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Levi H, Elkon R, Shamir R. The predictive capacity of polygenic risk scores for disease risk is only moderately influenced by imputation panels tailored to the target population. Bioinformatics 2024; 40:btae036. [PMID: 38265251 PMCID: PMC10868313 DOI: 10.1093/bioinformatics/btae036] [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: 08/31/2023] [Revised: 12/20/2023] [Accepted: 01/20/2024] [Indexed: 01/25/2024] Open
Abstract
MOTIVATION Polygenic risk scores (PRSs) predict individuals' genetic risk of developing complex diseases. They summarize the effect of many variants discovered in genome-wide association studies (GWASs). However, to date, large GWASs exist primarily for the European population and the quality of PRS prediction declines when applied to other ethnicities. Genetic profiling of individuals in the discovery set (on which the GWAS was performed) and target set (on which the PRS is applied) is typically done by SNP arrays that genotype a fraction of common SNPs. Therefore, a key step in GWAS analysis and PRS calculation is imputing untyped SNPs using a panel of fully sequenced individuals. The imputation results depend on the ethnic composition of the imputation panel. Imputing genotypes with a panel of individuals of the same ethnicity as the genotyped individuals typically improves imputation accuracy. However, there has been no systematic investigation into the influence of the ethnic composition of imputation panels on the accuracy of PRS predictions when applied to ethnic groups that differ from the population used in the GWAS. RESULTS We estimated the effect of imputation of the target set on prediction accuracy of PRS when the discovery and the target sets come from different ethnic groups. We analyzed binary phenotypes on ethnically distinct sets from the UK Biobank and other resources. We generated ethnically homogenous panels, imputed the target sets, and generated PRSs. Then, we assessed the prediction accuracy obtained from each imputation panel. Our analysis indicates that using an imputation panel matched to the ethnicity of the target population yields only a marginal improvement and only under specific conditions. AVAILABILITY AND IMPLEMENTATION The source code used for executing the analyses is this paper is available at https://github.com/Shamir-Lab/PRS-imputation-panels.
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Affiliation(s)
- Hagai Levi
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ron Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
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Lau W, Ali A, Maude H, Andrew T, Swallow DM, Maniatis N. The hazards of genotype imputation when mapping disease susceptibility variants. Genome Biol 2024; 25:7. [PMID: 38172955 PMCID: PMC10763476 DOI: 10.1186/s13059-023-03140-3] [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: 11/08/2022] [Accepted: 12/04/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND The cost-free increase in statistical power of using imputation to infer missing genotypes is undoubtedly appealing, but is it hazard-free? This case study of three type-2 diabetes (T2D) loci demonstrates that it is not; it sheds light on why this is so and raises concerns as to the shortcomings of imputation at disease loci, where haplotypes differ between cases and reference panel. RESULTS T2D-associated variants were previously identified using targeted sequencing. We removed these significantly associated SNPs and used neighbouring SNPs to infer them by imputation. We compared imputed with observed genotypes, examined the altered pattern of T2D-SNP association, and investigated the cause of imputation errors by studying haplotype structure. Most T2D variants were incorrectly imputed with a low density of scaffold SNPs, but the majority failed to impute even at high density, despite obtaining high certainty scores. Missing and discordant imputation errors, which were observed disproportionately for the risk alleles, produced monomorphic genotype calls or false-negative associations. We show that haplotypes carrying risk alleles are considerably more common in the T2D cases than the reference panel, for all loci. CONCLUSIONS Imputation is not a panacea for fine mapping, nor for meta-analysing multiple GWAS based on different arrays and different populations. A total of 80% of the SNPs we have tested are not included in array platforms, explaining why these and other such associated variants may previously have been missed. Regardless of the choice of software and reference haplotypes, imputation drives genotype inference towards the reference panel, introducing errors at disease loci.
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Affiliation(s)
- Winston Lau
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, UK
| | - Aminah Ali
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, UK
| | - Hannah Maude
- Department of Metabolism, Digestion and Reproduction, Section of Genetics and Genomics, London, UK
| | - Toby Andrew
- Department of Metabolism, Digestion and Reproduction, Section of Genetics and Genomics, London, UK
| | - Dallas M Swallow
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, UK
| | - Nikolas Maniatis
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, UK.
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Shi M, Tanikawa C, Munter HM, Akiyama M, Koyama S, Tomizuka K, Matsuda K, Lathrop GM, Terao C, Koido M, Kamatani Y. Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels. Brief Bioinform 2023; 25:bbad509. [PMID: 38221906 PMCID: PMC10788679 DOI: 10.1093/bib/bbad509] [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: 07/14/2023] [Revised: 11/20/2023] [Accepted: 12/13/2023] [Indexed: 01/16/2024] Open
Abstract
Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value.
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Affiliation(s)
- Mingyang Shi
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Chizu Tanikawa
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Hans Markus Munter
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Masato Akiyama
- Department of Ocular Pathology and Imaging Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Satoshi Koyama
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Kohei Tomizuka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
| | - Gregory Mark Lathrop
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University, Montreal, Québec, Canada
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yoichiro Kamatani
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
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Yang L, Lin Z, Gao Y, Zhang J, Peng H, Li Y, Che J, Zhao L, Zhang J. Populational pan-ethnic screening panel enabled by deep whole genome sequencing. NPJ Genom Med 2023; 8:38. [PMID: 37985665 PMCID: PMC10661700 DOI: 10.1038/s41525-023-00383-8] [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: 06/19/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
Birth defect is a global threat to the public health systems. Mitigating neonatal anomalies is hampered by elusive molecular mechanisms of pathogenic mutations and poor subsequent translation into preventative measures. Applying appropriate strategies in China to promote reproductive health is particularly challenging, as the Chinese population compromises complex genomic diversity due to the inclusion of many ethnic groups with distinct genetic backgrounds. To investigate and evaluate the feasibility of implementing a pan-ethnic screening strategy, and guide future reproductive counselling, high-quality variants associated with autosome recessive (AR) diseases derived from the largest publicly available cohort of the Chinese population were re-analysed using a bottom-up approach. The analyses of gene carrier rates (GCRs) across distinct ethnic groups revealed that substantial heterogeneity existed potentially due to diverse evolutionary selection. The sampling population, sequencing coverage and underlying population structure contributed to the differential variants observed between ChinaMAP and the East Asian group in gnomAD. Beyond characteristics of GCR, potential druggable targets were additionally explored according to genomic features and functional roles of investigated genes, demonstrating that phase separation could be a therapeutic target for autosomal recessive diseases. A further examination of estimated GCR across ethnic groups indicated that most genes shared by at least two populations could be utilised to direct the design of a pan-ethnic screening application once sequencing and interpreting costs become negligible. To this end, a list of autosomal recessive disease genes is proposed based on the prioritised rank of GCR to formulate a tiered screening strategy.
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Affiliation(s)
- Linfeng Yang
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, BGI-Shijiazhuang Medical Laboratory, Shijiazhuang, China
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - Zhe Lin
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, BGI-Shijiazhuang Medical Laboratory, Shijiazhuang, China
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - Yong Gao
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, BGI-Shijiazhuang Medical Laboratory, Shijiazhuang, China
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - Jianguo Zhang
- Hebei Industrial Technology Research Institute of Genomics in Maternal and Child Health, BGI-Shijiazhuang Medical Laboratory, Shijiazhuang, China
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | | | - Yaqing Li
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | | | - Lijian Zhao
- BGI Genomics, BGI-Shenzhen, Shenzhen, China.
- Medical Technology College of Hebei Medical University, Shijiazhuang, China.
| | - Jilin Zhang
- Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong S.A.R, China.
- Department of Precision Diagnostic and Therapeutic Technology, The City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, China.
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong S.A.R, China.
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Ba R, Durand A, Mauduit V, Chauveau C, Le Bas-Bernardet S, Salle S, Guérif P, Morin M, Petit C, Douillard V, Rousseau O, Blancho G, Kerleau C, Vince N, Giral M, Gourraud PA, Limou S. KiT-GENIE, the French genetic biobank of kidney transplantation. Eur J Hum Genet 2023; 31:1291-1299. [PMID: 36737541 PMCID: PMC10620190 DOI: 10.1038/s41431-023-01294-z] [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: 10/04/2022] [Revised: 12/16/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
KiT-GENIE is a monocentric DNA biobank set up to consolidate the very rich and homogeneous DIVAT French cohort of kidney donors and recipients (D/R) in order to explore the molecular factors involved in kidney transplantation outcomes. We collected DNA samples for kidney transplantations performed in Nantes, and we leveraged GWAS genotyping data for securing high-quality genetic data with deep SNP and HLA annotations through imputations and for inferring D/R genetic ancestry. Overall, the biobank included 4217 individuals (n = 1945 D + 2,272 R, including 1969 D/R pairs), 7.4 M SNPs and over 200 clinical variables. KiT-GENIE represents an accurate snapshot of kidney transplantation clinical practice in Nantes between 2002 and 2018, with an enrichment in living kidney donors (17%) and recipients with focal segmental glomerulosclerosis (4%). Recipients were predominantly male (63%), of European ancestry (93%), with a mean age of 51yo and 86% experienced their first graft over the study period. D/R pairs were 93% from European ancestry, and 95% pairs exhibited at least one HLA allelic mismatch. The mean follow-up time was 6.7 years with a hindsight up to 25 years. Recipients experienced biopsy-proven rejection and graft loss for 16.6% and 21.3%, respectively. KiT-GENIE constitutes one of the largest kidney transplantation genetic cohorts worldwide to date. It includes homogeneous high-quality clinical and genetic data for donors and recipients, hence offering a unique opportunity to investigate immunogenetic and genetic factors, as well as donor-recipient interactions and mismatches involved in rejection, graft survival, primary disease recurrence and other comorbidities.
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Affiliation(s)
- Rokhaya Ba
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Axelle Durand
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Vincent Mauduit
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Christine Chauveau
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Stéphanie Le Bas-Bernardet
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Sonia Salle
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Pierrick Guérif
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Martin Morin
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Clémence Petit
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Venceslas Douillard
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Olivia Rousseau
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Gilles Blancho
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Clarisse Kerleau
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Nicolas Vince
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Magali Giral
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
- CHU Nantes, Nantes Université, Service de Néphrologie-Immunologie Clinique, ITUN, F-44000, Nantes, France
| | - Pierre-Antoine Gourraud
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France
| | - Sophie Limou
- Nantes Université, Centrale Nantes, CHU Nantes, Inserm, Centre de Recherche Translationnelle en Transplantation et Immunologie, UMR 1064, F-44000, Nantes, France.
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Mahmood K, Thomas M, Qu C, Hsu L, Buchanan DD, Peters U. Elucidating the Risk of Colorectal Cancer for Variants in Hereditary Colorectal Cancer Genes. Gastroenterology 2023; 165:1070-1076.e3. [PMID: 37453563 PMCID: PMC10866455 DOI: 10.1053/j.gastro.2023.06.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/07/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Khalid Mahmood
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; University of Melbourne Center for Cancer Research, Victorian Comprehensive Cancer Center, Parkville, Victoria, Australia; Melbourne Bioinformatics, The University of Melbourne, Parkville, Victoria, Australia
| | - Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Biostatistics, University of Washington, Seattle, Washington.
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia; University of Melbourne Center for Cancer Research, Victorian Comprehensive Cancer Center, Parkville, Victoria, Australia; Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, Victoria, Australia.
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington; Department of Epidemiology, University of Washington, Seattle, Washington.
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Jiang MZ, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, Wang FF, Manansala R, Gogarten SM, Li Z, Polfus LM, Salimi S, Bis JC, Pankratz N, Yanek LR, Durda P, Tracy RP, Rich SS, Rotter JI, Mitchell BD, Lewis JP, Psaty BM, Pratte KA, Silverman EK, Kaplan RC, Avery C, North K, Mathias RA, Faraday N, Lin H, Wang B, Carson AP, Norwood AF, Gibbs RA, Kooperberg C, Lundin J, Peters U, Dupuis J, Hou L, Fornage M, Benjamin EJ, Reiner AP, Bowler RP, Lin X, Auer PL, Raffield LM. Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.10.555215. [PMID: 37745480 PMCID: PMC10515765 DOI: 10.1101/2023.09.10.555215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
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Affiliation(s)
- Min-Zhi Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
| | - Sheila M. Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
- Regeneron Genetics Center, Tarrytown, NY, 10591, USA
| | - Xihao Li
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Eric Van Buren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Adrienne Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Erin Buth
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Fei Fei Wang
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Regina Manansala
- Centre for Health Economics Research & Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO) WHO Collaborating Centre, University of Antwerp, Antwerp, BE
| | | | - Zilin Li
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Linda M. Polfus
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, 90033, USA
| | - Shabnam Salimi
- Department of Epidemiology and Public Health, Division of Gerontology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA, 98195, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Lisa R. Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, 1830 E Monument St Rm 8024, Baltimore, MD, 21287, USA
| | - Peter Durda
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT, 05446, USA
| | - Russell P. Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT, 05446, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, 200 Jeanette Lancaster Way, Charlottesville, VA, 22903, 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, 1124 W. Carson Street, Torrance, CA, 90502, USA
| | - Braxton D. Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD, 21201, USA
| | - Joshua P. Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD, 21201, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA, 98195, USA
- Departments of Epidemiology and Health Systems and Population Health, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98101, USA
| | - Katherine A. Pratte
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Edwin K. Silverman
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rasika A. Mathias
- Department of Medicine, Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, 5501 Hopkins Bayview Cir JHAAC Room 3B53, Baltimore, MD, 21287, USA
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA, 01655, USA
| | - Biqi Wang
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA, 01655, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS, 39213, USA
| | - Arnita F. Norwood
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS, 39213, USA
| | - Richard A. Gibbs
- Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, H3A 1G1, Canada
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Emelia J. Benjamin
- Department of Medicine, Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, 02118, USA
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA, 01702, USA
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98105, USA
| | - Russell P. Bowler
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Paul L. Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
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8
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Shen S, Li Z, Jiang Y, Duan W, Li H, Du S, Esteller M, Shen H, Hu Z, Zhao Y, Christiani DC, Chen F. A Large-Scale Exome-Wide Association Study Identifies Novel Germline Mutations in Lung Cancer. Am J Respir Crit Care Med 2023; 208:280-289. [PMID: 37167549 PMCID: PMC10395715 DOI: 10.1164/rccm.202212-2199oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 05/11/2023] [Indexed: 05/13/2023] Open
Abstract
Rationale: Genome-wide association studies have identified common variants of lung cancer. However, the contribution of rare exome-wide variants, especially protein-coding variants, to cancers remains largely unexplored. Objectives: To evaluate the role of human exomes in genetic predisposition to lung cancer. Methods: We performed exome-wide association studies to detect the association of exomes with lung cancer in 30,312 patients and 652,902 control subjects. A scalable and accurate implementation of a generalized mixed model was used to detect the association signals for loss-of-function, missense, and synonymous variants and gene-level sets. Furthermore, we performed association and Bayesian colocalization analyses to evaluate their relationships with intermediate exposures. Measurements and Main Results: We systematically analyzed 216,739 single-nucleotide variants in the human exome. The loss-of-function variants exhibited the most notable effects on lung cancer risk. We identified four novel variants, including two missense variants (rs202197044TET3 [Pmeta (P values of meta-analysis) = 3.60 × 10-8] and rs202187871POT1 [Pmeta = 2.21 × 10-8]) and two synonymous variants (rs7447927TMEM173 [Pmeta = 1.32 × 10-9] and rs140624366ATRN [Pmeta = 2.97 × 10-9]). rs202197044TET3 was significantly associated with emphysema (odds ratio, 3.55; Pfdr = 0.015), whereas rs7447927POT1 was strongly associated with telomere length (β = 1.08; Pfdr (FDR corrected P value) = 3.76 × 10-53). Functional evidence of expression of quantitative trait loci, splicing quantitative trait loci, and isoform expression was found for the four novel genes. Gene-level association tests identified several novel genes, including POT1 (protection of telomeres 1), RTEL1, BSG, and ZNF232. Conclusions: Our findings provide insights into the genetic architecture of human exomes and their role in lung cancer predisposition.
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Affiliation(s)
- Sipeng Shen
- Department of Biostatistics and
- Jiangsu Key Lab of Cancer Biomarkers, Prevention, and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine
- China International Cooperation Center of Environment and Human Health
| | | | | | - Weiwei Duan
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, and
| | | | - Sha Du
- Department of Biostatistics and
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
- Centro de Investigacion Biomedica en Red Cancer, Madrid, Spain
- Institucio Catalana de Recerca i Estudis Avançats, Barcelona, Spain
- Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health
- Jiangsu Key Lab of Cancer Biomarkers, Prevention, and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health
- Jiangsu Key Lab of Cancer Biomarkers, Prevention, and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine
| | - Yang Zhao
- Department of Biostatistics and
- Key Laboratory of Biomedical Big Data, Nanjing Medical University, Nanjing, China
| | - David C. Christiani
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, Massachusetts; and
- Pulmonary and Critical Care Division, Massachusetts General Hospital, Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Feng Chen
- Department of Biostatistics and
- Jiangsu Key Lab of Cancer Biomarkers, Prevention, and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine
- China International Cooperation Center of Environment and Human Health
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9
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Bocher O, Willer CJ, Zeggini E. Unravelling the genetic architecture of human complex traits through whole genome sequencing. Nat Commun 2023; 14:3520. [PMID: 37316478 DOI: 10.1038/s41467-023-39259-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 06/06/2023] [Indexed: 06/16/2023] Open
Affiliation(s)
- Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany
| | - Cristen J Willer
- Department of Internal Medicine, Division of Cardiology, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Computational Medicine and Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764, Neuherberg, Germany.
- Technical University of Munich (TUM) and Klinikum Rechts der Isar, TUM School of Medicine, Ismaninger Str. 22, 81675, Munich, Germany.
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10
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Romero-Molina C, Garretti F, Andrews SJ, Marcora E, Goate AM. Microglial efferocytosis: Diving into the Alzheimer's disease gene pool. Neuron 2022; 110:3513-3533. [PMID: 36327897 DOI: 10.1016/j.neuron.2022.10.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 10/04/2022] [Accepted: 10/07/2022] [Indexed: 11/07/2022]
Abstract
Genome-wide association studies and functional genomics studies have linked specific cell types, genes, and pathways to Alzheimer's disease (AD) risk. In particular, AD risk alleles primarily affect the abundance or structure, and thus the activity, of genes expressed in macrophages, strongly implicating microglia (the brain-resident macrophages) in the etiology of AD. These genes converge on pathways (endocytosis/phagocytosis, cholesterol metabolism, and immune response) with critical roles in core macrophage functions such as efferocytosis. Here, we review these pathways, highlighting relevant genes identified in the latest AD genetics and genomics studies, and describe how they may contribute to AD pathogenesis. Investigating the functional impact of AD-associated variants and genes in microglia is essential for elucidating disease risk mechanisms and developing effective therapeutic approaches.
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Affiliation(s)
- Carmen Romero-Molina
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Francesca Garretti
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shea J Andrews
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Edoardo Marcora
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Alison M Goate
- Ronald M. Loeb Center for Alzheimer's Disease, 1 Gustave L. Levy Place, New York, NY 10029-6574, USA; Department of Genetics & Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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