A number of differences in peoples’ genomes, termed single nucleotide polymorphisms or SNPs, have been associated with disease.

For example, certain SNPs in the DNA repair gene BRCA1 are very highly associated with breast cancer.

The majority of diseases for which there is a genetic component, however, are not associated with one particular genetic mutation, but alterations in a number of genes across the whole genome.

Genome-wide association studies (GWAS) are beginning to elucidate these complex interactions, with at least 165 associations now known.

This represents a huge range of new drug targets, validated in humans, with the promise of many more as more GWAS are performed.

The Oxford invention

A world-leading team in Oxford have developed a range of programmes for statistical analysis of genome-wide data using novel algorithms.

The programmes allow the user to gain an understanding of the complex interrelationships between many genetic variants and diseases and the conditions with which they are associated.

Reliable methods for analysing genome-wide data to understand these complexities have long been desired by geneticists in industry and academia alike.

The Oxford genome analysis software suite (OGWASS) has already elicited great interest from both academic groups and industry and we are now able to offer commercial licenses to this leading suite, in whole or in part.

OGWASS is comprised of the following software programmes:

CHIAMO (3507) – this programme incorporates a novel algorithm for calling of overall genotypes from SNP intensity data. Also, it allows the calling of genotypes in multiple cohorts at once using a hierarchical model. To our knowledge, this is a unique benefit.

GTOOL (3590) – program for converting genetic data between different file formats and for creating subsets of a given dataset. Our new algorithms use a new data format that allows genotypes to be stored with uncertainty. This is the only program that can convert data from and into this new format.

IMPUTE 1, 2 and 4 (14128) – a novel algorithm for imputation/prediction of unobserved and missing SNP alleles in a dataset consisting of genotype data on a set of individuals based upon a panel of known haplotype data and a recombination map. The idea of imputing alleles has now become very popular in genetics studies of human disease and is being used to enable researchers to find new disease genes and share data. IMPUTE allows more precise and efficient prediction than other algorithms available.

Sparse Decomposition of Arrays (SDA) (14872) – software that decomposes 3D arrays (or tensor) of gene expression measurements to identify gene networks that are associated with genetic variation.

IMPUTE 5 (16992) – software program for imputation/estimation of unobserved and missing SNP alleles in a dataset, consisting of genotype data on a set of individuals based upon a panel of known haplotype data and a recombination map.

Request more information if you would like to discuss the suite or any of the programmes further.