Parkinson’s GWAS improves when subclassifying patients by pathology, instead of clinical traits

Simón-Sánchez and Gasser discuss this in a stimulating article in the most recent issue of Neurology:

Beecham et al [2015] argue that one of the reasons for this “missing heritability” may be the uncontrolled degree of heterogeneity of [Parkinson’s Disease (PD)] in studies relying on clinical diagnosis alone. To reduce heterogeneity, they performed a GWAS in which only PD cases with autopsy-confirmed Lewy body (LB) pathology, and controls with exclusion of PD neuropathology, were included. Despite the relatively small number of samples used, they found evidence suggesting that common variation in a small region of chromosome 1p32 is associated with LB PD with a p-value just below genome-wide significance level and an odds ratio of 0.64 for the protective allele, which is comparable with that found for SNCA and MAPT in other studies. This association peak lies within PARK10, which was originally identified in a set of Icelandic families.

This “splitting” approach is likely to be valuable in other neurodegenerative diseases, as well.

Age-related changes in white matter are likely not due to demyelination?

That’s the conclusion of an important new paper from Billiet et al that integrated a bunch of different imaging modalities.

First, they used DTI to measure fractional anisotropy, which is a non-specific measure of white matter, measuring axon density, axon diameter, and myelin content. They also used myelin water fraction (MWF) to measure myelin content and the orientation dispersion index (ODI) to measure dendrite/axon dispersion. These were their findings:

  • Total white matter volume does not change in aging
  • Myelin content (via MWF) is not significantly altered in aging in most regions, and in the regions where there is a change, it increases
  • There is a widespread decrease in fractional anisotropy in aging that is especially strong in frontal regions
  • Changes in myelin content (MWF) do not correlate well with this decrease in fractional anisotropy
  • Changes in neurite orientation (ODI) dispersion do correlate with this decrease in fractional anisotropy

Thus, their conclusion is that age-related decreases in the “diffusibility” of white matter (or whatever fractional anisotropy is measuring) are due to changes in axons rather than to changes in myelin.

It’s all cross-sectional, n = 59, and they call this conclusion “highly speculative,” but if true it suggests that axons changes are more early/causal/fundamental to aging than changes in myelin. We need more data on this, especially from older individuals (their study went up to only 70) and from people with dementias, such as Alzheimer’s disease.

Billiet T, Vandenbulcke M, Mädler B, et al. Age-related microstructural differences quantified using myelin water imaging and advanced diffusion MRI. Neurobiol Aging. 2015

Journal club: How well do stem cell division rates predict cancer rates?

Summary: ~ 600 words on a moderately controversial recent paper that has been discussed endlessly elsewhere. Reading this is unlikely to be a good use of your time.

What Tomasetti et al did was to:

  • Choose a set of ~ 30 cancers
  • Find the lifetime incidence for each of those cancers
  • Independently, find estimates for the number of stems cells and the rate of stem cell division in each of the tissues from which those cancers arise, and then use these to calculate the lifetime number of stem cell divisions in that tissue
  • Find the correlation of the lifetime incidence of the cancer with the lifetime number of stem cell divisions in that tissue

In order to make sense of their results, I decided to reproduce their analysis. First, I manually copied and cleaned up the formatting of the data from their pdf (!) [1]. One of the first things I did after loading it into R was to plot it:

This doesn’t look like the plot in their paper, but I realized that was because I hadn’t taken the logs. Once I do, it looks way more similar (although I still flipped the axes):

log log

The Pearson correlation coefficient is much higher in the log-log data (0.80) than in the non-transformed data (0.53). The Spearman correlation coefficient is the same in both (0.81), since the ranks don’t change. This is a good example of how the Spearman correlation is more robust.

So what does figure one mean? I interpret it as showing that there is a positive relationship between the rate of stem cell division in a tissue and the rate of cancer in that tissue (variance explained of ~ 0.66). This then suggests that cancers arising from tissues with a higher rate of stem cell division are more due to the “luck” of whether or not one of those divisions happened to include a mutation, as opposed to a genetic predisposition.

Of course, the rate of stem cell divisions and/or mutations could still be influenced by an environmental factor, but it’d be less likely that any hypothetical environmental factor would affect the risk of mutation in all stem cells in all tissues at the same rates.

However, this does not suggest that “66% of cancers are caused by bad luck”, for many reasons, including the fact that the residuals are not weighted by the proportion that each individual cancer makes up of total cancer rates.

The next section of their paper ranks the cancers based on a score meant to quantify the amount of cancer that occurs above and beyond what you’d expect from the stem cell division rate in that tissue, and then clusters the cancers into two groups based on this ranking.

It’s slightly troubling that they call this one-dimensional k-means clustering “machine learning”. K-means clustering is in fact commonly considered a machine learning method, but at one dimension it reduces to a single breakpoint estimation, which is not quite in the spirit of high-dimensional ML connotation space. [Editor’s note: this was slightly edited for clarity/correctness].

All in all, while the data being in pdf format was slightly annoying and I wasn’t 100% sure of the implementation details of their k-means clustering [2], I was able to reproduce pretty much all of their results. This was a useful exercise for me and it is a good sign for the longevity of their insights. Simplicity is the ultimate sophistication.

1: My code for this post is on github.

2: Since I haven’t read through every line of the paper with a hyperdontic comb, let me state that this is probably my fault for missing something.

Is Alzheimers just normal aging? Part 1

A paper by Robinson et al provides a data point against, evaluating the post-mortem brains of around 150 people 90+ years old. They split their cohort three groups: those with dementia, those with cognitive impairment but no dementia, and those without cognitive impairment. Here is their main result table.

robinson et al

Thus, in their study, we can see that

a) there are pathological differences between dementia and other cognitive impairment, and

b) of the pathology types that they measured, there are no clear pathological differences between cognitive impairment and non-cognitive impairment.

To me this seems to be pretty good data emphasizing the standard theory that Alzheimers is distinct from normal aging. And that normal aging can also present with cognitive impairment, although these individuals can still convert to Alzheimers later in life.

How might nucleosome integrity affect aging?

Attention conservation notice: Rampant speculation on a paper outside of my field, which is unlikely to be simultaneously both new and interesting.

Using a zebrafish egg extract system, Ziehurt et al have shown that in the absence of nucleosomes (ie, in the “DNA beads” condition, as opposed to the “nucleosome beads” condition), there is no nuclear pore complex (NPC) formation. For example, these DNA extracts lack lamin B3.

Without nuclear pore complexes, cells can’t shuttle necessary proteins and other biomolecules in and out of the nucleus, which is… not good for the nucleus. This could be adaptive for the organism, though, if there is foreign or damaged DNA in the cell.

How does this relate to aging?

1) In general, nucleosome-free regions of the nucleus become more common in aging.

2) A different lamin protein called lamin A, which also acts at the NPC, is mutated in the most severe aging accelerating disease I’m aware of: progeria.

3) So, it’s reasonable to ask: in normal aging, does the loss of nucleosome integrity (because of, perhaps, the hand-wavy “accumulated damage”?) lead to lack of NPC accessibility?

From here, another reasonable question is whether you could find a biomarker of NPC integrity from some sort of high throughput method, such as a DNA sequencing method, that would allow you to measure nucleosome integrity easily across aging and intervention states.