TY - JOUR
T1 - Network-guided search for genetic heterogeneity between gene pairs
AU - International Headache Genetics Consortium
AU - Gumpinger, Anja C.
AU - Rieck, Bastian
AU - Grimm, Dominik G.
AU - Borgwardt, Karsten
N1 - Publisher Copyright:
© 2020 The Author(s). Published by Oxford University Press.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Motivation: Correlating genetic loci with a disease phenotype is a common approach to improve our understanding of the genetics underlying complex diseases. Standard analyses mostly ignore two aspects, namely genetic heterogeneity and interactions between loci. Genetic heterogeneity, the phenomenon that genetic variants at different loci lead to the same phenotype, promises to increase statistical power by aggregating low-signal variants. Incorporating interactions between loci results in a computational and statistical bottleneck due to the vast amount of candidate interactions. Results: We propose a novel method SiNIMin that addresses these two aspects by finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple Arabidopsis thaliana phenotypes, and, with an adapted variant of SiNIMin, for a study of rare variants in migraine patients.
AB - Motivation: Correlating genetic loci with a disease phenotype is a common approach to improve our understanding of the genetics underlying complex diseases. Standard analyses mostly ignore two aspects, namely genetic heterogeneity and interactions between loci. Genetic heterogeneity, the phenomenon that genetic variants at different loci lead to the same phenotype, promises to increase statistical power by aggregating low-signal variants. Incorporating interactions between loci results in a computational and statistical bottleneck due to the vast amount of candidate interactions. Results: We propose a novel method SiNIMin that addresses these two aspects by finding pairs of interacting genes that are, upon combination, associated with a phenotype of interest under a model of genetic heterogeneity. We guide the interaction search using biological prior knowledge in the form of protein-protein interaction networks. Our method controls type I error and outperforms state-of-the-art methods with respect to statistical power. Additionally, we find novel associations for multiple Arabidopsis thaliana phenotypes, and, with an adapted variant of SiNIMin, for a study of rare variants in migraine patients.
UR - http://www.scopus.com/inward/record.url?scp=85104209795&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btaa581
DO - 10.1093/bioinformatics/btaa581
M3 - Article
C2 - 32573681
AN - SCOPUS:85104209795
SN - 1367-4803
VL - 37
SP - 57
EP - 65
JO - Bioinformatics
JF - Bioinformatics
IS - 1
ER -