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Lyu C, Joehanes R, Huan T, Levy D, Li Y, Wang M, Liu X, Liu C, Ma J. Enhancing selection of alcohol consumption-associated genes by random forest. Br J Nutr 2024:1-10. [PMID: 38606596 DOI: 10.1017/s0007114524000795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2024]
Abstract
Machine learning methods have been used in identifying omics markers for a variety of phenotypes. We aimed to examine whether a supervised machine learning algorithm can improve identification of alcohol-associated transcriptomic markers. In this study, we analysed array-based, whole-blood derived expression data for 17 873 gene transcripts in 5508 Framingham Heart Study participants. By using the Boruta algorithm, a supervised random forest (RF)-based feature selection method, we selected twenty-five alcohol-associated transcripts. In a testing set (30 % of entire study participants), AUC (area under the receiver operating characteristics curve) of these twenty-five transcripts were 0·73, 0·69 and 0·66 for non-drinkers v. moderate drinkers, non-drinkers v. heavy drinkers and moderate drinkers v. heavy drinkers, respectively. The AUC of the selected transcripts by the Boruta method were comparable to those identified using conventional linear regression models, for example, AUC of 1958 transcripts identified by conventional linear regression models (false discovery rate < 0·2) were 0·74, 0·66 and 0·65, respectively. With Bonferroni correction for the twenty-five Boruta method-selected transcripts and three CVD risk factors (i.e. at P < 6·7e-4), we observed thirteen transcripts were associated with obesity, three transcripts with type 2 diabetes and one transcript with hypertension. For example, we observed that alcohol consumption was inversely associated with the expression of DOCK4, IL4R, and SORT1, and DOCK4 and SORT1 were positively associated with obesity, and IL4R was inversely associated with hypertension. In conclusion, using a supervised machine learning method, the RF-based Boruta algorithm, we identified novel alcohol-associated gene transcripts.
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Affiliation(s)
- Chenglin Lyu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA02118, USA
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA02118, USA
| | - Roby Joehanes
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA01702, USA
| | - Tianxiao Huan
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA01702, USA
| | - Daniel Levy
- Framingham Heart Study and Population Sciences Branch, NHLBI, Framingham, MA01702, USA
| | - Yi Li
- Department of Biostatistics, Boston University School of Public Health, Boston, MA02118, USA
| | - Mengyao Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA02118, USA
| | - Xue Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA02118, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA02118, USA
| | - Jiantao Ma
- Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA02111, USA
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Cao TV, Sutherland HG, Benton MC, Haupt LM, Lea RA, Griffiths LR. Exploring the Functional Basis of Epigenetic Aging in Relation to Body Fat Phenotypes in the Norfolk Island Cohort. Curr Issues Mol Biol 2023; 45:7862-7877. [PMID: 37886940 PMCID: PMC10605526 DOI: 10.3390/cimb45100497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/18/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
DNA methylation is an epigenetic factor that is modifiable and can change over a lifespan. While many studies have identified methylation sites (CpGs) related to aging, the relationship of these to gene function and age-related disease phenotypes remains unclear. This research explores this question by testing for the conjoint association of age-related CpGs with gene expression and the relation of these to body fat phenotypes. The study included blood-based gene transcripts and intragenic CpG methylation data from Illumina 450 K arrays in 74 healthy adults from the Norfolk Island population. First, a series of regression analyses were performed to detect associations between gene transcript level and intragenic CpGs and their conjoint relationship with age. Second, we explored how these age-related expression CpGs (eCpGs) correlated with obesity-related phenotypes, including body fat percentage, body mass index, and waist-to-hip ratio. We identified 35 age-related eCpGs associated with age. Of these, ten eCpGs were associated with at least one body fat phenotype. Collagen Type XI Alpha 2 Chain (COL11A2), Complement C1s (C1s), and four and a half LIM domains 2 (FHL2) genes were among the most significant genes with multiple eCpGs associated with both age and multiple body fat phenotypes. The COL11A2 gene contributes to the correct assembly of the extracellular matrix in maintaining the healthy structural arrangement of various components, with the C1s gene part of complement systems functioning in inflammation. Moreover, FHL2 expression was upregulated under hypermethylation in both blood and adipose tissue with aging. These results suggest new targets for future studies and require further validation to confirm the specific function of these genes on body fat regulation.
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Affiliation(s)
- Thao Van Cao
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Heidi G. Sutherland
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Miles C. Benton
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Larisa M. Haupt
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
- ARC Training Centre for Cell and Tissue Engineering Technologies, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia
- Max Planck Queensland Centre for the Materials Sciences of Extracellular Matrices, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia
| | - Rodney A. Lea
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
| | - Lyn R. Griffiths
- Centre for Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Kelvin Grove, QLD 4059, Australia; (T.V.C.); (H.G.S.); (M.C.B.); (L.M.H.); (L.R.G.)
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3
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Ma J, Huang A, Yan K, Li Y, Sun X, Joehanes R, Huan T, Levy D, Liu C. Blood transcriptomic biomarkers of alcohol consumption and cardiovascular disease risk factors: the Framingham Heart Study. Hum Mol Genet 2023; 32:649-658. [PMID: 36130209 PMCID: PMC9896471 DOI: 10.1093/hmg/ddac237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 08/19/2022] [Accepted: 09/15/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The relations of alcohol consumption and gene expression remain to be elucidated. MATERIALS AND METHODS We examined cross-sectional associations between alcohol consumption and whole blood derived gene expression levels and between alcohol-associated genes and obesity, hypertension, and diabetes in 5531 Framingham Heart Study (FHS) participants. RESULTS We identified 25 alcohol-associated genes. We further showed cross-sectional associations of 16 alcohol-associated genes with obesity, nine genes with hypertension, and eight genes with diabetes at P < 0.002. For example, we observed decreased expression of PROK2 (β = -0.0018; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) and PAX5 (β = -0.0014; 95%CI: -0.0021, -0.0007; P = 6.5e - 5) per 1 g/day increase in alcohol consumption. Consistent with our previous observation on the inverse association of alcohol consumption with obesity and positive association of alcohol consumption with hypertension, we found that PROK2 was positively associated with obesity (OR = 1.42; 95%CI: 1.17, 1.72; P = 4.5e - 4) and PAX5 was negatively associated with hypertension (OR = 0.73; 95%CI: 0.59, 0.89; P = 1.6e - 3). We also observed that alcohol consumption was positively associated with expression of ABCA13 (β = 0.0012; 95%CI: 0.0007, 0.0017; P = 1.3e - 6) and ABCA13 was positively associated with diabetes (OR = 2.57; 95%CI: 1.73, 3.84; P = 3.5e - 06); this finding, however, was inconsistent with our observation of an inverse association between alcohol consumption and diabetes. CONCLUSIONS We showed strong cross-sectional associations between alcohol consumption and expression levels of 25 genes in FHS participants. Nonetheless, complex relationships exist between alcohol-associated genes and CVD risk factors.
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Affiliation(s)
- Jiantao Ma
- Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA 02111, USA
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Allen Huang
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02142, USA
| | - Kaiyu Yan
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Yi Li
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Xianbang Sun
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Tianxiao Huan
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Daniel Levy
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University, Boston, MA 02118, USA
- Boston University’s and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA 01702, USA
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Connell JR, Benton MC, Lea RA, Sutherland HG, Haupt LM, Wright KM, Griffiths LR. Evaluating the suitability of current mitochondrial DNA interpretation guidelines for multigenerational whole mitochondrial genome comparisons. J Forensic Sci 2022; 67:1766-1775. [PMID: 35855536 PMCID: PMC9543078 DOI: 10.1111/1556-4029.15097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/06/2022] [Accepted: 06/30/2022] [Indexed: 12/03/2022]
Abstract
Sanger sequencing of the mitochondrial DNA (mtDNA) control region was previously the only method available for forensic casework involving degraded samples from skeletal remains. The introduction of Next Generation Sequencing (NGS) has transformed genetic data generation and human identification using mtDNA. Whole mitochondrial genome (mtGenome) analysis is now being introduced into forensic laboratories around the world to analyze historical remains. Research into large pedigrees using the mtGenome is critical to evaluate currently available interpretation guidelines for mtDNA analysis, which were developed for comparisons using the control region. This study included mtGenomes from 225 individuals from the last four generations of the Norfolk Island (NI) genetic isolate pedigree consisting of 49 distinct maternal lineages. The data from these individuals were arranged into 2339 maternally related pairs separated by up to 18 meioses. Our results show that 97.3% of maternally related pairs were concordant at all nucleotide positions, resulting in the correct interpretation of “Cannot Exclude”; 2.7% of pairs produced an “Inconclusive” result, and there were no instances of false exclusion. While these results indicate that existing guidelines are suitable for multigenerational whole mtGenome analysis, we recommend caution be taken when classifying heteroplasmic changes as differences for human identification. Our data showed the classification of heteroplasmic changes as differences increases the prevalence of inconclusive identification by 6%, with false exclusions observed in 0.34% of pairs examined. Further studies of multigenerational pedigrees, however, are needed to validate mtGenome interpretation guidelines for historical case work to more fully utilize emerging advancements.
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Affiliation(s)
- Jasmine R Connell
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Kelvin Grove, Qld, Australia
| | - Miles C Benton
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Kelvin Grove, Qld, Australia.,Human Genomics, Kenepuru Science Centre, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Rodney A Lea
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Kelvin Grove, Qld, Australia
| | - Heidi G Sutherland
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Kelvin Grove, Qld, Australia
| | - Larisa M Haupt
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Kelvin Grove, Qld, Australia
| | - Kirsty M Wright
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Kelvin Grove, Qld, Australia.,Unrecovered War Casualties-Army, Australian Defence Force, Russell Offices, Russell, ACT, Australia.,Royal Australian Air Force (RAAF), Headquarters History and Heritage, Unrecovered War Casualties-Air Force, Russell, ACT, Australia
| | - Lyn R Griffiths
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Kelvin Grove, Qld, Australia
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5
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Pedigree derived mutation rate across the entire mitochondrial genome of the Norfolk Island population. Sci Rep 2022; 12:6827. [PMID: 35473946 PMCID: PMC9042960 DOI: 10.1038/s41598-022-10530-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/17/2022] [Indexed: 11/09/2022] Open
Abstract
Estimates of mutation rates for various regions of the human mitochondrial genome (mtGenome) vary widely, depending on whether they are inferred using a phylogenetic approach or obtained directly from pedigrees. Traditionally, only the control region, or small portions of the coding region have been targeted for analysis due to the cost and effort required to produce whole mtGenome Sanger profiles. Here, we report one of the first pedigree derived mutation rates for the entire human mtGenome. The entire mtGenome from 225 individuals originating from Norfolk Island was analysed to estimate the pedigree derived mutation rate and compared against published mutation rates. These individuals were from 45 maternal lineages spanning 345 generational events. Mutation rates for various portions of the mtGenome were calculated. Nine mutations (including two transitions and seven cases of heteroplasmy) were observed, resulting in a rate of 0.058 mutations/site/million years (95% CI 0.031-0.108). These mutation rates are approximately 16 times higher than estimates derived from phylogenetic analysis with heteroplasmy detected in 13 samples (n = 225, 5.8% individuals). Providing one of the first pedigree derived estimates for the entire mtGenome, this study provides a better understanding of human mtGenome evolution and has relevance to many research fields, including medicine, anthropology and forensics.
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Tran NK, Lea RA, Holland S, Nguyen Q, Raghubar AM, Sutherland HG, Benton MC, Haupt LM, Blackburn NB, Curran JE, Blangero J, Mallett AJ, Griffiths LR. Multi-phenotype genome-wide association studies of the Norfolk Island isolate implicate pleiotropic loci involved in chronic kidney disease. Sci Rep 2021; 11:19425. [PMID: 34593906 PMCID: PMC8484585 DOI: 10.1038/s41598-021-98935-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/14/2021] [Indexed: 11/14/2022] Open
Abstract
Chronic kidney disease (CKD) is a persistent impairment of kidney function. Genome-wide association studies (GWAS) have revealed multiple genetic loci associated with CKD susceptibility but the complete genetic basis is not yet clear. Since CKD shares risk factors with cardiovascular diseases and diabetes, there may be pleiotropic loci at play but may go undetected when using single phenotype GWAS. Here, we used multi-phenotype GWAS in the Norfolk Island isolate (n = 380) to identify new loci associated with CKD. We performed a principal components analysis on different combinations of 29 quantitative traits to extract principal components (PCs) representative of multiple correlated phenotypes. GWAS of a PC derived from glomerular filtration rate, serum creatinine, and serum urea identified a suggestive peak (pmin = 1.67 × 10-7) that mapped to KCNIP4. Inclusion of other secondary CKD measurements with these three kidney function traits identified the KCNIP4 locus with GWAS significance (pmin = 1.59 × 10-9). Finally, we identified a group of two SNPs with increased minor allele frequencies as potential functional variants. With the use of genetic isolate and the PCA-based multi-phenotype GWAS approach, we have revealed a potential pleotropic effect locus for CKD. Further studies are required to assess functional relevance of this locus.
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Affiliation(s)
- Ngan K Tran
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), 60 Musk Ave., Kelvin Grove, QLD, 4059, Australia
| | - Rodney A Lea
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), 60 Musk Ave., Kelvin Grove, QLD, 4059, Australia
| | - Samuel Holland
- Institute for Molecular Bioscience & Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience & Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Arti M Raghubar
- Institute for Molecular Bioscience & Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Heidi G Sutherland
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), 60 Musk Ave., Kelvin Grove, QLD, 4059, Australia
| | - Miles C Benton
- Institute of Environmental Science and Research, Kenepuru, New Zealand
| | - Larisa M Haupt
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), 60 Musk Ave., Kelvin Grove, QLD, 4059, Australia
| | - Nicholas B Blackburn
- School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
- Department of Human Genetics, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
- Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Joanne E Curran
- School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
- Department of Human Genetics, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - John Blangero
- School of Medicine, South Texas Diabetes and Obesity Institute, The University of Texas Rio Grande Valley, Brownsville, TX, USA
- Department of Human Genetics, School of Medicine, The University of Texas Rio Grande Valley, Brownsville, TX, USA
| | - Andrew J Mallett
- Institute for Molecular Bioscience & Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
- Department of Renal Medicine, Townsville University Hospital, Townsville, QLD, Australia
- College of Medicine & Dentistry, James Cook University, Townsville, QLD, Australia
| | - Lyn R Griffiths
- School of Biomedical Sciences, Centre for Genomics and Personalised Health, Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), 60 Musk Ave., Kelvin Grove, QLD, 4059, Australia.
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Identification of Distant Regulatory Elements Using Expression Quantitative Trait Loci Mapping for Heat-Responsive Genes in Oysters. Genes (Basel) 2021; 12:genes12071040. [PMID: 34356056 PMCID: PMC8303352 DOI: 10.3390/genes12071040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 06/27/2021] [Accepted: 06/28/2021] [Indexed: 11/17/2022] Open
Abstract
Many marine ectotherms, especially those inhabiting highly variable intertidal zones, develop high phenotypic plasticity in response to rapid climate change by modulating gene expression levels. Herein, we examined the regulatory architecture of heat-responsive gene expression plasticity in oysters using expression quantitative trait loci (eQTL) analysis. Using a backcross family of Crassostrea gigas and its sister species Crassostrea angulata under acute stress, 56 distant regulatory regions accounting for 6–26.6% of the gene expression variation were identified for 19 heat-responsive genes. In total, 831 genes and 164 single nucleotide polymorphisms (SNPs) that could potentially regulate expression of the target genes were screened in the eQTL region. The association between three SNPs and the corresponding target genes was verified in an independent family. Specifically, Marker13973 was identified for heat shock protein (HSP) family A member 9 (HspA9). Ribosomal protein L10a (RPL10A) was detected approximately 2 kb downstream of the distant regulatory SNP. Further, Marker14346-48 and Marker14346-85 were in complete linkage disequilibrium and identified for autophagy-related gene 7 (ATG7). Nuclear respiratory factor 1 (NRF1) was detected approximately 3 kb upstream of the two SNPs. These results suggested regulatory relationships between RPL10A and HSPA9 and between NRF1 and ATG7. Our findings indicate that distant regulatory mutations play an important role in the regulation of gene expression plasticity by altering upstream regulatory factors in response to heat stress. The identified eQTLs provide candidate biomarkers for predicting the persistence of oysters under future climate change scenarios.
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Cao VT, Lea RA, Sutherland HG, Benton MC, Pishva RS, Haupt LM, Griffiths LR. A genome-wide methylation study of body fat traits in the Norfolk Island isolate. Nutr Metab Cardiovasc Dis 2021; 31:1556-1563. [PMID: 33810959 DOI: 10.1016/j.numecd.2021.01.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 01/27/2021] [Accepted: 01/27/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND AND AIMS Natural variation in body fat is explained by both genetic and environmental effects. Epigenetic mechanisms such as DNA methylation can mediate these effects causing changes in gene expression leading to onset of obesity. Studies of genetic isolates have the potential to provide new epigenetic insights with advantages such as reduced genetic diversity and environmental exposures. METHODS AND RESULTS This was an exploratory study of genome-wide DNA methylation in relation to body fat traits in 47 healthy adults from the genetic isolate of Norfolk Island. Quantitative body fat traits (body fat percentage, body mass index, hip circumference, waist circumference, waist-hip-ratio and weight) were carefully measured. DNA methylation data was obtained from peripheral blood using Illumina 450K arrays. Multi-trait analysis was performed using Principal Component Analysis (PCA). CpG by trait association testing was performed using stepwise linear regressions. Two components were identified that explained approximately 89% of the phenotypic variance. In total, 5 differential methylated positions (DMPs) were identified at genome-wide significance (P≤ 2.4 × 10-7), which mapped to GOT2-CDH8, LYSMD3, HIBADH, ADGRD1 and EBF4 genes. Gene set enrichment analysis of 848 genes containing suggestive DMPs (P≤ 1.0 × 10-4) implicated the Cadherin (28 genes, Padj = 6.76 × 10-7) and Wnt signaling pathways (38 genes, Padj = 7.78 × 10-6). CONCLUSION This study provides new insights into the epigenetically influenced genes and pathways underlying body fat variation in a healthy cohort and provides targets for consideration in future studies of obesity risk.
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Affiliation(s)
- Van T Cao
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, Queensland 4059, Australia.
| | - Rodney A Lea
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, Queensland 4059, Australia.
| | - Heidi G Sutherland
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, Queensland 4059, Australia.
| | - Miles C Benton
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, Queensland 4059, Australia; Human Genomics, Institute of Environmental Science and Research, Kenepuru, Wellington, New Zealand.
| | - Reza S Pishva
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, Queensland 4059, Australia.
| | - Larisa M Haupt
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, Queensland 4059, Australia.
| | - Lyn R Griffiths
- Queensland University of Technology (QUT), Centre for Genomics and Personalised Health, Genomics Research Centre, School of Biomedical Sciences, Institute of Health and Biomedical Innovation, 60 Musk Ave., Kelvin Grove, Queensland 4059, Australia.
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Ostinelli G, Vijay J, Vohl MC, Grundberg E, Tchernof A. AKR1C2 and AKR1C3 expression in adipose tissue: Association with body fat distribution and regulatory variants. Mol Cell Endocrinol 2021; 527:111220. [PMID: 33675863 PMCID: PMC8052191 DOI: 10.1016/j.mce.2021.111220] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 02/15/2021] [Accepted: 02/17/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Changes in androgen dynamics within adipose tissue have been proposed as modulators of body fat accumulation. In this context, AKR1C2 likely plays a significant role by inactivating 5α-dihydrotestosterone. AIM To characterize AKR1C2 expression patterns across adipose depots and cell populations and to provide insight into the link with body fat distribution and genetic regulation. METHODS We used RNA sequencing data from severely obese patients to assess patterns of AKR1C2 and AKR1C3 expression in abdominal adipose tissue depots and cell fractions. We additionally used data from 856 women to assess AKR1C2 heritability and to link its expression in adipose tissue with body fat distribution. Further, we used public resources to study AKR1C2 genetic regulation as well as reference epigenome data for regulatory element profiling and functional interpretation of genetic data. RESULTS We found that mature adipocytes and adipocyte-committed adipocyte progenitor cells (APCs) had enriched expression of AKR1C2. We found adipose tissue AKR1C2 and AKR1C3 expression to be significantly and positively associated with percentage trunk fat mass in women. We identified strong genetic regulation of AKR1C2 by rs28571848 and rs34477787 located on the binding sites of two nuclear transcription factors, namely retinoid acid-related orphan receptor alpha and the glucocorticoid receptor. CONCLUSION We confirm the link between AKR1C2, adipogenic differentiation and adipose tissue distribution. We provide insight into genetic regulation of AKR1C2 by identifying regulatory variants mapping to binding sites for the glucocorticoid receptor and retinoid acid-related orphan receptor alpha which may in part mediate the effect of AKR1C2 expression on body fat distribution.
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Affiliation(s)
- Giada Ostinelli
- Centre de Recherche de l'Institut Universitaire de Cardiologie et Pneumologie de Québec-Université Laval, 2725 Chemin Sainte-Foy, G1V 4G5, Québec City, Québec, Canada; École de Nutrition, Université Laval, 2425 Rue de l'Agriculture, G1V 0A6, Québec City, Québec, Canada
| | - Jinchu Vijay
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada
| | - Marie-Claude Vohl
- École de Nutrition, Université Laval, 2425 Rue de l'Agriculture, G1V 0A6, Québec City, Québec, Canada; Centre Nutrition, Santé et Societé (NUTRISS)-Insitut sur la Nutrition et les Aliments Fonctionnells (INAF), Université Laval, Québec City, Québec, Canada
| | - Elin Grundberg
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada; Children's Mercy Research Institute, Children's Mercy Kansas City, Kansas City, MO, USA.
| | - Andre Tchernof
- Centre de Recherche de l'Institut Universitaire de Cardiologie et Pneumologie de Québec-Université Laval, 2725 Chemin Sainte-Foy, G1V 4G5, Québec City, Québec, Canada; École de Nutrition, Université Laval, 2425 Rue de l'Agriculture, G1V 0A6, Québec City, Québec, Canada.
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10
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Alderawi A, Caramori G, Baker EH, Hitchings AW, Rahman I, Rossios C, Adcock I, Cassolari P, Papi A, Ortega VE, Curtis JL, Dunmore S, Kirkham P. FN3K expression in COPD: a potential comorbidity factor for cardiovascular disease. BMJ Open Respir Res 2020; 7:e000714. [PMID: 33208304 PMCID: PMC7677354 DOI: 10.1136/bmjresp-2020-000714] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 02/07/2023] Open
Abstract
INTRODUCTION Cigarette smoking and oxidative stress are common risk factors for the multi-morbidities associated with chronic obstructive pulmonary disease (COPD). Elevated levels of advanced glycation endproducts (AGE) increase the risk of cardiovascular disease (CVD) comorbidity and mortality. The enzyme fructosamine-3-kinase (FN3K) reduces this risk by lowering AGE levels. METHODS The distribution and expression of FN3K protein in lung tissues from stable COPD and control subjects, as well as an animal model of COPD, was assessed by immunohistochemistry. Serum FN3K protein and AGE levels were assessed by ELISA in patients with COPD exacerbations receiving metformin. Genetic variants within the FN3K and FN3K-RP genes were evaluated for associations with cardiorespiratory function in the Subpopulations and Intermediate Outcome Measures in COPD Study cohort. RESULTS This pilot study demonstrates that FN3K expression in the blood and human lung epithelium is distributed at either high or low levels irrespective of disease status. The percentage of lung epithelial cells expressing FN3K was higher in control smokers with normal lung function, but this induction was not observed in COPD patients nor in a smoking model of COPD. The top five nominal FN3K polymorphisms with possible association to decreased cardiorespiratory function (p<0.008-0.02), all failed to reach the threshold (p<0.0028) to be considered highly significant following multi-comparison analysis. Metformin enhanced systemic levels of FN3K in COPD subjects independent of their high-expression or low-expression status. DISCUSSION The data highlight that low and high FN3K expressors exist within our study cohort and metformin induces FN3K levels, highlighting a potential mechanism to reduce the risk of CVD comorbidity and mortality.
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Affiliation(s)
- Amr Alderawi
- Department of Biomedical Sciences and Physiology, University of Wolverhampton, Wolverhampton, UK
| | - Gaetano Caramori
- Pneumologia, Dipartimento di Scienze Biomediche, Odontoiatriche e delle Immagini Morfologiche e Funzionali (BIOMORF), Università di Messina, Messina, Italy
| | - Emma H Baker
- Basic Medical Sciences, St Georges, University of London, London, UK
| | | | - Irfan Rahman
- Environmental Medicine, University of Rochester Medical Center, Rochester, New York, USA
| | - Christos Rossios
- Airways Diseases Section, Faculty of Medicine, Imperial College London, National Heart and Lung Institute, London, UK
| | - Ian Adcock
- Airways Diseases Section, Faculty of Medicine, Imperial College London, National Heart and Lung Institute, London, UK
| | - Paolo Cassolari
- Clinical and Experimental Medicine, Research Centre on Asthma and COPD, University of Ferrara, Ferrara, Italy
| | - Alberto Papi
- Clinical and Experimental Medicine, Research Centre on Asthma and COPD, University of Ferrara, Ferrara, Italy
| | - Victor E Ortega
- Internal Medicine, Wake Forest Health Sciences, Winston-Salem, North Carolina, USA
| | - Jeffrey L Curtis
- Department of Internal Medicine, University of Michigan Health System, Ann Arbor, Michigan, USA
| | - Simon Dunmore
- Department of Biomedical Sciences and Physiology, University of Wolverhampton, Wolverhampton, UK
| | - Paul Kirkham
- Department of Biomedical Sciences and Physiology, University of Wolverhampton, Wolverhampton, UK
- Airways Diseases Section, Faculty of Medicine, Imperial College London, National Heart and Lung Institute, London, UK
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11
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Transcriptome-wide Profiling of Cerebral Cavernous Malformations Patients Reveal Important Long noncoding RNA molecular signatures. Sci Rep 2019; 9:18203. [PMID: 31796831 PMCID: PMC6890746 DOI: 10.1038/s41598-019-54845-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 11/20/2019] [Indexed: 12/13/2022] Open
Abstract
Cerebral cavernous malformations (CCMs) are low-flow vascular malformations in the brain associated with recurrent hemorrhage and seizures. The current treatment of CCMs relies solely on surgical intervention. Henceforth, alternative non-invasive therapies are urgently needed to help prevent subsequent hemorrhagic episodes. Long non-coding RNAs (lncRNAs) belong to the class of non-coding RNAs and are known to regulate gene transcription and involved in chromatin remodeling via various mechanism. Despite accumulating evidence demonstrating the role of lncRNAs in cerebrovascular disorders, their identification in CCMs pathology remains unknown. The objective of the current study was to identify lncRNAs associated with CCMs pathogenesis using patient cohorts having 10 CCM patients and 4 controls from brain. Executing next generation sequencing, we performed whole transcriptome sequencing (RNA-seq) analysis and identified 1,967 lncRNAs and 4,928 protein coding genes (PCGs) to be differentially expressed in CCMs patients. Among these, we selected top 6 differentially expressed lncRNAs each having significant correlative expression with more than 100 differentially expressed PCGs. The differential expression status of the top lncRNAs, SMIM25 and LBX2-AS1 in CCMs was further confirmed by qRT-PCR analysis. Additionally, gene set enrichment analysis of correlated PCGs revealed critical pathways related to vascular signaling and important biological processes relevant to CCMs pathophysiology. Here, by transcriptome-wide approach we demonstrate that lncRNAs are prevalent in CCMs disease and are likely to play critical roles in regulating important signaling pathways involved in the disease progression. We believe, that detailed future investigations on this set of identified lncRNAs can provide useful insights into the biology and, ultimately, contribute in preventing this debilitating disease.
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12
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Ion torrent high throughput mitochondrial genome sequencing (HTMGS). PLoS One 2019; 14:e0224847. [PMID: 31730669 PMCID: PMC6857855 DOI: 10.1371/journal.pone.0224847] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 10/22/2019] [Indexed: 12/13/2022] Open
Abstract
The implementation and popularity of next generation sequencing (NGS) has led to the development of various rapid whole mitochondrial genome sequencing techniques. We summarise an efficient and cost-effective NGS approach for mitochondrial genomic DNA in humans using the Ion Torrent platform, and further discuss our bioinformatics pipeline for streamlined variant calling. Ion 316 chips were utilised with the Ion Torrent semi-conductor platform Personal Genome Machine (PGM) to perform tandem sequencing of mitochondrial genomes from the core pedigree (n = 315) of the Norfolk Island Health Study. Key improvements from commercial methods focus on the initial PCR step, which currently requires extensive optimisation to ensure the accurate and reproducible elongation of each section of the complete mitochondrial genome. Dual-platform barcodes were incorporated into our protocol thereby extending its potential application onto Illumina-based systems. Our bioinformatics pipeline consists of a modified version of GATK best practices tailored for mitochondrial genomic data. When compared with current commercial methods, our method, termed high throughput mitochondrial genome sequencing (HTMGS), allows high multiplexing of samples and the use of alternate library preparation reagents at a lower cost per sample (~1.7 times) when compared to current commercial methodologies. Our HTMGS methodology also provides robust mitochondrial sequencing data (>450X average coverage) that can be applied and modified to suit various study designs. On average, we were able to identify ~30 variants per sample with 572 variants observed across 315 samples. We have developed a high throughput sequencing and analysis method targeting complete mitochondrial genomes; with the potential to be platform agnostic with analysis options that adhere to current best practices.
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13
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Benton MC, Lea RA, Macartney-Coxson D, Sutherland HG, White N, Kennedy D, Mengersen K, Haupt LM, Griffiths LR. Genome-wide allele-specific methylation is enriched at gene regulatory regions in a multi-generation pedigree from the Norfolk Island isolate. Epigenetics Chromatin 2019; 12:60. [PMID: 31594537 PMCID: PMC6781349 DOI: 10.1186/s13072-019-0304-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 09/12/2019] [Indexed: 02/08/2023] Open
Abstract
Background Allele-specific methylation (ASM) occurs when DNA methylation patterns exhibit asymmetry among alleles. ASM occurs at imprinted loci, but its presence elsewhere across the human genome is indicative of wider importance in terms of gene regulation and disease risk. Here, we studied ASM by focusing on blood-based DNA collected from 24 subjects comprising a 3-generation pedigree from the Norfolk Island genetic isolate. We applied a genome-wide bisulphite sequencing approach with a genotype-independent ASM calling method to map ASM across the genome. Regions of ASM were then tested for enrichment at gene regulatory regions using Genomic Association Test (GAT) tool. Results In total, we identified 1.12 M CpGs of which 147,170 (13%) exhibited ASM (P ≤ 0.05). When including contiguous ASM signal spanning ≥ 2 CpGs, this condensed to 12,761 ASM regions (AMRs). These AMRs tagged 79% of known imprinting regions and most (98.1%) co-localised with known single nucleotide variants. Notably, miRNA and lncRNA showed a 3.3- and 1.8-fold enrichment of AMRs, respectively (P < 0.005). Also, the 5′ UTR and start codons each showed a 3.5-fold enrichment of AMRs (P < 0.005). There was also enrichment of AMRs observed at subtelomeric regions of many chromosomes. Five out of 11 large AMRs localised to the protocadherin cluster on chromosome 5. Conclusions This study shows ASM extends far beyond genomic imprinting in humans and that gene regulatory regions are hotspots for ASM. Future studies of ASM in pedigrees should help to clarify transgenerational inheritance patterns in relation to genotype and disease phenotypes.
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Affiliation(s)
- Miles C Benton
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.,Human Genomics, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Rodney A Lea
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
| | - Donia Macartney-Coxson
- Human Genomics, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Heidi G Sutherland
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Nicole White
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Daniel Kennedy
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.,ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Kerry Mengersen
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Larisa M Haupt
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Lyn R Griffiths
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
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Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2019; 24:296-307. [PMID: 30864331 PMCID: PMC6417797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Transcriptome-wide association studies (TWAS) have recently gained great attention due to their ability to prioritize complex trait-associated genes and promote potential therapeutics development for complex human diseases. TWAS integrates genotypic data with expression quantitative trait loci (eQTLs) to predict genetically regulated gene expression components and associates predictions with a trait of interest. As such, TWAS can prioritize genes whose differential expressions contribute to the trait of interest and provide mechanistic explanation of complex trait(s). Tissue-specific eQTL information grants TWAS the ability to perform association analysis on tissues whose gene expression profiles are otherwise hard to obtain, such as liver and heart. However, as eQTLs are tissue context-dependent, whether and how the tissue-specificity of eQTLs influences TWAS gene prioritization has not been fully investigated. In this study, we addressed this question by adopting two distinct TWAS methods, PrediXcan and UTMOST, which assume single tissue and integrative tissue effects of eQTLs, respectively. Thirty-eight baseline laboratory traits in 4,360 antiretroviral treatment-naïve individuals from the AIDS Clinical Trials Group (ACTG) studies comprised the input dataset for TWAS. We performed TWAS in a tissue-specific manner and obtained a total of 430 significant gene-trait associations (q-value < 0.05) across multiple tissues. Single tissue-based analysis by PrediXcan contributed 116 of the 430 associations including 64 unique gene-trait pairs in 28 tissues. Integrative tissue-based analysis by UTMOST found the other 314 significant associations that include 50 unique gene-trait pairs across all 44 tissues. Both analyses were able to replicate some associations identified in past variant-based genome-wide association studies (GWAS), such as high-density lipoprotein (HDL) and CETP (PrediXcan, q-value = 3.2e-16). Both analyses also identified novel associations. Moreover, single tissue-based and integrative tissuebased analysis shared 11 of 103 unique gene-trait pairs, for example, PSRC1-low-density lipoprotein (PrediXcan's lowest q-value = 8.5e-06; UTMOST's lowest q-value = 1.8e-05). This study suggests that single tissue-based analysis may have performed better at discovering gene-trait associations when combining results from all tissues. Integrative tissue-based analysis was better at prioritizing genes in multiple tissues and in trait-related tissue. Additional exploration is needed to confirm this conclusion. Finally, although single tissue-based and integrative tissue-based analysis shared significant novel discoveries, tissue context-dependency of eQTLs impacted TWAS gene prioritization. This study provides preliminary data to support continued work on tissue contextdependency of eQTL studies and TWAS.
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Expression QTL analysis of glaucoma endophenotypes in the Norfolk Island isolate provides evidence that immune-related genes are associated with optic disc size. J Hum Genet 2017; 63:83-87. [PMID: 29215094 DOI: 10.1038/s10038-017-0374-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 09/25/2017] [Accepted: 09/25/2017] [Indexed: 11/08/2022]
Abstract
Primary open-angle glaucoma (POAG) is influenced by both genetic and environmental factors. Despite significant progress in identifying genetic variants associated with POAG, there remains a substantial amount of unexplained heritability. Study design features that may enhance knowledge of the genetic architecture include focusing on multiple quantitative traits related to ocular disorders (i.e. endophenotypes), targeting genetic variants that directly influence gene expression (i.e. cis-eQTLs) and utilising genetically isolated populations to reduce genetic and environmental noise and thus enhance association signals. In this study we performed heritability and blood-based eQTL association analysis of five key POAG endophenotypes in 330 individuals from the Norfolk Island (NI) isolate. Results showed evidence of heritability for all five traits, with H2 estimates ranging from 0.35 for intraocular pressure (IOP) to 0.82 for central corneal thickness (CCT) (P < 0.05). The primary finding was for BTN3A2, whereby both cis-SNP and transcript were significantly associated with disc size within a conditional regression model. Specifically, this model included rs853676 (β = 0.23,P = 0.008) and transcript (β = 0.23, P = 0.03). We also observed a cis-SNP association between optic disc size and LPCAT2 independent of transcript (P = 0.0004). These genes have specific functions in immune system pathways and suggest a role for an inherited immune component of POAG risk. This study also demonstrates an alternate approach to understanding the functional genetic basis of POAG and ocular health more generally.
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Matovinovic E, Kho PF, Lea RA, Benton MC, Eccles DA, Haupt LM, Hewitt AW, Sherwin JC, Mackey DA, Griffiths LR. Genome-wide linkage and association analysis of primary open-angle glaucoma endophenotypes in the Norfolk Island isolate. Mol Vis 2017; 23:660-665. [PMID: 28966548 PMCID: PMC5620381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Accepted: 09/26/2017] [Indexed: 12/04/2022] Open
Abstract
PURPOSE Primary open-angle glaucoma (POAG) refers to a group of heterogeneous diseases involving optic nerve damage. Two well-established risk factors for POAG are elevated intraocular pressure (IOP) and a thinner central corneal thickness (CCT). These endophenotypes exhibit a high degree of heritability across populations. Large-scale genome-wide association studies (GWASs) of outbred populations have robustly implicated several susceptibility gene variants for both IOP and CCT. Despite this progress, a substantial amount of genetic variance remains unexplained. Population-specific variants that might be rare in outbred populations may also influence POAG endophenotypes. The Norfolk Island population is a founder-effect genetic isolate that has been well characterized for POAG endophenotypes. This population is therefore a suitable candidate for mapping new variants that influence these complex traits. METHODS Three hundred and thirty participants from the Norfolk Island Eye Study (NIES) core pedigree provided DNA. Ocular measurements of CCT and IOP were also taken for analysis. Heritability analyses and genome-wide linkage analyses of short tandem repeats (STRs) were conducted using SOLAR. Pedigree-based GWASs of single-nucleotide polymorphisms (SNPs) were performed using the GenABEL software. RESULTS CCT was the most heritable endophenotype in this cohort (h2 = 0.77, p = 6×10-6), while IOP showed a heritability of 0.39 (p = 0.008). A genome-wide linkage analysis of these POAG phenotypes identified a maximum logarithm of the odds (LOD) score of 1.9 for CCT on chromosome 20 (p = 0.0016) and 1.3 for IOP on chromosome 15 (p = 0.0072). The GWAS results revealed a study-wise significant association for IOP at rs790357, which is located within DLG2 on chr11q14.1 (p = 1.02×10-7). DLG2 is involved in neuronal signaling and development, and while it has not previously been associated with IOP, it has been associated with myopia. An analysis of 12 known SNPs for IOP showed that rs12419342 in RAPSN on chromosome 11 was nominally associated in Norfolk Island (NI; p = 0.0021). For CCT, an analysis of 26 known SNPs showed rs9938149 in BANP-ZNF469 on chromosome 16 was nominally associated in NI (p = 0.002). CONCLUSIONS These study results indicate that CCT and IOP exhibit a substantial degree of heritability in the NI pedigree, indicating a genetic component. A genome-wide linkage analysis of POAG endophenotypes did not reveal any major effect loci, but the GWASs did implicate several known loci, as well as a potential new locus in DLG2, suggesting a role for neuronal signaling in development in IOP and perhaps POAG. These results also highlight the need to target rarer variants via whole genome sequencing in this genetic isolate.
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Affiliation(s)
- Elizabeth Matovinovic
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Pik Fang Kho
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Rodney A. Lea
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Miles C. Benton
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - David A. Eccles
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Larisa M. Haupt
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Alex W. Hewitt
- Centre for Eye Research Australia & Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia,Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, Australia
| | - Justin C. Sherwin
- Centre for Eye Research Australia & Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia
| | - David A. Mackey
- Menzies Institute for Medical Research, School of Medicine, University of Tasmania, Hobart, Australia,Lions Eye Institute, Centre for Ophthalmology and Visual Science, University of Western Australia, Perth, Australia
| | - Lyn R. Griffiths
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Australia
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17
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Benton MC, Sutherland HG, Macartney-Coxson D, Haupt LM, Lea RA, Griffiths LR. Methylome-wide association study of whole blood DNA in the Norfolk Island isolate identifies robust loci associated with age. Aging (Albany NY) 2017; 9:753-768. [PMID: 28255110 PMCID: PMC5391229 DOI: 10.18632/aging.101187] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 02/28/2017] [Indexed: 01/07/2023]
Abstract
Epigenetic regulation of various genomic functions, including gene expression, provide mechanisms whereby an organism can dynamically respond to changes in its environment and modify gene expression accordingly. One epigenetic mechanism implicated in human aging and age-related disorders is DNA methylation. Isolated populations such as Norfolk Island (NI) should be advantageous for the identification of epigenetic factors related to aging due to reduced genetic and environmental variation. Here we conducted a methylome-wide association study of age using whole blood DNA in 24 healthy female individuals from the NI genetic isolate (aged 24-47 years). We analysed 450K methylation array data using a machine learning approach (GLMnet) to identify age-associated CpGs. We identified 497 CpG sites, mapping to 422 genes, associated with age, with 11 sites previously associated with age. The strongest associations identified were for a single CpG site in MYOF and an extended region within the promoter of DDO. These hits were validated in curated public data from 2316 blood samples (MARMAL-AID). This study is the first to report robust age associations for MYOF and DDO, both of which have plausible functional roles in aging. This study also illustrates the value of genetic isolates to reveal new associations with epigenome-level data.
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Affiliation(s)
- Miles C Benton
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, 4059, Australia
| | - Heidi G Sutherland
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, 4059, Australia
| | - Donia Macartney-Coxson
- Kenepuru Science Centre, Institute of Environmental Science and Research, Wellington 5240, New Zealand
| | - Larisa M Haupt
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, 4059, Australia
| | - Rodney A Lea
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, 4059, Australia
| | - Lyn R Griffiths
- Genomics Research Centre, Institute of Health and Biomedical Innovation, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, Queensland, 4059, Australia
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Joshi AD, Andersson C, Buch S, Stender S, Noordam R, Weng LC, Weeke PE, Auer PL, Boehm B, Chen C, Choi H, Curhan G, Denny JC, De Vivo I, Eicher JD, Ellinghaus D, Folsom AR, Fuchs C, Gala M, Haessler J, Hofman A, Hu F, Hunter DJ, Janssen HL, Kang JH, Kooperberg C, Kraft P, Kratzer W, Lieb W, Lutsey PL, Murad SD, Nordestgaard BG, Pasquale LR, Reiner AP, Ridker PM, Rimm E, Rose LM, Shaffer CM, Schafmayer C, Tamimi RM, Uitterlinden AG, Völker U, Völzke H, Wakabayashi Y, Wiggs JL, Zhu J, Roden DM, Stricker BH, Tang W, Teumer A, Hampe J, Tybjærg-Hansen A, Chasman DI, Chan AT, Johnson AD. Four Susceptibility Loci for Gallstone Disease Identified in a Meta-analysis of Genome-Wide Association Studies. Gastroenterology 2016; 151:351-363.e28. [PMID: 27094239 PMCID: PMC4959966 DOI: 10.1053/j.gastro.2016.04.007] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Revised: 04/06/2016] [Accepted: 04/07/2016] [Indexed: 01/01/2023]
Abstract
BACKGROUND & AIMS A genome-wide association study (GWAS) of 280 cases identified the hepatic cholesterol transporter ABCG8 as a locus associated with risk for gallstone disease, but findings have not been reported from any other GWAS of this phenotype. We performed a large-scale, meta-analysis of GWASs of individuals of European ancestry with available prior genotype data, to identify additional genetic risk factors for gallstone disease. METHODS We obtained per-allele odds ratio (OR) and standard error estimates using age- and sex-adjusted logistic regression models within each of the 10 discovery studies (8720 cases and 55,152 controls). We performed an inverse variance weighted, fixed-effects meta-analysis of study-specific estimates to identify single-nucleotide polymorphisms that were associated independently with gallstone disease. Associations were replicated in 6489 cases and 62,797 controls. RESULTS We observed independent associations for 2 single-nucleotide polymorphisms at the ABCG8 locus: rs11887534 (OR, 1.69; 95% confidence interval [CI], 1.54-1.86; P = 2.44 × 10(-60)) and rs4245791 (OR, 1.27; P = 1.90 × 10(-34)). We also identified and/or replicated associations for rs9843304 in TM4SF4 (OR, 1.12; 95% CI, 1.08-1.16; P = 6.09 × 10(-11)), rs2547231 in SULT2A1 (encodes a sulfoconjugation enzyme that acts on hydroxysteroids and cholesterol-derived sterol bile acids) (OR, 1.17; 95% CI, 1.12-1.21; P = 2.24 × 10(-10)), rs1260326 in glucokinase regulatory protein (OR, 1.12; 95% CI, 1.07-1.17; P = 2.55 × 10(-10)), and rs6471717 near CYP7A1 (encodes an enzyme that catalyzes conversion of cholesterol to primary bile acids) (OR, 1.11; 95% CI, 1.08-1.15; P = 8.84 × 10(-9)). Among individuals of African American and Hispanic American ancestry, rs11887534 and rs4245791 were associated positively with gallstone disease risk, whereas the association for the rs1260326 variant was inverse. CONCLUSIONS In this large-scale GWAS of gallstone disease, we identified 4 loci in genes that have putative functions in cholesterol metabolism and transport, and sulfonylation of bile acids or hydroxysteroids.
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Affiliation(s)
- Amit D. Joshi
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA,Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital Boston, MA,To whom correspondence should be addressed: Amit D. Joshi, MBBS, PhD, Clinical and Translational Epidemiology Unit, Division of Gastroenterology, Massachusetts General Hospital, 55 Fruit Street, Boston, Massachusetts 02114, USA. Tel: +1 617 724 7558; Charlotte Andersson, MD, PhD, The Framingham Heart Study, 73 Mt Wayte Avenue, Framingham, Massachusetts 01702, USA. , Andrew T. Chan, MD, MPH, Massachusetts General Hospital and Harvard Medical School, Clinical and Translational Epidemiology Unit, Division of Gastroenterology, GRJ-825C, Boston, Massachusetts 02114, USA. Tel:+1 617 724 0283; Fax: +1 617 726 3673; , Andrew D. Johnson, PhD, Division of Intramural Research, National Heart, Lung and Blood Institute, Cardiovascular Epidemiology and Human Genomics Branch, The Framingham Heart Study, 73 Mt. Wayte Ave., Suite #2, Framingham, MA, 01702, USA. Tel: +1 508 663 4082; Fax: +1 508 626 1262;
| | - Charlotte Andersson
- The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts.
| | - Stephan Buch
- Medical Department 1, University Hospital Dresden, TU Dresden, Dresden Germany
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark
| | - Raymond Noordam
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands,Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Lu-Chen Weng
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN
| | - Peter E. Weeke
- Department of Medicine, Vanderbilt University, Nashville, TN,Department of Cardiology, The Heart Centre, Rigshospitalet, Copenhagen University Hospital, Denmark
| | - Paul L. Auer
- Joseph J. Zilber School of Public Health, University of Wisconsin, Milwaukee,Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Bernhard Boehm
- Department of Internal Medicine I, Ulm University Hospital, Ulm, Germany
| | - Constance Chen
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA
| | - Hyon Choi
- Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston, MA
| | - Gary Curhan
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA,Renal Division, Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Joshua C. Denny
- Department of Medicine, Vanderbilt University, Nashville, TN,Department of Biomedical Informatics, Vanderbilt University, Nashville, TN
| | - Immaculata De Vivo
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA,Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - John D. Eicher
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA,Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA
| | - David Ellinghaus
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Aaron R. Folsom
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN
| | - Charles Fuchs
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA,Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA
| | - Manish Gala
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Frank Hu
- Department of Epidemiology, Harvard School of Public Health, Boston, MA,Department of Nutrition, Harvard School of Public Health, Boston, MA
| | - David J. Hunter
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA,Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - Harry L.A. Janssen
- Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, the Netherlands,Toronto Centre for Liver Disease, Toronto Western and General Hospital, University Health Network, Toronto, Canada
| | - Jae H. Kang
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Peter Kraft
- Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA,Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - Wolfgang Kratzer
- Department of Internal Medicine I, Ulm University Hospital, Ulm, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Christian Albrechts Universität Kiel, Niemannsweg 11, Kiel, Germany
| | - Pamela L. Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN
| | - Sarwa Darwish Murad
- Department of Gastroenterology and Hepatology, Erasmus MC, Rotterdam, the Netherlands
| | - Børge G. Nordestgaard
- The Copenhagen General Population Study and,Department of Clinical Biochemistry, Herlev Hospital, Herlev Denmark,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Louis R. Pasquale
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA,Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA
| | - Alex P. Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Paul M Ridker
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Eric Rimm
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA,Department of Epidemiology, Harvard School of Public Health, Boston, MA,Department of Nutrition, Harvard School of Public Health, Boston, MA
| | - Lynda M. Rose
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | | | - Clemens Schafmayer
- Department of General, Abdominal, Thoracic and Transplantation Surgery, University of Kiel, Kiel, Germany
| | - Rulla M. Tamimi
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA,Department of Epidemiology, Harvard School of Public Health, Boston, MA
| | - André G Uitterlinden
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands,Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Germany
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany,German Center for Cardiovascular Research, Partner Site Greifswald,German Center for Diabetes Research, Site Greifswald
| | - Yoshiyuki Wakabayashi
- The National Heart, Lung, and Blood Institute, DNA Sequencing Core Laboratory, Bethesda, MD
| | - Janey L. Wiggs
- Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA
| | - Jun Zhu
- The National Heart, Lung, and Blood Institute, DNA Sequencing Core Laboratory, Bethesda, MD
| | - Dan M. Roden
- Department of Medicine, Vanderbilt University, Nashville, TN
| | - Bruno H. Stricker
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands,Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, MN
| | - Alexander Teumer
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Jochen Hampe
- Medical Department 1, University Hospital Dresden, TU Dresden, Dresden Germany
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen, Denmark,Department of Clinical Biochemistry, Herlev Hospital, Herlev Denmark
| | - Daniel I. Chasman
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Andrew T. Chan
- Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA,Clinical and Translational Epidemiology Unit, Massachusetts General Hospital Boston, MA,Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA,To whom correspondence should be addressed: Amit D. Joshi, MBBS, PhD, Clinical and Translational Epidemiology Unit, Division of Gastroenterology, Massachusetts General Hospital, 55 Fruit Street, Boston, Massachusetts 02114, USA. Tel: +1 617 724 7558; Charlotte Andersson, MD, PhD, The Framingham Heart Study, 73 Mt Wayte Avenue, Framingham, Massachusetts 01702, USA. , Andrew T. Chan, MD, MPH, Massachusetts General Hospital and Harvard Medical School, Clinical and Translational Epidemiology Unit, Division of Gastroenterology, GRJ-825C, Boston, Massachusetts 02114, USA. Tel:+1 617 724 0283; Fax: +1 617 726 3673; , Andrew D. Johnson, PhD, Division of Intramural Research, National Heart, Lung and Blood Institute, Cardiovascular Epidemiology and Human Genomics Branch, The Framingham Heart Study, 73 Mt. Wayte Ave., Suite #2, Framingham, MA, 01702, USA. Tel: +1 508 663 4082; Fax: +1 508 626 1262;
| | - Andrew D. Johnson
- The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA,Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Framingham, MA,To whom correspondence should be addressed: Amit D. Joshi, MBBS, PhD, Clinical and Translational Epidemiology Unit, Division of Gastroenterology, Massachusetts General Hospital, 55 Fruit Street, Boston, Massachusetts 02114, USA. Tel: +1 617 724 7558; Charlotte Andersson, MD, PhD, The Framingham Heart Study, 73 Mt Wayte Avenue, Framingham, Massachusetts 01702, USA. , Andrew T. Chan, MD, MPH, Massachusetts General Hospital and Harvard Medical School, Clinical and Translational Epidemiology Unit, Division of Gastroenterology, GRJ-825C, Boston, Massachusetts 02114, USA. Tel:+1 617 724 0283; Fax: +1 617 726 3673; , Andrew D. Johnson, PhD, Division of Intramural Research, National Heart, Lung and Blood Institute, Cardiovascular Epidemiology and Human Genomics Branch, The Framingham Heart Study, 73 Mt. Wayte Ave., Suite #2, Framingham, MA, 01702, USA. Tel: +1 508 663 4082; Fax: +1 508 626 1262;
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Abstract
PURPOSE OF REVIEW The review highlights recent advances in our understanding of the interactions between genetic polymorphisms in genes that metabolize choline and the dietary requirements of choline and how these interactions relate to human health and disease. RECENT FINDINGS The importance of choline as an essential nutrient has been well established, but our appreciation of the interaction between our underlying genetic architecture and dietary choline requirements is only beginning. It has been shown in both human and animal studies that choline deficiencies contribute to diseases such as nonalcoholic fatty liver disease and various neurodegenerative diseases. An adequate supply of dietary choline is important for optimum development, highlighted by the increased maternal requirements during fetal development and in breast-fed infants. We discuss recent studies investigating variants in PEMT and MTHFR1 that are associated with a variety of birth defects. In addition to genetic interactions, we discuss several recent studies that uncover changes in fetal global methylation patterns in response to maternal dietary choline intake that result in changes in gene expression in the offspring. In contrast to the developmental role of adequate choline, there is now an appreciation of the role choline has in cardiovascular disease through the gut microbiota-mediated metabolite trimethylamine N-oxide. This pathway highlights some of our understanding of how the microbiome affects nutrient processing and bioavailability. Finally, to better characterize the genetic architecture regulating choline requirements, we discuss recent results focused on identifying polymorphisms that regulate choline and its derivative products. SUMMARY Here we discuss recent studies that have advanced our understanding of how specific alleles in key choline metabolism genes are related to dietary choline requirements and human disease.
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Affiliation(s)
- Tangi Smallwood
- Department of Genetics, University of North Carolina Chapel Hill, North Carolina 27599
| | - Hooman Allayee
- Department of Preventive Medicine and Institute for Genetic Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033
| | - Brian J. Bennett
- Department of Genetics, University of North Carolina Chapel Hill, North Carolina 27599
- Nutrition Research Institute, University of North Carolina Kannapolis, North Carolina 28081
- Department of Nutrition, University of North Carolina Chapel Hill, North Carolina 27599
- Corresponding author: Brian J. Bennett, 500 Laureate Way, Suite 2303, Kannapolis NC 28081, Phone: 704-250-5044, Fax: 704-250-5000,
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20
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Benton MC, Lea RA, Macartney-Coxson D, Bellis C, Carless MA, Curran JE, Hanna M, Eccles D, Chambers GK, Blangero J, Griffiths LR. Serum bilirubin concentration is modified by UGT1A1 haplotypes and influences risk of type-2 diabetes in the Norfolk Island genetic isolate. BMC Genet 2015; 16:136. [PMID: 26628212 PMCID: PMC4667444 DOI: 10.1186/s12863-015-0291-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2015] [Accepted: 11/02/2015] [Indexed: 02/06/2023] Open
Abstract
Background Located in the Pacific Ocean between Australia and New Zealand, the unique population isolate of Norfolk Island has been shown to exhibit increased prevalence of metabolic disorders (type-2 diabetes, cardiovascular disease) compared to mainland Australia. We investigated this well-established genetic isolate, utilising its unique genomic structure to increase the ability to detect related genetic markers. A pedigree-based genome-wide association study of 16 routinely collected blood-based clinical traits in 382 Norfolk Island individuals was performed. Results A striking association peak was located at chromosome 2q37.1 for both total bilirubin and direct bilirubin, with 29 SNPs reaching statistical significance (P < 1.84 × 10−7). Strong linkage disequilibrium was observed across a 200 kb region spanning the UDP-glucuronosyltransferase family, including UGT1A1, an enzyme known to metabolise bilirubin. Given the epidemiological literature suggesting negative association between CVD-risk and serum bilirubin we further explored potential associations using stepwise multivariate regression, revealing significant association between direct bilirubin concentration and type-2 diabetes risk. In the Norfolk Island cohort increased direct bilirubin was associated with a 28 % reduction in type-2 diabetes risk (OR: 0.72, 95 % CI: 0.57-0.91, P = 0.005). When adjusted for genotypic effects the overall model was validated, with the adjusted model predicting a 30 % reduction in type-2 diabetes risk with increasing direct bilirubin concentrations (OR: 0.70, 95 % CI: 0.53-0.89, P = 0.0001). Conclusions In summary, a pedigree-based GWAS of blood-based clinical traits in the Norfolk Island population has identified variants within the UDPGT family directly associated with serum bilirubin levels, which is in turn implicated with reduced risk of developing type-2 diabetes within this population. Electronic supplementary material The online version of this article (doi:10.1186/s12863-015-0291-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- M C Benton
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
| | - R A Lea
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
| | - D Macartney-Coxson
- Kenepuru Science Centre, Institute of Environmental Science and Research, Wellington, 5240, New Zealand.
| | - C Bellis
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia. .,Texas Biomedical Research Institute, San Antonio, TX, 78227-5301, USA.
| | - M A Carless
- Texas Biomedical Research Institute, San Antonio, TX, 78227-5301, USA.
| | - J E Curran
- Texas Biomedical Research Institute, San Antonio, TX, 78227-5301, USA.
| | - M Hanna
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
| | - D Eccles
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
| | - G K Chambers
- School of Biological Sciences, Victoria University of Wellington, Wellington, 6140, New Zealand.
| | - J Blangero
- South Texas Diabetes and Obesity Institute, University of Texas, Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA.
| | - L R Griffiths
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, 4059, Australia.
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21
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Benton MC, Lea RA, Macartney-Coxson D, Hanna M, Eccles DA, Carless MA, Chambers GK, Bellis C, Goring HH, Curran JE, Harper JL, Gibson G, Blangero J, Griffiths LR. A Phenomic Scan of the Norfolk Island Genetic Isolate Identifies a Major Pleiotropic Effect Locus Associated with Metabolic and Renal Disorder Markers. PLoS Genet 2015; 11:e1005593. [PMID: 26474483 PMCID: PMC4608754 DOI: 10.1371/journal.pgen.1005593] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 09/18/2015] [Indexed: 11/23/2022] Open
Abstract
Multiphenotype genome-wide association studies (GWAS) may reveal pleiotropic genes, which would remain undetected using single phenotype analyses. Analysis of large pedigrees offers the added advantage of more accurately assessing trait heritability, which can help prioritise genetically influenced phenotypes for GWAS analysis. In this study we performed a principal component analysis (PCA), heritability (h2) estimation and pedigree-based GWAS of 37 cardiovascular disease -related phenotypes in 330 related individuals forming a large pedigree from the Norfolk Island genetic isolate. PCA revealed 13 components explaining >75% of the total variance. Nine components yielded statistically significant h2 values ranging from 0.22 to 0.54 (P<0.05). The most heritable component was loaded with 7 phenotypic measures reflecting metabolic and renal dysfunction. A GWAS of this composite phenotype revealed statistically significant associations for 3 adjacent SNPs on chromosome 1p22.2 (P<1x10-8). These SNPs form a 42kb haplotype block and explain 11% of the genetic variance for this renal function phenotype. Replication analysis of the tagging SNP (rs1396315) in an independent US cohort supports the association (P = 0.000011). Blood transcript analysis showed 35 genes were associated with rs1396315 (P<0.05). Gene set enrichment analysis of these genes revealed the most enriched pathway was purine metabolism (P = 0.0015). Overall, our findings provide convincing evidence for a major pleiotropic effect locus on chromosome 1p22.2 influencing risk of renal dysfunction via purine metabolism pathways in the Norfolk Island population. Further studies are now warranted to interrogate the functional relevance of this locus in terms of renal pathology and cardiovascular disease risk. While many large genetic association studies have identified genes playing a role in complex disorders, there is still concern over the amount of missing genetic heritability. With this in mind, we have used a data reduction approach alongside pedigree-based association to obtain highly heritable components which explain 'hidden' variance of multiphenotypes within a large pedigree from the Norfolk Island genetic isolate. The most heritable of these components involved 7 traits reflecting metabolic and renal functionality, association of which locates to an intergenic region on chromosome 1p22.2. By integrating gene expression information, we identified enrichment of a purine metabolism pathway, further strengthening the metabolic nature of the observed association. Adding additional support to our approach, we show association of the tagging SNP (rs1396315) in an independent US population. The findings presented here are of particular interest as they implicate pleiotropic effect loci and newly associated biological pathways underlying cardiovascular disease risk.
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Affiliation(s)
- Miles C. Benton
- Genomics Research Centre, Institute of Biomedical Health and Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Rodney A. Lea
- Genomics Research Centre, Institute of Biomedical Health and Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Donia Macartney-Coxson
- Biomarkers Group, Kenepuru Science Centre, Institute of Environmental Science and Research, Wellington, New Zealand
| | - Michelle Hanna
- Genomics Research Centre, Institute of Biomedical Health and Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - David A. Eccles
- Genomics Research Centre, Institute of Biomedical Health and Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Melanie A. Carless
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Geoffrey K. Chambers
- School of Biological Science, Victoria University of Wellington, Wellington, New Zealand
| | - Claire Bellis
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Harald H. Goring
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Joanne E. Curran
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | | | - Gregory Gibson
- School of Biology, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - John Blangero
- Texas Biomedical Research Institute, San Antonio, Texas, United States of America
| | - Lyn R. Griffiths
- Genomics Research Centre, Institute of Biomedical Health and Innovation, Queensland University of Technology, Brisbane, Queensland, Australia
- * E-mail:
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22
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Benton MC, Stuart S, Bellis C, Macartney-Coxson D, Eccles D, Curran JE, Chambers G, Blangero J, Lea RA, Grffiths LR. 'Mutiny on the Bounty': the genetic history of Norfolk Island reveals extreme gender-biased admixture. INVESTIGATIVE GENETICS 2015; 6:11. [PMID: 26339467 PMCID: PMC4558825 DOI: 10.1186/s13323-015-0028-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2015] [Accepted: 08/28/2015] [Indexed: 11/17/2022]
Abstract
Background The Pacific Oceania region was one of the last regions of the world to be settled via human migration. Here we outline a settlement of this region that has given rise to a uniquely admixed population. The current Norfolk Island population has arisen from a small number of founders with mixed Caucasian and Polynesian ancestry, descendants of a famous historical event. The ‘Mutiny on the Bounty’ has been told in history books, songs and the big screen, but recently this story can be portrayed through comprehensive molecular genetics. Written history details betrayal and murder leading to the founding of Pitcairn Island by European mutineers and the Polynesian women who left Tahiti with them. Investigation of detailed genealogical records supports historical accounts. Findings Using genetics, we show distinct maternal Polynesian mitochondrial lineages in the present day population, as well as a European centric Y-chromosome phylogeny. These results comprehensively characterise the unique gender-biased admixture of this genetic isolate and further support the historical records relating to Norfolk Island. Conclusions Our results significantly refine previous population genetic studies investigating Polynesian versus Caucasian diversity in the Norfolk Island population and add information that is beneficial to future disease and gene mapping studies. Electronic supplementary material The online version of this article (doi:10.1186/s13323-015-0028-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Miles C Benton
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Q Block, 66 Musk Avenue, Kelvin Grove Campus, Brisbane, QLD 4001 Australia
| | - Shani Stuart
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Q Block, 66 Musk Avenue, Kelvin Grove Campus, Brisbane, QLD 4001 Australia
| | - Claire Bellis
- Texas Biomedical Research Institute, San Antonio, TX 78227 USA
| | - Donia Macartney-Coxson
- Kenepuru Science Centre, Institute of Environmental Science and Research, Wellington, 5240 New Zealand
| | - David Eccles
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Q Block, 66 Musk Avenue, Kelvin Grove Campus, Brisbane, QLD 4001 Australia
| | - Joanne E Curran
- Texas Biomedical Research Institute, San Antonio, TX 78227 USA
| | - Geoff Chambers
- School of Biological Sciences, Victoria University of Wellington, Wellington, 6140 New Zealand
| | - John Blangero
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX 78520 USA
| | - Rod A Lea
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Q Block, 66 Musk Avenue, Kelvin Grove Campus, Brisbane, QLD 4001 Australia
| | - Lyn R Grffiths
- Genomics Research Centre, Institute of Health and Biomedical Innovation, Queensland University of Technology, Q Block, 66 Musk Avenue, Kelvin Grove Campus, Brisbane, QLD 4001 Australia
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23
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Abstract
Migraine has been defined as a common disabling primary headache disorder. Epidemiology studies have provided with the undeniable evidence of genetic components as active players in the development of the disease under a polygenic model in which multiple risk alleles exert modest individual effects. Our objective was to test the contribution of a polygenic effect to migraine risk in the Norfolk Island population using a panel of SNPs reported to be disease associated in published migraine GWAS. We also investigated whether individual SNPs were associated with gene expression levels measured in whole blood. Polygenic scores were calculated in a total of 285 related individuals (74 cases, 211 controls) from the Norfolk Island using 51 SNPs previously reported to be associated with migraine in published GWAS. The association between polygenic score and migraine case-control status was tested using logistic regression. Results indicate that a migraine polygenic risk score was associated with migraine case-control status in this population (P = 0.016). This supports the hypothesis that multiple SNPs with weak effects collectively contribute to migraine risk in this population. Amongst the SNPs included in the polygenic model, four were associated with the expression of the USMG5 gene, including rs171251 (P = 0.012). Results from this study provide evidence for a polygenic contribution to migraine risk in an isolated population and highlight specific SNPs that regulate the expression of USMG5, a gene critical for mitochondrial function.
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24
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Turpeinen H, Seppälä I, Lyytikäinen LP, Raitoharju E, Hutri-Kähönen N, Levula M, Oksala N, Waldenberger M, Klopp N, Illig T, Mononen N, Laaksonen R, Raitakari O, Kähönen M, Lehtimäki T, Pesu M. A genome-wide expression quantitative trait loci analysis of proprotein convertase subtilisin/kexin enzymes identifies a novel regulatory gene variant for FURIN expression and blood pressure. Hum Genet 2015; 134:627-36. [PMID: 25813623 DOI: 10.1007/s00439-015-1546-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2014] [Accepted: 03/18/2015] [Indexed: 01/11/2023]
Abstract
Proprotein convertase subtilisin/kexin (PCSK) enzymes cleave and convert their immature substrates into biologically active forms. Polymorphisms in the PCSK genes have been reported to associate with human diseases and phenotypes, including hypercholesterolemia and blood pressure (BP), and targeting PCSKs is considered a promising future form of drug therapy. PCSK processing is readily induced upon upregulation of the enzyme, but the genetic factors contributing to PCSK expression have not been thoroughly characterized. To gain a comprehensive understanding of the genetic regulation of PCSK expression, we performed, for the first time, a genome-wide expression quantitative trait loci (eQTL) analysis using mRNA expression in >1400 human peripheral blood samples from the Cardiovascular Risk in Young Finns Study and ca. ten million single-nucleotide polymorphisms (SNPs). The expression data showed clear expression for FURIN, PCSK5, PCSK7 and MBTPS1 (membrane-bound transcription factor peptidase, site 1) mRNAs in virtually all tested samples. A discovery analysis demonstrated a genome-wide significant (p < 5 × 10(-8)) association with the selected PCSK probes for 1024 variants, which were located at ten independent loci. Of these loci, 5/10 could be confirmed to regulate PCSK expression in two additional and independent sample sets. Finally, a phenotypic analysis demonstrated that a novel cis-eQTL SNP rs4702 for FURIN is strongly associated with both diastolic (p = 0.012) and systolic (p = 0.035) BP levels, as well as peripheral vascular resistance (p = 0.003). These findings indicate that the expression of the PCSK enzymes is regulated by genetic factors, which have biological roles in health and disease.
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Affiliation(s)
- Hannu Turpeinen
- BioMediTech, University of Tampere, Biokatu 8, 33580, Tampere, Finland,
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Ganesh S, Chasman D, Larson M, Guo X, Verwoert G, Bis J, Gu X, Smith A, Yang ML, Zhang Y, Ehret G, Rose L, Hwang SJ, Papanicolau G, Sijbrands E, Rice K, Eiriksdottir G, Pihur V, Ridker P, Vasan R, Newton-Cheh C, Raffel LJ, Amin N, Rotter JI, Liu K, Launer LJ, Xu M, Caulfield M, Morrison AC, Johnson AD, Vaidya D, Dehghan A, Li G, Bouchard C, Harris TB, Zhang H, Boerwinkle E, Siscovick DS, Gao W, Uitterlinden AG, Rivadeneira F, Hofman A, Willer CJ, Franco OH, Huo Y, Witteman JC, Munroe PB, Gudnason V, Palmas W, van Duijn C, Fornage M, Levy D, Psaty BM, Chakravarti A, Newton-Cheh C, Johnson T, Gateva V, Tobin M, Bochud M, Coin L, Najjar S, Zhao J, Heath S, Eyheramendy S, Papadakis K, Voight B, Scott L, Zhang F, Farrall M, Tanaka T, Wallace C, Chambers J, Khaw KT, Nilsson P, van der Harst P, Polidoro S, Grobbee D, Onland-Moret N, Bots M, Wain L, Elliott K, Teumer A, Luan J, Lucas G, Kuusisto J, Burton P, Hadley D, McArdle W, Brown M, Dominiczak A, Newhouse S, Samani N, Webster J, Zeggini E, Beckmann J, Bergmann S, Lim N, Song K, Vollenweider P, Waeber G, Waterworth D, Yuan X, Groop L, Orho-Melander M, Allione A, Di Gregorio A, Guarrera S, Panico S, Ricceri F, Romanazzi V, Sacerdote C, Vineis P, Barroso I, Sandhu M, Luben R, Crawford G, Jousilahti P, Perola M, Boehnke M, Bonnycastle L, Collins F, Jackson A, Mohlke K, Stringham H, Valle T, Willer C, Bergman R, Morken M, Döring A, Gieger C, Illig T, Meitinger T, Org E, Pfeufer A, Wichmann H, Kathiresan S, Marrugat J, O’Donnell C, Schwartz S, Siscovick D, Subirana I, Freimer N, Hartikainen AL, McCarthy M, O’Reilly P, Peltonen L, Pouta A, de Jong P, Snieder H, van Gilst W, Clarke R, Goel A, Hamsten A, Peden J, Seedorf U, Syvänen AC, Tognoni G, Lakatta E, Sanna S, Scheet P, Schlessinger D, Scuteri A, Dörr M, Ernst F, Felix S, Homuth G, Lorbeer R, Reffelmann T, Rettig R, Völker U, Galan P, Gut I, Hercberg S, Lathrop G, Zeleneka D, Deloukas P, Soranzo N, Williams F, Zhai G, Salomaa V, Laakso M, Elosua R, Forouhi N, Völzke H, Uiterwaal C, van der Schouw Y, Numans M, Matullo G, Navis G, Berglund G, Bingham S, Kooner J, Paterson A, Connell J, Bandinelli S, Ferrucci L, Watkins H, Spector T, Tuomilehto J, Altshuler D, Strachan D, Laan M, Meneton P, Wareham N, Uda M, Jarvelin MR, Mooser V, Melander O, Loos R, Elliott P, Abecasis G, Caulfield M, Munroe P. Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations. Am J Hum Genet 2014; 95:49-65. [PMID: 24975945 DOI: 10.1016/j.ajhg.2014.06.002] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2014] [Accepted: 06/03/2014] [Indexed: 01/11/2023] Open
Abstract
Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.
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