Recent technological advances may soon enable the study of hundreds
of thousands of human single-nucleotide polymorphisms (SNPs) at the population
level.1 Because strategies for analyzing
these data have not kept pace with the laboratory methods that generate the
data, however, it is unlikely that these advances will immediately lead to
an improved understanding of the genetic contribution to common human diseases.
In addition, the underlying genetics of common diseases such as sporadic breast
cancer or essential hypertension are far more complex than that of rare mendelian
diseases such as cystic fibrosis and sickle cell anemia. As a result, several
important technical challenges will need to be overcome to identify susceptibility
genes that can be used to improve the prevention, diagnosis, and treatment
of common diseases. These challenges include developing statistical methods
to analyze genetic data, selecting appropriate genetic variables, and interpreting
interactions between individual genes.
Moore JH, Ritchie MD. The Challenges of Whole-Genome Approaches to Common Diseases. JAMA. 2004;291(13):1642–1643. doi:10.1001/jama.291.13.1642
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