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      Evaluation of DNA Variants Associated with Androgenetic Alopecia and Their Potential to Predict Male Pattern Baldness

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          Abstract

          Androgenetic alopecia, known in men as male pattern baldness (MPB), is a very conspicuous condition that is particularly frequent among European men and thus contributes markedly to variation in physical appearance traits amongst Europeans. Recent studies have revealed multiple genes and polymorphisms to be associated with susceptibility to MPB. In this study, 50 candidate SNPs for androgenetic alopecia were analyzed in order to verify their potential to predict MPB. Significant associations were confirmed for 29 SNPs from chromosomes X, 1, 5, 7, 18 and 20. A simple 5-SNP prediction model and an extended 20-SNP model were developed based on a discovery panel of 305 males from various European populations fitting one of two distinct phenotype categories. The first category consisted of men below 50 years of age with significant baldness and the second; men aged 50 years or older lacking baldness. The simple model comprised the five best predictors: rs5919324 near AR, rs1998076 in the 20p11 region, rs929626 in EBF1, rs12565727 in TARDBP and rs756853 in HDAC9. The extended prediction model added 15 SNPs from five genomic regions that improved overall prevalence-adjusted predictive accuracy measured by area under the receiver characteristic operating curve (AUC). Both models were evaluated for predictive accuracy using a test set of 300 males reflecting the general European population. Applying a 65% probability threshold, high prediction sensitivity of 87.1% but low specificity of 42.4% was obtained in men aged <50 years. In men aged ≥50, prediction sensitivity was slightly lower at 67.7% while specificity reached 90%. Overall, the AUC=0.761 calculated for men at or above 50 years of age indicates these SNPs offer considerable potential for the application of genetic tests to predict MPB patterns, adding a highly informative predictive system to the emerging field of forensic analysis of externally visible characteristics.

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          Aging of blood can be tracked by DNA methylation changes at just three CpG sites

          Background Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age. Results We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs - located in the genes ITGA2B, ASPA and PDE4C - were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita - two diseases associated with progressive bone marrow failure and severe telomere attrition - are predicted to be prematurely aged. Conclusions Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.
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            Epigenetic Predictor of Age

            From the moment of conception, we begin to age. A decay of cellular structures, gene regulation, and DNA sequence ages cells and organisms. DNA methylation patterns change with increasing age and contribute to age related disease. Here we identify 88 sites in or near 80 genes for which the degree of cytosine methylation is significantly correlated with age in saliva of 34 male identical twin pairs between 21 and 55 years of age. Furthermore, we validated sites in the promoters of three genes and replicated our results in a general population sample of 31 males and 29 females between 18 and 70 years of age. The methylation of three sites—in the promoters of the EDARADD, TOM1L1, and NPTX2 genes—is linear with age over a range of five decades. Using just two cytosines from these loci, we built a regression model that explained 73% of the variance in age, and is able to predict the age of an individual with an average accuracy of 5.2 years. In forensic science, such a model could estimate the age of a person, based on a biological sample alone. Furthermore, a measurement of relevant sites in the genome could be a tool in routine medical screening to predict the risk of age-related diseases and to tailor interventions based on the epigenetic bio-age instead of the chronological age.
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              Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions.

              Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene-gene interaction or epistasis) and environmental factors (gene-environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2-15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                22 May 2015
                2015
                : 10
                : 5
                : e0127852
                Affiliations
                [1 ]Institute of Forensic Research, Section of Forensic Genetics, Krakow, Poland
                [2 ]Department of Genetics and Evolution, Jagiellonian University, Krakow, Poland
                [3 ]Faculty of Life Sciences, King’s College, London, United Kingdom
                [4 ]Section of Forensic Genetics, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
                [5 ]Department of Human Biological Traces, Netherlands Forensic Institute, The Hague, The Netherlands
                [6 ]Forensic Genetics Unit, Institute of Forensic Medicine, Faculty of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
                [7 ]Genomic Medicine Group, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Institute of Health Carlos III, Madrid, Spain
                [8 ]Institute of Legal Medicine, Medical University of Innsbruck, Innsbruck, Austria
                [9 ]Department of Analytical Biochemistry, Jagiellonian University Medical College, Krakow, Poland
                [10 ]Department of Dermatology, Medical College of Jagiellonian University, Krakow, Poland
                [11 ]Institute of Legal Medicine, Medical Faculty, University of Cologne, Cologne, Germany
                [12 ]Forensic Science Program, The Pennsylvania State University, University Park, Pennsylvania, United States of America
                National Cancer Institute, National Institutes of Health, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: WB CP WP MM EP. Performed the experiments: EP MM CB DB AM-L MS SA JDA MvdB ME JS NW. Analyzed the data: EP MM WB CB DB. Contributed reagents/materials/analysis tools: AC NM TS DS-C PS. Wrote the paper: WB MM EP CP.

                ¶ Membership of the EUROFORGEN-NoE Consortium is listed in the Acknowledgments.

                Article
                PONE-D-15-03618
                10.1371/journal.pone.0127852
                4441445
                26001114
                de6651c9-a318-4e1f-a369-983da9459a70

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication

                History
                : 25 January 2015
                : 20 April 2015
                Page count
                Figures: 3, Tables: 3, Pages: 18
                Funding
                The work leading to these results was financially supported from a grant from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 285487 (EUROFORGEN-NoE), www.euroforgen.eu.
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                All relevant data are within the paper and its Supporting Information files.

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