Methods: Modern immunogenomic data are highly polymorphism across large numbers of functionally related loci, but the methods historically used for immunogenomic data analysis were developed for small numbers of loci with low levels of polymorphism, characteristics of early molecular and serological immunological data. Recently developed genomic analysis tools (PLINK) are intended specifically for GWAS-derived SNP data and SNP-derived CNV data, which are minimally polymorphic across many hundreds of thousands of loci; for the most part, these variants have no known function, and LD between all but the most proximal of them is negligible. As a result, it is not easy to apply these recently developed methods to the analysis of HLA and KIR data.

Results: Tools and data-resources specific for immunogenomic research are available at www.immunogenomics.org/software.html.

Online tools include iHAP, which performs rapid EM Haplotype estimation on large numbers of loci (>20) for large numbers of samples (>10,000), allowing estimation of haplotypes comprising both SNP and HLA or KIR data; and UNCL, which translates HLA allele names from version 2 to version 3 of the nomenclature.

Locally-run tools include the ANTT, which translates allele names between any nomenclatures, and validates data against any reference list.

Data-resources such as the GFM Browser, which allows the browsing and comparison of global allele-frequency distribution maps, are also available.

Conclusions: Modern genomic analysis methods are being tailored to the specifics of the immunogenomic research community at immunogenomics.org.

Methods: Phase One of the project is underway, and consists of an ASHI-sponsored survey of current HLA and KIR data-management practices. This survey is intended to determine current HLA and KIR data-capture and data transmission standards, current HLA ambiguity resolution practices, and current primary data-analysis methods. The survey includes synthetic datasets, which project participants use to demonstrate their ambiguity resolution and primary data-analysis practices.

Investigators can access the survey online (http://www.surveymonkey.com/s/IDAWG).

Results: Beginning in June of 2011, Phase Two of the project will use the information provided by the community to determine the effects of the various practices in use on common applications for these data, including registry searches, disease-association studies, and population/anthropology studies. The results of the survey and the impact of current practices on common applications will be presented at the 16th IHIWS in Liverpool, UK in May 2012.

Conclusions: Consensus is emerging in the genomics community regarding the need for community data-reporting and analysis standards in genetic studies. The diverse, international nature of immunogenomic research has resulted in a proliferation of data management and analysis approaches with no discussion of their long-term impact on the field. All members of the immunogenomics community are invited to participate in this discussion. More information can be found at immunogenomics.org.