Identification and Functional Validation of Human Infertility Alleles

人类不育等位基因的鉴定和功能验证

基本信息

批准号：
8973021
负责人：
John C Schimenti
金额：
$ 59.96万
依托单位：
CORNELL UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2020-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8973021
关键词：
Address Alleles Amino Acids Behavior Benign Biological Assay CDK2 gene Cell Count Chromosomes Clustered Regularly Interspaced Short Palindromic Repeats Computer Simulation DNA Sequence DNA Sequence Alteration Data Data Aggregation Databases Diagnosis Disease Fertility Frequencies Future Generations Gametogenesis Gene Frequency Generations Genes Genetic Genetic Polymorphism Genetically Engineered Mouse Genomics Germ Cells Goals Heterogeneity Histology Human In Vitro Infertility Knowledge Laboratories Medicine Meiosis Methods Minor Modeling Mus Mutation Oocytes Orthologous Gene Patients Phenotype Population Process Proteins Reproduction Resources Single Nucleotide Polymorphism Sperm Motility Technology Test Result Testing Ursidae Family Validation Variant Woman Work base clinically significant cost empowered exome sequencing functional genomics gene therapy genetic disorder diagnosis genetic linkage analysis genetic variant genome editing genome wide association study genome-wide improved in vitro Assay in vitro testing interdisciplinary approach knockout gene meetings men mouse model multidisciplinary mutant mutant mouse model personalized medicine protein function protein protein interaction public health relevance reproductive sex sperm morphology web site

项目摘要

DESCRIPTION (provided by applicant): It is thought that nearly half of infertility cases have a genetic basis. Despite extensive knowledge gained from gene knockouts in mice, the genetic causes for the vast majority of idiopathic human infertilities are unknown. Traditional methods for studying inheritance, such as GWAS or linkage analysis, have been confounded by heterogeneity of infertility phenotypes and hundreds of genes involved in reproduction. Finally, we do not know the proportion of cases in which genetically-based infertilities are caused by de novo mutations vs. inheritance of alleles segregating in the human population. This project proposes a new, comprehensive, and multidisciplinary approach to this problem that will screen and identify nonsynonymous single nucleotide polymorphisms (nsSNPs) that functionally disrupt gametogenesis. The complementary expertise of two laboratories - one which has developed massively parallel in silico and in vitro methods to predict and validate disease-causing nsSNPs, and the other a leader in discovery and characterization of infertility genes using mouse mutant models - will together be brought together to bear on the problem. The Specific Aims are to: 1) Use computational approaches and high-throughput in vitro assays to identify nsSNPs in human infertility genes that are likely to disrupt protein function. These alleles will be precisely modeed in mice using CRISPR/Cas genome editing. 2) Phenotype the mouse models to identify those SNPs which impact gametogenesis and fertility. Overall, ~125 mouse models corresponding to nsSNPs in >300 known reproduction genes will be made. Already, 2 of 5 nsSNPs tested caused infertility. Establishing a database of experimentally-validated benign and deleterious SNPs in reproduction genes will revolutionize and empower the human reproductive genetics field as we enter the era of personalized medicine by improving the identification of causative alleles in exome-sequenced patients.

描述（由申请人提供）：据认为，近一半的不孕症病例具有遗传基础，尽管通过小鼠基因敲除获得了广泛的知识，但绝大多数人类特发性不孕症的遗传原因尚不清楚。诸如 GWAS 或连锁分析等，已被不孕表型的异质性和数百个参与生殖的基因所混淆。最后，我们不知道基于遗传的病例比例。不孕症是由从头突变与人群中分离的等位基因遗传引起的。该项目提出了一种新的、全面的、多学科的方法来解决这个问题，该方法将筛选和识别功能上破坏配子发生的非同义单核苷酸多态性（nsSNP）。两个实验室的专业知识 - 一个实验室开发了大规模并行计算机和体外方法来预测和验证致病的 nsSNP，另一个实验室处于领先地位使用小鼠突变模型发现和表征不育基因 - 将聚集在一起解决该问题。具体目标是：1）使用计算方法和高通量体外测定来识别人类不育基因中的 nsSNP。可能会破坏蛋白质功能。将使用 CRISPR/Cas 基因组编辑在小鼠中精确模拟这些等位基因。 2) 对小鼠模型进行表型分析，以识别影响配子发生和生育能力的 SNP。将制作出与超过 300 个已知生殖基因中的 nsSNP 相对应的约 125 个小鼠模型，已测试的 5 个 nsSNP 中的 2 个会导致不育。建立经过实验验证的生殖基因中的良性和有害 SNP 数据库将彻底改变并增强人类生殖遗传学领域的能力。随着我们通过改进外显子组测序患者致病等位基因的识别进入个性化医疗时代。