In my 100+ years career of genetics/genomics, there are what is called “Quantitative Trait Loci” (QTL) and “Other.” QTLs are phenotypes that can be quantified [e.g. height, body mass index (BMI), systolic blood pressure, and hemoglobin A1c]. “Other” includes autism spectrum disorder (ASD), schizophrenia, major depressive disorder (MDD), “hypertension,” and even type-2 diabetes (T2D). This latter category reflects a gradient –– largely due to the holistic individual patient who is different from the next person (and, in fact, can be different from himself, 10 years later). In other words, the “Other” category is a can of worms.
Also falling into this “Other” category is Genetic Risk Prediction for: complex diseases (e.g. will this patient become diabetic at age 40 or age 70?); risk for cancer caused by environmental toxicants (e.g. why is one smoker afflicted with bronchiogenic carcinoma after 1 pack-a-day for 20 years, whereas his neighbor has no lung cancer after smoking 3 packs-a-day for 50 years?); and toxicity as a drug response [e.g. why does one T2D patient respond favorably to Januvia (sitagliptin, a reversible inhibitor of dipeptidyl-peptidase-4), whereas another T2D patient taking the same drug at the same dose develop acute pancreatitis?]. Also falling into this “Other” category iS RISK ASSESSMENT; usually these measurements are not quantifiable, represent a gradient, and therefore “prediction of individual risk” must be concluded to be a “soft science.” Such is the task of the EPA and other governmental agencies.
This message [in the above two paragraphs] is what must be conveyed to the EPA, and to every other governmental agency and committee attempting to “create honest, unbiased policy” based on all available scientific data. If data are not shared –– or if data are “adjusted” (as the IPCC and NOAA have been doing for the past 30 years to global atmospheric temperatures) –– these issues obviously can become very expensive to the taxpayer, frustrating, and problematic. And this is where the division lies, where the rubber meets the road, between quantitative science and subjective politics. This is my answer to Glenn’s query and Jim’s comments [below].
Although I did notice [in the 27-page ‘EPA Transparency’ document; attached again] mention of the LNT Model, I was surprised and disappointed that there was no mention of the highly controversial “PM2.5 issue” [atmospheric particulate matter (PM) that has a diameter of less than 2.5 micrometers –– about 3% the diameter of a human hair), which has recently become “government policy” in southern California, leading to $28 billion in new taxes to those living in that region. 🙁
THANK YOU Daniel. Because you are much closer to the issues involving the EPA than I am, do you have any recommendations/suggestions as to whether or how we should respond to their call for comment?
Thanks again for your diligence in keeping us informed on these (and many other) issues!
Thank you very much for distributing this notice. It is very important that we all submit our own public comments in support of EPA ‘Strengthening Transparency in Regulatory Science’. The need for this new rule is largely driven by the “secret science” PM2.5 epidemiology, which has been used to justify and implement multi-billion-dollar EPA regulations.
The claim that PM2.5 causes premature deaths is not valid. My 2017 reanalysis of ACS Cancer prevention study data found no relationship and clearly demonstrate the importance of access to the underlying raw data. In 2016, you sent strong comments to the South Coast Air Quality Management District, SCAQMD (located in southern California), and now we need to send strong comments to the EPA itself.
Subject: Strengthening Transparency in Regulatory Science
Along the lines of trying to combat fraud and corruption in scientific research, these GEITP pages from time to time have shared news on this complex subject. Yes, there is a fine line between science and politics –– when one is dealing with attempts to establish governmental policy based on questionable scientific conclusions. These GEITP pages have tried hard to focus on the SCIENCE and stay away from the politics.
A number of times these GEITP pages have covered the topic of the linear no-threshold (LNT) model –– pushed forward with insufficient scientific evidence (and amidst the objection of many solid scientists of those days) in the 1940s-50s. The conclusion has then led to more than six decades of questionable “cancer” and “toxicity” research, funded by billions of taxpayer US dollars to pay for massive projects, and a great deal of time and experimental effort. I see that the LNT Model is specifically mentioned on pages 9 and 25.
The [attached] report proposes a regulation intended to strengthen the transparency of U.S. Environmental Protection Agency (EPA) regulatory science. When EPA develops regulations –– including regulations for which the public is likely to bear the cost of compliance, with regard to those scientific studies that are pivotal to the action being taken –– the document suggests that the EPA should ensure that the data underlying those are publicly available in a manner sufficient for independent validation. The EPA is now requesting comments on this proposal, in order to determine how best this policy can be disseminated and implemented in light of existing laws and prior Federal policies. This same issue of “reproducibility” and “transparency” in the past several years has also become increasingly important in the publication of articles in top-rate scientific journals.