Genome-wide association studies (GWAS) suggests that a complex disease is typically affected by many genetic variants with small or moderate effects. Identification of these risk variants remains to be a very challenging problem. Traditional approach
es focusing on a single GWAS dataset alone ignore relevant information that could potentially improve our ability to detect these variants. We proposed a novel statistical approach, named GPA, to performing integrative analysis of multiple GWAS datasets and functional annotations. Hypothesis testing procedures were developed to facilitate statistical inference of pleiotropy and enrichment of functional annotation. We applied our approach to perform systematic analysis of five psychiatric disorders. Not only did GPA identify many weak signals missed by the original single phenotype analysis, but also revealed interesting genetic architectures of these disorders. We also applied GPA to the bladder cancer GWAS data with the ENCODE DNase-seq data from 125 cell lines and showed that GPA can detect cell lines that are more biologically relevant to the phenotype of interest.
By measuring the dynamic and traditional magnetization relaxations we investigate the vortex dynamics of the newly discovered superconductor SmFeAsO_0.9F_0.1 with Tc = 55K. It is found that the relaxation rate is rather large reflecting a small chara
cteristic pinning energy. Moreover it shows a weak temperature dependence in wide temperature region, which resembles the behavior of the cuprate superconductors. Combining with the resistive data under different magnetic fields, a vortex phase diagram is obtained. Our results strongly suggest that the model of collective vortex pinning applies to this new superconductor very well.
