Sparse haplotype-based fine-scale local ancestry inference at scale reveals recent selection on immune responses

Increasingly efficient methods for inferring the ancestral origin of genome regions are needed to gain new insights into genetic function and history as biobanks grow in scale. Here we describe two near-linear time algorithms to learn ancestry harnessing the strengths of a Positional Burrows-Wheeler Transform (PBWT). SparsePainter is a faster, sparse replacement of previous model-based `chromosome painting' algorithms to identify recently shared haplotypes, whilst PBWTpaint uses further approximations to obtain lightning-fast estimation optimized for genome-wide relatedness estimation. The computational efficiency gains of these tools for fine-scale local ancestry inference offer the possibility to analyse large-scale genomic datasets in completely novel ways. Application to the UK Biobank shows that haplotypes better represent ancestries than principal components, whilst linkage-disequilibrium of ancestry identifies signals of recent changes to population-specific selection for many genomic regions associated with immune responses, suggesting new avenues for understanding the pathogen-immune system interplay on a historical timescale. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Y.Y. was supported by China Scholarship Council [grant number 202108060092]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All participants gave their written consent and UK Biobank received ethical approval from the NHS Research Ethics Service (11/NW/0382). Our analysis was conducted under application number 81499. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The phased 1000 Genomes Project data build GRCh37/hg19 are available at https://bochet.gcc.biostat.washington.edu/beagle/1000\_Genomes\_phase3_v5a/b37.vcf/. The UK Biobank data can be accessed by approved researchers through https://www.ukbiobank.ac.uk. We used the UK Biobank data under project 81499. The UK map data are available at https://gadm.org. [https://bochet.gcc.biostat.washington.edu/beagle/1000\_Genomes\_phase3_v5a/b37.vcf/][1] [1]: https://bochet.gcc.biostat.washington.edu/beagle/1000_Genomes_phase3_v5a/b37.vcf/