Our research aims to promote newly developed flexible statistical methods for analyzing lung cancer data to discover additive gene-by-environment interactions affecting the cancer, where interaction is measured using difference of disease risks. Existing methods for additive gene-by-environment interaction assume very specific genetic models. We provide methods which is more flexible compared to these methods in the sense that our methods provide more reliable results when one has no knowledge of the actual genetic model. We further show that if we let gene and environment be independent in the whole population, our method can better detect a truly exisitng interaction. The project is currently estimated to continue for three years. Through this project, we aim to extend our understanding on the relative importance of various environmental factors on the development of lung cancer.
The environmental variables that we are interested in are given below.
– Addictions
– Alcohol
– Alcohol use
– Alcoholic beverages yesterday
– Anxiety
– Arterial stiffness
– Body size measures
– Bone size, mineral and density by DXA
– Bone-densitometry of heel
– Brain MRI
– Bread/pasta/rice yesterday
– Breathing
– Broad WGS pilot
– COPD outcomes
– Cancer register
– Cancer screening
– Dementia outcomes
– Depression
– Diet
– Diet questionnaire performance
– Early life factors
– Education
– Ethnicity
– Family history
– Female-specific factors
– Medical conditions
– Medical information
– Medication
– Medications
– Mental distress
– Mental health
– Residential air pollution
– Residential noise pollution