连锁不平衡
全基因组关联研究
关联映射
绘图(图形)
基因座(遗传学)
遗传关联
人口
假阳性悖论
基因组
遗传学
计算生物学
计算机科学
数据挖掘
生物
单倍型
统计
人工智能
数学
等位基因
基因
单核苷酸多态性
医学
环境卫生
基因型
作者
Jiabo Wang,Jianming Yu,Alexander E. Lipka,Zhiwu Zhang
出处
期刊:Methods in molecular biology
日期:2022-01-01
卷期号:: 63-80
被引量:2
标识
DOI:10.1007/978-1-0716-2237-7_5
摘要
With increasing marker density, estimation of recombination rate between a marker and a causal mutation using linkage analysis becomes less important. Instead, linkage disequilibrium (LD) becomes the major indicator for gene mapping through genome-wide association studies (GWAS). In addition to the linkage between the marker and the causal mutation, many other factors may contribute to the LD, including population structure and cryptic relationships among individuals. As statistical methods and software evolve to improve statistical power and computing speed in GWAS, the corresponding outputs must also evolve to facilitate the interpretation of input data, the analytical process, and final association results. In this chapter, our descriptions focus on (1) considerations in creating a Manhattan plot displaying the strength of LD and locations of markers across a genome; (2) criteria for genome-wide significance threshold and the different appearance of Manhattan plots in single-locus and multiple-locus models; (3) exploration of population structure and kinship among individuals; (4) quantile–quantile (QQ) plot; (5) LD decay across the genome and LD between the associated markers and their neighbors; (6) exploration of individual and marker information on Manhattan and QQ plots via interactive visualization using HTML. The ultimate objective of this chapter is to help users to connect input data to GWAS outputs to balance power and false positives, and connect GWAS outputs to the selection of candidate genes using LD extent.
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