Assessment of gene-by-sex interaction effect on bone mineral density

To access publisher's full text version of this article. Please click on the hyperlink in Additional Links field.Sexual dimorphism in various bone phenotypes, including bone mineral density (BMD), is widely observed; however, the extent to which genes explain these sex differences is unclear. To identify variants with different effects by sex, we examined gene-by-sex autosomal interactions genome-wide, and performed expression quantitative trait loci (eQTL) analysis and bioinformatics network analysis. We conducted an autosomal genome-wide meta-analysis of gene-by-sex interaction on lumbar spine (LS) and femoral neck (FN) BMD in 25,353 individuals from 8 cohorts. In a second stage, we followed up the 12 top single-nucleotide polymorphisms (SNPs; p < 1 × 10(-5) ) in an additional set of 24,763 individuals. Gene-by-sex interaction and sex-specific effects were examined in these 12 SNPs. We detected one novel genome-wide significant interaction associated with LS-BMD at the Chr3p26.1-p25.1 locus, near the GRM7 gene (male effect = 0.02 and p = 3.0 × 10(-5) ; female effect = -0.007 and p = 3.3 × 10(-2) ), and 11 suggestive loci associated with either FN- or LS-BMD in discovery cohorts. However, there was no evidence for genome-wide significant (p < 5 × 10(-8) ) gene-by-sex interaction in the joint analysis of discovery and replication cohorts. Despite the large collaborative effort, no genome-wide significant evidence for gene-by-sex interaction was found to influence BMD variation in this screen of autosomal markers. If they exist, gene-by-sex interactions for BMD probably have weak effects, accounting for less than 0.08% of the variation in these traits per implicated SNP. © 2012 American Society for Bone and Mineral Research.Medtronic NIH R01 AG18728 R01HL088119 R01AR046838 U01 HL084756 R01 AR43351 P01-HL45522 R01-MH-078111 R01-MH-083824 Nutrition and Obesity Research Center of Maryland P30DK072488 NIAMS/NIH F32AR059469 Instituto de Salud Carlos III-FIS (Spanish Health Ministry) PI 06/0034 PI08/0183 Canadian Institutes of Health Research (CIHR) NHLBI HHSN268201200036C N01-HC-85239 N01-HC-85079 N01-HC-85086 N01-HC-35129 N01 HC15103 N01 HC-55222 N01-HC-75150 N01-HC-45133 HL080295 HL087652 HL105756 NIA AG-023629 AG-15928 AG-20098 AG-027058 N01AG62101 N01AG62103 N01AG62106 1R01AG032098-01A1 National Center of Advancing Translational Technologies CTSI UL1TR000124 National Institute of Diabetes and Digestive and Kidney Diseases DK063491 EUROSPAN (European Special Populations Research Network) European Commission FP6 STRP grant 018947 LSHG-CT-2006-01947 Netherlands Organisation for Scientific Research Erasmus MC Centre for Medical Systems Biology (CMSB) Netherlands Brain Foundation (HersenStichting Nederland) US National Institute for Arthritis, Musculoskeletal and Skin Diseases National Institute on Aging R01 AR/AG41398 R01 AR050066 R21 AR056405 National Heart, Lung, and Blood Institute's Framingham Heart Study N01-HC-25195 Affymetrix, Inc. N02-HL-6-4278 Canadian Institutes of Health Research from Institute of Aging 165446 Institute of Genetics 179433 Institute of Musculoskeletal health 221765 Intramural Research Program of the NIH, National Institute on Aging National Institutes of Health HHSN268200782096C Hong Kong Research Grant Council HKU 768610M Bone Health Fund of HKU Foundation KC Wong Education Foundation Small Project Funding 201007176237 Matching Grant CRCG Grant Osteoporosis and Endocrine Research Fund Genomics Strategic Research Theme of The University of Hong Kong Netherlands Organisation of Scientific Research NWO Investments 175.010.2005.011 911-03-012 Research Institute for Diseases in the Elderly 014-93-015 Netherlands Genomics Initiative (NGI)/Netherlands Consortium for Healthy Aging (NCHA) 050-060-810 Erasmus Medical Center and Erasmus University, Rotterdam Netherlands Organization for the Health Research and Development (ZonMw) Research Institute for Diseases in the Elderly (RIDE) Ministry of Education, Culture and Science Ministry for Health, Welfare and Sports European Commission (DG XII) Municipality of Rotterdam German Bundesministerium fur Forschung und Technology 01 AK 803 A-H 01 IG 07015

Genetic Sharing with Cardiovascular Disease Risk Factors and Diabetes Reveals Novel Bone Mineral Density Loci.

Bone Mineral Density (BMD) is a highly heritable trait, but genome-wide association studies have identified few genetic risk factors. Epidemiological studies suggest associations between BMD and several traits and diseases, but the nature of the suggestive comorbidity is still unknown. We used a novel genetic pleiotropy-informed conditional False Discovery Rate (FDR) method to identify single nucleotide polymorphisms (SNPs) associated with BMD by leveraging cardiovascular disease (CVD) associated disorders and metabolic traits. By conditioning on SNPs associated with the CVD-related phenotypes, type 1 diabetes, type 2 diabetes, systolic blood pressure, diastolic blood pressure, high density lipoprotein, low density lipoprotein, triglycerides and waist hip ratio, we identified 65 novel independent BMD loci (26 with femoral neck BMD and 47 with lumbar spine BMD) at conditional FDR < 0.01. Many of the loci were confirmed in genetic expression studies. Genes validated at the mRNA levels were characteristic for the osteoblast/osteocyte lineage, Wnt signaling pathway and bone metabolism. The results provide new insight into genetic mechanisms of variability in BMD, and a better understanding of the genetic underpinnings of clinical comorbidity

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Genomic analyses inform on migration events during the peopling of Eurasia.

High-coverage whole-genome sequence studies have so far focused on a limited number of geographically restricted populations, or been targeted at specific diseases, such as cancer. Nevertheless, the availability of high-resolution genomic data has led to the development of new methodologies for inferring population history and refuelled the debate on the mutation rate in humans. Here we present the Estonian Biocentre Human Genome Diversity Panel (EGDP), a dataset of 483 high-coverage human genomes from 148 populations worldwide, including 379 new genomes from 125 populations, which we group into diversity and selection sets. We analyse this dataset to refine estimates of continent-wide patterns of heterozygosity, long- and short-distance gene flow, archaic admixture, and changes in effective population size through time as well as for signals of positive or balancing selection. We find a genetic signature in present-day Papuans that suggests that at least 2% of their genome originates from an early and largely extinct expansion of anatomically modern humans (AMHs) out of Africa. Together with evidence from the western Asian fossil record, and admixture between AMHs and Neanderthals predating the main Eurasian expansion, our results contribute to the mounting evidence for the presence of AMHs out of Africa earlier than 75,000 years ago.Support was provided by: Estonian Research Infrastructure Roadmap grant no 3.2.0304.11-0312; Australian Research Council Discovery grants (DP110102635 and DP140101405) (D.M.L., M.W. and E.W.); Danish National Research Foundation; the Lundbeck Foundation and KU2016 (E.W.); ERC Starting Investigator grant (FP7 - 261213) (T.K.); Estonian Research Council grant PUT766 (G.C. and M.K.); EU European Regional Development Fund through the Centre of Excellence in Genomics to Estonian Biocentre (R.V.; M.Me. and A.Me.), and Centre of Excellence for Genomics and Translational Medicine Project No. 2014-2020.4.01.15-0012 to EGC of UT (A.Me.) and EBC (M.Me.); Estonian Institutional Research grant IUT24-1 (L.S., M.J., A.K., B.Y., K.T., C.B.M., Le.S., H.Sa., S.L., D.M.B., E.M., R.V., G.H., M.K., G.C., T.K. and M.Me.) and IUT20-60 (A.Me.); French Ministry of Foreign and European Affairs and French ANR grant number ANR-14-CE31-0013-01 (F.-X.R.); Gates Cambridge Trust Funding (E.J.); ICG SB RAS (No. VI.58.1.1) (D.V.L.); Leverhulme Programme grant no. RP2011-R-045 (A.B.M., P.G. and M.G.T.); Ministry of Education and Science of Russia; Project 6.656.2014/K (S.A.F.); NEFREX grant funded by the European Union (People Marie Curie Actions; International Research Staff Exchange Scheme; call FP7-PEOPLE-2012-IRSES-number 318979) (M.Me., G.H. and M.K.); NIH grants 5DP1ES022577 05, 1R01DK104339-01, and 1R01GM113657-01 (S.Tis.); Russian Foundation for Basic Research (grant N 14-06-00180a) (M.G.); Russian Foundation for Basic Research; grant 16-04-00890 (O.B. and E.B); Russian Science Foundation grant 14-14-00827 (O.B.); The Russian Foundation for Basic Research (14-04-00725-a), The Russian Humanitarian Scientific Foundation (13-11-02014) and the Program of the Basic Research of the RAS Presidium “Biological diversity” (E.K.K.); Wellcome Trust and Royal Society grant WT104125AIA & the Bristol Advanced Computing Research Centre (http://www.bris.ac.uk/acrc/) (D.J.L.); Wellcome Trust grant 098051 (Q.A.; C.T.-S. and Y.X.); Wellcome Trust Senior Research Fellowship grant 100719/Z/12/Z (M.G.T.); Young Explorers Grant from the National Geographic Society (8900-11) (C.A.E.); ERC Consolidator Grant 647787 ‘LocalAdaptatio’ (A.Ma.); Program of the RAS Presidium “Basic research for the development of the Russian Arctic” (B.M.); Russian Foundation for Basic Research grant 16-06-00303 (E.B.); a Rutherford Fellowship (RDF-10-MAU-001) from the Royal Society of New Zealand (M.P.C.)

Genetic Structure of Dagestan Populations: A Study of 11 Alu Insertion Polymorphisms

We examined genetic variation in nine populations of Dagestan using 11 autosomal Alu insertion polymorphisms to investigate the genetic structure of indigenous groups and to assess their genetic relationship with world populations. Genetic differentiation among mountain inhabitants (GST 2%) is comparable to that for European populations. Traces of genetic drift are detectable only for endogamous and small Ando-Didospeaking ethnic groups, and they coincide with the most linguistically diverse region of Dagestan. Multidimensional scaling analyses among West Eurasian populations revealed that mountain inhabitants of Dagestan are closely related to Anatolian and Cyprus Turks. Thus our frequency data are consistent with the available Y-chromosome data, according to which the Middle East and the Caucasus share a considerable portion of the gene pool. Overall, our results corroborate the initially suggested genetic contribution of Middle Eastern populations to Caucasus populations

Peak Bone Mass Formation: Modern View of the Problem

Peak bone mass is the amount of bone tissue that is formed when a stable skeletal state is achieved at a young age. To date, there are no established peak bone mass standards nor clear data on the age at which peak bone mass occurs. At the same time, the level of peak bone mass at a young age is an important predictor of the onset of primary osteoporosis. The purpose of this review is to analyze the results of studies of levels of peak bone mass in general, the age of its onset, as well as factors influencing its formation. Factors such as hormonal levels, body composition, physical activity, nutrition, heredity, smoking, lifestyle, prenatal predictors, intestinal microbiota, and vitamin and micronutrient status were considered, and a comprehensive scheme of the influence of these factors on the level of peak bone mass was created. Determining the standards and timing of the formation of peak bone mass, and the factors affecting it, will help in the development of measures to prevent its shortage and the consequent prevention of osteoporosis and concomitant diseases

Osteogenesis Imperfecta: Search for Mutations in Patients from the Republic of Bashkortostan (Russia)

Osteogenesis imperfecta (OI) is an inherited disease of bone characterized by increased bone fragility. Here, we report the results of the molecular architecture of osteogenesis imperfecta research in patients from Bashkortostan Republic, Russia. In total, 16 mutations in COL1A1, 11 mutations in COL1A2, and 1 mutation in P3H1 and IFIMT5 genes were found in isolated states; 11 of them were not previously reported in literature. We found mutations in CLCN7, ALOX12B, PLEKHM1, ERCC4, ARSB, PTH1R, and TGFB1 that were not associated with OI pathogenesis in patients with increased bone fragility. Additionally, we found combined mutations (c.2869C>T, p. Gln957* in COL1A1 and c.1197+5G>A in COL1A2; c.579delT, p. Gly194fs in COL1A1 and c.1197+5G>A in COL1A2; c.2971G>C, p. Gly991Arg in COL1A2 and c.212G>C, p.Ser71Thr in FGF23; c.-14C>T in IFITM5 and c.1903C>T, p. Arg635* in LAMB3) in 4 patients with typical OI clinic phenotypes

Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis

Osteoporosis (OP) is a multifactorial bone disease belonging to the metabolic osteopathies group. Using the polygenic score (PGS) approach, we combined the effects of bone mineral density (BMD) DNA loci, affecting osteoporosis pathogenesis, based on GEFOS/GENOMOS consortium GWAS meta-analysis. We developed models to predict the risk of low fractures in women from the Volga-Ural region of Russia with efficacy of 74% (AUC = 0.740; OR (95% CI) = 2.9 (2.353–3.536)), as well as the formation of low BMD with efficacy of 79% (AUC = 0.790; OR (95% CI) = 3.94 (2.993–5.337)). In addition, we propose a model that predicts fracture risk and low BMD in a comorbid condition with 85% accuracy (AUC = 0.850; OR (95% CI) = 6.6 (4.411–10.608)) in postmenopausal women

Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem

Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus