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Front Genet. 2018 May 16;9:173. doi: 10.3389/fgene.2018.00173. eCollection 2018.

Health Profiles of Mosaic Versus Non-mosaic FMR1 Premutation Carrier Mothers of Children With Fragile X Syndrome.

Author information

1
Waisman Center, University of Wisconsin-Madison, Madison, WI, United States.
2
Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States.
3
Department of Pediatrics, Rush University Medical Center, Chicago, IL, United States.
4
Department of Pathology, Rush University Medical Center, Chicago, IL, United States.
5
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, United States.
6
Wisconsin State Laboratory of Hygiene, Madison, WI, United States.
7
Marshfield Clinic Research Institute, Marshfield, WI, United States.
8
Department of Neurological Sciences and Biochemistry, Rush University Medical Center, Chicago, IL, United States.

Abstract

The FMR1 premutation is of increasing interest to the FXS community, as questions about a primary premutation phenotype warrant research attention. 100 FMR1 premutation carrier mothers (mean age = 58; 67-138 CGG repeats) of adults with fragile X syndrome were studied with respect to their physical and mental health, motor, and neurocognitive characteristics. We explored the correlates of CGG repeat mosaicism in women with expanded alleles. Mothers provided buccal swabs from which DNA was extracted and the FMR1 CGG genotyping was performed (Amplidex Kit, Asuragen). Mothers were categorized into three groups: Group 1: premutation non-mosaic (n = 45); Group 2: premutation mosaic (n = 41), and Group 3: premutation/full mutation mosaic (n = 14). Group 2 mothers had at least two populations of cells with different allele sizes in the premutation range besides their major expanded allele. Group 3 mothers had a very small population of cells in the full mutation range (>200 CGGs) in addition to one or multiple populations of cells with different allele sizes in the premutation range. Machine learning (random forest) was used to identify symptoms and conditions that correctly classified mothers with respect to mosaicism; follow-up comparisons were made to characterize the three groups. In categorizing mosaicism, the random forest yielded significantly better classification than random classification, with overall area under the receiver operating characteristic curve (AUROC) of 0.737. Among the most important symptoms and conditions that contributed to the classification were anxiety, menopause symptoms, executive functioning limitations, and difficulty walking several blocks, with the women who had full mutation mosaicism (Group 3) unexpectedly having better health. Although only 14 premutation carrier mothers in the present sample also had a small population of full mutation cells, their profile of comparatively better health, mental health, and executive functioning was unexpected. This preliminary finding should prompt additional research on larger numbers of participants with more extensive phenotyping to confirm the clinical correlates of low-level full mutation mosaicism in premutation carriers and to probe possible mechanisms.

KEYWORDS:

CGG repeats; FMR1 premutation; genotype–phenotype correlations; machine learning; mosaicism

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