Single-nucleotide variants (SNVs) are changes at specific positions in a DNA sequence that can help classify and explain differences in disease susceptibility across populations. While single-cell sequencing provides valuable insights into cellular differences within diverse tissue samples, current tools can only detect a small number of SNVs, limiting information on genetic ancestry.
To help bridge this gap, researchers led by Ken Chen, Ph.D., developed a more sensitive computational tool called Monopogen that accurately detects SNVs from single-cell sequencing data. As a proof of concept, the researchers applied the tool to uncover genetic determinants of cardiac health, accurately determining ancestry and classifying differences among human populations. This tool can potentially be used to learn more about the genetic drivers of cellular processes and to identify risks of complex diseases in different populations, informing better prevention and treatment strategies.