Genetic analyses of maternal and teacher ratings on attention problems in 7-year-old Dutch twins

The goal of the present study is to examine genetic and environmental influences on maternal and teacher ratings of Attention Problems (AP) in 7-year-old children. Teachers completed the Teacher Report Form (N=2259 pairs), and mothers the Child Behavior Checklist (N=2057 pairs). Higher correlations were found in twins rated by the same teacher than in twins rated by different teachers. This can be explained by rater bias or by a greater environmental sharing in twins, who are in the same classroom. We further found that 41% of the variation in maternal and teacher ratings is explained by a common factor. The heritability of this common factor is 78%. The heritabilities of the rater specific factors of mothers and teachers are 76% and 39%, respectively. Because Attention Problems that are persistent over situations may indicate more serious behavior problems than context dependent Attention Problems, we believe that gene finding strategies should focus on this common phenotype

Genetic and environmental influences on the stability of withdrawn behavior in children: A longitudinal, multi-informant twin study.

We examined the contribution of genetic and environmental influences on the stability of withdrawn behavior (WB) in childhood using a longitudinal multiple rater twin design. Maternal and paternal ratings on the withdrawn subscale of the Child Behavior Checklist (CBCL) were obtained from 14,889 families when the twins were 3, 7, 10 and 12 years old. A longitudinal psychometric model was fitted to the data and the fit of transmission and common factor models were evaluated for each variance component. WB showed considerable stability throughout childhood, with correlation coefficients ranging from about .30 for the 9-year time interval to .65 for shorter time intervals. Individual differences in WB as observed by the mother and the father were found to be largely influenced by genetic effects at all four time points, in both boys (50–66%) and girls (38–64%). Shared environmental influences explained a small to modest proportion (0–24%) of the variance at all ages and were slightly more pronounced in girls. Non-shared environmental influences were of moderate importance to the variance and slightly increased with age, from 22–28% at age 3 to 35–41% at age 12 years. The stability of WB was largely explained by genetic effects, accounting for 74% of stability in boys and 65% in girls. Shared environmental effects explained 7% (boys) and 17% (girls) of the behavioral stability. Most shared environmental effects were common to both raters, suggesting little influence of rater bias in the assessment of WB. The shared environmental effects common to both raters were best described by a common factor model, indicating that these effects are stable and persistent throughout childhood. Non-shared environmental effects accounted for the remaining covariance over time

Inverse magnetic catalysis in dense holographic matter

We study the chiral phase transition in a magnetic field at finite temperature and chemical potential within the Sakai-Sugimoto model, a holographic top-down approach to (large-N_c) QCD. We consider the limit of a small separation of the flavor D8-branes, which corresponds to a dual field theory comparable to a Nambu-Jona Lasinio (NJL) model. Mapping out the surface of the chiral phase transition in the parameter space of magnetic field strength, quark chemical potential, and temperature, we find that for small temperatures the addition of a magnetic field decreases the critical chemical potential for chiral symmetry restoration - in contrast to the case of vanishing chemical potential where, in accordance with the familiar phenomenon of magnetic catalysis, the magnetic field favors the chirally broken phase. This "inverse magnetic catalysis" (IMC) appears to be associated with a previously found magnetic phase transition within the chirally symmetric phase that shows an intriguing similarity to a transition into the lowest Landau level. We estimate IMC to persist up to 10^{19} G at low temperatures.Comment: 42 pages, 11 figures, v3: extended discussion; new appendix D; references added; version to appear in JHE

Chiral perturbation theory in a magnetic background - finite-temperature effects

We consider chiral perturbation theory for SU(2) at finite temperature $T$ in a constant magnetic background $B$. We compute the thermal mass of the pions and the pion decay constant to leading order in chiral perturbation theory in the presence of the magnetic field. The magnetic field gives rise to a splitting between $M_{\pi^0}$ and $M_{\pi^{\pm}}$ as well as between $F_{\pi^0}$ and $F_{\pi^{\pm}}$. We also calculate the free energy and the quark condensate to next-to-leading order in chiral perturbation theory. Both the pion decay constants and the quark condensate are decreasing slower as a function of temperature as compared to the case with vanishing magnetic field. The latter result suggests that the critical temperature $T_c$ for the chiral transition is larger in the presence of a constant magnetic field. The increase of $T_c$ as a function of $B$ is in agreement with most model calculations but in disagreement with recent lattice calculations.Comment: 24 pages and 9 fig

Power calculations using exact data simulation: A useful tool for genetic study designs.

Statistical power calculations constitute an essential first step in the planning of scientific studies. If sufficient summary statistics are available, power calculations are in principle straightforward and computationally light. In designs, which comprise distinct groups (e.g., MZ & DZ twins), sufficient statistics can be calculated within each group, and analyzed in a multi-group model. However, when the number of possible groups is prohibitively large (say, in the hundreds), power calculations on the basis of the summary statistics become impractical. In that case, researchers may resort to Monte Carlo based power studies, which involve the simulation of hundreds or thousands of replicate samples for each specified set of population parameters. Here we present exact data simulation as a third method of power calculation. Exact data simulation involves a transformation of raw data so that the data fit the hypothesized model exactly. As in power calculation with summary statistics, exact data simulation is computationally light, while the number of groups in the analysis has little bearing on the practicality of the method. The method is applied to three genetic designs for illustrative purposes

Meta-analysis of four new genome scans for lipid parameters and analysis of positional candidates in positive linkage regions

Lipid levels in plasma strongly influence the risk for coronary heart disease. To localise and subsequently identify genes affecting lipid levels, we performed four genome-wide linkage scans followed by combined linkage/association analysis. Genome-scans were performed in 701 dizygotic twin pairs from four samples with data on plasma levels of HDL- and LDL-cholesterol and their major protein constituents, apolipoprotein AI (ApoAI) and Apolipoprotein B (ApoB). To maximise power, the genome scans were analysed simultaneously using a well-established meta-analysis method that was newly applied to linkage analysis. Overall LOD scores were estimated using the means of the sample-specific quantitative trait locus (QTL) effects inversely weighted by the standard errors obtained using an inverse regression method. Possible heterogeneity was accounted for with a random effects model. Suggestive linkage for HDL-C was observed on 8p23.1 and 12q21.2 and for ApoAI on 1q21.3. For LDL-C and ApoB, linkage regions frequently coincided (2p24.1, 2q32.1, 19p13.2 and 19q13.31). Six of the putative QTLs replicated previous findings. After fine mapping, three maximum LOD scores mapped within 1cM of major candidate genes, namely APOB (LOD =2.1), LDLR (LOD =1.9) and APOE (LOD =1.7). APOB haplotypes explained 27% of the QTL effect observed for LDL-C on 2p24.1 and reduced the LOD-score by 0.82. Accounting for the effect of the LDLR and APOE haplotypes did not change the LOD score close to the LDLR gene but abolished the linkage signal at the APOE gene. In conclusion, application of a new meta-analysis approach maximised the power to detect QTLs for lipid levels and improved the precision of their location estimate. © 2005 Nature Publishing Group. All rights reserved

Genetic Structure, Nestmate Recognition and Behaviour of Two Cryptic Species of the Invasive Big-Headed Ant Pheidole megacephala

info:eu-repo/semantics/publishe

Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

BACKGROUND: Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. RESULTS: Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. CONCLUSION: This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity