Priority index: database of genetic targets in immune-mediated disease.

We describe a comprehensive and unique database 'Priority index' (Pi; http://pi.well.ox.ac.uk) of prioritized genes encoding potential therapeutic targets that encompasses all major immune-mediated diseases. We provide targets at the gene level, each receiving a 5-star rating supported by: genomic evidence arising from disease genome-wide associations and functional immunogenomics, annotation evidence using ontologies restricted to genes with genomic evidence, and network evidence from protein interactions. Target genes often act together in related molecular pathways. The underlying Pi approach is unique in identifying a network of highly rated genes that mediate pathway crosstalk. In the Pi website, disease-centric pages are specially designed to enable the users to browse a complete list of prioritized genes and also a manageable list of nodal genes at the pathway crosstalk level; both switchable by clicks. Moreover, target genes are cross-referenced and supported using additional information, particularly regarding tractability, including druggable pockets viewed in 3D within protein structures. Target genes highly rated across diseases suggest drug repurposing opportunity, while genes in a particular disease reveal disease-specific targeting potential. To facilitate the ease of such utility, cross-disease comparisons involving multiple diseases are also supported. This facility, together with the faceted search, enhances integrative mining of the Pi resource to accelerate early-stage therapeutic target identification and validation leveraging human genetics.

Challenges for the standardized reporting of NGS HLA genotyping: Surveying gaps between clinical and research laboratories.

Next generation sequencing (NGS) is being applied for HLA typing in research and clinical settings. NGS HLA typing has made it feasible to sequence exons, introns and untranslated regions simultaneously, with significantly reduced labor and reagent cost per sample, rapid turnaround time, and improved HLA genotype accuracy. NGS technologies bring challenges for cost-effective computation, data processing and exchange of NGS-based HLA data. To address these challenges, guidelines and specifications such as Genotype List (GL) String, Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING), and Histoimmunogenetics Markup Language (HML) were proposed to streamline and standardize reporting of HLA genotypes. As part of the 17th International HLA and Immunogenetics Workshop (IHIW), we implemented standards and systems for HLA genotype reporting that included GL String, MIRING and HML, and found that misunderstanding or misinterpretations of these standards led to inconsistencies in the reporting of NGS HLA genotyping results. This may be due in part to a historical lack of centralized data reporting standards in the histocompatibility and immunogenetics community. We have worked with software and database developers, clinicians and scientists to address these issues in a collaborative fashion as part of the Data Standard Hackathons (DaSH) for NGS. Here we report several categories of challenges to the consistent exchange of NGS HLA genotyping data we have observed. We hope to address these challenges in future DaSH for NGS efforts.

High-Throughput Immunogenetics for Clinical and Research Applications in Immunohematology: Potential and Challenges.

Analysis and interpretation of Ig and TCR gene rearrangements in the conventional, low-throughput way have their limitations in terms of resolution, coverage, and biases. With the advent of high-throughput, next-generation sequencing (NGS) technologies, a deeper analysis of Ig and/or TCR (IG/TR) gene rearrangements is now within reach, which impacts on all main applications of IG/TR immunogenetic analysis. To bridge the generation gap from low- to high-throughput analysis, the EuroClonality-NGS Consortium has been formed, with the main objectives to develop, standardize, and validate the entire workflow of IG/TR NGS assays for 1) clonality assessment, 2) minimal residual disease detection, and 3) repertoire analysis. This concerns the preanalytical (sample preparation, target choice), analytical (amplification, NGS), and postanalytical (immunoinformatics) phases. Here we critically discuss pitfalls and challenges of IG/TR NGS methodology and its applications in hemato-oncology and immunology.

Standard methods for the management of immunogenetic data.

In this chapter, we outline some basic principles for the consistent management of immunogenetic data. These include the preparation of a single master data file that can serve as the basis for all subsequent analyses, a focus on the quality and homogeneity of the data to be analyzed, the documentation of the coding systems used to represent the data, and the application of nomenclature standards specific for each immunogenetic system being evaluated. The data management principles discussed here are intended to provide a foundation for the data analysis methods detailed in Chaps. 13 and 14 . The relationship between the data management and analysis methods covered in these three chapters is illustrated in Fig. 3.The application of these data management principles is a first step toward consistent and reproducible data analyses. While it may take extra time and effort to apply them, we feel that it is better to take this approach than to assume that low data quality can be compensated for by large sample sizes.In addition to their relevance for analytical reproducibility, it is important to consider these data management principles from an ethical perspective. The reliability of the data collected and generated as part of a research study should be as important a component of the ethical review of a research application as the security of those data. Finally, in addition to ensuring the integrity of the data from collection to publication, the application of these data management principles will provide a means to foster research integrity and to improve the potential for collaborative data sharing.

Antihuman leukocyte antigen­specific antibody strength determined by complement-dependent or solid-phase assays can predict positive donor-specific crossmatches.

CONTEXT: The association of circulating donor-specific antibody (DSA) strength with crossmatch results is of potential interest to predict allograft outcome. OBJECTIVES: To systematically investigate the aforementioned association and to attempt to define a cutoff value for DSA strength that can predict a positive crossmatch result. DESIGN: We analyzed DSA strength and crossmatch results from the 2006 to 2008 proficiency testing samples of the American Society of Histocompatibility and Immunogenetics (n  =  50). We further validated our findings in candidates for potential kidney transplant (n  =  19). RESULTS: Proficiency test samples with positive antihuman globulin T-cell crossmatch results had significantly higher DSA strength, as assessed by Luminex (Austin, Texas) mean fluorescent intensity (MFI; MFI [SD], 7860 [4770]), compared with samples with negative crossmatch results (MFI [SD], 2900 [1820]; P  =  .001). Similarly, higher Luminex values were observed in samples from candidates for transplant with positive antihuman globulin T-cell crossmatch results (MFI [SD], 7910 [2370] versus 2840 [1960]; P < .001). The MFI value of 6540 had 61% and 75% sensitivity and 92% and 94% specificity for predicting positive antihuman globulin T-cell crossmatches in proficiency test samples and in candidates for transplant, respectively. CONCLUSIONS: The DSA strength correlates well with crossmatch results. An MFI of 6540 predicted a positive antihuman globulin T-cell crossmatch.

Recovering probabilities for nucleotide trimming processes for T cell receptor TRA and TRG V-J junctions analyzed with IMGT tools.

BACKGROUND: Nucleotides are trimmed from the ends of variable (V), diversity (D) and joining (J) genes during immunoglobulin (IG) and T cell receptor (TR) rearrangements in B cells and T cells of the immune system. This trimming is followed by addition of nucleotides at random, forming the N regions (N for nucleotides) of the V-J and V-D-J junctions. These processes are crucial for creating diversity in the immune response since the number of trimmed nucleotides and the number of added nucleotides vary in each B or T cell. IMGT sequence analysis tools, IMGT/V-QUEST and IMGT/JunctionAnalysis, are able to provide detailed and accurate analysis of the final observed junction nucleotide sequences (tool "output"). However, as trimmed nucleotides can potentially be replaced by identical N region nucleotides during the process, the observed "output" represents a biased estimate of the "true trimming process." RESULTS: A probabilistic approach based on an analysis of the standardized tool "output" is proposed to infer the probability distribution of the "true trimmming process" and to provide plausible biological hypotheses explaining this process. We collated a benchmark dataset of TR alpha (TRA) and TR gamma (TRG) V-J rearranged sequences and junctions analysed with IMGT/V-QUEST and IMGT/JunctionAnalysis, the nucleotide sequence analysis tools from IMGT, the international ImMunoGeneTics information system, http://imgt.cines.fr. The standardized description of the tool output is based on the IMGT-ONTOLOGY axioms and concepts. We propose a simple first-order model that attempts to transform the observed "output" probability distribution into an estimate closer to the "true trimming process" probability distribution. We use this estimate to test the hypothesis that Poisson processes are involved in trimming. This hypothesis was not rejected at standard confidence levels for three of the four trimming processes: TRAV, TRAJ and TRGV. CONCLUSION: By using trimming of rearranged TR genes as a benchmark, we show that a probabilistic approach, applied to IMGT standardized tool "outputs" opens the way to plausible hypotheses on the events involved in the "true trimming process" and eventually to an exact quantification of trimming itself. With increasing high-throughput of standardized immunogenetics data, similar probabilistic approaches will improve understanding of processes so far only characterized by the "output" of standardized tools.