Genomics is failing to account for diversity, leaving many populations behind in precision medicine. A study by Alice B. Popejoy and Stephanie M. Fullerton shows that while the proportion of non-European participants in genome-wide association studies (GWAS) has increased from 3.7% in 2009 to nearly 20% in 2016, populations of African, Latin American, Hispanic, and indigenous ancestry remain underrepresented. Despite this increase, these groups make up less than 4% of all samples analyzed. The study highlights that the majority of the growth in sample numbers comes from studies in East and South Asia, while other populations have seen little change. This bias is due to historical, logistical, and systemic factors, including a preference for existing European cohorts and the difficulty in recruiting diverse populations. The underrepresentation of these groups means that genetic associations found in European populations may not apply to others, potentially leading to ineffective or unsafe treatments. For example, a variant associated with diabetes may have different effects in people of African ancestry compared to Europeans. The study also notes that while whole-genome and exome sequencing are becoming more common, they still face challenges in diversity. Efforts to address this include trans-ethnic studies and initiatives like the Human Heredity and Health in Africa Consortium. The authors argue that more fundamental changes are needed, including funding incentives for diverse populations and a cultural shift in genomics research to ensure equitable benefits.Genomics is failing to account for diversity, leaving many populations behind in precision medicine. A study by Alice B. Popejoy and Stephanie M. Fullerton shows that while the proportion of non-European participants in genome-wide association studies (GWAS) has increased from 3.7% in 2009 to nearly 20% in 2016, populations of African, Latin American, Hispanic, and indigenous ancestry remain underrepresented. Despite this increase, these groups make up less than 4% of all samples analyzed. The study highlights that the majority of the growth in sample numbers comes from studies in East and South Asia, while other populations have seen little change. This bias is due to historical, logistical, and systemic factors, including a preference for existing European cohorts and the difficulty in recruiting diverse populations. The underrepresentation of these groups means that genetic associations found in European populations may not apply to others, potentially leading to ineffective or unsafe treatments. For example, a variant associated with diabetes may have different effects in people of African ancestry compared to Europeans. The study also notes that while whole-genome and exome sequencing are becoming more common, they still face challenges in diversity. Efforts to address this include trans-ethnic studies and initiatives like the Human Heredity and Health in Africa Consortium. The authors argue that more fundamental changes are needed, including funding incentives for diverse populations and a cultural shift in genomics research to ensure equitable benefits.