Alzheimer’s disease has presented numerous challenges to researchers due to the nature of the disease and the limited research methodologies available to scientists. Several technical aspects of genetic studies—important due to the genetic risk associated with Alzheimer’s development—have made genotype-phenotype correlation analyses problematic and incomplete.
Although Alzheimer’s is known to be largely heritable, a substantial proportion of the actual genetic risk for the disease has remained unexplained. This knowledge gap is known to researchers as the “missing (or hidden) heritability” problem. For example, while heritability explained 79% of late-onset Alzheimer disease risk in a Swedish twin study, common risk variants identified by pervious genetic studies explained only 20% to 50% of late-onset Alzheimer disease.
The complexity of the human genome and shortcomings of earlier research are additional limiting factors, and some genetic phenomena were not surveyed completely in prior studies. For example, there are many incompletely mapped genomic regions, areas with repetitive sequences, that could not be studied previously.
Recent advances in sequencing technologies have enabled more comprehensive studies. Such developments allow for more precise and accurate identification of genetic material than was available in earlier gene variant studies.
A new paper in the Journal of Neuropathology & Experimental Neurologystudies Mucin 6, or MUC6, a gene associated with Alzheimer’s and which has particular aspects that may help explain a large part of the missing genetic risk information for developing the disease.
Researchers analyzed Alzheimer’s Disease Sequencing Project data derived from more than 10,000 research volunteers who agreed to have their genetic data evaluated in combination with their disease status, with the goal of identifying genetic variation associated with late-onset Alzheimer disease.
This study used newer sequencing methods in Genome-Wide Associated Studies (GWAS) to provide more complete genetic analysis of neurological tissue, and of particular note the tandem repeats in the VNTR region ofMUC6, unique within and to the human genome, which provided information that was previously unobtainable.
Although the underlying mechanisms are mostly still unknown, researchers on the study believe that it’s possible to draw credible and testable hypotheses based on these results. For example, the genetic variant that was associated with Alzheimer’s disease risk may implicate a biochemical pathway in the brain that then represents a potential therapeutic target, a topic for future studies.
Corresponding authors Yuriko Katsumata and Peter Nelson, both from the University of Kentucky, are cautiously optimistic about these results. Dr. Nelson said of this study, “Our findings were made in a group of patients that is relatively small for a genetics study – [by comparison] some recent studies included hundreds of thousands of research subjects! That small sample size means two things: first, we should exercise caution and we need to make sure the phenomenon can be replicated in other groups; and second, it implies that there is a very large effect size, [meaning] the genetic variation is strongly associated with the disease.”
While any treatments derived from this new information might be a long way off, the ability to more thoroughly analyze patient genetic data will hopefully advance the field of Alzheimer’s research.