ASD cohorts (220 patients). For molecular modeling, we used a homology modeling approach via the SWISS-MODEL. RESULTS We found that there was no contribution of the broken genes or regulator DNA sequences to ASD, whereas the SNVs on the JMJD1C, CNKSR2 and DDX11 genes were the most convincing genetic risk factors for underlying ASD phenotypes. CONCLUSIONS Genetic etiologies of ASD should be analyzed comprehensively by taking into account of the all chromosomal structural abnormalities and de novo or inherited CNV/SNVs with all possible inheritance patterns.
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Affiliation(s)
- Bayram Toraman
- Faculty of Medicine Department of Medical Biology, Karadeniz Technical University, Trabzon, Turkey
| | - Samiye Çilem Bilginer
- Faculty of Medicine Child and Adolescent Psychiatry Department, Karadeniz Technical University, Trabzon, Turkey
| | - Selma Tural Hesapçıoğlu
- Child and Adolescent Psychiatry Department, Yildirim Beyazit University Faculty of Medicine, Ankara, Turkey
| | - Zeynep Göker
- Ministry of Health Ankara City Hospital, Child-Adolescent and Mental Health, Cankaya, Ankara, Turkey
| | - Hüseyin Okan Soykam
- Department of Biostatistics and Bioinformatics, Acibadem Mehmet Ali Aydinlar University, Institute of Health Sciences, İstanbul, Turkey
| | - Bekir Ergüner
- Sabanci University Faculty of Engineering and Natural Sciences, Molecular Biology, Genetics and Bio engineering, Istanbul, Turkey
| | - Tuba Dinçer
- Faculty of Medicine Department of Medical Biology, Karadeniz Technical University, Trabzon, Turkey
| | - Gökhan Yıldız
- Faculty of Medicine Department of Medical Biology, Karadeniz Technical University, Trabzon, Turkey
| | - Serbülent Ünsal
- Graduate School of Health Science, Biostatistics and Medical Informatics Department, PhD Candidate, Karadeniz Technical University, Trabzon, Turkey
| | - Burak Kaan Kasap
- Graduate School of Health Science, Medical Biology Department, PhD Candidate, Karadeniz Technical University, Trabzon, Turkey
| | - Sema Kandil
- Faculty of Medicine Child and Adolescent Psychiatry Department, Karadeniz Technical University, Trabzon, Turkey
| | - Ersan Kalay
- Faculty of Medicine Department of Medical Biology, Karadeniz Technical University, Trabzon, Turkey
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26
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Lázaro-Muñoz G, Torgerson L, Pereira S. Return of results in a global survey of psychiatric genetics researchers: practices, attitudes, and knowledge. Genet Med 2021; 23:298-305. [PMID: 33033403 PMCID: PMC8374879 DOI: 10.1038/s41436-020-00986-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/30/2022] Open
Abstract
PURPOSE Patient-participants in psychiatric genetics research may be at an increased risk for negative psychosocial impacts related to the return of genetic research results. Examining psychiatric genetics researchers' return of results practices and perspectives can aid the development of empirically informed and ethically sound guidelines. METHODS A survey of 407 psychiatric genetics researchers from 39 countries was conducted to examine current return of results practices, attitudes, and knowledge. RESULTS Most respondents (61%) reported that their studies generated medically relevant genomic findings. Although 24% have returned results to individual participants, 52% of those involved in decisions about return of results plan to return or continue to return results. Respondents supported offering "medically actionable" results related to psychiatric disorders (82%), and the majority agreed non-medically actionable risks for Huntington (71%) and Alzheimer disease (64%) should be offered. About half (49%) of respondents supported offering reliable polygenic risk scores for psychiatric conditions. Despite plans to return, only 14% of researchers agreed there are adequate guidelines for returning results, and 59% rated their knowledge about how to manage the process for returning results as poor. CONCLUSION Psychiatric genetics researchers support returning a wide range of results to patient-participants, but they lack adequate knowledge and guidelines.
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Affiliation(s)
- Gabriel Lázaro-Muñoz
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA.
| | - Laura Torgerson
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA
| | - Stacey Pereira
- Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, TX, USA
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27
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Prasad A, Bhargava H, Gupta A, Shukla N, Rajagopal S, Gupta S, Sharma A, Valadi J, Nigam V, Suravajhala P. Next Generation Sequencing. Adv Bioinformatics 2021. [DOI: 10.1007/978-981-33-6191-1_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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28
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Searles Quick VB, Wang B, State MW. Leveraging large genomic datasets to illuminate the pathobiology of autism spectrum disorders. Neuropsychopharmacology 2021; 46:55-69. [PMID: 32668441 PMCID: PMC7688655 DOI: 10.1038/s41386-020-0768-y] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Revised: 06/26/2020] [Accepted: 07/06/2020] [Indexed: 12/15/2022]
Abstract
"Big data" approaches in the form of large-scale human genomic studies have led to striking advances in autism spectrum disorder (ASD) genetics. Similar to many other psychiatric syndromes, advances in genotyping technology, allowing for inexpensive genome-wide assays, has confirmed the contribution of polygenic inheritance involving common alleles of small effect, a handful of which have now been definitively identified. However, the past decade of gene discovery in ASD has been most notable for the application, in large family-based cohorts, of high-density microarray studies of submicroscopic chromosomal structure as well as high-throughput DNA sequencing-leading to the identification of an increasingly long list of risk regions and genes disrupted by rare, de novo germline mutations of large effect. This genomic architecture offers particular advantages for the illumination of biological mechanisms but also presents distinctive challenges. While the tremendous locus heterogeneity and functional pleiotropy associated with the more than 100 identified ASD-risk genes and regions is daunting, a growing armamentarium of comprehensive, large, foundational -omics databases, across species and capturing developmental trajectories, are increasingly contributing to a deeper understanding of ASD pathology.
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Affiliation(s)
- Veronica B Searles Quick
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Belinda Wang
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Matthew W State
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, USA.
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29
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Kimura H, Mori D, Aleksic B, Ozaki N. Elucidation of molecular pathogenesis and drug development for psychiatric disorders from rare disease-susceptibility variants. Neurosci Res 2020; 170:24-31. [PMID: 33316300 DOI: 10.1016/j.neures.2020.11.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 11/19/2020] [Accepted: 11/26/2020] [Indexed: 10/22/2022]
Abstract
Recent rapid progress in genome analysis and large-scale consortia has made it possible to discover variants with a variety of allele frequencies and effect sizes associated with psychiatric disorders. Among psychiatric disorder-susceptibility variants, rare variants with large effect sizes detected by sequencing analysis or array comparative genomic hybridization would be particularly useful for elucidating pathophysiology by developing disease models, such as genome-edited mouse or induced pluripotent stem cells. In the last decade, investigations of rare variants with large effect size have revealed an important role of neurodevelopment in the pathogenesis of psychiatric disorders. In future research, integration of recent evidence concerning the contribution of the immune system or gut microbiota will enhance our understanding of psychiatric disorders and facilitate novel drug development.
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Affiliation(s)
- Hiroki Kimura
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - Daisuke Mori
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan; Brain & Mind Research Center, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Branko Aleksic
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Norio Ozaki
- Department of Psychiatry, Nagoya University Graduate School of Medicine, Nagoya, Japan; Brain & Mind Research Center, Nagoya University Graduate School of Medicine, Nagoya, Japan; Medical Genomics Center, Nagoya University Hospital, Nagoya, Japan
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30
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Mansolf M, Vreeker A, Reise SP, Freimer NB, Glahn DC, Gur RE, Moore TM, Pato CN, Pato MT, Palotie A, Holm M, Suvisaari J, Partonen T, Kieseppä T, Paunio T, Boks M, Kahn R, Ophoff RA, Bearden CE, Loohuis LO, Teshiba T, deGeorge D, Bilder RM. Extensions of Multiple-Group Item Response Theory Alignment: Application to Psychiatric Phenotypes in an International Genomics Consortium. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 2020; 80:870-909. [PMID: 32855563 PMCID: PMC7425327 DOI: 10.1177/0013164419897307] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Large-scale studies spanning diverse project sites, populations, languages, and measurements are increasingly important to relate psychological to biological variables. National and international consortia already are collecting and executing mega-analyses on aggregated data from individuals, with different measures on each person. In this research, we show that Asparouhov and Muthén's alignment method can be adapted to align data from disparate item sets and response formats. We argue that with these adaptations, the alignment method is well suited for combining data across multiple sites even when they use different measurement instruments. The approach is illustrated using data from the Whole Genome Sequencing in Psychiatric Disorders consortium and a real-data-based simulation is used to verify accurate parameter recovery. Factor alignment appears to increase precision of measurement and validity of scores with respect to external criteria. The resulting parameter estimates may further inform development of more effective and efficient methods to assess the same constructs in prospectively designed studies.
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Affiliation(s)
- Maxwell Mansolf
- University of California, Los Angeles, Los Angeles, CA, USA
- Maxwell Mansolf, Department of Psychology, University of California, Los Angeles, 502 Portola Plaza, Los Angeles, CA 90095, USA.
| | | | | | | | | | | | | | - Carlos N. Pato
- SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Michele T. Pato
- SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | - Aarno Palotie
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard University, Boston, MA, USA
- University of Helsinki, Helsinki, Finland
| | - Minna Holm
- National Institute for Health and Welfare, Finland, Helsinki
| | - Jaana Suvisaari
- National Institute for Health and Welfare, Finland, Helsinki
| | - Timo Partonen
- National Institute for Health and Welfare, Finland, Helsinki
| | | | - Tiina Paunio
- University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Finland, Helsinki
| | - Marco Boks
- University Medical Center Utrecht, Utrecht, Netherlands
| | - René Kahn
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Roel A. Ophoff
- University of California, Los Angeles, Los Angeles, CA, USA
| | | | | | - Terri Teshiba
- University of California, Los Angeles, Los Angeles, CA, USA
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31
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A comparative study of single nucleotide variant detection performance using three massively parallel sequencing methods. PLoS One 2020; 15:e0239850. [PMID: 32986766 PMCID: PMC7521702 DOI: 10.1371/journal.pone.0239850] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 09/14/2020] [Indexed: 12/22/2022] Open
Abstract
Massively parallel sequencing (MPS) has revolutionised clinical genetics and research within human genetics by enabling the detection of variants in multiple genes in several samples at the same time. Today, multiple approaches for MPS of DNA are available, including targeted gene sequencing (TGS) panels, whole exome sequencing (WES), and whole genome sequencing (WGS). As MPS is becoming an integrated part of the work in genetic laboratories, it is important to investigate the variant detection performance of the various MPS methods. We compared the results of single nucleotide variant (SNV) detection of three MPS methods: WGS, WES, and HaloPlex target enrichment sequencing (HES) using matched DNA of 10 individuals. The detection performance was investigated in 100 genes associated with cardiomyopathies and channelopathies. The results showed that WGS overall performed better than those of WES and HES. WGS had a more uniform and widespread coverage of the investigated regions compared to WES and HES, which both had a right-skewed coverage distribution and difficulties in covering regions and genes with high GC-content. WGS and WES showed roughly the same high sensitivities for detection of SNVs, whereas HES showed a lower sensitivity due to a higher number of false negative results.
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32
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Polygenic inheritance, GWAS, polygenic risk scores, and the search for functional variants. Proc Natl Acad Sci U S A 2020; 117:18924-18933. [PMID: 32753378 DOI: 10.1073/pnas.2005634117] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The reconciliation between Mendelian inheritance of discrete traits and the genetically based correlation between relatives for quantitative traits was Fisher's infinitesimal model of a large number of genetic variants, each with very small effects, whose causal effects could not be individually identified. The development of genome-wide genetic association studies (GWAS) raised the hope that it would be possible to identify single polymorphic variants with identifiable functional effects on complex traits. It soon became clear that, with larger and larger GWAS on more and more complex traits, most of the significant associations had such small effects, that identifying their individual functional effects was essentially hopeless. Polygenic risk scores that provide an overall estimate of the genetic propensity to a trait at the individual level have been developed using GWAS data. These provide useful identification of groups of individuals with substantially increased risks, which can lead to recommendations of medical treatments or behavioral modifications to reduce risks. However, each such claim will require extensive investigation to justify its practical application. The challenge now is to use limited genetic association studies to find individually identifiable variants of significant functional effect that can help to understand the molecular basis of complex diseases and traits, and so lead to improved disease prevention and treatment. This can best be achieved by 1) the study of rare variants, often chosen by careful candidate assessment, and 2) the careful choice of phenotypes, often extremes of a quantitative variable, or traits with relatively high heritability.
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33
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Closing in on Mechanisms of Open Neural Tube Defects. Trends Neurosci 2020; 43:519-532. [PMID: 32423763 DOI: 10.1016/j.tins.2020.04.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 04/02/2020] [Accepted: 04/22/2020] [Indexed: 11/24/2022]
Abstract
Neural tube defects (NTDs) represent a failure of the neural plate to complete the developmental transition to a neural tube. NTDs are the most common birth anomaly of the CNS. Following mandatory folic acid fortification of dietary grains, a dramatic reduction in the incidence of NTDs was observed in areas where the policy was implemented, yet the genetic drivers of NTDs in humans, and the mechanisms by which folic acid prevents disease, remain disputed. Here, we discuss current understanding of human NTD genetics, recent advances regarding potential mechanisms by which folic acid might modify risk through effects on the epigenome and transcriptome, and new approaches to study refined phenotypes for a greater appreciation of the developmental and genetic causes of NTDs.
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34
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Ross PJ, Mok RSF, Smith BS, Rodrigues DC, Mufteev M, Scherer SW, Ellis J. Modeling neuronal consequences of autism-associated gene regulatory variants with human induced pluripotent stem cells. Mol Autism 2020; 11:33. [PMID: 32398033 PMCID: PMC7218542 DOI: 10.1186/s13229-020-00333-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/03/2020] [Indexed: 12/27/2022] Open
Abstract
Genetic factors contribute to the development of autism spectrum disorder (ASD), and although non-protein-coding regions of the genome are being increasingly implicated in ASD, the functional consequences of these variants remain largely uncharacterized. Induced pluripotent stem cells (iPSCs) enable the production of personalized neurons that are genetically matched to people with ASD and can therefore be used to directly test the effects of genomic variation on neuronal gene expression, synapse function, and connectivity. The combined use of human pluripotent stem cells with genome editing to introduce or correct specific variants has proved to be a powerful approach for exploring the functional consequences of ASD-associated variants in protein-coding genes and, more recently, long non-coding RNAs (lncRNAs). Here, we review recent studies that implicate lncRNAs, other non-coding mutations, and regulatory variants in ASD susceptibility. We also discuss experimental design considerations for using iPSCs and genome editing to study the role of the non-protein-coding genome in ASD.
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Affiliation(s)
- P Joel Ross
- Department of Biology, University of Prince Edward Island, Charlottetown, PE, Canada.
| | - Rebecca S F Mok
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Brandon S Smith
- Department of Biology, University of Prince Edward Island, Charlottetown, PE, Canada
| | - Deivid C Rodrigues
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada
| | - Marat Mufteev
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Stephen W Scherer
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.,Genetics & Genome Biology Program and The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada.,McLaughlin Centre, University of Toronto, Toronto, ON, Canada
| | - James Ellis
- Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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35
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Kamitaki N, Sekar A, Handsaker RE, de Rivera H, Tooley K, Morris DL, Taylor KE, Whelan CW, Tombleson P, Loohuis LMO, Boehnke M, Kimberly RP, Kaufman KM, Harley JB, Langefeld CD, Seidman CE, Pato MT, Pato CN, Ophoff RA, Graham RR, Criswell LA, Vyse TJ, McCarroll SA. Complement genes contribute sex-biased vulnerability in diverse disorders. Nature 2020; 582:577-581. [PMID: 32499649 PMCID: PMC7319891 DOI: 10.1038/s41586-020-2277-x] [Citation(s) in RCA: 133] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Accepted: 02/28/2020] [Indexed: 12/18/2022]
Abstract
Many common illnesses differentially affect men and women for unknown reasons. The autoimmune diseases lupus and Sjögren’s syndrome affect nine times more women than men1, whereas schizophrenia affects men more frequently and severely2. All three illnesses have their strongest common genetic associations in the Major Histocompatibility Complex (MHC) locus, an association that in lupus and Sjögren’s syndrome has long been thought to arise from alleles of the human leukocyte antigen (HLA) genes at that locus3–6. Here we show that the complement component 4 (C4) genes, which are also in the MHC locus and were recently found to increase risk for schizophrenia7, generate 7-fold variation in risk for lupus (95% CI: 5.88–8.61; p < 10−117 in total) and 16-fold variation in risk for Sjögren’s syndrome (95% CI: 8.59–30.89; p < 10−23 in total) among individuals with common C4 genotypes, with C4A protecting more strongly than C4B in both illnesses. The same alleles that increase risk for schizophrenia greatly reduced risk for lupus and Sjögren’s syndrome. In all three illnesses, C4 alleles acted more strongly in men than in women: common combinations of C4A and C4B generated 14-fold variation in risk for lupus, 31-fold variation in risk for Sjögren’s syndrome, and 1.7-fold variation in schizophrenia risk among men (vs. 6-fold, 15-fold, and 1.26-fold among women respectively). At a protein level, both C4 and its effector C3 were present at greater levels in men than women in cerebrospinal fluid (p < 10−5 for both C4 and C3) and plasma8,9 among adults ages 20–50, corresponding to the ages of differential disease vulnerability. Sex differences in complement protein levels may help explain the larger effects of C4 alleles in men, women’s greater risk of SLE and Sjögren’s, and men’s greater vulnerability in schizophrenia. These results implicate the complement system as a source of sexual dimorphism in vulnerability to diverse illnesses.
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Affiliation(s)
- Nolan Kamitaki
- Department of Genetics, Harvard Medical School, Boston, MA, USA. .,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Aswin Sekar
- Department of Genetics, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Robert E Handsaker
- Department of Genetics, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Heather de Rivera
- Department of Genetics, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Katherine Tooley
- Department of Genetics, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David L Morris
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Kimberly E Taylor
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, UCSF School of Medicine, San Francisco, CA, USA
| | - Christopher W Whelan
- Department of Genetics, Harvard Medical School, Boston, MA, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip Tombleson
- Department of Medical and Molecular Genetics, King's College London, London, UK
| | - Loes M Olde Loohuis
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | | | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Robert P Kimberly
- Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kenneth M Kaufman
- Center for Autoimmune Genomics and Etiology (CAGE), Department of Pediatrics, Cincinnati Children's Medical Center & University of Cincinnati and the US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA
| | - John B Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Department of Pediatrics, Cincinnati Children's Medical Center & University of Cincinnati and the US Department of Veterans Affairs Medical Center, Cincinnati, OH, USA
| | - Carl D Langefeld
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Christine E Seidman
- Department of Genetics, Harvard Medical School, Boston, MA, USA.,Howard Hughes Medical Institute, Chevy Chase, MD, USA.,Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | | | | | - Roel A Ophoff
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.,Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
| | | | - Lindsey A Criswell
- Rosalind Russell/Ephraim P. Engleman Rheumatology Research Center, Division of Rheumatology, UCSF School of Medicine, San Francisco, CA, USA
| | - Timothy J Vyse
- Department of Medical and Molecular Genetics, King's College London, London, UK.
| | - Steven A McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA, USA. .,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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36
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Rees E, Owen MJ. Translating insights from neuropsychiatric genetics and genomics for precision psychiatry. Genome Med 2020; 12:43. [PMID: 32349784 PMCID: PMC7189552 DOI: 10.1186/s13073-020-00734-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 04/03/2020] [Indexed: 12/30/2022] Open
Abstract
The primary aim of precision medicine is to tailor healthcare more closely to the needs of individual patients. This requires progress in two areas: the development of more precise treatments and the ability to identify patients or groups of patients in the clinic for whom such treatments are likely to be the most effective. There is widespread optimism that advances in genomics will facilitate both of these endeavors. It can be argued that of all medical specialties psychiatry has most to gain in these respects, given its current reliance on syndromic diagnoses, the minimal foundation of existing mechanistic knowledge, and the substantial heritability of psychiatric phenotypes. Here, we review recent advances in psychiatric genomics and assess the likely impact of these findings on attempts to develop precision psychiatry. Emerging findings indicate a high degree of polygenicity and that genetic risk maps poorly onto the diagnostic categories used in the clinic. The highly polygenic and pleiotropic nature of psychiatric genetics will impact attempts to use genomic data for prediction and risk stratification, and also poses substantial challenges for conventional approaches to gaining biological insights from genetic findings. While there are many challenges to overcome, genomics is building an empirical platform upon which psychiatry can now progress towards better understanding of disease mechanisms, better treatments, and better ways of targeting treatments to the patients most likely to benefit, thus paving the way for precision psychiatry.
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Affiliation(s)
- Elliott Rees
- grid.5600.30000 0001 0807 5670MRC Centre for Neuropsychiatric Genetics and Genomics, Neuroscience and Mental Health Research Institute and Division of Psychological Medicine and Clinical Neuroscience, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
| | - Michael J. Owen
- grid.5600.30000 0001 0807 5670MRC Centre for Neuropsychiatric Genetics and Genomics, Neuroscience and Mental Health Research Institute and Division of Psychological Medicine and Clinical Neuroscience, Cardiff University, Hadyn Ellis Building, Maindy Road, Cardiff, CF24 4HQ UK
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37
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Halvorsen M, Huh R, Oskolkov N, Wen J, Netotea S, Giusti-Rodriguez P, Karlsson R, Bryois J, Nystedt B, Ameur A, Kähler AK, Ancalade N, Farrell M, Crowley JJ, Li Y, Magnusson PKE, Gyllensten U, Hultman CM, Sullivan PF, Szatkiewicz JP. Increased burden of ultra-rare structural variants localizing to boundaries of topologically associated domains in schizophrenia. Nat Commun 2020; 11:1842. [PMID: 32296054 PMCID: PMC7160146 DOI: 10.1038/s41467-020-15707-w] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 03/24/2020] [Indexed: 01/13/2023] Open
Abstract
Despite considerable progress in schizophrenia genetics, most findings have been for large rare structural variants and common variants in well-imputed regions with few genes implicated from exome sequencing. Whole genome sequencing (WGS) can potentially provide a more complete enumeration of etiological genetic variation apart from the exome and regions of high linkage disequilibrium. We analyze high-coverage WGS data from 1162 Swedish schizophrenia cases and 936 ancestry-matched population controls. Our main objective is to evaluate the contribution to schizophrenia etiology from a variety of genetic variants accessible to WGS but not by previous technologies. Our results suggest that ultra-rare structural variants that affect the boundaries of topologically associated domains (TADs) increase risk for schizophrenia. Alterations in TAD boundaries may lead to dysregulation of gene expression. Future mechanistic studies will be needed to determine the precise functional effects of these variants on biology. Common variants identified by large-scale genomewide association studies cannot account fully account for the heritability of schizophrenia (SCZ). Here, the authors report high-coverage whole-genome sequencing of 1162 SCZ cases and 936 controls and explore the contribution of different types of variants to SCZ.
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Affiliation(s)
- Matthew Halvorsen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Ruth Huh
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Nikolay Oskolkov
- Department of Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Lund University, 22362, Lund, Sweden
| | - Jia Wen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Sergiu Netotea
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, 41258, Göteborg, Sweden
| | | | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Julien Bryois
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Björn Nystedt
- Department of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Uppsala University, 75237, Uppsala, Sweden
| | - Adam Ameur
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185, Uppsala, Sweden
| | - Anna K Kähler
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden
| | - NaEshia Ancalade
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Martilias Farrell
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - James J Crowley
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Clinical Neuroscience, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Biostatistics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Ulf Gyllensten
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, 75185, Uppsala, Sweden
| | - Christina M Hultman
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden
| | - Patrick F Sullivan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA. .,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 17177, Stockholm, Sweden. .,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27599, USA.
| | - Jin P Szatkiewicz
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA. .,Department of Psychiatry, University of North Carolina, Chapel Hill, NC, 27599, USA.
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38
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Merikangas KR, Merikangas AK. Harnessing Progress in Psychiatric Genetics to Advance Population Mental Health. Am J Public Health 2020; 109:S171-S175. [PMID: 31242010 DOI: 10.2105/ajph.2019.304948] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Advances in genomics and neuroscience have ushered in unprecedented opportunities to increase our understanding of the biological underpinnings of mental disorders, yet there has been limited progress in translating knowledge on genetic risk factors to reduce the burden of these conditions in the population. We describe the challenges and opportunities afforded by the growth of large-scale population health databases, progress in genomics, and collaborative efforts in epidemiology and neuroscience to develop informed population-wide interventions for mental disorders. Future progress is likely to benefit from the following efforts: expansion of large collaborative studies of mental disorders to include more systematically ascertained multiethnic samples from biobanks and registries, harmonization of phenotypic characterization in registry and population samples to extend clinical diagnosis to transdiagnostic concepts, systematic investigation of the influences of both specific and nonspecific environmental factors that may combine with genetic susceptibility to confer increased risk of specific mental disorders, and implementation of study designs that can inform gene-environment interactions. Such data can ultimately be combined to develop comprehensive models of risks of, interventions for, and outcomes of mental disorders. With its focus on phenotypic characterization, sampling, study designs, and analytic methods, epidemiology will be central to progress in translating genomics to public health.
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Affiliation(s)
- Kathleen Ries Merikangas
- Kathleen Ries Merikangas is with the Genetic Epidemiology Research Branch, Division of Intramural Research Program, National Institute of Mental Health, Bethesda, MD. Alison K. Merikangas is with the Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Alison K Merikangas
- Kathleen Ries Merikangas is with the Genetic Epidemiology Research Branch, Division of Intramural Research Program, National Institute of Mental Health, Bethesda, MD. Alison K. Merikangas is with the Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA
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39
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Nehme R, Barrett LE. Using human pluripotent stem cell models to study autism in the era of big data. Mol Autism 2020; 11:21. [PMID: 32293529 PMCID: PMC7087382 DOI: 10.1186/s13229-020-00322-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Accepted: 02/21/2020] [Indexed: 12/18/2022] Open
Abstract
Advances in human pluripotent stem cell (hPSC) biology coupled with protocols to generate diverse brain cell types in vitro have provided neuroscientists with opportunities to dissect basic and disease mechanisms in increasingly relevant cellular substrates. At the same time, large data collections and analyses have facilitated unprecedented insights into autism genetics, normal human genetic variation, and the molecular landscape of the developing human brain. While such insights have enabled the investigation of key mechanistic questions in autism, they also highlight important limitations associated with the use of existing hPSC models. In this review, we discuss four such issues which influence the efficacy of hPSC models for studying autism, including (i) sources of variance, (ii) scale and format of study design, (iii) divergence from the human brain in vivo, and (iv) regulatory policies and compliance governing the use of hPSCs. Moreover, we advocate for a set of immediate and long-term priorities to address these issues and to accelerate the generation and reproducibility of data in order to facilitate future fundamental as well as therapeutic discoveries.
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Affiliation(s)
- Ralda Nehme
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Lindy E Barrett
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA.
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40
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Senthil G, Lehner T. Schizophrenia research in the era of Team Science and big data. Schizophr Res 2020; 217:13-16. [PMID: 31324441 DOI: 10.1016/j.schres.2019.07.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 07/03/2019] [Accepted: 07/06/2019] [Indexed: 12/21/2022]
Abstract
The last decade has provided new insights into the genetic architecture of schizophrenia. For the first time researchers have identified genetic factors conferring risk that can be mapped to tissue and cell specific perturbations of the molecular machinery underlying disease processes. However, it has also become clear that attempts to gain mechanistic insights into disease processes that span multiple levels of biological complexity, from genes to cells to circuits to behaviors, are inherently difficult and will require interdisciplinary efforts. Here we discuss the opportunities and pitfalls of developing causal models of SCZ that will lead to novel treatments and prevention strategies. We make the case that integrated large-scale Team Science efforts will be necessary to achieve this goal and that a systems level approach that includes genetics and integrative modelling is needed.
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Affiliation(s)
- Geetha Senthil
- National Institute of Mental Health, United States of America
| | - Thomas Lehner
- National Institute of Mental Health, United States of America.
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41
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Sul JH, Service SK, Huang AY, Ramensky V, Hwang SG, Teshiba TM, Park Y, Ori APS, Zhang Z, Mullins N, Olde Loohuis LM, Fears SC, Araya C, Araya X, Spesny M, Bejarano J, Ramirez M, Castrillón G, Gomez-Makhinson J, Lopez MC, Montoya G, Montoya CP, Aldana I, Escobar JI, Ospina-Duque J, Kremeyer B, Bedoya G, Ruiz-Linares A, Cantor RM, Molina J, Coppola G, Ophoff RA, Macaya G, Lopez-Jaramillo C, Reus V, Bearden CE, Sabatti C, Freimer NB. Contribution of common and rare variants to bipolar disorder susceptibility in extended pedigrees from population isolates. Transl Psychiatry 2020; 10:74. [PMID: 32094344 PMCID: PMC7039961 DOI: 10.1038/s41398-020-0758-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 09/24/2019] [Accepted: 11/04/2019] [Indexed: 12/13/2022] Open
Abstract
Current evidence from case/control studies indicates that genetic risk for psychiatric disorders derives primarily from numerous common variants, each with a small phenotypic impact. The literature describing apparent segregation of bipolar disorder (BP) in numerous multigenerational pedigrees suggests that, in such families, large-effect inherited variants might play a greater role. To identify roles of rare and common variants on BP, we conducted genetic analyses in 26 Colombia and Costa Rica pedigrees ascertained for bipolar disorder 1 (BP1), the most severe and heritable form of BP. In these pedigrees, we performed microarray SNP genotyping of 838 individuals and high-coverage whole-genome sequencing of 449 individuals. We compared polygenic risk scores (PRS), estimated using the latest BP1 genome-wide association study (GWAS) summary statistics, between BP1 individuals and related controls. We also evaluated whether BP1 individuals had a higher burden of rare deleterious single-nucleotide variants (SNVs) and rare copy number variants (CNVs) in a set of genes related to BP1. We found that compared with unaffected relatives, BP1 individuals had higher PRS estimated from BP1 GWAS statistics (P = 0.001 ~ 0.007) and displayed modest increase in burdens of rare deleterious SNVs (P = 0.047) and rare CNVs (P = 0.002 ~ 0.033) in genes related to BP1. We did not observe rare variants segregating in the pedigrees. These results suggest that small-to-moderate effect rare and common variants are more likely to contribute to BP1 risk in these extended pedigrees than a few large-effect rare variants.
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Affiliation(s)
- Jae Hoon Sul
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Susan K. Service
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA
| | - Alden Y. Huang
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Vasily Ramensky
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA ,Federal State Institution “National Medical Research Center for Preventive Medicine” of the Ministry of Healthcare of the Russian Federation. Petroverigskiy lane 10, Moscow, 101990 Russia
| | - Sun-Goo Hwang
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Terri M. Teshiba
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA
| | - YoungJun Park
- grid.19006.3e0000 0000 9632 6718Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Anil P. S. Ori
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA
| | - Zhongyang Zhang
- grid.59734.3c0000 0001 0670 2351Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Niamh Mullins
- grid.13097.3c0000 0001 2322 6764King’s College London, Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, Denmark Hill, London, SE5 8AF UK ,grid.59734.3c0000 0001 0670 2351Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Loes M. Olde Loohuis
- grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA
| | - Scott C. Fears
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Carmen Araya
- grid.412889.e0000 0004 1937 0706Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, 11501 Costa Rica
| | - Xinia Araya
- grid.412889.e0000 0004 1937 0706Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, 11501 Costa Rica
| | - Mitzi Spesny
- Division of Pediatric Pulmonology, Hospital Nacional de Nin ~os, San Jose, Costa Rica
| | - Julio Bejarano
- grid.412889.e0000 0004 1937 0706Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, 11501 Costa Rica
| | - Margarita Ramirez
- grid.412889.e0000 0004 1937 0706Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, 11501 Costa Rica
| | - Gabriel Castrillón
- Instituto de Alta Tecnologia Medica, Medellín, Antioquia, Colombia ,grid.15474.330000 0004 0477 2438Department of Neuroradiology, Klinikum rechts der Isar, TUM, Munich, Germany
| | - Juliana Gomez-Makhinson
- grid.412881.60000 0000 8882 5269Grupo de Investigación en Psiquiatría (Research Group in Psychiatry; GIPSI), Departamento de Psiquiatría Facultad de Medicina, Universidad de Antioquia, Medellín, 050011 Colombia
| | - Maria C. Lopez
- grid.412881.60000 0000 8882 5269Grupo de Investigación en Psiquiatría (Research Group in Psychiatry; GIPSI), Departamento de Psiquiatría Facultad de Medicina, Universidad de Antioquia, Medellín, 050011 Colombia
| | - Gabriel Montoya
- grid.412881.60000 0000 8882 5269Grupo de Investigación en Psiquiatría (Research Group in Psychiatry; GIPSI), Departamento de Psiquiatría Facultad de Medicina, Universidad de Antioquia, Medellín, 050011 Colombia
| | - Claudia P. Montoya
- grid.412881.60000 0000 8882 5269Grupo de Investigación en Psiquiatría (Research Group in Psychiatry; GIPSI), Departamento de Psiquiatría Facultad de Medicina, Universidad de Antioquia, Medellín, 050011 Colombia
| | - Ileana Aldana
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Javier I. Escobar
- grid.430387.b0000 0004 1936 8796Department of Psychiatry and Family Medicine, Rutgers-Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 08901 USA
| | - Jorge Ospina-Duque
- grid.412881.60000 0000 8882 5269Grupo de Investigación en Psiquiatría (Research Group in Psychiatry; GIPSI), Departamento de Psiquiatría Facultad de Medicina, Universidad de Antioquia, Medellín, 050011 Colombia
| | - Barbara Kremeyer
- grid.83440.3b0000000121901201Department of Genetics, Evolution and Environment, University College London, London, WC1E 6BT UK
| | - Gabriel Bedoya
- grid.412881.60000 0000 8882 5269Laboratory of Molecular Genetics, Institute of Biology, University of Antioquia, Medellín, 050010 Colombia
| | - Andres Ruiz-Linares
- grid.8547.e0000 0001 0125 2443Ministry of Education Key Laboratory of Contemporary Anthropology and Collaborative Innovation Center of Genetics and Development, Fudan University, Shanghai, 200438 China ,grid.5399.60000 0001 2176 4817Aix Marseille Univ, CNRS, EFS, ADES, Marseille, France
| | - Rita M. Cantor
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095 USA
| | | | - Giovanni Coppola
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Roel A. Ophoff
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095 USA ,grid.7692.a0000000090126352Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | - Gabriel Macaya
- grid.412889.e0000 0004 1937 0706Cell and Molecular Biology Research Center, Universidad de Costa Rica, San Pedro de Montes de Oca, San José, 11501 Costa Rica
| | - Carlos Lopez-Jaramillo
- grid.412881.60000 0000 8882 5269Grupo de Investigación en Psiquiatría (Research Group in Psychiatry; GIPSI), Departamento de Psiquiatría Facultad de Medicina, Universidad de Antioquia, Medellín, 050011 Colombia ,Mood Disorders Program, Hospital San Vicente Fundacion, Medellín, 050011 Colombia
| | - Victor Reus
- grid.266102.10000 0001 2297 6811Department of Psychiatry and UCSF Weill Institute for Neurosciences, University of California, San Francisco, CA 94143 USA
| | - Carrie E. Bearden
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Psychology, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Chiara Sabatti
- grid.168010.e0000000419368956Department of Health Research and Policy, Division of Biostatistics, Stanford University, Stanford, CA 94305 USA
| | - Nelson B. Freimer
- grid.19006.3e0000 0000 9632 6718Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA 90095 USA ,grid.19006.3e0000 0000 9632 6718Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, University California Los Angeles, Los Angeles, CA USA ,grid.19006.3e0000 0000 9632 6718Department of Human Genetics, University of California Los Angeles, Los Angeles, CA 90095 USA
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42
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Solis-Lemus CR, Fischer ST, Todor A, Liu C, Leslie EJ, Cutler DJ, Ghosh D, Epstein MP. Leveraging Family History in Case-Control Analyses of Rare Variation. Genetics 2020; 214:295-303. [PMID: 31843756 PMCID: PMC7017020 DOI: 10.1534/genetics.119.302846] [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: 10/26/2019] [Accepted: 12/10/2019] [Indexed: 11/18/2022] Open
Abstract
Standard methods for case-control association studies of rare variation often treat disease outcome as a dichotomous phenotype. However, both theoretical and experimental studies have demonstrated that subjects with a family history of disease can be enriched for risk variation relative to subjects without such history. Assuming family history information is available, this observation motivates the idea of replacing the standard dichotomous outcome variable used in case-control studies with a more informative ordinal outcome variable that distinguishes controls (0), sporadic cases (1), and cases with a family history (2), with the expectation that we should observe increasing number of risk variants with increasing category of the ordinal variable. To leverage this expectation, we propose a novel rare-variant association test that incorporates family history information based on our previous GAMuT framework for rare-variant association testing of multivariate phenotypes. We use simulated data to show that, when family history information is available, our new method outperforms standard rare-variant association methods, like burden and SKAT tests, that ignore family history. We further illustrate our method using a rare-variant study of cleft lip and palate.
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Affiliation(s)
| | - S Taylor Fischer
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, 30329 Georgia
| | - Andrei Todor
- Department of Human Genetics, Emory University, Atlanta, 30030 Georgia
| | - Cuining Liu
- Department of Biostatistics and Informatics, University of Colorado, Aurora, 80045 Colorado
| | | | - David J Cutler
- Department of Human Genetics, Emory University, Atlanta, 30030 Georgia
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, University of Colorado, Aurora, 80045 Colorado
| | - Michael P Epstein
- Department of Human Genetics, Emory University, Atlanta, 30030 Georgia
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43
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Zhang F, Flickinger M, Taliun SAG, Abecasis GR, Scott LJ, McCaroll SA, Pato CN, Boehnke M, Kang HM. Ancestry-agnostic estimation of DNA sample contamination from sequence reads. Genome Res 2020; 30:185-194. [PMID: 31980570 PMCID: PMC7050530 DOI: 10.1101/gr.246934.118] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 03/11/2019] [Indexed: 11/24/2022]
Abstract
Detecting and estimating DNA sample contamination are important steps to ensure high-quality genotype calls and reliable downstream analysis. Existing methods rely on population allele frequency information for accurate estimation of contamination rates. Correctly specifying population allele frequencies for each individual in early stage of sequence analysis is impractical or even impossible for large-scale sequencing centers that simultaneously process samples from multiple studies across diverse populations. On the other hand, incorrectly specified allele frequencies may result in substantial bias in estimated contamination rates. For example, we observed that existing methods often fail to identify 10% contaminated samples at a typical 3% contamination exclusion threshold when genetic ancestry is misspecified. Such an incomplete screening of contaminated samples substantially inflates the estimated rate of genotyping errors even in deeply sequenced genomes and exomes. We propose a robust statistical method that accurately estimates DNA contamination and is agnostic to genetic ancestry of the intended or contaminating sample. Our method integrates the estimation of genetic ancestry and DNA contamination in a unified likelihood framework by leveraging individual-specific allele frequencies projected from reference genotypes onto principal component coordinates. Our method can also be used for estimating genetic ancestries, similar to LASER or TRACE, but simultaneously accounting for potential contamination. We demonstrate that our method robustly estimates contamination rates and genetic ancestries across populations and contamination scenarios. We further demonstrate that, in the presence of contamination, genetic ancestry inference can be substantially biased with existing methods that ignore contamination, while our method corrects for such biases.
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Affiliation(s)
- Fan Zhang
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan 48109-2218, USA
| | - Matthew Flickinger
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109-2029, USA
| | - Sarah A Gagliano Taliun
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109-2029, USA
| | | | - Gonçalo R Abecasis
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109-2029, USA
| | - Laura J Scott
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109-2029, USA
| | - Steven A McCaroll
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA.,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA
| | - Carlos N Pato
- SUNY Downstate Medical Center, Brooklyn, New York 11203, USA
| | - Michael Boehnke
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109-2029, USA
| | - Hyun Min Kang
- Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan 48109-2029, USA.,Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109-2029, USA
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44
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Li M, Shen L, Chen L, Huai C, Huang H, Wu X, Yang C, Ma J, Zhou W, Du H, Fan L, He L, Wan C, Qin S. Novel genetic susceptibility loci identified by family based whole exome sequencing in Han Chinese schizophrenia patients. Transl Psychiatry 2020; 10:5. [PMID: 32066673 PMCID: PMC7026419 DOI: 10.1038/s41398-020-0708-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 12/07/2019] [Accepted: 12/19/2019] [Indexed: 12/14/2022] Open
Abstract
Schizophrenia (SCZ) is a highly heritable psychiatric disorder that affects approximately 1% of population around the world. However, early relevant studies did not reach clear conclusions of the genetic mechanisms of SCZ, suggesting that additional susceptibility loci that exert significant influence on SCZ are yet to be revealed. So, in order to identify novel susceptibility genes that account for the genetic risk of SCZ, we performed a systematic family-based study using whole exome sequencing (WES) in 65 Han Chinese families. The analysis of 51 SCZ trios with both unaffected parents identified 22 exonic and 1 splice-site de novo mutations (DNMs) on a total of 23 genes, and showed that 12 genes carried rare protein-altering compound heterozygous mutations in more than one trio. In addition, we identified 26 exonic or splice-site single nucleotide polymorphisms (SNPs) on 18 genes with nominal significance (P < 5 × 10-4) using a transmission disequilibrium test (TDT) in all the families. Moreover, TDT result confirmed a SCZ susceptibility locus on 3p21.1, encompassing the multigenetic region NEK4-ITIH1-ITIH3-ITIH4. Through several different strategies to predict the potential pathogenic genes in silico, we revealed 4 previous discovered susceptibility genes (TSNARE1, PBRM1, STAB1 and OLIG2) and 4 novel susceptibility loci (PSEN1, TLR5, MGAT5B and SSPO) in Han Chinese SCZ patients. In summary, we identified a list of putative candidate genes for SCZ using a family-based WES approach, thus improving our understanding of the pathology of SCZ and providing critical clues to future functional validation.
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Affiliation(s)
- Mo Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Lu Shen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Luan Chen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Cong Huai
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Xi Wu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Chao Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Jingsong Ma
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Wei Zhou
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Huihui Du
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China
| | - Lingzi Fan
- Psychiatric Hospital of Zhumadian City, Henan, China
| | - Lin He
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.
- The Third Affiliated Hospital, Guangzhou Medical University, Guangdong, China.
| | - Chunling Wan
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.
| | - Shengying Qin
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China.
- Collaborative Innovation Center, Jining Medical University, Shandong, China.
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45
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Raffield LM, Iyengar AK, Wang B, Gaynor SM, Spracklen CN, Zhong X, Kowalski MH, Salimi S, Polfus LM, Benjamin EJ, Bis JC, Bowler R, Cade BE, Choi WJ, Comellas AP, Correa A, Cruz P, Doddapaneni H, Durda P, Gogarten SM, Jain D, Kim RW, Kral BG, Lange LA, Larson MG, Laurie C, Lee J, Lee S, Lewis JP, Metcalf GA, Mitchell BD, Momin Z, Muzny DM, Pankratz N, Park CJ, Rich SS, Rotter JI, Ryan K, Seo D, Tracy RP, Viaud-Martinez KA, Yanek LR, Zhao LP, Lin X, Li B, Li Y, Dupuis J, Reiner AP, Mohlke KL, Auer PL. Allelic Heterogeneity at the CRP Locus Identified by Whole-Genome Sequencing in Multi-ancestry Cohorts. Am J Hum Genet 2020; 106:112-120. [PMID: 31883642 PMCID: PMC7042494 DOI: 10.1016/j.ajhg.2019.12.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Accepted: 12/02/2019] [Indexed: 12/19/2022] Open
Abstract
Whole-genome sequencing (WGS) can improve assessment of low-frequency and rare variants, particularly in non-European populations that have been underrepresented in existing genomic studies. The genetic determinants of C-reactive protein (CRP), a biomarker of chronic inflammation, have been extensively studied, with existing genome-wide association studies (GWASs) conducted in >200,000 individuals of European ancestry. In order to discover novel loci associated with CRP levels, we examined a multi-ancestry population (n = 23,279) with WGS (∼38× coverage) from the Trans-Omics for Precision Medicine (TOPMed) program. We found evidence for eight distinct associations at the CRP locus, including two variants that have not been identified previously (rs11265259 and rs181704186), both of which are non-coding and more common in individuals of African ancestry (∼10% and ∼1% minor allele frequency, respectively, and rare or monomorphic in 1000 Genomes populations of East Asian, South Asian, and European ancestry). We show that the minor (G) allele of rs181704186 is associated with lower CRP levels and decreased transcriptional activity and protein binding in vitro, providing a plausible molecular mechanism for this African ancestry-specific signal. The individuals homozygous for rs181704186-G have a mean CRP level of 0.23 mg/L, in contrast to individuals heterozygous for rs181704186 with mean CRP of 2.97 mg/L and major allele homozygotes with mean CRP of 4.11 mg/L. This study demonstrates the utility of WGS in multi-ethnic populations to drive discovery of complex trait associations of large effect and to identify functional alleles in noncoding regulatory regions.
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Affiliation(s)
- Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Apoorva K Iyengar
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Biqi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA
| | - Sheila M Gaynor
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | | | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Madeline H Kowalski
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Shabnam Salimi
- Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21201, USA
| | - Linda M Polfus
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA 90089, USA
| | - Emelia J Benjamin
- Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA; Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
| | - Joshua C Bis
- Department of Medicine, Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98101, USA
| | - Russell Bowler
- Department of Medicine, Division of Pulmonary, Critical Care & Sleep Medicine, National Jewish Health, Denver, CO 80206, USA
| | - Brian E Cade
- Department of Medicine, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Medicine, Division of Sleep Medicine, Harvard Medical School, Boston, MA 02115, USA
| | | | - Alejandro P Comellas
- Department of Medicine, Division of Pulmonary and Critical Care, University of Iowa, Iowa City, IA 52242, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Pedro Cruz
- Illumina Laboratory Services, Illumina Inc., San Diego, CA 92122, USA
| | - Harsha Doddapaneni
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Peter Durda
- Department of Pathology & Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05446, USA
| | | | - Deepti Jain
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | | | - Brian G Kral
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA; Division of Cardiology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
| | - Cecelia Laurie
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Jiwon Lee
- Department of Medicine, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA
| | | | - Joshua P Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Ginger A Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA; Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD 21201, USA
| | - Zeineen Momin
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Donna M Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Stephen S Rich
- Department of Public Health Sciences, Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Kathleen Ryan
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | | | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT 05446, USA; Department of Biochemistry, Larner College of Medicine, University of Vermont, Burlington, VT 05446, USA
| | | | - Lisa R Yanek
- GeneSTAR Research Program, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Lue Ping Zhao
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Statistics, Harvard University, Cambridge, MA 02138, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN 37232, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA 01702, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Paul L Auer
- Joseph J. Zilber School of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI 53205, USA.
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46
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Inoue F, Kreimer A, Ashuach T, Ahituv N, Yosef N. Identification and Massively Parallel Characterization of Regulatory Elements Driving Neural Induction. Cell Stem Cell 2019; 25:713-727.e10. [PMID: 31631012 PMCID: PMC6850896 DOI: 10.1016/j.stem.2019.09.010] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Revised: 07/15/2019] [Accepted: 09/26/2019] [Indexed: 12/16/2022]
Abstract
Epigenomic regulation and lineage-specific gene expression act in concert to drive cellular differentiation, but the temporal interplay between these processes is largely unknown. Using neural induction from human pluripotent stem cells (hPSCs) as a paradigm, we interrogated these dynamics by performing RNA sequencing (RNA-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and assay for transposase accessible chromatin using sequencing (ATAC-seq) at seven time points during early neural differentiation. We found that changes in DNA accessibility precede H3K27ac, which is followed by gene expression changes. Using massively parallel reporter assays (MPRAs) to test the activity of 2,464 candidate regulatory sequences at all seven time points, we show that many of these sequences have temporal activity patterns that correlate with their respective cell-endogenous gene expression and chromatin changes. A prioritization method incorporating all genomic and MPRA data further identified key transcription factors involved in driving neural fate. These results provide a comprehensive resource of genes and regulatory elements that orchestrate neural induction and illuminate temporal frameworks during differentiation.
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Affiliation(s)
- Fumitaka Inoue
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Anat Kreimer
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA; Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Tal Ashuach
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA.
| | - Nir Yosef
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA; Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA.
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47
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Sanders SJ, Sahin M, Hostyk J, Thurm A, Jacquemont S, Avillach P, Douard E, Martin CL, Modi ME, Moreno-De-Luca A, Raznahan A, Anticevic A, Dolmetsch R, Feng G, Geschwind DH, Glahn DC, Goldstein DB, Ledbetter DH, Mulle JG, Pasca SP, Samaco R, Sebat J, Pariser A, Lehner T, Gur RE, Bearden CE. A framework for the investigation of rare genetic disorders in neuropsychiatry. Nat Med 2019; 25:1477-1487. [PMID: 31548702 PMCID: PMC8656349 DOI: 10.1038/s41591-019-0581-5] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 07/31/2019] [Indexed: 02/07/2023]
Abstract
De novo and inherited rare genetic disorders (RGDs) are a major cause of human morbidity, frequently involving neuropsychiatric symptoms. Recent advances in genomic technologies and data sharing have revolutionized the identification and diagnosis of RGDs, presenting an opportunity to elucidate the mechanisms underlying neuropsychiatric disorders by investigating the pathophysiology of high-penetrance genetic risk factors. Here we seek out the best path forward for achieving these goals. We think future research will require consistent approaches across multiple RGDs and developmental stages, involving both the characterization of shared neuropsychiatric dimensions in humans and the identification of neurobiological commonalities in model systems. A coordinated and concerted effort across patients, families, researchers, clinicians and institutions, including rapid and broad sharing of data, is now needed to translate these discoveries into urgently needed therapies.
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Affiliation(s)
- Stephan J Sanders
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Joseph Hostyk
- Institute for Genomic Medicine, Columbia University Medical Center, Hammer Health Sciences, New York, NY, USA
| | - Audrey Thurm
- National Institute of Mental Health, Bethesda, MD, USA
| | - Sebastien Jacquemont
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, Quebec, Canada
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Elise Douard
- CHU Sainte-Justine Research Centre, University of Montreal, Montreal, Quebec, Canada
| | - Christa L Martin
- Geisinger Autism & Developmental Medicine Institute, Danville, PA, USA
| | - Meera E Modi
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | | | | | - Alan Anticevic
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Ricardo Dolmetsch
- Department of Neuroscience, Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Guoping Feng
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel H Geschwind
- Center for Autism Research and Treatment, Semel Institute for Neuroscience and Human Behavior and Departments of Neurology and Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - David C Glahn
- Tommy Fuss Center for Neuropsychiatric Disease Research, Boston Children's Hospital, Boston, MA, USA
| | - David B Goldstein
- Institute for Genomic Medicine, Columbia University Medical Center, Hammer Health Sciences, New York, NY, USA
| | - David H Ledbetter
- Geisinger Autism & Developmental Medicine Institute, Danville, PA, USA
| | - Jennifer G Mulle
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Sergiu P Pasca
- Department of Psychiatry and Behavioral Sciences, Stanford University, Palo Alto, CA, USA
| | - Rodney Samaco
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Jonathan Sebat
- Beyster Center for Genomics of Psychiatric Diseases, University of California, San Diego, La Jolla, CA, USA
| | - Anne Pariser
- National Center for Advancing Translational Sciences, Bethesda, MD, USA
| | - Thomas Lehner
- National Institute of Mental Health, Bethesda, MD, USA
| | - Raquel E Gur
- Department of Psychiatry, Neuropsychiatry Section, and the Lifespan Brain Institute, Perelman School of Medicine and Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
| | - Carrie E Bearden
- Semel Institute for Neuroscience and Human Behavior, Departments of Psychiatry and Biobehavioral Sciences and Psychology, University of California, Los Angeles, Los Angeles, CA, USA.
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48
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Variant calling and quality control of large-scale human genome sequencing data. Emerg Top Life Sci 2019; 3:399-409. [DOI: 10.1042/etls20190007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 06/28/2019] [Accepted: 07/16/2019] [Indexed: 12/12/2022]
Abstract
Abstract
Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.
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Sestan N, State MW. Lost in Translation: Traversing the Complex Path from Genomics to Therapeutics in Autism Spectrum Disorder. Neuron 2019; 100:406-423. [PMID: 30359605 DOI: 10.1016/j.neuron.2018.10.015] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Revised: 09/29/2018] [Accepted: 10/08/2018] [Indexed: 12/24/2022]
Abstract
Recent progress in the genomics of non-syndromic autism spectrum disorder (nsASD) highlights rare, large-effect, germline, heterozygous de novo coding mutations. This distinguishes nsASD from later-onset psychiatric disorders where gene discovery efforts have predominantly yielded common alleles of small effect. These differences point to distinctive opportunities for clarifying the neurobiology of nsASD and developing novel treatments. We argue that the path ahead also presents key challenges, including distinguishing human pathophysiology from the potentially pleiotropic neurobiology mediated by established risk genes. We present our view of some of the conceptual limitations of traditional studies of model organisms, suggest a strategy focused on investigating the convergence of multiple nsASD genes, and propose that the detailed characterization of the molecular and cellular landscapes of developing human brain is essential to illuminate disease mechanisms. Finally, we address how recent advances are leading to novel strategies for therapeutics that target various points along the path from genes to behavior.
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Affiliation(s)
- Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Departments of Genetics, of Psychiatry, and of Comparative Medicine, Program in Cellular Neuroscience, Neurodegeneration and Repair, and Yale Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA.
| | - Matthew W State
- Department of Psychiatry, Langley Porter Psychiatric Institute, Quantitative Biosciences Institute, Institute for Human Genetics, and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA.
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Sanders SJ. Next-Generation Sequencing in Autism Spectrum Disorder. Cold Spring Harb Perspect Med 2019; 9:cshperspect.a026872. [PMID: 30420340 DOI: 10.1101/cshperspect.a026872] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Autism spectrum disorder (ASD) is a common disorder that causes substantial distress. Heritability studies consistently show a strong genetic contribution, raising the hope that identifying ASD-associated genetic variants will offer insights into neurobiology and ultimately therapeutics. Next-generation sequencing (NGS) enabled the identification of disruptive variants throughout protein-coding regions of the genome. Alongside large cohorts and novel statistical methods, these NGS methods revolutionized ASD gene discovery. NGS methods have also contributed substantially to functional genetic data, such as gene expression, used to understand the neurobiological consequences of disrupting these ASD-associated genes. These functional data are also critical for annotating the noncoding genome as whole-genome sequencing (WGS) begins to provide initial insights outside of protein-coding regions. NGS methods still have a major role to play, as do similarly transformative advances in stem cell and gene-editing methods, in translating genetic discoveries into a first generation of ASD therapeutics.
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Affiliation(s)
- Stephan J Sanders
- Department of Psychiatry, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California 94158
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