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1
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Haykal D. Leveraging Single Nucleotide Polymorphism Profiling for Precision Skin Care: How SNPs Shape Individual Responses in Cosmetic Dermatology. J Cosmet Dermatol 2025; 24:e16750. [PMID: 39737554 DOI: 10.1111/jocd.16750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2024] [Accepted: 12/15/2024] [Indexed: 01/01/2025]
Abstract
INTRODUCTION Single-nucleotide polymorphisms (SNPs) represent a significant genetic variation influencing individual responses to cosmetic dermatology treatments. SNP profiling offers a pathway to personalized skincare by enabling practitioners to predict patient outcomes, customize interventions, and mitigate risks. BACKGROUND The integration of genetic insights into dermatology has gained traction, with SNP analysis revealing predispositions in skin characteristics, such as collagen degradation, pigmentation, and inflammatory responses. Key SNPs, including MMP1, SOD2, TYR, and IL-6, are pivotal in determining skin health and treatment outcomes. Despite its promise, the adoption of SNP profiling in cosmetic dermatology is in its infancy, requiring further exploration of its practical applications. RESULTS SNPs significantly influence skin responses to aesthetic treatments, offering insights for personalized care. Variations in MMP1 correlate with collagen degradation, suggesting collagen-stimulating therapies, while SOD2 SNPs highlight the need for antioxidant support. TYR variations affect pigmentation risks in light-based treatments, and IL-6 SNPs reveal inflammatory predispositions, guiding anti-inflammatory protocols. AI integration enhances SNP profiling by improving prediction accuracy and treatment customization. Challenges remain, including standardization, ethical considerations, and cost-effectiveness. Combining genetic insights with epigenetics and leveraging AI technologies can amplify precision and safety in dermatologic care. CONCLUSION SNP profiling marks a transformative step toward precision medicine in cosmetic dermatology, enabling tailored treatments that enhance efficacy and minimize adverse effects. Integrating AI-driven SNP analysis with epigenetic insights provides a comprehensive approach to patient care, fostering a new era of personalized skincare that respects genetic and environmental interactions. This paradigm shift holds the potential to redefine dermatologic practices, improving outcomes and patient satisfaction.
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Affiliation(s)
- Diala Haykal
- Centre Médical Laser Palaiseau, Palaiseau, France
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Sun L, Bian J, Xin Y, Jiang L, Zheng L. Epi-SSA: A novel epistasis detection method based on a multi-objective sparrow search algorithm. PLoS One 2024; 19:e0311223. [PMID: 39446852 PMCID: PMC11500897 DOI: 10.1371/journal.pone.0311223] [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: 06/26/2024] [Accepted: 09/16/2024] [Indexed: 10/26/2024] Open
Abstract
Genome-wide association studies typically considers epistatic interactions as a crucial factor in exploring complex diseases. However, the current methods primarily concentrate on the detection of two-order epistatic interactions, with flaws in accuracy. In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions. To evaluate its performance, we conducted a comprehensive comparison between Epi-SSA and seven other methods using five simulation datasets: DME 100, DNME 100, DME 1000, DNME 1000 and DNME3 100. The DME 100 dataset encompasses eight second-order epistasis disease models with marginal effects, each comprising 100 simulated data instances, featuring 100 SNPs per instance, alongside 800 case and 800 control samples. The DNME 100 encompasses eight second-order epistasis disease models without marginal effects and retains other properties consistent with DME 100. Experiments on the DME 100 and DNME 100 datasets were designed to evaluate the algorithms' capacity to detect epistasis across varying disease models. The DME 1000 and DNME 1000 datasets extend the complexity with 1000 SNPs per simulated data instance, while retaining other properties consistent with DME 100 and DNME 100. These experiments aimed to gauge the algorithms' adaptability in detecting epistasis as the number of SNPs in the data increases. The DNME3 100 dataset introduces a higher level of complexity with six third-order epistasis disease models, otherwise paralleling the structure of DNME 100, serving to test the algorithms' proficiency in identifying higher-order epistasis. The highest average F-measures achieved by the seven other existing methods on the five datasets are 0.86, 0.86, 0.41, 0.56, and 0.79 respectively, while the average F-measures of Epi-SSA on the five datasets are 0.92, 0.97, 0.79, 0.86, and 0.97 respectively. The experimental results demonstrate that the Epi-SSA algorithm outperforms other methods in a variety of epistasis detection tasks. As the number of SNPs in the data set increases and the order of epistasis rises, the advantages of the Epi-SSA algorithm become increasingly pronounced. In addition, we applied Epi-SSA to the analysis of the WTCCC dataset, uncovering numerous genes and gene pairs that might play a significant role in the pathogenesis of seven complex diseases. It is worthy of note that some of these genes have been relatedly reported in the Comparative Toxicogenomics Database (CTD). Epi-SSA is a potent tool for detecting epistatic interactions, which aids us in further comprehending the pathogenesis of common and complex diseases. The source code of Epi-SSA can be obtained at https://osf.io/6sqwj/.
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Affiliation(s)
- Liyan Sun
- College of Computer Science and Technology, Changchun University, Changchun City, Jilin Province, China
| | - Jingwen Bian
- School of Cultural and Media Studies, Changchun University of Science and Technology, Changchun City, Jilin Province, China
| | - Yi Xin
- College of Computer Science and Technology, Changchun University, Changchun City, Jilin Province, China
| | - Linqing Jiang
- College of Computer Science and Technology, Changchun University, Changchun City, Jilin Province, China
| | - Linxuan Zheng
- College of Computer Science and Technology, Changchun University, Changchun City, Jilin Province, China
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3
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Ahmed IA, Kharboush TG, Al-Amodi HS, Kamel HFM, Darwish E, Mosbeh A, Galbt HA, Abdel-Kareim AM, Abdelsattar S. Interleukin-1 Beta rs16944 and rs1143634 and Interleukin-6 Receptor rs12083537 Single Nucleotide Polymorphisms as Potential Predictors of COVID-19 Severity. Pathogens 2024; 13:915. [PMID: 39452786 PMCID: PMC11510688 DOI: 10.3390/pathogens13100915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Revised: 10/08/2024] [Accepted: 10/15/2024] [Indexed: 10/26/2024] Open
Abstract
Host genetic variation has been recognized as a key predictor of diverse clinical sequelae among severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients. Insights into the link between the Interleukin-6 receptor (IL-6R) and Interleukin-1 beta (IL-1β) genetic variation and severe coronavirus disease 2019 (COVID-19) are crucial for developing new predictors and therapeutic targets. We aimed to investigate the association of IL-6R rs12083537, IL-1β rs16944, and IL-1β rs1143634 SNPs with the severity of COVID-19. Our study was conducted on 300 COVID-19-negative individuals (control group) and 299 COVID-19-positive cases, classified into mild, moderate, and severe subgroups. Analyses of IL-1β (rs16944, rs1143634) and IL-6R (rs12083537) SNPs' genotypes were performed using qPCR genotyping assays. The IL-1β (rs16944) CC genotype and IL-6R (rs12083537) GG genotype were substantially related to COVID-19 severity, which was also associated with comorbidities and some laboratory parameters (p < 0.001). The IL-1β (rs1143634) TT genotype was found to be protective. Likewise, the IL-1β (rs16944) CC genotype was associated with increased mortality. IL-1β rs16944 and IL-6R rs12083537 SNPs are promising potential predictors of SARS-CoV-2 disease severity. Meanwhile, the rs1143634 SNP T allele was protective against severity and mortality risk.
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Affiliation(s)
- Inas A. Ahmed
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Benha University, Benha 13518, Egypt
- Central Laboratory for Research, Faculty of Medicine, Benha University, Benha 13518, Egypt
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Benha National University, El-Obour 11828, Egypt
| | - Taghrid G. Kharboush
- Department of Medical Microbiology and Immunology, Faculty of Medicine, Benha University, Benha 13518, Egypt;
| | - Hiba S. Al-Amodi
- Department of Biochemistry, Faculty of Medicine, Umm Al-Qura University, P.O. Box 715, Makkah 21955, Saudi Arabia; (H.S.A.-A.); (H.F.M.K.)
| | - Hala F. M. Kamel
- Department of Biochemistry, Faculty of Medicine, Umm Al-Qura University, P.O. Box 715, Makkah 21955, Saudi Arabia; (H.S.A.-A.); (H.F.M.K.)
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt
| | - Ehab Darwish
- Department of Tropical Medicine, Faculty of Medicine, Zagazig University, Zagazig 44511, Egypt;
- Department of Internal Medicine, Faculty of Medicine, King Faisal University, AI-Ahsa 31982, Saudi Arabia
| | - Asmaa Mosbeh
- Department of Pathology, National Liver Institute, Menoufia University, Menoufia 32511, Egypt;
| | - Hossam A. Galbt
- Department of Clinical Pathology, National Liver Institute, Menoufia University, Menoufia 32511, Egypt;
| | - Amal M. Abdel-Kareim
- Department of Zoology, Faculty of Science, Benha University, Benha 13518, Egypt;
| | - Shimaa Abdelsattar
- Department of Clinical Biochemistry and Molecular Diagnostics, National Liver Institute, Menoufia University, Menoufia 32511, Egypt;
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Thomas M, Mackes N, Preuss-Dodhy A, Wieland T, Bundschus M. Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review. JMIR BIOINFORMATICS AND BIOTECHNOLOGY 2024; 5:e54332. [PMID: 38935957 PMCID: PMC11165293 DOI: 10.2196/54332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/26/2024] [Accepted: 03/29/2024] [Indexed: 06/29/2024]
Abstract
BACKGROUND Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation. OBJECTIVE This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets. METHODS We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success. RESULTS From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants. CONCLUSIONS On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.
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Chatterjee R, Chowdhury AR, Mukherjee D, Chakravortty D. From Eberthella typhi to Salmonella Typhi: The Fascinating Journey of the Virulence and Pathogenicity of Salmonella Typhi. ACS OMEGA 2023; 8:25674-25697. [PMID: 37521659 PMCID: PMC10373206 DOI: 10.1021/acsomega.3c02386] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 06/30/2023] [Indexed: 08/01/2023]
Abstract
Salmonella Typhi (S. Typhi), the invasive typhoidal serovar of Salmonella enterica that causes typhoid fever in humans, is a severe threat to global health. It is one of the major causes of high morbidity and mortality in developing countries. According to recent WHO estimates, approximately 11-21 million typhoid fever illnesses occur annually worldwide, accounting for 0.12-0.16 million deaths. Salmonella infection can spread to healthy individuals by the consumption of contaminated food and water. Typhoid fever in humans sometimes is accompanied by several other critical extraintestinal complications related to the central nervous system, cardiovascular system, pulmonary system, and hepatobiliary system. Salmonella Pathogenicity Island-1 and Salmonella Pathogenicity Island-2 are the two genomic segments containing genes encoding virulent factors that regulate its invasion and systemic pathogenesis. This Review aims to shed light on a comparative analysis of the virulence and pathogenesis of the typhoidal and nontyphoidal serovars of S. enterica.
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Affiliation(s)
- Ritika Chatterjee
- Department
of Microbiology and Cell Biology, Division of Biological Sciences, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Atish Roy Chowdhury
- Department
of Microbiology and Cell Biology, Division of Biological Sciences, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Debapriya Mukherjee
- Department
of Microbiology and Cell Biology, Division of Biological Sciences, Indian Institute of Science, Bangalore, Karnataka 560012, India
| | - Dipshikha Chakravortty
- Department
of Microbiology and Cell Biology, Division of Biological Sciences, Indian Institute of Science, Bangalore, Karnataka 560012, India
- Centre
for Biosystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India
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6
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Polymorphisms in genes expressed during amelogenesis and their association with dental caries: a case–control study. Clin Oral Investig 2022; 27:1681-1695. [PMID: 36422720 PMCID: PMC10102052 DOI: 10.1007/s00784-022-04794-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/13/2022] [Indexed: 11/25/2022]
Abstract
Abstract
Objectives
Dental caries is a widespread multifactorial disease, caused by the demineralization of hard dental tissues. Susceptibility to dental caries is partially genetically conditioned; this study was aimed at finding an association of selected single nucleotide polymorphisms (SNPs) in genes encoding proteins involved in amelogenesis with this disease in children.
Materials and methods
In this case–control study, 15 SNPs in ALOX15, AMBN, AMELX, KLK4, TFIP11, and TUFT1 genes were analyzed in 150 children with primary dentition and 611 children with permanent teeth with/without dental caries from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) cohort.
Results
Dental caries in primary dentition was associated with SNPs in AMELX (rs17878486) and KLK4 (rs198968, rs2242670), and dental caries in permanent dentition with SNPs in AMELX (rs17878486) and KLK4 (rs2235091, rs2242670, rs2978642), (p ≤ 0.05). No significant differences between cases and controls were observed in the allele or genotype frequencies of any of the selected SNPs in ALOX15, AMBN, TFIP11, and TUFT1 genes (p > 0.05). Some KLK4 haplotypes were associated with dental caries in permanent dentition (p ≤ 0.05).
Conclusions
Based on this study, we found that although the SNPs in AMELX and KLK4 are localized in intronic regions and their functional significance has not yet been determined, they are associated with susceptibility to dental caries in children.
Clinical relevance
AMELX and KLK4 variants could be considered in the risk assessment of dental caries, especially in permanent dentition, in the European Caucasian population.
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7
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Context-Dependent Substitution Dynamics in Plastid DNA Across a Wide Range of Taxonomic Groups. J Mol Evol 2022; 90:44-55. [PMID: 35037071 DOI: 10.1007/s00239-021-10040-2] [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: 09/20/2021] [Accepted: 12/01/2021] [Indexed: 10/19/2022]
Abstract
The influence of neighboring base composition, or context, on substitution bias at fourfold degenerate coding sites and in intergenic regions in plastid DNA is compared across the angiosperms, gymnosperms, ferns, liverworts, chlorophytes, stramenopiles and rhodophytes. An influence of flanking base G + C content on the relative rates of transitions and transversions is observed in all lineages and extends up to four nucleotides from the site of substitution in some. Despite finding context effects in all lineages, significant differences were observed between lineages. Overall, the data suggest that context is a general factor affecting mutation bias in plastid DNA but that the dynamics of the influence have evolved over time. It is also shown that, although there are similar effects of context on substitution bias at fourfold degenerate coding sites and at sites within intergenic regions, there are also small but significant differences, suggesting that there could be some selection on some of these sites and that there could be some difference in the mutation and/or repair process between coding and noncoding DNA.
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Nauwelaerts SJD, Van Geel D, Delvoye M, De Cremer K, Bernard A, Roosens NHC, De Keersmaecker SCJ. Selection of a Noninvasive Source of Human DNA Envisaging Genotyping Assays in Epidemiological Studies: Urine or Saliva? J Biomol Tech 2021; 31:27-35. [PMID: 32042275 DOI: 10.7171/jbt.20-3101-004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Genetic epidemiology requires an appropriate approach to measure genetic variation within the population. The aim of this study was to evaluate the characteristics and genotyping results of DNA extracted from 2 human DNA sources, selected for their rapid and noninvasive sampling, and the use of simple and standardized protocols that are essential for large-scale epidemiologic studies. Saliva and urine samples were collected at the same day from 20 subjects aged 9-10 yr. Genomic DNA was extracted using commercial kits. Quantitative and qualitative evaluation was done by assessing the yield, the purity, and integrity of the extracted DNA. As a proof-of-concept, genotyping was performed targeting CC16 A38G and uteroglobin-related protein 1 (UGRP1)-112G/A. Saliva was found to provide the highest yield and concentration of total DNA extracted. Salivary DNA showed higher purity and a significantly less degraded state compared to urinary DNA. Consequently, the salivary DNA gave better genotyping results than urinary DNA. Therefore, if the choice exists, saliva is the preferred noninvasive matrix for genotyping purposes in large-scale genetic epidemiologic studies. Only in particular cases using urine could nevertheless be considered useful, although specific limitations need to be taken into account.
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Affiliation(s)
- Sarah J D Nauwelaerts
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium.,Louvain Centre for Toxicology and Applied Pharmacology, Université Catholique de Louvain Woluwe, 1200 Brussels, Belgium
| | - Dirk Van Geel
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium
| | - Maud Delvoye
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium
| | - Koen De Cremer
- Platform Chromatography and Mass Spectrometry, Sciensano, 1050 Brussels, Belgium; and
| | - Alfred Bernard
- Louvain Centre for Toxicology and Applied Pharmacology, Université Catholique de Louvain Woluwe, 1200 Brussels, Belgium
| | - Nancy H C Roosens
- Transversal Activities in Applied Genomics, Sciensano, 1050 Brussels, Belgium
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Mosaad YM, Hammad A, AlHarrass MF, Sallam R, Shouma A, Hammad E, Ahmed EO, Abdel-Azeem HA, Sherif D, Fawzy I, Elbahnasawy A, Abdel Twab H. ARID5B rs10821936 and rs10994982 gene polymorphism and susceptibility to juvenile systemic lupus erythematosus and lupus nephritis. Lupus 2021; 30:1226-1232. [PMID: 33888010 DOI: 10.1177/09612033211010338] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The prevalence of SLE and the spectrum of clinical manifestations vary widely in different races and geographical populations. OBJECTIVE To investigate the possible role of ARID5B rs10821936 and rs10994982 polymorphism as a risk factor for the development of SLE in children (jSLE) and to evaluate their role in relation to clinical manifestations especially lupus nephritis (LN). METHODS DNA extraction and Real-time PCR genotyping of ARID5B rs10821936 and rs10994982 were done for 104 jSLE and 282 healthy controls. RESULTS The C allele and C containing genotypes (CC, CT and CC+CT) of ARID5B rs10821936 were higher in children with SLE (p = 0.009, OR = 1.56, 0.037, OR = 2.35, 0.016, OR = 1.81 and 0.008 OR = 1.88 respectively). ARID5B rs10994982 alleles, genotypes and haplotypes are not associated with jSLE (p > 0.05). The ARID5B rs10821936 and rs10994982 genotypes showed non-significant associations with LN, proliferative versus non proliferative and biopsy grades (p > 0.05). CONCLUSION ARID5B rs10821936 SNP may be a susceptibility risk factor for juvenile SLE in the studied cohort of Egyptian children.
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Affiliation(s)
- Youssef M Mosaad
- Clinical Immunology Unit, Clinical Pathology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Ayman Hammad
- Pediatric Nephrology Unit, Department of Pediatrics, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Mohamed F AlHarrass
- Clinical Immunology Unit, Clinical Pathology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Rehab Sallam
- Rheumatology and Rehabilitation Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Amany Shouma
- Pediatric Cardiology Unit, Department of Pediatrics, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Enas Hammad
- Rheumatology and Rehabilitation Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Engy Osman Ahmed
- Pediatric Pulmonology and Allergy Unit, Department of Pediatrics, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Heba A Abdel-Azeem
- Dermatology, Andrology & STDs, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Doaa Sherif
- Microbiology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Iman Fawzy
- Laboratory Medicine Department, Mansoura Fever Hospital, Ministry of Health, Mansoura, Egypt
| | - Amany Elbahnasawy
- Rheumatology and Rehabilitation Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
| | - Hosam Abdel Twab
- Clinical Immunology Unit, Clinical Pathology Department, Faculty of Medicine, Mansoura University, Mansoura, Egypt
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Wang LN, Peng XL, Xu M, Zheng YB, Jiao YY, Yu JM, Fu YH, Zheng YP, Zhu WY, Dong ZJ, He JS. Evaluation of the Safety and Immune Efficacy of Recombinant Human Respiratory Syncytial Virus Strain Long Live Attenuated Vaccine Candidates. Virol Sin 2021; 36:706-720. [PMID: 33559831 PMCID: PMC8379332 DOI: 10.1007/s12250-021-00345-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 09/18/2020] [Indexed: 11/30/2022] Open
Abstract
Human respiratory syncytial virus (RSV) infection is the leading cause of lower respiratory tract illness (LRTI), and no vaccine against LRTI has proven to be safe and effective in infants. Our study assessed attenuated recombinant RSVs as vaccine candidates to prevent RSV infection in mice. The constructed recombinant plasmids harbored (5′ to 3′) a T7 promoter, hammerhead ribozyme, RSV Long strain antigenomic cDNA with cold-passaged (cp) mutations or cp combined with temperature-sensitive attenuated mutations from the A2 strain (A2cpts) or further combined with SH gene deletion (A2cptsΔSH), HDV ribozyme (δ), and a T7 terminator. These vectors were subsequently co-transfected with four helper plasmids encoding N, P, L, and M2-1 viral proteins into BHK/T7-9 cells, and the recovered viruses were then passaged in Vero cells. The rescued recombinant RSVs (rRSVs) were named rRSV-Long/A2cp, rRSV-Long/A2cpts, and rRSV-Long/A2cptsΔSH, respectively, and stably passaged in vitro, without reversion to wild type (wt) at sites containing introduced mutations or deletion. Although rRSV-Long/A2cpts and rRSV-Long/A2cptsΔSH displayed temperature-sensitive (ts) phenotype in vitro and in vivo, all rRSVs were significantly attenuated in vivo. Furthermore, BALB/c mice immunized with rRSVs produced Th1-biased immune response, resisted wtRSV infection, and were free from enhanced respiratory disease. We showed that the combination of ΔSH with attenuation (att) mutations of cpts contributed to improving att phenotype, efficacy, and gene stability of rRSV. By successfully introducing att mutations and SH gene deletion into the RSV Long parent and producing three rRSV strains, we have laid an important foundation for the development of RSV live attenuated vaccines.
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Affiliation(s)
- Li-Nan Wang
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Xiang-Lei Peng
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Min Xu
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Yuan-Bo Zheng
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Yue-Ying Jiao
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Jie-Mei Yu
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Yuan-Hui Fu
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Yan-Peng Zheng
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China
| | - Wu-Yang Zhu
- National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Zhong-Jun Dong
- School of Medicine and Institute for Immunology, Beijing Key Lab for Immunological Research on Chronic Diseases, Tsinghua University, Beijing, 100084, China
| | - Jin-Sheng He
- College of Life Sciences and Bioengineering, School of Science, Beijing Jiaotong University, Beijing, 100044, China.
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Sun L, Liu G, Su L, Wang R. HS-MMGKG: A Fast Multi-objective Harmony Search Algorithm for Two-locus Model Detection in GWAS. Curr Bioinform 2019. [DOI: 10.2174/1574893614666190409110843] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Background::
Genome-Wide Association Study (GWAS) plays a very important role in
identifying the causes of a disease. Because most of the existing methods for genetic-interaction
detection in GWAS are designed for a single-correlation model, their performances vary
considerably for different disease models. These methods usually have high computation cost and
low accuracy.
Method::
We present a new multi-objective heuristic optimization methodology named HSMMGKG
for detecting genetic interactions. In HS-MMGKG, we use harmony search with five
objective functions to improve the efficiency and accuracy. A new strategy based on p-value and
MDR is adopted to generate more reasonable results. The Boolean representation in BOOST is
modified to calculate the five functions rapidly. These strategies take less time complexity and
have higher accuracy while detecting the potential models.
Results::
We compared HS-MMGKG with CSE, MACOED and FHSA-SED using 26 simulated
datasets. The experimental results demonstrate that our method outperforms others in accuracy and
computation time. Our method has identified many two-locus SNP combinations that are
associated with seven diseases in WTCCC dataset. Some of the SNPs have direct evidence in CTD
database. The results may be helpful to further explain the pathogenesis.
Conclusion::
It is anticipated that our proposed algorithm could be used in GWAS which is helpful
in understanding disease mechanism, diagnosis and prognosis.
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Affiliation(s)
- Liyan Sun
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
| | - Guixia Liu
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
| | - Lingtao Su
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
| | - Rongquan Wang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, China
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Sankarasubramanian J, Vishnu US, Gunasekaran P, Rajendhran J. Development and evaluation of a core genome multilocus sequence typing (cgMLST) scheme for Brucella spp. INFECTION GENETICS AND EVOLUTION 2019; 67:38-43. [DOI: 10.1016/j.meegid.2018.10.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2017] [Revised: 09/29/2018] [Accepted: 10/27/2018] [Indexed: 10/28/2022]
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13
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Sun L, Liu G, Su L, Wang R. SEE: a novel multi-objective evolutionary algorithm for identifying SNP epistasis in genome-wide association studies. BIOTECHNOL BIOTEC EQ 2019. [DOI: 10.1080/13102818.2019.1593052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Affiliation(s)
- Liyan Sun
- Department of Computational Intelligence, College of Computer Science and Technology, Jilin University, Changchun, P.R. China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, P.R. China
| | - Guixia Liu
- Department of Computational Intelligence, College of Computer Science and Technology, Jilin University, Changchun, P.R. China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, P.R. China
| | - Lingtao Su
- Department of Computational Intelligence, College of Computer Science and Technology, Jilin University, Changchun, P.R. China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, P.R. China
| | - Rongquan Wang
- Department of Computational Intelligence, College of Computer Science and Technology, Jilin University, Changchun, P.R. China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, P.R. China
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14
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Gal-Mor O. Persistent Infection and Long-Term Carriage of Typhoidal and Nontyphoidal Salmonellae. Clin Microbiol Rev 2019; 32:e00088-18. [PMID: 30487167 PMCID: PMC6302356 DOI: 10.1128/cmr.00088-18] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
The ability of pathogenic bacteria to affect higher organisms and cause disease is one of the most dramatic properties of microorganisms. Some pathogens can establish transient colonization only, but others are capable of infecting their host for many years or even for a lifetime. Long-term infection is called persistence, and this phenotype is fundamental for the biology of important human pathogens, including Helicobacter pylori, Mycobacterium tuberculosis, and Salmonella enterica Both typhoidal and nontyphoidal serovars of the species Salmonella enterica can cause persistent infection in humans; however, as these two Salmonella groups cause clinically distinct diseases, the characteristics of their persistent infections in humans differ significantly. Here, following a general summary of Salmonella pathogenicity, host specificity, epidemiology, and laboratory diagnosis, I review the current knowledge about Salmonella persistence and discuss the relevant epidemiology of persistence (including carrier rate, duration of shedding, and host and pathogen risk factors), the host response to Salmonella persistence, Salmonella genes involved in this lifestyle, as well as genetic and phenotypic changes acquired during prolonged infection within the host. Additionally, I highlight differences between the persistence of typhoidal and nontyphoidal Salmonella strains in humans and summarize the current gaps and limitations in our understanding, diagnosis, and curing of persistent Salmonella infections.
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Affiliation(s)
- Ohad Gal-Mor
- Infectious Diseases Research Laboratory, Sheba Medical Center, Tel-Hashomer, Israel
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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15
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Rajagopalan RM, Fujimura JH. Variations on a Chip: Technologies of Difference in Human Genetics Research. JOURNAL OF THE HISTORY OF BIOLOGY 2018; 51:841-873. [PMID: 30338423 DOI: 10.1007/s10739-018-9543-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
In this article we examine the history of the production of microarray technologies and their role in constructing and operationalizing views of human genetic difference in contemporary genomics. Rather than the "turn to difference" emerging as a post-Human Genome Project (HGP) phenomenon, interest in individual and group differences was a central, motivating concept in human genetics throughout the twentieth century. This interest was entwined with efforts to develop polymorphic "genetic markers" for studying human traits and diseases. We trace the technological, methodological and conceptual strategies in the late twentieth century that established single nucleotide polymorphisms (SNPs) as key focal points for locating difference in the genome. By embedding SNPs in microarrays, researchers created a technology that they used to catalog and assess human genetic variation. In the process of making genetic markers and array-based technologies to track variation, scientists also made commitments to ways of describing, cataloging and "knowing" human genetic differences that refracted difference through a continental geographic lens. We show how difference came to matter in both senses of the term: difference was made salient to, and inscribed on, genetic matter(s), as a result of the decisions, assessments and choices of collaborative and hybrid research collectives in medical genomics research.
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Affiliation(s)
- Ramya M Rajagopalan
- Institute for Practical Ethics, University of California, San Diego, 9500 Gilman Drive, MC 0406, San Diego, CA, 92093, USA.
| | - Joan H Fujimura
- Department of Sociology and Holtz Center for Science and Technology Studies, University of Wisconsin-Madison, 8128 Sewell Social Sciences Building 1180 Observatory Drive, Madison, WI, 53706, USA
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16
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Pengelly RJ, Collins A. Linkage disequilibrium maps to guide contig ordering for genome assembly. Bioinformatics 2018; 35:541-545. [DOI: 10.1093/bioinformatics/bty687] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2018] [Revised: 07/13/2018] [Accepted: 08/03/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Reuben J Pengelly
- Genetic Epidemiology & Bioinformatics, Faculty of Medicine, University of Southampton, Southampton, UK
| | - Andrew Collins
- Genetic Epidemiology & Bioinformatics, Faculty of Medicine, University of Southampton, Southampton, UK
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D'Aquila P, Crocco P, De Rango F, Indiveri C, Bellizzi D, Rose G, Passarino G. A Genetic Variant of ASCT2 Hampers In Vitro RNA Splicing and Correlates with Human Longevity. Rejuvenation Res 2018; 21:193-199. [DOI: 10.1089/rej.2017.1948] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Affiliation(s)
- Patrizia D'Aquila
- Department of Biology, Ecology and Earth Science, University of Calabria, Rende, Italy
| | - Paolina Crocco
- Department of Biology, Ecology and Earth Science, University of Calabria, Rende, Italy
| | - Francesco De Rango
- Department of Biology, Ecology and Earth Science, University of Calabria, Rende, Italy
| | - Cesare Indiveri
- Department of Biology, Ecology and Earth Science, University of Calabria, Rende, Italy
| | - Dina Bellizzi
- Department of Biology, Ecology and Earth Science, University of Calabria, Rende, Italy
| | - Giuseppina Rose
- Department of Biology, Ecology and Earth Science, University of Calabria, Rende, Italy
| | - Giuseppe Passarino
- Department of Biology, Ecology and Earth Science, University of Calabria, Rende, Italy
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18
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Kim SA, Cho CS, Kim SR, Bull SB, Yoo YJ. A new haplotype block detection method for dense genome sequencing data based on interval graph modeling of clusters of highly correlated SNPs. Bioinformatics 2018; 34:388-397. [PMID: 29028986 PMCID: PMC5860363 DOI: 10.1093/bioinformatics/btx609] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 09/11/2017] [Accepted: 09/28/2017] [Indexed: 11/13/2022] Open
Abstract
Motivation Linkage disequilibrium (LD) block construction is required for research in population genetics and genetic epidemiology, including specification of sets of single nucleotide polymorphisms (SNPs) for analysis of multi-SNP based association and identification of haplotype blocks in high density sequencing data. Existing methods based on a narrow sense definition do not allow intermediate regions of low LD between strongly associated SNP pairs and tend to split high density SNP data into small blocks having high between-block correlation. Results We present Big-LD, a block partition method based on interval graph modeling of LD bins which are clusters of strong pairwise LD SNPs, not necessarily physically consecutive. Big-LD uses an agglomerative approach that starts by identifying small communities of SNPs, i.e. the SNPs in each LD bin region, and proceeds by merging these communities. We determine the number of blocks using a method to find maximum-weight independent set. Big-LD produces larger LD blocks compared to existing methods such as MATILDE, Haploview, MIG ++, or S-MIG ++ and the LD blocks better agree with recombination hotspot locations determined by sperm-typing experiments. The observed average runtime of Big-LD for 13 288 240 non-monomorphic SNPs from 1000 Genomes Project autosome data (286 East Asians) is about 5.83 h, which is a significant improvement over the existing methods. Availability and implementation Source code and documentation are available for download at http://github.com/sunnyeesl/BigLD. Contact yyoo@snu.ac.kr. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sun Ah Kim
- Department of Statistics, Seoul National University, Seoul, South Korea
| | - Chang-Sung Cho
- Department of Mathematics Education, Seoul National University, Seoul, South Korea
| | - Suh-Ryung Kim
- Department of Mathematics Education, Seoul National University, Seoul, South Korea
| | - Shelley B Bull
- Prosserman Centre for Health Research, The Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - Yun Joo Yoo
- Department of Mathematics Education, Seoul National University, Seoul, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea
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Tamura T, Osawa M, Kakimoto Y, Ochiai E, Suzuki T, Nakamura T. Combined effects of multiple linked loci on pairwise sibling tests. Int J Legal Med 2016; 131:95-102. [PMID: 27878372 DOI: 10.1007/s00414-016-1491-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2016] [Accepted: 11/02/2016] [Indexed: 11/24/2022]
Abstract
The advanced multiplex STR system, PowerPlex Fusion, includes four linked locus pairs. The conventional Identifiler system has one pair of linked loci. Therefore, sibling tests conducted using the advanced system might be more affected by linkage than those conducted using the conventional system. This study simulated single and combined effects of the four linked locus pairs on pairwise sibling tests. Simulated genotypes of 100,000 pairs of full siblings and nonrelatives were constructed according to allele frequencies of the Japanese population. The single linkage effect was evaluated for simulated genotype data by calculating both the likelihood ratio accounting for the linkage between two loci and the likelihood ratio ignoring the linkage. The combined effect was obtained by multiplication of the respective single effects. Furthermore, we investigated the possibility that ignoring the linkage affects subject classification by introducing a scale of the likelihood ratio into sibling tests. The single effect in the Identifiler analysis was 0.645-1.746 times if the linkage was ignored. Overestimations and underestimations were predictable from the identical-by-state status at two linked loci. The combined effect in the PowerPlex Fusion analysis was 0.217-7.390 times. Ignoring the linkage rarely caused a false conclusive or inconclusive result, even from PowerPlex Fusion analysis. Application of the advanced system improved sibling tests considerably. The additional examined loci were more beneficial than the adverse effect of the linkage derived from the four linked locus pairs.
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Affiliation(s)
- Tomonori Tamura
- Department of Forensic Medicine, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259-1193, Japan.,Scientific Crime Laboratory, Kanagawa Prefectural Police, Yamashita-cho 155, Naka-ku, Yokohama, 231-0023, Japan
| | - Motoki Osawa
- Department of Forensic Medicine, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259-1193, Japan.
| | - Yu Kakimoto
- Department of Forensic Medicine, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259-1193, Japan
| | - Eriko Ochiai
- Department of Forensic Medicine, Tokai University School of Medicine, Shimokasuya 143, Isehara, Kanagawa, 259-1193, Japan
| | - Takanori Suzuki
- Scientific Crime Laboratory, Kanagawa Prefectural Police, Yamashita-cho 155, Naka-ku, Yokohama, 231-0023, Japan
| | - Takashi Nakamura
- Scientific Crime Laboratory, Kanagawa Prefectural Police, Yamashita-cho 155, Naka-ku, Yokohama, 231-0023, Japan
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20
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PGAdb-builder: A web service tool for creating pan-genome allele database for molecular fine typing. Sci Rep 2016; 6:36213. [PMID: 27824078 PMCID: PMC5099940 DOI: 10.1038/srep36213] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 10/12/2016] [Indexed: 01/07/2023] Open
Abstract
With the advance of next generation sequencing techniques, whole genome sequencing (WGS) is expected to become the optimal method for molecular subtyping of bacterial isolates. To use WGS as a general subtyping method for disease outbreak investigation and surveillance, the layout of WGS-based typing must be comparable among laboratories. Whole genome multilocus sequence typing (wgMLST) is an approach that achieves this requirement. To apply wgMLST as a standard subtyping approach, a pan-genome allele database (PGAdb) for the population of a bacterial organism must first be established. We present a free web service tool, PGAdb-builder (http://wgmlstdb.imst.nsysu.edu.tw), for the construction of bacterial PGAdb. The effectiveness of PGAdb-builder was tested by constructing a pan-genome allele database for Salmonella enterica serovar Typhimurium, with the database being applied to create a wgMLST tree for a panel of epidemiologically well-characterized S. Typhimurium isolates. The performance of the wgMLST-based approach was as high as that of the SNP-based approach in Leekitcharoenphon’s study used for discerning among epidemiologically related and non-related isolates.
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21
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Ahola-Olli AV, Pitkänen N, Kettunen J, Oikonen MK, Mikkilä V, Lehtimäki T, Kähönen M, Pahkala K, Niinikoski H, Kangas AJ, Soininen P, Ala-Korpela M, Viikari JS, Rönnemaa T, Simell O, Raitakari OT. Interactions between genetic variants and dietary lipid composition: effects on circulating LDL cholesterol in children. Am J Clin Nutr 2014; 100:1569-77. [PMID: 25411292 DOI: 10.3945/ajcn.114.085027] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Elevated serum low-density lipoprotein (LDL) cholesterol is a predictor of cardiovascular disease events, and the quality of dietary fat is known to influence serum concentrations of LDL cholesterol in children. Interindividual differences in response to diet exist, but the underlying genetic factors remain largely unknown. OBJECTIVE We aimed to identify genetic variants that modify the variation in serum lipid response to dietary fat quality. DESIGN We used data from 2 longitudinal Finnish cohorts designed to study risk factors for cardiovascular diseases. Large-scale genotyping was performed with Metabochip in a long-term randomized controlled dietary intervention trial, the Special Turku Coronary Risk Factor Intervention Project (STRIP), for discovery of genetic polymorphisms. The observational Cardiovascular Risk in Young Finns Study (YFS) with genome-wide genetic data was used as a replication sample for the initial findings. Dietary records were used to calculate the ratio of unsaturated to saturated fats. Interaction models and multiple follow-ups were used in the analysis. RESULTS In the STRIP cohort, a variant within the PARK2 locus, rs9364628, showed moderate interaction with dietary fat quality and a consistent direction of effect in both scans on serum LDL-cholesterol concentration in children aged 5 and 7 y (P < 0.0084 and P < 0.0057, respectively). In the YFS cohort, we were unable to replicate the initial discovery signal, but rs12207186 within the PARK2 locus and dietary lipid quality had a stronger interaction effect on serum LDL-cholesterol concentration (P < 9.44 × 10(-5)) than did rs9364628 in children aged 6 y. CONCLUSION This genotyping study involving 2 cohorts of healthy Finnish children indicates a possible interaction between PARK2 variants and dietary fat quality on serum LDL-cholesterol concentration. This trial was registered at clinicaltrials.gov as NCT00223600.
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Affiliation(s)
- Ari V Ahola-Olli
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Niina Pitkänen
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Johannes Kettunen
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Mervi K Oikonen
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Vera Mikkilä
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Terho Lehtimäki
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Mika Kähönen
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Katja Pahkala
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Harri Niinikoski
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Antti J Kangas
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Pasi Soininen
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Mika Ala-Korpela
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Jorma S Viikari
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Tapani Rönnemaa
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Olli Simell
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
| | - Olli T Raitakari
- From the Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland (AVA-O, NP, MKO, VM, KP, and TR); the Department of Food and Environmental Sciences (VM) and the Institute for Molecular Medicine Finland (FIMM) (JK), University of Helsinki, Helsinki, Finland; the Department of Clinical Chemistry, Fimlab Laboratories, Pirkanmaa Hospital District, School of Medicine, University of Tampere, Tampere, Finland (TL); the Department of Clinical Physiology, Tampere University Hospital, Tampere, Finland (MK); Paavo Nurmi Centre, Sports & Exercise Medicine Unit, Department of Physical Activity and Health, University of Turku, Turku, Finland (KP); the Department of Pediatrics, Turku University Hospital, Turku, Finland (HN and OS); Computational Medicine, Institute of Health Sciences, University of Oulu, Oulu, Finland (AJK, PS, and MA-K); NMR Metabolomics Laboratory, School of Pharmacy, University of Eastern Finland, Kuopio, Finland (PS and MA-K); Oulu University Hospital, Oulu, Finland (MA-K); Computational Medicine, School of Social and Community Medicine and the Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom (MA-K); the Department of Medicine, University of Turku and Division of Medicine, Turku University Hospital, Turku, Finland (JSV and TR); and the Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland (OTR)
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Wang W, Zhao Y, Jin Y. Gold-nanorod-based colorimetric and fluorescent approach for sensitive and specific assay of disease-related gene and mutation. ACS APPLIED MATERIALS & INTERFACES 2013; 5:11741-11746. [PMID: 24151993 DOI: 10.1021/am4034119] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Sensitive and specific detection of disease-related gene and single nucleotide polymorphism (SNP) is of great importance in cancer diagnosis. Here, a colorimetric and fluorescent approach is described for detection of the p53 gene and SNP in homogeneous solution by using gold nanorods (GNRs) as both colorimetric probe and fluorescence quencher. Hairpin oligonucleotide was utilized as DNA probe to ensure highly sequence-specific detection of target DNA. In the presence of target DNA, the formation of DNA duplex greatly changed the electrostatic interaction between GNR and DNAs, leading to an obvious change in fluorescence and colorimetric response. The detection limit of fluorescent and colorimetric assay is 0.26 pM and 0.3 nM, respectively. Both fluorescence and colorimetric strategies were able to effectively discriminate complementary DNA from single-base mismatched DNA, which is meaningful for cancer diagnosis. More important, target DNA can be detected as low as 10 nM by the naked eye. Furthermore, transmission electron microscopy and fluorescence anisotropy measurements demonstrated that the color change as well as fluorescence quenching is ascribed to the DNA hybridization-induced aggregation of GNRs. Therefore, the assay provided a fast, sensitive, cost-effective, and specific sensing platform for detecting disease-related gene and SNP.
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Affiliation(s)
- Wenhong Wang
- Key Laboratory of Applied Surface and Colloid Chemistry, Ministry of Education, Key Laboratory of Analytical Chemistry for Life Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University , Xi'an 710062, China
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23
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Küchler EC, Deeley K, Ho B, Linkowski S, Meyer C, Noel J, Kouzbari MZ, Bezamat M, Granjeiro JM, Antunes LS, Antunes LA, de Abreu FV, Costa MC, Tannure PN, Seymen F, Koruyucu M, Patir A, Mereb JC, Poletta FA, Castilla EE, Orioli IM, Marazita ML, Vieira AR. Genetic mapping of high caries experience on human chromosome 13. BMC MEDICAL GENETICS 2013; 14:116. [PMID: 24192446 PMCID: PMC3907033 DOI: 10.1186/1471-2350-14-116] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/25/2013] [Accepted: 10/31/2013] [Indexed: 02/04/2023]
Abstract
Background Our previous genome-wide linkage scan mapped five loci for caries experience. The purpose of this study was to fine map one of these loci, the locus 13q31.1, in order to identify genetic contributors to caries. Methods Seventy-two pedigrees from the Philippines were studied. Caries experience was recorded and DNA was extracted from blood samples obtained from all subjects. Sixty-one single nucleotide polymorphisms (SNPs) in 13q31.1 were genotyped. Association between caries experience and alleles was tested. We also studied 1,481 DNA samples obtained from saliva of subjects from the USA, 918 children from Brazil, and 275 children from Turkey, in order to follow up the results found in the Filipino families. We used the AliBaba2.1 software to determine if the nucleotide changes of the associated SNPs changed the prediction of the presence of transcription-binding site sequences and we also analyzed the gene expression of the genes selected based on binding predictions. Mutation analysis was also performed in 33 Filipino individuals of a segment of 13q31.1 that is highly conserved in mammals. Results Statistically significant association with high caries experience was found for 11 markers in 13q31.1 in the Filipino families. Haplotype analysis also confirmed these results. In the populations used for follow-up purposes, associations were found between high caries experience and a subset of these markers. Regarding the prediction of the transcription-binding site, the base change of the SNP rs17074565 was found to change the predicted-binding of genes that could be involved in the pathogenesis of caries. When the sequence has the allele C of rs17074565, the potential transcription factors binding the sequence are GR and GATA1. When the subject carries the G allele of rs17074565, the potential transcription factor predicted to bind to the sequence is GATA3. The expression of GR in whole saliva was higher in individuals with low caries experience when compared to individuals with high caries experience (p = 0.046). No mutations were found in the highly conserved sequence. Conclusions Genetic factors contributing to caries experience may exist in 13q31.1. The rs17074565 is located in an intergenic region and is predicted to disrupt the binding sites of two different transcription factors that might be involved with caries experience. GR expression in saliva may be a biomarker for caries risk and should be further explored.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Alexandre R Vieira
- Department of Oral Biology, University of Pittsburgh, 614 Salk Hall, Pittsburgh, PA, USA.
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Windelinckx A, De Mars G, Huygens W, Peeters MW, Vincent B, Wijmenga C, Lambrechts D, Aerssens J, Vlietinck R, Beunen G, Thomis MAI. Identification and prioritization of NUAK1 and PPP1CC as positional candidate loci for skeletal muscle strength phenotypes. Physiol Genomics 2011; 43:981-92. [PMID: 21750233 DOI: 10.1152/physiolgenomics.00200.2010] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Muscle strength is an important determinant in elite sports performance as well as in the activities of daily living. Muscle metabolism also plays a role in the genesis, and therefore prevention, of common pathological conditions and chronic diseases. Even though heritability estimates between 31 and 78% suggest a significant genetic component in muscle strength, only a limited number of genes influencing muscle strength have been identified. This study aimed to identify and prioritize positional candidate genes within a skeletal muscle strength quantitative trait locus on chromosome 12q22-23 for follow-up. A two-staged gene-centered fine-mapping approach using 122 single nucleotide polymorphisms (SNPs) in stage 1 identified a family-based association (n=500) between several tagSNPs located in the ATPase, Ca2+ transporting, cardiac muscle, slow twitch 2 (ATP2A2; rs3026468), the NUAK family, SNF1-like kinase, 1 (NUAK1; rs10861553 and rs3741886), and the protein phosphatase 1, catalytic subunit, gamma isoform (PPP1CC; rs1050587 and rs7901769) genes and knee torque production (P values up to 0.00092). In stage 2, family-based association tests on additional putatively functional SNPs (e.g., exonic SNPs, SNPs in transcription factor binding sites or in conserved regions) in an enlarged sample (n=536; 464 individuals overlap with stage 1) did not identify additional associations with muscle strength characteristics. Further in-depth analyses will be necessary to elucidate the exact role of ATP2A2, PPP1CC, and NUAK1 in muscle strength and to find out which functional polymorphisms are at the base of the interindividual strength differences.
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Affiliation(s)
- An Windelinckx
- Research Center for Exercise and Health, Department of Biomedical Kinesiology, Faculty of Kinesiology and Rehabilitation Sciences, Katholieke Universiteit Leuven, Leuven, Belgium
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Abstract
Many forms of cardiovascular disease (CVD) demonstrate heritability and thus a genetic contribution is likely. This is most evident when considering the "simple" Mendelian traits such as hypertrophic cardiomyopathy. However, family history also influences our assessment of patients with complex traits such as coronary artery disease, hypertension, and common forms of hypercholesterolemia, as observed in clinical practice. Recent research has led to advances in our understanding of the genetic basis of both the simple and complex forms of CVD. This review presents the current state of knowledge regarding major gene disorders, as well as more common, complex forms of CVD such as coronary artery disease. It discusses the fundamental approaches being used to identify the genetic basis of the various disease states, as well as the practical implications of the discoveries to clinicians. It also focuses on our need to assess the extent by which genetic analysis can alter our calculation of an individual's risk of disease, and our ability to successfully target treatment that will modify this process.
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Yang HC, Lin HC, Kang M, Chen CH, Lin CW, Li LH, Wu JY, Chen YT, Pan WH. SAQC: SNP array quality control. BMC Bioinformatics 2011; 12:100. [PMID: 21501472 PMCID: PMC3101186 DOI: 10.1186/1471-2105-12-100] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2010] [Accepted: 04/18/2011] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Genome-wide single-nucleotide polymorphism (SNP) arrays containing hundreds of thousands of SNPs from the human genome have proven useful for studying important human genome questions. Data quality of SNP arrays plays a key role in the accuracy and precision of downstream data analyses. However, good indices for assessing data quality of SNP arrays have not yet been developed. RESULTS We developed new quality indices to measure the quality of SNP arrays and/or DNA samples and investigated their statistical properties. The indices quantify a departure of estimated individual-level allele frequencies (AFs) from expected frequencies via standardized distances. The proposed quality indices followed lognormal distributions in several large genomic studies that we empirically evaluated. AF reference data and quality index reference data for different SNP array platforms were established based on samples from various reference populations. Furthermore, a confidence interval method based on the underlying empirical distributions of quality indices was developed to identify poor-quality SNP arrays and/or DNA samples. Analyses of authentic biological data and simulated data show that this new method is sensitive and specific for the detection of poor-quality SNP arrays and/or DNA samples. CONCLUSIONS This study introduces new quality indices, establishes references for AFs and quality indices, and develops a detection method for poor-quality SNP arrays and/or DNA samples. We have developed a new computer program that utilizes these methods called SNP Array Quality Control (SAQC). SAQC software is written in R and R-GUI and was developed as a user-friendly tool for the visualization and evaluation of data quality of genome-wide SNP arrays. The program is available online (http://www.stat.sinica.edu.tw/hsinchou/genetics/quality/SAQC.htm).
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Affiliation(s)
- Hsin-Chou Yang
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
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Nielsen DA, Ji F, Yuferov V, Ho A, He C, Ott J, Kreek MJ. Genome-wide association study identifies genes that may contribute to risk for developing heroin addiction. Psychiatr Genet 2010; 20:207-14. [PMID: 20520587 PMCID: PMC3832188 DOI: 10.1097/ypg.0b013e32833a2106] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
OBJECTIVES We have used genome-wide association studies to identify variants that are associated with vulnerability to develop heroin addiction. METHODS DNA from 325 methadone stabilized, former severe heroin addicts and 250 control individuals were pooled by ethnicity (Caucasian and African-American) and analyzed using the Affymetrix GeneChip Mapping 100 K Set. Genome-wide association tests were conducted. RESULTS The strongest association with vulnerability to develop heroin addiction, with experiment-wise significance (P=0.035), was found in Caucasians with the variant rs10494334, a variant in an unannotated region of the genome (1q23.3). In African Americans, the variant most significantly associated with the heroin addiction vulnerability was rs950302, found in the cytosolic dual specificity phosphatase 27 gene DUSP27 (point-wise P=0.0079). Furthermore, analysis of the top 500 variants with the most significant associations (point-wise P< or =0.0036) in Caucasians showed that three of these variants are clustered in the regulating synaptic membrane exocytosis protein 2 gene RIMS2. Of the top 500 variants in African-Americans (point-wise P< or =0.0238), three variants are in the cardiomyopathy associated 3 gene CMYA3. CONCLUSION This study identifies new genes and variants that may increase an individual's vulnerability to develop heroin addiction.
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Affiliation(s)
- David A Nielsen
- Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York 10065, USA
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Yang P, Qiu G, Wang S, Su Z, Chen J, Wang S, Kong F, Lu L, Ezaki T, Xu H. The mutations of Th1 cell-specificT-boxtranscription factor may be associated with a predominant Th2 phenotype in gastric cancers. Int J Immunogenet 2010; 37:111-5. [DOI: 10.1111/j.1744-313x.2010.00899.x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Lawson HA, Cheverud JM. Metabolic syndrome components in murine models. Endocr Metab Immune Disord Drug Targets 2010; 10:25-40. [PMID: 20088816 PMCID: PMC2854879 DOI: 10.2174/187153010790827948] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2009] [Accepted: 11/20/2009] [Indexed: 01/04/2023]
Abstract
Animal models have enriched understanding of the physiological basis of metabolic disorders and advanced identification of genetic risk factors underlying the metabolic syndrome (MetS). Murine models are especially appropriate for this type of research, and are an excellent resource not only for identifying candidate genomic regions, but also for illuminating the possible molecular mechanisms or pathways affected in individual components of MetS. In this review, we briefly discuss findings from mouse models of metabolic disorders, particularly in light of issues raised by the recent flood of human genome-wide association studies (GWAS) results. We describe how mouse models are revealing that genotype interacts with environment in important ways, indicating that the underlying genetics of MetS is highly context dependant. Further we show that epistasis, imprinting and maternal effects each contribute to the genetic architecture underlying variation in metabolic traits, and mouse models provide an opportunity to dissect these aspects of the genetic architecture that are difficult if not impossible to ascertain in humans. Finally we discuss how knowledge gained from mouse models can be used in conjunction with comparative genomic methods and bioinformatic resources to inform human MetS research.
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Affiliation(s)
- Heather A Lawson
- The Department of Anatomy and Neurobiology, Washington University School of Medicine in St Louis, MO, USA.
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Jowett JBM, Curran JE, Johnson MP, Carless MA, Göring HHH, Dyer TD, Cole SA, Comuzzie AG, MacCluer JW, Moses EK, Blangero J. Genetic variation at the FTO locus influences RBL2 gene expression. Diabetes 2010; 59:726-32. [PMID: 20009087 PMCID: PMC2828652 DOI: 10.2337/db09-1277] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE Genome-wide association studies that compare the statistical association between thousands of DNA variations and a human trait have detected 958 loci across 127 different diseases and traits. However, these statistical associations only provide evidence for genomic regions likely to harbor a causal gene(s) and do not directly identify such genes. We combined gene variation and expression data in a human cohort to identify causal genes. RESEARCH DESIGN AND METHODS Global gene transcription activity was obtained for each individual in a large human cohort (n = 1,240). These quantitative transcript data were tested for correlation with genotype data generated from the same individuals to identify gene expression patterns influenced by the variants. RESULTS Variant rs8050136 lies within intron 1 of the FTO gene on chromosome 16 and marks a locus strongly associated with type 2 diabetes and obesity and widely replicated across many populations. We report that genetic variation at this locus does not influence FTO gene expression levels (P = 0.38), but is strongly correlated with expression of RBL2 (P = 2.7 x 10(-5)), approximately 270,000 base pairs distant to FTO. CONCLUSIONS These data suggest that variants at FTO influence RBL2 gene expression at large genetic distances. This observation underscores the complexity of human transcriptional regulation and highlights the utility of large human cohorts in which both genetic variation and global gene expression data are available to identify disease genes. Expedient identification of genes mediating the effects of genome-wide association study-identified loci will enable mechanism-of-action studies and accelerate understanding of human disease processes under genetic influence.
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Affiliation(s)
- Jeremy B M Jowett
- Department of Genomics and Systems Biology, Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia.
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Ren L, Wang WP, Gao YZ, Yu XW, Xie HP. Typing SNP based on the near-infrared spectroscopy and artificial neural network. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2009; 73:106-111. [PMID: 19264539 DOI: 10.1016/j.saa.2009.01.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2008] [Revised: 01/08/2009] [Accepted: 01/27/2009] [Indexed: 05/27/2023]
Abstract
Based on the near-infrared spectra (NIRS) of the measured samples as the discriminant variables of their genotypes, the genotype discriminant model of SNP has been established by using back-propagation artificial neural network (BP-ANN). Taking a SNP (857G>A) of N-acetyltransferase 2 (NAT2) as an example, DNA fragments containing the SNP site were amplified by the PCR method based on a pair of primers to obtain the three-genotype (GG, AA, and GA) modeling samples. The NIRS-s of the amplified samples were directly measured in transmission by using quartz cell. Based on the sample spectra measured, the two BP-ANN-s were combined to obtain the stronger ability of the three-genotype classification. One of them was established to compress the measured NIRS variables by using the resilient back-propagation algorithm, and another network established by Levenberg-Marquardt algorithm according to the compressed NIRS-s was used as the discriminant model of the three-genotype classification. For the established model, the root mean square error for the training and the prediction sample sets were 0.0135 and 0.0132, respectively. Certainly, this model could rightly predict the three genotypes (i.e. the accuracy of prediction samples was up to 100%) and had a good robust for the prediction of unknown samples. Since the three genotypes of SNP could be directly determined by using the NIRS-s without any preprocessing for the analyzed samples after PCR, this method is simple, rapid and low-cost.
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Affiliation(s)
- Li Ren
- College of Pharmaceutical Sciences, Department of Forensic Medicine, Medical School, Soochow University, Suzhou 215123, PR China
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Poland GA, Ovsyannikova IG, Jacobson RM. Personalized vaccines: the emerging field of vaccinomics. Expert Opin Biol Ther 2009; 8:1659-67. [PMID: 18847302 DOI: 10.1517/14712598.8.11.1659] [Citation(s) in RCA: 118] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The next 'golden age' in vaccinology will be ushered in by the new science of vaccinomics. In turn, this will inform and allow the development of personalized vaccines, based on our increasing understanding of immune response phenotype: genotype information. Rapid advances in developing such data are already occurring for hepatitis B, influenza, measles, mumps, rubella, anthrax and smallpox vaccines. In addition, newly available data suggest that some vaccine-related adverse events may also be genetically determined and, therefore, predictable. This paper reviews the basis and logic of personalized vaccines, and describes recent advances in the field.
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Affiliation(s)
- Gregory A Poland
- Mayo Clinic College of Medicine, Mayo Vaccine Research Group, Program in TranslationalImmunovirology and Biodefense, Mayo Clinic, Rochester, Minnesota 55905, USA.
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Jiang R, Tang W, Wu X, Fu W. A random forest approach to the detection of epistatic interactions in case-control studies. BMC Bioinformatics 2009; 10 Suppl 1:S65. [PMID: 19208169 PMCID: PMC2648748 DOI: 10.1186/1471-2105-10-s1-s65] [Citation(s) in RCA: 157] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND The key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the "combination explosion" course prohibits their applications to genome-wide analysis. It is therefore indispensable to develop new methods that are able to reduce the search space for epistatic interactions from an astronomic number of all possible combinations of genetic variants to a manageable set of candidates. RESULTS We studied case-control data from the viewpoint of binary classification. More precisely, we treated single nucleotide polymorphism (SNP) markers as categorical features and adopted the random forest to discriminate cases against controls. On the basis of the gini importance given by the random forest, we designed a sliding window sequential forward feature selection (SWSFS) algorithm to select a small set of candidate SNPs that could minimize the classification error and then statistically tested up to three-way interactions of the candidates. We compared this approach with three existing methods on three simulated disease models and showed that our approach is comparable to, sometimes more powerful than, the other methods. We applied our approach to a genome-wide case-control dataset for Age-related Macular Degeneration (AMD) and successfully identified two SNPs that were reported to be associated with this disease. CONCLUSION Besides existing pure statistical approaches, we demonstrated the feasibility of incorporating machine learning methods into genome-wide case-control studies. The gini importance offers yet another measure for the associations between SNPs and complex diseases, thereby complementing existing statistical measures to facilitate the identification of epistatic interactions and the understanding of epistasis in the pathogenesis of complex diseases.
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Affiliation(s)
- Rui Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST/Department of Automation, Tsinghua University, Beijing 100084, PR China.
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Li X, Luo J, Xiao P, Shi X, Tang C, Lu Z. Genotyping of multiple single nucleotide polymorphisms with hyperbranched rolling circle amplification and microarray. Clin Chim Acta 2009; 399:40-4. [DOI: 10.1016/j.cca.2008.08.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2008] [Revised: 08/10/2008] [Accepted: 08/13/2008] [Indexed: 10/21/2022]
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Yin BC, Li H, Ye BC. Microarray-based estimation of SNP allele-frequency in pooled DNA using the Langmuir kinetic model. BMC Genomics 2008; 9:605. [PMID: 19087310 PMCID: PMC2640397 DOI: 10.1186/1471-2164-9-605] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2008] [Accepted: 12/16/2008] [Indexed: 11/20/2022] Open
Abstract
Background High throughput genotyping of single nucleotide polymorphisms (SNPs) for genome-wide association requires technologies for generating millions of genotypes with relative ease but also at a reasonable cost and with high accuracy. In this work, we have developed a theoretical approach to estimate allele frequency in pooled DNA samples, based on the physical principles of DNA immobilization and hybridization on solid surface using the Langmuir kinetic model and quantitative analysis of the allelic signals. Results This method can successfully distinguish allele frequencies differing by 0.01 in the actual pool of clinical samples, and detect alleles with a frequency as low as 2%. The accuracy of measuring known allele frequencies is very high, with the strength of correlation between measured and actual frequencies having an r2 = 0.9992. These results demonstrated that this method could allow the accurate estimation of absolute allele frequencies in pooled samples of DNA in a feasible and inexpensive way. Conclusion We conclude that this novel strategy for quantitative analysis of the ratio of SNP allelic sequences in DNA pools is an inexpensive and feasible alternative for detecting polymorphic differences in candidate gene association studies and genome-wide linkage disequilibrium scans.
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Affiliation(s)
- Bin-Cheng Yin
- Laboratory of Biosystems and Microanalysis, State Key Laboratory of Bioreactor Engineering, East China University of Science & Technology, Shanghai, PR China.
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Germolec D, Burns-Naas L, Gerberick G, Ladics G, Ryan C, Pruett S, Yucesoy B, Luebke R. Immunotoxicogenomics. Genomics 2008. [DOI: 10.3109/9781420067064-12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
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Allelic association: linkage disequilibrium structure and gene mapping. Mol Biotechnol 2008; 41:83-9. [PMID: 18841501 DOI: 10.1007/s12033-008-9110-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2008] [Accepted: 09/12/2008] [Indexed: 10/21/2022]
Abstract
The linkage disequilibrium (LD) structure of the human genome is now well understood and characterised for a number of human populations. The LD structure underpins the design and execution of candidate gene and genome-wide association mapping studies. Successful association mapping studies completed to date provide vital new insights into the genetic influences on common diseases, such as diabetes, some cancers and heart disease. The LD structure also presents new avenues of research into the genetic history of human populations, the effects of natural selection and the impact of recombination on the genomic landscape. This review introduces this exciting and complex field by encompassing this range of topics.
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FTO gene associates to metabolic syndrome in women with polycystic ovary syndrome. Biochem Biophys Res Commun 2008; 373:230-4. [DOI: 10.1016/j.bbrc.2008.06.039] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2008] [Accepted: 06/05/2008] [Indexed: 12/31/2022]
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Nielsen DA, Ji F, Yuferov V, Ho A, Chen A, Levran O, Ott J, Kreek MJ. Genotype patterns that contribute to increased risk for or protection from developing heroin addiction. Mol Psychiatry 2008; 13:417-28. [PMID: 18195715 PMCID: PMC3810149 DOI: 10.1038/sj.mp.4002147] [Citation(s) in RCA: 74] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2007] [Accepted: 12/06/2007] [Indexed: 11/09/2022]
Abstract
A genome-wide association study was conducted using microarray technology to identify genes that may be associated with the vulnerability to develop heroin addiction, using DNA from 104 individual former severe heroin addicts (meeting Federal criteria for methadone maintenance) and 101 individual control subjects, all Caucasian. Using separate analyses for autosomal and X chromosomal variants, we found that the strongest associations of allele frequency with heroin addiction were with the autosomal variants rs965972, located in the Unigene cluster Hs.147755 (experiment-wise q=0.053), and rs1986513 (q=0.187). The three variants exhibiting the strongest association with heroin addiction by genotype frequency were rs1714984, located in an intron of the gene for the transcription factor myocardin (P=0.000022), rs965972 (P=0.000080) and rs1867898 (P=0.000284). One genotype pattern (AG-TT-GG) was found to be significantly associated with developing heroin addiction (odds ratio (OR)=6.25) and explained 27% of the population attributable risk for heroin addiction in this cohort. Another genotype pattern (GG-CT-GG) of these variants was found to be significantly associated with protection from developing heroin addiction (OR=0.13), and lacking this genotype pattern explained 83% of the population attributable risk for developing heroin addiction. Evidence was found for involvement of five genes in heroin addiction, the genes coding for the mu opioid receptor, the metabotropic receptors mGluR6 and mGluR8, nuclear receptor NR4A2 and cryptochrome 1 (photolyase-like). This approach has identified several new genes potentially associated with heroin addiction and has confirmed the role of OPRM1 in this disease.
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Affiliation(s)
- D A Nielsen
- Laboratory of the Biology of Addictive Diseases, The Rockefeller University, New York, NY 10065, USA.
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Ribas G, Milne RL, Gonzalez-Neira A, Benítez J. Haplotype patterns in cancer-related genes with long-range linkage disequilibrium: no evidence of association with breast cancer or positive selection. Eur J Hum Genet 2007; 16:252-60. [DOI: 10.1038/sj.ejhg.5201953] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
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Linkage disequilibrium maps and location databases. Methods Mol Biol 2007. [PMID: 17984536 DOI: 10.1007/978-1-59745-389-9_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Effective application of association mapping for complex traits requires characterization of linkage disequilibrium (LD) patterns that reflect the dominant process of recombination and its duration in addition to the more subtle influences of mutation, selection, and genetic drift. Maps expressed in linkage disequilibrium units (LDUs) reflect the influences of these factors with the use of a modified version of Malecot's isolation-by-distance model. As a result, LDU maps are analogous to linkage maps in so far as their provision of an additive metric that is related to recombination and facilitates association-mapping studies. However, unlike linkage maps, LDUs also reflect the partly cumulative effects of multiple historical bottlenecks that account for substantial variations in LD patterns between populations. This chapter provides an overview of the data requirements and methodology used to construct LDU maps, their applications outside association mapping, and their integration into location databases.
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Wollstein A, Herrmann A, Wittig M, Nothnagel M, Franke A, Nürnberg P, Schreiber S, Krawczak M, Hampe J. Efficacy assessment of SNP sets for genome-wide disease association studies. Nucleic Acids Res 2007; 35:e113. [PMID: 17726055 PMCID: PMC2034459 DOI: 10.1093/nar/gkm621] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The power of a genome-wide disease association study depends critically upon the properties of the marker set used, particularly the number and physical spacing of markers, and the level of inter-marker association due to linkage disequilibrium. Extending our previously devised theoretical framework for the entropy-based selection of genetic markers, we have developed a local measure of the efficacy of a marker set, relative to including a maximally polymorphic single nucleotide polymorphism (SNP) at the map position of interest. Using this quantitative criterion, we evaluated five currently available SNP sets, namely Affymetrix 100K and 500K, and Illumina 100K, 300K and 550K in the CEU, YRI and JPT + CHB HapMap populations. At 50% relative efficacy, the commercial marker sets cover between 19 and 68% of the human genome, depending upon the population under study. An optimal technology-independent 500K marker set constructed from HapMap for Caucasians, in contrast, would achieve 73% coverage at the same relative efficacy.
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Affiliation(s)
- Andreas Wollstein
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Alexander Herrmann
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Michael Wittig
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Michael Nothnagel
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Andre Franke
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Peter Nürnberg
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Stefan Schreiber
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Michael Krawczak
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
| | - Jochen Hampe
- Cologne Center for Genomics, Cologne, Institute of Clinical Molecular Biology, Christian-Albrechts University, Ist Department of Medicine and Institute of Medical Informatics and Statistics, Christian-Albrechts University, University Hospital Schleswig-Holstein Campus Kiel, Kiel, Germany
- *To whom correspondence should be addressed. +49 431 597 1246+49 431 597 1842
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Zhang Y, Liu JS. Bayesian inference of epistatic interactions in case-control studies. Nat Genet 2007; 39:1167-73. [PMID: 17721534 DOI: 10.1038/ng2110] [Citation(s) in RCA: 366] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2007] [Accepted: 07/02/2007] [Indexed: 12/22/2022]
Abstract
Epistatic interactions among multiple genetic variants in the human genome may be important in determining individual susceptibility to common diseases. Although some existing computational methods for identifying genetic interactions have been effective for small-scale studies, we here propose a method, denoted 'bayesian epistasis association mapping' (BEAM), for genome-wide case-control studies. BEAM treats the disease-associated markers and their interactions via a bayesian partitioning model and computes, via Markov chain Monte Carlo, the posterior probability that each marker set is associated with the disease. Testing this on an age-related macular degeneration genome-wide association data set, we demonstrate that the method is significantly more powerful than existing approaches and that genome-wide case-control epistasis mapping with many thousands of markers is both computationally and statistically feasible.
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Affiliation(s)
- Yu Zhang
- Department of Statistics, the Pennsylvania State University, Thomas Building 422A, University Park, Pennsylvania 16802, USA
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Seno H, Satoh K, Tsuji S, Shiratsuchi T, Harada Y, Hamajima N, Sugano K, Kawano S, Chiba T. Novel interleukin-4 and interleukin-1 receptor antagonist gene variations associated with non-cardia gastric cancer in Japan: comprehensive analysis of 207 polymorphisms of 11 cytokine genes. J Gastroenterol Hepatol 2007; 22:729-37. [PMID: 17444864 DOI: 10.1111/j.1440-1746.2007.04934.x] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND AND AIM Helicobacter pylori (H. pylori)-induced chronic atrophic gastritis is a high-risk factor for gastric cancer. Immune responses to H. pylori are involved in gastric mucosal inflammation, and might affect clinical outcome, including the development of gastric cancer. The present study examines the significance of gene polymorphisms of various cytokines in the development of gastric cancer following H. pylori infection. METHODS One hundred Japanese non-cardia gastric cancer patients and 93 dyspeptic patients as controls were enrolled in the study (age range 50-75 years). All patients were positive for H. pylori. Genomic DNA was extracted from peripheral whole blood leukocytes, and we comprehensively analyzed 207 single nucleotide polymorphisms (SNP) in 11 cytokine genes; interleukin (IL)-1alpha, IL-1beta, IL-1 receptor antagonist (RN), IL-4, IL-4R, IL-8, IL-10, IL-12, TNF-alpha, TNF-beta, and IFN-gamma, using either invader assay (163 SNP), direct sequencing (22 SNP), or PCR-restriction fragment length polymorphism (22 SNP). RESULTS Among the 207 SNP examined, the IL-4 gene diplotypes (984 and 2983 AA/GA) had a significant negative association with gastric cancer development (odds ratio =0.3, 95% confidence interval =0.1-0.9). When we adopted the dyspeptic patients over 66 years of age as the controls, the IL-1RN gene diplotypes (-1102 and 6110 CG/GA) also had a significant negative association (odds ratio =0.2, 95% confidence interval =0.1-0.7). CONCLUSION A comprehensive analysis of 207 SNP of 11 cytokine genes revealed that variations in IL-4 and IL-1RN genes are negatively associated with the risk of developing gastric cancer following H. pylori infection. Distinct host cytokine responses in the gastric mucosa might have a role in H. pylori-induced carcinogenesis.
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Affiliation(s)
- Hiroshi Seno
- Department of Gastroenterology and Hepatology, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Rosenberg PS, Che A, Chen BE. Multiple hypothesis testing strategies for genetic case-control association studies. Stat Med 2007; 25:3134-49. [PMID: 16252274 DOI: 10.1002/sim.2407] [Citation(s) in RCA: 34] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The genetic case-control association study of unrelated subjects is a leading method to identify single nucleotide polymorphisms (SNPs) and SNP haplotypes that modulate the risk of complex diseases. Association studies often genotype several SNPs in a number of candidate genes; we propose a two-stage approach to address the inherent statistical multiple comparisons problem. In the first stage, each gene's association with disease is summarized by a single p-value that controls a familywise error rate. In the second stage, summary p-values are adjusted for multiplicity using a false discovery rate (FDR) controlling procedure. For the first stage, we consider marginal and joint tests of SNPs and haplotypes within genes, and we construct an omnibus test that combines SNP and haplotype analysis. Simulation studies show that when disease susceptibility is conferred by a SNP, and all common SNPs in a gene are genotyped, marginal analysis of SNPs using the Simes test has similar or higher power than marginal or joint haplotype analysis. Conversely, haplotype analysis can be more powerful when disease susceptibility is conferred by a haplotype. The omnibus test tracks the more powerful of the two approaches, which is generally unknown. Multiple testing balances the desire for statistical power against the implicit costs of false positive results, which up to now appear to be common in the literature.
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Affiliation(s)
- Philip S Rosenberg
- Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Rockville, MD 20852-7244, USA.
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Abstract
The basis for recent developments on the characterization of the linkage-disequilibrium structure of the genome and the application of association mapping to genes for common human diseases is described. Patterns of linkage disequilibrium are now understood, for a number of human populations, in unprecedented detail. This information not only provides a vital resource for the design and execution of powerful association-mapping studies, but opens new avenues of research into the genetic history of human populations and the effects of natural selection, mutation, and recombination on the genomic landscape.
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Santarpia L, Valenzise M, Di Pasquale G, Arrigo T, San Martino G, Cicciò MP, Trimarchi F, De Luca F, Benvenga S. TTF-2/FOXE1 gene polymorphisms in Sicilian patients with permanent primary congenital hypothyroidism. J Endocrinol Invest 2007; 30:13-9. [PMID: 17318017 DOI: 10.1007/bf03347390] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Thyroid transcription factor-2 (TTF-2/FOXE1) is a polyalanine domain protein that regulates thyroid embryogenesis, but very few patients with permanent primary congenital hypothyroidism (pCH) harbor germline mutations of this or other transcription factors that are involved in thyroid development that might explain the etiology of pCH. Variations within the polyalanine tract are found in a variety of genes and are often reported in association with malformation syndromes; pCH is frequently associated with thyroid malformations and extra-thyroidal malformations. Therefore, in this study we investigated whether alanine (Ala) length polymorphisms and non-polymorphic mutations of the TTF-2 gene in pCH patients might be involved in the pathogenesis of pCH. The entire coding region of the TTF-2 gene was analyzed in 57 Sicilian patients and 142 healthy controls. We found that the homozygous Ala14 polymorphism (Ala14/14) was less frequent in the pCH group than in the controls. In contrast, significantly more pCH patients than controls harbored the Ala16 polymorphism (Ala16/16 and Ala14/16). However, neither the Ala14/14 nor the Ala16 polymorphism was related to extra-thyroidal malformations. Two of the 57 patients carried Ala11/14 and Ala12/14, and one Ala14/14 patient also had the silent polymorphism 387 C/T (Leu129Leu). Other than known polymorphic variants we found no mutation in the TTF-2 gene. Therefore, this study demonstrates that mutations in the TTF-2 gene are rare in pCH patients and suggests that variations in the length of the Ala-tract could at least partially explain the etiology of pCH but not that of extra-thyroidal malformations.
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Affiliation(s)
- L Santarpia
- Sezione di Endocrinologia del Dipartimento Clinico Sperimentale di Medicina e Farmacologia, Università di Messina, Messina, Italy
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LDMAP: the construction of high-resolution linkage disequilibrium maps of the human genome. Methods Mol Biol 2007; 376:47-57. [PMID: 17984537 DOI: 10.1007/978-1-59745-389-9_4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The precise characterization of the linkage disequilibrium (LD) landscape from high-density single-nucleotide polymorphism (SNP) data underpins the association mapping of diseases and other studies. We describe the algorithm and implementation of a powerful approach for constructing LD genetic maps with meaningful map distances. The computational problems posed by the enormous number of SNPs typed in the HapMap data are addressed by developing segmental map construction with the potential for parallelization, which we are developing. There is remarkably little loss of information (1-2%) through this approach, but the computation times are dramatically reduced (more than fourfold for sequential map assembly). These developments enable the construction of very high-density genome-wide LD maps using data from more than 3 million SNPs in HapMap. We anticipate that a whole-genome LD map will be useful for disease gene mapping, genomic research, and population genetics.
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Abstract
For some time, investigators have appreciated that genetic association studies in cancer are complex because of the multi-stage process of cancer and the daunting challenge of analysing genetic variants in population and family studies. Because of recent technological advances and annotation of common genetic variation in the human genome, it is now possible for investigators to study genetic variation and cancer risk in many different settings. While these studies hold great promise for unravelling multiple genetic risk factors that contribute to the set of complex diseases called cancer, it is also imperative that study design and methods of interpretation be carefully considered. Replication of results in sufficiently large, well-powered studies is critical if genetic variation is to realise the promise of personalised medicine--namely, using genetic data to individualise medical decisions. In this regard, the plausibility of validated genetic variants can only be realised by the study of gene-gene and gene-environment interactions. The genetic association study in cancer has come a long way from the days of restriction fragment length polymorphisms, and now promises to scan an entire genome 'agnostically' in search of genetic markers for a disease or outcome. Moreover, the application and interpretation of these studies should be conducted cautiously.
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Affiliation(s)
- Sharon A Savage
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephen J Chanock
- Pediatric Oncology Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Core Genotyping Facility, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
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Kuntsi J, Neale BM, Chen W, Faraone SV, Asherson P. The IMAGE project: methodological issues for the molecular genetic analysis of ADHD. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2006; 2:27. [PMID: 16887023 PMCID: PMC1559631 DOI: 10.1186/1744-9081-2-27] [Citation(s) in RCA: 94] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2006] [Accepted: 08/03/2006] [Indexed: 01/25/2023]
Abstract
The genetic mechanisms involved in attention deficit hyperactivity disorder (ADHD) are being studied with considerable success by several centres worldwide. These studies confirm prior hypotheses about the role of genetic variation within genes involved in the regulation of dopamine, norepinephrine and serotonin neurotransmission in susceptibility to ADHD. Despite the importance of these findings, uncertainties remain due to the very small effects sizes that are observed. We discuss possible reasons for why the true strength of the associations may have been underestimated in research to date, considering the effects of linkage disequilibrium, allelic heterogeneity, population differences and gene by environment interactions. With the identification of genes associated with ADHD, the goal of ADHD genetics is now shifting from gene discovery towards gene functionality--the study of intermediate phenotypes ('endophenotypes'). We discuss methodological issues relating to quantitative genetic data from twin and family studies on candidate endophenotypes and how such data can inform attempts to link molecular genetic data to cognitive, affective and motivational processes in ADHD. The International Multi-centre ADHD Gene (IMAGE) project exemplifies current collaborative research efforts on the genetics of ADHD. This European multi-site project is well placed to take advantage of the resources that are emerging following the sequencing of the human genome and the development of international resources for whole genome association analysis. As a result of IMAGE and other molecular genetic investigations of ADHD, we envisage a rapid increase in the number of identified genetic variants and the promise of identifying novel gene systems that we are not currently investigating, opening further doors in the study of gene functionality.
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Affiliation(s)
- Jonna Kuntsi
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Benjamin M Neale
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Wai Chen
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Stephen V Faraone
- SUNY Upstate Medical University, 750 East Adams St., Syracuse, NY 13210, USA
| | - Philip Asherson
- MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, UK
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