The various challenges associated with AI-driven genetic testing

Sub: Sci

Sec : Awareness in IT

Why in NEWS

AI has facilitated genetic information processing at higher speed, but this rapid analysis amplifies the risk of data security breaches and leaks.

The Human Genome Project (HGP)

The Human Genome Project, a 13-year public initiative starting in 1990, aimed to decipher the complete DNA sequence of the human genome
Identify all human genes, estimated 20,000-25,000 genes within the genome.
HGP objective was to create technologies for storing, organizing, and analyzing the vast amount of genomic information. Address ethical, legal, and social implications (ELSI).
The human genome contains approximately 3 billion base pairs. Genes are not evenly distributed across the genome.
A significant portion of the genome consists of repetitive DNA sequences with unknown functions.
Over 9% of the DNA sequence is identical in all humans, with the remaining 0.1% accounting for individual differences.

AI in GENOMICS

AI significantly accelerates genetic information processing, leading to analysis of much larger datasets.
John Hopkins researchers used machine learning to analyse junk DNA (non- coding DNA) revealing associations with tumors and opening new avenues for cancer research.
AI helps uncover complex patterns and insights within vast genetic datasets that would be impossible to detect manually.
AI algorithms predict genetic disease-causing traits, interpret gene-environment interactions, and offer personalized health recommendations.
AI models can be continuously updated with the latest scientific research, ensuring analyses are based on current knowledge.

Challenges with AI

Genetic tests cannot reliably predict complex outcomes like school success or job prospects. Genetics is only one factor (around 30%).
Diagnoses can change, and some results are inconclusive (variations of unknown significance), sometimes requiring further testing or family history.
Genetic testing for Alzheimer’s identifies risk genes, but doesn’t guarantee the disease. People can develop Alzheimer’s without having the associated genes.
Genetic testing raises ethical questions, especially regarding unexpected findings and predictions of mental health conditions.
The goal of genetic testing should be to provide insights for proactive health measures, not to make definitive diagnoses.
Environment, diet, and education are as important as genetics in shaping a child’s development.

Measures to reduce the risk of genetic data breaches and protect the privacy of individuals.

Implement strong encryption methods both in transit and at rest.
Limit access to genetic data to only authorized personnel. Implement multi-factor authentication for all users with access to sensitive data.
Store genetic data in secure, controlled environments, such as dedicated servers or cloud platforms with robust security measures.
Conduct regular security audits to identify vulnerabilities in systems and processes.
Whenever possible, anonymize or de-identify genetic data used for AI training and analysis. This reduces the risk of linking data back to individuals.
Secure software development practices to minimize vulnerabilities in AI algorithms and software applications.
Train all employees who handle genetic data on security best practices, data privacy regulations, and the importance of protecting sensitive data.
Be transparent with users about how their genetic data is being used and obtain their informed consent before collecting or analyzing their data.