Mott group
Research Overview
Our research focuses on development of methods, algorithms and software for mapping multifactorial trait loci. We analyse complex traits in mouse models of human disease, and in other model organisms
The group has current research foci in the areas of comparative genomics, ancestral haplotype construction, database development, QTL linkage and association methods, variability in patterns of linkage disequilibrium, microarray analysis and multivariate modelling of quantitative traits.
More extensive sumaries of our research projects can be found under Research Projects.
Publications
Yalcin B, Flint J, Mott R. 2005. Using progenitor strain information to identify quantitative trait nucleotides in outbred mice. Genetics, 171 (2), pp. 673-81. Read abstract | View on PubMed
We have developed a fast and economical strategy for dissecting the genetic architecture of quantitative trait loci at a molecular level. The method uses two pieces of information: mapping data from crosses that involve more than two inbred strains and sequence variants in the progenitor strains within the interval containing a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that any sequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals except at a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis. We apply the method to a 4.8-Mb region on mouse chromosome 1 that contains a QTL influencing anxiety segregating in a heterogeneous stock and show that, under the assumption that a single QTN is present and lies in a region conserved between the human and mouse genomes, it is possible to reduce the number of variants likely to be the quantitative trait nucleotide from many thousands to <20. Hide abstract
Valdar W, Solberg LC, Gauguier D, Burnett S, Klenerman P, Cookson WO, Taylor MS, Rawlins JN, Mott R, Flint J. 2006. Genome-wide genetic association of complex traits in heterogeneous stock mice. Nature genetics, 38 (8), pp. 879-87. Read abstract | View on PubMed
Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits. Hide abstract
Yalcin B, Willis-Owen SA, Fullerton J, Meesaq A, Deacon RM, Rawlins JN, Copley RR, Morris AP, Flint J, Mott R. 2004. Genetic dissection of a behavioral quantitative trait locus shows that Rgs2 modulates anxiety in mice. Nature genetics, 36 (11), pp. 1197-202. Read abstract | View on PubMed
Here we present a strategy to determine the genetic basis of variance in complex phenotypes that arise from natural, as opposed to induced, genetic variation in mice. We show that a commercially available strain of outbred mice, MF1, can be treated as an ultrafine mosaic of standard inbred strains and accordingly used to dissect a known quantitative trait locus influencing anxiety. We also show that this locus can be subdivided into three regions, one of which contains Rgs2, which encodes a regulator of G protein signaling. We then use quantitative complementation to show that Rgs2 is a quantitative trait gene. This combined genetic and functional approach should be applicable to the analysis of any quantitative trait. Hide abstract
Linnell J, Mott R, Field S, Kwiatkowski DP, Ragoussis J, Udalova IA. 2004. Quantitative high-throughput analysis of transcription factor binding specificities. Nucleic acids research, 32 (4), pp. e44. Read abstract | View on PubMed
We present a general high-throughput approach to accurately quantify DNA-protein interactions, which can facilitate the identification of functional genetic polymorphisms. The method tested here on two structurally distinct transcription factors (TFs), NF-kappaB and OCT-1, comprises three steps: (i) optimized selection of DNA variants to be tested experimentally, which we show is superior to selecting variants at random; (ii) a quantitative protein-DNA binding assay using microarray and surface plasmon resonance technologies; (iii) prediction of binding affinity for all DNA variants in the consensus space using a statistical model based on principal coordinates analysis. For the protein-DNA binding assay, we identified a polyacrylamide/ester glass activation chemistry which formed exclusive covalent bonds with 5'-amino-modified DNA duplexes and hindered non-specific electrostatic attachment of DNA. Full accessibility of the DNA duplexes attached to polyacrylamide-modified slides was confirmed by the high degree of data correlation with the electromobility shift assay (correlation coefficient 93%). This approach offers the potential for high-throughput determination of TF binding profiles and predicting the effects of single nucleotide polymorphisms on TF binding affinity. New DNA binding data for OCT-1 are presented. Hide abstract
Mangiarini L, Sathasivam K, Mahal A, Mott R, Seller M, Bates GP. 1997. Instability of highly expanded CAG repeats in mice transgenic for the Huntington's disease mutation. Nature genetics, 15 (2), pp. 197-200. Read abstract | View on PubMed
Funding Sources
The Wellcome Trust, BBSRC
Research Area
Statistical Genetics
Keywords
Statistical Genetics Arabidopsis Human Methods
Newt
Pinfox