High Throughput GWAS Analysis
Genome Wide Association Studies, or GWAS for short, are gradually becoming more commonplace. The main goals of these studies are to find genetic variants, or SNPs, that are correlated between Case or Control individuals. There have been recent publications announcing successful results in this field, including some of the first results from consortia who are currently running some of the largest GWA studies.
Current challenges for dealing with the GWA data include:
- How to handle and process large data sets.
- How to flexibly assess the quality of the data that is generated.
- How to cope with an area whose analysis methods are continually evolving.
- How to bring in context from other areas.
- How to build flexible analyses and comparing different statistical methods.
- How to integrate scripts and 3rd party tools in a maintainable environment.
In this paper we’ll briefly describe how InforSense has met some of these challenges
which scientists have to solve when faced with analysing the results from a GWA study.
Current challenges for dealing with the GWA data include:
- How to handle and process large data sets.
- How to flexibly assess the quality of the data that is generated.
- How to cope with an area whose analysis methods are continually evolving.
- How to bring in context from other areas.
- How to build flexible analyses and comparing different statistical methods.
- How to integrate scripts and 3rd party tools in a maintainable environment.
In this paper we’ll briefly describe how InforSense has met some of these challenges
which scientists have to solve when faced with analysing the results from a GWA study.
