Epigenome-wide association study takes a bird’s eye view on obesity
Epigenetics is a field in biology that investigates biological processes and mechanisms which have direct impact on DNA and DNA regulation, compared to genetics which is occupied with understanding what is written in our DNA.
One can imagine “epigenetic” modifications in DNA as little “Post-it”-notes sticking to certain positions, telling the body to ignore the information of a gene, or pay special attention to it and its neighbors. A kind of editorial process that does not change the content, only the delivery of a given speech.
Epigenetics has gained huge prominence in the last two decades once people found out that although humans share very high DNA sequence identity amongst each other, epigenetic markers (chemical additions to DNA) vary significantly and dynamically, furthermore there is evidence that epigenetic marker patterns can be inherited by the offspring.
Especially extreme environmental conditions like starvation or obesity cause profound changes to epigenetic modifications that can be inherited, prompting e.g. kids of obese parents to suffer from insulin insensitivity to metabolic syndrome without a “genetic” predisposition.
Yet, many aspects of epigenetics are still in the dark, since it has the potential to completely alter genetic output, the complexity of epigenetics is tightly linked to genetics.
Since the dawn of the genomic era, starting with the monumental completion of the Human Genome Project in 2001, big data found its way into biology. One of the first successful attempts to make good use of these data were so-called genome-wide association studies (GWAS), where scientists and statisticians looked at characteristic single-nucleotide polymorphisms in DNA sequences between huge cohorts of people and tried to link them a phenotype.
Imagine you have a cohort of ten thousand people, and within those, 500 suffer from depression. Within those 500, you find that 350 have a different letter in their DNA sequences coding for a receptor protein in the brain, whereas in the other 9500 non-depressives, only 5 have the non-standard letter. From these data, scientists would predict that the protein in question is maybe implicated in the development or maintenance of depression.
Now the big-data power comes with numbers, because with GWAS you can not only look for one gene in one condition, you can look to at all genes over as many conditions (disease, height, personality trait…) as you wish simultaneously. The more humans in your cohort, the more powerful the method. It is a great tool for discovery and hypothesis generation, yet one has to stress that it is still up for the scientists to do the footwork and actually find out what the biology behind the gene is, GWAS just gave a point to start.
If GWAS worked so well for genetics, could it also work for epigenetics?
This week, we got an answer to this question, in a recent paper published in Nature, the collaborative effort of many scientists and consortia all over the world put together a comprehensive epigenome-wide association study of body-mass-index (BMI) and its implications to adiposity.
By collecting data from 5,387 individuals from the EPICOR (n = 514), KORA (n = 2,193) and LOLIPOP (n = 2,680) population studies, the authors looked for DNA methylation in CpG-sites (CpG sites are highly methylated but rare genomic regions usually indicative of being promoters for coding genes) in blood samples.
What the authors discovered were 187 loci that show significant DNA methylation changes corresponding to changes in body mass index. Furthermore, they observed that most of the methylation changes were caused by adiposity rather than being the cause of it. Not surprisingly, most methylated loci identify to genes involved in lipid and lipoprotein metabolism, substrate transport and inflammatory pathways. Like GWAS, epi-GWAS provides a bird’s eye view on the epi-genomic landscape of complex diseases and the complicated interplay of many factors leading to a disease phenotype.
Most encouragingly, however, is the finding that disturbances in methylation patterns can be used as a predictor of future type 2 diabetes development; thus maybe allowing early treatment intervention.
Our observations thus provide insight into the regulatory pathways that may link adiposity to metabolic and cardiovascular disease, asthma and a wide range of cancers, although our study is limited in the tissues examined, and further studies are needed to include additional biologically relevant tissues. Our prospective population studies show that DNA methylation identifies people at high risk of incident type 2 diabetes, independent of conventional risk factors. -Wahl S. et al., 2016, Nature
With this studies, the authors provide evidence that epi-genome wide association studies constitute a “big data treasure-trove” which
(..) may prove useful in risk stratification and personalized medicine, to help to tackle the current global epidemic of obesity and its associated cardiovascular and metabolic disturbances.-Wahl S. et al., 2016, Nature
Information is power. Big data and biology have just started looking at each other; let us hope for many more productive mixes. In the case adiposity, most disease-associated methylation patterns are not inherited but acquired by our lifestyle.
This also means that we can be the masters of what the editorial “Post-it”-notes say, if we choose to put in the work to change lifestyle. We cannot choose what genes we inherit, but we are definitely responsible of how to tinker with them via epigenetics, for better or worse.
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