Individual specific effects in omics data sets
Lead Research Organisation:
University of Glasgow
Department Name: College of Medical, Veterinary, Life Sci
Abstract
Studentship strategic priority area:Statistics Biological informatics
Keywords: Statistics, Omics, Bayesian Inference, Generalized Linear Models
Abstract:
New omics technologies have yielded vast data sets on the degree of molecular variation in individuals, However, the great promise made of these technologies in biomedical research remain largely unfulfilled. This is primarily due to the lack of robust statistical techniques that can account for the complex variation between individuals. Recently, we have found evidence of a previously unaccounted source of individual-specific variability in gene expression data from myotonic dystrophy patients. In this project we will use these novel insights to develop new generally applicable statistical and computational mathematics approaches that account for these individual-specific effects (ISE) and investigate their significance across a range of studies and disorders. Accounting for such ISE has the potential to dramatically increase the power and reproducibility of all omics studies. The student will develop these methods using generalized linear models and Bayesian analyses and apply them to in-house and publicly available data sets.
Keywords: Statistics, Omics, Bayesian Inference, Generalized Linear Models
Abstract:
New omics technologies have yielded vast data sets on the degree of molecular variation in individuals, However, the great promise made of these technologies in biomedical research remain largely unfulfilled. This is primarily due to the lack of robust statistical techniques that can account for the complex variation between individuals. Recently, we have found evidence of a previously unaccounted source of individual-specific variability in gene expression data from myotonic dystrophy patients. In this project we will use these novel insights to develop new generally applicable statistical and computational mathematics approaches that account for these individual-specific effects (ISE) and investigate their significance across a range of studies and disorders. Accounting for such ISE has the potential to dramatically increase the power and reproducibility of all omics studies. The student will develop these methods using generalized linear models and Bayesian analyses and apply them to in-house and publicly available data sets.
Organisations
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509668/1 | 01/10/2016 | 30/09/2021 | |||
| 2137878 | Studentship | EP/N509668/1 | 03/10/2016 | 02/10/2020 | Adam Kurkiewicz |
| Description | Key finding 1. Myotonic dystrophy splicing biomarkers Myotonic dystrophy is a rare genetic disease affecting about 1 in 10000 people of European descent. Thanks to the advances in research, starting with the discovery of the disease-causing mutation in the 90s, we now have a very good understanding of the disease mechanism and what it does to the body (better than e.g. cancer, but worse than e.g. diabetes). Despite these research advances, there's currently no cure available for the disease. As a result, clinical management of the disease is based on easing the symptoms, but not on treating the underlying cause. However, as of March 2020, there are multiple clinical trials ongoing and there's a widespread expectation in the field that effective medication will become available within the next decade. Unfortunately, evaluating efficacy of a drug in a clinical trial for myotonic dystrophy is very difficult, because of the lack of objective methods to measure how affected a patient is. With this context in mind, we validated a set of previously described myotonic dystrophy RNA biomarkers in muscle specifically in the context of a clinical trial. We have made our findings publicly available through a bioarxiv pre-print: https://www.biorxiv.org/content/10.1101/728881v1 Our research is currently undergoing peer-review for publication in PLOS ONE, and initial reception from the peer-reviewers has been very supportive and enthusiastic. We expect the research to be published in the next 3-6 months. Key finding 2. Myotonic Dystrophy type 1 splicing biomarkers A fraction of patients with myotonic dystrophy type 1 have an atypical form of the mutation responsible for the disease. We call these patients "variant repeat" patients, which is a somewhat technical reference to the atypical form of the mutation present in these patients. In an attempt to understand the genetic mechanism through which this atypical form of the mutation emerges and sustains itself in the population we've created two new computational models to measure various properties of the mutation and help us cast some light on the basic biology behind the molecular behaviour of this mutation. Our research confirms several existing hypotheses about the phenomenon and allows us to formally propose a new mechanism behind the sustenance of this atypical mutation in the population of Myotonic Dystrophy type 1 patients. We are currently at a final stage of writing a manuscript for the publication of this finding, and we expect to make it publicly available on bioarxiv by the end of April 2020, with further prospects for peer-reviewed publication. Key finding 3. Coronary artery bypass graft failure Towards the end of my PhD I have undertaken an independent collaboration with Dr. Angela Bradshaw working on coronary artery bypass graft failure. Using single cell sequencing, which is a novel technique I learned during my internship at Peking University (Beijing, China), I have lead an effort towards discovery of a TGFB1-induced differentiation event in vascular smooth muscle cells, with cells developing along two different lineages, the "ALK1 lineage", which we believe contributes to vein graft failure and the "ALK 5 lineage", which we believe contributes to the correct remodelling of the vein to adapt it from a low-pressure to a high-pressure vascular environment. This finding has been combined with existing research in Dr Bradshaw's lab and we expect it to be published in a leading cardiovascular or biomolecular journal. A pre-print has been made available by Dr Bradshaw on bioarxiv: https://www.biorxiv.org/content/10.1101/860320v1 The manuscript is currently under consideration by EMBO Molecular Medicine. We expect a decision on publication to be made within the next 6 months. |
| Exploitation Route | Key finding 1. Companies evaluating candidate drugs for Myotonic Dystrophy type 1 can evaluate their efficacy using the statistical framework proposed in our research. Key finding 2. Developing a basic science understanding of the atypical forms of the mutation underlying Myotonic Dystrophy type 1 will help us understand how emerging treatments for the condition may affect 3-5% of individuals with an atypical form of the disease, and help us confirm or dispel concerns over potentially reduced efficacy of the these treatments. Key finding 3. Discovery of the protein ALK1 as a candidate drug target for the vein graft disease has potentially huge impact on the lives of millions of patients who have received this treatment, as roughly 50% of vein grafts fail within 10 years from the surgery. If our findings lead to successful drug development, we could substantially improve the lives of as many as 150,000 patients per year in the United States alone. |
| Sectors | Pharmaceuticals and Medical Biotechnology |
| URL | https://www.biorxiv.org/content/10.1101/728881v1 |
| Description | As part of this award I've developed a good understanding of data analysis in single cell sequencing. As a result I've been able to form a consulting company, Biomage, which now is working on the development of a standalone software product for single cell data analysis: https://biomage.net/ |
| First Year Of Impact | 2019 |
| Sector | Pharmaceuticals and Medical Biotechnology |
| Impact Types | Economic |
| Company Name | BIOMAGE LTD |
| Description | Biomage is a biotech software company behind the Biomage Single Cell Platform. The Biomage Single Cell platform turns vast sequencing data sets into meaningful biology. Researchers studying cancer, cardiovascular health and developmental biology will use the platform to make discoveries about complex biological systems. |
| Year Established | 2019 |
| Impact | We have received funding for incubation of the startup at Harvard Medical School in Boston, Massachusetts. All co-founders in the business will relocate to Boston to work on the company starting on the 31st of April supervised by Prof. Peter Kharchenko. The funding received is from the Blavatnik Foundation and will allow for the employment of 4 full-time people for the business incubation period of 2 years. |
| Website | https://biomage.net/ |