About this Research Topic
In the advent of genome explorations based on single nucleotide polymorphisms (SNPs) and next generation sequencing (NGS) technologies during the last decade, new knowledge on the role of genetic variation has been the key element in the advancement of pulmonary medicine.
Information obtained from the human genome importantly contributes to understanding the etiopathogenesis of complex pulmonary disease with immune (inflammatory, autoimmune) features, which range from frequent disorders such as COPD or asthma bronchiale to less frequent disease such as sarcoidosis or idiopathic pulmonary fibrosis. While there have been numerous reports on disease susceptibility or protective variants, only recently genetic data have been related to distinct phases of the disease course or to specific phenotypes and endotypes.
Therefore, at present there is an urgent need for translation of these finding to clinical practice. Application of genetic data for disease stratification, prognostication and last but not least for assessment of treatment response is desirable. In this context, providing evidence whether and how environmental interactions or epigenetic factors could be utilized for these purposes also deserves investigation.
The aim of this research topic is to provide state of the art knowledge on translational genetics of complex, immune-mediated respiratory disease with and to delineate areas for further research in this field. Contributions from investigators in respiratory medicine and related disciplines, not limited to immunology, genetics, bioinformatics, medical databases and big data analysis would be welcome in any manuscript format. Given the topic, we stimulate multi-disciplinary and multi-center contributions. The ultimate goal would be to present, discuss and share the results, methods and approaches and in this way to help moving forward the process of translating genetic findings into clinical management of complex pulmonary diseases.
Keywords: pulmonary translational medicine, genetic variation, prediction, disease course, personalized medicine