Chapter 25 Chapter 25 Genetic Determinants of Osteoporosis Copyright © 2013 Elsevier Inc. All rights reserved.

FIGURE 25.1 Schematic depiction of the genetic architecture of osteoporosis as a complex trait. BMD: bone mineral density. 2

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.2 Top-down and bottom-up approaches to identify complex disease risk gene variants. + and – indicate high and low effectiveness, respectively; +/– indicates intermediate effectiveness. bp: base pairs. 3

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.3 A schematic flow-diagram depicts the different steps in a candidate gene polymorphism analysis. At the top, genome-wide association analysis is indicated that will identify multiple areas across the genome as linkage disequilibrium blocks within candidate genes. This generates independent evidence for candidacy of a gene/region, but also is used in concordance with biological evidence based on three independent sources, to implicate a gene in the disease of interest. DNA: deoxyribonucleic acid. 4

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.4 Depiction of how “functional” deoxyribonucleic acid (DNA) polymorphisms might affect physiological processes at different levels of organization, that ultimately result in an association that is seen after many years (for age-related disorders this can be 70 years) of “exposure” to the risk factor. mRNA: messenger ribonucleic acid. 5

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.5 Genomic structure and LD map of the human vitamin D receptor (VDR) gene. a Physical organization of the 12q12 area containing the VDR gene mostly based on the celera database (47032–47145 Kb at chromosome 12q12). The arrows for each gene indicate the transcription direction (distance in Kb). b The genomic structure of the human VDR gene. Black boxes indicate the coding exons of the VDR gene, the gray boxes indicate 5’ exons and 3’-UTR. c Sequenced areas and position of the 62 variations. Gray boxes in the 3’- UTR indicate destabilizing elements. d Haplotype map of the VDR gene in caucasians, Asians, and African- Americans, based on SNPs with a minor allele frequency (MAF) of ≥ 5% in each of the different ethnic populations. common haplotype alleles in each block with a frequency >3% are presented below the blocks. SNPs and alleles in red indicate the haplotype tagging SNPs (htSNPs). Fracture risk haplotype alleles are underlined. correspondence to caucasians for the previous Bsm-Apa-Taq haplotype allele definition in block 5 is shown. Source: derived from Fang etal. (2005) [35]. 6

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.6 Hypothetical example of the importance of genewide genotype combinations. Three adjacent single nucleotide polymorphisms (SNPs) in different parts of a gene are shown for two individuals (A and B indicated at the bottom). The subjects, A and B, have identical genotypes, i.e., they are both heterozygous for all three SNPs. However, they have different allele combinations on the same chromosome (numbered 1–4): 1+2 for subject A and 3+4 for subject B. The promoter area regulates production of mRNA while the 3’UTR is involved in degradation of messenger ribonucleic acid (mRNA) and their interaction/combined effects regulates the net availability of the mRNA for translation into the protein. In this case the example is shown for a promoter polymorphism which has two alleles + and –, of which the + allele is the high producer variant in certain target cells. Of the two different 3’UTR variants + and –, the + is the more stable 3’UTR resulting in more mRNA being maintained. Hence, a “good” promoter allele and a “good” 3’UTR allele on the same chromosome, result in more protein being produced. The protein itself can occur in two variants: a less active “risk” form (–) and a more active form (+), and both A and B are again heterozygous for this polymorphism. The combined result of the particular allele combinations is that individual A has less of the “risk” protein than individual B in the target cell. This could not have been predicted by analyzing single SNPs and/or only looking at genotypes of individual SNPs, but is only evident upon analysis of the gene-wide genotype combinations. 7

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.7 Schematic overview of osteoporosis candidate genes from the pre-genome-wide association studies era including gene structure and position of the polymorphisms studied in GENOMOS. (A) VDR, (B) COLIA1, (c) ESR1, and (D) LRP5. 8

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.8 Osteoporosis genome-wide association studies (GWAS) time line indicating when studies were performed using the GWAS approach and some of their characteristics. 9

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE 25.9 The typical Manhattan plot obtained in GEFOS 2 showing the p value for association with bone mineral density (BMD) (femoral neck (FN) and lumbar spine (LS)) plotted on the y axis, against the chromosomal position of the SNPs on the x axis, as different colors for each chromosome. The GWAS discovery dataset contained 32,961 samples with GWAS data and top hits were replicated in 50,933 samples from the GENOMOS collection. Source: figure from Estrada etal. (2012) [127]. 10

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE Karyogram showing location of single nucleotide polymorphisms (SNPs) primarily found to be associated with bone mineral density and in a secondary analysis found to be associated with risk of any type of fracture as identified in the GEFOS-2 meta-analysis [127]. The rs numbers of SNPs are presented in green while the name of the closest prime candidate gene are given in black. 11

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE Overview of areas in clinical practice where genetic markers might eventually find some applications. BMD: bone mineral density; DXA: dual-energy x-ray absorptiometry; Fx: fracture. 12

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE Combined effect of bone mineral density (BMD)-decreasing alleles identified in Estrada et al. [127] and fracture risk-increasing risk alleles modeled in the population-based PERF study (n = 2836 women). (A–c) Effects are shown for baseline femoral neck (FN)-BMD standardized residuals (z-scores) (A), risk for osteoporosis (B) and risk for any type of fracture (c). The genetic score of each individual in a and b was based on the 63 single nucleotide polymorphisms (SNPs) showing genome-wide significant association with BMD (55 main and 8 secondary signals) and in c was based on the 16 BMD SNPs associated with fracture. Both genetic scores are weighted for relative effect sizes estimated without the PERF study. Weighted allele counts summed for each individual were divided by the mean effect size, making them equivalent to the per cent of alleles carried by each individual, and sorted into five bins as histograms (in gray), which show the numbers of individuals in each genetic score category (left y axis). Diamonds (right y axis) represent mean FN-BMD standardized levels in A, risk estimates in the form of odds ratios (OR) and osteoporosis (defined as NHANES T score of ≤ −2.5) in B and any type of fracture in c, using the middle category as reference (OR = 1). Vertical lines above and below the diamonds represent 95% confidence limits. Source: figure from Estrada etal. (2012) [127]. 13

Copyright © 2013 Elsevier Inc. All rights reserved. FIGURE The area under the receiver-operator characteristic (ROC) curves (area under the curve (AUC)) of two different models predicting the risk of osteoporosis (T score < 2.5) in the 2836 genotyped women of the PERF study. Model 1, represented by the solid black line, includes only the genetic score (AUC 0.59, 95% confidence interval (cI) 0.56 to 0.61). Model 2, represented by the dashed red line, includes age and weight (AUC 0.75, 95% cI 0.73 to 0.77]. Source: figure from Estrada et al. (2012) [127]. 14