Statistical Methods for Next Generation Genome-Wide Association Studies

下一代全基因组关联研究的统计方法

• 批准号: FT220100069
• 负责人: A/Prof Loic Yengo
• 金额: $ 63.05万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: ARC Future Fellowships
• 财政年份: 2023
• 资助国家: 澳大利亚
• 起止时间: 2023-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/FT220100069
• 关键词: Statistical; Methods; Next; Generation; Genome

This project aims to develop cutting-edge statistical methods to analyse large genomic datasets and identify genetic variants associated with inter-individual differences in various human traits. Knowledge of trait-associated DNA variants is instrumental in understanding how natural selection has shaped human traits. By integrating genomic data from diverse and underrepresented populations, this project further expects to contribute to the equitable use of genomic technologies in humans, regardless of geographical origins. Expected outcomes of this research include novel analysis methods and software tools, which should broadly and significantly benefit gene discovery in other species, including those of agricultural relevance.

该项目旨在开发尖端的统计方法，以分析大型基因组数据集，并确定与人类各种特征的个体间差异有关的遗传变异。了解与特征相关的DNA变异有助于理解自然选择是如何塑造人类特征的。通过整合来自不同和代表性不足的人群的基因组数据，该项目进一步期望促进基因组技术在人类中的公平使用，而不考虑其地理来源。这项研究的预期结果包括新的分析方法和软件工具，这应该会广泛和显著地有利于其他物种的基因发现，包括那些与农业相关的物种。