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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Johnson WC, Van Den Berg D, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Barr RG, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. Am J Hum Genet 2024; 111:133-149. [PMID: 38181730 PMCID: PMC10806864 DOI: 10.1016/j.ajhg.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/29/2023] [Accepted: 11/29/2023] [Indexed: 01/07/2024] Open
Abstract
Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue. The interpretation of age iQTLs, however, warrants caution because the moderation effect of age on the genotype and molecular phenotype association could be mediated by changes in cell-type composition. Finally, we show that cell-type iQTLs contribute to cell-type-specific enrichment of diseases that, in combination with additional functional data, could guide future functional studies. Overall, this study highlights the use of iQTLs to gain insights into the context specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA.
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA; Department of Pediatrics, Dr. von Hauner Children's Hospital, University Hospital LMU Munich, Munich, Germany; Computational Health Center, Institute of Translational Genomics, Helmholtz Munich, Neuherberg, Germany
| | - Brielin C Brown
- New York Genome Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA
| | - Daniel C Nachun
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Russell P Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D Smith
- Northwest Genomics Center, University of Washington, Seattle, WA, USA
| | - Thomas W Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R Graham Barr
- Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA; Department of Systems Biology, Columbia University, New York, NY, USA; Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
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Kasela S, Aguet F, Kim-Hellmuth S, Brown BC, Nachun DC, Tracy RP, Durda P, Liu Y, Taylor KD, Craig Johnson W, Berg DVD, Gabriel S, Gupta N, Smith JD, Blackwell TW, Rotter JI, Ardlie KG, Manichaikul A, Rich SS, Graham Barr R, Lappalainen T. Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.26.546528. [PMID: 37425716 PMCID: PMC10326995 DOI: 10.1101/2023.06.26.546528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Bulk tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, while context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell type proportions, we demonstrate that cell type iQTLs could be considered as proxies for cell type-specific QTL effects. The interpretation of age iQTLs, however, warrants caution as the moderation effect of age on the genotype and molecular phenotype association may be mediated by changes in cell type composition. Finally, we show that cell type iQTLs contribute to cell type-specific enrichment of diseases that, in combination with additional functional data, may guide future functional studies. Overall, this study highlights iQTLs to gain insights into the context-specificity of regulatory effects.
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Affiliation(s)
- Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Sarah Kim-Hellmuth
- New York Genome Center, New York, NY, USA
- Department of Pediatrics, Dr. von Hauner Children’s Hospital, University Hospital LMU Munich, Munich, Germany
- Computational Health Center, Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Brielin C. Brown
- New York Genome Center, New York, NY, USA
- Data Science Institute, Columbia University, New York, NY, USA
| | | | - Russell P. Tracy
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Peter Durda
- Pathology and Laboratory Medicine, The University of Vermont, Larner College of Medicine, Burlington, VT, USA
| | - Yongmei Liu
- Department of Medicine, Duke University, Durham, NC, USA
| | - Kent D. Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - W. Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - David Van Den Berg
- Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | | | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua D. Smith
- Northwest Genomic Center, University of Washington, Seattle, WA, USA
| | - Thomas W. Blackwell
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Jerome I. Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Ani Manichaikul
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S. Rich
- Center for Public health Genomics, University of Virginia, Charlottesville, VA, USA
| | - R. Graham Barr
- Epidemiology and Medicine, Columbia University Medical Center, New York, NY, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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Meza-Alvarado JC, Page RA, Mallard B, Bromhead C, Palmer BR. VEGF-A related SNPs: a cardiovascular context. Front Cardiovasc Med 2023; 10:1190513. [PMID: 37288254 PMCID: PMC10242119 DOI: 10.3389/fcvm.2023.1190513] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/27/2023] [Indexed: 06/09/2023] Open
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death worldwide. Currently, cardiovascular disease risk algorithms play a role in primary prevention. However, this is complicated by a lack of powerfully predictive biomarkers that could be observed in individuals before the onset of overt symptoms. A key potential biomarker for heart disease is the vascular endothelial growth factor (VEGF-A), a molecule that plays a pivotal role in blood vessel formation. This molecule has a complex biological role in the cardiovascular system due to the processes it influences, and its production is impacted by various CVD risk factors. Research in different populations has shown single nucleotide polymorphisms (SNPs) may affect circulating VEGF-A plasma levels, with some variants associated with the development of CVDs, as well as CVD risk factors. This minireview aims to give an overview of the VEGF family, and of the SNPs reported to influence VEGF-A levels, cardiovascular disease, and other risk factors used in CVD risk assessments.
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Affiliation(s)
| | | | | | | | - B. R. Palmer
- School of Health Sciences, Massey University, Wellington, New Zealand
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Genetic scores for predicting longevity in the Croatian oldest-old population. PLoS One 2023; 18:e0279971. [PMID: 36735720 PMCID: PMC9897585 DOI: 10.1371/journal.pone.0279971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 12/19/2022] [Indexed: 02/04/2023] Open
Abstract
Longevity is a hallmark of successful ageing and a complex trait with a significant genetic component. In this study, 43 single nucleotide polymorphisms (SNPs) were chosen from the literature and genotyped in a Croatian oldest-old sample (85+ years, sample size (N) = 314), in order to determine whether any of these SNPs have a significant effect on reaching the age thresholds for longevity (90+ years, N = 212) and extreme longevity (95+ years, N = 84). The best models were selected for both survival ages using multivariate logistic regression. In the model for reaching age 90, nine SNPs explained 20% of variance for survival to that age, while the 95-year model included five SNPs accounting for 9.3% of variance. The two SNPs that showed the most significant association (p ≤ 0.01) with longevity were TERC rs16847897 and GHRHR rs2267723. Unweighted and weighted Genetic Longevity Scores (uGLS and wGLS) were calculated and their predictive power was tested. All four scores showed significant correlation with age at death (p ≤ 0.01). They also passed the ROC curve test with at least 50% predictive ability, but wGLS90 stood out as the most accurate score, with a 69% chance of accurately predicting survival to the age of 90.
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Pan X, Peng H, Zhang J, Wu Y, Hu Z, Peng XE. Genetic variants in promoter region of TFR2 is associated with the risk of non-alcoholic fatty liver disease in a Chinese Han population: a case-control study. Gastroenterol Rep (Oxf) 2022; 10:goac060. [PMID: 36324614 PMCID: PMC9619830 DOI: 10.1093/gastro/goac060] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/15/2022] [Accepted: 10/09/2022] [Indexed: 11/04/2022] Open
Abstract
Background Iron overload is frequently observed in non-alcoholic fatty liver disease (NAFLD). Transferrin receptor 2 (TFR2) is an important key factor in iron regulation. We aimed to investigate whether TFR2 single nucleotide polymorphisms (SNPs) contribute to susceptibility to NAFLD in a Chinese Han population. Methods Five tag SNPs (rs10247962, rs4434553, rs2075672, rs1052897, and rs3757859) in the TFR2 gene were selected and genotyped in a case–control study on participants who visited two affiliated hospitals of Fujian Medical University between June 2011 and August 2017. Propensity score matching and inverse probability of treatment weighting analyses were used to verify the risk associated with TFR2 SNPs. Results Logistic regression analyses suggested that subjects with the rs4434553 GA or GG genotype had a lower risk of NAFLD than those carrying the AA genotype (odds ratio = 0.630, 95% confidence interval = 0.504–0.788). Moreover, the rs4434553 GA or GG genotype was negatively correlated with body mass index, hepatic steatosis index, and serum ferritin (b = −0.363, P = 0.008; b = −1.040, P = 0.009; b = −35.258, P = 0.015, respectively), and positively associated with serum hepcidin level (b = 35.308, P < 0.001). Moreover, rs10247962 and rs1052897 had multiplicative interactions with age in relation to the risk of NAFLD (P for interactions, 0.041 and 0.034, respectively). The cumulative effects of the rs10247962, rs1052897, and rs4434553 SNPs were positively associated with the risk of NAFLD (adjusted Ptrend = 0.012). Conclusions In this Chinese Han population, the rs4434553 polymorphism in TFR2 may be an independent influencing factor associated with the susceptibility to NAFLD. The ageing effect on the development of NAFLD may be inhibited by SNPs rs10247962 and rs1052897.
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Affiliation(s)
| | | | - Junchao Zhang
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, P. R. China
| | - Yunli Wu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, P. R. China
| | - Zhijian Hu
- Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fuzhou, P. R. China
| | - Xian-E Peng
- Corresponding author. Department of Epidemiology and Health Statistics, Xuefu North Road 1, Shangjie Town, Minhou Country, Fuzhou, Fujian 350108, China. Tel and Fax: +86-591-22862648;
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Shrestha Palikhe N, Mackenzie CA, Licskai C, Kim RB, Vliagoftis H, Cameron L. The CRTh2 polymorphism rs533116 G > A associates with asthma severity in older females. Front Med (Lausanne) 2022; 9:970495. [PMID: 36314028 PMCID: PMC9606418 DOI: 10.3389/fmed.2022.970495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/22/2022] [Indexed: 12/03/2022] Open
Abstract
Background CRTh2 is G protein coupled receptor for prostaglandin D2 (PGD)2 expressed by immune cells that drive type 2 inflammation such as CD4+ T cells (Th2), eosinophils and group 2 innate lymphoid cells (ILC2) as well as structural cells including smooth muscle and epithelium. CRTh2-expressing cells are increased in the blood and airways of asthmatics and severe asthma is characterized by increased activity of the PGD2-CRTh2 pathway. The CRTh2 single nucleotide polymorphism (SNP) rs533116 G > A is associated with development of asthma and increased Th2 cell differentiation. Objective To examine whether CRTh2 rs533116G > A associates with asthma severity. Since severe asthma is more common in females than males, we performed a sex-stratified analysis. Methods Clinical data from asthmatics (n = 170) were obtained from clinic visits and chart review. Asthma severity was assessed according to ERS/ATS guidelines. Peripheral blood cells were characterized by flow cytometry and qRT-PCR. Genotyping was performed by TaqMan assay. Results Older females (≥45 years) homozygous for minor A allele of rs533116 were more likely to have severe asthma, lower FEV1, a higher prescribed dose of inhaled corticosteroid and more type 2 inflammation than females carrying GA or GG genotypes. Comparing females and males with the AA genotype also revealed that women had more type 2 inflammation. Conclusions and significance The polymorphism CRTh2 rs533116 G > A associates with severe asthma and type 2 inflammation in older females. This study reveals a gene-sex-aging interaction influencing the effect of CRTh2 on asthma severity.
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Affiliation(s)
- Nami Shrestha Palikhe
- Division of Pulmonary Medicine, Department of Medicine and Alberta Respiratory Centre, University of Alberta, Edmonton, AB, Canada
| | - Constance A. Mackenzie
- Division of Respirology, Department of Medicine, Western University, London, ON, Canada,Division of Clinical Pharmacology, Department of Medicine, Western University, London, ON, Canada,Division of Clinical Pharmacology and Toxicology, Ontario Poison Centre, The Hospital for Sick Children, Toronto, ON, Canada
| | - Christopher Licskai
- Division of Respirology, Department of Medicine, Western University, London, ON, Canada
| | - Richard B. Kim
- Division of Clinical Pharmacology, Department of Medicine, Western University, London, ON, Canada
| | - Harissios Vliagoftis
- Division of Pulmonary Medicine, Department of Medicine and Alberta Respiratory Centre, University of Alberta, Edmonton, AB, Canada
| | - Lisa Cameron
- Division of Pulmonary Medicine, Department of Medicine and Alberta Respiratory Centre, University of Alberta, Edmonton, AB, Canada,Department of Pathology and Laboratory Medicine, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada,*Correspondence: Lisa Cameron,
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Lelièvre SA. Can the epigenome contribute to risk stratification for cancer onset? NAR Cancer 2021; 3:zcab043. [PMID: 34734185 PMCID: PMC8559165 DOI: 10.1093/narcan/zcab043] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 10/10/2021] [Accepted: 10/27/2021] [Indexed: 12/12/2022] Open
Abstract
The increasing burden of cancer requires identifying and protecting individuals at highest risk. The epigenome provides an indispensable complement to genetic alterations for a risk stratification approach for the following reasons: gene transcription necessary for cancer onset is directed by epigenetic modifications and many risk factors studied so far have been associated with alterations related to the epigenome. The risk level depends on the plasticity of the epigenome during phases of life particularly sensitive to environmental and dietary impacts. Modifications in the activity of DNA regulatory regions and altered chromatin compaction may accumulate, hence leading to the increase of cancer risk. Moreover, tissue architecture directs the unique organization of the epigenome for each tissue and cell type, which allows the epigenome to control cancer risk in specific organs. Investigations of epigenetic signatures of risk should help identify a continuum of alterations leading to a threshold beyond which the epigenome cannot maintain homeostasis. We propose that this threshold may be similar in the population for a given tissue, but the pace to reach this threshold will depend on the combination of germline inheritance and the risk and protective factors encountered, particularly during windows of epigenetic susceptibility, by individuals.
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Affiliation(s)
- Sophie A Lelièvre
- Institut de Cancérologie de l'Ouest (ICO)-Western Cancer Institute, Scientific Direction for Translational Research, Angers, France
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