MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, CB2 0QH, UK.
Department of Computer Science, University of Bristol, Bristol, BS8 1UB, UK.
Nat Commun. 2023 Feb 17;14(1):919. doi: 10.1038/s41467-023-36634-6.
Cohort-wide sequencing studies have revealed that the largest category of variants is those deemed 'rare', even for the subset located in coding regions (99% of known coding variants are seen in less than 1% of the population. Associative methods give some understanding how rare genetic variants influence disease and organism-level phenotypes. But here we show that additional discoveries can be made through a knowledge-based approach using protein domains and ontologies (function and phenotype) that considers all coding variants regardless of allele frequency. We describe an ab initio, genetics-first method making molecular knowledge-based interpretations for exome-wide non-synonymous variants for phenotypes at the organism and cellular level. By using this reverse approach, we identify plausible genetic causes for developmental disorders that have eluded other established methods and present molecular hypotheses for the causal genetics of 40 phenotypes generated from a direct-to-consumer genotype cohort. This system offers a chance to extract further discovery from genetic data after standard tools have been applied.
全队列测序研究表明，最大的一类变体是那些被认为是“罕见的”，即使是位于编码区域的亚组（已知编码变体的 99%仅见于不到 1%的人群。关联方法使我们对罕见遗传变体如何影响疾病和机体水平表型有了一些了解。但在这里，我们通过使用基于知识的方法（利用蛋白质结构域和本体论（功能和表型））来展示，该方法考虑了所有编码变体，而不论等位基因频率如何，还可以进行其他发现。我们描述了一种从头开始、以遗传学为基础的方法，对机体和细胞水平的表型进行外显子全范围非 synonymous变体的分子知识解读。通过使用这种反向方法，我们为其他已建立的方法无法确定的发育障碍确定了合理的遗传原因，并为直接面向消费者的基因型队列生成的 40 种表型的因果遗传学提供了分子假说。该系统为在应用标准工具后从遗传数据中提取更多发现提供了机会。
