Qatar’s genetic diversity offers unique research opportunity, says expert

Qatar’s distinctive demographic landscape — a tight-knit native populace alongside one of the globe’s most ethnically varied expatriate societies — is proving a boon for genomic research, according to a leading scientist from the nation. Dr Jithesh Puthenveetil, a professor at Hamad Bin Khalifa University (HBKU) and associate dean for education and student affairs, is partnering with the Hamad Medical Corporation and the Qatar Precision Health Institute on a pioneering project.

This endeavor merges genomics, precision medicine, and artificial intelligence to predict patient responses to life-saving antibiotics before a prescription is ever issued.

Dr Puthenveetil highlighted the dual nature of Qatar’s population — a genetically distinct native group, shaped by centuries of familial lineage, and a multicultural expatriate community drawn from every continent. This blend of homogeneity and diversity presents both opportunities and challenges. "The homogeneous Qatari population is advantageous for studying specific genetic diseases, yet the diverse expatriate community allows us to explore a broad spectrum of genetic variants," he explained.

The research heavily relies on data from the Qatar Precision Health Institute, which boasts a substantial cohort of Qatari participants and has recently expanded to include patients reflecting Qatar’s global makeup. Dr Puthenveetil anticipates that the study’s findings will be significant both locally and globally. "Our diverse participant pool enables us to pinpoint genetic factors unique to Qatari individuals while also uncovering markers relevant to a wide array of ethnic groups," he stated.

"The data will not only aid in identifying genetic variants affecting Qataris but will also contribute to broader insights applicable to global populations."

With the goal of recruiting approximately 600 participants from across Qatar, the team will examine how individuals respond to medications and use AI to integrate and analyze this data, ultimately developing a predictive model for medication response. "By combining all the information and integrating it through AI, we aim to create a model that can predict whether a future patient will respond positively or adversely to an antibiotic, even before the medication is prescribed," he concluded.