Common human diseases such as stroke pose a great burden on the Public Health in US. It has been established that genes ultimately determine, in concert with the environment, susceptibility to these diseases and survival. Therefore, understanding the genetic basis of stroke risk is a crucial step toward better disease prevention, diagnosis, and more effective treatment. High transcranial doppler (TCD) velocities define a subgroup of children with sickle cell disease who are at increased risk for developing ischemic stroke. The genetic factors leading to the development of a high TCD velocity and ultimately to stroke are not well characterized. Association studies offer great promise for unraveling the genetic basis of complex diseases. The ongoing study by Dr. Kutlar aims at understanding the genetic basis of stroke risk in African American children with sickle cell disease through this approach. One potential problem of this approach is the presence of population structure in the samples, which can lead to systematic bias and affect the validity of association results. This problem is especially important in admixed populations such as African Americans. Therefore, we proposed to do further analysis of the data with emphasis on the effects of population structure on association studies.
The aims are (i) to quantify the effects of population structure on large-scale association studies and evaluate properties of the structured association methods for their use in genome-wide association studies. Both the empirical data sets and coalescent-based simulation approach will be used to achieve this aim;(ii) to develop novel haplotype reconstruction and haplotype-tagging SNPs selection methods accounting for population structure for genome-wide association studies. The approach we take is in the Bayesian framework and allows for incorporation of prior knowledge into data analysis and construction of flexible models for complex system. The new results and methods developed in this proposal will be valuable for studying the genetic basis of stroke risk and other neurological diseases through association approaches.
Stroke is the third leading cause of death and a leading cause of disability in the United States. The proposed research aims at developing new methods and applying them to find genetic loci for stroke risk, which can lead to better disease prevention, diagnosis, and more effective treatment of stroke.
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