EDGE CMT: deleterious recessive variation - from experimental data to predictive models

EDGE CMT：有害的隐性变异 - 从实验数据到预测模型

• 批准号: 10675239
• 负责人: Matthew Rockman
• 金额: $ 38.09万
• 依托单位: NEW YORK UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-17 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10675239
• 关键词: Alleles; Architecture; Biology; Caenorhabditis; Caenorhabditis elegans; Characteristics; Data; Frequencies; Genes; Genetic Variation; Genome; Health; Measures; Molecular; Nematoda; Partner in relationship; Population; Population Biology; Sampling; Site; System; Systems Biology; Transcript; Variant; Work; design; fitness; predictive modeling; rare variant; segregation; sex

In population samples, segregating recessive variants at low frequency are largely inaccessible to study. This project uses an experimental system in which rare alleles have been made common, recessive alleles can be homozygosed on demand, and cumulative effects of large numbers of small-effect variants can be measured in an unbiased way. The project centers on the nematode Caenorhabditis becei, which has all the experimental virtues of C. elegans but with a mating system and population biology that make it better suited to questions about genetic diversity in general and recessive variation in particular. This project will measure sex-specific competitive fitnesses and sex- and allele-specific transcript abundances in a specially designed panel of C. becei to reveal the architecture of segregating recessive variation and its molecular characteristics. The data will address basic questions in evolutionary biology while generating generalizable gene- and variant-scale models that predict whether variants are likely to have recessive effects.

在种群样本中，低频下的隐性变体在很大程度上无法学习。 该项目使用一个实验系统，在该系统中，稀有等位基因已成为常见，隐性 等位基因可以按需纯合子，以及大量小型变体的累积影响 可以以公正的方式进行测量。该项目集中在线虫Caenorhabditis becei上，该炎 具有秀丽隐杆线虫的所有实验美德，但具有交配系统和种群生物学，使 它更适合有关遗传多样性的问题，尤其是隐性变异。这 项目将衡量特定性别的竞争健身以及性别和等位基因特定的成绩单丰度 在专门设计的C. becei面板中，揭示了隔离隐性变化和 它的分子特征。数据将解决进化生物学中的基本问题，而 生成可推广的基因和变体尺度模型，以预测变体是否可能具有 隐性效应。