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Published in: Journal of Bone and Mineral Metabolism 6/2016

01-11-2016 | Original Article

Genetic risk score based on the lifetime prevalence of femoral fracture in 924 consecutive autopsies of Japanese males

Authors: Heying Zhou, Seijiro Mori, Tatsuro Ishizaki, Masashi Tanaka, Kumpei Tanisawa, Makiko Naka Mieno, Motoji Sawabe, Tomio Arai, Masaaki Muramatsu, Yoshiji Yamada, Hideki Ito

Published in: Journal of Bone and Mineral Metabolism | Issue 6/2016

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Abstract

A genetic risk score (GRS) was developed for predicting fracture risk based on lifetime prevalence of femoral fractures in 924 consecutive autopsies of Japanese males. A total of 922 non-synonymous single nucleotide polymorphisms (SNPs) located in 62 osteoporosis susceptibility genes were genotyped and evaluated for their association with the prevalence of femoral fracture in autopsy cases. GRS values were calculated as the sum of risk allele counts (unweighted GRS) or the sum of weighted scores estimated from logistic regression coefficients (weighted GRS). Five SNPs (α-ʟ-iduronidase rs3755955, C7orf58 rs190543052, homeobox C4 rs75256744, G patch domain-containing gene 1 rs2287679, and Werner syndrome rs2230009) showed a significant association (P < 0.05) with the prevalence of femoral fracture in 924 male subjects. Both the unweighted and weighted GRS adequately predicted fracture prevalence; areas under receiver-operating characteristic curves were 0.750 [95 % confidence interval (CI) 0.660–0.840] and 0.770 (95 % CI 0.681–0.859), respectively. Multiple logistic regression analysis revealed that the odds ratio (OR) for the association between fracture prevalence and unweighted GRS ≥3 (n = 124) was 8.39 (95 % CI 4.22–16.69, P < 0.001) relative to a score <3 (n = 797). Likewise, the OR for a weighted GRS of 6–15 (n = 135) was 7.73 (95 % CI 3.89–15.36, P < 0.001) relative to scores of 0–5 (n = 786). The GRS based on risk allele profiles of the five SNPs could help identify at-risk individuals and enable implementation of preventive measures for femoral fracture.
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Literature
1.
2.
go back to reference Soen S, Fukunaga M, Sugimoto T, Sone T, Fujiwara S, Endo N, Gorai I, Shiraki M, Hagino H, Hosoi T, Ohta H, Yoneda T, Tomomitsu T (2013) Diagnostic criteria for primary osteoporosis: year 2012 revision. J Bone Miner Metab 31:247–257CrossRefPubMed Soen S, Fukunaga M, Sugimoto T, Sone T, Fujiwara S, Endo N, Gorai I, Shiraki M, Hagino H, Hosoi T, Ohta H, Yoneda T, Tomomitsu T (2013) Diagnostic criteria for primary osteoporosis: year 2012 revision. J Bone Miner Metab 31:247–257CrossRefPubMed
3.
5.
go back to reference Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Center JR, Nguyen TV, Bagger Y, Gulcher JR, Eisman JA, Christiansen C, Sigurdsson G, Kong A, Thorsteinsdottir U, Stefansson K (2008) Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358:2355–2365CrossRefPubMed Styrkarsdottir U, Halldorsson BV, Gretarsdottir S, Gudbjartsson DF, Walters GB, Ingvarsson T, Jonsdottir T, Saemundsdottir J, Center JR, Nguyen TV, Bagger Y, Gulcher JR, Eisman JA, Christiansen C, Sigurdsson G, Kong A, Thorsteinsdottir U, Stefansson K (2008) Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358:2355–2365CrossRefPubMed
6.
go back to reference Rivadeneira F, Styrkarsdottir U, Estrada K, Halldórsson BV, Hsu YH, Genetic Factors for Osteoporosis (GEFOS) Consortium et al (2009) Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 41:1199–1206CrossRefPubMedPubMedCentral Rivadeneira F, Styrkarsdottir U, Estrada K, Halldórsson BV, Hsu YH, Genetic Factors for Osteoporosis (GEFOS) Consortium et al (2009) Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 41:1199–1206CrossRefPubMedPubMedCentral
7.
go back to reference Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL et al (2012) Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 44:491–501CrossRefPubMedPubMedCentral Estrada K, Styrkarsdottir U, Evangelou E, Hsu YH, Duncan EL et al (2012) Genome-wide meta-analysis identifies 56 bone mineral density loci and reveals 14 loci associated with risk of fracture. Nat Genet 44:491–501CrossRefPubMedPubMedCentral
8.
go back to reference ERICA Research Group (1991) Prediction of coronary heart disease in Europe: the 2nd report of the WHO-ERICA Project. Eur Heart J 12:291–297 ERICA Research Group (1991) Prediction of coronary heart disease in Europe: the 2nd report of the WHO-ERICA Project. Eur Heart J 12:291–297
10.
go back to reference Lindström J, Tuomilehto J (2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26:725–731CrossRefPubMed Lindström J, Tuomilehto J (2003) The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care 26:725–731CrossRefPubMed
11.
go back to reference Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J (2006) Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol 5:735–741CrossRefPubMed Kivipelto M, Ngandu T, Laatikainen T, Winblad B, Soininen H, Tuomilehto J (2006) Risk score for the prediction of dementia risk in 20 years among middle aged people: a longitudinal, population-based study. Lancet Neurol 5:735–741CrossRefPubMed
12.
go back to reference Grove ML, Yu B, Cochran BJ, Haritunians T, Bis JC et al (2013) Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS One 8:e68095CrossRefPubMedPubMedCentral Grove ML, Yu B, Cochran BJ, Haritunians T, Bis JC et al (2013) Best practices and joint calling of the HumanExome BeadChip: the CHARGE Consortium. PLoS One 8:e68095CrossRefPubMedPubMedCentral
13.
go back to reference Piccolo SR, Abo RP, Allen-Brady K, Camp NJ, Knight S, Anderson JL, Horne BD (2009) Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study. BMC Proc 15:S46CrossRef Piccolo SR, Abo RP, Allen-Brady K, Camp NJ, Knight S, Anderson JL, Horne BD (2009) Evaluation of genetic risk scores for lipid levels using genome-wide markers in the Framingham Heart Study. BMC Proc 15:S46CrossRef
14.
go back to reference Reitz C, Tang MX, Schupf N, Manly JJ, Mayeux R, Luchsinger JA (2010) A summary risk score for the prediction of Alzheimer disease in elderly persons. Arch Neurol 67:835–841PubMedPubMedCentral Reitz C, Tang MX, Schupf N, Manly JJ, Mayeux R, Luchsinger JA (2010) A summary risk score for the prediction of Alzheimer disease in elderly persons. Arch Neurol 67:835–841PubMedPubMedCentral
15.
go back to reference Zhou H, Mori S, Tanaka M, Sawabe M, Arai T, Muramatsu M, Naka Mieno M, Shinkai S, Yamada Y, Miyachi M, Murakami H, Sanada K, Ito H (2015) A missense single nucleotide polymorphism, V114I of the Werner syndrome gene, is associated with risk of osteoporosis and femoral fracture in the Japanese population. J Bone Miner Metab (in press) Zhou H, Mori S, Tanaka M, Sawabe M, Arai T, Muramatsu M, Naka Mieno M, Shinkai S, Yamada Y, Miyachi M, Murakami H, Sanada K, Ito H (2015) A missense single nucleotide polymorphism, V114I of the Werner syndrome gene, is associated with risk of osteoporosis and femoral fracture in the Japanese population. J Bone Miner Metab (in press)
17.
go back to reference Duan Y, Beck TJ, Wang XF, Seeman E (2003) Structural and biomechanical basis of sexual dimorphism in femoral neck fragility has its origins in growth and aging. J Bone Miner Res 18:1766–1774CrossRefPubMed Duan Y, Beck TJ, Wang XF, Seeman E (2003) Structural and biomechanical basis of sexual dimorphism in femoral neck fragility has its origins in growth and aging. J Bone Miner Res 18:1766–1774CrossRefPubMed
18.
go back to reference Karasik D, Ferrari SL (2008) Contribution of gender-specific genetic factors to osteoporosis risk. Ann Hum Genet 72:696–714CrossRefPubMed Karasik D, Ferrari SL (2008) Contribution of gender-specific genetic factors to osteoporosis risk. Ann Hum Genet 72:696–714CrossRefPubMed
19.
go back to reference Bie H, Yin J, He X, Kermode AR, Goddard-Borger ED, Withers SG, James MN (2013) Insights into mucopolysaccharidosis I from the structure and action of α-l-iduronidase. Nat Chem Biol 9:739–745CrossRefPubMedPubMedCentral Bie H, Yin J, He X, Kermode AR, Goddard-Borger ED, Withers SG, James MN (2013) Insights into mucopolysaccharidosis I from the structure and action of α-l-iduronidase. Nat Chem Biol 9:739–745CrossRefPubMedPubMedCentral
20.
go back to reference Moayyeri AL, Hsu YH, Karasik D, Estrada K, Xiao SM et al (2014) Genetic determinants of heel bone properties: genome-wide association meta-analysis and replication in the GEFOS/GENOMOS consortium. Hum Mol Genet 23:3054–3068CrossRefPubMedPubMedCentral Moayyeri AL, Hsu YH, Karasik D, Estrada K, Xiao SM et al (2014) Genetic determinants of heel bone properties: genome-wide association meta-analysis and replication in the GEFOS/GENOMOS consortium. Hum Mol Genet 23:3054–3068CrossRefPubMedPubMedCentral
21.
go back to reference Medina-Gomez C, Kemp JP, Estrada K, Eriksson J, Liu J et al (2012) Meta-analysis of genome-wide scans for total body BMD in children and adults reveals allelic heterogeneity and age-specific effects at the WNT16 locus. PLoS Genet 8:e1002718CrossRefPubMedPubMedCentral Medina-Gomez C, Kemp JP, Estrada K, Eriksson J, Liu J et al (2012) Meta-analysis of genome-wide scans for total body BMD in children and adults reveals allelic heterogeneity and age-specific effects at the WNT16 locus. PLoS Genet 8:e1002718CrossRefPubMedPubMedCentral
Metadata
Title
Genetic risk score based on the lifetime prevalence of femoral fracture in 924 consecutive autopsies of Japanese males
Authors
Heying Zhou
Seijiro Mori
Tatsuro Ishizaki
Masashi Tanaka
Kumpei Tanisawa
Makiko Naka Mieno
Motoji Sawabe
Tomio Arai
Masaaki Muramatsu
Yoshiji Yamada
Hideki Ito
Publication date
01-11-2016
Publisher
Springer Japan
Published in
Journal of Bone and Mineral Metabolism / Issue 6/2016
Print ISSN: 0914-8779
Electronic ISSN: 1435-5604
DOI
https://doi.org/10.1007/s00774-015-0718-7

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