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.

在群体样本中，低频率的分离隐性变异在很大程度上是无法研究的。 这个项目使用了一个实验系统，在这个系统中，罕见的等位基因被变成了常见的隐性基因。 等位基因可以按需纯合，大量小效应变异的累积效应 可以以公正的方式进行测量。该项目的中心是线虫小杆线虫， 具有C的所有实验优点。但它的交配系统和种群生物学 它更适合于一般的遗传多样性问题，特别是隐性变异。这 该项目将测量性别特异性竞争适应性以及性别和等位基因特异性转录丰度 在一个特别设计的C.因为它揭示了分离隐性变异的结构， 其分子特征。这些数据将解决进化生物学中的基本问题， 生成可推广的基因和变异规模模型，预测变异是否可能具有 隐性效应