Francis Collins, NIH Dir., Getting it all backwards

Compare the scientific reality with the Director of the NIH’s goals and claims. Immunity is more acquired than genetic and if you are going to collect and analyze data based on genetic immunity, you’re going to miss the boat.

It might be time to consider reality given the Autism-from-vaccines crisis (the CDC does not call this brain damage from the live viral vaccines autism, they call it brain damage – see the MMR monograph; dont vaccinate an already immunosuppressed kid), and the epidemic of tick bite sepsis, now affecting 1 million people per year, according to the CDC’s own reporting (300,000 cases/0.15 accuracy of the testing, and half remain disabled/incurable).

Collins needs to go. We need someone who knows what they are talking about at the top. The HHS.gov needs a complete overhaul to get rid of redundancy and underqualified people on the dot gov dole. No one has the time or extra lives for this nonsense.

“Environment, more than genetics, shapes immune system

“By Emily ConoverJan. 15, 2015 , 12:15 PM

“Why did you get the flu this winter, but your co-workers didn’t? The answer, according to a new study of twins, may have less to do with your genes and more to do with your environment—including your past exposure to pathogens and vaccines.

“Our immune system is incredibly complex, with diverse armies of white blood cells and signal-sending proteins coursing through our veins, ready to mount an attack on would-be invaders. Everyone’s immune system is slightly different—a unique mixture of hundreds of these cells and proteins. But the main driver of this variation is unclear. Although scientists know that our immune system can adapt to our environment—that’s why vaccines work, for instance—it is also built by our genes.

“To unravel the competing influences of nature and nurture, researchers led by immunologist Mark Davis of Stanford University in Palo Alto, California, turned to the gold standard test: a twin study. Identical twins are nearly the same genetically, whereas fraternal twins share only about half of their genes. If a trait is hereditary, identical twins will be more likely to share it than fraternal twins, allowing scientists to tease out the genetic component.”

http://www.sciencemag.org/news/2015/01/environment-more-genetics-shapes-immune-system

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“Mining the Big Data Mountain

“Posted on May 6th, 2014 by Dr. Francis Collins

“Biomedical researchers and clinicians are generating an enormous, ever-expanding trove of digital data through DNA sequencing, biomedical imaging, and by replacing a patient’s medical chart with a lifelong electronic medical record. What can be done with all of this “Big Data”?

“Besides being handy for patients and doctors, Big Data may provide priceless raw material for the next era of biomedical research. Today, I want to share an example of research that is leveraging the power of Big Data.

“NIH-funded researcher Atul Butte of Stanford University recently mined mountains of existing data to find new links among genes, diseases, and traits. In this instance, traits are defined as any detectable physical or behavioral characteristic, such as cholesterol levels or other blood chemistries; bone density; or body weight. Butte reasoned that a trait that was closely linked to a disease through specific genes might be useful as a predictive marker of disease risk.

“To discover these new links, he tapped into the VARiants Informing MEDicine (VARIMED) database, a resource that he began building in 2008 to interpret the clinical consequences of DNA variation in patients [1]. To create VARIMED, Butte and his colleagues read scientific papers on human genetics—including many genome-wide association studies (GWAS), which identify common genetic variants that are associated with disease risk—and noted the genes, variations, and traits mentioned in each paper and the connections between them. Over the years, the privately funded database grew; today it contains findings from more than 9,000 studies.

“In their most recent study, Butte’s team examined the genetic architecture of each disease—all of the genetic variations that influence disease risk—and made a list of the gene-disease pairs. They found 801 genes that were reliably linked to 69 diseases [2]. Next, the researchers made a list of genes associated with particular traits; 796 genes were reliably linked to 85 traits. Finally, they searched for overlaps between the two lists: were there any genes that influenced both a disease and a trait?

“Butte’s team found 120 diseases and traits that were linked by the activity of just a few genes. Many of these disease-trait associations were already known to the biomedical community, but about 20 percent of these connections were novel.

“The next question was whether these traits can be used to predict whether an individual would develop a particular disease. Butte tested his hypothesis by taking five of the new disease-trait connections and examining patient data from Stanford Hospital and Clinics and New York’s Mount Sinai Medical Center and Columbia University Medical Center, all of which had at least a decade’s worth of electronic medical records (EMRs).

“For example, one of the disease-trait pairs was gastric cancer-magnesium, which is a trace mineral found in blood serum. This pair was connected by three genes—MUC1THBS3, and TRIM46—previously implicated in gastric cancer and also known to influence magnesium levels in blood serum.

“However, it wasn’t clear whether serum magnesium levels were actually predictive for gastric cancer. To find out, Butte’s team analyzed the EMRs of 704 patients who had a magnesium measurement one year before they were diagnosed with gastric cancer. For controls, he examined the EMRs of more than 324,000 patients who had magnesium measurements, but no diagnosis of gastric cancer. The comparison revealed that patients with elevated magnesium levels were significantly more likely to be diagnosed with gastric cancer than patients whose levels were normal….”

https://directorsblog.nih.gov/2014/05/06/mining-the-big-data-mountain/

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