A Bayesian spatio-temporal nearest neighbor Gaussian process model for pooled genetic data

2026-06-17

AI Summary

Inferring multi-locus haplotype frequencies from large-scale pooled genetic data is computationally challenging and has traditionally been limited to three or fewer markers. This work proposes a Bayesian spatiotemporal nearest-neighbor Gaussian process (NNGP) modeling framework, integrated with sequential Monte Carlo squared (SMC²) and particle Gibbs with ancestor sampling, to enable efficient and scalable inference of haplotype frequencies. By reducing computational complexity to linear order, the method overcomes the scalability bottleneck of existing approaches. It has been successfully applied to African malaria drug-resistance data involving three to six genetic markers, substantially enhancing both the scalability and practical utility of haplotype frequency estimation.

Abstract

Large scale genetic datasets often aggregate the total allele counts of distinct genetic markers. Inferring haplotype frequencies (i.e.\ the frequency of multimarker alleles) from these pooled data is a challenge. Previous spatio-temporal modelling in this context has been limited to 3 markers due to the computational cost. In this work, we propose a nearest neighbor Gaussian process (NNGP) model to improve scaling with the number of markers and observations. To infer the parameters of our model, we develop a novel sequential Monte Carlo squared algorithm, which uses particle Gibbs with ancestor sampling to mutate the NNGP function values. The latter has a linear cost in the number of observations and the number of NNGPs, and can be applied to a broad range of NNGP models. As a case study, we analyse genetic data relating to antimalarial drug resistance in Africa, and show our scaling results empirically on a 3 and 6 genetic marker dataset.

Problem

• haplotype frequency inference
• pooled genetic data
• spatio-temporal modeling
• computational scalability
• multimarker alleles

Innovation

• nearest neighbor Gaussian process
• sequential Monte Carlo squared
• haplotype frequency inference
• pooled genetic data
• spatio-temporal modeling