A suitable sequence of bioinformatics tools for NGS diagnosis of familial hypercholesterolemia

Abstract

Background and Aims : Familial hypercholesterolemia (FH) is the most common monogenic congenital metabolic disease with an estimated frequency of 1: 200 - 500 in the population. Aberrations of the LDL receptor gene (LDLR) or mutation in his ligand apolipoprotein B100 (FDB) are the most common cause of FH. To date, over 2000 different mutations have been described in the LDLR. More rarely the pathology could be detected in other genes (APOB, LDLRAP , PCSK9, etc.). Early diagnosis using molecular genetic methods enables appropriate treatment and belongs to the basic pillars of routine practise. Considering various types of possible mutations, next - generation sequencing (NGS) method using hybridization-based enriched libraries can provide a potent solution for required multilevel genetic analysis. Each level of data analysis requires specific bioinformatic tool although the sensitivity to detect certain types of variants may partially overlap.Methods: We present three bioinformatic tools (FreeBayes, Manta and CNVkit) as an example of suitable unique combination of optimal NGS data analysis pipeline - specifically for the custom Hyperlipoproteinemia 1 (HLP) SureSelect library panel (Agilent Technologies) covering 53 HLP genes and 55 SNPs.Results: The spectrum of causal mutations detected in our FH and HLP patients cohort were as follows: 81% of single-nucleotide variants, 5% of minor and 14% of gross (two and more exons) intragenic rearrangements. Mutations in LDLR covered 74,4% of causal variants, 11,6% of mutations were in APOB and 14% in another genes from the HLP panel.Conclusions: The presented combination of bioinformatic tools represents effective diagnostic algorithm with increase diagnostic yield.*This work was supported by the Cooperatio Program, research area "Metabolic Diseases 207037" Background and Aims : Familial hypercholesterolemia (FH) is the most common monogenic congenital metabolic disease with an estimated frequency of 1: 200 - 500 in the population. Aberrations of the LDL receptor gene (LDLR) or mutation in his ligand apolipoprotein B100 (FDB) are the most common cause of FH. To date, over 2000 different mutations have been described in the LDLR. More rarely the pathology could be detected in other genes (APOB, LDLRAP , PCSK9, etc.). Early diagnosis using molecular genetic methods enables appropriate treatment and belongs to the basic pillars of routine practise. Considering various types of possible mutations, next - generation sequencing (NGS) method using hybridization-based enriched libraries can provide a potent solution for required multilevel genetic analysis. Each level of data analysis requires specific bioinformatic tool although the sensitivity to detect certain types of variants may partially overlap. Methods: We present three bioinformatic tools (FreeBayes, Manta and CNVkit) as an example of suitable unique combination of optimal NGS data analysis pipeline - specifically for the custom Hyperlipoproteinemia 1 (HLP) SureSelect library panel (Agilent Technologies) covering 53 HLP genes and 55 SNPs. Results: The spectrum of causal mutations detected in our FH and HLP patients cohort were as follows: 81% of single-nucleotide variants, 5% of minor and 14% of gross (two and more exons) intragenic rearrangements. Mutations in LDLR covered 74,4% of causal variants, 11,6% of mutations were in APOB and 14% in another genes from the HLP panel. Conclusions: The presented combination of bioinformatic tools represents effective diagnostic algorithm with increase diagnostic yield. *This work was supported by the Cooperatio Program, research area "Metabolic Diseases 207037"