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Brandenburg JT, Chen WC, Boua PR, Govender MA, Agongo G, Micklesfield LK, Sorgho H, Tollman S, Asiki G, Mashinya F, Hazelhurst S, Morris AP, Fabian J, Ramsay M. Genetic association and transferability for urinary albumin-creatinine ratio as a marker of kidney disease in four Sub-Saharan African populations and non-continental individuals of African ancestry. Front Genet 2024; 15:1372042. [PMID: 38812969 PMCID: PMC11134365 DOI: 10.3389/fgene.2024.1372042] [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: 01/18/2024] [Accepted: 04/12/2024] [Indexed: 05/31/2024] Open
Abstract
Background Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts. Methods Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. Results Two genome-wide significant (P < 5 × 10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. Conclusion This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.
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Affiliation(s)
- Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Wenlong Carl Chen
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Cancer Registry, National Health Laboratory Service, Johannesburg, South Africa
| | - Palwende Romuald Boua
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Nanoro, Burkina Faso
| | | | - Godfred Agongo
- Navrongo Health Research Centre, Navrongo, Ghana
- Department of Biochemistry and Forensic Sciences, School of Chemical and Biochemical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
| | - Lisa K. Micklesfield
- SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Hermann Sorgho
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Nanoro, Burkina Faso
| | - Stephen Tollman
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gershim Asiki
- African Population and Health Research Center, Nairobi, Kenya
- Department of Women’s and Children’s Health, Karolinska Institute, Stockholm, Sweden
| | - Felistas Mashinya
- Department of Pathology and Medical Sciences, School of Healthcare Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, United Kingdom
| | - June Fabian
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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Eoli A, Ibing S, Schurmann C, Nadkarni GN, Heyne HO, Böttinger E. A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease. Sci Rep 2024; 14:9642. [PMID: 38671065 PMCID: PMC11053134 DOI: 10.1038/s41598-024-59747-4] [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: 10/09/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n = 31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.
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Affiliation(s)
- A Eoli
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482.
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA.
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany.
| | - S Ibing
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
| | - C Schurmann
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
| | - G N Nadkarni
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, New York City, NY, USA
| | - H O Heyne
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
| | - E Böttinger
- Digital Engineering Faculty, University of Potsdam, Potsdam, Germany, Prof.-Dr.-Helmert-Str. 2-3, 14482
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Hasso Plattner Institute for Digital Engineering gGmbH, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany
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Weon B, Jang Y, Jo J, Jin W, Ha S, Ko A, Oh YK, Lim CS, Lee JP, Won S, Lee J. Association between dyslipidemia and the risk of incident chronic kidney disease affected by genetic susceptibility: Polygenic risk score analysis. PLoS One 2024; 19:e0299605. [PMID: 38626061 PMCID: PMC11020804 DOI: 10.1371/journal.pone.0299605] [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: 08/13/2023] [Accepted: 02/13/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND The effect of dyslipidemia on kidney disease outcomes has been inconclusive, and it requires further clarification. Therefore, we aimed to investigate the effects of genetic factors on the association between dyslipidemia and the risk of chronic kidney disease (CKD) using polygenic risk score (PRS). METHODS We analyzed data from 373,523 participants from the UK Biobank aged 40-69 years with no history of CKD. Baseline data included plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride, as well as genome-wide genotype data for PRS. Our primary outcome, incident CKD, was defined as a composite of estimated glomerular filtration rate < 60 ml/min/1.73 m2 and CKD diagnosis according to International Classification of Disease-10 codes. The effects of the association between lipid levels and PRS on incident CKD were assessed using the Cox proportional hazards model. To investigate the effect of this association, we introduced multiplicative interaction terms into a multivariate analysis model and performed subgroup analysis stratified by PRS tertiles. RESULTS In total, 4,424 participants developed CKD. In the multivariable analysis, PRS was significantly predictive of the risk of incident CKD as both a continuous variable and a categorized variable. In addition, lower total cholesterol, LDL-C, HDL-C, and higher triglyceride levels were significantly associated with the risk of incident CKD. There were interactions between triglycerides and intermediate and high PRS, and the interactions were inversely associated with the risk of incident CKD. CONCLUSIONS This study showed that PRS presented significant predictive power for incident CKD and individuals in the low-PRS group had a higher risk of triglyceride-related incident CKD.
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Affiliation(s)
- Boram Weon
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | | | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Wencheng Jin
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seounguk Ha
- Korea Medical Institute, Seoul, Republic of Korea
| | - Ara Ko
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yun Kyu Oh
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Rexsoft Corporation, Seoul, Republic of Korea
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Brandenburg JT, Chen WC, Boua PR, Govender MA, Agongo G, Micklesfield LK, Sorgho H, Tollman S, Asiki G, Mashinya F, Hazelhurst S, Morris AP, Fabian J, Ramsay M. Genetic Association and Transferability for Urinary Albumin-Creatinine Ratio as a Marker of Kidney Disease in four Sub-Saharan African Populations and non-continental Individuals of African Ancestry. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.17.24301398. [PMID: 38293229 PMCID: PMC10827237 DOI: 10.1101/2024.01.17.24301398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts. METHODS Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. RESULTS Two genome-wide significant (P<5x10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. CONCLUSION This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes. METHODS Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. RESULTS Two genome-wide significant (P<5x10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. CONCLUSION This study contributes novel insights into the genetic architecture of kidney function in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations.
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Arunachalam V, Lea R, Hoy W, Lee S, Mott S, Savige J, Mathews JD, McMorran BJ, Nagaraj SH. Novel genetic markers for chronic kidney disease in a geographically isolated population of Indigenous Australians: Individual and multiple phenotype genome-wide association study. Genome Med 2024; 16:29. [PMID: 38347632 PMCID: PMC10860247 DOI: 10.1186/s13073-024-01299-3] [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: 04/30/2023] [Accepted: 01/30/2024] [Indexed: 02/15/2024] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) is highly prevalent among Indigenous Australians, especially those in remote regions. The Tiwi population has been isolated from mainland Australia for millennia and exhibits unique genetic characteristics that distinguish them from other Indigenous and non-Indigenous populations. Notably, the rate of end-stage renal disease is up to 20 times greater in this population compared to non-Indigenous populations. Despite the identification of numerous genetic loci associated with kidney disease through GWAS, the Indigenous population such as Tiwi remains severely underrepresented and the increased prevalence of CKD in this population may be due to unique disease-causing alleles/genes. METHODS We used albumin-to-creatinine ratio (ACR) and estimated glomerular filtration rate (eGFR) to estimate the prevalence of kidney disease in the Tiwi population (N = 492) in comparison to the UK Biobank (UKBB) (N = 134,724) database. We then performed an exploratory factor analysis to identify correlations among 10 CKD-related phenotypes and identify new multi-phenotype factors. We subsequently conducted a genome-wide association study (GWAS) on all single and multiple phenotype factors using mixed linear regression models, adjusted for age, sex, population stratification, and genetic relatedness between individuals. RESULTS Based on ACR, 20.3% of the population was at severely increased risk of CKD progression and showed elevated levels of ACR compared to the UKBB population independent of HbA1c. A GWAS of ACR revealed novel association loci in the genes MEG3 (chr14:100812018:T:A), RAB36 (rs11704318), and TIAM2 (rs9689640). Additionally, multiple phenotypes GWAS of ACR, eGFR, urine albumin, and serum creatinine identified a novel variant that mapped to the gene MEIS2 (chr15:37218869:A:G). Most of the identified variants were found to be either absent or rare in the UKBB population. CONCLUSIONS Our study highlights the Tiwi population's predisposition towards elevated ACR, and the collection of novel genetic variants associated with kidney function. These associations may prove valuable in the early diagnosis and treatment of renal disease in this underrepresented population. Additionally, further research is needed to comprehensively validate the functions of the identified variants/genes.
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Affiliation(s)
- Vignesh Arunachalam
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Rodney Lea
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Wendy Hoy
- Centre of chronic disease, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Simon Lee
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Susan Mott
- Centre of chronic disease, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Judith Savige
- Royal Melbourne Hospital, The University of Melbourne, Melbourne, VIC, Australia
| | - John D Mathews
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Brendan J McMorran
- National Centre for Indigenous Genomics, The John Curtin of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.
- Translational Research Institute, Queensland University of Technology, Brisbane, QLD, Australia.
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Eoli A, Ibing S, Schurmann C, Nadkarni GN, Heyne H, Böttinger E. A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease. RESEARCH SQUARE 2023:rs.3.rs-3424565. [PMID: 37886494 PMCID: PMC10602158 DOI: 10.21203/rs.3.rs-3424565/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.
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Affiliation(s)
- Andrea Eoli
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai
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Eoli A, Ibing S, Schurmann C, Nadkarni G, Heyne H, Böttinger E. A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.12.23296926. [PMID: 37873472 PMCID: PMC10593036 DOI: 10.1101/2023.10.12.23296926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.
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Affiliation(s)
- A. Eoli
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - S. Ibing
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - C. Schurmann
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Current address: Bayer AG, Research & Development, Pharmaceuticals, Berlin, Germany
| | - G.N. Nadkarni
- Windreich Dept. of Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- The Charles Bronfman Institute of Personalized Medicine, New York City, NY, USA
| | - H.O. Heyne
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Windreich Dept. of Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - E. Böttinger
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Windreich Dept. of Artificial Intelligence & Human Health, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
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Hill C, Duffy S, Coulter T, Maxwell AP, McKnight AJ. Harnessing Genomic Analysis to Explore the Role of Telomeres in the Pathogenesis and Progression of Diabetic Kidney Disease. Genes (Basel) 2023; 14:609. [PMID: 36980881 PMCID: PMC10048490 DOI: 10.3390/genes14030609] [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: 02/09/2023] [Revised: 02/20/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
The prevalence of diabetes is increasing globally, and this trend is predicted to continue for future decades. Research is needed to uncover new ways to manage diabetes and its co-morbidities. A significant secondary complication of diabetes is kidney disease, which can ultimately result in the need for renal replacement therapy, via dialysis or transplantation. Diabetic kidney disease presents a substantial burden to patients, their families and global healthcare services. This review highlights studies that have harnessed genomic, epigenomic and functional prediction tools to uncover novel genes and pathways associated with DKD that are useful for the identification of therapeutic targets or novel biomarkers for risk stratification. Telomere length regulation is a specific pathway gaining attention recently because of its association with DKD. Researchers are employing both observational and genetics-based studies to identify telomere-related genes associated with kidney function decline in diabetes. Studies have also uncovered novel functions for telomere-related genes beyond the immediate regulation of telomere length, such as transcriptional regulation and inflammation. This review summarises studies that have revealed the potential to harness therapeutics that modulate telomere length, or the associated epigenetic modifications, for the treatment of DKD, to potentially slow renal function decline and reduce the global burden of this disease.
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Affiliation(s)
- Claire Hill
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
| | - Seamus Duffy
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
| | - Tiernan Coulter
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
| | - Alexander Peter Maxwell
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
- Regional Nephrology Unit, Belfast City Hospital, Belfast BT9 7AB, UK
| | - Amy Jayne McKnight
- Centre for Public Health, Queen’s University of Belfast, Belfast BT12 6BA, UK
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Dauda B, Molina SJ, Allen DS, Fuentes A, Ghosh N, Mauro M, Neale BM, Panofsky A, Sohail M, Zhang SR, Lewis ACF. Ancestry: How researchers use it and what they mean by it. Front Genet 2023; 14:1044555. [PMID: 36755575 PMCID: PMC9900027 DOI: 10.3389/fgene.2023.1044555] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 01/10/2023] [Indexed: 01/24/2023] Open
Abstract
Background: Ancestry is often viewed as a more objective and less objectionable population descriptor than race or ethnicity. Perhaps reflecting this, usage of the term "ancestry" is rapidly growing in genetics research, with ancestry groups referenced in many situations. The appropriate usage of population descriptors in genetics research is an ongoing source of debate. Sound normative guidance should rest on an empirical understanding of current usage; in the case of ancestry, questions about how researchers use the concept, and what they mean by it, remain unanswered. Methods: Systematic literature analysis of 205 articles at least tangentially related to human health from diverse disciplines that use the concept of ancestry, and semi-structured interviews with 44 lead authors of some of those articles. Results: Ancestry is relied on to structure research questions and key methodological approaches. Yet researchers struggle to define it, and/or offer diverse definitions. For some ancestry is a genetic concept, but for many-including geneticists-ancestry is only tangentially related to genetics. For some interviewees, ancestry is explicitly equated to ethnicity; for others it is explicitly distanced from it. Ancestry is operationalized using multiple data types (including genetic variation and self-reported identities), though for a large fraction of articles (26%) it is impossible to tell which data types were used. Across the literature and interviews there is no consistent understanding of how ancestry relates to genetic concepts (including genetic ancestry and population structure), nor how these genetic concepts relate to each other. Beyond this conceptual confusion, practices related to summarizing patterns of genetic variation often rest on uninterrogated conventions. Continental labels are by far the most common type of label applied to ancestry groups. We observed many instances of slippage between reference to ancestry groups and racial groups. Conclusion: Ancestry is in practice a highly ambiguous concept, and far from an objective counterpart to race or ethnicity. It is not uniquely a "biological" construct, and it does not represent a "safe haven" for researchers seeking to avoid evoking race or ethnicity in their work. Distinguishing genetic ancestry from ancestry more broadly will be a necessary part of providing conceptual clarity.
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Affiliation(s)
- Bege Dauda
- Center for Global Genomics and Health Equity, University of Pennsylvania, Philadelphia, PA, United States
| | - Santiago J. Molina
- Department of Sociology, Northwestern University, Evanston, IL, United States
| | - Danielle S. Allen
- Edmond & Lily Safra Center for Ethics, Harvard University, Cambridge, MA, United States
| | - Agustin Fuentes
- Department of Anthropology, Princeton University, Princeton, NJ, United States
| | - Nayanika Ghosh
- Department of the History of Science, Harvard University, Cambridge, MA, United States
| | - Madelyn Mauro
- Edmond & Lily Safra Center for Ethics, Harvard University, Cambridge, MA, United States
| | - Benjamin M. Neale
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, United States
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Aaron Panofsky
- Institute for Society & Genetics, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Public Policy, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Sociology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Mashaal Sohail
- Centro de Ciencias Genomicas (CCG), Universidad Nacional Autonoma de Mexico (UNAM), Cuernavaca, Morelos, Mexico
| | - Sarah R. Zhang
- University of California, Berkeley, Berkeley, CA, United States
| | - Anna C. F. Lewis
- Edmond & Lily Safra Center for Ethics, Harvard University, Cambridge, MA, United States
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, United States
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10
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Al-Hamed MH, Hussein MH, Shah Y, Al-Mojalli H, Alsabban E, Alshareef T, Altayyar A, Elshouny S, Ali W, Abduljabbar M, AlOtaibi A, AlShammasi A, Akili R, Abouelhoda M, Sayer JA, Dasouki MJ, Imtiaz F. Exome sequencing unravels genetic variants associated with chronic kidney disease in Saudi Arabian patients. Hum Mutat 2022; 43:e24-e37. [PMID: 36177613 DOI: 10.1002/humu.24480] [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: 01/30/2022] [Revised: 09/10/2022] [Accepted: 09/26/2022] [Indexed: 01/25/2023]
Abstract
The use of genetic testing within nephrology is increasing and its diagnostic yield depends on the methods utilized, patient selection criteria, and population characteristics. We performed exome sequencing (ES) analysis on 102 chronic kidney disease (CKD) patients with likely genetic kidney disease. Patients had diverse CKD subtypes with/without consanguinity, positive family history, and possible hereditary renal syndrome with extra-renal abnormalities or progressive kidney disease of unknown etiology. The identified genetic variants associated with the observed kidney phenotypes were then confirmed and reported. End-stage kidney disease was reported in 51% of the cohort and a family history of kidney disease in 59%, while known consanguinity was reported in 54%. Pathogenic/likely pathogenic variants were identified in 43 patients with a diagnostic yield of 42%, and clinically associated variants of unknown significance (VUS) were identified in further 21 CKD patients (21%). A total of eight novel predicted pathogenic variants and eight VUS were detected. The clinical utility of ES within the nephrology clinic was demonstrated allowing patient management to be disease-specific. In this cohort, ES detected a diagnostic molecular abnormality in 42% of patients with CKD phenotypes. Positive family history and high rates of consanguinity likely contributed to this high diagnostic yield.
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Affiliation(s)
- Mohamed H Al-Hamed
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.,Saudi Diagnostics Laboratory, KFSH&RC, Riyadh, Saudi Arabia
| | - Maged H Hussein
- Medicine Department, Nephrology Section, KFSH&RC, Riyadh, Saudi Arabia
| | - Yaser Shah
- Organ Transplant Centre of Excellence, Adult Transplant Nephrology, KFSH&RC, Riyadh, Saudi Arabia
| | - Hamad Al-Mojalli
- Organ Transplant Centre of Excellence, Adult Transplant Nephrology, KFSH&RC, Riyadh, Saudi Arabia
| | | | | | - Ali Altayyar
- Medicine Department, Nephrology Section, KFSH&RC, Riyadh, Saudi Arabia
| | - Samir Elshouny
- Medicine Department, Nephrology Section, KFSH&RC, Riyadh, Saudi Arabia
| | - Wafaa Ali
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Mai Abduljabbar
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Afaf AlOtaibi
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Amal AlShammasi
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Rana Akili
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Mohamed Abouelhoda
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - John A Sayer
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.,Renal Services, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Majed J Dasouki
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Faiqa Imtiaz
- Centre for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.,Saudi Diagnostics Laboratory, KFSH&RC, Riyadh, Saudi Arabia
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11
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Gulamali FF, Sawant AS, Nadkarni GN. Machine learning for risk stratification in kidney disease. Curr Opin Nephrol Hypertens 2022; 31:548-552. [PMID: 36004937 PMCID: PMC9529795 DOI: 10.1097/mnh.0000000000000832] [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] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW Risk stratification for chronic kidney is becoming increasingly important as a clinical tool for both treatment and prevention measures. The goal of this review is to identify how machine learning tools contribute and facilitate risk stratification in the clinical setting. RECENT FINDINGS The two key machine learning paradigms to predictively stratify kidney disease risk are genomics-based and electronic health record based approaches. These methods can provide both quantitative information such as relative risk and qualitative information such as characterizing risk by subphenotype. SUMMARY The four key methods to stratify chronic kidney disease risk are genomics, multiomics, supervised and unsupervised machine learning methods. Polygenic risk scores utilize whole genome sequencing data to generate an individual's relative risk compared with the population. Multiomic methods integrate information from multiple biomarkers to generate trajectories and prognostic different outcomes. Supervised machine learning methods can directly utilize the growing compendia of electronic health records such as laboratory results and notes to generate direct risk predictions, while unsupervised machine learning methods can cluster individuals with chronic kidney disease into subphenotypes with differing approaches to care.
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12
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A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients. Cancers (Basel) 2022; 14:cancers14092111. [PMID: 35565241 PMCID: PMC9103317 DOI: 10.3390/cancers14092111] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice. Abstract Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.
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13
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Whitcher B, Thanaj M, Cule M, Liu Y, Basty N, Sorokin EP, Bell JD, Thomas EL. Precision MRI phenotyping enables detection of small changes in body composition for longitudinal cohorts. Sci Rep 2022; 12:3748. [PMID: 35260612 PMCID: PMC8904801 DOI: 10.1038/s41598-022-07556-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 02/18/2022] [Indexed: 12/23/2022] Open
Abstract
Longitudinal studies provide unique insights into the impact of environmental factors and lifespan issues on health and disease. Here we investigate changes in body composition in 3088 free-living participants, part of the UK Biobank in-depth imaging study. All participants underwent neck-to-knee MRI scans at the first imaging visit and after approximately two years (second imaging visit). Image-derived phenotypes for each participant were extracted using a fully-automated image processing pipeline, including volumes of several tissues and organs: liver, pancreas, spleen, kidneys, total skeletal muscle, iliopsoas muscle, visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue, as well as fat and iron content in liver, pancreas and spleen. Overall, no significant changes were observed in BMI, body weight, or waist circumference over the scanning interval, despite some large individual changes. A significant decrease in grip strength was observed, coupled to small, but statistically significant, decrease in all skeletal muscle measurements. Significant increases in VAT and intermuscular fat in the thighs were also detected in the absence of changes in BMI, waist circumference and ectopic-fat deposition. Adjusting for disease status at the first imaging visit did not have an additional impact on the changes observed. In summary, we show that even after a relatively short period of time significant changes in body composition can take place, probably reflecting the obesogenic environment currently inhabited by most of the general population in the United Kingdom.
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Affiliation(s)
- Brandon Whitcher
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Marjola Thanaj
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Madeleine Cule
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Yi Liu
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Nicolas Basty
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Elena P Sorokin
- Calico Life Sciences LLC, South San Francisco, California, USA
| | - Jimmy D Bell
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - E Louise Thomas
- Research Centre for Optimal Health, School of Life Sciences, University of Westminster, London, UK.
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14
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Park NH. The Chromosomes and the Kidney. PHYSICIAN ASSISTANT CLINICS 2022. [DOI: 10.1016/j.cpha.2021.11.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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15
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African Genomic Medicine Portal: A Web Portal for Biomedical Applications. J Pers Med 2022; 12:jpm12020265. [PMID: 35207753 PMCID: PMC8879570 DOI: 10.3390/jpm12020265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/22/2022] [Accepted: 01/26/2022] [Indexed: 11/17/2022] Open
Abstract
Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.
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16
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Horimoto AR, Xue D, Cai J, Lash JP, Daviglus ML, Franceschini N, Thornton TA. Genome-Wide Admixture Mapping of Estimated Glomerular Filtration Rate and Chronic Kidney Disease Identifies European and African Ancestry-of-Origin Loci in Hispanic and Latino Individuals in the United States. J Am Soc Nephrol 2022; 33:77-87. [PMID: 34670813 PMCID: PMC8763178 DOI: 10.1681/asn.2021050617] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 09/08/2021] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Admixture mapping is a powerful approach for gene mapping of complex traits that leverages the diverse genetic ancestry in populations with recent admixture, such as Hispanic or Latino individuals in the United States. These individuals have an increased risk of CKD. METHODS We performed genome-wide admixture mapping for both CKD and eGFR in a sample of 12,601 participants from the Hispanic Community Health Study/Study of Latinos, with validation in a sample of 8191 Black participants from the Women's Health Initiative (WHI). We also compared the findings with those from a conventional genome-wide association study. RESULTS Three novel ancestry-of-origin loci were identified on chromosomes 2, 14, and 15 for CKD and eGFR. The chromosome 2 locus comprises two European ancestry regions encompassing the FSHR and NRXN1 genes, with European ancestry at this locus associated with increased CKD risk. The chromosome 14 locus, found within the DLK1-DIO3 imprinted domain, was associated with lower eGFR and driven by European ancestry. The eGFR-associated locus on chromosome 15 included intronic variants of RYR3 and was within an African-specific genomic region associated with higher eGFR. The genome-wide association study failed to identify significant associations in these regions. We validated the chromosome 14 and 15 loci associated with eGFR in the WHI Black participants. CONCLUSIONS This study provides evidence of shared ancestry-specific genomic regions influencing eGFR in Hispanic or Latino individuals and Black individuals and illustrates the potential for leveraging genetic ancestry in recently admixed populations for the discovery of novel candidate loci for kidney phenotypes.
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Affiliation(s)
| | - Diane Xue
- Institute for Public Health Genetics, University of Washington, Seattle, Washington
| | - Jianwen Cai
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - James P. Lash
- Department of Medicine, University of Illinois at Chicago, Chicago, Illinois
| | - Martha L. Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, Illinois
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - Timothy A. Thornton
- Department of Biostatistics, University of Washington, Seattle, Washington,Department of Statistics, University of Washington, Seattle, Washington
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17
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Wilke RA, Larson EA. Air, Land, and Sea: Gene-Environment Interaction in Chronic Disease. Am J Med 2021; 134:1476-1482. [PMID: 34343516 PMCID: PMC8922305 DOI: 10.1016/j.amjmed.2021.07.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 06/29/2021] [Accepted: 07/05/2021] [Indexed: 11/28/2022]
Abstract
Each of us reflects a unique convergence of DNA and the environment. Over the past 2 decades, huge biobanks linked to electronic medical records have positioned the clinical and scientific communities to understand the complex genetic architecture underlying many common diseases. Although these efforts are producing increasingly accurate gene-based risk prediction algorithms for use in routine clinical care, the algorithms often fail to include environmental factors. This review explores the concept of heritability (genetic vs nongenetic determinants of disease), with emphasis on the role of environmental factors as risk determinants for common complex diseases influenced by air and water quality. Efforts to define patient exposure to specific toxicants in practice-based data sets will deepen our understanding of diseases with low heritability, and improved land management practices will reduce the burden of disease.
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Affiliation(s)
- Russell A Wilke
- Professor and Vice Chair, Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls; Professor and Chair, Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls.
| | - Eric A Larson
- Professor and Vice Chair, Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls; Professor and Chair, Department of Internal Medicine, Sanford School of Medicine, University of South Dakota, Sioux Falls
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18
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Saracyn M, Kisiel B, Franaszczyk M, Brodowska-Kania D, Żmudzki W, Małecki R, Niemczyk L, Dyrla P, Kamiński G, Płoski R, Niemczyk S. Diabetic kidney disease: Are the reported associations with single-nucleotide polymorphisms disease-specific? World J Diabetes 2021; 12:1765-1777. [PMID: 34754377 PMCID: PMC8554375 DOI: 10.4239/wjd.v12.i10.1765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/26/2021] [Accepted: 09/07/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The genetic backgrounds of diabetic kidney disease (DKD) and end-stage kidney disease (ESKD) have not been fully elucidated. AIM To examine the individual and cumulative effects of single-nucleotide polymorphisms (SNPs) previously associated with DKD on the risk for ESKD of diabetic etiology and to determine if any associations observed were specific for DKD. METHODS Fourteen SNPs were genotyped in hemodialyzed 136 patients with diabetic ESKD (DKD group) and 121 patients with non-diabetic ESKD (NDKD group). Patients were also re-classified on the basis of the primary cause of chronic kidney disease (CKD). The distribution of alleles was compared between diabetic and non-diabetic groups as well as between different sub-phenotypes. The weighted multilocus genetic risk score (GRS) was calculated to estimate the cumulative risk conferred by all SNPs. The GRS distribution was then compared between the DKD and NDKD groups as well as in the groups according to the primary cause of CKD. RESULTS One SNP (rs841853; SLC2A1) showed a nominal association with DKD (P = 0.048; P > 0.05 after Bonferroni correction). The GRS was higher in the DKD group (0.615 ± 0.260) than in the NDKD group (0.590 ± 0.253), but the difference was not significant (P = 0.46). The analysis of associations between GRS and individual factors did not show any significant correlation. However, the GRS was significantly higher in patients with glomerular disease than in those with tubulointerstitial disease (P = 0.014) and in those with a combined group (tubulointerstitial, vascular, and cystic and congenital disease) (P = 0.018). CONCLUSION Our results suggest that selected SNPs that were previously associated with DKD may not be specific for DKD and may confer risk for CKD of different etiology, particularly those affecting renal glomeruli.
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Affiliation(s)
- Marek Saracyn
- Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland
- Department of Endocrinology and Isotope Therapy, Military Institute of Medicine, Warsaw 04-141, Poland
| | - Bartłomiej Kisiel
- Clinical Research Support Center, Military Institute of Medicine, Warsaw 04-141, Poland
| | - Maria Franaszczyk
- Department of Medical Biology, Molecular Biology Laboratory, Institute of Cardiology, Warsaw 04-628, Poland
| | - Dorota Brodowska-Kania
- Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland
- Department of Endocrinology and Isotope Therapy, Military Institute of Medicine, Warsaw 04-141, Poland
| | - Wawrzyniec Żmudzki
- Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland
| | - Robert Małecki
- Department of Nephrology, Międzyleski Specialist Hospital in Warsaw, Warsaw 04-749, Poland
| | - Longin Niemczyk
- Department of Nephrology, Dialysis and Internal Diseases, Warsaw Medical University, Warsaw 02-097, Poland
| | - Przemysław Dyrla
- Department of Gastroenterology, Military Institute of Medicine, Warsaw 04-141, Poland
| | - Grzegorz Kamiński
- Department of Endocrinology and Isotope Therapy, Military Institute of Medicine, Warsaw 04-141, Poland
| | - Rafał Płoski
- Department of Medical Genetics, Medical University of Warsaw, Warsaw 02-106, Poland
| | - Stanisław Niemczyk
- Department of Internal Diseases, Nephrology and Dialysis, Military Institute of Medicine, Warsaw 04-141, Poland
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19
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Ali I, Ibrahim ST, Chinnadurai R, Green D, Taal M, Whetton TD, Kalra PA. A Paradigm to Discover Biomarkers Associated With Chronic Kidney Disease Progression. Biomark Insights 2020; 15:1177271920976146. [PMID: 33311975 PMCID: PMC7716058 DOI: 10.1177/1177271920976146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 11/02/2020] [Indexed: 11/16/2022] Open
Abstract
Biomarker discovery in the field of risk prediction in chronic kidney disease (CKD) embraces the prospect of improving our ability to risk stratify future adverse outcomes and thereby guide patient care in a new era of personalised medicine. However, many studies that report biomarkers predictive of CKD progression share a key methodological limitation: failure to characterise patients' renal progression precisely. This weakens any observable association between a biomarker and an outcome poorly defined by a patient's change in renal function over time. In this commentary, we discuss the need for a better approach in this research arena and describe a compelling strategy that has the advantage of offering robust and meaningful biomarker exploration relevant to CKD progression.
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Affiliation(s)
- Ibrahim Ali
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.,Division of Cardiovascular Sciences, University of Manchester, Manchester, UK
| | - Sara T Ibrahim
- Internal Medicine and Nephrology Department, University of Alexandria, Alexandria, Egypt
| | - Rajkumar Chinnadurai
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK
| | - Darren Green
- Department of Acute Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK
| | - Maarten Taal
- Faculty of Medicine and Health Sciences, University of Nottingham, Nottingham, UK
| | - Tony D Whetton
- Division of Cancer Sciences, University of Manchester, Manchester, UK
| | - Philip A Kalra
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Stott Lane, Salford, UK.,Division of Cardiovascular Sciences, University of Manchester, Manchester, UK
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20
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Stone RC, Chen V, Burgess J, Pannu S, Tomic-Canic M. Genomics of Human Fibrotic Diseases: Disordered Wound Healing Response. Int J Mol Sci 2020; 21:ijms21228590. [PMID: 33202590 PMCID: PMC7698326 DOI: 10.3390/ijms21228590] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 11/08/2020] [Accepted: 11/11/2020] [Indexed: 02/06/2023] Open
Abstract
Fibrotic disease, which is implicated in almost half of all deaths worldwide, is the result of an uncontrolled wound healing response to injury in which tissue is replaced by deposition of excess extracellular matrix, leading to fibrosis and loss of organ function. A plethora of genome-wide association studies, microarrays, exome sequencing studies, DNA methylation arrays, next-generation sequencing, and profiling of noncoding RNAs have been performed in patient-derived fibrotic tissue, with the shared goal of utilizing genomics to identify the transcriptional networks and biological pathways underlying the development of fibrotic diseases. In this review, we discuss fibrosing disorders of the skin, liver, kidney, lung, and heart, systematically (1) characterizing the initial acute injury that drives unresolved inflammation, (2) identifying genomic studies that have defined the pathologic gene changes leading to excess matrix deposition and fibrogenesis, and (3) summarizing therapies targeting pro-fibrotic genes and networks identified in the genomic studies. Ultimately, successful bench-to-bedside translation of observations from genomic studies will result in the development of novel anti-fibrotic therapeutics that improve functional quality of life for patients and decrease mortality from fibrotic diseases.
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Affiliation(s)
- Rivka C. Stone
- Wound Healing and Regenerative Medicine Research Program, Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami-Miller School of Medicine, Miami, FL 33136, USA; (V.C.); (J.B.)
- Correspondence: (R.C.S.); (M.T.-C.)
| | - Vivien Chen
- Wound Healing and Regenerative Medicine Research Program, Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami-Miller School of Medicine, Miami, FL 33136, USA; (V.C.); (J.B.)
| | - Jamie Burgess
- Wound Healing and Regenerative Medicine Research Program, Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami-Miller School of Medicine, Miami, FL 33136, USA; (V.C.); (J.B.)
- Medical Scientist Training Program in Biomedical Sciences, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Sukhmani Pannu
- Department of Dermatology, Tufts Medical Center, Boston, MA 02116, USA;
| | - Marjana Tomic-Canic
- Wound Healing and Regenerative Medicine Research Program, Dr. Phillip Frost Department of Dermatology and Cutaneous Surgery, University of Miami-Miller School of Medicine, Miami, FL 33136, USA; (V.C.); (J.B.)
- John P. Hussman Institute for Human Genomics, University of Miami-Miller School of Medicine, Miami, FL 33136, USA
- Correspondence: (R.C.S.); (M.T.-C.)
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21
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Abstract
The past few years have seen major advances in genome-wide association studies (GWAS) of CKD and kidney function-related traits in several areas: increases in sample size from >100,000 to >1 million, enabling the discovery of >250 associated genetic loci that are highly reproducible; the inclusion of participants not only of European but also of non-European ancestries; and the use of advanced computational methods to integrate additional genomic and other unbiased, high-dimensional data to characterize the underlying genetic architecture and prioritize potentially causal genes and variants. Together with other large-scale biobank and genetic association studies of complex traits, these GWAS of kidney function-related traits have also provided novel insight into the relationship of kidney function to other diseases with respect to their genetic associations, genetic correlation, and directional relationships. A number of studies also included functional experiments using model organisms or cell lines to validate prioritized potentially causal genes and/or variants. In this review article, we will summarize these recent GWAS of CKD and kidney function-related traits, explain approaches for downstream characterization of associated genetic loci and the value of such computational follow-up analyses, and discuss related challenges along with potential solutions to ultimately enable improved treatment and prevention of kidney diseases through genetics.
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Affiliation(s)
- Adrienne Tin
- Division of Nephrology, University of Mississippi Medical Center, Jackson, Mississippi
- MIND Center, University of Mississippi Medical Center, Jackson, Mississippi
| | - Anna Köttgen
- MIND Center, University of Mississippi Medical Center, Jackson, Mississippi
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center–University of Freiburg, Freiburg, Germany
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22
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Batai K, Hooker S, Kittles RA. Leveraging genetic ancestry to study health disparities. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2020; 175:363-375. [PMID: 32935870 PMCID: PMC8246846 DOI: 10.1002/ajpa.24144] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Revised: 07/22/2020] [Accepted: 08/20/2020] [Indexed: 12/14/2022]
Abstract
Research to understand human genomic variation and its implications in health has great potential to contribute in the reduction of health disparities. Biological anthropology can play important roles in genomics and health disparities research using a biocultural approach. This paper argues that racial/ethnic categories should not be used as a surrogate for sociocultural factors or global genomic clusters in biomedical research or clinical settings, because of the high genetic heterogeneity that exists within traditional racial/ethnic groups. Genetic ancestry is used to show variation in ancestral genomic contributions to recently admixed populations in the United States, such as African Americans and Hispanic/Latino Americans. Genetic ancestry estimates are also used to examine the relationship between ancestry‐related biological and sociocultural factors affecting health disparities. To localize areas of genomes that contribute to health disparities, admixture mapping and genome‐wide association studies (GWAS) are often used. Recent GWAS have identified many genetic variants that are highly differentiated among human populations that are associated with disease risk. Some of these are population‐specific variants. Many of these variants may impact disease risk and help explain a portion of the difference in disease burden among racial/ethnic groups. Genetic ancestry is also of particular interest in precision medicine and disparities in drug efficacy and outcomes. By using genetic ancestry, we can learn about potential biological differences that may contribute to the heterogeneity observed across self‐reported racial groups. Special Issue: Race reconciled II: Interpreting and communicating biological variation and race in 2021
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Affiliation(s)
- Ken Batai
- Department of Urology, University of Arizona, Tucson, Arizona, USA
| | - Stanley Hooker
- Division of Health Equities, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California, USA
| | - Rick A Kittles
- Division of Health Equities, Department of Population Sciences, City of Hope Comprehensive Cancer Center, Duarte, California, USA
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23
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Li J, Wu X, Luo M, Yan M, Wang Q, Li P, Niu W. Association of Serum Thyroid Hormones with the Risk and Severity of Chronic Kidney Disease Among 3563 Chinese Adults. Med Sci Monit 2020; 26:e922910. [PMID: 32569261 PMCID: PMC7331475 DOI: 10.12659/msm.922910] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background Chronic kidney disease (CKD) is a global health problem with an increasing prevalence. We explored the association of serum thyroid hormones with the risk and severity of CKD among Chinese adults. Material/Methods This retrospective study involved 3563 participants. CKD was diagnosed according to the clinical practice guidelines of the 2012 Kidney Disease Improving Global Outcomes guidelines. Effect-size estimates are expressed as odds ratio (OR) and 95% confidence interval (CI). Results Given the strong magnitude of correlation, only 3 thyroid hormones were analyzed: free triiodothyronine (FT3), free thyroxin (FT4), and thyroid-stimulating hormone (TSH). After propensity score matching on age, sex, diabetes, and hypertension, per 0.2 pg/mL increase in FT3 was significantly associated with 35–38% reduced risk of CKD at stage 1–4, and per 0.3 ng/dL increase in FT4 was only significantly associated with 21% reduced risk of CKD at stage 5 (OR, 95% CI: 0.79, 0.69–0.89), and per 0.5 μIU/mL increment in TSH increased the risk of CKD stage 5 by 8% (1.08, 1.02–1.14). Importantly, 3 thyroid hormones acted interactively, particularly for the interaction between FT3 and FT4 in predicting CKD at stage 5 (OR, 95% CI: 1.81, 1.30–2.55 for high FT3-low FT4, 17.72, 7.18–43.74 for low FT3-high FT4, and 22.28, 9.68–51.30 for low FT3-low FT4). Conclusions Our findings indicate that serum FT3 can be used as an early-stage biomarker for CKD, and FT4 and TSH can be used as advanced-stage biomarkers among Chinese adults.
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Affiliation(s)
- Jialin Li
- Graduate School, Beijing University of Chinese Medicine, Beijing, China (mainland)
| | - Xi'ai Wu
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China (mainland)
| | - Minjing Luo
- Graduate School, Beijing University of Chinese Medicine, Beijing, China (mainland)
| | - Meihua Yan
- Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China (mainland)
| | - Qian Wang
- Graduate School, Beijing University of Chinese Medicine, Beijing, China (mainland)
| | - Ping Li
- Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China (mainland)
| | - Wenquan Niu
- Institute of Clinical Medical Sciences, China-Japan Friendship Hospital, Beijing, China (mainland)
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24
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Abstract
PURPOSE OF REVIEW Accurate risk stratification in patients with chronic kidney disease (CKD) is highly desirable to help guide earlier, targeted treatment in high-risk individuals. In this review, we report recent developments in our understanding of risk factors and risk prediction in patients with CKD. RECENT FINDINGS A large meta-analysis has shown that conventional cardiovascular risk factors continue to play an important role in disease progression and adverse outcomes in patients with advanced CKD where the estimated glomerular filtration rate (eGFR) is < 30 ml/min/1.73 m). Several studies have shed light on novel biomarkers in CKD, including peptides (LG1 M), genes (MUC1) and metabolic factors (urinary oxalate excretion). Cortical oxygenation measured by BOLD-MRI also provides a novel radiological measure predictive of future eGFR decline. A new risk prediction score for patients with CKD G4-5 has been developed, offering an aid to decision-making in these patients. SUMMARY Ongoing work across various disciplines continues to unravel the determinants of CKD progression. A few notable risk prediction tools in CKD have now surfaced but whether they can be utilised to offer improved care remains a key unanswered question.
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25
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Tan X, Tang H, Gong L, Xie L, Lei Y, Luo Z, He C, Ma J, Han S. Integrating Genome-Wide Association Studies and Gene Expression Profiles With Chemical-Genes Interaction Networks to Identify Chemicals Associated With Colorectal Cancer. Front Genet 2020; 11:385. [PMID: 32391058 PMCID: PMC7193025 DOI: 10.3389/fgene.2020.00385] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 03/27/2020] [Indexed: 12/14/2022] Open
Abstract
Colorectal cancer (CRC) is the third most common cancer and has the second highest mortality rate in global cancer. Exploring the associations between chemicals and CRC has great significance in prophylaxis and therapy of tumor diseases. This study aims to explore the relationships between CRC and environmental chemicals on genetic basis by bioinformatics analysis. The genome-wide association study (GWAS) datasets for CRC were obtained from the UK Biobank. The GWAS data for colon cancer (category C18) includes 2,581 individuals and 449,683 controls, while that of rectal cancer (category C20) includes 1,244 individuals and 451,020 controls. In addition, we derived CRC gene expression datasets from the NCBI-GEO (GSE106582). The chemicals related gene sets were acquired from the comparative toxicogenomics database (CTD). Transcriptome-wide association study (TWAS) analysis was applied to CRC GWAS summary data and calculated the expression association testing statistics by FUSION software. We performed chemicals related gene set enrichment analysis (GSEA) by integrating GWAS summary data, mRNA expression profiles of CRC and the CTD chemical-gene interaction networks to identify relationships between chemicals and genes of CRC. We observed several significant correlations between chemicals and CRC. Meanwhile, we also detected 5 common chemicals between colon and rectal cancer, including methylnitronitrosoguanidine, isoniazid, PD 0325901, sulindac sulfide, and importazole. Our study performed TWAS and GSEA analysis, linked prior knowledge to newly generated data and thereby helped identifying chemicals related to tumor genes, which provides new clues for revealing the associations between environmental chemicals and cancer.
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Affiliation(s)
- Xinyue Tan
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Hanmin Tang
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Liuyun Gong
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Lina Xie
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Yutiantian Lei
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Zhenzhen Luo
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Chenchen He
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Jinlu Ma
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
| | - Suxia Han
- Department of Oncology, The First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
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26
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Pendergrass SA, Buyske S, Jeff JM, Frase A, Dudek S, Bradford Y, Ambite JL, Avery CL, Buzkova P, Deelman E, Fesinmeyer MD, Haiman C, Heiss G, Hindorff LA, Hsu CN, Jackson RD, Lin Y, Le Marchand L, Matise TC, Monroe KR, Moreland L, North KE, Park SL, Reiner A, Wallace R, Wilkens LR, Kooperberg C, Ritchie MD, Crawford DC. A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans. PLoS One 2019; 14:e0226771. [PMID: 31891604 PMCID: PMC6938343 DOI: 10.1371/journal.pone.0226771] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/03/2019] [Indexed: 12/11/2022] Open
Abstract
We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.
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Affiliation(s)
| | - Steven Buyske
- Department of Statistics, Rutgers University, Piscataway, New Jersey, United States of America
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Janina M. Jeff
- Illumina, Inc., San Diego, California, United States of America
| | - Alex Frase
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Scott Dudek
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jose-Luis Ambite
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | - Christy L. Avery
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Ewa Deelman
- Information Sciences Institute; University of Southern California, Marina del Rey, California, United States of America
| | | | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Gerardo Heiss
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Lucia A. Hindorff
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Chun-Nan Hsu
- Center for Research in Biological Systems, Department of Neurosciences, University of California, San Diego, La Jolla, California, United States of America
| | | | - Yi Lin
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Kristine R. Monroe
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Larry Moreland
- University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Kari E. North
- Department of Epidemiology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Sungshim L. Park
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, California, United States of America
| | - Alex Reiner
- Department of Epidemiology, University of Washington, Seattle, Washington, United States of America
| | - Robert Wallace
- Departments of Epidemiology and Internal Medicine, University of Iowa, Iowa City, Iowa, United States of America
| | - Lynne R. Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Dana C. Crawford
- Cleveland Institute for Computational Biology, Cleveland, Ohio, United States of America
- Departments of Population and Quantitative Health Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, United States of America
- * E-mail:
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