‘Phantom Heritability’ Indicates Poor Predictive Value of Gene Tests

Helen Wallace, GeneWatch UK (photo credit: jurvetson)

Last week, a paper on “phantom heritability” was published by a research group led by Eric Lander, one of the leading contributors to understanding the implications of the Human Genome Project (HGP) for common, complex diseases such as heart disease and cancer (1). The paper has created excitement amongst scientists who have been critical of the claims made for the likely impacts of the HGP on health and medicine. This is not because the paper makes a startling new discovery, but because it is an important step towards recognition of these long standing criticisms.

DNA Sequencing Machines

The new paper has fundamental implications for the future of health policy and the role of genetic science. Nevertheless, it has so far been ignored by the mainstream press, with only one specialist publication reporting on its findings (2). This is unfortunate because the paper undermines frequently reported claims that sequencing the human genome will lead to a “genetic revolution” in healthcare, in which the entire population will have their genomes mapped or sequenced in order to predict and prevent the diseases they will get. It casts doubt on this idea because it recognises that common diseases are in fact more complex than enthusiasts for this approach have previously admitted: meaning that there will be inherent, biological limitations to the predictive value of whole genome sequencing, however much research is done. Specifically, the paper finds that interactions between multiple genes may give rise to “phantom heritability”, so that genetic differences between individuals appear more important that they really are in determining who will develop a particular disease or complex trait.

My own paper, published five years ago, reached the same conclusions (3). I found that both gene-gene and gene-environment interactions could reduce the calculated heritability for a given trait to values considerably below the heritability predicted by the standard twin-studies method, which was originally developed by the eugenicist Ronald Fisher in 1918. I further showed that, in the absence of any gene-environment interaction, selecting a high risk group for intervention using a genetic test was no better at reducing the incidence of a disease in a population than randomly selecting the same number of people from the population: i.e. such a test would have no added value in reducing the incidence of a disease. Changing the “equal environments” assumption (which presumes that identical and non-identical twins share their environments to the same extent) also reduces heritability, as many authors have previously noted.  In general, common diseases will not involve both a high heritability and a strong gene-environment interaction, so the usefulness of targeting lifestyle advice at people based on their genetic make-up is likely to be limited, even if all the genetic and environmental factors involved in a disease were known.

This has major implications for the idea that whole genome sequences should be integrated into electronic medical records and used routinely in clinical care (4): what is the point, if most of this information is likely to be clinically useless for most people? The implications for health policy are also significant: although whole genome sequencing undoubtedly will have some applications in the context of rare genetic and familial disorders, prevention of common diseases will not be delivered by genetic susceptibility testing. Instead, what is required is a return to traditional epidemiology (focused on identifying modifiable environmental risk factors, rather than genetic ones) and to population-based public health interventions (such as controls on tobacco marketing, pollution, or salt- and fat-content in processed foods). Some pharmacogenetic tests may be useful to predict response to some specific drugs, but these tests are also only useful in specific situations when people need to take those drugs: they do not suggest a need to sequence the whole genomes of the healthy population.

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