Epigenetic effects on genetic diseases

This 2017 review provided evidence for epigenetic effects on a disease widely considered to be of genetic origins:

“For a T1D [type 1 diabetes] identical twin the concordance rate (both twins affected) is consistently less than 100%, which implies a non-genetically determined effect. However, the concordance rate declines with age at diagnosis of the index twin, indicating that in adult-onset T1D the genetic impact is limited, and certainly lower than that in childhood-onset disease.

Genes associated with T1D are well-established and have four broad functions. However, T1D is unlikely to be a single disease since there is disease heterogeneity. The incidence of T1D has even increased several-fold in the last 30 years-a timeframe which rules out genetic evolution. In addition, studies of the incidence of T1D in migrant populations have shown a convergence towards the risk of the host population.

Alongside histone modifications and transcription factors, several cis-regulatory elements, including enhancers, promoters, silencers and insulators, are crucial to the function of the genome. There are more than a million enhancers; therefore, many more than there are genes, so that a number of genes are regulated by the same enhancer, which may co-localise with CpGs. Gene enhancers can be found upstream or downstream of genes and do not necessarily act on the closest promoter. Enhancers may be accompanied by insulators, which are located between the enhancers and promoters of adjacent genes and can limit phenotypic gene expression despite genetic activation.”

The review was weak in a few areas:

1. The authors repeated a laughable claim for gross national product as a non-genetic effect for Type 1 diabetes.

2. They also made other hyperbolic statements such as “This observation illustrates the power of epigenetic analysis to identify those cells which are actively using the genes associated with a given tissue, given that all cells contain every gene.” that were out of place with the review’s evidential bases.

https://link.springer.com/article/10.1007/s11892-017-0916-x “The Role of Epigenetics in Type 1 Diabetes”

What are we to believe?

This 2017 blog post from Antiwar.com’s Justin Raimondo outlines the latest instance of exploiting beliefs:

“Neither the sources of this story nor those who are reporting it can be trusted. Journalism is not a means of discovering knowledge, but a weapon to be deployed in a political-ideological conflict.”

Similar to the development of other beliefs, the reporting of the referenced story discouraged inquiries into “Information about the real world..giving us a highly distorted version of events.” It followed the blueprint of Using citations to develop beliefs instead of evidence in that once the faulty information became widely cited, refuting evidence would be ignored, and the false belief was used for other purposes.

http://original.antiwar.com/justin/2017/08/10/what-are-we-to-believe/ “What Are We To Believe? Fake news plus phony “intelligence” equals disaster”

This post has somehow become a target for spammers, and I’ve disabled comments. Readers can comment on other posts and indicate that they want their comment to apply here, and I’ll re-enable comments.

Using citations to develop beliefs instead of evidence

This 2009 Harvard study analyzed how citations were used as tools to establish a belief.

The researched data was gathered from 1992 to 2007 on a specific subject of Alzheimer’s research. The belief was:

“β amyloid is produced by inclusion body myositis myofibres or is uniquely present in inclusion body myositis muscle.”

The author used social network analysis to determine:

“Four primary data papers, five model papers, and one review paper constituted the 10 most authoritative papers that the claim was true.

The supportive papers received 94% of the 214 citations to these primary data, whereas the six papers containing data that weakened or refuted the claim received only 6% of these citations.

95% of all citation paths flow through four review papers by the same research group.

Amplification of a claim is instead introduced into belief systems through the citing of review papers and other papers that lack data addressing the claim.”

Some of the benefits believers received included:

  1. It became easier to build models if a researcher believed:

    “Animal and cell culture experiments are valid models of inclusion body myositis”


    “The uncited data suggest that the animal and cell culture experiments are no more models of inclusion body myositis than any other neuromuscular disease in which muscle regeneration occurs.”

  2. Believers used exaggerations in their confirming research that diverted the original claim’s meaning. As an example:

    “Three supportive citations developed into 7,848 supportive citation paths—chains of false claim in the network.”

  3. Citation biases and diversions could be used to support proposals for new funding.

Just imagine how compressed this phenomenon’s timeframe is now with our social networks! The tools available for creating memes and widespread nonfactual distortions are children’s play.

A few questions for the current year:

  1. What do we believe in that isn’t thoroughly investigated, where we haven’t found the time or inclination to search for opposing results?
  2. What causes us to believe these things?
  3. What are the positive and negative consequences of our beliefs?

http://www.bmj.com/content/339/bmj.b2680 “How citation distortions create unfounded authority: analysis of a citation network”

Hat tip to Jon in the comments section of Neuroskeptic’s blog post “The Ethics of Citation” http://blogs.discovermagazine.com/neuroskeptic/2017/03/12/the-ethics-of-citation