全基因组关联研究
荟萃分析
遗传关联
复制(统计)
计算生物学
单核苷酸多态性
计算机科学
SNP公司
数据科学
联想(心理学)
生物
遗传学
医学
心理学
基因
基因型
内科学
病毒学
心理治疗师
作者
Joel Defo,Denis Awany,Raj Ramesar
摘要
Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.
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