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Cui R, Wu J, Yan K, Luo S, Hu Y, Feng W, Lu B, Wang J. Phased genome assemblies reveal haplotype-specific genetic load in the critically endangered Chinese Bahaba (Teleostei, Sciaenidae). Mol Ecol 2024; 33:e17250. [PMID: 38179694 DOI: 10.1111/mec.17250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 12/06/2023] [Accepted: 12/11/2023] [Indexed: 01/06/2024]
Abstract
While haplotype-specific genetic load shapes the evolutionary trajectory of natural and captive populations, mixed-haplotype assembly and genotyping hindered its characterization in diploids. Herein, we produced two phased genome assemblies of the critically endangered fish Chinese Bahaba (Bahaba taipingensis, Sciaenidae, Teleostei) and resequenced 20 whole genomes to quantify population genetic load at a haplotype level. We identified frame-shifting variants as the most deleterious type, followed by mutations in the 5'-UTR, 3'-UTR and missense mutations at conserved amino acids. Phased haplotypes revealed gene deletions and high-impact deleterious variants. We estimated ~1.12% of genes missing or interrupted per haplotype, with a significant overlap of disrupted genes (30.35%) between haplotype sets. Relative proportions of deleterious variant categories differed significantly between haplotypes. Simulations suggested that purifying selection struggled to purge slightly deleterious genetic load in captive breeding compared to genotyping interventions, and that higher inter-haplotypic variance of genetic load predicted more efficient purging by artificial selection. Combining the knowledge of haplotype-resolved genetic load with predictive modelling will be immensely useful for understanding the evolution of deleterious variants and guiding conservation planning.
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Affiliation(s)
- Rongfeng Cui
- School of Ecology & State Key Laboratory of Biocontrol, Sun Yat-sen University, Shenzhen, China
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, China
| | - Jinxian Wu
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, Guangdong-Macao Joint Laboratory for Aquaculture Breeding Development and Innovation, School of Life Sciences, South China Normal University, Guangzhou, China
| | - Kuoqiu Yan
- Huangjing Marine Biotechnology Co. Ltd., Huizhou, China
| | - Sujun Luo
- Dongguan Forestry Affairs Center, Dongguan, China
| | - Yuting Hu
- Dongguan Forestry Affairs Center, Dongguan, China
| | - Wei Feng
- Dongguan Forestry Affairs Center, Dongguan, China
| | - Bingqian Lu
- Dongguan Forestry Affairs Center, Dongguan, China
| | - Junjie Wang
- Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, Guangdong-Macao Joint Laboratory for Aquaculture Breeding Development and Innovation, School of Life Sciences, South China Normal University, Guangzhou, China
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Rougemont Q, Leroy T, Rondeau EB, Koop B, Bernatchez L. Allele surfing causes maladaptation in a Pacific salmon of conservation concern. PLoS Genet 2023; 19:e1010918. [PMID: 37683018 PMCID: PMC10545117 DOI: 10.1371/journal.pgen.1010918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 10/02/2023] [Accepted: 08/11/2023] [Indexed: 09/10/2023] Open
Abstract
How various factors, including demography, recombination or genome duplication, may impact the efficacy of natural selection and the burden of deleterious mutations, is a central question in evolutionary biology and genetics. In this study, we show that key evolutionary processes, including variations in i) effective population size (Ne) ii) recombination rates and iii) chromosome inheritance, have influenced the genetic load and efficacy of selection in Coho salmon (Oncorhynchus kisutch), a widely distributed salmonid species on the west coast of North America. Using whole genome resequencing data from 14 populations at different migratory distances from their southern glacial refugium, we found evidence supporting gene surfing, wherein reduced Ne at the postglacial recolonization front, leads to a decrease in the efficacy of selection and a surf of deleterious alleles in the northernmost populations. Furthermore, our results indicate that recombination rates play a prime role in shaping the load along the genome. Additionally, we identified variation in polyploidy as a contributing factor to within-genome variation of the load. Overall, our results align remarkably well with expectations under the nearly neutral theory of molecular evolution. We discuss the fundamental and applied implications of these findings for evolutionary and conservation genomics.
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Affiliation(s)
- Quentin Rougemont
- Centre d’Ecologie Fonctionnelle et Evolutive, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Thibault Leroy
- GenPhySE, INRAE, INP, ENVT, Université de Toulouse, Auzeville- Tolosane, France
| | - Eric B. Rondeau
- Department of Fisheries and Ocean, Pacific Biological Station, Nanaimo, Canada
| | - Ben Koop
- Department of Biology, University of Victoria, Victoria, Canada
| | - Louis Bernatchez
- Département de Biologie, Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, Canada
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3
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Fierheller CT, Alenezi WM, Serruya C, Revil T, Amuzu S, Bedard K, Subramanian DN, Fewings E, Bruce JP, Prokopec S, Bouchard L, Provencher D, Foulkes WD, El Haffaf Z, Mes-Masson AM, Tischkowitz M, Campbell IG, Pugh TJ, Greenwood CMT, Ragoussis J, Tonin PN. Molecular Genetic Characteristics of FANCI, a Proposed New Ovarian Cancer Predisposing Gene. Genes (Basel) 2023; 14:genes14020277. [PMID: 36833203 PMCID: PMC9956348 DOI: 10.3390/genes14020277] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023] Open
Abstract
FANCI was recently identified as a new candidate ovarian cancer (OC)-predisposing gene from the genetic analysis of carriers of FANCI c.1813C>T; p.L605F in OC families. Here, we aimed to investigate the molecular genetic characteristics of FANCI, as they have not been described in the context of cancer. We first investigated the germline genetic landscape of two sisters with OC from the discovery FANCI c.1813C>T; p.L605F family (F1528) to re-affirm the plausibility of this candidate. As we did not find other conclusive candidates, we then performed a candidate gene approach to identify other candidate variants in genes involved in the FANCI protein interactome in OC families negative for pathogenic variants in BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, and FANCI, which identified four candidate variants. We then investigated FANCI in high-grade serous ovarian carcinoma (HGSC) from FANCI c.1813C>T carriers and found evidence of loss of the wild-type allele in tumour DNA from some of these cases. The somatic genetic landscape of OC tumours from FANCI c.1813C>T carriers was investigated for mutations in selected genes, copy number alterations, and mutational signatures, which determined that the profiles of tumours from carriers were characteristic of features exhibited by HGSC cases. As other OC-predisposing genes such as BRCA1 and BRCA2 are known to increase the risk of other cancers including breast cancer, we investigated the carrier frequency of germline FANCI c.1813C>T in various cancer types and found overall more carriers among cancer cases compared to cancer-free controls (p = 0.007). In these different tumour types, we also identified a spectrum of somatic variants in FANCI that were not restricted to any specific region within the gene. Collectively, these findings expand on the characteristics described for OC cases carrying FANCI c.1813C>T; p.L605F and suggest the possible involvement of FANCI in other cancer types at the germline and/or somatic level.
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Affiliation(s)
- Caitlin T. Fierheller
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Wejdan M. Alenezi
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Department of Medical Laboratory Technology, Taibah University, Medina 42353, Saudi Arabia
| | - Corinne Serruya
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Timothée Revil
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
| | - Setor Amuzu
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
| | - Karine Bedard
- Laboratoire de Diagnostic Moléculaire, Centre Hospitalier de l’Université de Montréal (CHUM), Montreal, QC H2X 3E4, Canada
- Département de Pathologie et Biologie Cellulaire, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Deepak N. Subramanian
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Eleanor Fewings
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge CB2 1TN, UK
| | - Jeffrey P. Bruce
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Stephenie Prokopec
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
- Department of Medical Biology, Centres Intégrés Universitaires de Santé et de Services Sociaux du Saguenay-Lac-Saint-Jean Hôpital Universitaire de Chicoutimi, Saguenay, QC G7H 7K9, Canada
- Centre de Recherche du Centre Hospitalier l’Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Diane Provencher
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal and Institut du Cancer de Montréal, Montreal, QC H2X 0A9, Canada
- Division of Gynecologic Oncology, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - William D. Foulkes
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Department of Medicine, McGill University, Montreal, QC H3G 2M1, Canada
| | - Zaki El Haffaf
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC H2X 0A9, Canada
| | - Anne-Marie Mes-Masson
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal and Institut du Cancer de Montréal, Montreal, QC H2X 0A9, Canada
- Department of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge CB2 1TN, UK
| | - Ian G. Campbell
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Trevor J. Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Celia M. T. Greenwood
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H4A 3T2, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, QC H3A 1Y7, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
| | - Patricia N. Tonin
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Department of Medicine, McGill University, Montreal, QC H3G 2M1, Canada
- Correspondence:
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Zheng Z, Hu H, Lei W, Zhang J, Zhu M, Li Y, Zhang X, Ma J, Wan D, Ma T, Ren G, Ru D. Somatic mutations during rapid clonal domestication of
Populus alba
var.
pyramidalis. Evol Appl 2022; 15:1875-1887. [DOI: 10.1111/eva.13486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 09/08/2022] [Accepted: 09/15/2022] [Indexed: 12/01/2022] Open
Affiliation(s)
- Zeyu Zheng
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Hongyin Hu
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Weixiao Lei
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Jin Zhang
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Mingjia Zhu
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Ying Li
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Xu Zhang
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Jianchao Ma
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Dongshi Wan
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Tao Ma
- Key Laboratory of Bio‐Resource and Eco‐Environment of Ministry of Education, College of Life Sciences, State Key Laboratory of Hydraulics and Mountain River Engineering Sichuan University Chengdu China
| | - Guangpeng Ren
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
| | - Dafu Ru
- State Key Laboratory of Grassland Agro‐Ecosystems, College of Ecology Lanzhou University Lanzhou China
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5
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How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia. Genes (Basel) 2022; 13:genes13061040. [PMID: 35741803 PMCID: PMC9222307 DOI: 10.3390/genes13061040] [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: 05/12/2022] [Revised: 05/27/2022] [Accepted: 06/08/2022] [Indexed: 12/10/2022] Open
Abstract
Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project is to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene–gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve the threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach the threshold for schizophrenia at a recurring frequency in the population.
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Harwood MP, Alves I, Edgington H, Agbessi M, Bruat V, Soave D, Lamaze FC, Favé MJ, Awadalla P. Recombination affects allele-specific expression of deleterious variants in human populations. SCIENCE ADVANCES 2022; 8:eabl3819. [PMID: 35559670 PMCID: PMC9106294 DOI: 10.1126/sciadv.abl3819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 03/29/2022] [Indexed: 06/15/2023]
Abstract
How the genetic composition of a population changes through stochastic processes, such as genetic drift, in combination with deterministic processes, such as selection, is critical to understanding how phenotypes vary in space and time. Here, we show how evolutionary forces affecting selection, including recombination and effective population size, drive genomic patterns of allele-specific expression (ASE). Integrating tissue-specific genotypic and transcriptomic data from 1500 individuals from two different cohorts, we demonstrate that ASE is less often observed in regions of low recombination, and loci in high or normal recombination regions are more efficient at using ASE to underexpress harmful mutations. By tracking genetic ancestry, we discriminate between ASE variability due to past demographic effects, including subsequent bottlenecks, versus local environment. We observe that ASE is not randomly distributed along the genome and that population parameters influencing the efficacy of natural selection alter ASE levels genome wide.
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Affiliation(s)
- Michelle P. Harwood
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Isabel Alves
- Université de Nantes, CHU Nantes, CNRS, INSERM, L’Institut du thorax, F-44000 Nantes, France
| | | | | | - Vanessa Bruat
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - David Soave
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Mathematics, Wilfrid Laurier University, Waterloo, ON, Canada
| | - Fabien C. Lamaze
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Institut universitaire de cardiologie et de pneumologie de Québec, Université Laval, Québec, QC, Canada
| | | | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
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7
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Jantzen R, Payette Y, de Malliard T, Labbé C, Noisel N, Broët P. Five-year absolute risk estimates of colorectal cancer based on CCRAT model and polygenic risk scores: A validation study using the Quebec population-based cohort CARTaGENE. Prev Med Rep 2022; 25:101678. [PMID: 35127357 PMCID: PMC8800052 DOI: 10.1016/j.pmedr.2021.101678] [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: 08/22/2021] [Revised: 10/21/2021] [Accepted: 12/24/2021] [Indexed: 11/06/2022] Open
Abstract
The objective was to evaluate the predictive performance of the Colorectal Cancer Risk Assessment Tool (CCRAT) and three polygenic risk scores (Hsu et al., 2015; Law et al., 2019, Archambault et al., 2020) to predict the occurrence of colorectal cancer at five years in a Quebec population-based cohort. By using the CARTaGENE cohort, we computed the absolute risk of colorectal cancer with the CCRAT model, the polygenic risk scores (PRS) and combined clinico-genetic models (CCRAT + PRS). We also tailored the CCRAT model by using the marginal age-specific colorectal incidence rates in Canada and the risk score distribution. We reported the calibration and the discrimination. Performances of the PRSs, combined and tailored CCRAT models were compared to the original CCRAT model. The expected-to-observed ratio of the original CCRAT model was 0.54 [0.43–0.68]. The c-index was 74.79 [68.3–80.5]. The tailored CCRAT model improved the expected-to-observed ratio (0.74 [0.59–0.94]) and c-index (76.39 [69.7–82.1]). All PRS improved the expected-to-observed ratios (around 0.83, confidence intervals including one). PRSs’ c-indexes were not significantly different from CCRAT models. Results from the combined models were close to those from the PRS models, Archambault combined model’s c-index being significantly higher than the original and tailored CCRAT models (78.67 [70.8–86.5]; p < 0.001 and p = 0.028, respectively). In this Quebec cohort, CCRAT model has a good discrimination with a poor calibration. While the tailored CCRAT provides some gain in calibration, clinico-genetic models improved both calibration and discrimination. However, better calibrations must be obtained before a practical use among the inhabitants of Quebec province.
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8
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Fine human genetic map based on UK10K data set. Hum Genet 2022; 141:273-281. [DOI: 10.1007/s00439-021-02415-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 12/03/2021] [Indexed: 11/04/2022]
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9
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de Klerk A, Swanepoel P, Lourens R, Zondo M, Abodunran I, Lytras S, MacLean OA, Robertson D, Kosakovsky Pond SL, Zehr JD, Kumar V, Stanhope MJ, Harkins G, Murrell B, Martin DP. Conserved recombination patterns across coronavirus subgenera. Virus Evol 2022; 8:veac054. [PMID: 35814334 PMCID: PMC9261289 DOI: 10.1093/ve/veac054] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 03/03/2022] [Accepted: 06/10/2022] [Indexed: 11/12/2022] Open
Abstract
Recombination contributes to the genetic diversity found in coronaviruses and is known to be a prominent mechanism whereby they evolve. It is apparent, both from controlled experiments and in genome sequences sampled from nature, that patterns of recombination in coronaviruses are non-random and that this is likely attributable to a combination of sequence features that favour the occurrence of recombination break points at specific genomic sites, and selection disfavouring the survival of recombinants within which favourable intra-genome interactions have been disrupted. Here we leverage available whole-genome sequence data for six coronavirus subgenera to identify specific patterns of recombination that are conserved between multiple subgenera and then identify the likely factors that underlie these conserved patterns. Specifically, we confirm the non-randomness of recombination break points across all six tested coronavirus subgenera, locate conserved recombination hot- and cold-spots, and determine that the locations of transcriptional regulatory sequences are likely major determinants of conserved recombination break-point hotspot locations. We find that while the locations of recombination break points are not uniformly associated with degrees of nucleotide sequence conservation, they display significant tendencies in multiple coronavirus subgenera to occur in low guanine-cytosine content genome regions, in non-coding regions, at the edges of genes, and at sites within the Spike gene that are predicted to be minimally disruptive of Spike protein folding. While it is apparent that sequence features such as transcriptional regulatory sequences are likely major determinants of where the template-switching events that yield recombination break points most commonly occur, it is evident that selection against misfolded recombinant proteins also strongly impacts observable recombination break-point distributions in coronavirus genomes sampled from nature.
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Affiliation(s)
- Arné de Klerk
- Institute of Infectious Diseases and Molecular Medicine, Division Of Computational Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7701, South Africa
| | - Phillip Swanepoel
- Institute of Infectious Diseases and Molecular Medicine, Division Of Computational Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7701, South Africa
| | - Rentia Lourens
- Division of Neurosurgery, Neuroscience Institute, Department of Surgery, University of Cape Town, Cape Town, 7701, South Africa
| | - Mpumelelo Zondo
- Institute of Infectious Diseases and Molecular Medicine, Division Of Computational Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7701, South Africa
| | - Isaac Abodunran
- Institute of Infectious Diseases and Molecular Medicine, Division Of Computational Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7701, South Africa
| | - Spyros Lytras
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Oscar A MacLean
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - David Robertson
- MRC-University of Glasgow Centre for Virus Research, University of Glasgow, Glasgow G61 1QH, UK
| | - Sergei L Kosakovsky Pond
- Department of Biology, Temple University, Institute for Genomics and Evolutionary Medicine, Philadelphia, PA 19122, USA
| | - Jordan D Zehr
- Department of Biology, Temple University, Institute for Genomics and Evolutionary Medicine, Philadelphia, PA 19122, USA
| | - Venkatesh Kumar
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 14186, Sweden
| | - Michael J Stanhope
- Department of Population and Ecosystem Health, College of Veterinary Medicine, Cornell University, Ithaca, NY, 14853, USA
| | - Gordon Harkins
- South African National Bioinformatics Institute, University of the Western Cape, Cape Town, 7535, South Africa
| | - Ben Murrell
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 14186, Sweden
| | - Darren P Martin
- Institute of Infectious Diseases and Molecular Medicine, Division Of Computational Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town 7701, South Africa
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10
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Estimated prevalence of Niemann-Pick type C disease in Quebec. Sci Rep 2021; 11:22621. [PMID: 34799641 PMCID: PMC8604933 DOI: 10.1038/s41598-021-01966-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 11/08/2021] [Indexed: 12/30/2022] Open
Abstract
Niemann–Pick type C (NP-C) disease is an autosomal recessive disease caused by variants in the NPC1 or NPC2 genes. It has a large range of symptoms depending on age of onset, thus making it difficult to diagnose. In adults, symptoms appear mainly in the form of psychiatric problems. The prevalence varies from 0.35 to 2.2 per 100,000 births depending on the country. The aim of this study is to calculate the estimated prevalence of NP-C in Quebec to determine if it is underdiagnosed in this population. The CARTaGENE database is a unique database that regroups individuals between 40 and 69 years old from metropolitan regions of Quebec. RNA-sequencing data was available for 911 individuals and exome sequencing for 198 individuals. We used a bioinformatic pipeline on those individuals to extract the variants in the NPC1/2 genes. The prevalence in Quebec was estimated assuming Hardy–Weinberg Equilibrium. Two pathogenic variants were used. The variant p.Pro543Leu was found in three heterozygous individuals that share a common haplotype, which suggests a founder French-Canadian pathogenic variant. The variant p.Ile1061Thr was found in two heterozygous individuals. Both variants have previously been reported and are usually associated with infantile onset. The estimated prevalence calculated using those two variants is 0.61:100,000 births. This study represents the first estimate of NP-C in Quebec. The estimated prevalence for NP-C is likely underestimated due to misdiagnosis or missed cases. It is therefore important to diagnose all NP-C patients to initiate early treatment.
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11
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Mitani T, Isikay S, Gezdirici A, Gulec EY, Punetha J, Fatih JM, Herman I, Akay G, Du H, Calame DG, Ayaz A, Tos T, Yesil G, Aydin H, Geckinli B, Elcioglu N, Candan S, Sezer O, Erdem HB, Gul D, Demiral E, Elmas M, Yesilbas O, Kilic B, Gungor S, Ceylan AC, Bozdogan S, Ozalp O, Cicek S, Aslan H, Yalcintepe S, Topcu V, Bayram Y, Grochowski CM, Jolly A, Dawood M, Duan R, Jhangiani SN, Doddapaneni H, Hu J, Muzny DM, Marafi D, Akdemir ZC, Karaca E, Carvalho CMB, Gibbs RA, Posey JE, Lupski JR, Pehlivan D. High prevalence of multilocus pathogenic variation in neurodevelopmental disorders in the Turkish population. Am J Hum Genet 2021; 108:1981-2005. [PMID: 34582790 PMCID: PMC8546040 DOI: 10.1016/j.ajhg.2021.08.009] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Neurodevelopmental disorders (NDDs) are clinically and genetically heterogenous; many such disorders are secondary to perturbation in brain development and/or function. The prevalence of NDDs is > 3%, resulting in significant sociocultural and economic challenges to society. With recent advances in family-based genomics, rare-variant analyses, and further exploration of the Clan Genomics hypothesis, there has been a logarithmic explosion in neurogenetic "disease-associated genes" molecular etiology and biology of NDDs; however, the majority of NDDs remain molecularly undiagnosed. We applied genome-wide screening technologies, including exome sequencing (ES) and whole-genome sequencing (WGS), to identify the molecular etiology of 234 newly enrolled subjects and 20 previously unsolved Turkish NDD families. In 176 of the 234 studied families (75.2%), a plausible and genetically parsimonious molecular etiology was identified. Out of 176 solved families, deleterious variants were identified in 218 distinct genes, further documenting the enormous genetic heterogeneity and diverse perturbations in human biology underlying NDDs. We propose 86 candidate disease-trait-associated genes for an NDD phenotype. Importantly, on the basis of objective and internally established variant prioritization criteria, we identified 51 families (51/176 = 28.9%) with multilocus pathogenic variation (MPV), mostly driven by runs of homozygosity (ROHs) - reflecting genomic segments/haplotypes that are identical-by-descent. Furthermore, with the use of additional bioinformatic tools and expansion of ES to additional family members, we established a molecular diagnosis in 5 out of 20 families (25%) who remained undiagnosed in our previously studied NDD cohort emanating from Turkey.
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Affiliation(s)
- Tadahiro Mitani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sedat Isikay
- Department of Pediatric Neurology, Faculty of Medicine, University of Gaziantep, Gaziantep 27310, Turkey
| | - Alper Gezdirici
- Department of Medical Genetics, Basaksehir Cam and Sakura City Hospital, Istanbul 34480, Turkey
| | - Elif Yilmaz Gulec
- Department of Medical Genetics, Kanuni Sultan Suleyman Training and Research Hospital, 34303 Istanbul, Turkey
| | - Jaya Punetha
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jawid M Fatih
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Isabella Herman
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Section of Pediatric Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Gulsen Akay
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Haowei Du
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel G Calame
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Section of Pediatric Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Akif Ayaz
- Department of Medical Genetics, Adana City Training and Research Hospital, Adana 01170, Turkey; Departments of Medical Genetics, School of Medicine, Istanbul Medipol University, Istanbul 34810, Turkey
| | - Tulay Tos
- University of Health Sciences Zubeyde Hanim Research and Training Hospital of Women's Health and Diseases, Department of Medical Genetics, Ankara 06080, Turkey
| | - Gozde Yesil
- Istanbul Faculty of Medicine, Department of Medical Genetics, Istanbul University, Istanbul 34093, Turkey
| | - Hatip Aydin
- Centre of Genetics Diagnosis, Zeynep Kamil Maternity and Children's Training and Research Hospital, Istanbul, Turkey; Private Reyap Istanbul Hospital, Istanbul 34515, Turkey
| | - Bilgen Geckinli
- Centre of Genetics Diagnosis, Zeynep Kamil Maternity and Children's Training and Research Hospital, Istanbul, Turkey; Department of Medical Genetics, School of Medicine, Marmara University, Istanbul 34722, Turkey
| | - Nursel Elcioglu
- Department of Pediatric Genetics, School of Medicine, Marmara University, Istanbul 34722, Turkey; Eastern Mediterranean University Medical School, Magosa, Mersin 10, Turkey
| | - Sukru Candan
- Medical Genetics Section, Balikesir Ataturk Public Hospital, Balikesir 10100, Turkey
| | - Ozlem Sezer
- Department of Medical Genetics, Samsun Education and Research Hospital, Samsun 55100, Turkey
| | - Haktan Bagis Erdem
- Department of Medical Genetics, University of Health Sciences, Diskapi Yildirim Beyazit Training and Research Hospital, Ankara 06110, Turkey
| | - Davut Gul
- Department of Medical Genetics, Gulhane Military Medical School, Ankara 06010, Turkey
| | - Emine Demiral
- Department of Medical Genetics, School of Medicine, University of Inonu, Malatya 44280, Turkey
| | - Muhsin Elmas
- Department of Medical Genetics, Afyon Kocatepe University, School of Medicine, Afyon 03218, Turkey
| | - Osman Yesilbas
- Division of Critical Care Medicine, Department of Pediatrics, School of Medicine, Bezmialem Foundation University, Istanbul 34093, Turkey; Department of Pediatrics, Division of Pediatric Critical Care Medicine, Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey
| | - Betul Kilic
- Department of Pediatrics and Pediatric Neurology, Faculty of Medicine, Inonu University, Malatya 34218, Turkey
| | - Serdal Gungor
- Department of Pediatrics and Pediatric Neurology, Faculty of Medicine, Inonu University, Malatya 34218, Turkey
| | - Ahmet C Ceylan
- Department of Medical Genetics, University of Health Sciences, Ankara Training and Research Hospital, Ankara 06110, Turkey
| | - Sevcan Bozdogan
- Department of Medical Genetics, Cukurova University Faculty of Medicine, Adana 01330, Turkey
| | - Ozge Ozalp
- Department of Medical Genetics, Adana City Training and Research Hospital, Adana 01170, Turkey
| | - Salih Cicek
- Department of Medical Genetics, Konya Training and Research Hospital, Konya 42250, Turkey
| | - Huseyin Aslan
- Department of Medical Genetics, Adana City Training and Research Hospital, Adana 01170, Turkey
| | - Sinem Yalcintepe
- Department of Medical Genetics, School of Medicine, Trakya University, Edirne 22130, Turkey
| | - Vehap Topcu
- Department of Medical Genetics, Ankara City Hospital, Ankara 06800, Turkey
| | - Yavuz Bayram
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | | | - Angad Jolly
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA
| | - Moez Dawood
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ruizhi Duan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Shalini N Jhangiani
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Harsha Doddapaneni
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jianhong Hu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Donna M Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Dana Marafi
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zeynep Coban Akdemir
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ender Karaca
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Claudia M B Carvalho
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA; Texas Children's Hospital, Houston, TX 77030, USA.
| | - Davut Pehlivan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Section of Pediatric Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, TX 77030, USA.
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12
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Gamache I, Legault MA, Grenier JC, Sanchez R, Rhéaume E, Asgari S, Barhdadi A, Zada YF, Trochet H, Luo Y, Lecca L, Murray M, Raychaudhuri S, Tardif JC, Dubé MP, Hussin J. A sex-specific evolutionary interaction between ADCY9 and CETP. eLife 2021; 10:69198. [PMID: 34609279 PMCID: PMC8594919 DOI: 10.7554/elife.69198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 10/04/2021] [Indexed: 12/14/2022] Open
Abstract
Pharmacogenomic studies have revealed associations between rs1967309 in the adenylyl cyclase type 9 (ADCY9) gene and clinical responses to the cholesteryl ester transfer protein (CETP) modulator dalcetrapib, however, the mechanism behind this interaction is still unknown. Here, we characterized selective signals at the locus associated with the pharmacogenomic response in human populations and we show that rs1967309 region exhibits signatures of positive selection in several human populations. Furthermore, we identified a variant in CETP, rs158477, which is in long-range linkage disequilibrium with rs1967309 in the Peruvian population. The signal is mainly seen in males, a sex-specific result that is replicated in the LIMAA cohort of over 3400 Peruvians. Analyses of RNA-seq data further suggest an epistatic interaction on CETP expression levels between the two SNPs in multiple tissues, which also differs between males and females. We also detected interaction effects of the two SNPs with sex on cardiovascular phenotypes in the UK Biobank, in line with the sex-specific genotype associations found in Peruvians at these loci. We propose that ADCY9 and CETP coevolved during recent human evolution due to sex-specific selection, which points toward a biological link between dalcetrapib’s pharmacogene ADCY9 and its therapeutic target CETP.
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Affiliation(s)
- Isabel Gamache
- Université de Montréal, Montréal, Canada.,Montreal Heart Institute, Montréal, Canada
| | - Marc-André Legault
- Université de Montréal, Montréal, Canada.,Montreal Heart Institute, Montréal, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montréal, Canada
| | | | | | - Eric Rhéaume
- Université de Montréal, Montréal, Canada.,Montreal Heart Institute, Montréal, Canada
| | - Samira Asgari
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States
| | - Amina Barhdadi
- Montreal Heart Institute, Montréal, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montréal, Canada
| | - Yassamin Feroz Zada
- Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montréal, Canada
| | - Holly Trochet
- Université de Montréal, Montréal, Canada.,Montreal Heart Institute, Montréal, Canada
| | - Yang Luo
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States
| | - Leonid Lecca
- Socios En Salud, Lima, Peru.,Harvard Medical School, Boston, United States
| | - Megan Murray
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, United States.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, United States.,Centre for Genetics and Genomics Versus Arthritis, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.,Department of Biomedical Informatics, Harvard Medical School, Boston, United States.,Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, United States
| | - Jean-Claude Tardif
- Université de Montréal, Montréal, Canada.,Montreal Heart Institute, Montréal, Canada
| | - Marie-Pierre Dubé
- Université de Montréal, Montréal, Canada.,Montreal Heart Institute, Montréal, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montréal, Canada
| | - Julie Hussin
- Université de Montréal, Montréal, Canada.,Montreal Heart Institute, Montréal, Canada
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13
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Skead K, Ang Houle A, Abelson S, Agbessi M, Bruat V, Lin B, Soave D, Shlush L, Wright S, Dick J, Morris Q, Awadalla P. Interacting evolutionary pressures drive mutation dynamics and health outcomes in aging blood. Nat Commun 2021; 12:4921. [PMID: 34389724 PMCID: PMC8363714 DOI: 10.1038/s41467-021-25172-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Accepted: 07/27/2021] [Indexed: 01/10/2023] Open
Abstract
Age-related clonal hematopoiesis (ARCH) is characterized by age-associated accumulation of somatic mutations in hematopoietic stem cells (HSCs) or their pluripotent descendants. HSCs harboring driver mutations will be positively selected and cells carrying these mutations will rise in frequency. While ARCH is a known risk factor for blood malignancies, such as Acute Myeloid Leukemia (AML), why some people who harbor ARCH driver mutations do not progress to AML remains unclear. Here, we model the interaction of positive and negative selection in deeply sequenced blood samples from individuals who subsequently progressed to AML, compared to healthy controls, using deep learning and population genetics. Our modeling allows us to discriminate amongst evolutionary classes with high accuracy and captures signatures of purifying selection in most individuals. Purifying selection, acting on benign or mildly damaging passenger mutations, appears to play a critical role in preventing disease-predisposing clones from rising to dominance and is associated with longer disease-free survival. Through exploring a range of evolutionary models, we show how different classes of selection shape clonal dynamics and health outcomes thus enabling us to better identify individuals at a high risk of malignancy.
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Affiliation(s)
- Kimberly Skead
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Armande Ang Houle
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Sagi Abelson
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | | | - Vanessa Bruat
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Boxi Lin
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - David Soave
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Mathematics, Wilfrid Laurier University, Waterloo, ON, Canada
| | - Liran Shlush
- Department of Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Stephen Wright
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada
| | - John Dick
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Quaid Morris
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Vector Institute for Artificial Intelligence, Toronto, ON, Canada.
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Toronto, ON, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
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14
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Sall S, Thompson W, Santos A, Dwyer DS. Analysis of Major Depression Risk Genes Reveals Evolutionary Conservation, Shared Phenotypes, and Extensive Genetic Interactions. Front Psychiatry 2021; 12:698029. [PMID: 34335334 PMCID: PMC8319724 DOI: 10.3389/fpsyt.2021.698029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/21/2021] [Indexed: 12/29/2022] Open
Abstract
Major depressive disorder (MDD) affects around 15% of the population at some stage in their lifetime. It can be gravely disabling and it is associated with increased risk of suicide. Genetics play an important role; however, there are additional environmental contributions to the pathogenesis. A number of possible risk genes that increase liability for developing symptoms of MDD have been identified in genome-wide association studies (GWAS). The goal of this study was to characterize the MDD risk genes with respect to the degree of evolutionary conservation in simpler model organisms such as Caenorhabditis elegans and zebrafish, the phenotypes associated with variation in these genes and the extent of network connectivity. The MDD risk genes showed higher conservation in C. elegans and zebrafish than genome-to-genome comparisons. In addition, there were recurring themes among the phenotypes associated with variation of these risk genes in C. elegans. The phenotype analysis revealed enrichment for essential genes with pleiotropic effects. Moreover, the MDD risk genes participated in more interactions with each other than did randomly-selected genes from similar-sized gene sets. Syntenic blocks of risk genes with common functional activities were also identified. By characterizing evolutionarily-conserved counterparts to the MDD risk genes, we have gained new insights into pathogenetic processes relevant to the emergence of depressive symptoms in man.
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Affiliation(s)
- Saveen Sall
- Department of Psychiatry and Behavioral Medicine, Louisiana State University Health Shreveport, Shreveport, LA, United States
| | - Willie Thompson
- Department of Psychiatry and Behavioral Medicine, Louisiana State University Health Shreveport, Shreveport, LA, United States
| | - Aurianna Santos
- Department of Psychiatry and Behavioral Medicine, Louisiana State University Health Shreveport, Shreveport, LA, United States
| | - Donard S. Dwyer
- Department of Psychiatry and Behavioral Medicine, Louisiana State University Health Shreveport, Shreveport, LA, United States
- Department of Pharmacology, Toxicology and Neuroscience, Louisiana State University Health Shreveport, Shreveport, LA, United States
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15
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Garcia JA, Lohmueller KE. Negative linkage disequilibrium between amino acid changing variants reveals interference among deleterious mutations in the human genome. PLoS Genet 2021; 17:e1009676. [PMID: 34319975 PMCID: PMC8351996 DOI: 10.1371/journal.pgen.1009676] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 08/09/2021] [Accepted: 06/22/2021] [Indexed: 11/18/2022] Open
Abstract
Evolutionary forces like Hill-Robertson interference and negative epistasis can lead to deleterious mutations being found on distinct haplotypes. However, the extent to which these forces depend on the selection and dominance coefficients of deleterious mutations and shape genome-wide patterns of linkage disequilibrium (LD) in natural populations with complex demographic histories has not been tested. In this study, we first used forward-in-time simulations to predict how negative selection impacts LD. Under models where deleterious mutations have additive effects on fitness, deleterious variants less than 10 kb apart tend to be carried on different haplotypes relative to pairs of synonymous SNPs. In contrast, for recessive mutations, there is no consistent ordering of how selection coefficients affect LD decay, due to the complex interplay of different evolutionary effects. We then examined empirical data of modern humans from the 1000 Genomes Project. LD between derived alleles at nonsynonymous SNPs is lower compared to pairs of derived synonymous variants, suggesting that nonsynonymous derived alleles tend to occur on different haplotypes more than synonymous variants. This result holds when controlling for potential confounding factors by matching SNPs for frequency in the sample (allele count), physical distance, magnitude of background selection, and genetic distance between pairs of variants. Lastly, we introduce a new statistic HR(j) which allows us to detect interference using unphased genotypes. Application of this approach to high-coverage human genome sequences confirms our finding that nonsynonymous derived alleles tend to be located on different haplotypes more often than are synonymous derived alleles. Our findings suggest that interference may play a pervasive role in shaping patterns of LD between deleterious variants in the human genome, and consequently influences genome-wide patterns of LD.
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Affiliation(s)
- Jesse A. Garcia
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, United States of America
| | - Kirk E. Lohmueller
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, United States of America
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, United States of America
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, California, United States of America
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16
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Franklin C, Dwyer DS. Candidate risk genes for bipolar disorder are highly conserved during evolution and highly interconnected. Bipolar Disord 2021; 23:400-408. [PMID: 32959503 DOI: 10.1111/bdi.12996] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 07/24/2020] [Accepted: 09/12/2020] [Indexed: 12/21/2022]
Abstract
OBJECTIVES Bipolar disorder (BPD) is a highly heritable psychiatric disorder whose genetic complexity and pathogenetic mechanisms are still being unraveled. The main goal of this work was to characterize BPD risk-gene candidates (identified by Nurnberger et al., JAMA Psychiatry 71:657, 2014, and Stahl et al., Nat. Genet. 51:793, 2019) with respect to their evolutionary conservation, associated phenotypes, and extent of gene-gene interactions. METHODS Database searches and BLAST were used to identify homologous counterparts of human BPD risk genes in C. elegans, zebrafish, and Drosophila. Phenotypes associated with the C. elegans genes were annotated and searched. With GeneMANIA, we characterized and quantified gene-gene interactions among members of the BPD gene set in comparison to randomly chosen gene sets of the same size. RESULTS BPD risk genes are highly conserved across species and are enriched for essential genes and genes associated with lethality and altered life span. They are significantly more interactive with each other in comparison to random genes. We identified syntenic blocks of risk genes, which provided potential insights into molecular pathways and co-morbidities associated with BPD including coronary disease, obesity, and decreased life expectancy. CONCLUSIONS BPD risk genes appear to be special in terms of their degree of conservation, interconnectedness, and pleiotropic effects that extend beyond a role in brain function. Key hub genes or pleiotropic regulatory components may represent attractive targets for future drug discovery.
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Affiliation(s)
- Claire Franklin
- School of Medicine, LSU Health Shreveport, Shreveport, LA, USA.,LSU Health Sciences Center New Orleans, Shreveport, LA, USA
| | - Donard S Dwyer
- Departments of Psychiatry and Behavioral Medicine and Pharmacology, Toxicology and Neuroscience, LSU Health Shreveport, Shreveport, LA, USA
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17
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Dehghani N, Bras J, Guerreiro R. How understudied populations have contributed to our understanding of Alzheimer's disease genetics. Brain 2021; 144:1067-1081. [PMID: 33889936 DOI: 10.1093/brain/awab028] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/30/2020] [Accepted: 11/17/2020] [Indexed: 12/14/2022] Open
Abstract
The majority of genome-wide association studies have been conducted using samples with a broadly European genetic background. As a field, we acknowledge this limitation and the need to increase the diversity of populations studied. A major challenge when designing and conducting such studies is to assimilate large samples sizes so that we attain enough statistical power to detect variants associated with disease, particularly when trying to identify variants with low and rare minor allele frequencies. In this review, we aimed to illustrate the benefits to genetic characterization of Alzheimer's disease, in researching currently understudied populations. This is important for both fair representation of world populations and the translatability of findings. To that end, we conducted a literature search to understand the contributions of studies, on different populations, to Alzheimer's disease genetics. Using both PubMed and Alzforum Mutation Database, we systematically quantified the number of studies reporting variants in known disease-causing genes, in a worldwide manner, and discuss the contributions of research in understudied populations to the identification of novel genetic factors in this disease. Additionally, we compared the effects of genome-wide significant single nucleotide polymorphisms across populations by focusing on loci that show different association profiles between populations (a key example being APOE). Reports of variants in APP, PSEN1 and PSEN2 can initially determine whether patients from a country have been studied for Alzheimer's disease genetics. Most genome-wide significant associations in non-Hispanic white genome-wide association studies do not reach genome-wide significance in such studies of other populations, with some suggesting an opposite effect direction; this is likely due to much smaller sample sizes attained. There are, however, genome-wide significant associations first identified in understudied populations which have yet to be replicated. Familial studies in understudied populations have identified rare, high effect variants, which have been replicated in other populations. This work functions to both highlight how understudied populations have furthered our understanding of Alzheimer's disease genetics, and to help us gauge our progress in understanding the genetic architecture of this disease in all populations.
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Affiliation(s)
- Nadia Dehghani
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, USA
| | - Jose Bras
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, USA.,Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
| | - Rita Guerreiro
- Department of Neurodegenerative Science, Van Andel Institute, Grand Rapids, Michigan, USA.,Division of Psychiatry and Behavioral Medicine, Michigan State University College of Human Medicine, Grand Rapids, MI, USA
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18
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Jantzen R, Payette Y, de Malliard T, Labbé C, Noisel N, Broët P. Validation of breast cancer risk assessment tools on a French-Canadian population-based cohort. BMJ Open 2021; 11:e045078. [PMID: 33846154 PMCID: PMC8047995 DOI: 10.1136/bmjopen-2020-045078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVES Evaluate the accuracy of the Breast Cancer Risk Assessment Tool (BCRAT), International Breast Cancer Intervention Study risk evaluation tool (IBIS), Polygenic Risk Scores (PRS) and combined scores (BCRAT+PRS and IBIS +PRS) to predict the occurrence of invasive breast cancers at 5 years in a French-Canadian population. DESIGN Population-based cohort study. SETTING We used the population-based cohort CARTaGENE, composed of 43 037 Quebec residents aged between 40 and 69 years and broadly representative of the population recorded on the Quebec administrative health insurance registries. PARTICIPANTS 10 200 women recruited in 2009-2010 were included for validating BCRAT and IBIS and 4555 with genetic information for validating the PRS and combined scores. OUTCOME MEASURES We computed the absolute risks of breast cancer at 5 years using BCRAT, IBIS, four published PRS and combined models. We reported the overall calibration performance, goodness-of-fit test and discriminatory accuracy. RESULTS 131 (1.28%) women developed a breast cancer at 5 years for validating BCRAT and IBIS and 58 (1.27%) for validating PRS and combined scores. Median follow-up was 5 years. BCRAT and IBIS had an overall expected-to-observed ratio of 1.01 (0.85-1.19) and 1.02 (0.86-1.21) but with significant differences when partitioning by risk groups (p<0.05). IBIS' c-index was significantly higher than BCRAT (63.42 (59.35-67.49) vs 58.63 (54.05-63.21), p=0.013). PRS scores had a global calibration around 0.82, with a CI including one, and non-significant goodness-of-fit tests. PRS' c-indexes were non-significantly higher than BCRAT and IBIS, the highest being 64.43 (58.23-70.63). Combined models did not improve the results. CONCLUSIONS In this French-Canadian population-based cohort, BCRAT and IBIS have good mean calibration that could be improved for risk subgroups, and modest discriminatory accuracy. Despite this modest discriminatory power, these tools can be of interest for primary care physicians for delivering a personalised message to their high-risk patients, regarding screening and lifestyle counselling.
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Affiliation(s)
- Rodolphe Jantzen
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Yves Payette
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | | | - Catherine Labbé
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
| | - Nolwenn Noisel
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
| | - Philippe Broët
- CARTaGENE, Research Center, CHU Sainte-Justine, Montreal, Quebec, Canada
- Université de Montréal, Montréal, Québec, Canada
- CESP, INSERM, University Paris-Saclay, Villejuif, France
- Assistance Publique-Hôpitaux de Paris (AP-HP), Hôpitaux Universitaires Paris-Sud, Hôpital Paul Brousse, Villejuif, France
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Dwyer DS. Genomic Chaos Begets Psychiatric Disorder. Complex Psychiatry 2020; 6:20-29. [PMID: 34883501 PMCID: PMC7673594 DOI: 10.1159/000507988] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/06/2020] [Indexed: 12/21/2022] Open
Abstract
The processes that created the primordial genome are inextricably linked to current day vulnerability to developing a psychiatric disorder as summarized in this review article. Chaos and dynamic forces including duplication, transposition, and recombination generated the protogenome. To survive early stages of genome evolution, self-organization emerged to curb chaos. Eventually, the human genome evolved through a delicate balance of chaos/instability and organization/stability. However, recombination coldspots, silencing of transposable elements, and other measures to limit chaos also led to retention of variants that increase risk for disease. Moreover, ongoing dynamics in the genome creates various new mutations that determine liability for psychiatric disorders. Homologous recombination, long-range gene regulation, and gene interactions were all guided by spooky action-at-a-distance, which increased variability in the system. A probabilistic system of life was required to deal with a changing environment. This ensured the generation of outliers in the population, which enhanced the probability that some members would survive unfavorable environmental impacts. Some of the outliers produced through this process in man are ill suited to cope with the complex demands of modern life. Genomic chaos and mental distress from the psychological challenges of modern living will inevitably converge to produce psychiatric disorders in man.
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Affiliation(s)
- Donard S. Dwyer
- Departments of Psychiatry and Behavioral Medicine and Pharmacology, Toxicology and Neuroscience, LSU Health Shreveport, Shreveport, Louisiana, USA
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20
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Wang L, Li J. 'Artificial spermatid'-mediated genome editing†. Biol Reprod 2020; 101:538-548. [PMID: 31077288 DOI: 10.1093/biolre/ioz087] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 04/27/2019] [Accepted: 05/10/2019] [Indexed: 12/12/2022] Open
Abstract
For years, extensive efforts have been made to use mammalian sperm as the mediator to generate genetically modified animals; however, the strategy of sperm-mediated gene transfer (SMGT) is unable to produce stable and diversified modifications in descendants. Recently, haploid embryonic stem cells (haESCs) have been successfully derived from haploid embryos carrying the genome of highly specialized gametes, and can stably maintain haploidy (through periodic cell sorting based on DNA quantity) and both self-renewal and pluripotency in long-term cell culture. In particular, haESCs derived from androgenetic haploid blastocysts (AG-haESCs), carrying only the sperm genome, can support the generation of live mice (semi-cloned, SC mice) through oocyte injection. Remarkably, after removal of the imprinted control regions H19-DMR (differentially methylated region of DNA) and IG-DMR in AG-haESCs, the double knockout (DKO)-AG-haESCs can stably produce SC animals with high efficiency, and so can serve as a sperm equivalent. Importantly, DKO-AG-haESCs can be used for multiple rounds of gene modifications in vitro, followed by efficient generation of live and fertile mice with the expected genetic traits. Thus, DKO-AG-haESCs (referred to as 'artificial spermatids') combed with CRISPR-Cas technology can be used as the genetically tractable fertilization agent, to efficiently create genetically modified offspring, and is a versatile genetic tool for in vivo analyses of gene function.
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Affiliation(s)
- Lingbo Wang
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.,Obstetrics and Gynecology Hospital, NHC Key Laboratory of Reproduction Regulation (Shanghai Institute of Planned Parenthood Research), School of Life Sciences, Fudan University, Shanghai, China
| | - Jinsong Li
- State Key Laboratory of Cell Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
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21
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Reay WR, Cairns MJ. Pairwise common variant meta-analyses of schizophrenia with other psychiatric disorders reveals shared and distinct gene and gene-set associations. Transl Psychiatry 2020; 10:134. [PMID: 32398653 PMCID: PMC7217970 DOI: 10.1038/s41398-020-0817-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/20/2020] [Accepted: 04/23/2020] [Indexed: 01/09/2023] Open
Abstract
The complex aetiology of schizophrenia is postulated to share components with other psychiatric disorders. We investigated pleiotropy amongst the common variant genomics of schizophrenia and seven other psychiatric disorders using a multimarker association test. Transcriptomic imputation was then leveraged to investigate the functional significance of variation mapped to these genes, prioritising several interesting functional candidates. Gene-based analysis of common variation revealed 67 schizophrenia-associated genes shared with other psychiatric phenotypes, including bipolar disorder, major depressive disorder, ADHD and autism-spectrum disorder. In addition, we uncovered 78 genes significantly enriched with common variant associations for schizophrenia that were not linked to any of these seven disorders (P > 0.05). Multivariable gene-set association suggested that common variation enrichment within biologically constrained genes observed for schizophrenia also occurs across several psychiatric phenotypes. Pairwise meta-analysis of schizophrenia and each psychiatric phenotype was implemented and identified 330 significantly associated genes (PMeta < 2.7 × 10-6) that were only nominally associated with each disorder individually (P < 0.05). These analyses consolidate the overlap between the genomic architecture of schizophrenia and other psychiatric disorders, uncovering several candidate pleiotropic genes which warrant further investigation.
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Affiliation(s)
- William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.
- Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia.
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22
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Cordellier M, Schneider JM, Uhl G, Posnien N. Sex differences in spiders: from phenotype to genomics. Dev Genes Evol 2020; 230:155-172. [PMID: 32052129 PMCID: PMC7127994 DOI: 10.1007/s00427-020-00657-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Accepted: 01/31/2020] [Indexed: 01/26/2023]
Abstract
Sexual reproduction is pervasive in animals and has led to the evolution of sexual dimorphism. In most animals, males and females show marked differences in primary and secondary sexual traits. The formation of sex-specific organs and eventually sex-specific behaviors is defined during the development of an organism. Sex determination processes have been extensively studied in a few well-established model organisms. While some key molecular regulators are conserved across animals, the initiation of sex determination is highly diverse. To reveal the mechanisms underlying the development of sexual dimorphism and to identify the evolutionary forces driving the evolution of different sexes, sex determination mechanisms must thus be studied in detail in many different animal species beyond the typical model systems. In this perspective article, we argue that spiders represent an excellent group of animals in which to study sex determination mechanisms. We show that spiders are sexually dimorphic in various morphological, behavioral, and life history traits. The availability of an increasing number of genomic and transcriptomic resources and functional tools provides a great starting point to scrutinize the extensive sexual dimorphism present in spiders on a mechanistic level. We provide an overview of the current knowledge of sex determination in spiders and propose approaches to reveal the molecular and genetic underpinnings of sexual dimorphism in these exciting animals.
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Affiliation(s)
- Mathilde Cordellier
- Department of Biology, Institute of Zoology, Universität Hamburg, Martin-Luther-King Platz 3, 20146, Hamburg, Germany.
| | - Jutta M Schneider
- Department of Biology, Institute of Zoology, Universität Hamburg, Martin-Luther-King Platz 3, 20146, Hamburg, Germany.
| | - Gabriele Uhl
- Zoological Institute and Museum, Research Group General and Systematic Zoology, Universität Greifswald, Loitzer Straße 26, 17489, Greifswald, Germany.
| | - Nico Posnien
- Department of Developmental Biology, Göttingen Center for Molecular Biosciences (GZMB), University Göttingen, Justus-von-Liebig-Weg 11, 37077, Göttingen, Germany.
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23
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Abstract
Bacterial and archaeal flagellins are remarkable in having a shared region with variation in housekeeping proteins and a region with extreme diversity, perhaps greater than for any other protein. Analysis of the 113,285 available full-gene sequences of flagellin genes from published bacterial and archaeal sequences revealed the nature and enormous extent of flagellin diversity. There were 35,898 unique amino acid sequences that were resolved into 187 clusters. Analysis of the Escherichia coli and Salmonella enterica flagellins revealed that the variation occurs at two levels. The first is the division of the variable regions into sequence forms that are so divergent that there is no meaningful alignment even within species, and these corresponded to the E. coli or S. enterica H-antigen groups. The second level is variation within these groups, which is extensive in both species. Shared sequence would allow PCR of the variable regions and thus strain-level analysis of microbiome DNA. Flagellin, the agent of prokaryotic flagellar motion, is very widely distributed and is the H antigen of serology. Flagellin molecules have a variable region that confers serotype specificity, encoded by the middle of the gene, and also conserved regions encoded by the two ends of the gene. We collected all available prokaryotic flagellin protein sequences and found the variable region diversity to be at two levels. In each species investigated, there are hypervariable region (HVR) forms without detectable homology in protein sequences between them. There is also considerable variation within HVR forms, indicating that some have been diverging for thousands of years and that interphylum horizontal gene transfers make a major contribution to the evolution of such atypical diversity. IMPORTANCE Bacterial and archaeal flagellins are remarkable in having a shared region with variation in housekeeping proteins and a region with extreme diversity, perhaps greater than for any other protein. Analysis of the 113,285 available full-gene sequences of flagellin genes from published bacterial and archaeal sequences revealed the nature and enormous extent of flagellin diversity. There were 35,898 unique amino acid sequences that were resolved into 187 clusters. Analysis of the Escherichia coli and Salmonella enterica flagellins revealed that the variation occurs at two levels. The first is the division of the variable regions into sequence forms that are so divergent that there is no meaningful alignment even within species, and these corresponded to the E. coli or S. enterica H-antigen groups. The second level is variation within these groups, which is extensive in both species. Shared sequence would allow PCR of the variable regions and thus strain-level analysis of microbiome DNA.
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24
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Cacheiro P, Muñoz-Fuentes V, Murray SA, Dickinson ME, Bucan M, Nutter LMJ, Peterson KA, Haselimashhadi H, Flenniken AM, Morgan H, Westerberg H, Konopka T, Hsu CW, Christiansen A, Lanza DG, Beaudet AL, Heaney JD, Fuchs H, Gailus-Durner V, Sorg T, Prochazka J, Novosadova V, Lelliott CJ, Wardle-Jones H, Wells S, Teboul L, Cater H, Stewart M, Hough T, Wurst W, Sedlacek R, Adams DJ, Seavitt JR, Tocchini-Valentini G, Mammano F, Braun RE, McKerlie C, Herault Y, de Angelis MH, Mallon AM, Lloyd KCK, Brown SDM, Parkinson H, Meehan TF, Smedley D. Human and mouse essentiality screens as a resource for disease gene discovery. Nat Commun 2020; 11:655. [PMID: 32005800 PMCID: PMC6994715 DOI: 10.1038/s41467-020-14284-2] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2019] [Accepted: 12/12/2019] [Indexed: 12/31/2022] Open
Abstract
The identification of causal variants in sequencing studies remains a considerable challenge that can be partially addressed by new gene-specific knowledge. Here, we integrate measures of how essential a gene is to supporting life, as inferred from viability and phenotyping screens performed on knockout mice by the International Mouse Phenotyping Consortium and essentiality screens carried out on human cell lines. We propose a cross-species gene classification across the Full Spectrum of Intolerance to Loss-of-function (FUSIL) and demonstrate that genes in five mutually exclusive FUSIL categories have differing biological properties. Most notably, Mendelian disease genes, particularly those associated with developmental disorders, are highly overrepresented among genes non-essential for cell survival but required for organism development. After screening developmental disorder cases from three independent disease sequencing consortia, we identify potentially pathogenic variants in genes not previously associated with rare diseases. We therefore propose FUSIL as an efficient approach for disease gene discovery.
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Grants
- UM1 HG008900 NHGRI NIH HHS
- UM1 HG006504 NHGRI NIH HHS
- MC_UP_1502/1 Medical Research Council
- UM1 HG006542 NHGRI NIH HHS
- UM1 OD023221 NIH HHS
- MC_U142684171 Medical Research Council
- MR/S006753/1 Medical Research Council
- UM1 HG006370 NHGRI NIH HHS
- UM1 HG006493 NHGRI NIH HHS
- U54 HG006370 NHGRI NIH HHS
- U54 HG006364 NHGRI NIH HHS
- MC_U142684172 Medical Research Council
- UM1 HG006348 NHGRI NIH HHS
- U42 OD011174 NIH HHS
- U42 OD011175 NIH HHS
- Wellcome Trust
- This work was supported by NIH grant U54 HG006370. IMPC-related mouse production and phenotyping was funded by the Government of Canada through Genome Canada and Ontario Genomics (OGI-051) for NorCOMM2 (C.M.) and the National Institutes of Health and OD, NCRR, NIDDK and NHLBI for KOMP and KOMP2 Projects U42 OD011175 and UM1OD023221 (C.M., K.C.K.L), Infrafrontier grant 01KX1012, EU Horizon2020: IPAD-MD funding 653961 (M.H.d.A); EUCOMM: LSHM-CT-2005-018931, EUCOMMTOOLS: FP7-HEALTH-F4-2010-261492 (W.G.W). UM1 HG006348; U42 OD011174; U54 HG005348 (A.L.B), NIH U54706HG006364 (A.L.B). Wellcome Trust grants WT098051 and WT206194 (D.A). The French National Centre for Scientific Research (CNRS), the French National Institute of Health and Medical Research (INSERM), the University of Strasbourg and the “Centre Europeen de Recherche en Biomedecine”, and the French state funds through the “Agence Nationale de la Recherche” under the frame programme Investissements d’Avenir labelled (ANR-10-IDEX-0002-02, ANR-10-LABX-0030-INRT, ANR-10-INBS-07 PHENOMIN (J.H.). This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. We are also grateful for the data access provided by the DDD and CMG projects. The DDD study presents independent research commissioned by the Health Innovation Challenge Fund [grant number HICF-1009-003], a parallel funding partnership between Wellcome and the Department of Health, and the Wellcome Sanger Institute [grant number WT098051]. The views expressed in this publication are those of the author(s) and not necessarily those of Wellcome or the Department of Health. The study has UK Research Ethics Committee approval (10/H0305/83, granted by the Cambridge South REC, and GEN/284/12 granted by the Republic of Ireland REC). The research team acknowledges the support of the National Institute for Health Research, through the Comprehensive Clinical Research Network. The Centers for Mendelian Genomics are funded by the National Human Genome Research Institute, the National Heart, Lung, and Blood Institute, and the National Eye Institute. Broad Institute (UM1 HG008900), Johns Hopkins University School of Medicine/Baylor College of Medicine (UM1 HG006542), University of Washington (UM1 HG006493), Yale University (UM1 HG006504).
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Affiliation(s)
- Pilar Cacheiro
- Clinical Pharmacology, William Harvey Research Institute, School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Violeta Muñoz-Fuentes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | | | - Mary E Dickinson
- Departments of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, 77030, USA
- Departments of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Maja Bucan
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauryl M J Nutter
- The Centre for Phenogenomics, The Hospital for Sick Children, Toronto, ON, M5T 3H7, Canada
| | | | - Hamed Haselimashhadi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Ann M Flenniken
- The Centre for Phenogenomics, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, M5T 3H7, Canada
| | - Hugh Morgan
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Henrik Westerberg
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Tomasz Konopka
- Clinical Pharmacology, William Harvey Research Institute, School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Chih-Wei Hsu
- Departments of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Audrey Christiansen
- Departments of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Denise G Lanza
- Departments of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Arthur L Beaudet
- Departments of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Jason D Heaney
- Departments of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Helmut Fuchs
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Valerie Gailus-Durner
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Tania Sorg
- Université de Strasbourg, CNRS, INSERM, Institut Clinique de la Souris, PHENOMIN-ICS, 67404, Illkirch, France
| | - Jan Prochazka
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Vestec, 252 50, Prague, Czech Republic
| | - Vendula Novosadova
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Vestec, 252 50, Prague, Czech Republic
| | | | | | - Sara Wells
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Lydia Teboul
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Heather Cater
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Michelle Stewart
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Tertius Hough
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Wolfgang Wurst
- Institute of Developmental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, 85764, Neuherberg, Germany
- Department of Developmental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85764, Neuherberg, Germany
- Deutsches Institut für Neurodegenerative Erkrankungen (DZNE) Site Munich, Munich Cluster for Systems Neurology (SyNergy), Adolf-Butenandt-Institut, Ludwig-Maximilians-Universität München, 80336, Munich, Germany
| | - Radislav Sedlacek
- Czech Centre for Phenogenomics, Institute of Molecular Genetics of the Czech Academy of Sciences, Vestec, 252 50, Prague, Czech Republic
| | - David J Adams
- Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| | - John R Seavitt
- Departments of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Glauco Tocchini-Valentini
- Monterotondo Mouse Clinic, Italian National Research Council (CNR), Institute of Cell Biology and Neurobiology, 00015, Monterotondo Scalo, Italy
| | - Fabio Mammano
- Monterotondo Mouse Clinic, Italian National Research Council (CNR), Institute of Cell Biology and Neurobiology, 00015, Monterotondo Scalo, Italy
| | | | - Colin McKerlie
- The Centre for Phenogenomics, The Hospital for Sick Children, Toronto, ON, M5T 3H7, Canada
- Translational Medicine, The Hospital for Sick Children, Toronto, ON, M5T 3H7, Canada
| | - Yann Herault
- Université de Strasbourg, CNRS, INSERM, Institut de Génétique, Biologie Moléculaire et Cellulaire, Institut Clinique de la Souris, IGBMC, PHENOMIN-ICS, 67404, Illkirch, France
| | - Martin Hrabě de Angelis
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
- Department of Experimental Genetics, Center of Life and Food Sciences Weihenstephan, Technische Universität München, 85354, Freising-Weihenstephan, Germany
- German Center for Diabetes Research (DZD), 85764, Neuherberg, Germany
| | - Ann-Marie Mallon
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - K C Kent Lloyd
- Mouse Biology Program, University of California, Davis, CA, 95618, USA
| | - Steve D M Brown
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire, OX11 0RD, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Terrence F Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Damian Smedley
- Clinical Pharmacology, William Harvey Research Institute, School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK.
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Castellano D, Eyre-Walker A, Munch K. Impact of Mutation Rate and Selection at Linked Sites on DNA Variation across the Genomes of Humans and Other Homininae. Genome Biol Evol 2020; 12:3550-3561. [PMID: 31596481 PMCID: PMC6944223 DOI: 10.1093/gbe/evz215] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/03/2019] [Indexed: 12/23/2022] Open
Abstract
DNA diversity varies across the genome of many species. Variation in diversity across a genome might arise from regional variation in the mutation rate, variation in the intensity and mode of natural selection, and regional variation in the recombination rate. We show that both noncoding and nonsynonymous diversity are positively correlated to a measure of the mutation rate and the recombination rate and negatively correlated to the density of conserved sequences in 50 kb windows across the genomes of humans and nonhuman homininae. Interestingly, we find that although noncoding diversity is equally affected by these three genomic variables, nonsynonymous diversity is mostly dominated by the density of conserved sequences. The positive correlation between diversity and our measure of the mutation rate seems to be largely a direct consequence of regions with higher mutation rates having more diversity. However, the positive correlation with recombination rate and the negative correlation with the density of conserved sequences suggest that selection at linked sites also affect levels of diversity. This is supported by the observation that the ratio of the number of nonsynonymous to noncoding polymorphisms is negatively correlated to a measure of the effective population size across the genome. We show these patterns persist even when we restrict our analysis to GC-conservative mutations, demonstrating that the patterns are not driven by GC biased gene conversion. In conclusion, our comparative analyses describe how recombination rate, gene density, and mutation rate interact to produce the patterns of DNA diversity that we observe along the hominine genomes.
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Affiliation(s)
- David Castellano
- Bioinformatics Research Centre, Aarhus University, Denmark
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr Aiguader 88, Barcelona, Spain
| | - Adam Eyre-Walker
- School of Life Sciences, University of Sussex, Brighton, United Kingdom
| | - Kasper Munch
- Bioinformatics Research Centre, Aarhus University, Denmark
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Khramtsova EA, Davis LK, Stranger BE. The role of sex in the genomics of human complex traits. Nat Rev Genet 2019; 20:173-190. [PMID: 30581192 DOI: 10.1038/s41576-018-0083-1] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nearly all human complex traits and disease phenotypes exhibit some degree of sex differences, including differences in prevalence, age of onset, severity or disease progression. Until recently, the underlying genetic mechanisms of such sex differences have been largely unexplored. Advances in genomic technologies and analytical approaches are now enabling a deeper investigation into the effect of sex on human health traits. In this Review, we discuss recent insights into the genetic models and mechanisms that lead to sex differences in complex traits. This knowledge is critical for developing deeper insight into the fundamental biology of sex differences and disease processes, thus facilitating precision medicine.
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Affiliation(s)
- Ekaterina A Khramtsova
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA.,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Lea K Davis
- Division of Medical Genetics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. .,Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA. .,Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL, USA. .,Center for Data Intensive Science, University of Chicago, Chicago, IL, USA.
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27
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Exploiting selection at linked sites to infer the rate and strength of adaptation. Nat Ecol Evol 2019; 3:977-984. [PMID: 31061475 PMCID: PMC6693860 DOI: 10.1038/s41559-019-0890-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 03/28/2019] [Indexed: 12/18/2022]
Abstract
Genomic data encodes past evolutionary events and has the potential to reveal the strength, rate, and biological drivers of adaptation. However, jointly estimating adaptation rate (a) and adaptation strength remains challenging because evolutionary processes such as demography, linkage, and non-neutral polymorphism can confound inference. Here, we exploit the influence of background selection to reduce the fixation rate of weakly-beneficial alleles to jointly infer the strength and rate of adaptation. We develop an MK-based method (ABC-MK) to infer adaptation rate and strength, and estimate α = 0.135 in human protein-coding sequences, 72% of which is contributed by weakly-adaptive variants. We show that in this adaptation regime α is reduced ≈ 25% by linkage genome-wide. Moreover, we show that virus-interacting proteins (VIPs) undergo adaptation that is both stronger and nearly twice as frequent as the genome average (α = 0.224, 56% due to strongly-beneficial alleles). Our results suggest that while most adaptation in human proteins is weakly-beneficial, adaptation to viruses is often strongly-beneficial. Our method provides a robust framework for estimating adaptation rate and strength across species.
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28
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Vergara-Lope A, Ennis S, Vorechovsky I, Pengelly RJ, Collins A. Heterogeneity in the extent of linkage disequilibrium among exonic, intronic, non-coding RNA and intergenic chromosome regions. Eur J Hum Genet 2019; 27:1436-1444. [PMID: 31053778 DOI: 10.1038/s41431-019-0419-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Revised: 03/04/2019] [Accepted: 04/16/2019] [Indexed: 11/09/2022] Open
Abstract
Whole-genome sequence data enable construction of high-resolution linkage disequilibrium (LD) maps revealing the LD structure of functional elements within genic and subgenic sequences. The Malecot-Morton model defines LD map distances in linkage disequilibrium units (LDUs), analogous to the centimorgan scale of linkage maps. For whole-genome sequence-derived LD maps, we introduce the ratio of corresponding map lengths kilobases/LDU to describe the extent of LD within genome components. The extent of LD is highly variable across the genome ranging from ~38 kb for intergenic sequences to ~858 kb for centromeric regions. LD is ~16% more extensive in genic, compared with intergenic sequences, reflecting relatively increased selection and/or reduced recombination in genes. The LD profile across 18,268 autosomal genes reveals reduced extent of LD, consistent with elevated recombination, in exonic regions near the 5' end of genes but more extensive LD, compared with intronic sequences, across more centrally located exons. Genes classified as essential and genes linked to Mendelian phenotypes show more extensive LD compared with genes associated with complex traits, perhaps reflecting differences in selective pressure. Significant differences between exonic, intronic and intergenic components demonstrate that fine-scale LD structure provides important insights into genome function, which cannot be revealed by LD analysis of much lower resolution array-based genotyping and conventional linkage maps.
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Affiliation(s)
- Alejandra Vergara-Lope
- Human Genetics, Faculty of Medicine, University of Southampton, Duthie Building (808), Southampton General Hospital, Tremona Road, Southampton, SO16 6YD, UK
| | - Sarah Ennis
- Human Genetics, Faculty of Medicine, University of Southampton, Duthie Building (808), Southampton General Hospital, Tremona Road, Southampton, SO16 6YD, UK
| | - Igor Vorechovsky
- Human Genetics, Faculty of Medicine, University of Southampton, Duthie Building (808), Southampton General Hospital, Tremona Road, Southampton, SO16 6YD, UK
| | - Reuben J Pengelly
- Human Genetics, Faculty of Medicine, University of Southampton, Duthie Building (808), Southampton General Hospital, Tremona Road, Southampton, SO16 6YD, UK
| | - Andrew Collins
- Human Genetics, Faculty of Medicine, University of Southampton, Duthie Building (808), Southampton General Hospital, Tremona Road, Southampton, SO16 6YD, UK.
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29
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Lian Q, Tang D, Bai Z, Qi J, Lu F, Huang S, Zhang C. Acquisition of deleterious mutations during potato polyploidization. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2019; 61:7-11. [PMID: 30474354 DOI: 10.1111/jipb.12748] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 11/12/2018] [Indexed: 06/09/2023]
Abstract
Potatoes (Solanum tuberosum L.) represent an important tuber crop, worldwide. During its prolonged clonal propagation, numerous deleterious mutations have accumulated in the potato genome, leading to severe inbreeding depression; however, the shaping of this mutation burden during polyploidization and improvement is largely unknown. Here, we sequenced 20 diploid landraces of the Stenotomum group, eight tetraploid landraces, and 20 tetraploid modern cultivars, to analyze variations in their deleterious mutations. We show that deleterious mutations accumulated rapidly during the polyploidization of tetraploid potatoes. This study provides a foundation for future potato improvement.
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Affiliation(s)
- Qun Lian
- Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute, the Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Inner Mongolia Potato Engineering and Technology Research Centre, Inner Mongolia University, Hohhot 010021, China
| | - Die Tang
- Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute, the Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Zhiyan Bai
- Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute, the Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Jianjian Qi
- Inner Mongolia Potato Engineering and Technology Research Centre, Inner Mongolia University, Hohhot 010021, China
| | - Fei Lu
- The State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, the Chinese Academy of Sciences, Beijing 100101, China
- CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Shanghai Institutes for Biological Sciences, the Chinese Academy of Sciences, Shanghai 200032, China
| | - Sanwen Huang
- Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute, the Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Chunzhi Zhang
- Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute, the Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
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30
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Fadason T, Schierding W, Lumley T, O'Sullivan JM. Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities. Nat Commun 2018; 9:5198. [PMID: 30518762 PMCID: PMC6281603 DOI: 10.1038/s41467-018-07692-y] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 11/14/2018] [Indexed: 02/06/2023] Open
Abstract
Clinical studies of non-communicable diseases identify multimorbidities that suggest a common set of predisposing factors. Despite the fact that humans have ~24,000 genes, we do not understand the genetic pathways that contribute to the development of multimorbid non-communicable disease. Here we create a multimorbidity atlas of traits based on pleiotropy of spatially regulated genes. Using chromatin interaction and expression Quantitative Trait Loci (eQTL) data, we analyse 20,782 variants (p < 5 × 10-6) associated with 1351 phenotypes to identify 16,248 putative spatial eQTL-eGene pairs that are involved in 76,013 short- and long-range regulatory interactions (FDR < 0.05) in different human tissues. Convex biclustering of spatial eGenes that are shared among phenotypes identifies complex interrelationships between nominally different phenotype-associated SNPs. Our approach enables the simultaneous elucidation of variant interactions with target genes that are drivers of multimorbidity, and those that contribute to unique phenotype associated characteristics.
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Affiliation(s)
- Tayaza Fadason
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, 1010, New Zealand
| | - William Schierding
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, 1010, New Zealand
| | - Thomas Lumley
- The Department of Biostatistics, The University of Auckland, Auckland, 1010, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, 1023, New Zealand.
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, 1010, New Zealand.
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31
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Kasap M, Rajani V, Rajani J, Dwyer DS. Surprising conservation of schizophrenia risk genes in lower organisms reflects their essential function and the evolution of genetic liability. Schizophr Res 2018; 202:120-128. [PMID: 30017463 DOI: 10.1016/j.schres.2018.07.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 07/02/2018] [Accepted: 07/04/2018] [Indexed: 12/12/2022]
Abstract
Schizophrenia is a devastating psychiatric illness that affects approximately 1% of the population. Genetic variation in multiple genes causes elevated risk for the disorder, but the molecular basis is inadequately understood and it is not clear how risk genes have evolved and persisted in the genome. To address these issues, we have identified orthologs/homologs of 344 schizophrenia risk genes (from the Psychiatric Genomics Consortium dataset) in lower organisms, including C. elegans, Drosophila and zebrafish, along with phenotypes produced by genetic disruption in C. elegans. Schizophrenia risk genes were evolutionarily conserved at significantly higher rates in C. elegans (81%) and zebrafish (88%) than genes in general for these two species (40-70%). The risk-gene equivalents were highly (~3-fold) enriched for essential genes consistent with polygenic mutation threshold models, which propose that genetic susceptibility results from the inevitable expression of harmful combinations of risk variants in the population. Most notably, numerous examples of cross-species synteny revealed how blocks of risk genes geared toward a shared biological purpose coalesced into proximity during evolution. We obtained initial evidence that schizophrenia risk genes affected different stages of development, potentially allowing differential modulation by the environment. Taken together, studies of the conservation of schizophrenia risk genes in simple model organisms provided novel insights into the molecular basis for genetic susceptibility to a complex human psychiatric disorder.
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Affiliation(s)
- Merve Kasap
- Department of Pharmacology, Toxicology and Neuroscience, LSU Health Shreveport, USA
| | - Vivek Rajani
- Department of Psychiatry, LSU Health Shreveport, Shreveport, LA 71130, USA
| | - Jackie Rajani
- Department of Psychiatry, Rosalind Franklin University, Chicago, IL 60064, USA
| | - Donard S Dwyer
- Department of Pharmacology, Toxicology and Neuroscience, LSU Health Shreveport, USA; Department of Psychiatry, LSU Health Shreveport, Shreveport, LA 71130, USA.
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32
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Houle AA, Gibling H, Lamaze FC, Edgington HA, Soave D, Fave MJ, Agbessi M, Bruat V, Stein LD, Awadalla P. Aberrant PRDM9 expression impacts the pan-cancer genomic landscape. Genome Res 2018; 28:1611-1620. [PMID: 30341163 PMCID: PMC6211651 DOI: 10.1101/gr.231696.117] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2017] [Accepted: 10/04/2018] [Indexed: 12/15/2022]
Abstract
The binding of PRDM9 to chromatin is a key step in the induction of DNA double-strand breaks associated with meiotic recombination hotspots; it is normally expressed solely in germ cells. We interrogated 1879 cancer samples in 39 different cancer types and found that PRDM9 is unexpectedly expressed in 20% of these tumors even after stringent gene homology correction. The expression levels of PRDM9 in tumors are significantly higher than those found in healthy neighboring tissues and in healthy nongerm tissue databases. Recurrently mutated regions located within 5 Mb of the PRDM9 loci, as well as differentially expressed genes in meiotic pathways, correlate with PRDM9 expression. In samples with aberrant PRDM9 expression, structural variant breakpoints frequently neighbor the DNA motif recognized by PRDM9, and there is an enrichment of structural variants at sites of known meiotic PRDM9 activity. This study is the first to provide evidence of an association between aberrant expression of the meiosis-specific gene PRDM9 with genomic instability in cancer.
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Affiliation(s)
- Armande Ang Houle
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada.,University of Toronto, Department of Molecular Genetics, Toronto, Ontario M5S 1A8, Canada
| | - Heather Gibling
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada.,University of Toronto, Department of Molecular Genetics, Toronto, Ontario M5S 1A8, Canada
| | - Fabien C Lamaze
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada.,University of Toronto, Department of Molecular Genetics, Toronto, Ontario M5S 1A8, Canada
| | - Hilary A Edgington
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada.,University of Toronto, Department of Molecular Genetics, Toronto, Ontario M5S 1A8, Canada
| | - David Soave
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada
| | - Marie-Julie Fave
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada
| | - Mawusse Agbessi
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada
| | - Vanessa Bruat
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada
| | - Lincoln D Stein
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada.,University of Toronto, Department of Molecular Genetics, Toronto, Ontario M5S 1A8, Canada
| | - Philip Awadalla
- Ontario Institute for Cancer Research, Department of Computational Biology, Toronto, Ontario M5G 0A3, Canada.,University of Toronto, Department of Molecular Genetics, Toronto, Ontario M5S 1A8, Canada
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33
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34
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Gene-by-environment interactions in urban populations modulate risk phenotypes. Nat Commun 2018; 9:827. [PMID: 29511166 PMCID: PMC5840419 DOI: 10.1038/s41467-018-03202-2] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Accepted: 01/26/2018] [Indexed: 01/21/2023] Open
Abstract
Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G × E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ∼1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual’s response to environmental challenges. Individuals with different genotypes may respond differently to environmental variation. Here, Favé et al. find substantial impacts of different environment exposures on the transcriptome and clinical endophenotypes when controlling for genetic ancestry by analyzing data from ∼1000 individuals from a founder population in Quebec.
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35
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Pardiñas AF, Holmans P, Pocklington AJ, Escott-Price V, Ripke S, Carrera N, Legge SE, Bishop S, Cameron D, Hamshere ML, Han J, Hubbard L, Lynham A, Mantripragada K, Rees E, MacCabe JH, McCarroll SA, Baune BT, Breen G, Byrne EM, Dannlowski U, Eley TC, Hayward C, Martin NG, McIntosh AM, Plomin R, Porteous DJ, Wray NR, Caballero A, Geschwind DH, Huckins LM, Ruderfer DM, Santiago E, Sklar P, Stahl EA, Won H, Agerbo E, Als TD, Andreassen OA, Bækvad-Hansen M, Mortensen PB, Pedersen CB, Børglum AD, Bybjerg-Grauholm J, Djurovic S, Durmishi N, Pedersen MG, Golimbet V, Grove J, Hougaard DM, Mattheisen M, Molden E, Mors O, Nordentoft M, Pejovic-Milovancevic M, Sigurdsson E, Silagadze T, Hansen CS, Stefansson K, Stefansson H, Steinberg S, Tosato S, Werge T, Collier DA, Rujescu D, Kirov G, Owen MJ, O'Donovan MC, Walters JTR. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat Genet 2018; 50:381-389. [PMID: 29483656 PMCID: PMC5918692 DOI: 10.1038/s41588-018-0059-2] [Citation(s) in RCA: 950] [Impact Index Per Article: 158.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 01/07/2018] [Indexed: 12/13/2022]
Abstract
Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.
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Affiliation(s)
- Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Peter Holmans
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Andrew J Pocklington
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Valentina Escott-Price
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry and Psychotherapy, Charité, Campus Mitte, Berlin, Germany
| | - Noa Carrera
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Sophie E Legge
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Sophie Bishop
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Darren Cameron
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Marian L Hamshere
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Jun Han
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Leon Hubbard
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Amy Lynham
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Kiran Mantripragada
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Elliott Rees
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - James H MacCabe
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Steven A McCarroll
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bernhard T Baune
- Discipline of Psychiatry, University of Adelaide, Adelaide, South Australia, Australia
| | - Gerome Breen
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR Biomedical Research Centre for Mental Health, Maudsley Hospital and Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Enda M Byrne
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Udo Dannlowski
- Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany
| | - Thalia C Eley
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Caroline Hayward
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Nicholas G Martin
- School of Psychology, University of Queensland, Brisbane, Queensland, Australia
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew M McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK
| | - Robert Plomin
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - David J Porteous
- Medical Genetics Section, Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK
| | - Naomi R Wray
- Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Queensland, Australia
| | - Armando Caballero
- Departamento de Bioquímica, Genética e Inmunología. Facultad de Biología, Universidad de Vigo, Vigo, Spain
| | - Daniel H Geschwind
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Laura M Huckins
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Douglas M Ruderfer
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Enrique Santiago
- Departamento de Biología Funcional. Facultad de Biología, Universidad de Oviedo, Oviedo, Spain
| | - Pamela Sklar
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eli A Stahl
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hyejung Won
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Esben Agerbo
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Thomas D Als
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- iSEQ, Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
| | - Ole A Andreassen
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Marie Bækvad-Hansen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Preben Bo Mortensen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
- iSEQ, Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark
| | - Carsten Bøcker Pedersen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Anders D Børglum
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- iSEQ, Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
| | - Jonas Bybjerg-Grauholm
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Srdjan Djurovic
- NORMENT, KG Jebsen Centre for Psychosis Research, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Naser Durmishi
- Department of Child and Adolescent Psychiatry, University Clinic of Psychiatry, Skopje, Macedonia
| | - Marianne Giørtz Pedersen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- National Centre for Register-Based Research, Aarhus University, Aarhus, Denmark
| | - Vera Golimbet
- Department of Clinical Genetics, Mental Health Research Center, Moscow, Russia
| | - Jakob Grove
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- iSEQ, Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
| | - David M Hougaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Manuel Mattheisen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- iSEQ, Center for Integrative Sequencing, Aarhus University, Aarhus, Denmark
- Department of Biomedicine-Human Genetics, Aarhus University, Aarhus, Denmark
| | - Espen Molden
- Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole Mors
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital, Risskov, Denmark
| | - Merete Nordentoft
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Teimuraz Silagadze
- Department of Psychiatry and Drug Addiction, Tbilisi State Medical University (TSMU), Tbilisi, Georgia
| | - Christine Søholm Hansen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | | | | | | | - Sarah Tosato
- Section of Psychiatry, Department of Public Health and Community Medicine, University of Verona, Verona, Italy
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Institute of Biological Psychiatry, MHC Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - David A Collier
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Discovery Neuroscience Research, Eli Lilly and Company, Lilly Research Laboratories, Windlesham, UK
| | - Dan Rujescu
- Department of Psychiatry, University of Halle, Halle, Germany
- Department of Psychiatry, University of Munich, Munich, Germany
| | - George Kirov
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK.
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Alves I, Houle AA, Hussin JG, Awadalla P. The impact of recombination on human mutation load and disease. Philos Trans R Soc Lond B Biol Sci 2017; 372:20160465. [PMID: 29109227 PMCID: PMC5698626 DOI: 10.1098/rstb.2016.0465] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2017] [Indexed: 12/21/2022] Open
Abstract
Recombination promotes genomic integrity among cells and tissues through double-strand break repair, and is critical for gamete formation and fertility through a strict regulation of the molecular mechanisms associated with proper chromosomal disjunction. In humans, congenital defects and recurrent structural abnormalities can be attributed to aberrant meiotic recombination. Moreover, mutations affecting genes involved in recombination pathways are directly linked to pathologies including infertility and cancer. Recombination is among the most prominent mechanism shaping genome variation, and is associated with not only the structuring of genomic variability, but is also tightly linked with the purging of deleterious mutations from populations. Together, these observations highlight the multiple roles of recombination in human genetics: its ability to act as a major force of evolution, its molecular potential to maintain genome repair and integrity in cell division and its mutagenic cost impacting disease evolution.This article is part of the themed issue 'Evolutionary causes and consequences of recombination rate variation in sexual organisms'.
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Affiliation(s)
- Isabel Alves
- Ontario Institute of Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, Canada M5G 0A3
| | - Armande Ang Houle
- Ontario Institute of Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, Canada M5G 0A3
- Department of Molecular Genetics, University of Toronto, Medical Science Building, 1 King's College Circle, Toronto, Ontario, Canada M5S 1A8
| | - Julie G Hussin
- Montreal Heart Institute, Department of Medicine, University of Montreal, 5000 Rue Bélanger, Montréal, Quebec, Canada H1T 1C8
- The Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK
| | - Philip Awadalla
- Ontario Institute of Cancer Research, 661 University Avenue, Suite 510, Toronto, Ontario, Canada M5G 0A3
- Department of Molecular Genetics, University of Toronto, Medical Science Building, 1 King's College Circle, Toronto, Ontario, Canada M5S 1A8
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37
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Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection. Nat Genet 2017; 49:1421-1427. [PMID: 28892061 DOI: 10.1038/ng.3954] [Citation(s) in RCA: 247] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 08/16/2017] [Indexed: 12/14/2022]
Abstract
Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average N = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (P = 2.38 × 10-104); the youngest 20% of common SNPs explain 3.9 times more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.
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38
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Pengelly RJ, Vergara-Lope A, Alyousfi D, Jabalameli MR, Collins A. Understanding the disease genome: gene essentiality and the interplay of selection, recombination and mutation. Brief Bioinform 2017; 20:267-273. [DOI: 10.1093/bib/bbx110] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Indexed: 12/24/2022] Open
Affiliation(s)
- Reuben J Pengelly
- Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Alejandra Vergara-Lope
- Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Dareen Alyousfi
- Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK
| | - M Reza Jabalameli
- Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Andrew Collins
- Genetic Epidemiology and Genomic Informatics, Faculty of Medicine, University of Southampton, Southampton, UK
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Allele-specific expression reveals interactions between genetic variation and environment. Nat Methods 2017; 14:699-702. [PMID: 28530654 PMCID: PMC5501199 DOI: 10.1038/nmeth.4298] [Citation(s) in RCA: 93] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Accepted: 04/20/2017] [Indexed: 12/13/2022]
Abstract
Identifying interactions between genetics and the environment (GxE) remains challenging. We have developed EAGLE, a hierarchical Bayesian model for identifying GxE interactions based on association between environment and allele-specific expression (ASE). Combining RNA-sequencing of whole blood and extensive environmental annotations collected from 922 human individuals, we identified 35 GxE interactions, compared to only four using standard GxE testing. EAGLE provides new opportunities to identify GxE interactions using functional genomic data.
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40
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Variation in Recombination Rate: Adaptive or Not? Trends Genet 2017; 33:364-374. [DOI: 10.1016/j.tig.2017.03.003] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 03/06/2017] [Accepted: 03/07/2017] [Indexed: 01/30/2023]
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41
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Chakravorty S, Hegde M. Gene and Variant Annotation for Mendelian Disorders in the Era of Advanced Sequencing Technologies. Annu Rev Genomics Hum Genet 2017; 18:229-256. [PMID: 28415856 DOI: 10.1146/annurev-genom-083115-022545] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Comprehensive annotations of genetic and noncoding regions and corresponding accurate variant classification for Mendelian diseases are the next big challenge in the new genomic era of personalized medicine. Progress in the development of faster and more accurate pipelines for genome annotation and variant classification will lead to the discovery of more novel disease associations and candidate therapeutic targets. This ultimately will facilitate better patient recruitment in clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics that aims to increase understanding of overall genomic complexity, complex inheritance patterns of disease, and patient-phenotype-specific genomic associations. We describe the emerging field of translational functional genomics, which integrates other functional "-omics" approaches that support next-generation sequencing genomic data in order to facilitate personalized diagnostics, disease management, biomarker discovery, and medicine. We also discuss the utility of this integrated approach for diagnostic clinics and medical databases and its role in the future of personalized medicine.
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Affiliation(s)
- Samya Chakravorty
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322;
| | - Madhuri Hegde
- Department of Human Genetics, Emory University School of Medicine, Atlanta, Georgia 30322;
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42
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Ramu P, Esuma W, Kawuki R, Rabbi IY, Egesi C, Bredeson JV, Bart RS, Verma J, Buckler ES, Lu F. Cassava haplotype map highlights fixation of deleterious mutations during clonal propagation. Nat Genet 2017; 49:959-963. [DOI: 10.1038/ng.3845] [Citation(s) in RCA: 140] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 03/21/2017] [Indexed: 12/20/2022]
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43
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da Silva J, Galbraith JD. Hill-Robertson interference maintained by Red Queen dynamics favours the evolution of sex. J Evol Biol 2017; 30:994-1010. [PMID: 28295769 DOI: 10.1111/jeb.13068] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2016] [Revised: 02/17/2017] [Accepted: 03/06/2017] [Indexed: 12/21/2022]
Abstract
Although it is well established theoretically that selective interference among mutations (Hill-Robertson interference) favours meiotic recombination, genomewide mean rates of mutation and strengths of selection appear too low to support this as the mechanism favouring recombination in nature. A possible solution to this discrepancy between theory and observation is that selection is at least intermittently very strong due to the antagonistic coevolution between a host and its parasites. The Red Queen theory posits that such coevolution generates fitness epistasis among loci, which generates negative linkage disequilibrium among beneficial mutations, which in turn favours recombination. This theory has received only limited support. However, Red Queen dynamics without epistasis may provide the ecological conditions that maintain strong and frequent selective interference in finite populations that indirectly selects for recombination. This hypothesis is developed here through the simulation of Red Queen dynamics. This approach required the development of a method to calculate the exact frequencies of multilocus haplotypes after recombination. Simulations show that recombination is favoured by the moderately weak selection of many loci involved in the interaction between a host and its parasites, which results in substitution rates that are compatible with empirical estimates. The model also reproduces the previously reported rapid increase in the rate of outcrossing in Caenorhabditis elegans coevolving with a bacterial pathogen.
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Affiliation(s)
- J da Silva
- Department of Genetics and Evolution, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - J D Galbraith
- Department of Genetics and Evolution, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
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44
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Loehlein Fier H, Prokopenko D, Hecker J, Cho MH, Silverman EK, Weiss ST, Tanzi RE, Lange C. On the association analysis of genome-sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows. Genet Epidemiol 2017; 41:332-340. [PMID: 28318110 DOI: 10.1002/gepi.22040] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 12/20/2016] [Accepted: 02/04/2017] [Indexed: 12/16/2022]
Abstract
For the association analysis of whole-genome sequencing (WGS) studies, we propose an efficient and fast spatial-clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed-window approaches, our approach is likely to group nearby variants together. Given existing biological evidence that disease-associated mutations tend to physically cluster in specific regions along the chromosome, the identification of meaningful groups of nearby located variants could thus lead to a potential power gain for association analysis. Our algorithm defines consecutive genomic regions based on the physical positions of the variants, assuming an inhomogeneous Poisson process and groups together nearby variants. As parameters are estimated locally, the algorithm takes the differing variant density along the chromosome into account and provides locally optimal partitioning of variants into consecutive regions. An R-implementation of the algorithm is provided. We discuss the theoretical advances of our algorithm compared to existing, window-based approaches and show the performance and advantage of our introduced algorithm in a simulation study and by an application to Alzheimer's disease WGS data. Our analysis identifies a region in the ITGB3 gene that potentially harbors disease susceptibility loci for Alzheimer's disease. The region-based association signal of ITGB3 replicates in an independent data set and achieves formally genome-wide significance. Software Implementation: An implementation of the algorithm in R is available at: https://github.com/heidefier/cluster_wgs_data.
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Affiliation(s)
- Heide Loehlein Fier
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.,Working Group of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Dmitry Prokopenko
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Julian Hecker
- Working Group of Genomic Mathematics, University of Bonn, Bonn, Germany
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Rudolph E Tanzi
- Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, United States of America
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America.,Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
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45
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Hodgkinson A, Grenier JC, Gbeha E, Awadalla P. A haplotype-based normalization technique for the analysis and detection of allele specific expression. BMC Bioinformatics 2016; 17:364. [PMID: 27618913 PMCID: PMC5020486 DOI: 10.1186/s12859-016-1238-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Accepted: 09/02/2016] [Indexed: 12/17/2022] Open
Abstract
Background Allele specific expression (ASE) has become an important phenotype, being utilized for the detection of cis-regulatory variation, nonsense mediated decay and imprinting in the personal genome, and has been used to both identify disease loci and consider the penetrance of damaging alleles. The detection of ASE using high throughput technologies relies on aligning short-read sequencing data, a process that has inherent biases, and there is still a need to develop fast and accurate methods to detect ASE given the unprecedented growth of sequencing information in big data projects. Results Here, we present a new approach to normalize RNA sequencing data in order to call ASE events with high precision in a short time-frame. Using simulated datasets we find that our approach dramatically improves reference allele quantification at heterozygous sites versus default mapping methods and also performs well compared to existing techniques for ASE detection, such as filtering methods and mapping to parental genomes, without the need for complex and time consuming manipulation. Finally, by sequencing the exomes and transcriptomes of 96 well-phenotyped individuals of the CARTaGENE cohort, we characterise the levels of ASE across individuals and find a significant association between the proportion of sites undergoing ASE within the genome and smoking. Conclusions The correct treatment and analysis of RNA sequencing data is vital to control for mapping biases and detect genuine ASE signals. By normalising RNA sequencing information after mapping, we show that this approach can be used to identify biologically relevant signals in personal genomes. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1238-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alan Hodgkinson
- CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada. .,Department of Medical and Molecular Genetics, Guy's Hospital, King's College London, London, SE1 9RT, UK.
| | - Jean-Christophe Grenier
- CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada
| | - Elias Gbeha
- CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada.,Ontario Institute of Cancer Research, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Philip Awadalla
- CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, 3175 Chemin de la Cote Sainte Catherine, Montreal, QC, Canada.,Ontario Institute of Cancer Research, Toronto, ON, Canada.,Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
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46
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Xue C, Chen H, Yu F. Base-Biased Evolution of Disease-Associated Mutations in the Human Genome. Hum Mutat 2016; 37:1209-1214. [PMID: 27507420 DOI: 10.1002/humu.23065] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2016] [Revised: 08/02/2016] [Accepted: 08/07/2016] [Indexed: 11/08/2022]
Abstract
Understanding the evolution of disease-associated mutations is fundamental to analyze pathogenetics of diseases. Mutation, recombination (by GC-biased gene conversion, gBGC), and selection have been known to shape the evolution of disease-associated mutations, but how these evolutionary forces work together is still an open question. In this study, we analyzed several human large-scale datasets (1000 Genomes, ESP6500, ExAC and ClinVar), and found that base-biased mutagenesis generates more GC→AT than AT→GC mutations, while gBGC promotes the fixation of AT→GC mutations to balance the impact of base-biased mutation on genome. Due to this effect of gBGC, purifying selection removes more deleterious AT→GC mutations than GC→AT from population, but many high-frequency (fixed and nearly fixed) deleterious AT→GC mutations are remained possibly due to high genetic load. As a special subset, disease-associated mutations follow this evolutionary rule, in which disease-associated GC→AT mutations are more enriched in rare mutations compared with AT→GC, while disease-associated AT→GC are more enriched in mutations with high frequency. Thus, we presented a base-biased evolutionary framework that explains the base-biased generation and accumulation of disease-associated mutations in human populations.
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Affiliation(s)
- Cheng Xue
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas
| | - Hua Chen
- Center for Computational Genomics, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Fuli Yu
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas.
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47
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Zhang M, Zhou L, Bawa R, Suren H, Holliday J. Recombination Rate Variation, Hitchhiking, and Demographic History Shape Deleterious Load in Poplar. Mol Biol Evol 2016; 33:2899-2910. [DOI: 10.1093/molbev/msw169] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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48
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Merner ND, Mercado A, Khanna AR, Hodgkinson A, Bruat V, Awadalla P, Gamba G, Rouleau GA, Kahle KT. Gain-of-function missense variant in SLC12A2, encoding the bumetanide-sensitive NKCC1 cotransporter, identified in human schizophrenia. J Psychiatr Res 2016; 77:22-6. [PMID: 26955005 DOI: 10.1016/j.jpsychires.2016.02.016] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Accepted: 02/19/2016] [Indexed: 12/12/2022]
Abstract
Perturbations of γ-aminobutyric acid (GABA) neurotransmission in the human prefrontal cortex have been implicated in the pathogenesis of schizophrenia (SCZ), but the mechanisms are unclear. NKCC1 (SLC12A2) is a Cl(-)-importing cation-Cl(-) cotransporter that contributes to the maintenance of depolarizing GABA activity in immature neurons, and variation in SLC12A2 has been shown to increase the risk for schizophrenia via alterations of NKCC1 mRNA expression. However, no disease-causing mutations or functional variants in NKCC1 have been identified in human patients with SCZ. Here, by sequencing three large French-Canadian (FC) patient cohorts of SCZ, autism spectrum disorders (ASD), and intellectual disability (ID), we identified a novel heterozygous NKCC1 missense variant (p.Y199C) in SCZ. This variant is located in an evolutionarily conserved residue in the critical N-terminal regulatory domain and exhibits high predicted pathogenicity. No NKCC1 variants were detected in ASD or ID, and no KCC3 variants were identified in any of the three neurodevelopmental disorder cohorts. Functional experiments show Y199C is a gain-of-function variant, increasing Cl(-)-dependent and bumetanide-sensitive NKCC1 activity even in conditions in which the transporter is normally functionally silent (hypotonicity). These data are the first to describe a functional missense variant in SLC12A2 in human SCZ, and suggest that genetically encoded dysregulation of NKCC1 may be a risk factor for, or contribute to the pathogenesis of, human SCZ.
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Affiliation(s)
- Nancy D Merner
- Harrison School of Pharmacy, Department of Drug Discovery and Development, Auburn University, Auburn, AL, USA
| | - Adriana Mercado
- Department of Nephrology, Instituto Nacional de Cardiología Ignacio Chávez, Mexico City, Mexico
| | - Arjun R Khanna
- Department of Neurobiology, Harvard Medical School, Boston, MA 02125, USA
| | - Alan Hodgkinson
- CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, Montréal, Quebec, Canada
| | - Vanessa Bruat
- CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, Montréal, Quebec, Canada
| | - Philip Awadalla
- CHU Sainte Justine Research Centre, Department of Pediatrics, Faculty of Medicine, Universite de Montreal, Montréal, Quebec, Canada; CARTaGENE, 3333 Queen Mary Road, Office 493, Montreal, Quebec, Canada
| | - Gerardo Gamba
- Molecular Physiology Uinit, Instituto de Investigaciones Biomédicas, Univesidad Nacional Autónoma de México and Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico
| | - Guy A Rouleau
- Montreal Neurological Hospital and Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada.
| | - Kristopher T Kahle
- Department of Neurosurgery and Pediatrics, Interdepartmental Neuroscience Program, Program on Neurogenetics, Yale School of Medicine, New Haven, CT 06510, USA.
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49
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Kukurba KR, Parsana P, Balliu B, Smith KS, Zappala Z, Knowles DA, Favé MJ, Davis JR, Li X, Zhu X, Potash JB, Weissman MM, Shi J, Kundaje A, Levinson DF, Awadalla P, Mostafavi S, Battle A, Montgomery SB. Impact of the X Chromosome and sex on regulatory variation. Genome Res 2016; 26:768-77. [PMID: 27197214 PMCID: PMC4889977 DOI: 10.1101/gr.197897.115] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2015] [Accepted: 04/18/2016] [Indexed: 02/07/2023]
Abstract
The X Chromosome, with its unique mode of inheritance, contributes to differences between the sexes at a molecular level, including sex-specific gene expression and sex-specific impact of genetic variation. Improving our understanding of these differences offers to elucidate the molecular mechanisms underlying sex-specific traits and diseases. However, to date, most studies have either ignored the X Chromosome or had insufficient power to test for the sex-specific impact of genetic variation. By analyzing whole blood transcriptomes of 922 individuals, we have conducted the first large-scale, genome-wide analysis of the impact of both sex and genetic variation on patterns of gene expression, including comparison between the X Chromosome and autosomes. We identified a depletion of expression quantitative trait loci (eQTL) on the X Chromosome, especially among genes under high selective constraint. In contrast, we discovered an enrichment of sex-specific regulatory variants on the X Chromosome. To resolve the molecular mechanisms underlying such effects, we generated chromatin accessibility data through ATAC-sequencing to connect sex-specific chromatin accessibility to sex-specific patterns of expression and regulatory variation. As sex-specific regulatory variants discovered in our study can inform sex differences in heritable disease prevalence, we integrated our data with genome-wide association study data for multiple immune traits identifying several traits with significant sex biases in genetic susceptibilities. Together, our study provides genome-wide insight into how genetic variation, the X Chromosome, and sex shape human gene regulation and disease.
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Affiliation(s)
- Kimberly R Kukurba
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Princy Parsana
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Brunilda Balliu
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Kevin S Smith
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Zachary Zappala
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - David A Knowles
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Marie-Julie Favé
- Sainte-Justine University Hospital Research Centre, Department of Pediatrics, University of Montreal, Montreal, Québec H3T 1J4, Canada
| | - Joe R Davis
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Xin Li
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Xiaowei Zhu
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - James B Potash
- Department of Psychiatry, University of Iowa Hospitals & Clinics, Iowa City, Iowa 52242, USA
| | - Myrna M Weissman
- Department of Psychiatry, Columbia University and New York State Psychiatric Institute, New York, New York 10032, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland 20892, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Computer Science, Stanford University, Stanford, California 94305, USA
| | - Douglas F Levinson
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Philip Awadalla
- Sainte-Justine University Hospital Research Centre, Department of Pediatrics, University of Montreal, Montreal, Québec H3T 1J4, Canada
| | - Sara Mostafavi
- Department of Statistics, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Alexis Battle
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Computer Science, Stanford University, Stanford, California 94305, USA;
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An expanded sequence context model broadly explains variability in polymorphism levels across the human genome. Nat Genet 2016; 48:349-55. [PMID: 26878723 PMCID: PMC4811712 DOI: 10.1038/ng.3511] [Citation(s) in RCA: 141] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2015] [Accepted: 01/22/2016] [Indexed: 12/15/2022]
Abstract
The rate of single nucleotide polymorphism varies substantially across the human genome and fundamentally influences evolution and incidence of genetic disease. Previous studies have only considered the immediate flanking nucleotides around a polymorphic site –the site’s trinucleotide sequence context– to study polymorph levels across the genome. Moreover, the impact of larger sequence contexts has not been fully clarified, even though context substantially influences rates of polymorphism. Using a new statistical framework and data from the 1000 Genomes Project, we demonstrate that a heptanucleotide context explains >81% of variability in substitution probabilities, revealing new mutation-promoting motifs at ApT dinucleotide, CAAT, and TACG sequences. Our approach also identifies previously undocumented variability in C-to-T substitutions at CpG sites, which is not immediately explained by differential methylation intensity. Using our model, we present informative substitution intolerance scores for genes and a new intolerance score for amino acids, and we demonstrate clinical use of the model in neuropsychiatric diseases.
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