Whole Genome Investigation of Genomic Aberrations of Rare Genetic Conditions Using Joint Analysis

Abstract

Genomic analysis is a method designed to assign meaning to the nucleotides of DNA and translate them into readable information. The decreasing cost and increasing efficiency of genomic sequencing have enabled the integration of this practice into modern medicine (Johansen Taber, Dickinson, & Wilson, 2014; Park & Kim, 2016). Today’s unprecedented access to sequencing technology has opened the door for advances in the technology used for modern genomic sequencing. Whole genome sequencing (WGS), one of the most advanced methods that exists today, has quickly become the industry standing for genomic sequencing (Nakagawa & Fujita, 2018; Napoli, Chau, & Figg, 2022; Ng & Kirkness, 2010). While a promising advancement in the field of genomics, WGS can present limitation in analysis of large data. Often WGS sequencing has a low yield of actionable findings or diagnosis for patients. Actionable diagnoses are key to offering early and effective medical interventions (Dewey et al., 2014; Khera et al., 2018; Stavropoulos et al., 2016). WGS can provide a great insight into an individual’s genetics, however the data on genetic inheritance can often indicate more actionable information (Kovesdi & Patocs, 2019). Utilizing the inheritance pattern of genomic abnormalities, as well as their content, can provide a more informative result at mutational effects and the possible translation of those effects into phenotypes (Wong, Sealfon, Theesfeld, & Troyanskaya, 2021). The most effective way for researchers or clinical geneticists to utilize the inheritance pattern for diagnostic purposes is to perform a joint family-based genomic analysis. Joint Analysis utilizes genomic information of the proband, the index individual, as well as their unaffected family members, often first degree, to examine genome wide patterns of inheritance. A comparison between the DNA of the proband and each parent can elucidate the possible effect of genetic variants in the proband (Kovesdi & Patocs, 2019). Joint Analysis can be used in tandem with variant classification systems to identify mutations of interest within affected individuals. In order to classify individual variants, there are several factors to consider. A first consideration is the quality and confidence of variant calls. Genomic pipelines have a system that ranks and quantifies confidence and accuracy of calls as they are being generated (Koboldt, 2020; Panoutsopoulou & Walter, 2018). Secondary steps of variant classification relate to functional studies of specific variants in literature and genomic databases. The gnomAD and Clinvar genomic databases are examples of commonly used genomic databases for respectively, allele frequency of the variant in population and reported classification of the variant by other genetic groups (Chen et al., 2022). The variant classification process is regulated by the American College of Medical Geneticists (ACMG) and the ACMG variant classification guidelines (Richards et al., 2015). This set of guidelines, consisting of 28 different rules, considers a variety of variant specific genomic factors. These factors are In silico or computational predictions, genomic location, population frequency, and previously reported functional or clinical data. The ACMG rules classify variants into five main categories: Pathogenic, Likely Pathogenic, Uncertain, Likely Benign, and Benign (Richards et al., 2015). Utilizing the ACMG criteria for classification provides a more accurate and systematic method to identify actionable and diagnostic variants. In this research proposed herein, we plan to utilize WGS Joint Analysis combined with the ACMG classification system to investigate a cohort of 157 individuals (probands and family members) from 50 families ranging in age from >20 to 59 years old. This research will identify Pathogenic and Likely Pathogenic variants that exist within the probands. We expect Pathogenic and Likely Pathogenic variants to be informative and provide insight into the proband’s phenotypic presentations. The Joint Analysis step will be utilized to identify less obvious genomic variations and should help to improve the efficacy of variant analysis. Therefore, using Joint Analysis combined with ACMG classification in this research presents an effective method for identifying actionable variants in individuals with complex phenotypes.