A Bioinformatics Crash Course for Interpreting Genomics Data

Abstract

Reductions in genotyping costs and improvements in computational power have made conducting genome-wide association studies (GWAS) standard practice for many complex diseases. GWAS is the assessment of genetic variants across the genome of many individuals to determine which, if any, genetic variants are associated with a specific trait. As with any analysis, there are evolving best practices that should be followed to ensure scientific rigor and reliability in the conclusions. This article presents a brief summary for many of the key bioinformatics considerations when either planning or evaluating GWAS. This review is meant to serve as a guide to those without deep expertise in bioinformatics and GWAS and give them tools to critically evaluate this popular approach to investigating complex diseases. In addition, a checklist is provided that can be used by investigators to evaluate whether a GWAS has appropriately accounted for the many potential sources of bias and generally followed current best practices.

The potential mechanisms of neuroblastoma in children based on bioinformatics big data

“…The levels of retinyl ester in cells is affected by the retinol substance inside and outside the cell and the physiological state of the cell. Recent studies have shown that the amount of retinoyl ester is decreased in bladder cancer, prostate cancer, kidney cancer, squamous cell carcinoma, and other tumor cells, and the degree of reduction is positively correlated with the degree of malignancy of tumor cells, indicating that the synthesis of retinoyl ester is closely related to the occurrence of tumors ( 10 ). Retinol dehydrogenase reductase in neuroblastoma cells can be induced by retinoic acid, which can increase the content of retinoyl ester in NB cells.…”