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教程:一个统计遗传学指南,用于识别导致复杂疾病的HLA等位基因

Saori Sakaue^1,2,3, Saisriram Gurajala^1,2,3, Michelle Curtis^1,2,3

¹Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Nature protocols

|July 26, 2023

概括

本教程提供了从基因型数据中归因人类白细胞抗原 (HLA) 基因的基本准则. 它详细介绍了协会测试和精细映射的最佳实践,以了解HLA.

科学领域:

基因组学就是基因组学.
免疫遗传学免疫遗传学
计算生物学计算生物学

背景情况:

人类白细胞抗原 (HLA) 位点显著影响复杂疾病,往往解释了比其他遗传位点结合起来更大的遗传性.
在基HLA归算方法允许从现有的微阵列数据准确估计HLA等位基因,促进大规模的遗传研究.
现有研究强调了HLA归算在识别自身免疫性疾病和艾滋病毒感染控制中的因果差异方面的实用性.

研究的目的:

为从基因型数据进行人类白细胞抗原 (HLA) 归算提供全面的指导方针.
为协会测试和HLA等位基因,氨基酸和单元类型的精细映射提供最佳实践建议.
建立一个标准化的管道和在线资源,用于适用于大规模基因型化数据的HLA归算分析.

主要方法:

关于输入基因型化数据质量控制的详细指南.
描述本地和基于网络 (密歇根计算服务器) 的HLA等位基和氨基酸计算方法.
包括一个多祖先的HLA归算参考面板,以提高准确性.

主要成果:

一个全面的教程,包括HLA归算的逐步说明和脚本.
关联测试的最佳实践建议,以确定与特征相关的HLA变异.
一个可访问的在线指南和软件库 (https://github.com/immunogenomics/HLA_analyses_tutorial) 用于实际应用.

结论:

本教程解决了在HLA归算,关联测试和精细映射中需要标准化准则的需求.
提供的资源将使研究人员能够有效地利用大规模的基因型化数据来研究与HLA相关的疾病.
这项工作旨在推进对HLA在人类疾病中的作用的理解,在全球不同的人口中发挥作用.

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