Attribution bias in individual and team sport : a comparison using a within-groups design in indoor bowls

This study compared the success/failure attributions made by the same individuals competing alone and within teams (of four players) in indoor bowls competition. This is the first sport-attribution study to examine individual/ team differences using such a within-groups design. The benefit of this design is that it controls for: (a) possible personality differences between players in individual and in team sports, and (b) situational differences that exist between different sports. Solicited causal attributions were rated along locus, controllability, stability, and globality scales. In addition, multiple loci scales (referring to the: self, rest of team, whole team, opposition, and external circumstances) were used to assess the locus and controllability dimensions. Results were generally consistent with predictions. In the individual competition, winners as compared to losers, made more internal, controllable, stable, and global attributions. In team competition, winners as compared to losers, made more internal and controllable attributions (from the self, rest of team and whole team perspective), and more stable attributions. Also, moderate externality effects were shown in both individual and team competition. The results were interpreted as showing self-serving biases in the individual competition, and both self- and team-serving biases in the team competition. In team competition, however, team-serving interests clearly dominated.· Other variables examined in relation to player's causal attributions, included player's pre-game perceived importance of outcome and expectations of success, and post-game perceptions of success. Discussion focusses on the individual/ team differences in causal attributions and the related attributional variables

Genetic polymorphisms in CYP17 , CYP3A4 , CYP19A1 , SRD5A2 , IGF-1 , and IGFBP-3 and prostate cancer risk in African-American men: The Flint Men's Health Study

BACKGROUND Association studies have examined the significance of several candidate genes based on biological pathways relevant to prostate carcinogenesis, including both the androgen and insulin-like growth factor pathways. Clinical and epidemiologic evidence suggest that androgens, specifically testosterone and dihydrotestosterone (DHT) are important not only in normal prostate growth but in the pathogenesis of prostate cancer. Similarly, the insulin-like growth factor-1 (IGF-1) signaling pathway regulates both cellular proliferation and apoptosis. Therefore, genes involved in the biosynthesis, activation, metabolism and degradation of androgens and the stimulation of mitogenic and antiapoptotic activities of prostate epithelial cells represent important candidates for affecting the development and progression of prostate cancer. METHODS Using resources from the Flint Men's Health Study, a population-based case control study of African-American men aged 40–79, we evaluated the associations between selected single-nucleotide polymorphisms (SNPs) in the CYP17 , CYP3A4 , CYP19A1 , SDR5A2 , IGF1 , and IGFBP3 genes and prostate cancer diagnosis in 473 men (131 prostate cancer cases and 342 disease-free controls). RESULTS We found a significant association between prostate cancer and selected CYP17 SNP genotypes, with the heterozygous genotype conferring decreased risk. Suggestive evidence for association between IGF1 SNPs and prostate cancer were also found. No significant associations were observed between SNPs in the other genes and prostate cancer. CONCLUSIONS These findings suggest that variation in or around CYP17 and/or IGF1 may be associated with prostate cancer development in the African-American population. Additional studies are needed to determine whether these polymorphisms are indeed associated with prostate cancer risk in African Americans. Prostate 68: 296–305, 2008. © 2007 Wiley-Liss, Inc.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/57913/1/20696_ftp.pd

Testing Genetic Association With Rare Variants in Admixed Populations: Rare Variant Analysis in Admixed Populations

Recent studies suggest that rare variants play an important role in the etiology of many traits. Although a number of methods have been developed for genetic association analysis of rare variants, they all assume a relatively homogeneous population under study. Such an assumption may not be valid for samples collected from admixed populations such as African Americans and Hispanic Americans as there is a great extent of local variation in ancestry in these populations. To ensure valid and more powerful rare variant association tests performed in admixed populations, we have developed a local ancestry-based weighted dosage test, which is able to take into account local ancestry of rare alleles, uncertainties in rare variant imputation when imputed data are included, and the direction of effect that rare variants exert on phenotypic outcome. We used simulated sequence data to show that our proposed test has controlled type I error rates, whereas naïve application of existing rare variants tests and tests that adjust for global ancestry lead to inflated type I error rates. We showed that our test has higher power than tests without proper adjustment of ancestry. We also applied the proposed method to a candidate gene study on low-density lipoprotein cholesterol. Our results suggest that it is important to appropriately control for potential population stratification induced by local ancestry difference in the analysis of rare variants in admixed populations

Exploring pleiotropy using principal components

A standard multivariate principal components (PCs) method was utilized to identify clusters of variables that may be controlled by a common gene or genes (pleiotropy). Heritability estimates were obtained and linkage analyses performed on six individual traits (total cholesterol (Chol), high and low density lipoproteins, triglycerides (TG), body mass index (BMI), and systolic blood pressure (SBP)) and on each PC to compare our ability to identify major gene effects. Using the simulated data from Genetic Analysis Workshop 13 (Cohort 1 and 2 data for year 11), the quantitative traits were first adjusted for age, sex, and smoking (cigarettes per day). Adjusted variables were standardized and PCs calculated followed by orthogonal transformation (varimax rotation). Rotated PCs were then subjected to heritability and quantitative multipoint linkage analysis. The first three PCs explained 73% of the total phenotypic variance. Heritability estimates were above 0.60 for all three PCs. We performed linkage analyses on the PCs as well as the individual traits. The majority of pleiotropic and trait-specific genes were not identified. Standard PCs analysis methods did not facilitate the identification of pleiotropic genes affecting the six traits examined in the simulated data set. In addition, genes contributing 20% of the variance in traits with over 0.60 heritability estimates could not be identified in this simulated data set using traditional quantitative trait linkage analyses. Lack of identification of pleiotropic and trait-specific genes in some cases may reflect their low contribution to the traits/PCs examined or more importantly, characteristics of the sample group analyzed, and not simply a failure of the PC approach itself

Does Functional Gain and Pain Relief After TKR and THR Differ by Patient Obese Status?

Introduction: Obesity is an important predictor of functional status and pain after total knee (TKR) and total hip (THR) replacement. However, variations in pre-post TKR and THR changes in function and pain by obesity status remain to be examined. Material & Methods: Pre- and 6 month post surgery data were collected on 2,964 primary TKR and 2,040 primary THR patients between 5/2011 and 3/2013. Data included demographics, comorbidities, operative joint pain severity based on the Knee Injury or Hip Disability and Osteoarthritis Outcome Score (KOOS/HOOS), WOMAC pain (higher is better), physical function (SF-36 PCS, higher is better), mental health (SF-36 MCS), and musculoskeletal burden of illness. Pre-post changes in PCS and pain were analyzed using descriptive statistics. Results: TKR patients were average 67 years, 61% women, 93% whites, 13% under or normal weight, 33% overweight, 29% obese, 15% severely obese, 9% morbidly obese. Greater level of obesity was associated with lower PCS at baseline and 6 month, lower pain scores at baseline but larger improvement post-op. Pre to-6 month PCS did not differ by obesity status. At 6 months morbidly obese patients had slightly lower/worse pain score. THR patients were average 65 years, 62% women, 95% whites, 27% under/normal weight, 38% overweight, 23% obese, 9% severely obese, 4% morbidly obese. Greater level of obesity was associated with lower PCS at baseline and 6 month, poorer baseline pain score but larger improvement post-op. Mean changes in pre-to-6 month PCS did not differ by obesity status. Conclusion: At 6 months after TKR, severely obese patients (BMI\u3e35) reported improvements in both pain and function equal to or greater than patients with BMI35 had lower mean functional gain than those with BM

Gene-centric meta-analyses of 108,912 individuals confirm known body mass index loci and reveal three novel signals

Recent genetic association studies have made progress in uncovering components of the genetic architecture of body mass index (BMI). We used the ITMAT-Broad-CARe (IBC) array comprising up to 49,320 single nucleotide polymorphisms (SNPs) across ~2,100 metabolic and cardiovascular-related loci to genotype up to 108,912 individuals of European ancestry (EA), African Americans, Hispanics, and East Asians, from 46 studies, to provide additional insight into SNPs underpinning BMI. We used a five-phase study design: Phase I focused on meta-analysis of EA studies providing individual level genotype data; Phase II performed a replication of cohorts providing summary level EA data; Phase III meta-analyzed results from the first two phases; associated SNPs from Phase III were used for replication in Phase IV; finally in Phase V, a multi-ethnic meta-analysis of all samples from four ethnicities was performed. At an array-wide significance (P<2.40E-06), we identify novel BMI associations in loci TOMM40-APOE-APOC1 (rs2075650, P=2.95E-10), SREBF2 (a sterol regulatory element binding transcription factor gene, rs5996074, P=9.43E-07) and NTRK2 (a BDNF receptor, rs1211166, P=1.04E-06) in the Phase IV meta-analysis. Of ten loci with previous evidence for BMI association represented on IBC array, eight were replicated, with the remaining two showing nominal significance. Conditional analyses revealed two independent BMI associated signals in BDNF and MC4R regions. Of the 11 array-wide significant SNPs, three are associated with gene expression levels in both primary B-cells and monocytes; with rs4788099 in SH2B1 notably being associated with the expression of multiple genes in cis. These multi-ethnic meta-analyses expand our knowledge of BMI genetics

Genetic Epidemiology of Body Mass Index and Body Mass Change From Adolescence to Young Adulthood

The complex interplay between genes and environment affecting body mass gain over lifecycle periods of risk is not well understood. We use longitudinal sibling cohort data to examine the role of shared household environment, additive genetic, and shared genetic effects on Body Mass Index (BMI) and BMI change. In the National Longitudinal Study of Adolescent Health, siblings and twin pairs sharing households for ≥10 years as adolescents (N=5524; mean=16.5±1.7 years) were followed into young adulthood (N = 4368; mean=22.4±1.8 years). Using a variance component approach, we quantified genetic and household effects on BMI in siblings and non-siblings sharing household environments over time. Adjusting for race, age, sex, and age by sex interaction, we detected a heritability of 0.43±0.05 for BMI change. Significant household effects were noted during the young adulthood time period only (0.11±0.06). We find evidence for shared genetic effects between BMI and BMI change during adolescence [Genetic Correlation (ρG)=0.61±0.03] and young adulthood (ρG=0.23±0.06). Our findings support a complex etiology of BMI and BMI change