Efficient computation of the joint sample frequency spectra for multiple populations

TL;DR

This paper introduces new formulas and algorithms for efficiently computing the joint sample frequency spectrum across multiple populations with complex demographic histories, improving stability and scalability.

Contribution

It provides novel analytic formulas and algorithms for the expected joint SFS, enabling efficient inference in complex multi-population models.

Findings

Enhanced numerical stability in computations

Reduced computational complexity for large samples

Successful application to empirical data with many populations

Abstract

A wide range of studies in population genetics have employed the sample frequency spectrum (SFS), a summary statistic which describes the distribution of mutant alleles at a polymorphic site in a sample of DNA sequences. In particular, recently there has been growing interest in analyzing the joint SFS data from multiple populations to infer parameters of complex demographic histories, including variable population sizes, population split times, migration rates, admixture proportions, and so on. Although much methodological progress has been made, existing SFS-based inference methods suffer from numerical instability and high computational complexity when multiple populations are involved and the sample size is large. In this paper, we present new analytic formulas and algorithms that enable efficient computation of the expected joint SFS for multiple populations related by a complex demographic model with arbitrary population size histories (including piecewise exponential growth). Our results are implemented in a new software package called momi (MOran Models for Inference). Through an empirical study involving tens of populations, we demonstrate our improvements to numerical stability and computational complexity.