As the cost of sequencing a person’s genome has sharply declined, the enthusiasm for using that genomic knowledge to predict susceptibility to gene-based illness has grown. In fact, it’s been one of the most common topics of medicine in the public media for more than a year. This includes intense debates about whether it is desirable to know one’s genetic weaknesses and the ethics of predicting major health problems based on genetic background. Behind much of that coverage was the assumption that a personal genome sequence provides genetic information reliable enough to make accurate predictions.
As is often the case with a medical advance surrounded with hype, there are doubts and concerns. A major new study by Johns Hopkins medical research in Science Translational Medicine [02 April 2012, paywalled, The Predictive Capacity of Personal Genome Sequencing] involving thousands of identical twins is outspoken about the failure of personal genomic analysis to identify a person’s risk for most common diseases. In fact, it flat out warns people not to uncritically accept negative genome test results.
Since the personal genome sequencing business is turning into a major growth industry and the stake of research on the links between genes and disease is enormous, this study is bound to be not only something of a knowledge-bomb, but also instantly controversial.
The basis of the study is recorded data on thousands of identical twins in the registries of Sweden, Denmark, Finland, and Norway along with the Twins Registry of the American National Academy of Science. Because identical twins are thought to share identical genomes, it should follow that if one twin presents a genetic based disease, statistics should indicate a prevalence of the same disease in the identical twin. The researchers used information on 24 diseases (cancer, autoimmune, cardiovascular, genitourinary, neurological and obesity-associated). Statistical models were created to predict the risk of each disease based on typical doctors’ diagnosis.
The results need a careful reading: A whole genome sequencing could indicate an increased risk of at least one disease, but most people would get negative test results for the majority of diseases in the study. Put another way, even for diseases with links to a genetic foundation, the presence of those genes in one individual is not a satisfactory predictor for another individual. Statistically, for example, if 2% of women show a genetic predisposition for ovarian cancer, it does not mean that the other 98% who test negative for the gene won’t get ovarian cancer.
You may have noticed that this is not a blanket denial of genomic analysis. The researchers are careful to say that genomic information can be very helpful for people with families who have a strong history of a particular disease. In addition, certain diseases that are shown to be particularly related to genetic variation such as coronary heart disease in men, thyroid autoimmunity, type 1 diabetes and Alzheimer’s disease are more likely to be accurately predicted by genomic analysis. This list is likely to grow as medical research advances.
However, in the broader perspective the ability of genomic analysis to predict a limited set of diseases leaves most people and most diseases unpredictable by this method. For example, while some hereditary cancers are gene influenced, hereditary cancer is rare. Most cancer is caused by genetic mutations acquired by environmental exposure, lifestyle choices (like smoking) and random errors in genes that occur during cell division. As one of the lead researchers, Bert Vogelstein of Johns Hopkins Kimmel Cancer Center (Maryland, USA) puts it:
We believe that genomic tests will not be substitutes for current disease prevention strategies. Prudent screening, early diagnosis and prevention strategies, such as not smoking and removing early cancers, will be the keys to cutting disease death rates.