169 research outputs found
Genetic evidence for a normal-weight "metabolically obese" phenotype linking insulin resistance, hypertension, coronary artery disease, and type 2 diabetes
PublishedJournal ArticleResearch Support, Non-U.S. Gov'tThe mechanisms that predispose to hypertension, coronary artery disease (CAD), and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy-a reduction in subcutaneous adipose tissue-it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, CAD, and T2D. We aimed to test the hypothesis that common alleles associated with IR also influence the wider clinical and biochemical profile of monogenic IR. We selected 19 common genetic variants associated with fasting insulin-based measures of IR. We used hierarchical clustering and results from genome-wide association studies of eight nondisease outcomes of monogenic IR to group these variants. We analyzed genetic risk scores against disease outcomes, including 12,171 T2D cases, 40,365 CAD cases, and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (β = 0.018; P = 4 × 10(-29)), lower HDL cholesterol (β = -0.020; P = 7 × 10(-37)), greater hepatic steatosis (β = 0.021; P = 3 × 10(-4)), higher alanine transaminase (β = 0.002; P = 3 × 10(-5)), lower sex-hormone-binding globulin (β = -0.010; P = 9 × 10(-13)), and lower adiponectin (β = -0.015; P = 2 × 10(-26)). The same risk alleles were associated with lower BMI (per-allele β = -0.008; P = 7 × 10(-8)) and increased visceral-to-subcutaneous adipose tissue ratio (β = -0.015; P = 6 × 10(-7)). Individuals carrying ≥17 fasting insulin-raising alleles (5.5% population) were slimmer (0.30 kg/m(2)) but at increased risk of T2D (odds ratio [OR] 1.46; per-allele P = 5 × 10(-13)), CAD (OR 1.12; per-allele P = 1 × 10(-5)), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg [per-allele P = 2 × 10(-5)] and 0.67 mmHg [per-allele P = 2 × 10(-4)], respectively) compared with individuals carrying ≤9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the "metabolic syndrome" and point to reduced subcutaneous adiposity as a central mechanism
Meta-analysis of genome-wide studies identifies MEF2C SNPs associated with bone mineral density at forearm
Background: Forearm fractures affect 1.7 million individuals worldwide each year and most occur earlier in life than hip fractures. While the heritability of forearm bone mineral density (BMD) and fracture is high, their genetic determinants are largely unknown. Aim: To identify genetic variants associated with forearm BMD and forearm fractures. Methods: BMD at distal radius, measured by dualenergy x-ray absorptiometry, was tested for association with common genetic variants. We conducted a metaanalysis of genome-wide association studies for BMD in 5866 subjects of European descent and then selected the variants for replication in 715 Mexican American samples. Gene-based association was carried out to supplement the single-nucleotide polymorphism (SNP) association test. We then tested the BMD-associated SNPs for association with forearm fracture in 2023 cases and 3740 controls. Results: We found that five SNPs in the introns of MEF2C were associated with forearm BMD at a genome-wide significance level (
Identification of new therapeutic targets for osteoarthritis through genome-wide analyses of UK Biobank data
Osteoarthritis is the most common musculoskeletal disease and the leading cause of disability globally. Here, we performed a genome-wide association study for osteoarthritis (77,052 cases and 378,169 controls), analyzing four phenotypes: knee osteoarthritis, hip osteoarthritis, knee and/or hip osteoarthritis, and any osteoarthritis. We discovered 64 signals, 52 of them novel, more than doubling the number of established disease loci. Six signals fine-mapped to a single variant. We identified putative effector genes by integrating expression quantitative trait loci (eQTL) colocalization, fine-mapping, and human rare-disease, animal-model, and osteoarthritis tissue expression data. We found enrichment for genes underlying monogenic forms of bone development diseases, and for the collagen formation and extracellular matrix organization biological pathways. Ten of the likely effector genes, including TGFB1 (transforming growth factor beta 1), FGF18 (fibroblast growth factor 18), CTSK (cathepsin K), and IL11 (interleukin 11), have therapeutics approved or in clinical trials, with mechanisms of action supportive of evaluation for efficacy in osteoarthritis
Parent-of-origin-specific allelic associations among 106 genomic loci for age at menarche.
Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P < 5 × 10(-8)) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1-WDR25, MKRN3-MAGEL2 and KCNK9) demonstrating parent-of-origin-specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and γ-aminobutyric acid-B2 receptor signalling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition
Genetic Evidence for a Normal-Weight "Metabolically Obese" Phenotype Linking Insulin Resistance, Hypertension, Coronary Artery Disease, and Type 2 Diabetes
The mechanisms that predispose to hypertension, coronary artery disease (CAD), and type 2 diabetes (T2D) in individuals of normal weight are poorly understood. In contrast, in monogenic primary lipodystrophy—a reduction in subcutaneous adipose tissue—it is clear that it is adipose dysfunction that causes severe insulin resistance (IR), hypertension, CAD, and T2D. We aimed to test the hypothesis that common alleles associated with IR also influence the wider clinical and biochemical profile of monogenic IR. We selected 19 common genetic variants associated with fasting insulin–based measures of IR. We used hierarchical clustering and results from genome-wide association studies of eight nondisease outcomes of monogenic IR to group these variants. We analyzed genetic risk scores against disease outcomes, including 12,171 T2D cases, 40,365 CAD cases, and 69,828 individuals with blood pressure measurements. Hierarchical clustering identified 11 variants associated with a metabolic profile consistent with a common, subtle form of lipodystrophy. A genetic risk score consisting of these 11 IR risk alleles was associated with higher triglycerides (β = 0.018; P = 4 × 10−29), lower HDL cholesterol (β = −0.020; P = 7 × 10−37), greater hepatic steatosis (β = 0.021; P = 3 × 10−4), higher alanine transaminase (β = 0.002; P = 3 × 10−5), lower sex-hormone-binding globulin (β = −0.010; P = 9 × 10−13), and lower adiponectin (β = −0.015; P = 2 × 10−26). The same risk alleles were associated with lower BMI (per-allele β = −0.008; P = 7 × 10−8) and increased visceral-to-subcutaneous adipose tissue ratio (β = −0.015; P = 6 × 10−7). Individuals carrying ≥17 fasting insulin–raising alleles (5.5% population) were slimmer (0.30 kg/m2) but at increased risk of T2D (odds ratio [OR] 1.46; per-allele P = 5 × 10−13), CAD (OR 1.12; per-allele P = 1 × 10−5), and increased blood pressure (systolic and diastolic blood pressure of 1.21 mmHg [per-allele P = 2 × 10−5] and 0.67 mmHg [per-allele P = 2 × 10−4], respectively) compared with individuals carrying ≤9 risk alleles (5.5% population). Our results provide genetic evidence for a link between the three diseases of the “metabolic syndrome” and point to reduced subcutaneous adiposity as a central mechanism
Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry
Hip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with ∼2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p ≤ 2.6 × 10 –8 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 × 10 –5 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12% to 22% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility
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
Rare coding variants and X-linked loci associated with age at menarche
More than 100 loci have been identified for age at menarche by genome-wide association studies; however, collectively these explain only ∼3% of the trait variance. Here we test two overlooked sources of variation in 192,974 European ancestry women: low-frequency protein-coding variants and X-chromosome variants. Five missense/nonsense variants (in ALMS1/LAMB2/TNRC6A/TACR3/PRKAG1) are associated with age at menarche (minor allele frequencies 0.08–4.6%; effect sizes 0.08–1.25 years per allele; P<5 × 10−8). In addition, we identify common X-chromosome loci at IGSF1 (rs762080, P=9.4 × 10−13) and FAAH2 (rs5914101, P=4.9 × 10−10). Highlighted genes implicate cellular energy homeostasis, post-transcriptional gene silencing and fatty-acid amide signalling. A frequently reported mutation in TACR3 for idiopathic hypogonatrophic hypogonadism (p.W275X) is associated with 1.25-year-later menarche (P=2.8 × 10−11), illustrating the utility of population studies to estimate the penetrance of reportedly pathogenic mutations. Collectively, these novel variants explain ∼0.5% variance, indicating that these overlooked sources of variation do not substantially explain the ‘missing heritability' of this complex trait
Rare coding variants and X-linked loci associated with age at menarche.
More than 100 loci have been identified for age at menarche by genome-wide association studies; however, collectively these explain only ∼3% of the trait variance. Here we test two overlooked sources of variation in 192,974 European ancestry women: low-frequency protein-coding variants and X-chromosome variants. Five missense/nonsense variants (in ALMS1/LAMB2/TNRC6A/TACR3/PRKAG1) are associated with age at menarche (minor allele frequencies 0.08-4.6%; effect sizes 0.08-1.25 years per allele; P<5 × 10(-8)). In addition, we identify common X-chromosome loci at IGSF1 (rs762080, P=9.4 × 10(-13)) and FAAH2 (rs5914101, P=4.9 × 10(-10)). Highlighted genes implicate cellular energy homeostasis, post-transcriptional gene silencing and fatty-acid amide signalling. A frequently reported mutation in TACR3 for idiopathic hypogonatrophic hypogonadism (p.W275X) is associated with 1.25-year-later menarche (P=2.8 × 10(-11)), illustrating the utility of population studies to estimate the penetrance of reportedly pathogenic mutations. Collectively, these novel variants explain ∼0.5% variance, indicating that these overlooked sources of variation do not substantially explain the 'missing heritability' of this complex trait.UK sponsors (see article for overseas ones):
This work made use of data and samples generated by the 1958 Birth Cohort (NCDS). Access to these resources was enabled via the 58READIE Project funded by Wellcome Trust and Medical Research Council (grant numbers WT095219MA and G1001799). A full list of the financial, institutional and personal contributions to the development of the 1958 Birth Cohort Biomedical resource is available at http://www2.le.ac.uk/projects/birthcohort. Genotyping was undertaken as part of the Wellcome Trust Case-Control Consortium (WTCCC) under Wellcome Trust award 076113, and a full list of the investigators who contributed to the generation of the data is available at www.wtccc.org.uk
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The Fenland Study is funded by the Wellcome Trust and the Medical Research Council, as well as by the Support for Science Funding programme and CamStrad.
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SIBS - CRUK ref: C1287/A8459 SEARCH - CRUK ref: A490/A10124 EMBRACE is supported by Cancer Research UK Grants C1287/A10118, C1287/A16563 and C1287/A17523. Genotyping was supported by Cancer Research - UK grant C12292/A11174D
and C8197/A16565. Gareth Evans and Fiona Lalloo are supported by an NIHR grant to the Biomedical Research Centre, Manchester.
The Investigators at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust are supported by an NIHR grant to the Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. Ros Eeles and Elizabeth Bancroft are supported by Cancer Research UK Grant C5047/A8385.
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Generation Scotland - Scottish Executive Health Department, Chief Scientist Office, grant number CZD/16/6. Exome array genotyping for GS:SFHS was funded by the Medical Research Council UK. 23andMe - This work was supported in part by NIH Award 2R44HG006981-02 from the National Human Genome Research Institute.This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/ncomms875
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