SALAI-Net: species-agnostic local ancestry inference network.
SALAI-Net: species-agnostic local ancestry inference network.
复制标题
SALAI-Net：与物种无关的本地祖先推理网络。
DOI：
10.1093/bioinformatics/btac464
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Ioannidis,AlexanderG
中科院分区：
文献类型：
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作者：
OriolSabat,Benet;MasMontserrat,Daniel;Giro-I-Nieto,Xavier;Ioannidis,AlexanderG
局部祖先推断（LAI）是沿着DNA序列沿着祖先标签的高分辨率预测。LAI在人类历史和迁移研究中非常重要，并且开始在精准医学应用中发挥作用，包括祖先调整的全基因组关联研究（GWAS）和多基因风险评分（PRS）。现有的LAI模型不能很好地在物种、染色体甚至祖先群体之间推广，需要针对每个不同的设置进行重新训练。此外，这样的方法可能缺乏可解释性，这是一个重要的元素，在每个这些application.ResultsWe目前SALAI网，一个便携式的统计LAI方法，可以应用于任何一组的物种和祖先（物种不可知），只需要单倍型数据，没有其他生物参数。受血统同一性方法的启发，SALAI-Net通过执行参考匹配方法来估计每个DNA片段的群体标签，这导致了一种可解释的快速技术。我们以人类的全基因组数据为基准，测试了这些模型在人类数据上训练时推广到狗品种的能力。SALAI-Net在平衡精度方面优于以前的方法，同时在不同的设置，物种和数据集之间进行推广。此外，它比竞争方法快两个数量级，使用的RAM内存也少得多。可用性和实现我们提供了一个开源实现，并在github.com/AI-sandbox/SALAI-Net上提供了公开数据的链接。数据可通过以下网站公开获取：https://www.internationalgenome.org（1000个基因组）、https://www.simonsfoundation.org/simons-genome-diversity-project（西蒙斯基因组多样性项目）、ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516（人类基因组多样性项目）和https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733（犬科动物基因组）。https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html
MotivationLocal ancestry inference (LAI) is the high resolution prediction of ancestry labels along a DNA sequence. LAI is important in the study of human history and migrations, and it is beginning to play a role in precision medicine applications including ancestry-adjusted genome-wide association studies (GWASs) and polygenic risk scores (PRSs). Existing LAI models do not generalize well between species, chromosomes or even ancestry groups, requiring re-training for each different setting. Furthermore, such methods can lack interpretability, which is an important element in each of these applications.ResultsWe present SALAI-Net, a portable statistical LAI method that can be applied on any set of species and ancestries (species-agnostic), requiring only haplotype data and no other biological parameters. Inspired by identity by descent methods, SALAI-Net estimates population labels for each segment of DNA by performing a reference matching approach, which leads to an interpretable and fast technique. We benchmark our models on whole-genome data of humans and we test these models’ ability to generalize to dog breeds when trained on human data. SALAI-Net outperforms previous methods in terms of balanced accuracy, while generalizing between different settings, species and datasets. Moreover, it is up to two orders of magnitude faster and uses considerably less RAM memory than competing methods.Availability and implementationWe provide an open source implementation and links to publicly available data at github.com/AI-sandbox/SALAI-Net. Data is publicly available as follows: https://www.internationalgenome.org (1000 Genomes), https://www.simonsfoundation.org/simons-genome-diversity-project (Simons Genome Diversity Project), https://www.sanger.ac.uk/resources/downloads/human/hapmap3.html (HapMap), ftp://ngs.sanger.ac.uk/production/hgdp/hgdp_wgs.20190516 (Human Genome Diversity Project) and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA448733 (Canid genomes).Supplementary informationSupplementary data are available fromBioinformaticsonline.