Hypothesis-free phenotype prediction within a genetics-first framework.
Hypothesis-free phenotype prediction within a genetics-first framework.
复制标题
DOI：
10.1038/s41467-023-36634-6
复制
发表时间：
2023-02-17
影响因子：
16.6
通讯作者：
Gough, Julian
中科院分区：
文献类型：
--
作者：
Lu, Chang;Zaucha, Jan;Gam, Rihab;Fang, Hai;Smithers, Ben E.;Oates, Matt;Bernabe-Rubio, Miguel;Williams, James;Zelenka, Natalie;Pandurangan, Arun Prasad;Tandon, Himani;Shihab, Hashem;Kalaivani, Raju;Sung, Minkyung J.;Sardar, Adam;Tzovoras, Bastian Greshake;Danovi, Davide;Gough, Julian
队列范围的测序研究表明，最大的变异类别是那些被认为是“罕见”的变异，即使是位于编码区的子集（99%的已知编码变异在不到1%的人群中可见）。关联方法提供了一些了解罕见的遗传变异如何影响疾病和生物体水平的表型。但在这里，我们表明，额外的发现可以通过基于知识的方法，使用蛋白质结构域和本体（功能和表型），考虑所有的编码变异，无论等位基因频率。我们描述了一种从头开始，遗传学第一的方法，使分子知识为基础的解释外显子组范围内的非同义变异的表型在生物体和细胞水平。通过使用这种反向的方法，我们确定了可能的遗传原因的发育障碍，逃避了其他既定的方法，并提出了分子假说的因果遗传学的40个表型产生的直接消费者基因型队列。该系统提供了一个机会，在应用标准工具后，从遗传数据中提取进一步的发现。基于个体遗传变异的信息系统地预测表型或疾病风险仍然是一个未解决的挑战。在这里，作者开发了一种基于知识的方法，用于直接从人类基因组执行和评估无假设的表型预测。
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. Systematically predicting phenotypes or disease risks based on the information of an individual’s genetic variation remains an unsolved challenge. Here, the authors develop a knowledge-based approach for performing and evaluating hypothesis-free phenotype prediction directly from a human genome.
登录
查看更多内容
影响因子：
14.9
作者：
Mistry J;Chuguransky S;Williams L;Qureshi M;Salazar GA;Sonnhammer ELL;Tosatto SCE;Paladin L;Raj S;Richardson LJ;Finn RD;Bateman A
通讯作者：
Bateman A
影响因子：
12.3
作者：
McLaren W;Gil L;Hunt SE;Riat HS;Ritchie GR;Thormann A;Flicek P;Cunningham F
通讯作者：
Cunningham F
影响因子：
4.6
作者：
Davis JJ;Boisvert S;Brettin T;Kenyon RW;Mao C;Olson R;Overbeek R;Santerre J;Shukla M;Wattam AR;Will R;Xia F;Stevens R
通讯作者：
Stevens R
影响因子：
48
作者：
Ma J;Yu MK;Fong S;Ono K;Sage E;Demchak B;Sharan R;Ideker T
通讯作者：
Ideker T
影响因子：
14.9
作者：
Fang H;Gough J
通讯作者：
Gough J