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Crewe, United Kingdom

Young R.S.,MRC Human Genetics Unit | Ponting C.P.,University of Oxford
Essays in Biochemistry | Year: 2013

It is now clear that eukaryotic cells produce many thousands of non-coding RNAs. The least well-studied of these are longer than 200 nt and are known as lncRNAs (long non-coding RNAs). These loci are of particular interest as their biological relevance remains uncertain. Sequencing projects have identified thousands of these loci in a variety of species, from flies to humans. Genome-wide scans for functionality, such as evolutionary and expression analyses, suggest that many of these molecules have functional roles to play in the cell. Nevertheless, only a handful of lncRNAs have been experimentally investigated, and most of these appear to possess roles in regulating gene expression at a variety of different levels. Several lncRNAs have also been implicated in cancer. This evidence suggests that lncRNAs represent a new class of non-coding gene whose importance should become clearer upon further experimental investigation. © The Authors Journal compilation © 2013 Biochemical Society.

Agency: GTR | Branch: MRC | Program: | Phase: Intramural | Award Amount: 474.04K | Year: 2009

Inherited disorders that cause brain and eye malformations in humans are a significant cause of childhood disability. The aim of my research is to identify genes that cause such disorders, to unravel how those genes function in normal biology, and to understand what goes wrong when they are mutated. We have identified three genes that cause Micro syndrome, a condition in which children have severe progressive limb spasticity (cerebral palsy), learning difficulties, cataracts and small eyes. The relationship between these genes is currently not clear and we are undertaking cellular and protein studies to elucidate this. Because many genes and their functions are strongly conserved during evolution, we are using model organisms (mice) to investigate how these genes affect brain and eye development and function. The ultimate aim is that such studies may lead to the development of therapies for common neurological diseases as the pathways that are perturbed by these genetic diseases are likely to have roles in non-genetic disease.

Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 107.31K | Year: 2010

Gene interactions are thought to be important in shaping complex trait variation in agricultural, model organism and human disease genetics. They have been poorly explored, however, because of the lack of high throughput tools to analyse many different traits. With the support from the GridQTL project funded by BBSRC, we have developed a tool that can perform high throughput analyses of gene interactions in experimental populations genotyped with low density genetic markers. The tool however is not applicable to large datasets provided by genome-wide association studies in natural/commercial populations. Such datasets typically include hundreds of thousands of genetic markers and thousands of individuals with a large number of phenotypic traits. Genome-wide association studies have become increasingly popular for the investigation of the genetics of complex traits in livestock, plant, and human sectors. Despite much effort, a comprehensive analysis of gene interactions in those large datasets is still intractable for even a single trait (at levels of CPU months) due to their excessive computing demand and the lack of algorithms to handle billions of tests of marker combinations. A new high throughput analysis tool has become a necessity to study gene interactions in these large datasets. We propose the development of Epicluster, a novel tool to support routine high throughput analysis of gene interactions in large association study datasets. Instead of directly testing billions of marker combinations exhaustively, Epicluster will effectively select candidate markers with consistent genotype distribution patterns that differentiate the group of individuals with high trait values from the group with low trait values. It then performs comprehensive statistical tests only among the selected candidate markers and thus can improve the speed of analysing gene interactions for one trait to CPU hours. Epicluster development will adapt a bi-clustering algorithm that has been successfully applied in gene expression studies. A proof of principal test showed that the bi-clustering algorithm could cluster a large dataset with 500,000 markers in minutes. On completion Epicluster will be implemented as distributed software (i.e. automated analysis) to be used in high performance computer environments. In summary we expect Epicluster to herald a breakthrough in gene interaction analyses in large datasets across species. Hence Epicluster will facilitate a fuller understanding of the importance of gene interactions in complex traits.

Agency: GTR | Branch: MRC | Program: | Phase: Intramural | Award Amount: 485.59K | Year: 2010

Improving our understanding of genetic differences between species allows us to better interpret genetic risk in people.|We are all at risk of developing a wide range of diseases, some very common, including heart disease, diabetes, dementia and cancer. But such risks differ hugely between individuals, and are to a large degree influenced by the sequence of DNA in our cells.|The big question is which of the many thousands of DNA differences between individuals are responsible for increasing or decreasing their risk of developing a given disease. The historic record of evolution can provide some answers. We can read it as the differences in DNA between species, for example human versus mouse. The pattern of differences between species can reveal functionally important regions of DNA. Contrasting the between species pattern with the differences between people can point to the critically important changes that influence disease risk.|More broadly, we compare how DNA has changed between species with the differences between people. This allows us to study why and where DNA changes (mutations) arise, and what the functional consequences of those changes are. We are applying these methods to understand the genetic basis of many rare and common diseases.

Agency: GTR | Branch: BBSRC | Program: | Phase: Research Grant | Award Amount: 364.90K | Year: 2009

When people think of an atlas, they think of a world map, showing oceans, country borders, timelines, labelled with names of cities, rivers, etc. The atlas proposed here will show patterns of gene expression layered onto anatomical structures of the developing chick embryo, labelled with the names of structures, genes, etc. Just as a world atlas helps us to understand where we live, an atlas of the developing embryo will help us to comprehend how we form. We all develop from a single fertilized cell which multiplies into a mass of cells, this undergoes complex changes in shape, while growing to form the different organs and tissues that make up our bodies. By imaging chick embryos at different times during development, we will make a detailed map of anatomical structures as they form. The earliest stages are relatively simple as they are flat, but older embryos become increasingly more complicated and we will use a 3D imaging technique called Optical Projection Tomography to image them. An atlas is not very useful without a systematic way to name its features so we will create a standard set of words, which will make it possible to query the atlas using tools based on computer science. But the overt structure of developing embryos hides a further level of anatomy, special groups of cells called organizers. These organizers instruct cells around them so that the correct structures are made in the right place at the right time. Organizers are not always easy to identify; the polarizing region responsible for patterning the digits of the limb for example looks just like the tissue all around it. About half a dozen organizers have been discovered, many through transplantation experiments in chick embryos, and we now know that they are best distinguished by specific genes that are active (expressed) in their cells. In our project we plan to examine exactly which genes are expressed in four well defined organizers and produce a 3D map of their precise expression patterns in the whole embryo throughout development. Gene expression patterns of ~1000 genes will be mapped. This is a significant number of genes with which to begin to populate the chicken Atlas to be made publicly available to everyone over the internet. To determine what genes are expressed in these four different regions of early chick embryos (hypoblast, Hensens node, floor plate of the neural tube and limb polarizing region) we will dissect out these tissues and use microarrays to screen for all the genes they express and identify shared sets of genes. Genes expressed in the same place (synexpression groups) are likely to be involved in the same biological process, so we hope to uncover sets of genes which work together to define an organizer. But why focus on chicks rather than animals closer to humans? Amazingly, organizers and other signalling centres act in similar ways in different species as diverse as fish and man. Thus discoveries in the chick are relevant to human development and chicks are much easier to obtain and dissect than mouse embryos, so these two models are very complementary. The chick atlas however will be based on the same system developed for the mouse thus allowing comparisons. Conserved patterns of expression in chick and mouse will provide strong evidence for genes being functionally related while subtle differences can cast light on why a chick and mouse do not look the same. We will create a database to organize and manage this huge collection of data on gene expression patterns, anatomical structures, genes, etc. and develop new computer tools to query and analyse the data to discover new relationships and new functions for genes in development. This research will lead to a deeper understanding of the basic biological processes which will in turn help understanding of health issues such as congenital abnormalities, cancers and tissue repair.

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