Next-Generation Algorithms in Statistical Genetics Based on Modern Machine Learning

项目总结/摘要 技术的进步使得能够收集数百万人类基因组的大规模数据集。的 这一提议背后的长期愿景是利用现代数据集和机器学习（ML）， 了解遗传学和环境如何决定对改善人类健康重要的特征和结果。 现代机器学习在数百万非结构化数据点（基因组、临床记录、图像）的庞大数据集上蓬勃发展， 并将对研究基因型-表型联系的统计遗传学产生重大影响。改善- 在统计遗传学方面的进展反过来又有可能阐明疾病的遗传基础，并支持每 超声波治疗 该提案通过两个方面推进了上述愿景：（1）开发新的机器学习（ML）算法， 由统计遗传学中的问题激发的rithms;（2）为科学家创建开源软件系统 和临床医生基于上述算法。具体来说，我们描述了一个计划的发展计算- 在统计遗传学的三个广泛领域的方法：建模连锁不平衡和结构， 遗传变异，分析全基因组关联研究数据，预测遗传和环境风险， 心理因素在每个领域内，我们的目标是为关键应用问题开发开源软件，包括： 遗传估算、单体型分析、低通测序、因果变异鉴定和风险评分。 我们的研究旨在为基于现代ML的统计遗传学奠定基础， ML在其他应用领域可能不会追求的方向。我们的方法将支持技术 这些研究在医疗保健方面有直接的应用，并有助于揭示影响疾病的新的遗传因素， 提高从预防医学到药物基因组学领域基因组预测的准确性; 显著降低基因组测序分析的成本，并最终改善人类健康。

PROJECT SUMMARY/ABSTRACT Advances in technology are enabling the collection of massive datasets of millions of human genomes. The long-term vision underlying this proposal is to leverage modern datasets and machine learning (ML) to under- stand how genetics and the environment determine traits and outcomes important to improving human health. Modern ML thrives on vast datasets of millions of unstructured datapoints (genomes, clinical notes, images), and stands to greatly impact statistical genetics, the field which studies the genotype-phenotype link. Improve- ments in statistical genetics have in turn the potential to elucidate the genetic basis of disease and support per- sonalized medical therapies. This proposal advances the above vision via two thrusts: (1) developing novel machine learning (ML) algo- rithms motivated by problems in statistical genetics; (2) creating open-source software systems for scientists and clinicians based on the above algorithms. Specifically, we describe a plan for the development of computa- tional methods in three broad areas in statistical genetics: modeling linkage disequilibrium and the structure of genetic variation, analyzing genome-wide association study data, and predicting risk from genetic and environ- mental factors. Within in each area, we aim to develop open-source software for key applied problems includ- ing genetic imputation, haplotyping, low-pass sequencing, causal variant identification, and risk scoring. Our research seeks to establish a foundation for statistical genetics based on modern ML and also advance ML in directions that may not be pursued in other application domains. Our methods will support technologies that have immediate applications in healthcare and that help reveal novel genetic factors that influence dis- ease; improve the accuracy of genomic prediction in domains from preventive medicine to pharmacogenomics; significantly reduce the cost of genomic sequencing assays, and ultimately improve human health.