Machine learning for high-dimensional data in genetics and neuroscience

我最近的工作是合作创建人类大脑结构和功能的遗传基础目录（L. Elliott等人）。自然,2018)。我还参与开发了新软件（Bycroft等人）。Nature. 2018)对新一代大型联盟（如UK Biobank、China Kadoorie Biobank和AllofUs，每个联盟的研究对象均超过50万）进行高效的全基因组关联研究（GWAS）。目前的方法揭示了这些数据中的关联，但现代机器学习（ML）需要在发现的大量关联中找到模式，并将关联扩展到表型预测。本提案涉及四个目标，旨在推进遗传和生物信息学的ML方法在大规模GWAS中的应用。我的第一个目标是开发基于GWAS关联结果的ML技术。GWAS的关联模式是利用当前的方法来估计表型的遗传力。为了实现这一目标，我将使用多任务学习的ML学科技术来进一步分析这些关联，揭示涉及变异组和表型组的潜在遗传过程。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，但深度学习在GWAS中的应用还没有明确的结果。这是由于在GWAS的制定中缺乏上下文敏感信息。我将研究将上下文敏感信息添加到深度神经网络输入层的方法。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（voxel）水平上对大脑图像进行GWAS。最近在体向GWAS方面的研究没有产生统计学上显著的结果。通过使用来自大规模联合体的数据和有效的方法来控制大脑图像信号中的空间噪声，我将创建人类大脑结构遗传变异的精细地图。这项工作将有助于更好地了解大脑的变异。我的第四个目标是推进与亲属矩阵相关的方法。亲属矩阵是研究对象对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，但计算亲属矩阵的计算成本阻碍了它的广泛采用。我将开发新的和有效的方法来计算和近似大规模财团的亲属矩阵，允许更准确的GWAS结果。该研究计划将支持高素质人才的培训，并且在该研究计划中开发的软件将在开源许可下发布。我预计这个研究项目将为新一代大规模联盟的ML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.