A Composite of Multiple Signals Distinguishes Causal Variants in Regions of Positive Selection

Pinpointing Genetic Selection The human genome contains hundreds of regions with evidence of recent positive natural selection, yet, for all but a handful of cases, the underlying advantageous mutation remains unknown. Current methods to detect the signal of selection often results in the identification of a broad genomic region containing many candidate regions that vary among individuals. By combining existing statistical methods, Grossman et al. (p. 883, published online 7 January) developed a method, termed Composite of Multiple Signals, which can increase the ability to pinpoint the specific variant under selection. Several candidate regions under selection in human populations were identified. Combining statistical methods detects signals of selection with increased sensitivity and a lower false-positive rate. The human genome contains hundreds of regions whose patterns of genetic variation indicate recent positive natural selection, yet for most the underlying gene and the advantageous mutation remain unknown. We developed a method, composite of multiple signals (CMS), that combines tests for multiple signals of selection and increases resolution by up to 100-fold. By applying CMS to candidate regions from the International Haplotype Map, we localized population-specific selective signals to 55 kilobases (median), identifying known and novel causal variants. CMS can not just identify individual loci but implicates precise variants selected by evolution.