Machine-Learning Approaches for Classifying Haplogroup from Y Chromosome STR Data

Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs) are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs). Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup) and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.Y染色体非重组区域的遗传变异蕴含着男性谱系的祖先溯源信息。由于单核苷酸多态性（single nucleotide polymorphisms, SNPs）突变率极低，因此是将Y染色体明确归类为相关谱系集合（即单倍群，haplogroups）的首选遗传标记，而这类单倍群在全球多数地区往往呈现出地理分布结构。然而，若要准确推断单倍群归属，需开展大量SNP基因分型检测，这一过程不仅成本高昂且耗时费力。本文提出了一种全新的替代方案，用于将待测Y染色体分配至对应单倍群。研究表明，通过应用现代机器学习算法，仅需对少量Y染色体连锁短串联重复序列（short tandem repeats, STRs）进行分型评分，即可高精度推断样本所属的正确Y染色体单倍群。本模型的训练基于一个多样化的基准真值数据集，该数据集包含SNP检测结果（对应单倍群）与相应STR分型评分的配对样本。我们联合运用多种独立的机器学习方法构建标准化分类函数，最终得到一套集成化高通量分析系统，可经济高效且精准地将大批量样本自动归类至相应单倍群。