Phenotypic Dissection of Bone Mineral Density Reveals Skeletal Site Specificity and Facilitates the Identification of Novel Loci in the Genetic Regulation of Bone Mass Attainment

English version České info

The heritability of bone mineral density (BMD) varies across skeletal sites, reflecting different relative contributions of genetic and environmental influences. To investigate whether the genes underlying bone acquisition act in a site-specific manner, we quantified the shared genetic influences across axial and appendicular skeletal sites by estimating the genetic and residual correlation of BMD at the upper limb, lower limb and the skull. Our results suggest that different skeletal sites as measured by total-body Dual-Energy X-Ray Absorptiometry are to a certain extent under distinct genetic and environmental influences. To further explore the basis for these differences, genome-wide association meta-analyses were performed to identify genetic loci that are preferentially associated with one or more skeletal regions. Variants at 13 loci (including RIN3, a novel BMD associated locus) reached genome-wide significance and several displayed evidence of differential association with BMD across the different skeletal sites in particular CPED1 and WNT16. Our results suggest that it may be advantageous to decompose the total-body BMD measures and perform GWAS at separate skeletal regions. By allowing for site-specific differences, new genetic variants affecting BMD and future risk of osteoporosis may be uncovered.

Vyšlo v časopise: Phenotypic Dissection of Bone Mineral Density Reveals Skeletal Site Specificity and Facilitates the Identification of Novel Loci in the Genetic Regulation of Bone Mass Attainment. PLoS Genet 10(6): e32767. doi:10.1371/journal.pgen.1004423
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004423

Souhrn

Zdroje

1. CummingsSR, BlackDM, NevittMC, BrownerW, CauleyJ, et al. (1993) Bone density at various sites for prediction of hip fractures. The Lancet 341 : 72–75.

2. FerrariS, RizzoliR, SlosmanD, BonjourJP (1998) Familial resemblance for bone mineral mass is expressed before puberty. J Clin Endocrinol Metab 83 : 358–361.

3. CooperC, WestlakeS, HarveyN, JavaidK, DennisonE, et al. (2006) Review: developmental origins of osteoporotic fracture. Osteoporos Int 17 : 337–347.

4. EstradaK, StyrkarsdottirU, EvangelouE, HsuYH, DuncanEL, 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 : 491–501.

5. StyrkarsdottirU, HalldorssonBV, GretarsdottirS, GudbjartssonDF, WaltersGB, et al. (2008) Multiple genetic loci for bone mineral density and fractures. N Engl J Med 358 : 2355–2365.

6. RivadeneiraF, StyrkarsdottirU, EstradaK, HalldorssonBV, HsuYH, et al. (2009) Twenty bone-mineral-density loci identified by large-scale meta-analysis of genome-wide association studies. Nat Genet 41 : 1199–1206.

7. Medina-GomezC, KempJP, EstradaK, ErikssonJ, LiuJ, 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: e1002718.

8. TimpsonNJ, TobiasJH, RichardsJB, SoranzoN, DuncanEL, et al. (2009) Common variants in the region around Osterix are associated with bone mineral density and growth in childhood. Hum Mol Genet 18 : 1510–1517.

9. ClarkEM, NessAR, BishopNJ, TobiasJH (2006) Association between bone mass and fractures in children: a prospective cohort study. J Bone Miner Res 21 : 1489–1495.

10. LewieckiEM (2009) Managing osteoporosis: challenges and strategies. Cleve Clin J Med 76 : 457–466.

11. TruswellAS (1958) Osteopetrosis with syndactyly; a morphological variant of Albers-Schonberg's disease. J Bone Joint Surg Br 40-B: 209–218.

12. BeightonP (1988) Sclerosteosis. J Med Genet 25 : 200–203.

13. PaternosterL, LorentzonM, LehtimakiT, ErikssonJ, KahonenM, et al. (2013) Genetic determinants of trabecular and cortical volumetric bone mineral densities and bone microstructure. PLoS Genet 9: e1003247.

14. StyrkarsdottirU, ThorleifssonG, SulemP, GudbjartssonDF, SigurdssonA, et al. (2013) Nonsense mutation in the LGR4 gene is associated with several human diseases and other traits. Nature 497 : 517–520.

15. KollerDL, ZhengHF, KarasikD, Yerges-ArmstrongL, LiuCT, et al. (2013) Meta-analysis of genome-wide studies identifies WNT16 and ESR1 SNPs associated with bone mineral density in premenopausal women. J Bone Miner Res 28 : 547–558.

16. NgPC, HenikoffS (2003) SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res 31 : 3812–3814.

17. BoyleAP, HongEL, HariharanM, ChengY, SchaubMA, et al. (2012) Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22 : 1790–1797.

18. Morgan EF, Barnes GL, Einhorn TA (2013) The bone organ system: Form and Function; Marcus R, Feldam D, Dempster DW, Luckey M, Cauley JA, editors: Academic Press.

19. Gilbert SF (2000) Osteogenesis: The Development of Bones. Developmental Biology. 6th ed. Sunderland (MA): Sinauer Associates.

20. AlbaghaOM, WaniSE, ViscontiMR, AlonsoN, GoodmanK, et al. (2011) Genome-wide association identifies three new susceptibility loci for Paget's disease of bone. Nat Genet 43 : 685–689.

21. DunfordJE, RogersMJ, EbetinoFH, PhippsRJ, CoxonFP (2006) Inhibition of protein prenylation by bisphosphonates causes sustained activation of Rac, Cdc42, and Rho GTPases. J Bone Miner Res 21 : 684–694.

22. LorentzonM, LandinK, MellstromD, OhlssonC (2006) Leptin is a negative independent predictor of areal BMD and cortical bone size in young adult Swedish men. J Bone Miner Res 21 : 1871–1878.

23. TurnerAS, MailletJM, MallinckrodtC, CordainL (1997) Bone mineral density of the skull in premenopausal women. Calcif Tissue Int 61 : 110–113.

24. BoydA, GoldingJ, MacleodJ, LawlorDA, FraserA, et al. (2012) Cohort Profile: The ‘Children of the 90s’–the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol 42(1): 111–27.

25. JaddoeVW, van DuijnCM, FrancoOH, van der HeijdenAJ, van IizendoornMH, et al. (2012) The Generation R Study: design and cohort update 2012. Eur J Epidemiol 27 : 739–756.

26. PurcellS, NealeB, Todd-BrownK, ThomasL, FerreiraMA, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81 : 559–575.

27. PriceAL, PattersonNJ, PlengeRM, WeinblattME, ShadickNA, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38 : 904–909.

28. LiY, WillerC, SannaS, AbecasisG (2009) Genotype imputation. Annu Rev Genomics Hum Genet 10 : 387–406.

29. LiY, WillerCJ, DingJ, ScheetP, AbecasisGR (2010) MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 34 : 816–834.

30. FrazerKA, BallingerDG, CoxDR, HindsDA, StuveLL, et al. (2007) A second generation human haplotype map of over 3.1 million SNPs. Nature 449 : 851–U853.

31. PennisiE (2010) Genomics. 1000 Genomes Project gives new map of genetic diversity. Science 330 : 574–575.

32. YangJ, LeeSH, GoddardME, VisscherPM (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88 : 76–82.

33. LeeSH, YangJ, GoddardME, VisscherPM, WrayNR (2012) Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood. Bioinformatics 28 : 2540–2542.

34. BostonRC, SumnerAE (2003) STATA: a statistical analysis system for examining biomedical data. Adv Exp Med Biol 537 : 353–369.

35. EstradaK, AbuseirisA, GrosveldFG, UitterlindenAG, KnochTA, et al. (2009) GRIMP: a web -⁠ and grid-based tool for high-speed analysis of large-scale genome-wide association using imputed data. Bioinformatics 25 : 2750–2752.

36. WillerCJ, LiY, AbecasisGR (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26 : 2190–2191.

37. PruimRJ, WelchRP, SannaS, TeslovichTM, ChinesPS, et al. (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26 : 2336–2337.

38. International HapMapC (2003) The International HapMap Project. Nature 426 : 789–796.

39. JohnsonAD, HandsakerRE, PulitSL, NizzariMM, O'DonnellCJ, et al. (2008) SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 24 : 2938–2939.

40. Team RDC (2010) R: A language and environment for statistical computing. ISBN 3-900051-07-0.

41. Wickham H (2009) ggplot2: Elegant graphics for data analysis.: Springer New York.

42. NyholtDR (2004) A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet 74 : 765–769.

43. NealeMC, PsychiatryMCoVDo, PsychiatricVIf, GeneticsB (1997) MX: Statistical Modeling: Department of Psychiatry, Medical College of Virginia.

44. GrundbergE, AdoueV, KwanT, GeB, DuanQL, et al. (2011) Global analysis of the impact of environmental perturbation on cis-regulation of gene expression. PLoS Genet 7: e1001279.

45. GrundbergE, KwanT, GeB, LamKC, KokaV, et al. (2009) Population genomics in a disease targeted primary cell model. Genome Res 19 : 1942–1952.

46. JemtlandR, HoldenM, ReppeS, OlstadOK, ReinholtFP, et al. (2011) Molecular disease map of bone characterizing the postmenopausal osteoporosis phenotype. J Bone Miner Res 26 : 1793–1801.

47. KalajzicI, KalajzicZ, KaliternaM, GronowiczG, ClarkSH, et al. (2002) Use of type I collagen green fluorescent protein transgenes to identify subpopulations of cells at different stages of the osteoblast lineage. J Bone Miner Res 17 : 15–25.

48. LangmeadB, TrapnellC, PopM, SalzbergSL (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome biology 10: R25.

49. LiB, DeweyCN (2011) RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12 : 323.

50. BullardJH, PurdomE, HansenKD, DudoitS (2010) Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics 11 : 94.

51. DrabekK, van de PeppelJ, EijkenM, van LeeuwenJP (2011) GPM6B regulates osteoblast function and induction of mineralization by controlling cytoskeleton and matrix vesicle release. J Bone Miner Res 26 : 2045–2051.