Disease insights through cross-species phenotype comparisons.

Melissa A Haendel, Nicole Vasilevsky, Matthew Brush, Harry S Hochheiser, Julius Jacobsen, Anika Oellrich, Christopher J Mungall, Nicole Washington, Sebastian Köhler, Suzanna E Lewis, Peter N Robinson, Damian Smedley
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引用次数: 26

Abstract

New sequencing technologies have ushered in a new era for diagnosis and discovery of new causative mutations for rare diseases. However, the sheer numbers of candidate variants that require interpretation in an exome or genomic analysis are still a challenging prospect. A powerful approach is the comparison of the patient's set of phenotypes (phenotypic profile) to known phenotypic profiles caused by mutations in orthologous genes associated with these variants. The most abundant source of relevant data for this task is available through the efforts of the Mouse Genome Informatics group and the International Mouse Phenotyping Consortium. In this review, we highlight the challenges in comparing human clinical phenotypes with mouse phenotypes and some of the solutions that have been developed by members of the Monarch Initiative. These tools allow the identification of mouse models for known disease-gene associations that may otherwise have been overlooked as well as candidate genes may be prioritized for novel associations. The culmination of these efforts is the Exomiser software package that allows clinical researchers to analyse patient exomes in the context of variant frequency and predicted pathogenicity as well the phenotypic similarity of the patient to any given candidate orthologous gene.

Abstract Image

Abstract Image

Abstract Image

通过跨物种表型比较了解疾病。
新的测序技术开创了诊断和发现罕见疾病新致病突变的新时代。然而,需要外显子组解释或基因组分析的候选变异的绝对数量仍然是一个具有挑战性的前景。一种有效的方法是将患者的表型集(表型谱)与由与这些变异相关的同源基因突变引起的已知表型谱进行比较。通过小鼠基因组信息学小组和国际小鼠表型联盟的努力,可以获得这项任务最丰富的相关数据来源。在这篇综述中,我们强调了比较人类临床表型与小鼠表型的挑战,以及一些由君主倡议成员开发的解决方案。这些工具允许识别已知疾病基因关联的小鼠模型,否则可能会被忽视,以及候选基因可能会优先考虑新的关联。这些努力的高潮是Exomiser软件包,它允许临床研究人员在变异频率的背景下分析患者外显子组,预测致病性以及患者与任何给定候选同源基因的表型相似性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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