. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA. 2010 May 12;303(18):1832-40. PubMed.

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  1. The methodology in this study is slightly different from the one we are used to: a GWAS developed on a large population sample with a selection of SNPs that reach statistical significance (p -8) and are then replicated in large, independent samples. In this study, the first stage involved the selection of 2,708 SNPs with genetic association (p -3) from a meta-analysis of six different studies (four constituting the CHARGE consortium). Then these 2,708 SNPs were evaluated in our GWAS EADI consortium. A meta-analysis of the six studies together with the EADI consortium allowed the selection of 38 SNPs in 10 loci that have a p -5. In the third stage, the most significant SNPs from these 10 loci were meta-analyzed with the GWAS GERAD consortium, allowing the discovery of two SNPs reaching the threshold for genomewide significance. Finally, the two new loci, one on chromosome 2 in the vicinity of BIN1, and one on chromosome 19 within BLOC1S3/EXOC3L2/MARK4, were confirmed in an independent Spanish case-control study. Thus, the major conclusion of this paper is that two new potential genetic susceptibility factors are identified that deserve in-depth analyses and other independent replications.

    Another interest of this study is that two cohorts from the CHARGE consortium—the Rotterdam Study and the CHS—allowed us to estimate risk of incident Alzheimer disease in the general population. The risk estimation improvement associated with these genetic susceptibility factors is weak compared to the information from classical risk factors (age and gender). This conclusion is also observed with other diseases like type 2 diabetes (Talmud et al., 2010). Should these results suggest that GWASs identify loci that have little influence on disease risk and thus do not add significant information to our knowledge of the disease?

    If we examine the estimation obtained in the Rotterdam Study of the AUC for a prediction model that includes only age and gender, it already reaches 0.826, which means age and gender explain a very large part of the risk. Thus, adding one or two susceptibility genes or any other risk factor will not, in a general population sample, largely improve this prediction. Therefore, in that situation, the interest of these risk factors as a screening tool for risk prediction is not obvious in the general population. However, that does not mean that genes coming out from GWASs are not helpful. Let us take the example of HMG CoA reductase enzyme, whose genetic variability explains less than 10 percent of the variance of the LDL-cholesterol blood level. This enzyme is today the major target for one of the most active classes of therapeutics for cardiovascular risk reduction—the statins. Thus, it is very difficult to estimate the importance of a new hit or a new pathway from the predictive value of its genetic variability in the general population. The genes identified in AD GWASs—i.e., CLU, PICALM, CR1, and now BIN1 and BLOC1S3/EXOC3L2/MARK4—need further in-depth investigation to understand how they interfere with Alzheimer disease.

    A last remark about the individual clinical interest of these genetic susceptibility risk factors comes from a recent article (Ashley et al., 2010). It describes the potential use of an individual whole-genome sequence in the estimation of the risk of a 40-year-old man with a family history of coronary artery disease and sudden death, and with clinical characteristics within normal limits. In this patient's genome, several common variants were found associated with increased coronary and type 2 diabetes risks, together with rare variants in three genes associated with sudden cardiac death, offering potentially useful information for the possible care of this patient. This personal genome case, which will probably not be isolated in the near future due to the dramatically decreasing costs of whole-genome sequencing, gives an example of clinical utility of the genes emerging from GWASs.

    Even if the results of GWASs do not seem to add significant information to the risk prediction in the general population, they pave the way for personalized medicine and tailored care of the diseases.

    References:

    . Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study. BMJ. 2010;340:b4838. PubMed.

    . Clinical assessment incorporating a personal genome. Lancet. 2010 May 1;375(9725):1525-35. PubMed.