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1. Increased secretion of adipocyte-derived extracellular vesicles is associated with adipose tissue inflammation and the mobilization of excess lipid in human obesity.

Author: Matilainen, Johanna, Berg, Viivi, Vaittinen, Maija, Impola, Ulla, Mustonen, Anne-Mari, Männistö, Ville, Malinen, Marjo, Luukkonen, Veera, Rosso, Natalia, Turunen, Tanja, Käkelä, Pirjo, Palmisano, Silvia, Arasu, Uma, Sihvo, Sanna, Aaltonen, Niina, Härkönen, Kai, Caddeo, Andrea, Kaminska, Dorota, Pajukanta, Päivi, Kaikkonen, Minna, Tiribelli, Claudio, Käkelä, Reijo, Laitinen, Saara, Pihlajamäki, Jussi, Nieminen, Petteri, and Rilla, Kirsi
Subjects: Adipocyte, Adipose tissue, Extracellular vesicles, Fatty acids, Inflammation, Obesity, Humans, Extracellular Vesicles, Obesity, Adipocytes, Inflammation, Adipose Tissue, Lipid Metabolism, Female, Male, Adult, Fatty Acids
Abstract: BACKGROUND: Obesity is a worldwide epidemic characterized by adipose tissue (AT) inflammation. AT is also a source of extracellular vesicles (EVs) that have recently been implicated in disorders related to metabolic syndrome. However, our understanding of mechanistic aspect of obesitys impact on EV secretion from human AT remains limited. METHODS: We investigated EVs from human Simpson Golabi Behmel Syndrome (SGBS) adipocytes, and from AT as well as plasma of subjects undergoing bariatric surgery. SGBS cells were treated with TNFα, palmitic acid, and eicosapentaenoic acid. Various analyses, including nanoparticle tracking analysis, electron microscopy, high-resolution confocal microscopy, and gas chromatography-mass spectrometry, were utilized to study EVs. Plasma EVs were analyzed with imaging flow cytometry. RESULTS: EVs from mature SGBS cells differed significantly in size and quantity compared to preadipocytes, disagreeing with previous findings in mouse adipocytes and indicating that adipogenesis promotes EV secretion in human adipocytes. Inflammatory stimuli also induced EV secretion, and altered EV fatty acid (FA) profiles more than those of cells, suggesting the role of EVs as rapid responders to metabolic shifts. Visceral AT (VAT) exhibited higher EV secretion compared to subcutaneous AT (SAT), with VAT EV counts positively correlating with plasma triacylglycerol (TAG) levels. Notably, the plasma EVs of subjects with obesity contained a higher number of adiponectin-positive EVs than those of lean subjects, further demonstrating higher AT EV secretion in obesity. Moreover, plasma EV counts of people with obesity positively correlated with body mass index and TNF expression in SAT, connecting increased EV secretion with AT expansion and inflammation. Finally, EVs from SGBS adipocytes and AT contained TAGs, and EV secretion increased despite signs of less active lipolytic pathways, indicating that AT EVs could be involved in the mobilization of excess lipids into circulation. CONCLUSIONS: We are the first to provide detailed FA profiles of human AT EVs. We report that AT EV secretion increases in human obesity, implicating their role in TAG transport and association with adverse metabolic parameters, thereby emphasizing their role in metabolic disorders. These findings promote our understanding of the roles that EVs play in human AT biology and metabolic disorders.
Published: 2024

2. Hepatokine ITIH3 protects against hepatic steatosis by downregulating mitochondrial bioenergetics and de novo lipogenesis.

Author: Sotomayor-Rodriguez, Irene, Rahman, Afra, Péterfy, Miklós, Pajukanta, Päivi, Pihlajamäki, Jussi, Chella Krishnan, Karthickeyan, Talari, Noble, Mattam, Ushodaya, and Kaminska, Dorota
Subjects: Biochemistry, Biological sciences, Cell biology, Physiology
Abstract: Recent studies demonstrate that liver secretory proteins, also known as hepatokines, regulate normal development, obesity, and simple steatosis to non-alcoholic steatohepatitis (NASH) progression. Using a panel of ∼100 diverse inbred strains of mice and a cohort of bariatric surgery patients, we found that one such hepatokine, inter-trypsin inhibitor heavy chain 3 (ITIH3), was progressively lower in severe non-alcoholic fatty liver disease (NAFLD) disease states highlighting an inverse relationship between Itih3/ITIH3 expression and NAFLD severity. Follow-up animal and cell culture models demonstrated that hepatic ITIH3 overexpression lowered liver triglyceride and lipid droplet accumulation, respectively. Conversely, ITIH3 knockdown in mice increased the liver triglyceride in two independent NAFLD models. Mechanistically, ITIH3 reduced mitochondrial respiration and this, in turn, reduced liver triglycerides, via downregulated de novo lipogenesis. This was accompanied by increased STAT1 signaling and Stat3 expression, both of which are known to protect against NAFLD/NASH. Our findings indicate hepatokine ITIH3 as a potential biomarker and/or treatment for NAFLD.
Published: 2024

3. Age-dependent genes in adipose stem and precursor cells affect regulation of fat cell differentiation and link aging to obesity via cellular and genetic interactions.

Author: Kar, Asha, Alvarez, Marcus, Garske, Kristina, Huang, Huiling, Lee, Seung, Deal, Milena, Das, Sankha, Koka, Amogha, Jamal, Zoeb, Mohlke, Karen, Laakso, Markku, Heinonen, Sini, Pietiläinen, Kirsi, and Pajukanta, Päivi
Subjects: Adipose stem and precursor cells (ASPCs), Aging, Gene-age interactions, Obesity, Polygenic risk score (PRS), Humans, Cell Differentiation, Obesity, Adipose Tissue, Adipocytes, Aging, Transcription Factors
Abstract: BACKGROUND: Age and obesity are dominant risk factors for several common cardiometabolic disorders, and both are known to impair adipose tissue function. However, the underlying cellular and genetic factors linking aging and obesity on adipose tissue function have remained elusive. Adipose stem and precursor cells (ASPCs) are an understudied, yet crucial adipose cell type due to their deterministic adipocyte differentiation potential, which impacts the capacity to store fat in a metabolically healthy manner. METHODS: We integrated subcutaneous adipose tissue (SAT) bulk (n=435) and large single-nucleus RNA sequencing (n=105) data with the UK Biobank (UKB) (n=391,701) data to study age-obesity interactions originating from ASPCs by performing cell-type decomposition, differential expression testing, cell-cell communication analyses, and construction of polygenic risk scores for body mass index (BMI). RESULTS: We found that the SAT ASPC proportions significantly decrease with age in an obesity-dependent way consistently in two independent cohorts, both showing that the age dependency of ASPC proportions is abolished by obesity. We further identified 76 genes (72 SAT ASPC marker genes and 4 transcription factors regulating ASPC marker genes) that are differentially expressed by age in SAT and functionally enriched for developmental processes and adipocyte differentiation (i.e., adipogenesis). The 76 age-perturbed ASPC genes include multiple negative regulators of adipogenesis, such as RORA, SMAD3, TWIST2, and ZNF521, form tight clusters of longitudinally co-expressed genes during human adipogenesis, and show age-based differences in cellular interactions between ASPCs and adipose cell types. Finally, our genetic data demonstrate that cis-regional variants of these genes interact with age as predictors of BMI in an obesity-dependent way in the large UKB, while no such gene-age interaction on BMI is observed with non-age-dependent ASPC marker genes, thus independently confirming our cellular ASPC results at the biobank level. CONCLUSIONS: Overall, we discover that obesity prematurely induces a decrease in ASPC proportions and identify 76 developmentally important ASPC genes that implicate altered negative regulation of fat cell differentiation as a mechanism for aging and directly link aging to obesity via significant cellular and genetic interactions.
Published: 2024

4. Deep learning-based phenotype imputation on population-scale biobank data increases genetic discoveries.

Author: An, Ulzee, Pazokitoroudi, Ali, Alvarez, Marcus, Huang, Lianyun, Bacanu, Silviu, Schork, Andrew, Kendler, Kenneth, Pajukanta, Päivi, Flint, Jonathan, Cai, Na, Dahl, Andy, Sankararaman, Sriram, and Zaitlen, Noah
Subjects: Humans, Genome-Wide Association Study, Genotype, Biological Specimen Banks, Deep Learning, Polymorphism, Single Nucleotide, Phenotype
Abstract: Biobanks that collect deep phenotypic and genomic data across many individuals have emerged as a key resource in human genetics. However, phenotypes in biobanks are often missing across many individuals, limiting their utility. We propose AutoComplete, a deep learning-based imputation method to impute or fill-in missing phenotypes in population-scale biobank datasets. When applied to collections of phenotypes measured across ~300,000 individuals from the UK Biobank, AutoComplete substantially improved imputation accuracy over existing methods. On three traits with notable amounts of missingness, we show that AutoComplete yields imputed phenotypes that are genetically similar to the originally observed phenotypes while increasing the effective sample size by about twofold on average. Further, genome-wide association analyses on the resulting imputed phenotypes led to a substantial increase in the number of associated loci. Our results demonstrate the utility of deep learning-based phenotype imputation to increase power for genetic discoveries in existing biobank datasets.
Published: 2023

5. Applying an evolutionary mismatch framework to understand disease susceptibility.

Author: Lea, Amanda, Clark, Andrew, Dahl, Andrew, Devinsky, Orrin, Garcia, Angela, Golden, Christopher, Kamau, Joseph, Kraft, Thomas, Lim, Yvonne, Martins, Dino, Mogoi, Donald, Pajukanta, Päivi, Perry, George, Pontzer, Herman, Trumble, Benjamin, Urlacher, Samuel, Venkataraman, Vivek, Wallace, Ian, Lieberman, Daniel, Ayroles, Julien, and Gurven, Michael
Subjects: Humans, Disease Susceptibility, Diabetes Mellitus, Type 2, Biological Evolution, Cardiovascular Diseases, Genomics
Abstract: Noncommunicable diseases (NCDs) are on the rise worldwide. Obesity, cardiovascular disease, and type 2 diabetes are among a long list of lifestyle diseases that were rare throughout human history but are now common. The evolutionary mismatch hypothesis posits that humans evolved in environments that radically differ from those we currently experience; consequently, traits that were once advantageous may now be mismatched and disease causing. At the genetic level, this hypothesis predicts that loci with a history of selection will exhibit genotype by environment (GxE) interactions, with different health effects in ancestral versus modern environments. To identify such loci, we advocate for combining genomic tools in partnership with subsistence-level groups experiencing rapid lifestyle change. In these populations, comparisons of individuals falling on opposite extremes of the matched to mismatched spectrum are uniquely possible. More broadly, the work we propose will inform our understanding of environmental and genetic risk factors for NCDs across diverse ancestries and cultures.
Published: 2023

6. Cross-tissue omics analysis discovers ten adipose genes encoding secreted proteins in obesity-related non-alcoholic fatty liver disease.

Author: Darci-Maher, Nicholas, Alvarez, Marcus, Arasu, Uma Thanigai, Selvarajan, Ilakya, Lee, Seung Hyuk T, Pan, David Z, Miao, Zong, Das, Sankha Subhra, Kaminska, Dorota, Örd, Tiit, Benhammou, Jihane N, Wabitsch, Martin, Pisegna, Joseph R, Männistö, Ville, Pietiläinen, Kirsi H, Laakso, Markku, Sinsheimer, Janet S, Kaikkonen, Minna U, Pihlajamäki, Jussi, and Pajukanta, Päivi
Subjects: Liver, Humans, Obesity, Genome-Wide Association Study, Non-alcoholic Fatty Liver Disease, Biomarkers, Adipogenesis, Dual-tissue transcriptomics screening, Liver histology, Non-alcoholic fatty liver disease, Serum biomarkers, cis regulatory variants, Chronic Liver Disease and Cirrhosis, Digestive Diseases, Liver Disease, Prevention, Nutrition, Biotechnology, Genetics, Aetiology, 2.1 Biological and endogenous factors, Metabolic and endocrine, Clinical Sciences, Public Health and Health Services
Abstract: BackgroundNon-alcoholic fatty liver disease (NAFLD) is a fast-growing, underdiagnosed, epidemic. We hypothesise that obesity-related inflammation compromises adipose tissue functions, preventing efficient fat storage, and thus driving ectopic fat accumulation into the liver.MethodsTo identify adipose-based mechanisms and potential serum biomarker candidates (SBCs) for NAFLD, we utilise dual-tissue RNA-sequencing (RNA-seq) data in adipose tissue and liver, paired with histology-based NAFLD diagnosis, from the same individuals in a cohort of obese individuals. We first scan for genes that are differentially expressed (DE) for NAFLD in obese individuals' subcutaneous adipose tissue but not in their liver; encode proteins secreted to serum; and show preferential adipose expression. Then the identified genes are filtered to key adipose-origin NAFLD genes by best subset analysis, knockdown experiments during human preadipocyte differentiation, recombinant protein treatment experiments in human liver HepG2 cells, and genetic analysis.FindingsWe discover a set of genes, including 10 SBCs, that may modulate NAFLD pathogenesis by impacting adipose tissue function. Based on best subset analysis, we further follow-up on two SBCs CCDC80 and SOD3 by knockdown in human preadipocytes and subsequent differentiation experiments, which show that they modulate crucial adipogenesis genes, LPL, SREBPF1, and LEP. We also show that treatment of the liver HepG2 cells with the CCDC80 and SOD3 recombinant proteins impacts genes related to steatosis and lipid processing, including PPARA, NFE2L2, and RNF128. Finally, utilizing the adipose NAFLD DE gene cis-regulatory variants associated with serum triglycerides (TGs) in extensive genome-wide association studies (GWASs), we demonstrate a unidirectional effect of serum TGs on NAFLD with Mendelian Randomization (MR) analysis. We also demonstrate that a single SNP regulating one of the SBC genes, rs2845885, produces a significant MR result by itself. This supports the conclusion that genetically regulated adipose expression of the NAFLD DE genes may contribute to NAFLD through changes in serum TG levels.InterpretationOur results from the dual-tissue transcriptomics screening improve the understanding of obesity-related NAFLD by providing a targeted set of 10 adipose tissue-active genes as new serum biomarker candidates for the currently grossly underdiagnosed fatty liver disease.FundingThe work was supported by NIH grants R01HG010505 and R01DK132775. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The KOBS study (J. P.) was supported by the Finnish Diabetes Research Foundation, Kuopio University Hospital Project grant (EVO/VTR grants 2005-2019), and the Academy of Finland grant (Contract no. 138006). This study was funded by the European Research Council under the European Union's Horizon 2020 research and innovation program (Grant No. 802825 to M. U. K.). K. H. P. was funded by the Academy of Finland (grant numbers 272376, 266286, 314383, and 335443), the Finnish Medical Foundation, Gyllenberg Foundation, Novo Nordisk Foundation (grant numbers NNF10OC1013354, NNF17OC0027232, and NNF20OC0060547), Finnish Diabetes Research Foundation, Finnish Foundation for Cardiovascular Research, University of Helsinki, and Helsinki University Hospital and Government Research Funds. I. S. was funded by the Instrumentarium Science Foundation. Personal grants to U. T. A. were received from the Matti and Vappu Maukonen Foundation, Ella och Georg Ehrnrooths Stiftelse and the Finnish Foundation for Cardiovascular Research.
Published: 2023

7. Role of Matrix Gla Protein in Transforming Growth Factor-β Signaling and Nonalcoholic Steatohepatitis in Mice.

Author: Hui, Simon, Gong, Lili, Swichkow, Chantle, Blencowe, Montgomery, Kaminska, Dorota, Diamante, Graciel, Pan, Calvin, Dalsania, Meet, French, Samuel, Magyar, Clara, Pajukanta, Päivi, Pihlajamäki, Jussi, Boström, Kristina, Yang, Xia, and Lusis, Aldons
Subjects: Gene Network Analysis, Liver Fibrosis, Nonalcoholic Steatohepatitis, Systems Genetics, Animals, Humans, Mice, Liver Cirrhosis, Non-alcoholic Fatty Liver Disease, Transforming Growth Factor beta, Transforming Growth Factors, Matrix Gla Protein
Abstract: BACKGROUND & AIMS: Nonalcoholic steatohepatitis (NASH) is a complex disease involving both genetic and environmental factors in its onset and progression. We analyzed NASH phenotypes in a genetically diverse cohort of mice, the Hybrid Mouse Diversity Panel, to identify genes contributing to disease susceptibility. METHODS: A systems genetics approach, involving integration of genetic, transcriptomic, and phenotypic data, was used to identify candidate genes and pathways in a mouse model of NASH. The causal role of Matrix Gla Protein (MGP) was validated using heterozygous MGP knockout (Mgp+/-) mice. The mechanistic role of MGP in transforming growth factor-beta (TGF-β) signaling was examined in the LX-2 stellate cell line by using a loss of function approach. RESULTS: Local cis-acting regulation of MGP was correlated with fibrosis, suggesting a causal role in NASH, and this was validated using loss of function experiments in 2 models of diet-induced NASH. Using single-cell RNA sequencing, Mgp was found to be primarily expressed in hepatic stellate cells and dendritic cells in mice. Knockdown of MGP expression in stellate LX-2 cells led to a blunted response to TGF-β stimulation. This was associated with reduced regulatory SMAD phosphorylation and TGF-β receptor ALK1 expression as well as increased expression of inhibitory SMAD6. Hepatic MGP expression was found to be significantly correlated with the severity of fibrosis in livers of patients with NASH, suggesting relevance to human disease. CONCLUSIONS: MGP regulates liver fibrosis and TGF-β signaling in hepatic stellate cells and contributes to NASH pathogenesis.
Published: 2023

8. Human liver single nucleus and single cell RNA sequencing identify a hepatocellular carcinoma-associated cell-type affecting survival

Author: Alvarez, Marcus, Benhammou, Jihane N, Darci-Maher, Nicholas, French, Samuel W, Han, Steven B, Sinsheimer, Janet S, Agopian, Vatche G, Pisegna, Joseph R, and Pajukanta, Päivi
Subjects: Biological Sciences, Bioinformatics and Computational Biology, Digestive Diseases, Cancer, Clinical Research, Liver Cancer, Rare Diseases, Genetics, Human Genome, Liver Disease, Biotechnology, 2.1 Biological and endogenous factors, Aetiology, Good Health and Well Being, Biomarkers, Tumor, Carcinoma, Hepatocellular, Gene Expression Regulation, Neoplastic, Humans, Liver Neoplasms, Prognosis, Sequence Analysis, RNA, Clinical Sciences
Abstract: BackgroundHepatocellular carcinoma (HCC) is a common primary liver cancer with poor overall survival. We hypothesized that there are HCC-associated cell-types that impact patient survival.MethodsWe combined liver single nucleus (snRNA-seq), single cell (scRNA-seq), and bulk RNA-sequencing (RNA-seq) data to search for cell-type differences in HCC. To first identify cell-types in HCC, adjacent non-tumor tissue, and normal liver, we integrated single-cell level data from a healthy liver cohort (n = 9 non-HCC samples) collected in the Strasbourg University Hospital; an HCC cohort (n = 1 non-HCC, n = 14 HCC-tumor, and n = 14 adjacent non-tumor samples) collected in the Singapore General Hospital and National University; and another HCC cohort (n = 3 HCC-tumor and n = 3 adjacent non-tumor samples) collected in the Dumont-UCLA Liver Cancer Center. We then leveraged these single cell level data to decompose the cell-types in liver bulk RNA-seq data from HCC patients' tumor (n = 361) and adjacent non-tumor tissue (n = 49) from the Cancer Genome Atlas (TCGA) multi-center cohort. For replication, we decomposed 221 HCC and 209 adjacent non-tumor liver microarray samples from the Liver Cancer Institute (LCI) cohort collected by the Liver Cancer Institute and Zhongshan Hospital of Fudan University.ResultsWe discovered a tumor-associated proliferative cell-type, Prol (80.4% tumor cells), enriched for cell cycle and mitosis genes. In the liver bulk tissue from the TCGA cohort, the proportion of the Prol cell-type is significantly increased in HCC and associates with a worse overall survival. Independently from our decomposition analysis, we reciprocally show that Prol nuclei/cells significantly over-express both tumor-elevated and survival-decreasing genes obtained from the bulk tissue. Our replication analysis in the LCI cohort confirmed that an increased estimated proportion of the Prol cell-type in HCC is a significant marker for a shorter overall survival. Finally, we show that somatic mutations in the tumor suppressor genes TP53 and RB1 are linked to an increase of the Prol cell-type in HCC.ConclusionsBy integrating liver single cell, single nucleus, and bulk expression data from multiple cohorts we identified a proliferating cell-type (Prol) enriched in HCC tumors, associated with a decreased overall survival, and linked to TP53 and RB1 somatic mutations.
Published: 2022

9. Hyperinsulinemia Is Highly Associated With Markers of Hepatocytic Senescence in Two Independent Cohorts.

Author: Meijnikman, Abraham, van Olden, Casper, Aydin, Ömrüm, Herrema, Hilde, Kaminska, Dorota, Lappa, Dimitra, Männistö, Ville, Tremaroli, Valentina, Olofsson, Louise, de Brauw, Maurits, van de Laar, Arnold, Verheij, Joanne, Gerdes, Victor, Schwartz, Thue, Nielsen, Jens, Bäckhed, Fredrik, Pajukanta, Päivi, Pihlajamäki, Jussi, Tchkonia, Tamar, Kirkland, James, Kuipers, Folkert, Nieuwdorp, Max, and Groen, Albert
Subjects: Biomarkers, Diabetes Mellitus, Type 2, Humans, Hyperinsulinism, Insulin, Insulin Resistance, Liver, Non-alcoholic Fatty Liver Disease
Abstract: Cellular senescence is an essentially irreversible growth arrest that occurs in response to various cellular stressors and may contribute to development of type 2 diabetes mellitus and nonalcoholic fatty liver disease (NAFLD). In this article, we investigated whether chronically elevated insulin levels are associated with cellular senescence in the human liver. In 107 individuals undergoing bariatric surgery, hepatic senescence markers were assessed by immunohistochemistry as well as transcriptomics. A subset of 180 participants from the ongoing Finnish Kuopio OBesity Surgery (KOBS) study was used as validation cohort. We found plasma insulin to be highly associated with various markers of cellular senescence in liver tissue. The liver transcriptome of individuals with high insulin revealed significant upregulation of several genes associated with senescence: p21, TGFβ, PI3K, HLA-G, IL8, p38, Ras, and E2F. Insulin associated with hepatic senescence independently of NAFLD and plasma glucose. By using transcriptomic data from the KOBS study, we could validate the association of insulin with p21 in the liver. Our results support a potential role for hyperinsulinemia in induction of cellular senescence in the liver. These findings suggest possible benefits of lowering insulin levels in obese individuals with insulin resistance.
Published: 2022

10. Single nucleus RNA-sequencing integrated into risk variant colocalization discovers 17 cell-type-specific abdominal obesity genes for metabolic dysfunction-associated steatotic liver disease

Author: Lee, Seung Hyuk T., Garske, Kristina M., Arasu, Uma Thanigai, Kar, Asha, Miao, Zong, Alvarez, Marcus, Koka, Amogha, Darci-Maher, Nicholas, Benhammou, Jihane N., Pan, David Z., Örd, Tiit, Kaminska, Dorota, Männistö, Ville, Heinonen, Sini, Wabitsch, Martin, Laakso, Markku, Agopian, Vatche G., Pisegna, Joseph R., Pietiläinen, Kirsi H., Pihlajamäki, Jussi, Kaikkonen, Minna U., and Pajukanta, Päivi
Published: 2024
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11. Hepatokine ITIH3 protects against hepatic steatosis by downregulating mitochondrial bioenergetics and de novo lipogenesis

Author: Talari, Noble Kumar, Mattam, Ushodaya, Kaminska, Dorota, Sotomayor-Rodriguez, Irene, Rahman, Afra P., Péterfy, Miklós, Pajukanta, Päivi, Pihlajamäki, Jussi, and Chella Krishnan, Karthickeyan
Published: 2024
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12. Increased body mass index is linked to systemic inflammation through altered chromatin co-accessibility in human preadipocytes

Author: Garske, Kristina M., Kar, Asha, Comenho, Caroline, Balliu, Brunilda, Pan, David Z., Bhagat, Yash V., Rosenberg, Gregory, Koka, Amogha, Das, Sankha Subhra, Miao, Zong, Sinsheimer, Janet S., Kaprio, Jaakko, Pietiläinen, Kirsi H., and Pajukanta, Päivi
Published: 2023
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13. Identification of 90 NAFLD GWAS loci and establishment of NAFLD PRS and causal role of NAFLD in coronary artery disease.

Author: Miao, Zong, Garske, Kristina, Pan, David, Koka, Amogha, Kaminska, Dorota, Männistö, Ville, Sinsheimer, Janet, Pihlajamäki, Jussi, and Pajukanta, Päivi
Subjects: GWAS, Mendelian randomization, NAFLD, UK Biobank, polygenic risk score
Abstract: The prevalence of non-alcoholic fatty liver disease (NAFLD), now also known as metabolic dysfunction-associated fatty liver disease (MAFLD), is rapidly increasing worldwide due to the ongoing obesity epidemic. However, currently the NALFD diagnosis requires non-readily available imaging technologies or liver biopsy, which has drastically limited the sample sizes of NAFLD studies and hampered the discovery of its genetic component. Here we utilized the large UK Biobank (UKB) to accurately estimate the NAFLD status in UKB based on common serum traits and anthropometric measures. Scoring all individuals in UKB for NAFLD risk resulted in 28,396 NAFLD cases and 108,652 healthy individuals at a >90% confidence level. Using this imputed NAFLD status to perform the largest NAFLD genome-wide association study (GWAS) to date, we identified 94 independent (R2 < 0.2) NAFLD GWAS loci, of which 90 have not been identified before; built a polygenic risk score (PRS) model to predict the genetic risk of NAFLD; and used the GWAS variants of imputed NAFLD for a tissue-aware Mendelian randomization analysis that discovered a significant causal effect of NAFLD on coronary artery disease (CAD). In summary, we accurately estimated the NAFLD status in UKB using common serum traits and anthropometric measures, which empowered us to identify 90 GWAS NAFLD loci, build NAFLD PRS, and discover a significant causal effect of NAFLD on CAD.
Published: 2022

14. β2-spectrin (SPTBN1) as a therapeutic target for diet-induced liver disease and preventing cancer development.

Author: Rao, Shuyun, Yang, Xiaochun, Ohshiro, Kazufumi, Zaidi, Sobia, Wang, Zhanhuai, Shetty, Kirti, Xiang, Xiyan, Hassan, Md, Mohammad, Taj, Latham, Patricia, Nguyen, Bao-Ngoc, Wong, Linda, Yu, Herbert, Al-Abed, Yousef, Mishra, Bibhuti, Vacca, Michele, Guenigault, Gareth, Allison, Michael, Vidal-Puig, Antonio, Benhammou, Jihane, Alvarez, Marcus, Pajukanta, Päivi, Pisegna, Joseph, and Mishra, Lopa
Subjects: Animals, Diet, High-Fat, Liver, Mice, Mice, Inbred C57BL, Neoplasms, Non-alcoholic Fatty Liver Disease, Spectrin
Abstract: The prevalence of nonalcoholic steatohepatitis (NASH) and liver cancer is increasing. De novo lipogenesis and fibrosis contribute to disease progression and cancerous transformation. Here, we found that β2-spectrin (SPTBN1) promotes sterol regulatory element (SRE)–binding protein (SREBP)–stimulated lipogenesis and development of liver cancer in mice fed a high-fat diet (HFD) or a western diet (WD). Either hepatocyte-specific knockout of SPTBN1 or siRNA-mediated therapy protected mice from HFD/WD-induced obesity and fibrosis, lipid accumulation, and tissue damage in the liver. Biochemical analysis suggested that HFD/WD induces SPTBN1 and SREBP1 cleavage by CASPASE-3 and that the cleaved products interact to promote expression of genes with sterol response elements. Analysis of human NASH tissue revealed increased SPTBN1 and CASPASE-3 expression. Thus, our data indicate that SPTBN1 represents a potential target for therapeutic intervention in NASH and liver cancer.
Published: 2021

15. Electrical impedance tomography for non-invasive identification of fatty liver infiltrate in overweight individuals.

Author: Chang, Chih-Chiang, Huang, Zi-Yu, Shih, Shu-Fu, Luo, Yuan, Ko, Arthur, Cui, Qingyu, Sumner, Jennifer, Cavallero, Susana, Das, Swarna, Gao, Wei, Sinsheimer, Janet, Bui, Alex, Jacobs, Jonathan P, Pajukanta, Päivi, Wu, Holden, Tai, Yu-Chong, Li, Zhaoping, and Hsiai, Tzung K
Subjects: Digestive Diseases, Clinical Research, Biomedical Imaging, Chronic Liver Disease and Cirrhosis, Liver Disease, Oral and gastrointestinal
Abstract: Non-alcoholic fatty liver disease (NAFLD) is one of the most common causes of cardiometabolic diseases in overweight individuals. While liver biopsy is the current gold standard to diagnose NAFLD and magnetic resonance imaging (MRI) is a non-invasive alternative still under clinical trials, the former is invasive and the latter costly. We demonstrate electrical impedance tomography (EIT) as a portable method for detecting fatty infiltrate. We enrolled 19 overweight subjects to undergo liver MRI scans, followed by EIT measurements. The MRI images provided the a priori knowledge of the liver boundary conditions for EIT reconstruction, and the multi-echo MRI data quantified liver proton-density fat fraction (PDFF%) to validate fat infiltrate. Using the EIT electrode belts, we circumferentially injected pairwise current to the upper abdomen, followed by acquiring the resulting surface-voltage to reconstruct the liver conductivity. Pearson's correlation analyses compared EIT conductivity or MRI PDFF with body mass index, age, waist circumference, height, and weight variables. We reveal that the correlation between liver EIT conductivity or MRI PDFF with demographics is statistically insignificant, whereas liver EIT conductivity is inversely correlated with MRI PDFF (R = -0.69, p = 0.003, n = 16). As a pilot study, EIT conductivity provides a portable method for operator-independent and cost-effective detection of hepatic steatosis.
Published: 2021

16. Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes.

Author: Pan, David Z, Miao, Zong, Comenho, Caroline, Rajkumar, Sandhya, Koka, Amogha, Lee, Seung Hyuk T, Alvarez, Marcus, Kaminska, Dorota, Ko, Arthur, Sinsheimer, Janet S, Mohlke, Karen L, Mancuso, Nicholas, Muñoz-Hernandez, Linda Liliana, Herrera-Hernandez, Miguel, Tusié-Luna, Maria Teresa, Aguilar-Salinas, Carlos, Pietiläinen, Kirsi H, Pihlajamäki, Jussi, Laakso, Markku, Garske, Kristina M, and Pajukanta, Päivi
Subjects: Master transcription factor, Polygenic risk score, Trans regulation of genes, Transcriptional regulation of abdominal obesity, Type 2 diabetes, Waist-hip ratio adjusted for body mass index, Genetics, Clinical Sciences
Abstract: BackgroundObesity predisposes individuals to multiple cardiometabolic disorders, including type 2 diabetes (T2D). As body mass index (BMI) cannot reliably differentiate fat from lean mass, the metabolically detrimental abdominal obesity has been estimated using waist-hip ratio (WHR). Waist-hip ratio adjusted for body mass index (WHRadjBMI) in turn is a well-established sex-specific marker for abdominal fat and adiposity, and a predictor of adverse metabolic outcomes, such as T2D. However, the underlying genes and regulatory mechanisms orchestrating the sex differences in obesity and body fat distribution in humans are not well understood.MethodsWe searched for genetic master regulators of WHRadjBMI by employing integrative genomics approaches on human subcutaneous adipose RNA sequencing (RNA-seq) data (n ~ 1400) and WHRadjBMI GWAS data (n ~ 700,000) from the WHRadjBMI GWAS cohorts and the UK Biobank (UKB), using co-expression network, transcriptome-wide association study (TWAS), and polygenic risk score (PRS) approaches. Finally, we functionally verified our genomic results using gene knockdown experiments in a human primary cell type that is critical for adipose tissue function.ResultsHere, we identified an adipose gene co-expression network that contains 35 obesity GWAS genes and explains a significant amount of polygenic risk for abdominal obesity and T2D in the UKB (n = 392,551) in a sex-dependent way. We showed that this network is preserved in the adipose tissue data from the Finnish Kuopio Obesity Study and Mexican Obesity Study. The network is controlled by a novel adipose master transcription factor (TF), TBX15, a WHRadjBMI GWAS gene that regulates the network in trans. Knockdown of TBX15 in human primary preadipocytes resulted in changes in expression of 130 network genes, including the key adipose TFs, PPARG and KLF15, which were significantly impacted (FDR < 0.05), thus functionally verifying the trans regulatory effect of TBX15 on the WHRadjBMI co-expression network.ConclusionsOur study discovers a novel key function for the TBX15 TF in trans regulating an adipose co-expression network of 347 adipose, mitochondrial, and metabolically important genes, including PPARG, KLF15, PPARA, ADIPOQ, and 35 obesity GWAS genes. Thus, based on our converging genomic, transcriptional, and functional evidence, we interpret the role of TBX15 to be a main transcriptional regulator in the adipose tissue and discover its importance in human abdominal obesity.
Published: 2021

17. Integrative analysis of liver-specific non-coding regulatory SNPs associated with the risk of coronary artery disease.

Author: Selvarajan, Ilakya, Toropainen, Anu, Garske, Kristina, López Rodríguez, Maykel, Ko, Arthur, Miao, Zong, Kaminska, Dorota, Õunap, Kadri, Örd, Tiit, Ravindran, Aarthi, Liu, Oscar, Moreau, Pierre, Jawahar Deen, Ashik, Männistö, Ville, Pan, Calvin, Levonen, Anna-Liisa, Lusis, Aldons, Heikkinen, Sami, Romanoski, Casey, Pihlajamäki, Jussi, Pajukanta, Päivi, and Kaikkonen, Minna
Subjects: CRISPR, GWAS, SNP, STARR-seq, cholesterol, coronary artery disease, enhancer, functional genomics, hepatocyte, liver, Alleles, Chromatin, Coronary Artery Disease, Enhancer Elements, Genetic, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genomics, Humans, Liver, Male, Molecular Sequence Annotation, Organ Specificity, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Protein Binding, Quantitative Trait Loci, Risk Factors
Abstract: Genetic factors underlying coronary artery disease (CAD) have been widely studied using genome-wide association studies (GWASs). However, the functional understanding of the CAD loci has been limited by the fact that a majority of GWAS variants are located within non-coding regions with no functional role. High cholesterol and dysregulation of the liver metabolism such as non-alcoholic fatty liver disease confer an increased risk of CAD. Here, we studied the function of non-coding single-nucleotide polymorphisms in CAD GWAS loci located within liver-specific enhancer elements by identifying their potential target genes using liver cis-eQTL analysis and promoter Capture Hi-C in HepG2 cells. Altogether, 734 target genes were identified of which 121 exhibited correlations to liver-related traits. To identify potentially causal regulatory SNPs, the allele-specific enhancer activity was analyzed by (1) sequence-based computational predictions, (2) quantification of allele-specific transcription factor binding, and (3) STARR-seq massively parallel reporter assay. Altogether, our analysis identified 1,277 unique SNPs that display allele-specific regulatory activity. Among these, susceptibility enhancers near important cholesterol homeostasis genes (APOB, APOC1, APOE, and LIPA) were identified, suggesting that altered gene regulatory activity could represent another way by which genetic variation regulates serum lipoprotein levels. Using CRISPR-based perturbation, we demonstrate how the deletion/activation of a single enhancer leads to changes in the expression of many target genes located in a shared chromatin interaction domain. Our integrative genomics approach represents a comprehensive effort in identifying putative causal regulatory regions and target genes that could predispose to clinical manifestation of CAD by affecting liver function.
Published: 2021

18. Cross-Tissue Single-Nucleus RNA Sequencing Discovers Tissue-Resident Adipocytes Involved in Propanoate Metabolism in the Human Heart

Author: Das, Sankha Subhra, Kar, Asha, Rajkumar, Sandhya, Lee, Seung Hyuk T., Alvarez, Marcus, Pietiläinen, Kirsi H., and Pajukanta, Päivi
Published: 2023
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19. RIPK1 gene variants associate with obesity in humans and can be therapeutically silenced to reduce obesity in mice.

Author: Karunakaran, Denuja, Turner, Adam, Duchez, Anne-Claire, Soubeyrand, Sebastien, Rasheed, Adil, Smyth, David, Cook, David, Nikpay, Majid, Kandiah, Joshua, Pan, Calvin, Geoffrion, Michele, Lee, Richard, Boytard, Ludovic, Wyatt, Hailey, Nguyen, My-Anh, Lau, Paulina, Laakso, Markku, Ramkhelawon, Bhama, Alvarez, Marcus, Pietiläinen, Kirsi, Pajukanta, Päivi, Vanderhyden, Barbara, Liu, Peter, Berger, Scott, Gough, Peter, Bertin, John, Harper, Mary-Ellen, Lusis, Aldons, McPherson, Ruth, and Rayner, Katey
Subjects: Adipocytes, Adipose Tissue, Animals, Basic-Leucine Zipper Transcription Factors, Energy Metabolism, Gene Silencing, Glucose Tolerance Test, Humans, Male, Mice, Mice, Inbred C57BL, Mice, Obese, Obesity, Polymorphism, Genetic, Receptor-Interacting Protein Serine-Threonine Kinases, Subcutaneous Fat
Abstract: Obesity is a major public health burden worldwide and is characterized by chronic low-grade inflammation driven by the cooperation of the innate immune system and dysregulated metabolism in adipose tissue and other metabolic organs. Receptor-interacting serine/threonine-protein kinase 1 (RIPK1) is a central regulator of inflammatory cell function that coordinates inflammation, apoptosis and necroptosis in response to inflammatory stimuli. Here we show that genetic polymorphisms near the human RIPK1 locus associate with increased RIPK1 gene expression and obesity. We show that one of these single nucleotide polymorphisms is within a binding site for E4BP4 and increases RIPK1 promoter activity and RIPK1 gene expression in adipose tissue. Therapeutic silencing of RIPK1 in vivo in a mouse model of diet-induced obesity dramatically reduces fat mass, total body weight and improves insulin sensitivity, while simultaneously reducing macrophage and promoting invariant natural killer T cell accumulation in adipose tissue. These findings demonstrate that RIPK1 is genetically associated with obesity, and reducing RIPK1 expression is a potential therapeutic approach to target obesity and related diseases.
Published: 2020

20. A saturated map of common genetic variants associated with human height

Author: Yengo, Loïc, Vedantam, Sailaja, Marouli, Eirini, Sidorenko, Julia, Bartell, Eric, Sakaue, Saori, Graff, Marielisa, Eliasen, Anders U., Jiang, Yunxuan, Raghavan, Sridharan, Miao, Jenkai, Arias, Joshua D., Graham, Sarah E., Mukamel, Ronen E., Spracklen, Cassandra N., Yin, Xianyong, Chen, Shyh-Huei, Ferreira, Teresa, Highland, Heather H., Ji, Yingjie, Karaderi, Tugce, Lin, Kuang, Lüll, Kreete, Malden, Deborah E., Medina-Gomez, Carolina, Machado, Moara, Moore, Amy, Rüeger, Sina, Sim, Xueling, Vrieze, Scott, Ahluwalia, Tarunveer S., Akiyama, Masato, Allison, Matthew A., Alvarez, Marcus, Andersen, Mette K., Ani, Alireza, Appadurai, Vivek, Arbeeva, Liubov, Bhaskar, Seema, Bielak, Lawrence F., Bollepalli, Sailalitha, Bonnycastle, Lori L., Bork-Jensen, Jette, Bradfield, Jonathan P., Bradford, Yuki, Braund, Peter S., Brody, Jennifer A., Burgdorf, Kristoffer S., Cade, Brian E., Cai, Hui, Cai, Qiuyin, Campbell, Archie, Cañadas-Garre, Marisa, Catamo, Eulalia, Chai, Jin-Fang, Chai, Xiaoran, Chang, Li-Ching, Chang, Yi-Cheng, Chen, Chien-Hsiun, Chesi, Alessandra, Choi, Seung Hoan, Chung, Ren-Hua, Cocca, Massimiliano, Concas, Maria Pina, Couture, Christian, Cuellar-Partida, Gabriel, Danning, Rebecca, Daw, E. Warwick, Degenhard, Frauke, Delgado, Graciela E., Delitala, Alessandro, Demirkan, Ayse, Deng, Xuan, Devineni, Poornima, Dietl, Alexander, Dimitriou, Maria, Dimitrov, Latchezar, Dorajoo, Rajkumar, Ekici, Arif B., Engmann, Jorgen E., Fairhurst-Hunter, Zammy, Farmaki, Aliki-Eleni, Faul, Jessica D., Fernandez-Lopez, Juan-Carlos, Forer, Lukas, Francescatto, Margherita, Freitag-Wolf, Sandra, Fuchsberger, Christian, Galesloot, Tessel E., Gao, Yan, Gao, Zishan, Geller, Frank, Giannakopoulou, Olga, Giulianini, Franco, Gjesing, Anette P., Goel, Anuj, Gordon, Scott D., Gorski, Mathias, Grove, Jakob, Guo, Xiuqing, Gustafsson, Stefan, Haessler, Jeffrey, Hansen, Thomas F., Havulinna, Aki S., Haworth, Simon J., He, Jing, Heard-Costa, Nancy, Hebbar, Prashantha, Hindy, George, Ho, Yuk-Lam A., Hofer, Edith, Holliday, Elizabeth, Horn, Katrin, Hornsby, Whitney E., Hottenga, Jouke-Jan, Huang, Hongyan, Huang, Jie, Huerta-Chagoya, Alicia, Huffman, Jennifer E., Hung, Yi-Jen, Huo, Shaofeng, Hwang, Mi Yeong, Iha, Hiroyuki, Ikeda, Daisuke D., Isono, Masato, Jackson, Anne U., Jäger, Susanne, Jansen, Iris E., Johansson, Ingegerd, Jonas, Jost B., Jonsson, Anna, Jørgensen, Torben, Kalafati, Ioanna-Panagiota, Kanai, Masahiro, Kanoni, Stavroula, Kårhus, Line L., Kasturiratne, Anuradhani, Katsuya, Tomohiro, Kawaguchi, Takahisa, Kember, Rachel L., Kentistou, Katherine A., Kim, Han-Na, Kim, Young Jin, Kleber, Marcus E., Knol, Maria J., Kurbasic, Azra, Lauzon, Marie, Le, Phuong, Lea, Rodney, Lee, Jong-Young, Leonard, Hampton L., Li, Shengchao A., Li, Xiaohui, Li, Xiaoyin, Liang, Jingjing, Lin, Honghuang, Lin, Shih-Yi, Liu, Jun, Liu, Xueping, Lo, Ken Sin, Long, Jirong, Lores-Motta, Laura, Luan, Jian’an, Lyssenko, Valeriya, Lyytikäinen, Leo-Pekka, Mahajan, Anubha, Mamakou, Vasiliki, Mangino, Massimo, Manichaikul, Ani, Marten, Jonathan, Mattheisen, Manuel, Mavarani, Laven, McDaid, Aaron F., Meidtner, Karina, Melendez, Tori L., Mercader, Josep M., Milaneschi, Yuri, Miller, Jason E., Millwood, Iona Y., Mishra, Pashupati P., Mitchell, Ruth E., Møllehave, Line T., Morgan, Anna, Mucha, Soeren, Munz, Matthias, Nakatochi, Masahiro, Nelson, Christopher P., Nethander, Maria, Nho, Chu Won, Nielsen, Aneta A., Nolte, Ilja M., Nongmaithem, Suraj S., Noordam, Raymond, Ntalla, Ioanna, Nutile, Teresa, Pandit, Anita, Christofidou, Paraskevi, Pärna, Katri, Pauper, Marc, Petersen, Eva R. B., Petersen, Liselotte V., Pitkänen, Niina, Polašek, Ozren, Poveda, Alaitz, Preuss, Michael H., Pyarajan, Saiju, Raffield, Laura M., Rakugi, Hiromi, Ramirez, Julia, Rasheed, Asif, Raven, Dennis, Rayner, Nigel W., Riveros, Carlos, Rohde, Rebecca, Ruggiero, Daniela, Ruotsalainen, Sanni E., Ryan, Kathleen A., Sabater-Lleal, Maria, Saxena, Richa, Scholz, Markus, Sendamarai, Anoop, Shen, Botong, Shi, Jingchunzi, Shin, Jae Hun, Sidore, Carlo, Sitlani, Colleen M., Slieker, Roderick C., Smit, Roelof A. J., Smith, Albert V., Smith, Jennifer A., Smyth, Laura J., Southam, Lorraine, Steinthorsdottir, Valgerdur, Sun, Liang, Takeuchi, Fumihiko, Tallapragada, Divya Sri Priyanka, Taylor, Kent D., Tayo, Bamidele O., Tcheandjieu, Catherine, Terzikhan, Natalie, Tesolin, Paola, Teumer, Alexander, Theusch, Elizabeth, Thompson, Deborah J., Thorleifsson, Gudmar, Timmers, Paul R. H. J., Trompet, Stella, Turman, Constance, Vaccargiu, Simona, van der Laan, Sander W., van der Most, Peter J., van Klinken, Jan B., van Setten, Jessica, Verma, Shefali S., Verweij, Niek, Veturi, Yogasudha, Wang, Carol A., Wang, Chaolong, Wang, Lihua, Wang, Zhe, Warren, Helen R., Bin Wei, Wen, Wickremasinghe, Ananda R., Wielscher, Matthias, Wiggins, Kerri L., Winsvold, Bendik S., Wong, Andrew, Wu, Yang, Wuttke, Matthias, Xia, Rui, Xie, Tian, Yamamoto, Ken, Yang, Jingyun, Yao, Jie, Young, Hannah, Yousri, Noha A., Yu, Lei, Zeng, Lingyao, Zhang, Weihua, Zhang, Xinyuan, Zhao, Jing-Hua, Zhao, Wei, Zhou, Wei, Zimmermann, Martina E., Zoledziewska, Magdalena, Adair, Linda S., Adams, Hieab H. H., Aguilar-Salinas, Carlos A., Al-Mulla, Fahd, Arnett, Donna K., Asselbergs, Folkert W., Åsvold, Bjørn Olav, Attia, John, Banas, Bernhard, Bandinelli, Stefania, Bennett, David A., Bergler, Tobias, Bharadwaj, Dwaipayan, Biino, Ginevra, Bisgaard, Hans, Boerwinkle, Eric, Böger, Carsten A., Bønnelykke, Klaus, Boomsma, Dorret I., Børglum, Anders D., Borja, Judith B., Bouchard, Claude, Bowden, Donald W., Brandslund, Ivan, Brumpton, Ben, Buring, Julie E., Caulfield, Mark J., Chambers, John C., Chandak, Giriraj R., Chanock, Stephen J., Chaturvedi, Nish, Chen, Yii-Der Ida, Chen, Zhengming, Cheng, Ching-Yu, Christophersen, Ingrid E., Ciullo, Marina, Cole, John W., Collins, Francis S., Cooper, Richard S., Cruz, Miguel, Cucca, Francesco, Cupples, L. Adrienne, Cutler, Michael J., Damrauer, Scott M., Dantoft, Thomas M., de Borst, Gert J., de Groot, Lisette C. P. G. M., De Jager, Philip L., de Kleijn, Dominique P. V., Janaka de Silva, H., Dedoussis, George V., den Hollander, Anneke I., Du, Shufa, Easton, Douglas F., Elders, Petra J. M., Eliassen, A. Heather, Ellinor, Patrick T., Elmståhl, Sölve, Erdmann, Jeanette, Evans, Michele K., Fatkin, Diane, Feenstra, Bjarke, Feitosa, Mary F., Ferrucci, Luigi, Ford, Ian, Fornage, Myriam, Franke, Andre, Franks, Paul W., Freedman, Barry I., Gasparini, Paolo, Gieger, Christian, Girotto, Giorgia, Goddard, Michael E., Golightly, Yvonne M., Gonzalez-Villalpando, Clicerio, Gordon-Larsen, Penny, Grallert, Harald, Grant, Struan F. A., Grarup, Niels, Griffiths, Lyn, Gudnason, Vilmundur, Haiman, Christopher, Hakonarson, Hakon, Hansen, Torben, Hartman, Catharina A., Hattersley, Andrew T., Hayward, Caroline, Heckbert, Susan R., Heng, Chew-Kiat, Hengstenberg, Christian, Hewitt, Alex W., Hishigaki, Haretsugu, Hoyng, Carel B., Huang, Paul L., Huang, Wei, Hunt, Steven C., Hveem, Kristian, Hyppönen, Elina, Iacono, William G., Ichihara, Sahoko, Ikram, M. Arfan, Isasi, Carmen R., Jackson, Rebecca D., Jarvelin, Marjo-Riitta, Jin, Zi-Bing, Jöckel, Karl-Heinz, Joshi, Peter K., Jousilahti, Pekka, Jukema, J. Wouter, Kähönen, Mika, Kamatani, Yoichiro, Kang, Kui Dong, Kaprio, Jaakko, Kardia, Sharon L. R., Karpe, Fredrik, Kato, Norihiro, Kee, Frank, Kessler, Thorsten, Khera, Amit V., Khor, Chiea Chuen, Kiemeney, Lambertus A. L. M., Kim, Bong-Jo, Kim, Eung Kweon, Kim, Hyung-Lae, Kirchhof, Paulus, Kivimaki, Mika, Koh, Woon-Puay, Koistinen, Heikki A., Kolovou, Genovefa D., Kooner, Jaspal S., Kooperberg, Charles, Köttgen, Anna, Kovacs, Peter, Kraaijeveld, Adriaan, Kraft, Peter, Krauss, Ronald M., Kumari, Meena, Kutalik, Zoltan, Laakso, Markku, Lange, Leslie A., Langenberg, Claudia, Launer, Lenore J., Le Marchand, Loic, Lee, Hyejin, Lee, Nanette R., Lehtimäki, Terho, Li, Huaixing, Li, Liming, Lieb, Wolfgang, Lin, Xu, Lind, Lars, Linneberg, Allan, Liu, Ching-Ti, Liu, Jianjun, Loeffler, Markus, London, Barry, Lubitz, Steven A., Lye, Stephen J., Mackey, David A., Mägi, Reedik, Magnusson, Patrik K. E., Marcus, Gregory M., Vidal, Pedro Marques, Martin, Nicholas G., März, Winfried, Matsuda, Fumihiko, McGarrah, Robert W., McGue, Matt, McKnight, Amy Jayne, Medland, Sarah E., Mellström, Dan, Metspalu, Andres, Mitchell, Braxton D., Mitchell, Paul, Mook-Kanamori, Dennis O., Morris, Andrew D., Mucci, Lorelei A., Munroe, Patricia B., Nalls, Mike A., Nazarian, Saman, Nelson, Amanda E., Neville, Matt J., Newton-Cheh, Christopher, Nielsen, Christopher S., Nöthen, Markus M., Ohlsson, Claes, Oldehinkel, Albertine J., Orozco, Lorena, Pahkala, Katja, Pajukanta, Päivi, Palmer, Colin N. A., Parra, Esteban J., Pattaro, Cristian, Pedersen, Oluf, Pennell, Craig E., Penninx, Brenda W. J. H., Perusse, Louis, Peters, Annette, Peyser, Patricia A., Porteous, David J., Posthuma, Danielle, Power, Chris, Pramstaller, Peter P., Province, Michael A., Qi, Qibin, Qu, Jia, Rader, Daniel J., Raitakari, Olli T., Ralhan, Sarju, Rallidis, Loukianos S., Rao, Dabeeru C., Redline, Susan, Reilly, Dermot F., Reiner, Alexander P., Rhee, Sang Youl, Ridker, Paul M., Rienstra, Michiel, Ripatti, Samuli, Ritchie, Marylyn D., Roden, Dan M., Rosendaal, Frits R., Rotter, Jerome I., Rudan, Igor, Rutters, Femke, Sabanayagam, Charumathi, Saleheen, Danish, Salomaa, Veikko, Samani, Nilesh J., Sanghera, Dharambir K., Sattar, Naveed, Schmidt, Börge, Schmidt, Helena, Schmidt, Reinhold, Schulze, Matthias B., Schunkert, Heribert, Scott, Laura J., Scott, Rodney J., Sever, Peter, Shiroma, Eric J., Shoemaker, M. Benjamin, Shu, Xiao-Ou, Simonsick, Eleanor M., Sims, Mario, Singh, Jai Rup, Singleton, Andrew B., Sinner, Moritz F., Smith, J. Gustav, Snieder, Harold, Spector, Tim D., Stampfer, Meir J., Stark, Klaus J., Strachan, David P., ‘t Hart, Leen M., Tabara, Yasuharu, Tang, Hua, Tardif, Jean-Claude, Thanaraj, Thangavel A., Timpson, Nicholas J., Tönjes, Anke, Tremblay, Angelo, Tuomi, Tiinamaija, Tuomilehto, Jaakko, Tusié-Luna, Maria-Teresa, Uitterlinden, Andre G., van Dam, Rob M., van der Harst, Pim, Van der Velde, Nathalie, van Duijn, Cornelia M., van Schoor, Natasja M., Vitart, Veronique, Völker, Uwe, Vollenweider, Peter, Völzke, Henry, Wacher-Rodarte, Niels H., Walker, Mark, Wang, Ya Xing, Wareham, Nicholas J., Watanabe, Richard M., Watkins, Hugh, Weir, David R., Werge, Thomas M., Widen, Elisabeth, Wilkens, Lynne R., Willemsen, Gonneke, Willett, Walter C., Wilson, James F., Wong, Tien-Yin, Woo, Jeong-Taek, Wright, Alan F., Wu, Jer-Yuarn, Xu, Huichun, Yajnik, Chittaranjan S., Yokota, Mitsuhiro, Yuan, Jian-Min, Zeggini, Eleftheria, Zemel, Babette S., Zheng, Wei, Zhu, Xiaofeng, Zmuda, Joseph M., Zonderman, Alan B., Zwart, John-Anker, Chasman, Daniel I., Cho, Yoon Shin, Heid, Iris M., McCarthy, Mark I., Ng, Maggie C. Y., O’Donnell, Christopher J., Rivadeneira, Fernando, Thorsteinsdottir, Unnur, Sun, Yan V., Tai, E. Shyong, Boehnke, Michael, Deloukas, Panos, Justice, Anne E., Lindgren, Cecilia M., Loos, Ruth J. F., Mohlke, Karen L., North, Kari E., Stefansson, Kari, Walters, Robin G., Winkler, Thomas W., Young, Kristin L., Loh, Po-Ru, Yang, Jian, Esko, Tõnu, Assimes, Themistocles L., Auton, Adam, Abecasis, Goncalo R., Willer, Cristen J., Locke, Adam E., Berndt, Sonja I., Lettre, Guillaume, Frayling, Timothy M., Okada, Yukinori, Wood, Andrew R., Visscher, Peter M., and Hirschhorn, Joel N.
Published: 2022
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21. Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM.

Author: Alvarez, Marcus, Rahmani, Elior, Jew, Brandon, Garske, Kristina M, Miao, Zong, Benhammou, Jihane N, Ye, Chun Jimmie, Pisegna, Joseph R, Pietiläinen, Kirsi H, Halperin, Eran, and Pajukanta, Päivi
Abstract: Single-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. We observe that snRNA-seq is commonly subject to contamination by high amounts of ambient RNA, which can lead to biased downstream analyses, such as identification of spurious cell types if overlooked. We present a novel approach to quantify contamination and filter droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: (1) human differentiating preadipocytes in vitro, (2) fresh mouse brain tissue, and (3) human frozen adipose tissue (AT) from six individuals. All three data sets showed evidence of extranuclear RNA contamination, and we observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq, our clustering strategy also successfully filtered single-cell RNA-seq data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.
Published: 2020

22. Publisher Correction: Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

Author: Jew, Brandon, Alvarez, Marcus, Rahmani, Elior, Miao, Zong, Ko, Arthur, Garske, Kristina M, Sul, Jae Hoon, Pietiläinen, Kirsi H, Pajukanta, Päivi, and Halperin, Eran
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Published: 2020

23. Accurate estimation of cell composition in bulk expression through robust integration of single-cell information.

Author: Jew, Brandon, Alvarez, Marcus, Rahmani, Elior, Miao, Zong, Ko, Arthur, Garske, Kristina M, Sul, Jae Hoon, Pietiläinen, Kirsi H, Pajukanta, Päivi, and Halperin, Eran
Abstract: We present Bisque, a tool for estimating cell type proportions in bulk expression. Bisque implements a regression-based approach that utilizes single-cell RNA-seq (scRNA-seq) or single-nucleus RNA-seq (snRNA-seq) data to generate a reference expression profile and learn gene-specific bulk expression transformations to robustly decompose RNA-seq data. These transformations significantly improve decomposition performance compared to existing methods when there is significant technical variation in the generation of the reference profile and observed bulk expression. Importantly, compared to existing methods, our approach is extremely efficient, making it suitable for the analysis of large genomic datasets that are becoming ubiquitous. When applied to subcutaneous adipose and dorsolateral prefrontal cortex expression datasets with both bulk RNA-seq and snRNA-seq data, Bisque replicates previously reported associations between cell type proportions and measured phenotypes across abundant and rare cell types. We further propose an additional mode of operation that merely requires a set of known marker genes.
Published: 2020

24. Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.

Author: Mefford, Joel, Park, Danny, Zheng, Zhili, Ko, Arthur, Ala-Korpela, Mika, Laakso, Markku, Pajukanta, Päivi, Yang, Jian, Witte, John, and Zaitlen, Noah
Subjects: Humans, Genetic Predisposition to Disease, Linear Models, Risk Factors, Multifactorial Inheritance, Phenotype, Genome-Wide Association Study, BLUP, PCA, PRS, linear mixed model, polygenic risk score, Prevention, Genetics, Mathematical Sciences, Biological Sciences, Information and Computing Sciences, Bioinformatics
Abstract: Large-scale cohorts with combined genetic and phenotypic data, coupled with methodological advances, have produced increasingly accurate genetic predictors of complex human phenotypes called polygenic risk scores (PRSs). In addition to the potential translational impacts of identifying at-risk individuals, PRS are being utilized for a growing list of scientific applications, including causal inference, identifying pleiotropy and genetic correlation, and powerful gene-based and mixed-model association tests. Existing PRS approaches rely on external large-scale genetic cohorts that have also measured the phenotype of interest. They further require matching on ancestry and genotyping platform or imputation quality. In this work, we present a novel reference-free method to produce a PRS that does not rely on an external cohort. We show that naive implementations of reference-free PRS either result in substantial overfitting or prohibitive increases in computational time. We show that our algorithm avoids both of these issues and can produce informative in-sample PRSs over a single cohort without overfitting. We then demonstrate several novel applications of reference-free PRSs, including detection of pleiotropy across 246 metabolic traits and efficient mixed-model association testing.
Published: 2020

25. Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution.

Author: Wu, Ying, Broadaway, K, Raulerson, Chelsea, Scott, Laura, Pan, Calvin, Ko, Arthur, He, Aiqing, Tilford, Charles, Fuchsberger, Christian, Locke, Adam, Stringham, Heather, Jackson, Anne, Narisu, Narisu, Kuusisto, Johanna, Pajukanta, Päivi, Collins, Francis, Boehnke, Michael, Laakso, Markku, Lusis, Aldons, Civelek, Mete, and Mohlke, Karen
Subjects: Adipose Tissue, Adult, Bayes Theorem, Body Fat Distribution, Body Mass Index, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Male, Metabolic Syndrome, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Subcutaneous Fat, Waist-Hip Ratio
Abstract: Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist-hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.
Published: 2019

26. Adipose Tissue Gene Expression Associations Reveal Hundreds of Candidate Genes for Cardiometabolic Traits.

Author: Raulerson, Chelsea, Ko, Arthur, Kidd, John, Currin, Kevin, Brotman, Sarah, Cannon, Maren, Wu, Ying, Spracklen, Cassandra, Jackson, Anne, Stringham, Heather, Welch, Ryan, Fuchsberger, Christian, Locke, Adam, Narisu, Narisu, Lusis, Aldons, Civelek, Mete, Furey, Terrence, Kuusisto, Johanna, Collins, Francis, Boehnke, Michael, Scott, Laura, Lin, Dan-Yu, Love, Michael, Laakso, Markku, Pajukanta, Päivi, and Mohlke, Karen
Subjects: GWAS, adipocyte, colocalization, diabetes, eQTL, lipid, obesity, secondary signal, trans-ancestry, waist-hip ratio, Adipose Tissue, Alleles, Body Mass Index, Diabetes Mellitus, Type 2, Finland, Gene Expression, Genome-Wide Association Study, Humans, Male, Obesity, Quantitative Trait Loci, Waist-Hip Ratio
Abstract: Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic traits including type 2 diabetes (T2D), lipid levels, body fat distribution, and adiposity, although most causal genes remain unknown. We used subcutaneous adipose tissue RNA-seq data from 434 Finnish men from the METSIM study to identify 9,687 primary and 2,785 secondary cis-expression quantitative trait loci (eQTL;
Published: 2019

27. ACE2 expression in adipose tissue is associated with cardio-metabolic risk factors and cell type composition—implications for COVID-19

Author: El-Sayed Moustafa, Julia S., Jackson, Anne U., Brotman, Sarah M., Guan, Li, Villicaña, Sergio, Roberts, Amy L., Zito, Antonino, Bonnycastle, Lori, Erdos, Michael R., Narisu, Narisu, Stringham, Heather M., Welch, Ryan, Yan, Tingfen, Lakka, Timo, Parker, Stephen, Tuomilehto, Jaakko, Seow, Jeffrey, Graham, Carl, Huettner, Isabella, Acors, Sam, Kouphou, Neophytos, Wadge, Samuel, Duncan, Emma L., Steves, Claire J., Doores, Katie J., Malim, Michael H., Collins, Francis S., Pajukanta, Päivi, Boehnke, Michael, Koistinen, Heikki A., Laakso, Markku, Falchi, Mario, Bell, Jordana T., Scott, Laura J., Mohlke, Karen L., and Small, Kerrin S.
Published: 2022
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28. Hepatokine ITIH3 Protects Against Hepatic Steatosis by Downregulating Mitochondrial Bioenergetics and De Novo Lipogenesis

Author: Kumar Talari, Noble, primary, Mattam, Ushodaya, additional, Kaminska, Dorota, additional, Sotomayor-Rodriguez, Irene, additional, Rahman, Afra, additional, Péterfy, Miklós, additional, and Pajukanta, Päivi, additional
Published: 2024
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29. Twin pair analysis uncovers novel links between DNA methylation, mitochondrial DNA quantity and obesity

Author: Heikkinen, Aino, primary, Esser, Vivienne F C, additional, Lundgren, Sara, additional, Lee, Seung Hyuk T, additional, Hakkarainen, Antti, additional, Lundbom, Jesper, additional, Kuula, Juho, additional, Groop, Per-Henrik, additional, Heinonen, Sini, additional, Pajukanta, Päivi, additional, Kaprio, Jaakko, additional, Li, Shuai, additional, and Ollikainen, Miina, additional
Published: 2024
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30. Single-cell dissection of the obesity-exercise axis in adipose-muscle tissues implies a critical role for mesenchymal stem cells

Author: Yang, Jiekun, Vamvini, Maria, Nigro, Pasquale, Ho, Li-Lun, Galani, Kyriakitsa, Alvarez, Marcus, Tanigawa, Yosuke, Renfro, Ashley, Carbone, Nicholas P., Laakso, Markku, Agudelo, Leandro Z., Pajukanta, Päivi, Hirshman, Michael F., Middelbeek, Roeland J.W., Grove, Kevin, Goodyear, Laurie J., and Kellis, Manolis
Published: 2022
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31. Reverse gene-environment interaction approach to identify variants influencing body-mass index in humans.

Author: Garske, Kristina M, Pan, David Z, Miao, Zong, Bhagat, Yash V, Comenho, Caroline, Robles, Christopher R, Benhammou, Jihane N, Alvarez, Marcus, Ko, Arthur, Ye, Chun Jimmie, Pisegna, Joseph R, Mohlke, Karen L, Sinsheimer, Janet S, Laakso, Markku, and Pajukanta, Päivi
Subjects: Cells, Cultured, Humans, Obesity, Dietary Fats, Body Mass Index, Models, Genetic, Gene-Environment Interaction, Human Genome, Nutrition, Clinical Research, Genetics, 1.1 Normal biological development and functioning, Metabolic and endocrine
Abstract: Identifying gene-environment interactions (GxEs) contributing to human cardiometabolic disorders is challenging. Here we apply a reverse GxE candidate search by deriving candidate variants from promoter-enhancer interactions that respond to dietary fatty acid challenge through altered chromatin accessibility in human primary adipocytes. We then test all variants residing in the lipid-responsive open chromatin sites within adipocyte promoter-enhancer contacts for interaction effects between the genotype and dietary saturated fat intake on body mass index (BMI) in the UK Biobank. We discover 14 novel GxE variants in 12 lipid-responsive promoters, including well-known lipid genes (LIPE, CARM1, and PLIN2) and novel genes, such as LDB3, for which we provide further functional and integrative genomics evidence. We further identify 24 GxE variants in enhancers, totaling 38 new GxE variants for BMI in the UK Biobank, demonstrating that molecular genomics data produced in physiologically relevant contexts can discover new functional GxE mechanisms in humans.
Published: 2019

32. Reverse GWAS: Using genetics to identify and model phenotypic subtypes.

Author: Dahl, Andy, Cai, Na, Ko, Arthur, Laakso, Markku, Pajukanta, Päivi, Flint, Jonathan, and Zaitlen, Noah
Subjects: Algorithms, Blood Glucose, Cluster Analysis, Computer Simulation, Coronary Disease, Depressive Disorder, Major, Diabetes Mellitus, Type 2, Genome-Wide Association Study, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Lipids, Models, Genetic, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Prediabetic State, Quantitative Trait Loci
Abstract: Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automated statistical approaches to subtype definition valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a novel decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show that modelling these features can be crucial for power and calibration. We validate RGWAS in practice by recovering a recently discovered stress subtype in major depression. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests subtypes may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting the subtypes have potential translational value.
Published: 2019

33. A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context

Author: Gallois, Apolline, Mefford, Joel, Ko, Arthur, Vaysse, Amaury, Julienne, Hanna, Ala-Korpela, Mika, Laakso, Markku, Zaitlen, Noah, Pajukanta, Päivi, and Aschard, Hugues
Subjects: Human Genome, Genetics, 2.1 Biological and endogenous factors, Aetiology, Aged, Amino Acids, Cohort Studies, Fatty Acids, Gene Regulatory Networks, Genetic Pleiotropy, Genome-Wide Association Study, Humans, Lipoproteins, HDL, Lipoproteins, IDL, Lipoproteins, LDL, Lipoproteins, VLDL, Magnetic Resonance Spectroscopy, Male, Metabolic Syndrome, Metabolome, Middle Aged, Polymorphism, Single Nucleotide
Abstract: Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level.
Published: 2019

34. Genetic and environmental perturbations lead to regulatory decoherence

Author: Lea, Amanda, Subramaniam, Meena, Ko, Arthur, Lehtimäki, Terho, Raitoharju, Emma, Kähönen, Mika, Seppälä, Ilkka, Mononen, Nina, Raitakari, Olli T, Ala-Korpela, Mika, Pajukanta, Päivi, Zaitlen, Noah, and Ayroles, Julien F
Subjects: Genetics, Human Genome, 2.1 Biological and endogenous factors, Aetiology, Computer Simulation, Environmental Exposure, Gene Expression Profiling, Genetic Variation, Humans, Individuality, Magnetic Resonance Spectroscopy, Metabolic Diseases, Metabolome, Quantitative Trait Loci, co-expression, decanalization, decoherence, genetics, genomics, human, human biology, medicine, metabolic syndrome, Biochemistry and Cell Biology
Abstract: Correlation among traits is a fundamental feature of biological systems that remains difficult to study. To address this problem, we developed a flexible approach that allows us to identify factors associated with inter-individual variation in correlation. We use data from three human cohorts to study the effects of genetic and environmental variation on correlations among mRNA transcripts and among NMR metabolites. We first show that environmental exposures (infection and disease) lead to a systematic loss of correlation, which we define as 'decoherence'. Using longitudinal data, we show that decoherent metabolites are better predictors of whether someone will develop metabolic syndrome than metabolites commonly used as biomarkers of this disease. Finally, we demonstrate that correlation itself is under genetic control by mapping hundreds of 'correlation quantitative trait loci (QTLs)'. Together, this work furthers our understanding of how and why coordinated biological processes break down, and points to a potential role for decoherence in disease.Editorial noteThis article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
Published: 2019

35. Phenotype-Specific Enrichment of Mendelian Disorder Genes near GWAS Regions across 62 Complex Traits

Author: Freund, Malika Kumar, Burch, Kathryn S, Shi, Huwenbo, Mancuso, Nicholas, Kichaev, Gleb, Garske, Kristina M, Pan, David Z, Miao, Zong, Mohlke, Karen L, Laakso, Markku, Pajukanta, Päivi, Pasaniuc, Bogdan, and Arboleda, Valerie A
Subjects: Biological Sciences, Genetics, Human Genome, Brain Disorders, 2.1 Biological and endogenous factors, Aetiology, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Multifactorial Inheritance, Phenotype, Polymorphism, Single Nucleotide, Promoter Regions, Genetic, Quantitative Trait Loci, Transcription Initiation Site, GWAS, Hi-C, Mendelian, body mass index, common disease, complex traits, monogenic, polygenic, statistical genetics, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract: Although recent studies provide evidence for a common genetic basis between complex traits and Mendelian disorders, a thorough quantification of their overlap in a phenotype-specific manner remains elusive. Here, we have quantified the overlap of genes identified through large-scale genome-wide association studies (GWASs) for 62 complex traits and diseases with genes containing mutations known to cause 20 broad categories of Mendelian disorders. We identified a significant enrichment of genes linked to phenotypically matched Mendelian disorders in GWAS gene sets; of the total 1,240 comparisons, a higher proportion of phenotypically matched or related pairs (n = 50 of 92 [54%]) than phenotypically unmatched pairs (n = 27 of 1,148 [2%]) demonstrated significant overlap, confirming a phenotype-specific enrichment pattern. Further, we observed elevated GWAS effect sizes near genes linked to phenotypically matched Mendelian disorders. Finally, we report examples of GWAS variants localized at the transcription start site or physically interacting with the promoters of genes linked to phenotypically matched Mendelian disorders. Our results are consistent with the hypothesis that genes that are disrupted in Mendelian disorders are dysregulated by non-coding variants in complex traits and demonstrate how leveraging findings from related Mendelian disorders and functional genomic datasets can prioritize genes that are putatively dysregulated by local and distal non-coding GWAS variants.
Published: 2018

36. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author: Ramdas, Shweta, Judd, Jonathan, Graham, Sarah E., Kanoni, Stavroula, Wang, Yuxuan, Surakka, Ida, Wenz, Brandon, Clarke, Shoa L., Chesi, Alessandra, Wells, Andrew, Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W., Locke, Adam E., Marouli, Eirini, Zajac, Greg J.M., Wu, Kuan-Han H., Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T., Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F., Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M., Rasheed, Humaira, Havulinna, Aki S., Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A., Lingren, Todd, Feng, QiPing, Kullo, Iftikhar J., Narita, Akira, Takayama, Jun, Martin, Hilary C., Hunt, Karen A., Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E., Campbell, Archie, Lin, Kuang, Millwood, Iona Y., Rasheed, Asif, Hindy, George, Faul, Jessica D., Zhao, Wei, Weir, David R., Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R., Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M., Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Wood, Andrew R., Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E., Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A., Yao, Jie, Manichaikul, Ani, Lee, Wen-Jane, Hsiung, Chao Agnes, Warren, Helen R., Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L., Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Mauro, Pala, Matteo, Floris, McDaid, Aaron F., Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, Sattar, Naveed, Møllehave, Line T., Munz, Matthias, Zeng, Lingyao, Huang, Jianfeng, Yang, Bin, Poveda, Alaitz, Kurbasic, Azra, Schönherr, Sebastian, Forer, Lukas, Scholz, Markus, Galesloot, Tessel E., Bradfield, Jonathan P., Ruotsalainen, Sanni E., Daw, E. Warwick, Zmuda, Joseph M., Mitchell, Jonathan S., Fuchsberger, Christian, Christensen, Henry, Brody, Jennifer A., Le, Phuong, Feitosa, Mary F., Wojczynski, Mary K., Hemerich, Daiane, Preuss, Michael, Mangino, Massimo, Christofidou, Paraskevi, Verweij, Niek, Benjamins, Jan W., Engmann, Jorgen, Noah, Tsao L., Verma, Anurag, Slieker, Roderick C., Lo, Ken Sin, Zilhao, Nuno R., Kleber, Marcus E., Delgado, Graciela E., Huo, Shaofeng, Ikeda, Daisuke D., Iha, Hiroyuki, Yang, Jian, Liu, Jun, Demirkan, Ayşe, Leonard, Hampton L., Marten, Jonathan, Emmel, Carina, Schmidt, Börge, Smyth, Laura J., Cañadas-Garre, Marisa, Wang, Chaolong, Nakatochi, Masahiro, Wong, Andrew, Hutri-Kähönen, Nina, Sim, Xueling, Xia, Rui, Huerta-Chagoya, Alicia, Fernandez-Lopez, Juan Carlos, Lyssenko, Valeriya, Nongmaithem, Suraj S., Sankareswaran, Alagu, Irvin, Marguerite R., Oldmeadow, Christopher, Kim, Han-Na, Ryu, Seungho, Timmers, Paul R.H.J., Arbeeva, Liubov, Dorajoo, Rajkumar, Lange, Leslie A., Prasad, Gauri, Lorés-Motta, Laura, Pauper, Marc, Long, Jirong, Li, Xiaohui, Theusch, Elizabeth, Takeuchi, Fumihiko, Spracklen, Cassandra N., Loukola, Anu, Bollepalli, Sailalitha, Warner, Sophie C., Wang, Ya Xing, Wei, Wen B., Nutile, Teresa, Ruggiero, Daniela, Sung, Yun Ju, Chen, Shufeng, Liu, Fangchao, Yang, Jingyun, Kentistou, Katherine A., Banas, Bernhard, Morgan, Anna, Meidtner, Karina, Bielak, Lawrence F., Smith, Jennifer A., Hebbar, Prashantha, Farmaki, Aliki-Eleni, Hofer, Edith, Lin, Maoxuan, Concas, Maria Pina, Vaccargiu, Simona, van der Most, Peter J., Pitkänen, Niina, Cade, Brian E., van der Laan, Sander W., Chitrala, Kumaraswamy Naidu, Weiss, Stefan, Bentley, Amy R., Doumatey, Ayo P., Adeyemo, Adebowale A., Lee, Jong Young, Petersen, Eva R.B., Nielsen, Aneta A., Choi, Hyeok Sun, Nethander, Maria, Freitag-Wolf, Sandra, Southam, Lorraine, Rayner, Nigel W., Wang, Carol A., Lin, Shih-Yi, Wang, Jun-Sing, Couture, Christian, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Cuellar-Partida, Gabriel, Vestergaard, Henrik, Hidalgo, Bertha, Giannakopoulou, Olga, Cai, Qiuyin, Obura, Morgan O., van Setten, Jessica, He, Karen Y., Tang, Hua, Terzikhan, Natalie, Shin, Jae Hun, Jackson, Rebecca D., Reiner, Alexander P., Martin, Lisa Warsinger, Chen, Zhengming, Li, Liming, Kawaguchi, Takahisa, Thiery, Joachim, Bis, Joshua C., Launer, Lenore J., Li, Huaixing, Nalls, Mike A., Raitakari, Olli T., Ichihara, Sahoko, Wild, Sarah H., Nelson, Christopher P., Campbell, Harry, Jäger, Susanne, Nabika, Toru, Al-Mulla, Fahd, Niinikoski, Harri, Braund, Peter S., Kolcic, Ivana, Kovacs, Peter, Giardoglou, Tota, Katsuya, Tomohiro, de Kleijn, Dominique, de Borst, Gert J., Kim, Eung Kweon, Adams, Hieab H.H., Ikram, M. Arfan, Zhu, Xiaofeng, Asselbergs, Folkert W., Kraaijeveld, Adriaan O., Beulens, Joline W.J., Shu, Xiao-Ou, Rallidis, Loukianos S., Pedersen, Oluf, Hansen, Torben, Mitchell, Paul, Hewitt, Alex W., Kähönen, Mika, Pérusse, Louis, Bouchard, Claude, Tönjes, Anke, Ida Chen, Yii-Der, Pennell, Craig E., Mori, Trevor A., Lieb, Wolfgang, Franke, Andre, Ohlsson, Claes, Mellström, Dan, Cho, Yoon Shin, Lee, Hyejin, Yuan, Jian-Min, Koh, Woon-Puay, Rhee, Sang Youl, Woo, Jeong-Taek, Heid, Iris M., Stark, Klaus J., Zimmermann, Martina E., Völzke, Henry, Homuth, Georg, Evans, Michele K., Zonderman, Alan B., Polasek, Ozren, Pasterkamp, Gerard, Hoefer, Imo E., Redline, Susan, Pahkala, Katja, Oldehinkel, Albertine J., Snieder, Harold, Biino, Ginevra, Schmidt, Reinhold, Schmidt, Helena, Bandinelli, Stefania, Dedoussis, George, Thanaraj, Thangavel Alphonse, Peyser, Patricia A., Kato, Norihiro, Schulze, Matthias B., Girotto, Giorgia, Böger, Carsten A., Jung, Bettina, Joshi, Peter K., Bennett, David A., De Jager, Philip L., Lu, Xiangfeng, Mamakou, Vasiliki, Brown, Morris, Caulfield, Mark J., Munroe, Patricia B., Guo, Xiuqing, Ciullo, Marina, Jonas, Jost B., Samani, Nilesh J., Kaprio, Jaakko, Pajukanta, Päivi, Tusié-Luna, Teresa, Aguilar-Salinas, Carlos A., Adair, Linda S., Bechayda, Sonny Augustin, de Silva, H. Janaka, Wickremasinghe, Ananda R., Krauss, Ronald M., Wu, Jer-Yuarn, Zheng, Wei, den Hollander, Anneke I., Bharadwaj, Dwaipayan, Correa, Adolfo, Wilson, James G., Lind, Lars, Heng, Chew-Kiat, Nelson, Amanda E., Golightly, Yvonne M., Wilson, James F., Penninx, Brenda, Kim, Hyung-Lae, Attia, John, Scott, Rodney J., Rao, D.C., Arnett, Donna K., Walker, Mark, Scott, Laura J., Koistinen, Heikki A., Chandak, Giriraj R., Mercader, Josep M., Villalpando, Clicerio Gonzalez, Orozco, Lorena, Fornage, Myriam, Tai, E. Shyong, van Dam, Rob M., Lehtimäki, Terho, Chaturvedi, Nish, Yokota, Mitsuhiro, Liu, Jianjun, Reilly, Dermot F., McKnight, Amy Jayne, Kee, Frank, Jöckel, Karl-Heinz, McCarthy, Mark I., Palmer, Colin N.A., Vitart, Veronique, Hayward, Caroline, Simonsick, Eleanor, van Duijn, Cornelia M., Jin, Zi-Bing, Lu, Fan, Hishigaki, Haretsugu, Lin, Xu, März, Winfried, Gudnason, Vilmundur, Tardif, Jean-Claude, Lettre, Guillaume, t Hart, Leen M., Elders, Petra J.M., Rader, Daniel J., Damrauer, Scott M., Kumari, Meena, Kivimaki, Mika, van der Harst, Pim, Spector, Tim D., Loos, Ruth J.F., Province, Michael A., Parra, Esteban J., Cruz, Miguel, Psaty, Bruce M., Brandslund, Ivan, Pramstaller, Peter P., Rotimi, Charles N., Christensen, Kaare, Ripatti, Samuli, Widén, Elisabeth, Hakonarson, Hakon, Grant, Struan F.A., Kiemeney, Lambertus, de Graaf, Jacqueline, Loeffler, Markus, Kronenberg, Florian, Gu, Dongfeng, Erdmann, Jeanette, Schunkert, Heribert, Franks, Paul W., Linneberg, Allan, Jukema, J. Wouter, Khera, Amit V., Männikkö, Minna, Jarvelin, Marjo-Riitta, Kutalik, Zoltan, Francesco, Cucca, Mook-Kanamori, Dennis O., Willems van Dijk, Ko, Watkins, Hugh, Strachan, David P., Grarup, Niels, Sever, Peter, Poulter, Neil, Huey-Herng Sheu, Wayne, Rotter, Jerome I., Dantoft, Thomas M., Karpe, Fredrik, Neville, Matt J., Timpson, Nicholas J., Cheng, Ching-Yu, Wong, Tien-Yin, Khor, Chiea Chuen, Li, Hengtong, Sabanayagam, Charumathi, Peters, Annette, Gieger, Christian, Hattersley, Andrew T., Pedersen, Nancy L., Magnusson, Patrik K.E., Boomsma, Dorret I., de Geus, Eco J.C., Cupples, L. Adrienne, van Meurs, Joyce B.J., Ikram, Arfan, Ghanbari, Mohsen, Gordon-Larsen, Penny, Huang, Wei, Kim, Young Jin, Tabara, Yasuharu, Wareham, Nicholas J., Langenberg, Claudia, Zeggini, Eleftheria, Tuomilehto, Jaakko, Kuusisto, Johanna, Laakso, Markku, Ingelsson, Erik, Abecasis, Goncalo, Chambers, John C., Kooner, Jaspal S., de Vries, Paul S., Morrison, Alanna C., Hazelhurst, Scott, Ramsay, Michèle, North, Kari E., Daviglus, Martha, Kraft, Peter, Martin, Nicholas G., Whitfield, John B., Abbas, Shahid, Saleheen, Danish, Walters, Robin G., Holmes, Michael V., Black, Corri, Smith, Blair H., Baras, Aris, Justice, Anne E., Buring, Julie E., Ridker, Paul M., Chasman, Daniel I., Kooperberg, Charles, Tamiya, Gen, Yamamoto, Masayuki, van Heel, David A., Trembath, Richard C., Wei, Wei-Qi, Jarvik, Gail P., Namjou, Bahram, Hayes, M. Geoffrey, Ritchie, Marylyn D., Jousilahti, Pekka, Salomaa, Veikko, Hveem, Kristian, Åsvold, Bjørn Olav, Kubo, Michiaki, Kamatani, Yoichiro, Okada, Yukinori, Murakami, Yoshinori, Kim, Bong-Jo, Thorsteinsdottir, Unnur, Stefansson, Kari, Zhang, Jifeng, Chen, Y. Eugene, Ho, Yuk-Lam, Lynch, Julie A., Tsao, Philip S., Chang, Kyong-Mi, Cho, Kelly, O'Donnell, Christopher J., Gaziano, John M., Wilson, Peter, Mohlke, Karen L., Frayling, Timothy M., Hirschhorn, Joel N., Kathiresan, Sekar, Boehnke, Michael, Struan Grant, Natarajan, Pradeep, Sun, Yan V., Morris, Andrew P., Deloukas, Panos, Peloso, Gina, Assimes, Themistocles L., Willer, Cristen J., Zhu, Xiang, and Brown, Christopher D.
Published: 2022
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37. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author: Kanoni, Stavroula, Graham, Sarah E., Wang, Yuxuan, Surakka, Ida, Ramdas, Shweta, Zhu, Xiang, Clarke, Shoa L., Bhatti, Konain Fatima, Vedantam, Sailaja, Winkler, Thomas W., Locke, Adam E., Marouli, Eirini, Zajac, Greg J. M., Wu, Kuan-Han H., Ntalla, Ioanna, Hui, Qin, Klarin, Derek, Hilliard, Austin T., Wang, Zeyuan, Xue, Chao, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F., Holm, Hilma, Olafsson, Isleifur, Hwang, Mi Yeong, Han, Sohee, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M., Rasheed, Humaira, Havulinna, Aki S., Veturi, Yogasudha, Pacheco, Jennifer Allen, Rosenthal, Elisabeth A., Lingren, Todd, Feng, QiPing, Kullo, Iftikhar J., Narita, Akira, Takayama, Jun, Martin, Hilary C., Hunt, Karen A., Trivedi, Bhavi, Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E., Campbell, Archie, Lin, Kuang, Millwood, Iona Y., Rasheed, Asif, Hindy, George, Faul, Jessica D., Zhao, Wei, Weir, David R., Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Choudhury, Ananyo, Sengupta, Dhriti, Mahajan, Anubha, Brown, Michael R., Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M., Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Wood, Andrew R., Ji, Yingji, Gao, Zishan, Haworth, Simon, Yousri, Noha A., Mitchell, Ruth E., Chai, Jin Fang, Aadahl, Mette, Bjerregaard, Anne A., Yao, Jie, Manichaikul, Ani, Hwu, Chii-Min, Hung, Yi-Jen, Warren, Helen R., Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L., Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Mauro, Pala, Matteo, Floris, McDaid, Aaron F., Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, Sattar, Naveed, Møllehave, Line T., Munz, Matthias, Zeng, Lingyao, Huang, Jianfeng, Yang, Bin, Poveda, Alaitz, Kurbasic, Azra, Lamina, Claudia, Forer, Lukas, Scholz, Markus, Galesloot, Tessel E., Bradfield, Jonathan P., Ruotsalainen, Sanni E., Daw, EWarwick, Zmuda, Joseph M., Mitchell, Jonathan S., Fuchsberger, Christian, Christensen, Henry, Brody, Jennifer A., Vazquez-Moreno, Miguel, Feitosa, Mary F., Wojczynski, Mary K., Wang, Zhe, Preuss, Michael H., Mangino, Massimo, Christofidou, Paraskevi, Verweij, Niek, Benjamins, Jan W., Engmann, Jorgen, Tsao, Noah L., Verma, Anurag, Slieker, Roderick C., Lo, Ken Sin, Zilhao, Nuno R., Le, Phuong, Kleber, Marcus E., Delgado, Graciela E., Huo, Shaofeng, Ikeda, Daisuke D., Iha, Hiroyuki, Yang, Jian, Liu, Jun, Demirkan, Ayşe, Leonard, Hampton L., Marten, Jonathan, Frank, Mirjam, Schmidt, Börge, Smyth, Laura J., Cañadas-Garre, Marisa, Wang, Chaolong, Nakatochi, Masahiro, Wong, Andrew, Hutri-Kähönen, Nina, Sim, Xueling, Xia, Rui, Huerta-Chagoya, Alicia, Fernandez-Lopez, Juan Carlos, Lyssenko, Valeriya, Nongmaithem, Suraj S., Bayyana, Swati, Stringham, Heather M., Irvin, Marguerite R., Oldmeadow, Christopher, Kim, Han-Na, Ryu, Seungho, Timmers, Paul R. H. J., Arbeeva, Liubov, Dorajoo, Rajkumar, Lange, Leslie A., Prasad, Gauri, Lorés-Motta, Laura, Pauper, Marc, Long, Jirong, Li, Xiaohui, Theusch, Elizabeth, Takeuchi, Fumihiko, Spracklen, Cassandra N., Loukola, Anu, Bollepalli, Sailalitha, Warner, Sophie C., Wang, Ya Xing, Wei, Wen B., Nutile, Teresa, Ruggiero, Daniela, Sung, Yun Ju, Chen, Shufeng, Liu, Fangchao, Yang, Jingyun, Kentistou, Katherine A., Banas, Bernhard, Nardone, Giuseppe Giovanni, Meidtner, Karina, Bielak, Lawrence F., Smith, Jennifer A., Hebbar, Prashantha, Farmaki, Aliki-Eleni, Hofer, Edith, Lin, Maoxuan, Concas, Maria Pina, Vaccargiu, Simona, van der Most, Peter J., Pitkänen, Niina, Cade, Brian E., van der Laan, Sander W., Chitrala, Kumaraswamy Naidu, Weiss, Stefan, Bentley, Amy R., Doumatey, Ayo P., Adeyemo, Adebowale A., Lee, Jong Young, Petersen, Eva R. B., Nielsen, Aneta A., Choi, Hyeok Sun, Nethander, Maria, Freitag-Wolf, Sandra, Southam, Lorraine, Rayner, Nigel W., Wang, Carol A., Lin, Shih-Yi, Wang, Jun-Sing, Couture, Christian, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Cuellar-Partida, Gabriel, Vestergaard, Henrik, Hidalgo, Bertha, Giannakopoulou, Olga, Cai, Qiuyin, Obura, Morgan O., van Setten, Jessica, Li, Xiaoyin, Liang, Jingjing, Tang, Hua, Terzikhan, Natalie, Shin, Jae Hun, Jackson, Rebecca D., Reiner, Alexander P., Martin, Lisa Warsinger, Chen, Zhengming, Li, Liming, Kawaguchi, Takahisa, Thiery, Joachim, Bis, Joshua C., Launer, Lenore J., Li, Huaixing, Nalls, Mike A., Raitakari, Olli T., Ichihara, Sahoko, Wild, Sarah H., Nelson, Christopher P., Campbell, Harry, Jäger, Susanne, Nabika, Toru, Al-Mulla, Fahd, Niinikoski, Harri, Braund, Peter S., Kolcic, Ivana, Kovacs, Peter, Giardoglou, Tota, Katsuya, Tomohiro, de Kleijn, Dominique, de Borst, Gert J., Kim, Eung Kweon, Adams, Hieab H. H., Ikram, M. Arfan, Zhu, Xiaofeng, Asselbergs, Folkert W., Kraaijeveld, Adriaan O., Beulens, Joline W. J., Shu, Xiao-Ou, Rallidis, Loukianos S., Pedersen, Oluf, Hansen, Torben, Mitchell, Paul, Hewitt, Alex W., Kähönen, Mika, Pérusse, Louis, Bouchard, Claude, Tönjes, Anke, Chen, Yii-Der Ida, Pennell, Craig E., Mori, Trevor A., Lieb, Wolfgang, Franke, Andre, Ohlsson, Claes, Mellström, Dan, Cho, Yoon Shin, Lee, Hyejin, Yuan, Jian-Min, Koh, Woon-Puay, Rhee, Sang Youl, Woo, Jeong-Taek, Heid, Iris M., Stark, Klaus J., Zimmermann, Martina E., Völzke, Henry, Homuth, Georg, Evans, Michele K., Zonderman, Alan B., Polasek, Ozren, Pasterkamp, Gerard, Hoefer, Imo E., Redline, Susan, Pahkala, Katja, Oldehinkel, Albertine J., Snieder, Harold, Biino, Ginevra, Schmidt, Reinhold, Schmidt, Helena, Bandinelli, Stefania, Dedoussis, George, Thanaraj, Thangavel Alphonse, Kardia, Sharon L. R., Peyser, Patricia A., Kato, Norihiro, Schulze, Matthias B., Girotto, Giorgia, Böger, Carsten A., Jung, Bettina, Joshi, Peter K., Bennett, David A., De Jager, Philip L., Lu, Xiangfeng, Mamakou, Vasiliki, Brown, Morris, Caulfield, Mark J., Munroe, Patricia B., Guo, Xiuqing, Ciullo, Marina, Jonas, Jost B., Samani, Nilesh J., Kaprio, Jaakko, Pajukanta, Päivi, Tusié-Luna, Teresa, Aguilar-Salinas, Carlos A., Adair, Linda S., Bechayda, Sonny Augustin, de Silva, H. Janaka, Wickremasinghe, Ananda R., Krauss, Ronald M., Wu, Jer-Yuarn, Zheng, Wei, Hollander, Anneke Iden, Bharadwaj, Dwaipayan, Correa, Adolfo, Wilson, James G., Lind, Lars, Heng, Chew-Kiat, Nelson, Amanda E., Golightly, Yvonne M., Wilson, James F., Penninx, Brenda, Kim, Hyung-Lae, Attia, John, Scott, Rodney J., Rao, D. C., Arnett, Donna K., Hunt, Steven C., Walker, Mark, Koistinen, Heikki A., Chandak, Giriraj R., Mercader, Josep M., Costanzo, Maria C., Jang, Dongkeun, Burtt, Noël P., Villalpando, Clicerio Gonzalez, Orozco, Lorena, Fornage, Myriam, Tai, EShyong, van Dam, Rob M., Lehtimäki, Terho, Chaturvedi, Nish, Yokota, Mitsuhiro, Liu, Jianjun, Reilly, Dermot F., McKnight, Amy Jayne, Kee, Frank, Jöckel, Karl-Heinz, McCarthy, Mark I., Palmer, Colin N. A., Vitart, Veronique, Hayward, Caroline, Simonsick, Eleanor, van Duijn, Cornelia M., Jin, Zi-Bing, Qu, Jia, Hishigaki, Haretsugu, Lin, Xu, März, Winfried, Gudnason, Vilmundur, Tardif, Jean-Claude, Lettre, Guillaume, Hart, Leen M.‘t, Elders, Petra J. M., Damrauer, Scott M., Kumari, Meena, Kivimaki, Mika, van der Harst, Pim, Spector, Tim D., Loos, Ruth J. F., Province, Michael A., Parra, Esteban J., Cruz, Miguel, Psaty, Bruce M., Brandslund, Ivan, Pramstaller, Peter P., Rotimi, Charles N., Christensen, Kaare, Ripatti, Samuli, Widén, Elisabeth, Hakonarson, Hakon, Grant, Struan F. A., Kiemeney, Lambertus A. L. M., de Graaf, Jacqueline, Loeffler, Markus, Kronenberg, Florian, Gu, Dongfeng, Erdmann, Jeanette, Schunkert, Heribert, Franks, Paul W., Linneberg, Allan, Jukema, J. Wouter, Khera, Amit V., Männikkö, Minna, Jarvelin, Marjo-Riitta, Kutalik, Zoltan, Francesco, Cucca, Mook-Kanamori, Dennis O., van Dijk, Ko Willems, Watkins, Hugh, Strachan, David P., Grarup, Niels, Sever, Peter, Poulter, Neil, Chuang, Lee-Ming, Rotter, Jerome I., Dantoft, Thomas M., Karpe, Fredrik, Neville, Matt J., Timpson, Nicholas J., Cheng, Ching-Yu, Wong, Tien-Yin, Khor, Chiea Chuen, Li, Hengtong, Sabanayagam, Charumathi, Peters, Annette, Gieger, Christian, Hattersley, Andrew T., Pedersen, Nancy L., Magnusson, Patrik K. E., Boomsma, Dorret I., Willemsen, Allegonda H. M., Cupples, LAdrienne, van Meurs, Joyce B. J., Ghanbari, Mohsen, Gordon-Larsen, Penny, Huang, Wei, Kim, Young Jin, Tabara, Yasuharu, Wareham, Nicholas J., Langenberg, Claudia, Zeggini, Eleftheria, Kuusisto, Johanna, Laakso, Markku, Ingelsson, Erik, Abecasis, Goncalo, Chambers, John C., Kooner, Jaspal S., de Vries, Paul S., Morrison, Alanna C., Hazelhurst, Scott, Ramsay, Michèle, North, Kari E., Daviglus, Martha, Kraft, Peter, Martin, Nicholas G., Whitfield, John B., Abbas, Shahid, Saleheen, Danish, Walters, Robin G., Holmes, Michael V., Black, Corri, Smith, Blair H., Baras, Aris, Justice, Anne E., Buring, Julie E., Ridker, Paul M., Chasman, Daniel I., Kooperberg, Charles, Tamiya, Gen, Yamamoto, Masayuki, van Heel, David A., Trembath, Richard C., Wei, Wei-Qi, Jarvik, Gail P., Namjou, Bahram, Hayes, M. Geoffrey, Ritchie, Marylyn D., Jousilahti, Pekka, Salomaa, Veikko, Hveem, Kristian, Åsvold, Bjørn Olav, Kubo, Michiaki, Kamatani, Yoichiro, Okada, Yukinori, Murakami, Yoshinori, Kim, Bong-Jo, Thorsteinsdottir, Unnur, Stefansson, Kari, Zhang, Jifeng, Chen, YEugene, Ho, Yuk-Lam, Lynch, Julie A., Rader, Daniel J., Tsao, Philip S., Chang, Kyong-Mi, Cho, Kelly, O’Donnell, Christopher J., Gaziano, John M., Wilson, Peter W. F., Frayling, Timothy M., Hirschhorn, Joel N., Kathiresan, Sekar, Mohlke, Karen L., Sun, Yan V., Morris, Andrew P., Boehnke, Michael, Brown, Christopher D., Natarajan, Pradeep, Deloukas, Panos, Willer, Cristen J., Assimes, Themistocles L., and Peloso, Gina M.
Published: 2022
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38. The power of genetic diversity in genome-wide association studies of lipids

Author: Graham, Sarah E., Clarke, Shoa L., Wu, Kuan-Han H., Kanoni, Stavroula, Zajac, Greg J. M., Ramdas, Shweta, Surakka, Ida, Ntalla, Ioanna, Vedantam, Sailaja, Winkler, Thomas W., Locke, Adam E., Marouli, Eirini, Hwang, Mi Yeong, Han, Sohee, Narita, Akira, Choudhury, Ananyo, Bentley, Amy R., Ekoru, Kenneth, Verma, Anurag, Trivedi, Bhavi, Martin, Hilary C., Hunt, Karen A., Hui, Qin, Klarin, Derek, Zhu, Xiang, Thorleifsson, Gudmar, Helgadottir, Anna, Gudbjartsson, Daniel F., Holm, Hilma, Olafsson, Isleifur, Akiyama, Masato, Sakaue, Saori, Terao, Chikashi, Kanai, Masahiro, Zhou, Wei, Brumpton, Ben M., Rasheed, Humaira, Ruotsalainen, Sanni E., Havulinna, Aki S., Veturi, Yogasudha, Feng, QiPing, Rosenthal, Elisabeth A., Lingren, Todd, Pacheco, Jennifer Allen, Pendergrass, Sarah A., Haessler, Jeffrey, Giulianini, Franco, Bradford, Yuki, Miller, Jason E., Campbell, Archie, Lin, Kuang, Millwood, Iona Y., Hindy, George, Rasheed, Asif, Faul, Jessica D., Zhao, Wei, Weir, David R., Turman, Constance, Huang, Hongyan, Graff, Mariaelisa, Mahajan, Anubha, Brown, Michael R., Zhang, Weihua, Yu, Ketian, Schmidt, Ellen M., Pandit, Anita, Gustafsson, Stefan, Yin, Xianyong, Luan, Jian’an, Zhao, Jing-Hua, Matsuda, Fumihiko, Jang, Hye-Mi, Yoon, Kyungheon, Medina-Gomez, Carolina, Pitsillides, Achilleas, Hottenga, Jouke Jan, Willemsen, Gonneke, Wood, Andrew R., Ji, Yingji, Gao, Zishan, Haworth, Simon, Mitchell, Ruth E., Chai, Jin Fang, Aadahl, Mette, Yao, Jie, Manichaikul, Ani, Warren, Helen R., Ramirez, Julia, Bork-Jensen, Jette, Kårhus, Line L., Goel, Anuj, Sabater-Lleal, Maria, Noordam, Raymond, Sidore, Carlo, Fiorillo, Edoardo, McDaid, Aaron F., Marques-Vidal, Pedro, Wielscher, Matthias, Trompet, Stella, Sattar, Naveed, Møllehave, Line T., Thuesen, Betina H., Munz, Matthias, Zeng, Lingyao, Huang, Jianfeng, Yang, Bin, Poveda, Alaitz, Kurbasic, Azra, Lamina, Claudia, Forer, Lukas, Scholz, Markus, Galesloot, Tessel E., Bradfield, Jonathan P., Daw, E. Warwick, Zmuda, Joseph M., Mitchell, Jonathan S., Fuchsberger, Christian, Christensen, Henry, Brody, Jennifer A., Feitosa, Mary F., Wojczynski, Mary K., Preuss, Michael, Mangino, Massimo, Christofidou, Paraskevi, Verweij, Niek, Benjamins, Jan W., Engmann, Jorgen, Kember, Rachel L., Slieker, Roderick C., Lo, Ken Sin, Zilhao, Nuno R., Le, Phuong, Kleber, Marcus E., Delgado, Graciela E., Huo, Shaofeng, Ikeda, Daisuke D., Iha, Hiroyuki, Yang, Jian, Liu, Jun, Leonard, Hampton L., Marten, Jonathan, Schmidt, Börge, Arendt, Marina, Smyth, Laura J., Cañadas-Garre, Marisa, Wang, Chaolong, Nakatochi, Masahiro, Wong, Andrew, Hutri-Kähönen, Nina, Sim, Xueling, Xia, Rui, Huerta-Chagoya, Alicia, Fernandez-Lopez, Juan Carlos, Lyssenko, Valeriya, Ahmed, Meraj, Jackson, Anne U., Yousri, Noha A., Irvin, Marguerite R., Oldmeadow, Christopher, Kim, Han-Na, Ryu, Seungho, Timmers, Paul R. H. J., Arbeeva, Liubov, Dorajoo, Rajkumar, Lange, Leslie A., Chai, Xiaoran, Prasad, Gauri, Lorés-Motta, Laura, Pauper, Marc, Long, Jirong, Li, Xiaohui, Theusch, Elizabeth, Takeuchi, Fumihiko, Spracklen, Cassandra N., Loukola, Anu, Bollepalli, Sailalitha, Warner, Sophie C., Wang, Ya Xing, Wei, Wen B., Nutile, Teresa, Ruggiero, Daniela, Sung, Yun Ju, Hung, Yi-Jen, Chen, Shufeng, Liu, Fangchao, Yang, Jingyun, Kentistou, Katherine A., Gorski, Mathias, Brumat, Marco, Meidtner, Karina, Bielak, Lawrence F., Smith, Jennifer A., Hebbar, Prashantha, Farmaki, Aliki-Eleni, Hofer, Edith, Lin, Maoxuan, Xue, Chao, Zhang, Jifeng, Concas, Maria Pina, Vaccargiu, Simona, van der Most, Peter J., Pitkänen, Niina, Cade, Brian E., Lee, Jiwon, van der Laan, Sander W., Chitrala, Kumaraswamy Naidu, Weiss, Stefan, Zimmermann, Martina E., Lee, Jong Young, Choi, Hyeok Sun, Nethander, Maria, Freitag-Wolf, Sandra, Southam, Lorraine, Rayner, Nigel W., Wang, Carol A., Lin, Shih-Yi, Wang, Jun-Sing, Couture, Christian, Lyytikäinen, Leo-Pekka, Nikus, Kjell, Cuellar-Partida, Gabriel, Vestergaard, Henrik, Hildalgo, Bertha, Giannakopoulou, Olga, Cai, Qiuyin, Obura, Morgan O., van Setten, Jessica, Li, Xiaoyin, Schwander, Karen, Terzikhan, Natalie, Shin, Jae Hun, Jackson, Rebecca D., Reiner, Alexander P., Martin, Lisa Warsinger, Chen, Zhengming, Li, Liming, Highland, Heather M., Young, Kristin L., Kawaguchi, Takahisa, Thiery, Joachim, Bis, Joshua C., Nadkarni, Girish N., Launer, Lenore J., Li, Huaixing, Nalls, Mike A., Raitakari, Olli T., Ichihara, Sahoko, Wild, Sarah H., Nelson, Christopher P., Campbell, Harry, Jäger, Susanne, Nabika, Toru, Al-Mulla, Fahd, Niinikoski, Harri, Braund, Peter S., Kolcic, Ivana, Kovacs, Peter, Giardoglou, Tota, Katsuya, Tomohiro, Bhatti, Konain Fatima, de Kleijn, Dominique, de Borst, Gert J., Kim, Eung Kweon, Adams, Hieab H. H., Ikram, M. Arfan, Zhu, Xiaofeng, Asselbergs, Folkert W., Kraaijeveld, Adriaan O., Beulens, Joline W. J., Shu, Xiao-Ou, Rallidis, Loukianos S., Pedersen, Oluf, Hansen, Torben, Mitchell, Paul, Hewitt, Alex W., Kähönen, Mika, Pérusse, Louis, Bouchard, Claude, Tönjes, Anke, Chen, Yii-Der Ida, Pennell, Craig E., Mori, Trevor A., Lieb, Wolfgang, Franke, Andre, Ohlsson, Claes, Mellström, Dan, Cho, Yoon Shin, Lee, Hyejin, Yuan, Jian-Min, Koh, Woon-Puay, Rhee, Sang Youl, Woo, Jeong-Taek, Heid, Iris M., Stark, Klaus J., Völzke, Henry, Homuth, Georg, Evans, Michele K., Zonderman, Alan B., Polasek, Ozren, Pasterkamp, Gerard, Hoefer, Imo E., Redline, Susan, Pahkala, Katja, Oldehinkel, Albertine J., Snieder, Harold, Biino, Ginevra, Schmidt, Reinhold, Schmidt, Helena, Chen, Y. Eugene, Bandinelli, Stefania, Dedoussis, George, Thanaraj, Thangavel Alphonse, Kardia, Sharon L. R., Kato, Norihiro, Schulze, Matthias B., Girotto, Giorgia, Jung, Bettina, Böger, Carsten A., Joshi, Peter K., Bennett, David A., De Jager, Philip L., Lu, Xiangfeng, Mamakou, Vasiliki, Brown, Morris, Caulfield, Mark J., Munroe, Patricia B., Guo, Xiuqing, Ciullo, Marina, Jonas, Jost B., Samani, Nilesh J., Kaprio, Jaakko, Pajukanta, Päivi, Adair, Linda S., Bechayda, Sonny Augustin, de Silva, H. Janaka, Wickremasinghe, Ananda R., Krauss, Ronald M., Wu, Jer-Yuarn, Zheng, Wei, den Hollander, Anneke I., Bharadwaj, Dwaipayan, Correa, Adolfo, Wilson, James G., Lind, Lars, Heng, Chew-Kiat, Nelson, Amanda E., Golightly, Yvonne M., Wilson, James F., Penninx, Brenda, Kim, Hyung-Lae, Attia, John, Scott, Rodney J., Rao, D. C., Arnett, Donna K., Hunt, Steven C., Walker, Mark, Koistinen, Heikki A., Chandak, Giriraj R., Yajnik, Chittaranjan S., Mercader, Josep M., Tusié-Luna, Teresa, Aguilar-Salinas, Carlos A., Villalpando, Clicerio Gonzalez, Orozco, Lorena, Fornage, Myriam, Tai, E. Shyong, van Dam, Rob M., Lehtimäki, Terho, Chaturvedi, Nish, Yokota, Mitsuhiro, Liu, Jianjun, Reilly, Dermot F., McKnight, Amy Jayne, Kee, Frank, Jöckel, Karl-Heinz, McCarthy, Mark I., Palmer, Colin N. A., Vitart, Veronique, Hayward, Caroline, Simonsick, Eleanor, van Duijn, Cornelia M., Lu, Fan, Qu, Jia, Hishigaki, Haretsugu, Lin, Xu, März, Winfried, Parra, Esteban J., Cruz, Miguel, Gudnason, Vilmundur, Tardif, Jean-Claude, Lettre, Guillaume, ’t Hart, Leen M., Elders, Petra J. M., Damrauer, Scott M., Kumari, Meena, Kivimaki, Mika, van der Harst, Pim, Spector, Tim D., Loos, Ruth J. F., Province, Michael A., Psaty, Bruce M., Brandslund, Ivan, Pramstaller, Peter P., Christensen, Kaare, Ripatti, Samuli, Widén, Elisabeth, Hakonarson, Hakon, Grant, Struan F. A., Kiemeney, Lambertus A. L. M., de Graaf, Jacqueline, Loeffler, Markus, Kronenberg, Florian, Gu, Dongfeng, Erdmann, Jeanette, Schunkert, Heribert, Franks, Paul W., Linneberg, Allan, Jukema, J. Wouter, Khera, Amit V., Männikkö, Minna, Jarvelin, Marjo-Riitta, Kutalik, Zoltan, Cucca, Francesco, Mook-Kanamori, Dennis O., van Dijk, Ko Willems, Watkins, Hugh, Strachan, David P., Grarup, Niels, Sever, Peter, Poulter, Neil, Rotter, Jerome I., Dantoft, Thomas M., Karpe, Fredrik, Neville, Matt J., Timpson, Nicholas J., Cheng, Ching-Yu, Wong, Tien-Yin, Khor, Chiea Chuen, Sabanayagam, Charumathi, Peters, Annette, Gieger, Christian, Hattersley, Andrew T., Pedersen, Nancy L., Magnusson, Patrik K. E., Boomsma, Dorret I., de Geus, Eco J. C., Cupples, L. Adrienne, van Meurs, Joyce B. J., Ghanbari, Mohsen, Gordon-Larsen, Penny, Huang, Wei, Kim, Young Jin, Tabara, Yasuharu, Wareham, Nicholas J., Langenberg, Claudia, Zeggini, Eleftheria, Kuusisto, Johanna, Laakso, Markku, Ingelsson, Erik, Abecasis, Goncalo, Chambers, John C., Kooner, Jaspal S., de Vries, Paul S., Morrison, Alanna C., North, Kari E., Daviglus, Martha, Kraft, Peter, Martin, Nicholas G., Whitfield, John B., Abbas, Shahid, Saleheen, Danish, Walters, Robin G., Holmes, Michael V., Black, Corri, Smith, Blair H., Justice, Anne E., Baras, Aris, Buring, Julie E., Ridker, Paul M., Chasman, Daniel I., Kooperberg, Charles, Wei, Wei-Qi, Jarvik, Gail P., Namjou, Bahram, Hayes, M. Geoffrey, Ritchie, Marylyn D., Jousilahti, Pekka, Salomaa, Veikko, Hveem, Kristian, Åsvold, Bjørn Olav, Kubo, Michiaki, Kamatani, Yoichiro, Okada, Yukinori, Murakami, Yoshinori, Thorsteinsdottir, Unnur, Stefansson, Kari, Ho, Yuk-Lam, Lynch, Julie A., Rader, Daniel J., Tsao, Philip S., Chang, Kyong-Mi, Cho, Kelly, O’Donnell, Christopher J., Gaziano, John M., Wilson, Peter, Rotimi, Charles N., Hazelhurst, Scott, Ramsay, Michèle, Trembath, Richard C., van Heel, David A., Tamiya, Gen, Yamamoto, Masayuki, Kim, Bong-Jo, Mohlke, Karen L., Frayling, Timothy M., Hirschhorn, Joel N., Kathiresan, Sekar, Boehnke, Michael, Natarajan, Pradeep, Peloso, Gina M., Brown, Christopher D., Morris, Andrew P., Assimes, Themistocles L., Deloukas, Panos, Sun, Yan V., and Willer, Cristen J.
Published: 2021
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39. Author Correction: Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS.

Author: Pan, David Z, Garske, Kristina M, Alvarez, Marcus, Bhagat, Yash V, Boocock, James, Nikkola, Elina, Miao, Zong, Raulerson, Chelsea K, Cantor, Rita M, Civelek, Mete, Glastonbury, Craig A, Small, Kerrin S, Boehnke, Michael, Lusis, Aldons J, Sinsheimer, Janet S, Mohlke, Karen L, Laakso, Markku, Pajukanta, Päivi, and Ko, Arthur
Abstract: In the original version of this Article, Supplementary Table 10 contained incorrect primer sequences for the mobility shift assay for SNP rs4776984. These errors have now been fixed and the corrected version of the Supplementary Information PDF is available to download from the HTML version of the Article.
Published: 2018

40. Integration of human adipocyte chromosomal interactions with adipose gene expression prioritizes obesity-related genes from GWAS.

Author: Pan, David Z, Garske, Kristina M, Alvarez, Marcus, Bhagat, Yash V, Boocock, James, Nikkola, Elina, Miao, Zong, Raulerson, Chelsea K, Cantor, Rita M, Civelek, Mete, Glastonbury, Craig A, Small, Kerrin S, Boehnke, Michael, Lusis, Aldons J, Sinsheimer, Janet S, Mohlke, Karen L, Laakso, Markku, Pajukanta, Päivi, and Ko, Arthur
Subjects: Adipose Tissue, Cells, Cultured, Chromosomes, Human, Adipocytes, Humans, Obesity, Genetic Predisposition to Disease, Body Mass Index, Cohort Studies, Genomics, Gene Expression Regulation, Phenotype, Polymorphism, Single Nucleotide, Gene Library, Quantitative Trait Loci, Aged, Middle Aged, Finland, Male, Adiposity, Gene Regulatory Networks, Promoter Regions, Genetic, Primary Cell Culture
Abstract: Increased adiposity is a hallmark of obesity and overweight, which affect 2.2 billion people world-wide. Understanding the genetic and molecular mechanisms that underlie obesity-related phenotypes can help to improve treatment options and drug development. Here we perform promoter Capture Hi-C in human adipocytes to investigate interactions between gene promoters and distal elements as a transcription-regulating mechanism contributing to these phenotypes. We find that promoter-interacting elements in human adipocytes are enriched for adipose-related transcription factor motifs, such as PPARG and CEBPB, and contribute to heritability of cis-regulated gene expression. We further intersect these data with published genome-wide association studies for BMI and BMI-related metabolic traits to identify the genes that are under genetic cis regulation in human adipocytes via chromosomal interactions. This integrative genomics approach identifies four cis-eQTL-eGene relationships associated with BMI or obesity-related traits, including rs4776984 and MAP2K5, which we further confirm by EMSA, and highlights 38 additional candidate genes.
Published: 2018

41. Partitioned polygenic risk scores identify distinct types of metabolic dysfunction-associated steatotic liver disease

Author: Romeo, Stefano, primary, Jamialahmadi, Oveis, additional, De Vincentis, Antonio, additional, Tavaglione, Federica, additional, Malvestiti, Francesco, additional, Li-Gao, Ruifang, additional, Mancina, Rosellina, additional, Alvarez, Marcus, additional, Gelev, Kyla, additional, Maurotti, Samantha, additional, Vespasiani-Gentilucci, Umberto, additional, Rosendaal, Frits, additional, Kozlitina, Julia, additional, Pajukanta, Päivi, additional, Pattou, François, additional, and Valenti, Luca, additional
Published: 2024
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42. Molecular pathways behind acquired obesity: Adipose tissue and skeletal muscle multiomics in monozygotic twin pairs discordant for BMI

Author: van der Kolk, Birgitta W., Saari, Sina, Lovric, Alen, Arif, Muhammad, Alvarez, Marcus, Ko, Arthur, Miao, Zong, Sahebekhtiari, Navid, Muniandy, Maheswary, Heinonen, Sini, Oghabian, Ali, Jokinen, Riikka, Jukarainen, Sakari, Hakkarainen, Antti, Lundbom, Jesper, Kuula, Juho, Groop, Per-Henrik, Tukiainen, Taru, Lundbom, Nina, Rissanen, Aila, Kaprio, Jaakko, Williams, Evan G., Zamboni, Nicola, Mardinoglu, Adil, Pajukanta, Päivi, and Pietiläinen, Kirsi H.
Published: 2021
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43. Family-specific aggregation of lipid GWAS variants confers the susceptibility to familial hypercholesterolemia in a large Austrian family

Author: Nikkola, Elina, Ko, Arthur, Alvarez, Marcus, Cantor, Rita M, Garske, Kristina, Kim, Elliot, Gee, Stephanie, Rodriguez, Alejandra, Muxel, Reinhard, Matikainen, Niina, Söderlund, Sanni, Motazacker, Mahdi M, Borén, Jan, Lamina, Claudia, Kronenberg, Florian, Schneider, Wolfgang J, Palotie, Aarno, Laakso, Markku, Taskinen, Marja-Riitta, and Pajukanta, Päivi
Subjects: Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Digestive Diseases, Human Genome, Prevention, Genetics, Heart Disease, Cardiovascular, Atherosclerosis, Aetiology, 2.1 Biological and endogenous factors, Apolipoprotein B-100, Austria, Biomarkers, Cholesterol, DNA Mutational Analysis, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Heredity, Humans, Hyperlipoproteinemia Type II, Lipoprotein(a), Male, Middle Aged, Mutation, Pedigree, Phenotype, Polymorphism, Single Nucleotide, Receptors, LDL, Risk Factors, Exome Sequencing, Familial hypercholesterolemia, LDL cholesterol, Genetic risk score, Lipoprotein, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract: Background and aimsHypercholesterolemia confers susceptibility to cardiovascular disease (CVD). Both serum total cholesterol (TC) and LDL-cholesterol (LDL-C) exhibit a strong genetic component (heritability estimates 0.41-0.50). However, a large part of this heritability cannot be explained by the variants identified in recent extensive genome-wide association studies (GWAS) on lipids. Our aim was to find genetic causes leading to high LDL-C levels and ultimately CVD in a large Austrian family presenting with what appears to be autosomal dominant inheritance for familial hypercholesterolemia (FH).MethodsWe utilized linkage analysis followed by whole-exome sequencing and genetic risk score analysis using an Austrian multi-generational family with various dyslipidemias, including elevated TC and LDL-C, and one family branch with elevated lipoprotein (a) (Lp(a)).ResultsWe did not find evidence for genome-wide significant linkage for LDL-C or apparent causative variants in the known FH genes rather, we discovered a particular family-specific combination of nine GWAS LDL-C SNPs (p = 0.02 by permutation), and putative less severe familial hypercholesterolemia mutations in the LDLR and APOB genes in a subset of the affected family members. Separately, high Lp(a) levels observed in one branch of the family were explained primarily by the LPA locus, including short (
Published: 2017

44. Genetic Regulation of Adipose Gene Expression and Cardio-Metabolic Traits

Author: Civelek, Mete, Wu, Ying, Pan, Calvin, Raulerson, Chelsea K, Ko, Arthur, He, Aiqing, Tilford, Charles, Saleem, Niyas K, Stančáková, Alena, Scott, Laura J, Fuchsberger, Christian, Stringham, Heather M, Jackson, Anne U, Narisu, Narisu, Chines, Peter S, Small, Kerrin S, Kuusisto, Johanna, Parks, Brian W, Pajukanta, Päivi, Kirchgessner, Todd, Collins, Francis S, Gargalovic, Peter S, Boehnke, Michael, Laakso, Markku, Mohlke, Karen L, and Lusis, Aldons J
Subjects: Prevention, Genetics, Human Genome, Biotechnology, Aetiology, 2.1 Biological and endogenous factors, Aged, Animals, Cardiovascular Diseases, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Genome-Wide Association Study, Genotyping Techniques, Humans, Male, Metabolic Syndrome, Mice, Middle Aged, Nuclear Proteins, Phenotype, Quantitative Trait Loci, Reproducibility of Results, Subcutaneous Fat, Trans-Activators, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract: Subcutaneous adipose tissue stores excess lipids and maintains energy balance. We performed expression quantitative trait locus (eQTL) analyses by using abdominal subcutaneous adipose tissue of 770 extensively phenotyped participants of the METSIM study. We identified cis-eQTLs for 12,400 genes at a 1% false-discovery rate. Among an approximately 680 known genome-wide association study (GWAS) loci for cardio-metabolic traits, we identified 140 coincident cis-eQTLs at 109 GWAS loci, including 93 eQTLs not previously described. At 49 of these 140 eQTLs, gene expression was nominally associated (p < 0.05) with levels of the GWAS trait. The size of our dataset enabled identification of five loci associated (p < 5 × 10-8) with at least five genes located >5 Mb away. These trans-eQTL signals confirmed and extended the previously reported KLF14-mediated network to 55 target genes, validated the CIITA regulation of class II MHC genes, and identified ZNF800 as a candidate master regulator. Finally, we observed similar expression-clinical trait correlations of genes associated with GWAS loci in both humans and a panel of genetically diverse mice. These results provide candidate genes for further investigation of their potential roles in adipose biology and in regulating cardio-metabolic traits.
Published: 2017

45. Molecular Characterization of the Lipid Genome-Wide Association Study Signal on Chromosome 18q11.2 Implicates HNF4A-Mediated Regulation of the TMEM241 Gene

Author: Rodríguez, Alejandra, Gonzalez, Luis, Ko, Arthur, Alvarez, Marcus, Miao, Zong, Bhagat, Yash, Nikkola, Elina, Cruz-Bautista, Ivette, Arellano-Campos, Olimpia, Muñoz-Hernández, Linda L, Ordóñez-Sánchez, Maria-Luisa, Rodriguez-Guillen, Rosario, Mohlke, Karen L, Laakso, Markku, Tusie-Luna, Teresa, Aguilar-Salinas, Carlos A, and Pajukanta, Päivi
Subjects: Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Genetics, Biotechnology, Human Genome, Digestive Diseases, 2.1 Biological and endogenous factors, Aetiology, Aged, Binding Sites, Chromosomes, Human, Pair 18, Finland, Genes, Reporter, Genetic Markers, Genome-Wide Association Study, Genotype, Hep G2 Cells, Hepatocyte Nuclear Factor 4, Humans, Linkage Disequilibrium, Lipid Metabolism, Male, Membrane Proteins, Mexico, Middle Aged, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Transcription, Genetic, Transfection, Triglycerides, United States, Up-Regulation, chromatin, dyslipidemias, functional genomics, gene expression and regulation, genome-wide association study, mechanisms, triglycerides, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract: ObjectiveWe recently identified a locus on chromosome 18q11.2 for high serum triglycerides in Mexicans. We hypothesize that the lead genome-wide association study single-nucleotide polymorphism rs9949617, or its linkage disequilibrium proxies, regulates 1 of the 5 genes in the triglyceride-associated region.Approach and resultsWe performed a linkage disequilibrium analysis and found 9 additional variants in linkage disequilibrium (r(2)>0.7) with the lead single-nucleotide polymorphism. To select the variants for functional analyses, we annotated the 10 variants using DNase I hypersensitive sites, transcription factor and chromatin states and identified rs17259126 as the lead candidate variant for functional in vitro validation. Using luciferase transcriptional reporter assay in liver HepG2 cells, we found that the G allele exhibits a significantly lower effect on transcription (P
Published: 2016

46. The Contribution of GWAS Loci in Familial Dyslipidemias.

Author: Ripatti, Pietari, Rämö, Joel T, Söderlund, Sanni, Surakka, Ida, Matikainen, Niina, Pirinen, Matti, Pajukanta, Päivi, Sarin, Antti-Pekka, Service, Susan K, Laurila, Pirkka-Pekka, Ehnholm, Christian, Salomaa, Veikko, Wilson, Richard K, Palotie, Aarno, Freimer, Nelson B, Taskinen, Marja-Riitta, and Ripatti, Samuli
Subjects: Humans, Hyperlipidemia, Familial Combined, Triglycerides, Lipoproteins, LDL, Apolipoproteins B, Adult, Middle Aged, Female, Male, Dyslipidemias, Cholesterol, HDL, Coronary Artery Disease, Genome-Wide Association Study, Cholesterol, HDL, Hyperlipidemia, Familial Combined, Lipoproteins, LDL, Genetics, Developmental Biology
Abstract: Familial combined hyperlipidemia (FCH) is a complex and common familial dyslipidemia characterized by elevated total cholesterol and/or triglyceride levels with over five-fold risk of coronary heart disease. The genetic architecture and contribution of rare Mendelian and common variants to FCH susceptibility is unknown. In 53 Finnish FCH families, we genotyped and imputed nine million variants in 715 family members with DNA available. We studied the enrichment of variants previously implicated with monogenic dyslipidemias and/or lipid levels in the general population by comparing allele frequencies between the FCH families and population samples. We also constructed weighted polygenic scores using 212 lipid-associated SNPs and estimated the relative contributions of Mendelian variants and polygenic scores to the risk of FCH in the families. We identified, across the whole allele frequency spectrum, an enrichment of variants known to elevate, and a deficiency of variants known to lower LDL-C and/or TG levels among both probands and affected FCH individuals. The score based on TG associated SNPs was particularly high among affected individuals compared to non-affected family members. Out of 234 affected FCH individuals across the families, seven (3%) carried Mendelian variants and 83 (35%) showed high accumulation of either known LDL-C or TG elevating variants by having either polygenic score over the 90th percentile in the population. The positive predictive value of high score was much higher for affected FCH individuals than for similar sporadic cases in the population. FCH is highly polygenic, supporting the hypothesis that variants across the whole allele frequency spectrum contribute to this complex familial trait. Polygenic SNP panels improve identification of individuals affected with FCH, but their clinical utility remains to be defined.
Published: 2016

47. Integrative approaches for large-scale transcriptome-wide association studies

Author: Gusev, Alexander, Ko, Arthur, Shi, Huwenbo, Bhatia, Gaurav, Chung, Wonil, Penninx, Brenda WJH, Jansen, Rick, de Geus, Eco JC, Boomsma, Dorret I, Wright, Fred A, Sullivan, Patrick F, Nikkola, Elina, Alvarez, Marcus, Civelek, Mete, Lusis, Aldons J, Lehtimäki, Terho, Raitoharju, Emma, Kähönen, Mika, Seppälä, Ilkka, Raitakari, Olli T, Kuusisto, Johanna, Laakso, Markku, Price, Alkes L, Pajukanta, Päivi, and Pasaniuc, Bogdan
Subjects: Biological Sciences, Genetics, Obesity, Human Genome, Cancer, Generic health relevance, Animals, Gene Expression Regulation, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Mice, Phenotype, Quantitative Trait Loci, Transcriptome, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract: Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in ∼ 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.
Published: 2016

48. Correction to: Identification of TBX15 as an adipose master trans regulator of abdominal obesity genes

Author: Pan, David Z., Miao, Zong, Comenho, Caroline, Rajkumar, Sandhya, Koka, Amogha, Lee, Seung Hyuk T., Alvarez, Marcus, Kaminska, Dorota, Ko, Arthur, Sinsheimer, Janet S., Mohlke, Karen L., Mancuso, Nicholas, Muñoz-Hernandez, Linda Liliana, Herrera-Hernandez, Miguel, Tusié-Luna, Maria Teresa, Aguilar-Salinas, Carlos, Pietiläinen, Kirsi H., Pihlajamäki, Jussi, Laakso, Markku, Garske, Kristina M., and Pajukanta, Päivi
Published: 2021
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49. Further evidence supporting a potential role for ADH1B in obesity

Author: Morales, Liza D., Cromack, Douglas T., Tripathy, Devjit, Fourcaudot, Marcel, Kumar, Satish, Curran, Joanne E., Carless, Melanie, Göring, Harald H. H., Hu, Shirley L., Lopez-Alvarenga, Juan Carlos, Garske, Kristina M., Pajukanta, Päivi, Small, Kerrin S., Glastonbury, Craig A., Das, Swapan K., Langefeld, Carl, Hanson, Robert L., Hsueh, Wen-Chi, Norton, Luke, Arya, Rector, Mummidi, Srinivas, Blangero, John, DeFronzo, Ralph A., Duggirala, Ravindranath, and Jenkinson, Christopher P.
Published: 2021
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50. Remote Ischemic Conditioning Alters Methylation and Expression of Cell Cycle Genes in Aneurysmal Subarachnoid Hemorrhage

Author: Nikkola, Elina, Laiwalla, Azim, Ko, Arthur, Alvarez, Marcus, Connolly, Mark, Ooi, Yinn Cher, Hsu, William, Bui, Alex, Pajukanta, Päivi, and Gonzalez, Nestor R
Subjects: Genetics, Biotechnology, Human Genome, Stroke, Adult, Aged, DNA Methylation, Female, Gene Expression, Genes, cdc, Humans, Intracranial Aneurysm, Ischemic Preconditioning, Longitudinal Studies, Male, Middle Aged, Pilot Projects, Subarachnoid Hemorrhage, Transcriptome, aneurysm, DNA methylation, genomics, preconditioning, subarachnoid hemorrhage, transcriptome, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Neurosciences, Neurology & Neurosurgery
Abstract: Background and purposeRemote ischemic conditioning (RIC) is a phenomenon in which short periods of nonfatal ischemia in 1 tissue confers protection to distant tissues. Here we performed a longitudinal human pilot study in patients with aneurysmal subarachnoid hemorrhage undergoing RIC by limb ischemia to compare changes in DNA methylation and transcriptome profiles before and after RIC.MethodsThirteen patients underwent 4 RIC sessions over 2 to 12 days after rupture of an intracranial aneurysm. We analyzed whole blood transcriptomes using RNA sequencing and genome-wide DNA methylomes using reduced representation bisulfite sequencing, both before and after RIC. We tested differential expression and differential methylation using an intraindividual paired study design and then overlapped the differential expression and differential methylation results for analyses of functional categories and protein-protein interactions.ResultsWe observed 164 differential expression genes and 3493 differential methylation CpG sites after RIC, of which 204 CpG sites overlapped with 103 genes, enriched for pathways of cell cycle (P
Published: 2015

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