Machine learning for high-dimensional data in genetics and neuroscience

我最近的工作是合作创建一个人脑结构和功能的遗传基础目录(L.Elliott等人)。大自然。2018年)。我还参与开发了新软件(Bycroft等人，大自然。2018年)对新一代大型联合体，如英国生物库、中国嘉道理生物库和阿洛夫斯(每个拥有超过50万个主题)进行高效的全基因组关联研究(GWAS)。目前的方法在这些数据中发现关联，但现代机器学习(ML)S需要在发现的大量关联中发现模式，并将关联扩展到表型预测。这项建议涉及四个目标，旨在推动遗传学和生物信息学的ML方法在大规模GWAs中的应用。我的第一个目标是开发对GWAS的关联结果进行操作的ML技术。目前的方法利用遗传相关中的模式来估计表型的遗传力。为了这个目标，我的工作将使用多任务学习的ML学科的技术来进一步分析这些关联，揭示涉及一组变异和一组表型的潜在遗传过程。我的第二个目标是使用ML技术，如深度学习和贝叶斯非参数学来预测表型。最近人们对卷积神经网络的深度学习很感兴趣，但还没有明确的深度学习在GWA中的应用。这是因为在制定全球气候变化框架时缺乏对环境敏感的信息。我将研究将上下文敏感信息添加到深度神经网络的输入层的方法。这项工作可能会对深度学习产生开创性的影响。我的第三个目标是在体积像素(体素)的水平上对大脑图像进行GWA。最近在体素GWAS方面的工作没有产生统计上的显著结果。通过使用来自大型财团的数据，以及控制大脑图像信号中空间噪声的有效方法，我将创建人类大脑结构遗传变异的精细地图。这项工作将使人们能够更好地理解大脑的变异。我的第四个目标是改进与亲属关系矩阵相关的方法。亲属关系矩阵是编码研究对象对之间遗传相似性的基本单位。它被用来控制研究参与者的社会经济和地理差异，但计算亲属关系矩阵的计算成本阻碍了它的广泛采用。我将开发新的、高效的方法来计算和近似大规模联盟的亲属关系矩阵，从而获得更准确的GWAS结果。该研究计划将支持高素质人才的培训，该研究计划中开发的软件将在开放源码许可下发布。我预计这项研究计划将为ML关于新一代大型财团的研究奠定基础，并在神经科学的广泛领域产生影响。

My recent work was a collaborative creation of a catalogue of the genetic basis of human brain structure and function (L. Elliott et al. Nature. 2018). I also co-created new software (Bycroft et al. Nature. 2018) to conduct efficient genome wide association studies (GWAS) on a new generation of large-scale consortia such as UK Biobank, the China Kadoorie Biobank and AllofUs (each with more than 0.5 million subjects). Current methods uncover associations in such data, but modern machine learning (ML) s required to find patterns in the large number of associations found, and also to extend associations to phenotype predictions. This proposal involves four aims designed to advance ML methods for genetics and bioinformatics in application to large-scale GWAS. My first aim is to develop ML techniques that operate on association results from GWAS. Patterns in the associations of GWAS are exploited by current methods to estimate heritability of phenotypes. My work towards this aim will use techniques from ML disciplines of multi-task learning to further analyse these associations, uncovering latent genetic processes involving groups of variants and groups of phenotypes. My second aim is to use ML techniques such as deep learning and Bayesian nonparametrics to predict phenotypes. There is much recent interest in deep learning with convolutional neural networks, but there is no definitive application of deep learning to GWAS. This is due to the lack of context sensitive information in the formulation of GWAS. I will investigate methods for adding context sensitive information to the input layers of deep neural network. This work could have groundbreaking impact in deep learning. My third aim is to conduct a GWAS on brain images at the level of the volumetric pixel (voxel). Recent work in voxelwise GWAS has not produced statistically significant results. By using data from large-scale consortia, and efficient methods for controlling for spatial noise in brain image signals, I will create fine-scale maps of genetic variation in human brain structure. This work will allow greater understanding of brain variation. My fourth aim is to advance methods related to kinship matrices. The kinship matrix is a fundamental unit encoding genetic similarity between pairs of subjects of a study. It is used to control for socioeconomic and geographical variation within study participants, but the computational cost of computing the kinship matrix precludes its' widespread adoption. I will develop new and efficient methods for computing and approximating the kinship matrix on large-scale consortia, allowing more accurate GWAS results. This research program will support the training of highly qualified personnel, and the software developed in this research program will be released under open source licenses. I anticipate that this research program will form a foundation for ML on the new generation of large-scale consortia, with impact in broad areas of neuroscience.