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Heilmann S.,University of Bonn | Kiefer A.K.,23 and Me | Fricker N.,University of Bonn | Drichel D.,German Center for Neurodegenerative Diseases | And 24 more authors.
Journal of Investigative Dermatology | Year: 2013

The pathogenesis of androgenetic alopecia (AGA, male-pattern baldness) is driven by androgens, and genetic predisposition is the major prerequisite. Candidate gene and genome-wide association studies have reported that single-nucleotide polymorphisms (SNPs) at eight different genomic loci are associated with AGA development. However, a significant fraction of the overall heritable risk still awaits identification. Furthermore, the understanding of the pathophysiology of AGA is incomplete, and each newly associated locus may provide novel insights into contributing biological pathways. The aim of this study was to identify unknown AGA risk loci by replicating SNPs at the 12 genomic loci that showed suggestive association (5 × 10-8


Corral-Frias N.S.,Washington University in St. Louis | Pizzagalli D.A.,Harvard University | Carre J.M.,Nipissing University | Michalski L.J.,Washington University in St. Louis | And 13 more authors.
Genes, Brain and Behavior | Year: 2016

Identifying mechanisms through which individual differences in reward learning emerge offers an opportunity to understand both a fundamental form of adaptive responding as well as etiological pathways through which aberrant reward learning may contribute to maladaptive behaviors and psychopathology. One candidate mechanism through which individual differences in reward learning may emerge is variability in dopaminergic reinforcement signaling. A common functional polymorphism within the catechol-O-methyl transferase gene (COMT; rs4680, Val158Met) has been linked to reward learning, where homozygosity for the Met allele (linked to heightened prefrontal dopamine function and decreased dopamine synthesis in the midbrain) has been associated with relatively increased reward learning. Here, we used a probabilistic reward learning task to asses response bias, a behavioral form of reward learning, across three separate samples that were combined for analyses (age: 21.80 ± 3.95; n = 392; 268 female; European-American: n = 208). We replicate prior reports that COMT rs4680 Met allele homozygosity is associated with increased reward learning in European-American participants (β = 0.20, t = 2.75, P < 0.01; ΔR2 = 0.04). Moreover, a meta-analysis of 4 studies, including the current one, confirmed the association between COMT rs4680 genotype and reward learning (95% CI -0.11 to -0.03; z = 3.2; P < 0.01). These results suggest that variability in dopamine signaling associated with COMT rs4680 influences individual differences in reward which may potentially contribute to psychopathology characterized by reward dysfunction. © 2016 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.


Nalls M.A.,U.S. National Institute on Aging | McLean C.Y.,23 and Me | Rick J.,University of Pennsylvania | Eberly S.,University of Rochester | And 44 more authors.
The Lancet Neurology | Year: 2015

Background: Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. Methods: We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). Findings: In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003). Interpretation: Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. Funding: National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation. © 2015 Elsevier Ltd.


PubMed | Washington University in St. Louis, Nipissing University, Center for Addiction and Mental Health Toronto, U.S. National Institute on Drug Abuse and 5 more.
Type: Journal Article | Journal: Genes, brain, and behavior | Year: 2016

Identifying mechanisms through which individual differences in reward learning emerge offers an opportunity to understand both a fundamental form of adaptive responding as well as etiological pathways through which aberrant reward learning may contribute to maladaptive behaviors and psychopathology. One candidate mechanism through which individual differences in reward learning may emerge is variability in dopaminergic reinforcement signaling. A common functional polymorphism within the catechol-O-methyl transferase gene (COMT; rs4680, Val(158) Met) has been linked to reward learning, where homozygosity for the Met allele (linked to heightened prefrontal dopamine function and decreased dopamine synthesis in the midbrain) has been associated with relatively increased reward learning. Here, we used a probabilistic reward learning task to asses response bias, a behavioral form of reward learning, across three separate samples that were combined for analyses (age: 21.80 3.95; n = 392; 268 female; European-American: n = 208). We replicate prior reports that COMT rs4680 Met allele homozygosity is associated with increased reward learning in European-American participants ( = 0.20, t = 2.75, P < 0.01; R(2) = 0.04). Moreover, a meta-analysis of 4 studies, including the current one, confirmed the association between COMT rs4680 genotype and reward learning (95% CI -0.11 to -0.03; z = 3.2; P < 0.01). These results suggest that variability in dopamine signaling associated with COMT rs4680 influences individual differences in reward which may potentially contribute to psychopathology characterized by reward dysfunction.


News Article | November 17, 2016
Site: www.prnewswire.co.uk

Genotyping market is growing with a CAGR of 23% owing to growing prevalence of genetic diseases such as haemophilia, thalassemia, etc. across globe. Moreover, the increasing adoption of personalized medicine is also propelling growth in the global genotyping market. North America held the largest market share of around 30% in 2015 owing to presence of some key players such as Illumina, Thermo fisher, etc. who are investing a huge amount for the development of new technology in the genome development which is leading North America share in the global genotyping market. To have a brief overview of the report please click on the following link Biotech investments are expected to continue to gain traction in the life sciences sector. The biotech companies such as Agilent, Biogenic-Q are looking forward for various developments in drug discovery and development. Rising treatments for rheumatoid arthritis, Hepatitis C, and cancer figures are most prominently added in the list of the most sold genetic drugs. Biotech investments were around $325 billion in 2015 and are projected to grow to about $430 billion by 2019. In addition, biotech's share globally on the basis of prescription drug and over-the-counter pharma sales is likely to increase from 23% in 2015 to 26% in 2019. The growing DNA sequencing segment is having huge impact on the growth of the genotyping market. DNA sequencing finds its most of the applications in drug discovery and as the biotechnological investment is rising this can positively affect genotyping market revenue in the near future. Moreover, the key players such as ME, Agilent, Biogenic-Q, etc. are developing new products in the market to capture more market potential, July 2016, 23 and Me launched its new Genotyping Services for Research (GSR) platform, providing scientists with an end-to-end service. June 2016, Agilent Technologies Inc. introduced a wide range of systems, software and technologies designed to improve both the speed and accuracy of mass spectrometry. To contact us please click on the following link The demand for personalized medicine is increasing day by day. In 2015 oncology diseases amounted a large share of 38% for the demand of personalized medicines followed by psychiatry at 17%, infectious diseases 10%, cardiology 7% and neurology 6%. In 2015 1 out of 5 FDA approvals of personalized medicines were for targeted therapies. Personalized medicines are very effective around 62% effectiveness in having Depression followed by 60% in Asthma, 57% in Diabetes, Alzheimer is accounted at 30%.  The rising funding by government and private firms in the research in 2015, biotech investment accounted at around $325 billion and development of personalized medicine is propelling the growth in the global market. However, the increasing aging population is having a huge impact on the demand of personalized medicine that is impacting the growth of global genotyping market. According to WHO the world aged population by the end of 2050 is expected to reach 2 billion that is 900 million in 2015. Now around the world 125 million people are aged 80 years or more and it is expected that by the end of 2050 almost 125 million people would reach the age of 80 years or more in China alone and 434 million people across the globe. Occams Business Research & Consulting has been in the business (Market Research) for the past three years. OBRC, based in India, is formed by two women analysts, Shyamal Moghe and Sathya Durga, who started the company after years of working as analysts and project managers for companies such as Frost & Sullivan, Smart Analyst etc. and have an excellent track record for the best customer satisfaction.


News Article | November 13, 2016
Site: www.npr.org

DNA Is Not Destiny When It Comes To Heart Risk You can't choose your parents, so you can't help it if you're born with genes that increase your risk of heart disease. But a study finds that you can reduce that risk greatly with a healthful lifestyle. Scientists have been wondering whether that's the case. To find out, one international consortium looked at data from four large studies that had isolated genetic risk factors for heart disease. They identified genetic markers that seem to put people at nearly twice the risk for heart disease. The scientists then dug further into their data to look at behavior that helps the heart, as well as at the influence of obesity. Specifically, they looked at smoking habits, obesity, diet and exercise. People who were healthy — based on at least three of those criteria — were considered, for the purposes of the study, to be following a healthful lifestyle. The scientists were pleased to discover that the benefits of those good habits were strong, even for people who carried genetic traits that raised their risk. (Healthful habits actually benefited everyone, regardless of inherited risk.) People with unlucky genes, heart-wise, but good health habits were half as likely to develop coronary artery disease as those with unlucky genes and an unhealthful lifestyle, according to the study. The New England Journal of Medicine published the results online Sunday to coincide with a presentation of the findings at the American Heart Association's scientific sessions in New Orleans. "At least for heart attack it's pretty clear that DNA is not destiny," senior author Dr. Sekar Kathiresan, who heads the Center for Human Genetic Research at Massachusetts General Hospital, told Shots. "You have pretty good control over your own health." Kathiresan and colleagues in the United States and Sweden based their conclusion on four big studies, involving more than 55,000 people. Two of those studies have been following people for more than 20 years, including the Atherosclerosis Risk in Communities study in the United States, and a similar study in Sweden. People with unlucky genes were at about twice the risk of getting heart disease as a group, the scientists found. But those with healthful habits basically cut that risk in half. Participants in the study who had an increased genetic risk and poor health habits had a 10 percent chance of having a heart attack or similar event over the course of 10 years. And those with unlucky genes and good health habits had a 5 percent chance. That 5 percent risk was within the same ballpark of many people who had a comparatively good genetic profile, Kathiresan told Shots. The genetic test of heart risk that the researchers used "is not a test that a physician can order," he said. But materials published online along with the paper identify approximately 50 gene variants that, collectively, increase a person's risk of heart disease. And if you're determined to see how you rank in terms of genetic risk relative to the general population, a test from 23 and Me does scan these genes. Alternatively, Kathiresan said, you can assume you may have inherited a risk factor for heart disease if a parent or a sibling died young as a result of heart disease. One limitation of this study, the scientists note, is that most of the participants were white, so the results may not apply to every group. Researchers hope to soon expand their research to include a more racially diverse population.

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