Dataset Information

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.

SUBMITTER: Greenbaum J

PROVIDER: S-EPMC5488332 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Increased detection of genetic loci associated with risk predictors of osteoporotic fracture using a pleiotropic cFDR method.

Greenbaum Jonathan J Wu Kehao K Zhang Lan L Shen Hui H Zhang Jigang J Deng Hong-Wen HW

Bone 20170331

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 ...[more]

PMID: 28373146

Similar Datasets

Project description:AimsClinical and epidemiological findings point to an association between type 2 diabetes (T2D) and osteoporosis. Genome-wide association studies (GWASs) have been fruitful in identifying some loci potentially associated with osteoporosis and T2D respectively. However, the total genetic variance for each of these two diseases and the shared genetic determination between them are largely unknown. The aim of this study was to identify novel genetic variants for osteoporosis and/or T2D.MethodsFirst, using a pleiotropic conditional false discovery rate (cFDR) method, we analyzed two GWAS summary data of femoral neck bone mineral density (FN_BMD, n = 53,236) and T2D (n = 159,208) to identify novel shared genetic loci. FN_BMD is an important risk factor for osteoporosis. Next, to explore the potential functions of the identified potential pleiotropic genes, differential expression analysis was performed for them in monocytes and peripheral blood mononuclear cells (PBMCs) as these cells are relevant to the etiology of osteoporosis and/or T2D. Further, weighted gene co-expression analysis (WGCNA) was conducted to identify functional connections between novel pleiotropic genes and known osteoporosis/T2D susceptibility genes by using transcriptomic expression datasets in bone biopsies (E-MEXP-1618) and pancreatic islets (GSE50397). Finally, multi-trait fine mapping for the detected pleiotropic risk loci were conducted to identify the SNPs that have the highest probability of being causal for both FN_BMD and T2D.ResultsWe identified 27 significant SNPs with cFDR<0.05 for FN_BMD and 61 SNPs for T2D respectively. Four loci, rs7068487 (PLEKHA1), rs10885421 (TCF7L2), rs944082 (GNG12-AS1 (WLS)) and rs2065929 (PIFO||PGCP1), were found to be potentially pleiotropic and shared between FN_BMD and T2D (ccFDR<0.05). PLEKHA1 was found differentially expressed in circulating monocytes between high and low BMD subjects, and PBMCs between diabetic and non-diabetic conditions. WGCNA showed that PLEKHA1 and TCF7L2 were interconnected with multiple osteoporosis and T2D associated genes in bone biopsy and pancreatic islets, such as JAG, EN1 and CPE. Fine mapping showed that rs11200594 was a potentially causal variant in the locus of PLEKHA1. rs11200594 is also an eQTL of PLEKHA1 in multiple tissue (e.g. peripheral blood cells, adipose and ovary) and is in strong LD with a number of functional variants.ConclusionsFour potential pleiotropic loci were identified for shared genetic determination of osteoporosis and T2D. Our study highlights PLEKHA1 as an important potentially pleiotropic gene. The findings may help us gain a better understanding of the shared genetic determination between these two important disorders.

Project description:Using data from the Study of Osteoporotic Fractures (SOF), several clinical characteristics predictive of near-term (1-year) risk of hip and non-vertebral fracture among elderly osteoporotic women were identified, and a subset of those for hip fracture was incorporated into a risk assessment tool. Additional research is needed to validate study findings.IntroductionWhile several risk factors are known to contribute to long-term fracture risk in women with osteoporosis, factors predicting fracture risk over a shorter time horizon, such as over a 1-year period, are less well-established.MethodsWe utilized a repeated-observations design and data from the Study of Osteoporotic Fractures to identify factors contributing to near-term risk of hip fracture and any non-vertebral fracture, respectively, among osteoporotic women aged ?65 years. Potential predictors of hip fracture and any non-vertebral fracture over the 1-year period subsequent to each qualifying SOF exam were examined using multivariable frailty models. Because the discriminative ability of the hip fracture model was acceptable, a corresponding risk-prediction tool was also developed.ResultsStudy population included 2499 women with osteoporosis, who contributed 6811 observations. Incidence of fracture in the 1-year period subsequent to each exam was 2.2% for hip fracture and 6.6% for any non-vertebral fracture. Independent predictors of hip fracture included low total hip T-score, prior fracture, and risk factors for falls (multivariable model c-statistic = 0.71 (95% CI 0.67-0.76)). Independent predictors of any non-vertebral fracture included age, total hip T-score, prior falls, prior fracture, walking speed, Parkinson's disease or stroke, and smoking (multivariable model c-statistic = 0.62 (0.59-0.65)).ConclusionsSeveral clinical characteristics predictive of hip and non-vertebral fracture within a 1-year follow-up period among elderly women with osteoporosis were identified, and a subset of those for hip fracture was incorporated into a risk assessment tool. Assessment of these risk factors may help guide osteoporosis treatment choices by identifying patients in whom there is urgency to treat. Additional research is needed to validate the findings of this study and the accuracy of the risk assessment tool.

Dataset Information

Increased detection of genetic loci associated with risk predictors of osteoporotic fracture using a pleiotropic cFDR method.

Publications

Increased detection of genetic loci associated with risk predictors of osteoporotic fracture using a pleiotropic cFDR method.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets