4th January 2012
A new study published in Proceedings of the National Academy of Sciences today undermines claims that people's genetic make-up can be used to predict and prevent the diseases they will get (1). This claim has been central to the idea of a genetic revolution in healthcare, in which every adult and child is assumed to have their whole genome sequenced and stored in a database or on their mobile phone.
The study supports earlier findings by GeneWatch UK that much of the heritability of common diseases, calculated using twin studies, may not exist (2). Scientists have been puzzled by the failure of large genetic studies to find genes which explain the "missing heritability" of common diseases such as heart disease and cancer and traits such as height. Typically, 85 to 95 per cent of the expected heritability has not been found. Today's new study confirms that one explanation may be that interactions between multiple genes would reduce the predicted heritability. These interactions were not properly accounted for by the eugenicist Ronald Fisher who developed the original twin studies method in 1918, and later analysis has not corrected Fisher's error.
"Claims of a genetic revolution in healthcare have long been based on false assumptions" said Dr Helen Wallace, Director of GeneWatch UK, "If heritability is much lower than expected this means that genetic differences play only a small role in explaining why some individuals get a disease which others do not. Genetic testing can help people with rare disorders but will never be useful to predict and prevent the common diseases that most people get."
Companies which sell technologies to analyse the human genome, such as Life Technologies and Illumina, have previously claimed that it will become routine to sequence the DNA of every baby at birth in order to predict their risk of future illnesses and tailor lifestyle advice and preventive medication to their personal genetic make-up. Companies selling gene tests online to consumers, such as 23andMe and DeCode Genetics, rely on convincing a large market of healthy people that they should buy such tests. The idea that everyone should have their whole genome read and stored also led to the New Labour Government's decision to spend billions on creating a database of electronic medical records in the NHS (3).
"Past policy decisions have been based on hubris, lobbying by vested interests and failure to check unsubstantiated claims" said Dr Wallace, "Sequencing the whole genome of every person is a science fantasy that will not deliver benefits to health".
Some genetic studies will remain useful to identify disease mechanisms, because even small differences in risk can provide clues about the biological pathways involved in a disease. But the medical value of genetic testing will be likely to remain restricted to situations where an individual is thought to be at high risk of an inherited disease, or displaying unexplained symptoms.
The study does not assess the usefulness of genetic screening to predict adverse drug reactions. However, many such tests also have low predictive value and are only useful prior to taking a few specific drugs.
For further information contact:
Dr Helen Wallace: 01298-24300 (office); 07903-311584 (mobile).
Notes for Editors:
(1) Zuk et al. (2012) The mystery of missing heritability: Genetic interactions create phantom heritability. PNAS Early Edition. On: http://www.pnas.org/content/early/2012/01/04/1119675109.full.pdf ; Reported in: Broad-Led Analysis Suggests Genetic Interactions Could Account for Substantial Portion of 'Missing Heritability'. January 03, 2012. On: http://www.genomeweb.com/node/1007726?hq_e=el&hq_m=1168863&hq_l=2&hq_v=b6b3a85825
(2) Wallace HM (2006) A model of gene-gene and gene-environment interactions and its implications for targeting environmental interventions by genotype. Theoretical Biology and Medical Modelling 2006, 3:35. http://www.tbiomed.com/content/3/1/35
(3) GeneWatch UK (2009) Is 'early health' good health? On: http://www.genewatch.org/uploads/f03c6d66a9b354535738483c1c3d49e4/Data_mining_brief_fin_3.doc