Inconsistent analytic strategies reduce robustness in fear extinction via skin conductance response

Robustness of fear conditioning and extinction paradigms has become increasingly important for many researchers interested in improving the study of anxiety and trauma disorders. We recently illustrated the wide variability in data analysis techniques in this paradigm, which we argued may result in lack of robustness. In the current study, we resampled data from six of our own fear acquisition and extinction datasets, with skin conductance as the outcome. In the resampled and original datasets, we found that effect sizes that were calculated using discrepant statistical strategies, sourced from a non-exhaustive search of high-impact articles, were often poorly correlated. The main contributors to poor correlations were selection of trials from different stages of each experimental phase and use of averaged compared to trial-by-trial analysis. These findings reinforce the importance of focusing on robustness in psychophysiological measurement of fear acquisition and extinction in the laboratory and may guide prospective researchers in which decisions may most impact the robustness of their results

Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis

We conducted DNA methylation association analyses using Illumina 450K data from whole blood for an Australian amyotrophic lateral sclerosis (ALS) case–control cohort (782 cases and 613 controls). Analyses used mixed linear models as implemented in the OSCA software. We found a significantly higher proportion of neutrophils in cases compared to controls which replicated in an independent cohort from the Netherlands (1159 cases and 637 controls). The OSCA MOMENT linear mixed model has been shown in simulations to best account for confounders. When combined in a methylation profile score, the 25 most-associated probes identified by MOMENT significantly classified case–control status in the Netherlands sample (area under the curve, AUC = 0.65, CI95% = [0.62–0.68], p = 8.3 × 10−22). The maximum AUC achieved was 0.69 (CI95% = [0.66–0.71], p = 4.3 × 10−34) when cell-type proportion was included in the predictor

The effects of sex and method of blood pressure measurement on genetic associations with blood pressure in the PAMELA study

Background Phenotypic accuracy and specificity are essential for a successful genetic association study. Blood pressure (BP) measurements show heterogeneity depending on the method and time of measurement, sexual dimorphism and measurement errors, making genetic dissection difficult. Methods and results We studied 1550 adults aged 25-74 years, not on any antihypertensive treatment, resident in Monza, Italy (PAMELA study) all of whom had home, clinic and ambulatory BPs measured. We analysed 3705 single nucleotide polymorphisms (SNPs) (1324 typed and 2381 imputed) across 168 genes for association with these traits. No SNP achieved an experiment wide significance level of P less than 3 x 10(-4) for any of the phenotypes studied. We selected 28 top candidate SNPs for further analysis of phenotypic heterogeneity and sexual dimorphism using a gene-centric strategy calculating empirical P values by permutations within each gene by including genic SNPs with an r(2) less than 0.5. The association signals were not consistent across all the BP phenotypes, whether compared by genes or by physiological pathways. The top SNPs in WNK1, ADRA1A, ADRA1B, DRD1, NOS1 and PON3 showed significant sex interaction for BP and when analysed separately by sex showed evidence of dimorphism with opposite direction of effect for the same allele in the two sexes. Conclusion In the largest study of its kind, we show that sex and BP measurement methods have a significant impact on association signals. These findings might explain previous inconsistencies in studies on cardiovascular candidate genes and should have major implications for the design and interpretation of association studies. J Hypertens 28:465-477 (c) 2010 Wolters Kluwer Health vertical bar Lippincott Williams & Wilkin

Optimizing Rubisco and its regulation for greater resource use efficiency

Rubisco catalyses the carboxylation of ribulose-1,5-bisphosphate (RuBP), enabling net CO2 assimilation in photosynthesis. The properties and regulation of Rubisco are not optimal for biomass production in current and projected future environments. Rubisco is relatively inefficient, and large amounts of the enzyme are needed to support photosynthesis, requiring large investments in nitrogen. The competing oxygenation of RuBP by Rubisco decreases photosynthetic efficiency. Additionally, Rubisco is inhibited by some sugar phosphates and depends upon interaction with Rubisco activase (Rca) to be reactivated. Rca activity is modulated by the chloroplast redox status and ADP/ATP ratios, thereby mediating Rubisco activation and photosynthetic induction in response to irradiance. The extreme thermal sensitivity of Rca compromises net CO2 assimilation at moderately high temperatures. Given its central role in carbon assimilation, the improvement of Rubisco function and regulation is tightly linked with irradiance, nitrogen and water use efficiencies. Although past attempts have had limited success, novel technologies and an expanding knowledge base make the challenge of improving Rubisco activity in crops an achievable goal. Strategies to optimize Rubisco and its regulation are addressed in relation to their potential to improve crop resource use efficiency and climate resilience of photosynthesis

Polygenic risk score analysis for amyotrophic lateral sclerosis leveraging cognitive performance, educational attainment and schizophrenia

Amyotrophic Lateral Sclerosis (ALS) is recognised to be a complex neurodegenerative disease involving both genetic and non-genetic risk factors. The underlying causes and risk factors for the majority of cases remain unknown; however, ever-larger genetic data studies and methodologies promise an enhanced understanding. Recent analyses using published summary statistics from the largest ALS genome-wide association study (GWAS) (20,806 ALS cases and 59,804 healthy controls) identified that schizophrenia (SCZ), cognitive performance (CP) and educational attainment (EA) related traits were genetically correlated with ALS. To provide additional evidence for these correlations, we built single and multi-trait genetic predictors using GWAS summary statistics for ALS and these traits, (SCZ, CP, EA) in an independent Australian cohort (846 ALS cases and 665 healthy controls). We compared methods for generating the risk predictors and found that the combination of traits improved the prediction (Nagelkerke-R2) of the case–control logistic regression. The combination of ALS, SCZ, CP, and EA, using the SBayesR predictor method gave the highest prediction (Nagelkerke-R2) of 0.027 (P value = 4.6 × 10−8), with the odds-ratio for estimated disease risk between the highest and lowest deciles of individuals being 3.15 (95% CI 1.96–5.05). These results support the genetic correlation between ALS, SCZ, CP and EA providing a better understanding of the complexity of ALS