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Tag Archives: BMJ

Biotech Trends Update — Personalized Medicine: The Limits of Genomic Analysis

A great report on GenomeWeb yesterday by Andrea Anderson reviews two JAMA papers that failed to show a clinically useful role for SNP genetic testing in predicting heart disease risk.  Instead,

“traditional risk information based on factors such as family history and plasma biomarker levels were better for predicting heart disease.”

Anderson ties these results back to a January paper in the British Medical Journal that found that

“non-genetic factors were more useful for predicting type 2 diabetes than a set of 20 SNPs.”

The GenomeWeb article quotes the lead author of one JAMA paper as finding the results “surprising and a little disappointing;” but I am inclined to think some context is missing, since only the most die-hard genetic determinist should be either surprised or disappointed. 

Two factors suggest that these conditions, and many others, will resist accurate prediction based on genomic sequence analysis:

  1. They are genetically complex.  The prospective studies looked at data sets with between 12 and 101 SNPs.  Simple calculations suggest that the number of genetic permutations is itself staggering, never mind the physiological complexity of multigenic interactions.
  2. There are massive environmental components. Diet and exercise, among many other factors, will have a tremendous impact on clinical outcomes.  These habits are learned, not inherited, and are even “contagious” within social groupings.

My bottom line: In an age when genomic sequences are becoming increasingly accessible, it should be reassuring to know that even your medical future is not written in stone.  We always suspected as much. Now we have the genetic data to prove it.

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