maasMDS：面向混合基因组精细结构的多样本阵列祖源特异性多维标度方法
maasMDS: Multi-array ancestry-specific multidimensional scaling for fine-scale structure in admixed genomes
maasMDS 是一个结合局部祖源掩蔽与距离校准的框架，可在整合不同基因分型阵列样本的同时解析指定祖源成分内部的群体结构。该方法在嵌入前基于共享观测位点校准祖源特异性成对距离，从而缓解阵列驱动的聚类，且无需丢弃大量变异，比事后 Procrustes 对齐或标准插补协调更忠实地解析精细关系。在 Indigenous American 队列分析中，maasMDS 恢复了与地区殖民史一致的少数欧洲祖源片段的次大陆来源，并揭示墨西哥和中美洲原住民群体内在地理梯度上的渐变，而这些在不限制祖源或不做多阵列校正时会被掩盖。
来源：Bioinformatics Advances · doi.org
Motivation
Fine-scale population structure in admixed cohorts is often obscured by genome-wide dimensionality reduction, which averages signals from different genetic ancestry sources present in the same individual, and by batch effects when datasets are merged across heterogeneous genotyping arrays with sparse SNP overlap.
Results
We present multi-array ancestry-specific multidimensional scaling (maasMDS), a framework that combines local-ancestry masking with distance calibration to integrate samples genotyped on different arrays while resolving structure within a chosen ancestry component. By calibrating ancestry-specific pairwise distances on shared observed loci before embedding, maasMDS mitigates array-driven clustering without discarding large numbers of variants and more faithfully resolves fine-scale relationships than post hoc Procrustes alignment or standard imputation-based harmonization. Across analyses of Indigenous American cohorts, maasMDS recovers sub-continental origins of minority European ancestry segments consistent with regional colonial histories and reveals geographic clines within Indigenous populations of Mexico and Central America that are obscured without ancestry restriction or multi-array correction.
Availability and Implementation
maasMDS is implemented in the snputils package with both a Python API and CLI. Source code is available at https://github.com/AI-sandbox/snputils.