Affiliation:
1. Developmental Biology & Medicine, Faculty of Biology, Medicine & Health, University of Manchester and Manchester Academic Health Science Centre, Manchester, United Kingdom
2. Department of Paediatric Endocrinology, Manchester University NHS Foundation Trust, Manchester, United Kingdom
3. Informatics, Imaging & Data Science, Faculty of Biology, Medicine & Health, University of Manchester, Manchester, United Kingdom
Abstract
Abstract
Background
Children with short stature of undefined aetiology (SS-UA) may have undiagnosed genetic conditions.
Purpose
To identify mutations causing short stature (SS) and genes related to SS, using candidate gene sequence data from the European EPIGROW study.
Methods
First, we selected exonic single nucleotide polymorphisms (SNPs), in cases and not controls, with minor allele frequency (MAF) < 2%, whose carriage fitted the mode of inheritance. Known mutations were identified using Ensembl and gene-specific databases. Variants were classified as pathogenic, likely pathogenic, or variant of uncertain significance using criteria from the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. If predicted by ≥ 5/10 algorithms (eg, Polyphen2) to be deleterious, this was considered supporting evidence of pathogenicity. Second, gene-based burden testing determined the difference in SNP frequencies between cases and controls across all and then rare SNPs. For genotype/phenotype relationships, we used PLINK, based on haplotype, MAF > 2%, genotype present in > 75%, and Hardy Weinberg equilibrium P > 10–4.
Results
First, a diagnostic yield of 10% (27/263) was generated by 2 pathogenic (nonsense in ACAN) and a further 25 likely pathogenic mutations, including previously known missense mutations in FANCB, IGFIR, MMP13, NPR2, OBSL1, and PTPN11. Second, genes related to SS: all methods identified PEX2. Another 7 genes (BUB1B, FANCM, CUL7, FANCA, PTCH1, TEAD3, BCAS3) were identified by both gene-based approaches and 6 (A2M, EFEMP1, PRKCH, SOS2, RNF135, ZBTB38) were identified by gene-based testing for all SNPs and PLINK.
Conclusions
Such panels improve diagnosis in SS-UA, extending known disease phenotypes. Fourteen genes related to SS included some known to cause growth disorders as well as novel targets.
Funder
MRC Clinical Academic Research Partnership
Subject
Endocrinology, Diabetes and Metabolism