2009 EFI Abstract
Accepted for poster presentation in the Wine & Cheese Poster Session (Population Genetics), Foyer Grandfloor + 1st Floor, on Monday May 11, from 13:30 - 15:00, at the European Federation For Immunogenetics (EFI) 23rd European Immunogenetics and Histocompatibility Conference and 17th Annual Meeting of the German Society for Immunogenetics in Ulm, Germany.
The Immunogenomics Data-Analysis Working Group
Jill A. Hollenbach1, Henry Erlich2, Michael Feolo3, Marcelo Fernandez-Vina4, Martin Maiers5, Hazael Maldonado-Torres6, Diogo Meyer7, Derek Middleton8, Rich Single9, Glenys Thomson10, Steven J. Mack1
1 Children's Hospital Oakland Research Institute, Oakland, United States of America
The goal of the immunogenomics data analysis working group is to facilitate the sharing of immunogenomic data (HLA, KIR, etc.) and analyses, fostering consistent analytical interpretation by the immunogenomics and larger genomics community. The working group will develop methods, standards, tools and recommendations intended to; 1) record, store and transmit immunogenomic data without obscuring the limitations of the typing method used, allow easy identification of allelic equivalency under successive nomenclatures, make data both human-readable (e.g., flat-text file) and machine-readable (e.g., XML file), conform to extant nomenclature rules, all without the use of proprietary platforms; 2) document ambiguity reduction (AR) methods used, permit reproducible AR, and permit equivalency under different AR methods; 3) foster portability between extant analysis tools and methods for maximum access to investigators (e.g., web-based tools); 4) encourage consistent data formats in future analytical methods, promoting widespread accessibility and application; 5) foster methodological consistency in the analysis of low frequency alleles and heterogeneous data, haplotype estimation, Hardy-Weinberg testing of highly polymorphic data, the application of measures of and adjustment for linkage disequilibrium, tests for selection and measures of population differentiation, the calculation of odds ratios, relative risks, etc., corrections for multiple testing, mitigation of false positive readings; and 6) develop novel methods of data analysis for highly polymorphic loci in disease association and population studies (e.g., peptide and nucleotide-level analyses, multidimensional scaling analyses, and neural network analyses). We envision an international collaborative effort by investigators particularly interested in issues of immunogenomics data management and analysis, with the goal of presenting our recommendations on these topics at the 16th IHWC and eventually producing a reference manual.