Genome-wide meta-analysis uncovers novel loci influencing circulating leptin levels.

Leptin is an adipocyte-secreted hormone, the circulating levels of which correlate closely with overall adiposity. Although rare mutations in the leptin (LEP) gene are well known to cause leptin deficiency and severe obesity, no common loci regulating circulating leptin levels have been uncovered. Therefore, we performed a genome-wide association study (GWAS) of circulating leptin levels from 32,161 individuals and followed up loci reaching P<10(-6) in 19,979 additional individuals. We identify five loci robustly associated (P<5 × 10(-8)) with leptin levels in/near LEP, SLC32A1, GCKR, CCNL1 and FTO. Although the association of the FTO obesity locus with leptin levels is abolished by adjustment for BMI, associations of the four other loci are independent of adiposity. The GCKR locus was found associated with multiple metabolic traits in previous GWAS and the CCNL1 locus with birth weight. Knockdown experiments in mouse adipose tissue explants show convincing evidence for adipogenin, a regulator of adipocyte differentiation, as the novel causal gene in the SLC32A1 locus influencing leptin levels. Our findings provide novel insights into the regulation of leptin production by adipose tissue and open new avenues for examining the influence of variation in leptin levels on adiposity and metabolic health

Improvement of adhesion between polyethylene and regenerated cellulose fibers by surface fibrillation

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An Adaptive and Memory Efficient Algorithm for Genotype Imputation

Genome wide association studies have proven to be a highly successful method for identification of genetic loci for complex phenotypes in both humans and model organisms. These large scale studies rely oil the collection of hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome. Standard high-throughput genotyping technologies capture only a fraction of the total genetic variation. Recent efforts have shown that it is possible to "impute" with high accuracy the genotypes of SNPs that are not collected ill the study provided that they are present in a reference data set which contains both SNPs collected in the Study as well as other SNPs. We here introduce a novel HMM based technique to solve the imputation problem that addresses several shortcomings of existing methods. First, our method is adaptive which lets it estimate population genetic parameters from the data and be applied to model organisms that have very different evolutionary histories. Compared to traditional methods. Our method is tip to tell times more accurate on model organisms such as mouse. Second, our algorithm scales in memory usage in the number of collected markers as opposed to the number of known SNPs. This issue is very relevant due to the size of the reference data sets currently being generated. We compare our method over mouse and human data sets to existing methods and show that each has either comparable or better performance and much lower memory usage. The method is available for download at http://genetics.cs.ucla.edu/eminim.N

Photoneutron yields from tungsten in the energy range of the giant dipole resonance

Photoneutron production on the nuclei of high-Z components of medical accelerator heads can lead to a significant secondary dose during a course of bremsstrahlung radiotherapy, However, a quantitative evaluation of secondary neutron dose requires improved data on the photoreaction yields. These have been measured as a function of photon energy, neutron energy and neutron angle for <sup>nat</sup>W, using tagged photons at the MAX-Lab photonuclear facility in Sweden. This work presents neutron yields for <sup>nat</sup>W(γ, n) and compares these with the predictions of the Monte Carlo code MCNP-GN, developed specifically to simulate photoneutron production at medical accelerators