Recent whole exome and genome sequencing studies, including our own, have revealed that both healthy individuals and those affected by genetic diseases carry many rare DNA sequence variants. They classify them correctly as either bona fide causal mutations for genetic diseases, or merely benign polymorphisms. New approaches are clearly needed to filter through candidate sequence variants identified in whole exome/genome studies to identify causal mutations and evaluate their epistatic interactions in human genetic diseases, particularly complex traits. Building upon the experience of the PIs, we propose an innovative hybrid computational-experimental systems.The high-throughput 3DIP strategy can classify coding genetic variants as causative mutations or benign polymorphisms and evaluate epistatic interactions that can be broadly applied to human genetic diseases, including complex traits. Our overall goal is to develop a high-throughput approach that can accurately validate coding genetic variants as causal mutations or merely benign polymorphisms to improve exome study success rates. Our long-term goal is to apply this approach to improve the medical outcomes of patients and their family members who carry variants of uncertain significance in genetic disease risk genes.

Public Health Relevance

Recent advances in DNA sequencing have globally transformed our understanding of human genetic diseases by revealing the ubiquity of rare DNA sequence variants: All our genomes are riddled with rare loss-of-function and gain-of-function gene variants. The almost overwhelming number of background rare variants has become the rate limiting step to identify causative mutations underlying important genetic diseases. This project proposes a new approach to help distinguish causative mutations from merely benign polymorphisms. DESCRIPTION (provided by applicant): Recent whole exome and genome sequencing studies, including our own, have revealed that both healthy individuals and those affected by genetic diseases carry many rare DNA sequence variants. They classify them correctly as either bona fide causal mutations for genetic diseases, or merely benign polymorphisms. New approaches are clearly needed to filter through candidate sequence variants identified in whole exome/genome studies to identify causal mutations and evaluate their epistatic interactions in human genetic diseases, particularly complex traits. Building upon the experience of the PIs, we propose an innovative hybrid computational-experimental systems.The high-throughput 3DIP strategy can classify coding genetic variants as causative mutations or benign polymorphisms and evaluate epistatic interactions that can be broadly applied to human genetic diseases, including complex traits. Our overall goal is to develop a high-throughput approach that can accurately validate coding genetic variants as causal mutations or merely benign polymorphisms to improve exome study success rates. Our long-term goal is to apply this approach to improve the medical outcomes of patients and their family members who carry variants of uncertain significance in genetic disease risk genes.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM104424-02
Application #
8706913
Study Section
Special Emphasis Panel (ZGM1-GDB-7 (CP))
Program Officer
Krasnewich, Donna M
Project Start
2013-09-01
Project End
2017-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
$411,062
Indirect Cost
$81,775
Name
Weill Medical College of Cornell University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
060217502
City
New York
State
NY
Country
United States
Zip Code
10065
Guo, Yu; Alexander, Katherine; Clark, Andrew G et al. (2016) Integrated network analysis reveals distinct regulatory roles of transcription factors and microRNAs. RNA 22:1663-1672
Vo, Tommy V; Das, Jishnu; Meyer, Michael J et al. (2016) A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human. Cell 164:310-23
Meyer, Michael J; Lapcevic, Ryan; Romero, Alfonso E et al. (2016) mutation3D: Cancer Gene Prediction Through Atomic Clustering of Coding Variants in the Structural Proteome. Hum Mutat 37:447-56
Bastos de Oliveira, Francisco Meirelles; Kim, Dongsung; Cussiol, José Renato et al. (2015) Phosphoproteomics reveals distinct modes of Mec1/ATR signaling during DNA replication. Mol Cell 57:1124-32
Das, Jishnu; Gayvert, Kaitlyn M; Bunea, Florentina et al. (2015) ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers. BMC Genomics 16:263
Pu, Mintie; Ni, Zhuoyu; Wang, Minghui et al. (2015) Trimethylation of Lys36 on H3 restricts gene expression change during aging and impacts life span. Genes Dev 29:718-31
Das, Jishnu; Lee, Hao Ran; Sagar, Adithya et al. (2014) Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks. Hum Mutat 35:585-93
Das, Jishnu; Fragoza, Robert; Lee, Hao Ran et al. (2014) Exploring mechanisms of human disease through structurally resolved protein interactome networks. Mol Biosyst 10:9-17
Wei, Xiaomu; Das, Jishnu; Fragoza, Robert et al. (2014) A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations. PLoS Genet 10:e1004819
Das, Jishnu; Gayvert, Kaitlyn M; Yu, Haiyuan (2014) Predicting cancer prognosis using functional genomics data sets. Cancer Inform 13:85-8

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