Most general population risk for schizophrenia is accounted for by common variations in the genome, but each variant explains only a tiny fraction of increased risk. In contrast, approximately 1%−2% of individuals with the diagnosis of schizophrenia have a microstructural chromosomal abnormality resulting in either a deletion or duplication of a segment of the genome, typically involving more than 100,000 nucleotides and many genes. These so-called copy number variations (CNVs) typically arise during meiosis and are found on one parental chromosome. They tend to be much more clinically penetrant than common variants, presumably because they involve a dosage alteration of genes within the CNV region, although they account for much less population risk. Close to a dozen CNVs have been associated with the diagnosis of schizophrenia, and as with common variants, they span the genome and do not converge on a simple common biology. Although the more penetrant schizophrenia associated CNVs are of potential value in genetic counseling, and some involve a high probability of intellectual compromise, none are more likely to result in the diagnosis of schizophrenia than not.
Because common risk-associated variants have little or no value in genetic counseling and limited application in clinical research, a so-called polygenic risk score (PRS) has been developed as a tool for assigning to an individual a measure of his or her overall genomic risk for illness, and PRSs tend to account for severalfold increases in individual liability. Indeed, PRSs near the upper end of the distribution of scores carry risk odds ratios similar to most of the risk-associated CNVs. The PRS is calculated as a weighted sum of risk-associated alleles found in the latest genome-wide association study (GWAS) of a particular trait or diagnosis. Because it is a value given to an individual, it can be used as a predictor variable in clinical studies. Three articles in this issue of the Journal illustrate some applications of the schizophrenia PRS in clinical research, including in the context of risk-associated CNVs.
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