Asthma has rapidly become one of the most common chronic illness, espescially in developed western countries. It is of particular interest to genetic researchers whether the genetic-makeup of an individual predisposes one to develope asthma. With findings from various recent studies, it was found that asthma is a complex disease that involves and caused by interactions between genes and environmental factors. Although some of these environmental factors have been well-established, little is known about the underlying genetic causes of this disease. This research primarily investigates whether variation in the candidate gene, PDH Finger Protein 11 gene, also better known as PHF11 gene, affects asthma-related phenotypes. The data used is the Busselton data, whereby it includes data of individuals and their families.

The population in the Busselton data is family-based and so, there exist some kind of correlation between family members since they possess similar genes and were exposed to the same type of environment. To analyze such data, appropriate statistical tools have to be used. The mixed effects model is one such approach that can appropriately model such familial data. However, a more complicated but effective approach is employing the use of the variance component framework, which effectively makes use of the mixed effects model and partitioning of the various variance components to model the correlation structure in the data.

Although these methods provide inferences and insights to the role of the PHF11 gene in influencing asthma and asthma-related phenotypes, the disadvantage of using complex methods is that it may be too computational intensive. Therefore, the secondary objective of this research is to investigate if the use of simpler statistical methods for modeling the Busselton data can produce similar results as the "Gold Standard" method, which is also known as Quantitative Transmission Disequilibrium Tests (QTDT) and is commonly used in genetic association studies.