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GWAS Meta-Analysis for Complex Diseases

Description: The analysis of genetic determinants in human disease, particularly in complex traits such as diabetes, obesity, tuberculosis and heart diseases has advanced markedly in recent years due to the availability of high-density maps of Single Nucleotide Polymorphism (SNP) markers, new insights into human genome structure from NGS, the International HapMap Project and other novel analytical methods. Furthermore, Genome-wide Association studies (GWAS) are becoming the method of choice for studying disease etiology with an increasing number of GWAS studies reporting rapid progress towards uncovering the genetic markers for complex diseases. The high-throughput nature of this technology has allowed for whole genomes to be sequenced and has greatly facilitated the rapid and cost-effective detection of pathogenic mutations in human disorders. The sheer size and complexity of such data highlights the need for understanding from a data analysis point of view. This, in turn, will aid the development of increasingly sophisticated statistical and machine learning methods for studying the genetic origins of complex diseases. This project aims at discussing current GWAS approaches in the context of African populations and will investigate the possibility of combining hypothesis-driven pathway-based analysis with standard disease scoring approaches.

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