Phenocopy sometimes muddies-up the genetic factors of disease risk, but it soon might get cleaned up.
Nature can fool us in many ways. We’ve all seen mimicry in butterflies and camouflage in chameleons. They can trick our eyes into seeing double or not seeing what is present. These mysteries are left for the naturalists. However, not too many people have heard of a more subtle, but equally tricky phenomenon: phenocopy; geneticists must contend with this.
“Phenocopy has traditionally been defined as sort of a non-genetic basis for disease,” says Jason Moore, PhD, associate professor of genetics at Dartmouth University, Hanover, N.H. “In other words, you have some environmental agent that is determining disease in a subset of the people you are studying. And, if you are looking at genetic risk factors, then those people look like they may have a genetic basis when, in fact, they may have an environmental basis.”
That’s the classic definition of phenocopy, says Moore, who adds that he is not sure if this definition holds true any more. This is definitely the case in his research program, where he does not see the environment and the genome as being independent of each other, but as working together. The goal of Moore’s research is to understand the role of genes in determining susceptibility to cardiovascular disease, psychiatric diseases, and cancer. His research program aims to develop the computational tools for looking at gene-environment interactions because, he says, genes don’t work in isolation, but work in the context of the environment.
“I think that there are very few diseases that have a purely environmental cause that doesn’t also involve the genetics of the individual,” he says. In Moore’s eyes, phenocopy is what is left unexplained after measuring the genetic and environmental sources of disease risk. Because of its character as a residual factor, phenocopy can add a lot of noise (variation) into experimental data and can consequently complicate a genetic analysis.
Phenocopy and linkage
And, a phenocopy can wreak havoc on studies of hereditary patterns in families. “There is actually a lot of literature about the effects of phenocopy on the results of linkage analysis,” says Andrew Paterson, MbChB, Canada Research Chair in Genetics of Complex Diseases, and scientist, Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. Paterson’s definition of phenocopy differs slightly from others. “A phenocopy is someone in a pedigree [analysis] who has the same disease as the one we are studying, but the cause of it is either due to another genetic location or some other factor, say environmental . . . If an individual in the study does not have the right allele at the locus of interest, then they can be considered a phenocopy from that gene’s perspective,” says Paterson.
Paterson gives the example of a study involving families afflicted with a form of deafness that is due to either a genetic or an environmental cause. This can complicate the pedigree analysis and generally diminish the ability of the experimenter to find a gene responsible for the form of deafness afflicting a majority of individuals in the study. In this case, as well as aforementioned ones, phenocopies effectively add noise into the data because affected individuals are not affected due to the presence of the gene, but due to some unexplained factor. This eventually weakens the genetic linkage data by weakening the power of the assay to detect the effect of the gene on deafness. “And this problem tends to get bigger, the bigger the pedigrees are because there is greater chance that there will be additional phenocopies in the family. And, the more common the disease is, the more problems phenocopy produces,” says Paterson.
However, researchers can adjust their data to the presence of phenocopies. One way Paterson adjusts for phenocopies in an experiment is by allowing for a proportion of the individuals in the family to have the disease from some other cause—either another gene or the environment.
According to Paterson, there are also ways to sort out who has the disease gene and who is a phenocopy. “We can use, but there is no specific test for any particular disease. I mean the people who have unusual characteristics to the disease may be the ones who are more likely to be the phenocopies.”
He also says that phenocopy was a very big problem in studies of Mendelian diseases, but that it is less of a problem for association diseases because of the fact that there are typically fewer phenocopies in the data set when using unrelated individuals. Phenocopies do not cause a dramatic reduction in power in this case, so it’s becoming less of problem for genome-wide association studies.
Not only does phenocopy complicate genetic research, but it can also complicate the clinical diagnosis of diseases that have both a genetic and environmental component. One of the physician-researchers who can attest to this is Kalish Bhatia, FRCP, professor of clinical neurology, Institute of Neurology, University College of London, Queens Square, London, UK. Bhatia encounters phenocopy in his research, particularly in patients who have suspected Huntington’s Disease (HD) when the diagnosis is based on clinical features alone. But, these HD patients eventually turn out to be phenocopies when they are determined to be negative for the HD gene.
Distinguishing an HD gene-carrier from an HD-phenocopy can be difficult even for an expert. Of course, there is the HD gene test and also non-genetic methods to distinguish the two groups of patients, Bhatia says. “For example, if you do a brain scan, maybe 60% of HD patients may have abnormalities on a brain scan in the basal ganglia, but the phenocopy patients do not have this atrophy,” he describes. So other lab tests can rule out phenocopies for a given gene when a genetic test for that gene does not exist.
Bhatia explains why phenocopy is no longer such a devastating problem in the clinic. “It’s funny that the more genes we are discovering, the more we are realizing that phenocopies are present; I am sure that will happen in more genetic conditions.”
The future of phenocopy
So will the effects of phenocopy ever be completely accounted for in a genetic study? Moore believes that, to some degree, there will always be some things left unexplained, things that can’t be measured. “There will always be a source of noise, a chance of chance events that sort of muddy-up any given data set,” he says. “That’s something that is never going to go away.”
But it seems progress is being made because more of the previously-unexplained noise or variation can now be measured and explained. As evidence of this progress, Moore points out that researchers can now carry out genome-wide association studies with one million SNPs across the human genome. And that, he says, is going to remove that source of noise from the measured genetic factors.
“The better the genome, the better we can measure the environment and the more info we will have for predicting disease susceptibility, the less phenocopy will be an issue. But [phenocopy] will never go away because we can’t measure everything,” says Moore.
Another resource to aid in the measurement of unexplained variation in a genetic study is coming out of an initiative from the National Institute of Environmental Health Sciences at the National Institutes of Health, Bethesda, Md. The goal of the initiative: to develop the technologies and tools needed to better measure environmental factors of human disease risk, something that Moore says researchers currently don’t do very well.
“It’s a very difficult thing to do—to measure in real-time someone’s environmental exposure,” says Moore. “It’s very complicated. It is a lot of info that needs to be measured, that needs to be measured over an individual’s lifespan.”
Despite the fact that the phenomenon of phenocopy continues to muddy-up the data from genetic studies, it is clear that researchers are getting a handle on it—cleaning it up, so to speak. And this trend is likely to continue as genomic and genetic researchers tease out the genetic components of human disease. Overall, this gene discovery should make genetic study data less noisy as it attempts to explain residual variability in experimental data.
This article was published in G & P magazine: Vol. 7, No. 11, November, 2007, pp. G2-G5.
Filed Under: Genomics/Proteomics