New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

One science-driven approach for the regulatory implementation of alternative methods: A multi-sector perspective

EU regulations call for the use of alternative methods to animal testing. During the last decade, an increasing number of alternative approaches have been formally adopted. In parallel, new 3Rs-relevant technologies and mechanistic approaches have increasingly contributed to hazard identification and risk assessment evolution. In this changing landscape, an EPAA meeting reviewed the challenges that different industry sectors face in the implementation of alternative methods following a science-driven approach. Although clear progress was acknowledged in animal testing reduction and refinement thanks to an integration of scientifically robust approaches, the following challenges were identified: i) further characterization of toxicity pathways; ii) development of assays covering current scientific gaps, iii) better characterization of links between in vitro readouts and outcome in the target species; iv) better definition of alternative method applicability domains, and v) appropriate implementation of the available approaches. For areas having regulatory adopted alternative methods (e.g., vaccine batch testing), harmonised acceptance across geographical regions was considered critical for broader application. Overall, the main constraints to the application of non-animal alternatives are the still existing gaps in scientific knowledge and technological limitations. The science-driven identification of most appropriate methods is key for furthering a multi-sectorial decrease in animal testing.JRC.F.3-Chemicals Safety and Alternative Method

Log₂ transformation (L2T) versus variance-stabilizing transformation (VST).

<p>The panels show the association results for the random phenotype (A–C) and for body mass index (BMI) (D–F) on each mRNA probe adjusted for sex, age, <i>RNA amplification batch</i>, <i>RNA integrity number</i> (<i>RIN</i>) and the sample <i>storage time</i> based on L2T expression values (x-axis) and on VST values (y-axis) in the SHIP-TREND cohort. The upper panels (A, D) show the betas, the middle panels (B, E) show the standard errors (SEs) and the lower panels (C, F) show the negative log₁₀ association p-values. The corresponding squared Pearson product-moment correlation coefficient between the plotted values is given in the upper right corner of each plot. Each spot represents a probe and is colored according to its mean L2T expression value from all samples. The color code is given in the legend located in the lower right corner of each plot. Although betas and SEs differ between both transformations, the association p-values are highly correlated.</p

<i>Eigen-R²</i> results for SHIP-TREND, KORA F4 and GHS.

*<p><i>Storage time</i>: <i>Time between blood donation and RNA isolation</i> (SHIP-TREND and KORA F4) or <i>time between RNA isolation and RNA amplification</i> (GHS).</p><p>The first six lines of the Table represent technical parameters. A dash indicates that the parameter was not available in the cohort.</p

Unexplained variance after adjustment for principle components (PCs).

<p>The panels show the percentage of adjusted unexplained variance (y-axis) of the regression on the log₂ transformed (L2T) gene expression levels and body mass index (BMI) (A) or the random phenotype (B) over the first 100 PCs (x-axis). With both phenotypes the unexplained variance decreases continuously with the addition of further PCs to the regression model. Results are given separately for the SHIP-TREND, KORA F4 and GHS cohorts.</p

Cohort characteristics.

*<p><i>Storage time</i>: <i>Time between blood donation and RNA isolation</i> (SHIP-TREND and KORA F4) <i>or time between RNA isolation and RNA amplification</i> (GHS).</p><p>A dash indicates that the variable was not available in the cohort.</p

Mean standard errors (SEs) for SHIP-TREND, KORA F4 and GHS after different covariate adjustments for the random phenotype and body mass index (BMI).

<p>50 PCs: the first 50 principal components (PCs) of the principle component analysis (PCA) over the gene expression levels; BMI: body mass index in [kg/m²]; cell types: percentage of lymphocytes, neutrophils, monocytes, eosinophils and basophils; detected genes: <i>number of detected genes</i> (detection p-value<0.01); Mean SE: mean standard error of phenotypes' beta from all probes of the corresponding association analysis; non-technical: all non-technical parameters having an <i>Eigen-R²</i> value>0.3% in SHIP-TREND; PC1: the first PC of the PCA; random phenotype: the random phenotype ∼N (0,1); technical: <i>RNA amplification batch</i>, <i>RNA integrity number (RIN)</i>, <i>storage time</i>.</p><p>A dash indicates that the parameter was not available in the cohort.</p

Effects of SNPs within probes on signal intensities.

<p>The effects on measured log₂ transformed (L2T) gene expression levels per mismatch allele of SNPs located within probes (y-axis) are plotted against the mean L2T expression level of the samples for each probe (x-axis). Each spot represents a SNP-probe combination; associations with significant p-values after Bonferroni correction (p<2.3×10⁻⁵) are colored in red and p-values below 0.05 are colored in orange. To increase legibility the y-axis was limited from −3 to 3 excluding 176 non-significant results out of 1237 successful association results (minimum and maximum effect sizes were −174.1 and 188.7, respectively). Surprisingly, in almost 45% of the associations a positive effect per mismatch allele on expression signal intensity was observed.</p

Workflow – from blood sampling to measured mRNA intensities.

<p>From left to right: Whole blood was collected and stored in PAXgene tubes until isolation of RNA from whole blood cells in both SHIP-TREND and KORA F4. In GHS, monocytes were separated from whole blood and RNA was isolated from monocytes within 24 hours after blood sampling, subsequently storing the isolated RNA until amplification. The sample <i>storage time</i> refers to the duration the whole blood (SHIP-TREND and KORA F4) or isolated RNA (GHS) was stored before further processing, shown as mean ± standard deviation in days. The samples were processed in 96 well plates both after isolation and amplification of the RNA. The corresponding plate layouts were called <i>RNA isolation batch</i> and <i>RNA amplification batch</i>, respectively. Finally, the RNA was hybridized and the arrays were scanned, quality controlled and analyzed.</p