Genotyping experiments are widely used in clinical and basic research laboratories to identify associations between genetic variations and normal/abnormal phenotypes. Genotyping assay techniques vary from single genomic regions that are interrogated using PCR reactions to high throughput assays examining genome-wide sequence and structural variation. The resulting genotype data may include millions of markers of thousands of individuals, requiring various statistical, modeling or other data analysis methodologies to interpret the results. To date, there are no standards for reporting genotyping experiments. Here we present the Minimum Information about a Genotyping Experiment (MIGen) standard, defining the minimum information required for reporting genotyping experiments. MIGen standard covers experimental design, subject description, genotyping procedure, quality control and data analysis. MIGen is a registered project under MIBBI (Minimum Information for Biological and Biomedical Investigations) and is being developed by an interdisciplinary group of experts in basic biomedical science, clinical science, biostatistics and bioinformatics. To accommodate the wide variety of techniques and methodologies applied in current and future genotyping experiment, MIGen leverages foundational concepts from the Ontology for Biomedical Investigations (OBI) for the description of the various types of planned processes and implements a hierarchical document structure. The adoption of MIGen by the research community will facilitate consistent genotyping data interpretation and independent data validation. MIGen can also serve as a framework for the development of data models for capturing and storing genotyping results and experiment metadata in a structured way, to facilitate the exchange of metadata.

We describe a novel approach to genetic association analyses with proteins sub-divided into biologically relevant smaller sequence features (SFs), and their variant types (VTs). SFVT analyses are particularly informative for study of highly polymorphic proteins such as the human leukocyte antigen (HLA), given the nature of its genetic variation: the high level of polymorphism, the pattern of amino acid variability, and that most HLA variation occurs at functionally important sites, as well as its known role in organ transplant rejection, autoimmune disease development and response to infection. Further, combinations of variable amino acid sites shared by several HLA alleles (shared epitopes) are most likely better descriptors of the actual causative genetic variants. In a cohort of systemic sclerosis patients/controls, SFVT analysis shows that a combination of SFs implicating specific amino acid residues in peptide binding pockets 4 and 7 of HLA-DRB1 explains much of the molecular determinant of risk.

The immune response HLA class II DRB1 gene provides the major genetic contribution to Juvenile Idiopathic Arthritis (JIA), with a hierarchy of predisposing through intermediate to protective effects. With JIA, and the many other HLA associated diseases, it is difficult to identify the combinations of biologically relevant amino acid (AA) residues directly involved in disease due to the high level of HLA polymorphism, the pattern of AA variability, including varying degrees of linkage disequilibrium (LD), and the fact that most HLA variation occurs at functionally important sites. In a subset of JIA patients with the clinical phenotype oligoarticular-persistent (OP), we have applied a recently developed novel approach to genetic association analyses with genes/proteins sub-divided into biologically relevant smaller sequence features (SFs), and their “alleles” which are called variant types (VTs). With SFVT analysis, association tests are performed on variation at biologically relevant SFs based on structural (e.g., beta-strand 1) and functional (e.g., peptide binding site) features of the protein. We have extended the SFVT analysis pipeline to additionally include pairwise comparisons of DRB1 alleles within serogroup classes, our extension of the Salamon Unique Combinations algorithm, and LD patterns of AA variability to evaluate the SFVT results; all of which contributed additional complementary information. With JIA-OP, we identified a set of single AA SFs, and SFs in which they occur, particularly pockets of the peptide binding site, that account for the major disease risk attributable to HLA DRB1. These are (in numeric order): AAs 13 (pockets 4 and 6), 37 and 57 (both pocket 9), 67 (pocket 7), 74 (pocket 4), and 86 (pocket 1), and to a lesser extent 30 (pockets 6 and 7) and 71 (pockets 4, 5, and 7).

The Minimum Information for Biological and Biomedical Investigations (MIBBI) project provides a resource for those exploring the range of extant minimum information checklists and fosters coordinated development of such checklists.