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Fu-Ying Chen

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Sep 2026

Distinct genotype-phenotype patterns in non-syndromic hereditary hypotrichosis: a multicenter Chinese cohort.

BACKGROUND Non-syndromic hereditary hypotrichosis (NSHH) is a genetically heterogeneous disorder characterized by sparse or absent hair growth. Comprehensive genotype-phenotype correlation analyses remain limited, particularly in the Chinese population. OBJECTIVES To define the genetic and phenotypic spectrum of NSHH in a Chinese cohort and to explore genotype-phenotype correlations with potential clinical utility. METHODS This observational multicenter study included 47 unrelated families (107 affected individuals) with clinically and genetically confirmed NSHH. Genetic analysis was performed using next-generation sequencing with Sanger validation. Clinical features, including age at onset, severity, hair shaft morphology, and extracranial hair involvement, were systematically analysed and correlated with causative genes. RESULTS Eleven causative genes were identified in 47 unrelated families, with LIPH (n=18, 38.3%), LSS (n=10, 21.27%), and HRURF (n=7, 14.89%) being the most prevalent. NSHH showed marked phenotypic heterogeneity with genotype-dependent patterns in age at onset, severity, and hair shaft morphology. Congenital onset was common in LIPH, LSS, and HRURF, whereas postnatal onset was observed in APCDD1, KRT86, and HR. Preliminary genotype-phenotype analysis of LIPH demonstrated allele-specific effects, with c.736T>A associated variants linked to more severe hypotrichosis compared with c.742C>A. In HRURF, two recurrently affected regions were observed, involving the start codon and the region encoding amino acids 23-28. Based on these findings, a preliminary phenotype-driven candidate-gene prioritisation framework was proposed. Exploratory analysis of topical minoxidil showed variable treatment responses. CONCLUSIONS This study defines the genetic architecture and genotype-phenotype correlations of NSHH in the Chinese population and provides a preliminary phenotype-driven framework that may assist clinical evaluation and candidate-gene prioritisation.

An-Qi Zhao, Qiao-Yu Cao, Han Chen et al. · 0 citations

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