TY - JOUR
T1 - The GENDULF algorithm
T2 - mining transcriptomics to uncover modifier genes for monogenic diseases
AU - Auslander, Noam
AU - Ramos, Daniel M.
AU - Zelaya, Ivette
AU - Karathia, Hiren
AU - Crawford, Thomas O.
AU - Schäffer, Alejandro A.
AU - Sumner, Charlotte J.
AU - Ruppin, Eytan
N1 - Funding Information:
We thank the Cancer Data Science Lab (CDSL) members for helpful discussions and comments. We also thank Giovanni Coppola for helpful discussion and Qing Wang for sequencing library preparation. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute and National Library of Medicine. This work was also supported in part by NIH (NINDS) grants R01NS096770 and 5F31NS105376.
Funding Information:
We thank the Cancer Data Science Lab (CDSL) members for helpful discussions and comments. We also thank Giovanni Coppola for helpful discussion and Qing Wang for sequencing library preparation. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Cancer Institute and National Library of Medicine. This work was also supported in part by NIH (NINDS) grants R01NS096770 and 5F31NS105376.
Publisher Copyright:
© 2020 The Authors. Published under the terms of the CC BY 4.0 license
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - cystic fibrosis
KW - digenic inheritance
KW - gene expression
KW - modifier gene
KW - spinal muscular atrophy
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U2 - 10.15252/msb.20209701
DO - 10.15252/msb.20209701
M3 - Article
C2 - 33438800
AN - SCOPUS:85098259677
VL - 16
JO - Molecular Systems Biology
JF - Molecular Systems Biology
SN - 1744-4292
IS - 12
M1 - e9701
ER -