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Chen AS, Liu H, Wu Y, Luo S, Patz EF, Glass C, Su L, Du M, Christiani DC, Wei Q. Genetic variants in DDO and PEX5L in peroxisome-related pathways predict non-small cell lung cancer survival. Mol Carcinog 2022; 61:619-628. [PMID: 35502931 DOI: 10.1002/mc.23400] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 03/05/2022] [Accepted: 03/10/2022] [Indexed: 01/14/2023]
Abstract
Peroxisomes play a role in lipid metabolism and regulation of reactive oxygen species, but its role in development and progression of non-small cell lung cancer (NSCLC) is not well understood. Here, we investigated the associations between 9708 single-nucleotide polymorphisms (SNPs) in 113 genes in the peroxisome-related pathways and survival of NSCLC patients from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) and the Harvard Lung Cancer Susceptibility (HLCS) study. In 1185 NSCLC patients from the PLCO trial, we found that 213 SNPs were significantly associated with NSCLC overall survival (OS) (p ≤ 0.05, Bayesian false discovery probability [BFDP] ≤ 0.80), of which eight SNPs were validated in the HLCS data set. In a multivariate Cox proportional hazards regression model, two independent SNPs (rs9384742 DDO and rs9825224 PEX5L) were significantly associated with NSCLC survival (hazards ratios [HR] of 1.17 with 95% CI [confidence interval] of 1.06-1.28 and 0.86 with 95% CI of 0.77-0.96, respectively). Patients with one or two protective genotypes had a significantly higher OS (HR: 0.787 [95% CI: 0.620-0.998] and 0.691 [95% CI: 0.543-0.879], respectively). Further expression quantitative trait loci analysis using whole blood and lung tissue showed that the minor allele of rs9384742 DDO was significantly associated with decreased messenger RNA (mRNA) expression levels and that DDO expression was also decreased in NSCLC tumor tissue. Additionally, high PEX5L expression levels were significantly associated with lower survival of NSCLC. Our data suggest that variants in these peroxisome-related genes may influence gene regulation and are potential predictors of NSCLC OS, once validated by additional studies.
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Affiliation(s)
- Allan S Chen
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Hongliang Liu
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Yufeng Wu
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA
| | - Edward F Patz
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA.,Departments of Radiology, Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina, USA
| | - Carolyn Glass
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA.,Department of Pathology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Li Su
- Departments of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Mulong Du
- Departments of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA
| | - David C Christiani
- Departments of Environmental Health and Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA.,Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Qingyi Wei
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina, USA.,Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA.,Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA.,Duke Global Health Institute, Duke University, Durham, North Carolina, USA
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52
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Multivariate genome-wide association study models to improve prediction of Crohn’s disease risk and identification of potential novel variants. Comput Biol Med 2022; 145:105398. [DOI: 10.1016/j.compbiomed.2022.105398] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 03/09/2022] [Accepted: 03/09/2022] [Indexed: 12/21/2022]
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Gong H, Han B. Genotype calling and haplotype inference from low coverage sequence data in heterozygous plant genome using HetMap. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2157-2166. [PMID: 35504967 DOI: 10.1007/s00122-022-04105-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/12/2022] [Indexed: 06/14/2023]
Abstract
This study developed a new genotyping method that can accurately infer heterozygous genotype information from the complex plant genome sequence data, which helped discover new alleles in the association studies. Many software packages and pipelines had been developed to handle the sequence data of the model species. However, Genotyping from complex heterozygous plant genome needs further improvement on the previous methods. Here we present a new pipeline available at https://github.com/Ncgrhg/HetMapv1 ) for variant calling and missing genotype imputation from low coverage sequence data for heterozygous plant genomes. To check the performance of the HetMap on the real sequence data, HetMap was applied to both the F1 hybrid rice population, which consists of 1495 samples and the wild rice population with 446 samples. The high coverage sequence data of four hybrid rice accessions and two wild rice accessions, which were also included in low coverage sequence data, were used to validate the accuracy of genotype inference. The validation results showed that HetMap archieved significant improvement in heterozygous genotype inference accuracy (13.65% for hybrid rice, 26.05% for wild rice) and total accuracy compared with similar software packages. The application of the new genotype with the genome-wide association study also showed improvement of association power in wild rice awn length phenotype. It could archive high genotype inference accuracy in low sequence coverage in a small population with both the natural and constructed recombination population. HetMap provided a powerful tool for the heterozygous plant genome sequence data analysis, which may help to discover new phenotype regions for the plant species with the complex heterozygous genome.
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Affiliation(s)
- Hao Gong
- School of Life Science, Huizhou University, Huizhou, 516007, China.
| | - Bin Han
- State Key Laboratory of Plant Molecular Genetics, National Center for Gene Research, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200233, China.
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Zeng Q, Zhao B, Wang H, Wang M, Teng M, Hu J, Bao Z, Wang Y. Aquaculture Molecular Breeding Platform (AMBP): a comprehensive web server for genotype imputation and genetic analysis in aquaculture. Nucleic Acids Res 2022; 50:W66-W74. [PMID: 35639514 PMCID: PMC9252723 DOI: 10.1093/nar/gkac424] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/19/2022] [Accepted: 05/09/2022] [Indexed: 12/26/2022] Open
Abstract
It is of vital importance to understand the population structure, dissect the genetic bases of performance traits, and make proper strategies for selection in breeding programs. However, there is no single webserver covering the specific needs in aquaculture. We present Aquaculture Molecular Breeding Platform (AMBP), the first web server for genetic data analysis in aquatic species of farming interest. AMBP integrates the haplotype reference panels of 18 aquaculture species, which greatly improves the accuracy of genotype imputation. It also supports multiple tools to infer genetic structures, dissect the genetic architecture of performance traits, estimate breeding values, and predict optimum contribution. All the tools are coherently linked in a web-interface for users to generate interpretable results and evaluate statistical appropriateness. The webserver supports standard VCF and PLINK (PED, MAP) files, and implements automated pipelines for format transformation and visualization to simplify the process of analysis. As a demonstration, we applied the webserver to Pacific white shrimp and Atlantic salmon datasets. In summary, AMBP constitutes comprehensive resources and analytical tools for exploring genetic data and guiding practical breeding programs. AMBP is available at http://mgb.qnlm.ac.
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Affiliation(s)
- Qifan Zeng
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China.,Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanog Inst, Ocean Univ China, Sanya 572000, Peoples R China.,Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Baojun Zhao
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Hao Wang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Mengqiu Wang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Mingxuan Teng
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Jingjie Hu
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China.,Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanog Inst, Ocean Univ China, Sanya 572000, Peoples R China
| | - Zhenmin Bao
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China.,Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanog Inst, Ocean Univ China, Sanya 572000, Peoples R China.,Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
| | - Yangfan Wang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China.,Laboratory for Marine Fisheries Science and Food Production Processes, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China
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55
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See GM, Fix JS, Schwab CR, Spangler ML. Imputation of non-genotyped F1 dams to improve genetic gain in swine crossbreeding programs. J Anim Sci 2022; 100:6572187. [PMID: 35451025 PMCID: PMC9126202 DOI: 10.1093/jas/skac148] [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: 12/23/2021] [Accepted: 04/20/2022] [Indexed: 11/12/2022] Open
Abstract
This study investigated using imputed genotypes from non-genotyped animals which were not in the pedigree for the purpose of genetic selection and improving genetic gain for economically relevant traits. Simulations were used to mimic a 3-breed crossbreeding system that resembled a modern swine breeding scheme. The simulation consisted of three purebred (PB) breeds A, B, and C each with 25 and 425 mating males and females, respectively. Males from A and females from B were crossed to produce AB females (n = 1,000), which were crossed with males from C to produce crossbreds (CB; n = 10,000). The genome consisted of three chromosomes with 300 quantitative trait loci and ~9,000 markers. Lowly heritable reproductive traits were simulated for A, B, and AB (h2 = 0.2, 0.2, and 0.15, respectively), whereas a moderately heritable carcass trait was simulated for C (h2 = 0.4). Genetic correlations between reproductive traits in A, B, and AB were moderate (rg = 0.65). The goal trait of the breeding program was AB performance. Selection was practiced for four generations where AB and CB animals were first produced in generations 1 and 2, respectively. Non-genotyped AB dams were imputed using FImpute beginning in generation 2. Genotypes of PB and CB were used for imputation. Imputation strategies differed by three factors: 1) AB progeny genotyped per generation (2, 3, 4, or 6), 2) known or unknown mates of AB dams, and 3) genotyping rate of females from breeds A and B (0% or 100%). PB selection candidates from A and B were selected using estimated breeding values for AB performance, whereas candidates from C were selected by phenotype. Response to selection using imputed genotypes of non-genotyped animals was then compared to the scenarios where true AB genotypes (trueGeno) or no AB genotypes/phenotypes (noGeno) were used in genetic evaluations. The simulation was replicated 20 times. The average increase in genotype concordance between unknown and known sire imputation strategies was 0.22. Genotype concordance increased as the number of genotyped CB increased with little additional gain beyond 9 progeny. When mates of AB were known and more than 4 progeny were genotyped per generation, the phenotypic response in AB did not differ (P > 0.05) from trueGeno yet was greater (P < 0.05) than noGeno. Imputed genotypes of non-genotyped animals can be used to increase performance when 4 or more progeny are genotyped and sire pedigrees of CB animals are known.
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Affiliation(s)
- Garrett M See
- Department of Animal Science, University of Nebraska - Lincoln, Lincoln, NE 68588, USA
| | | | | | - Matthew L Spangler
- Department of Animal Science, University of Nebraska - Lincoln, Lincoln, NE 68588, USA
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56
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Chen L, Yang S, Araya S, Quigley C, Taliercio E, Mian R, Specht JE, Diers BW, Song Q. Genotype imputation for soybean nested association mapping population to improve precision of QTL detection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1797-1810. [PMID: 35275252 PMCID: PMC9110473 DOI: 10.1007/s00122-022-04070-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 02/25/2022] [Indexed: 06/14/2023]
Abstract
KEY MESSAGE Software for high imputation accuracy in soybean was identified. Imputed dataset could significantly reduce the interval of genomic regions controlling traits, thus greatly improve the efficiency of candidate gene identification. Genotype imputation is a strategy to increase marker density of existing datasets without additional genotyping. We compared imputation performance of software BEAGLE 5.0, IMPUTE 5 and AlphaPlantImpute and tested software parameters that may help to improve imputation accuracy in soybean populations. Several factors including marker density, extent of linkage disequilibrium (LD), minor allele frequency (MAF), etc., were examined for their effects on imputation accuracy across different software. Our results showed that AlphaPlantImpute had a higher imputation accuracy than BEAGLE 5.0 or IMPUTE 5 tested in each soybean family, especially if the study progeny were genotyped with an extremely low number of markers. LD extent, MAF and reference panel size were positively correlated with imputation accuracy, a minimum number of 50 markers per chromosome and MAF of SNPs > 0.2 in soybean line were required to avoid a significant loss of imputation accuracy. Using the software, we imputed 5176 soybean lines in the soybean nested mapping population (NAM) with high-density markers of the 40 parents. The dataset containing 423,419 markers for 5176 lines and 40 parents was deposited at the Soybase. The imputed NAM dataset was further examined for the improvement of mapping quantitative trait loci (QTL) controlling soybean seed protein content. Most of the QTL identified were at identical or at similar position based on initial and imputed datasets; however, QTL intervals were greatly narrowed. The resulting genotypic dataset of NAM population will facilitate QTL mapping of traits and downstream applications. The information will also help to improve genotyping imputation accuracy in self-pollinated crops.
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Affiliation(s)
- Linfeng Chen
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Soybean Research Institute, Nanjing Agricultural University, Nanjing, 210095, China
| | - Shouping Yang
- National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean, Ministry of Agriculture, State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Soybean Research Institute, Nanjing Agricultural University, Nanjing, 210095, China.
| | - Susan Araya
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA
| | - Charles Quigley
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA
| | - Earl Taliercio
- Soybean and Nitrogen Fixation Research, USDA-ARS, Raleigh, NC, 27607, USA
| | - Rouf Mian
- Soybean and Nitrogen Fixation Research, USDA-ARS, Raleigh, NC, 27607, USA
| | - James E Specht
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, 68583, USA
| | - Brian W Diers
- Department of Crop Sciences, National Soybean Research Center, University of Illinois, 1101 West Peabody Drive, Urbana, IL, 61801, USA
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center, Beltsville, MD, 20705, USA.
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57
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Avery CL, Howard AG, Ballou AF, Buchanan VL, Collins JM, Downie CG, Engel SM, Graff M, Highland HM, Lee MP, Lilly AG, Lu K, Rager JE, Staley BS, North KE, Gordon-Larsen P. Strengthening Causal Inference in Exposomics Research: Application of Genetic Data and Methods. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:55001. [PMID: 35533073 PMCID: PMC9084332 DOI: 10.1289/ehp9098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 05/11/2023]
Abstract
Advances in technologies to measure a broad set of exposures have led to a range of exposome research efforts. Yet, these efforts have insufficiently integrated methods that incorporate genetic data to strengthen causal inference, despite evidence that many exposome-associated phenotypes are heritable. Objective: We demonstrate how integration of methods and study designs that incorporate genetic data can strengthen causal inference in exposomics research by helping address six challenges: reverse causation and unmeasured confounding, comprehensive examination of phenotypic effects, low efficiency, replication, multilevel data integration, and characterization of tissue-specific effects. Examples are drawn from studies of biomarkers and health behaviors, exposure domains where the causal inference methods we describe are most often applied. Discussion: Technological, computational, and statistical advances in genotyping, imputation, and analysis, combined with broad data sharing and cross-study collaborations, offer multiple opportunities to strengthen causal inference in exposomics research. Full application of these opportunities will require an expanded understanding of genetic variants that predict exposome phenotypes as well as an appreciation that the utility of genetic variants for causal inference will vary by exposure and may depend on large sample sizes. However, several of these challenges can be addressed through international scientific collaborations that prioritize data sharing. Ultimately, we anticipate that efforts to better integrate methods that incorporate genetic data will extend the reach of exposomics research by helping address the challenges of comprehensively measuring the exposome and its health effects across studies, the life course, and in varied contexts and diverse populations. https://doi.org/10.1289/EHP9098.
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Affiliation(s)
- Christy L Avery
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Annie Green Howard
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anna F Ballou
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Victoria L Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason M Collins
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Carolina G Downie
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Stephanie M Engel
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Moa P Lee
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Adam G Lilly
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Sociology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kun Lu
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Julia E Rager
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Brooke S Staley
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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58
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Genotyping, the Usefulness of Imputation to Increase SNP Density, and Imputation Methods and Tools. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2467:113-138. [PMID: 35451774 DOI: 10.1007/978-1-0716-2205-6_4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Imputation has become a standard practice in modern genetic research to increase genome coverage and improve accuracy of genomic selection and genome-wide association study as a large number of samples can be genotyped at lower density (and lower cost) and, imputed up to denser marker panels or to sequence level, using information from a limited reference population. Most genotype imputation algorithms use information from relatives and population linkage disequilibrium. A number of software for imputation have been developed originally for human genetics and, more recently, for animal and plant genetics considering pedigree information and very sparse SNP arrays or genotyping-by-sequencing data. In comparison to human populations, the population structures in farmed species and their limited effective sizes allow to accurately impute high-density genotypes or sequences from very low-density SNP panels and a limited set of reference individuals. Whatever the imputation method, the imputation accuracy, measured by the correct imputation rate or the correlation between true and imputed genotypes, increased with the increasing relatedness of the individual to be imputed with its denser genotyped ancestors and as its own genotype density increased. Increasing the imputation accuracy pushes up the genomic selection accuracy whatever the genomic evaluation method. Given the marker densities, the most important factors affecting imputation accuracy are clearly the size of the reference population and the relationship between individuals in the reference and target populations.
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59
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Sun Q, Liu W, Rosen JD, Huang L, Pace RG, Dang H, Gallins PJ, Blue EE, Ling H, Corvol H, Strug LJ, Bamshad MJ, Gibson RL, Pugh EW, Blackman SM, Cutting GR, O'Neal WK, Zhou YH, Wright FA, Knowles MR, Wen J, Li Y. Leveraging TOPMed imputation server and constructing a cohort-specific imputation reference panel to enhance genotype imputation among cystic fibrosis patients. HGG ADVANCES 2022; 3:100090. [PMID: 35128485 PMCID: PMC8804187 DOI: 10.1016/j.xhgg.2022.100090] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 01/06/2022] [Indexed: 11/25/2022] Open
Abstract
Cystic fibrosis (CF) is a severe genetic disorder that can cause multiple comorbidities affecting the lungs, the pancreas, the luminal digestive system and beyond. In our previous genome-wide association studies (GWAS), we genotyped approximately 8,000 CF samples using a mixture of different genotyping platforms. More recently, the Cystic Fibrosis Genome Project (CFGP) performed deep (approximately 30×) whole genome sequencing (WGS) of 5,095 samples to better understand the genetic mechanisms underlying clinical heterogeneity among patients with CF. For mixtures of GWAS array and WGS data, genotype imputation has proven effective in increasing effective sample size. Therefore, we first performed imputation for the approximately 8,000 CF samples with GWAS array genotype using the Trans-Omics for Precision Medicine (TOPMed) freeze 8 reference panel. Our results demonstrate that TOPMed can provide high-quality imputation for patients with CF, boosting genomic coverage from approximately 0.3-4.2 million genotyped markers to approximately 11-43 million well-imputed markers, and significantly improving polygenic risk score (PRS) prediction accuracy. Furthermore, we built a CF-specific CFGP reference panel based on WGS data of patients with CF. We demonstrate that despite having approximately 3% the sample size of TOPMed, our CFGP reference panel can still outperform TOPMed when imputing some CF disease-causing variants, likely owing to allele and haplotype differences between patients with CF and general populations. We anticipate our imputed data for 4,656 samples without WGS data will benefit our subsequent genetic association studies, and the CFGP reference panel built from CF WGS samples will benefit other investigators studying CF.
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Affiliation(s)
- Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jonathan D. Rosen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Le Huang
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rhonda G. Pace
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hong Dang
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Paul J. Gallins
- Bioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Elizabeth E. Blue
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Brotman Baty Institute, Seattle, WA 98195, USA
| | - Hua Ling
- Center for Inherited Disease Research (CIDR), Johns Hopkins University, Baltimore, MD 21205, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Harriet Corvol
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, Assistance Publique-Hôpitaux de Paris (APHP), Hôpital Trousseau, Service de Pneumologie Pédiatrique, Paris, France
| | - Lisa J. Strug
- Departments of Statistical Sciences and Computer Science and Division of Biostatistics, University of Toronto, Toronto, ON, Canada
- Program in Genetics and Genome Biology and The Centre for Applied Genomics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Michael J. Bamshad
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA
- Brotman Baty Institute, Seattle, WA 98195, USA
| | - Ronald L. Gibson
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
| | - Elizabeth W. Pugh
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Scott M. Blackman
- Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Garry R. Cutting
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Wanda K. O'Neal
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yi-Hui Zhou
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Fred A. Wright
- Bioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Michael R. Knowles
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Cystic Fibrosis Genome Project
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Bioinformatics Research Center and Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Center for Inherited Disease Research (CIDR), Johns Hopkins University, Baltimore, MD 21205, USA
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Sorbonne Université, Inserm, Centre de Recherche Saint-Antoine, Assistance Publique-Hôpitaux de Paris (APHP), Hôpital Trousseau, Service de Pneumologie Pédiatrique, Paris, France
- Departments of Statistical Sciences and Computer Science and Division of Biostatistics, University of Toronto, Toronto, ON, Canada
- Program in Genetics and Genome Biology and The Centre for Applied Genomics, The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Department of Pediatrics, University of Washington, Seattle, WA 98105, USA
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
- Division of Genetic Medicine, Seattle Children's Hospital, Seattle, WA 98105, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Biological Sciences, North Carolina State University, Raleigh, NC 27695, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Brotman Baty Institute, Seattle, WA 98195, USA
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60
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Kenny D, Carthy TR, Murphy CP, Sleator RD, Evans RD, Berry DP. The Association Between Genomic Heterozygosity and Carcass Merit in Cattle. Front Genet 2022; 13:789270. [PMID: 35281838 PMCID: PMC8908906 DOI: 10.3389/fgene.2022.789270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/25/2022] [Indexed: 12/16/2022] Open
Abstract
The objective of the present study was to quantify the association between both pedigree and genome-based measures of global heterozygosity and carcass traits, and to identify single nucleotide polymorphisms (SNPs) exhibiting non-additive associations with these traits. The carcass traits of interest were carcass weight (CW), carcass conformation (CC) and carcass fat (CF). To define the genome-based measures of heterozygosity, and to quantify the non-additive associations between SNPs and the carcass traits, imputed, high-density genotype data, comprising of 619,158 SNPs, from 27,213 cattle were used. The correlations between the pedigree-based heterosis coefficient and the three defined genomic measures of heterozygosity ranged from 0.18 to 0.76. The associations between the different measures of heterozygosity and the carcass traits were biologically small, with positive associations for CW and CC, and negative associations for CF. Furthermore, even after accounting for the pedigree-based heterosis coefficient of an animal, part of the remaining variability in some of the carcass traits could be captured by a genomic heterozygosity measure. This signifies that the inclusion of both a heterosis coefficient based on pedigree information and a genome-based measure of heterozygosity could be beneficial to limiting bias in predicting additive genetic merit. Finally, one SNP located on Bos taurus (BTA) chromosome number 5 demonstrated a non-additive association with CW. Furthermore, 182 SNPs (180 SNPs on BTA 2 and two SNPs on BTA 21) demonstrated a non-additive association with CC, while 231 SNPs located on BTA 2, 5, 11, 13, 14, 18, 19 and 21 demonstrated a non-additive association with CF. Results demonstrate that heterozygosity both at a global level and at the level of individual loci contribute little to the variability in carcass merit.
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Affiliation(s)
- David Kenny
- Animal and Grassland Research and Innovation Centre, Teagasc, Fermoy, Ireland
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | - Tara R. Carthy
- Animal and Grassland Research and Innovation Centre, Teagasc Grange, Dunsany, Ireland
| | - Craig P. Murphy
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | - Roy D. Sleator
- Department of Biological Sciences, Munster Technological University, Cork, Ireland
| | | | - Donagh P. Berry
- Animal and Grassland Research and Innovation Centre, Teagasc, Fermoy, Ireland
- *Correspondence: Donagh P. Berry,
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61
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Baud A, McPeek S, Chen N, Hughes KA. Indirect Genetic Effects: A Cross-disciplinary Perspective on Empirical Studies. J Hered 2022; 113:1-15. [PMID: 34643239 PMCID: PMC8851665 DOI: 10.1093/jhered/esab059] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Indirect genetic effects (IGE) occur when an individual's phenotype is influenced by genetic variation in conspecifics. Opportunities for IGE are ubiquitous, and, when present, IGE have profound implications for behavioral, evolutionary, agricultural, and biomedical genetics. Despite their importance, the empirical study of IGE lags behind the development of theory. In large part, this lag can be attributed to the fact that measuring IGE, and deconvoluting them from the direct genetic effects of an individual's own genotype, is subject to many potential pitfalls. In this Perspective, we describe current challenges that empiricists across all disciplines will encounter in measuring and understanding IGE. Using ideas and examples spanning evolutionary, agricultural, and biomedical genetics, we also describe potential solutions to these challenges, focusing on opportunities provided by recent advances in genomic, monitoring, and phenotyping technologies. We hope that this cross-disciplinary assessment will advance the goal of understanding the pervasive effects of conspecific interactions in biology.
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Affiliation(s)
- Amelie Baud
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,the Universitat Pompeu Fabra (UPF), Barcelona,Spain
| | - Sarah McPeek
- the Department of Biology, University of Virginia, Charlottesville, VA 22904, USA
| | - Nancy Chen
- the Department of Biology, University of Rochester, Rochester, NY 14627,USA
| | - Kimberly A Hughes
- the Department of Biological Science, Florida State University, Tallahassee, FL 32303,USA
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62
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Mdyogolo S, MacNeil MD, Neser FWC, Scholtz MM, Makgahlela ML. Assessing accuracy of genotype imputation in the Afrikaner and Brahman cattle breeds of South Africa. Trop Anim Health Prod 2022; 54:90. [PMID: 35133512 DOI: 10.1007/s11250-022-03102-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 02/01/2022] [Indexed: 11/26/2022]
Abstract
Imputation may be used to rescue genomic data from animals that would otherwise be eliminated due to a lower than desired call rate. The aim of this study was to compare the accuracy of genotype imputation for Afrikaner, Brahman, and Brangus cattle of South Africa using within- and multiple-breed reference populations. A total of 373, 309, and 101 Afrikaner, Brahman, and Brangus cattle, respectively, were genotyped using the GeneSeek Genomic Profiler 150 K panel that contained 141,746 markers. Markers with MAF ≤ 0.02 and call rates ≤ 0.95 or that deviated from Hardy Weinberg Equilibrium frequency with a probability of ≤ 0.0001 were excluded from the data as were animals with a call rate ≤ 0.90. The remaining data included 99,086 SNPs and 360 Afrikaner, 75,291 SNPs and 288 animals Brahman, and 97,897 SNPs and 99 Brangus animals. A total of 7986, 7002, and 7000 SNP from 50 Afrikaner and Brahman and 30 Brangus cattle, respectively, were masked and then imputed using BEAGLE v3 and FImpute v2. The within-breed imputation yielded accuracies ranging from 89.9 to 96.6% for the three breeds. The multiple-breed imputation yielded corresponding accuracies from 69.21 to 88.35%. The results showed that population homogeneity and numerical representation for within and across breed strategies, respectively, are crucial components for improving imputation accuracies.
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Affiliation(s)
- S Mdyogolo
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa.
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa.
| | - M D MacNeil
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
- Delta G, Miles City, MT, USA
| | - F W C Neser
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - M M Scholtz
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
| | - M L Makgahlela
- Department of Animal Breeding and Genetics, Agricultural Research Council, Irene, South Africa
- Department of Animal, Wildlife and Grassland Sciences, University of the Free State, Bloemfontein, South Africa
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63
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SNP characteristics and validation success in genome wide association studies. Hum Genet 2022; 141:229-238. [PMID: 34981173 PMCID: PMC8855685 DOI: 10.1007/s00439-021-02407-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 11/27/2021] [Indexed: 02/03/2023]
Abstract
Genome wide association studies (GWASs) have identified tens of thousands of single nucleotide polymorphisms (SNPs) associated with human diseases and characteristics. A significant fraction of GWAS findings can be false positives. The gold standard for true positives is an independent validation. The goal of this study was to identify SNP features associated with validation success. Summary statistics from the Catalog of Published GWASs were used in the analysis. Since our goal was an analysis of reproducibility, we focused on the diseases/phenotypes targeted by at least 10 GWASs. GWASs were arranged in discovery-validation pairs based on the time of publication, with the discovery GWAS published before validation. We used four definitions of the validation success that differ by stringency. Associations of SNP features with validation success were consistent across the definitions. The strongest predictor of SNP validation was the level of statistical significance in the discovery GWAS. The magnitude of the effect size was associated with validation success in a non-linear manner. SNPs with risk allele frequencies in the range 30-70% showed a higher validation success rate compared to rarer or more common SNPs. Missense, 5'UTR, stop gained, and SNPs located in transcription factor binding sites had a higher validation success rate compared to intergenic, intronic and synonymous SNPs. There was a positive association between validation success and the level of evolutionary conservation of the sites. In addition, validation success was higher when discovery and validation GWASs targeted the same ethnicity. All predictors of validation success remained significant in a multivariate logistic regression model indicating their independent contribution. To conclude, we identified SNP features predicting validation success of GWAS hits. These features can be used to select SNPs for validation and downstream functional studies.
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64
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Deng T, Zhang P, Garrick D, Gao H, Wang L, Zhao F. Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data. Front Genet 2022; 12:704118. [PMID: 35046990 PMCID: PMC8762119 DOI: 10.3389/fgene.2021.704118] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/03/2021] [Indexed: 11/17/2022] Open
Abstract
Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.
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Affiliation(s)
- Tianyu Deng
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Pengfei Zhang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Dorian Garrick
- A. L. Rae Centre of Genetics and Breeding, Massey University, Hamilton, New Zealand
| | - Huijiang Gao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lixian Wang
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fuping Zhao
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China
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65
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Li G, Zhang G, Li Y. DNA Methylation Imputation Across Platforms. Methods Mol Biol 2022; 2432:137-151. [PMID: 35505213 DOI: 10.1007/978-1-0716-1994-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In this chapter, we will provide a review on imputation in the context of DNA methylation, specifically focusing on a penalized functional regression (PFR) method we have previously developed. We will start with a brief review of DNA methylation, genomic and epigenomic contexts where imputation has proven beneficial in practice, and statistical or computational methods proposed for DNA methylation in the recent literature (Subheading 1). The rest of the chapter (Subheadings 2-4) will provide a detailed review of our PFR method proposed for across-platform imputation, which incorporates nonlocal information using a penalized functional regression framework. Subheading 2 introduces commonly employed technologies for DNA methylation measurement and describes the real dataset we have used in the development of our method: the acute myeloid leukemia (AML) dataset from The Cancer Genome Atlas (TCGA) project. Subheading 3 comprehensively reviews our method, encompassing data harmonization prior to model building, the actual building of penalized functional regression model, post-imputation quality filter, and imputation quality assessment. Subheading 4 shows the performance of our method in both simulation and the TCGA AML dataset, demonstrating that our penalized functional regression model is a valuable across-platform imputation tool for DNA methylation data, particularly because of its ability to boost statistical power for subsequent epigenome-wide association study. Finally, Subheading 5 provides future perspectives on imputation for DNA methylation data.
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Affiliation(s)
- Gang Li
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Guosheng Zhang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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66
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Castela Forte J, Folkertsma P, Gannamani R, Kumaraswamy S, Mount S, de Koning TJ, van Dam S, Wolffenbuttel BHR. Development and Validation of Decision Rules Models to Stratify Coronary Artery Disease, Diabetes, and Hypertension Risk in Preventive Care: Cohort Study of Returning UK Biobank Participants. J Pers Med 2021; 11:1322. [PMID: 34945794 PMCID: PMC8707007 DOI: 10.3390/jpm11121322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 10/23/2021] [Accepted: 11/20/2021] [Indexed: 12/25/2022] Open
Abstract
Many predictive models exist that predict risk of common cardiometabolic conditions. However, a vast majority of these models do not include genetic risk scores and do not distinguish between clinical risk requiring medical or pharmacological interventions and pre-clinical risk, where lifestyle interventions could be first-choice therapy. In this study, we developed, validated, and compared the performance of three decision rule algorithms including biomarkers, physical measurements, and genetic risk scores for incident coronary artery disease (CAD), diabetes (T2D), and hypertension against commonly used clinical risk scores in 60,782 UK Biobank participants. The rules models were tested for an association with incident CAD, T2D, and hypertension, and hazard ratios (with 95% confidence interval) were calculated from survival models. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), and Net Reclassification Index (NRI). The higher risk group in the decision rules model had a 40-, 40.9-, and 21.6-fold increased risk of CAD, T2D, and hypertension, respectively (p < 0.001 for all). Risk increased significantly between the three strata for all three conditions (p < 0.05). Based on genetic risk alone, we identified not only a high-risk group, but also a group at elevated risk for all health conditions. These decision rule models comprising blood biomarkers, physical measurements, and polygenic risk scores moderately improve commonly used clinical risk scores at identifying individuals likely to benefit from lifestyle intervention for three of the most common lifestyle-related chronic health conditions. Their utility as part of digital data or digital therapeutics platforms to support the implementation of lifestyle interventions in preventive and primary care should be further validated.
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Affiliation(s)
- José Castela Forte
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
| | - Pytrik Folkertsma
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Rahul Gannamani
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Sridhar Kumaraswamy
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
| | - Sarah Mount
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
| | - Tom J. de Koning
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
- Pediatrics, Department of Clinical Sciences, Lund University, Sölvegatan 19-BMC F12, 221 84 Lund, Sweden
| | - Sipko van Dam
- Ancora Health B.V., Herestraat 106, 9711 LM Groningen, The Netherlands; (P.F.); (R.G.); (S.K.); (S.M.); (S.v.D.)
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Bruce H. R. Wolffenbuttel
- Department of Endocrinology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
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67
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Ortuño FM, Loucera C, Casimiro-Soriguer CS, Lepe JA, Camacho Martinez P, Merino Diaz L, de Salazar A, Chueca N, García F, Perez-Florido J, Dopazo J. Highly accurate whole-genome imputation of SARS-CoV-2 from partial or low-quality sequences. Gigascience 2021; 10:giab078. [PMID: 34865008 PMCID: PMC8643610 DOI: 10.1093/gigascience/giab078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 10/26/2021] [Accepted: 11/12/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The current SARS-CoV-2 pandemic has emphasized the utility of viral whole-genome sequencing in the surveillance and control of the pathogen. An unprecedented ongoing global initiative is producing hundreds of thousands of sequences worldwide. However, the complex circumstances in which viruses are sequenced, along with the demand of urgent results, causes a high rate of incomplete and, therefore, useless sequences. Viral sequences evolve in the context of a complex phylogeny and different positions along the genome are in linkage disequilibrium. Therefore, an imputation method would be able to predict missing positions from the available sequencing data. RESULTS We have developed the impuSARS application, which takes advantage of the enormous number of SARS-CoV-2 genomes available, using a reference panel containing 239,301 sequences, to produce missing data imputation in viral genomes. ImpuSARS was tested in a wide range of conditions (continuous fragments, amplicons or sparse individual positions missing), showing great fidelity when reconstructing the original sequences, recovering the lineage with a 100% precision for almost all the lineages, even in very poorly covered genomes (<20%). CONCLUSIONS Imputation can improve the pace of SARS-CoV-2 sequencing production by recovering many incomplete or low-quality sequences that would be otherwise discarded. ImpuSARS can be incorporated in any primary data processing pipeline for SARS-CoV-2 whole-genome sequencing.
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Affiliation(s)
- Francisco M Ortuño
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013 Sevilla, Spain
| | - Carlos Loucera
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013 Sevilla, Spain
| | - Carlos S Casimiro-Soriguer
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013 Sevilla, Spain
| | - Jose A Lepe
- Unidad Clínica Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen del Rocío, 41013 Sevilla, Spain
| | - Pedro Camacho Martinez
- Unidad Clínica Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen del Rocío, 41013 Sevilla, Spain
| | - Laura Merino Diaz
- Unidad Clínica Enfermedades Infecciosas, Microbiología y Medicina Preventiva, Hospital Universitario Virgen del Rocío, 41013 Sevilla, Spain
| | - Adolfo de Salazar
- Servicio de Microbiología, Hospital Universitario San Cecilio, 18016 Granada, Spain
| | - Natalia Chueca
- Servicio de Microbiología, Hospital Universitario San Cecilio, 18016 Granada, Spain
| | - Federico García
- Servicio de Microbiología, Hospital Universitario San Cecilio, 18016 Granada, Spain
| | - Javier Perez-Florido
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013 Sevilla, Spain
| | - Joaquin Dopazo
- Clinical Bioinformatics Area, Fundación Progreso y Salud (FPS), CDCA, Hospital Virgen del Rocio, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013 Sevilla, Spain
- FPS/ELIXIR-es, Hospital Virgen del Rocío, Sevilla 42013, Spain
- CIBER de Enfermedades Infecciosas (CIBERINFEC), Hospital Universitario San Cecilio, 18016 Granada, Spain
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68
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Zuanetti DA, Milan LA. A new Bayesian approach for QTL mapping of family data. J Bioinform Comput Biol 2021; 20:2150030. [PMID: 34806951 DOI: 10.1142/s021972002150030x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we propose a new Bayesian approach for QTL mapping of family data. The main purpose is to model a phenotype as a function of QTLs' effects. The model considers the detailed familiar dependence and it does not rely on random effects. It combines the probability for Mendelian inheritance of parents' genotype and the correlation between flanking markers and QTLs. This is an advance when compared with models which use only Mendelian segregation or only the correlation between markers and QTLs to estimate transmission probabilities. We use the Bayesian approach to estimate the number of QTLs, their location and the additive and dominance effects. We compare the performance of the proposed method with variance component and LASSO models using simulated and GAW17 data sets. Under tested conditions, the proposed method outperforms other methods in aspects such as estimating the number of QTLs, the accuracy of the QTLs' position and the estimate of their effects. The results of the application of the proposed method to data sets exceeded all of our expectations.
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Affiliation(s)
- Daiane Aparecida Zuanetti
- Departamento de Estatística, UFSCar, Rodovia Washington Luis, KM 235, São Carlos, São Paulo 13565-905, Brazil
| | - Luis Aparecido Milan
- Departamento de Estatística, UFSCar, Rodovia Washington Luis, KM 235, São Carlos, São Paulo 13565-905, Brazil
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69
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Long EM, Bradbury PJ, Romay MC, Buckler ES, Robbins KR. Genome-wide Imputation Using the Practical Haplotype Graph in the Heterozygous Crop Cassava. G3-GENES GENOMES GENETICS 2021; 12:6423990. [PMID: 34751380 PMCID: PMC8728015 DOI: 10.1093/g3journal/jkab383] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 10/14/2021] [Indexed: 11/13/2022]
Abstract
Genomic applications such as genomic selection and genome-wide association have become increasingly common since the advent of genome sequencing. The cost of sequencing has decreased in the past two decades; however, genotyping costs are still prohibitive to gathering large datasets for these genomic applications, especially in nonmodel species where resources are less abundant. Genotype imputation makes it possible to infer whole-genome information from limited input data, making large sampling for genomic applications more feasible. Imputation becomes increasingly difficult in heterozygous species where haplotypes must be phased. The practical haplotype graph (PHG) is a recently developed tool that can accurately impute genotypes, using a reference panel of haplotypes. We showcase the ability of the PHG to impute genomic information in the highly heterozygous crop cassava (Manihot esculenta). Accurately phased haplotypes were sampled from runs of homozygosity across a diverse panel of individuals to populate PHG, which proved more accurate than relying on computational phasing methods. The PHG achieved high imputation accuracy, using sparse skim-sequencing input, which translated to substantial genomic prediction accuracy in cross-validation testing. The PHG showed improved imputation accuracy, compared to a standard imputation tool Beagle, especially in predicting rare alleles.
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Affiliation(s)
- Evan M Long
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Peter J Bradbury
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,United States Department of Agriculture-Agricultural Research Service, Robert W. Holley, Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA.,Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,United States Department of Agriculture-Agricultural Research Service, Robert W. Holley, Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - Kelly R Robbins
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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Stahl K, Gola D, König IR. Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data. Front Genet 2021; 12:724037. [PMID: 34630519 PMCID: PMC8493217 DOI: 10.3389/fgene.2021.724037] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 08/26/2021] [Indexed: 01/02/2023] Open
Abstract
Despite the widespread use of genotype imputation tools and the availability of different approaches, late developments of currently used programs have not been compared comprehensively. We therefore assessed the performance of 35 combinations of phasing and imputation programs, including versions of SHAPEIT, Eagle, Beagle, minimac, PBWT, and IMPUTE, for genetic imputation of completely missing SNPs with a HRC reference panel regarding quality and speed. We used a data set comprising 1,149 fully sequenced individuals from the German population, subsetting the SNPs to approximate the Illumina Infinium-Omni5 array. Five hundred fifty-three thousand two hundred and thirty-four SNPs across two selected chromosomes were utilized for comparison between imputed and sequenced genotypes. We found that all tested programs with the exception of PBWT impute genotypes with very high accuracy (mean error rate < 0.005). PBTW hardly ever imputes the less frequent allele correctly (mean concordance for genotypes including the minor allele <0.0002). For all programs, imputation accuracy drops for rare alleles with a frequency <0.05. Even though overall concordance is high, concordance drops with genotype probability, indicating that low genotype probabilities are rare. The mean concordance of SNPs with a genotype probability <95% drops below 0.9, at which point disregarding imputed genotypes might prove favorable. For fast and accurate imputation, a combination of Eagle2.4.1 using a reference panel for phasing and Beagle5.1 for imputation performs best. Replacing Beagle5.1 with minimac3, minimac4, Beagle4.1, or IMPUTE4 results in a small gain in accuracy at a high cost of speed.
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Affiliation(s)
- Katharina Stahl
- Department of Genetic Epidemiology, University Medical Center, University of Göttingen, Göttingen, Germany.,Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Lübeck, Germany
| | - Damian Gola
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Lübeck, Germany
| | - Inke R König
- Institut für Medizinische Biometrie und Statistik, Universität zu Lübeck, Universitätsklinikum Schleswig-Holstein, Lübeck, Germany.,German Center for Cardiovascular Research, Partner Site Hamburg/Kiel/Lübeck, Lübeck, Germany
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71
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Denault WRP, Gjessing HK, Juodakis J, Jacobsson B, Jugessur A. Wavelet Screening: a novel approach to analyzing GWAS data. BMC Bioinformatics 2021; 22:484. [PMID: 34620077 PMCID: PMC8499521 DOI: 10.1186/s12859-021-04356-5] [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: 03/10/2021] [Accepted: 09/06/2021] [Indexed: 11/13/2022] Open
Abstract
Background Traditional methods for single-variant genome-wide association study (GWAS) incur a substantial multiple-testing burden because of the need to test for associations with a vast number of single-nucleotide polymorphisms (SNPs) simultaneously. Further, by ignoring more complex joint effects of nearby SNPs within a given region, these methods fail to consider the genomic context of an association with the outcome. Results To address these shortcomings, we present a more powerful method for GWAS, coined ‘Wavelet Screening’ (WS), that greatly reduces the number of tests to be performed. This is achieved through the use of a sliding-window approach based on wavelets to sequentially screen the entire genome for associations. Wavelets are oscillatory functions that are useful for analyzing the local frequency and time behavior of signals. The signals can then be divided into different scale components and analyzed separately. In the current setting, we consider a sequence of SNPs as a genetic signal, and for each screened region, we transform the genetic signal into the wavelet space. The null and alternative hypotheses are modeled using the posterior distribution of the wavelet coefficients. WS is enhanced by using additional information from the regression coefficients and by taking advantage of the pyramidal structure of wavelets. When faced with more complex genetic signals than single-SNP associations, we show via simulations that WS provides a substantial gain in power compared to both the traditional GWAS modeling and another popular regional association test called SNP-set (Sequence) Kernel Association Test (SKAT). To demonstrate feasibility, we applied WS to a large Norwegian cohort (N=8006) with genotypes and information available on gestational duration. Conclusions WS is a powerful and versatile approach to analyzing whole-genome data and lends itself easily to investigating various omics data types. Given its broader focus on the genomic context of an association, WS may provide additional insight into trait etiology by revealing genes and loci that might have been missed by previous efforts.
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Affiliation(s)
- William R P Denault
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway. .,Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway. .,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.
| | - Håkon K Gjessing
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
| | - Julius Juodakis
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Bo Jacobsson
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Department of Obstetrics and Gynecology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Astanand Jugessur
- Department of Genetics and Bioinformatics, Norwegian Institute of Public Health, Oslo, Norway.,Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.,Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway
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72
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Hong S, Dobricic V, Ohlei O, Bos I, Vos SJB, Prokopenko D, Tijms BM, Andreasson U, Blennow K, Vandenberghe R, Gabel S, Scheltens P, Teunissen CE, Engelborghs S, Frisoni G, Blin O, Richardson JC, Bordet R, Lleó A, Alcolea D, Popp J, Clark C, Peyratout G, Martinez-Lage P, Tainta M, Dobson RJB, Legido-Quigley C, Sleegers K, Van Broeckhoven C, Tanzi RE, Ten Kate M, Wittig M, Franke A, Lill CM, Barkhof F, Lovestone S, Streffer J, Zetterberg H, Visser PJ, Bertram L. TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels. Alzheimers Dement 2021; 17:1628-1640. [PMID: 33991015 DOI: 10.1002/alz.12330] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/16/2021] [Accepted: 02/13/2021] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Neurofilament light (NfL), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are biomarkers for Alzheimer's disease (AD) to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. METHODS We performed genome-wide association studies (GWAS) using DNA and cerebrospinal fluid (CSF) samples from the EMIF-AD Multimodal Biomarker Discovery study for discovery, and the Alzheimer's Disease Neuroimaging Initiative study for validation analyses. GWAS were performed for all three CSF biomarkers using linear regression models adjusting for relevant covariates. RESULTS We identify novel genome-wide significant associations between DNA variants in TMEM106B and CSF levels of NfL, and between CPOX and YKL-40. We confirm previous work suggesting that YKL-40 levels are associated with DNA variants in CHI3L1. DISCUSSION Our study provides important new insights into the genetic architecture underlying interindividual variation in three AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.
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Affiliation(s)
- Shengjun Hong
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Ulf Andreasson
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospital Leuven, Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, the Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Center for Neurosciences, UZ Brussel and Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Giovanni Frisoni
- University of Geneva, Geneva, Switzerland
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- AIX Marseille University, INS, Ap-hm, Marseille, France
| | | | - Regis Bordet
- Inserm, CHU Lille, University of Lille, Lille, France
| | - Alberto Lleó
- Memory Unit, Neurology Department. Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Daniel Alcolea
- Memory Unit, Neurology Department. Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Julius Popp
- Centre for Gerontopsychiatric Medicine, Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zürich, Switzerland
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Christopher Clark
- Centre for Gerontopsychiatric Medicine, Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zürich, Switzerland
| | - Gwendoline Peyratout
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Mikel Tainta
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark and Institute of Pharmaceutical Sciences, King's College London, London, UK
| | - Kristel Sleegers
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, the Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Michael Wittig
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Christina M Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Ageing Epidemiology Research Unit, School of Public Health, Imperial College, London, United Kingdom
| | - Frederik Barkhof
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | | | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Translational Medicine Neuroscience, UCB Biopharma SPRL, Braine l'Alleud, Belgium
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
- Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Instutet, Stockholm, Sweden
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
- Department of Psychology, University of Oslo, Oslo, Norway
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73
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Genetic variants associated with cardiometabolic abnormalities during treatment with selective serotonin reuptake inhibitors: a genome-wide association study. THE PHARMACOGENOMICS JOURNAL 2021; 21:574-585. [PMID: 33824429 DOI: 10.1038/s41397-021-00234-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/19/2021] [Accepted: 03/11/2021] [Indexed: 02/02/2023]
Abstract
Selective serotonin reuptake inhibitors (SSRIs) are prescribed both to patients with schizophrenia and bipolar disorder. Previous studies have shown associations between SSRI treatment and cardiometabolic alterations. The aim of the present study was to investigate genetic variants associated with cardiometabolic adverse effects in patients treated with SSRIs in a naturalistic setting, using a genome-wide cross-sectional approach in a genetically homogeneous sample. We included and genotyped 1981 individuals with schizophrenia or bipolar disorder, of whom 1180 had information available on the outcomes low-density lipoprotein cholesterol (LDL-cholesterol), high-density lipoprotein cholesterol (HDL-cholesterol), triglycerides, and body mass index (BMI) and investigated interactions between SNPs and SSRI use (N = 246) by conducting a genome-wide GxE analysis. We report 13 genome-wide significant interaction effects of SNPs and SSRI serum concentrations on LDL-cholesterol, HDL-cholesterol, and BMI, located in four distinct genomic loci. This study provides new insight into the pharmacogenetics of SSRI but warrants replication in independent populations.
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74
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Liu S, Yu T. Kernel density estimation in mixture models with known mixture proportions. Stat Med 2021; 40:6360-6372. [PMID: 34474504 DOI: 10.1002/sim.9187] [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: 07/08/2020] [Revised: 06/18/2021] [Accepted: 08/17/2021] [Indexed: 11/11/2022]
Abstract
In this article, we consider the density estimation for data with a mixture structure, where the component densities are assumed unknown, but for each observation, the probabilities of its membership to the subpopulations are known or estimable from other resources. Data of this kind arise from practice and have wide applications. Motivated from the classical kernel density estimation method for a single population, we propose a weighted kernel density estimation method to estimate the component density functions nonparametrically. Within the framework of the EM algorithm, we derive an algorithm that computes our proposed estimates effectively. Via extensive simulation studies, we demonstrate that our methods outperform the existing methods in most occasions. We further compare our methods with existing methods by real data examples.
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Affiliation(s)
- Siyun Liu
- Department of Statistics and Data Science, National University of Singapore, Singapore
| | - Tao Yu
- Department of Statistics and Data Science, National University of Singapore, Singapore
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75
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Yan G, Liu X, Xiao S, Xin W, Xu W, Li Y, Huang T, Qin J, Xie L, Ma J, Zhang Z, Huang L. An imputed whole-genome sequence-based GWAS approach pinpoints causal mutations for complex traits in a specific swine population. SCIENCE CHINA-LIFE SCIENCES 2021; 65:781-794. [PMID: 34387836 DOI: 10.1007/s11427-020-1960-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 05/19/2021] [Indexed: 01/08/2023]
Abstract
Sequencing-based genome-wide association studies (GWAS) have facilitated the identification of causal associations between genetic variants and traits in diverse species. However, it is cost-prohibitive for the majority of research groups to sequence a large number of samples. Here, we carried out genotype imputation to increase the density of single nucleotide polymorphisms in a large-scale Swine F2 population using a reference panel including 117 individuals, followed by a series of GWAS analyses. The imputation accuracies reached 0.89 and 0.86 for allelic concordance and correlation, respectively. A quantitative trait nucleotide (QTN) affecting the chest vertebrate was detected directly, while the investigation of another QTN affecting the residual glucose failed due to the presence of similar haplotypes carrying wild-type and mutant allelesin the reference panel used in this study. A high imputation accuracy was confirmed by Sanger sequencing technology for the most significant loci. Two candidate genes, CPNE5 and MYH3, affecting meat-related traits were proposed. Collectively, we illustrated four scenarios in imputation-based GWAS that may be encountered by researchers, and our results will provide an extensive reference for future genotype imputation-based GWAS analyses in the future.
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Affiliation(s)
- Guorong Yan
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
- Institute of Photomedicine, Shanghai Skin Disease Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Xianxian Liu
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Shijun Xiao
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Wenshui Xin
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Wenwu Xu
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Yiping Li
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Tao Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Jiangtao Qin
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Lei Xie
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
| | - Junwu Ma
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China.
| | - Zhiyan Zhang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China.
| | - Lusheng Huang
- State Key Laboratory for Pig Genetic Improvement and Production Technology, Jiangxi Agricultural University, Nanchang, 330045, China
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76
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Zhang Z, Xiao X, Zhou W, Zhu D, Amos CI. False positive findings during genome-wide association studies with imputation: Influence of allele frequency and imputation accuracy. Hum Mol Genet 2021; 31:146-155. [PMID: 34368847 DOI: 10.1093/hmg/ddab203] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 06/21/2021] [Accepted: 07/12/2021] [Indexed: 11/12/2022] Open
Abstract
Genotype imputation is widely used in genetic studies to boost the power of GWAS, to combine multiple studies for meta-analysis and to perform fine mapping. With advances of imputation tools and large reference panels, genotype imputation has become mature and accurate. However, the uncertain nature of imputed genotypes can cause bias in the downstream analysis. Many studies have compared the performance of popular imputation approaches, but few investigated bias characteristics of downstream association analyses. Herein, we showed that the imputation accuracy is diminished if the real genotypes contain minor alleles. Although these genotypes are less common, which is particularly true for loci with low minor allele frequency, a large discordance between imputed and observed genotypes significantly inflated the association results, especially in data with a large portion of uncertain SNPs. The significant discordance of p-values happened as the p-value approached 0 or the imputation quality was poor. Although elimination of poorly imputed SNPs can remove false positive (FP) SNPs, it sacrificed, sometimes, more than 80% true positive (TP) SNPs. For top ranked SNPs, removing variants with moderate imputation quality cannot reduce the proportion of FP SNPs, and increasing sample size in reference panels did not greatly benefit the results as well. Additionally, samples with a balanced ratio between cases and controls can dramatically improve the number of TP SNPs observed in the imputation based GWAS. These results raise concerns about results from analysis of association studies when rare variants are studied, particularly when case-control studies are unbalanced.
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Affiliation(s)
- Zhihui Zhang
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030.,Institute of Clinical and Translational Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX 77030
| | - Xiangjun Xiao
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030
| | - Wen Zhou
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030
| | - Dakai Zhu
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030
| | - Christopher I Amos
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030.,Institute of Clinical and Translational Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX 77030
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Li G, Raffield L, Logue M, Miller MW, Santos HP, O’Shea TM, Fry RC, Li Y. CUE: CpG impUtation ensemble for DNA methylation levels across the human methylation450 (HM450) and EPIC (HM850) BeadChip platforms. Epigenetics 2021; 16:851-861. [PMID: 33016200 PMCID: PMC8330997 DOI: 10.1080/15592294.2020.1827716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/08/2020] [Accepted: 09/09/2020] [Indexed: 10/23/2022] Open
Abstract
DNA methylation at CpG dinucleotides is one of the most extensively studied epigenetic marks. With technological advancements, geneticists can profile DNA methylation with multiple reliable approaches. However, profiling platforms can differ substantially in the CpGs they assess, consequently hindering integrated analysis across platforms. Here, we present CpG impUtation Ensemble (CUE), which leverages multiple classical statistical and modern machine learning methods, to impute from the Illumina HumanMethylation450 (HM450) BeadChip to the Illumina HumanMethylationEPIC (HM850) BeadChip. Data were analysed from two population cohorts with methylation measured both by HM450 and HM850: the Extremely Low Gestational Age Newborns (ELGAN) study (n = 127, placenta) and the VA Boston Posttraumatic Stress Disorder (PTSD) genetics repository (n = 144, whole blood). Cross-validation results show that CUE achieves the lowest predicted root-mean-square error (RMSE) (0.026 in PTSD) and the highest accuracy (99.97% in PTSD) compared with five individual methods tested, including k-nearest-neighbours, logistic regression, penalized functional regression, random forest, and XGBoost. Finally, among all 339,033 HM850-only CpG sites shared between ELGAN and PTSD, CUE successfully imputed 289,604 (85.4%) sites, where success was defined as RMSE < 0.05 and accuracy >95% in PTSD. In summary, CUE is a valuable tool for imputing CpG methylation from the HM450 to HM850 platform.
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Affiliation(s)
- Gang Li
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Mark Logue
- National Center for PTSD: Behavioral Sciences Division at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
- Biomedical Genetics, Boston University School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Mark W. Miller
- National Center for PTSD: Behavioral Sciences Division at VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Hudson P. Santos
- School of Nursing, University of North Carolina, Chapel Hill, NC, USA
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - T. Michael O’Shea
- Department of Pediatrics, School of Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Rebecca C. Fry
- Institute for Environmental Health Solutions, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Curriculum in Toxicology and Environmental Medicine, University of North Carolina, Chapel Hill, NC, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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78
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Lu C, Greshake Tzovaras B, Gough J. A survey of direct-to-consumer genotype data, and quality control tool ( GenomePrep) for research. Comput Struct Biotechnol J 2021; 19:3747-3754. [PMID: 34285776 PMCID: PMC8267563 DOI: 10.1016/j.csbj.2021.06.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 06/23/2021] [Accepted: 06/24/2021] [Indexed: 01/07/2023] Open
Abstract
Review of the offerings from commercial genotyping companies. Analysis of consumer genotype data SNP arrays. Quality control analysis of over 7000 open genomes. Open source tools and web service providing quality control report of genotype arrays.
Two major forces have contributed to the fast growth of human genetic data. One from medical research supported by governments and academic institutes; the other from direct-to-consumer (DTC) sequencing companies. While the former benefits from meticulously designed sequencing standards and quality control procedures, the latter comes in various formats and sequencing methods which are subject to changes over time and the particular needs of different companies. Thanks to the general public who shared their DNA data without constraint, here we provide a review for over 7000 genomes made public between 2011 and 2020, and produced by over six DTC sequencing companies. An open source tool-kit to systematically parse, quality check and filter genome files and statistically problematic alleles is provided to prepare consumer DNA datasets for research. The GenomePrep output is available in two common DNA datafile formats to enable further analysis with other tools. We also provide for download the combined output for all OpenSNP array genomes processed in this paper in a single data freeze file.
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Affiliation(s)
- Chang Lu
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, UK
| | | | - Julian Gough
- MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge Biomedical Campus, Cambridge, UK
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79
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Tang H, He Z. Advances and challenges in quantitative delineation of the genetic architecture of complex traits. QUANTITATIVE BIOLOGY 2021; 9:168-184. [PMID: 35492964 PMCID: PMC9053444 DOI: 10.15302/j-qb-021-0249] [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] [Indexed: 11/16/2022]
Abstract
Background Genome-wide association studies (GWAS) have been widely adopted in studies of human complex traits and diseases. Results This review surveys areas of active research: quantifying and partitioning trait heritability, fine mapping functional variants and integrative analysis, genetic risk prediction of phenotypes, and the analysis of sequencing studies that have identified millions of rare variants. Current challenges and opportunities are highlighted. Conclusion GWAS have fundamentally transformed the field of human complex trait genetics. Novel statistical and computational methods have expanded the scope of GWAS and have provided valuable insights on the genetic architecture underlying complex phenotypes.
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Affiliation(s)
- Hua Tang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Zihuai He
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA
- Quantitative Sciences Unit, Department of Medicine, Stanford University, Stanford, CA 94305, USA
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80
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Dissecting polygenic signals from genome-wide association studies on human behaviour. Nat Hum Behav 2021; 5:686-694. [PMID: 33986517 DOI: 10.1038/s41562-021-01110-y] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 03/31/2021] [Indexed: 02/03/2023]
Abstract
Genome-wide association studies on human behavioural traits are producing large amounts of polygenic signals with significant predictive power and potentially useful biological clues. Behavioural traits are more distal and are less directly under biological control compared with physical characteristics, which makes the associated genetic effects harder to interpret. The results of genome-wide association studies for human behaviour are likely made up of a composite of signals from different sources. While sample sizes continue to increase, we outline additional steps that need to be taken to better delineate the origin of the increasingly stronger polygenic signals. In addition to genetic effects on the traits themselves, the major sources of polygenic signals are those that are associated with correlated traits, environmental effects and ascertainment bias. Advances in statistical approaches that disentangle polygenic effects from different traits as well as extending data collection to families and social circles with better geographical coverage will probably contribute to filling the gap of knowledge between genetic effects and behavioural outcomes.
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81
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Genomic and functional evaluation of TNFSF14 in multiple sclerosis susceptibility. J Genet Genomics 2021; 48:497-507. [PMID: 34353742 DOI: 10.1016/j.jgg.2021.03.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/24/2021] [Accepted: 03/05/2021] [Indexed: 11/24/2022]
Abstract
Among multiple sclerosis (MS) susceptibility genes, the strongest non-human leukocyte antigen (HLA) signal in the Italian population maps to the TNFSF14 gene encoding LIGHT, a glycoprotein involved in dendritic cell (DC) maturation. Through fine-mapping in a large Italian dataset (4,198 patients with MS and 3,903 controls), we show that the TNFSF14 intronic SNP rs1077667 is the primarily MS-associated variant in the region. Expression quantitative trait locus (eQTL) analysis indicates that the MS risk allele is significantly associated with reduced TNFSF14 messenger RNA levels in blood cells, which is consistent with the allelic imbalance in RNA-Seq reads (P < 0.0001). The MS risk allele is associated with reduced levels of TNFSF14 gene expression (P < 0.01) in blood cells from 84 Italian patients with MS and 80 healthy controls (HCs). Interestingly, patients with MS are lower expressors of TNFSF14 compared to HC (P < 0.007). Individuals homozygous for the MS risk allele display an increased percentage of LIGHT-positive peripheral blood myeloid DCs (CD11c+, P = 0.035) in 37 HCs, as well as in in vitro monocyte-derived DCs from 22 HCs (P = 0.04). Our findings suggest that the intronic variant rs1077667 alters the expression of TNFSF14 in immune cells, which may play a role in MS pathogenesis.
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82
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Charon C, Allodji R, Meyer V, Deleuze JF. Impact of pre- and post-variant filtration strategies on imputation. Sci Rep 2021; 11:6214. [PMID: 33737531 PMCID: PMC7973508 DOI: 10.1038/s41598-021-85333-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 02/22/2021] [Indexed: 01/04/2023] Open
Abstract
Quality control (QC) methods for genome-wide association studies and fine mapping are commonly used for imputation, however they result in loss of many single nucleotide polymorphisms (SNPs). To investigate the consequences of filtration on imputation, we studied the direct effects on the number of markers, their allele frequencies, imputation quality scores and post-filtration events. We pre-phrased 1031 genotyped individuals from diverse ethnicities and compared the imputed variants to 1089 NCBI recorded individuals for additional validation. Without QC-based variant pre-filtration, we observed no impairment in the imputation of SNPs that failed QC whereas with pre-filtration there was an overall loss of information. Significant differences between frequencies with and without pre-filtration were found only in the range of very rare (5E-04-1E-03) and rare variants (1E-03-5E-03) (p < 1E-04). Increasing the post-filtration imputation quality score from 0.3 to 0.8 reduced the number of single nucleotide variants (SNVs) < 0.001 2.5 fold with or without QC pre-filtration and halved the number of very rare variants (5E-04). Thus, to maintain confidence and enough SNVs, we propose here a two-step filtering procedure which allows less stringent filtering prior to imputation and post-imputation in order to increase the number of very rare and rare variants compared to conservative filtration methods.
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Affiliation(s)
- Céline Charon
- CEA Paris-Saclay, Institut François Jacob, Centre National de Recherche en Génomique Humaine, 2 rue Gaston Crémieux, Evry, 91057, France.
| | - Rodrigue Allodji
- Radiation Epidemiology Group CESP, Inserm Unit 1018, Gustave Roussy Université Paris Saclay, 114 rue Edouard Vaillant, Villejuif, 94805, France
| | - Vincent Meyer
- CEA Paris-Saclay, Institut François Jacob, Centre National de Recherche en Génomique Humaine, 2 rue Gaston Crémieux, Evry, 91057, France
| | - Jean-François Deleuze
- CEA Paris-Saclay, Institut François Jacob, Centre National de Recherche en Génomique Humaine, 2 rue Gaston Crémieux, Evry, 91057, France
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83
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Mancin E, Sosa-Madrid BS, Blasco A, Ibáñez-Escriche N. Genotype Imputation to Improve the Cost-Efficiency of Genomic Selection in Rabbits. Animals (Basel) 2021; 11:ani11030803. [PMID: 33805619 PMCID: PMC8000098 DOI: 10.3390/ani11030803] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/04/2021] [Accepted: 03/05/2021] [Indexed: 01/19/2023] Open
Abstract
Simple Summary Genotyping costs are still the major limitation for the uptake of genomic selection by the rabbit meat industry, as a large number of genetic markers are needed for improving the prediction of breeding values by genomic data. In this study, several genotyping strategies were examined through simulation scenarios to disentangle the best feasible options of implementing genomic selection in rabbit breeding programs. Most scenarios emphasized the genotyping of candidate animals with a low Single Nucleotide Polymorphism (SNP) density platform. Imputation accuracies were high for the scenarios with ancestors genotyped at high or medium SNP-densities. However, the scenario with male ancestors genotyped at high SNP-density and only dams genotyped at medium SNP-density showed the best economically feasible strategy, taking into account the trade-off among genotyping costs, the accuracy of breeding values and response to selection. The results confirmed that by combining the imputation technique with a mindful selection of the animals to be genotyped, it is possible to achieve better performance than Best Linear Unbiased Prediction (BLUP), reducing genotyping cost at the same time. Abstract Genomic selection uses genetic marker information to predict genomic breeding values (gEBVs), and can be a suitable tool for selecting low-hereditability traits such as litter size in rabbits. However, genotyping costs in rabbits are still too high to enable genomic prediction in selective breeding programs. One method for decreasing genotyping costs is the genotype imputation, where parents are genotyped at high SNP-density (HD) and the progeny are genotyped at lower SNP-density, followed by imputation to HD. The aim of this study was to disentangle the best imputation strategies with a trade-off between genotyping costs and the accuracy of breeding values for litter size. A selection process, mimicking a commercial breeding rabbit selection program for litter size, was simulated. Two different Quantitative Trait Nucleotide (QTN) models (QTN_5 and QTN_44) were generated 36 times each. From these simulations, seven different scenarios (S1–S7) and a further replicate of the third scenario (S3_A) were created. Scenarios consist of a different combination of genotyping strategies. In these scenarios, ancestors and progeny were genotyped with a mix of three different platforms, containing 200,000, 60,000, and 600 SNPs under a cost of EUR 100, 50 and 11 per animal, respectively. Imputation accuracy (IA) was measured as a Pearson’s correlation between true genotype and imputed genotype, whilst the accuracy of gEBVs was the correlation between true breeding value and the estimated one. The relationships between IA, the accuracy of gEBVs, genotyping costs, and response to selection were examined under each QTN model. QTN_44 presented better performance, according to the results of genomic prediction, but the same ranks between scenarios remained in both QTN models. The highest IA (0.99) and the accuracy of gEBVs (0.26; QTN_44, and 0.228; QTN_5) were observed in S1 where all ancestors were genotyped at HD and progeny at medium SNP-density (MD). Nevertheless, this was the most expensive scenario compared to the others in which the progenies were genotyped at low SNP-density (LD). Scenarios with low average costs presented low IA, particularly when female ancestors were genotyped at LD (S5) or non-genotyped (S7). The S3_A, imputing whole-genomes, had the lowest accuracy of gEBVs (0.09), even worse than Best Linear Unbiased Prediction (BLUP). The best trade-off between genotyping costs and the accuracy of gEBVs (0.234; QTN_44 and 0.199) was in S6, in which dams were genotyped with MD whilst grand-dams were non-genotyped. However, this relationship would depend mainly on the distribution of QTN and SNP across the genome, suggesting further studies on the characterization of the rabbit genome in the Spanish lines. In summary, genomic selection with genotype imputation is feasible in the rabbit industry, considering only genotyping strategies with suitable IA, accuracy of gEBVs, genotyping costs, and response to selection.
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Affiliation(s)
- Enrico Mancin
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, viale dell’Università 16, 35020 Legnaro, PD, Italy;
| | - Bolívar Samuel Sosa-Madrid
- Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain;
- Correspondence: (B.S.S.-M.); (N.I.-E.); Tel.: +34-963877438 (N.I.-E.)
| | - Agustín Blasco
- Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Noelia Ibáñez-Escriche
- Institute for Animal Science and Technology, Universitat Politècnica de València, 46022 Valencia, Spain;
- Correspondence: (B.S.S.-M.); (N.I.-E.); Tel.: +34-963877438 (N.I.-E.)
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84
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Torkamaneh D, Belzile F. Accurate Imputation of Untyped Variants from Deep Sequencing Data. Methods Mol Biol 2021; 2243:271-281. [PMID: 33606262 DOI: 10.1007/978-1-0716-1103-6_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
Abstract
The quality, statistical power, and resolution of genome-wide association studies (GWAS) are largely dependent on the comprehensiveness of genotypic data. Over the last few years, despite the constant decrease in the price of sequencing, whole-genome sequencing (WGS) of association panels comprising a large number of samples remains cost-prohibitive. Therefore, most GWAS populations are still genotyped using low-coverage genotyping methods resulting in incomplete datasets. Imputation of untyped variants is a powerful method to maximize the number of SNPs identified in study samples, it increases the power and resolution of GWAS and allows to integrate genotyping datasets obtained from various sources. Here, we describe the key concepts underlying imputation of untyped variants, including the architecture of reference panels, and review some of the associated challenges and how these can be addressed. We also discuss the need and available methods to rigorously assess the accuracy of imputed data prior to their use in any genetic study.
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Affiliation(s)
- Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, Canada.,Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.,Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - François Belzile
- Département de Phytologie, Université Laval, Québec City, QC, Canada. .,Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada.
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85
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Chen Z, Boehnke M, Wen X, Mukherjee B. Revisiting the genome-wide significance threshold for common variant GWAS. G3 (BETHESDA, MD.) 2021; 11:jkaa056. [PMID: 33585870 PMCID: PMC8022962 DOI: 10.1093/g3journal/jkaa056] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/05/2020] [Indexed: 11/23/2022]
Abstract
Over the last decade, GWAS meta-analyses have used a strict P-value threshold of 5 × 10-8 to classify associations as significant. Here, we use our current understanding of frequently studied traits including lipid levels, height, and BMI to revisit this genome-wide significance threshold. We compare the performance of studies using the P = 5 × 10-8 threshold in terms of true and false positive rate to other multiple testing strategies: (1) less stringent P-value thresholds, (2) controlling the FDR with the Benjamini-Hochberg and Benjamini-Yekutieli procedure, and (3) controlling the Bayesian FDR with posterior probabilities. We applied these procedures to re-analyze results from the Global Lipids and GIANT GWAS meta-analysis consortia and supported them with extensive simulation that mimics the empirical data. We observe in simulated studies with sample sizes ∼20,000 and >120,000 that relaxing the P-value threshold to 5 × 10-7 increased discovery at the cost of 18% and 8% of additional loci being false positive results, respectively. FDR and Bayesian FDR are well controlled for both sample sizes with a few exceptions that disappear under a less stringent definition of true positives and the two approaches yield similar results. Our work quantifies the value of using a relaxed P-value threshold in large studies to increase their true positive discovery but also show the excess false positive rates due to such actions in modest-sized studies. These results may guide investigators considering different thresholds in replication studies and downstream work such as gene-set enrichment or pathway analysis. Finally, we demonstrate the viability of FDR-controlling procedures in GWAS.
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Affiliation(s)
- Zhongsheng Chen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA
| | - Xiaoquan Wen
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA
| | - Bhramar Mukherjee
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109-2029, USA
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86
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Li JH, Mazur CA, Berisa T, Pickrell JK. Low-pass sequencing increases the power of GWAS and decreases measurement error of polygenic risk scores compared to genotyping arrays. Genome Res 2021; 31:529-537. [PMID: 33536225 PMCID: PMC8015847 DOI: 10.1101/gr.266486.120] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 02/01/2021] [Indexed: 01/08/2023]
Abstract
Low-pass sequencing (sequencing a genome to an average depth less than 1× coverage) combined with genotype imputation has been proposed as an alternative to genotyping arrays for trait mapping and calculation of polygenic scores. To empirically assess the relative performance of these technologies for different applications, we performed low-pass sequencing (targeting coverage levels of 0.5× and 1×) and array genotyping (using the Illumina Global Screening Array [GSA]) on 120 DNA samples derived from African- and European-ancestry individuals that are part of the 1000 Genomes Project. We then imputed both the sequencing data and the genotyping array data to the 1000 Genomes Phase 3 haplotype reference panel using a leave-one-out design. We evaluated overall imputation accuracy from these different assays as well as overall power for GWAS from imputed data and computed polygenic risk scores for coronary artery disease and breast cancer using previously derived weights. We conclude that low-pass sequencing plus imputation, in addition to providing a substantial increase in statistical power for genome-wide association studies, provides increased accuracy for polygenic risk prediction at effective coverages of ∼0.5× and higher compared to the Illumina GSA.
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Affiliation(s)
- Jeremiah H Li
- Gencove, Incorporated, New York, New York 10016, USA
| | - Chase A Mazur
- Gencove, Incorporated, New York, New York 10016, USA
| | - Tomaz Berisa
- Gencove, Incorporated, New York, New York 10016, USA
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Torkamaneh D, Laroche J, Valliyodan B, O'Donoughue L, Cober E, Rajcan I, Vilela Abdelnoor R, Sreedasyam A, Schmutz J, Nguyen HT, Belzile F. Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:324-334. [PMID: 32794321 DOI: 10.1101/534578] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 07/24/2020] [Accepted: 08/07/2020] [Indexed: 05/27/2023]
Abstract
Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.
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Affiliation(s)
- Davoud Torkamaneh
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Jérôme Laroche
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
| | - Babu Valliyodan
- National Center for Soybean Biotechnology and Division of Plant Sciences, University of Missouri, Columbia, MO, USA
| | - Louise O'Donoughue
- CÉROM, Centre de recherche Sur Les Grains Inc., Saint-Mathieu de Beloeil, QC, Canada
| | - Elroy Cober
- Agriculture and Agri-Food Canada, Ottawa, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Ricardo Vilela Abdelnoor
- Brazilian Corporation of Agricultural Research (Embrapa Soja), Warta County, PR, Brazil
- Londrina State University (UEL), Londrina, PR, Brazil
| | | | - Jeremy Schmutz
- Institute for Biotechnology, HudsonAlpha, Huntsville, AL, USA
- Department of Energy, Joint Genome Institute, Walnut Creek, CA, USA
| | - Henry T Nguyen
- National Center for Soybean Biotechnology and Division of Plant Sciences, University of Missouri, Columbia, MO, USA
| | - François Belzile
- Département de Phytologie, Université Laval, Québec City, QC, Canada
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC, Canada
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Torkamaneh D, Laroche J, Valliyodan B, O’Donoughue L, Cober E, Rajcan I, Vilela Abdelnoor R, Sreedasyam A, Schmutz J, Nguyen HT, Belzile F. Soybean (Glycine max) Haplotype Map (GmHapMap): a universal resource for soybean translational and functional genomics. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:324-334. [PMID: 32794321 PMCID: PMC7868971 DOI: 10.1111/pbi.13466] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Revised: 07/24/2020] [Accepted: 08/07/2020] [Indexed: 05/10/2023]
Abstract
Here, we describe a worldwide haplotype map for soybean (GmHapMap) constructed using whole-genome sequence data for 1007 Glycine max accessions and yielding 14.9 million variants as well as 4.3 M tag single-nucleotide polymorphisms (SNPs). When sampling random subsets of these accessions, the number of variants and tag SNPs plateaued beyond approximately 800 and 600 accessions, respectively. This suggests extensive coverage of diversity within the cultivated soybean. GmHapMap variants were imputed onto 21 618 previously genotyped accessions with up to 96% success for common alleles. A local association analysis was performed with the imputed data using markers located in a 1-Mb region known to contribute to seed oil content and enabled us to identify a candidate causal SNP residing in the NPC1 gene. We determined gene-centric haplotypes (407 867 GCHs) for the 55 589 genes and showed that such haplotypes can help to identify alleles that differ in the resulting phenotype. Finally, we predicted 18 031 putative loss-of-function (LOF) mutations in 10 662 genes and illustrated how such a resource can be used to explore gene function. The GmHapMap provides a unique worldwide resource for applied soybean genomics and breeding.
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Affiliation(s)
- Davoud Torkamaneh
- Département de PhytologieUniversité LavalQuébec CityQCCanada
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
- Department of Plant AgricultureUniversity of GuelphGuelphONCanada
| | - Jérôme Laroche
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
| | - Babu Valliyodan
- National Center for Soybean Biotechnology and Division of Plant SciencesUniversity of MissouriColumbiaMOUSA
| | - Louise O’Donoughue
- CÉROMCentre de recherche Sur Les Grains Inc.Saint‐Mathieu de BeloeilQCCanada
| | - Elroy Cober
- Agriculture and Agri‐Food CanadaOttawaONCanada
| | - Istvan Rajcan
- Department of Plant AgricultureUniversity of GuelphGuelphONCanada
| | - Ricardo Vilela Abdelnoor
- Brazilian Corporation of Agricultural Research (Embrapa Soja)Warta CountyPRBrazil
- Londrina State University (UEL)LondrinaPRBrazil
| | | | - Jeremy Schmutz
- Institute for BiotechnologyHudsonAlphaHuntsvilleALUSA
- Department of EnergyJoint Genome InstituteWalnut CreekCAUSA
| | - Henry T. Nguyen
- National Center for Soybean Biotechnology and Division of Plant SciencesUniversity of MissouriColumbiaMOUSA
| | - François Belzile
- Département de PhytologieUniversité LavalQuébec CityQCCanada
- Institut de Biologie Intégrative et des Systèmes (IBIS)Université LavalQuébec CityQCCanada
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Lagou V, Mägi R, Hottenga JJ, Grallert H, Perry JRB, Bouatia-Naji N, Marullo L, Rybin D, Jansen R, Min JL, Dimas AS, Ulrich A, Zudina L, Gådin JR, Jiang L, Faggian A, Bonnefond A, Fadista J, Stathopoulou MG, Isaacs A, Willems SM, Navarro P, Tanaka T, Jackson AU, Montasser ME, O'Connell JR, Bielak LF, Webster RJ, Saxena R, Stafford JM, Pourcain BS, Timpson NJ, Salo P, Shin SY, Amin N, Smith AV, Li G, Verweij N, Goel A, Ford I, Johnson PCD, Johnson T, Kapur K, Thorleifsson G, Strawbridge RJ, Rasmussen-Torvik LJ, Esko T, Mihailov E, Fall T, Fraser RM, Mahajan A, Kanoni S, Giedraitis V, Kleber ME, Silbernagel G, Meyer J, Müller-Nurasyid M, Ganna A, Sarin AP, Yengo L, Shungin D, Luan J, Horikoshi M, An P, Sanna S, Boettcher Y, Rayner NW, Nolte IM, Zemunik T, Iperen EV, Kovacs P, Hastie ND, Wild SH, McLachlan S, Campbell S, Polasek O, Carlson O, Egan J, Kiess W, Willemsen G, Kuusisto J, Laakso M, Dimitriou M, Hicks AA, Rauramaa R, Bandinelli S, Thorand B, Liu Y, Miljkovic I, Lind L, Doney A, Perola M, Hingorani A, Kivimaki M, Kumari M, Bennett AJ, Groves CJ, Herder C, Koistinen HA, Kinnunen L, Faire UD, Bakker SJL, Uusitupa M, Palmer CNA, Jukema JW, Sattar N, Pouta A, Snieder H, Boerwinkle E, Pankow JS, Magnusson PK, Krus U, Scapoli C, de Geus EJCN, Blüher M, Wolffenbuttel BHR, Province MA, Abecasis GR, Meigs JB, Hovingh GK, Lindström J, Wilson JF, Wright AF, Dedoussis GV, Bornstein SR, Schwarz PEH, Tönjes A, Winkelmann BR, Boehm BO, März W, Metspalu A, Price JF, Deloukas P, Körner A, Lakka TA, Keinanen-Kiukaanniemi SM, Saaristo TE, Bergman RN, Tuomilehto J, Wareham NJ, Langenberg C, Männistö S, Franks PW, Hayward C, Vitart V, Kaprio J, Visvikis-Siest S, Balkau B, Altshuler D, Rudan I, Stumvoll M, Campbell H, van Duijn CM, Gieger C, Illig T, Ferrucci L, Pedersen NL, Pramstaller PP, Boehnke M, Frayling TM, Shuldiner AR, Peyser PA, Kardia SLR, Palmer LJ, Penninx BW, Meneton P, Harris TB, Navis G, Harst PVD, Smith GD, Forouhi NG, Loos RJF, Salomaa V, Soranzo N, Boomsma DI, Groop L, Tuomi T, Hofman A, Munroe PB, Gudnason V, Siscovick DS, Watkins H, Lecoeur C, Vollenweider P, Franco-Cereceda A, Eriksson P, Jarvelin MR, Stefansson K, Hamsten A, Nicholson G, Karpe F, Dermitzakis ET, Lindgren CM, McCarthy MI, Froguel P, Kaakinen MA, Lyssenko V, Watanabe RM, Ingelsson E, Florez JC, Dupuis J, Barroso I, Morris AP, Prokopenko I. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability. Nat Commun 2021; 12:24. [PMID: 33402679 PMCID: PMC7785747 DOI: 10.1038/s41467-020-19366-9] [Citation(s) in RCA: 71] [Impact Index Per Article: 23.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 09/22/2020] [Indexed: 12/20/2022] Open
Abstract
Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.
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Affiliation(s)
- Vasiliki Lagou
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Department of Microbiology and Immunology, Laboratory of Adaptive Immunity, KU Leuven, Leuven, Belgium
- VIB-KU Leuven Center for Brain and Disease Research, Leuven, Belgium
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jouke- Jan Hottenga
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, VU University medical center, Amsterdam, the Netherlands
| | - Harald Grallert
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
| | - John R B Perry
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Nabila Bouatia-Naji
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- INSERM U970, Paris Cardiovascular Research Center PARCC, 75006, Paris, France
| | - Letizia Marullo
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Denis Rybin
- Boston University Data Coordinating Center, Boston, MA, USA
| | - Rick Jansen
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Josine L Min
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Antigone S Dimas
- Institute for Bioinnovation, Biomedical Sciences Research Center Al. Fleming, Vari, Greece
| | - Anna Ulrich
- Department of Medicine, Imperial College London, London, UK
| | | | - Jesper R Gådin
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Longda Jiang
- Department of Medicine, Imperial College London, London, UK
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | | | - Amélie Bonnefond
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Department of Medicine, Imperial College London, London, UK
| | - Joao Fadista
- Department of Epidemiology Research, Statens Serum Institut, Copenhagen, Denmark
| | | | - Aaron Isaacs
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- CARIM School for Cardiovascular Diseases and Maastricht Centre for Systems Biology (MaCSBio, Maastricht University, Maastricht, the Netherlands
- Department of Physiology, Maastricht University, Maastricht, the Netherlands
| | - Sara M Willems
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Pau Navarro
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Toshiko Tanaka
- Translational Gerontology Branch, Longitudinal Study Section, National Institute on Aging, Baltimore, MD, USA
| | - Anne U Jackson
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - May E Montasser
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Jeff R O'Connell
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rebecca J Webster
- Laboratory for Cancer Medicine, Harry Perkins Institute of Medical Research, University of Western Australia Centre for Medical Research, Nedlands, WA, Australia
| | - Richa Saxena
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Departmentartment of Anesthesia, Critical Care and Pain Medicine, MGH, Boston, MA, USA
| | - Jeanette M Stafford
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Beate St Pourcain
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Perttu Salo
- Public Health Genomics Unit, Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - So-Youn Shin
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Najaf Amin
- Department of Epidemiology Erasmus MC, Rotterdam, the Netherlands
| | - Albert V Smith
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Guo Li
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anuj Goel
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Paul C D Johnson
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
- Institute of Biodiversity, Animal Health & Comparative Medicine, University of Glasgow, Glasgow, UK
| | - Toby Johnson
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Karen Kapur
- Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland
| | | | - Rona J Strawbridge
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Evelin Mihailov
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Tove Fall
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Ross M Fraser
- Usher Institute, University of Edinburgh, Edinburgh, UK
- Synpromics Ltd, Roslin Innovation Centre, Easter Bush Campus, Edinburgh, EH25 9RG, UK
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Genentech, 340 Point San Bruno Boulevard, South San Francisco, CA, 94080, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala Universitet, Uppsala, Sweden
| | - Marcus E Kleber
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Günther Silbernagel
- Division of Angiology, Department of Internal Medicine, Medical University of Graz, Graz, Austria
| | - Julia Meyer
- Institute of Genetic Epidemiology,Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Martina Müller-Nurasyid
- Institute of Genetic Epidemiology,Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology and Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-University, Munich, Germany
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI, University Medical Center, Johannes Gutenberg University, 55101, Mainz, Germany
| | - Andrea Ganna
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Antti-Pekka Sarin
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Public Health Genomics Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Loic Yengo
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, 4072, Australia
| | - Dmitry Shungin
- Department of Public Health & Clinical Medicine, Umeå University, Umeå, Sweden
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Odontology, Umeå University, Umeå, Sweden
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Momoko Horikoshi
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- RIKEN, Center for Integrative Medical Sciences, Laboratory for Endocrinology, Metabolism and Kidney Disease, Yokohama, Japan
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Serena Sanna
- Istituto di Ricerca Genetica e Biomedica, CNR, Monserrato, Italy
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Yvonne Boettcher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | - N William Rayner
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Wellcome Trust Sanger Institute, Hinxton, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Ilja M Nolte
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Erik van Iperen
- Department of Clinical Epidemiology and Biostatistics, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Peter Kovacs
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | - Nicholas D Hastie
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Sarah H Wild
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Susan Campbell
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Ozren Polasek
- Faculty of Medicine, University of Split, Split, Croatia
| | - Olga Carlson
- Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, MD, USA
| | - Josephine Egan
- Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, MD, USA
| | - Wieland Kiess
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
- Pediatric Research Center, Department of Women's & Child Health, University of Leipzig, Leipzig, Germany
| | - Gonneke Willemsen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Johanna Kuusisto
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Markku Laakso
- Department of Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Maria Dimitriou
- Department of Dietetics-Nutrition, Harokopio University, Athens, Greece
| | - Andrew A Hicks
- Center for Biomedicine, European Academy Bozen/Bolzano (EURAC) (Affiliated Institute of the University of LübeckLübeckGermany), Bolzano, Italy
| | - Rainer Rauramaa
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital, Kuopio, Finland
| | | | - Barbara Thorand
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Iva Miljkovic
- Department of Epidemiology, Center for Aging and Population Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Akademiska sjukhuset, Uppsala, Sweden
| | - Alex Doney
- Pat McPherson Centre for Pharmacogenetics and Pharmacogenomics, Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Markus Perola
- Public Health Genomics Unit, Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Estonian Genome Center, University of Tartu, Tartu, Estonia
| | - Aroon Hingorani
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, London, UK
| | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, London, UK
- University of Essex, Wivenhoe Park, Colchester, Essex, UK
| | - Amanda J Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Christian Herder
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Division of Endocrinology and Diabetology, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Heikki A Koistinen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
- Department of Medicine, University of Helsinki and Helsinki University Central Hospital, P.O. Box 340, Haartmaninkatu 4, Helsinki, FI-00029, Finland
- Minerva Foundation Institute for Medical Research, Biomedicum 2U, Tukholmankatu 8, Helsinki, FI-00290, Finland
| | - Leena Kinnunen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
| | - Ulf de Faire
- Division of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Stephan J L Bakker
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Matti Uusitupa
- Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Colin N A Palmer
- Pat McPherson Centre for Pharmacogenetics and Pharmacogenomics, Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - J Wouter Jukema
- Dept of Cardiology, Leiden University Medical Center, Leiden, the Netherlands
- Netherlands Heart Institute, Utrecht, the Netherlands
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Anneli Pouta
- Department of Government Services, Finnish Institute for Health and Welfare, Helsinki, Finland
- PEDEGO Research Unit, Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Eric Boerwinkle
- IMM Center for Human Genetics, University of Texas Health Science Center at Houston, Houston, TX, USA
- Division of Epidemiology, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MiI, USA
| | - Patrik K Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ulrika Krus
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
| | - Chiara Scapoli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| | - Eco J C N de Geus
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, VU University medical center, Amsterdam, the Netherlands
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig, Germany
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Bruce H R Wolffenbuttel
- Department of Endocrinology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Goncalo R Abecasis
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - James B Meigs
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- General Medicine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - G Kees Hovingh
- Department of Vascular Medicine, Amsterdam UMC, Amsterdam, the Netherlands
- Novo Nordisk A/S, Copenhagen, Denmark
| | - Jaana Lindström
- Finnish Institute for Health and Welfare, Diabetes Prevention Unit, Helsinki, Finland
| | - James F Wilson
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Alan F Wright
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | | | - Stefan R Bornstein
- Department of Medicine, Division for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Peter E H Schwarz
- Department for Prevention and Care of Diabetes, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of the Helmholtz Center Munich at University Hospital and Faculty of Medicine, TU Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | | | - Bernhard O Boehm
- Lee Kong Chian School of Medicine, Nanyang Technological University Singapore and Imperial College London, Singapore, Singapore
| | - Winfried März
- Vth Department of Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
- Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | | | - Panos Deloukas
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Wellcome Trust Sanger Institute, Hinxton, UK
- Princess Al-Jawhara Al-Brahim Centre of Excellence in Research of Hereditary Disorders (PACER-HD, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Antje Körner
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Timo A Lakka
- Kuopio Research Institute of Exercise Medicine, Kuopio, Finland
- Institute of Biomedicine/Physiology, University of Eastern Finland, Kuopio Campus, Kuopio, Finland
| | - Sirkka M Keinanen-Kiukaanniemi
- Faculty of Medicine, Center for Life Course Health Research, University of Oulu, Oulu, Finland
- Unit of General Practice, Oulu University Hospital, Oulu, Finland
| | - Timo E Saaristo
- Finnish Diabetes Association, Tampere, Finland
- Pirkanmaa Hospital District, Tampere, Finland
| | - Richard N Bergman
- Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jaakko Tuomilehto
- Department of Chronic Disease Prevention, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Centre for Vascular Prevention, Danube-University Krems, Krems, Austria
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Satu Männistö
- Department of Public Health Solutions, Finnish Institute for Health and Welfare, P.O. Box 30, Helsinki, FI-00271, Finland
| | - Paul W Franks
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
- Department of Public Health & Clinical Medicine, Units of Medicine and Nutritional Research, Umeå University, Umeå, Sweden
| | - Caroline Hayward
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Veronique Vitart
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | | | - Beverley Balkau
- Inserm, CESP Center for Research in Epidemiology and Public Health, U1018, Villejuif, France
- Univ Paris-Saclay, Univ Paris Sud, UVSQ, UMRS 1018, UMRS 1018, Villejuif, France
| | - David Altshuler
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Igor Rudan
- Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Leipzig, Germany
- IFB AdiposityDiseases, University of Leipzig, Leipzig, Germany
| | | | - Cornelia M van Duijn
- Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- Centre for Medical Systems Biology, Leiden, the Netherlands
| | - Christian Gieger
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD, München-Neuherberg, Germany
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Thomas Illig
- Research Unit of Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum München Research Center for Environmental Health, Neuherberg, Germany
- Hannover Unified Biobank, Hannover Medical School, Hannover, Germany
- Institute of Human Genetics, Hannover Medical School, Hannover, Germany
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland, USA
| | - Nancy L Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Peter P Pramstaller
- Center for Biomedicine, European Academy Bozen/Bolzano (EURAC) (Affiliated Institute of the University of LübeckLübeckGermany), Bolzano, Italy
- Department of Neurology, General Central Hospital, Bolzano, Italy
- Department of Neurology, University of Lübeck, Lübeck, Germany
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Timothy M Frayling
- Genetics of Complex Traits, Peninsula Medical School, University of Exeter, Exeter, UK
| | - Alan R Shuldiner
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland, School of Medicine, Baltimore, MD, USA
- The Regeneron Genetics Center, Regeneron Pharmaceuticals, Tarrytown, NY, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Lyle J Palmer
- School of Public Health, University of Adelaide, Adelaide, Australia
| | - Brenda W Penninx
- Department of Psychiatry, VU University Medical Center, Amsterdam, the Netherlands
| | - Pierre Meneton
- U872 Institut National de la Santé et de la Recherche Médicale, Centre de Recherche des Cordeliers, 75006, Paris, France
| | - Tamara B Harris
- Geriatric Epidemiology Section, Laboratory of Epidemiology, Demography, and Biometry, National Institute on Aging, Bethesda, MD, USA
| | - Gerjan Navis
- Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
| | - Ruth J F Loos
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
| | - Leif Groop
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
- Endocrinology, Abdominal Centre, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, University of Helsinki and Folkhälsan Research Center, Helsinki, Finland
| | - Albert Hofman
- Department of Epidemiology Erasmus MC, Rotterdam, the Netherlands
- Netherlands Consortium for healthy ageing, the Hague, the Netherlands
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, USA
| | - Patricia B Munroe
- William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- NIHR Barts Cardiovascular Biomedical Research Unit, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine University of Iceland, Reykjavik, Iceland
| | - David S Siscovick
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Hugh Watkins
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Cecile Lecoeur
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
| | - Peter Vollenweider
- Department of Medicine, University Hospital Lausanne, Lausanne, Switzerland
| | - Anders Franco-Cereceda
- Cardiothoracic Surgery Unit, Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Per Eriksson
- Cardiovascular Medicine Unit, Center for Molecular Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Karolinska University Hospital, Solna, Sweden
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Biostatistics and HPA-MRC Center, School of Public Health, Imperial College London, London, UK
- Institue of Health Sciences, University of Oulu, Oulu, Finland
| | - Kari Stefansson
- deCODE Genetics, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavík, Iceland
| | - Anders Hamsten
- Cardiovascular Medicine Unit, Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital Solna, Stockholm, Sweden
- Department of Cardiology, Karolinska University Hospital Solna, Stockholm, Sweden
| | | | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Emmanouil T Dermitzakis
- Department of Genetic Medicine and Development, University of Geneva Medical School, Geneva, Switzerland
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Broad Institute of Harvard and Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, UK
- Genentech, 340 Point San Bruno Boulevard, South San Francisco, CA, 94080, USA
| | - Philippe Froguel
- University of Lille Nord de France, Lille, France
- CNRS UMR8199, Institut Pasteur de Lille, Lille, France
- Department of Medicine, Imperial College London, London, UK
| | - Marika A Kaakinen
- Department of Medicine, Imperial College London, London, UK
- School of Biosciences and Medicine, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology Research Unit, University Hospital Malmö, Lund University, Malmö, Sweden
- Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Richard M Watanabe
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
- Department of Physiology & Neuroscience, Keck School of Medicine of USC, Los Angeles, CA, USA
- USC Diabetes and Obesity Research Institute, Los Angeles, CA, USA
| | - Erik Ingelsson
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, 94305, USA
| | - Jose C Florez
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, UK
- University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Wellcome Trust-MRC Institute of Metabolic Science, Cambridge, UK
- Exeter Centre of ExcEllence in Diabetes (ExCEED), University of Exeter Medical School, Exeter, UK
| | - Andrew P Morris
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Estonian Genome Center, University of Tartu, Tartu, Estonia
- Department of Biostatistics, University of Liverpool, Liverpool, UK
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Inga Prokopenko
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
- Department of Medicine, Imperial College London, London, UK.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
- School of Biosciences and Medicine, Department of Clinical and Experimental Medicine, University of Surrey, Guildford, UK.
- Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa, Russian Federation.
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90
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Zhang W, Luo C, Scossa F, Zhang Q, Usadel B, Fernie AR, Mei H, Wen W. A phased genome based on single sperm sequencing reveals crossover pattern and complex relatedness in tea plants. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 105:197-208. [PMID: 33118252 DOI: 10.1111/tpj.15051] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 05/27/2023]
Abstract
For diploid organisms that are highly heterozygous, a phased haploid genome can greatly aid in functional genomic, population genetic and breeding studies. Based on the genome sequencing of 135 single sperm cells of the elite tea cultivar 'Fudingdabai', we herein phased the genome of Camellia sinensis, one of the most popular beverage crops worldwide. High-resolution genetic and recombination maps of Fudingdabai were constructed, which revealed that crossover (CO) positions were frequently located in the 5' and 3' ends of annotated genes, while CO distributions across the genome were random. The low CO frequency in tea can be explained by strong CO interference, and CO simulation revealed the proportion of interference insensitive CO ranged from 5.2% to 11.7%. We furthermore developed a method to infer the relatedness between tea accessions and detected complex kinship and genetic signatures of 106 tea accessions. Among them, 59 accessions were closely related with Fudingdabai and 31 of them were first-degree relatives. We additionally identified genes displaying allele specific expression patterns between the two haplotypes of Fudingdabai and genes displaying significantly differential expression levels between Fudingdabai and other haplotypes. These results lay the foundation for further investigation of genetic and epigenetic factors underpinning the regulation of gene expression and provide insights into the evolution of tea plants as well as a valuable genetic resource for future breeding efforts.
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Affiliation(s)
- Weiyi Zhang
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Cheng Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Federico Scossa
- Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, 14476, Germany
- Council for Agricultural Research and Economics, Research Center for Genomics and Bioinformatics, Via Ardeatina 546, Rome, 00178, Italy
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Björn Usadel
- Institute for Biological Data Science, Heinrich Heine University, Düsseldorf, Germany
- Institute of Bio- and Geosciences, IBG-4: Bioinformatics, CEPLAS, Forschungszentrum Jülich, Leo-Brandt-Straße, Jülich, 52425, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, 14476, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Hanwei Mei
- Shanghai Agrobiological Gene Center, Shanghai, 201106, China
| | - Weiwei Wen
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
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91
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Jin J, Robeson H, Fagan P, Orloff MS. Association of PARP1-specific polymorphisms and haplotypes with non-small cell lung cancer subtypes. PLoS One 2020; 15:e0243509. [PMID: 33284833 PMCID: PMC7721167 DOI: 10.1371/journal.pone.0243509] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 11/20/2020] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE The carcinogenesis role of PARP1 in lung cancer is still not clear. Analysis at allelic levels cannot fully explain the function of PARP1 on lung cancer. Our study aims to further explore the relation between PARP1 haplotypes and lung cancer. MATERIALS AND METHODS DNA and RNA were extracted from non-small cell lung cancer (NSCLC) tumor and adjacent normal fresh frozen tissue. Five PARP1-SNPs were genotyped and PARP1-specific SNPs were imputed using IMPUTE and SHAPEIT software. The SNPs were subjected to allelic, haplotype and SNP-SNP interaction analyses. Correlation between SNPs and mRNA/protein expressions were performed. RESULTS SNP imputation inferred the ungenotyped SNPs and increased the power for association analysis. Tumor tissue samples are more likely to carry rs1805414 (OR = 1.85; 95% CI: 1.12-3.06; P-value: 0.017) and rs1805404 (OR = 2.74; 95%CI 1.19-6.32; P-value: 0.015) compared to normal tissues. Our study is the first study to show that haplotypes comprising of 5 SNPs on PARP1 (rs1136410, rs3219073, rs1805414, rs1805404, rs1805415) is able to differentiate the NSCLC tumor from normal tissues. Interaction between rs3219073, rs1805415, and rs1805414 were significantly associated with the NSCLC tumor with OR ranging from 3.61-6.75; 95%CI from 1.82 to 19.9; P-value<0.001. CONCLUSION PARP1 haplotypes may serve as a better predictor in lung cancer development and prognosis compared to single alleles.
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Affiliation(s)
- Jing Jin
- Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Heather Robeson
- Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Pebbles Fagan
- Department of Health Behavior and Health Education, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Center for the Studies of Tobacco, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
| | - Mohammed S. Orloff
- Department of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
- Center for the Studies of Tobacco, Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America
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92
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Niu Y, Xie C, Du Z, Zeng J, Chen H, Jin L, Zhang Q, Yu H, Wang Y, Ping J, Yang C, Liu X, Li Y, Zhou G. Genome-wide association study identifies 7q11.22 and 7q36.3 associated with noise-induced hearing loss among Chinese population. J Cell Mol Med 2020; 25:411-420. [PMID: 33242228 PMCID: PMC7810922 DOI: 10.1111/jcmm.16094] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 10/02/2020] [Accepted: 10/28/2020] [Indexed: 12/25/2022] Open
Abstract
Noise-induced hearing loss (NIHL) seriously affects the life quality of humans and causes huge economic losses to society. To identify novel genetic loci involved in NIHL, we conducted a genome-wide association study (GWAS) for this symptom in Chinese populations. GWAS scan was performed in 89 NIHL subjects (cases) and 209 subjects with normal hearing who have been exposed to a similar noise environment (controls), followed by a replication study consisting of 53 cases and 360 controls. We identified that four candidate pathways were nominally significantly associated with NIHL, including the Erbb, Wnt, hedgehog and intraflagellar transport pathways. In addition, two novel index single-nucleotide polymorphisms, rs35075890 in the intron of AUTS2 gene at 7q11.22 (combined P = 1.3 × 10-6 ) and rs10081191 in the intron of PTPRN2 gene at 7q36.3 (combined P = 2.1 × 10-6 ), were significantly associated with NIHL. Furthermore, the expression quantitative trait loci analyses revealed that in brain tissues, the genotypes of rs35075890 are significantly associated with the expression levels of AUTS2, and the genotypes of rs10081191 are significantly associated with the expressions of PTPRN2 and WDR60. In conclusion, our findings highlight two novel loci at 7q11.22 and 7q36.3 conferring susceptibility to NIHL.
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Affiliation(s)
- Yuguang Niu
- Department of Otolaryngology, the First Medical Center of PLA General Hospital, Beijing, China
| | - Chengyong Xie
- Medical College of Guizhou University, Guiyang city, China
| | - Zhenhua Du
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Jifeng Zeng
- Department of Otolaryngology, the No. 954 Hospital of PLA, Shannan City, China
| | - Hongxia Chen
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Liang Jin
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Qing Zhang
- Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, China
| | - Huiying Yu
- Outpatient Department, the Fifth Medical Center of PLA General Hospital, Beijing, China
| | - Yahui Wang
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Jie Ping
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Chenning Yang
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Xinyi Liu
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Yuanfeng Li
- State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China
| | - Gangqiao Zhou
- Medical College of Guizhou University, Guiyang city, China.,State Key Laboratory of Proteomics, National Center for Protein Sciences, Beijing Institute of Radiation Medicine, Beijing, China.,Collaborative Innovation Center for Personalized Cancer Medicine, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing City, China
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93
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Hong S, Prokopenko D, Dobricic V, Kilpert F, Bos I, Vos SJB, Tijms BM, Andreasson U, Blennow K, Vandenberghe R, Cleynen I, Gabel S, Schaeverbeke J, Scheltens P, Teunissen CE, Niemantsverdriet E, Engelborghs S, Frisoni G, Blin O, Richardson JC, Bordet R, Molinuevo JL, Rami L, Kettunen P, Wallin A, Lleó A, Sala I, Popp J, Peyratout G, Martinez-Lage P, Tainta M, Dobson RJB, Legido-Quigley C, Sleegers K, Van Broeckhoven C, Ten Kate M, Barkhof F, Zetterberg H, Lovestone S, Streffer J, Wittig M, Franke A, Tanzi RE, Visser PJ, Bertram L. Genome-wide association study of Alzheimer's disease CSF biomarkers in the EMIF-AD Multimodal Biomarker Discovery dataset. Transl Psychiatry 2020; 10:403. [PMID: 33223526 PMCID: PMC7680793 DOI: 10.1038/s41398-020-01074-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/23/2020] [Accepted: 05/18/2020] [Indexed: 12/11/2022] Open
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the elderly. Susceptibility to AD is considerably determined by genetic factors which hitherto were primarily identified using case-control designs. Elucidating the genetic architecture of additional AD-related phenotypic traits, ideally those linked to the underlying disease process, holds great promise in gaining deeper insights into the genetic basis of AD and in developing better clinical prediction models. To this end, we generated genome-wide single-nucleotide polymorphism (SNP) genotyping data in 931 participants of the European Medical Information Framework Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) sample to search for novel genetic determinants of AD biomarker variability. Specifically, we performed genome-wide association study (GWAS) analyses on 16 traits, including 14 measures derived from quantifications of five separate amyloid-beta (Aβ) and tau-protein species in the cerebrospinal fluid (CSF). In addition to confirming the well-established effects of apolipoprotein E (APOE) on diagnostic outcome and phenotypes related to Aβ42, we detected novel potential signals in the zinc finger homeobox 3 (ZFHX3) for CSF-Aβ38 and CSF-Aβ40 levels, and confirmed the previously described sex-specific association between SNPs in geminin coiled-coil domain containing (GMNC) and CSF-tau. Utilizing the results from independent case-control AD GWAS to construct polygenic risk scores (PRS) revealed that AD risk variants only explain a small fraction of CSF biomarker variability. In conclusion, our study represents a detailed first account of GWAS analyses on CSF-Aβ and -tau-related traits in the EMIF-AD MBD dataset. In subsequent work, we will utilize the genomics data generated here in GWAS of other AD-relevant clinical outcomes ascertained in this unique dataset.
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Affiliation(s)
- Shengjun Hong
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Fabian Kilpert
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, The Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, The Netherlands
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, The Netherlands
| | - Ulf Andreasson
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospital Leuven, Leuven, Belgium
| | - Isabelle Cleynen
- Laboratory for Complex Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Jolien Schaeverbeke
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, The Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands
| | - Ellis Niemantsverdriet
- Department of Biomedical Sciences, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Department of Neurology and Center for Neurosciences, UZ Brussel and Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Giovanni Frisoni
- University of Geneva, Geneva, Switzerland
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- AIX Marseille University, INS, Ap-hm, Marseille, France
| | | | - Regis Bordet
- University of Lille, Inserm, CHU Lille, Lille, France
| | - José Luis Molinuevo
- Alzheimer's disease and other cognitive disorders unit, Hospital Clinic I Universitari, Barcelona, Spain
| | - Lorena Rami
- Alzheimer's disease and other cognitive disorders unit, Hospital Clinic I Universitari, Barcelona, Spain
| | - Petronella Kettunen
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Anders Wallin
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Alberto Lleó
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Isabel Sala
- Memory Unit, Neurology Department, Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Julius Popp
- Geriatric Psychiatry, Department of Mental Health and Psychiatry, Geneva University Hospitals, Geneva, Switzerland
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Gwendoline Peyratout
- Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Mikel Tainta
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK
- Health Data Research UK London, University College London, 222 Euston Road, London, UK
- Institute of Health Informatics, University College London, 222 Euston Road, London, UK
- The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, 222 Euston Road, London, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark
- Institute of Pharmaceutical Sciences, King's College London, London, UK
| | - Kristel Sleegers
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | | | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
- Translational Medicine Neuroscience, UCB Biopharma SPRL, Braine l'Alleud, Belgium
| | - Michael Wittig
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, The Netherlands
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), Institutes of Neurogenetics and Cardiogenetics, University of Lübeck, Lübeck, Germany.
- Department of Psychology, University of Oslo, Oslo, Norway.
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94
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Hermisdorff IDC, Costa RB, de Albuquerque LG, Pausch H, Kadri NK. Investigating the accuracy of imputing autosomal variants in Nellore cattle using the ARS-UCD1.2 assembly of the bovine genome. BMC Genomics 2020; 21:772. [PMID: 33167856 PMCID: PMC7654006 DOI: 10.1186/s12864-020-07184-8] [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] [Received: 06/27/2020] [Accepted: 10/26/2020] [Indexed: 11/22/2022] Open
Abstract
Background Imputation accuracy among other things depends on the size of the reference panel, the marker’s minor allele frequency (MAF), and the correct placement of single nucleotide polymorphism (SNP) on the reference genome assembly. Using high-density genotypes of 3938 Nellore cattle from Brazil, we investigated the accuracy of imputation from 50 K to 777 K SNP density using Minimac3, when map positions were determined according to the bovine genome assemblies UMD3.1 and ARS-UCD1.2. We assessed the effect of reference and target panel sizes on the pre-phasing based imputation quality using ten-fold cross-validation. Further, we compared the reliability of the model-based imputation quality score (Rsq) from Minimac3 to the empirical imputation accuracy. Results The overall accuracy of imputation measured as the squared correlation between true and imputed allele dosages (R2dose) was almost identical using either the UMD3.1 or ARS-UCD1.2 genome assembly. When the size of the reference panel increased from 250 to 2000, R2dose increased from 0.845 to 0.917, and the number of polymorphic markers in the imputed data set increased from 586,701 to 618,660. Advantages in both accuracy and marker density were also observed when larger target panels were imputed, likely resulting from more accurate haplotype inference. Imputation accuracy increased from 0.903 to 0.913, and the marker density in the imputed data increased from 593,239 to 595,570 when haplotypes were inferred in 500 and 2900 target animals. The model-based imputation quality scores from Minimac3 (Rsq) were systematically higher than empirically estimated accuracies. However, both metrics were positively correlated and the correlation increased with the size of the reference panel and MAF of imputed variants. Conclusions Accurate imputation of BovineHD BeadChip markers is possible in Nellore cattle using the new bovine reference genome assembly ARS-UCD1.2. The use of large reference and target panels improves the accuracy of the imputed genotypes and provides genotypes for more markers segregating at low frequency for downstream genomic analyses. The model-based imputation quality score from Minimac3 (Rsq) can be used to detect poorly imputed variants but its reliability depends on the size of the reference panel and MAF of the imputed variants. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-020-07184-8.
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Affiliation(s)
- Isis da Costa Hermisdorff
- School of Veterinary Medicine and Animal Science, Federal University of Bahia (UFBA), Salvador, Brazil.,Animal Genomics, ETH Zurich, Zurich, Switzerland
| | - Raphael Bermal Costa
- School of Veterinary Medicine and Animal Science, Federal University of Bahia (UFBA), Salvador, Brazil
| | - Lucia Galvão de Albuquerque
- Animal Science Department, School of Agricultural and Veterinary Sciences, São Paulo State University (Unesp), Jaboticabal, São Paulo, Brazil
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95
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Vergara C, Duggal P, Thio CL, Valencia A, O'Brien TR, Latanich R, Timp W, Johnson EO, Kral AH, Mangia A, Goedert JJ, Piazzola V, Mehta SH, Kirk GD, Peters MG, Donfield SM, Edlin BR, Busch MP, Alexander G, Murphy EL, Kim AY, Lauer GM, Chung RT, Cramp ME, Cox AL, Khakoo SI, Rosen HR, Alric L, Wheelan SJ, Wojcik GL, Thomas DL, Taub MA. Multi-ancestry fine mapping of interferon lambda and the outcome of acute hepatitis C virus infection. Genes Immun 2020; 21:348-359. [PMID: 33116245 PMCID: PMC7657970 DOI: 10.1038/s41435-020-00115-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 10/08/2020] [Accepted: 10/12/2020] [Indexed: 02/06/2023]
Abstract
Clearance of acute infection with hepatitis C virus (HCV) is associated with the chr19q13.13 region containing the rs368234815 (TT/ΔG) polymorphism. We fine-mapped this region to detect possible causal variants that may contribute to HCV clearance. First, we performed sequencing of IFNL1-IFNL4 region in 64 individuals sampled according to rs368234815 genotype: TT/clearance (N = 16) and ΔG/persistent (N = 15) (genotype-outcome concordant) or TT/persistent (N = 19) and ΔG/clearance (N = 14) (discordant). 25 SNPs had a difference in counts of alternative allele >5 between clearance and persistence individuals. Then, we evaluated those markers in an association analysis of HCV clearance conditioning on rs368234815 in two groups of European (692 clearance/1 025 persistence) and African ancestry (320 clearance/1 515 persistence) individuals. 10/25 variants were associated (P < 0.05) in the conditioned analysis leaded by rs4803221 (P value = 4.9 × 10-04) and rs8099917 (P value = 5.5 × 10-04). In the European ancestry group, individuals with the haplotype rs368234815ΔG/rs4803221C were 1.7× more likely to clear than those with the rs368234815ΔG/rs4803221G haplotype (P value = 3.6 × 10-05). For another nearby SNP, the haplotype of rs368234815ΔG/rs8099917T was associated with HCV clearance compared to rs368234815ΔG/rs8099917G (OR: 1.6, P value = 1.8 × 10-04). We identified four possible causal variants: rs368234815, rs12982533, rs10612351 and rs4803221. Our results suggest a main signal of association represented by rs368234815, with contributions from rs4803221, and/or nearby SNPs including rs8099917.
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Affiliation(s)
- Candelaria Vergara
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Priya Duggal
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - Chloe L Thio
- Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Ana Valencia
- Johns Hopkins University, School of Medicine, Baltimore, MD, USA
- Universidad Pontificia Bolivariana, Medellin, Colombia
| | - Thomas R O'Brien
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Rachel Latanich
- Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Winston Timp
- Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | | | - Alex H Kral
- RTI International, Research Triangle Park, NC, USA
| | - Alessandra Mangia
- Liver Unit IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Italy
| | - James J Goedert
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Valeria Piazzola
- Liver Unit IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, Italy
| | - Shruti H Mehta
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - Gregory D Kirk
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - Marion G Peters
- Division of Gastroenterology, Department of Medicine, School of Medicine, University of California, San Francisco, CA, USA
| | | | - Brian R Edlin
- Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Michael P Busch
- University of California San Francisco and Vitalant Research Institute, San Francisco, CA, USA
| | - Graeme Alexander
- University College London Institute for Liver and Digestive Health, The Royal Free Hospital, London, UK
| | - Edward L Murphy
- University of California San Francisco and Vitalant Research Institute, San Francisco, CA, USA
| | - Arthur Y Kim
- Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Georg M Lauer
- Liver Center and Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Raymond T Chung
- Liver Center and Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Andrea L Cox
- Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Salim I Khakoo
- University of Southampton, Southampton General Hospital, Southampton, UK
| | | | - Laurent Alric
- Department of Internal Medicine and Digestive Diseases, Centre Hospitalier Universitaire Rangueil, UMR 152, Institut de Recherche pour le Développement Toulouse 3 University, Toulouse, France
| | - Sarah J Wheelan
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
- Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Genevieve L Wojcik
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
| | - David L Thomas
- Johns Hopkins University, School of Medicine, Baltimore, MD, USA
| | - Margaret A Taub
- Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD, USA
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96
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Hui R, D'Atanasio E, Cassidy LM, Scheib CL, Kivisild T. Evaluating genotype imputation pipeline for ultra-low coverage ancient genomes. Sci Rep 2020; 10:18542. [PMID: 33122697 PMCID: PMC7596702 DOI: 10.1038/s41598-020-75387-w] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 10/12/2020] [Indexed: 01/12/2023] Open
Abstract
Although ancient DNA data have become increasingly more important in studies about past populations, it is often not feasible or practical to obtain high coverage genomes from poorly preserved samples. While methods of accurate genotype imputation from > 1 × coverage data have recently become a routine, a large proportion of ancient samples remain unusable for downstream analyses due to their low coverage. Here, we evaluate a two-step pipeline for the imputation of common variants in ancient genomes at 0.05-1 × coverage. We use the genotype likelihood input mode in Beagle and filter for confident genotypes as the input to impute missing genotypes. This procedure, when tested on ancient genomes, outperforms a single-step imputation from genotype likelihoods, suggesting that current genotype callers do not fully account for errors in ancient sequences and additional quality controls can be beneficial. We compared the effect of various genotype likelihood calling methods, post-calling, pre-imputation and post-imputation filters, different reference panels, as well as different imputation tools. In a Neolithic Hungarian genome, we obtain ~ 90% imputation accuracy for heterozygous common variants at coverage 0.05 × and > 97% accuracy at coverage 0.5 ×. We show that imputation can mitigate, though not eliminate reference bias in ultra-low coverage ancient genomes.
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Affiliation(s)
- Ruoyun Hui
- McDonald Institute for Archaeological Research, University of Cambridge, Cambridge, UK
| | - Eugenia D'Atanasio
- Department of Human Genetics, Katholieke Universiteit Leuven, Herestraat 49 - box 602, 3000, Leuven, Belgium
- Istituto di Biologia e Patologia Molecolari, Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Lara M Cassidy
- Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland
| | - Christiana L Scheib
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia
- St John's College, St John's Street, Cambridge, CB2 1TP, UK
| | - Toomas Kivisild
- Department of Human Genetics, Katholieke Universiteit Leuven, Herestraat 49 - box 602, 3000, Leuven, Belgium.
- Estonian Biocentre, Institute of Genomics, University of Tartu, Tartu, Estonia.
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97
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Hebbar P, Abubaker JA, Abu-Farha M, Alsmadi O, Elkum N, Alkayal F, John SE, Channanath A, Iqbal R, Pitkaniemi J, Tuomilehto J, Sladek R, Al-Mulla F, Thanaraj TA. Genome-wide landscape establishes novel association signals for metabolic traits in the Arab population. Hum Genet 2020; 140:505-528. [PMID: 32902719 PMCID: PMC7889551 DOI: 10.1007/s00439-020-02222-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Accepted: 09/01/2020] [Indexed: 02/07/2023]
Abstract
While the Arabian population has a high prevalence of metabolic disorders, it has not been included in global studies that identify genetic risk loci for metabolic traits. Determining the transferability of such largely Euro-centric established risk loci is essential to transfer the research tools/resources, and drug targets generated by global studies to a broad range of ethnic populations. Further, consideration of populations such as Arabs, that are characterized by consanguinity and a high level of inbreeding, can lead to identification of novel risk loci. We imputed published GWAS data from two Kuwaiti Arab cohorts (n = 1434 and 1298) to the 1000 Genomes Project haplotypes and performed meta-analysis for associations with 13 metabolic traits. We compared the observed association signals with those established for metabolic traits. Our study highlighted 70 variants from 9 different genes, some of which have established links to metabolic disorders. By relaxing the genome-wide significance threshold, we identified ‘novel’ risk variants from 11 genes for metabolic traits. Many novel risk variant association signals were observed at or borderline to genome-wide significance. Furthermore, 349 previously established variants from 187 genes were validated in our study. Pleiotropic effect of risk variants on multiple metabolic traits were observed. Fine-mapping illuminated rs7838666/CSMD1 rs1864163/CETP and rs112861901/[INTS10,LPL] as candidate causal variants influencing fasting plasma glucose and high-density lipoprotein levels. Computational functional analysis identified a variety of gene regulatory signals around several variants. This study enlarges the population ancestry diversity of available GWAS and elucidates new variants in an ethnic group burdened with metabolic disorders.
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Affiliation(s)
- Prashantha Hebbar
- Dasman Diabetes Institute, P.O. Box 1180, 15462, Dasman, Kuwait.,Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | | | | | | | - Naser Elkum
- Sidra Medical and Research Center, Doha, Qatar
| | - Fadi Alkayal
- Dasman Diabetes Institute, P.O. Box 1180, 15462, Dasman, Kuwait
| | - Sumi Elsa John
- Dasman Diabetes Institute, P.O. Box 1180, 15462, Dasman, Kuwait
| | | | - Rasheeba Iqbal
- Dasman Diabetes Institute, P.O. Box 1180, 15462, Dasman, Kuwait
| | - Janne Pitkaniemi
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Jaakko Tuomilehto
- Department of Public Health, University of Helsinki, Helsinki, Finland.,Department of Public Health Solutions, National Institute for Health and Welfare, Helsinki, Finland
| | - Robert Sladek
- McGill University and Genome Quebec Innovation Centre, Montreal, Canada
| | - Fahd Al-Mulla
- Dasman Diabetes Institute, P.O. Box 1180, 15462, Dasman, Kuwait.
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98
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Rovira P, Demontis D, Sánchez-Mora C, Zayats T, Klein M, Mota NR, Weber H, Garcia-Martínez I, Pagerols M, Vilar-Ribó L, Arribas L, Richarte V, Corrales M, Fadeuilhe C, Bosch R, Martin GE, Almos P, Doyle AE, Grevet EH, Grimm O, Halmøy A, Hoogman M, Hutz M, Jacob CP, Kittel-Schneider S, Knappskog PM, Lundervold AJ, Rivero O, Rovaris DL, Salatino-Oliveira A, da Silva BS, Svirin E, Sprooten E, Strekalova T, Arias-Vasquez A, Sonuga-Barke EJS, Asherson P, Bau CHD, Buitelaar JK, Cormand B, Faraone SV, Haavik J, Johansson SE, Kuntsi J, Larsson H, Lesch KP, Reif A, Rohde LA, Casas M, Børglum AD, Franke B, Ramos-Quiroga JA, Soler Artigas M, Ribasés M. Shared genetic background between children and adults with attention deficit/hyperactivity disorder. Neuropsychopharmacology 2020; 45:1617-1626. [PMID: 32279069 PMCID: PMC7419307 DOI: 10.1038/s41386-020-0664-5] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 02/25/2020] [Accepted: 03/03/2020] [Indexed: 12/14/2022]
Abstract
Attention deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate symptoms of inattention, impulsivity, and hyperactivity that persist into adulthood in the majority of the diagnosed children. Despite several risk factors during childhood predicting the persistence of ADHD symptoms into adulthood, the genetic architecture underlying the trajectory of ADHD over time is still unclear. We set out to study the contribution of common genetic variants to the risk for ADHD across the lifespan by conducting meta-analyses of genome-wide association studies on persistent ADHD in adults and ADHD in childhood separately and jointly, and by comparing the genetic background between them in a total sample of 17,149 cases and 32,411 controls. Our results show nine new independent loci and support a shared contribution of common genetic variants to ADHD in children and adults. No subgroup heterogeneity was observed among children, while this group consists of future remitting and persistent individuals. We report similar patterns of genetic correlation of ADHD with other ADHD-related datasets and different traits and disorders among adults, children, and when combining both groups. These findings confirm that persistent ADHD in adults is a neurodevelopmental disorder and extend the existing hypothesis of a shared genetic architecture underlying ADHD and different traits to a lifespan perspective.
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Affiliation(s)
- Paula Rovira
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain
| | - Ditte Demontis
- grid.7048.b0000 0001 1956 2722Department of Biomedicine (Human Genetics), and Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark ,grid.452548.a0000 0000 9817 5300The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark ,Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Cristina Sánchez-Mora
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.413448.e0000 0000 9314 1427Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain ,grid.5841.80000 0004 1937 0247Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
| | - Tetyana Zayats
- grid.7914.b0000 0004 1936 7443Department of Biomedicine, University of Bergen, Bergen, Norway ,grid.32224.350000 0004 0386 9924Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, and Harvard Medical School, Boston, MA USA ,grid.66859.34Stanley Center for Psychiatric Research, Broad Institute of MIT, and Harvard, Cambridge, MA USA
| | - Marieke Klein
- grid.10417.330000 0004 0444 9382Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.7692.a0000000090126352University Medical Center Utrecht, UMC Utrecht Brain Center, Department of Psychiatry, Utrecht, The Netherlands
| | - Nina Roth Mota
- grid.10417.330000 0004 0444 9382Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.414449.80000 0001 0125 3761ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil ,grid.10417.330000 0004 0444 9382Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Heike Weber
- grid.8379.50000 0001 1958 8658Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Würzburg, Germany ,grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Iris Garcia-Martínez
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.438280.5Banc de Sang i Teixits (BST), Barcelona, Spain ,grid.7080.f0000 0001 2296 0625Grup de Medicina Transfusional, Vall d’Hebron Institut de Recerca, Universitat Autònoma de Barcelona (VHIR-UAB), Barcelona, Spain
| | - Mireia Pagerols
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain
| | - Laura Vilar-Ribó
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain
| | - Lorena Arribas
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain
| | - Vanesa Richarte
- grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.413448.e0000 0000 9314 1427Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain ,grid.7080.f0000 0001 2296 0625Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain
| | - Montserrat Corrales
- grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.413448.e0000 0000 9314 1427Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain ,grid.7080.f0000 0001 2296 0625Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain
| | - Christian Fadeuilhe
- grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.413448.e0000 0000 9314 1427Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain ,grid.7080.f0000 0001 2296 0625Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain
| | - Rosa Bosch
- grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.413448.e0000 0000 9314 1427Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain ,grid.7080.f0000 0001 2296 0625Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain
| | - Gemma Español Martin
- grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.7080.f0000 0001 2296 0625Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.430994.30000 0004 1763 0287Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Barcelona, Catalonia Spain
| | - Peter Almos
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Alysa E. Doyle
- grid.32224.350000 0004 0386 9924Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, Harvard Medical School, Boston, MA USA
| | - Eugenio Horacio Grevet
- grid.414449.80000 0001 0125 3761ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil ,grid.8532.c0000 0001 2200 7498Department of Psychiatry, Faculty of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Oliver Grimm
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Anne Halmøy
- grid.7914.b0000 0004 1936 7443Department of Biomedicine, University of Bergen, Bergen, Norway ,grid.412008.f0000 0000 9753 1393Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Martine Hoogman
- grid.10417.330000 0004 0444 9382Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mara Hutz
- grid.8532.c0000 0001 2200 7498Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Christian P. Jacob
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Sarah Kittel-Schneider
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Per M. Knappskog
- grid.412008.f0000 0000 9753 1393Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway ,grid.7914.b0000 0004 1936 7443Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Astri J. Lundervold
- grid.7914.b0000 0004 1936 7443Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
| | - Olga Rivero
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany
| | - Diego Luiz Rovaris
- grid.414449.80000 0001 0125 3761ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil ,grid.8532.c0000 0001 2200 7498Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil ,grid.11899.380000 0004 1937 0722Department of Physiology and Biophysics, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil
| | - Angelica Salatino-Oliveira
- grid.8532.c0000 0001 2200 7498Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Bruna Santos da Silva
- grid.414449.80000 0001 0125 3761ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil ,grid.8532.c0000 0001 2200 7498Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Evgeniy Svirin
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany ,grid.448878.f0000 0001 2288 8774Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russia
| | - Emma Sprooten
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Tatyana Strekalova
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany ,grid.448878.f0000 0001 2288 8774Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russia ,grid.5012.60000 0001 0481 6099Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | | | | | - Alejandro Arias-Vasquez
- grid.10417.330000 0004 0444 9382Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Edmund J. S. Sonuga-Barke
- grid.13097.3c0000 0001 2322 6764Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, London, UK ,grid.7048.b0000 0001 1956 2722Department of Child and Adolescent Psychiatry, Aarhus University, Aarhus, Denmark
| | - Philip Asherson
- grid.13097.3c0000 0001 2322 6764Social Genetic and Developmental Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London, UK
| | - Claiton Henrique Dotto Bau
- grid.414449.80000 0001 0125 3761ADHD Outpatient Program, Adult Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil ,grid.8532.c0000 0001 2200 7498Department of Genetics, Institute of Biosciences, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Jan K. Buitelaar
- grid.10417.330000 0004 0444 9382Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Centre, Nijmegen, The Netherlands ,grid.461871.d0000 0004 0624 8031Karakter Child and Adolescent Psychiatry, Nijmegen, The Netherlands
| | - Bru Cormand
- grid.5841.80000 0004 1937 0247Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain ,grid.5841.80000 0004 1937 0247Institut de Biomedicina de la Universitat de Barcelona (IBUB), Barcelona, Catalonia Spain ,grid.452372.50000 0004 1791 1185Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain ,grid.411160.30000 0001 0663 8628Institut de Recerca Sant Joan de Déu (IRSJD), Esplugues de Llobregat, Catalonia Spain
| | - Stephen V. Faraone
- grid.411023.50000 0000 9159 4457Departments of Psychiatry, of Neuroscience, and Physiology, SUNY Upstate Medical University, Syracuse, NY USA
| | - Jan Haavik
- grid.7914.b0000 0004 1936 7443Department of Biomedicine, University of Bergen, Bergen, Norway ,grid.412008.f0000 0000 9753 1393Division of Psychiatry, Haukeland University Hospital, Bergen, Norway
| | - Stefan E. Johansson
- grid.412008.f0000 0000 9753 1393Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Bergen, Norway ,grid.7914.b0000 0004 1936 7443Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Jonna Kuntsi
- grid.13097.3c0000 0001 2322 6764Social Genetic and Developmental Psychiatry, Institute of Psychiatry Psychology and Neuroscience, King’s College London, London, UK
| | - Henrik Larsson
- grid.15895.300000 0001 0738 8966School of medical Sciences, Örebro University, Örebro, Sweden ,grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Klaus-Peter Lesch
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Center of Mental Health, University of Würzburg, Würzburg, Germany ,grid.448878.f0000 0001 2288 8774Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, IM Sechenov First Moscow State Medical University, Moscow, Russia ,grid.5012.60000 0001 0481 6099Department of Neuroscience, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Andreas Reif
- grid.411088.40000 0004 0578 8220Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - Luis Augusto Rohde
- grid.8532.c0000 0001 2200 7498Division of Child Psychiatry, Hospital de Clinicas de Porto Alegre, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Miquel Casas
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.413448.e0000 0000 9314 1427Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain ,grid.7080.f0000 0001 2296 0625Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain
| | - Anders D. Børglum
- grid.7048.b0000 0001 1956 2722Department of Biomedicine (Human Genetics), and Centre for Integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark ,grid.452548.a0000 0000 9817 5300The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark ,Center for Genomics and Personalized Medicine, Aarhus, Denmark
| | - Barbara Franke
- grid.10417.330000 0004 0444 9382Department of Human Genetics, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands ,grid.10417.330000 0004 0444 9382Department of Psychiatry, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Josep Antoni Ramos-Quiroga
- grid.7080.f0000 0001 2296 0625Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d’Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain ,grid.411083.f0000 0001 0675 8654Department of Psychiatry, Hospital Universitari Vall d’Hebron, Barcelona, Catalonia Spain ,grid.413448.e0000 0000 9314 1427Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain ,grid.7080.f0000 0001 2296 0625Department of Psychiatry and Legal Medicine, Universitat Autònoma de Barcelona, Barcelona, Catalonia Spain
| | - María Soler Artigas
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain. .,Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain. .,Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain. .,Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
| | - Marta Ribasés
- Psychiatric Genetics Unit, Group of Psychiatry, Mental Health, and Addiction, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Catalonia, Spain. .,Department of Psychiatry, Hospital Universitari Vall d'Hebron, Barcelona, Catalonia, Spain. .,Biomedical Network Research Centre on Mental Health (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain. .,Department of Genetics, Microbiology, and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
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99
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Neuner SM, Tcw J, Goate AM. Genetic architecture of Alzheimer's disease. Neurobiol Dis 2020; 143:104976. [PMID: 32565066 PMCID: PMC7409822 DOI: 10.1016/j.nbd.2020.104976] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 05/30/2020] [Accepted: 06/13/2020] [Indexed: 02/06/2023] Open
Abstract
Advances in genetic and genomic technologies over the last thirty years have greatly enhanced our knowledge concerning the genetic architecture of Alzheimer's disease (AD). Several genes including APP, PSEN1, PSEN2, and APOE have been shown to exhibit large effects on disease susceptibility, with the remaining risk loci having much smaller effects on AD risk. Notably, common genetic variants impacting AD are not randomly distributed across the genome. Instead, these variants are enriched within regulatory elements active in human myeloid cells, and to a lesser extent liver cells, implicating these cell and tissue types as critical to disease etiology. Integrative approaches are emerging as highly effective for identifying the specific target genes through which AD risk variants act and will likely yield important insights related to potential therapeutic targets in the coming years. In the future, additional consideration of sex- and ethnicity-specific contributions to risk as well as the contribution of complex gene-gene and gene-environment interactions will likely be necessary to further improve our understanding of AD genetic architecture.
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Affiliation(s)
- Sarah M Neuner
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Julia Tcw
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alison M Goate
- Nash Department of Neuroscience, Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA.
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100
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Quick C, Anugu P, Musani S, Weiss ST, Burchard EG, White MJ, Keys KL, Cucca F, Sidore C, Boehnke M, Fuchsberger C. Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations. Genet Epidemiol 2020; 44:537-549. [PMID: 32519380 PMCID: PMC7449570 DOI: 10.1002/gepi.22326] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 04/02/2020] [Accepted: 05/22/2020] [Indexed: 01/03/2023]
Abstract
A key aim for current genome-wide association studies (GWAS) is to interrogate the full spectrum of genetic variation underlying human traits, including rare variants, across populations. Deep whole-genome sequencing is the gold standard to fully capture genetic variation, but remains prohibitively expensive for large sample sizes. Array genotyping interrogates a sparser set of variants, which can be used as a scaffold for genotype imputation to capture a wider set of variants. However, imputation quality depends crucially on reference panel size and genetic distance from the target population. Here, we consider sequencing a subset of GWAS participants and imputing the rest using a reference panel that includes both sequenced GWAS participants and an external reference panel. We investigate how imputation quality and GWAS power are affected by the number of participants sequenced for admixed populations (African and Latino Americans) and European population isolates (Sardinians and Finns), and identify powerful, cost-effective GWAS designs given current sequencing and array costs. For populations that are well-represented in existing reference panels, we find that array genotyping alone is cost-effective and well-powered to detect common- and rare-variant associations. For poorly represented populations, sequencing a subset of participants is often most cost-effective, and can substantially increase imputation quality and GWAS power.
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Affiliation(s)
- Corbin Quick
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Pramod Anugu
- University of Mississippi Medical CenterJacksonMississippi
| | - Solomon Musani
- University of Mississippi Medical CenterJacksonMississippi
| | - Scott T. Weiss
- Harvard Medical SchoolBostonMassachusetts
- Channing Department of Network MedicineBrigham and Women's HospitalBostonCalifornia
- Partners HealthCare Personalized MedicineBostonMassachusetts
| | - Esteban G. Burchard
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
- Department of Bioengineering and Therapeutic SciencesUniversity of California San FranciscoSan FranciscoCalifornia
| | - Marquitta J. White
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Kevin L. Keys
- Department of MedicineUniversity of California San FranciscoSan FranciscoCalifornia
| | - Francesco Cucca
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
- Dipartimento di Scienze BiomedicheUniversità di SassariSassariItaly
| | - Carlo Sidore
- Istituto di Ricerca Genetica e Biomedica (IRGB), CNRMonserratoItaly
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
| | - Christian Fuchsberger
- Department of Biostatistics and Center for Statistical GeneticsUniversity of Michigan School of Public HealthAnn ArborMichigan
- Department of Genetics and Pharmacology, Institute of Genetic EpidemiologyMedical University of InnsbruckInnsbruckAustria
- Institute for Biomedicine, Eurac ResearchAffiliated Institute of the University of LübeckBolzanoItaly
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