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arXiv · 2606.19743

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

Imke Botha·Tianxiao Hao·Lucinda E. Harrison·Nick Golding·Daniel J. Weiss·Jennifer A. Flegg
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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.

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Imke Botha, Tianxiao Hao, Lucinda E. Harrison, Nick Golding, Daniel J. Weiss, Jennifer A. Flegg. 2026-06-18. A Bayesian spatio-temporal nearest neighbor Gaussian process model for pooled genetic data. https://arxiv.org/abs/2606.19743

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