Identifying Highly Penetrant Disease Causal Mutations Using Next Generation Sequencing: Guide to Whole Process.

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Rapid technological advancements present challenges for geneticists, requiring adaptation to new bioinformatics tools and vast public data.
• Diverse methods and file formats in sequence analysis impede efficient investigation into the genetic basis of human disorders.
• Many researchers possess basic data analysis skills but underutilize available tools and databases, lacking full understanding of data generation.

Purpose of the Study:

• To document key approaches and provide an analysis schema for efficient and reliable discovery of highly penetrant causal mutations/genes.
• To compare analytical methods for identifying highly penetrant variants in consanguineous versus nonconsanguineous populations.
• To differentiate analytical strategies for Mendelian disorders versus common-complex disorders.

Main Methods:

• Review of current bioinformatics tools and publicly available genetic data resources.
• Development of a systematic analysis schema for genetic disorder research.
• Comparative analysis of variant identification strategies based on population relatedness and disease type.

Main Results:

• Identification of key bioinformatics approaches and data resources relevant to genetic analysis.
• Proposal of an integrated schema to streamline the process of identifying disease-causing mutations.
• Comparative insights into variant detection efficacy across different population structures and disease complexities.

Conclusions:

• An efficient and reliable analysis schema is crucial for navigating the complexities of modern genetic data.
• Understanding data generation and maximizing the use of available tools enhances the discovery of causal mutations/genes.
• Tailored analytical approaches are necessary for Mendelian and common-complex disorders, as well as for consanguineous and nonconsanguineous datasets.