IDAWG Ongoing International Workshop Project:
Reporting Guidelines for Immunogenomic Studies

STrengthening the REporting of Immunogenomic Studies: The STREIS Statement

The genomic research community has recognized the need for community data-reporting and analysis standards in genetic disease association studies.

For example, the Human Genome Epidemiology Network (HuGENet) was established in 1998 by the Office of Public Health Genomics to advance the synthesis and interpretation of data on human genetic variation in disease association.

The publications of the 'Strengthening the Reporting of Observational Studies in Epidemiology' (STROBE) and 'Strengthening the Reporting of Genetic Association studies' (STREGA) statements represent further advances in these efforts, enumerating specific areas in which adoption of community-based reporting guidelines can improve the consistent interpretation of genetic studies, particularly for genome-wide association studies (GWAS) and meta-analyses of GWAS data.

The STROBE and STREGA statements represent significant progress toward the widespread adoption of such reporting standards in the field of genetic epidemiology, and can be applied to gene-based population and evolutionary studies as well.

Many of the data-reporting issues described in these statements are pertinent to histocompatibility and immunogenetic studies.

However, these statements pertain primarily to large cohorts and single nucleotide polymorphism (SNP) based studies.

The high level of polymorphism associated with the HLA and KIR loci, the variety of HLA and KIR genotyping systems in use, the complexities of transplantation studies, and the unique role played by the MHC region in predisposition to disease (in both the strength and complexity of associations) require specific consideration for the development of reporting standards and recommendations that go beyond those defined in the STROBE and STREGA statements.

The goal of the immunogenomics data-analysis working group (IDAWG) is to develop consensus-based community data standards for the HLA and KIR gene systems.

The integration of standards for both immunogenetic systems will allow for consistent, reproducible, and easily combined analyses for each system, and will facilitate the immunogenomic analysis of KIR and HLA interactions.

The first step in achieving this approach is to build on the principles outlined in the STREGA statement and develop a set of community-based documentation guidelines intended to strengthen the reporting of immunogenomic studies.

Consistent reporting of the manner in which the data in immunogenomic studies are managed and analyzed will facilitate the reproducibility of studies, enhance data-sharing and meta-analyses and make immunogenomic research more accessible to the larger genomics community.

While the guidelines described in the STROBE and STREGA statements need to be applied to immunogenomic studies, a STREIS statement is also needed to extend these guidelines as described in the table below.

If you are interested in participating in the development of the STREIS statement, please complete the IDAWG survey of HLA and KIR data-management practices, and send us an email describing your interest.

Proposed STREIS Extensions of the STREGA Statement
STROBE ItemItemStrobe GuidelineSTREGA GuidelineProposed STREIS Extension
Variables7(a) Clearly define all outcomes, exposures, predictors, potential cofounders, and effect modifiers. Give diagnostic criteria, if applicable. (b) Clearly define genetic exposures (genetic variants) using a widely-used nomenclature system. Identify variables likely to be associated with population stratification (confounding by ethnic origin) (c) Describe HLA alleles in accordance with WHO Nomenclature Committee for Factors of the HLA System. Identify the IMGT/HLA Database release number pertinent to the data.

(d) Describe KIR alleles in accordance with the IPD-KIR Database. Identify the IPD-KIR Database release number pertinent to the data.

Data sources/ measurement8(a) For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group. (b) Describe laboratory methods, including source and storage of DNA, genotyping methods and platforms (including the allele calling algorithm used, and its version), error rates and call rates. State the laboratory/center where genotyping was done. Describe comparability of laboratory methods if there is more than one group. Specify whether genotypes were assigned using all of the data from the study simultaneously or in smaller batches. (c) Provide access to the primary, ambiguous genotype data for each individual.

(d) Describe the system(s) used to store, manage, and validate genotype and allele data, and to prepare data for analysis.

(e) Use objective terms, identifying the assessed features of each gene, to describe genotyping systems and genotyping results. Avoid using subjective terms (e.g. low-, intermediate-, high-, or allele-resolution), that that may change over time, to describe genotyping systems and results.

(f) Document all methods applied to resolve ambiguity.

(g) Define any codes used to represent ambiguities.

(h) Describe any binning or combining of alleles into common categories that were performed.

Statistical Methods12(a) Describe all statistical methods, including those used to control for confounding. State software version used and options (or settings) chosen. (b) Discuss any modifications made to the data in order to have them comport to the expectations of a method for the purpose of analysis.

(c) Document any caveats associated with each analysis as they pertain to immunogenomic data.

Limitations19Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias. (a) Discuss the impact of any modifications made to the data for the purpose of analysis.

(b) Discuss any caveats associated with each analysis as they pertain to immunogenomic data.

(c) Discuss any potential impact of ambiguity resolution on the results.