I thought about ordering about a testkit. But the interpretation of the results are not based on enough information, just on the available information. Maybe something for in the future with a lower price. A price of $99 would be in my gadget range.
--
Although companies offering genomic profiles did not specify how they selected polymorphisms for inclusion in the profiles, they probably did so on the basis of statistically significant results from association studies. Because positive results from single gene-disease association studies are often not replicated in subsequent studies,12 one study showing a statistically significant association is considered insufficient evidence of genetic association.10 Our review of meta-analyses found significant associations with disease risk for fewer than half of the 56 genes that are tested in commercially available genomic profiles. Various polymorphisms of these genes were associated with risk for 28 different disorders. Many of these disorders were unrelated to the ostensible target condition, and the associations were generally modest.
Before interpreting our results, we need to clarify four issues regarding the review strategy we used. First, our paper addressed predictive genomic profiles that are sold online and that aim to personalize nutrition and other lifestyle health recommendations. The review did not assess the scientific basis of gene-expression profiles and pharmacogenomic applications. Although there may be applications that have stronger scientific support than others, there are clearly promising developments in this area.[13] and [14] Second, the information on genes and polymorphisms in this study was obtained from company websites and online sample reports. As of November 2007, all seven companies were still selling the profiles, but two no longer specified on their Web sites which genes they were actually testing. Although these companies may now use other polymorphisms to profile the disease risk of their clients, the scientific evidence for the disease risk associated with these other polymorphisms is likely to be similar to that for the polymorphisms we reviewed. Third, we limited our search for meta-analyses to those on the association between polymorphisms and disease susceptibility, and we excluded those on associations with intermediate, quantitative phenotypes or risk factors such as blood pressure or bone mass density because the need for preventive intervention varies with the level of these traits. We did include meta-analyses of associations between polymorphisms and risk for conditions defined by clinically relevant thresholds, such as hypertension or osteoporotic fractures. Because the genetic profiles of the companies are offered to the general public, we restricted our search to meta-analyses of studies that included healthy or general-population controls. Because the predictive value of genetic testing depends on disease risk, genotype frequencies, and odds ratios for the association between disease risk and polymorphisms in a particular genetic profile, all of which may differ between populations, the profiles should be evaluated in the target population.15 This explains why genetic testing for APOE, Factor II [MIM 176930], and Factor V [MIM 227400] can have lower predictive value in a general population context but be very informative to persons with a family history. Fourth, we did not exclude meta-analyses on the basis of quality criteria, even though there were obvious differences in quality among meta-analyses. The authors of larger meta-analyses often selected studies according to a set of strict criteria, whereas the authors of smaller ones often combined all available studies. In addition, more than a quarter of the meta-analyses in Table S2 reported statistically significant heterogeneity in effect sizes among studies. Several of the meta-analyses that found a significant association involving heterogeneous study populations did not find a significant association when the analyses were restricted to a subgroup of more homogenous studies.[16] and [17] Application of strict quality criteria would have reduced the number of meta-analyses in the present review substantially.
These methodological choices partly explain why we found no meta-analyses for 24 of the 56 genes. There were meta-analyses available for many of these genes, but these meta-analyses could not evidence the utility of genomic profiling in the general population. For example, we found several meta-analyses of pharmacogenomic studies (e.g., for CYP2C9 [MIM 601130] and CYP2C19 [MIM 124020][18] and [19]), several meta-analyses on diseases that do not affect the average individual in the general population (such as IL-10 [MIM 124092] and recurrent pregnancy loss20), and meta-analyses on health traits (e.g., smoking behavior and CYP2A6 [122720]21). Furthermore, for many genes we found meta-analyses on other polymorphisms (e.g., IL-10 G[−1082]A22 and LPL Asn291Ser23) or on related genes (e.g., Leptin Receptor gene (LEPR; [MIM 601007]), but not for Leptin [MIM 164160]).24
This review shows that the excess disease risk associated with many genetic variants included in genomic profiles has not been investigated in meta-analyses or has been found to be minimal or not significant. These results raise concern about the validity of combining tests for many different genetic variants into profiles, especially when the companies offering them do not describe how they create a composite profile from the results of tests for single genetic markers. One company reports that they use complex mathematical algorithms to produce personalized diet and lifestyle recommendations. Another recommends basic nutritional or lifestyle-change support for homozygous negatives, added support for heterozygous positives, and maximum support for homozygous positives, which suggests that they are using single genetic markers as the basis for their recommendations. This reliance on single genetic markers is particularly worrisome given the limited predictive value of results from testing single susceptibility genes with small effects.[25], [26] and [27] To be meaningful, a genetic risk profile should combine information about the disease risk associated with multiple genes, and creating such a profile would require extensive knowledge of gene-gene interactions, which are even less well understood than the disease risk associated with individual polymorphisms.
How the companies we examined use their clients' genetic profiles to tailor individualized nutrition-supplement and lifestyle recommendations is another intriguing puzzle. Evidence on gene-diet interactions is still preliminary because trials designed to test these interactions have thus far yielded mainly inconclusive results.28 Furthermore, several genes, such as ACE [MIM 106180], APOE, and MTHFR, increase people's risk for some diseases and decrease their risk for others (Table S2). For example, MTHFR 677TT was associated with an increased risk for depression, stroke, coronary artery disease, gastric cancer, schizophrenia, and venous thrombosis, but it was associated with a decreased risk for colorectal cancer. Hence, the putative health effects of preventive interventions tailored to a person's MTHFR genotype may not be entirely beneficial. Finally, when profiles are composed of low-risk susceptibility genes, people with purportedly “high-risk” profiles may be at only slightly higher risk of disease than are people with “low-risk” profiles. One possible danger of marketing lifestyle recommendations to people with “high-risk” profiles is that those with “low-risk” profiles could be led to mistakenly believe that they have little need to make healthy lifestyle changes. The predictive value of genomic profiling may simply be insufficient for targeting interventions when low-risk groups will receive no intervention at all.29 It also needs to be investigated whether genomic profiling can usefully identify the better from the worse responders in the choice between two treatments.
Although genomic profiling may have potential to enhance the effectiveness and efficiency of preventive interventions, to date the scientific evidence for most associations between genetic variants and disease risk is insufficient to support useful applications. Despite advances in nutrigenomics and pharmacogenomics research,30 it could take years, if not decades, before lifestyle and medical interventions can be responsibly and effectively tailored to individual genomic profiles.
http://www.sciencedi...8793cf3a7486bb3
Edited by drmz, 29 July 2009 - 10:48 AM.
