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Damena D, Barry A, Morrison R, Gaoussou S, Mahamar A, Attaher O, Issiaka D, Dicko Y, Dicko A, Duffy P, Fried M. A novel locus in CSMD1 gene is associated with increased susceptibility to severe malaria in Malian children. Front Genet 2024; 15:1390786. [PMID: 38854427 PMCID: PMC11157005 DOI: 10.3389/fgene.2024.1390786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/02/2024] [Indexed: 06/11/2024] Open
Abstract
Background Plasmodium falciparum malaria is still a leading cause of child mortality in sub-Saharan Africa. The clinical manifestations of malaria range from asymptomatic infection to severe disease. The variation in clinical presentation is partly attributed to host genetic factors with estimated narrow-sense heritability of 23%. Here, we investigate the associations between candidate gene polymorphisms and the likelihood of severe malaria (SM) in a cohort of Malian children. Methods Based on our previous genome-wide association studies (GWAS) analysis, candidate genes were selected for in-depth analysis using several criteria including gene-level GWAS scores, functional overlap with malaria pathogenesis, and evidence of association with protection or susceptibility to other infectious or inflammatory diseases. Single Nucleotide Polymorphisms (SNPs) residing within these genes were selected mainly based on p-values from previous severe malaria susceptibility GWAS studies and minor allele frequency (MAF) in West African populations. Results Of 182 candidate genes reported in our previous study, 11 genes and 22 SNPs residing in these genes were selected. The selected SNPs were genotyped using KASP technology in 477 DNA samples (87 SM and 390 controls). Logistic regression analysis revealed that a common intron variant, rs13340578 in CUB and Sushi Multi Domain (CSMD1) gene, is associated with increased odds of SM in recessive mode of inheritance (MAF = 0.42, OR = 1.8, 95% CI = [1.78, 1.84], p = 0.029). The SNP is in linkage disequilibrium (LD) with multiple variants with regulatory features. Conclusion Taken together, the current study showed that an intron variant rs13340578, residing in CSMD1 gene, is associated with increased susceptibility to malaria. This finding suggests that modified regulation of complement may contribute to malaria disease severity. Further studies are needed to identify the causal variants and the underlying molecular mechanisms.
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Affiliation(s)
- Delesa Damena
- Molecular Pathogenesis and Biomarkers Section, Laboratory of Malaria Immunology and Vaccinology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Amadou Barry
- Malaria Research and Training Center, University of Sciences Techniques and Technologies of Bamako, Bamako, Mali
| | - Robert Morrison
- Pathogenesis and Immunity Section, Laboratory of Malaria Immunology and Vaccinology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Santara Gaoussou
- Malaria Research and Training Center, University of Sciences Techniques and Technologies of Bamako, Bamako, Mali
| | - Almahamoudou Mahamar
- Malaria Research and Training Center, University of Sciences Techniques and Technologies of Bamako, Bamako, Mali
| | - Oumar Attaher
- Malaria Research and Training Center, University of Sciences Techniques and Technologies of Bamako, Bamako, Mali
| | - Djibrilla Issiaka
- Malaria Research and Training Center, University of Sciences Techniques and Technologies of Bamako, Bamako, Mali
| | - Yahia Dicko
- Malaria Research and Training Center, University of Sciences Techniques and Technologies of Bamako, Bamako, Mali
| | - Alassane Dicko
- Malaria Research and Training Center, University of Sciences Techniques and Technologies of Bamako, Bamako, Mali
| | - Patrick Duffy
- Pathogenesis and Immunity Section, Laboratory of Malaria Immunology and Vaccinology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Michal Fried
- Molecular Pathogenesis and Biomarkers Section, Laboratory of Malaria Immunology and Vaccinology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
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Park N, Kim H, Oh J, Kim J, Heo C, Kim J. PAPipe: A Pipeline for Comprehensive Population Genetic Analysis. Mol Biol Evol 2024; 41:msae040. [PMID: 38427787 PMCID: PMC10919927 DOI: 10.1093/molbev/msae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 02/07/2024] [Accepted: 02/16/2024] [Indexed: 03/03/2024] Open
Abstract
Advancements in next-generation sequencing (NGS) technologies have led to a substantial increase in the availability of population genetic variant data, thus prompting the development of various population analysis tools to enhance our understanding of population structure and evolution. The tools that are currently used to analyze population genetic variant data generally require different environments, parameters, and formats of the input data, which can act as a barrier preventing the wide-spread usage of such tools by general researchers who may not be familiar with bioinformatics. To address this problem, we have developed an automated and comprehensive pipeline called PAPipe to perform nine widely used population genetic analyses using population NGS data. PAPipe seamlessly interconnects and serializes multiple steps, such as read trimming and mapping, genetic variant calling, data filtering, and format converting, along with nine population genetic analyses such as principal component analysis, phylogenetic analysis, population tree analysis, population structure analysis, linkage disequilibrium decay analysis, selective sweep analysis, population admixture analysis, sequentially Markovian coalescent analysis, and fixation index analysis. PAPipe also provides an easy-to-use web interface that allows for the parameters to be set and the analysis results to be browsed in intuitive manner. PAPipe can be used to generate extensive results that provide insights that can help enhance user convenience and data usability. PAPipe is freely available at https://github.com/jkimlab/PAPipe.
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Affiliation(s)
- Nayoung Park
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Hyeonji Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Jeongmin Oh
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Jinseok Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Charyeong Heo
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Republic of Korea
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3
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Winters NP, Wafula EK, Knollenberg BJ, Hämälä T, Timilsena PR, Perryman M, Zhang D, Sheaffer LL, Praul CA, Ralph PE, Prewitt S, Leandro-Muñoz ME, Delgadillo-Duran DA, Altman NS, Tiffin P, Maximova SN, dePamphilis CW, Marden JH, Guiltinan MJ. A combination of conserved and diverged responses underlies Theobroma cacao's defense response to Phytophthora palmivora. BMC Biol 2024; 22:38. [PMID: 38360697 PMCID: PMC10870529 DOI: 10.1186/s12915-024-01831-2] [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: 04/06/2023] [Accepted: 01/23/2024] [Indexed: 02/17/2024] Open
Abstract
BACKGROUND Plants have complex and dynamic immune systems that have evolved to resist pathogens. Humans have worked to enhance these defenses in crops through breeding. However, many crops harbor only a fraction of the genetic diversity present in wild relatives. Increased utilization of diverse germplasm to search for desirable traits, such as disease resistance, is therefore a valuable step towards breeding crops that are adapted to both current and emerging threats. Here, we examine diversity of defense responses across four populations of the long-generation tree crop Theobroma cacao L., as well as four non-cacao Theobroma species, with the goal of identifying genetic elements essential for protection against the oomycete pathogen Phytophthora palmivora. RESULTS We began by creating a new, highly contiguous genome assembly for the P. palmivora-resistant genotype SCA 6 (Additional file 1: Tables S1-S5), deposited in GenBank under accessions CP139290-CP139299. We then used this high-quality assembly to combine RNA and whole-genome sequencing data to discover several genes and pathways associated with resistance. Many of these are unique, i.e., differentially regulated in only one of the four populations (diverged 40 k-900 k generations). Among the pathways shared across all populations is phenylpropanoid biosynthesis, a metabolic pathway with well-documented roles in plant defense. One gene in this pathway, caffeoyl shikimate esterase (CSE), was upregulated across all four populations following pathogen treatment, indicating its broad importance for cacao's defense response. Further experimental evidence suggests this gene hydrolyzes caffeoyl shikimate to create caffeic acid, an antimicrobial compound and known inhibitor of Phytophthora spp. CONCLUSIONS Our results indicate most expression variation associated with resistance is unique to populations. Moreover, our findings demonstrate the value of using a broad sample of evolutionarily diverged populations for revealing the genetic bases of cacao resistance to P. palmivora. This approach has promise for further revealing and harnessing valuable genetic resources in this and other long-generation plants.
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Affiliation(s)
- Noah P Winters
- IGDP Ecology, The Pennsylvania State University, 422 Huck Life Sciences Building, University Park, PA, 16803, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Eric K Wafula
- Department of Biology, The Pennsylvania State University, University Park, PA, USA
| | | | - Tuomas Hämälä
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, USA
- Department of Ecology and Genetics, University of Oulu, Oulu, Finland
| | - Prakash R Timilsena
- Department of Biology, The Pennsylvania State University, University Park, PA, USA
| | - Melanie Perryman
- Department of Plant Science, The Pennsylvania State University, University Park, PA, USA
| | - Dapeng Zhang
- Sustainable Perennial Crops Laboratory, U.S. Department of Agriculture-Agricultural Research Service, Beltsville, MD, USA
| | - Lena L Sheaffer
- Department of Plant Science, The Pennsylvania State University, University Park, PA, USA
| | - Craig A Praul
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
| | - Paula E Ralph
- Department of Biology, The Pennsylvania State University, University Park, PA, USA
| | - Sarah Prewitt
- Department of Plant Science, The Pennsylvania State University, University Park, PA, USA
| | | | | | - Naomi S Altman
- Department of Statistics, The Pennsylvania State University, University Park, PA, USA
| | - Peter Tiffin
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, USA
| | - Siela N Maximova
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
- Department of Plant Science, The Pennsylvania State University, University Park, PA, USA
| | - Claude W dePamphilis
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
- Department of Biology, The Pennsylvania State University, University Park, PA, USA
- IGDP Plant Biology, The Pennsylvania State University, University Park, PA, USA
| | - James H Marden
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA
- Department of Biology, The Pennsylvania State University, University Park, PA, USA
| | - Mark J Guiltinan
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, USA.
- Department of Biology, The Pennsylvania State University, University Park, PA, USA.
- IGDP Plant Biology, The Pennsylvania State University, University Park, PA, USA.
- Department of Plant Science, The Pennsylvania State University, University Park, PA, USA.
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4
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Yadav A, Srivastava S, Tyagi S, Krishna N, Katara P. In-silico mining to glean SNPs of pharmaco-clinical importance: an investigation with reference to the Indian populated SNPs. In Silico Pharmacol 2023; 11:17. [PMID: 37484779 PMCID: PMC10356698 DOI: 10.1007/s40203-023-00154-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 07/11/2023] [Indexed: 07/25/2023] Open
Abstract
Drugs pharmacology is defined by pharmacokinetics and pharmacodynamics and both of them are affected by genetic variability. Genetic variability varies from population to population, and sometimes even within the population, it exists. Single nucleotide polymorphisms (SNPs) are one of the major genetic variability factors which are found to be associated with the pharmacokinetics and pharmacodynamics process of a drug and are responsible for variable drug response and clinical phenotypes. Studies of SNPs can help to perform genome-wide association studies for their association with pharmacological and clinical events, at the same time; their information can direct genome-wide association studies for their use as biomarkers. With the aim to mine and characterize Indian populated SNPs of pharmacological and clinical importance. Two hundred six candidate SNPs belonging to 43 genes were retrieved from Indian Genome Variation Database. The distribution pattern of considered SNPs was observed against all five world super-populations (AFR, AMR, EAS, EUR, and SAS). Further, their annotation was done through SNP-nexus by considering Human genome reference builds - hg38, pharmacological and clinical information was supplemented by PharmGKB and ClinVar database. At last, to find out the association between SNPs linkage disequilibrium was observed in terms of r2. Overall, the study reported 53 pharmaco-clinical active SNPs and found 24 SNP-pairs as potential markers, and recommended their clinical and experimental validation. Supplementary Information The online version contains supplementary material available at 10.1007/s40203-023-00154-4.
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Affiliation(s)
- Anamika Yadav
- Computational Omics Lab, Centre of Bioinformatics, University of Allahabad, Prayagraj, 211002 India
| | - Shivani Srivastava
- Computational Omics Lab, Centre of Bioinformatics, University of Allahabad, Prayagraj, 211002 India
- Centre of Biotechnology, University of Allahabad, Prayagraj, 211002 India
| | - Shivani Tyagi
- Computational Omics Lab, Centre of Bioinformatics, University of Allahabad, Prayagraj, 211002 India
| | - Neelam Krishna
- Computational Omics Lab, Centre of Bioinformatics, University of Allahabad, Prayagraj, 211002 India
| | - Pramod Katara
- Computational Omics Lab, Centre of Bioinformatics, University of Allahabad, Prayagraj, 211002 India
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5
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Wilder CL, Lefaudeux D, Mathenge R, Kishimoto K, Zuniga Munoz A, Nguyen MA, Meyer AS, Cheng QJ, Hoffmann A. A stimulus-contingent positive feedback loop enables IFN-β dose-dependent activation of pro-inflammatory genes. Mol Syst Biol 2023; 19:e11294. [PMID: 36929731 PMCID: PMC10167482 DOI: 10.15252/msb.202211294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/18/2023] Open
Abstract
Type I interferons (IFN) induce powerful antiviral and innate immune responses via the transcription factor, IFN-stimulated gene factor (ISGF3). However, in some pathological contexts, type I IFNs are responsible for exacerbating inflammation. Here, we show that a high dose of IFN-β also activates an inflammatory gene expression program in contrast to IFN-λ3, a type III IFN, which elicits only the common antiviral gene program. We show that the inflammatory gene program depends on a second, potentiated phase in ISGF3 activation. Iterating between mathematical modeling and experimental analysis, we show that the ISGF3 activation network may engage a positive feedback loop with its subunits IRF9 and STAT2. This network motif mediates stimulus-specific ISGF3 dynamics that are dependent on ligand, dose, and duration of exposure, and when engaged activates the inflammatory gene expression program. Our results reveal a previously underappreciated dynamical control of the JAK-STAT/IRF signaling network that may produce distinct biological responses and suggest that studies of type I IFN dysregulation, and in turn therapeutic remedies, may focus on feedback regulators within it.
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Affiliation(s)
- Catera L Wilder
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
- Present address:
Department of Bioengineering and Therapeutic SciencesUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Diane Lefaudeux
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
- Present address:
Novadiscovery S.A.LyonFrance
| | - Raisa Mathenge
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
- Present address:
Division of RheumatologyUniversity of California, San FranciscoSan FranciscoCAUSA
| | - Kensei Kishimoto
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
- Present address:
Department of Molecular, Cell, and Cancer BiologyUniversity of Massachusetts Chan Medical SchoolWorcesterMAUSA
| | - Alma Zuniga Munoz
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
- Present address:
Department of Physiology and BiophysicsUniversity of Southern CaliforniaLos AngelesCAUSA
| | - Minh A Nguyen
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
- Present address:
Division of Genetics and Genomics, Department of PediatricsBoston Children's HospitalBostonMAUSA
| | - Aaron S Meyer
- Department of BioengineeringUniversity of CaliforniaLos AngelesCAUSA
| | - Quen J Cheng
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
- Division of Infectious Diseases, Department of MedicineUniversity of CaliforniaLos AngelesCAUSA
| | - Alexander Hoffmann
- Department of Microbiology, Immunology, and Molecular GeneticsUniversity of CaliforniaLos AngelesCAUSA
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6
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Bykova M, Hou Y, Eng C, Cheng F. Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes. Hum Mol Genet 2022; 31:R105-R113. [PMID: 36018824 PMCID: PMC9989738 DOI: 10.1093/hmg/ddac208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/18/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.
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Affiliation(s)
- Marina Bykova
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
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7
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Fedorova L, Khrunin A, Khvorykh G, Lim J, Thornton N, Mulyar OA, Limborska S, Fedorov A. Analysis of Common SNPs across Continents Reveals Major Genomic Differences between Human Populations. Genes (Basel) 2022; 13:genes13081472. [PMID: 36011383 PMCID: PMC9408407 DOI: 10.3390/genes13081472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 12/03/2022] Open
Abstract
Common alleles tend to be more ancient than rare alleles. These common SNPs appeared thousands of years ago and reflect intricate human evolution including various adaptations, admixtures, and migration events. Eighty-four thousand abundant region-specific alleles (ARSAs) that are common in one continent but absent in the rest of the world have been characterized by processing 3100 genomes from 230 populations. Also computed were 17,446 polymorphic sites with regional absence of common alleles (RACAs), which are widespread globally but absent in one region. A majority of these region-specific SNPs were found in Africa. America has the second greatest number of ARSAs (3348) and is even ahead of Europe (1911). Surprisingly, East Asia has the highest number of RACAs (10,524) and the lowest number of ARSAs (362). ARSAs and RACAs have distinct compositions of ancestral versus derived alleles in different geographical regions, reflecting their unique evolution. Genes associated with ARSA and RACA SNPs were identified and their functions were analyzed. The core 100 genes shared by multiple populations and associated with region-specific natural selection were examined. The largest part of them (42%) are related to the nervous system. ARSA and RACA SNPs are important for both association and human evolution studies.
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Affiliation(s)
| | - Andrey Khrunin
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Gennady Khvorykh
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Jan Lim
- CRI Genetics LLC, Santa Monica, CA 90404, USA
| | | | | | - Svetlana Limborska
- Institute of Molecular Genetics of National Research Centre, “Kurchatov Institute”, 123182 Moscow, Russia
| | - Alexei Fedorov
- CRI Genetics LLC, Santa Monica, CA 90404, USA
- Department of Medicine, University of Toledo, Toledo, OH 43606, USA
- Correspondence: ; Tel.: +1-419-383-5270
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8
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Association of the 5HTR2C gene Ser23 variation with childhood allergic asthma. THE EUROBIOTECH JOURNAL 2022. [DOI: 10.2478/ebtj-2022-0013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Abstract
Objective: Allergic asthma is the most frequently observed subtype of individuals with asthma. The effects of serotonin plays in the pathophysiology of asthma has not been clearly determined. Thus, this study aimed to investigate the association between the 5HTR2C gene rs 6318 G>C polymorphism and allergic asthma in pediatric patients in Cyprus.
Methods: This study included total number of 177 individuals with 118 control and 59 pediatric patients (43 atopic and 16 non-atopic asthma patient). A skin prick test was performed for each patient to confirm asthma diagnosis and to evaluate atopic status. Genotyping for the 5HTR2C was completed by Real Time-PCR analysis.
Results: The genotype distribution frequencies were not in agreement with the Hardy-Weinberg Equilibrium in the patients’ group (p<0.00001). The frequency of the risk allele (allele C) was not significantly different between the patient and control groups (p=0.255). The genotypic distribution between atopic asthma and non-atopic asthma within the patientsts groups was not in agreement with the Hardy-Weinberg Equilibrium (p=0.006). However, risk allele presence showed a statistically significant association with atopy-related asthma (p=0.037).
Discussion & Conclusion: Overall, despite the finding of no association between the 5HTR2C rs6318 C allele and childhood asthma, the current results indicated that there is a strong association between the 5HTR2C rs6318 C variant and childhood atopic asthma.
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9
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Xie NG, Zhang K, Song P, Li R, Luo J, Zhang DY. High-Throughput Variant Detection Using a Color-Mixing Strategy. J Mol Diagn 2022; 24:878-892. [PMID: 35718091 PMCID: PMC9379672 DOI: 10.1016/j.jmoldx.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 04/04/2022] [Accepted: 04/25/2022] [Indexed: 11/16/2022] Open
Abstract
Many diseases are related to multiple genetic alterations along a single gene. Probing for highly multiple (>10) variants in a single quantitative PCR tube is not possible because of a limited number of fluorescence channels and one variant per channel, so many more tubes are needed. Herein, a novel color-mixing strategy that uses fluorescence combinations as digital color codes to probe multiple variants simultaneously was experimentally validated. The color-mixing strategy relies on a simple intratube assay that can probe for 15 variants as part of an intertube assay that can probe for an exponentially increased number of variants. This strategy is achieved by using multiplex double-stranded toehold probes modified with fluorophores and quenchers; the probes are designed to be quenched or luminous after binding to wild-type or variant templates. The color-mixing strategy was used to probe for 21 pathogenic variants in thalassemia and to distinguish between heterozygous and homozygous variants in six tubes, with a specificity of 99% and a sensitivity of 94%. To support tuberculosis diagnosis, the same strategy was applied to simultaneously probe in Mycobacterium tuberculosis for rifampicin-resistance mutations occurring within one 81-bp region and one 48-bp region in the rpoB gene, plus five isoniazid-resistance mutations in the inhA and katG genes.
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Affiliation(s)
- Nina Guanyi Xie
- Department of Bioengineering, Rice University, Houston, Texas
| | - Kerou Zhang
- Department of Bioengineering, Rice University, Houston, Texas
| | - Ping Song
- Department of Bioengineering, Rice University, Houston, Texas
| | | | | | - David Yu Zhang
- Department of Bioengineering, Rice University, Houston, Texas.
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10
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Barrios N, González-Lagos C, Dreger DL, Parker HG, Nourdin-Galindo G, Hogan AN, Gómez MA, Ostrander EA. Patagonian sheepdog: Genomic analyses trace the footprints of extinct UK herding dogs to South America. PLoS Genet 2022; 18:e1010160. [PMID: 35482674 PMCID: PMC9049511 DOI: 10.1371/journal.pgen.1010160] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 03/21/2022] [Indexed: 11/26/2022] Open
Abstract
Most modern dog breeds were developed within the last two hundred years, following strong and recent human selection based predominantly on aesthetics, with few modern breeds constructed solely to maximize their work potential. In many cases, these working breeds represent the last remnants of now lost populations. The Patagonian sheepdog (PGOD), a rare herding breed, is a remarkable example of such a population. Maintained as an isolated population for over 130 years, the PGOD offers a unique opportunity to understand the genetic relationship amongst modern herding breeds, determine key genomic structure of the founder PGOD populations, and investigate how canine genomic data can mirror human migration patterns. We thus analyzed the population structure of 159 PGOD, comparing them with 1514 dogs representing 175 established breeds. Using 150,069 SNPs from a high-density SNP genotyping array, we establish the genomic composition, ancestry, and genetic diversity of the population, complementing genomic data with the PGOD's migratory history to South America. Our phylogenetic analysis reveals that PGODs are most closely related to modern herding breeds hailing from the United Kingdom. Admixture models illustrate a greater degree of diversity and genetic heterogeneity within the very small PGOD population than in Western European herding breeds, suggesting the PGOD predates the 200-year-old construction of most pure breeds known today. We thus propose that PGODs originated from the foundational herding dogs of the UK, prior to the Victorian explosion of breeds, and that they are the closest link to a now-extinct population of herding dogs from which modern herding breeds descended.
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Grants
- Agencia Nacional de Investigación y Desarrollo, FONDECYT, Chile.
- Agencia Nacional de Investigación y Desarrollo, Beca Doctorado Nacional, 2018, Chile.
- Agencia Nacional de Investigación y Desarrollo, REDI, Chile.
- Agencia Nacional de Investigación y Desarrollo, PIA/BASAL, Chile.
- Intramural Program of the National Human Genome Research Institute of the National Institute of Health, Bethesda MD, USA.
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Affiliation(s)
- Natasha Barrios
- Instituto de Farmacología y Morfofisiología, Facultad de Ciencias Veterinarias, Universidad Austral de Chile, Valdivia, Chile
- Escuela de Graduados, Facultad de Ciencias Veterinarias, Universidad Austral de Chile, Valdivia, Chile
| | - César González-Lagos
- Departamento de Ciencias, Facultad de Artes Liberales, Universidad Adolfo Ibáñez, Santiago, Chile
- Center of Applied Ecology and Sustainability (CAPES), Santiago, Chile
| | - Dayna L. Dreger
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Heidi G. Parker
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | | | - Andrew N. Hogan
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Marcelo A. Gómez
- Instituto de Farmacología y Morfofisiología, Facultad de Ciencias Veterinarias, Universidad Austral de Chile, Valdivia, Chile
| | - Elaine A. Ostrander
- Cancer Genetics and Comparative Genomics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
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11
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Whole-genome resequencing reveals genomic footprints of Italian sweet and hot pepper heirlooms giving insight into genes underlying key agronomic and qualitative traits. BMC Genom Data 2022; 23:21. [PMID: 35337259 PMCID: PMC8957157 DOI: 10.1186/s12863-022-01039-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Background Pepper is a major crop species of the Solanaceae family, largely appreciated for its high nutritional and healthy contribution to human diets. In the Mediterranean basin, the favorable pedoclimatic conditions enhanced the selection of several diversified landraces cultivated pepper (Capsicum annuum), for whom Italy can be considered a main pole of diversification. Hence, a survey of traditional C. annuum genetic resources is essential for deep understanding of such diversity and for applications in genomics assisted breeding. Here, we report whole-genome resequencing analyses of two sweet and two pungent genotypes highly diffused in South Italy and representative of the variability for shape, colour and nutritional properties. Results The four genomes were reconstructed at a chromosomal scale using a reference-guided approach, based on a dataset of 2.6 billion paired-end reads, corresponding to 20× genome coverage and a mapping rate above 99% for a final genomes size of approximately 3 Gb. After five iterations of variant calling, a total of 29,258,818 single nucleotide polymorphisms (SNPs) and 1,879,112 InDels, were identified. Substantial differences were observed among the four genomes based on geographical origin, with chromosomes 9 and 11 showing more polymorphisms in the accessions with higher fruit weight and absence of pungency. Among the identified variants, a small private indel (T - > TA) shared between sweet and big fruits accessions induces a frameshift with the generation of a new stop codon in a gene annotated as extensin, whereas two private SNPs within hot types were identified in 1-aminocyclopropane-1-carboxylate oxidase (ACO), a key gene involved in fruit ripening. The estimation of repetitive elements highlights a preponderant presence of Long Terminal Repeats (LTRs), the majority of which belonged to Gypsy superfamily. By comparing the four genomes with publicly available references including ‘CM334’ and Zunla-1 highlight the presence of 49,475 shared gene families. Conclusions The new genomic sequences aim to enrich the whole genome information of pepper local varieties, providing a valuable tool for precision gene mapping, marker discovery, comparative studies. Such knowledge widens the frontiers to understand the selection history of Italian pepper landraces toward the recognition of specificity local agri-food products marks. Supplementary Information The online version contains supplementary material available at 10.1186/s12863-022-01039-9.
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12
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Granot-Hershkovitz E, Sun Q, Argos M, Zhou H, Lin X, Browning SR, Sofer T. AFA: Ancestry-specific allele frequency estimation in admixed populations: The Hispanic Community Health Study/Study of Latinos. HGG ADVANCES 2022; 3:100096. [PMID: 35300209 PMCID: PMC8920934 DOI: 10.1016/j.xhgg.2022.100096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 02/18/2022] [Indexed: 11/22/2022] Open
Abstract
Allele frequency estimates in admixed populations, such as Hispanics and Latinos, rely on the sample's specific admixture composition and thus may differ between two seemingly similar populations. However, ancestry-specific allele frequencies, i.e., pertaining to the ancestral populations of an admixed group, may be particularly useful for prioritizing genetic variants for genetic discovery and personalized genomic health. We developed a method, ancestry-specific allele frequency estimation in admixed populations (AFA), to estimate the frequencies of biallelic variants in admixed populations with an unlimited number of ancestries. AFA uses maximum-likelihood estimation by modeling the conditional probability of having an allele given proportions of genetic ancestries. It can be applied using either local ancestry interval proportions encompassing the variant (local-ancestry-specific allele frequency estimations in admixed populations [LAFAs]) or global proportions of genetic ancestries (global-ancestry-specific allele frequency estimations in admixed populations [GAFAs]), which are easier to compute and are more widely available. Simulations and comparisons to existing software demonstrated the high accuracy of the method. We implemented AFA on high-quality imputed data of ∼9,000 Hispanics and Latinos from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an understudied, admixed population with three predominant continental ancestries: Amerindian, European, and African. Comparison of the European and African estimated frequencies to the respective gnomAD frequencies demonstrated high correlations (Pearson R2 = 0.97-0.99). We provide a genome-wide dataset of the estimated ancestry-specific allele frequencies for available variants with allele frequency between 5% and 95% in at least one of the three ancestral populations. Association analysis of Amerindian-enriched variants with cardiometabolic traits identified five loci associated with lipid traits in Hispanics and Latinos, demonstrating the utility of ancestry-specific allele frequencies in admixed populations.
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Affiliation(s)
- Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA,Department of Medicine, Harvard Medical School, Boston, MA 02115, USA,Corresponding author
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Maria Argos
- School of Public Health, The University of Illinois, Chicago, Chicago, IL 60612, USA
| | - Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Sharon R. Browning
- Department of Biostatistics, University of Washington, Seattle, WA 98105, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA,Department of Medicine, Harvard Medical School, Boston, MA 02115, USA,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA,Corresponding author
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13
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Taliun D, Harris DN, Kessler MD, Carlson J, Szpiech ZA, Torres R, Taliun SAG, Corvelo A, Gogarten SM, Kang HM, Pitsillides AN, LeFaive J, Lee SB, Tian X, Browning BL, Das S, Emde AK, Clarke WE, Loesch DP, Shetty AC, Blackwell TW, Smith AV, Wong Q, Liu X, Conomos MP, Bobo DM, Aguet F, Albert C, Alonso A, Ardlie KG, Arking DE, Aslibekyan S, Auer PL, Barnard J, Barr RG, Barwick L, Becker LC, Beer RL, Benjamin EJ, Bielak LF, Blangero J, Boehnke M, Bowden DW, Brody JA, Burchard EG, Cade BE, Casella JF, Chalazan B, Chasman DI, Chen YDI, Cho MH, Choi SH, Chung MK, Clish CB, Correa A, Curran JE, Custer B, Darbar D, Daya M, de Andrade M, DeMeo DL, Dutcher SK, Ellinor PT, Emery LS, Eng C, Fatkin D, Fingerlin T, Forer L, Fornage M, Franceschini N, Fuchsberger C, Fullerton SM, Germer S, Gladwin MT, Gottlieb DJ, Guo X, Hall ME, He J, Heard-Costa NL, Heckbert SR, Irvin MR, Johnsen JM, Johnson AD, Kaplan R, Kardia SLR, Kelly T, Kelly S, Kenny EE, Kiel DP, Klemmer R, Konkle BA, Kooperberg C, Köttgen A, Lange LA, Lasky-Su J, Levy D, Lin X, Lin KH, Liu C, Loos RJF, Garman L, Gerszten R, Lubitz SA, Lunetta KL, Mak ACY, Manichaikul A, Manning AK, Mathias RA, McManus DD, McGarvey ST, Meigs JB, Meyers DA, Mikulla JL, Minear MA, Mitchell BD, Mohanty S, Montasser ME, Montgomery C, Morrison AC, Murabito JM, Natale A, Natarajan P, Nelson SC, North KE, O'Connell JR, Palmer ND, Pankratz N, Peloso GM, Peyser PA, Pleiness J, Post WS, Psaty BM, Rao DC, Redline S, Reiner AP, Roden D, Rotter JI, Ruczinski I, Sarnowski C, Schoenherr S, Schwartz DA, Seo JS, Seshadri S, Sheehan VA, Sheu WH, Shoemaker MB, Smith NL, Smith JA, Sotoodehnia N, Stilp AM, Tang W, Taylor KD, Telen M, Thornton TA, Tracy RP, Van Den Berg DJ, Vasan RS, Viaud-Martinez KA, Vrieze S, Weeks DE, Weir BS, Weiss ST, Weng LC, Willer CJ, Zhang Y, Zhao X, Arnett DK, Ashley-Koch AE, Barnes KC, Boerwinkle E, Gabriel S, Gibbs R, Rice KM, Rich SS, Silverman EK, Qasba P, Gan W, Papanicolaou GJ, Nickerson DA, Browning SR, Zody MC, Zöllner S, Wilson JG, Cupples LA, Laurie CC, Jaquish CE, Hernandez RD, O'Connor TD, Abecasis GR. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 2021; 590:290-299. [PMID: 33568819 PMCID: PMC7875770 DOI: 10.1038/s41586-021-03205-y] [Citation(s) in RCA: 899] [Impact Index Per Article: 299.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 01/07/2021] [Indexed: 02/08/2023]
Abstract
The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes)1. In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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Affiliation(s)
- Daniel Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Daniel N Harris
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Michael D Kessler
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jedidiah Carlson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zachary A Szpiech
- Department of Biology, Pennsylvania State University, University Park, PA, USA
- Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA
| | - Raul Torres
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Sarah A Gagliano Taliun
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | | | - Hyun Min Kang
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | - Jonathon LeFaive
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Seung-Been Lee
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Xiaowen Tian
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Brian L Browning
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Sayantan Das
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | | | | | - Douglas P Loesch
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Amol C Shetty
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Albert V Smith
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiaoming Liu
- USF Genomics, College of Public Health, University of South Florida, Tampa, FL, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Dean M Bobo
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - François Aguet
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Paul L Auer
- Zilber School of Public Health, University of Wisconsin Milwaukee, Milwaukee, WI, USA
| | | | - R Graham Barr
- Department of Medicine, Columbia University Medical Center, New York, NY, USA
- Department of Epidemiology, Columbia University Medical Center, New York, NY, USA
| | | | | | - Rebecca L Beer
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Emelia J Benjamin
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - John Blangero
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Michael Boehnke
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Esteban G Burchard
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Brian E Cade
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - James F Casella
- Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA
- Division of Pediatric Hematology, Johns Hopkins University, Baltimore, MD, USA
| | - Brandon Chalazan
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Mina K Chung
- Department of Cardiovascular Medicine, Heart & Vascular Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Cardiovascular and Metabolic Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Clary B Clish
- Metabolomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Pediatrics, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Population Health Science, University of Mississippi Medical Center, Jackson, MS, USA
| | - Joanne E Curran
- Department of Human Genetics, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Brian Custer
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Dawood Darbar
- Department of Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Michelle Daya
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | | | - Dawn L DeMeo
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Susan K Dutcher
- McDonnell Genome Institute, Washington University, St Louis, MO, USA
- Department of Genetics, Washington University, St Louis, MO, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Celeste Eng
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Diane Fatkin
- Molecular Cardiology Division, Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- Faculty of Medicine, University of New South Wales, Kensington, New South Wales, Australia
- Cardiology Department, St Vincent's Hospital, Darlinghurst, New South Wales, Australia
| | - Tasha Fingerlin
- National Jewish Health, Center for Genes, Environment and Health, Denver, CO, USA
| | - Lukas Forer
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Christian Fuchsberger
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
- Institute for Biomedicine, Eurac Research, Bolzano, Italy
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
| | | | - Mark T Gladwin
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Daniel J Gottlieb
- VA Boston Healthcare System, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Michael E Hall
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jiang He
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
- Tulane University Translational Science Institute, Tulane University, New Orleans, LA, USA
| | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Susan R Heckbert
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jill M Johnsen
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Andrew D Johnson
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Robert Kaplan
- Albert Einstein College of Medicine, New York, NY, USA
| | - Sharon L R Kardia
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University, New Orleans, LA, USA
| | - Shannon Kelly
- Department of Epidemiology, Vitalant Research Institute, San Francisco, CA, USA
- Department of Pediatrics, UCSF Benioff Children's Hospital, Oakland, CA, USA
- Division of Pediatric Hematology, UCSF Benioff Children's Hospital, Oakland, CA, USA
| | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Douglas P Kiel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert Klemmer
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Barbara A Konkle
- Department of Medicine, University of Washington, Seattle, WA, USA
- Bloodworks Northwest Research Institute, Seattle, WA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Leslie A Lange
- Department of Medicine, University of Colorado at Denver, Aurora, CO, USA
| | - Jessica Lasky-Su
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | - Daniel Levy
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA, USA
| | - Xihong Lin
- Biostatistics and Statistics, Harvard University, Boston, MA, USA
| | - Keng-Han Lin
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lori Garman
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | | | | | - Kathryn L Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Angel C Y Mak
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Alisa K Manning
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Mongan Institute, Massachusetts General Hospital, Boston, MA, USA
- Metabolism Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rasika A Mathias
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David D McManus
- Cardiovascular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Stephen T McGarvey
- International Health Institute, Brown University, Providence, RI, USA
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Anthropology, Brown University, Providence, RI, USA
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Harvard Medical School, The Broad Institute of MIT and Harvard, Boston, MA, USA
| | | | - Julie L Mikulla
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Mollie A Minear
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Braxton D Mitchell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, MD, USA
| | - Sanghamitra Mohanty
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
- Department of Internal Medicine, Dell Medical School, Austin, TX, USA
| | - May E Montasser
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Courtney Montgomery
- Department of Genes and Human Disease, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joanne M Murabito
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Andrea Natale
- Texas Cardiac Arrhythmia Institute, St David's Medical Center, Austin, TX, USA
| | - Pradeep Natarajan
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sarah C Nelson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Kari E North
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Jeffrey R O'Connell
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jacob Pleiness
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Services, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - D C Rao
- Division of Biostatistics, Washington University in St Louis, St Louis, MO, USA
| | - Susan Redline
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Alexander P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Dan Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ingo Ruczinski
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sebastian Schoenherr
- Institute of Genetic Epidemiology, Department of Genetics and Pharmacology, Medical University of Innsbruck, Innsbruck, Austria
| | | | - Jeong-Sun Seo
- Precision Medicine Center, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
- Macrogen Inc, Seoul, Republic of Korea
- Gong Wu Genomic Medicine Institute, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Sudha Seshadri
- Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center at San Antonio, San Antonio, TX, USA
| | - Vivien A Sheehan
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA
- Aflac Cancer and Blood Disorders Center, Children's Healthcare of Atlanta, Atlanta, GA, USA
| | - Wayne H Sheu
- Taichung Veterans General Hospital Taiwan, Taichung City, Taiwan
| | | | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation, Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | | | - Russell P Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - David J Van Den Berg
- Center for Genetic Epidemiology, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ramachandran S Vasan
- Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | | | - Scott Vrieze
- Department of Psychology, University of Minnesota, Minneapolis, MN, USA
| | - Daniel E Weeks
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bruce S Weir
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Scott T Weiss
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Brigham and Women's Hospital, Boston, MA, USA
| | | | - Cristen J Willer
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine-Cardiology, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Yingze Zhang
- Pittsburgh Heart, Lung, Blood and Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA, USA
- Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xutong Zhao
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Donna K Arnett
- Department of Epidemiology, University of Kentucky, Lexington, KY, USA
| | - Allison E Ashley-Koch
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Kathleen C Barnes
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Eric Boerwinkle
- University of Texas Health Science Center at Houston, Houston, TX, USA
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Stacey Gabriel
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Pankaj Qasba
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Weiniu Gan
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - George J Papanicolaou
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Northwest Genomics Center, Seattle, WA, USA
- Brotman Baty Institute, Seattle, WA, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | | | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - James G Wilson
- Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS, USA
| | - L Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
- Framingham Heart Study, Framingham, MA, USA.
| | - Cathy C Laurie
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Cashell E Jaquish
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Ryan D Hernandez
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
| | - Timothy D O'Connor
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
- Program in Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - Gonçalo R Abecasis
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
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14
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Wohlers I, Künstner A, Munz M, Olbrich M, Fähnrich A, Calonga-Solís V, Ma C, Hirose M, El-Mosallamy S, Salama M, Busch H, Ibrahim S. An integrated personal and population-based Egyptian genome reference. Nat Commun 2020; 11:4719. [PMID: 32948767 PMCID: PMC7501257 DOI: 10.1038/s41467-020-17964-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 07/24/2020] [Indexed: 02/05/2023] Open
Abstract
A small number of de novo assembled human genomes have been reported to date, and few have been complemented with population-based genetic variation, which is particularly important for North Africa, a region underrepresented in current genome-wide references. Here, we combine long- and short-read whole-genome sequencing data with recent assembly approaches into a de novo assembly of an Egyptian genome. The assembly demonstrates well-balanced quality metrics and is complemented with variant phasing via linked reads into haploblocks, which we associate with gene expression changes in blood. To construct an Egyptian genome reference, we identify genome-wide genetic variation within a cohort of 110 Egyptian individuals. We show that differences in allele frequencies and linkage disequilibrium between Egyptians and Europeans may compromise the transferability of European ancestry-based genetic disease risk and polygenic scores, substantiating the need for multi-ethnic genome references. Thus, the Egyptian genome reference will be a valuable resource for precision medicine.
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Affiliation(s)
- Inken Wohlers
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Axel Künstner
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Matthias Munz
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Michael Olbrich
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Anke Fähnrich
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Verónica Calonga-Solís
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
- Department of Genetics, Federal University of Paraná (UFPR), Centro Politécnico, Jardim das Américas, 81531-990, Curitiba, Brazil
| | - Caixia Ma
- Novogene (UK) Company Limited, 25 Cambridge Science Park, Milton Road, CB4 0FW, Cambridge, UK
| | - Misa Hirose
- Genetics Division, Lübeck Institute of Experimental Dermatology, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany
| | - Shaaban El-Mosallamy
- Medical Experimental Research Center (MERC), Mansoura University, Elgomhouria St., Dakahlia Governorate, 35516, Mansoura, Egypt
| | - Mohamed Salama
- Medical Experimental Research Center (MERC), Mansoura University, Elgomhouria St., Dakahlia Governorate, 35516, Mansoura, Egypt
- Institute of Global Health and Human Ecology, The American University in Cairo, AUC avenue, 11835, Cairo, Egypt
| | - Hauke Busch
- Medical Systems Biology Division, Lübeck Institute of Experimental Dermatology and Institute for Cardiogenetics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
| | - Saleh Ibrahim
- Genetics Division, Lübeck Institute of Experimental Dermatology, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
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15
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Vorster E, Essop FB, Rodda JL, Krause A. Spinal Muscular Atrophy in the Black South African Population: A Matter of Rearrangement? Front Genet 2020; 11:54. [PMID: 32117462 PMCID: PMC7033609 DOI: 10.3389/fgene.2020.00054] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Accepted: 01/17/2020] [Indexed: 12/14/2022] Open
Abstract
Spinal muscular atrophy (SMA) is a neuromuscular disorder, characterized by muscle atrophy and impaired mobility. A homozygous deletion of survival motor neuron 1 (SMN1), exon 7 is the main cause of SMA in ~94% of patients worldwide, but only accounts for 51% of South African (SA) black patients. SMN1 and its highly homologous centromeric copy, survival motor neuron 2 (SMN2), are located in a complex duplicated region. Unusual copy number variations (CNVs) have been reported in black patients, suggesting the presence of complex pathogenic rearrangements. The aim of this study was to further investigate the genetic cause of SMA in the black SA population. Multiplex ligation-dependent probe amplification (MLPA) testing was performed on 197 unrelated black patients referred for SMA testing (75 with a homozygous deletion of SMN1, exon 7; 50 with a homozygous deletion of SMN2, exon 7; and 72 clinically suggestive patients with no homozygous deletions). Furthermore, 122 black negative controls were tested. For comparison, 68 white individuals (30 with a homozygous deletion of SMN1, exon 7; 8 with a homozygous deletion of SMN2, exon 7 and 30 negative controls) were tested. Multiple copies (>2) of SMN1, exon 7 were observed in 50.8% (62/122) of black negative controls which could mask heterozygous SMN1 deletions and potential pathogenic CNVs. MLPA is not a reliable technique for detecting carriers in the black SA population. Large deletions extending into the rest of SMN1 and neighboring genes were more frequently observed in black patients with homozygous SMN1, exon 7 deletions when compared to white patients. Homozygous SMN2, exon 7 deletions were commonly observed in black individuals. No clear pathogenic CNVs were identified in black patients but discordant copy numbers of exons suggest complex rearrangements, which may potentially interrupt the SMN1 gene. Only 8.3% (6/72) of clinically suggestive patients had heterozygous deletions of SMN1, exon 7 (1:0) which is lower than previous SA reports of 69.5%. This study emphasizes the lack of understanding of the architecture of the SMN region as well as the cause of SMA in the black SA population. These factors need to be taken into account when counseling and performing diagnostic testing in black populations.
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Affiliation(s)
- Elana Vorster
- National Health Laboratory Service and School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - Fahmida B Essop
- National Health Laboratory Service and School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
| | - John L Rodda
- Department of Paediatrics, University of the Witwatersrand, Johannesburg, South Africa
| | - Amanda Krause
- National Health Laboratory Service and School of Pathology, University of the Witwatersrand, Johannesburg, South Africa
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16
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Zwane AA, Schnabel RD, Hoff J, Choudhury A, Makgahlela ML, Maiwashe A, Van Marle-Koster E, Taylor JF. Genome-Wide SNP Discovery in Indigenous Cattle Breeds of South Africa. Front Genet 2019; 10:273. [PMID: 30988672 PMCID: PMC6452414 DOI: 10.3389/fgene.2019.00273] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Accepted: 03/12/2019] [Indexed: 01/30/2023] Open
Abstract
Single nucleotide polymorphism arrays have created new possibilities for performing genome-wide studies to detect genomic regions harboring sequence variants that affect complex traits. However, the majority of validated SNPs for which allele frequencies have been estimated are limited primarily to European breeds. The objective of this study was to perform SNP discovery in three South African indigenous breeds (Afrikaner, Drakensberger, and Nguni) using whole genome sequencing. DNA was extracted from blood and hair samples, quantified and prepared at 50 ng/μl concentration for sequencing at the Agricultural Research Council Biotechnology Platform using an Illumina HiSeq 2500. The fastq files were used to call the variants using the Genome Analysis Tool Kit. A total of 1,678,360 were identified as novel using Run 6 of 1000 Bull Genomes Project. Annotation of the identified variants classified them into functional categories. Within the coding regions, about 30% of the SNPs were non-synonymous substitutions that encode for alternate amino acids. The study of distribution of SNP across the genome identified regions showing notable differences in the densities of SNPs among the breeds and highlighted many regions of functional significance. Gene ontology terms identified genes such as MLANA, SYT10, and CDC42EP5 that have been associated with coat color in mouse, and ADAMS3, DNAJC3, and PAG5 genes have been associated with fertility in cattle. Further analysis of the variants detected 688 candidate selective sweeps (ZHp Z-scores ≤ -4) across all three breeds, of which 223 regions were assigned as being putative selective sweeps (ZHp scores ≤-5). We also identified 96 regions with extremely low ZHp Z-scores (≤-6) in Afrikaner and Nguni. Genes such as KIT and MITF that have been associated with skin pigmentation in cattle and CACNA1C, which has been associated with biopolar disorder in human, were identified in these regions. This study provides the first analysis of sequence data to discover SNPs in indigenous South African cattle breeds. The information will play an important role in our efforts to understand the genetic history of our cattle and in designing appropriate breed improvement programmes.
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Affiliation(s)
- Avhashoni A. Zwane
- Department of Animal Breeding and Genetics, Agricultural Research Council-Animal Production, Irene, South Africa
- Department of Animal and Wildlife Sciences, University of Pretoria, Pretoria, South Africa
| | - Robert D. Schnabel
- Division of Animal Sciences, University of Missouri, Columbia, MO, United States
- Informatics Institute, University of Missouri, Columbia, MO, United States
| | - Jesse Hoff
- Division of Animal Sciences, University of Missouri, Columbia, MO, United States
| | - Ananyo Choudhury
- Sydney Brenner Institute of Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Mahlako Linah Makgahlela
- Department of Animal Breeding and Genetics, Agricultural Research Council-Animal Production, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - Azwihangwisi Maiwashe
- Department of Animal Breeding and Genetics, Agricultural Research Council-Animal Production, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - Este Van Marle-Koster
- Department of Animal and Wildlife Sciences, University of Pretoria, Pretoria, South Africa
| | - Jeremy F. Taylor
- Division of Animal Sciences, University of Missouri, Columbia, MO, United States
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17
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Dnyansagar R, Zimmermann B, Moran Y, Praher D, Sundberg P, Møller LF, Technau U. Dispersal and speciation: The cross Atlantic relationship of two parasitic cnidarians. Mol Phylogenet Evol 2018; 126:346-355. [PMID: 29702219 DOI: 10.1016/j.ympev.2018.04.035] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2017] [Revised: 03/12/2018] [Accepted: 04/23/2018] [Indexed: 01/11/2023]
Affiliation(s)
- Rohit Dnyansagar
- Department of Molecular Evolution and Development, University of Vienna, Austria
| | - Bob Zimmermann
- Department of Molecular Evolution and Development, University of Vienna, Austria
| | - Yehu Moran
- Department of Molecular Evolution and Development, University of Vienna, Austria; Department of Ecology, Evolution and Behavior, Hebrew University of Jerusalem, Israel
| | - Daniela Praher
- Department of Molecular Evolution and Development, University of Vienna, Austria
| | - Per Sundberg
- Department of Marine Sciences, University of Gothenburg, Sweden
| | - Lene Friis Møller
- Danish Shellfish Centre, DTU Aqua, Technical University of Denmark, Denmark
| | - Ulrich Technau
- Department of Molecular Evolution and Development, University of Vienna, Austria.
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18
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Yuasa I, Akane A, Yamamoto T, Matsusue A, Endoh M, Nakagawa M, Umetsu K, Ishikawa T, Iino M. Japaneseplex: A forensic SNP assay for identification of Japanese people using Japanese-specific alleles. Leg Med (Tokyo) 2018; 33:17-22. [PMID: 29705644 DOI: 10.1016/j.legalmed.2018.04.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 02/15/2018] [Accepted: 04/23/2018] [Indexed: 11/17/2022]
Abstract
It is sometimes necessary to determine whether a forensic biological sample came from a Japanese person. In this study, we developed a 60-locus SNP assay designed for the differentiation of Japanese people from other East Asians using entirely and nearly Japanese-specific alleles. This multiplex assay consisted of 6 independent PCR reactions followed by single nucleotide extension. The average number and standard deviation of Japanese-specific alleles possessed by an individual were 0.81 ± 0.93 in 108 Koreans from Seoul, 8.87 ± 2.89 in 103 Japanese from Tottori, 17.20 ± 3.80 in 88 Japanese from Okinawa, and 0 in 220 Han Chinese from Wuxi and Changsha. The Koreans had 0-4 Japanese-specific alleles per individual, whereas the Japanese had 4-26 Japanese-specific alleles. Almost all Japanese were distinguished from the Koreans and other people by the factorial correspondence and principal component analyses. The Snipper program was also useful to estimate the degree of Japaneseness. The method described here was successfully applied to the differentiation of Japanese from non-Japanese people in forensic cases. This Japanese-specific SNP assay was named Japaneseplex.
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Affiliation(s)
- Isao Yuasa
- Division of Legal Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan.
| | - Atsushi Akane
- Department of Legal Medicine, Kansai Medical University, Hirakata, Japan
| | - Toshimichi Yamamoto
- Department of Legal Medicine and Bioethics, Nagoya University School of Medicine, Nagoya, Japan
| | - Aya Matsusue
- Department of Forensic Medicine, Fukuoka University School of Medicine, Fukuoka, Japan
| | - Minoru Endoh
- Division of Legal Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
| | - Mayumi Nakagawa
- Department of Pathobiological Science and Technology, Faculty of Medicine, Tottori University, Yonago, Japan
| | - Kazuo Umetsu
- Department of Forensic Medicine, Yamagata University School of Medicine, Yamagata, Japan
| | - Takaki Ishikawa
- Department of Legal Medicine, Osaka City University Medical School, Osaka, Japan
| | - Morio Iino
- Division of Legal Medicine, Faculty of Medicine, Tottori University, Yonago 683-8503, Japan
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19
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Abstract
Human genetic diversity is the result of population genetic forces. This genetic variation influences disease risk and contributes to health disparities. Natural selection is an important influence on human genetic variation. Because immune and inflammatory function genes are enriched for signals of positive selection, the prevalence of rheumatic disease-risk alleles seen in different populations is partially the result of differing selective pressures (eg, due to pathogens). This review summarizes the genetic regions associated with susceptibility to different rheumatic diseases and concomitant evidence for natural selection, including known agents of selection exerting selective pressure in these regions.
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Affiliation(s)
- Paula S Ramos
- Division of Rheumatology and Immunology, Department of Medicine, Medical University of South Carolina, 96 Jonathan Lucas Street, Suite 816, Charleston, SC 29425, USA; Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
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20
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Cascella R, Strafella C, Caputo V, Errichiello V, Zampatti S, Milano F, Potenza S, Mauriello S, Novelli G, Ricci F, Cusumano A, Giardina E. Towards the application of precision medicine in Age-Related Macular Degeneration. Prog Retin Eye Res 2017; 63:132-146. [PMID: 29197628 DOI: 10.1016/j.preteyeres.2017.11.004] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Revised: 11/21/2017] [Accepted: 11/27/2017] [Indexed: 12/11/2022]
Abstract
The review essentially describes genetic and non-genetic variables contributing to the onset and progression of exudative Age-related Macular Degeneration (AMD) in Italian population. In particular, AMD susceptibility within Italian population is contributed to by genetic variants, accounting for 23% of disease and non-genetic variants, accounting for 10% of AMD. Our data highlighted prominent differences concerning genetic and non-genetic contributors to AMD in our cohort with respect to worldwide populations. Among genetic variables, SNPs of CFH, ARMS2, IL-8, TIMP3, SLC16A8, RAD51B, VEGFA and COL8A1 were significantly associated with the risk of AMD in the Italian cohort. Surprisingly, other susceptibility variants described in European, American and Asiatic populations, did not reach the significance threshold in our cohort. As expected, advanced age, smoking and dietary habits were associated with the disease. In addition, we also describe a number of gene-gene and gene-phenotype interactions. In fact, AMD-associated genes may be involved in the alteration of Bruch's membrane and induction of angiogenesis, contributing to exacerbate the damage caused by aging and environmental factors. Our review provides an overview of genetic and non-genetic factors characterizing AMD susceptibility in Italian population, outlining the differences with respect to the worldwide populations. Altogether, these data reflect historical, geographic, demographic and lifestyle peculiarities of Italian population. The role of epigenetics, pharmacogenetics, comorbities and genetic counseling in the management of AMD patients have been described, in the perspective of the application of a "population-specific precision medicine" approach addressed to prevent AMD onset and improve patients' quality of life.
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Affiliation(s)
- Raffaella Cascella
- Molecular Genetics Laboratory UILDM, Santa Lucia Foundation, Via Ardeatina 354, 00142, Rome, Italy; Department of Chemical Pharmaceutical and Biomolecular Technologies, Catholic University "Our Lady of Good Counsel" Laprakë, Rruga Dritan Hoxha, 1000, Tirane, Albania
| | - Claudia Strafella
- Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy; Emotest Laboratory, Via Patria Montenuovo Licola 60, 80078, Pozzuoli, Italy
| | - Valerio Caputo
- Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy
| | - Valeria Errichiello
- Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy
| | - Stefania Zampatti
- Molecular Genetics Laboratory UILDM, Santa Lucia Foundation, Via Ardeatina 354, 00142, Rome, Italy; Neuromed IRCCS, Via Atinense, 18, 86077, Pozzilli, Italy
| | - Filippo Milano
- Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy
| | - Saverio Potenza
- Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy
| | - Silvestro Mauriello
- Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy
| | - Giuseppe Novelli
- Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy
| | - Federico Ricci
- UOSD Retinal Pathology PTV Foundation "Policlinico Tor Vergata", Viale Oxford, 81, 00133, Rome, Italy
| | - Andrea Cusumano
- UOSD Retinal Pathology PTV Foundation "Policlinico Tor Vergata", Viale Oxford, 81, 00133, Rome, Italy
| | - Emiliano Giardina
- Molecular Genetics Laboratory UILDM, Santa Lucia Foundation, Via Ardeatina 354, 00142, Rome, Italy; Department of Biomedicine and Prevention, ''Tor Vergata'' University, Via Montpellier 1, 00133, Rome, Italy.
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21
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Merker S, Reif A, Ziegler GC, Weber H, Mayer U, Ehlis AC, Conzelmann A, Johansson S, Müller-Reible C, Nanda I, Haaf T, Ullmann R, Romanos M, Fallgatter AJ, Pauli P, Strekalova T, Jansch C, Vasquez AA, Haavik J, Ribasés M, Ramos-Quiroga JA, Buitelaar JK, Franke B, Lesch KP. SLC2A3 single-nucleotide polymorphism and duplication influence cognitive processing and population-specific risk for attention-deficit/hyperactivity disorder. J Child Psychol Psychiatry 2017; 58:798-809. [PMID: 28224622 DOI: 10.1111/jcpp.12702] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/14/2016] [Indexed: 12/19/2022]
Abstract
BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is a common, highly heritable neurodevelopmental disorder with profound cognitive, behavioral, and psychosocial impairments with persistence across the life cycle. Our initial genome-wide screening approach for copy number variants (CNVs) in ADHD implicated a duplication of SLC2A3, encoding glucose transporter-3 (GLUT3). GLUT3 plays a critical role in cerebral glucose metabolism, providing energy for the activity of neurons, which, in turn, moderates the excitatory-inhibitory balance impacting both brain development and activity-dependent neural plasticity. We therefore aimed to provide additional genetic and functional evidence for GLUT3 dysfunction in ADHD. METHODS Case-control association analyses of SLC2A3 single-nucleotide polymorphisms (SNPs) and CNVs were conducted in several European cohorts of patients with childhood and adult ADHD (SNP, n = 1,886 vs. 1,988; CNV, n = 1,692 vs. 1,721). These studies were complemented by SLC2A3 expression analyses in peripheral cells, functional EEG recordings during neurocognitive tasks, and ratings of food energy content. RESULTS Meta-analysis of all cohorts detected an association of SNP rs12842 with ADHD. While CNV analysis detected a population-specific enrichment of SLC2A3 duplications only in German ADHD patients, the CNV + rs12842 haplotype influenced ADHD risk in both the German and Spanish cohorts. Duplication carriers displayed elevated SLC2A3 mRNA expression in peripheral blood cells and altered event-related potentials reflecting deficits in working memory and cognitive response control, both endophenotypic traits of ADHD, and an underestimation of energy units of high-caloric food. CONCLUSIONS Taken together, our results indicate that both common and rare SLC2A3 variation impacting regulation of neuronal glucose utilization and energy homeostasis may result in neurocognitive deficits known to contribute to ADHD risk.
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Affiliation(s)
- Sören Merker
- Division of Molecular Psychiatry, ADHD Clinical Research Unit, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Georg C Ziegler
- Division of Molecular Psychiatry, ADHD Clinical Research Unit, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Heike Weber
- Division of Molecular Psychiatry, ADHD Clinical Research Unit, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany.,Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt, Frankfurt, Germany
| | - Ute Mayer
- Division of Molecular Psychiatry, ADHD Clinical Research Unit, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Ann-Christine Ehlis
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Annette Conzelmann
- Department of Psychology I, University of Würzburg, Würzburg, Germany.,Department of Child and Adolescent Psychiatry, University of Tübingen, Tübingen, Germany
| | - Stefan Johansson
- K.G. Jebsen Centre for Neuropsychiatric Disorders, Department of Clinical Science, University of Bergen, Bergen, Norway
| | | | - Indrajit Nanda
- Department of Human Genetics, Biozentrum, University of Würzburg, Würzburg, Germany
| | - Thomas Haaf
- Department of Human Genetics, Biozentrum, University of Würzburg, Würzburg, Germany
| | - Reinhard Ullmann
- Max-Planck Institute for Molecular Genetics, Berlin, Germany.,Bundeswehr Institute of Radiobiology, University of Ulm, Ulm, Germany
| | - Marcel Romanos
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University of Würzburg, Würzburg, Germany
| | - Andreas J Fallgatter
- Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Paul Pauli
- Department of Psychology I, University of Würzburg, Würzburg, Germany
| | - Tatyana Strekalova
- Division of Molecular Psychiatry, ADHD Clinical Research Unit, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany.,Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Department of Translational Neuroscience, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Charline Jansch
- Division of Molecular Psychiatry, ADHD Clinical Research Unit, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Alejandro Arias Vasquez
- Departments of Human Genetics and Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands.,Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jan Haavik
- Division of Psychiatry, Haukeland University Hospital, Bergen, Norway.,Department of Biomedicine, K.G. Jebsen Centre for Neuropsychiatric Disorders, University of Bergen, Bergen, Norway
| | - Marta Ribasés
- Psychiatric Genetics Unit, Institute Vall d'Hebron Research (VHIR), Universitat Autonoma de Barcelona, Barcelona, Spain.,Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Spain
| | - Josep Antoni Ramos-Quiroga
- Psychiatric Genetics Unit, Institute Vall d'Hebron Research (VHIR), Universitat Autonoma de Barcelona, Barcelona, Spain.,Biomedical Network Research Centre on Mental Health (CIBERSAM), Barcelona, Spain.,Department of Psychiatry and Legal Medicine, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Jan K Buitelaar
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Barbara Franke
- Departments of Human Genetics and Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Klaus-Peter Lesch
- Division of Molecular Psychiatry, ADHD Clinical Research Unit, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany.,Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.,Department of Translational Neuroscience, School of Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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22
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Dutta R, Mainsah J, Yatskiv Y, Chakrabortty S, Brennan P, Khuder B, Qiu S, Fedorova L, Fedorov A. Intricacies in arrangement of SNP haplotypes suggest "Great Admixture" that created modern humans. BMC Genomics 2017; 18:433. [PMID: 28583085 PMCID: PMC5741169 DOI: 10.1186/s12864-017-3776-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Accepted: 05/09/2017] [Indexed: 12/22/2022] Open
Abstract
Background Inferring history from genomic sequences is challenging and problematic because chromosomes are mosaics of thousands of small Identicalby-descent (IBD) fragments, each of them having their own unique story. However, the main events in recent evolution might be deciphered from comparative analysis of numerous loci. A paradox of why humans, whose effective population size is only 104, have nearly three million frequent SNPs is formulated and examined. Results We studied 5398 loci evenly covering all human autosomes. Common haplotypes built from frequent SNPs that are present in people from various populations have been examined. We demonstrated highly non-random arrangement of alleles in common haplotypes. Abundance of mutually exclusive pairs of common haplotypes that have different alleles at every polymorphic position (so-called Yin/Yang haplotypes) was found in 56% of loci. A novel widely spread category of common haplotypes named Mosaic has been described. Mosaic consists of numerous pieces of Yin/Yang haplotypes and represents an ancestral stage of one of them. Scenarios of possible appearance of large number of frequent human SNPs and their habitual arrangement in Yin/Yang common haplotypes have been evaluated with an advanced genomic simulation algorithm. Conclusions Computer modeling demonstrated that the observed arrangement of 2.9 million frequent SNPs could not originate from a sole stand-alone population. A “Great Admixture” event has been proposed that can explain peculiarities with frequent SNP distributions. This Great Admixture presumably occurred 100–300 thousand years ago between two ancestral populations that had been separated from each other about a million years ago. Our programs and algorithms can be applied to other species to perform evolutionary and comparative genomics. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3776-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Rajib Dutta
- Program in Biomedical Sciences, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA.,Department of Medicine, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA
| | - Joseph Mainsah
- Program in Bioinformatics and Proteomics/Genomics, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA
| | - Yuriy Yatskiv
- Program in Bioinformatics and Proteomics/Genomics, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA
| | - Sharmistha Chakrabortty
- Program in Bioinformatics and Proteomics/Genomics, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA
| | - Patrick Brennan
- Program in Bioinformatics and Proteomics/Genomics, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA
| | - Basil Khuder
- Program in Bioinformatics and Proteomics/Genomics, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA
| | - Shuhao Qiu
- Department of Medicine, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA
| | | | - Alexei Fedorov
- Program in Bioinformatics and Proteomics/Genomics, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA. .,Department of Medicine, University of Toledo, Health Science Campus, Toledo, 43614, OH, USA.
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23
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Li X, Jian Y, Xie C, Wu J, Xu Y, Zou C. Fast diffusion of domesticated maize to temperate zones. Sci Rep 2017; 7:2077. [PMID: 28522839 PMCID: PMC5437101 DOI: 10.1038/s41598-017-02125-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 04/06/2017] [Indexed: 11/09/2022] Open
Abstract
Adaptation to a temperate climate was a prerequisite for the spread of maize across a broad geographical range. To explicitly explore the demographic process underlying maize adaptation, we used a diffusion-based method to model the differentiation between temperate and tropical populations using the Non-Stiff Stalk group as a proxy for temperate maize. Based on multiple sequential Markovian coalescent approaches, we estimate that tropical and temperate maize diverged approximately 3'000 to 5'000 years ago and the population size shrank after the split. Using composite likelihood approaches, we identified a distinct tropical-temperate divergence event initiated 4'958 years ago (95% confidence interval (CI): 4'877-5'039) from an ancestral population whose effective size was 24,162 (95% CI: 23,914-24,409). We found that continuous gene flow between tropical and temperate maize accompanied the differentiation of temperate maize. Long identical-by-descent tracts shared by tropical and temperate inbred lines have been identified, which might be the result of gene flow between tropical and temperate maize or artificial selection during domestication and crop improvement. Understanding the demographic history of maize diffusion not only provides evidence for population dynamics of maize, but will also assist the identification of regions under selection and the genetic basis of complex traits of agronomic importance.
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Affiliation(s)
- Xiaolong Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- Centre of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yinqiao Jian
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Chuanxiao Xie
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jun Wu
- Centre of Pear Engineering Technology Research, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, 210095, China
| | - Yunbi Xu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
- International Maize and Wheat Improvement Center (CIMMYT), El Batán, 56130, Texcoco, Mexico.
| | - Cheng Zou
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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24
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He H, Lei L, Chen E, Xu X, Wang L, Pan J, Yang F, Wang M, Dong J, Yang J. The screening of the functional microRNA binding site SNPs in sporadic colorectal cancer genes. Cancer Biol Ther 2017; 18:407-413. [PMID: 28494187 DOI: 10.1080/15384047.2017.1323584] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Sporadic colorectal cancer (sCRC) is one of the most commonly diagnosed cancers worldwide, but few genetic markers have been identified and used for its early detection. MicroRNAs are diverse cellular regulators in cancer pathogenesis that bind to the 3'-untranslated region (3'-UTR) of their target mRNAs, and variants within the miRNA target sites on sCRC-related genes may influence its pathogenesis. To investigate this possibility, we used a bioinformatical method to screen SNPs for putative changes in miRNA recognition sites within the 3'-UTR of sCRC-related genes. The rs11466537 single nucleotide polymorphism was predicted to modify the regulation of hsa-miR-1193 on the Transforming Growth Factor β Receptor II (TGFBR2) gene. Additionally, luciferase reporter assays indicated that hsa-miR-1193 bound the T allele more strongly than the A allele of rs11466537 (with A being the less frequent variant), and real time-polymerase chain reaction and western blot analysis showed that TGFBR2 is significantly repressed by hsa-miR-1193. Furthermore, overexpression of hsa-miR-1193 promoted HT-29 cell proliferation, while the loss of hsa-miR-1193 inhibited the process. Finally, the rs11466537 genotyping result revealed that the frequency of A allele carriers was 1.5% in the control blood samples, but 0 in the sCRC patients' normal colon tissue samples. Our results demonstrated that hsa-miR-1193 may be involved in sCRC tumourigenesis at least in part by suppression of TGFBR2, and the A allele of rs11466537 disturbed the regulation of hsa-miR-1193 on TGFBR2.
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Affiliation(s)
- Hongjuan He
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Lei Lei
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Erfei Chen
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Xiaona Xu
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Lili Wang
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Junqiang Pan
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Fangfang Yang
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Min Wang
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Jing Dong
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
| | - Jin Yang
- a College of Life Science, Institute of Preventive Genomic Medicine, Northwest University , Xi'an , Shaanxi , China
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25
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Chen F, Dow M, Ding S, Lu Y, Jiang X, Tang H, Wang S. PREMIX: PRivacy-preserving EstiMation of Individual admiXture. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:1747-1755. [PMID: 28269933 PMCID: PMC5333197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper we proposed a framework: PRivacy-preserving EstiMation of Individual admiXture (PREMIX) using Intel software guard extensions (SGX). SGX is a suite of software and hardware architectures to enable efficient and secure computation over confidential data. PREMIX enables multiple sites to securely collaborate on estimating individual admixture within a secure enclave inside Intel SGX. We implemented a feature selection module to identify most discriminative Single Nucleotide Polymorphism (SNP) based on informativeness and an Expectation Maximization (EM)-based Maximum Likelihood estimator to identify the individual admixture. Experimental results based on both simulation and 1000 genome data demonstrated the efficiency and accuracy of the proposed framework. PREMIX ensures a high level of security as all operations on sensitive genomic data are conducted within a secure enclave using SGX.
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Affiliation(s)
- Feng Chen
- Department of Biomedical Informatics, UC San Diego, La Jolla, CA
| | - Michelle Dow
- Department of Biomedical Informatics, UC San Diego, La Jolla, CA
| | - Sijie Ding
- Department of Electrical and Computer Engineering, UC San Diego, La Jolla, CA
| | - Yao Lu
- Department of Electrical and Computer Engineering, UC San Diego, La Jolla, CA
| | - Xiaoqian Jiang
- Department of Biomedical Informatics, UC San Diego, La Jolla, CA
| | - Hua Tang
- Department of Genetics, Stanford University, Stanford, CA
| | - Shuang Wang
- Department of Biomedical Informatics, UC San Diego, La Jolla, CA
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26
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Forensically relevant SNaPshot® assays for human DNA SNP analysis: a review. Int J Legal Med 2016; 131:21-37. [DOI: 10.1007/s00414-016-1490-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2016] [Accepted: 10/31/2016] [Indexed: 10/20/2022]
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27
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Polimanti R, Yang BZ, Zhao H, Gelernter J. Evidence of Polygenic Adaptation in the Systems Genetics of Anthropometric Traits. PLoS One 2016; 11:e0160654. [PMID: 27537407 PMCID: PMC4990182 DOI: 10.1371/journal.pone.0160654] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2015] [Accepted: 07/25/2016] [Indexed: 12/26/2022] Open
Abstract
Many signals of natural selection have been identified in the human genome. However, except for some single-locus mechanisms, most molecular processes generating these adaptation signals are still unknown. We developed an approach that integrates datasets related to genome-wide association studies (GWAS) with information about systems biology and genetic signatures of natural selection to identify evidence of polygenic adaptation. Specifically, we focused on five anthropometric measurements: body mass index (BMI), height, waist-to-hip ratio adjusted for BMI (WHR), and waist circumference adjusted for BMI (WC), and sex differences for WHR and WC. We performed an enrichment analysis for signals of natural selection in protein interaction networks associated with anthropometric traits in European populations. The adaptation signals-enriched gene networks associated highlighted epistatic interactions in the context of polygenic selection for the investigated traits. These polygenic mechanisms indicated intriguing selective mechanisms related to the anthropometric traits: adult locomotory behavior for BMI, infection resistance for height, interplay between lipid transport and immune systems for WHR, and female-specific polygenic adaptation for WHR and WC. In conclusion, we observed evidence of polygenic adaptation in the context of systems genetics of anthropometric traits that indicates polygenic mechanisms related to the natural selection in European populations.
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Affiliation(s)
- Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, West Haven, Connecticut, United States of America
- VA CT Healthcare Center, West Haven, Connecticut, United States of America
| | - Bao Zhu Yang
- Department of Psychiatry, Yale University School of Medicine, West Haven, Connecticut, United States of America
- VA CT Healthcare Center, West Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, United States of America
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America
| | - Joel Gelernter
- Department of Psychiatry, Yale University School of Medicine, West Haven, Connecticut, United States of America
- VA CT Healthcare Center, West Haven, Connecticut, United States of America
- Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, United States of America
- Department of Neurobiology, Yale University School of Medicine, New Haven, Connecticut, United States of America
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28
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Tyagi A, Yadav A, Tripathi AM, Roy S. High light intensity plays a major role in emergence of population level variation in Arabidopsis thaliana along an altitudinal gradient. Sci Rep 2016; 6:26160. [PMID: 27211014 PMCID: PMC4876511 DOI: 10.1038/srep26160] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2016] [Accepted: 04/27/2016] [Indexed: 12/29/2022] Open
Abstract
Environmental conditions play an important role in the emergence of genetic variations in natural populations. We identified genome-wide patterns of nucleotide variations in the coding regions of natural Arabidopsis thaliana populations. These populations originated from 700 m to 3400 m a.m.s.l. in the Western Himalaya. Using a pooled RNA-Seq approach, we identified the local and global level population-specific SNPs. The biological functions of the SNP-containing genes were primarily related to the high light intensity prevalent at high-altitude regions. The novel SNPs identified in these genes might have arisen de novo in these populations. In another approach, the FSTs of SNP-containing genes were correlated with the corresponding climatic factors. ‘Radiation in the growing season’ was the only environmental factor found to be strongly correlated with the gene-level FSTs. In both the approaches, the high light intensity was identified as the primary abiotic stress associated with the variations in these populations. The differential gene expression analysis between field and controlled condition grown plants also showed high light intensity as the primary abiotic stress, particularly for the high altitude populations. Our results provide a genome-wide perspective of nucleotide variations in populations along altitudinal gradient and their putative role in emergence of these variations.
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Affiliation(s)
- Antariksh Tyagi
- Genetics and Molecular Biology Division, CSIR-National Botanical Research Institute, Lucknow, 226001, India
| | - Amrita Yadav
- Genetics and Molecular Biology Division, CSIR-National Botanical Research Institute, Lucknow, 226001, India
| | - Abhinandan Mani Tripathi
- Genetics and Molecular Biology Division, CSIR-National Botanical Research Institute, Lucknow, 226001, India.,Academy of Scientific and Innovative Research (AcSIR), Anusandhan Bhawan, 2 Rafi Marg, New Delhi, 110 001, India
| | - Sribash Roy
- Genetics and Molecular Biology Division, CSIR-National Botanical Research Institute, Lucknow, 226001, India.,Academy of Scientific and Innovative Research (AcSIR), Anusandhan Bhawan, 2 Rafi Marg, New Delhi, 110 001, India
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29
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Gonçalves AC, Alves R, Baldeiras I, Cortesão E, Carda JP, Branco CC, Oliveiros B, Loureiro L, Pereira A, Nascimento Costa JM, Sarmento-Ribeiro AB, Mota-Vieira L. Genetic variants involved in oxidative stress, base excision repair, DNA methylation, and folate metabolism pathways influence myeloid neoplasias susceptibility and prognosis. Mol Carcinog 2016; 56:130-148. [PMID: 26950655 DOI: 10.1002/mc.22478] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2015] [Revised: 01/22/2016] [Accepted: 02/17/2016] [Indexed: 12/27/2022]
Abstract
Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) share common features: elevated oxidative stress, DNA repair deficiency, and aberrant DNA methylation. We performed a hospital-based case-control study to evaluate the association in variants of genes involved in oxidative stress, folate metabolism, DNA repair, and DNA methylation with susceptibility and prognosis of these malignancies. To that end, 16 SNPs (one per gene: CAT, CYBA, DNMT1, DNMT3A, DNMT3B, GPX1, KEAP1, MPO, MTRR, NEIL1, NFE2F2, OGG1, SLC19A1, SOD1, SOD2, and XRCC1) were genotyped in 191 patients (101 MDS and 90 AML) and 261 controls. We also measured oxidative stress (reactive oxygen species/total antioxidant status ratio), DNA damage (8-hydroxy-2'-deoxyguanosine), and DNA methylation (5-methylcytosine) in 50 subjects (40 MDS and 10 controls). Results showed that five genes (GPX1, NEIL1, NFE2L2, OGG1, and SOD2) were associated with MDS, two (DNMT3B and SLC19A1) with AML, and two (CYBA and DNMT1) with both diseases. We observed a correlation of CYBA TT, GPX1 TT, and SOD2 CC genotypes with increased oxidative stress levels, as well as NEIL1 TT and OGG1 GG genotypes with higher DNA damage. The 5-methylcytosine levels were negatively associated with DNMT1 CC, DNMT3A CC, and MTRR AA genotypes, and positively with DNMT3B CC genotype. Furthermore, DNMT3A, MTRR, NEIL1, and OGG1 variants modulated AML transformation in MDS patients. Additionally, DNMT3A, OGG1, GPX1, and KEAP1 variants influenced survival of MDS and AML patients. Altogether, data suggest that genetic variability influence predisposition and prognosis of MDS and AML patients, as well AML transformation rate in MDS patients. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ana Cristina Gonçalves
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal
| | - Raquel Alves
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal
| | - Inês Baldeiras
- Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal.,Department of Neurology, Laboratory of Neurochemistry, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.,Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal
| | - Emília Cortesão
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Clinical Hematology Department, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal
| | - José Pedro Carda
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Clinical Hematology Department, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal
| | - Claudia C Branco
- Molecular Genetics and Pathology Unit, Hospital of Divino Espírito Santo of Ponta Delgada, EPE, Ponta Delgada, São Miguel Island, Azores, Portugal.,Azores Genetics Research Group, Instituto Gulbenkian de Ciência, Oeiras, Portugal.,Faculty of Sciences, BioISI-Biosystems and Integrative Sciences Institute, University of Lisboa, Lisbon, Portugal
| | - Bárbara Oliveiros
- Laboratory for Biostatistics and Medical Informatics, FMUC, Coimbra, Portugal
| | - Luísa Loureiro
- Department of Medicine, Hospital Distrital da Figueira da Foz, EPE (HDFF, EPE), Figueira da Foz, Portugal
| | - Amélia Pereira
- Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Department of Medicine, Hospital Distrital da Figueira da Foz, EPE (HDFF, EPE), Figueira da Foz, Portugal
| | - José Manuel Nascimento Costa
- Department of Oncology, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal.,Faculty of Medicine, University Clinic of Oncology, University of Coimbra-FMUC, Coimbra, Portugal
| | - Ana Bela Sarmento-Ribeiro
- Laboratory of Oncobiology and Hematology (LOH) and University Clinic of Hematology, Faculty of Medicine, University of Coimbra-FMUC, Coimbra, Portugal.,Center of Investigation in Environment, Genetics and Oncobiology (CIMAGO), FMUC, Coimbra, Portugal.,Center for Neuroscience and Cell Biology and Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Coimbra, Portugal.,Clinical Hematology Department, Centro Hospitalar e Universitário de Coimbra, EPE (CHUC, EPE), Coimbra, Portugal
| | - Luisa Mota-Vieira
- Molecular Genetics and Pathology Unit, Hospital of Divino Espírito Santo of Ponta Delgada, EPE, Ponta Delgada, São Miguel Island, Azores, Portugal.,Azores Genetics Research Group, Instituto Gulbenkian de Ciência, Oeiras, Portugal.,Faculty of Sciences, BioISI-Biosystems and Integrative Sciences Institute, University of Lisboa, Lisbon, Portugal
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30
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Ramos PS, Shedlock AM, Langefeld CD. Genetics of autoimmune diseases: insights from population genetics. J Hum Genet 2015; 60:657-64. [PMID: 26223182 PMCID: PMC4660050 DOI: 10.1038/jhg.2015.94] [Citation(s) in RCA: 106] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Revised: 06/12/2015] [Accepted: 06/19/2015] [Indexed: 12/14/2022]
Abstract
Human genetic diversity is the result of population genetic forces. This genetic variation influences disease risk and contributes to health disparities. Autoimmune diseases (ADs) are a family of complex heterogeneous disorders with similar underlying mechanisms characterized by immune responses against self. Collectively, ADs are common, exhibit gender and ethnic disparities, and increasing incidence. As natural selection is an important influence on human genetic variation, and immune function genes are enriched for signals of positive selection, it is thought that the prevalence of AD risk alleles seen in different population is partially the result of differing selective pressures (for example, due to pathogens). With the advent of high-throughput technologies, new analytical methodologies and large-scale projects, evidence for the role of natural selection in contributing to the heritable component of ADs keeps growing. This review summarizes the genetic regions associated with susceptibility to different ADs and concomitant evidence for selection, including known agents of selection exerting selective pressure in these regions. Examples of specific adaptive variants with phenotypic effects are included as an evidence of natural selection increasing AD susceptibility. Many of the complexities of gene effects in different ADs can be explained by population genetics phenomena. Integrating AD susceptibility studies with population genetics to investigate how natural selection has contributed to genetic variation that influences disease risk will help to identify functional variants and elucidate biological mechanisms. As such, the study of population genetics in human population holds untapped potential for elucidating the genetic causes of human disease and more rapidly focusing to personalized medicine.
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Affiliation(s)
- Paula S Ramos
- Division of Rheumatology and Immunology, Department of Medicine, and Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew M Shedlock
- Department of Biology, College of Charleston, Charleston, SC, USA
- Hollings Marine Laboratory Center for Marine Biomedicine and College of Graduate Studies, Medical University of South Carolina, Charleston, SC, USA
| | - Carl D Langefeld
- Division of Public Health Sciences, Department of Biostatistical Sciences; and Center for Public Health Genomics, Wake Forest School of Medicine, Winston-Salem, NC, USA
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Contribution of GABRG2 Polymorphisms to Risk of Epilepsy and Febrile Seizure: a Multicenter Cohort Study and Meta-analysis. Mol Neurobiol 2015; 53:5457-67. [DOI: 10.1007/s12035-015-9457-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2015] [Accepted: 09/25/2015] [Indexed: 10/23/2022]
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Starlard-Davenport A, Orloff MS, Dhakal I, Penney RB, Kadlubar SA. Genotypic and allelic variability in CYP19A1 among populations of African and European ancestry. PLoS One 2015; 10:e0117347. [PMID: 25647083 PMCID: PMC4315570 DOI: 10.1371/journal.pone.0117347] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2014] [Accepted: 12/23/2014] [Indexed: 01/08/2023] Open
Abstract
CYP19A1 facilitates the bioconversion of estrogens from androgens. CYP19A1 intron single nucleotide polymorphisms (SNPs) may alter mRNA splicing, resulting in altered CYP19A1 activity, and potentially influencing disease susceptibility. Genetic studies of CYP19A1 SNPs have been well documented in populations of European ancestry; however, studies in populations of African ancestry are limited. In the present study, ten 'candidate' intronic SNPs in CYP19A1 from 125 African Americans (AA) and 277 European Americans (EA) were genotyped and their frequencies compared. Allele frequencies were also compared with HapMap and ASW 1000 Genomes populations. We observed significant differences in the minor allele frequencies between AA and EA in six of the ten SNPs including rs10459592 (p<0.0001), rs12908960 (p<0.0001), rs1902584 (p = 0.016), rs2470144 (p<0.0001), rs1961177 (p<0.0001), and rs6493497 (p = 0.003). While there were no significant differences in allele frequencies between EA and CEU in the HapMap population, a 1.2- to 19-fold difference in allele frequency for rs10459592 (p = 0.004), rs12908960 (p = 0.0006), rs1902584 (p<0.0001), rs2470144 (p = 0.0006), rs1961177 (p<0.0001), and rs6493497 (p = 0.0092) was observed between AA and the Yoruba (YRI) population. Linkage disequilibrium (LD) blocks and haplotype clusters that is unique to the EA population but not AA was also observed. In summary, we demonstrate that differences in the allele frequencies of CYP19A1 intron SNPs are not consistent between populations of African and European ancestry. Thus, investigations into whether CYP19A1 intron SNPs contribute to variations in cancer incidence, outcomes and pharmacological response seen in populations of different ancestry may prove beneficial.
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Affiliation(s)
- Athena Starlard-Davenport
- Department of Medical Genetics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
| | - Mohammed S. Orloff
- Department of Epidemiology, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
| | - Ishwori Dhakal
- Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
| | - Rosalind B. Penney
- Department of Environmental and Occupational Health, College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
| | - Susan A. Kadlubar
- Department of Medical Genetics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States of America
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Kotze MJ, Lückhoff HK, Peeters AV, Baatjes K, Schoeman M, van der Merwe L, Grant KA, Fisher LR, van der Merwe N, Pretorius J, van Velden DP, Myburgh EJ, Pienaar FM, van Rensburg SJ, Yako YY, September AV, Moremi KE, Cronje FJ, Tiffin N, Bouwens CSH, Bezuidenhout J, Apffelstaedt JP, Hough FS, Erasmus RT, Schneider JW. Genomic medicine and risk prediction across the disease spectrum. Crit Rev Clin Lab Sci 2015; 52:120-37. [PMID: 25597499 DOI: 10.3109/10408363.2014.997930] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Genomic medicine is based on the knowledge that virtually every medical condition, disease susceptibility or response to treatment is caused, regulated or influenced by genes. Genetic testing may therefore add value across the disease spectrum, ranging from single-gene disorders with a Mendelian inheritance pattern to complex multi-factorial diseases. The critical factors for genomic risk prediction are to determine: (1) where the genomic footprint of a particular susceptibility or dysfunction resides within this continuum, and (2) to what extent the genetic determinants are modified by environmental exposures. Regarding the small subset of highly penetrant monogenic disorders, a positive family history and early disease onset are mostly sufficient to determine the appropriateness of genetic testing in the index case and to inform pre-symptomatic diagnosis in at-risk family members. In more prevalent polygenic non-communicable diseases (NCDs), the use of appropriate eligibility criteria is required to ensure a balance between benefit and risk. An additional screening step may therefore be necessary to identify individuals most likely to benefit from genetic testing. This need provided the stimulus for the development of a pathology-supported genetic testing (PSGT) service as a new model for the translational implementation of genomic medicine in clinical practice. PSGT is linked to the establishment of a research database proven to be an invaluable resource for the validation of novel and previously described gene-disease associations replicated in the South African population for a broad range of NCDs associated with increased cardio-metabolic risk. The clinical importance of inquiry concerning family history in determining eligibility for personalized genotyping was supported beyond its current limited role in diagnosing or screening for monogenic subtypes of NCDs. With the recent introduction of advanced microarray-based breast cancer subtyping, genetic testing has extended beyond the genome of the host to also include tumor gene expression profiling for chemotherapy selection. The decreasing cost of next generation sequencing over recent years, together with improvement of both laboratory and computational protocols, enables the mapping of rare genetic disorders and discovery of shared genetic risk factors as novel therapeutic targets across diagnostic boundaries. This article reviews the challenges, successes, increasing inter-disciplinary integration and evolving strategies for extending PSGT towards exome and whole genome sequencing (WGS) within a dynamic framework. Specific points of overlap are highlighted between the application of PSGT and exome or WGS, as the next logical step in genetically uncharacterized patients for whom a particular disease pattern and/or therapeutic failure are not adequately accounted for during the PSGT pre-screen. Discrepancies between different next generation sequencing platforms and low concordance among variant-calling pipelines caution against offering exome or WGS as a stand-alone diagnostic approach. The public reference human genome sequence (hg19) contains minor alleles at more than 1 million loci and variant calling using an advanced major allele reference genome sequence is crucial to ensure data integrity. Understanding that genomic risk prediction is not deterministic but rather probabilistic provides the opportunity for disease prevention and targeted treatment in a way that is unique to each individual patient.
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Affiliation(s)
- Maritha J Kotze
- Division of Anatomical Pathology, Department of Pathology, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town , South Africa
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