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Dr. Robin F Chan was awarded a prestigious 2019 NARSAD Young Investigator Grant (Brain & Behavior Research Foundation) to use leading-edge technologies to investigate how epigenetic markers influence gene expression in major depressive disorder. Using the latest in CRISPR-Cas9 tools, Dr. Chan will directly modify specific DNA methylation sites linked to depression and test their functional effects. By using a cell culture model of hESC-derived neurons, the effects of these DNA methylation markers can be tested in a manner more closely resembling human brain. Hence this research has implications for developing new forms of treatment for depression.

Dr. Robin F Chan was awarded a Local SMRT Grant (Pacific Biosciences) to evaluate long-read sequencing technology for detected novel DNA methylation modifications in human brain. Recent evidence suggests that the novel DNA modification, N6-methyl-2’-deoxyadenosine (m6dA), plays important roles in behavioral responses. However, current methods of detecting m6dA in DNA are often subject to biases or lack fine resolution. Therefore, Dr. Chan proposes to use SMRT sequencing from PacBio to determine the location and properties of m6dA in human brain for the first time.

Currently, considerable effort is devoted to finding novel drug targets. Problematically, the development of new medications is extremely expensive and takes many years. A parallel and potentially more immediate route to enhance treatment efficacy and minimize common adverse effects is to tailor the use of existing drugs to individual patients. 

Initial studies aimed to predict drug response were hampered by the fact that deep mechanistic knowledge of drug action is typically lacking. With advances in genomic technologies, it has become possible to bypass the need for such knowledge by studying genetic variation on a genome-wide scale. This has yielded multiple encouraging findings. Other relevant developments involve the possibility to combine population-based genetic studies with electronic medical records, perform high-throughput screening of new potential drugs, and change the genome at sites that cause disease. 

Although the tests that are currently on the market generally lack robust prognostic power, in this invited editorial we argue the field of precision medicine has undergone a proof of concept. New research tools may generate a next generation of tests that meet the widespread clinical demand for precision medicine.

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To better understand childhood trauma (e.g., witnessing violent events, abuse or neglect) we capitalized on a study that started at Duke University about 25 years ago involving 1,420 9- to 13-year-old children. The children were interviewed on a regular basis and this continues today as the participants are in their 30s.

The study suggested that childhood trauma is more common than is often assumed. Furthermore, results show it can cast a long and wide-ranging shadow as it is associated with elevated risk for adult psychiatric disorders and diminished physical health, financial and academic success, and social life. 
We are currently working on developing a biomarker that would enable early interventions by identifying children at risk for negative effects later in life. 

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The methylation of certain regions in our genome tends to change as we age. These age-sensitive methylation sites can be used to estimate someone’s biological age. We found that individuals with major depressive disorder or a history of childhood trauma may be biologically older than individuals without depression or childhood trauma. These patterns were found in DNA from blood and replicated in DNA from post mortem brain samples.

On average, individuals with major depression or childhood trauma die earlier and have more age-related diseases. Biological age may therefore represent a biomarker to identify patients who may benefit from early interventions seeking to reduce the risk of age-related diseases.

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Our studies typically measure the methylation status of > 20 million sites. It is common for computer programs to store data in computer memory. This, however, is not feasible for data sets of this size (e.g. 20 million sites across 1000 samples would already occupy 160 GB, which far exceeds the capacity of most computers). Furthermore, existing software lack important features needed for thorough data analyses. 

We present a new software package called RaMWAS that addresses these challenges. RaMWAS uses a specially developed system of data processing that avoids loading all data into memory. We have made RaMWAS fast by employing efficient algorithms and by parallelizing most tasks across multiple CPU cores. Finally, we implemented a full set of tools for quality control of data, analyses aimed at detecting disease sites, and added advanced options such as the possibility to create risk scores that can used as biomarkers to diagnose disease or predict drug response. 

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Cells in the human brain appear to survive and function for decades. Molecular changes to existing cells are therefore essential for development and to effectively respond to the environment. DNA methylation represents an important set of such molecular changes and aberrant methylation patterns have already been associated with a variety of psychiatric disorders.

However, the brain methylome is very large (~1 billion sites) and complex. Although methods exist to measure the brain methylome, they are too expensive to be used in studies aimed at finding the methylation changes in brain that are associated with psychiatric disorders. 

We have therefore developed an approach that allows comprehensive and cost-effective investigations of the entire brain methylome. Specifically, we showed that this approach has approximately the same performance as the expensive approaches but achieves this at <5% of the costs. Being the only viable option currently available for comprehensive brain methylome studies, this approach may be critical for moving the field forward.

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