SPASIBA

SPASIBA performs spatial continuous assignment of samples' geographic origins from genotype data (from genotyping arrays or next-generation sequencing) using a geostatistical modeling approach.

Key Features:

• Geostatistical Modeling: Employs a geostatistical model trained on georeferenced genotypes to estimate geographic origins from genetic variation.
• Integrated Nested Laplace Approximation (INLA): Uses the Integrated Nested Laplace Approximation (INLA) framework for statistical inference, avoiding computationally intensive Monte Carlo simulations.
• Comparative Performance: Includes simulation studies comparing SPASIBA to the SPA method, reporting superior accuracy and reliability across scales.
• Application Range: Validated on genotype datasets from Florida Scrub-jay (Aphelocoma coerulescens), Arabidopsis thaliana, and humans spanning 41 to 197,146 single nucleotide polymorphisms (SNPs).
• Data-scale Suitability: Capable of handling large-scale genotype datasets and well-suited for medium-sized datasets such as those generated by reduced-representation sequencing.

Scientific Applications:

• Epidemiology: Infers geographic origins and dispersal patterns relevant to tracking epidemiological outbreaks.
• Forensics: Predicts geographic provenance from genetic data to assist profiling in forensic investigations.
• Wildlife Management: Locates geographic origins of wildlife samples to identify poaching hotspots and inform conservation actions.

Methodology:

Implemented as an R package, SPASIBA trains a geostatistical model on georeferenced genotype data and performs statistical inference using INLA, with simulation-based comparisons to the SPA method.