SCCT

SCCT detects recent positive selection in genomic sequences by analyzing conditional coalescent trees and counting unbalanced mutations in genealogies to localize causal variants using deep sequencing data while addressing limitations of the integrated haplotype score (iHS) and Fay and Wu's H.

Key Features:

• Novel Methodology: Employs conditional coalescent trees to count unbalanced mutations in genealogies for detecting recent positive selection.
• Sequencing data support: Operates on deep sequencing data.
• Robustness to Demographic Events: Maintains robustness to population bottlenecks, expansions, and stratification that bias other methods.
• Superior Localization Capability: Localizes causal variants within linked regions with a 20–40% higher success rate than state-of-the-art methods in simulations.
• Empirical Validation: Successfully localized validated functional causal variants in ADH1B, MCM6, APOL1, and HBB on empirical datasets.
• Computational Efficiency: Executes 24–66 times faster than the REHH package and over 10,000 times faster than the original iHS implementation.

Scientific Applications:

• Natural selection and human evolution: Detects and localizes recent positive selection events to inform studies of natural selection and human evolution.
• Identification of functional causal variants: Facilitates identification of functional causal variants associated with adaptive traits in population genetics and evolutionary biology.

Methodology:

Constructs conditional coalescent trees and counts unbalanced mutations in genealogies; performance was assessed using extensive simulation studies and empirical comparisons to REHH and iHS.