Modifier genes are believed to account for the clinical variability observed in many Mendelian disorders, but their identification remains challenging due to the limited availability of genomics data from large patient cohorts. Here, we present GENDULF (GENetic moDULators identiFication), one of the first methods to facilitate prediction of disease modifiers using healthy and diseased tissue gene expression data. GENDULF is designed for monogenic diseases in which the mechanism is loss of function leading to reduced expression of the mutated gene. When applied to cystic fibrosis, GENDULF successfully identifies multiple, previously established disease modifiers, including EHF, SLC6A14, and CLCA1. It is then utilized in spinal muscular atrophy (SMA) and predicts U2AF1 as a modifier whose low expression correlates with higher SMN2 pre‐mRNA exon 7 retention. Indeed, knockdown of U2AF1 in SMA patient‐derived cells leads to increased full‐length SMN2 transcript and SMN protein expression. Taking advantage of the increasing availability of transcriptomic data, GENDULF is a novel addition to existing strategies for prediction of genetic disease modifiers, providing insights into disease pathogenesis and uncovering novel therapeutic targets.

中文翻译：

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GENDULF 算法：挖掘转录组学以发现单基因疾病的修饰基因


人们认为修饰基因可以解释许多孟德尔疾病中观察到的临床变异，但由于来自大型患者群体的基因组数据有限，修饰基因的识别仍然具有挑战性。在这里，我们提出了 GENDULF（基因调节因子识别），这是利用健康和患病组织基因表达数据促进疾病修饰因子预测的首批方法之一。 GENDULF 专为单基因疾病而设计，其机制是功能丧失导致突变基因表达减少。当应用于囊性纤维化时，GENDULF 成功识别了多种先前确定的疾病修饰因子，包括EHF 、 SLC6A14和CLCA1 。然后将其用于脊髓性肌萎缩症 (SMA)，并预测U2AF1作为修饰剂，其低表达与较高的 SMN2 前 mRNA 外显子 7 保留相关。事实上，在 SMA 患者来源的细胞中敲低 U2AF1 会导致全长 SMN2 转录物和 SMN 蛋白表达增加。利用转录组数据不断增加的优势，GENDULF 是对现有遗传疾病修饰因子预测策略的新补充，提供了对疾病发病机制的见解并发现了新的治疗靶点。