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Nusinovici S, Zhou L, Wang X, Tham YC, Wang X, Wong TY, Chakravarthy U, Cheng CY. Contributions of Lipid-Related Metabolites and Complement Proteins to Early and Intermediate Age-Related Macular Degeneration. OPHTHALMOLOGY SCIENCE 2024; 4:100538. [PMID: 39051044 PMCID: PMC11268342 DOI: 10.1016/j.xops.2024.100538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/08/2024] [Accepted: 04/19/2024] [Indexed: 07/27/2024]
Abstract
Objective Our objective was to determine the effects of lipids and complement proteins on early and intermediate age-related macular degeneration (AMD) stages using machine learning models by integrating metabolomics and proteomic data. Design Nested case-control study. Subjects and Controls The analyses were performed in a subset of the Singapore Indian Chinese Cohort (SICC) Eye Study. Among the 6753 participants, we randomly selected 155 Indian and 155 Chinese cases of AMD and matched them with 310 controls on age, sex, and ethnicity. Methods We measured 35 complement proteins and 56 lipids using mass spectrometry and nuclear magnetic resonance, respectively. We first selected the most contributing lipids and complement proteins to early and intermediate AMD using random forest models. Then, we estimated their effects using a multinomial model adjusted for potential confounders. Main Outcome Measures Age-related macular degeneration was classified using the Beckman classification system. Results Among the 310 individuals with AMD, 166 (53.5%) had early AMD, and 144 (46.5%) had intermediate AMD. First, high-density lipoprotein (HDL) particle diameter was positively associated with both early and intermediate AMD (odds ratio [OR]early = 1.69; 95% confidence interval [CI],1.11-2.55 and ORintermediate = 1.72; 95% CI, 1.11-2.66 per 1-standard deviation increase in HDL diameter). Second, complement protein 2 (C2), complement C1 inhibitor (IC1), complement protein 6 (C6), complement protein 1QC (C1QC) and complement factor H-related protein 1 (FHR1), were associated with AMD. C2 was positively associated with both early and intermediate AMD (ORearly = 1.58; 95% CI, 1.08-2.30 and ORintermediate = 1.56; 95% CI, 1.04-2.34). C6 was positively (ORearly = 1.41; 95% CI, 1.03-1.93) associated with early AMD. However, IC1 was negatively associated with early AMD (ORearly = 0.62; 95% CI, 0.38-0.99), whereas C1QC (ORintermediate = 0.63; 95% CI, 0.42-0.93) and FHR1 (ORintermediate = 0.73; 95% CI, 0.54-0.98) were both negatively associated with intermediate AMD. Conclusions Although both HDL diameter and C2 levels show associations with both early and intermediate AMD, dysregulations of IC1, C6, C1QC, and FHR1 are only observed at specific stages of AMD. These findings underscore the complexity of complement system dysregulation in AMD, which appears to vary depending on the disease severity. Financial Disclosures The authors have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Simon Nusinovici
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Lei Zhou
- School of Optometry; Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong
| | - Xinyue Wang
- School of Optometry; Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hong Kong
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Xiaomeng Wang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A∗STAR), Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Tsinghua Medicine, Tsinghua University, Beijing, China
| | | | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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Sun Y, Zhang R, Tian L, Pan Y, Sun X, Huang Z, Fan J, Chen J, Zhang K, Li S, Chen W, Bazzano LA, Kelly TN, He J, Bundy JD, Li C. Novel Metabolites Associated With Blood Pressure After Dietary Interventions. Hypertension 2024; 81:1966-1975. [PMID: 39005213 PMCID: PMC11324412 DOI: 10.1161/hypertensionaha.124.22999] [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: 03/07/2024] [Accepted: 06/21/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND The blood pressure (BP) etiologic study is complex due to multifactorial influences, including genetic, environmental, lifestyle, and their intricate interplays. We used a metabolomics approach to capture internal pathways and external exposures and to study BP regulation mechanisms after well-controlled dietary interventions. METHODS In the ProBP trail (Protein and Blood Pressure), a double-blinded crossover randomized controlled trial, participants underwent dietary interventions of carbohydrate, soy protein, and milk protein, receiving 40 g daily for 8 weeks, with 3-week washout periods. We measured plasma samples collected at baseline and at the end of each dietary intervention. Multivariate linear models were used to evaluate the association between metabolites and systolic/diastolic BP. Nominally significant metabolites were examined for enriching biological pathways. Significant ProBP findings were evaluated for replication among 1311 participants of the BHS (Bogalusa Heart Study), a population-based study conducted in the same area as ProBP. RESULTS After Bonferroni correction for 77 independent metabolite clusters (α=6.49×10-4), 18 metabolites were significantly associated with BP at baseline or the end of a dietary intervention, of which 11 were replicated in BHS. Seven emerged as novel discoveries, which are as follows: 1-linoleoyl-GPE (18:2), 1-oleoyl-GPE (18:1), 1-stearoyl-2-linoleoyl-GPC (18:0/18:2), 1-palmitoyl-2-oleoyl-GPE (16:0/18:1), maltose, N-stearoyl-sphinganine (d18:0/18:0), and N6-carbamoylthreonyladenosine. Pathway enrichment analyses suggested dietary protein intervention might reduce BP through pathways related to G protein-coupled receptors, incretin function, selenium micronutrient network, and mitochondrial biogenesis. CONCLUSIONS Seven novel metabolites were identified to be associated with BP at the end of different dietary interventions. The beneficial effects of protein interventions might be mediated through specific metabolic pathways.
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Affiliation(s)
- Yixi Sun
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Ling Tian
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Yang Pan
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago (Y.P., X.S., T.N.K.)
| | - Xiao Sun
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago (Y.P., X.S., T.N.K.)
| | - Zhijie Huang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Jia Fan
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Jing Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Kai Zhang
- Department of Environmental Health Sciences, University of Albany, State University of New York, Rensselaer (K.Z.)
| | - Shengxu Li
- Children's Minnesota Research Institute, Children's Minnesota, Minneapolis (S.L.)
| | - Wei Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Lydia A Bazzano
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Tanika N Kelly
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois at Chicago (Y.P., X.S., T.N.K.)
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Joshua D Bundy
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
| | - Changwei Li
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA (Y.S., R.Z., L.T., Z.H., J.F., J.C., W.C., L.B., J.H., J.D.B., C.L.)
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Dabiri H, Mortezaei Z. Genome-wide association study of therapeutic response to statin drugs in cardiovascular disease. Sci Rep 2024; 14:18005. [PMID: 39097628 PMCID: PMC11297937 DOI: 10.1038/s41598-024-68665-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 07/26/2024] [Indexed: 08/05/2024] Open
Abstract
Cardiovascular disease (CVD) is one of the main causes of death in the world. The increased level of blood cholesterol is significantly correlated to CVD incidents. Statins are a group of drugs that decrease the synthesis of cholesterol in the liver by inhibiting the final enzyme of the pathway named HMG-CoA reductase. Several investigations showed that different patients give different responses to the administration of statin drugs according to their genetic background. In this research study, using Genome-Wide Association Studies (GWAS) data analysis methods, such as the SimpleM statistical approach and genomic connection matrix, we tried to discover the novel candidate SNPs that were involved in response to statin drugs. The investigation was carried out using 3,221 cardiovascular patients' data about genotypes and phenotypes of two important parameters including total cholesterol, and LDL level, in response to statin administration. Functional annotation of nearest genes to candidate SNPs was also carried out by using comprehensive databases and tools such as BioMart-Ensembl, UCSC, NCBI, and WebGestalt software. Our results represented eight novel SNPs (rs10820084, rs4803750, rs10989887, rs1966503, rs17502794, rs10785232, rs484071, rs4785621) significantly associated with statin response in different individual cardiovascular patients for the first time. In addition, the groups of genes that are close to the SNPs were also represented and evaluated in detail. Our results illustrated that some of the genes such as BAAT, BCL3, and CMTM6 have a direct functional impact on cholesterol level or LDL biosynthesis which confirmed the effects of neighbor SNPs on the response to statin drugs. Today, finding the loci, genes, and molecular mechanisms involved in the response to drugs is of great importance in pharmacogenomics and personalized medicine.
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Affiliation(s)
- Hamed Dabiri
- Human Genetics Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Zahra Mortezaei
- Human Genetics Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran.
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Tahir M, Yude B, Mehmood T, Bashir S, Zhenping Y, Awais M. Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine. Sci Rep 2024; 14:16815. [PMID: 39039135 PMCID: PMC11263374 DOI: 10.1038/s41598-024-66687-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Accepted: 07/03/2024] [Indexed: 07/24/2024] Open
Abstract
Machine learning has emerged as a leading field in artificial intelligence, demonstrating expert-level performance in various domains. Astronomy has benefited from machine learning techniques, particularly in classifying and identifying stars based on their features. This study focuses on the spectra-based classification of 11,408 B-type and 2422 hot subdwarf stars. The study employs baseline correction using Asymmetric Least Squares (ALS) to enhance classification accuracy. It applies the Pan-Core concept to identify 500 unique patterns or ranges for both types of stars. These patterns are the foundation for creating Support Vector Machine (SVM) models, including the linear (L-SVM), polynomial (P-SVM), and radial basis (R-SVM) kernels. Parameter tuning for the SVM models is achieved through cross-validation. Evaluation of the SVM models on test data reveals that the linear kernel SVM achieves the highest accuracy (87.0%), surpassing the polynomial kernel SVM (84.1%) and radial kernel SVM (80.1%). The average calibrated accuracy falls within the range of 90-95%. These results demonstrate the potential of using spectrum-based classification to aid astronomers in improving and expanding their understanding of stars, with a specific focus on the identification of hot subdwarf stars. This study presents a valuable investigation for astronomers, as it enables the classification of stars based on their spectra, leveraging machine learning techniques to enhance their knowledge and insights in astronomy.
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Affiliation(s)
- Muhammad Tahir
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, Shandong, China
| | - Bu Yude
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, Shandong, China.
| | - Tahir Mehmood
- School of Natural Sciences (SNS), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Saima Bashir
- School of Mathematics and Statistics, Shandong University, Weihai, 264209, Shandong, China
| | - Yi Zhenping
- School of Mechanical, Electrical, and Information Engineering, Shandong University, Weihai, 264209, Shandong, China
| | - Muhammad Awais
- School of Computer Science, Minhaj University, Lahore, Punjab, Pakistan
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Shibata M, Terada A, Kawaguchi T, Kamatani Y, Okada D, Nagashima K, Ohmura K, Matsuda F, Kawaguchi S, Sese J, Yamada R. Identification of epistatic SNP combinations in rheumatoid arthritis using LAMPLINK and Japanese cohorts. J Hum Genet 2024:10.1038/s10038-024-01269-y. [PMID: 39014190 DOI: 10.1038/s10038-024-01269-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 06/16/2024] [Accepted: 06/20/2024] [Indexed: 07/18/2024]
Abstract
Genome-wide association studies have enabled the identification of important genetic factors in many trait studies. However, only a fraction of the heritability can be explained by known genetic factors, even in the most common diseases. Genetic loci combinations, or epistatic contributions expressed by combinations of single nucleotide polymorphisms (SNPs), have been argued to be one of the critical factors explaining some of the missing heritability, especially in oligogenic/polygenic diseases. Rheumatoid arthritis (RA) is a complex disease with more than 100 reported SNP associations, as well as various HLA haplotypes and amino acids; however, many associations between RA and inter-chromosomal SNP combinations are unknown. To discover novel associations of epistatic interactions with high odds ratios in RA, we applied the LAMPLINK method, a systematic enumerative procedure for identifying high-order SNP combinations, to a Japanese RA cohort (discovery cohort; 4024 patients with RA and 7731 controls). We validated the identified associations in a different Japanese cohort (validation cohort; 810 RA patients and 6303 controls). In this study, we identified 90 significant genetic associations in the discovery cohort. Among these, 74 (82.2%) associations were replicated in the validation cohort, and eight combinations were inter-chromosomal, all of which comprised rs7765379 or rs35265698 located in the HLA region. These two SNPs exhibited strong correlations with valine at amino acid position 11 in HLA-DRB1 (HLA-DRB1-11-Val). Finally, we discovered that rs9624 showed an association with RA through an epistatic interaction with HLA-DRB1-11-Val. Overall, LAMPLINK showed high reliability for identifying epistatic genetic contributions hidden in complex traits.
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Affiliation(s)
- Mio Shibata
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Takahisa Kawaguchi
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoichiro Kamatani
- Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Daigo Okada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kazuhisa Nagashima
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shuji Kawaguchi
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
| | - Jun Sese
- Humanome Lab. Inc., Tokyo, Japan.
- Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.
| | - Ryo Yamada
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Hu W, Wang W, Liao H, Bulloch G, Zhang X, Shang X, Huang Y, Hu Y, Yu H, Yang X, He M, Zhu Z. Metabolic profiling reveals circulating biomarkers associated with incident and prevalent Parkinson's disease. NPJ Parkinsons Dis 2024; 10:130. [PMID: 38982064 PMCID: PMC11233508 DOI: 10.1038/s41531-024-00713-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/19/2024] [Indexed: 07/11/2024] Open
Abstract
The metabolic profile predating the onset of Parkinson's disease (PD) remains unclear. We aim to investigate the metabolites associated with incident and prevalent PD and their predictive values in the UK Biobank participants with metabolomics and genetic data at the baseline. A panel of 249 metabolites was quantified using a nuclear magnetic resonance analytical platform. PD was ascertained by self-reported history, hospital admission records and death registers. Cox proportional hazard models and logistic regression models were used to investigate the associations between metabolites and incident and prevalent PD, respectively. Area under receiver operating characteristics curves (AUC) were used to estimate the predictive values of models for future PD. Among 109,790 participants without PD at the baseline, 639 (0.58%) individuals developed PD after one year from the baseline during a median follow-up period of 12.2 years. Sixty-eight metabolites were associated with incident PD at nominal significance (P < 0.05), spanning lipids, lipid constituent of lipoprotein subclasses and ratios of lipid constituents. After multiple testing corrections (P < 9 × 10-4), polyunsaturated fatty acids (PUFA) and omega-6 fatty acids remained significantly associated with incident PD, and PUFA was shared by incident and prevalent PD. Additionally, 14 metabolites were exclusively associated with prevalent PD, including amino acids, fatty acids, several lipoprotein subclasses and ratios of lipids. Adding these metabolites to the conventional risk factors yielded a comparable predictive performance to the risk-factor-based model (AUC = 0.766 vs AUC = 0.768, P = 0.145). Our findings suggested metabolic profiles provided additional knowledge to understand different pathways related to PD before and after its onset.
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Affiliation(s)
- Wenyi Hu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Huan Liao
- Neural Regeneration Group, Institute of Reconstructive Neurobiology, University of Bonn, Bonn, Germany
| | - Gabriella Bulloch
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
| | - Xiayin Zhang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia
| | - Yu Huang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yijun Hu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xiaohong Yang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Mingguang He
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia.
- School of Optometry, The Hong Kong Polytechnic University, Hong Kong SAR, China.
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Centre for Eye Research Australia; Ophthalmology, University of Melbourne, Melbourne, VIC, Australia.
- Department of Surgery (Ophthalmology), The University of Melbourne, Melbourne, VIC, Australia.
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Corlouer E, Sauvage C, Leveugle M, Nesi N, Laperche A. Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield stability. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:164. [PMID: 38898332 PMCID: PMC11186914 DOI: 10.1007/s00122-024-04664-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 05/28/2024] [Indexed: 06/21/2024]
Abstract
KEY MESSAGE A comprehensive environmental characterization allowed identifying stable and interactive QTL for seed yield: QA09 and QC09a were detected across environments; whereas QA07a was specifically detected on the most stressed environments. A main challenge for rapeseed consists in maintaining seed yield while adapting to climate changes and contributing to environmental-friendly cropping systems. Breeding for cultivar adaptation is one of the keys to meet this challenge. Therefore, we propose to identify the genetic determinant of seed yield stability for winter oilseed rape using GWAS coupled with a multi-environmental trial and to interpret them in the light of environmental characteristics. Due to a comprehensive characterization of a multi-environmental trial using 79 indicators, four contrasting envirotypes were defined and used to identify interactive and stable seed yield QTL. A total of four QTLs were detected, among which, QA09 and QC09a, were stable (detected at the multi-environmental trial scale or for different envirotypes and environments); and one, QA07a, was specifically detected into the most stressed envirotype. The analysis of the molecular diversity at QA07a showed a lack of genetic diversity within modern lines compared to older cultivars bred before the selection for low glucosinolate content. The results were discussed in comparison with other studies and methods as well as in the context of breeding programs.
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Affiliation(s)
- Erwan Corlouer
- IGEPP, INRAE, Institut Agro, Université de Rennes, 35650, Le Rheu, France
| | | | | | - Nathalie Nesi
- IGEPP, INRAE, Institut Agro, Université de Rennes, 35650, Le Rheu, France
| | - Anne Laperche
- IGEPP, INRAE, Institut Agro, Université de Rennes, 35650, Le Rheu, France.
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Laurençon M, Legrix J, Wagner MH, Demilly D, Baron C, Rolland S, Ducournau S, Laperche A, Nesi N. Genomic and phenomic predictions help capture low-effect alleles promoting seed germination in oilseed rape in addition to QTL analyses. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:156. [PMID: 38858297 PMCID: PMC11164772 DOI: 10.1007/s00122-024-04659-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/25/2024] [Indexed: 06/12/2024]
Abstract
KEY MESSAGE Phenomic prediction implemented on a large diversity set can efficiently predict seed germination, capture low-effect favorable alleles that are not revealed by GWAS and identify promising genetic resources. Oilseed rape faces many challenges, especially at the beginning of its developmental cycle. Achieving rapid and uniform seed germination could help to ensure a successful establishment and therefore enabling the crop to compete with weeds and tolerate stresses during the earliest developmental stages. The polygenic nature of seed germination was highlighted in several studies, and more knowledge is needed about low- to moderate-effect underlying loci in order to enhance seed germination effectively by improving the genetic background and incorporating favorable alleles. A total of 17 QTL were detected for seed germination-related traits, for which the favorable alleles often corresponded to the most frequent alleles in the panel. Genomic and phenomic predictions methods provided moderate-to-high predictive abilities, demonstrating the ability to capture small additive and non-additive effects for seed germination. This study also showed that phenomic prediction estimated phenotypic values closer to phenotypic values than GEBV. Finally, as the predictive ability of phenomic prediction was less influenced by the genetic structure of the panel, it is worth using this prediction method to characterize genetic resources, particularly with a view to design prebreeding populations.
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Affiliation(s)
- Marianne Laurençon
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Julie Legrix
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Marie-Hélène Wagner
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Didier Demilly
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Cécile Baron
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sophie Rolland
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
| | - Sylvie Ducournau
- Groupe d'Etude et de Contrôle des Variétés Et des Semences (GEVES), 49070, Beaucouzé, France
| | - Anne Laperche
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France.
| | - Nathalie Nesi
- Institute of Genetics, Environment and Plant Protection (IGEPP), INRAE - Institut Agro Rennes-Angers - Université de Rennes, 35650, Le Rheu, France
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Xu IRL, Danzi MC, Ruiz A, Raposo J, De Jesus YA, Reilly MM, Cortese A, Shy ME, Scherer SS, Hermann D, Fridman V, Baets J, Saporta M, Seyedsadjadi R, Stojkovic T, Claeys KG, Patel P, Feely S, Rebelo A, Dohrn MF, Züchner S. A study concept of expeditious clinical enrollment for genetic modifier studies in Charcot-Marie-Tooth neuropathy 1A. J Peripher Nerv Syst 2024; 29:202-212. [PMID: 38581130 PMCID: PMC11209807 DOI: 10.1111/jns.12621] [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: 01/24/2024] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Caused by duplications of the gene encoding peripheral myelin protein 22 (PMP22), Charcot-Marie-Tooth disease type 1A (CMT1A) is the most common hereditary neuropathy. Despite this shared genetic origin, there is considerable variability in clinical severity. It is hypothesized that genetic modifiers contribute to this heterogeneity, the identification of which may reveal novel therapeutic targets. In this study, we present a comprehensive analysis of clinical examination results from 1564 CMT1A patients sourced from a prospective natural history study conducted by the RDCRN-INC (Inherited Neuropathy Consortium). Our primary objective is to delineate extreme phenotype profiles (mild and severe) within this patient cohort, thereby enhancing our ability to detect genetic modifiers with large effects. METHODS We have conducted large-scale statistical analyses of the RDCRN-INC database to characterize CMT1A severity across multiple metrics. RESULTS We defined patients below the 10th (mild) and above the 90th (severe) percentiles of age-normalized disease severity based on the CMT Examination Score V2 and foot dorsiflexion strength (MRC scale). Based on extreme phenotype categories, we defined a statistically justified recruitment strategy, which we propose to use in future modifier studies. INTERPRETATION Leveraging whole genome sequencing with base pair resolution, a future genetic modifier evaluation will include single nucleotide association, gene burden tests, and structural variant analysis. The present work not only provides insight into the severity and course of CMT1A, but also elucidates the statistical foundation and practical considerations for a cost-efficient and straightforward patient enrollment strategy that we intend to conduct on additional patients recruited globally.
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Affiliation(s)
- Isaac R. L. Xu
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Matt C. Danzi
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Ariel Ruiz
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Jacquelyn Raposo
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Yeisha Arcia De Jesus
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
| | - Mary M Reilly
- Centre for Neuromuscular Diseases, Department of Neuromuscular Diseases, UCL Queen Square
| | - Andrea Cortese
- Centre for Neuromuscular Diseases, Department of Neuromuscular Diseases, UCL Queen Square
| | - Michael E Shy
- Department of Neurology, University of Iowa, Iowa City, Iowa, USA
| | - Steven S. Scherer
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, United States
| | - David Hermann
- Department of Neurology, University of Rochester Medical Center, 601 Elmwood Avenue, Box 673, Rochester, New York, 14642, USA
| | - Vera Fridman
- Department of Neurology, University of Colorado Anschutz Medical Campus, 12631 E 17th Avenue, Mailstop B185, Room 5113C, Aurora, CO, 80045, USA
| | - Jonathan Baets
- Department of Neurology, Neuromuscular Reference Centre, Antwerp University Hospital, Antwerp, Belgium
- Faculty of Medicine and Health Sciences, Translational Neurosciences, University of Antwerp, Antwerp, Belgium
- Laboratory of Neuromuscular Pathology, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Mario Saporta
- Department of Neurology, University of Miami Miller School of Medicine, United States
| | - Reza Seyedsadjadi
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tanya Stojkovic
- AP-HP, Centre de référence des maladies neuromusculaires Nord/Est/Ile de France, Hôpital Pitié-Salpêtrière, 47-83, boulevard de l’Hôpital, 75013 Paris, France
| | - Kristl G. Claeys
- Department of Neurology, University Hospitals Leuven, Leuven, Belgium
- Department of Neurosciences, Laboratory for Muscle Diseases and Neuropathies, KU Leuven, Leuven, Belgium
| | - Pooja Patel
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Shawna Feely
- Department of Neurology, University of Iowa, Iowa City, Iowa, USA
| | - Adriana Rebelo
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
| | | | - Maike F. Dohrn
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
- Department of Neurology, Medical Faculty of the RWTH Aachen University, Aachen, Germany
| | - Stephan Züchner
- Dr. John T. Macdonald Foundation, Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami, Miller School of Medicine, Miami, FL, USA
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10
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McGee EE, Zeleznik OA, Balasubramanian R, Hu J, Rosner BA, Wactawski-Wende J, Clish CB, Avila-Pacheco J, Willett WC, Rexrode KM, Tamimi RM, Eliassen AH. Differences in metabolomic profiles between Black and White women in the U.S.: Analyses from two prospective cohorts. Eur J Epidemiol 2024; 39:653-665. [PMID: 38703248 DOI: 10.1007/s10654-024-01111-x] [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: 11/22/2023] [Accepted: 02/26/2024] [Indexed: 05/06/2024]
Abstract
There is growing interest in incorporating metabolomics into public health practice. However, Black women are under-represented in many metabolomics studies. If metabolomic profiles differ between Black and White women, this under-representation may exacerbate existing Black-White health disparities. We therefore aimed to estimate metabolomic differences between Black and White women in the U.S. We leveraged data from two prospective cohorts: the Nurses' Health Study (NHS; n = 2077) and Women's Health Initiative (WHI; n = 2128). The WHI served as the replication cohort. Plasma metabolites (n = 334) were measured via liquid chromatography-tandem mass spectrometry. Observed metabolomic differences were estimated using linear regression and metabolite set enrichment analyses. Residual metabolomic differences in a hypothetical population in which the distributions of 14 risk factors were equalized across racial groups were estimated using inverse odds ratio weighting. In the NHS, Black-White differences were observed for most metabolites (75 metabolites with observed differences ≥ |0.50| standard deviations). Black women had lower average levels than White women for most metabolites (e.g., for N6, N6-dimethlylysine, mean Black-White difference = - 0.98 standard deviations; 95% CI: - 1.11, - 0.84). In metabolite set enrichment analyses, Black women had lower levels of triglycerides, phosphatidylcholines, lysophosphatidylethanolamines, phosphatidylethanolamines, and organoheterocyclic compounds, but higher levels of phosphatidylethanolamine plasmalogens, phosphatidylcholine plasmalogens, cholesteryl esters, and carnitines. In a hypothetical population in which distributions of 14 risk factors were equalized, Black-White metabolomic differences persisted. Most results replicated in the WHI (88% of 272 metabolites available for replication). Substantial differences in metabolomic profiles exist between Black and White women. Future studies should prioritize racial representation.
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Affiliation(s)
- Emma E McGee
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA.
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Raji Balasubramanian
- Division of Women's Health, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jie Hu
- Division of Women's Health, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Bernard A Rosner
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, Buffalo, NY, USA
| | - Clary B Clish
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Julian Avila-Pacheco
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA
| | - Walter C Willett
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Kathryn M Rexrode
- Division of Women's Health, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rulla M Tamimi
- Department of Population Health Sciences, Weill Cornell Medical College, New York, USA
| | - A Heather Eliassen
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, 02115, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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11
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Papadimitriou N, Kim A, Kawaguchi ES, Morrison J, Diez-Obrero V, Albanes D, Berndt SI, Bézieau S, Bien SA, Bishop DT, Bouras E, Brenner H, Buchanan DD, Campbell PT, Carreras-Torres R, Chan AT, Chang-Claude J, Conti DV, Devall MA, Dimou N, Drew DA, Gruber SB, Harrison TA, Hoffmeister M, Huyghe JR, Joshi AD, Keku TO, Kundaje A, Küry S, Le Marchand L, Lewinger JP, Li L, Lynch BM, Moreno V, Newton CC, Obón-Santacana M, Ose J, Pellatt AJ, Peoples AR, Platz EA, Qu C, Rennert G, Ruiz-Narvaez E, Shcherbina A, Stern MC, Su YR, Thomas DC, Thomas CE, Tian Y, Tsilidis KK, Ulrich CM, Um CY, Visvanathan K, Wang J, White E, Woods MO, Schmit SL, Macrae F, Potter JD, Hopper JL, Peters U, Murphy N, Hsu L, Gunter MJ, Gauderman WJ. Genome-wide interaction study of dietary intake of fibre, fruits, and vegetables with risk of colorectal cancer. EBioMedicine 2024; 104:105146. [PMID: 38749303 PMCID: PMC11112268 DOI: 10.1016/j.ebiom.2024.105146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 04/15/2024] [Accepted: 04/19/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Consumption of fibre, fruits and vegetables have been linked with lower colorectal cancer (CRC) risk. A genome-wide gene-environment (G × E) analysis was performed to test whether genetic variants modify these associations. METHODS A pooled sample of 45 studies including up to 69,734 participants (cases: 29,896; controls: 39,838) of European ancestry were included. To identify G × E interactions, we used the traditional 1--degree-of-freedom (DF) G × E test and to improve power a 2-step procedure and a 3DF joint test that investigates the association between a genetic variant and dietary exposure, CRC risk and G × E interaction simultaneously. FINDINGS The 3-DF joint test revealed two significant loci with p-value <5 × 10-8. Rs4730274 close to the SLC26A3 gene showed an association with fibre (p-value: 2.4 × 10-3) and G × fibre interaction with CRC (OR per quartile of fibre increase = 0.87, 0.80, and 0.75 for CC, TC, and TT genotype, respectively; G × E p-value: 1.8 × 10-7). Rs1620977 in the NEGR1 gene showed an association with fruit intake (p-value: 1.0 × 10-8) and G × fruit interaction with CRC (OR per quartile of fruit increase = 0.75, 0.65, and 0.56 for AA, AG, and GG genotype, respectively; G × E -p-value: 0.029). INTERPRETATION We identified 2 loci associated with fibre and fruit intake that also modify the association of these dietary factors with CRC risk. Potential mechanisms include chronic inflammatory intestinal disorders, and gut function. However, further studies are needed for mechanistic validation and replication of findings. FUNDING National Institutes of Health, National Cancer Institute. Full funding details for the individual consortia are provided in acknowledgments.
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Affiliation(s)
- Nikos Papadimitriou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Andre Kim
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Eric S Kawaguchi
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - John Morrison
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Virginia Diez-Obrero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona, 08908, Spain; Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | - Stephanie A Bien
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumour Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Parkville, Australia; University of Melbourne Centre for Cancer Research, The University of Melbourne, Parkville, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Robert Carreras-Torres
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), 17190 Salt, Girona, Spain
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - David V Conti
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew A Devall
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA; Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, VA, USA
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research and Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Amit D Joshi
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University, Stanford, CA, USA; Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Sébastien Küry
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes, France
| | | | - Juan Pablo Lewinger
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | - Brigid M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia
| | - Victor Moreno
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona, 08908, Spain; Department of Clinical Sciences, Faculty of Medicine and Health Sciences and Universitat de Barcelona Institute of Complex Systems (UBICS), University of Barcelona (UB), L'Hospitalet de Llobregat, 08908, Barcelona, Spain
| | - Christina C Newton
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Mireia Obón-Santacana
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain; Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain; Consortium for Biomedical Research in Epidemiology and Public Health, Barcelona, 08908, Spain
| | - Jennifer Ose
- Huntsman Cancer Institute, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Andrew J Pellatt
- Department of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anita R Peoples
- Huntsman Cancer Institute, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel; Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel; Clalit National Cancer Control Center, Haifa, Israel
| | - Edward Ruiz-Narvaez
- Department of Nutritional Sciences, University of Michigan School of Public Health, Ann Arbor, MI, USA
| | - Anna Shcherbina
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Mariana C Stern
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Duncan C Thomas
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Claire E Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Yu Tian
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany; School of Public Health, Capital Medical University, Beijing, China
| | - Konstantinos K Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece; Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
| | - Cornelia M Ulrich
- Huntsman Cancer Institute, Salt Lake City, UT, USA; Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jun Wang
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA; Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Finlay Macrae
- The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia; Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA.
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France; Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK.
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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12
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Zeeb M, Pasin C, Cavassini M, Bieler-Aeschlimann M, Frischknecht P, Kusejko K, Fellay J, Blanquart F, Metzner KJ, Neumann K, Jörimann L, Tschumi J, Bernasconi E, Huber M, Kovari H, Leuzinger K, Notter J, Perreau M, Rauch A, Ramette A, Stöckle M, Yerly S, Günthard HF, Kouyos RD. Self-reported neurocognitive complaints in the Swiss HIV Cohort Study: a viral genome-wide association study. Brain Commun 2024; 6:fcae188. [PMID: 38961872 PMCID: PMC11220509 DOI: 10.1093/braincomms/fcae188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/03/2024] [Accepted: 05/30/2024] [Indexed: 07/05/2024] Open
Abstract
People with HIV may report neurocognitive complaints, with or without associated neurocognitive impairment, varying between individuals and populations. While the HIV genome could play a major role, large systematic viral genome-wide screens to date are lacking. The Swiss HIV Cohort Study biannually enquires neurocognitive complaints. We quantified broad-sense heritability estimates using partial 'pol' sequences from the Swiss HIV Cohort Study resistance database and performed a viral near full-length genome-wide association study for the longitudinal area under the curve of neurocognitive complaints. We performed all analysis (i) restricted to HIV Subtype B and (ii) including all HIV subtypes. From 8547 people with HIV with neurocognitive complaints, we obtained 6966 partial 'pol' sequences and 2334 near full-length HIV sequences. Broad-sense heritability estimates for presence of memory loss complaints ranged between 1% and 17% (Subtype B restricted 1-22%) and increased with the stringency of the phylogenetic distance thresholds. The genome-wide association study revealed one amino acid (Env L641E), after adjusting for multiple testing, positively associated with memory loss complaints (P = 4.3 * 10-6). Other identified mutations, while insignificant after adjusting for multiple testing, were reported in other smaller studies (Tat T64N, Env *291S). We present the first HIV genome-wide association study analysis of neurocognitive complaints and report a first estimate for the heritability of neurocognitive complaints through HIV. Moreover, we could identify one mutation significantly associated with the presence of memory loss complaints. Our findings indicate that neurocognitive complaints are polygenetic and highlight advantages of a whole genome approach for pathogenicity determination.
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Affiliation(s)
- Marius Zeeb
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Chloé Pasin
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Matthias Cavassini
- Division of Infectious Diseases, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Mélanie Bieler-Aeschlimann
- Division of Infectious Diseases, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Paul Frischknecht
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Katharina Kusejko
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Jacques Fellay
- Division of Infectious Diseases, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
- Global Health Institute, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - François Blanquart
- Centre interdisciplinaire de recherche en biologie, Collége de France, 75231 Paris, France
| | - Karin J Metzner
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Kathrin Neumann
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Lisa Jörimann
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Jasmin Tschumi
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Enos Bernasconi
- Division of Infectious Diseases, Ente Ospedaliero Cantonale, 6500 Lugano, Switzerland
- Division of Infectious Diseases, University of Geneva and University of Southern Switzerland, 6900 Lugano, Switzerland
| | - Michael Huber
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Helen Kovari
- Center for Infectious Diseases, Klinik im Park, 8027 Zurich, Switzerland
| | - Karoline Leuzinger
- Division Infection Diagnostics, Department Biomedicine, University of Basel, 4001 Basel Switzerland
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, 4031 Basel, Switzerland
| | - Julia Notter
- Division of Infectious Diseases and Hospital Epidemiology, Cantonal Hospital St. Gallen, 9007 St. Gallen, Switzerland
| | - Matthieu Perreau
- Division of Immunology and Allergy, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
| | - Andri Rauch
- Department of Infectious Diseases, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Alban Ramette
- Institute for Infectious Diseases and Multidisciplinary Center for Infectious Diseases, University of Bern, 3012 Bern, Switzerland
| | - Marcel Stöckle
- Division of Infectious Diseases and Hospital Epidemiology, University Hospital Basel, 4031 Basel, Switzerland
| | - Sabine Yerly
- Laboratory of Virology and Division of Infectious Diseases, Geneva University Hospital, University of Geneva, 1205 Geneva, Switzerland
| | - Huldrych F Günthard
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
| | - Roger D Kouyos
- Department of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, 8091 Zurich, Switzerland
- Institute of Medical Virology, University of Zurich, 8057 Zurich, Switzerland
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13
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Downie CG, Highland HM, Alotaibi M, Welch BM, Howard AG, Cheng S, Miller N, Jain M, Kaplan RC, Lilly AG, Long T, Sofer T, Thyagarajan B, Yu B, North KE, Avery CL. Genome-wide association study reveals shared and distinct genetic architecture underlying fatty acid and bioactive oxylipin metabolites in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.21.24307719. [PMID: 38826448 PMCID: PMC11142272 DOI: 10.1101/2024.05.21.24307719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Bioactive fatty acid-derived oxylipin molecules play key roles in mediating inflammation and oxidative stress, which underlie many chronic diseases. Circulating levels of fatty acids and oxylipins are influenced by both environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biological pathways. Thus, we performed a genome wide association study (GWAS) of n=81 fatty acids and oxylipins in n=11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years, standard deviation = 13.8 years). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Heritability estimates ranged from 0% to 47.9%, and 48 of the 81oxylipins and fatty acids were significantly heritable. Moreover, 40 (49.4%) of the 81 oxylipins and fatty acids had at least one genome-wide significant (p< 6.94E-11) variant resulting in 19 independent genetic loci involved in fatty acid and oxylipin synthesis, as well as downstream pathways. Four loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including the desaturase-encoding FADS and the OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with four or more fatty acids and oxylipins. The majority of the 15 remaining loci (87.5%) (lead variant MAF range = 0.03-0.45, mean = 0.23) were only associated with one oxylipin or fatty acid, demonstrating evidence of distinct genetic effects. Finally, while most loci identified in two-degree-of-freedom tests were previously identified in our main effects analyses, we also identified an additional rare variant (MAF = 0.002) near CARS2, a locus previously implicated in inflammation. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating future multi-omics work to characterize these compounds and elucidate their roles in disease pathways.
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Affiliation(s)
- Carolina G Downie
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Heather M Highland
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Mona Alotaibi
- Division of Pulmonary, Critical Care and Sleep Medicine, University of California, San Diego, San Diego, CA
| | - Barrett M Welch
- School of Public Health, University of Nevada, Reno, Reno, NV
| | - Annie Green Howard
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Mohit Jain
- Sapient Bioanalytics, San Diego, CA
- Departments of Medicine and Pharmacology, University of California, San Diego, San Diego, CA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY; Public Health Sciences Division, Fred Hutchison Cancer Center, Seattle, WA
| | - Adam G Lilly
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Tao Long
- Sapient Bioanalytics, San Diego, CA
| | - Tamar Sofer
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical Center, Minneapolis, MN
| | - Bing Yu
- Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, TX
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
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14
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Moix S, Sadler MC, Kutalik Z, Auwerx C. Breaking down causes, consequences, and mediating effects of telomere length variation on human health. Genome Biol 2024; 25:125. [PMID: 38760657 PMCID: PMC11101352 DOI: 10.1186/s13059-024-03269-9] [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: 11/01/2023] [Accepted: 05/07/2024] [Indexed: 05/19/2024] Open
Abstract
BACKGROUND Telomeres form repeated DNA sequences at the ends of chromosomes, which shorten with each cell division. Yet, factors modulating telomere attrition and the health consequences thereof are not fully understood. To address this, we leveraged data from 326,363 unrelated UK Biobank participants of European ancestry. RESULTS Using linear regression and bidirectional univariable and multivariable Mendelian randomization (MR), we elucidate the relationships between leukocyte telomere length (LTL) and 142 complex traits, including diseases, biomarkers, and lifestyle factors. We confirm that telomeres shorten with age and show a stronger decline in males than in females, with these factors contributing to the majority of the 5.4% of LTL variance explained by the phenome. MR reveals 23 traits modulating LTL. Smoking cessation and high educational attainment associate with longer LTL, while weekly alcohol intake, body mass index, urate levels, and female reproductive events, such as childbirth, associate with shorter LTL. We also identify 24 traits affected by LTL, with risk for cardiovascular, pulmonary, and some autoimmune diseases being increased by short LTL, while longer LTL increased risk for other autoimmune conditions and cancers. Through multivariable MR, we show that LTL may partially mediate the impact of educational attainment, body mass index, and female age at childbirth on proxied lifespan. CONCLUSIONS Our study sheds light on the modulators, consequences, and the mediatory role of telomeres, portraying an intricate relationship between LTL, diseases, lifestyle, and socio-economic factors.
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Affiliation(s)
- Samuel Moix
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
| | - Marie C Sadler
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland
| | - Zoltán Kutalik
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland.
| | - Chiara Auwerx
- Department of Computational Biology, UNIL, Lausanne, 1015, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, 1015, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, 1015, Switzerland.
- Center for Integrative Genetics, UNIL, Lausanne, 1015, Switzerland.
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15
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Carland C, Zhao L, Salman O, Cohen JB, Zamani P, Xiao Q, Dongre A, Wang Z, Ebert C, Greenawalt D, van Empel V, Richards AM, Doughty RN, Rietzschel E, Javaheri A, Wang Y, Schafer PH, Hersey S, Carayannopoulos LN, Seiffert D, Chang C, Gordon DA, Ramirez‐Valle F, Mann DL, Cappola TP, Chirinos JA. Urinary Proteomics and Outcomes in Heart Failure With Preserved Ejection Fraction. J Am Heart Assoc 2024; 13:e033410. [PMID: 38639358 PMCID: PMC11179922 DOI: 10.1161/jaha.123.033410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 03/01/2024] [Indexed: 04/20/2024]
Abstract
BACKGROUND Although several studies have addressed plasma proteomics in heart failure with preserved ejection fraction, limited data are available on the prognostic value of urinary proteomics. The objective of our study was to identify urinary proteins/peptides associated with death and heart failure admission in patients with heart failure with preserved ejection fraction. METHODS AND RESULTS The study population included participants enrolled in TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist Trial). The relationship between urine protein levels and the risk of death or heart failure admission was assessed using Cox regression, in both nonadjusted analyses and adjusting for urine creatinine levels, and the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) score. A total of 426 (12.4%) TOPCAT participants had urinary protein data and were included. There were 40 urinary proteins/peptides significantly associated with death or heart failure admission in nonadjusted analyses, 21 of which were also significant adjusted analyses. Top proteins in the adjusted analysis included ANGPTL2 (angiopoietin-like protein 2) (hazard ratio [HR], 0.5731 [95% CI, 0.47-0.7]; P=3.13E-05), AMY2A (α amylase 2A) (HR, 0.5496 [95% CI, 0.44-0.69]; P=0.0001), and DNASE1 (deoxyribonuclease-1) (HR, 0.5704 [95% CI, 0.46-0.71]; P=0.0002). Higher urinary levels of proteins involved in fibrosis (collagen VI α-1, collagen XV α-1), metabolism (pancreatic α-amylase 2A/B, mannosidase α class 1A member 1), and inflammation (heat shock protein family D member 1, inducible T cell costimulatory ligand) were associated with a lower risk of death or heart failure admission. CONCLUSIONS Our study identifies several novel associations between urinary proteins/peptides and outcomes in heart failure with preserved ejection fraction. Many of these associations are independent of clinical risk scores and may aid in risk stratification in this patient population.
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Affiliation(s)
- Corinne Carland
- Hospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Lei Zhao
- Bristol‐Myers Squibb CompanyLawrencevilleNJUSA
| | - Oday Salman
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Jordana B. Cohen
- Hospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Payman Zamani
- Hospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Qing Xiao
- Bristol‐Myers Squibb CompanyLawrencevilleNJUSA
| | | | | | | | | | - Vanessa van Empel
- Department of CardiologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - A. Mark Richards
- Cardiovascular Research Institute, National University of SingaporeSingapore
- Christchurch Heart Institute, University of OtagoChristchurchNew Zealand
| | - Robert N. Doughty
- Christchurch Heart Institute, University of OtagoChristchurchNew Zealand
| | - Ernst Rietzschel
- Department of Cardiovascular DiseasesGhent University Hospital and Ghent UniversityGhentBelgium
| | - Ali Javaheri
- Washington University School of MedicineSt. LouisMOUSA
| | - Yixin Wang
- Bristol‐Myers Squibb CompanyLawrencevilleNJUSA
| | | | | | | | | | | | | | | | | | - Thomas P. Cappola
- Hospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Julio A. Chirinos
- Hospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
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16
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Brümmer A, Bergmann S. Disentangling genetic effects on transcriptional and post-transcriptional gene regulation through integrating exon and intron expression QTLs. Nat Commun 2024; 15:3786. [PMID: 38710690 DOI: 10.1038/s41467-024-48244-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 04/23/2024] [Indexed: 05/08/2024] Open
Abstract
Expression quantitative trait loci (eQTL) studies typically consider exon expression of genes and discard intronic RNA sequencing reads despite their information on RNA metabolism. Here, we quantify genetic effects on exon and intron levels of genes and their ratio in lymphoblastoid cell lines, revealing thousands of cis-QTLs of each type. While genetic effects are often shared between cis-QTL types, 7814 (47%) are not detected as top cis-QTLs at exon levels. We show that exon levels preferentially capture genetic effects on transcriptional regulation, while exon-intron-ratios better detect those on co- and post-transcriptional processes. Considering all cis-QTL types substantially increases (by 71%) the number of colocalizing variants identified by genome-wide association studies (GWAS). It further allows dissecting the potential gene regulatory processes underlying GWAS associations, suggesting comparable contributions by transcriptional (50%) and co- and post-transcriptional regulation (46%) to complex traits. Overall, integrating intronic RNA sequencing reads in eQTL studies expands our understanding of genetic effects on gene regulatory processes.
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Affiliation(s)
- Anneke Brümmer
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Bioinformatics Competence Center, University of Lausanne, Lausanne, Switzerland.
| | - Sven Bergmann
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa.
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17
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Hughes DA, Li-Gao R, Bull CJ, de Mutsert R, Rosendaal FR, Mook-Kanamori DO, Willems van Dijk K, Timpson NJ. The association between body mass index and metabolite response to a liquid mixed meal challenge: a Mendelian randomization study. Am J Clin Nutr 2024; 119:1354-1370. [PMID: 38494119 PMCID: PMC11130664 DOI: 10.1016/j.ajcnut.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/30/2024] [Accepted: 03/12/2024] [Indexed: 03/19/2024] Open
Abstract
BACKGROUND Metabolite abundance is a dynamic trait that varies in response to environmental stimuli and phenotypic traits, such as food consumption and body mass index (BMI, kg/m2). OBJECTIVES In this study, we used the Netherlands Epidemiology of Obesity (NEO) study data to identify observational and causal associations between BMI and metabolite response to a liquid meal. METHODS A liquid meal challenge was performed, and Nightingale Health metabolite profiles were collected in 5744 NEO participants. Observational and one-sample Mendelian randomization (MR) analysis were conducted to estimate the effect of BMI on metabolites (n = 229) in the fasting, postprandial, and response (or change in abundance) states. RESULTS We observed 473 associations with BMI (175 fasting, 188 postprandial, and 110 response) in observational analyses. In MR analyses, we observed 20 metabolite traits (5 fasting, 12 postprandial, and 3 response) to be associated with BMI. MR associations included the glucogenic amino acid alanine, which was inversely associated with BMI in the response state (β: -0.081; SE: 0.023; P = 5.91 × 10-4), suggesting that as alanine increased in postprandial abundance, that increase was attenuated with increasing BMI. CONCLUSIONS Overall, this study showed that MR estimates were strongly correlated with observational effect estimates, suggesting that the broad associations seen between BMI and metabolite variation has a causal underpinning. Specific effects in previously unassessed postprandial and response states are detected, and these may likely mark novel life course risk exposures driven by regular nutrition.
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Affiliation(s)
- David A Hughes
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Caroline J Bull
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Renée de Mutsert
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frits R Rosendaal
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands; Division of Endocrinology, Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom; Population Health Science, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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18
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Wang Y, Sarnowski C, Lin H, Pitsillides AN, Heard‐Costa NL, Choi SH, Wang D, Bis JC, Blue EE, Boerwinkle E, De Jager PL, Fornage M, Wijsman EM, Seshadri S, Dupuis J, Peloso GM, DeStefano AL. Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data. Alzheimers Dement 2024; 20:3290-3304. [PMID: 38511601 PMCID: PMC11095439 DOI: 10.1002/alz.13705] [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: 08/30/2023] [Revised: 12/08/2023] [Accepted: 12/21/2023] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.
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Affiliation(s)
- Yanbing Wang
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Chloé Sarnowski
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- Human Genetics CenterDepartment of EpidemiologySchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | | | - Nancy L. Heard‐Costa
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
| | - Seung Hoan Choi
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Dongyu Wang
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Joshua C. Bis
- Cardiovascular Health Research UnitDepartment of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Elizabeth E. Blue
- Department of MedicineDivision of Medical GeneticsUniversity of WashingtonSeattleWashingtonUSA
- Brotman Baty InstituteSeattleWashingtonUSA
| | | | - Eric Boerwinkle
- Human Genetics CenterDepartment of EpidemiologySchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Philip L. De Jager
- Center for Translational & Computational NeuroimmunologyDepartment of NeurologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Myriam Fornage
- Human Genetics CenterDepartment of EpidemiologySchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Brown Foundation Institute of Molecular MedicineMcGovern Medical SchoolUniversity of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Ellen M. Wijsman
- Division of Medical Genetics and Department Biostatistics Statistical Genetics LabUniversity of WashingtonHans Rosling Center for Population HealthSeattleWashingtonUSA
| | - Sudha Seshadri
- The Framingham Heart StudyFraminghamMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesThe University of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
| | - Josée Dupuis
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- Department of Epidemiology, Biostatistics and Occupational HealthSchool of Population and Global HealthMcGill UniversityMontrealQuebecCanada
| | - Gina M. Peloso
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Anita L. DeStefano
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
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19
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Liang Z, Xi N, Liu T, Li M, Sang M, Zou C, Chen Z, Yuan G, Pan G, Ma L, Shen Y. A combination of QTL mapping and genome-wide association study revealed the key gene for husk number in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:112. [PMID: 38662228 DOI: 10.1007/s00122-024-04617-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 04/07/2024] [Indexed: 04/26/2024]
Abstract
KEY MESSAGE Two key genes Zm00001d021232 and Zm00001d048138 were identified by QTL mapping and GWAS. Additionally, they were verified to be significantly associated with maize husk number (HN) using gene-based association study. As a by-product of maize production, maize husk is an important industrial raw material. Husk layer number (HN) is an important trait that affects the yield of maize husk. However, the genetic mechanism underlying HN remains unclear. Herein, a total of 13 quantitative trait loci (QTL) controlling HN were identified in an IBM Syn 10 DH population across different locations. Among these, three QTL were individually repeatedly detected in at least two environments. Meanwhile, 26 unique single nucleotide polymorphisms (SNPs) were detected to be significantly (p < 2.15 × 10-6) associated with HN in an association pool. Of these SNPs, three were simultaneously detected across multiple environments or environments and best linear unbiased prediction (BLUP). We focused on these environment-stable and population-common genetic loci for excavating the candidate genes responsible for maize HN. Finally, 173 initial candidate genes were identified, of which 22 were involved in both multicellular organism development and single-multicellular organism process and thus confirmed as the candidate genes for HN. Gene-based association analyses revealed that the variants in four genes were significantly (p < 0.01/N) correlated with HN, of which Zm00001d021232 and Zm00001d048138 were highly expressed in husks and early developing ears among different maize tissues. Our study contributes to the understanding of genetic and molecular mechanisms of maize husk yield and industrial development in the future.
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Affiliation(s)
- Zhenjuan Liang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Na Xi
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Tao Liu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Minglin Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Mengxiang Sang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Chaoying Zou
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Zhong Chen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Guangsheng Yuan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Guangtang Pan
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Langlang Ma
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Yaou Shen
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China.
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20
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Ochs-Balcom HM, Preus L, Du Z, Elston RC, Teerlink CC, Jia G, Guo X, Cai Q, Long J, Ping J, Li B, Stram DO, Shu XO, Sanderson M, Gao G, Ahearn T, Lunetta KL, Zirpoli G, Troester MA, Ruiz-Narváez EA, Haddad SA, Figueroa J, John EM, Bernstein L, Hu JJ, Ziegler RG, Nyante S, Bandera EV, Ingles SA, Mancuso N, Press MF, Deming SL, Rodriguez-Gil JL, Yao S, Ogundiran TO, Ojengbede O, Bolla MK, Dennis J, Dunning AM, Easton DF, Michailidou K, Pharoah PDP, Sandler DP, Taylor JA, Wang Q, O’Brien KM, Weinberg CR, Kitahara CM, Blot W, Nathanson KL, Hennis A, Nemesure B, Ambs S, Sucheston-Campbell LE, Bensen JT, Chanock SJ, Olshan AF, Ambrosone CB, Olopade OI, the Ghana Breast Health Study Team, Conti DV, Palmer J, García-Closas M, Huo D, Zheng W, Haiman C. Novel breast cancer susceptibility loci under linkage peaks identified in African ancestry consortia. Hum Mol Genet 2024; 33:687-697. [PMID: 38263910 PMCID: PMC11000665 DOI: 10.1093/hmg/ddae002] [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: 09/14/2023] [Revised: 01/02/2024] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Expansion of genome-wide association studies across population groups is needed to improve our understanding of shared and unique genetic contributions to breast cancer. We performed association and replication studies guided by a priori linkage findings from African ancestry (AA) relative pairs. METHODS We performed fixed-effect inverse-variance weighted meta-analysis under three significant AA breast cancer linkage peaks (3q26-27, 12q22-23, and 16q21-22) in 9241 AA cases and 10 193 AA controls. We examined associations with overall breast cancer as well as estrogen receptor (ER)-positive and negative subtypes (193,132 SNPs). We replicated associations in the African-ancestry Breast Cancer Genetic Consortium (AABCG). RESULTS In AA women, we identified two associations on chr12q for overall breast cancer (rs1420647, OR = 1.15, p = 2.50×10-6; rs12322371, OR = 1.14, p = 3.15×10-6), and one for ER-negative breast cancer (rs77006600, OR = 1.67, p = 3.51×10-6). On chr3, we identified two associations with ER-negative disease (rs184090918, OR = 3.70, p = 1.23×10-5; rs76959804, OR = 3.57, p = 1.77×10-5) and on chr16q we identified an association with ER-negative disease (rs34147411, OR = 1.62, p = 8.82×10-6). In the replication study, the chr3 associations were significant and effect sizes were larger (rs184090918, OR: 6.66, 95% CI: 1.43, 31.01; rs76959804, OR: 5.24, 95% CI: 1.70, 16.16). CONCLUSION The two chr3 SNPs are upstream to open chromatin ENSR00000710716, a regulatory feature that is actively regulated in mammary tissues, providing evidence that variants in this chr3 region may have a regulatory role in our target organ. Our study provides support for breast cancer variant discovery using prioritization based on linkage evidence.
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Affiliation(s)
- Heather M Ochs-Balcom
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, 270 Farber Hall, Buffalo, NY 14214, United States
| | - Leah Preus
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, 270 Farber Hall, Buffalo, NY 14214, United States
| | - Zhaohui Du
- Department of Preventive Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
- Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave, N. Seattle, WA 98109, United States
| | - Robert C Elston
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, United States
| | - Craig C Teerlink
- Department of Internal Medicine, University of Utah School of Medicine, 30 North Mario Capecchi Dr, 3rd Floor North, Salt Lake City, UT 84112, United States
| | - Guochong Jia
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Xingyi Guo
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Jie Ping
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University, 707 Light Hall 2215 Garland Avenue, Nashville, TN 37232, United States
| | - Daniel O Stram
- Department of Preventive Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Maureen Sanderson
- Department of Family and Community Medicine, Meharry Medical College, 1005 Dr. DB Todd Jr, Blvd. Nashville, TN 37208, United States
| | - Guimin Gao
- Department of Public Health Sciences, University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, United States
| | - Thomas Ahearn
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, MD 20892, United States
| | - Kathryn L Lunetta
- Department of Biostatistics, Boston University, 715 Albany St, Boston, MA 02118, United States
| | - Gary Zirpoli
- Slone Epidemiology Center, Boston University, L-7, 72 East Concord Street, Boston, MA 02118, United States
| | - Melissa A Troester
- Department of Epidemiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 135 Dauer Drive, CB 7435, Chapel Hill, NC 27599, United States
| | - Edward A Ruiz-Narváez
- Department of Nutritional Sciences, University of Michigan School of Public Health, 1860 SPH I, 1415 Washington Heights, Ann Arbor, MI 48109, United States
| | - Stephen A Haddad
- Slone Epidemiology Center, Boston University, L-7, 72 East Concord Street, Boston, MA 02118, United States
| | - Jonine Figueroa
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, MD 20892, United States
- Usher Institute of Population Health Sciences and Informatics, The University of Edinburgh Medical School, 9 Little France Road, Edinburgh, EH16 4UX, United Kingdom
- Cancer Research UK Edinburgh Centre, Crewe Rd S, Edinburgh, EH4 2XR, United Kingdom
| | - Esther M John
- Department of Epidemiology & Population Health, Stanford University School of Medicine, 3145 Porter Dr, Suite E223, MC 5393, Palo Alto, CA 94304, United States
- Department of Medicine (Oncology), Stanford University School of Medicine, 291 Campus Drive Li Ka Shing Building, Stanford, CA 94305, United States
| | - Leslie Bernstein
- Division of Biomarkers of Early Detection and Prevention Department of Population Sciences, Beckman Research Institute of the City of Hope, City of Hope Comprehensive Cancer Center, 1500 East Duarte Road, Duarte, CA 91010, United States
| | - Jennifer J Hu
- Sylvester Comprehensive Cancer Center and Department of Public Health Sciences, University of Miami Miller School of Medicine, 1120 NW 14th St, CRB 1511, Miami, FL 33136, United States
| | - Regina G Ziegler
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, MD 20892, United States
| | - Sarah Nyante
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 130 Mason Farm Rd., Chapel Hill, NC 27599, United States
| | - Elisa V Bandera
- Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, 120 Albany Street, Tower 2, 8th Floor, New Brunswick, NJ 08903, United States
| | - Sue A Ingles
- Department of Preventive Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Nicholas Mancuso
- Department of Preventive Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Michael F Press
- Department of Pathology, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, 1441 Eastlake Ave., Los Angeles, CA 90033, United States
| | - Sandra L Deming
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Jorge L Rodriguez-Gil
- Genomics, Development and Disease Section, Genetic Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 31 Center Dr, Bethesda, MD 20894, United States
- Medical Scientist Training Program, School of Medicine and Public Health, University of Wisconsin-Madison, 750 Highland Ave., Madison, WI 53705, United States
| | - Song Yao
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263, United States
| | - Temidayo O Ogundiran
- Department of Surgery, College of Medicine, University of Ibadan, Queen Elizabeth II Road, Ibadan, 200285, Nigeria
| | - Oladosu Ojengbede
- Center for Population and Reproductive Health, College of Medicine, University of Ibadan, UCH, Queen Elizabeth II Road, Ibadan, 200285, Nigeria
| | - Manjeet K Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, 2 Worts Causeway, Cambridge, CB1 8RN, United Kingdom
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, 2 Worts Causeway, Cambridge, CB1 8RN, United Kingdom
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, United Kingdom
| | - Douglas F Easton
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, United Kingdom
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology & Genetics, Iroon Avenue 6, 2371 Ayius Dometios, Nicosia, Cyprus
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, United Kingdom
| | - Dale P Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, United States
| | - Jack A Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, United States
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, 2 Worts Causeway, Cambridge, CB1 8RN, United Kingdom
| | - Katie M O’Brien
- Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, United States
| | - Clarice R Weinberg
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, PO Box 12233, Research Triangle Park, NC 27709, United States
| | - Cari M Kitahara
- Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD 20892, United States
| | - William Blot
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
- International Epidemiology Institute, 1455 Research Boulevard, Rockville, MD 20850, United States
| | - Katherine L Nathanson
- Department of Medicine, Abramson Cancer Center, The Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19140, United States
| | - Anselm Hennis
- Chronic Disease Research Centre and Faculty of Medical Sciences, University of the West Indies, Jemmotts Lane, Avalon, Bridgetown, Barbados
| | - Barbara Nemesure
- Department of Family, Population and Preventive Medicine, Stony Brook University, 100 Nicolls Road, Stony Brook, NY 11794, United States
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, National Cancer Institute, 37 Convent Drive, Bethesda, MD 20892, United States
| | - Lara E Sucheston-Campbell
- College of Pharmacy, The Ohio State University, 217 Lloyd M. Parks Hall, 500 West 12th Ave., Columbus, OH 43210, United States
- College of Veterinary Medicine, The Ohio State University, 1900 Coffey Road, Columbus, OH 43210, United States
| | - Jeannette T Bensen
- Department of Epidemiology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, 135 Dauer Drive, CB 7435, Chapel Hill, NC 27599, United States
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, MD 20892, United States
| | - Andrew F Olshan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, 170 Rosenau Hall, CB #7400, 135 Dauer Drive, Chapel Hill, NC 27599, United States
| | - Christine B Ambrosone
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Elm and Carlton Streets, Buffalo, NY 14263, United States
| | - Olufunmilayo I Olopade
- Center for Clinical Cancer Genetics and Global Health, Department of Medicine, University of Chicago, 5841 S Maryland Avenue, Chicago, IL 60637, United States
| | | | - David V Conti
- Department of Preventive Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
| | - Julie Palmer
- Slone Epidemiology Center, Boston University, L-7, 72 East Concord Street, Boston, MA 02118, United States
| | - Montserrat García-Closas
- Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, 9609 Medical Center Drive, Bethesda, MD 20892, United States
| | - Dezheng Huo
- Department of Public Health Sciences, University of Chicago, 5841 S. Maryland Ave., Chicago, IL 60637, United States
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, and Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, 2525 West End Avenue, Nashville, TN 37203, United States
| | - Christopher Haiman
- Department of Preventive Population and Public Health Sciences, Keck School of Medicine and Norris Comprehensive Cancer Center, University of Southern California, 1450 Biggy Street, Los Angeles, CA 90033, United States
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21
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Fola AA, He Q, Xie S, Thimmapuram J, Bhide KP, Dorman J, Ciubotariu II, Mwenda MC, Mambwe B, Mulube C, Hawela M, Norris DE, Moss WJ, Bridges DJ, Carpi G. Genomics reveals heterogeneous Plasmodium falciparum transmission and selection signals in Zambia. COMMUNICATIONS MEDICINE 2024; 4:67. [PMID: 38582941 PMCID: PMC10998850 DOI: 10.1038/s43856-024-00498-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 03/28/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Genomic surveillance is crucial for monitoring malaria transmission and understanding parasite adaptation to interventions. Zambia lacks prior nationwide efforts in malaria genomic surveillance among African countries. METHODS We conducted genomic surveillance of Plasmodium falciparum parasites from the 2018 Malaria Indicator Survey in Zambia, a nationally representative household survey of children under five years of age. We whole-genome sequenced and analyzed 241 P. falciparum genomes from regions with varying levels of malaria transmission across Zambia and estimated genetic metrics that are informative about transmission intensity, genetic relatedness between parasites, and selection. RESULTS We provide genomic evidence of widespread within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in Zambia. Our analysis reveals country-level clustering of parasites from Zambia and neighboring regions, with distinct separation in West Africa. Within Zambia, identity by descent (IBD) relatedness analysis uncovers local spatial clustering and rare cases of long-distance sharing of closely related parasite pairs. Genomic regions with large shared IBD segments and strong positive selection signatures implicate genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Furthermore, differences in selection signatures, including drug resistance loci, are observed between eastern and western Zambian parasite populations, suggesting variable transmission intensity and ongoing drug pressure. CONCLUSIONS Our findings enhance our understanding of nationwide P. falciparum transmission in Zambia, establishing a baseline for analyzing parasite genetic metrics as they vary over time and space. These insights highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.
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Affiliation(s)
- Abebe A Fola
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- Department of Pathology and Laboratory Medicine, Brown University, Providence, RI, USA
| | - Qixin He
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Shaojun Xie
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jyothi Thimmapuram
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Ketaki P Bhide
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jack Dorman
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Ilinca I Ciubotariu
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Mulenga C Mwenda
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Brenda Mambwe
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Conceptor Mulube
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Moonga Hawela
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Douglas E Norris
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - William J Moss
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Giovanna Carpi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Purdue Institute for Inflammation, Immunology, & Infectious Disease, Purdue University, West Lafayette, IN, USA.
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22
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Kalnapenkis A, Jõeloo M, Lepik K, Kukuškina V, Kals M, Alasoo K, Mägi R, Esko T, Võsa U. Genetic determinants of plasma protein levels in the Estonian population. Sci Rep 2024; 14:7694. [PMID: 38565889 PMCID: PMC10987560 DOI: 10.1038/s41598-024-57966-3] [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] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
The proteome holds great potential as an intermediate layer between the genome and phenome. Previous protein quantitative trait locus studies have focused mainly on describing the effects of common genetic variations on the proteome. Here, we assessed the impact of the common and rare genetic variations as well as the copy number variants (CNVs) on 326 plasma proteins measured in up to 500 individuals. We identified 184 cis and 94 trans signals for 157 protein traits, which were further fine-mapped to credible sets for 101 cis and 87 trans signals for 151 proteins. Rare genetic variation contributed to the levels of 7 proteins, with 5 cis and 14 trans associations. CNVs were associated with the levels of 11 proteins (7 cis and 5 trans), examples including a 3q12.1 deletion acting as a hub for multiple trans associations; and a CNV overlapping NAIP, a sensor component of the NAIP-NLRC4 inflammasome which is affecting pro-inflammatory cytokine interleukin 18 levels. In summary, this work presents a comprehensive resource of genetic variation affecting the plasma protein levels and provides the interpretation of identified effects.
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Affiliation(s)
- Anette Kalnapenkis
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia.
| | - Maarja Jõeloo
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Kaido Lepik
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- University Center for Primary Care and Public Health, Lausanne, Switzerland
| | - Viktorija Kukuškina
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
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23
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Reyer H, Abou-Soliman I, Schulze M, Henne H, Reinsch N, Schoen J, Wimmers K. Genome-Wide Association Analysis of Semen Characteristics in Piétrain Boars. Genes (Basel) 2024; 15:382. [PMID: 38540441 PMCID: PMC10969825 DOI: 10.3390/genes15030382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/18/2024] [Accepted: 03/19/2024] [Indexed: 06/14/2024] Open
Abstract
Since artificial insemination is common practice in pig breeding, the quality and persistence of the semen are decisive for the usability of individual boars. In the current study, genome-wide association analyses were performed to investigate the genetic variability underlying phenotypic variations in semen characteristics. These traits comprise sperm morphology and sperm motility under different temporal and thermal storage conditions, in addition to standard semen quality parameters. Two consecutive samples of the fourth and fifth ejaculates from the same boar were comprehensively analyzed in a genotyped Piétrain boar population. A total of 13 genomic regions on different chromosomes were identified that contain single-nucleotide polymorphisms significantly associated with these traits. Subsequent analysis of the genomic regions revealed candidate genes described to be involved in spermatogenesis, such as FOXL3, GPER1, PDGFA, PRKAR1B, SNRK, SUN1, and TSPO, and sperm motility, including ARRDC4, CEP78, DNAAF5, and GPER1. Some of these genes were also associated with male fertility or infertility in mammals (e.g., CEP78, GPER1). The analyses based on these laboriously determined and valuable phenotypes contribute to a better understanding of the genetic background of male fertility traits in pigs and could prospectively contribute to the improvement of sperm quality through breeding approaches.
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Affiliation(s)
- Henry Reyer
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany; (I.A.-S.); (N.R.); (K.W.)
| | - Ibrahim Abou-Soliman
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany; (I.A.-S.); (N.R.); (K.W.)
- Department of Animal and Poultry Breeding, Desert Research Center, Cairo 11753, Egypt
| | - Martin Schulze
- Institute for Reproduction of Farm Animals Schönow, 16321 Bernau, Germany;
| | | | - Norbert Reinsch
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany; (I.A.-S.); (N.R.); (K.W.)
| | - Jennifer Schoen
- Leibniz Institute for Zoo and Wildlife Research (IZW), 10315 Berlin, Germany;
- Institute of Biotechnology, Technische Universität Berlin, 10623 Berlin, Germany
| | - Klaus Wimmers
- Research Institute for Farm Animal Biology (FBN), 18196 Dummerstorf, Germany; (I.A.-S.); (N.R.); (K.W.)
- Faculty of Agricultural and Environmental Sciences, University of Rostock, 18059 Rostock, Germany
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24
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Dib M, Levin MG, Zhao L, Diab A, Wang Z, Ebert C, Salman O, Azzo JD, Gan S, Zamani P, Cohen JB, Gill D, Burgess S, Zagkos L, van Empel V, Richards AM, Doughty R, Rietzschel ER, Kammerhoff K, Kvikstad E, Maranville J, Schafer P, Seiffert DA, Ramirez‐Valle F, Gordon DA, Chang C, Javaheri A, Mann DL, Cappola TP, Chirinos JA. Proteomic Associations of Adverse Outcomes in Human Heart Failure. J Am Heart Assoc 2024; 13:e031154. [PMID: 38420755 PMCID: PMC10944037 DOI: 10.1161/jaha.123.031154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 01/16/2024] [Indexed: 03/02/2024]
Abstract
BACKGROUND Identifying novel molecular drivers of disease progression in heart failure (HF) is a high-priority goal that may provide new therapeutic targets to improve patient outcomes. The authors investigated the relationship between plasma proteins and adverse outcomes in HF and their putative causal role using Mendelian randomization. METHODS AND RESULTS The authors measured 4776 plasma proteins among 1964 participants with HF with a reduced left ventricular ejection fraction enrolled in PHFS (Penn Heart Failure Study). Assessed were the observational relationship between plasma proteins and (1) all-cause death or (2) death or HF-related hospital admission (DHFA). The authors replicated nominally significant associations in the Washington University HF registry (N=1080). Proteins significantly associated with outcomes were the subject of 2-sample Mendelian randomization and colocalization analyses. After correction for multiple testing, 243 and 126 proteins were found to be significantly associated with death and DHFA, respectively. These included small ubiquitin-like modifier 2 (standardized hazard ratio [sHR], 1.56; P<0.0001), growth differentiation factor-15 (sHR, 1.68; P<0.0001) for death, A disintegrin and metalloproteinase with thrombospondin motifs-like protein (sHR, 1.40; P<0.0001), and pulmonary-associated surfactant protein C (sHR, 1.24; P<0.0001) for DHFA. In pathway analyses, top canonical pathways associated with death and DHFA included fibrotic, inflammatory, and coagulation pathways. Genomic analyses provided evidence of nominally significant associations between levels of 6 genetically predicted proteins with DHFA and 11 genetically predicted proteins with death. CONCLUSIONS This study implicates multiple novel proteins in HF and provides preliminary evidence of associations between genetically predicted plasma levels of 17 candidate proteins and the risk for adverse outcomes in human HF.
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Affiliation(s)
- Marie‐Joe Dib
- Division of Cardiovascular MedicineHospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Michael G. Levin
- Division of Cardiovascular MedicineHospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Lei Zhao
- Bristol‐Myers Squibb CompanyLawrencevilleNJUSA
| | - Ahmed Diab
- Washington University School of MedicineSt. LouisMOUSA
| | | | | | - Oday Salman
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Joe David Azzo
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Sushrima Gan
- Division of Cardiovascular MedicineHospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Payman Zamani
- Division of Cardiovascular MedicineHospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Jordana B. Cohen
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
- Renal‐Electrolyte and Hypertension Division, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPAUSA
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUnited Kingdom
| | - Stephen Burgess
- MRC Integrative Epidemiology UnitUniversity of BristolUnited Kingdom
- Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
| | - Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public HealthImperial College LondonLondonUnited Kingdom
| | - Vanessa van Empel
- Department of CardiologyMaastricht University Medical CenterMaastrichtThe Netherlands
| | - A. Mark Richards
- Department of CardiologyMaastricht University Medical CenterMaastrichtThe Netherlands
- Cardiovascular Research InstituteNational University of SingaporeSingapore
| | - Rob Doughty
- Christchurch Heart InstituteUniversity of OtagoChristchurchNew Zealand
| | | | | | | | | | | | | | | | | | | | - Ali Javaheri
- Washington University School of MedicineSt. LouisMOUSA
- John J. Cochran Veterans HospitalSt. LouisMOUSA
| | | | - Thomas P. Cappola
- Division of Cardiovascular MedicineHospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
| | - Julio A. Chirinos
- Division of Cardiovascular MedicineHospital of the University of PennsylvaniaPhiladelphiaPAUSA
- University of Pennsylvania Perelman School of MedicinePhiladelphiaPAUSA
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25
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Harrer P, Inderhees J, Zhao C, Schormair B, Tilch E, Gieger C, Peters A, Jöhren O, Fleming T, Nawroth PP, Berger K, Hermesdorf M, Winkelmann J, Schwaninger M, Oexle K. Phenotypic and genome-wide studies on dicarbonyls: major associations to glomerular filtration rate and gamma-glutamyltransferase activity. EBioMedicine 2024; 101:105007. [PMID: 38354534 PMCID: PMC10875252 DOI: 10.1016/j.ebiom.2024.105007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/16/2024] Open
Abstract
BACKGROUND The dicarbonyl compounds methylglyoxal (MG), glyoxal (GO) and 3-deoxyglucosone (3-DG) have been linked to various diseases. However, disease-independent phenotypic and genotypic association studies with phenome-wide and genome-wide reach, respectively, have not been provided. METHODS MG, GO and 3-DG were measured by LC-MS in 1304 serum samples of two populations (KORA, n = 482; BiDirect, n = 822) and assessed for associations with genome-wide SNPs (GWAS) and with phenome-wide traits. Redundancy analysis (RDA) was used to identify major independent trait associations. FINDINGS Mutual correlations of dicarbonyls were highly significant, being stronger between MG and GO (ρ = 0.6) than between 3-DG and MG or GO (ρ = 0.4). Significant phenotypic results included associations of all dicarbonyls with sex, waist-to-hip ratio, glomerular filtration rate (GFR), gamma-glutamyltransferase (GGT), and hypertension, of MG and GO with age and C-reactive protein, of GO and 3-DG with glucose and antidiabetics, of MG with contraceptives, of GO with ferritin, and of 3-DG with smoking. RDA revealed GFR, GGT and, in case of 3-DG, glucose as major contributors to dicarbonyl variance. GWAS did not identify genome-wide significant loci. SNPs previously associated with glyoxalase activity did not reach nominal significance. When multiple testing was restricted to the lead SNPs of GWASs on the traits selected by RDA, 3-DG was found to be associated (p = 2.3 × 10-5) with rs1741177, an eQTL of NF-κB inhibitor NFKBIA. INTERPRETATION This large-scale, population-based study has identified numerous associations, with GFR and GGT being of pivotal importance, providing unbiased perspectives on dicarbonyls beyond the current state. FUNDING Deutsche Forschungsgemeinschaft, Helmholtz Munich, German Centre for Cardiovascular Research (DZHK), German Federal Ministry of Research and Education (BMBF).
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Affiliation(s)
- Philip Harrer
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Julica Inderhees
- Institute for Experimental and Clinical Pharmacology and Toxicology, Center of Brain, Behavior and Metabolism, University of Lubeck, Lubeck, Germany; Bioanalytic Core Facility, Center for Brain, Behavior and Metabolism, University of Lübeck, Germany; German Centre for Cardiovascular Research (DZHK), Hamburg-Lübeck-Kiel, Germany
| | - Chen Zhao
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany
| | - Barbara Schormair
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany
| | - Erik Tilch
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Helmholtz Munich, Neuherberg, Germany; Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Munich, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany; Chair of Epidemiology, Institute for Medical Information Processing, Biometry and Epidemiology, Medical Faculty, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Olaf Jöhren
- Institute for Experimental and Clinical Pharmacology and Toxicology, Center of Brain, Behavior and Metabolism, University of Lubeck, Lubeck, Germany; Bioanalytic Core Facility, Center for Brain, Behavior and Metabolism, University of Lübeck, Germany
| | - Thomas Fleming
- Department of Internal Medicine, University of Heidelberg, Heidelberg, Germany
| | - Peter P Nawroth
- Department of Internal Medicine, University of Heidelberg, Heidelberg, Germany
| | - Klaus Berger
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Marco Hermesdorf
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Juliane Winkelmann
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; German Centre for Mental Health (DZPG), Munich-Augsburg, Germany
| | - Markus Schwaninger
- Institute for Experimental and Clinical Pharmacology and Toxicology, Center of Brain, Behavior and Metabolism, University of Lubeck, Lubeck, Germany; German Centre for Cardiovascular Research (DZHK), Hamburg-Lübeck-Kiel, Germany
| | - Konrad Oexle
- Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany; Neurogenetic Systems Analysis Group, Institute of Neurogenomics, Helmholtz Munich, Neuherberg, Germany.
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26
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Watson AE, Guitton B, Soriano A, Rivallan R, Vignes H, Farrera I, Huettel B, Arnaiz C, Falavigna VDS, Coupel-Ledru A, Segura V, Sarah G, Dufayard JF, Sidibe-Bocs S, Costes E, Andrés F. Target enrichment sequencing coupled with GWAS identifies MdPRX10 as a candidate gene in the control of budbreak in apple. FRONTIERS IN PLANT SCIENCE 2024; 15:1352757. [PMID: 38455730 PMCID: PMC10918860 DOI: 10.3389/fpls.2024.1352757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 02/02/2024] [Indexed: 03/09/2024]
Abstract
The timing of floral budbreak in apple has a significant effect on fruit production and quality. Budbreak occurs as a result of a complex molecular mechanism that relies on accurate integration of external environmental cues, principally temperature. In the pursuit of understanding this mechanism, especially with respect to aiding adaptation to climate change, a QTL at the top of linkage group (LG) 9 has been identified by many studies on budbreak, but the genes underlying it remain elusive. Here, together with a dessert apple core collection of 239 cultivars, we used a targeted capture sequencing approach to increase SNP resolution in apple orthologues of known or suspected A. thaliana flowering time-related genes, as well as approximately 200 genes within the LG9 QTL interval. This increased the 275 223 SNP Axiom® Apple 480 K array dataset by an additional 40 857 markers. Robust GWAS analyses identified MdPRX10, a peroxidase superfamily gene, as a strong candidate that demonstrated a dormancy-related expression pattern and down-regulation in response to chilling. In-silico analyses also predicted the residue change resulting from the SNP allele associated with late budbreak could alter protein conformation and likely function. Late budbreak cultivars homozygous for this SNP allele also showed significantly up-regulated expression of C-REPEAT BINDING FACTOR (CBF) genes, which are involved in cold tolerance and perception, compared to reference cultivars, such as Gala. Taken together, these results indicate a role for MdPRX10 in budbreak, potentially via redox-mediated signaling and CBF gene regulation. Moving forward, this provides a focus for developing our understanding of the effects of temperature on flowering time and how redox processes may influence integration of external cues in dormancy pathways.
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Affiliation(s)
- Amy E. Watson
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Baptiste Guitton
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Alexandre Soriano
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
- French Institute of Bioinformatics (IFB) - South Green Bioinformatics Platform, Bioversity, CIRAD, INRAE, IRD, Montpellier, France
| | - Ronan Rivallan
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
| | - Hélène Vignes
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
| | - Isabelle Farrera
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Bruno Huettel
- Genome Centre, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Catalina Arnaiz
- Centro de Biotecnología y Genómica de Plantas, Instituto de Investigación y Tecnología Agraria y Alimentaria, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Aude Coupel-Ledru
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Vincent Segura
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Gautier Sarah
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Jean-François Dufayard
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
- French Institute of Bioinformatics (IFB) - South Green Bioinformatics Platform, Bioversity, CIRAD, INRAE, IRD, Montpellier, France
| | - Stéphanie Sidibe-Bocs
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- CIRAD, UMR AGAP Institut, Montpellier, France
- French Institute of Bioinformatics (IFB) - South Green Bioinformatics Platform, Bioversity, CIRAD, INRAE, IRD, Montpellier, France
| | - Evelyne Costes
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Fernando Andrés
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
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27
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Fola AA, He Q, Xie S, Thimmapuram J, Bhide KP, Dorman J, Ciubotariu II, Mwenda MC, Mambwe B, Mulube C, Hawela M, Norris DE, Moss WJ, Bridges DJ, Carpi G. Genomics reveals heterogeneous Plasmodium falciparum transmission and population differentiation in Zambia and bordering countries. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.09.24302570. [PMID: 38370674 PMCID: PMC10871455 DOI: 10.1101/2024.02.09.24302570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Genomic surveillance plays a critical role in monitoring malaria transmission and understanding how the parasite adapts in response to interventions. We conducted genomic surveillance of malaria by sequencing 241 Plasmodium falciparum genomes from regions with varying levels of malaria transmission across Zambia. We found genomic evidence of high levels of within-host polygenomic infections, regardless of epidemiological characteristics, underscoring the extensive and ongoing endemic malaria transmission in the country. We identified country-level clustering of parasites from Zambia and neighboring countries, and distinct clustering of parasites from West Africa. Within Zambia, our identity by descent (IBD) relatedness analysis uncovered spatial clustering of closely related parasite pairs at the local level and rare cases of long-distance sharing. Genomic regions with large shared IBD segments and strong positive selection signatures identified genes involved in sulfadoxine-pyrimethamine and artemisinin combination therapies drug resistance, but no signature related to chloroquine resistance. Together, our findings enhance our understanding of P. falciparum transmission nationwide in Zambia and highlight the urgency of strengthening malaria control programs and surveillance of antimalarial drug resistance.
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Affiliation(s)
- Abebe A. Fola
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Qixin He
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Shaojun Xie
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jyothi Thimmapuram
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Ketaki P. Bhide
- Bioinformatics Core, Purdue University, Purdue University, West Lafayette, IN, USA
| | - Jack Dorman
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | | | - Brenda Mambwe
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Conceptor Mulube
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Moonga Hawela
- PATH-MACEPA, National Malaria Elimination Centre, Lusaka, Zambia
| | - Douglas E. Norris
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - William J. Moss
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Giovanna Carpi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
- The Johns Hopkins Malaria Research Institute, W. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Purdue Institute for Inflammation, Immunology, & Infectious Disease, Purdue University, West Lafayette, IN, USA
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28
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Lavoignat M, Cassan C, Pétriacq P, Gibon Y, Heumez E, Duque C, Momont P, Rincent R, Blancon J, Ravel C, Le Gouis J. Different wheat loci are associated to heritable free asparagine content in grain grown under different water and nitrogen availability. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:46. [PMID: 38332254 DOI: 10.1007/s00122-024-04551-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Accepted: 01/07/2024] [Indexed: 02/10/2024]
Abstract
KEY MESSAGE Different wheat QTLs were associated to the free asparagine content of grain grown in four different conditions. Environmental effects are a key factor when selecting for low acrylamide-forming potential. The amount of free asparagine in grain of a wheat genotype determines its potential to form harmful acrylamide in derivative food products. Here, we explored the variation in the free asparagine, aspartate, glutamine and glutamate contents of 485 accessions reflecting wheat worldwide diversity to define the genetic architecture governing the accumulation of these amino acids in grain. Accessions were grown under high and low nitrogen availability and in water-deficient and well-watered conditions, and plant and grain phenotypes were measured. Free amino acid contents of grain varied from 0.01 to 1.02 mg g-1 among genotypes in a highly heritable way that did not correlate strongly with grain yield, protein content, specific weight, thousand-kernel weight or heading date. Mean free asparagine content was 4% higher under high nitrogen and 3% higher in water-deficient conditions. After genotyping the accessions, single-locus and multi-locus genome-wide association study models were used to identify several QTLs for free asparagine content located on nine chromosomes. Each QTL was associated with a single amino acid and growing environment, and none of the QTLs colocalised with genes known to be involved in the corresponding amino acid metabolism. This suggests that free asparagine content is controlled by several loci with minor effects interacting with the environment. We conclude that breeding for reduced asparagine content is feasible, but should be firmly based on multi-environment field trials. KEY MESSAGE Different wheat QTLs were associated to the free asparagine content of grain grown in four different conditions. Environmental effects are a key factor when selecting for low acrylamide-forming potential.
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Affiliation(s)
- Mélanie Lavoignat
- Université Clermont Auvergne, INRAE, UMR1095 GDEC, 63000, Clermont-Ferrand, France
- AgroParisTech, 75005, Paris, France
| | - Cédric Cassan
- Université Bordeaux, INRAE, UMR 1332 BFP, 33883, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Pierre Pétriacq
- Université Bordeaux, INRAE, UMR 1332 BFP, 33883, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Yves Gibon
- Université Bordeaux, INRAE, UMR 1332 BFP, 33883, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | | | | | | | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
| | - Justin Blancon
- Université Clermont Auvergne, INRAE, UMR1095 GDEC, 63000, Clermont-Ferrand, France
| | - Catherine Ravel
- Université Clermont Auvergne, INRAE, UMR1095 GDEC, 63000, Clermont-Ferrand, France
| | - Jacques Le Gouis
- Université Clermont Auvergne, INRAE, UMR1095 GDEC, 63000, Clermont-Ferrand, France.
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29
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Azzo JD, Dib MJ, Zagkos L, Zhao L, Wang Z, Chang CP, Ebert C, Salman O, Gan S, Zamani P, Cohen JB, van Empel V, Richards AM, Javaheri A, Mann DL, Rietzschel E, Schafer P, Seiffert DA, Gill D, Burgess S, Ramirez-Valle F, Gordon DA, Cappola TP, Chirinos JA. Proteomic Associations of NT-proBNP (N-Terminal Pro-B-Type Natriuretic Peptide) in Heart Failure With Preserved Ejection Fraction. Circ Heart Fail 2024; 17:e011146. [PMID: 38299345 PMCID: PMC7615693 DOI: 10.1161/circheartfailure.123.011146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/20/2023] [Indexed: 02/02/2024]
Abstract
BACKGROUND NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels are variably elevated in heart failure with preserved ejection fraction (HFpEF), even in the presence of increased left ventricular filling pressures. NT-proBNP levels are prognostic in HFpEF and have been used as an inclusion criterion for several recent randomized clinical trials. However, the underlying biologic differences between HFpEF participants with high and low NT-proBNP levels remain to be fully understood. METHODS We measured 4928 proteins using an aptamer-based proteomic assay (SOMAScan) in available plasma samples from 2 cohorts: (1) Participants with HFpEF enrolled in the PHFS (Penn Heart Failure Study; n=253); (2) TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist Trial) participants in the Americas (n=218). We assessed the relationship between SOMAScan-derived plasma NT-proBNP and levels of other proteins available in the SOMAScan assay version 4 using robust linear regression, with correction for multiple comparisons, followed by pathway analysis. RESULTS NT-proBNP levels exhibited prominent proteome-wide associations in PHFS and TOPCAT cohorts. Proteins most strongly associated with NT-proBNP in both cohorts included SVEP1 (sushi, von Willebrand factor type-A, epidermal growth factor, and pentraxin domain containing 1; βTOPCAT=0.539; P<0.0001; βPHFS=0.516; P<0.0001) and ANGPT2 (angiopoietin 2; βTOPCAT=0.571; P<0.0001; βPHFS=0.459; P<0.0001). Canonical pathway analysis demonstrated consistent associations with multiple pathways related to fibrosis and inflammation. These included hepatic fibrosis and inhibition of matrix metalloproteases. Analyses using cut points corresponding to estimated quantitative concentrations of 360 pg/mL (and 480 pg/mL in atrial fibrillation) revealed similar proteomic associations. CONCLUSIONS Circulating NT-proBNP levels exhibit prominent proteomic associations in HFpEF. Our findings suggest that higher NT-proBNP levels in HFpEF are a marker of fibrosis and inflammation. These findings will aid the interpretation of NT-proBNP levels in HFpEF and may guide the selection of participants in future HFpEF clinical trials.
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Affiliation(s)
- Joe David Azzo
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Marie-Joe Dib
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia PA
| | - Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
| | - Lei Zhao
- Bristol-Myers Squibb Company, Lawrenceville, NJ
| | | | | | | | - Oday Salman
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
| | - Sushrima Gan
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia PA
| | - Payman Zamani
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia PA
| | - Jordana B. Cohen
- Bristol-Myers Squibb Company, Lawrenceville, NJ
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA
| | - Vanessa van Empel
- Department of Cardiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - A. Mark Richards
- Cardiovascular Research Institute, National University of Singapore, Singapore
- Christchurch Heart Institute, University of Otago, Christchurch, New Zealand
| | - Ali Javaheri
- Washington University School of Medicine, St. Louis, MO
- John J. Cochran Veterans Hospital, St. Louis, MO
| | | | - Ernst Rietzschel
- Department of Cardiovascular Diseases, Ghent University Hospital, Ghent, Belgium
| | | | | | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
| | - Stephen Burgess
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | | | | | - Thomas P. Cappola
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia PA
| | - Julio A. Chirinos
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia PA
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30
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Tong TYN, Clarke R, Schmidt JA, Huybrechts I, Noor U, Forouhi NG, Imamura F, Travis RC, Weiderpass E, Aleksandrova K, Dahm CC, van der Schouw YT, Overvad K, Kyrø C, Tjønneland A, Kaaks R, Katzke V, Schiborn C, Schulze MB, Mayen-Chacon AL, Masala G, Sieri S, de Magistris MS, Tumino R, Sacerdote C, Boer JMA, Verschuren WMM, Brustad M, Nøst TH, Crous-Bou M, Petrova D, Amiano P, Huerta JM, Moreno-Iribas C, Engström G, Melander O, Johansson K, Lindvall K, Aglago EK, Heath AK, Butterworth AS, Danesh J, Key TJ. Dietary amino acids and risk of stroke subtypes: a prospective analysis of 356,000 participants in seven European countries. Eur J Nutr 2024; 63:209-220. [PMID: 37804448 PMCID: PMC10799144 DOI: 10.1007/s00394-023-03251-4] [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: 03/31/2023] [Accepted: 09/08/2023] [Indexed: 10/09/2023]
Abstract
PURPOSE Previously reported associations of protein-rich foods with stroke subtypes have prompted interest in the assessment of individual amino acids. We examined the associations of dietary amino acids with risks of ischaemic and haemorrhagic stroke in the EPIC study. METHODS We analysed data from 356,142 participants from seven European countries. Dietary intakes of 19 individual amino acids were assessed using validated country-specific dietary questionnaires, calibrated using additional 24-h dietary recalls. Multivariable-adjusted Cox regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of ischaemic and haemorrhagic stroke in relation to the intake of each amino acid. The role of blood pressure as a potential mechanism was assessed in 267,642 (75%) participants. RESULTS After a median follow-up of 12.9 years, 4295 participants had an ischaemic stroke and 1375 participants had a haemorrhagic stroke. After correction for multiple testing, a higher intake of proline (as a percent of total protein) was associated with a 12% lower risk of ischaemic stroke (HR per 1 SD higher intake 0.88; 95% CI 0.82, 0.94). The association persisted after mutual adjustment for all other amino acids, systolic and diastolic blood pressure. The inverse associations of isoleucine, leucine, valine, phenylalanine, threonine, tryptophan, glutamic acid, serine and tyrosine with ischaemic stroke were each attenuated with adjustment for proline intake. For haemorrhagic stroke, no statistically significant associations were observed in the continuous analyses after correcting for multiple testing. CONCLUSION Higher proline intake may be associated with a lower risk of ischaemic stroke, independent of other dietary amino acids and blood pressure.
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Affiliation(s)
- Tammy Y N Tong
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Julie A Schmidt
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
- Departments of Clinical Epidemiology, Clinical Medicine, Aarhus University Hospital and Aarhus University, Aarhus, Denmark
| | - Inge Huybrechts
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC), World Health Organization (WHO), Lyon, France
| | - Urwah Noor
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Fumiaki Imamura
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
| | - Elisabete Weiderpass
- International Agency for Research on Cancer (IARC), World Health Organization (WHO), Lyon, France
| | - Krasimira Aleksandrova
- Department Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany
- Faculty of Human and Health Sciences, University of Bremen, Grazer Straße 2, 28359, Bremen, Germany
| | | | - Yvonne T van der Schouw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Kim Overvad
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Cecilie Kyrø
- Danish Cancer Society Research Center, Copenhagen, Denmark
| | | | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Verena Katzke
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Catarina Schiborn
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- Institute for Nutritional Science, University of Potsdam, Nuthetal, Germany
| | - Ana-Lucia Mayen-Chacon
- Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC), World Health Organization (WHO), Lyon, France
| | - Giovanna Masala
- Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Sabina Sieri
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori Di Milano, Milan, Italy
| | | | - Rosario Tumino
- Hyblean Association for Epidemiological Research AIRE-ONLUS, Ragusa, Italy
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital, Turin, Italy
| | - Jolanda M A Boer
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - W M Monique Verschuren
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- National Institute for Public Health and the Environment, Bilthoven, The Netherlands
| | - Magritt Brustad
- Department of Community Medicine, Faculty of Health Sciences, UiT the Arctic University of Norway, Tromsø, Norway
- The Public Dental Service Competence Centre of Northern Norway (TkNN), Tromsø, Norway
| | - Therese Haugdahl Nøst
- Department of Community Medicine, Faculty of Health Sciences, UiT the Arctic University of Norway, Tromsø, Norway
- K.G. Jebsen Centre for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marta Crous-Bou
- Unit of Nutrition and Cancer, Cancer Epidemiology Research Program, Catalan Institute of Oncology (ICO)-Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Dafina Petrova
- Escuela Andaluza de Salud Pública (EASP), Granada, Spain
- Instituto de Investigación Biosanitaria Ibs.GRANADA, Granada, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, Madrid, Spain
| | - Pilar Amiano
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, Madrid, Spain
- Ministry of Health of the Basque Government, Sub Directorate for Public Health and Addictions of Gipuzkoa, San Sebastian, Spain
- Biodonostia Health Research Institute, Epidemiology of Chronic and Communicable Diseases Group, San Sebastián, Spain
| | - José María Huerta
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain
| | - Conchi Moreno-Iribas
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, C. de Melchor Fernández Almagro, Madrid, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, SpainInstituto de Salud Pu´Blica de Navarra, IdiSNA, Navarre Institute for Health Research, Pamplona, Spain
| | - Gunnar Engström
- Department of Clinical Science in Malmö, Lund University, Clinical Research Center, Malmö, Sweden
| | - Olle Melander
- Department of Clinical Science in Malmö, Lund University, Clinical Research Center, Malmö, Sweden
- Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Kristina Johansson
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Kristina Lindvall
- Department of Epidemiology and Global Health, Umeå University, Umeå, Sweden
| | - Elom K Aglago
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Hills Road, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- National Institute for Health Research Blood and Transplant Research Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Hills Road, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, UK
| | - Timothy J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Richard Doll Building, Old Road Campus, Oxford, OX3 7LF, UK
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Visonà G, Bouzigon E, Demenais F, Schweikert G. Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery. Brief Bioinform 2024; 25:bbae014. [PMID: 38340090 PMCID: PMC10858647 DOI: 10.1093/bib/bbae014] [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: 10/16/2023] [Revised: 12/28/2023] [Accepted: 01/08/2024] [Indexed: 02/12/2024] Open
Abstract
MOTIVATION Genome-wide association studies (GWAS) have enabled large-scale analysis of the role of genetic variants in human disease. Despite impressive methodological advances, subsequent clinical interpretation and application remains challenging when GWAS suffer from a lack of statistical power. In recent years, however, the use of information diffusion algorithms with molecular networks has led to fruitful insights on disease genes. RESULTS We present an overview of the design choices and pitfalls that prove crucial in the application of network propagation methods to GWAS summary statistics. We highlight general trends from the literature, and present benchmark experiments to expand on these insights selecting as case study three diseases and five molecular networks. We verify that the use of gene-level scores based on GWAS P-values offers advantages over the selection of a set of 'seed' disease genes not weighted by the associated P-values if the GWAS summary statistics are of sufficient quality. Beyond that, the size and the density of the networks prove to be important factors for consideration. Finally, we explore several ensemble methods and show that combining multiple networks may improve the network propagation approach.
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Affiliation(s)
- Giovanni Visonà
- Empirical Inference, Max-Planck Institute for Intelligent Systems, Tübingen 72076, Germany
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Escamilla DM, Dietz N, Bilyeu K, Hudson K, Rainey KM. Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max). PLoS One 2024; 19:e0294123. [PMID: 38241340 PMCID: PMC10798547 DOI: 10.1371/journal.pone.0294123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 10/25/2023] [Indexed: 01/21/2024] Open
Abstract
The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits.
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Affiliation(s)
- Diana M. Escamilla
- Department of Agronomy, Purdue University, West Lafayette, Indiana, United States of America
| | - Nicholas Dietz
- Division of Plant Science and Technology, University of Missouri, Columbia, Missouri, United States of America
| | - Kristin Bilyeu
- Plant Genetics Research Unit, United States Department of Agriculture (USDA)−Agricultural Research Service (ARS), Columbia, Missouri, United States of America
| | - Karen Hudson
- USDA-ARS Crop Production and Pest Control Research Unit, West Lafayette, Indiana, United States of America
| | - Katy Martin Rainey
- Department of Agronomy, Purdue University, West Lafayette, Indiana, United States of America
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Dong Y, Li G, Zhang X, Feng Z, Li T, Li Z, Xu S, Xu S, Liu W, Xue J. Genome-Wide Association Study for Maize Hybrid Performance in a Typical Breeder Population. Int J Mol Sci 2024; 25:1190. [PMID: 38256265 PMCID: PMC10816832 DOI: 10.3390/ijms25021190] [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: 11/28/2023] [Revised: 01/14/2024] [Accepted: 01/16/2024] [Indexed: 01/24/2024] Open
Abstract
Maize is one of the major crops that has demonstrated success in the utilization of heterosis. Developing high-yield hybrids is a crucial part of plant breeding to secure global food demand. In this study, we conducted a genome-wide association study (GWAS) for 10 agronomic traits using a typical breeder population comprised 442 single-cross hybrids by evaluating additive, dominance, and epistatic effects. A total of 49 significant single nucleotide polymorphisms (SNPs) and 69 significant pairs of epistasis were identified, explaining 26.2% to 64.3% of the phenotypic variation across the 10 traits. The enrichment of favorable genotypes is significantly correlated to the corresponding phenotype. In the confident region of the associated site, 532 protein-coding genes were discovered. Among these genes, the Zm00001d044211 candidate gene was found to negatively regulate starch synthesis and potentially impact yield. This typical breeding population provided a valuable resource for dissecting the genetic architecture of yield-related traits. We proposed a novel mating strategy to increase the GWAS efficiency without utilizing more resources. Finally, we analyzed the enrichment of favorable alleles in the Shaan A and Shaan B groups, as well as in each inbred line. Our breeding practice led to consistent results. Not only does this study demonstrate the feasibility of GWAS in F1 hybrid populations, it also provides a valuable basis for further molecular biology and breeding research.
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Affiliation(s)
- Yuan Dong
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Guoliang Li
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization (MOE), China Agricultural University, Beijing 100193, China
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466 Seeland, Germany
| | - Xinghua Zhang
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Zhiqian Feng
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Ting Li
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Zhoushuai Li
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Shutu Xu
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
| | - Wenxin Liu
- National Maize Improvement Center of China, Key Laboratory of Crop Heterosis and Utilization (MOE), China Agricultural University, Beijing 100193, China
| | - Jiquan Xue
- Key Laboratory of Biology and Genetic Breeding of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling 712100, China
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Auwerx C, Jõeloo M, Sadler MC, Tesio N, Ojavee S, Clark CJ, Mägi R, Reymond A, Kutalik Z. Rare copy-number variants as modulators of common disease susceptibility. Genome Med 2024; 16:5. [PMID: 38185688 PMCID: PMC10773105 DOI: 10.1186/s13073-023-01265-5] [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: 07/17/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Copy-number variations (CNVs) have been associated with rare and debilitating genomic disorders (GDs) but their impact on health later in life in the general population remains poorly described. METHODS Assessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white British UK Biobank (UKBB) participants with replication in the Estonian Biobank. RESULTS We identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. We estimated that 16% of these associations are indirect, acting by increasing body mass index (BMI). Signals mapped to 45 unique, non-overlapping regions, nine of which being linked to known GDs. Number and identity of genes affected by CNVs modulated their pathogenicity, with many associations being supported by colocalization with both common and rare single-nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacted renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Finally, we show that while the total burden of rare CNVs-and especially deletions-strongly associated with disease risk, it only accounted for ~ 0.02% of the UKBB disease burden. These associations are mainly driven by CNVs at known GD CNV regions, whose pleiotropic effect on common diseases was broader than anticipated by our CNV-GWAS. CONCLUSIONS Our results shed light on the prominent role of rare CNVs in determining common disease susceptibility within the general population and provide actionable insights for anticipating later-onset comorbidities in carriers of recurrent CNVs.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
| | - Maarja Jõeloo
- Institute of Molecular and Cell Biology, University of Tartu, 51010, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Marie C Sadler
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland
| | - Nicolò Tesio
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Sven Ojavee
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Charlie J Clark
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
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Sun C, Lan F, Zhou Q, Guo X, Jin J, Wen C, Guo Y, Hou Z, Zheng J, Wu G, Li G, Yan Y, Li J, Ma Q, Yang N. Mechanisms of hepatic steatosis in chickens: integrated analysis of the host genome, molecular phenomics and gut microbiome. Gigascience 2024; 13:giae023. [PMID: 38837944 PMCID: PMC11152177 DOI: 10.1093/gigascience/giae023] [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: 05/14/2023] [Revised: 01/14/2024] [Accepted: 04/22/2024] [Indexed: 06/07/2024] Open
Abstract
Hepatic steatosis is the initial manifestation of abnormal liver functions and often leads to liver diseases such as nonalcoholic fatty liver disease in humans and fatty liver syndrome in animals. In this study, we conducted a comprehensive analysis of a large chicken population consisting of 705 adult hens by combining host genome resequencing; liver transcriptome, proteome, and metabolome analysis; and microbial 16S ribosomal RNA gene sequencing of each gut segment. The results showed the heritability (h2 = 0.25) and duodenal microbiability (m2 = 0.26) of hepatic steatosis were relatively high, indicating a large effect of host genetics and duodenal microbiota on chicken hepatic steatosis. Individuals with hepatic steatosis had low microbiota diversity and a decreased genetic potential to process triglyceride output from hepatocytes, fatty acid β-oxidation activity, and resistance to fatty acid peroxidation. Furthermore, we revealed a molecular network linking host genomic variants (GGA6: 5.59-5.69 Mb), hepatic gene/protein expression (PEMT, phosphatidyl-ethanolamine N-methyltransferase), metabolite abundances (folate, S-adenosylmethionine, homocysteine, phosphatidyl-ethanolamine, and phosphatidylcholine), and duodenal microbes (genus Lactobacillus) to hepatic steatosis, which could provide new insights into the regulatory mechanism of fatty liver development.
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Affiliation(s)
- Congjiao Sun
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Fangren Lan
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Qianqian Zhou
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Xiaoli Guo
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jiaming Jin
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Chaoliang Wen
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Yanxin Guo
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Zhuocheng Hou
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Jiangxia Zheng
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Guiqin Wu
- Beijing Engineering Research Centre of Layer, Beijing 101206, China
| | - Guangqi Li
- Beijing Engineering Research Centre of Layer, Beijing 101206, China
| | - Yiyuan Yan
- Beijing Engineering Research Centre of Layer, Beijing 101206, China
| | - Junying Li
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Qiugang Ma
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
| | - Ning Yang
- Department of Animal Genetics and Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China
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Ruberg S, Zhang Y, Showalter H, Shen L. A platform for comparing subgroup identification methodologies. Biom J 2024; 66:e2200164. [PMID: 37147787 DOI: 10.1002/bimj.202200164] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 02/24/2023] [Accepted: 03/01/2023] [Indexed: 05/07/2023]
Abstract
Since the advent of the phrase "subgroup identification," there has been an explosion of methodologies that seek to identify meaningful subgroups of patients with exceptional response in order to further the realization of personalized medicine. However, to perform fair comparison and understand what methods work best under different clinical trials situations, a common platform is needed for comparative effectiveness of these various approaches. In this paper, we describe a comprehensive project that created an extensive platform for evaluating subgroup identification methods as well as a publicly posted challenge that was used to elicit new approaches. We proposed a common data-generating model for creating virtual clinical trial datasets that contain subgroups of exceptional responders encompassing the many dimensions of the problem or null scenarios in which there are no such subgroups. Furthermore, we created a common scoring system for evaluating performance of purported methods for identifying subgroups. This makes it possible to benchmark methodologies in order to understand what methods work best under different clinical trial situations. The findings from this project produced considerable insights and allow us to make recommendations for how the statistical community can better compare and contrast old and new subgroup identification methodologies.
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Affiliation(s)
| | - Ying Zhang
- Global Statistical Sciences, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Hollins Showalter
- Global Statistical Sciences, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Lei Shen
- Global Statistical Sciences, Eli Lilly and Company, Indianapolis, Indiana, USA
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37
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Waples RS. Practical application of the linkage disequilibrium method for estimating contemporary effective population size: A review. Mol Ecol Resour 2024; 24:e13879. [PMID: 37873672 DOI: 10.1111/1755-0998.13879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/26/2023] [Accepted: 09/29/2023] [Indexed: 10/25/2023]
Abstract
The method to estimate contemporary effective population size (Ne ) based on patterns of linkage disequilibrium (LD) at unlinked loci has been widely applied to natural and managed populations. The underlying model makes many simplifying assumptions, most of which have been evaluated in numerous studies published over the last two decades. Here, these performance evaluations are reviewed and summarized, with a focus on information that facilitates practical application to real populations in nature. Potential sources of bias that are discussed include calculation of r2 (a measure of LD), adjustments for sampling error, physical linkage, age structure, migration and spatial structure, mutation and selection, mating systems, changes in abundance, rare alleles, missing data, genotyping errors, data filtering choices and methods for combining multiple Ne estimates. Factors that affect precision are reviewed, including pseudoreplication that limits the information gained from large genomics datasets, constraints imposed by small samples of individuals, and the challenges in obtaining robust estimates for large populations. Topics that merit further research include the potential to weight r2 values by allele frequency, lump samples of individuals, use genotypic likelihoods rather than called genotypes, prune large LD values and apply the method to species practising partial monogamy.
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Affiliation(s)
- Robin S Waples
- School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington, USA
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38
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Prunas O, Willemsen JE, Bont L, Pitzer VE, Warren JL, Weinberger DM. Incorporating Data from Multiple Endpoints in the Analysis of Clinical Trials: Example from RSV Vaccines. Epidemiology 2024; 35:103-112. [PMID: 37793120 DOI: 10.1097/ede.0000000000001680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
BACKGROUND To meet regulatory approval, interventions must demonstrate efficacy against a primary outcome in randomized clinical trials. However, when there are multiple clinically relevant outcomes, selecting a single primary outcome is challenging. Incorporating data from multiple outcomes may increase statistical power in clinical trials. We examined methods for analyzing data on multiple endpoints, inspired by real-world trials of interventions against respiratory syncytial virus (RSV). METHOD We developed a novel permutation test representing a weighted average of individual outcome test statistics ( wavP ) to evaluate intervention efficacy in a multiple endpoint analysis. We compared the power and type I error rate of this approach to the Bonferroni correction ( bonfT ) and the minP permutation test. We evaluated the different approaches using simulated data from three hypothetical trials varying the intervention efficacy, correlation, and incidence of the outcomes, and data from a real-world RSV clinical trial. RESULTS When the vaccine efficacy against different outcomes was similar, wavP yielded higher power than bonfT and minP ; in some scenarios the improvement in power was substantial. In settings where vaccine efficacy was notably larger against one endpoint compared with the others, all three methods had similar power. We developed an R package, PERmutation basEd ANalysis of mulTiple Endpoints (PERMEATE), to guide the selection of the most appropriate method for analyzing multiple endpoints in clinical trials. CONCLUSIONS Analyzing multiple endpoints using a weighted permutation method can increase power, whereas controlling the type I error rate compared with established methods under conditions mirroring real-world RSV clinical trials.
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Affiliation(s)
- Ottavia Prunas
- From the Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, CT
| | - Joukje E Willemsen
- Centre for Translational Immunology, University Medical Center Utrecht, Utrecht, the Netherlands
- Division of Infectious Diseases, Department of Pediatrics, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Louis Bont
- Division of Infectious Diseases, Department of Pediatrics, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Virginia E Pitzer
- From the Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, CT
| | - Joshua L Warren
- From the Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, CT
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT
| | - Daniel M Weinberger
- From the Department of Epidemiology of Microbial Diseases and Public Health Modeling Unit, Yale School of Public Health, Yale University, New Haven, CT
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Main LR, Song YE, Lynn A, Laux RA, Miskimen KL, Osterman MD, Cuccaro ML, Ogrocki PK, Lerner AJ, Vance JM, Fuzzell MD, Fuzzell SL, Hochstetler SD, Dorfsman DA, Caywood LJ, Prough MB, Adams LD, Clouse JE, Herington SD, Scott WK, Pericak-Vance MA, Haines JL. Genetic analysis of cognitive preservation in the midwestern Amish reveals a novel locus on chromosome 2. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.13.23299932. [PMID: 38168325 PMCID: PMC10760262 DOI: 10.1101/2023.12.13.23299932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
INTRODUCTION Alzheimer disease (AD) remains a debilitating condition with limited treatments and additional therapeutic targets needed. Identifying AD protective genetic loci may identify new targets and accelerate identification of therapeutic treatments. We examined a founder population to identify loci associated with cognitive preservation into advanced age. METHODS Genome-wide association and linkage analyses were performed on 946 examined and sampled Amish individuals, aged 76-95, who were either cognitively unimpaired (CU) or impaired (CI). RESULTS 12 SNPs demonstrated suggestive association (P≤5×10-4) with cognitive preservation. Genetic linkage analyses identified >100 significant (LOD≥3.3) SNPs, some which overlapped with the association results. Only one locus on chromosome 2 retained significance across multiple analyses. DISCUSSION A novel significant result for cognitive preservation on chromosome 2 includes the genes LRRTM4 and CTNNA2. Additionally, the lead SNP, rs1402906, impacts the POU3F2 transcription factor binding affinity, which regulates LRRTM4 and CTNNA2.
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Affiliation(s)
- Leighanne R Main
- Departments of Genetics and Genome Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Yeunjoo E Song
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Audrey Lynn
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Renee A Laux
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Kristy L Miskimen
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Michael D Osterman
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Michael L Cuccaro
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Paula K Ogrocki
- Department of Neurology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Neurology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Alan J Lerner
- Department of Neurology, University Hospitals Cleveland Medical Center, 11100 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Neurology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
| | - Jeffery M Vance
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - M Denise Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Sarada L Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Sherri D Hochstetler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
| | - Daniel A Dorfsman
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Laura J Caywood
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Michael B Prough
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Larry D Adams
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Jason E Clouse
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Sharlene D Herington
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - William K Scott
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Margaret A Pericak-Vance
- John P Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
- Dr. John T Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, 1501 NW 10th Ave, Miami, FL, USA, 33136
| | - Jonathan L Haines
- Departments of Genetics and Genome Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH, USA, 44106
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44016
- Cleveland Institute of Computational Biology, Case Western Reserve University School of Medicine, 10900 Euclid Ave, Cleveland, OH, USA, 44106
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Hernangomez-Laderas A, Cilleros-Portet A, Martínez Velasco S, Marí S, Legarda M, González-García BP, Tutau C, García-Santisteban I, Irastorza I, Fernandez-Jimenez N, Bilbao JR. Sex bias in celiac disease: XWAS and monocyte eQTLs in women identify TMEM187 as a functional candidate gene. Biol Sex Differ 2023; 14:86. [PMID: 38072919 PMCID: PMC10712119 DOI: 10.1186/s13293-023-00572-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Celiac disease (CeD) is an immune-mediated disorder that develops in genetically predisposed individuals upon gluten consumption. HLA risk alleles explain 40% of the genetic component of CeD, so there have been continuing efforts to uncover non-HLA loci that can explain the remaining heritability. As in most autoimmune disorders, the prevalence of CeD is significantly higher in women. Here, we investigated the possible involvement of the X chromosome on the sex bias of CeD. METHODS We performed a X chromosome-wide association study (XWAS) and a gene-based association study in women from the CeD Immunochip (7062 cases, 5446 controls). We also constructed a database of X chromosome cis-expression quantitative trait loci (eQTLs) in monocytes from unstimulated (n = 226) and lipopolysaccharide (LPS)-stimulated (n = 130) female donors and performed a Summary-data-based MR (SMR) analysis to integrate XWAS and eQTL information. We interrogated the expression of the potentially causal gene (TMEM187) in peripheral blood mononuclear cells (PBMCs) from celiac patients at onset, on a gluten-free diet, potential celiac patients and non-celiac controls. RESULTS The XWAS and gene-based analyses identified 13 SNPs and 25 genes, respectively, 22 of which had not been previously associated with CeD. The X chromosome cis-eQTL analysis found 18 genes with at least one cis-eQTL in naïve female monocytes and 8 genes in LPS-stimulated female monocytes, 2 of which were common to both situations and 6 were unique to LPS stimulation. SMR identified a potentially causal association of TMEM187 expression in naïve monocytes with CeD in women, regulated by CeD-associated, eQTL-SNPs rs7350355 and rs5945386. The CeD-risk alleles were correlated with lower TMEM187 expression. These results were replicated using eQTLs from LPS-stimulated monocytes. We observed higher levels of TMEM187 expression in PBMCs from female CeD patients at onset compared to female non-celiac controls, but not in male CeD individuals. CONCLUSION Using X chromosome genotypes and gene expression data from female monocytes, SMR has identified TMEM187 as a potentially causal candidate in CeD. Further studies are needed to understand the implication of the X chromosome in the higher prevalence of CeD in women.
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Affiliation(s)
- Alba Hernangomez-Laderas
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Ariadna Cilleros-Portet
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Silvia Martínez Velasco
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Sergi Marí
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - María Legarda
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Bárbara Paola González-García
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Carlos Tutau
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Iraia García-Santisteban
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
| | - Iñaki Irastorza
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain
- Pediatric Gastroenterology Unit, Cruces University Hospital, Barakaldo, Basque Country, Spain
| | - Nora Fernandez-Jimenez
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain.
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain.
| | - Jose Ramon Bilbao
- Department of Genetics, Physical Anthropology and Animal Physiology, University of the Basque Country (UPV/EHU), Leioa, Basque Country, Spain.
- Biobizkaia Health Research Institute, Barakaldo, Basque Country, Spain.
- Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM), Madrid, Spain.
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Morieri ML, Candido R, Frontoni S, Disoteo O, Solini A, Fadini GP. Clinical Features, Cardiovascular Risk Profile, and Therapeutic Trajectories of Patients with Type 2 Diabetes Candidate for Oral Semaglutide Therapy in the Italian Specialist Care. Diabetes Ther 2023; 14:2159-2172. [PMID: 37848758 PMCID: PMC10597935 DOI: 10.1007/s13300-023-01490-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 10/05/2023] [Indexed: 10/19/2023] Open
Abstract
INTRODUCTION This study aimed to address therapeutic inertia in the management of type 2 diabetes (T2D) by investigating the potential of early treatment with oral semaglutide. METHODS A cross-sectional survey was conducted between October 2021 and April 2022 among specialists treating individuals with T2D. A scientific committee designed a data collection form covering demographics, cardiovascular risk, glucose control metrics, ongoing therapies, and physician judgments on treatment appropriateness. Participants completed anonymous patient questionnaires reflecting routine clinical encounters. The preferred therapeutic regimen for each patient was also identified. RESULTS The analysis was conducted on 4449 patients initiating oral semaglutide. The population had a relatively short disease duration (42% < 5 years), and a minority (15.6%) had a history of cardiovascular events. Importantly, oral semaglutide was started in subjects with various disease durations and background therapies. Notably, its initiation was accompanied by de-prescription of sulfonylureas, pioglitazone, DPP-4 inhibitors, and insulin. Choice of oral semaglutide was influenced by patient profiles and ongoing glucose-lowering regimens. Factors such as younger age, higher HbA1c, and ongoing SGLT-2 inhibitor therapy drove the choice of oral semaglutide with the aim of improving glycemic control. Projected glycemic effectiveness analysis revealed that oral semaglutide could potentially lead HbA1c to target in > 60% of patients, and more often than sitagliptin or empagliflozin. CONCLUSION The study supports the potential of early implementation of oral semaglutide as a strategy to overcome therapeutic inertia and enhance T2D management.
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Affiliation(s)
- Mario Luca Morieri
- Division of Metabolic Diseases, Department of Medicine, University of Padova, Via Giustiniani 2, 35128, Padua, Italy
| | - Riccardo Candido
- Diabetes Centre, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), University of Trieste, Trieste, Italy
| | - Simona Frontoni
- Unit of Endocrinology, Diabetes and Metabolism, Department of Systems Medicine, S. Giovanni Calibita Fatebenefratelli Hospital, University of Rome Tor Vergata, Rome, Italy
| | | | - Anna Solini
- Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Pisa, Italy
| | - Gian Paolo Fadini
- Division of Metabolic Diseases, Department of Medicine, University of Padova, Via Giustiniani 2, 35128, Padua, Italy.
- Veneto Institute of Molecular Medicine, Padua, Italy.
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Lukic B, Curik I, Drzaic I, Galić V, Shihabi M, Vostry L, Cubric-Curik V. Genomic signatures of selection, local adaptation and production type characterisation of East Adriatic sheep breeds. J Anim Sci Biotechnol 2023; 14:142. [PMID: 37932811 PMCID: PMC10626677 DOI: 10.1186/s40104-023-00936-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/04/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND The importance of sheep breeding in the Mediterranean part of the eastern Adriatic has a long tradition since its arrival during the Neolithic migrations. Sheep production system is extensive and generally carried out in traditional systems without intensive systematic breeding programmes for high uniform trait production (carcass, wool and milk yield). Therefore, eight indigenous Croatian sheep breeds from eastern Adriatic treated here as metapopulation (EAS), are generally considered as multipurpose breeds (milk, meat and wool), not specialised for a particular type of production, but known for their robustness and resistance to certain environmental conditions. Our objective was to identify genomic regions and genes that exhibit patterns of positive selection signatures, decipher their biological and productive functionality, and provide a "genomic" characterization of EAS adaptation and determine its production type. RESULTS We identified positive selection signatures in EAS using several methods based on reduced local variation, linkage disequilibrium and site frequency spectrum (eROHi, iHS, nSL and CLR). Our analyses identified numerous genomic regions and genes (e.g., desmosomal cadherin and desmoglein gene families) associated with environmental adaptation and economically important traits. Most candidate genes were related to meat/production and health/immune response traits, while some of the candidate genes discovered were important for domestication and evolutionary processes (e.g., HOXa gene family and FSIP2). These results were also confirmed by GO and QTL enrichment analysis. CONCLUSIONS Our results contribute to a better understanding of the unique adaptive genetic architecture of EAS and define its productive type, ultimately providing a new opportunity for future breeding programmes. At the same time, the numerous genes identified will improve our understanding of ruminant (sheep) robustness and resistance in the harsh and specific Mediterranean environment.
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Affiliation(s)
- Boris Lukic
- Faculty of Agrobiotechnical Sciences Osijek, J.J, Strossmayer University of Osijek, Vladimira Preloga 1, 31000, Osijek, Croatia.
| | - Ino Curik
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia.
| | - Ivana Drzaic
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia
| | - Vlatko Galić
- Department of Maize Breeding and Genetics, Agricultural Institute Osijek, Južno predgrađe 17, 31000, Osijek, Croatia
| | - Mario Shihabi
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia
| | - Luboš Vostry
- Czech University of Life Sciences Prague, Kamýcká 129, 165 00, Praque, Czech Republic
| | - Vlatka Cubric-Curik
- Department of Animal Science, Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000, Zagreb, Croatia
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Jakubek YA, Zhou Y, Stilp A, Bacon J, Wong JW, Ozcan Z, Arnett D, Barnes K, Bis JC, Boerwinkle E, Brody JA, Carson AP, Chasman DI, Chen J, Cho M, Conomos MP, Cox N, Doyle MF, Fornage M, Guo X, Kardia SLR, Lewis JP, Loos RJF, Ma X, Machiela MJ, Mack TM, Mathias RA, Mitchell BD, Mychaleckyj JC, North K, Pankratz N, Peyser PA, Preuss MH, Psaty B, Raffield LM, Vasan RS, Redline S, Rich SS, Rotter JI, Silverman EK, Smith JA, Smith AP, Taub M, Taylor KD, Yun J, Li Y, Desai P, Bick AG, Reiner AP, Scheet P, Auer PL. Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing. Nat Genet 2023; 55:1912-1919. [PMID: 37904051 PMCID: PMC10632132 DOI: 10.1038/s41588-023-01553-1] [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: 11/04/2022] [Accepted: 09/27/2023] [Indexed: 11/01/2023]
Abstract
Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.
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Affiliation(s)
- Yasminka A Jakubek
- Department of Internal Medicine, University of Kentucky, Lexington, KY, USA
| | - Ying Zhou
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Adrienne Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jason Bacon
- Department of Computer Science, Department of Biological Sciences, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
| | - Justin W Wong
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Zuhal Ozcan
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | | | - Kathleen Barnes
- Division of Biomedical Informatics and Personalized Medicine, School of Medicine University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington Seattle, Seattle, WA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Jiawen Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Nancy Cox
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Margaret F Doyle
- Department of Pathology and Laboratory Medicine, The University of Vermont Larner College of Medicine, Colchester, VT, USA
| | - Myriam Fornage
- University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiuqing Guo
- 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
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Joshua P Lewis
- Department of Medicine, University of Maryland Baltimore, Baltimore, MD, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Xiaolong Ma
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA
| | | | - Taralynn M Mack
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rasika A Mathias
- Division of Allergy and Clinical Immunology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland Baltimore, Baltimore, MD, USA
| | - Josyf C Mychaleckyj
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina Chapel-Hill, Chapel Hill, NC, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce Psaty
- Cardiovascular Health Research Unit, Department of Medicine, Department of Epidemiology, Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Susan Redline
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia School of Medicine, Charlottesville, VA, 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
| | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, MI, USA
| | - Aaron P Smith
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, USA
| | - Margaret Taub
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MA, 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
| | - Jeong Yun
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Yun Li
- Department of Biostatistics, Department of Genetics, Department of Computer Science, University of North Carolina Chapel-Hill, Chapel Hill, NC, USA
| | - Pinkal Desai
- Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Alexander G Bick
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Paul Scheet
- Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA.
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Gan S, Zhao L, Salman O, Wang Z, Ebert C, Azzo JD, Dib MJ, Zamani P, Cohen JB, Kammerhoff K, Schafer P, Seiffert DA, Ramirez-Valle F, Gordon DA, Cvijic ME, Gunawardhana K, Liu L, Chang CP, Cappola TP, Chirinos JA. Proteomic Correlates of the Urinary Protein/Creatinine Ratio in Heart Failure With Preserved Ejection Fraction. Am J Cardiol 2023; 206:312-319. [PMID: 37734292 PMCID: PMC10874232 DOI: 10.1016/j.amjcard.2023.08.146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 09/23/2023]
Abstract
Proteinuria is common in heart failure with preserved ejection fraction (HFpEF), but its biologic correlates are poorly understood. We assessed the relation between 49 plasma proteins and the urinary protein/creatinine ratio (UPCR) in 365 participants in the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial. Linear regression and network analysis were used to represent relations between protein biomarkers and UPCR. Higher UPCR was associated with older age, a greater proportion of female gender, smaller prevalence of previous myocardial infarction, and greater prevalence of diabetes, insulin use, smoking, and statin use, in addition to a lower estimated glomerular filtration rate, hematocrit, and diastolic blood pressure. Growth differentiation factor 15 (GDF-15; β = 0.15, p <0.0001), followed by N-terminal proatrial natriuretic peptide (NT-proANP; β = 0.774, p <0.0001), adiponectin (β = 0.0005, p <0.0001), fibroblast growth factor 23 (FGF-23, β = 0.177; p <0.0001), and soluble tumor necrosis factor receptors I (β = 0.002, p <0.0001) and II (β = 0.093, p <0.0001) revealed the strongest associations with UPCR. Network analysis showed that UPCR is linked to various proteins primarily through FGF-23, which, along with GDF-15, indicated node characteristics with strong connectivity, whereas UPCR did not. In a model that included FGF-23 and UPCR, the former was predictive of the risk of death or heart-failure hospital admission (standardized hazard ratio 1.83, 95% confidence interval 1.49 to 2.26, p <0.0001) and/or all-cause death (standardized hazard ratio 1.59, 95% confidence interval 1.22 to 2.07, p = 0.0005), whereas UPCR was not prognostic. Proteinuria in HFpEF exhibits distinct proteomic correlates, primarily through its association with FGF-23, a well-known prognostic marker in HFpEF. However, in contrast to FGF-23, UPCR does not hold independent prognostic value.
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Affiliation(s)
- Sushrima Gan
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Cardiovascular Medicine, Hospital of The University of Pennsylvania, Philadelphia, Pennsylvania
| | - Lei Zhao
- Bristol-Myers Squibb Company, Lawrenceville, New Jersey
| | - Oday Salman
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Cardiovascular Medicine, Hospital of The University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zhaoqing Wang
- Bristol-Myers Squibb Company, Lawrenceville, New Jersey
| | | | - Joe David Azzo
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Cardiovascular Medicine, Hospital of The University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marie Joe Dib
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Cardiovascular Medicine, Hospital of The University of Pennsylvania, Philadelphia, Pennsylvania
| | - Payman Zamani
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Cardiovascular Medicine, Hospital of The University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jordana B Cohen
- Renal-Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Biostatistics, Epidemiology, and Informatics
| | | | - Peter Schafer
- Bristol-Myers Squibb Company, Lawrenceville, New Jersey
| | | | | | | | | | | | - Laura Liu
- Bristol-Myers Squibb Company, Lawrenceville, New Jersey
| | | | - Thomas P Cappola
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Cardiovascular Medicine, Hospital of The University of Pennsylvania, Philadelphia, Pennsylvania
| | - Julio A Chirinos
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Division of Cardiovascular Medicine, Hospital of The University of Pennsylvania, Philadelphia, Pennsylvania.
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45
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Dowell JA, Mason C. Candidate pathway association and genome-wide association approaches reveal alternative genetic architectures of carotenoid content in cultivated sunflower ( Helianthus annuus). APPLICATIONS IN PLANT SCIENCES 2023; 11:e11558. [PMID: 38106540 PMCID: PMC10719882 DOI: 10.1002/aps3.11558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/10/2023] [Accepted: 05/19/2023] [Indexed: 12/19/2023]
Abstract
Premise The explosion of available genomic data poses significant opportunities and challenges for genome-wide association studies. Current approaches via linear mixed models (LMM) are straightforward but prevent flexible assumptions of an a priori genomic architecture, while Bayesian sparse LMMs (BSLMMs) allow this flexibility. Complex traits, such as specialized metabolites, are subject to various hierarchical effects, including gene regulation, enzyme efficiency, and the availability of reactants. Methods To identify alternative genetic architectures, we examined the genetic architecture underlying the carotenoid content of an association mapping panel of Helianthus annuus individuals using multiple BSLMM and LMM frameworks. Results The LMMs of genome-wide single-nucleotide polymorphisms (SNPs) identified a single transcription factor responsible for the observed variations in the carotenoid content; however, a BSLMM of the SNPs with the bottom 1% of effect sizes from the results of the LMM identified multiple biologically relevant quantitative trait loci (QTLs) for carotenoid content external to the known (annotated) carotenoid pathway. A candidate pathway analysis (CPA) suggested a β-carotene isomerase to be the enzyme with the highest impact on the observed carotenoid content within the carotenoid pathway. Discussion While traditional LMM approaches suggested a single unknown transcription factor associated with carotenoid content variation in sunflower petals, BSLMM proposed several QTLs with interpretable biological relevance to this trait. In addition, the CPA allowed for the dissection of the regulatory vs. biosynthetic genetic architectures underlying this metabolic trait.
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Affiliation(s)
- Jordan A. Dowell
- Department of Plant SciencesUniversity of CaliforniaDavisCalifornia95616USA
- Present address:
Department of Biological SciencesLouisiana State UniversityBaton RougeLouisiana70803USA
| | - Chase Mason
- Department of BiologyUniversity of Central FloridaOrlandoFlorida32816USA
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Sormunen-Harju H, Huvinen E, Girchenko PV, Kajantie E, Villa PM, Hämäläinen EK, Lahti-Pulkkinen M, Laivuori H, Räikkönen K, Koivusalo SB. Metabolomic Profiles of Nonobese and Obese Women With Gestational Diabetes. J Clin Endocrinol Metab 2023; 108:2862-2870. [PMID: 37220084 PMCID: PMC10584006 DOI: 10.1210/clinem/dgad288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 05/04/2023] [Accepted: 05/17/2023] [Indexed: 05/25/2023]
Abstract
CONTEXT In non-pregnant population, nonobese individuals with obesity-related metabolome have increased risk for type 2 diabetes and cardiovascular diseases. The risk of these diseases is also increased after gestational diabetes. OBJECTIVE This work aimed to examine whether nonobese (body mass index [BMI] < 30) and obese (BMI ≥ 30) women with gestational diabetes mellitus (GDM) and obese non-GDM women differ in metabolomic profiles from nonobese non-GDM controls. METHODS Levels of 66 metabolic measures were assessed in early (median 13, IQR 12.4-13.7 gestation weeks), and across early, mid (20, 19.3-23.0), and late (28, 27.0-35.0) pregnancy blood samples in 755 pregnant women from the PREDO and RADIEL studies. The independent replication cohort comprised 490 pregnant women. RESULTS Nonobese and obese GDM, and obese non-GDM women differed similarly from the controls across early, mid, and late pregnancy in 13 measures, including very low-density lipoprotein-related measures, and fatty acids. In 6 measures, including fatty acid (FA) ratios, glycolysis-related measures, valine, and 3-hydroxybutyrate, the differences between obese GDM women and controls were more pronounced than the differences between nonobese GDM or obese non-GDM women and controls. In 16 measures, including HDL-related measures, FA ratios, amino acids, and inflammation, differences between obese GDM or obese non-GDM women and controls were more pronounced than the differences between nonobese GDM women and controls. Most differences were evident in early pregnancy, and in the replication cohort were more often in the same direction than would be expected by chance alone. CONCLUSION Differences between nonobese and obese GDM, or obese non-GDM women and controls in metabolomic profiles may allow detection of high-risk women for timely targeted preventive interventions.
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Affiliation(s)
- Heidi Sormunen-Harju
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, FI-00270 Helsinki, Finland
| | - Emilia Huvinen
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, FI-00270 Helsinki, Finland
| | - Polina V Girchenko
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, FI-00270 Helsinki, Finland
| | - Eero Kajantie
- Clinical Medicine Research Unit, MRC Oulu, Oulu University Hospital and University of Oulu, FI-90220 Oulu, Finland
- Population Health Unit, Finnish Institute for Health and Welfare, FI-00300 Helsinki, Finland
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway
- Children’s Hospital, Helsinki University Hospital and University of Helsinki, FI-00290 Helsinki, Finland
| | - Pia M Villa
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, FI-00270 Helsinki, Finland
| | - Esa K Hämäläinen
- Department of Clinical Chemistry, University of Eastern Finland, FI-70211 Kuopio, Finland
| | - Marius Lahti-Pulkkinen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, FI-00270 Helsinki, Finland
- Finnish National Institute for Health and Welfare, FI-00300 Helsinki, Finland
- University of Edinburgh, Edinburgh EH8 9YL, UK
| | - Hannele Laivuori
- Medical and Clinical Genetics, Helsinki University Hospital and University of Helsinki, FI-00270 Helsinki, Finland
- Institute for Molecular Medicine Finland, Helsinki Institute of Life Science, University of Helsinki, FI-00270 Helsinki, Finland
- Department of Obstetrics and Gynecology, Tampere University Hospital, FI-33520 Tampere, Finland
- Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, FI-33520 Tampere, Finland
| | - Katri Räikkönen
- Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, FI-00270 Helsinki, Finland
| | - Saila B Koivusalo
- Department of Obstetrics and Gynecology, Helsinki University Hospital and University of Helsinki, FI-00270 Helsinki, Finland
- Department of Obstetrics and Gynaecology, Turku University Hospital and University of Turku, FI-20520 Turku, Finland
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47
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Wang Y, Selvaraj MS, Li X, Li Z, Holdcraft JA, Arnett DK, Bis JC, Blangero J, Boerwinkle E, Bowden DW, Cade BE, Carlson JC, Carson AP, Chen YDI, Curran JE, de Vries PS, Dutcher SK, Ellinor PT, Floyd JS, Fornage M, Freedman BI, Gabriel S, Germer S, Gibbs RA, Guo X, He J, Heard-Costa N, Hildalgo B, Hou L, Irvin MR, Joehanes R, Kaplan RC, Kardia SL, Kelly TN, Kim R, Kooperberg C, Kral BG, Levy D, Li C, Liu C, Lloyd-Jone D, Loos RJ, Mahaney MC, Martin LW, Mathias RA, Minster RL, Mitchell BD, Montasser ME, Morrison AC, Murabito JM, Naseri T, O'Connell JR, Palmer ND, Preuss MH, Psaty BM, Raffield LM, Rao DC, Redline S, Reiner AP, Rich SS, Ruepena MS, Sheu WHH, Smith JA, Smith A, Tiwari HK, Tsai MY, Viaud-Martinez KA, Wang Z, Yanek LR, Zhao W, Rotter JI, Lin X, Natarajan P, Peloso GM. Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study. Am J Hum Genet 2023; 110:1704-1717. [PMID: 37802043 PMCID: PMC10577076 DOI: 10.1016/j.ajhg.2023.09.003] [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: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 10/08/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.
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Affiliation(s)
- Yuxuan Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Margaret Sunitha Selvaraj
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Xihao Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zilin Li
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, China; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jacob A Holdcraft
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Donna K Arnett
- Provost Office, University of South Carolina, Columbia, SC, USA; Department of Epidemiology and Biostatistics, University of South Carolina Arnold School of Public Health, Columbia, SC, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Brian E Cade
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Jenna C Carlson
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Yii-Der Ida Chen
- 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
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Susan K Dutcher
- The McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Myriam Fornage
- Center for Human Genetics, University of Texas Health at Houston, Houston, TX, USA
| | - Barry I Freedman
- Department of Internal Medicine, Nephrology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | | | | | - Richard A Gibbs
- Baylor College of Medicine Human Genome Sequencing Center, Houston, TX, USA
| | - Xiuqing Guo
- 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
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA; Tulane University Translational Science Institute, New Orleans, LA, USA
| | - Nancy Heard-Costa
- Framingham Heart Study, Framingham, MA, USA; Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Bertha Hildalgo
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL, USA
| | - Roby Joehanes
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Sharon Lr Kardia
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Tanika N Kelly
- Department of Medicine, Division of Nephrology, University of Illinois Chicago, Chicago, IL, USA
| | - Ryan Kim
- Psomagen, Inc. (formerly Macrogen USA), Rockville, MD, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Brian G Kral
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Levy
- Framingham Heart Study, Framingham, MA, USA; Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Changwei Li
- Tulane University Translational Science Institute, New Orleans, LA, USA; Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; Framingham Heart Study, Framingham, MA, USA
| | - Don Lloyd-Jone
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; NNF Center for Basic Metabolic Research, University of Copenhagen, Cophenhagen, Denmark
| | - Michael C Mahaney
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, USA
| | - Lisa W Martin
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ryan L Minster
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - May E Montasser
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Joanne M Murabito
- Framingham Heart Study, Framingham, MA, USA; Department of Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Take Naseri
- Naseri & Associates Public Health Consultancy Firm and Family Health Clinic, Apia, Samoa; International Health Institute, School of Public Health, Brown University, Providence, RI, USA
| | - Jeffrey R O'Connell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Nicholette D Palmer
- Department of Biochemistry, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Michael H Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA; Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dabeeru C Rao
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | | | - Wayne H-H Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute (NHRI), Miaoli County, Taiwan
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Albert Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama, Birmingham, AL, USA
| | - Michael Y Tsai
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | | | - Zhe Wang
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, 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
| | - Xihong Lin
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
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Coe K, Bostan H, Rolling W, Turner-Hissong S, Macko-Podgórni A, Senalik D, Liu S, Seth R, Curaba J, Mengist MF, Grzebelus D, Van Deynze A, Dawson J, Ellison S, Simon P, Iorizzo M. Population genomics identifies genetic signatures of carrot domestication and improvement and uncovers the origin of high-carotenoid orange carrots. NATURE PLANTS 2023; 9:1643-1658. [PMID: 37770615 PMCID: PMC10581907 DOI: 10.1038/s41477-023-01526-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/28/2023] [Indexed: 09/30/2023]
Abstract
Here an improved carrot reference genome and resequencing of 630 carrot accessions were used to investigate carrot domestication and improvement. The study demonstrated that carrot was domesticated during the Early Middle Ages in the region spanning western Asia to central Asia, and orange carrot was selected during the Renaissance period, probably in western Europe. A progressive reduction of genetic diversity accompanied this process. Genes controlling circadian clock/flowering and carotenoid accumulation were under selection during domestication and improvement. Three recessive genes, at the REC, Or and Y2 quantitative trait loci, were essential to select for the high α- and β-carotene orange phenotype. All three genes control high α- and β-carotene accumulation through molecular mechanisms that regulate the interactions between the carotenoid biosynthetic pathway, the photosynthetic system and chloroplast biogenesis. Overall, this study elucidated carrot domestication and breeding history and carotenoid genetics at a molecular level.
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Affiliation(s)
- Kevin Coe
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, USA
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Hamed Bostan
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, USA
| | - William Rolling
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Agricultural Research Service, Vegetable Crops Research Unit, US Department of Agriculture, Madison, WI, USA
| | | | - Alicja Macko-Podgórni
- Department of Plant Biology and Biotechnology, Faculty of Biotechnology and Horticulture, University of Agriculture in Krakow, Krakow, Poland
| | - Douglas Senalik
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Agricultural Research Service, Vegetable Crops Research Unit, US Department of Agriculture, Madison, WI, USA
| | - Su Liu
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, USA
| | - Romit Seth
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, USA
| | - Julien Curaba
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, USA
| | - Molla Fentie Mengist
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, USA
| | - Dariusz Grzebelus
- Department of Plant Biology and Biotechnology, Faculty of Biotechnology and Horticulture, University of Agriculture in Krakow, Krakow, Poland
| | - Allen Van Deynze
- Seed Biotechnology Center, University of California, Davis, CA, USA
| | - Julie Dawson
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Shelby Ellison
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Philipp Simon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin-Madison, Madison, WI, USA.
- Agricultural Research Service, Vegetable Crops Research Unit, US Department of Agriculture, Madison, WI, USA.
| | - Massimo Iorizzo
- Plants for Human Health Institute, North Carolina State University, Kannapolis, NC, USA.
- Department of Horticultural Science, North Carolina State University, Raleigh, NC, USA.
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49
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Changwei L, Bundy JD, Tian L, Zhang R, Chen J, Kelly TN, He J. Examination of Serum Metabolome Altered by Dietary Carbohydrate, Milk Protein, and Soy Protein Interventions Identified Novel Metabolites Associated with Blood Pressure: The ProBP Trial. Mol Nutr Food Res 2023; 67:e2300044. [PMID: 37650262 PMCID: PMC10592004 DOI: 10.1002/mnfr.202300044] [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: 01/25/2023] [Revised: 05/08/2023] [Indexed: 09/01/2023]
Abstract
SCOPE This study aims to discover metabolites of dietary carbohydrate, soy and milk protein supplements and evaluate their roles in blood pressure (BP) regulation in the protein and blood pressure (ProBP), a cross-over trial. METHODS AND RESULTS Plasma metabolites are profiled at pre-trial baseline and after 8 weeks of supplementation with carbohydrate, soy protein, and milk protein, respectively, among 80 ProBP participants. After Bonferroni correction (α = 6.49 × 10-4 ), dietary interventions significantly changed 40 metabolites. Changes of erucate (22:1n9), an omega-9 fatty acid, are positively associated with systolic BP changes (Beta = 1.90, p = 6·27 × 10-4 ). This metabolite is also associated with higher odds of hypertension among 1261 participants of an independent cohort (odds ratio per unit increase = 1.34; 95% confidence interval: 1.07-1.68). High levels of acylcholines dihomo-linolenoyl-choline (p = 4.71E-04) and oleoylcholine (p = 3.48E-04) at baseline predicted larger BP lowering effects of soy protein. Increasing cheese intake during the trial, as reflected by isobutyrylglycine and isovalerylglycine, reduces the BP lowering effect of soy protein. CONCLUSIONS The study identifies molecular signatures of dietary interventions. Erucate (22:1n9) increases systolic BP. Acylcholine enhances and cheese intake reduces the BP lowering effect of soy protein supplement.
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Affiliation(s)
- Li Changwei
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
| | - Joshua D Bundy
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
| | - Ling Tian
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
| | - Ruiyuan Zhang
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
| | - Jing Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
- Department of Medicine, Tulane University School of Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
| | - Tanika N Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
- Division of Nephrology, Department of Medicine, College of Medicine, University of Illinois Chicago, 820 S. Wood St, Room W420, Chicago, IL, 60612, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
- Department of Medicine, Tulane University School of Medicine, 1440 Canal Street Suite 2000, New Orleans, LA, 70112-2703, USA
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Poudel RS, Belay K, Nelson B, Brueggeman R, Underwood W. Population and genome-wide association studies of Sclerotinia sclerotiorum isolates collected from diverse host plants throughout the United States. Front Microbiol 2023; 14:1251003. [PMID: 37829452 PMCID: PMC10566370 DOI: 10.3389/fmicb.2023.1251003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/29/2023] [Indexed: 10/14/2023] Open
Abstract
Introduction Sclerotinia sclerotiorum is a necrotrophic fungal pathogen causing disease and economic loss on numerous crop plants. This fungus has a broad host range and can infect over 400 plant species, including important oilseed crops such as soybean, canola, and sunflower. S. sclerotiorum isolates vary in aggressiveness of lesion formation on plant tissues. However, the genetic basis for this variation remains to be determined. The aims of this study were to evaluate a diverse collection of S. sclerotiorum isolates collected from numerous hosts and U.S. states for aggressiveness of stem lesion formation on sunflower, to evaluate the population characteristics, and to identify loci associated with isolate aggressiveness using genome-wide association mapping. Methods A total of 219 S. sclerotiorum isolates were evaluated for stem lesion formation on two sunflower inbred lines and genotyped using genotyping-by-sequencing. DNA markers were used to assess population differentiation across hosts, regions, and climatic conditions and to perform a genome-wide association study of isolate aggressiveness. Results and discussion We observed a broad range of aggressiveness for lesion formation on sunflower stems, and only a moderate correlation between aggressiveness on the two lines. Population genetic evaluations revealed differentiation between populations from warmer climate regions compared to cooler regions. Finally, a genome-wide association study of isolate aggressiveness identified three loci significantly associated with aggressiveness on sunflower. Functional characterization of candidate genes at these loci will likely improve our understanding of the virulence strategies used by this pathogen to cause disease on a wide array of agriculturally important host plants.
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Affiliation(s)
- Roshan Sharma Poudel
- Department of Plant Pathology, North Dakota State University, Fargo, ND, United States
| | - Kassaye Belay
- Department of Plant Pathology, North Dakota State University, Fargo, ND, United States
| | - Berlin Nelson
- Department of Plant Pathology, North Dakota State University, Fargo, ND, United States
| | - Robert Brueggeman
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - William Underwood
- Edward T. Schafer Agricultural Research Center, Sunflower and Plant Biology Research Unit, USDA Agricultural Research Service, Fargo, ND, United States
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