On the Basics of Pedigree Visualization and Feature Extraction for the Autosomal Recessive Inheritance Pattern

Abstract

Pedigree analysis is carried out to understand the mode of inheritance of a particular disease, which includes recessive, dominant, partial dominant, autosomal, mitochondrial or sex-linked etc. It also determines the individual's probability of affecting in a given cross. Genetic disorders are transmitted from one generation to the next by following a particular inheritance pattern. Correct investigation of the transmission patterns of a trait under different circumstances is the crucial in genetic research. That is, identification of ancestry patterns for twins, full penetrance and reduced penetrance cases etc. This paper considers autosomal recessive case. Two simulated single nucleotide polymorphisms (SNPs) based genotype data sets for 14 and 47 individuals with three and four generations, respectively, were applied for this investigation. This evaluation looks for the probable features of ancestry patterns of a genetic disorder from one generation to the next based on the specified genetic conditions. Proper visualization of the pedigree charts for autosomal recessive case having different characteristics were demonstrated here. Since, sequencing of deoxyribonucleic acid (DNA), and handling of such massive amount of data depends on the availability of funding, dedicated software, high throughput data storage capacity etc. Hence, effective simulation for data generation would be the choice to cope with this situation for realizing the pipeline of such genetic research. The main objective of this paper is to provide a useful guideline for the introductory genetic researchers to whom real data sets are not available, and once available, dealing with this massive amount of sequencing data is a big challenge due to some limitations. This guideline will help to have an idea about such research. If opportunity is given, this idea could be applied for the real data sets, and the results would be similar.