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：
10224949
负责人：
John C Schimenti
金额：
$ 60.85万
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 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10224949
关键词：
Address Affect Age Alleles Amino Acids Aneuploidy Benign Biochemical Biological Assay CRISPR/Cas technology Code Collection Computational Biology Computing Methodologies Couples DNA Sequence Alteration DNA sequencing Databases Defect Development Diagnosis Disease susceptibility Embryo Embryo Loss Embryonic Development Enhancers Exhibits Fertility Fertilization Frequencies Funding Gametogenesis Generations Genes Genetic Genetic Polymorphism Genetic Recombination Genetically Engineered Mouse Genome Genomic medicine Genomics Genotype-Tissue Expression Project Germ Cells Goals Health Hereditary Disease Heterogeneity Human Infertility Link Litter Size MLH1 gene Maintenance Meiosis Methodology Methods Minor Mismatch Repair Modeling Mus Mutant Strains Mice Mutation Nature Nucleic Acid Regulatory Sequences Ovarian Patients Phenotype Plant Roots Population Pregnancy loss Privatization Process Proteins Proteomics RNA Recurrence Regulatory Element Reporter Reproduction Reproductive Health Resources Single Nucleotide Polymorphism Sum Technology Testing Time Transcriptional Regulation Transgenic Mice Transgenic Organisms Untranslated RNA Validation Variant Women&apos s Health accurate diagnosis age related base cell type chromosome missegregation cost de novo mutation exome sequencing experience genetic disorder diagnosis genetic pedigree genetic variant genome editing genome sequencing genome wide association study human model idiopathic infertility improved in vitro Assay in vivo innovation mouse model multidisciplinary novel offspring ovarian reserve predictive modeling primary ovarian insufficiency proband promoter protein function protein protein interaction reproductive sex subfertility vector

项目摘要

Abstract Approximately 10% of people in the U.S. suffer from infertility, about half of whom are thought to have a genetic basis. However, the underlying causes remain undetermined in the great majority of patients. Traditional methods for identifying inherited disease loci, such as GWAS, have been confounded by heterogeneity of infertility phenotypes and the large numbers of genes involved in reproduction. Nevertheless, there are probably numerous “infertility” alleles segregating in populations, affecting diverse processes at all stages of gamete development. Our goal is to identify these alleles, their nature, and their in vivo impacts to reproduction. Under previous funding, we used a radically different approach to the problem that involved prediction and modeling of human coding variants biochemically and in mice. Here, we propose to employ innovative strategies for identifying and characterizing infertility variants (particularly SNPs) that segregate as minor alleles in populations. A multidisciplinary team with expertise in high-throughput genomics, reproductive genetics, proteomics, computational biology, and transcriptional regulation has been assembled to identify both protein-coding and regulatory variants affecting “fertility” genes. The Specific Aims are to: 1) Use computational approaches and high-throughput in vitro assays to identify nonsynonymous SNPs in human reproduction genes that are likely to disrupt protein function. These alleles will be precisely modeled in mice using CRISPR/Cas9 genome editing, and thoroughly phenotyped to inform patient diagnosis. 2) Exploit subfertile mouse models of human variants, exhibiting decreased chiasmata, to understand mechanisms of premature ovarian insufficiency (POI) and recurrent pregnancy loss. 3) Identify human germ cell regulatory variants via indentification of active (eRNA-transcribing) enhancers using ChRO-seq technology, followed by mouse transgenic assays. We will also identify eQTL residing in gametogenesis promoters by exploiting GTEx, high-throughput vector-building technology, and expression assays. Successful execution of this project would constitute the most comprehensive study ever conducted to identify and validate both coding and non-coding genetic variants in human populations that contribute to infertility in both sexes. Since the variants are carried by millions of people collectively, this project can have a major and lasting impact on the field of reproductive genetics in the precision genomics era.

抽象的大约 10% 的美国人患有不孕症，其中大约一半被认为患有不孕症然而，大多数患者的根本原因尚未确定。识别遗传病位点的传统方法（例如 GWAS）受到以下因素的困扰：然而，不育表型的异质性和参与繁殖的大量基因。人群中可能有许多“不孕”等位基因分离，影响不同的过程我们的目标是确定这些等位基因、它们的性质以及它们对配子发育的体内影响。在之前的资助下，我们使用了一种完全不同的方法来解决所涉及的问题。在这里，我们建议采用生物化学和小鼠中的人类编码变体的预测和建模。用于识别和表征不孕变异（特别是 SNP）的创新策略，这些变异分离为人群中的次要等位基因，具有高通量基因组学、生殖专业知识的多学科团队。遗传学、蛋白质组学、计算生物学和转录调控已被整合起来，以识别影响“生育”基因的蛋白质编码和调控变异体的具体目标是： 1) 使用用于识别人类非同义 SNP 的计算方法和高通量体外测定可能破坏蛋白质功能的繁殖基因将在小鼠中精确建模。使用 CRISPR/Cas9 基因组编辑，并进行彻底的表型分析以告知患者诊断 2) 利用。人类变异的低生育力小鼠模型，表现出交叉减少，以了解卵巢早衰 (POI) 和复发性流产 3) 识别人类生殖细胞调节。使用 ChRO-seq 技术识别活性（eRNA 转录）增强子来识别变体，然后我们还将利用 GTEx 来鉴定配子发生启动子中的 eQTL，高通量载体构建技术和表达测定。该项目的成功执行将构成有史以来最全面的研究识别并验证人类群体中的编码和非编码遗传变异，这些变异有助于由于数百万人集体携带这种变异，因此该项目可以有一个效果。对精准基因组学时代的生殖遗传学领域产生重大而持久的影响。