Refining Mendelian disease analysis via detection of clinically relevant repeat variants

通过检测临床相关的重复变异来完善孟德尔疾病分析

• 批准号: 10586956
• 负责人: Vineet Bafna
• 金额: $ 56.98万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-14 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10586956
• 关键词: Accounting; Affect; Alleles; Anus; Bioinformatics; Clinical; Code; Complex; Cystic Kidney Diseases; Detection; Disease; Disease Progression; Fragile X Syndrome; Gases; Gene Duplication; Genome; Genotype; Goals; Hereditary Nonpolyposis Colorectal Neoplasms; Human Genome; Huntington Disease; Individual; Inherited Spinocerebellar Degenerations; Insulin-Dependent Diabetes Mellitus; Length; Malignant Neoplasms; Mendelian disorder; Methods; Minisatellite Repeats; Mutation; Nerve Degeneration; PMS2 gene; Pathogenicity; Sequence Analysis; Short Tandem Repeat; Single Nucleotide Polymorphism; Tandem Repeat Sequences; Technology; Variant; analysis pipeline; clinically relevant; frontotemporal lobar dementia amyotrophic lateral sclerosis; genetic testing; genetic variant; genome sequencing; hearing impairment; insertion/deletion mutation; novel; rare mendelian disorder; trait; whole genome

PROJECT SUMMARY Whole genome sequencing (WGS) has the potential to profile all clinically relevant genetic variants simultaneously. However, clinical variant discovery pipelines have focused largely on coding single nucleotide variants (SNVs), and to a lesser extent on regulatory SNVs and small indels, ignoring more complex classes of pathogenic variants such as repeats or structural rearrangements. Repeats can take many forms, but we consider three classes of repeats: short tandem repeats (STRs), variable number tandem repeats (VNTRs), and low-copy repeats or segmental duplications, together accounting for more than 8% of the human genome. These variant classes have been implicated in a number of Mendelian diseases. More than 30 disorders, primarily neurodegenerative, are caused by STR expansions, including Huntington’s Disease, Fragile X Syndrome, ALS/FTD, and hereditary ataxias. Similarly, VNTRs have been implicated in a range of psychiatric and other traits including medullary cystic kidney disease and type 1 diabetes. In many cases, the disease progression is correlated with germline repeat counts, but sequence variation within individual repeat units, and somatic instability of repeat length, has also been shown to be pathogenic in some cases. Finally, mutations in more than 100 duplicated genes have been implicated in rare Mendelian disorders and cancer, including PMS2 in Lynch Syndrome and STRC in hearing loss. Taken together, diseases associated with these repeat classes affect millions of individuals worldwide. Despite their relevance to disease, these repeat types are typically absent from sequence analysis pipelines due to the bioinformatics challenges they present. Over the last several years, we and others have made significant progress in developing methods to analyze clinically relevant repeats from short reads. However, important challenges remain, including the ability to genotype long, complex, imperfect, or GC rich repeats, to infer clinically relevant somatic variation, and the computational burden of existing methods. Further, existing frameworks for predicting the pathogenicity of individual SNVs or indels are not applicable to most repeats, and thus there is a need for prioritization methods to predict the impact of new repeat variants. The goal of this project is to make repeat analysis a standard component of existing Mendelian variant calling pipelines. To this end, we will develop novel methods for profiling repeat variants from long reads (Aim 1), extend our existing methods for short reads to consider more complex variant types (Aim 2), and establish a framework for prioritization of pathogenic repeat mutations (Aim 3).

项目摘要 全基因组测序（WGS）有可能分析所有临床相关的遗传变异 同步然而，临床变异发现管道主要集中在编码单核苷酸上， 变异（SNV），并在较小程度上对监管SNV和小插入缺失，忽略了更复杂的类别， 致病性变体，如重复序列或结构重排。 重复序列可以有多种形式，但我们考虑三类重复序列：短串联重复序列（STR）， 可变数目串联重复序列（VNTR）和低拷贝重复序列或片段重复序列一起 占人类基因组的8%以上。这些变体类与许多 孟德尔疾病超过30种疾病，主要是神经退行性疾病，是由STR扩增引起的， 包括亨廷顿氏病、脆性X综合征、ALS/FTD和遗传性共济失调。同样，VNTR具有 与一系列精神病和其他特征有关，包括髓质囊性肾病和1型糖尿病。 糖尿病在许多情况下，疾病进展与生殖系重复计数相关，但序列 单个重复单位内的变异和重复长度的体细胞不稳定性也被证明是 在某些情况下是致病的。最后，100多个重复基因的突变与罕见的 孟德尔疾病和癌症，包括Lynch综合征中的PMS 2和听力损失中的STRC。采取 与这些重复类别相关的疾病影响着全世界数百万人。 尽管它们与疾病相关，但这些重复类型通常不存在于序列分析中 由于它们所带来的生物信息学挑战，在过去的几年里，我们和其他人 在开发从短读段分析临床相关重复序列的方法方面取得了重大进展。 然而，重要的挑战仍然存在，包括基因型的能力长，复杂，不完善，或GC丰富 重复，以推断临床相关的体细胞变异，以及现有方法的计算负担。 此外，用于预测单个SNV或插入缺失的致病性的现有框架不适用于 大多数重复序列，因此需要优先化方法来预测新重复序列变体的影响。 该项目的目标是使重复分析成为现有孟德尔遗传算法的标准组成部分。 变量调用管道。为此，我们将开发新的方法，从长 reads（目标1），扩展我们现有的短读方法以考虑更复杂的变体类型（目标2）， 并建立致病性重复突变优先化的框架（目标3）。