全基因组关联研究
计算生物学
遗传关联
生物
基因组学
基因组
基因组选择
计算机科学
数据科学
遗传学
基因
单核苷酸多态性
基因型
作者
Laura E. Tibbs‐Cortes,Zhiwu Zhang,Jianming Yu
摘要
Abstract Genome‐wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome‐wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naïve methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare‐variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.
科研通智能强力驱动
Strongly Powered by AbleSci AI