Development of a specific pipeline of bioinformatics

Abstract

The better understanding of the genetic contribution for HF and the correlation between the HF genotypes and phenotypes will allow the prior identification of patients with the highest risk of CVD development, allowing a more efficient clinical and therapeutic follow-up, besides contributing to the development of more efficient and specific pharmacotherapies. Usually, the diagnosis of FH is performed exclusively by clinical parameter and/or by searching for genetic variants present in the LDLR, APOB and PCSK9 genes. Even with the advent of so many technologies for the detection of new mutations, when clinical diagnostic groups screen for mutations in LDLR in patients with FH, there is always a patient where no mutation was found. In this way the association with other gene candidates has been investigated, but they are very rare. An example is the recessive mutations in CYP7A1 found in some families and not in others and another are the variants of the SREBP-2 and SCAP gene that have been found in patients with HF associated with a milder phenotype. The new high-throughput sequencing technologies (NGS) sequenced the DNA on platforms capable of generating information about millions of base pairs in a single run, being a powerful alternative for structural and functional genomic studies. Depending on the purpose, different NGS methodologies can be used, such as total genome analysis, total exome analysis or exomic analysis directed to genes of interest. These technologies have allowed the discovery of new genetic alterations, besides the detection of several regions in a single step, which facilitates the characterization of the diseases associated with alterations in genes, as in HF that can be caused by mutation in the LDLR, APOB and PCSK9 genes or even be polygenic. Studies based on targeted sequencing methodologies for the LDLR, APOB, LDLRAP1 and PCSK9 genes has indicated mutations already related to HF, in addition to unknown pathogenicity mutations. Moreover, an efficacy of this technology is also seen when it is used for the detection of diseases without clinical evidence. Thus, NGS has been able to identify other genes related to HF, such as those of membrane transporters (NPC1L1 and ABCG5/8), the gene involved with the incorporation of dietary cholesterol (MTP), lipid neosynthesis (HMGCR, TRIB1), those related to the release of apoB-100 into the circulation (ANGPTL3, sara2, SORT1) and those related to the uptake of LDL (LDLRAP1) and other hepatic lipoproteins (APOE). The identification of these new genes is since NGS allows the analysis of a large number of genomic data in a short period, but one of the challenges is to prove the pathogenicity of the new mutations identified. However, in most cases, the effects of new mutations are based on results of in silico prediction tools, which are essential for NGS data analysis. Thus, through an extensive data file and the need for rapid interpretation for the advancement of studies, the development of an automated workflow represents an essential tool.