| Title: | Tracing human genetic histories and natural selection with precise local ancestry inference |
| Journal: | Nature Communications |
| Published: | 16 May 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40379651/ |
| DOI: | https://doi.org/10.1038/s41467-025-59936-3 |
Publication 15331
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Abstract
Local ancestry inference is crucial for unraveling demographic histories, discovering selection signals, and including admixed individuals in genomic studies for improved equity and portability. To date, the precision and resolution of local ancestry inference were limited by technical and dataset issues. To address them, we present Orchestra, a model we train on over 10,000 single-origin individuals from 35 worldwide populations that demonstrates superior accuracy in benchmarking analyzes. We employ Orchestra to shed light on the demographic history of Latin Americans, finding trace ancestries supported by historical records. We then deploy it to offer insight on the debated Ashkenazi Jewish origins, highlighting their South European heritage. Finally, Orchestra enables us to map selection signatures, identifying trace Scandinavian ancestry in British samples and unveiling an immune-rich region linked to respiratory infections passed down from the Viking conquests. Our work significantly advances the field of local ancestry inference, highlighting its use in admixed populations.</p>
7 Keywords
- Genetics, Population
- Genome, Human
- Humans
- Jews
- Models, Genetic
- Polymorphism, Single Nucleotide
- Selection, Genetic
16 Authors
- Jon Lerga-Jaso
- Biljana Novković
- Deepu Unnikrishnan
- Varuna Bamunusinghe
- Marcelinus R. Hatorangan
- Charlie Manson
- Haley Pedersen
- Alex Osama
- Andrew Terpolovsky
- Sandra Bohn
- Adriano De Marino
- Abdallah A. Mahmoud
- Karatuğ O. Bircan
- Umar Khan
- Manfred G. Grabherr
- Puya G. Yazdi
1 Application
| Application ID | Title |
|---|---|
| 84038 | Assessing the role of rare and common variation in human clinical conditions and risk prediction using improved methods for genotype imputation and genetic effect modeling across populations |
Enabling scientific discoveries that improve human health