Genome-wide association study identifies two susceptibility loci for osteosarcoma

Osteosarcoma is the most common primary bone malignancy of adolescents and young adults. To better understand the genetic etiology of osteosarcoma, we performed a multistage genome-wide association study consisting of 941 individuals with osteosarcoma (cases) and 3,291 cancer-free adult controls of European ancestry. Two loci achieved genome-wide significance: a locus in the GRM4 gene at 6p21.3 (encoding glutamate receptor metabotropic 4; rs1906953; P = 8.1 × 10⁻⁹) and a locus in the gene desert at 2p25.2 (rs7591996 and rs10208273; P = 1.0 × 10⁻⁸ and 2.9 × 10⁻⁷, respectively). These two loci warrant further exploration to uncover the biological mechanisms underlying susceptibility to osteosarcoma

The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape : A Large-Scale Genome-Wide Interaction Study

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age-and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to similar to 2.8M SNPs with BMI and WHRadjBMI in four strata (men 50y, women 50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR= 50y). No sex-dependent effects were identified for BMI. For WHRadjBMI, we identified 44 loci (27 previously established for main effects, 17 novel) with sex-specific effects, of which 28 showed larger effects in women than in men, five showed larger effects in men than in women, and 11 showed opposite effects between sexes. No age-dependent effects were identified for WHRadjBMI. This is the first genome-wide interaction meta-analysis to report convincing evidence of age-dependent genetic effects on BMI. In addition, we confirm the sex-specificity of genetic effects on WHRadjBMI. These results may providefurther insights into the biology that underlies weight change with age or the sexually dimorphism of body shape.Peer reviewe

WHY DO URALS PEOPLE DIE: ECONOMIC-POPULATION ASPECT

The analysis of dead-rate in Middle Urals, caused by suicides, alcohol and drugs intoxications in 1998, 2001 and 2003 is presented in the article. Quantitative estimation of social-economic hazard to society caused by premature people deaths is given on the basis of potential population foundations

A decision support system model for technology transfer

Technology transfer is the process by which technology originating at one institutional setting is adapted for use in another. A major impediment to the implementation of new technologies to assist with mangerial decision-making problems is a lack of communication between the technology and management communities. Development of a tool designed to bridge the technology transfer gap was the goal of this research. The result is a prototype software package which may be used on an interactive computer terminal by a manager for assistance in designing a decision support system (DSS). The four primary research tasks were: 1. Develop a conceptual model of the DSS design process. 2. Select and adapt, or create, appropriate software to mechanize the model. 3. Develop a knowledge base to describe the interactiveness of various organization variables and managerial decision-making needs. 4. Collect and analyze interview data and implement resultant production rules on the model. Tasks 1 and 2 were accomplished first to establish the feasibility of this effort. An interview instrument was developed for Task 3. And, corporate managers from several firms were interviewed to accomplish Task 4. Using this data, a prototype production rule model (called DECAIDS for DECISION AIDS) was constructed which supports managerial decision-making from the EMYCIN production rule system used at Stanford University. The purpose of this article is to introduce the need for a Decision Support System Model. A complete copy of this research can be obtained through University Microfilms International, 300 N. Zeeb Road, Ann Arbor, Michigan 48106, from the Naval Postgraduate School, Monterey, California 93940, or the Defense Technical Information Center, Cameron Station, Alexandria, Virginia 22314. The title is “An Interactive Decision Support System for Technology Transfer Pertaining to Organization and Management”, 1980. Copyright the Technology Transfer Society 1982

Population genetic differentiation of height and body mass index across Europe

Across-nation differences in the mean values for complex traits are common, but the reasons for these differences are unknown. Here we find that many independent loci contribute to population genetic differences in height and body mass index (BMI) in 9,416 individuals across 14 European countries. Using discovery data on over 250,000 individuals and unbiased effect size estimates from 17,500 sibling pairs, we estimate that 24% (95% credible interval (CI) = 9%, 41%) and 8% (95% CI = 4%, 16%) of the captured additive genetic variance for height and BMI, respectively, reflect population genetic differences. Population genetic divergence differed significantly from that in a null model (height, P < 3.94 × 10 -8; BMI, P < 5.95 × 10 -4), and we find an among-population genetic correlation for tall and slender individuals (r = -0.80, 95% CI = -0.95, -0.60), consistent with correlated selection for both phenotypes. Observed differences in height among populations reflected the predicted genetic means (r = 0.51; P < 0.001), but environmental differences across Europe masked genetic differentiation for BMI (P < 0.58)