tailieunhanh - báo cáo khoa học: " Simulating gene-environment interactions in complex human diseases"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Simulating gene-environment interactions in complex human diseases | Peng Genome Medicine 2010 2 21 http content 2 3 21 Genome Medicine MINIREVIEW L__ Simulating gene-environment interactions in complex human diseases Bo Peng Abstract Because little is currently known about how genes interact with environmental factors in human diseases and because of the large number of possible interactions between and within genetic and environmental factors it is difficult to simulate samples for a disease caused by multiple interacting genetic and environmental factors. A recent article by Amato and colleagues in BMC Bioinformatics describes a mathematical model to characterize gene-environment interactions and a computer program that simulates them using biologically meaningful inputs. Here I evaluate the advantages and limitations of the authors approach in terms of its usefulness for simulating genetic samples for real-world studies of geneenvironment interactions in complex human diseases. Introduction Simulated datasets with known underlying disease mechanisms have been widely used to develop efficient statistical methods for deciphering the complex interplay between the genetic and environmental factors responsible for complex human diseases such as hypertension diabetes and cancer 1-3 . Although genetic and environmental risk factors have been identified for various human diseases little is currently known about how genes interact with environmental factors in these diseases. Because the number of possible interactions between and within genetic and environmental factors is large it is difficult to specify and simulate samples for a disease caused by multiple interacting genetic and environmental factors. Consequently existing studies have focused on simple models with low-order interactions between a few genetic and environmental factors using specialized simulation programs. Here I discuss a Correspondence bpeng@ Department of Epidemiology The University of Texas MD Anderson Cancer Center .

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