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Critical Reviews™ in Eukaryotic Gene Expression
インパクトファクター: 1.841 5年インパクトファクター: 1.927 SJR: 0.649 SNIP: 0.516 CiteScore™: 1.96

ISSN 印刷: 1045-4403
ISSN オンライン: 2162-6502

Critical Reviews™ in Eukaryotic Gene Expression

DOI: 10.1615/CritRevEukarGeneExpr.v18.i4.20
pages 323-343

A Close Examination of Genes within Quantitative Trait Loci of Bone Mineral Density in Whole Mouse Genome

Qing Xiong
Department of Orthopaedic Surgery - Campbell Clinic and Pathology, University of Tennessee Health Science Center (UTHSC), Memphis, TN 38163, USA; Department of Computer Science and Technology, Southwest University, Chongqing 400715, P.R. China
Caili Han
Department of Orthopedic Surgery, Campbell Clinic, University of Tennessee Health Science Center, Memphis, TN 38163, USA; Department of Pathology, Hebei Medical University, HeBei 050017, P.R. China
Wesley G. Beamer
The Jackson Laboratory, Bar Harbor, ME 04609, USA
Weikuan Gu
Department of Orthopedics Surgery, Campbell Clinic, and Department of Pathology, University of Tennessee Health Science Center, Memphis, TN

要約

Bone mineral density (BMD) is one of the strongest determinants of osteoporotic fracture risk. Over the last decade, a large number of quantitative trait loci (QTL) that regulate BMD have been identified using the mouse model. In an attempt to examine the relationship between those QTL and gene distribution in the mouse genome, we searched PubMed with keywords “bone” and “QTL” for every publication up to January 2007; we obtained a total of 75 QTL of BMD. We next obtained genes within a QTL for measurements of BMD from the Ensembl database. We then evaluated the potential connection of every gene with bone biology with Online Mendelian Inheritance in Man (OMIM) and PubMed by using eight key words: bone mineral density, BMD, bone strength, bone size, osteoporosis, osteoblast, osteoclast, and fracture. We obtained a total of 15,084 genes for 75 BMD QTL covering 1,211,376,097 base pairs of genomic sequence. Although this very large number of genes exists within QTL regions, only 291 were identified as candidate genes according to our bioinformatics search. Importantly, the association between polymorphism of many candidate genes and BMD has been reported in human studies. Thus, updated genome information and resources should provide new insight for gene identification of QTL. Accordingly, the comprehensive search of candidate genes in the genome for known QTL may provide unexpected benefits for QTL studies.


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