Stephen V. Faraone

Bio: Stephen V. Faraone is an academic researcher from State University of New York Upstate Medical University. The author has contributed to research in topics: Attention deficit hyperactivity disorder & Bipolar disorder. The author has an hindex of 188, co-authored 1427 publications receiving 140298 citations. Previous affiliations of Stephen V. Faraone include University of Bergen & National Institute for Health Research.

The concept of target features in schizophrenia research.

Abstract: Target features are clinical or neurobiological characteristics that are expressions of the underlying predisposition to an illness. They comprise a wide range of phenomena, from the classic signs and symptoms of psychopathology to sophisticated measures of brain structure and function. For schizophrenia, many target features have been identified. These include eye tracking dysfunction, attentional impairment, allusive thinking, neurological signs, thought disorder, characteristic auditory evoked potentials, neuropsychological impairment, structural brain abnormalities and functional brain abnormalities. In their most pathological forms, these features are present among many schizophrenic patients, yet it is their presence among their non-psychotic relatives that shows them to be target features. We discuss the theoretical background for target features, present examples and describe how the discovery of target features has implications for schizophrenia research.

An analysis of patient adherence to treatment during a 1-year, open-label study of OROS methylphenidate in children with ADHD.

Abstract: Objective: Treatment adherence is an important aspect of ADHD symptom management, but there are many factors that may influence adherence. Method: This analysis assessed adherence to OROS methylphenidate during a 1-year, open-label study in children. Adherence was defined as the number of days medication was taken divided by the number of days in the study and determined to be high if ≥75%. Possible clinical and demographic factors associated with adherence, including use of planned medication breaks, were assessed. Results: Mean adherence was 86.4%. It was 91.6% for the subgroup of patients who reported not taking planned medication breaks (n = 252) and 77.7% for the subgroup taking planned medication breaks (n = 155). Overall, 75% of patients showed high adherence. Older age, low starting dose, minority ethnic status, and fewer ADHD symptoms were associated with low adherence. Conclusion: Various factors were found to be associated with low adherence, and the results of this analysis provide guidance to...

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Using Multivariate Statistics

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Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.