Abstract
It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer's disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 2177-2182 |
| Number of pages | 6 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781509030491 |
| DOIs | |
| State | Published - Dec 15 2017 |
| Event | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: Nov 13 2017 → Nov 16 2017 |
Other
| Other | 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 |
|---|---|
| Country | United States |
| City | Kansas City |
| Period | 11/13/17 → 11/16/17 |
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Keywords
- complex disease
- network robustness
- RNA binding
- single nucleotide polymorphism
- systems biology
- transcription factor
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
Cite this
VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies : Applied to Alzheimer's disease. / Liu, Qiao; Chen, Chen; Gao, Annie; Tong, Hanghang; Xie, Lei.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 2177-2182.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - VariFunNet, an integrated multiscale modeling framework to study the effects of rare non-coding variants in genome-wide association studies
T2 - Applied to Alzheimer's disease
AU - Liu, Qiao
AU - Chen, Chen
AU - Gao, Annie
AU - Tong, Hanghang
AU - Xie, Lei
PY - 2017/12/15
Y1 - 2017/12/15
N2 - It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer's disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.
AB - It is a grand challenge to reveal the causal effects of DNA variants in complex phenotypes. Although statistical techniques can establish correlations between genotypes and phenotypes in Genome-Wide Association Studies (GWAS), they often fail when the variant is rare. The emerging Network-based Association Studies aim to address this shortcoming in statistical analysis, but are mainly applied to coding variations. Increasing evidences suggest that non-coding variants play critical roles in the etiology of complex diseases. However, few computational tools are available to study the effect of rare non-coding variants on phenotypes. Here we have developed a multiscale modeling variant-to-function-to-network framework VariFunNet to address these challenges. VariFunNet first predict the functional variations of molecular interactions, which result from the non-coding variants. Then we incorporate the genes associated with the functional variation into a tissue-specific gene network, and identify subnetworks that transmit the functional variation to molecular phenotypes. Finally, we quantify the functional implication of the subnetwork, and prioritize the association of the non-coding variants with the phenotype. We have applied VariFunNet to investigating the causal effect of rare non-coding variants on Alzheimer's disease (AD). Among top 21 ranked causal non-coding variants, 16 of them are directly supported by existing evidences. The remaining 5 novel variants dysregulate multiple downstream biological processes, all of which are associated with the pathology of AD. Furthermore, we propose potential new drug targets that may modulate diverse pathways responsible for AD. These findings may shed new light on discovering new biomarkers and therapies for the prevention, diagnosis, and treatment of AD. Our results suggest that multiscale modeling is a potentially powerful approach to studying causal genotype-phenotype associations.
KW - complex disease
KW - network robustness
KW - RNA binding
KW - single nucleotide polymorphism
KW - systems biology
KW - transcription factor
UR - http://www.scopus.com/inward/record.url?scp=85045989760&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85045989760&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2017.8217995
DO - 10.1109/BIBM.2017.8217995
M3 - Conference contribution
AN - SCOPUS:85045989760
VL - 2017-January
SP - 2177
EP - 2182
BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -