Understanding heterogeneity and interaction in the context of whole genome genetic analysis

Interactions and heterogeneity play a vital role in the miscommunication between genotype and phenotype in complex diseases. Detection of genes that influence the risk of common, complex disorders involves many statistical and computational challenges. This led us to investigate and compare the common methods of linkage analysis in complex diseases. We applied various methods of linkage analysis on the simulated dataset from the Genetic Analysis Workshop (GAW) 14. As the disease modeled in this dataset resembled a qualitative disorder, we employed methods such as nonparametric linkage scans, association studies of susceptibility regions, and conditional studies (conditioning on a previously identified susceptibility locus). The goal of this project was to study the efficiencies and inadequacies of various methods in detecting interactions and heterogeneity in the simulated dataset.The methods used on this dataset showed very low percentage in the detection of interactions. We attribute this unsatisfactory performance of these methods mostly to the low prevalence of interactions in the imaginary populations studied. We also propose various ways of improving the power in these analyses like considering haplotype studies instead of targeting single markers and increasing the range of the flanking markers around regions of high LOD scores.Public Health Importance: Understanding the complexities involved in the genetics of diseases will provide new insight for disease prevention and health promotion. For over twenty years, public health agencies have focused more and more on newborn screening programs to detect and prevent rare genetic disorders. But common complex disorders pose a bigger problem because of their unique characteristics like heterogeneity, gene-gene interactions, multiple susceptible loci, incomplete penetrance, phenocopy and presence of environmental risk factors. By comparing common methods of linkage analysis in complex disorders in the simulated dataset of Genetic Analysis Workshop (GAW) 14, our study aims to come up with a better understanding of how heterogeneity and interaction work in the context of a whole genome genetic analysis. It is also expected to lay a foundation on which future public health researchers will be able to expand on our work