The test results we received are misleading and of little or no practical use to consumers. Comparing results for 15 diseases, we made the following observations: (1) each donor’s factual profile received disease risk predictions that varied across all four companies, indicating that identical DNA can yield contradictory results depending solely on the company it was sent to for analysis; (2) these risk predictions often conflicted with the donors’ factual illnesses and family medical histories; (3) none of the companies could provide the donors who submitted fictitious African American and Asian profiles with complete test results for their ethnicity but did not explicitly disclose this limitation prior to purchase; (4) one company provided donors with reports that showed conflicting predictions for the same DNA and profile, but did not explain how to interpret these different results; and (5) follow-up consultations offered by three of the companies provided only general information and not the expert advice the companies promised to provide. The experts we spoke with agreed that the companies’ claims and test results are both ambiguous and misleading. Further, they felt that consumers who are concerned about their health should consult directly with their physicians instead of purchasing these kinds of DTC genetic tests. See appendix I for comprehensive information on the test results we received for each donor. Different companies often provide different results for identical DNA: Each donor received risk predictions for the 15 diseases that varied from company to company, demonstrating that identical DNA samples produced contradictory results. Specifically, in reviewing the test results across all four companies for the donors’ factual profiles, we found that Donor 1 had contradictory results for 11 diseases, Donor 2 for 9 diseases, Donor 3 for 12 diseases, Donor 4 for 10 diseases, and Donor 5 for 9 diseases. Specific examples of these contradictory predictions are listed below; note that some of the diseases we compared were only tested by three of the four companies. To facilitate comparison among companies, we chose to use the terms “below average,” “average,” and “above average” to describe the risk predictions we received; the exact language used by each of the companies is reprinted in appendix I. For Donor 1, Company 1 predicted an above-average risk of developing leukemia, while Company 2 predicted a below-average risk, and Company 3 reported that she had an average risk for developing the disease. In addition, Companies 2 and 4 told the donor that her risk for contracting breast cancer was above average, but Companies 1 and 3 found her only to be at average risk. See figure 1. Companies 1 and 2 claimed that Donor 2 had an above-average risk of developing type 1 diabetes, while Company 3 reported that she was at below-average risk for the disease. Further, Company 2 predicted she was at above-average risk for restless leg syndrome, Company 1 claimed she was at below-average risk for the condition, and Company 4 found that she was at average risk. See figure 2. Company 4 claimed that Donor 3’s risk of developing prostate cancer was above-average, Company 3 found that he was at below-average risk, and Companies 1 and 2 found that he was at average risk. For hypertension, Company 3 found that he had an above-average risk of developing the condition, Company 2 found that he was at below- average risk, and Company 1 found he was at average risk. See figure 3. Donor 4 was told by Companies 1 and 4 that he was at above-average risk for celiac disease, but Company 2 reported that he was only at average risk. In addition, Companies 1 and 4 found that he was at below-average risk for multiple sclerosis, while Companies 2 and 3 found that he was at average risk. See figure 4. For Donor 5, Companies 2 and 3 reported an above-average risk for heart attacks, and Companies 1 and 4 identified only an average risk. Company 2 found him to be at below-average risk for atrial fibrillation, while Companies 1, 3, and 4 predicted an average risk. See figure 5. These contradictions can be attributed in part to the fact that the companies analyzed different genetic “markers” in assessing the donors’ risk for disease. As described in a recent article published in the science journal Nature, researchers determine which markers occur more frequently in patients with a specific disease by conducting “genome-wide association studies, which survey hundreds of thousands or millions of markers across control and disease populations.” DTC companies use these publicly available studies to decide which markers to include in their analyses, but none of the companies we investigated used the exact same markers in its tests. For example, Company 1 looked at 5 risk markers for prostate cancer, while Company 4 looked at 18 risk markers. In our post-test interviews, representatives from all four companies acknowledged that, in general, DTC genetic test companies test for different risk markers and that this could result in companies having different results for identical DNA. When we asked the representatives whether they thought that any DTC genetic test companies currently on the market were more accurate than others, all claimed that their own companies’ tests were better than those offered by their competitors. For example, Company 1 said that it offers consumers more information than other companies because its results are based on both preliminary research reports as well as clinical data. Company 2 claimed that other companies do not test for as many markers as it does and that while none of the companies are “wrong,” using more markers is “probably more accurate.” Company 2 also stated that disparate test results from different companies are “caused, in part, due to a lack of guidance from the federal government, CDC in particular.” Company 3 similarly claimed to test for more markers than other companies and stated that its test is “the best.” Company 3 also said that there is a movement within the DTC genetic test industry to standardize test results, but that such standardization is a work in progress. Finally, Company 4 claimed that it uses stricter criteria to select risk markers than other companies. Company 4 also told us that it has been involved in a collaborative effort with other DTC genetic test companies to develop standard sets of markers, but stated that there are many unresolved differences in philosophy and approach. When we asked genetics experts if any of the companies’ markers and disease predictions were actually more accurate than the others, they told us that there are too many uncertainties and ambiguities in this type of testing to rely on any of the results. Unlike well-established genetic testing for diseases like cystic fibrosis, the experts feel that these tests are “promising for research, but the application is premature.” In other words, “each company’s results could be internally consistent, but not tell the full story…. the science of risk prediction based on genetic markers is not fully worked out, and that the limitations inherent in this sort of risk prediction have not been adequately disclosed.” As one expert further noted, “the fact that different companies, using the same samples, predict different…directions of risk is telling and is important. It shows that we are nowhere near really being able to interpret .” We also asked our experts if any of our donors should be concerned if the companies all agreed on a risk prediction; for example, all four companies told Donor 1 she was at increased risk for Alzheimer’s disease. The experts told us this consensus means very little because there are so many demographic, environmental, and lifestyle factors that contribute to the occurrence of the types of diseases tested by the four companies. Risk predictions sometimes conflict with diagnosed medical conditions or family history: Four of our five donors received test results that conflicted with their factual medical conditions and family histories. When we asked the experts about these discrepancies, they told us that the results from these DTC tests are not conclusive because the tests are not diagnostic, as is noted on all of the companies Web sites. Because risks are probabilistic by definition, it is very likely that consumers will receive results from these companies that do not comport with their knowledge of their own medical histories. However, one expert noted that the discrepancies between actual health and the predications made by these companies also serve to illustrate the lack of robustness of such predictive tests. Moreover, experts fear that consumers may misinterpret the test results because they do not understand such distinctions. For example, a consumer with a strong family history of heart disease may be falsely reassured by below-average risk predictions related to heart attacks and consequently make poor health choices. In fact, one expert told us that “family history is still by far the most consistent risk factor for common chronic conditions. The presence of family history increases the risk of disease regardless of genetic variants and the current genetic variants do not explain the familial clustering of diseases.” Another expert stated that “the most accurate way for these companies to predict disease risks would be for them to charge consumers $500 for DNA and family medical history information, throw out the DNA, and then make predictions based solely on the family history information.” Examples we identified include the following: Donor 2 has a family history of heart disease yet all four companies predicted that she was at average risk for having a heart attack. Donor 2 also has a family history of type 1 diabetes, but Company 3 reported that she was at below-average risk for the disease. Donor 3 has a family history of heart disease, but Companies 1, 2, and 3 reported that he was at average risk for having a heart attack and Company 4 reported he was at below-average risk. Donor 4 had a pacemaker implanted 13 years ago to treat atrial fibrillation. However, Company 1 and 2 found that he was at below- average risk for developing atrial fibrillation, and Companies 3 and 4 claimed that he was at average risk. Donor 4 is also a colon cancer survivor, but Company 2 reported that he was at average risk of developing the disease. Donor 5 has Type 2 diabetes, but Companies 1, 2, and 3 indicated that he had an average risk of developing the disease. Donor 5 is also overweight, but all four companies found him to be at average risk for obesity.