Candidate: The candidate is an Assistant Professor of Pediatrics in the Divisions of Asthma Research at Cincinnati Children's Medical Center and the University of Cincinnati. Upon completion of his quantitative genetics training, the candidate pursed statistical and human genetics fellowship programs at the University of Alabama Section on Statistical Genetics and the Human Genetics Center of Medical College of Wisconsin. These projects have been published in peer-review journals. Dr. Baye's overall research interest includes the use of quantitative and statistical genetics methods to dissect complex diseases particularly asthma and asthma-related allergy disorders. The long-term goals are to reduce childhood morbidity and mortality associated with asthma. Environment: At the Cincinnati Children's Hospital Medical center, the applicant will work with a highly collaborative multidisciplinary research team and will be fostered in an excellent academic environment offering outstanding educational programs for junior faculty members. The institution, through the Divisions of Asthma Research, Asthma Center, Biostatistics and Epidemiology, Biomedical Informatics and the Genetic Variation and Gene Discovery Core, provides all resources and facilities that are needed for the applicant's proposed research and is providing full access to all necessary resources to the applicant. The applicant will be provided 90% protected time to conduct the research proposed in this application. Research: This project aims to develop a program of study that would lead to an in depth understanding of the genome of African American (AA) admixed populations and develop procedures and methods for utilizing this information and SNP markers for linkage disequilibrium admixture mapping to localize asthma liability genes. The burden of asthma is disproportionately high among individuals of African descent. Due to recent admixture, AAs have a mixed parental genome contribution, with an average ratio of 80:20 for the proportion from African descent and European descent. This generates linkage disequilibrium (LD) among alleles on all chromosomes, which is detectable even at substantial distances (20-30 cM) using Ancestry Informative Markers (AIMs). We plan to develop methods to exploit this LD that span along the genomes of AAs with asthma, in search for specific regions of unusually high African or European ancestry, thereby identifying the chromosomal segments that are likely to contain genes that are related to the disease liability. To achieve these goals, the following specific aims are proposed: 1) Investigate the genome of African American admixed population using our recently developed ancestry informative markers (Baye et al., 2009);2) Estimate admixture proportions and evaluate differences between asthma cases and controls;3) Apply these procedures to investigate whether genetic ancestral background at the 5q31 region is associated with asthma outcome measures. Hence, genotypic data from HapMap, Perlegen datasets and Greater Cincinnati Pediatric Clinic Cohort will be used to test the approach and enhance our ability to map asthma liability genes. Implications: This career development plan will foster Dr. Baye's development into an established independent expertise in complex disease and asthma genetics. The research will improve mapping of loci affecting complex traits in admixed populations, ultimately improving elucidation of the genetic architecture of complex human diseases.

Public Health Relevance

The burden of asthma is disproportionately high among individuals of ethnic minorities. This project aims to develop procedures and methods for utilize the genome of admixed African American populations to localize asthma liability genes by modeling individual estimates of genetic admixture and socioeconomic status on measures of asthma. By gaining a better understanding of asthma risk factors in admixed minority populations, this proposal is consistent with the goals established by Healthy People 2010, which are to eliminate health disparities among different segments of the population. (End of Abstract)

Agency
National Institute of Health (NIH)
Type
Research Scientist Development Award - Research & Training (K01)
Project #
5K01HL103165-05
Application #
8669058
Study Section
Special Emphasis Panel (ZHL1)
Program Officer
Tigno, Xenia
Project Start
Project End
Budget Start
Budget End
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
City
Cincinnati
State
OH
Country
United States
Zip Code
45229
Maglo, Koffi N; Mersha, Tesfaye B; Martin, Lisa J (2016) Population Genomics and the Statistical Values of Race: An Interdisciplinary Perspective on the Biological Classification of Human Populations and Implications for Clinical Genetic Epidemiological Research. Front Genet 7:22
Gupta, Jayanta; Johansson, Elisabet; Bernstein, Jonathan A et al. (2016) Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry. J Allergy Clin Immunol 138:676-99
Ghosh, Debajyoti; Ding, Lili; Sivaprasad, Umasundari et al. (2015) Multiple Transcriptome Data Analysis Reveals Biologically Relevant Atopic Dermatitis Signature Genes and Pathways. PLoS One 10:e0144316
Mersha, Tesfaye B; Abebe, Tilahun (2015) Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities. Hum Genomics 9:1
Mersha, Tesfaye B; Martin, Lisa J; Biagini Myers, Jocelyn M et al. (2015) Genomic architecture of asthma differs by sex. Genomics 106:15-22
Ding, Lili; Abebe, Tilahun; Beyene, Joseph et al. (2013) Rank-based genome-wide analysis reveals the association of ryanodine receptor-2 gene variants with childhood asthma among human populations. Hum Genomics 7:16
Feng, QiPing; Wilke, Russell A; Baye, Tesfaye M (2012) Individualized risk for statin-induced myopathy: current knowledge, emerging challenges and potential solutions. Pharmacogenomics 13:579-94
Amirisetty, Sushil; Hershey, Gurjit K Khurana; Baye, Tesfaye M (2012) AncestrySNPminer: a bioinformatics tool to retrieve and develop ancestry informative SNP panels. Genomics 100:57-63
Ding, Lili; Baye, Tesfaye M; He, Hua et al. (2011) Detection of associations with rare and common SNPs for quantitative traits: a nonparametric Bayes-based approach. BMC Proc 5 Suppl 9:S10
He, Hua; Zhang, Xue; Ding, Lili et al. (2011) Effect of population stratification analysis on false-positive rates for common and rare variants. BMC Proc 5 Suppl 9:S116

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