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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Weight-adjusted genome scan analysis for mapping quantitative trait Loci for menarchal age
Anya Rothenbuhler1, Delphine Fradin, Simon Heath
1Department of Pediatric Endocrinology and U56, Institut National de la Santé et de la Recherche Médicale, Hôpital Saint-Vincent de Paul, 75014 Paris, France.
The Journal of Clinical Endocrinology and Metabolism
|June 29, 2006
Summary
Genetic factors influence the age at menarche (AAM). This study identified several potential quantitative trait loci (QTLs) for AAM, particularly on chromosome 16, offering insights into reproductive health.
Area of Science:
- Genetics
- Human Reproduction
- Quantitative Trait Analysis
Background:
- Genetic factors are known to influence the variability of age at menarche (AAM).
- AAM is a critical multifactorial trait impacting human reproductive success.
- Premenarcheal fatness is a significant determinant of AAM.
Purpose of the Study:
- To map quantitative trait loci (QTLs) associated with the age at menarche (AAM).
- To investigate genetic linkages for AAM and AAM adjusted for menarcheal weight.
Main Methods:
- Genome scan analysis was performed on 98 sister pairs of European ancestry.
- Variance components model implemented in Merlin was used for linkage analysis.
- Microsatellite markers were utilized to evaluate linkage of AAM and weight-adjusted AAM.
Main Results:
- Nominal quantitative trait loci (QTLs) for AAM were identified on multiple chromosomes (1p, 1q, 7p, 8q, 16p, 19q, 20q).
- Strongly suggestive QTLs for AAM adjusted for menarcheal weight SDS were found on chromosome 16q21 (LOD=3.33), 16q12 (LOD=3.12), and 8p12 (LOD=2.18).
- Several other nominally significant QTLs were identified but considered hypothetical.
Conclusions:
- Several genomic regions potentially harbor determinants of age at menarche (AAM).
- Further research is required to confirm these QTLs and identify specific genomic polymorphisms.
- This study provides a foundation for understanding the genetic underpinnings of AAM variability.
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