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22 results on '"GWAS meta-analysis"'

1. Genome-wide association studies reveal differences in genetic susceptibility between single events vs. recurrent events of atrial fibrillation and myocardial infarction: the HUNT study

Author

Martina Hall, Anne Heidi Skogholt, Ida Surakka, Haavard Dalen, and Eivind Almaas

Subjects
GWAS meta-analysis, recurrent events, atrial fibrillation, myocardial infarction, networks, PheWAS, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Genetic research into atrial fibrillation (AF) and myocardial infarction (MI) has predominantly focused on comparing afflicted individuals with their healthy counterparts. However, this approach lacks granularity, thus overlooking subtleties within patient populations. In this study, we explore the distinction between AF and MI patients who experience only a single disease event and those experiencing recurrent events. Integrating hospital records, questionnaire data, clinical measurements, and genetic data from more than 500,000 HUNT and United Kingdom Biobank participants, we compare both clinical and genetic characteristics between the two groups using genome-wide association studies (GWAS) meta-analyses, phenome-wide association studies (PheWAS) analyses, and gene co-expression networks. We found that the two groups of patients differ in both clinical characteristics and genetic risks. More specifically, recurrent AF patients are significantly younger and have better baseline health, in terms of reduced cholesterol and blood pressure, than single AF patients. Also, the results of the GWAS meta-analysis indicate that recurrent AF patients seem to be at greater genetic risk for recurrent events. The PheWAS and gene co-expression network analyses highlight differences in the functions associated with the sets of single nucleotide polymorphisms (SNPs) and genes for the two groups. However, for MI patients, we found that those experiencing single events are significantly younger and have better baseline health than those with recurrent MI, yet they exhibit higher genetic risk. The GWAS meta-analysis mostly identifies genetic regions uniquely associated with single MI, and the PheWAS analysis and gene co-expression networks support the genetic differences between the single MI and recurrent MI groups. In conclusion, this work has identified novel genetic regions uniquely associated with single MI and related PheWAS analyses, as well as gene co-expression networks that support the genetic differences between the patient subgroups of single and recurrent occurrence for both MI and AF.

Published
2024
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2. The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes.

Author

Huerta-Chagoya, Alicia, Schroeder, Philip, Mandla, Ravi, Deutsch, Aaron J., Zhu, Wanying, Petty, Lauren, Yi, Xiaoyan, Cole, Joanne B., Udler, Miriam S., Dornbos, Peter, Porneala, Bianca, DiCorpo, Daniel, Liu, Ching-Ti, Li, Josephine H., Szczerbiński, Lukasz, Kaur, Varinderpal, Kim, Joohyun, Lu, Yingchang, Martin, Alicia, and Eizirik, Decio L.

Abstract

Aims/hypothesis: The Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population. Methods: We evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort. Results: Compared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10−9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance. Conclusions/interpretation: Our results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores. Data availability: Full summary statistics are available through the Common Metabolic Diseases Knowledge Portal (https://t2d.hugeamp.org/downloads.html) and through the GWAS catalog (https://www.ebi.ac.uk/gwas/, accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog (https://www.pgscatalog.org, publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445). [ABSTRACT FROM AUTHOR]

Published
2023
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3. Weighted Co-Expression Network Analysis Identifies RNF181 as a Causal Gene of Coronary Artery Disease.

Author

Dang, Ruoyu, Qu, Bojian, Guo, Kaimin, Zhou, Shuiping, Sun, He, Wang, Wenjia, Han, Jihong, Feng, Ke, Lin, Jianping, and Hu, Yunhui

Subjects
CORONARY artery disease, UBIQUITINATION, BIOLOGICAL networks, GENOME-wide association studies, GENETIC variation, GENE regulatory networks, GENES
Abstract

Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration. Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS). Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS. Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted. [ABSTRACT FROM AUTHOR]

Published
2022
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4. Weighted Co-Expression Network Analysis Identifies RNF181 as a Causal Gene of Coronary Artery Disease

Author

Ruoyu Dang, Bojian Qu, Kaimin Guo, Shuiping Zhou, He Sun, Wenjia Wang, Jihong Han, Ke Feng, Jianping Lin, and Yunhui Hu

Subjects
WGCNA (weighted gene co-expression network analyses), coronary artery disease, heart tissue, GWAS meta-analysis, diagnostic biomarkers, Genetics, QH426-470
Abstract

Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration.Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS).Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS.Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted.

Published
2022
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5. A genome-wide association meta-analysis of all-cause and vascular dementia.

Abstract

Introduction: Dementia is a multifactorial disease with Alzheimer's disease (AD) and vascular dementia (VaD) pathologies making the largest contributions. Yet, most genome-wide association studies (GWAS) focus on AD., Methods: We conducted a GWAS of all-cause dementia (ACD) and examined the genetic overlap with VaD. Our dataset includes 800,597 individuals, with 46,902 and 8702 cases of ACD and VaD, respectively. Known AD loci for ACD and VaD were replicated. Bioinformatic analyses prioritized genes that are likely functionally relevant and shared with closely related traits and risk factors., Results: For ACD, novel loci identified were associated with energy transport (SEMA4D), neuronal excitability (ANO3), amyloid deposition in the brain (RBFOX1), and magnetic resonance imaging markers of small vessel disease (SVD; HBEGF). Novel VaD loci were associated with hypertension, diabetes, and neuron maintenance (SPRY2, FOXA2, AJAP1, and PSMA3)., Discussion: Our study identified genetic risks underlying ACD, demonstrating overlap with neurodegenerative processes, vascular risk factors, and cerebral SVD., Highlights: We conducted the largest genome-wide association study of all-cause dementia (ACD) and vascular dementia (VaD). Known genetic variants associated with AD were replicated for ACD and VaD. Functional analyses identified novel loci for ACD and VaD. Genetic risks of ACD overlapped with neurodegeneration, vascular risk factors, and cerebral small vessel disease., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Published
2024
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6. Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum.

Author

Yang, Zhiyu, Wu, Hanrui, Lee, Phil H., Tsetsos, Fotis, Davis, Lea K., Yu, Dongmei, Lee, Sang Hong, Dalsgaard, Søren, Haavik, Jan, Barta, Csaba, Zayats, Tetyana, Eapen, Valsamma, Wray, Naomi R., Devlin, Bernie, Daly, Mark, Neale, Benjamin, Børglum, Anders D., Crowley, James J., Scharf, Jeremiah, and Mathews, Carol A.

Subjects
*TOURETTE syndrome, *GENOME-wide association studies, *AUTISM spectrum disorders, *HYPOTHALAMUS, *GENETIC correlations, *COMORBIDITY
Abstract

Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum. Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers). As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism–based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD. Our work underlines the value of redefining the framework for research across traditional diagnostic categories. [ABSTRACT FROM AUTHOR]

Published
2021
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7. Genome-wide association studies reveal differences in genetic susceptibility between single events vs. recurrent events of atrial fibrillation and myocardial infarction: the HUNT study.

Author

Hall M, Skogholt AH, Surakka I, Dalen H, and Almaas E

Abstract

Genetic research into atrial fibrillation (AF) and myocardial infarction (MI) has predominantly focused on comparing afflicted individuals with their healthy counterparts. However, this approach lacks granularity, thus overlooking subtleties within patient populations. In this study, we explore the distinction between AF and MI patients who experience only a single disease event and those experiencing recurrent events. Integrating hospital records, questionnaire data, clinical measurements, and genetic data from more than 500,000 HUNT and United Kingdom Biobank participants, we compare both clinical and genetic characteristics between the two groups using genome-wide association studies (GWAS) meta-analyses, phenome-wide association studies (PheWAS) analyses, and gene co-expression networks. We found that the two groups of patients differ in both clinical characteristics and genetic risks. More specifically, recurrent AF patients are significantly younger and have better baseline health, in terms of reduced cholesterol and blood pressure, than single AF patients. Also, the results of the GWAS meta-analysis indicate that recurrent AF patients seem to be at greater genetic risk for recurrent events. The PheWAS and gene co-expression network analyses highlight differences in the functions associated with the sets of single nucleotide polymorphisms (SNPs) and genes for the two groups. However, for MI patients, we found that those experiencing single events are significantly younger and have better baseline health than those with recurrent MI, yet they exhibit higher genetic risk. The GWAS meta-analysis mostly identifies genetic regions uniquely associated with single MI, and the PheWAS analysis and gene co-expression networks support the genetic differences between the single MI and recurrent MI groups. In conclusion, this work has identified novel genetic regions uniquely associated with single MI and related PheWAS analyses, as well as gene co-expression networks that support the genetic differences between the patient subgroups of single and recurrent occurrence for both MI and AF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Hall, Skogholt, Surakka, Dalen and Almaas.)

Published
2024
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9. Genome-Wide Association Study of Meiotic Recombination Phenotypes

Author

Ferdouse Begum, Reshmi Chowdhury, Vivian G. Cheung, Stephanie L. Sherman, and Eleanor Feingold

Subjects
crossover, nondisjunction, GWAS meta-analysis, hotspot/nonhotspot, average recombination, Genetics, QH426-470
Abstract

Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9. By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2. This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events.

Published
2016
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10. Parkinson Disease from Mendelian Forms to Genetic Susceptibility: New Molecular Insights into the Neurodegeneration Process.

Author

Karimi-Moghadam, Amin, Charsouei, Saeid, Bell, Benjamin, and Jabalameli, Mohammad Reza

Subjects
*PARKINSON'S disease, *MENDEL'S law, *GENETICS, *MOLECULAR biology, *NEURODEGENERATION
Abstract

Parkinson disease (PD) is known as a common progressive neurodegenerative disease which is clinically diagnosed by the manifestation of numerous motor and nonmotor symptoms. PD is a genetically heterogeneous disorder with both familial and sporadic forms. To date, researches in the field of Parkinsonism have identified 23 genes or loci linked to rare monogenic familial forms of PD with Mendelian inheritance. Biochemical studies revealed that the products of these genes usually play key roles in the proper protein and mitochondrial quality control processes, as well as synaptic transmission and vesicular recycling pathways within neurons. Despite this, large number of patients affected with PD typically tends to show sporadic forms of disease with lack of a clear family history. Recent genome-wide association studies (GWAS) meta-analyses on the large sporadic PD case-control samples from European populations have identified over 12 genetic risk factors. However, the genetic etiology that underlies pathogenesis of PD is also discussed, since it remains unidentified in 40% of all PD-affected cases. Nowadays, with the emergence of new genetic techniques, international PD genomics consortiums and public online resources such as PDGene, there are many hopes that future large-scale genetics projects provide further insights into the genetic etiology of PD and improve diagnostic accuracy and therapeutic clinical trial designs. [ABSTRACT FROM AUTHOR]

Published
2018
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11. Investigating Shared Genetic Basis Across Tourette Syndrome and Comorbid Neurodevelopmental Disorders Along the Impulsivity-Compulsivity Spectrum

Author

Jan Haavik, Naomi R. Wray, Phil Lee, Bernie Devlin, Anders D. Børglum, Lea K. Davis, Carol A. Mathews, Peristera Paschou, Barbara Franke, Jeremiah M. Scharf, Manuel Mattheisen, Zhiyu Yang, James J. Crowley, Stephen V. Faraone, Sang Hong Lee, Csaba Barta, Dongmei Yu, Søren Dalsgaard, Fotis Tsetsos, Jordan W. Smoller, Tetyana Zayats, Mark J. Daly, Benjamin M. Neale, Hanrui Wu, Valsamma Eapen, Yang, Zhiyu, Wu, Hanrui, Lee, Phil H, Tsetsos, Fotis, Lee, Sang Hong, and Paschou, Peristera

Subjects
0301 basic medicine, Obsessive-Compulsive Disorder, Autism Spectrum Disorder, Single-nucleotide polymorphism, Comorbidity, Biology, Impulsivity, ASD, behavioral disciplines and activities, Tourette syndrome, Genetic correlation, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, 0302 clinical medicine, mental disorders, medicine, ADHD, Humans, Biological Psychiatry, Genetic association, Genetics, Neurodevelopmental disorders Donders Center for Medical Neuroscience [Radboudumc 7], OCD, medicine.disease, Cross-disorder genetic analysis, Genetic architecture, 3. Good health, GWAS meta-analysis, 030104 developmental biology, Attention Deficit Disorder with Hyperactivity, Autism spectrum disorder, Impulsive Behavior, Etiology, medicine.symptom, 030217 neurology & neurosurgery, Genome-Wide Association Study, Tourette Syndrome
Abstract

BACKGROUND: Tourette syndrome (TS) is often found comorbid with other neurodevelopmental disorders across the impulsivity-compulsivity spectrum, with attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) as most prevalent. This points to the possibility of a common etiological thread along an impulsivity-compulsivity continuum.METHODS: Investigating the shared genetic basis across TS, ADHD, ASD, and OCD, we undertook an evaluation of cross-disorder genetic architecture and systematic meta-analysis, integrating summary statistics from the latest genome-wide association studies (93,294 individuals, 6,788,510 markers).RESULTS: As previously identified, a common unifying factor connects TS, ADHD, and ASD, while TS and OCD show the highest genetic correlation in pairwise testing among these disorders. Thanks to a more homogeneous set of disorders and a targeted approach that is guided by genetic correlations, we were able to identify multiple novel hits and regions that seem to play a pleiotropic role for the specific disorders analyzed here and could not be identified through previous studies. In the TS-ADHD-ASD genome-wide association study single nucleotide polymorphism-based and gene-based meta-analysis, we uncovered 13 genome-wide significant regions that host single nucleotide polymorphisms with a high posterior probability for association with all three studied disorders (m-value > 0.9), 11 of which were not identified in previous cross-disorder analysis. In contrast, we also identified two additional pleiotropic regions in the TS-OCD meta-analysis. Through conditional analysis, we highlighted genes and genetic regions that play a specific role in a TS-ADHD-ASD genetic factor versus TS-OCD. Cross-disorder tissue specificity analysis implicated the hypothalamus-pituitary-adrenal gland axis in TS-ADHD-ASD.CONCLUSIONS: Our work underlines the value of redefining the framework for research across traditional diagnostic categories.

Published
2021

12. A compendium of human genes regulating feeding behavior and body weight, its functional characterization and identification of GWAS genes involved in brain-specific PPI network.

Author

Ignatieva, Elena V., Afonnikov, Dmitry A., Saik, Olga V., Rogaev, Evgeny I., and Kolchanov, Nikolay A.

Subjects
*HUMAN genes, *PHYSIOLOGICAL aspects of food habits, *REGULATION of body weight, *GENETIC regulation, *OBESITY genetics, *HUMAN genetics, *CELLULAR signal transduction
Abstract

Background: Obesity is heritable. It predisposes to many diseases. The objectives of this study were to create a compendium of genes relevant to feeding behavior (FB) and/or body weight (BW) regulation; to construct and to analyze networks formed by associations between genes/proteins; and to identify the most significant genes, biological processes/pathways, and tissues/organs involved in BW regulation. Results: The compendium of genes controlling FB or BW includes 578 human genes. Candidate genes were identified from various sources, including previously published original research and review articles, GWAS meta-analyses, and OMIM (Online Mendelian Inheritance in Man). All genes were ranked according to knowledge about their biological role in body weight regulation and classified according to expression patterns or functional characteristics. Substantial and overrepresented numbers of genes from the compendium encoded cell surface receptors, signaling molecules (hormones, neuropeptides, cytokines), transcription factors, signal transduction proteins, cilium and BBSome components, and lipid binding proteins or were present in the brain-specific list of tissue-enriched genes identified with TSEA tool. We identified 27 pathways from KEGG, REACTOME and BIOCARTA whose genes were overrepresented in the compendium. Networks formed by physical interactions or homological relationships between proteins or interactions between proteins involved in biochemical/signaling pathways were reconstructed and analyzed. Subnetworks and clusters identified by the MCODE tool included genes/proteins associated with cilium morphogenesis, signal transduction proteins (particularly, G protein-coupled receptors, kinases or proteins involved in response to insulin stimulus) and transcription regulation (particularly nuclear receptors). We ranked GWAS genes according to the number of neighbors in three networks and revealed 22 GWAS genes involved in the brain-specific PPI network. On the base of the most reliable PPIs functioning in the brain tissue, new regulatory schemes interpreting relevance to BW regulation are proposed for three GWAS genes (ETV5, LRP1B, and NDUFS3). Conclusions: A compendium comprising 578 human genes controlling FB or BW was designed, and the most significant functional groups of genes, biological processes/pathways, and tissues/organs involved in BW regulation were revealed. We ranked genes from the GWAS meta-analysis set according to the number and quality of associations in the networks and then according to their involvement in the brain-specific PPI network and proposed new regulatory schemes involving three GWAS genes (ETV5, LRP1B, and NDUFS3) in BW regulation. The compendium is expected to be useful for pathology risk estimation and for design of new pharmacological approaches in the treatment of human obesity. [ABSTRACT FROM AUTHOR]

Published
2016
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13. Genome-Wide Association Study of Meiotic Recombination Phenotypes.

Author

Begum, Ferdouse, Chowdhury, Reshmi, Cheung, Vivian G., Sherman, Stephanie L., and Feingold, Eleanor

Subjects
*PHENOTYPES, *GAMETOGENESIS
Abstract

Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9. By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2. This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events. [ABSTRACT FROM AUTHOR]

Published
2016
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14. Weighted Co-Expression Network Analysis Identifies RNF181 as a Causal Gene of Coronary Artery Disease

Author

Ruoyu Dang, Bojian Qu, Kaimin Guo, Shuiping Zhou, He Sun, Wenjia Wang, Jihong Han, Ke Feng, Jianping Lin, and Yunhui Hu

Subjects
GWAS meta-analysis, diagnostic biomarkers, WGCNA (weighted gene co-expression network analyses), Genetics, Molecular Medicine, QH426-470, heart tissue, Genetics (clinical), coronary artery disease
Abstract

Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration.Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS).Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS.Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted.

Published
2021

15. GWAS of thyroid stimulating hormone highlights pleiotropic effects and inverse association with thyroid cancer

Author

Leigha Senter, Ben Michael Brumpton, Julius Gudmundsson, Anita Pandit, Peter Arvan, Sandya Liyanarachchi, Matthew D. Ringel, Juha Karjalainen, George Davey Smith, Anne Heidi Skogholt, Layal Chaker, Hannes Hjartarson, Tanmoy Roychowdhury, Albert de la Chapelle, Wei Zhou, Brooke N. Wolford, Kari Stefansson, Ulla T. Schultheiss, Cristen J. Willer, Whitney E. Hornsby, Lars G. Fritsche, Theo S. Plantinga, Sarah E. Graham, Marco Medici, Jonas B. Nielsen, Li Xu, Morgan Oneka, Jose I. Mayordomo, Kristian Hveem, Cintia Eliana Citterio, Serena Sanna, William Overton, Josh Weinstock, Eirin B. Haug, Lambertus A. Kiemeney, Gudmar Thorleifsson, Chad M. Brummett, Michael Boehnke, Mitja I. Kurki, Aarno Palotie, Omer Kabil, Mark J. Daly, Alexander Teumer, Bjørn Olav Åsvold, Peter N. Taylor, Ida Surakka, Erich M. Sturgis, Romana T. Netea-Maier, Silvia Naitza, Maiken Elvestad Gabrielsen, Huiling He, Anne R. Cappola, Jon Hrafnkelsson, Matthew Zawistowski, Humaira Rasheed, Jon G. Jonasson, Epidemiology, Internal Medicine, Institute for Molecular Medicine Finland, Helsinki Institute of Life Science HiLIFE, University of Helsinki, Centre of Excellence in Complex Disease Genetics, Research Programs Unit, Aarno Palotie / Principal Investigator, Genomics of Neurological and Neuropsychiatric Disorders, HUS Helsinki and Uusimaa Hospital District, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Læknadeild (HÍ), Faculty of Medicine (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, and University of Iceland

Subjects
0301 basic medicine, Multifactorial Inheritance, Goiter, endocrine system diseases, medicine.medical_treatment, Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], Thyrotropin, General Physics and Astronomy, Genome-wide association study, 02 engineering and technology, Genome-wide association studies, THYROID, Risk Factors, Thyroid stimulating hormone, Prevalence, lcsh:Science, Thyroid cancer, Multidisciplinary, TSH, Hormónar, Genetic analysis, Thyroid, COMMON VARIANTS, Genetic Pleiotropy, purl.org/becyt/ford/3.1 [https], Physical Chromosome Mapping, 021001 nanoscience & nanotechnology, CANCER, CONGENITAL HYPOTHYROIDISM, 3. Good health, Phenotype, medicine.anatomical_structure, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], purl.org/becyt/ford/3 [https], Thyroid function, Erfðarannsóknir, 0210 nano-technology, hormones, hormone substitutes, and hormone antagonists, medicine.medical_specialty, endocrine system, Science, Mutation, Missense, Development, Rare cancers Radboud Institute for Molecular Life Sciences [Radboudumc 9], Article, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, THYROGLOBULIN, All institutes and research themes of the Radboud University Medical Center, Thyroid-stimulating hormone, SDG 3 - Good Health and Well-being, Internal medicine, Mendelian randomization, medicine, Humans, Genetic Predisposition to Disease, Thyroid Neoplasms, GENOME-WIDE ASSOCIATION, Author Correction, MALIGNANCY, RECEPTOR, HYPERTENSION, business.industry, ENDOGENOUS SUBCLINICAL HYPERTHYROIDISM, SERUM THYROTROPIN CONCENTRATION, Efnaskipti, General Chemistry, Mendelian Randomization Analysis, medicine.disease, Metabolism, GWAS meta-analysis, 030104 developmental biology, Endocrinology, Genetic Loci, lcsh:Q, Thyroglobulin, Gene expression, 3111 Biomedicine, business, Genome-Wide Association Study
Abstract

Publsiher's version (útgefin grein), Thyroid stimulating hormone (TSH) is critical for normal development and metabolism. To better understand the genetic contribution to TSH levels, we conduct a GWAS meta-analysis at 22.4 million genetic markers in up to 119,715 individuals and identify 74 genome-wide significant loci for TSH, of which 28 are previously unreported. Functional experiments show that the thyroglobulin protein-altering variants P118L and G67S impact thyroglobulin secretion. Phenome-wide association analysis in the UK Biobank demonstrates the pleiotropic effects of TSH-associated variants and a polygenic score for higher TSH levels is associated with a reduced risk of thyroid cancer in the UK Biobank and three other independent studies. Two-sample Mendelian randomization using TSH index variants as instrumental variables suggests a protective effect of higher TSH levels (indicating lower thyroid function) on risk of thyroid cancer and goiter. Our findings highlight the pleiotropic effects of TSH-associated variants on thyroid function and growth of malignant and benign thyroid tumors., We acknowledge the University of Michigan Precision Health Initiative and Medical School Central Biorepository for providing biospecimen storage, management, processing, and distribution services, and the Center for Statistical Genetics in the Department of Biostatistics at the School of Public Health for genotype data curation, imputation, and management in support of this research (for MGI cohort). The biochemical work at the University of Michigan was done with funding from NIH R01 DK-40344. This research has been conducted using the UK Biobank Resource under application number 24460. The HUNT Study is a collaboration between the HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU, Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Regional Health Authority, and the Norwegian Institute of Public Health. The genotyping in HUNT was financed by the National Institutes of Health; University of Michigan; the Research Council of Norway; the Liaison Committee for Education, Research and Innovation in Central Norway; and the Joint Research Committee between St Olav’s Hospital and the Faculty of Medicine and Health Sciences, NTNU. We thank Sean Caron for web development for data sharing. Support for this research was provided by NIH grant DK062370 (M.B.). Ohio State Cohort was supported by NIH P50CA168505 SPORE (M.D.R. and A.d.l.C.). Support for the HUNT and MGI analysis was additionally provided by R35HL135824 (C.J.W.) and R01HL109946 (C.J.W.). W.Z. was supported by the National Human Genome Research Institute of the National Institutes of Health under award number T32HG010464. G.D.S., B.B., H.R. and E.B.H. work in the Medical Research Council Integrative Epidemiology Unit at the University of Bristol MC_UU_00011/1.

Published
2020

16. Parkinson Disease from Mendelian Forms to Genetic Susceptibility: New Molecular Insights into the Neurodegeneration Process

Author

Benjamin Bell, Mohammad Reza Jabalameli, Saeid Charsouei, and Amin Karimi-Moghadam

Subjects
0301 basic medicine, Genome-wide association study, Disease, Biology, 03 medical and health sciences, Cellular and Molecular Neuroscience, symbols.namesake, 0302 clinical medicine, medicine, Genetic predisposition, Autophagy, Animals, Humans, Genetic Predisposition to Disease, Neurodegeneration, Genetic association, Genetics, Review Paper, Genetic heterogeneity, Parkinsonism, Parkinson Disease, Cell Biology, General Medicine, medicine.disease, Mitochondria, 030104 developmental biology, GWAS meta-analysis, Oxidative stress, Mutation, Mendelian inheritance, symbols, Mitochondrial dysfunction, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Parkinson disease (PD) is known as a common progressive neurodegenerative disease which is clinically diagnosed by the manifestation of numerous motor and nonmotor symptoms. PD is a genetically heterogeneous disorder with both familial and sporadic forms. To date, researches in the field of Parkinsonism have identified 23 genes or loci linked to rare monogenic familial forms of PD with Mendelian inheritance. Biochemical studies revealed that the products of these genes usually play key roles in the proper protein and mitochondrial quality control processes, as well as synaptic transmission and vesicular recycling pathways within neurons. Despite this, large number of patients affected with PD typically tends to show sporadic forms of disease with lack of a clear family history. Recent genome-wide association studies (GWAS) meta-analyses on the large sporadic PD case–control samples from European populations have identified over 12 genetic risk factors. However, the genetic etiology that underlies pathogenesis of PD is also discussed, since it remains unidentified in 40% of all PD-affected cases. Nowadays, with the emergence of new genetic techniques, international PD genomics consortiums and public online resources such as PDGene, there are many hopes that future large-scale genetics projects provide further insights into the genetic etiology of PD and improve diagnostic accuracy and therapeutic clinical trial designs.

Published
2018

17. Genome-Wide Association Study of Meiotic Recombination Phenotypes

Author

Eleanor Feingold, Vivian G. Cheung, Ferdouse Begum, Stephanie L. Sherman, and Reshmi Chowdhury

Subjects
DNA Replication, Male, 0301 basic medicine, hotspot/nonhotspot, Genotype, Genome-wide association study, Investigations, QH426-470, Biology, Polymorphism, Single Nucleotide, Chromosomal crossover, Ligases, 03 medical and health sciences, Sex Factors, 0302 clinical medicine, Meiosis, Genetics, Humans, Crossing Over, Genetic, Nucleotide Motifs, Molecular Biology, Genetics (clinical), PRDM9, Genetic association, Recombination, Genetic, crossover, average recombination, Histone-Lysine N-Methyltransferase, Pedigree, Phenotype, GWAS meta-analysis, 030104 developmental biology, nondisjunction, Female, Homologous recombination, 030217 neurology & neurosurgery, Recombination, Genome-Wide Association Study, SNP array
Abstract

Meiotic recombination is an essential step in gametogenesis, and is one that also generates genetic diversity. Genome-wide association studies (GWAS) and molecular studies have identified genes that influence of human meiotic recombination. RNF212 is associated with total or average number of recombination events, and PRDM9 is associated with the locations of hotspots, or sequences where crossing over appears to cluster. In addition, a common inversion on chromosome 17 is strongly associated with recombination. Other genes have been identified by GWAS, but those results have not been replicated. In this study, using new datasets, we characterized additional recombination phenotypes to uncover novel candidates and further dissect the role of already known loci. We used three datasets totaling 1562 two-generation families, including 3108 parents with 4304 children. We estimated five different recombination phenotypes including two novel phenotypes (average recombination counts within recombination hotspots and outside of hotspots) using dense SNP array genotype data. We then performed gender-specific and combined-sex genome-wide association studies (GWAS) meta-analyses. We replicated associations for several previously reported recombination genes, including RNF212 and PRDM9. By looking specifically at recombination events outside of hotspots, we showed for the first time that PRDM9 has different effects in males and females. We identified several new candidate loci, particularly for recombination events outside of hotspots. These include regions near the genes SPINK6, EVC2, ARHGAP25, and DLGAP2. This study expands our understanding of human meiotic recombination by characterizing additional features that vary across individuals, and identifying regulatory variants influencing the numbers and locations of recombination events.

Published
2016

18. JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects

Author

David V. Conti, Sylvia Richardson, Paul J. Newcombe, Newcombe, Paul [0000-0002-5611-6702], Richardson, Sylvia [0000-0003-1998-492X], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, insulin, Multivariate statistics, Epidemiology, Computer science, Bayesian probability, Genome-wide association study, Feature selection, computer.software_genre, Polymorphism, Single Nucleotide, Correlation, fine‐mapping, 03 medical and health sciences, Bayes' theorem, symbols.namesake, Humans, Computer Simulation, Research Articles, Genetics (clinical), Models, Genetic, Bayes Theorem, Markov chain Monte Carlo, Fasting, Genomics, Stepwise regression, GWAS meta‐analysis, GWAS meta-analysis, Glucose, Phenotype, 030104 developmental biology, fine-mapping, symbols, Data mining, computer, Algorithms, Research Article, variable selection, Adenylyl Cyclases, Genome-Wide Association Study
Abstract

Recently, large scale genome‐wide association study (GWAS) meta‐analyses have boosted the number of known signals for some traits into the tens and hundreds. Typically, however, variants are only analysed one‐at‐a‐time. This complicates the ability of fine‐mapping to identify a small set of SNPs for further functional follow‐up. We describe a new and scalable algorithm, joint analysis of marginal summary statistics (JAM), for the re‐analysis of published marginal summary stactistics under joint multi‐SNP models. The correlation is accounted for according to estimates from a reference dataset, and models and SNPs that best explain the complete joint pattern of marginal effects are highlighted via an integrated Bayesian penalized regression framework. We provide both enumerated and Reversible Jump MCMC implementations of JAM and present some comparisons of performance. In a series of realistic simulation studies, JAM demonstrated identical performance to various alternatives designed for single region settings. In multi‐region settings, where the only multivariate alternative involves stepwise selection, JAM offered greater power and specificity. We also present an application to real published results from MAGIC (meta‐analysis of glucose and insulin related traits consortium) – a GWAS meta‐analysis of more than 15,000 people. We re‐analysed several genomic regions that produced multiple significant signals with glucose levels 2 hr after oral stimulation. Through joint multivariate modelling, JAM was able to formally rule out many SNPs, and for one gene, ADCY5, suggests that an additional SNP, which transpired to be more biologically plausible, should be followed up with equal priority to the reported index.

Published
2016

19. A compendium of human genes regulating feeding behavior and body weight, its functional characterization and identification of GWAS genes involved in brain-specific PPI network

Author

Dmitry A. Afonnikov, Olga V. Saik, Elena V. Ignatieva, Nikolay A. Kolchanov, and Evgeny I. Rogaev

Subjects
0301 basic medicine, Candidate gene, Databases, Factual, Gene regulatory network, Network, Biology, Database, 03 medical and health sciences, 0302 clinical medicine, Genetics, OMIM : Online Mendelian Inheritance in Man, Humans, Gene Regulatory Networks, Genetics(clinical), Protein Interaction Maps, KEGG, Gene, Transcription factor, Genetics (clinical), Regulation of gene expression, PPIs, Research, Body Weight, Brain, Feeding Behavior, GWAS meta-analysis, 030104 developmental biology, Gene Expression Regulation, Nuclear receptor, Body weight regulation, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Background Obesity is heritable. It predisposes to many diseases. The objectives of this study were to create a compendium of genes relevant to feeding behavior (FB) and/or body weight (BW) regulation; to construct and to analyze networks formed by associations between genes/proteins; and to identify the most significant genes, biological processes/pathways, and tissues/organs involved in BW regulation. Results The compendium of genes controlling FB or BW includes 578 human genes. Candidate genes were identified from various sources, including previously published original research and review articles, GWAS meta-analyses, and OMIM (Online Mendelian Inheritance in Man). All genes were ranked according to knowledge about their biological role in body weight regulation and classified according to expression patterns or functional characteristics. Substantial and overrepresented numbers of genes from the compendium encoded cell surface receptors, signaling molecules (hormones, neuropeptides, cytokines), transcription factors, signal transduction proteins, cilium and BBSome components, and lipid binding proteins or were present in the brain-specific list of tissue-enriched genes identified with TSEA tool. We identified 27 pathways from KEGG, REACTOME and BIOCARTA whose genes were overrepresented in the compendium. Networks formed by physical interactions or homological relationships between proteins or interactions between proteins involved in biochemical/signaling pathways were reconstructed and analyzed. Subnetworks and clusters identified by the MCODE tool included genes/proteins associated with cilium morphogenesis, signal transduction proteins (particularly, G protein–coupled receptors, kinases or proteins involved in response to insulin stimulus) and transcription regulation (particularly nuclear receptors). We ranked GWAS genes according to the number of neighbors in three networks and revealed 22 GWAS genes involved in the brain-specific PPI network. On the base of the most reliable PPIs functioning in the brain tissue, new regulatory schemes interpreting relevance to BW regulation are proposed for three GWAS genes (ETV5, LRP1B, and NDUFS3). Conclusions A compendium comprising 578 human genes controlling FB or BW was designed, and the most significant functional groups of genes, biological processes/pathways, and tissues/organs involved in BW regulation were revealed. We ranked genes from the GWAS meta-analysis set according to the number and quality of associations in the networks and then according to their involvement in the brain-specific PPI network and proposed new regulatory schemes involving three GWAS genes (ETV5, LRP1B, and NDUFS3) in BW regulation. The compendium is expected to be useful for pathology risk estimation and for design of new pharmacological approaches in the treatment of human obesity. Electronic supplementary material The online version of this article (doi:10.1186/s12863-016-0466-2) contains supplementary material, which is available to authorized users.

Published
2016

20. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study

Author

Winkler, Thomas W, Justice, Anne E, Graff, Mariaelisa, Barata, Llilda, Feitosa, Mary F, Chu, Su, Czajkowski, Jacek, Esko, Tõnu, Fall, Tove, Kilpeläinen, Tuomas O, Lu, Yingchang, Mägi, Reedik, Mihailov, Evelin, Pers, Tune H, Rüeger, Sina, Teumer, Alexander, Ehret, Georg B, Ferreira, Teresa, Heard-Costa, Nancy L, Karjalainen, Juha, Lagou, Vasiliki, Mahajan, Anubha, Neinast, Michael D, Prokopenko, Inga, Simino, Jeannette, Teslovich, Tanya M, Jansen, Rick, Westra, Harm-Jan, White, Charles C, Absher, Devin, Ahluwalia, Tarunveer S, Ahmad, Shafqat, Albrecht, Eva, Alves, Alexessander Couto, Bragg-Gresham, Jennifer L, de Craen, Anton JM, Bis, Joshua C, Bonnefond, Amélie, Boucher, Gabrielle, Cadby, Gemma, Cheng, Yu-Ching, Chiang, Charleston WK, Delgado, Graciela, Demirkan, Ayse, Dueker, Nicole, Eklund, Niina, Eiriksdottir, Gudny, Eriksson, Joel, Feenstra, Bjarke, Fischer, Krista, Frau, Francesca, Galesloot, Tessel E, Geller, Frank, Goel, Anuj, Gorski, Mathias, Grammer, Tanja B, Gustafsson, Stefan, Haitjema, Saskia, Hottenga, Jouke-Jan, Huffman, Jennifer E, Jackson, Anne U, Jacobs, Kevin B, Johansson, Åsa, Kaakinen, Marika, Kleber, Marcus E, Lahti, Jari, Mateo Leach, Irene, Lehne, Benjamin, Liu, Youfang, Lo, Ken Sin, Lorentzon, Mattias, Luan, Jian'an, Madden, Pamela AF, Mangino, Massimo, McKnight, Barbara, Medina-Gomez, Carolina, Monda, Keri L, Montasser, May E, Müller, Gabriele, Müller-Nurasyid, Martina, Nolte, Ilja M, Panoutsopoulou, Kalliope, Pascoe, Laura, Paternoster, Lavinia, Rayner, Nigel W, Renström, Frida, Rizzi, Federica, Rose, Lynda M, Ryan, Kathy A, Salo, Perttu, Sanna, Serena, Scharnagl, Hubert, Shi, Jianxin, Smith, Albert Vernon, Southam, Lorraine, Stančáková, Alena, Steinthorsdottir, Valgerdur, Strawbridge, Rona J, Sung, Yun Ju, Tachmazidou, Ioanna, Tanaka, Toshiko, Thorleifsson, Gudmar, Trompet, Stella, Pervjakova, Natalia, Tyrer, Jonathan P, Vandenput, Liesbeth, van der Laan, Sander W, van der Velde, Nathalie, van Setten, Jessica, van Vliet-Ostaptchouk, Jana V, Verweij, Niek, Vlachopoulou, Efthymia, Waite, Lindsay L, Wang, Sophie R, Wang, Zhaoming, Wild, Sarah H, Willenborg, Christina, Wilson, James F, Wong, Andrew, Yang, Jian, Yengo, Loïc, Yerges-Armstrong, Laura M, Yu, Lei, Zhang, Weihua, Zhao, Jing Hua, Andersson, Ehm A, Bakker, Stephan JL, Baldassarre, Damiano, Banasik, Karina, Barcella, Matteo, Barlassina, Cristina, Bellis, Claire, Benaglio, Paola, Blangero, John, Blüher, Matthias, Bonnet, Fabrice, Bonnycastle, Lori L, Boyd, Heather A, Bruinenberg, Marcel, Buchman, Aron S, Campbell, Harry, Chen, Yii-Der Ida, Chines, Peter S, Claudi-Boehm, Simone, Cole, John, Collins, Francis S, de Geus, Eco JC, de Groot, Lisette CPGM, Dimitriou, Maria, Duan, Jubao, Enroth, Stefan, Eury, Elodie, Farmaki, Aliki-Eleni, Forouhi, Nita G, Friedrich, Nele, Gejman, Pablo V, Gigante, Bruna, Glorioso, Nicola, Go, Alan S, Gottesman, Omri, Gräßler, Jürgen, Grallert, Harald, Grarup, Niels, Gu, Yu-Mei, Broer, Linda, Ham, Annelies C, Hansen, Torben, Harris, Tamara B, Hartman, Catharina A, Hassinen, Maija, Hastie, Nicholas, Hattersley, Andrew T, Heath, Andrew C, Henders, Anjali K, Hernandez, Dena, Hillege, Hans, Holmen, Oddgeir, Hovingh, Kees G, Hui, Jennie, Husemoen, Lise L, Hutri-Kähönen, Nina, Hysi, Pirro G, Illig, Thomas, De Jager, Philip L, Jalilzadeh, Shapour, Jørgensen, Torben, Jukema, J Wouter, Juonala, Markus, Kanoni, Stavroula, Karaleftheri, Maria, Khaw, Kay Tee, Kinnunen, Leena, Kittner, Steven J, Koenig, Wolfgang, Kolcic, Ivana, Kovacs, Peter, Krarup, Nikolaj T, Kratzer, Wolfgang, Krüger, Janine, Kuh, Diana, Kumari, Meena, Kyriakou, Theodosios, Langenberg, Claudia, Lannfelt, Lars, Lanzani, Chiara, Lotay, Vaneet, Launer, Lenore J, Leander, Karin, Lindström, Jaana, Linneberg, Allan, Liu, Yan-Ping, Lobbens, Stéphane, Luben, Robert, Lyssenko, Valeriya, Männistö, Satu, Magnusson, Patrik K, McArdle, Wendy L, Menni, Cristina, Merger, Sigrun, Milani, Lili, Montgomery, Grant W, Morris, Andrew P, Narisu, Narisu, Nelis, Mari, Ong, Ken K, Palotie, Aarno, Pérusse, Louis, Pichler, Irene, Pilia, Maria G, Pouta, Anneli, Rheinberger, Myriam, Ribel-Madsen, Rasmus, Richards, Marcus, Rice, Kenneth M, Rice, Treva K, Rivolta, Carlo, Salomaa, Veikko, Sanders, Alan R, Sarzynski, Mark A, Scholtens, Salome, Scott, Robert A, Scott, William R, Sebert, Sylvain, Sengupta, Sebanti, Sennblad, Bengt, Seufferlein, Thomas, Silveira, Angela, Slagboom, P Eline, Smit, Jan H, Sparsø, Thomas H, Stirrups, Kathleen, Stolk, Ronald P, Stringham, Heather M, Swertz, Morris A, Swift, Amy J, Syvänen, Ann-Christine, Tan, Sian-Tsung, Thorand, Barbara, Tönjes, Anke, Tremblay, Angelo, Tsafantakis, Emmanouil, van der Most, Peter J, Völker, Uwe, Vohl, Marie-Claude, Vonk, Judith M, Waldenberger, Melanie, Walker, Ryan W, Wennauer, Roman, Widén, Elisabeth, Willemsen, Gonneke, Wilsgaard, Tom, Wright, Alan F, Zillikens, M Carola, van Dijk, Suzanne C, van Schoor, Natasja M, Asselbergs, Folkert W, de Bakker, Paul IW, Beckmann, Jacques S, Beilby, John, Bennett, David A, Bergman, Richard N, Bergmann, Sven, Böger, Carsten A, Boehm, Bernhard O, Boerwinkle, Eric, Boomsma, Dorret I, Bornstein, Stefan R, Bottinger, Erwin P, Bouchard, Claude, Chambers, John C, Chanock, Stephen J, Chasman, Daniel I, Cucca, Francesco, Cusi, Daniele, Dedoussis, George, Erdmann, Jeanette, Eriksson, Johan G, Evans, Denis A, de Faire, Ulf, Farrall, Martin, Ferrucci, Luigi, Ford, Ian, Franke, Lude, Franks, Paul W, Froguel, Philippe, Gansevoort, Ron T, Gieger, Christian, Grönberg, Henrik, Gudnason, Vilmundur, Gyllensten, Ulf, Hall, Per, Hamsten, Anders, van der Harst, Pim, Hayward, Caroline, Heliövaara, Markku, Hengstenberg, Christian, Hicks, Andrew A, Hingorani, Aroon, Hofman, Albert, Hu, Frank, Huikuri, Heikki V, Hveem, Kristian, James, Alan L, Jordan, Joanne M, Jula, Antti, Kähönen, Mika, Kajantie, Eero, Kathiresan, Sekar, Kiemeney, Lambertus ALM, Kivimaki, Mika, Knekt, Paul B, Koistinen, Heikki A, Kooner, Jaspal S, Koskinen, Seppo, Kuusisto, Johanna, Maerz, Winfried, Martin, Nicholas G, Laakso, Markku, Lakka, Timo A, Lehtimäki, Terho, Lettre, Guillaume, Levinson, Douglas F, Lind, Lars, Lokki, Marja-Liisa, Mäntyselkä, Pekka, Melbye, Mads, Metspalu, Andres, Mitchell, Braxton D, Moll, Frans L, Murray, Jeffrey C, Musk, Arthur W, Nieminen, Markku S, Njølstad, Inger, Ohlsson, Claes, Oldehinkel, Albertine J, Oostra, Ben A, Palmer, Lyle J, Pankow, James S, Pasterkamp, Gerard, Pedersen, Nancy L, Pedersen, Oluf, Penninx, Brenda W, Perola, Markus, Peters, Annette, Polašek, Ozren, Pramstaller, Peter P, Psaty, Bruce M, Qi, Lu, Quertermous, Thomas, Raitakari, Olli T, Rankinen, Tuomo, Rauramaa, Rainer, Ridker, Paul M, Rioux, John D, Rivadeneira, Fernando, Rotter, Jerome I, Rudan, Igor, den Ruijter, Hester M, Saltevo, Juha, Sattar, Naveed, Schunkert, Heribert, Schwarz, Peter EH, Shuldiner, Alan R, Sinisalo, Juha, Snieder, Harold, Sørensen, Thorkild IA, Spector, Tim D, Staessen, Jan A, Stefania, Bandinelli, Thorsteinsdottir, Unnur, Stumvoll, Michael, Tardif, Jean-Claude, Tremoli, Elena, Tuomilehto, Jaakko, Uitterlinden, André G, Uusitupa, Matti, Verbeek, André LM, Vermeulen, Sita H, Viikari, Jorma S, Vitart, Veronique, Völzke, Henry, Vollenweider, Peter, Waeber, Gérard, Walker, Mark, Wallaschofski, Henri, Wareham, Nicholas J, Watkins, Hugh, Zeggini, Eleftheria, arcOGEN Consortium, CHARGE Consortium, DIAGRAM Consortium, GLGC Consortium, Global-BPGen Consortium, ICBP Consortium, MAGIC Consortium, Chakravarti, Aravinda, Clegg, Deborah J, Cupples, L Adrienne, Gordon-Larsen, Penny, Jaquish, Cashell E, Rao, DC, Abecasis, Goncalo R, Assimes, Themistocles L, Barroso, Inês, Berndt, Sonja I, Boehnke, Michael, Deloukas, Panos, Fox, Caroline S, Groop, Leif C, Hunter, David J, Ingelsson, Erik, Kaplan, Robert C, McCarthy, Mark I, Mohlke, Karen L, O'Connell, Jeffrey R, Schlessinger, David, Strachan, David P, Stefansson, Kari, van Duijn, Cornelia M, Hirschhorn, Joel N, Lindgren, Cecilia M, Heid, Iris M, North, Kari E, Borecki, Ingrid B, Kutalik, Zoltán, Loos, Ruth JF, Division of Statistical Genomics, Washington University School of Medicine, Institut Pasteur du Cambodge, Réseau International des Instituts Pasteur (RIIP), Estonian Genome and Medicine, University of Tartu, Institute of Molecular and Cell Biology, Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], Institute of Metabolic Science, MRC, Brown University, Center for Biological Sequence Analysis [Lyngby], Technical University of Denmark [Lyngby] (DTU), King‘s College London, Groningen Bioinformatics Centre, GBB, University of Groningen [Groningen], University of Queensland [Brisbane], National Institut of Food Science and Technology, University of Agriculture, Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Department of neurology, Leiden University Medical Center (LUMC), University of Washington [Seattle], Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (GI3M), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Genetic Epidemiology Unit, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Deptartment of Medical Biochemistry and Microbiology, Uppsala University, Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Limnology, Ecology, Department of Ecology and Evolutionary Biology, University of California, Core Genotyping Facility, National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), Division of Cancer Epidemiology and Genetics, Institute of Health Sciences and Biocenter Oulu, University of Oulu, Department of Chemical Engineering Taiwan (DCET - NTHU), National Tsing Hua University [Hsinchu] (NTHU), University of Oxford [Oxford], Department of Epidemiology, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Human Genetics, The Wellcome Trust Sanger Institute [Cambridge], Departments of Epidemiology and Nutrition, Harvard School of Public Health, Department of Clinical Sciences, Lund University [Lund]-Lund University Diabetes Centre, Genetic Epidemiology and Clinical Research Group, Umea University Hospital, Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Department of Medical Sciences, Department of Educational Psychology and Counseling, National Taiwan Normal University (NTNU), Laboratory for Cardiovascular Genomics and Informatics [Yokohama] (RIKEN IMS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN)-RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), deCODE Genetics, deCODE genetics [Reykjavik], Interuniversity Cardiology Institute Netherlands, Molecular Genetics Section, University of Groningen [Groningen]-University Medical Centre Groningen, Kidney Center, University Medical Center Groningen [Groningen] (UMCG), The University of Texas Health Science Center at Houston (UTHealth), Texas Biomedical Research Institute [San Antonio, TX], Medical Department III, Universität Leipzig [Leipzig], Foie, métabolismes et cancer, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Dept. of Epidemiology Research, Statens Serum Institut [Copenhagen], GNS Science, Blackett Laboratory, Imperial College London, Medstar Research Institute, University of Rochester [USA], MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, Institute of Epidemiology [Neuherberg] (EPI), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Dept Biochem & Mol Biol, Pennsylvania State University (Penn State), Penn State System-Penn State System, Genomic Research Laboratory, Service of Infectious Disease, Hôpitaux Universitaires de Genève (HUG), Epidemiology, University Medical Centre Groningen, Unit for Molecular Epidemiology, German Research Center for Environmental Health-Helmholtz-Zentrum München (HZM), Program in Translational NeuroPsychiatric Genomics, Brigham and Women's Hospital [Boston], Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Research Centre for Prevention and Health (RCPH), Department of Public Health [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Capital Region of Denmark, University of Copenhagen = Københavns Universitet (KU), Faculty of Medicine, Institute of Public Health, Aalborg University [Denmark] (AAU), Department of Nutrition-Dietetics, Harokopio University of Athens, Department of Medical Statistics, Epidemiology and Medical Informatics, University of Zagreb, MRC National Survey of Health and Development, MRC Unit for Lifelong Health and Ageing, Department of Epidemiology and Public Health, University College of London [London] (UCL), Department of Public health and Caring Sciences, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden, Research Center for Prevention and Health, Glostrup Hospital, Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Cambridge Institute of Public Health, University of Cambridge [UK] (CAM), Chronic Disease Epidemiology and Prevention Unit, National Institute for Health and Welfare [Helsinki], Queensland Institute of Medical Research, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Head of Medical Sequencing, Department of Biostatistics, University of Washington, Department of Chronic Disease Prevention, Molecular Epidemiology, Department of Genetics, Université Laval [Québec] (ULaval), Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, Netherlands Genomics Initiative, Netherlands Consortium for Healthy Aging [Leiden, Netherlands] (NCHA), Department of Internal Medicine, Université de Lausanne (UNIL), Carl Gustav Carus University Hospital, Loughborough University, Genomics and Bioinformatics Platform, Fondazione Filarete, Department of Medicine, Surgery, and Dentistry, University of Milano, Medizinische Klinik II, Universität zu Lübeck [Lübeck], Department of Genomic Medicine, University of Manchester [Manchester], The Wellcome Trust Centre for Human Genetics [Oxford], National Institutes of Health [Bethesda] (NIH), Department of Nephrology, University Medical Center, University of Groningen, Helmholtz-Zentrum München (HZM), Icelandic Heart Association, Kopavogur, Iceland., Faculty of Medicine, University of Iceland [Reykjavik], Genetics and Pathology, Department of child and adolescent psychiatry, Universität Duisburg-Essen [Essen], Department of Internal Medicine II, Division of Respirology, University of Regensburg, Regensburg, Germany, Universität Regensburg (UR), Franz-Volhard-Centrum für Klinische Forschung, ECRC, Department of Clinical Physiology, University of Tampere [Finland]-Tampere University Hospital, Finnish Institute of Occupational Health of Helsinki, Department of Clinical Chemistry, Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Department of Oncology, University of Tampere Medical School, University of Tampere, Department of Physiology, University of Eastern Finland-Institute of Biomedicine, Department of Medicine, Montreal, Developmental Brain and Behaviour Unit, University of Southampton, Division of Gastroenterology and Hepatology, Department of Immunology, Mayo Clinic, Centre for Bone and Arthritis Research, Institute of Medicine-University of Gothenburg (GU), Interdisciplinary Center for Psychiatric Epidemiology, Experimental Cardiology Laboratory, University Medical Center [Utrecht], Peter MacCallum Cancer Center, Faculty of Health Sciences, Aarhus University [Aarhus], Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Department of Health Services, University of Washington, Group Health Research Institute, Group Health Cooperative, Department of Epidemiology, University of Washington, Department of Medicine, University of Washington, Cardiovascular Health Research Unit, University of Washington, The Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku University Hospital (TYKS), Erasmus Medical Centre, Cedars-Sinai Medical Center, Department of Pathological Biochemistry, Royal Infirmary, Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, Department of Pediatrics, Augusta University - Medical College of Georgia, University System of Georgia (USG)-University System of Georgia (USG), Institute of Preventive Medicine, Copenhagen University Hospital-Bispebjerg and Frederiksberg Hospitals, Maastricht University [Maastricht], Department of Public Health, South Ostrobothnia Central Hospital, Department of Clinical and Preventive Medicine, Danube-University Krems, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands, Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Department of Epidemiology & Biostatistics, Radboud University Medical Center [Nijmegen], Institute for Community Medicine, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), MRC epidemiology Unit, Framingham Heart Study, National Heart, Lung, and Blood Institute [Bethesda] (NHLBI)-Boston University [Boston] (BU), Metabolic Disease Group, University of Cambridge Metabolic Research Laboratories, Department of Genetics and Biotechnology, Wellcome Trust, Divisions of Genetics and Endocrinology and Program in Genomics, Boston Children's Hospital, Metabolism Initiative and Program in Medical and Population Genetics, Endocrinology and Metabolism, The Churchill Hospital-Oxford Centre for Diabetes, Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, University of North Carolina System (UNC)-University of North Carolina System (UNC)-UNC Gillings School of Global Public Health-Carolina Center for Genome Sciences, Department of Medical Genetics, Epidemiology Unit, Addenbrooke's Hospital-Medical Research Council (MRC), P30 AG010161/AG/NIA NIH HHS/United States, Psychiatry, Epidemiology and Data Science, NCA - Neurobiology of mental health, EMGO - Lifestyle, overweight and diabetes, APH - Amsterdam Public Health, AMS - Amsterdam Movement Sciences, Geriatrics, ACS - Amsterdam Cardiovascular Sciences, Vascular Medicine, Other departments, Biological Psychology, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, EMGO+ - Lifestyle, Overweight and Diabetes, RS: CARIM - R3 - Vascular biology, MUMC+: DA BV AIOS Radiologie (9), Epidemiologie, Orthopedie, Institute for Molecular Medicine Finland, Helsinki Collegium for Advanced Studies, Behavioural Sciences, University of Helsinki, Transplantation Laboratory, Medicum, Research Programs Unit, Research Programme of Molecular Medicine, Aarno Palotie / Principal Investigator, Elisabeth Ingrid Maria Widen / Principal Investigator, Clinicum, Johan Eriksson / Principal Investigator, Department of General Practice and Primary Health Care, Children's Hospital, Lastentautien yksikkö, Marja-Liisa Lokki / Principal Investigator, Kardiologian yksikkö, Leif Groop Research Group, Quantitative Genetics, Developmental Psychology Research Group, Genomics of Neurological and Neuropsychiatric Disorders, Genomic Discoveries and Clinical Translation, Ehret, Georg Benedikt, Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Universiteit Leiden-Universiteit Leiden, Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (EGENODIA (GI3M)), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of California (UC), University of Oxford, Lund University [Lund], Universität Leipzig, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Helmholtz Zentrum München = German Research Center for Environmental Health, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Capital Region of Denmark, University of Copenhagen = Københavns Universitet (UCPH), Université de Lausanne = University of Lausanne (UNIL), Universität zu Lübeck = University of Lübeck [Lübeck], Universität Duisburg-Essen = University of Duisburg-Essen [Essen], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, University of Gothenburg (GU)-Institute of Medicine, Boston University [Boston] (BU)-National Heart, Lung, and Blood Institute [Bethesda] (NHLBI), Luan, Jian'an [0000-0003-3137-6337], Tyrer, Jonathan [0000-0003-3724-4757], Forouhi, Nita [0000-0002-5041-248X], Khaw, Kay-Tee [0000-0002-8802-2903], Langenberg, Claudia [0000-0002-5017-7344], Luben, Robert [0000-0002-5088-6343], Ong, Kenneth [0000-0003-4689-7530], Johnson, Kathleen [0000-0002-6823-3252], Wareham, Nicholas [0000-0003-1422-2993], Barroso, Ines [0000-0001-5800-4520], Apollo - University of Cambridge Repository, CHARGE Consortium, DIAGRAM Consortium, GLGC Consortium, Global-BPGen Consortium, ICBP Consortium, MAGIC Consortium, Biochemistry, Surgery, Internal Medicine, Public Health, Medical Oncology, Pathology, Erasmus MC other, Child and Adolescent Psychiatry / Psychology, and Clinical Genetics

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), Male, Cancer Research, endocrine system diseases, Biological pathways, QH426-470, Genome, Body Mass Index, Body Size, Genetics (clinical), ddc:616, Genetics & Heredity, Sex Characteristics, Loci, MAGIC Consortium, Mass index, Age Factors, Chromosome Mapping, Middle Aged, Genealogy, Self-reported height, Peripheral-blood, Scale (social sciences), ICBP Consortium, Female, Life Sciences & Biomedicine, Medical Genetics, Research Article, arcOGEN Consortium, musculoskeletal diseases, Adult, European Continental Ancestry Group, Natural menopause, Aged, Body Size/genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Waist-Hip Ratio, Biology, Body size, DIAGRAM Consortium, Age and sex, White People, Fat distribution, GLGC Consortium, 03 medical and health sciences, Life-course, Genetics, Weight-gain, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Medicinsk genetik, [SDV.GEN]Life Sciences [q-bio]/Genetics, 0604 Genetics, Science & Technology, nutritional and metabolic diseases, Correction, 030104 developmental biology, GWAS meta-analysis, Global-BPGen Consortium, Common SNPS, CHARGE Consortium, 3111 Biomedicine, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR, Author Summary Adult body size and body shape differ substantially between men and women and change over time. More than 100 genetic variants that influence body mass index (measure of body size) or waist-to-hip ratio (measure of body shape) have been identified. While there is evidence that some genetic loci affect body shape differently in men than in women, little is known about whether genetic effects differ in older compared to younger adults, and whether such changes differ between men and women. Therefore, we conducted a systematic genome-wide search, including 114 studies (>320,000 individuals), to specifically identify genetic loci with age- and or sex-dependent effects on body size and shape. We identified 15 loci of which the effect on BMI was different in older compared to younger adults, whereas we found no evidence for loci with different effects in men compared to women. The opposite was seen for body shape as we identified 44 loci of which the effect on waist-to-hip ratio differed between men and women, but no difference between younger and older adults were observed. Our observations may provide new insights into the biology that underlies weight change with age or the sexual dimorphism of body shape.

Published
2014

21. JAM: A Scalable Bayesian Framework for Joint Analysis of Marginal SNP Effects.

Author

Newcombe PJ, Conti DV, and Richardson S

Subjects
Adenylyl Cyclases genetics, Algorithms, Computer Simulation, Fasting metabolism, Genomics, Glucose metabolism, Humans, Insulin metabolism, Models, Genetic, Phenotype, Bayes Theorem, Genome-Wide Association Study methods, Polymorphism, Single Nucleotide genetics
Abstract

Recently, large scale genome-wide association study (GWAS) meta-analyses have boosted the number of known signals for some traits into the tens and hundreds. Typically, however, variants are only analysed one-at-a-time. This complicates the ability of fine-mapping to identify a small set of SNPs for further functional follow-up. We describe a new and scalable algorithm, joint analysis of marginal summary statistics (JAM), for the re-analysis of published marginal summary statistics under joint multi-SNP models. The correlation is accounted for according to estimates from a reference dataset, and models and SNPs that best explain the complete joint pattern of marginal effects are highlighted via an integrated Bayesian penalized regression framework. We provide both enumerated and Reversible Jump MCMC implementations of JAM and present some comparisons of performance. In a series of realistic simulation studies, JAM demonstrated identical performance to various alternatives designed for single region settings. In multi-region settings, where the only multivariate alternative involves stepwise selection, JAM offered greater power and specificity. We also present an application to real published results from MAGIC (meta-analysis of glucose and insulin related traits consortium) - a GWAS meta-analysis of more than 15,000 people. We re-analysed several genomic regions that produced multiple significant signals with glucose levels 2 hr after oral stimulation. Through joint multivariate modelling, JAM was able to formally rule out many SNPs, and for one gene, ADCY5, suggests that an additional SNP, which transpired to be more biologically plausible, should be followed up with equal priority to the reported index., (© 2016 The Authors Genetic Epidemiology Published by Wiley Periodicals, Inc.)

Published
2016
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22. Regionally Smoothed Meta-Analysis Methods for GWAS Datasets.

Author

Begum F, Sharker MH, Sherman SL, Tseng GC, and Feingold E

Subjects
Computer Simulation, Environmental Exposure, Genotype, Humans, Linkage Disequilibrium genetics, Male, Genetic Predisposition to Disease, Genome-Wide Association Study methods, Meta-Analysis as Topic, Polymorphism, Single Nucleotide genetics
Abstract

Genome-wide association studies are proven tools for finding disease genes, but it is often necessary to combine many cohorts into a meta-analysis to detect statistically significant genetic effects. Often the component studies are performed by different investigators on different populations, using different chips with minimal SNPs overlap. In some cases, raw data are not available for imputation so that only the genotyped single nucleotide polymorphisms (SNPs) results can be used in meta-analysis. Even when SNP sets are comparable, different cohorts may have peak association signals at different SNPs within the same gene due to population differences in linkage disequilibrium or environmental interactions. We hypothesize that the power to detect statistical signals in these situations will improve by using a method that simultaneously meta-analyzes and smooths the signal over nearby markers. In this study, we propose regionally smoothed meta-analysis methods and compare their performance on real and simulated data., (© 2015 WILEY PERIODICALS, INC.)

Published
2016
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