title: Increased detection of genetic loci associated with risk predictors of osteoporotic fracture using a pleiotropic cFDR method.
abstract: Although GWAS have been successful in identifying some osteoporosis associated loci, the findings explain only a small fraction of the total genetic variance. In this study we use a recently developed novel pleiotropic conditional false discovery rate (cFDR) method to identify novel genetic loci associated with two risk traits for osteoporotic fracture (the clinical outcome and end result of osteoporosis), Height (HT) and Femoral Neck (FNK) BMD. The cFDR method allows us to improve the detection of associated variants by incorporating any potentially shared genetic mechanisms between the two associated traits. We analyzed the summary statistics from two GWAS meta-analyses for single nucleotide polymorphisms (SNPs) that are associated with HT and FNK BMD. Using the cFDR method, we show enrichment in the identification of SNPs associated with each trait conditioned on their strength of association with the second trait. The findings revealed 18 SNPs that are associated with both HT and FNK BMD, 4 of which had not previously been reported to play a role in bone health. The novel SNPs located at KIF1B and the intergenic region between FERD3L and TWISTNB are noteworthy as these genes may be associated with processes that are functionally important in bone metabolism. By leveraging GWAS results from related phenotypes we identified several novel loci that may contribute to the proportion of variability explained for each trait, although we cannot speculate about these potential contributions to heritability based on this analysis alone.
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Greenbaum
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.
authors.forename: Kehao
authors.initials: K
authors.lastname: Wu
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.
authors.forename: Lan
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.
authors.forename: Hui
authors.initials: H
authors.lastname: Shen
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA.
authors.forename: Jigang
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA 70112, USA. Electronic address: hdeng2@tulane.edu.
authors.forename: Hong-Wen
authors.initials: HW
authors.lastname: Deng
mesh_terms: D015519:Bone Density / Q000235:genetics; D030541:Databases, Genetic; D056426:Genetic Loci / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D055106:Genome-Wide Association Study; D006801:Humans; D010024:Osteoporosis / Q000235:genetics; D058866:Osteoporotic Fractures / Q000235:genetics*; D012307:Risk Factors
keywords: Association; Bone mineral density; Human genetics; Osteoporosis