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.

Annotation of Human Exome Gene Variants with Consensus Pathogenicity

A novel approach is developed to address the challenge of annotating with phenotypic effects those exome variants for which relevant empirical data are lacking or minimal. The predictive annotation method is implemented as a stacked ensemble of supervised base-learners, including distributed random forest and gradient boosting machines. Ensemble models were trained and cross-validated on evidence-based categorical variant effect annotations from the ClinVar database, and were applied to 84 million non-synonymous single nucleotide variants (SNVs). The consensus model combined 39 functional mutation impacts, cross-species conservation score, and gene indispensability score. The indispensability score, accounting for differences in variant pathogenicities including in essential and mutation-tolerant genes, considerably improved the predictions. The consensus combination is consistent with as many input scores as possible while minimizing false predictions. The input scores are ranked based on their ability to predict effects. The score rankings and categorical phenotypic variant effect predictions are aimed for direct use in clinical and biological applications to prioritize human exome variants and mutations.

MAP: Mutation Arranger for Defining Phenotype-Related Single-Nucleotide Variant

Next-generation sequencing (NGS) is widely used to identify the causative mutations underlying diverse human diseases, including cancers, which can be useful for discovering the diagnostic and therapeutic targets. Currently, a number of single-nucleotide variant (SNV)-calling algorithms are available; however, there is no tool for visualizing the recurrent and phenotype-specific mutations for general researchers. In this study, in order to support defining the recurrent mutations or phenotype-specific mutations from NGS data of a group of cancers with diverse phenotypes, we aimed to develop a user-friendly tool, named mutation arranger for defining phenotype-related SNV (MAP). MAP is a user-friendly program with multiple functions that supports the determination of recurrent or phenotype-specific mutations and provides graphic illustration images to the users. Its operation environment, the Microsoft Windows environment, enables more researchers who cannot operate Linux to define clinically meaningful mutations with NGS data from cancer cohorts.

Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data

The analysis of whole-genome or exome sequencing data from trios and pedigrees has been successfully applied to the identification of disease-causing mutations. However, most methods used to identify and genotype genetic variants from next-generation sequencing data ignore the relationships between samples, resulting in significant Mendelian errors, false positives and negatives. Here we present a Bayesian network framework that jointly analyzes data from all members of a pedigree simultaneously using Mendelian segregation priors, yet providing the ability to detect de novo mutations in offspring, and is scalable to large pedigrees. We evaluated our method by simulations and analysis of whole-genome sequencing (WGS) data from a 17-individual, 3-generation CEPH pedigree sequenced to 50× average depth. Compared with singleton calling, our family caller produced more high-quality variants and eliminated spurious calls as judged by common quality metrics such as Ti/Tv, Het/Hom ratios, and dbSNP/SNP array data concordance, and by comparing to ground truth variant sets available for this sample. We identify all previously validated de novo mutations in NA12878, concurrent with a 7× precision improvement. Our results show that our method is scalable to large genomics and human disease studies.