Understanding the genetic etiology of diseases, both common and rare, is paramount to the application of genetics in personalized medicine. All diseases are the result of a combination of environmental and/or genetic factors. The amount of variation in a disease/trait that is due to a genetic contribution is called heritability. Conducing genetic/genomic studies is difficult without evidence of a strong genetic component by heritability measurements. Heritability can be measured in twins or other family structures, but is challenged by the resources required to collect the limited available families with appropriate phenotypic data. Moreover, even when these families are identified, heritability measurements are typically restricted to a single disease. The proposed research addresses these challenges by applying well-developed statistical methods in novel ways to simultaneously calculate heritability for many diseases using data available in the electronic medical record (EMR). The proposed project will test the hypothesis that thousands of clinical phenotypes, defined by patient medical records in families, can be used to measure heritability to direct further genetic/genomic studies. We call this novel bioinformatic method Phenome-wide Scan of Heritability (PheSH). The PheSH concept resulted from my work on Phenome-Wide Association Studies (PheWAS) conducted during my NLM-supported mentored training. Both PheWAS and PheSH are phenotype- independent approaches that allow for the genetic study of many clinical diseases or traits simultaneously. In the independent phase of my career, I plan to continue developing phenotype-independent techniques, including PheSH, to study the genetic etiology of human disease.

Public Health Relevance

All diseases are the result of a combination of genetic factors, also known as heritable factors, and/or environmental factors. This study is designed to measure the heritability of thousands of clinically significant diseases using multiple family structures and electronic medical records. The goal of this project is to identify genetic mutations that explain the strong heritability measurements so that genetics can be applied in 'personalized medicine.'

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Career Transition Award (K22)
Project #
5K22LM011938-02
Application #
8853944
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Ye, Jane
Project Start
2014-06-01
Project End
2017-05-31
Budget Start
2015-06-01
Budget End
2016-05-31
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost
Name
Marshfield Clinic Research Foundation
Department
Type
DUNS #
074776030
City
Marshfield
State
WI
Country
United States
Zip Code
54449
Blue, Elizabeth; Louie, Tin L; Chong, Jessica X et al. (2018) Variation in Cilia Protein Genes and Progression of Lung Disease in Cystic Fibrosis. Ann Am Thorac Soc 15:440-448
Huang, Xiayuan; Elston, Robert C; Rosa, Guilherme J et al. (2018) Applying family analyses to electronic health records to facilitate genetic research. Bioinformatics 34:635-642
Carter, Tonia C; Hebbring, Scott J; Liu, Jixia et al. (2018) Pilot screening study of targeted genetic polymorphisms for association with seasonal influenza hospital admission. J Med Virol 90:436-446
Liu, Jixia; Zhao, Ran; Ye, Zhan et al. (2017) Relationship of SULT1A1 copy number variation with estrogen metabolism and human health. J Steroid Biochem Mol Biol 174:169-175
Karnes, Jason H; Bastarache, Lisa; Shaffer, Christian M et al. (2017) Phenome-wide scanning identifies multiple diseases and disease severity phenotypes associated with HLA variants. Sci Transl Med 9:
Kim, TaeWon; Havighurst, Thomas; Kim, KyungMann et al. (2017) RNA-Binding Protein IGF2BP1 in Cutaneous Squamous Cell Carcinoma. J Invest Dermatol 137:772-775
Fritsche, Lars G; Igl, Wilmar; Bailey, Jessica N Cooke et al. (2016) A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat Genet 48:134-43
Simonti, Corinne N; Vernot, Benjamin; Bastarache, Lisa et al. (2016) The phenotypic legacy of admixture between modern humans and Neandertals. Science 351:737-41
Liu, Jixia; Ye, Zhan; Mayer, John G et al. (2016) Phenome-wide association study maps new diseases to the human major histocompatibility complex region. J Med Genet 53:681-9
Brilliant, Murray H; Vaziri, Kamyar; Connor Jr, Thomas B et al. (2016) Mining Retrospective Data for Virtual Prospective Drug Repurposing: L-DOPA and Age-related Macular Degeneration. Am J Med 129:292-8

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