全基因组关联研究
连锁不平衡
生物
特质
数量性状位点
计算生物学
虚假关系
样本量测定
混淆
遗传关联
遗传学
遗传建筑学
单核苷酸多态性
进化生物学
计算机科学
基因型
统计
机器学习
基因
数学
程序设计语言
作者
Arthur Korte,Ashley Farlow
出处
期刊:Plant Methods
[BioMed Central]
日期:2013-01-01
卷期号:9 (1): 29-29
被引量:1758
标识
DOI:10.1186/1746-4811-9-29
摘要
Over the last 10 years, high-density SNP arrays and DNA re-sequencing have illuminated the majority of the genotypic space for a number of organisms, including humans, maize, rice and Arabidopsis. For any researcher willing to define and score a phenotype across many individuals, Genome Wide Association Studies (GWAS) present a powerful tool to reconnect this trait back to its underlying genetics. In this review we discuss the biological and statistical considerations that underpin a successful analysis or otherwise. The relevance of biological factors including effect size, sample size, genetic heterogeneity, genomic confounding, linkage disequilibrium and spurious association, and statistical tools to account for these are presented. GWAS can offer a valuable first insight into trait architecture or candidate loci for subsequent validation.
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