The 1000 Genomes Project is an international research consortium whose aim is to produce a detailed map of human genetic variation to support disease studies with major sequencing effort. This project involves sequencing the genomes of at least a thousand people from around the world to facilitate the discovery and understanding of genetic variants such as single nucleotide polymorphisms and structural variants. The data generated from this project will help in the discovery of regions in the genome containing genetic variations associated with risk of human diseases as previously attempted by efforts such as the HapMap Project. However, there are significant challenges in the analysis, annotation, and applications of these data to guide the identifications of variants associated with diseases and various traits. In this application, we will focus on loss-of-function variants because they represent a major class of genetic variations that are potentially involved in complex traits, and we believe a comprehensive characterization of these variants and making the knowledge gained available to the general research community will facilitate the identifications of genes involved in complex traits. To accomplish this objective, we will develop a bioinformatics pipeline to identify loss-of-function variants from the 1000 genome data, associate them with other types of information accumulated in the literature and public databases, such as gene ontology, protein interactions, expression profiles, investigate the best approaches to attain the genotypes of these variants in population samples, and develop statistical methods to incorporate the annotation results to increase the statistical power to identify loss of function variants affecting complex traits. We will disseminate the methods and results to the public both through a stand-alone application focusing of loss of function variants as well as through collaboration with the UCSC Genome Browser team to add tracks on their browser to different types of information on these loss of function variants. We believe that this proposed project will generate very valuable resources to the scientific community that can significantly enhance our understanding of loss of function variants in human populations and use such knowledge to more effectively improve human health.

Public Health Relevance

The research proposal is developed to generate very valuable resources to the scientific community that can significantly enhance our understanding of loss of function variants in human populations and use such knowledge to more effectively improve human health.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project--Cooperative Agreements (U01)
Project #
1U01HG005718-01
Application #
7882977
Study Section
Special Emphasis Panel (ZHG1-HGR-M (J1))
Program Officer
Brooks, Lisa
Project Start
2010-09-13
Project End
2012-06-30
Budget Start
2010-09-13
Budget End
2011-06-30
Support Year
1
Fiscal Year
2010
Total Cost
$262,000
Indirect Cost
Name
Yale University
Department
Public Health & Prev Medicine
Type
Schools of Medicine
DUNS #
043207562
City
New Haven
State
CT
Country
United States
Zip Code
06520
Harmanci, Arif; Gerstein, Mark (2016) Quantification of private information leakage from phenotype-genotype data: linking attacks. Nat Methods 13:251-6
Lu, Qiongshi; Yao, Xinwei; Hu, Yiming et al. (2016) GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. Bioinformatics 32:542-8
Li, Gengxin; Zhao, Hongyu (2015) M(3)-S: a genotype calling method incorporating information from samples with known genotypes. BMC Bioinformatics 16:403
1000 Genomes Project Consortium; Auton, Adam; Brooks, Lisa D et al. (2015) A global reference for human genetic variation. Nature 526:68-74
Lu, Qiongshi; Hu, Yiming; Sun, Jiehuan et al. (2015) A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data. Sci Rep 5:10576
Hou, Lin; Ma, TianZhou; Zhao, HongYu (2014) Incorporating functional annotation information in prioritizing disease associated SNPs from genome wide association studies. Sci China Life Sci 57:1072-9
Li, Cong; Yang, Can; Gelernter, Joel et al. (2014) Improving genetic risk prediction by leveraging pleiotropy. Hum Genet 133:639-50
Khurana, Ekta; Fu, Yao; Colonna, Vincenza et al. (2013) Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342:1235587