Genomewide Association Studies and Assessment of the Risk of Disease

http://www.nejm.org/doi/full/10.1056/NEJMra0905980

Nearly 600 genomewide association studies covering 150 distinct diseases and traits have been published, with nearly 800 SNP–trait associations reported as significant (P<5×10−8)

Approximately 40% of trait-associated SNPs fall in intergenic regions, and another 40% are located in noncoding introns. These two findings have sharpened the focus on the potential roles of intronic, and particularly intergenic, regions in regulating gene expression. 1

Although intronic and intergenic SNPs are not overrepresented in associations as compared with randomly selected SNPs, they account for the great majority — more than 80% — of associated SNPs.

Given the lack of good representation of SNPs with a prevalence of less than 5% in current genomewide association arrays, a comprehensive catalogue of SNPs with a prevalence of 1 to 5% is being generated by the 1000 Genomes Project55 for potential inclusion in fine-mapping efforts and expanded genomewide association arrays. In the project's pilot effort, more than 11 million novel SNPs have been identified in what was initially low-depth coverage of 172 persons.

Annotation catalogues (maps of functions of variants), such as those related to transcription-factor binding (promoting gene expression) or to RNA interference (silencing genes), are currently in development and should facilitate the identification of functional variants underlying genomewide association signals.57

The importance of structural variation, including copy-number variants, inversions, and translocations, is an active area of investigation; several structural variants underlie genomewide association signals for autism, schizophrenia, Crohn's disease, and obesity.

For the prediction of complex diseases, genotypes at multiple SNPs are often combined into scores calculated according to the number of risk alleles carried, which is the approach that Kathiresan and colleagues used in predicting the risk of cardiovascular disease on the basis of nine SNPs associated with cholesterol levels

What is becoming clear from these early attempts at genetically based risk assessment is that currently known variants explain too little about the risk of disease occurrence to be of clinically useful predictive value.

Possible clinical uses of predictive scores — for example, in deciding which patients should be screened more intensively for breast cancer with the use of mammography69 or for statin-induced myopathy with the use of muscle enzyme assays70 — will require rigorous, preferably prospective, evaluation before being accepted into clinical practice.

The ability to assess risk for 120 conditions at the same time also raises the concern that predictive models will yield conflicting recommendations; if implemented, they could reduce a person's risk for development of one condition and exacerbate the risk for development of another.

Patients inquiring about genomewide association testing should be advised that at present the results of such testing have no value in predicting risk and are not clinically directive.

Much more remains to be learned about how variations in intronic and intergenic regions (where the vast majority of SNP–trait associations reside) influence gene expression, protein coding, and disease phenotypes.

The substantial challenges of incorporating such research into clinical care must be pursued if the potential of genomic medicine is to be realized.

Hardy J, Singleton A. Genomewide association studies and human disease. N Engl J Med 2009;360:1759-1768 Full Text | Web of Science | Medline http://www.ncbi.nlm.nih.gov/pubmed/19369657

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