New method published in Genome Medicine improves complex disease risk prediction

A Method recently published in Genome Medicine suggests that inclusion of phenotypic and genotypic information from close relatives in a genetic risk prediction model can lead to improved estimates of an individual’s disease risk.

Douglas Ruderfer, Joshua Korn and Shaun Purcell, from Massachusetts General Hospital, The Broad Institute of Harvard and MIT, and Harvard Medical School have developed a liability threshold model which predicts an individual’s risk of developing a complex disease, using their own genotype as well as that of a close relative whose disease phenotype is known. “Family-based genetic risk prediction of multifactorial disease” is published in the January issue of Genome Medicine.

The authors say, ‘we do not ask “how well do SNPs predict disease compared to family history”, but rather, “how well do SNPs predict disease given a positive family history, and to what extent does including genotype data from the affected relatives help?”.’  They test their model on a simulated dataset for Crohn’s disease, a multifactorial trait, and show that estimates of disease risk are modestly improved by this method.

The presence of a complex disease in a family member may be a motivator for genetic testing.  For many such diseases, the causal genetic variants will have a wide range of magnitudes, and the addition of a newly-discovered common variant with a small effect on disease risk will have a moderate effect on risk prediction.  However, the cumulative value of these low-magnitude data can be informative.  The improvement to disease risk prediction by this new model is also moderate, but genetic information from family members may be an important addition to genetic tests in the future.  Of course, whether individuals then change their behaviour to reflect their predicted risk is another story …

Why not visit Genome Medicine’s new website for more research and reviews in this exciting field?


Rebecca Furlong
Assistant Editor, Genome Medicine

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