Genetics of Osteoporosis Ralston, Stuart H; Uitterlinden, André G
Endocrine reviews,
2010-October, Letnik:
31, Številka:
5
Journal Article
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Osteoporosis is a common disease with a strong genetic component characterized by reduced bone mass, defects in the microarchitecture of bone tissue, and an increased risk of fragility fractures. ...Twin and family studies have shown high heritability of bone mineral density (BMD) and other determinants of fracture risk such as ultrasound properties of bone, skeletal geometry, and bone turnover. Osteoporotic fractures also have a heritable component, but this reduces with age as environmental factors such as risk of falling come into play. Susceptibility to osteoporosis is governed by many different genetic variants and their interaction with environmental factors such as diet and exercise. Notable successes in identification of genes that regulate BMD have come from the study of rare Mendelian bone diseases characterized by major abnormalities of bone mass where variants of large effect size are operative. Genome-wide association studies have also identified common genetic variants of small effect size that contribute to regulation of BMD and fracture risk in the general population. In many cases, the loci and genes identified by these studies had not previously been suspected to play a role in bone metabolism. Although there has been extensive progress in identifying the genes and loci that contribute to the regulation of BMD and fracture over the past 15 yr, most of the genetic variants that regulate these phenotypes remain to be discovered.
This review addresses recent developments in understanding the genetic basis of osteoporosis. We discuss the approaches that have been used to identify predisposing genes including association studies, linkage studies, genome wide association studies and studies in model organisms. Detailed discussion of specific candidate genes and loci is also included, focusing on those where robust statistical evidence has been gained to support their role in regulating susceptibility to osteoporosis.
Gut microbiota has been implicated in major diseases affecting the human population and has also been linked to triglycerides and high-density lipoprotein levels in the circulation. Recent ...development in metabolomics allows classifying the lipoprotein particles into more details. Here, we examine the impact of gut microbiota on circulating metabolites measured by Nuclear Magnetic Resonance technology in 2309 individuals from the Rotterdam Study and the LifeLines-DEEP cohort. We assess the relationship between gut microbiota and metabolites by linear regression analysis while adjusting for age, sex, body-mass index, technical covariates, medication use, and multiple testing. We report an association of 32 microbial families and genera with very-low-density and high-density subfractions, serum lipid measures, glycolysis-related metabolites, ketone bodies, amino acids, and acute-phase reaction markers. These observations provide insights into the role of microbiota in host metabolism and support the potential of gut microbiota as a target for therapeutic and preventive interventions.
Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. ...This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called 'missing heritability'. Here, we describe the online Meta-GWAS Accuracy and Power (MetaGAP) calculator (available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from the MetaGAP calculator with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51-62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36-38%). Hence, cross-study heterogeneity contributes to the missing heritability.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Standard genome-wide association studies (GWASs) rely on analyzing a single trait at a time. However, many human phenotypes are complex and composed by multiple correlated traits. Here we introduce ...C-GWAS, a method for combining GWAS summary statistics of multiple potentially correlated traits. Extensive computer simulations demonstrated increased statistical power of C-GWAS compared to the minimal p-values of multiple single-trait GWASs (MinGWAS) and the current state-of-the-art method for combining single-trait GWASs (MTAG). Applying C-GWAS to a meta-analysis dataset of 78 single trait facial GWASs from 10,115 Europeans identified 56 study-wide suggestively significant loci with multi-trait effects on facial morphology of which 17 are novel loci. Using data from additional 13,622 European and Asian samples, 46 (82%) loci, including 9 (53%) novel loci, were replicated at nominal significance with consistent allele effects. Functional analyses further strengthen the reliability of our C-GWAS findings. Our study introduces the C-GWAS method and makes it available as computationally efficient open-source R package for widespread future use. Our work also provides insights into the genetic architecture of human facial appearance.
Accumulation of advanced glycation end-products (AGEs) in skeletal muscle has been implicated in development of sarcopenia.
To obtain further insight in the pathophysiology of sarcopenia, we studied ...its relationship with skin AGEs in the general population.
In a cross-sectional analysis, 2744 participants of northern European background, mean age 74.1 years, were included from the Rotterdam Study. Skin AGEs were measured as skin autofluorescence (SAF) using AGE ReaderTM, appendicular skeletal mass index (ASMI) using insight dual-energy X-ray absorptiometry, hand grip strength (HGS) using a hydraulic hand dynamometer, and, in a subgroup, gait speed (GS) measured on an electronic walkway (n = 2080). We defined probable sarcopenia (low HGS) and confirmed sarcopenia (low HGS and low ASMI) based on the European Working Group on Sarcopenia in Older People (EWGSOP2) revised criteria cutoffs. Multivariate linear and logistic regression were performed adjusting for age, sex, body fat percentage, height, renal function, diabetes, and smoking status.
The prevalence of low ASMI was 7.7%; probable sarcopenia, 24%, slow GS, 3%; and confirmed sarcopenia, 3.5%. SAF was inversely associated with ASMI β -0.062 (95% CI -0.092, -0.032), HGS β -0.051 (95% CI -0.075, -0.026), and GS β -0.074 (95% CI -0.116, -0.033). A 1-unit increase in SAF was associated with higher odds of probable sarcopenia odds ratio (OR) 1.36 (95% CI 1.09, 1.68) and confirmed sarcopenia OR 2.01 (95% CI 1.33, 3.06).
Higher skin AGEs are associated with higher sarcopenia prevalence. We call for future longitudinal studies to explore the role of SAF as a potential biomarker of sarcopenia.
The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and ...potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.
Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed ...peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3' UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
The gut microbiota has been shown to play diverse roles in human health and disease although the underlying mechanisms have not yet been fully elucidated. Large cohort studies can provide further ...understanding into inter-individual differences, with more precise characterization of the pathways by which the gut microbiota influences human physiology and disease processes. Here, we aimed to profile the stool microbiome of children and adults from two population-based cohort studies, comprising 2,111 children in the age-range of 9 to 12 years (the Generation R Study) and 1,427 adult individuals in the range of 46 to 88 years of age (the Rotterdam Study). For the two cohorts, 16S rRNA gene profile datasets derived from the Dutch population were generated. The comparison of the two cohorts showed that children had significantly lower gut microbiome diversity. Furthermore, we observed higher relative abundances of genus Bacteroides in children and higher relative abundances of genus Blautia in adults. Predicted functional metagenome analysis showed an overrepresentation of the glycan degradation pathways, riboflavin (vitamin B2), pyridoxine (vitamin B6) and folate (vitamin B9) biosynthesis pathways in children. In contrast, the gut microbiome of adults showed higher abundances of carbohydrate metabolism pathways, beta-lactam resistance, thiamine (vitamin B1) and pantothenic (vitamin B5) biosynthesis pathways. A predominance of catabolic pathways in children (valine, leucine and isoleucine degradation) as compared to biosynthetic pathways in adults (valine, leucine and isoleucine biosynthesis) suggests a functional microbiome switch to the latter in adult individuals. Overall, we identified compositional and functional differences in gut microbiome between children and adults in a population-based setting. These microbiome profiles can serve as reference for future studies on specific human disease susceptibility in childhood, adulthood and specific diseased populations.
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