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.

Neuropsychological intra-individual variability explains unique genetic variance of ADHD and shows suggestive linkage to chromosomes 12, 13, and 17.

Abstract: Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neuropsychiatric disorder that is usually accompanied by neuropsychological impairments. The use of heritable, psychometrically robust traits that show association with the disorder of interest can increase the power of gene-finding studies. Due to the robust association of intra-individual variability with ADHD on a phenotypic and genetic level, intra-individual variability is a prime candidate for such an attempt. We aimed to combine intra-individual variability measures across tasks into one more heritable measure, to examine the relatedness to other cognitive factors, and to explore the genetic underpinnings through quantitative trait linkage analysis. Intra-individual variability measures from seven tasks were available for 238 ADHD families (350 ADHD-affected and 195 non-affected children) and 147 control families (271 children). Intra-individual variability measures from seven different tasks shared common variance and could be used to construct an aggregated measure. This aggregated measure was largely independent from other cognitive factors related to ADHD and showed suggestive linkage to chromosomes 12q24.3 (LOD = 2.93), 13q22.2 (LOD = 2.36), and 17p13.3 (LOD = 2.00). A common intra-individual variability construct can be extracted from very diverse neuropsychological tasks; this construct taps into unique genetic aspects of ADHD and may relate to loci conferring risk for ADHD (12q24.3 and 17p13.3) and possibly autism (12q24.3). Given that joining of data across sites boosts the power for genetic analyses, our findings are promising in showing that intra-individual variability measures are viable candidates for across site analyses where different tasks have been used.

Unipolar relatives in bipolar pedigrees: A search for elusive indicators of underlying bipolarity

Abstract: In an effort to identify features indicative of underlying bipolarity within the unipolar relatives of bipolar probands, we compared unipolar relatives of bipolars with unipolar relatives of controls. Using data from the Yale-NIMH Collaborative Study of Depression, we compared a number of demographic and clinical features individually, and then developed a logistic regression model for the differences found. Unipolar relatives of bipolars were generally similar to relatives of controls, but they were older and more likely to suffer from more severe, even psychotic, depression, and somewhat less likely to report a brief transition into their illness. A multiple logistic regression model for observed differences was highly statistically significant, but had limited ability to discriminate effectively between the two groups. These findings suggest that more stringent diagnostic criteria might be beneficial if unipolar relatives are counted as affected in linkage studies of bipolar disorder. The ability of this strategy to improve the “clinical phenotype” is limited, however, and other approaches may be needed to identify features of underlying bipolarity and thus to define “caseness” for unipolar relatives in linkage analyses of bipolar disorder. © 1996 Wiley-Liss, Inc.

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.