Large scale nutrigenetics and genomics in a tractable metazoan model

总结 个体可以以明显不同的方式对不同的营养素和饮食限制做出反应。有些人 有些人很容易发胖，但有些人无论吃什么都很瘦。此外，代谢性疾病可以 对于罕见的单基因孟德尔疾病和常见的 多因素代谢疾病，如肥胖症和2型糖尿病。在很大程度上，这种变化表明， 个体遗传差异极大地影响了患病的可能性以及疾病的严重程度， 包括人群中罕见和常见的代谢性疾病。如果有人能把它 确定罕见和常见的变异，有助于个人对饮食的反应，并获得 不同类型的代谢性疾病。罕见的变异通常通过连锁图谱和整体- 使用有受影响个体的家庭进行基因组测序。相比之下，常见的变体通常 通过使用大量患病和未患病人群的全基因组关联研究确定。 我们将为线虫的大量遗传个体开发个性化的代谢网络模型 C. elegans，既代表健康的代谢状态，又模仿人类代谢的先天缺陷。 通过我们的实验系统和方法，我们将能够预测罕见和常见的 受营养影响的各种代谢性状的变化。我们将广泛验证这些预测 使用CRISPR/Cas9介导的基因组编辑。

SUMMARY Individuals can respond to diverse nutrients and dietary restrictions in markedly different ways. Some people easily gain weight, but others remain thin no matter what they eat. Additionally, metabolic diseases can differ dramatically among individuals in a population, for both rare single-gene Mendelian diseases and common multifactorial metabolic diseases such as obesity and type 2 diabetes. In large part, this variability suggests that individual genetic differences greatly affect the likelihood to get sick as well as the severity of the illness for both rare and common metabolic diseases across a population. It would be extremely valuable if one could identify both rare and common variants that contribute to individual responses to diet and to the acquisition of different types of metabolic diseases. Rare variants are usually identified by linkage mapping and whole- genome sequencing using families with affected individuals. By contrast, common variants are usually identified by genome-wide association studies using large populations of people with and without a disease. We will develop personalized metabolic network models for a large set of genetic individuals of the nematode C. elegans, both representing healthy metabolic state and mimicking an inborn error of human metabolism. With our experimental system and approach we will be able to derive predictions of both rare and common variation in a variety of metabolic traits influenced by nutrition. We will extensively validate such predictions using CRISPR/Cas9-mediated genome editing.