Novel Statistical Methods with Application to Imaging Genetics

应用于影像遗传学的新统计方法

• 批准号: RGPIN-2017-06538
• 负责人: Kong, Dehan
• 金额: $ 1.53万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635768
• 关键词: Novel; Statistical; Methods; Application; Imaging

Imaging genetics is a rapidly evolving field that integrates individual medical images with genetic information to assess the impact of genetic variation on brain function and structure. The long term objective of this proposal is to develop novel statistical methods for imaging genetics studies. With the advent of both advanced imaging as well as genotyping techniques, many large biomedical studies have been conducted to collect imaging and genetic data. Conventional approaches for imaging genetics analysis suffer from issues such as heavy computational cost and algorithmic instability, which requires the development of fast and efficient statistical methods.

成像遗传学是一个快速发展的领域，它将个体医学图像与遗传信息相结合，以评估遗传变异对大脑功能和结构的影响。该提案的长期目标是开发用于成像遗传学研究的新的统计方法。随着先进的成像和基因分型技术的出现，许多大型生物医学研究已经进行，以收集成像和遗传数据。传统的成像遗传学分析方法存在计算成本高和算法不稳定等问题，需要开发快速有效的统计方法。