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Weight-Adjusted Genome Scan Analysis for Mapping Quantitative Trait Loci for Menarchal Age

Department of Pediatric Endocrinology and U561 (A.R., D.F., H.L., C.B., P.B.), Paris 5 René Descartes University, Institut National de la Santé et de la Recherche Médicale, Hôpital Saint-Vincent de Paul, 75014 Paris, France; and Centre National de Génotypage (S.H., M.L.), 91057 Evry, France

Address all correspondence and requests for reprints to: Pierre Bougnères, Department of Pediatric Endocrinology and U56, Institut National de la Santé et de la Recherche Médicale, Hôpital Saint-Vincent de Paul, 75014 Paris, France. E-mail: pierre.bougneres{at}wanadoo.fr.

Context: Twin and family studies indicate that genetic factors contribute to the variability of age at menarche (AAM), a multifactorial trait of major importance to human reproductive success. Individual variability of premenarcheal fatness is known to be an important determinant of AAM.

Objective: The objective of the study was mapping quantitative trait loci (QTLs) for AAM.

Design and Methods: AAM was assessed in 98 sister pairs of recent European ancestry whose growth charts were available. There was a negative correlation between menarcheal body weight SD score (SDS) and AAM (r = 0.47, P < 0.0001). We designed a genome scan approach and used the variance components model implemented in Merlin for quantitative traits to evaluate linkage of AAM and AAM adjusted for menarcheal weight SDS to 418 genome-wide microsatellites.

Results: Multipoint linkage analysis for AAM revealed nominal QTLs defined by LOD scores between 1.06 and 1.69 on chromosomes 1p, 1q, 7p, 8q, 16p, 19q, and 20q. The genome scan for AAM adjusted for menarcheal weight SDS revealed several QTLs with strongly suggestive LOD scores in 16q21 (LOD = 3.33), 16q12 (LOD = 3.12), and 8p12 (LOD = 2.18) and a number of other nominally significant QTLs yet viewed as hypothetical.

Conclusions: We found several regions that may contain determinants of AAM, but there is still a long series of steps to confirm these QTLs and identify the genomic polymorphisms implicated in AAM variability.

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