Statistical genomics

Lead Research Organisation: Rothamsted Research
Department Name: Unlisted

Abstract

The focus of this project is to develop new state-of-the-art statistical methodology for application in areas of genomics which focus on biomolecular sequence and/or structure and which are of relevance to agriculture. The new wave of high-throughput genomic and proteomic technologies present great and unprecedented opportunities for biological and agricultural science, but simultaneously present great challenges in how to manage, interpret and exploit the concomitant information deluge.

Rothamsted Research was the birthplace of mathematical statistics in the early part of the twentieth century, pioneered by the celebrated scientist and statistician R.A.Fisher. Since its inception, the purpose of mathematical statistics has been to distil meaning from experimental and observational data, to reveal underlying statistical and scientific truth. New biotechnology generates new forms of biological data, in hitherto unimaginable quantities. Moreover, such data are often collected to answer new kinds of biological question. To reliably extract information from these new types of data to address these new kinds of question, new statistical methods for experimental design, data analysis and inference are required.

Currently, the Statistical Genomics Group is involved in projects on soil metagenomics, crop genetic map integration, crop chromatin structure and fungal genome architecture. Each of these projects involves DNA sequence in some way, in each case the aim of the analysis being to reveal underlying structure of some sort. In each case, we are developing statistical methodology to address the intricate nature of the data and of the questions being asked of them.

Publications

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Gilks WR (2012) Three-point appraisal of genetic linkage maps. in TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik

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Jaramillo Ramirez GI (2012) Repellents inhibit P450 enzymes in Stegomyia (Aedes) aegypti. in PloS one

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Loza-Reyes E (2014) Classification of molecular sequence data using Bayesian phylogenetic mixture models in Computational Statistics & Data Analysis

 
Description This project developed statistical methods for analysing genomics datasets for crop species and their pests. These methods were applied to datasets generated in Rothamsted Research.
Exploitation Route The methods can be reapplied to other datasets from genetic and genomic experiments.
Sectors Agriculture, Food and Drink

 
Description Some of the methods developed in this project were applied to understanding mechanisms of fungicide resistance.
First Year Of Impact 2009
Sector Agriculture, Food and Drink
Impact Types Economic