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.

Biomarkers in the Diagnosis of ADHD – Promising Directions

Abstract: The etiology and pathogenesis of attention-deficit/hyperactivity disorder (ADHD) are unclear and a more valid diagnosis would certainly be welcomed. Starting from the literature, we built an hypothetical pyramid representing a putative set of biomarkers where, at the top, variants in DAT1 and DRD4 genes are the best candidates for their associations to neuropsychological tasks, activation in specific brain areas, methylphenidate response and gene expression levels. Interesting data come from the noradrenergic system (norepinephrine transporter, norepinephrine, 3-methoxy-4-hydroxyphenylglycol, monoamine oxidase, neuropeptide Y) for their altered peripheral levels, their association with neuropsychological tasks, symptomatology, drugs effect and brain function. Other minor putative genetic biomarkers could be dopamine beta hydroxylase and catechol-O-methyltransferase. In the bottom, we placed endophenotype biomarkers. A more deep integration of “omics” sciences along with more accurate clinical profiles and new high-throughput computational methods will allow us to identify a better list of biomarkers useful for diagnosis and therapies.

Impaired intellect and memory: a missing link between genetic risk and schizophrenia?

Abstract: Content The DSM-IV concept of schizophrenia offers diagnostic reliability but etiologic and pathologic heterogeneity, which probably contributes to the inconsistencies in genetic studies. One solution is to identify intermediate phenotypes, “narrower” constructs of liability, that hypothetically share genetic risk with the disorder. Although a variety of candidate intermediate phenotypes have emerged, few have explicitly quantified the extent of their genetic overlap with schizophrenia. Objective To quantify the net-shared genetic effects between schizophrenia and specific cognitive candidate intermediate phenotypes. Design Twin and family design. Setting Adult psychiatric research centers in the United States and the United Kingdom. Participants A total of 2056 participants: 657 patients with schizophrenia, 674 first-degree relatives (including co-twins), and 725 controls. Main Outcome Measures (1) Latent factors capturing the common variance between cognitive tasks, (2) separation of the latent factors into their genetic and environmental components, and (3) estimation of the net-shared genetic variance between the latent cognitive factors or intelligence and schizophrenia. Results Genetic factors contributed substantially to the total variance in cognition (immediate recall latent factor: 0.66; 95% confidence interval [CI], 0.62 to 0.85; delayed recall latent factor: 0.48; 0.42 to 0.55; and intelligence: 0.66; 0.62 to 0.71). The latent common factors for modality-specific immediate and delayed recall and intelligence showed similar levels of phenotypic covariance with schizophrenia (immediate recall: −0.35; delayed recall: −0.37; and intelligence: −0.38), with 72%, 86%, and 89%, respectively, due to shared genetic effects with schizophrenia. Environmental effects accounted for little phenotypic correlation between cognition and schizophrenia. Conclusions Using the largest international familial schizophrenia cohort to date, we showed that a substantial portion of the phenotypic correlation between schizophrenia and cognition is caused by shared genetic effects. However, because the phenotypic and genetic correlations are far from unity, the genetics of schizophrenia are clearly not merely the genetics of cognition.

The analysis of 51 genes in DSM-IV combined type attention deficit hyperactivity disorder: association signals in DRD4, DAT1 and 16 other genes

Abstract: Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder, starting in early childhood and persisting into adulthood in the majority of cases. Family and twin studies have demonstrated the importance of genetic factors and candidate gene association studies have identified several loci that exert small but significant effects on ADHD. To provide further clarification of reported associations and identify novel associated genes, we examined 1038 single-nucleotide polymorphisms (SNPs) spanning 51 candidate genes involved in the regulation of neurotransmitter pathways, particularly dopamine, norepinephrine and serotonin pathways, in addition to circadian rhythm genes. Analysis used within family tests of association in a sample of 776 DSM-IV ADHD combined type cases ascertained for the International Multi-centre ADHD Gene project. We found nominal significance with one or more SNPs in 18 genes, including the two most replicated findings in the literature: DRD4 and DAT1. Gene-wide tests, adjusted for the number of SNPs analysed in each gene, identified associations with TPH2, ARRB2, SYP, DAT1, ADRB2, HES1, MAOA and PNMT. Further studies will be needed to confirm or refute the observed associations and their generalisability to other samples.

Further Evidence of an Association Between Attention-Deficit/Hyperactivity Disorder and Cigarette Smoking: Findings from a High-Risk Sample of Siblings

Abstract: The authors investigated the relationship between attention-deficit/hyperactivity disorder (ADHD) and cigarette smoking in siblings of ADHD and non-ADHD probands. They conducted a 4-year follow-up of siblings from ADHD and control-group families. In the siblings of ADHD probands, ADHD was associated with higher rates and earlier onset of cigarette smoking. There was also a significant positive association between cigarette smoking and conduct disorder, major depression, and drug abuse in the siblings, even after adjusting for confounding variables. Moreover, smoking was found to be familial among ADHD families but not control-group families. Our findings indicate that ADHD is a risk factor for early initiation of cigarette smoking in the high-risk siblings of ADHD probands.

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.