Collaborative Meta-analysis: Associations of 150 Candidate Genes With Osteoporosis and Osteoporotic Fracture

Context Although variations in 150 genes have been tested for their influence on bone mineral density (BMD), these studies have generally not been tested for replication in large studies that assess all common genetic variation across these genes. Contribution In this analysis of genome-wide association results from 5 large populations, variations in only 9 of 150 genes were associated with BMD, and variations in only 4 of these genes were associated with fracture. Caution The findings do not apply to non-European populations, and the effect sizes were very modest. Implication Most genes previously tested for their influence on risk for low BMD have no consistent effect on that risk. The Editors Osteoporosis and osteoporotic fractures are heritable (13), common, and costly. One third to one half of women of European descent experience an osteoporotic fracture in their lifetime (4, 5), and the annual direct expenditure for osteoporotic fractures exceeds $17 billion in the United States (6). Morbidity and mortality associated with the disease will increase substantially as populations age (7). Bone mineral density (BMD) remains the single most clinically useful risk factor for osteoporotic fracture and is the metric on which most therapeutic decisions are based (8). Substantial efforts have been dedicated to understanding whether variants in genes known to influence bone physiology also influence risk for low BMD and possibly fracturescalled candidate genes (9). The candidate gene approach has relied on the assumption that if a gene is important for biological reasons, it may also affect the trait of clinical interest. Whether these genes do indeed influence propensity to osteoporosis and fracture has remained uncertain because many candidate gene studies lacked sufficient sample sizes and a replication group with which to validate findings (10). Moreover, replication efforts that evaluate 1 or a few variants at a time are highly susceptible to fragmented, selective reporting of only the most promising results (11, 12), and the selection of single or several variants in a gene does not provide information on whether variants elsewhere in the gene may influence the disease of interest. The notable exceptions to this is the set of variants from candidate genes tested in 18000 to 45000 participants by the GENOMOS (Genetic Markers for Osteoporosis) ConsortiumESR1 (13), COL1A1 (14), VDR (15), TGFB1 (16), and LRP5 and LRP6 (17)which systematically replicated these candidate genes. Recent advances in microarray technology have facilitated genome-wide association studies, which test genetic variation across the human genome in thousands of individuals without a priori hypotheses. Through the use of dense genotyping, large study samples, and replication studies to confirm results, these studies have led to the discovery of many common genetic variants that have robust statistical evidence for association with various traits and diseases (18). Genome-wide association studies have left the previous literature on candidate genes in a state of uncertainty (19) because they offer a means to reevaluate how many (if any) of the hundreds of previously proposed candidate gene associations are true. Specifically, genome-wide association data sets cover a large proportion of the common variation across the genome and can be used to systematically replicate previously proposed associations without selective reporting biases. Associations identified in these studies typically suggest a small effect size, an approach that becomes even more statistically efficient if large-scale genome-wide association data are pooled in meta-analyses of studies that use consistent phenotype definitions and analysis methods so that replication power is optimized (20, 21). It would be important to know whether even a small proportion of previously identified candidate genes for any disease are valid because they may indicate which biological pathways translate into clinical outcomes, and they might be important for future risk prediction tools. For osteoporosis, it is important to further understand whether candidate genes are associated not only with BMD but also with fracture. In this international collaborative meta-analysis of 19195 men and women, we used genotyping arrays to systematically assess all common single-nucleotide polymorphisms (SNPs) assessed in the common haplotype map of the human genome (HapMap) CEPH (Centre d'tude du polymorphisme humain) data set in all previously published candidate genes for osteoporosis. We aimed to identify which candidate genes and common genetic variants near those genes influence osteoporosis and to understand whether the candidate genes that influence BMD also alter the risk for fracture. Methods See the Appendix for a glossary of genetic terms and an overview of analytic techniques. Supplement. Supplemental Data Cohorts We performed a meta-analysis of SNP-level genome-wide association results from 5 large cohort populations of European descent: the Rotterdam (the Netherlands) (22), Framingham Heart and Offspring (the United States) (23), deCODE (Iceland) (24), Erasmus Rucphen Family (ERF) (the Netherlands) (25), and Twins United Kingdom (TwinsUK) (26) studies. These cohorts were assembled to identify genetic risk factors in the development of complex disorders or to study aging-related diseases and chronic disabling conditions; all participants (23016 total before exclusions based on genotyping quality control) were unselected for any trait or condition, and all studies collected cross-sectional data on lumbar spine and femoral neck BMD. Table 1 provides details on the cohorts; data on women and men were considered as separate data sets for inclusion in the meta-analysis. Table 1. Characteristics of Each Cohort Included in the Meta-analysis Gene and SNP Selection To identify studies of candidate genes and SNPs for this analysis, we searched the Human Genome Epidemiology (HuGE) Navigator, which provides a comprehensive, continuously updated archive of studies assessing the relationship between genetic variants and diseases published since 2000 (27). Very few genetic association studies were published before 2000, and it is unlikely that a gene proposed before that time would not have been studied again in at least 1 study since. We used the Phenopedia tool in the Navigator, which lists all studies associated with a particular phenotype, by using the search terms osteoporosis and osteoporosis, postmenopausal on 18 July 2008. During revision of our article, we updated the list of candidate gene studies for osteoporosis (between 18 July 2008 and 30 April 2009) by searching PubMed using the terms osteoporosis or osteoporosis, postmenopausal and gene or genetic or candidate gene in humans and found that 3 additional candidate genes (CA8, CA10, and PBX1) had been assessed for their association with BMD (28, 29). None of the SNPs in or near these 3 additional candidate genes achieved a P value less than 0.001 in their association with BMD at the lumbar spine or femoral neck (Appendix Table 1 lists all genes studied). Using the second generation of HapMap data (a registry of all common human genetic variants) (30), we then identified all SNPs within 50 kilobase pairs downstream of the stop codons and upstream of the start codons of autosomal genes identified through the HuGE Navigator search. We used autosomal genes only because we could not accurately impute genetic information on sex chromosomes (31). The stop and start codons were identified by using the Ensembl Genome Browser (Ensembl, Cambridge, United Kingdom). BMD Measurement All cohorts had measured BMD at the lumbar spine (L1 to L4 or L2 to L4) and femoral neck by using standard manufacturer protocols on a dual-energy x-ray absorptiometry machine (Table 1). Measurements were performed as follows: in the Rotterdam Study, at baseline between 1991 and 1992 (22); in ERF, between 2002 and 2003 (25); in the Icelandic population, at baseline (32); and in generations 1 and 2 of the Framingham Offspring Study, between 1992 and 1997 (33) and between 1996 and 2001 (34), respectively. In TwinsUK (26), all measurements were obtained from the most recent BMD data to better match the age distribution of the other cohorts. Genotyping and Quality Control Appendix Table 2 describes genotyping, imputation, and association testing in each cohort. Genotyping for the TwinsUK, Rotterdam, and deCODE studies has been described elsewhere (26, 32). After we assessed all polymorphic SNPs identified in autosomal chromosomes from the HapMap CEPH phase II panel (release, build 36) and aligned all genotypes to the positive strand, we imputed missing genotypes by using the MACH (Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan) (35) or IMPUTE (Oxford University, Oxford, United Kingdom) (36) software programs. These programs implement hidden Markov modelbased algorithms to impute missing genotypes (37). The imputation allows assaying of most of the common genetic variations (minor allele frequency >1%) in the genome and permits the sharing of data between cohorts that have used different gene array chips to genotype participants. Empirical evidence suggests that these 2 imputation algorithms tend to provide similar results (38, 39). Statistical Analysis of Single Studies (BMD) Appendix Table 2 and the Appendix outline the details of genome-wide association testing and imputation. To lessen population stratification, individuals were excluded from the analyses if they demonstrated evidence of non-European ancestry by use of the following: STRUCTURE (Chicago) program (40) in the TwinsUK (n = 20) and deCODE (n = 0) studies, identity by state-clustering analysis in the Rotterdam Study (n = 129) (41), GenABEL (Aulchenko Y, Struchalin M, Erasmus University Medical Center, Rotterdam, the Netherlands) (42) in the ERF study, and Eigenstrat principle component analysis (