In the past five years, genetic association studies have evaluated the contribution of common SNP variation to complex traits at an unprecedented level of detail. These genome wide studies relied not only on advances in genotyping technologies but also on improved study designs and advances in statistical and computational methods - ranging from the development of cost-effective two stage designs, to new strategies to control for population structure, to methods and software for genotype imputation and for cross study meta-analyses. In the next five years, great advances are again expected in genotyping and sequencing technologies. Effectively using these technologies to further our understanding of complex traits will require continued advances in methods for the design and analysis of genetic studies. In this application, we build on our record of developing practical useful analytical methods, computational tools, and study designs for human genetic studies. We set out to develop computational and statistical methods that will enable studies of complex traits in humans to effectively exploit these new technologies. Specifically, we will develop new methods and computational tools for genotype imputation and for the interpretation of short read sequence data, evaluate sequence and genotyping based design strategies for complex trait studies, and develop statistical methods that facilitate the prioritization of likely functional variants in genetic association studies. !

Public Health Relevance

In the next few years, continued advances in laboratory methods will allow geneticists to examine sequence variation in great detail and in progressively larger numbers of individuals. Here, we propose to develop statistical tools, computational methods and study designs that will allow geneticists to more fully exploit these new laboratory methods to study complex traits in humans. We expect methods developed here will lead directly to improved understanding of the molecular basis of many human traits and diseases - an important step in the path towards new treatments and therapies. !

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Research Project (R01)
Project #
5R01HG007022-02
Application #
8513389
Study Section
Special Emphasis Panel (ZRG1-GGG-H (02))
Program Officer
Brooks, Lisa
Project Start
2012-07-18
Project End
2017-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
2
Fiscal Year
2013
Total Cost
$591,256
Indirect Cost
$198,272
Name
University of Michigan Ann Arbor
Department
Type
Schools of Public Health
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Loh, Po-Ru; Danecek, Petr; Palamara, Pier Francesco et al. (2016) Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet 48:1443-1448
Das, Sayantan; Forer, Lukas; Schönherr, Sebastian et al. (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284-7
Wen, Xiaoquan; Lee, Yeji; Luca, Francesca et al. (2016) Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of Posteriors. Am J Hum Genet 98:1114-29
Tsoi, Lam C; Elder, James T; Abecasis, Goncalo R (2015) Graphical algorithm for integration of genetic and biological data: proof of principle using psoriasis as a model. Bioinformatics 31:1243-9
Lo, Yancy; Zhang, Lixin; Foxman, Betsy et al. (2015) Whole-genome sequencing of uropathogenic Escherichia coli reveals long evolutionary history of diversity and virulence. Infect Genet Evol 34:244-50
Lo, Yancy; Kang, Hyun M; Nelson, Matthew R et al. (2015) Comparing variant calling algorithms for target-exon sequencing in a large sample. BMC Bioinformatics 16:75
Danjou, Fabrice; Zoledziewska, Magdalena; Sidore, Carlo et al. (2015) Genome-wide association analyses based on whole-genome sequencing in Sardinia provide insights into regulation of hemoglobin levels. Nat Genet 47:1264-71
Flickinger, Matthew; Jun, Goo; Abecasis, Gonçalo R et al. (2015) Correcting for Sample Contamination in Genotype Calling of DNA Sequence Data. Am J Hum Genet 97:284-90
(2015) Large-scale genomic analyses link reproductive aging to hypothalamic signaling, breast cancer susceptibility and BRCA1-mediated DNA repair. Nat Genet 47:1294-303
Zoledziewska, Magdalena; Sidore, Carlo; Chiang, Charleston W K et al. (2015) Height-reducing variants and selection for short stature in Sardinia. Nat Genet 47:1352-6

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