AF: Medium: A High Performance Computing Foundation to Whole-Genome Prediction

个性化医疗的前提是基于对个体疾病遗传风险的预测。现代动物和植物育种计划基于基因型信息选择个体或品系，这避免了昂贵的后代测试过程，从而提高了效率。在这些科学领域，将基因型信息转化为疾病或育种目标风险的定量预测的能力是至关重要的。为了解决使用遗传标记的全基因组样本进行预测的技术障碍，迫切需要新的统计模型和高性能计算基础，以允许同时使用数百万个遗传标记和描述疾病（或育种目标）的各种变量。该项目提出了解决几个这样的障碍，通过一个综合的方法结合和开发技术的数据减少，并行计算和贝叶斯推理。这个跨学科的项目为研究生和本科生提供了获得基因组数据分析计算方面第一手研究经验的教育机会。该项目旨在了解全基因组标记如何帮助预测尚未指定的个体表型，以及如何更好地估计表型的总遗传贡献。所提出的研究的主要目标是开发：（1）并行算法，以减少数据，包括数以百万计的遗传标记到较低的维度;（2）稀疏预测建模与校正不均匀的标签问题，由于连锁不平衡;（3）快速算法多位点定位问题;和（4）协作预测方法，以联合预测多种表型。这些解决方案将在大规模生物数据的分析中进行测试，包括美国农业部收集的奶牛数据库和从人类疾病的多项遗传研究中汇总的数据集。该项目将产生用户友好的软件工具，可以广泛部署到研究复杂表型遗传学的生物研究领域。经验证的方法和软件将通过PI的实验室网站进行传播。

The premise of personalized medicine is based on prediction of an individual's genetic risk to disease. Modern animal and plant breeding programs select individuals or lines based on genotypic information which circumvents the costly process of progeny testing, leading to greater efficiency. In these scientific areas, the ability to translate genotypic information into a quantitative prediction of the risk to disease or breeding targets is a matter of utmost importance. To address the technical barriers in the prediction using a whole-genome sample of genetic markers, there is urgent need for new statistical models and high performance computing foundations that allow the concurrent use of millions of genetic markers and a large variety of variables describing a disease (or a breeding target). This project proposes to solve several such barriers by an integrative approach combining and developing techniques for data reduction, parallel computing and Bayesian inference. This interdisciplinary project provides educational opportunities for graduate and undergraduate students to get first-hand research experience in computational aspects of genomics data analysis.This project aims to understand how genome-wide markers help to predict not-yet-specified phenotypes of individuals and how the total genetic contribution can be better estimated for a phenotype. The primary goals of the proposed research are to develop: (1) parallel algorithms to reduce data that comprises millions of genetic markers into lower dimensions; (2) sparse predictive modeling with correction for the uneven tagging issue due to linkage disequilibrium; (3) fast algorithms for multi-locus mapping problems; and (4) collaborative prediction methods to jointly predict multiple phenotypes. The proposed solutions will be tested in the analysis of large-scale biological data, including a dairy cattle database collected by US Department of Agriculture and a dataset aggregated from multiple genetic studies of human diseases. This project will yield user-friendly software tools that can be broadly deployed to biological research areas that study genetics of complex phenotypes. The validated methods and software will be disseminated through the PI's laboratory website.