Genotype-Phenotype Association for 14 GFAP Variants in Alexander Disease

Alexander病中14种GFAP变异体的基因型-表型关联分析

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Abstract

BACKGROUND AND OBJECTIVES: Alexander disease is a rare monogenic disorder caused by dominant variants in GFAP (glial fibrillary acidic protein). Over 180 variants have been associated with the disease, with a wide spectrum of severity and clinical manifestations. Previous attempts at genotype-phenotype correlations have been hampered by the small numbers of cases that have been published for many of the variants. We sought to determine whether genotype-phenotype correlations could be discerned from available information. METHODS: We compiled a list of variants in GFAP for which 7 or more unrelated cases had been either published or identified through an ongoing natural history study and other sources (with a closing date of July 27, 2024). For each of these cases, we tabulated age at onset, age at death (or last contact), and sex. We used a Kruskal-Wallis test to evaluate statistical differences in age at onset in relation to variant. Differences in survival across variants were studied using Kaplan-Meier curves. RESULTS: Fourteen variants met our criteria for detailed analysis (10 with 7 or more unrelated cases and 4 additional variants involving 2 of the most commonly affected amino acids, R79 and R239) derived from a total of 231 cases. The variants seem to fall into 3 distinct groups-some with consistent early onsets (N77S, R79C and R79L, and most of the R239s), some with consistent late onsets (R70W and N386S), and some with more variable onsets (R416W). Pairwise comparison results found that R239H was associated with significantly earlier onsets than R239C. We found similar groupings for survival. Finally, we evaluated sex as a potential modifying factor for either age at onset or survival but found no significant association. DISCUSSION: Genotype-phenotype correlations do exist in Alexander disease, at least for a limited number of GFAP variants for which sufficient numbers of individual cases can be identified to allow valid statistical analysis.

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