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Crewe, United Kingdom

Wilkinson S.,University of Edinburgh | Haley C.,University of Edinburgh | Haley C.,MRC Human Genetics Unit | Alderson L.,Countrywide Livestock Ltd | Wiener P.,University of Edinburgh
Heredity | Year: 2011

Recently developed Bayesian genotypic clustering methods for analysing genetic data offer a powerful tool to evaluate the genetic structure of domestic farm animal breeds. The unit of study with these approaches is the individual instead of the population. We aimed to empirically evaluate various individual-based population genetic statistical methods for characterization of genetic diversity and structure of livestock breeds. Eighteen British pig populations, comprising 819 individuals, were genotyped at 46 microsatellite markers. Three Bayesian genotypic clustering approaches, principle component analysis (PCA) and phylogenetic reconstruction were applied to individual multilocus genotypes to infer the genetic structure and diversity of the British pig breeds. Comparisons of the three Bayesian genotypic clustering methods (, BAPS and STRUCTURAMA) revealed some broad similarities but also some notable differences. Overall, the methods agreed that majority of the British pig breeds are independent genetic units with little evidence of admixture. The three Bayesian genotypic clustering methods provided complementary, biologically credible clustering solutions but at different levels of resolution. BAPS detected finer genetic differentiation and in some cases, populations within breeds. Consequently, it estimated a greater number of underlying genetic populations (K, in the notation of Bayesian clustering methods). Two of the Bayesian methods (STRUCTURE and BAPS) and phylogenetic reconstruction provided similar success in assignment of individuals, supporting the use of these methods for breed assignment. © 2011 Macmillan Publishers Limited All rights reserved. Source

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. Source

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: 77.00 | Year: 2015

Each one of us carries mutations (genetic “blemishes”) which make us susceptible to diseases, such as infections or cancer. Finding these harmful mutations is difficult because they exist in a sea of more numerous, inconsequential DNA changes. We use the latest cutting-edge technologies and computational analyses to find the consequential mutations and to work out what changes they incur for molecules, cells, organs and individuals. Our studies not just consider proteins – the “work-horses of the cell” – but also RNAs which also help to orchestrate the cell’s function. We also try to understand how single cells change when their DNA is altered, or over time, or when their environments alter. In this way we are seeking to bridge between DNA mutations and disease, whilst understanding in which type of cell the disease is first manifested.

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.

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