AI 中文总结
本文通过跨代全息基因组模拟，探究整合微生物群与基因组数据对表型预测的增益，发现其价值高度依赖情境，并提供了评估框架。
AI 中文摘要
微生物群日益被认为是宿主生物学的一个活跃组成部分，影响各种宿主表型。高通量测序的进步和全息生物体视角的出现，提升了人们对全息基因组信息预测的期望。然而，整合微生物群和基因组数据是否以及在何种条件下能有意义地改善表型预测，仍不清楚。微生物群的生物学特征，包括但不限于传播机制、环境效应以及与宿主遗传学的相互作用，使其整合到经典评估框架中变得复杂。此外，微生物群数据集具有高维、高度离散、稀疏和成分性的特点。最后，分析选择，如用于聚合的分类粒度或预测模型中使用的相似性矩阵，可能影响下游推断和预测准确性。在此，我们通过一套全面的跨代全息基因组模拟来探索这些挑战。通过在广泛的参数空间中生成受控且对比鲜明的生物学场景，我们检验了微生物群粒度、方差结构和宿主调节如何影响（i）方差成分的估计和（ii）表型预测的准确性。我们表明，与基因组预测相比，全息基因组预测的附加价值高度依赖于情境。我们的结果提供了一个结构化框架，用于探究在育种应用中何时以及如何整合微生物群可能增强表型预测。
英文摘要
The microbiota is increasingly recognized as an active component of host biology, influencing various host phenotypes. Advances in high-throughput sequencing and the emergence of the holobiont perspective have raised expectations regarding hologenomic-informed prediction. Yet, whether and under which conditions integrating microbiota and genomic data meaningfully improves phenotypic prediction remains unclear. The biological characteristics of the microbiota, including but not limited to transmission mechanisms, environmental effects and interactions with host genetics, complicate their integration into classical evaluation frameworks. In addition, microbiota datasets are high-dimensional, highly dispersed, sparse and compositional. Finally, analytical choices such as the taxonomic granularity considered for aggregation or the similarity matrix used in prediction models may impact downstream inference and prediction accuracy. Here we explore these challenges using a comprehensive set of transgenerational hologenomic simulations. By generating controlled and contrasted biological scenarios across a broad parameter space, we examine how microbiota granularity, variance structure and host modulation influence (i) the estimation of variance components and (ii) the accuracy of phenotypic prediction. We show that the added value of hologenomic, compared to genomic prediction, is highly context dependent. Our results provide a structured framework to interrogate when and how integrating microbiota may enhance phenotypic prediction in breeding applications.
CommentsAccepted at the CIBB 2026 conference (https://cibb2026.teralab.ai/)
