DMS/NIGMS 2: A Stability Driven Recommendation System for Efficient Disease Mechanistic Discovery

概述。发现潜在的疾病机制的生物学特征对疾病的发展至关重要。 有效的治疗方法和疗法。通常，这是通过两个步骤完成的：在第一阶段，统计 分析被用来推荐后续研究的候选变异/基因。在第二阶段， 研究人员通过独立的队列进行昂贵的实验、临床试验或外部研究 验证或建立候选特征和疾病特征之间的因果关系。为了最大限度地降低成本， 建议应该带来高收益的实验，并且是可复制的。这些建议是 通常通过基于线性混合模型的GWAS方法生成。尽管取得了成功， 但是，仍然存在很大的遗传力差距，限制了这些关联在 临床实践。许多关键问题可能会导致遗漏遗传性，包括：需要更多 信息性、多模式特征；不明的非线性和上位性效应；链接不平衡 变异体之间的差异；以及变异性的异质性来源。为了应对这些挑战，我们提出了一个 基于决策树的真实性检验稳定性驱动的特征推荐系统 在实验中获得高产量的高效发现。我们建立在迭代随机森林(IRF)和 基于可预测性、可计算性和稳定性原则的真实数据科学框架 (PC)由PI开发，为第一阶段提出一些新的进展。我们建议：(1) 广义MDI(GMDI)--稳定性驱动的非线性特征改善IRF的重要措施 推荐；(2)依赖感知特征和交互发现；(3)有监督的局部特征 异质机制发现的重要性；以及(4)通过基因沉默进行验证 实验。重要的是，我们生成多模式特征来提取整个基因组的信息。 智力上的功绩。我们的建议：通过解决MDI的缺陷和改进基于MDI的方法 针对问题设置进行调整；在IRF中纳入gMDI和依赖结构；以及检测 异类噪声源。每个目标都将经过审查，并遵循真实的数据科学框架。 在案例研究中，我们将推荐用于基因沉默实验的基因和相互作用。这些遗嘱 对心肌肥厚相关性状潜在的遗传机制提供有价值的见解。结果 这项工作的进展将影响复杂疾病的机械性发现，并推动统计方法的进步。

Overview. It is crucial to uncover the biological features underlying disease mechanisms to develop effective treatments and therapies. Typically, this is done via a two-step process: in stage 1, statistical analyses are used to recommend candidate variants/genes for follow-up investigation. In stage 2, researchers conduct costly experiments, clinical trials, or external studies via independent cohorts to validate or establish causality between candidate features and disease traits. To minimize costs, recommendations should lead to high-yield experiments and be replicable. These recommendations are often generated through GWAS methods, based on linear mixed models. Despite the successes of GWAS, there still exists a substantial heritability gap limiting the applicability of these associations in clinical practice. A number of key issues can contribute to missing heritability including: the need for more informative, multi-modal features; unidentified non-linear and epistatic effects; linkage disequilibrium among variants; and heterogeneous sources of variability. To confront these challenges, we propose a reality-checked stability-driven feature recommendation system based on decision trees that aims at efficient discoveries for high yields in experimentation. We build upon iterative random forests (iRF) and the veridical data science framework based on the principles of Predictability, Computability and Stability (PCS) developed by the PI to propose a number of novel advances for stage 1. We propose: (1) generalized MDI (gMDI) a stability-driven non-linear feature important measure for improving iRF recommendations; (2) dependence-aware feature and interaction discovery; (3) supervised local feature importance for heterogeneous mechanistic discoveries; and (4) validation through gene-silencing experiments. Importantly, we generate multi-modal features to extract information across the genome. Intellectual Merit. Our proposals: improve MDI-based methods by addressing drawbacks of MDI and tailoring to problem settings; incorporate gMDI and dependence structure in iRF; and detect heterogeneous sources of noise. Each aim will be vetted and follow the veridical data science framework. In the case study, we will recommend genes and interactions for gene-silencing experiments. These will supply valuable insights into genetic mechanisms underlying traits related to cardiac hypertrophy. Results of this work will impact mechanistic discovery for complex diseases and advance statistical methodology.