A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits.

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

Optogenetic approaches in biotechnology and biomaterials.

Advances in genetic engineering, combined with the development of optical technologies, have allowed optogenetics to broaden its area of possible applications in recent years. However, the application of optogenetic tools in industry, including biotechnology and the production of biomaterials, is still limited, because each practical task requires the engineering of a specific optogenetic system. In this review, we discuss recent advances in the use of optogenetic tools in the production of biofuels and valuable chemicals, the synthesis of biomedical and polymer materials, and plant agrobiology. We also offer a comprehensive analysis of the properties and industrial applicability of light-controlled and other smart biomaterials. These data allow us to outline the prospects for the future use of optogenetics in bioindustry.