Genome sequencing data analysis for rare disease gene discovery

优先次序 工作流程 基因组 计算生物学 生物 数据科学 人口 疾病 注释 基因组学 精密医学 基因 遗传学 计算机科学 医学 人工智能 管理科学 经济 病理 环境卫生 数据库
作者
Umm-Kulthum Ismail Umlai,Dhinoth Kumar Bangarusamy,Xavier Estivill,Puthen V. Jithesh
出处
期刊:Briefings in Bioinformatics [Oxford University Press]
卷期号:23 (1) 被引量:6
标识
DOI:10.1093/bib/bbab363
摘要

Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.

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