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Busse E, Lee B, Nagamani SCS. Genetic Evaluation for Monogenic Disorders of Low Bone Mass and Increased Bone Fragility: What Clinicians Need to Know. Curr Osteoporos Rep 2024; 22:308-317. [PMID: 38600318 DOI: 10.1007/s11914-024-00870-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/23/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE OF REVIEW The purpose of this review is to outline the principles of clinical genetic testing and to provide practical guidance to clinicians in navigating genetic testing for patients with suspected monogenic forms of osteoporosis. RECENT FINDINGS Heritability assessments and genome-wide association studies have clearly shown the significant contributions of genetic variations to the pathogenesis of osteoporosis. Currently, over 50 monogenic disorders that present primarily with low bone mass and increased risk of fractures have been described. The widespread availability of clinical genetic testing offers a valuable opportunity to correctly diagnose individuals with monogenic forms of osteoporosis, thus instituting appropriate surveillance and treatment. Clinical genetic testing may identify the appropriate diagnosis in a subset of patients with low bone mass, multiple or unusual fractures, and severe or early-onset osteoporosis, and thus clinicians should be aware of how to incorporate such testing into their clinical practices.
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Affiliation(s)
- Emily Busse
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- Texas Children's Hospital, Houston, TX, USA.
| | - Sandesh C S Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
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Kingdom R, Beaumont RN, Wood AR, Weedon MN, Wright CF. Genetic modifiers of rare variants in monogenic developmental disorder loci. Nat Genet 2024; 56:861-868. [PMID: 38637616 PMCID: PMC11096126 DOI: 10.1038/s41588-024-01710-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/06/2024] [Indexed: 04/20/2024]
Abstract
Rare damaging variants in a large number of genes are known to cause monogenic developmental disorders (DDs) and have also been shown to cause milder subclinical phenotypes in population cohorts. Here, we show that carrying multiple (2-5) rare damaging variants across 599 dominant DD genes has an additive adverse effect on numerous cognitive and socioeconomic traits in UK Biobank, which can be partially counterbalanced by a higher educational attainment polygenic score (EA-PGS). Phenotypic deviators from expected EA-PGS could be partly explained by the enrichment or depletion of rare DD variants. Among carriers of rare DD variants, those with a DD-related clinical diagnosis had a substantially lower EA-PGS and more severe phenotype than those without a clinical diagnosis. Our results suggest that the overall burden of both rare and common variants can modify the expressivity of a phenotype, which may then influence whether an individual reaches the threshold for clinical disease.
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Affiliation(s)
- Rebecca Kingdom
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Caroline F Wright
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK.
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3
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Trégouët DA, Morange PE. Next-generation sequencing strategies in venous thromboembolism: in whom and for what purpose? J Thromb Haemost 2024:S1538-7836(24)00218-6. [PMID: 38641321 DOI: 10.1016/j.jtha.2024.04.004] [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: 02/13/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/21/2024]
Abstract
This invited review follows the oral presentation "To Sequence or Not to Sequence, That Is Not the Question; But 'When, Who, Which and What For?' Is" given during the State of the Art session "Translational Genomics in Thrombosis: From OMICs to Clinics" of the International Society on Thrombosis and Haemostasis 2023 Congress. Emphasizing the power of next-generation sequencing technologies and the diverse strategies associated with DNA variant analysis, this review highlights the unresolved questions and challenges in their implementation both for the clinical diagnosis of venous thromboembolism and in translational research.
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Affiliation(s)
- David-Alexandre Trégouët
- University of Bordeaux, Institut National de la Santé et de la Recherche Médicale, Bordeaux Population Health Research Center, Unité Mixte de Recherche 1219, Bordeaux, France.
| | - Pierre-Emmanuel Morange
- Cardiovascular and Nutrition Research Center (Centre de Recherche en CardioVasculaire et Nutrition), Institut National de la Santé et de la Recherche Médicale, Institut National de Recherche pour l'agriculture, l' Alimentation et l'Environnement, Aix-Marseille University, Marseille, France
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4
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Xie Y, Wu R, Li H, Dong W, Zhou G, Zhao H. Statistical methods for assessing the effects of de novo variants on birth defects. Hum Genomics 2024; 18:25. [PMID: 38486307 PMCID: PMC10938830 DOI: 10.1186/s40246-024-00590-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/26/2024] [Indexed: 03/18/2024] Open
Abstract
With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.
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Affiliation(s)
- Yuhan Xie
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Ruoxuan Wu
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Hongyu Li
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Weilai Dong
- Department of Genetics, Yale School of Medicine, New Haven, CT, 06520, USA
| | - Geyu Zhou
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
- Department of Genetics, Yale School of Medicine, New Haven, CT, 06520, USA.
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Andreoli L, Peeters H, Van Steen K, Dierickx K. Taking the risk. A systematic review of ethical reasons and moral arguments in the clinical use of polygenic risk scores. Am J Med Genet A 2024:e63584. [PMID: 38450933 DOI: 10.1002/ajmg.a.63584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 02/08/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024]
Abstract
Debates about the prospective clinical use of polygenic risk scores (PRS) have grown considerably in the last years. The potential benefits of PRS to improve patient care at individual and population levels have been extensively underlined. Nonetheless, the use of PRS in clinical contexts presents a number of unresolved ethical challenges and consequent normative gaps that hinder their optimal implementation. Here, we conducted a systematic review of reasons of the normative literature discussing ethical issues and moral arguments related to the use of PRS for the prevention and treatment of common complex diseases. In total, we have included and analyzed 34 records, spanning from 2013 to 2023. The findings have been organized in three major themes: in the first theme, we consider the potential harms of PRS to individuals and their kin. In the theme "Threats to health equity," we consider ethical concerns of social relevance, with a focus on justice issues. Finally, the theme "Towards best practices" collects a series of research priorities and provisional recommendations to be considered for an optimal clinical translation of PRS. We conclude that the use of PRS in clinical care reinvigorates old debates in matters of health justice; however, open questions, regarding best practices in clinical counseling, suggest that the ethical considerations applicable in monogenic settings will not be sufficient to face PRS emerging challenges.
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Affiliation(s)
- Lara Andreoli
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | | | - Kris Dierickx
- Department of Public Health and Primary Care, Centre for Biomedical Ethics and Law, KU Leuven, Leuven, Belgium
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Pan C, Cheng S, Liu L, Chen Y, Meng P, Yang X, Li C, Zhang J, Zhang Z, Zhang H, Cheng B, Wen Y, Jia Y, Zhang F. Identification of novel rare variants for anxiety: an exome-wide association study in the UK Biobank. Prog Neuropsychopharmacol Biol Psychiatry 2024; 130:110928. [PMID: 38154517 DOI: 10.1016/j.pnpbp.2023.110928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/19/2023] [Accepted: 12/23/2023] [Indexed: 12/30/2023]
Abstract
BACKGROUND Rare variants are believed to play a substantial role in the genetic architecture of mental disorders, particularly in coding regions. However, limited evidence supports the impact of rare variants on anxiety. METHODS Using whole-exome sequencing data from 200,643 participants in the UK Biobank, we investigated the contribution of rare variants to anxiety. Firstly, we computed genetic risk score (GRS) of anxiety utilizing genotype data and summary data from a genome-wide association study (GWAS) on anxiety disorder. Subsequently, we identified individuals within the lowest 50% GRS, a subgroup more likely to carry pathogenic rare variants. Within this subgroup, we classified individuals with the highest 10% 7-item Generalized Anxiety Disorder scale (GAD-7) score as cases (N = 1869), and those with the lowest 10% GAD-7 score were designated as controls (N = 1869). Finally, we conducted gene-based burden tests and single-variant association analyses to assess the relationship between rare variants and anxiety. RESULTS Totally, 47,800 variants with MAF ≤0.01 were annotated as non-benign coding variants, consisting of 42,698 nonsynonymous SNVs, 489 nonframeshift substitution, 236 frameshift substitution, 617 stop-gain and 40 stop-loss variants. After variation aggregation, 5066 genes were included in gene-based association analysis. Totally, 11 candidate genes were detected in burden test, such as RNF123 (PBonferroni adjusted = 3.40 × 10-6), MOAP1(PBonferroni adjusted = 4.35 × 10-4), CCDC110 (PBonferroni adjusted = 5.83 × 10-4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(PBonferroni adjusted = 3.16 × 10-10) and rs16942615(CAMTA2) (PBonferroni adjusted = 4.04 × 10-4). Notably, RNF123, CCDC110, DNAH2, and CSKMT gene were identified in both tests. CONCLUSIONS Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety.
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Affiliation(s)
- Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China.
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Andrews SV, Kukkle PL, Menon R, Geetha TS, Goyal V, Kandadai RM, Kumar H, Borgohain R, Mukherjee A, Wadia PM, Yadav R, Desai S, Kumar N, Joshi D, Murugan S, Biswas A, Pal PK, Oliver M, Nair S, Kayalvizhi A, Samson PL, Deshmukh M, Bassi A, Sandeep C, Mandloi N, Davis OB, Roberts MA, Leto DE, Henry AG, Di Paolo G, Muthane U, Das SK, Peterson AS, Sandmann T, Gupta R, Ramprasad VL. The Genetic Drivers of Juvenile, Young, and Early-Onset Parkinson's Disease in India. Mov Disord 2024; 39:339-349. [PMID: 38014556 DOI: 10.1002/mds.29676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 10/18/2023] [Accepted: 11/09/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Recent studies have advanced our understanding of the genetic drivers of Parkinson's disease (PD). Rare variants in more than 20 genes are considered causal for PD, and the latest PD genome-wide association study (GWAS) identified 90 independent risk loci. However, there remains a gap in our understanding of PD genetics outside of the European populations in which the vast majority of these studies were focused. OBJECTIVE The aim was to identify genetic risk factors for PD in a South Asian population. METHODS A total of 674 PD subjects predominantly with age of onset (AoO) ≤50 years (encompassing juvenile, young, or early-onset PD) were recruited from 10 specialty movement disorder centers across India over a 2-year period; 1376 control subjects were selected from the reference population GenomeAsia, Phase 2. We performed various case-only and case-control genetic analyses for PD diagnosis and AoO. RESULTS A genome-wide significant signal for PD diagnosis was identified in the SNCA region, strongly colocalizing with SNCA region signal from European PD GWAS. PD cases with pathogenic mutations in PD genes exhibited, on average, lower PD polygenic risk scores than PD cases lacking any PD gene mutations. Gene burden studies of rare, predicted deleterious variants identified BSN, encoding the presynaptic protein Bassoon that has been previously associated with neurodegenerative disease. CONCLUSIONS This study constitutes the largest genetic investigation of PD in a South Asian population to date. Future work should seek to expand sample numbers in this population to enable improved statistical power to detect PD genes in this understudied group. © 2023 Denali Therapeutics and The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Shan V Andrews
- Denali Therapeutics, South San Francisco, California, USA
| | - Prashanth L Kukkle
- Manipal Hospital, Bangalore, India
- Parkinson's Disease and Movement Disorders Clinic, Bangalore, India
| | | | | | - Vinay Goyal
- All India Institute of Medical Sciences (AIIMS), New Delhi, India
- Medanta Hospital, New Delhi, India
- Medanta, The Medicity, Gurgaon, India
| | - Rukmini Mridula Kandadai
- Nizams Institute of Medical Sciences (NIMS), Hyderabad, India
- Citi Neuro Centre, Hyderabad, India
| | | | - Rupam Borgohain
- Nizams Institute of Medical Sciences (NIMS), Hyderabad, India
- Citi Neuro Centre, Hyderabad, India
| | - Adreesh Mukherjee
- Bangur Institute of Neurosciences and Institute of Post Graduate Medical Education and Research (IPGME&R), Kolkata, India
| | | | - Ravi Yadav
- National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | - Soaham Desai
- Department of Neurology, Shree Krishna Hospital and Pramukhaswami Medical College, Bhaikaka University, Anand, India
| | - Niraj Kumar
- All India Institute of Medical Sciences, Rishikesh, India
- All India Institute of Medical Sciences, Bibinagar (Hyderabad Metropolitan Region), Bibinagar, India
| | - Deepika Joshi
- Department of Neurology, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India
| | | | - Atanu Biswas
- Bangur Institute of Neurosciences and Institute of Post Graduate Medical Education and Research (IPGME&R), Kolkata, India
| | - Pramod K Pal
- National Institute of Mental Health and Neurosciences (NIMHANS), Bangalore, India
| | | | | | | | | | | | | | | | | | - Oliver B Davis
- Denali Therapeutics, South San Francisco, California, USA
| | | | - Dara E Leto
- Denali Therapeutics, South San Francisco, California, USA
| | | | | | - Uday Muthane
- Parkinson and Ageing Research Foundation, Bangalore, India
| | - Shymal K Das
- Bangur Institute of Neurosciences and Institute of Post Graduate Medical Education and Research (IPGME&R), Kolkata, India
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LoPiccolo J, Gusev A, Christiani DC, Jänne PA. Lung cancer in patients who have never smoked - an emerging disease. Nat Rev Clin Oncol 2024; 21:121-146. [PMID: 38195910 PMCID: PMC11014425 DOI: 10.1038/s41571-023-00844-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 01/11/2024]
Abstract
Lung cancer is the most common cause of cancer-related deaths globally. Although smoking-related lung cancers continue to account for the majority of diagnoses, smoking rates have been decreasing for several decades. Lung cancer in individuals who have never smoked (LCINS) is estimated to be the fifth most common cause of cancer-related deaths worldwide in 2023, preferentially occurring in women and Asian populations. As smoking rates continue to decline, understanding the aetiology and features of this disease, which necessitate unique diagnostic and treatment paradigms, will be imperative. New data have provided important insights into the molecular and genomic characteristics of LCINS, which are distinct from those of smoking-associated lung cancers and directly affect treatment decisions and outcomes. Herein, we review the emerging data regarding the aetiology and features of LCINS, particularly the genetic and environmental underpinnings of this disease as well as their implications for treatment. In addition, we outline the unique diagnostic and therapeutic paradigms of LCINS and discuss future directions in identifying individuals at high risk of this disease for potential screening efforts.
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Affiliation(s)
- Jaclyn LoPiccolo
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
- The Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Alexander Gusev
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- The Eli and Edythe L. Broad Institute, Cambridge, MA, USA
| | - David C Christiani
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Massachusetts General Hospital, Boston, MA, USA
| | - Pasi A Jänne
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- The Lowe Center for Thoracic Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
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9
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Ross JP, Akçimen F, Liao C, Kwan K, Phillips DE, Schmilovich Z, Spiegelman D, Genge A, Dupré N, Dion PA, Farhan SMK, Rouleau GA. Rare-variant and polygenic analyses of amyotrophic lateral sclerosis in the French-Canadian genome. Genet Med 2024; 26:100967. [PMID: 37638500 DOI: 10.1016/j.gim.2023.100967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/29/2023] Open
Abstract
PURPOSE The genetic etiology of amyotrophic lateral sclerosis (ALS) includes few rare, large-effect variants and potentially many common, small-effect variants per case. The genetic risk liability for ALS might require a threshold comprised of a certain amount of variants. Here, we tested the degree to which risk for ALS was affected by rare variants in ALS genes, polygenic risk score, or both. METHODS 335 ALS cases and 356 controls from Québec, Canada were concurrently tested by microarray genotyping and targeted sequencing of ALS genes known at the time of study inception. ALS genome-wide association studies summary statistics were used to estimate an ALS polygenic risk score (PRS). Cases and controls were subdivided into rare-variant heterozygotes and non-heterozygotes. RESULTS Risk for ALS was significantly associated with PRS and rare variants independently in a logistic regression model. Although ALS PRS predicted a small amount of ALS risk overall, the effect was most pronounced between ALS cases and controls that were not heterozygous for a rare variant in the ALS genes surveyed. CONCLUSION Both PRS and rare variants in ALS genes impact risk for ALS. PRS for ALS is most informative when rare variants are not observed in ALS genes.
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Affiliation(s)
- Jay P Ross
- Department of Human Genetics, McGill University, Montréal, QC, Canada; Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Fulya Akçimen
- Department of Human Genetics, McGill University, Montréal, QC, Canada; Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Calwing Liao
- Department of Medicine, Harvard Medical School, Cambridge, MA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA
| | - Karina Kwan
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, QC, Canada
| | - Daniel E Phillips
- Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada; Department of Biology, McGill University, Montréal, QC, Canada
| | - Zoe Schmilovich
- Department of Human Genetics, McGill University, Montréal, QC, Canada; Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Dan Spiegelman
- Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Angela Genge
- Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada
| | - Nicolas Dupré
- Division of Neurosciences, CHU de Québec, Université Laval, Québec City, QC, Canada; Department of Medicine, Faculty of Medicine, Université Laval, Québec City, QC, Canada
| | - Patrick A Dion
- Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Sali M K Farhan
- Department of Human Genetics, McGill University, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada
| | - Guy A Rouleau
- Department of Human Genetics, McGill University, Montréal, QC, Canada; Montréal Neurological Institute and Hospital, McGill University, Montréal, QC, Canada; Department of Neurology and Neurosurgery, McGill University, Montréal, QC, Canada.
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10
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Lu T, Forgetta V, Zhou S, Richards JB, Greenwood CM. Identifying Rare Genetic Determinants for Improved Polygenic Risk Prediction of Bone Mineral Density and Fracture Risk. J Bone Miner Res 2023; 38:1771-1781. [PMID: 37830501 DOI: 10.1002/jbmr.4920] [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: 01/20/2023] [Revised: 09/13/2023] [Accepted: 10/06/2023] [Indexed: 10/14/2023]
Abstract
Osteoporosis and fractures severely impact the elderly population. Polygenic risk scores for bone mineral density have demonstrated potential clinical utility. However, the value of rare genetic determinants in risk prediction has not been assessed. With whole-exome sequencing data from 436,824 UK Biobank participants, we assigned White British ancestry individuals into a training data set (n = 317,434) and a test data set (n = 74,825). In the training data set, we developed a common variant-based polygenic risk score for heel ultrasound speed of sound (SOS). Next, we performed burden testing to identify genes harboring rare determinants of bone mineral density, targeting influential rare variants with predicted high deleteriousness. We constructed a genetic risk score, called ggSOS, to incorporate influential rare variants in significant gene burden masks into the common variant-based polygenic risk score. We assessed the predictive performance of ggSOS in the White British test data set, as well as in populations of non-White British European (n = 18,885), African (n = 7165), East Asian (n = 2236), South Asian (n = 9829), and other admixed (n = 1481) ancestries. Twelve genes in pivotal regulatory pathways of bone homeostasis harbored influential rare variants associated with SOS (p < 5.5 × 10-7 ), including AHNAK, BMP5, CYP19A1, FAM20A, FBXW5, KDM5B, KREMEN1, LGR4, LRP5, SMAD6, SOST, and WNT1. Among 4013 (5.4%) individuals in the test data set carrying these variants, a one standard deviation decrease in ggSOS was associated with 1.35-fold (95% confidence interval [CI] 1.16-1.57) increased hazard of major osteoporotic fracture. However, compared with a common variant-based polygenic risk score (C-index = 0.641), ggSOS had only marginally improved prediction accuracy in identifying at-risk individuals (C-index = 0.644), with overlapping confidence intervals. Similarly, ggSOS did not demonstrate substantially improved predictive performance in non-European ancestry populations. In summary, modeling the effects of rare genetic determinants may assist polygenic prediction of fracture risk among carriers of influential rare variants. Nonetheless, improved clinical utility is not guaranteed for population-level risk screening. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | | | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- 5 Prime Sciences Inc., Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Celia Mt Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
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Tsegaselassie W, Jian Y, Berhanu GG, Tianyuan L, April M, Tali E, Fasil TA, Timothy TA, Jordana C, Marguerite IR, Robert SM, Michael VW, Kristine Y, Myriam F, Donald LJM, Mario S, Daichi S, Yuichiro Y, Paul M, Adam B. Associations of cardiometabolic polygenic risk scores with cardiovascular disease in African Americans. RESEARCH SQUARE 2023:rs.3.rs-3228815. [PMID: 37693576 PMCID: PMC10491340 DOI: 10.21203/rs.3.rs-3228815/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background Cardiovascular disease (CVD) is a complex disease, and genetic factors contribute individually or cumulatively to CVD risk. While African American women and men are disproportionately affected by CVD, their lack of representation in genomic investigations may widen disparities in health. We investigated the associations of cardiometabolic polygenic risk scores (PRSs) with CVD risk in African Americans. Methods We used the Jackson Heart Study, a prospective cohort study of CVD in African American adults and the predicted atherosclerotic cardiovascular disease (ASCVD) 10-year risk. We included 40-79 years old adults without a history of coronary heart disease (CHD) or stroke at baseline. We derived genome-wide PRSs for systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol, LDL cholesterol, hemoglobin A1c (HbA1c), triglycerides, and C-reactive protein (CRP) separately for each of the participants, using African-origin UK Biobank participants' genome-wide association summary statistics. We estimated the associations between PRSs and 10-year predicted ASCVD risk adjusting for age, sex, study visit date, and genetic ancestry using linear and logistic regression models. Results Participants (n=2,077) were 63% female and 66% never-smokers. They had mean (SD) 56 (10) years of age, 127.8 (16.3) mmHg SBP, 76.3 (8.7) mmHg DBP, 200.4 (40.2) mg/dL total cholesterol, 51.7 (14.7) mg/dL HDL cholesterol, 127.2 (36.7) mg/dL LDL cholesterol, 6.0 (1.3) mmol/mol HbA1c, 108.9 (81.7) mg/dL triglycerides and 0.53 (1.1) CRP. Their median (interquartile range) predicted 10-year predicted ASCVD risk was 8.0 (4.0-15.0). Participants in the >75th percentile for HbA1c PRS had 1.42 percentage-point greater predicted 10-year ASCVD risk (1.42 [95% CI: 0.58-2.26]) and higher odds of ≥10% predicted 10-year ASCVD risk (OR: 1.46 [95% CI: 1.03-2.07]) compared with those in the <25th percentile for HbA1c PRS. Participants in the >75th percentile for SBP PRS had higher odds of ≥10% predicted 10-year ASCVD risk (OR: 1.52 [95% CI: 1.07-2.15]) compared with those in the <25th percentile for SBP PRS. Conclusion Among 40-79 years old African Americans without CHD and stroke, higher PRSs for HbA1c and SBP were associated with CVD risk. PRSs may help stratify individuals based on their clinical risk factors for CVD early prevention and clinical management.
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Affiliation(s)
| | | | | | - Lu Tianyuan
- Lady Davis Institute for Medical Research, Jewish General Hospital
| | | | | | | | | | | | | | | | | | | | | | | | - Sims Mario
- University of Mississippi Medical Center
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12
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Wąsowska A, Sendecki A, Boguszewska-Chachulska A, Teper S. Polygenic Risk Score and Rare Variant Burden Identified by Targeted Sequencing in a Group of Patients with Pigment Epithelial Detachment in Age-Related Macular Degeneration. Genes (Basel) 2023; 14:1707. [PMID: 37761846 PMCID: PMC10531282 DOI: 10.3390/genes14091707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/22/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
A subset of ophthalmic imaging examination results from 334 patients were subjected to reanalysis to identify a specific group of patients with pigment epithelial detachment (PED) in at least one eye. Overall, we found a subgroup of 47 patients manifesting PED and studied their genotypes in comparison to those of patients with age-related macular degeneration without PED and healthy controls. We established a polygenic risk score that allowed the explanation of 16.3% of the variation within the disease. The highest predictive value was achieved for a model consisting of six non-coding variants: rs760306 (BEST1), rs148662546 (BEST1), rs11569560 (C3), rs74600252 (GUCA1B), rs2240688 (PROM1), and rs185507582 (TCF4). The risk of PED occurrence was found to be the highest in the first tercile, showing a 7.89-fold higher risk compared to the third tercile for AMD without PED (95% CI: 2.87; 21.71, p < 0.001) and a 7.22-fold higher risk compared to the healthy controls (95% CI: 2.60; 20.06, p < 0.001). In addition, we focused on rare variants in targeted genes. The rare variants' burden was compared among the groups, but no statistical significance was observed in the number of rare variants, predicted functional effects, or pathogenicity classification.
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Affiliation(s)
- Anna Wąsowska
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland
- Genomed S.A., 02-972 Warszawa, Poland
| | - Adam Sendecki
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland
| | | | - Sławomir Teper
- Chair and Clinical Department of Ophthalmology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, 40-055 Katowice, Poland
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13
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Trajanoska K, Bhérer C, Taliun D, Zhou S, Richards JB, Mooser V. From target discovery to clinical drug development with human genetics. Nature 2023; 620:737-745. [PMID: 37612393 DOI: 10.1038/s41586-023-06388-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/29/2023] [Indexed: 08/25/2023]
Abstract
The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.
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Affiliation(s)
- Katerina Trajanoska
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Claude Bhérer
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Daniel Taliun
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - Sirui Zhou
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology and Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Vincent Mooser
- Canada Excellence Research Chair in Genomic Medicine, Department of Human Genetics, Faculty of Medicine and Health Sciences, Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Quebec, Canada.
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14
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Lu T, Silveira PP, Greenwood CMT. Development of risk prediction models for depression combining genetic and early life risk factors. Front Neurosci 2023; 17:1143496. [PMID: 37534032 PMCID: PMC10390723 DOI: 10.3389/fnins.2023.1143496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/03/2023] [Indexed: 08/04/2023] Open
Abstract
Background Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated. Methods Leveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression. Results In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively. Conclusion We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Patrícia Pelufo Silveira
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Center, McGill University, Montreal, QC, Canada
| | - Celia M. T. Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
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15
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Cai Q, Pan C, Shi S, Chu X, Qin X, He D, Zhang N, Zhao Y, Wei W, Zhang F. Exome-wide screening identifies novel susceptibility genes for subjective well-being. Psychiatry Clin Neurosci 2023; 77:414-415. [PMID: 37144917 DOI: 10.1111/pcn.13562] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/16/2023] [Accepted: 05/02/2023] [Indexed: 05/06/2023]
Affiliation(s)
- Qingqing Cai
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Sirong Shi
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Xiaoge Chu
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Xiaoyue Qin
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Dan He
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Na Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Yijing Zhao
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Wenming Wei
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission; School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, People's Republic of China
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16
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Andersen LVB, Larsen MJ, Davies H, Degasperi A, Nielsen HR, Jensen LA, Kroeldrup L, Gerdes AM, Lænkholm AV, Kruse TA, Nik-Zainal S, Thomassen M. Non-BRCA1/BRCA2 high-risk familial breast cancers are not associated with a high prevalence of BRCAness. Breast Cancer Res 2023; 25:69. [PMID: 37316882 DOI: 10.1186/s13058-023-01655-y] [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: 08/09/2022] [Accepted: 05/09/2023] [Indexed: 06/16/2023] Open
Abstract
BACKGROUND Familial breast cancer is in most cases unexplained due to the lack of identifiable pathogenic variants in the BRCA1 and BRCA2 genes. The somatic mutational landscape and in particular the extent of BRCA-like tumour features (BRCAness) in these familial breast cancers where germline BRCA1 or BRCA2 mutations have not been identified is to a large extent unknown. METHODS We performed whole-genome sequencing on matched tumour and normal samples from high-risk non-BRCA1/BRCA2 breast cancer families to understand the germline and somatic mutational landscape and mutational signatures. We measured BRCAness using HRDetect. As a comparator, we also analysed samples from BRCA1 and BRCA2 germline mutation carriers. RESULTS We noted for non-BRCA1/BRCA2 tumours, only a small proportion displayed high HRDetect scores and were characterized by concomitant promoter hypermethylation or in one case a RAD51D splice variant previously reported as having unknown significance to potentially explain their BRCAness. Another small proportion showed no features of BRCAness but had mutationally active tumours. The remaining tumours lacked features of BRCAness and were mutationally quiescent. CONCLUSIONS A limited fraction of high-risk familial non-BRCA1/BRCA2 breast cancer patients is expected to benefit from treatment strategies against homologue repair deficient cancer cells.
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Affiliation(s)
- Lars V B Andersen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Martin J Larsen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Helen Davies
- Hutchison Research Centre, Early Cancer Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
- Academic Laboratory of Medical Genetics, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - Andrea Degasperi
- Hutchison Research Centre, Early Cancer Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
- Academic Laboratory of Medical Genetics, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | | | - Louise A Jensen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lone Kroeldrup
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
| | - Anne-Marie Gerdes
- Department of Clinical Genetics, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Anne-Vibeke Lænkholm
- Department of Surgical Pathology, Zealand University Hospital, 4000, Roskilde, Denmark
| | - Torben A Kruse
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Serena Nik-Zainal
- Hutchison Research Centre, Early Cancer Institute, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XZ, UK
- Academic Laboratory of Medical Genetics, Lv 6 Addenbrooke's Treatment Centre, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
- European Sperm Bank, Copenhagen, Denmark
| | - Mads Thomassen
- Department of Clinical Genetics, Odense University Hospital, Odense, Denmark.
- Clinical Genome Center, Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
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17
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He D, Pan C, Zhao Y, Wei W, Qin X, Cai Q, Shi S, Chu X, Zhang N, Jia Y, Wen Y, Cheng B, Liu H, Feng R, Zhang F, Xu P. Exome-wide screening identifies novel rare risk variants for bone mineral density. Osteoporos Int 2023; 34:965-975. [PMID: 36849660 DOI: 10.1007/s00198-023-06710-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023]
Abstract
UNLABELLED Bone mineral density (BMD) is an independent risk factor of osteoporosis-related fractures. We performed gene-based burden tests to assess the association between rare variants and BMD, and identified several BMD candidate genes. PURPOSE BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD. METHODS Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively. RESULTS The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10-5, PBonferroni adjust = 2.11 × 10-3), C22orf31 (P = 3.49 × 10-4, PBonferroni adjust = 3.17 × 10-2), and SPATC1L (P = 1.09 × 10-5, PBonferroni adjust = 8.80 × 10-3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10-3, PBonferroni adjust = 3.91 × 10-2). CONCLUSIONS Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.
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Affiliation(s)
- D He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - C Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - W Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Q Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - S Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - N Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - B Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - H Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - R Feng
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China.
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
| | - P Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China.
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18
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Shams H, Shao X, Santaniello A, Kirkish G, Harroud A, Ma Q, Isobe N, Schaefer CA, McCauley JL, Cree BAC, Didonna A, Baranzini SE, Patsopoulos NA, Hauser SL, Barcellos LF, Henry RG, Oksenberg JR. Polygenic risk score association with multiple sclerosis susceptibility and phenotype in Europeans. Brain 2023; 146:645-656. [PMID: 35253861 PMCID: PMC10169285 DOI: 10.1093/brain/awac092] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 01/29/2022] [Accepted: 02/15/2022] [Indexed: 11/13/2022] Open
Abstract
Polygenic inheritance plays a pivotal role in driving multiple sclerosis susceptibility, an inflammatory demyelinating disease of the CNS. We developed polygenic risk scores (PRS) of multiple sclerosis and assessed associations with both disease status and severity in cohorts of European descent. The largest genome-wide association dataset for multiple sclerosis to date (n = 41 505) was leveraged to generate PRS scores, serving as an informative susceptibility marker, tested in two independent datasets, UK Biobank [area under the curve (AUC) = 0.73, 95% confidence interval (CI): 0.72-0.74, P = 6.41 × 10-146] and Kaiser Permanente in Northern California (KPNC, AUC = 0.8, 95% CI: 0.76-0.82, P = 1.5 × 10-53). Individuals within the top 10% of PRS were at higher than 5-fold increased risk in UK Biobank (95% CI: 4.7-6, P = 2.8 × 10-45) and 15-fold higher risk in KPNC (95% CI: 10.4-24, P = 3.7 × 10-11), relative to the median decile. The cumulative absolute risk of developing multiple sclerosis from age 20 onwards was significantly higher in genetically predisposed individuals according to PRS. Furthermore, inclusion of PRS in clinical risk models increased the risk discrimination by 13% to 26% over models based only on conventional risk factors in UK Biobank and KPNC, respectively. Stratifying disease risk by gene sets representative of curated cellular signalling cascades, nominated promising genetic candidate programmes for functional characterization. These pathways include inflammatory signalling mediation, response to viral infection, oxidative damage, RNA polymerase transcription, and epigenetic regulation of gene expression to be among significant contributors to multiple sclerosis susceptibility. This study also indicates that PRS is a useful measure for estimating susceptibility within related individuals in multicase families. We show a significant association of genetic predisposition with thalamic atrophy within 10 years of disease progression in the UCSF-EPIC cohort (P < 0.001), consistent with a partial overlap between the genetics of susceptibility and end-organ tissue injury. Mendelian randomization analysis suggested an effect of multiple sclerosis susceptibility on thalamic volume, which was further indicated to be through horizontal pleiotropy rather than a causal effect. In summary, this study indicates important, replicable associations of PRS with enhanced risk assessment and radiographic outcomes of tissue injury, potentially informing targeted screening and prevention strategies.
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Affiliation(s)
- Hengameh Shams
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Xiaorong Shao
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Adam Santaniello
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Gina Kirkish
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Adil Harroud
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Qin Ma
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Noriko Isobe
- Department of Neurology, Graduate School of medical Sciences, Kyushu University, Fukuoka, 812-8582, Japan
| | | | | | - Jacob L McCauley
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, Miami, FL, USA.,Dr. John T. Macdonald Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Bruce A C Cree
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Alessandro Didonna
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA.,Department of Anatomy and Cell Biology, East Carolina University, Greenville, NC 27834, USA
| | - Sergio E Baranzini
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Nikolaos A Patsopoulos
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham and Women's Hospital, Boston, 02115 MA, USA.,Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Harvard Medical School, Boston, MA 02115, USA.,Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Stephen L Hauser
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Lisa F Barcellos
- Division of Epidemiology and Biostatistics, School of Public Health, University of California Berkeley, Berkeley, CA 94720, USA
| | - Roland G Henry
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
| | - Jorge R Oksenberg
- Weill Institute for Neurosciences, Department of Neurology, University of California San Francisco, San Francisco, CA 94158, USA
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19
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Jans D, Cleynen I. The genetics of non-monogenic IBD. Hum Genet 2023; 142:669-682. [PMID: 36720734 DOI: 10.1007/s00439-023-02521-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/10/2023] [Indexed: 02/02/2023]
Abstract
Inflammatory bowel disease (IBD), with Crohn's disease and ulcerative colitis as main subtypes, is a prototypical multifactorial disease with both genetic and environmental factors involved. Genetically, IBD covers a wide spectrum from monogenic to polygenic forms. In polygenic disease, many genetic variants each contribute a small amount to disease risk. With the advent of genome-wide association studies (GWAS), it became possible to find these variants and corresponding genes, leading so far to the discovery of ca 240 loci associated with IBD. Together, these however explain only 20-25% of the heritability of IBD, leaving a large portion unaccounted for. This missing heritability might be hidden in common variants with even lower effect than the ones currently found through GWAS, but also in rare variants which can be found through large-scale sequencing studies or potentially in multiplex families. In this review, we will give an overview of the current knowledge about the genetics of non-monogenic IBD and how it differs from the monogenic form(s), and future perspectives. The history of IBD genetic studies from twin studies over linkage studies to GWAS, and finally large-scale sequencing studies and the revisiting of multiplex families will be discussed.
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Affiliation(s)
- Deborah Jans
- Laboratory for Complex Genetics, Department of Human Genetics, KU Leuven, Herestraat 49, box610, 3000, Louvain, Belgium
| | - Isabelle Cleynen
- Laboratory for Complex Genetics, Department of Human Genetics, KU Leuven, Herestraat 49, box610, 3000, Louvain, Belgium.
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20
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Dong L, Wang Y, Wang X, Luo T, Zhou Q, Zhao G, Li B, Xia L, Xia K, Li J. Interactions of genetic risks for autism and the broad autism phenotypes. Front Psychiatry 2023; 14:1110080. [PMID: 37102084 PMCID: PMC10123509 DOI: 10.3389/fpsyt.2023.1110080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/07/2023] [Indexed: 04/28/2023] Open
Abstract
Background Common polygenic risk and de novo variants (DNVs) capture a small proportion of autism spectrum disorder (ASD) liability, and ASD phenotypic heterogeneity remains difficult to explain. Integrating multiple genetic factors contribute to clarifying the risk and clinical presentation of ASD. Methods In our study, we investigated the individual and combined effects of polygenic risk, damaging DNVs (including those in ASD risk genes), and sex among 2,591 ASD simplex families in the Simons Simplex Collection. We also explored the interactions among these factors, along with the broad autism phenotypes of ASD probands and their unaffected siblings. Finally, we combined the effects of polygenic risk, damaging DNVs in ASD risk genes, and sex to explain the total liability of ASD phenotypic spectrum. Results Our findings revealed that both polygenic risk and damaging DNVs contribute to an increased risk for ASD, with females exhibiting higher genetic burdens than males. ASD probands that carry damaging DNVs in ASD risk genes showed reduced polygenic risk. The effects of polygenic risk and damaging DNVs on autism broad phenotypes were inconsistent; probands with higher polygenic risk exhibited improvement in some behaviors, such as adaptive/cognitive behaviors, while those with damaging DNVs exhibited more severe phenotypes. Siblings with higher polygenic risk and damaging DNVs tended to have higher scores on broader autism phenotypes. Females exhibited more severe cognitive and behavioral problems compared to males among both ASD probands and siblings. The combination of polygenic risk, damaging DNVs in ASD risk genes, and sex explained 1-4% of the total liability of adaptive/cognitive behavior measurements. Conclusion Our study revealed that the risk for ASD and the autism broad phenotypes likely arises from a combination of common polygenic risk, damaging DNVs (including those in ASD risk genes), and sex.
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Affiliation(s)
- Lijie Dong
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Yijing Wang
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Xiaomeng Wang
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Tengfei Luo
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
| | - Qiao Zhou
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Guihu Zhao
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Bin Li
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Lu Xia
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
- *Correspondence: Lu Xia,
| | - Kun Xia
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Kun Xia,
| | - Jinchen Li
- Bioinformatics Center and National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Centre for Medical Genetics and Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, China
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Jinchen Li,
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21
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Whole Exome Sequencing Study Identifies Novel Rare Risk Variants for Habitual Coffee Consumption Involved in Olfactory Receptor and Hyperphagia. Nutrients 2022; 14:nu14204330. [PMID: 36297015 PMCID: PMC9607528 DOI: 10.3390/nu14204330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/06/2022] Open
Abstract
Habitual coffee consumption is an addictive behavior with unknown genetic variations and has raised public health issues about its potential health-related outcomes. We performed exome-wide association studies to identify rare risk variants contributing to habitual coffee consumption utilizing the newly released UK Biobank exome dataset (n = 200,643). A total of 34,761 qualifying variants were imported into SKAT to conduct gene-based burden and robust tests with minor allele frequency <0.01, adjusting the polygenic risk scores (PRS) of coffee intake to exclude the effect of common coffee-related polygenic risk. The gene-based burden and robust test of the exonic variants found seven exome-wide significant associations, such as OR2G2 (PSKAT = 1.88 × 10−9, PSKAT-Robust = 2.91 × 10−17), VEZT1 (PSKAT = 3.72 × 10−7, PSKAT-Robust = 1.41 × 10−7), and IRGC (PSKAT = 2.92 × 10−5, PSKAT-Robust = 1.07 × 10−7). These candidate genes were verified in the GWAS summary data of coffee intake, such as rs12737801 (p = 0.002) in OR2G2, and rs34439296 (p = 0.008) in IRGC. This study could help to extend genetic insights into the pathogenesis of coffee addiction, and may point to molecular mechanisms underlying health effects of habitual coffee consumption.
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22
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Mapping the genetic architecture of cortical morphology through neuroimaging: progress and perspectives. Transl Psychiatry 2022; 12:447. [PMID: 36241627 PMCID: PMC9568576 DOI: 10.1038/s41398-022-02193-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 09/06/2022] [Accepted: 09/20/2022] [Indexed: 11/26/2022] Open
Abstract
Cortical morphology is a key determinant of cognitive ability and mental health. Its development is a highly intricate process spanning decades, involving the coordinated, localized expression of thousands of genes. We are now beginning to unravel the genetic architecture of cortical morphology, thanks to the recent availability of large-scale neuroimaging and genomic data and the development of powerful biostatistical tools. Here, we review the progress made in this field, providing an overview of the lessons learned from genetic studies of cortical volume, thickness, surface area, and folding as captured by neuroimaging. It is now clear that morphology is shaped by thousands of genetic variants, with effects that are region- and time-dependent, thereby challenging conventional study approaches. The most recent genome-wide association studies have started discovering common genetic variants influencing cortical thickness and surface area, yet together these explain only a fraction of the high heritability of these measures. Further, the impact of rare variants and non-additive effects remains elusive. There are indications that the quickly increasing availability of data from whole-genome sequencing and large, deeply phenotyped population cohorts across the lifespan will enable us to uncover much of the missing heritability in the upcoming years. Novel approaches leveraging shared information across measures will accelerate this process by providing substantial increases in statistical power, together with more accurate mapping of genetic relationships. Important challenges remain, including better representation of understudied demographic groups, integration of other 'omics data, and mapping of effects from gene to brain to behavior across the lifespan.
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23
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases. Genet Med 2022; 24:1545-1555. [PMID: 35460399 DOI: 10.1016/j.gim.2022.03.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases. METHODS Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history. RESULTS Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%. CONCLUSION Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - John Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, Gerald Bronfman Department of Oncology, McGill University, McGill University, Montreal, Quebec, Canada.
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24
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Cheng S, Cheng B, Liu L, Yang X, Meng P, Yao Y, Pan C, Zhang J, Li C, Zhang H, Chen Y, Zhang Z, Wen Y, Jia Y, Zhang F. Exome-wide screening identifies novel rare risk variants for major depression disorder. Mol Psychiatry 2022; 27:3069-3074. [PMID: 35365804 DOI: 10.1038/s41380-022-01536-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 11/09/2022]
Abstract
Despite thousands of common genetic loci of major depression disorders (MDD) have been identified by GWAS to date, a large proportion of genetic variation predisposing to MDD remains unaccounted for. By utilizing the newly released UK Biobank 200,643 exome dataset, we conducted an exome-wide association study to identify rare risk variants contributing to MDD. After quality control, 120,033 participants with MDD polygenic risk scores (PRS) values were included. The individuals with lower 30% quantile of the PRS value were filtered for case and control selecting. Then the cases were set as the individuals with upper 10% quantile of the PHQ depression score and lower 10% quantile were set as controls. Finally, 1612 cases and 1612 controls were included in this study. The variants were annotated by ANNOVRA software. After exclusions, 34,761 qualifying variants, including 148 frameshift variant, 335 non-frameshift variant, 33,758 nonsynonymous, 91 start-loss, 393 stop-gain, 36 stop-loss variants were imported into the SKAT R-package to perform single variants, gene-based burden and robust burden tests with minor allele frequency (MAF) < 0.01. Single variant association testing identified one variant, rs4057749 (P = 5.39 × 10-9), within OR8B4 gene at an exome-wide significance level. The gene-based burden test of the exonic variants identified genome-wide significant associations in OR8B4 (PSKAT = 6.23 × 10-5, PSKAT Robust = 4.49 × 10-5), TRAPPC11 (PSKAT = 0.014, PSKAT Robust = 0.015), SBK3 (PSKAT = 0.020, PSKAT Robust = 0.025) and TNRC6B (PSKAT = 0.026, PSKAT Robust = 0.036). We identified multiple novel rare risk variants contributing to MDD in the individuals with lower PRS of MDD. The findings can help to broaden the genetic insights of the MDD pathogenesis.
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Affiliation(s)
- Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yao Yao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Chuyu Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Jingxi Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Huijie Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yujing Chen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Zhen Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China. .,Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China. .,Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China.
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25
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Capturing additional genetic risk from family history for improved polygenic risk prediction. Commun Biol 2022; 5:595. [PMID: 35710731 PMCID: PMC9203758 DOI: 10.1038/s42003-022-03532-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/24/2022] [Indexed: 12/01/2022] Open
Abstract
Family history of complex traits may reflect transmitted rare pathogenic variants, intra-familial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. For 941 children in the Avon Longitudinal Study of Parents and Children cohort, a joint predictor combining a polygenic risk score for height and mid-parental height was able to explain ~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Marginal yet consistent risk prediction improvements were also achieved among ~400,000 European ancestry participants for 11 complex diseases in the UK Biobank. Our work showcases a paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada.
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Department of Human Genetics, McGill University, Montreal, QC, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada. .,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
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26
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Oheim R, Tsourdi E, Seefried L, Beller G, Schubach M, Vettorazzi E, Stürznickel J, Rolvien T, Ehmke N, Delsmann A, Genest F, Krüger U, Zemojtel T, Barvencik F, Schinke T, Jakob F, Hofbauer LC, Mundlos S, Kornak U. Genetic Diagnostics in Routine Osteological Assessment of Adult Low Bone Mass Disorders. J Clin Endocrinol Metab 2022; 107:e3048-e3057. [PMID: 35276006 PMCID: PMC9202726 DOI: 10.1210/clinem/dgac147] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Indexed: 12/17/2022]
Abstract
CONTEXT Many different inherited and acquired conditions can result in premature bone fragility/low bone mass disorders (LBMDs). OBJECTIVE We aimed to elucidate the impact of genetic testing on differential diagnosis of adult LBMDs and at defining clinical criteria for predicting monogenic forms. METHODS Four clinical centers broadly recruited a cohort of 394 unrelated adult women before menopause and men younger than 55 years with a bone mineral density (BMD) Z-score < -2.0 and/or pathological fractures. After exclusion of secondary causes or unequivocal clinical/biochemical hallmarks of monogenic LBMDs, all participants were genotyped by targeted next-generation sequencing. RESULTS In total, 20.8% of the participants carried rare disease-causing variants (DCVs) in genes known to cause osteogenesis imperfecta (COL1A1, COL1A2), hypophosphatasia (ALPL), and early-onset osteoporosis (LRP5, PLS3, and WNT1). In addition, we identified rare DCVs in ENPP1, LMNA, NOTCH2, and ZNF469. Three individuals had autosomal recessive, 75 autosomal dominant, and 4 X-linked disorders. A total of 9.7% of the participants harbored variants of unknown significance. A regression analysis revealed that the likelihood of detecting a DCV correlated with a positive family history of osteoporosis, peripheral fractures (> 2), and a high normal body mass index (BMI). In contrast, mutation frequencies did not correlate with age, prevalent vertebral fractures, BMD, or biochemical parameters. In individuals without monogenic disease-causing rare variants, common variants predisposing for low BMD (eg, in LRP5) were overrepresented. CONCLUSION The overlapping spectra of monogenic adult LBMD can be easily disentangled by genetic testing and the proposed clinical criteria can help to maximize the diagnostic yield.
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Affiliation(s)
- Ralf Oheim
- Ralf Oheim, MD, Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, Lottestraße 59, 22529 Hamburg, Germany.
| | - Elena Tsourdi
- Department of Medicine III, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
- Center for Healthy Aging, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
| | - Lothar Seefried
- Orthopedic Center for Musculoskeletal Research, Orthopedic Department, University of Würzburg, 97070 Würzburg, Germany
| | - Gisela Beller
- Centre of Muscle and Bone Research, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Max Schubach
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Eik Vettorazzi
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Julian Stürznickel
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
- Department of Orthopaedics and Trauma Surgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Tim Rolvien
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
- Department of Orthopaedics and Trauma Surgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Nadja Ehmke
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany
| | - Alena Delsmann
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
| | - Franca Genest
- Orthopedic Center for Musculoskeletal Research, Orthopedic Department, University of Würzburg, 97070 Würzburg, Germany
| | - Ulrike Krüger
- Core Facility Genomics, Berlin Institute of Health (BIH), 10178 Berlin, Germany
| | - Tomasz Zemojtel
- Core Facility Genomics, Berlin Institute of Health (BIH), 10178 Berlin, Germany
| | - Florian Barvencik
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
| | - Thorsten Schinke
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
| | - Franz Jakob
- Orthopedic Center for Musculoskeletal Research, Orthopedic Department, University of Würzburg, 97070 Würzburg, Germany
| | - Lorenz C Hofbauer
- Department of Medicine III, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
- Center for Healthy Aging, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
| | - Stefan Mundlos
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany
- BIH Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10178 Berlin, Germany
- Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Uwe Kornak
- Correspondence: Uwe Kornak, PhD, Institute of Human Genetics, Universitätsmedizin Göttingen, Heinrich-Düker-Weg 12, 37073 Göttingen, Germany.
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Dattani S, Howard DM, Lewis CM, Sham PC. Clarifying the causes of consistent and inconsistent findings in genetics. Genet Epidemiol 2022; 46:372-389. [PMID: 35652173 PMCID: PMC9544854 DOI: 10.1002/gepi.22459] [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: 02/04/2022] [Revised: 04/12/2022] [Accepted: 04/22/2022] [Indexed: 11/29/2022]
Abstract
As research in genetics has advanced, some findings have been unexpected or shown to be inconsistent between studies or datasets. The reasons these inconsistencies arise are complex. Results from genetic studies can be affected by various factors including statistical power, linkage disequilibrium, quality control, confounding and selection bias, as well as real differences from interactions and effect modifiers, which may be informative about the mechanisms of traits and disease. Statistical artefacts can manifest as differences between results but they can also conceal underlying differences, which implies that their critical examination is important for understanding the underpinnings of traits. In this review, we examine these factors and outline how they can be identified and conceptualised with structural causal models. We explain the consequences they have on genetic estimates, such as genetic associations, polygenic scores, family‐ and genome‐wide heritability, and describe methods to address them to aid in the estimation of true effects of genetic variation. Clarifying these factors can help researchers anticipate when results are likely to diverge and aid researchers' understanding of causal relationships between genes and complex traits.
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Affiliation(s)
- Saloni Dattani
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Psychiatry, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Hong Kong, China
| | - David M Howard
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Division of Psychiatry, Royal Edinburgh Hospital, University of Edinburgh, Edinburgh, UK
| | - Cathryn M Lewis
- Social Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.,Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Pak C Sham
- Department of Psychiatry, State Key Laboratory of Brain and Cognitive Sciences, and Centre for Panoromic Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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28
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Gouveia C, Gibbons E, Dehghani N, Eapen J, Guerreiro R, Bras J. Genome-wide association of polygenic risk extremes for Alzheimer's disease in the UK Biobank. Sci Rep 2022; 12:8404. [PMID: 35589863 PMCID: PMC9120074 DOI: 10.1038/s41598-022-12391-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 05/10/2022] [Indexed: 01/27/2023] Open
Abstract
In just over a decade, advances in genome-wide association studies (GWAS) have offered an approach to stratify individuals based on genetic risk for disease. Using recent Alzheimer's disease (AD) GWAS results as the base data, we determined each individual's polygenic risk score (PRS) in the UK Biobank dataset. Using individuals within the extreme risk distribution, we performed a GWAS that is agnostic of AD phenotype and is instead based on known genetic risk for disease. To interpret the functions of the new risk factors, we conducted phenotype analyses, including a phenome-wide association study. We identified 246 loci surpassing the significance threshold of which 229 were not reported in the base AD GWAS. These include loci that showed suggestive levels of association in the base GWAS and loci not previously suspected to be associated with AD. Among these, there are loci, such as IL34 and KANSL1, that have since been shown to be associated with AD in recent studies. We also show highly significant genetic correlations with multiple health-related outcomes that provide insights into prodromal symptoms and comorbidities. This is the first study to utilize PRS as a phenotype-agnostic group classification in AD genetic studies. We identify potential new loci for AD and detail phenotypic analysis of these PRS extremes.
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Affiliation(s)
- Catarina Gouveia
- Department of Neurodegenerative Science, Van Andel Research Institute, 333 Bostwick Ave. N.E., Grand Rapids, MI, 49503-2518, USA
| | - Elizabeth Gibbons
- Department of Neurodegenerative Science, Van Andel Research Institute, 333 Bostwick Ave. N.E., Grand Rapids, MI, 49503-2518, USA
| | - Nadia Dehghani
- Department of Neurodegenerative Science, Van Andel Research Institute, 333 Bostwick Ave. N.E., Grand Rapids, MI, 49503-2518, USA
| | - James Eapen
- Department of Neurodegenerative Science, Van Andel Research Institute, 333 Bostwick Ave. N.E., Grand Rapids, MI, 49503-2518, USA
| | - Rita Guerreiro
- Department of Neurodegenerative Science, Van Andel Research Institute, 333 Bostwick Ave. N.E., Grand Rapids, MI, 49503-2518, USA.,Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
| | - Jose Bras
- Department of Neurodegenerative Science, Van Andel Research Institute, 333 Bostwick Ave. N.E., Grand Rapids, MI, 49503-2518, USA. .,Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA.
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29
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Shi JW, Cao H, Hong L, Ma J, Cui L, Zhang Y, Song X, Liu J, Yang Y, Lv Q, Zhang L, Wang J, Xie M. Diagnostic yield of whole exome data in fetuses aborted for conotruncal malformations. Prenat Diagn 2022; 42:852-861. [PMID: 35420166 DOI: 10.1002/pd.6147] [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: 10/13/2021] [Revised: 04/01/2022] [Accepted: 04/07/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVE We investigated a custom congenital heart disease (CHD) geneset to assess the diagnostic value of whole-exome sequencing (WES) in karyotype- and copy number variation (CNV)-negative aborted fetuses with conotruncal defects (CTD), and to explore the impact of postnatal phenotyping on genetic diagnosis. METHODS We sequentially analyzed CNV-seq and WES data from 47 CTD fetuses detected by prenatal ultrasonography. Fetuses with either a confirmed aneuploidy or pathogenic CNV were excluded from the WES analyses, which were performed following the American College of Medical Genetics and Genomics recommendations and a custom CHD-geneset. Imaging and autopsy were applied to obtain postnatal phenotypic information about aborted fetuses. RESULTS CNV-seq identified aneuploidy in 7/47 cases while 13/47 fetuses were CNV-positive. Eighty-five rare deleterious variants in 61 genes (from custom geneset) were identified by WES in the remaining fetuses. Of these, five (likely) pathogenic variants (LPV/PV) were identified in five fetuses, revealing a 10.6% incremental diagnostic yield. Furthermore, RERE:c.2461_2472delGGGATGTGGCGA was reclassified as LPV based on postnatal phenotypic data. CONCLUSION We have developed and defined a CHD gene panel that can be utilized in a subset of fetuses with CTDs. We demonstrate the utility of incorporating both prenatal and postnatal phenotypic information may facilitate WES diagnostics. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Jia-Wei Shi
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Haiyan Cao
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Liu Hong
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Jing Ma
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Li Cui
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Yi Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Xiaoyan Song
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Juanjuan Liu
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Yali Yang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Qing Lv
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Li Zhang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Jing Wang
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Mingxing Xie
- Department of Ultrasound, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.,Clinical Research Center for Medical Imaging in Hubei Province, Wuhan, 430022, China.,Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
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30
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Irlmeier R, Hughey JJ, Bastarache L, Denny JC, Chen Q. Cox regression is robust to inaccurate EHR-extracted event time: an application to EHR-based GWAS. Bioinformatics 2022; 38:2297-2306. [PMID: 35157022 PMCID: PMC10060718 DOI: 10.1093/bioinformatics/btac086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 12/14/2021] [Accepted: 02/09/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Logistic regression models are used in genomic studies to analyze the genetic data linked to electronic health records (EHRs), and do not take full usage of the time-to-event information available in EHRs. Previous work has shown that Cox regression, which can account for left truncation and right censoring in EHRs, increased the power to detect genotype-phenotype associations compared to logistic regression. We extend this to evaluate the relative performance of Cox regression and various logistic regression models in the presence of positive errors in event time (delayed event time), relating to recorded event time accuracy. RESULTS One Cox model and three logistic regression models were considered under different scenarios of delayed event time. Extensive simulations and a genomic study application were used to evaluate the impact of delayed event time. While logistic regression does not model the time-to-event directly, various logistic regression models used in the literature were more sensitive to delayed event time than Cox regression. Results highlighted the importance to identify and exclude the patients diagnosed before entry time. Cox regression had similar or modest improvement in statistical power over various logistic regression models at controlled type I error. This was supported by the empirical data, where the Cox models steadily had the highest sensitivity to detect known genotype-phenotype associations under all scenarios of delayed event time. AVAILABILITY AND IMPLEMENTATION Access to individual-level EHR and genotype data is restricted by the IRB. Simulation code and R script for data process are at: https://github.com/QingxiaCindyChen/CoxRobustEHR.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rebecca Irlmeier
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Jacob J Hughey
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Biomedical Sciences, Vanderbilt University, Nashville, TN 37203, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Joshua C Denny
- All of Us Research Program, National Institutes of Health, Bethesda, MD 20892, USA
| | - Qingxia Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN 37203, USA.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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31
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Sotnikova EA, Kiseleva AV, Meshkov AN, Ershova AI, Ivanova AA, Kolchina MA, Kutsenko VA, Skripnikova IA, Drapkina OM. Biobank data for studying the genetic architecture of osteoporosis and developing genetic risk scores. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2021-3045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.
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Affiliation(s)
- E. A. Sotnikova
- National Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Research Center for Therapy and Preventive Medicine
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine; Russian National Research Medical University
| | - A. I. Ershova
- National Research Center for Therapy and Preventive Medicine
| | - A. A. Ivanova
- National Research Center for Therapy and Preventive Medicine
| | - M. A. Kolchina
- National Research Center for Therapy and Preventive Medicine
| | - V. A. Kutsenko
- National Medical Research Center for Therapy and Preventive Medicine; Lomonosov Moscow State University
| | - I. A. Skripnikova
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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32
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Escribe C, Lu T, Keller-Baruch J, Forgetta V, Xiao B, Richards JB, Bhatnagar S, Oualkacha K, Greenwood CMT. Block coordinate descent algorithm improves variable selection and estimation in error-in-variables regression. Genet Epidemiol 2021; 45:874-890. [PMID: 34468045 PMCID: PMC9292988 DOI: 10.1002/gepi.22430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 07/19/2021] [Accepted: 08/12/2021] [Indexed: 11/13/2022]
Abstract
Medical research increasingly includes high‐dimensional regression modeling with a need for error‐in‐variables methods. The Convex Conditioned Lasso (CoCoLasso) utilizes a reformulated Lasso objective function and an error‐corrected cross‐validation to enable error‐in‐variables regression, but requires heavy computations. Here, we develop a Block coordinate Descent Convex Conditioned Lasso (BDCoCoLasso) algorithm for modeling high‐dimensional data that are only partially corrupted by measurement error. This algorithm separately optimizes the estimation of the uncorrupted and corrupted features in an iterative manner to reduce computational cost, with a specially calibrated formulation of cross‐validation error. Through simulations, we show that the BDCoCoLasso algorithm successfully copes with much larger feature sets than CoCoLasso, and as expected, outperforms the naïve Lasso with enhanced estimation accuracy and consistency, as the intensity and complexity of measurement errors increase. Also, a new smoothly clipped absolute deviation penalization option is added that may be appropriate for some data sets. We apply the BDCoCoLasso algorithm to data selected from the UK Biobank. We develop and showcase the utility of covariate‐adjusted genetic risk scores for body mass index, bone mineral density, and lifespan. We demonstrate that by leveraging more information than the naïve Lasso in partially corrupted data, the BDCoCoLasso may achieve higher prediction accuracy. These innovations, together with an R package, BDCoCoLasso, make error‐in‐variables adjustments more accessible for high‐dimensional data sets. We posit the BDCoCoLasso algorithm has the potential to be widely applied in various fields, including genomics‐facilitated personalized medicine research.
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Affiliation(s)
- Célia Escribe
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, Québec, Canada
| | - Julyan Keller-Baruch
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada
| | - Bowei Xiao
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, Québec, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Sahir Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.,Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montréal, Montreal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada
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Kim S, Shin JY, Kwon NJ, Kim CU, Kim C, Lee CS, Seo JS. Evaluation of low-pass genome sequencing in polygenic risk score calculation for Parkinson's disease. Hum Genomics 2021; 15:58. [PMID: 34454617 PMCID: PMC8403377 DOI: 10.1186/s40246-021-00357-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 08/22/2021] [Indexed: 12/02/2022] Open
Abstract
Background Low-pass sequencing (LPS) has been extensively investigated for applicability to various genetic studies due to its advantages over genotype array data including cost-effectiveness. Predicting the risk of complex diseases such as Parkinson’s disease (PD) using polygenic risk score (PRS) based on the genetic variations has shown decent prediction accuracy. Although ultra-LPS has been shown to be effective in PRS calculation, array data has been favored to the majority of PRS analysis, especially for PD.
Results Using eight high-coverage WGS, we assessed imputation approaches for downsampled LPS data ranging from 0.5 × to 7.0 × . We demonstrated that uncertain genotype calls of LPS diminished imputation accuracy, and an imputation approach using genotype likelihoods was plausible for LPS. Additionally, comparing imputation accuracies between LPS and simulated array illustrated that LPS had higher accuracies particularly at rare frequencies. To evaluate ultra-low coverage data in PRS calculation for PD, we prepared low-coverage WGS and genotype array of 87 PD cases and 101 controls. Genotype imputation of array and downsampled LPS were conducted using a population-specific reference panel, and we calculated risk scores based on the PD-associated SNPs from an East Asian meta-GWAS. The PRS models discriminated cases and controls as previously reported when both LPS and genotype array were used. Also strong correlations in PRS models for PD between LPS and genotype array were discovered. Conclusions Overall, this study highlights the potentials of LPS under 1.0 × followed by genotype imputation in PRS calculation and suggests LPS as attractive alternatives to genotype array in the area of precision medicine for PD. Supplementary Information The online version contains supplementary material available at 10.1186/s40246-021-00357-w.
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Affiliation(s)
- Sungjae Kim
- Precision Medicine Institute, Seoul, 08511, Republic of Korea.,Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, 03080, Republic of Korea
| | - Jong-Yeon Shin
- Precision Medicine Institute, Seoul, 08511, Republic of Korea
| | - Nak-Jung Kwon
- Precision Medicine Institute, Seoul, 08511, Republic of Korea
| | | | - Changhoon Kim
- Precision Medicine Institute, Seoul, 08511, Republic of Korea
| | - Chong Sik Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Pungnap 2(i)-dong, Songpa-gu, Seoul, 05505, Republic of Korea.
| | - Jeong-Sun Seo
- Precision Medicine Institute, Seoul, 08511, Republic of Korea. .,Asian Genome Institute, Seoul National University Bundang Hospital, 172 Dolma-ro, Seongnam, Bundang-gu, Gyeonggi-do, 13605, Republic of Korea.
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Contextualizing genetic risk score for disease screening and rare variant discovery. Nat Commun 2021; 12:4418. [PMID: 34285202 PMCID: PMC8292385 DOI: 10.1038/s41467-021-24387-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 06/07/2021] [Indexed: 11/08/2022] Open
Abstract
Studies of the genetic basis of complex traits have demonstrated a substantial role for common, small-effect variant polygenic burden (PB) as well as large-effect variants (LEV, primarily rare). We identify sufficient conditions in which GWAS-derived PB may be used for well-powered rare pathogenic variant discovery or as a sample prioritization tool for whole-genome or exome sequencing. Through extensive simulations of genetic architectures and generative models of disease liability with parameters informed by empirical data, we quantify the power to detect, among cases, a lower PB in LEV carriers than in non-carriers. Furthermore, we uncover clinically useful conditions wherein the risk derived from the PB is comparable to the LEV-derived risk. The resulting summary-statistics-based methodology (with publicly available software, PB-LEV-SCAN) makes predictions on PB-based LEV screening for 36 complex traits, which we confirm in several disease datasets with available LEV information in the UK Biobank, with important implications on clinical decision-making.
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Lu T, Forgetta V, Wu H, Perry JRB, Ong KK, Greenwood CMT, Timpson NJ, Manousaki D, Richards JB. A Polygenic Risk Score to Predict Future Adult Short Stature Among Children. J Clin Endocrinol Metab 2021; 106:1918-1928. [PMID: 33788949 PMCID: PMC8266463 DOI: 10.1210/clinem/dgab215] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Indexed: 11/30/2022]
Abstract
CONTEXT Adult height is highly heritable, yet no genetic predictor has demonstrated clinical utility compared to mid-parental height. OBJECTIVE To develop a polygenic risk score for adult height and evaluate its clinical utility. DESIGN A polygenic risk score was constructed based on meta-analysis of genomewide association studies and evaluated on the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. SUBJECTS Participants included 442 599 genotyped White British individuals in the UK Biobank and 941 genotyped child-parent trios of European ancestry in the ALSPAC cohort. INTERVENTIONS None. MAIN OUTCOME MEASURES Standing height was measured using stadiometer; Standing height 2 SDs below the sex-specific population average was considered as short stature. RESULTS Combined with sex, a polygenic risk score captured 71.1% of the total variance in adult height in the UK Biobank. In the ALSPAC cohort, the polygenic risk score was able to identify children who developed adulthood short stature with an area under the receiver operating characteristic curve (AUROC) of 0.84, which is close to that of mid-parental height. Combining this polygenic risk score with mid-parental height or only one of the child's parent's height could improve the AUROC to at most 0.90. The polygenic risk score could also substitute mid-parental height in age-specific Khamis-Roche height predictors and achieve an equally strong discriminative power in identifying children with a short stature in adulthood. CONCLUSIONS A polygenic risk score could be considered as an alternative or adjunct to mid-parental height to improve screening for children at risk of developing short stature in adulthood in European ancestry populations.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
| | - Haoyu Wu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada
| | - John R B Perry
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Pediatrics, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada
- Department of Human Genetics, McGill University, Montréal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Despoina Manousaki
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Pediatrics, Université de Montréal, Montréal, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Human Genetics, McGill University, Montréal, Canada
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- Correspondence: J. Brent Richards, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, Québec, H3T 1E2, Canada. E-mail:
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Combined Effect of a Polygenic Risk Score and Rare Genetic Variants on Prostate Cancer Risk. Eur Urol 2021; 80:134-138. [PMID: 33941403 DOI: 10.1016/j.eururo.2021.04.013] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 04/12/2021] [Indexed: 02/08/2023]
Abstract
Although prostate cancer is known to have a strong genetic basis and is influenced by both common and rare variants, the ability to investigate the combined effect of such genetic risk factors has been limited to date. We conducted an investigation of 81 094 men from the UK Biobank, including 3568 prostate cancer cases, to examine the combined effect of rare pathogenic/likely pathogenic/deleterious (P/LP/D) germline variants and common prostate cancer risk variants, measured using a polygenic risk score (PRS), on prostate cancer risk. The absolute risk of prostate cancer for HOXB13, BRCA2, ATM, and CHEK2 P/LP/D carriers ranged from 9% to 56%, and the absolute risk in noncarriers ranged from 2% to 31%, by age 85 yr, for men in the lowest and highest PRS decile, respectively. The high-penetrant HOXB13 G84E prostate cancer risk variant was most common in cases in the lowest PRS quintile (4.4%) and least common in cases in the highest PRS quintile (0.5%; p = 0.005), whereas there was no statistically significant difference in frequencies by PRS in controls. While rare and common variants strongly and distinctly influence prostate cancer onset, consideration of rare and common variants in conjunction will lead to more precise estimates of a man's lifetime risk of prostate cancer. PATIENT SUMMARY: We found that the risk of prostate cancer conveyed by rare variants could vary depending on an individual's genetic profile of common risk variants. This implies that in order to comprehensively assess genetic risk of prostate cancer, it is important to consider both rare and common variants.
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