Identification of Novel Genetic Risk Factors for Focal Segmental Glomerulosclerosis in Children: Results From the Chronic Kidney Disease in Children (CKiD) Cohort.

RATIONALE & OBJECTIVE: Focal segmental glomerulosclerosis (FSGS) is a major cause of pediatric nephrotic syndrome, and African Americans exhibit an increased risk for developing FSGS compared with other populations. Predisposing genetic factors have previously been described in adults. Here we performed genomic screening of primary FSGS in a pediatric African American population. STUDY DESIGN: Prospective cohort with case-control genetic association study design. SETTING & PARTICIPANTS: 140 African American children with chronic kidney disease from the Chronic Kidney Disease in Children (CKiD) cohort, including 32 cases with FSGS. PREDICTORS: Over 680,000 common single-nucleotide polymorphisms (SNPs) were tested for association. We also ran a pathway enrichment analysis and a human leucocyte antigen (HLA)-focused association study. OUTCOME: Primary biopsy-proven pediatric FSGS. ANALYTICAL APPROACH: Multivariate logistic regression models. RESULTS: The genome-wide association study revealed 169 SNPs from 14 independent loci significantly associated with FSGS (false discovery rate [FDR]<5%). We observed notable signals for genetic variants within the APOL1 (P=8.6×10-7; OR, 25.8 [95% CI, 7.1-94.0]), ALMS1 (P=1.3×10-7; 13.0% in FSGS cases vs 0% in controls), and FGFR4 (P=4.3×10-6; OR, 24.8 [95% CI, 6.3-97.7]) genes, all of which had previously been associated with adult FSGS, kidney function, or chronic kidney disease. We also highlighted novel, functionally relevant genes, including GRB2 (which encodes a slit diaphragm protein promoting podocyte structure through actin polymerization) and ITGB1 (which is linked to renal injuries). Our results suggest a major role for immune responses and antigen presentation in pediatric FSGS through (1) associations with SNPs in PTPRJ (or CD148, P=3.5×10-7), which plays a role in T-cell receptor signaling, (2) HLA-DRB1∗11:01 association (P=6.1×10-3; OR, 4.5 [95% CI, 1.5-13.0]), and (3) signaling pathway enrichment (P=1.3×10-6). LIMITATIONS: Sample size and no independent replication cohort with genomic data readily available. CONCLUSIONS: Our genetic study has identified functionally relevant risk factors and the importance of immune regulation for pediatric primary FSGS, which contributes to a better description of its molecular pathophysiological mechanisms. PLAIN-LANGUAGE SUMMARY: We assessed the genetic risk factors for primary focal segmental glomerulosclerosis (FSGS) by simultaneously testing over 680,000 genetic markers spread across the genome in 140 children, including 32 with FSGS lesions. Fourteen independent genetic regions were significantly associated with pediatric FSGS, including APOL1 and ALMS1-NAT8, which were previously found to be associated with FSGS and chronic kidney diseases in adults. Novel genes with relevant biological functions were also highlighted, such as GRB2 and FGFR4, which play a role in the kidney filtration barrier and in kidney cell differentiation, respectively. Finally, we revealed the importance of immune regulation in pediatric FSGS through associations involving cell surface proteins presenting antigens to the immune system and interacting with T-cell receptors.

HLA-EPI: A new EPIsode in exploring donor/recipient epitopic compatibilities.

The HLA system plays a pivotal role both in transplantation and immunology. While classical HLA genotypes matching is made at the allelic level, recent progresses were developed to explore antibody-antigen recognition by studying epitopes. Donor to recipient matching at the epitopic level is becoming a trending topic in the transplantation research field because anti-HLA antibodies are epitope-specific rather than allele-specific. Indeed, different HLA alleles often share common epitopes. We present the HLA-Epi tool (hla.univ-nantes.fr) to study an HLA genotype at the epitope level. Using the international HLA epitope registry (Epregistry.com.br) as a reference, we developed HLA-Epi to easily determine epitopic and allelic compatibility levels between several HLA genotypes. The epitope database covers the most common HLA alleles (N = 2976 HLA alleles), representing more than 99% of the total observed frequency of HLA alleles. The freely accessible web tool HLA-Epi calculates an epitopic mismatch load between different sets of potential recipient-donor pairs at different resolution levels. We have characterized the epitopic mismatches distribution in a cohort of more than 10,000 kidney transplanted pairs from European ancestry, which showed low number of epitopic mismatches: 56.9 incompatibilities on average. HLA-Epi allows the exploration of epitope pairing matching to better understand epitopes contribution to immune responses regulation, particularly during transplantation. This free and ready-to-use bioinformatics tool not only addresses limitations of other related tools, but also offers a cost-efficient and reproducible strategy to analyze HLA epitopes as an alternative to HLA allele compatibility. In the future, this could improve sensitization prevention for allograft allocation decisions and reduce the risk of alloreactivity.

Surfing the Big Data Wave: Omics Data Challenges in Transplantation.

In both research and care, patients, caregivers, and researchers are facing a leap forward in the quantity of data that are available for analysis and interpretation, marking the daunting "big data era." In the biomedical field, this quantitative shift refers mostly to the -omics that permit measuring and analyzing biological features of the same type as a whole. Omics studies have greatly impacted transplantation research and highlighted their potential to better understand transplant outcomes. Some studies have emphasized the contribution of omics in developing personalized therapies to avoid graft loss. However, integrating omics data remains challenging in terms of analytical processes. These data come from multiple sources. Consequently, they may contain biases and systematic errors that can be mistaken for relevant biological information. Normalization methods and batch effects have been developed to tackle issues related to data quality and homogeneity. In addition, imputation methods handle data missingness. Importantly, the transplantation field represents a unique analytical context as the biological statistical unit is the donor-recipient pair, which brings additional complexity to the omics analyses. Strategies such as combined risk scores between 2 genomes taking into account genetic ancestry are emerging to better understand graft mechanisms and refine biological interpretations. The future omics will be based on integrative biology, considering the analysis of the system as a whole and no longer the study of a single characteristic. In this review, we summarize omics studies advances in transplantation and address the most challenging analytical issues regarding these approaches.

FaDA: A web application for regular laboratory data analyses.

Web-based data analysis and visualization tools are mostly designed for specific purposes, such as the analysis of data from whole transcriptome RNA sequencing or single-cell RNA sequencing. However, generic tools designed for the analysis of common laboratory data for noncomputational scientists are also needed. The importance of such web-based tools is emphasized by the continuing increases in the sample capacity of conventional laboratory tools such as quantitative PCR, flow cytometry or ELISA instruments. We present a web-based application FaDA, developed with the R Shiny package that provides users with the ability to perform statistical group comparisons, including parametric and nonparametric tests, with multiple testing corrections suitable for most standard wet-laboratory analyses. FaDA provides data visualizations such as heatmaps, principal component analysis (PCA) plots, correlograms and receiver operating curves (ROCs). Calculations are performed through the R language. The FaDA application provides a free and intuitive interface that allows biologists without bioinformatic skill to easily and quickly perform common laboratory data analyses. The application is freely accessible at https://shiny-bird.univ-nantes.fr/app/Fada.

SNP-HLA Reference Consortium (SHLARC): HLA and SNP data sharing for promoting MHC-centric analyses in genomics.

Genome-wide associations studies have repeatedly identified the major histocompatibility complex genomic region (6p21.3) as key in immune pathologies. Researchers have also aimed to extend the biological interpretation of associations by focusing directly on human leukocyte antigen (HLA) polymorphisms and their combination as haplotypes. To circumvent the effort and high costs of HLA typing, statistical solutions have been developed to infer HLA alleles from single-nucleotide polymorphism (SNP) genotyping data. Though HLA imputation methods have been developed, no unified effort has yet been undertaken to share large and diverse imputation models, or to improve methods. By training the HIBAG software on SNP + HLA data generated by the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) to create reference panels, we highlighted the importance of (a) the number of individuals in reference panels, with a twofold increase in accuracy (from 10 to 100 individuals) and (b) the number of SNPs, with a 1.5-fold increase in accuracy (from 500 to 24,504 SNPs). Results showed improved accuracy with CAAPA compared to the African American models available in HIBAG, highlighting the need for precise population-matching. The SNP-HLA Reference Consortium is an international endeavor to gather data, enhance HLA imputation and broaden access to highly accurate imputation models for the immunogenomics community.

Association of HLA-DRB1∗09:01 with tIgE levels among African-ancestry individuals with asthma.

BACKGROUND: Asthma is a complex chronic inflammatory disease of the airways. Association studies between HLA and asthma were first reported in the 1970s, and yet, the precise role of HLA alleles in asthma is not fully understood. Numerous genome-wide association studies were recently conducted on asthma, but were always limited to simple genetic markers (single nucleotide polymorphisms) and not complex HLA gene polymorphisms (alleles/haplotypes), therefore not capturing the biological relevance of this complex locus for asthma pathogenesis. OBJECTIVE: To run the first HLA-centric association study with asthma and specific asthma-related phenotypes in a large cohort of African-ancestry individuals. METHODS: We collected high-density genomics data for the Consortium on Asthma among African-ancestry Populations in the Americas (N = 4993) participants. Using computer-intensive machine-learning attribute bagging methods to infer HLA alleles, and Easy-HLA to infer HLA 5-gene haplotypes, we conducted a high-throughput HLA-centric association study of asthma susceptibility and total serum IgE (tIgE) levels in subjects with and without asthma. RESULTS: Among the 1607 individuals with asthma, 972 had available tIgE levels, with a mean tIgE level of 198.7 IU/mL. We could not identify any association with asthma susceptibility. However, we showed that HLA-DRB1∗09:01 was associated with increased tIgE levels (P = 8.5 × 10-4; weighted effect size, 0.51 [0.15-0.87]). CONCLUSIONS: We identified for the first time an HLA allele associated with tIgE levels in African-ancestry individuals with asthma. Our report emphasizes that by leveraging powerful computational machine-learning methods, specific/extreme phenotypes, and population diversity, we can explore HLA gene polymorphisms in depth and reveal the full extent of complex disease associations.

Easy-HLA: a validated web application suite to reveal the full details of HLA typing.

MOTIVATION: The HLA system plays a pivotal role in both clinical applications and immunology research. Typing HLA genes in patient and donor is indeed required in hematopoietic stem cell and solid-organ transplantation, and the histocompatibility complex region exhibits countless genetic associations with immune-related pathologies. Since the discovery of HLA antigens, the HLA system nomenclature and typing methods have constantly evolved, which leads to difficulties in using data generated with older methodologies. RESULTS: Here, we present Easy-HLA, a web-based software suite designed to facilitate analysis and gain knowledge from HLA typing, regardless of nomenclature or typing method. Easy-HLA implements a computational and statistical method of HLA haplotypes inference based on published reference populations containing over 600 000 haplotypes to upgrade missing or partial HLA information: 'HLA-Upgrade' tool infers high-resolution HLA typing and 'HLA-2-Haplo' imputes haplotype pairs and provides additional functional annotations (e.g. amino acids and KIR ligands). We validated both tools using two independent cohorts (total n = 2500). For HLA-Upgrade, we reached a prediction accuracy of 92% from low- to high-resolution of European genotypes. We observed a 96% call rate and 76% accuracy with HLA-2-Haplo European haplotype pairs prediction. In conclusion, Easy-HLA tools facilitate large-scale immunogenetic analysis and promotes the multi-faceted HLA expertise beyond allelic associations by providing new functional immunogenomics parameters. AVAILABILITY AND IMPLEMENTATION: Easy-HLA is a web application freely available (free account) at: https://hla.univ-nantes.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.