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Genetic information not always helpful （遗传信息其实没多大用）- O1 U J& c2 i! m8 c2 @
ANI
+ O W; C; L- b+ gJun 6, 2012, 12.00AM IST
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(Genetic information may…), M( I8 T2 r Q* x/ x& A# X7 D8 }
- Z3 k/ t% I( G, g/ B: w9 @[p=20, null, left]Detailed knowledge about your genetic makeup—the interplay between genetic variants and other genetic variants, or between genetic variants and environmental risk factors—may only change your estimated disease prediction risk for three common diseases by a few percentage points, which is typically not enough to make a difference in prevention or treatment plans, say researchers.! F- w8 Z+ ~7 |* x9 ]% s
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4 }4 \* { Q g# E- The study by Harvard School of Public Health (HSPH) researchers is the first to revisit claims in previous research that including such information in risk models would eventually help doctors either prevent or treat diseases.
- "While identifying a synergistic effect between even a single genetic variant and another risk factor is known to be extremely challenging and requires studies with a very large number of individuals, the benefit of such discovery for risk prediction purpose might be very limited," said lead author Hugues Aschard, research fellow in the Department of Epidemiology.
- Scientists have long hoped that using genetic information gleaned from the
- F2 A. l3 c% }4 p! P" k[color=rgb(51, 103, 151) !important]Human Genome Project
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and other genetic research could improve disease risk prediction enough to help aid in prevention and treatment. Others have been skeptical that such "personalized medicine" will be of clinical benefit.
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[p=20, null, left]Still others have argued that there will be benefits in the future, but that current risk prediction algorithms underperform because they don''t allow for potential synergistic effects—the interplay of multiple genetic risk markers and environmental factors—instead focusing only on individual genetic markers.[p=20, null, left]Aschard and his co-authors, including senior author Peter Kraft, HSPH associate professor of epidemiology, examined whether disease risk prediction would improve for [color=rgb(51, 103, 151) !important]breast cancer, type 2 [color=rgb(51, 103, 151) !important]diabetes, and [color=rgb(51, 103, 151) !important]rheumatoid arthritis if they included the effect of synergy in their statistical models. But they found no significant effect by doing so.[p=20, null, left]"Statistical models of synergy among genetic markers are not 'game changers' in terms of risk prediction in the general population," said Aschard.[p=20, null, left]The researchers conducted a simulation study by generating a broad range of possible statistical interactions among common environmental exposures and common genetic risk markers related to each of the three diseases. Then they estimated whether such interactions would significantly boost disease prediction risk when compared with models that didn''t include these interactions since, to date, using individual genetic markers in such predictions has provided only modest improvements.[p=20, null, left]For breast cancer, the researchers considered 15 common genetic variations associated with disease risk and environmental factors such as age of first menstruation, age at first birth, and number of close relatives who developed breast cancer.[p=20, null, left]For type 2 diabetes, they looked at 31 genetic variations along with factors such as [color=rgb(51, 103, 151) !important]obesity,[color=rgb(51, 103, 151) !important]smoking status, physical activity, and family history of the disease. For rheumatoid arthritis, they also included 31 genetic variations, as well as two environmental factors: smoking and breastfeeding.[p=20, null, left]But, for each of these disease models, researchers calculated that the increase in risk prediction sensitivity—when considering the potential interplay between various genetic and environmental factors—would only be between 1 percent and 3 percent at best.: J& q. q6 v7 E5 X) R3 ]
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/ a5 F0 b# i! Q6 l[p=20, null, left]"Overall, our findings suggest that the potential complexity of genetic and environmental factors related to disease will have to be understood on a much larger scale than initially expected to be useful for risk prediction. The road to efficient genetic risk prediction, if it exists, is likely to be long," said Aschard.[p=20, null, left]Kraft added "For most people, your doctor's advice before seeing your genetic test for a particular disease will be exactly the same as after seeing your tests."[p=20, null, left]The study appeared online and will appear in the June 8, 2012 print issue of The American Journal of Human Genetics.http://articles.timesofindia.indiatimes.com/2012-06-06/health/32057090_1_disease-risk-genetic-information-genetic-research [p=20, null, left]Does genetic information improve prediction of cardiovascular risk?7 X" c7 _& C7 X+ l% b
A single nucleotide polymorphism (SNP), or genetic variation, in the chromosome 9p21.3 region has recently been found to be strongly associated with coronary artery disease. However, it is not known whether checking for this genetic variation would improve upon conventional ways of evaluating a person’s risk for coronary artery disease. Fondation Leducq-supported investigators Paul M. Ridker and Julie E. Buring and their colleagues addressed this question in a study published in the January 20, 2009 issue of the Annals of Internal Medicine.
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The study followed 22,129 white women enrolled in the Women’s Genome Health Study for 10 years. Conventional risk factors that were assessed included clinical indices such as age, blood pressure, smoking history, and family history of premature heart disease, as well as laboratory indices such as cholesterol and C-reactive protein (CRP) levels. Each woman also underwent genetic analysis to determine if she had 0, 1 or 2 copies of the high-risk SNP in 9p21.3. Among the study population, 49.5% had 1 copy and 24.3% had 2 copies.
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Consistent with previous studies in men, having 1 or 2 copies of the 9p21.3 SNP was associated with a higher risk of coronary artery disease. In addition, the SNP was associated with stroke, abdominal aortic aneurysm, intracranial aneurysm and a family history of premature heart disease. However, when the conventional risk factors were taken into account, knowledge of the SNP status did not add any incremental information about the risk for coronary artery disease. Thus, although the SNP may be important in the pathophysiology of coronary artery disease, it is unlikely that screening for it will be useful in clinical practice. It is possible that presence of the SNP did not override the low cardiovascular risk in this middle-aged female population, or that the SNP was so common that it no longer added incremental value to the other risk factors. The results of this study may not apply in other populations. Finally, while screening for this particular SNP is unlikely to be clinical useful in itself, evaluating multiple genetic locations, including 9p21.3, may be.
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Drs. Buring and Ridker are members of the Leducq Transatlantic Network of Excellence on Atherothrombosis. This research was also supported by the National Heart, Lung and Blood Institute, the National Cancer Institute, the Donald W. Reynolds Foundation, Celera, Roche Diagnostics, and Amgen.
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7 y, m9 I; Q8 g+ p. s0 r5 @Click on the title to access the article in the Annals of Internal Medicine: Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3 http://www.fondationleducq.org/nivel3.aspx?idsec=853
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