1
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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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2
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Butty AM, Sargolzaei M, Miglior F, Stothard P, Schenkel FS, Gredler-Grandl B, Baes CF. Optimizing Selection of the Reference Population for Genotype Imputation From Array to Sequence Variants. Front Genet 2019; 10:510. [PMID: 31214246 PMCID: PMC6554347 DOI: 10.3389/fgene.2019.00510] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 05/10/2019] [Indexed: 11/29/2022] Open
Abstract
Imputation of high-density genotypes to whole-genome sequences (WGS) is a cost-effective method to increase the density of available markers within a population. Imputed genotypes have been successfully used for genomic selection and discovery of variants associated with traits of interest for the population. To allow for the use of imputed genotypes for genomic analyses, accuracy of imputation must be high. Accuracy of imputation is influenced by multiple factors, such as size and composition of the reference group, and the allele frequency of variants included. Understanding the use of imputed WGSs prior to the generation of the reference population is important, as accurate imputation might be more focused, for instance, on common or on rare variants. The aim of this study was to present and evaluate new methods to select animals for sequencing relying on a previously genotyped population. The Genetic Diversity Index method optimizes the number of unique haplotypes in the future reference population, while the Highly Segregating Haplotype selection method targets haplotype alleles found throughout the majority of the population of interest. First the WGSs of a dairy cattle population were simulated. The simulated sequences mimicked the linkage disequilibrium level and the variants’ frequency distribution observed in currently available Holstein sequences. Then, reference populations of different sizes, in which animals were selected using both novel methods proposed here as well as two other methods presented in previous studies, were created. Finally, accuracies of imputation obtained with different reference populations were compared against each other. The novel methods were found to have overall accuracies of imputation of more than 0.85. Accuracies of imputation of rare variants reached values above 0.50. In conclusion, if imputed sequences are to be used for discovery of novel associations between variants and traits of interest in the population, animals carrying novel information should be selected and, consequently, the Genetic Diversity Index method proposed here may be used. If sequences are to be used to impute the overall genotyped population, a reference population consisting of common haplotypes carriers selected using the proposed Highly Segregating Haplotype method is recommended.
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Affiliation(s)
- Adrien M Butty
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.,Select Sires Inc., Plain City, OH, United States
| | - Filippo Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Paul Stothard
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Flavio S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Birgit Gredler-Grandl
- Qualitas AG, Zug, Switzerland.,Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, Wageningen, Netherlands
| | - Christine F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada.,Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, Switzerland
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3
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Revisit Population-based and Family-based Genotype Imputation. Sci Rep 2019; 9:1800. [PMID: 30755687 PMCID: PMC6372660 DOI: 10.1038/s41598-018-38469-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 12/27/2018] [Indexed: 11/12/2022] Open
Abstract
Genome-Wide Association (GWA) with population-based imputation (PBI) has been successful in identifying common variants associated with complex diseases; however, much heritability remains to be explained and low frequency variants (LFV) may contribute. To identify LFV, a study of unrelated individuals may no longer be as efficient as a family study, where rare population variants can be frequent in families. Family-based imputation (FBI) provides an opportunity to evaluate LFV. To compare the performance of PBI and FBI, we conducted extensive simulations, generating genotypes using SeqSIMLA from various reference panels for families. We masked genotype information for variants unavailable in Framingham 550 K GWA genotype data in less informative subjects selected by GIGI-Pick. We implemented IMPUTE2 with duoHMM in SHAPEIT (Impute2_duoHMM) for PBI, MERLIN and GIGI for FBI and PedBLIMP for a hybrid approach. In general, FBI in both MERLIN and GIGI outperformed other approaches with imputation accuracy greater than 0.99 for the squared correlation and imputation quality scores (IQS) especially for LFV, although imputation accuracy from MERLIN depends on pedigree splitting for larger families. PBI performed worst with the exception of good imputation accuracy for common variants when a closely ancestry matched reference is used. In summary, linkage disequilibrium (LD) information from large available genotype resources provides good imputation for common variants with well-selected reference panels without requiring densely sequenced data in family members, while imputation of LFV with FBI benefits more from information on inheritance patterns within families yielding better imputation.
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4
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Vergara C, Bomotti SM, Valencia C, Klein BE, Lee KE, Klein R, Klein AP, Duggal P. Association analysis of exome variants and refraction, axial length, and corneal curvature in a European-American population. Hum Mutat 2018; 39:1973-1979. [PMID: 30157304 PMCID: PMC6497529 DOI: 10.1002/humu.23628] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 08/24/2018] [Accepted: 08/25/2018] [Indexed: 12/16/2022]
Abstract
Refractive errors, myopia, and hyperopia are common visual disorders greatly affecting older individuals. Refraction is determined by genetic factors but only a small percentage of its variation has been explained. We performed a genetic association analysis with three ocular phenotypes: spherical equivalent (a continous measure of refraction), axial length, and corneal curvature in 1,871 European-Americans from the Beaver Dam Eye Study. Individuals were genotyped on the Illumina exome array and imputed to the Haplotype Reference Consortium reference panel. After increasing the number of analyzed variants in targeted protein-coding regions 10-fold via imputation, we confirmed associations for two previously known loci with corneal curvature (chr4q12, rs2114039; g.55092626T > C, β = -0.03 (95% confidence interval [CI]): -0.06, -0.01, P value = 0.01) and spherical equivalent (chr15q14, rs634990; g.35006073T > C, β = -0.27, 95% CI: -0.45, -0.09, P value = 3.79 × 10-3 ). Despite increased single nucleotide polymorphism (SNP) density, we did not detect any novel significant variants after correction for multiple comparisons. In summary, we confirmed two previous loci associated with corneal curvature and spherical equivalent in a European-American population highlighting the potential biological role of those regions in these traits.
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Affiliation(s)
- Candelaria Vergara
- Division of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Samantha M. Bomotti
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Cristian Valencia
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Barbara E.K. Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Kristine E. Lee
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Ronald Klein
- Department of Ophthalmology and Visual Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Alison P. Klein
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland, USA
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
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5
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Ghoreishifar SM, Moradi-Shahrbabak H, Moradi-Shahrbabak M, Nicolazzi EL, Williams JL, Iamartino D, Nejati-Javaremi A. Accuracy of imputation of single-nucleotide polymorphism marker genotypes for water buffaloes (Bubalus bubalis) using different reference population sizes and imputation tools. Livest Sci 2018. [DOI: 10.1016/j.livsci.2018.08.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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6
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Jo Hodonsky C, Schurmann C, Schick UM, Kocarnik J, Tao R, van Rooij FJ, Wassel C, Buyske S, Fornage M, Hindorff LA, Floyd JS, Ganesh SK, Lin DY, North KE, Reiner AP, Loos RJ, Kooperberg C, Avery CL. Generalization and fine mapping of red blood cell trait genetic associations to multi-ethnic populations: The PAGE Study. Am J Hematol 2018; 93:10.1002/ajh.25161. [PMID: 29905378 PMCID: PMC6300146 DOI: 10.1002/ajh.25161] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Revised: 05/29/2018] [Accepted: 05/29/2018] [Indexed: 12/17/2022]
Abstract
Red blood cell (RBC) traits provide insight into a wide range of physiological states and exhibit moderate to high heritability, making them excellent candidates for genetic studies to inform underlying biologic mechanisms. Previous RBC trait genome-wide association studies were performed primarily in European- or Asian-ancestry populations, missing opportunities to inform understanding of RBC genetic architecture in diverse populations and reduce intervals surrounding putative functional SNPs through fine-mapping. Here, we report the first fine-mapping of six correlated (Pearson's r range: |0.04 - 0.92|) RBC traits in up to 19,036 African Americans and 19,562 Hispanic/Latinos participants of the Population Architecture using Genomics and Epidemiology (PAGE) consortium. Trans-ethnic meta-analysis of race/ethnic- and study-specific estimates for approximately 11,000 SNPs flanking 13 previously identified association signals as well as 150,000 additional array-wide SNPs was performed using inverse-variance meta-analysis after adjusting for study and clinical covariates. Approximately half of previously reported index SNP-RBC trait associations generalized to the trans-ethnic study population (p<1.7x10-4 ); previously unreported independent association signals within the ABO region reinforce the potential for multiple functional variants affecting the same locus. Trans-ethnic fine-mapping did not reveal additional signals at the HFE locus independent of the known functional variants. Finally, we identified a potential novel association in the Hispanic/Latino study population at the HECTD4/RPL6 locus for RBC count (p=1.9x10-7 ). The identification of a previously unknown association, generalization of a large proportion of known association signals, and refinement of known association signals all exemplify the benefits of genetic studies in diverse populations. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Chani Jo Hodonsky
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
| | - Claudia Schurmann
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ursula M Schick
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Jonathan Kocarnik
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
| | - Frank Ja van Rooij
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, 3000, the Netherlands
| | - Christina Wassel
- Department of Pathology and Laboratory Medicine, College of Medicine, University of Vermont, Burlington, VT
| | - Steve Buyske
- Department of Statistics and Biostatistics, Hill Center, Rutgers, The State University of New Jersey, 110 Frelinghuysen Rd. Piscataway, NY
| | - Myriam Fornage
- Institute of Molecular Medicine and Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX
| | - Lucia A Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National institutes of Health, Bethesda, MD
| | - James S Floyd
- Departments of Medicine, University of Washington, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Santhi K Ganesh
- Department of Human Genetics, University of Michigan Medical School, Ann Arbor, MI
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC
| | - Kari E North
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
| | - Alex P Reiner
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
- Department of Epidemiology, University of Washington, Seattle, WA
| | - Ruth Jf Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Genetics of Obesity and Related Metabolic Traits Program, The Icahn School of Medicine at Mount Sinai, New York, NY
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Christy L Avery
- Department of Epidemiology, University of North Carolina Gillings School of Public Health, Chapel Hill, NC
- Carolina Population Center, University of North Carolina, Chapel Hill, NC
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7
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Shirali M, Knott SA, Pong-Wong R, Navarro P, Haley CS. Haplotype Heritability Mapping Method Uncovers Missing Heritability of Complex Traits. Sci Rep 2018; 8:4982. [PMID: 29563569 PMCID: PMC5862984 DOI: 10.1038/s41598-018-23307-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 03/08/2018] [Indexed: 11/17/2022] Open
Abstract
We propose a novel approach to analyze genomic data that incorporates haplotype information for detecting rare variants within a regional heritability mapping framework. The performance of our approach was tested in a simulation study based on human genotypes. The phenotypes were simulated by generating regional variance using either SNP(s) or haplotype(s). Regional genomic relationship matrices, constructed with either a SNP-based or a haplotype-based estimator, were employed to estimate the regional variance. The results from the study show that haplotype heritability mapping captures the regional effect, with its relative performance decreasing with increasing analysis window size. The SNP-based regional mapping approach often misses the effect of causal haplotype(s); however, it has a greater power to detect simulated SNP-based-variants. Heritability estimates suggest that the haplotype heritability mapping estimates the simulated regional heritability accurately for all phenotypes and analysis windows. However, the SNP-based analysis overestimates the regional heritability and performs less well than our haplotype-based approach for the simulated rare haplotype-based-variant. We conclude that haplotype heritability mapping is a useful tool to capture the effect of rare variants, and explain a proportion of the missing heritability.
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Affiliation(s)
- Masoud Shirali
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Sara A Knott
- Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, EH9 3FL, UK
| | - Ricardo Pong-Wong
- The Roslin Institute and R (D) SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK
| | - Pau Navarro
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Chris S Haley
- MRC Human Genetics Unit, MRC Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK.
- The Roslin Institute and R (D) SVS, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, UK.
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8
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Lert-Itthiporn W, Suktitipat B, Grove H, Sakuntabhai A, Malasit P, Tangthawornchaikul N, Matsuda F, Suriyaphol P. Validation of genotype imputation in Southeast Asian populations and the effect of single nucleotide polymorphism annotation on imputation outcome. BMC MEDICAL GENETICS 2018; 19:23. [PMID: 29439659 PMCID: PMC5812212 DOI: 10.1186/s12881-018-0534-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 01/24/2018] [Indexed: 11/24/2022]
Abstract
Background Imputation involves the inference of untyped single nucleotide polymorphisms (SNPs) in genome-wide association studies. The haplotypic reference of choice for imputation in Southeast Asian populations is unclear. Moreover, the influence of SNP annotation on imputation results has not been examined. Methods This study was divided into two parts. In the first part, we applied imputation to genotyped SNPs from Southeast Asian populations from the Pan-Asian SNP database. Five percent of the total SNPs were removed. The remaining SNPs were applied to imputation with IMPUTE2. The imputed outcomes were verified with the removed SNPs. We compared imputation references from Chinese and Japanese haplotypes from the HapMap phase II (HMII) and the complete set of haplotypes from the 1000 Genomes Project (1000G). The second part was imputation accuracy and yield in Thai patient dataset. Half of the autosomal SNPs was removed to create Set 1. Another dataset, Set 2, was then created where we switched which half of the SNPs were removed. Both Set 1 and Set 2 were imputed with HMII to create a complete imputed SNPs dataset. The dataset was used to validate association testing, SNPs annotation and imputation outcome. Results The accuracy was highest for all populations when using the HMII reference, but at the cost of a lower yield. Thai genotypes showed the highest accuracy over other populations in both HMII and 1000G panels, although accuracy and yield varied across chromosomes. Imputation was tested in a clinical dataset to compare accuracy in gene-related regions, and coding regions were found to have a higher accuracy and yield. Conclusions This work provides the first evidence of imputation reference selection for Southeast Asian studies and highlights the effects of SNP locations respective to genes on imputation outcome. Researchers will need to consider the trade-off between accuracy and yield in future imputation studies. Electronic supplementary material The online version of this article (10.1186/s12881-018-0534-8) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Worachart Lert-Itthiporn
- Molecular Medicine Graduate Program, Faculty of Science, Mahidol University, Bangkok, Thailand.,Division of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Bhoom Suktitipat
- Integrative Computational BioScience Center, Department of Biochemistry, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.,Center of Excellence in Bioinformatics and Clinical Data Management, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Harald Grove
- Division of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand.,Center of Excellence in Bioinformatics and Clinical Data Management, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Anavaj Sakuntabhai
- Unité de Génétique Fonctionnelle des Maladies Infectieuses, Department Genome and Genetics, Institut Pasteur, Paris, France.,Centre National de la Recherche Scientifique, URA3012, Paris, France.,Systems Biology of Diseases Research Unit, Faculty of Science, Mahidol University, Bangkok, Thailand
| | - Prida Malasit
- Medical Biotechnology Research Unit, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Bangkok, Thailand.,Division of Dengue Hemorrhagic Fever Research, Department of Research and Development, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Nattaya Tangthawornchaikul
- Medical Biotechnology Research Unit, National Center for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Bangkok, Thailand.,Division of Dengue Hemorrhagic Fever Research, Department of Research and Development, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Prapat Suriyaphol
- Division of Bioinformatics and Data Management for Research, Department of Research and Development, Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand. .,Center of Excellence in Bioinformatics and Clinical Data Management, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
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9
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Liu N, Irvin MR, Zhi D, Patki A, Beasley TM, Nickerson DA, Hill CE, Chen J, Kimmel SE, Limdi NA. Influence of common and rare genetic variation on warfarin dose among African-Americans and European-Americans using the exome array. Pharmacogenomics 2017; 18:1059-1073. [PMID: 28686080 DOI: 10.2217/pgs-2017-0046] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
AIM We conducted a genome-wide association study using the Illumina Exome Array to identify coding SNPs that may explain additional warfarin dose variability. PATIENTS & METHODS Analysis was performed after adjustment for clinical variables and genetic factors known to influence warfarin dose among 1680 warfarin users (838 European-Americans and 842 African-Americans). Replication was performed in an independent sample. RESULTS We confirmed the influence of known genetic variants on warfarin dose variability. Our study is the first to show the association between rs12772169 and warfarin dose in African-Americans. In addition, genes COX15 and FGF5 showed significant association in European-Americans. CONCLUSION We identified some novel genes/SNPs that underpin warfarin dose response. Further replication is needed to confirm our findings.
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Affiliation(s)
- Nianjun Liu
- Department of Epidemiology & Biostatistics, School of Public Health - Bloomington, Indiana University, Bloomington, IN 47405, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Degui Zhi
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - T Mark Beasley
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, School of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Charles E Hill
- Department of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jinbo Chen
- Department of Biostatistics & Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Stephen E Kimmel
- Department of Biostatistics & Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nita A Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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10
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A combined reference panel from the 1000 Genomes and UK10K projects improved rare variant imputation in European and Chinese samples. Sci Rep 2016; 6:39313. [PMID: 28004816 PMCID: PMC5177868 DOI: 10.1038/srep39313] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Accepted: 11/16/2016] [Indexed: 02/04/2023] Open
Abstract
Imputation using the 1000 Genomes haplotype reference panel has been widely adapted to estimate genotypes in genome wide association studies. To evaluate imputation quality with a relatively larger reference panel and a reference panel composed of different ethnic populations, we conducted imputations in the Framingham Heart Study and the North Chinese Study using a combined reference panel from the 1000 Genomes (N = 1,092) and UK10K (N = 3,781) projects. For rare variants with 0.01% < MAF ≤ 0.5%, imputation in the Framingham Heart Study with the combined reference panel increased well-imputed genotypes (with imputation quality score ≥0.4) from 62.9% to 76.1% when compared to imputation with the 1000 Genomes. For the North Chinese samples, imputation of rare variants with 0.01% < MAF ≤ 0.5% with the combined reference panel increased well-imputed genotypes by from 49.8% to 61.8%. The predominant European ancestry of the UK10K and the combined reference panels may explain why there was less of an increase in imputation success in the North Chinese samples. Our results underscore the importance and potential of larger reference panels to impute rare variants, while recognizing that increasing ethnic specific variants in reference panels may result in better imputation for genotypes in some ethnic groups.
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11
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Kim YJ, Lee J, Kim BJ, Park T. PreCimp: Pre-collapsing imputation approach increases imputation accuracy of rare variants in terms of collapsed variables. Genet Epidemiol 2016; 41:41-50. [PMID: 27859580 DOI: 10.1002/gepi.22020] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Revised: 08/17/2016] [Accepted: 09/21/2016] [Indexed: 12/22/2022]
Abstract
Imputation is widely used for obtaining information about rare variants. However, one issue concerning imputation is the low accuracy of imputed rare variants as the inaccurate imputed rare variants may distort the results of region-based association tests. Therefore, we developed a pre-collapsing imputation method (PreCimp) to improve the accuracy of imputation by using collapsed variables. Briefly, collapsed variables are generated using rare variants in the reference panel, and a new reference panel is constructed by inserting pre-collapsed variables into the original reference panel. Following imputation analysis provides the imputed genotypes of the collapsed variables. We demonstrated the performance of PreCimp on 5,349 genotyped samples using a Korean population specific reference panel including 848 samples of exome sequencing, Affymetrix 5.0, and exome chip. PreCimp outperformed a traditional post-collapsing method that collapses imputed variants after single rare variant imputation analysis. Compared with the results of post-collapsing method, PreCimp approach was shown to relatively increase imputation accuracy about 3.4-6.3% when dosage r2 is between 0.6 and 0.8, 10.9-16.1% when dosage r2 is between 0.4 and 0.6, and 21.4 ∼ 129.4% when dosage r2 is below 0.4.
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Affiliation(s)
- Young Jin Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, Korea
| | - Juyoung Lee
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, Korea
| | - Bong-Jo Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, Korea
| | | | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Statistics, Seoul National University, Seoul, Korea
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Lent S, Deng X, Cupples LA, Lunetta KL, Liu CT, Zhou Y. Imputing rare variants in families using a two-stage approach. BMC Proc 2016; 10:209-214. [PMID: 27980638 PMCID: PMC5133481 DOI: 10.1186/s12919-016-0032-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Recent focus on studying rare variants makes imputation accuracy of rare variants an important issue. Many approaches have been proposed to increase imputation accuracy among rare variants, from reference panel selection to combinations of existing methods to multistage analyses. We aimed to bring the strengths of these new approaches together with our proposed two-stage imputation for family data. METHODS Our imputation methods were tested on the region from 46.75Mb to 49.25Mb on chromosome 3. We did quality control based on the proportion of missing genotypes per variant and individual, leaving 495 individuals with 761 genome-wide association studies (GWAS) variants only, 45 with 14,077 sequence variants only, and 419 with both GWAS and sequencing data. All data were prephased using SHAPEIT2 with a duo hidden Markov model algorithm prior to performing imputation. Imputations were performed 100 times, each time masking the sequence data for 1 individual and imputing it from the GWAS data. We used well-imputed genotypes, defined as a probability of greater than 0.9, above 2 different minor allele frequency cutoffs-0.01 and 0.05-from Impute2 as input for Merlin, and compared these results to Impute2 and Merlin separately. The imputed results were evaluated using correlation measurement and the imputation quality score. RESULTS Our method improved imputation accuracy, measured by imputation quality score, for variants with minor allele frequency between 0.01 and 0.40, but failed to improve accuracy for variants with minor allele frequency less than 0.01 when we used a minor allele frequency cutoff of 0.01 for the Impute2 results. In contrast, our 2-stage approach with a minor allele frequency cutoff of 0.05 performed the worst of all methods for variants with minor allele frequency between 0.01 and 0.40. CONCLUSIONS This method gave promising results, but may be further improved by changing the inclusion criteria of Impute2 variants. More analyses are needed on a larger region with different inclusion thresholds to assess the accuracy of this approach.
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Affiliation(s)
- Samantha Lent
- Department of Biostatistics, Boston University, Boston, MA USA
| | - Xuan Deng
- Department of Biostatistics, Boston University, Boston, MA USA
| | | | | | - C T Liu
- Department of Biostatistics, Boston University, Boston, MA USA
| | - Yanhua Zhou
- Department of Biostatistics, Boston University, Boston, MA USA
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13
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Long D, Deng X, Singh P, Loeb M, Lauring AS, Seielstad M. Identification of genetic variants associated with susceptibility to West Nile virus neuroinvasive disease. Genes Immun 2016; 17:298-304. [PMID: 27170560 PMCID: PMC5215919 DOI: 10.1038/gene.2016.21] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2016] [Revised: 04/10/2016] [Accepted: 04/12/2016] [Indexed: 11/09/2022]
Abstract
West Nile virus (WNV) infection results in a diverse spectrum of outcomes, and host genetics are likely to influence susceptibility to neuroinvasive disease (West Nile neuroinvasive disease (WNND)). We performed whole-exome sequencing of 44 individuals with WNND and identified alleles associated with severe disease by variant filtration in cases, kernel association testing in cases and controls and single-nucleotide polymorphism (SNP) imputation into a larger cohort of WNND cases and seropositive controls followed by genome-wide association analysis. Variant filtration prioritized genes based on the enrichment of otherwise rare variants, but did not unambiguously implicate variants shared by a majority of cases. Kernel association demonstrated enrichment for risk and protective alleles in the human leukocyte antigen (HLA)-A and HLA-DQB1 loci that have well understood roles in antiviral immunity. Two loci, HERC5 and an intergenic region between CD83 and JARID2, were implicated by multiple imputed SNPs and exceeded genome-wide significance in a discovery cohort (n=862). SNPs at two additional loci, TFCP2L1 and CACNA1H, achieved genome-wide significance after association testing of directly genotyped and imputed SNPs in a discovery cohort (n=862) and a separate replication cohort (n=1387). The context of these loci suggests that immunoregulatory, ion channel and endothelial barrier functions may be important elements of the host response to WNV.
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Affiliation(s)
- D Long
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - X Deng
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- Blood Systems Research Institute, San Francisco, CA, USA
| | - P Singh
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - M Loeb
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - A S Lauring
- Division of Infectious Diseases, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - M Seielstad
- Department of Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA
- Blood Systems Research Institute, San Francisco, CA, USA
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14
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Friedenberg SG, Meurs KM. Genotype imputation in the domestic dog. Mamm Genome 2016; 27:485-94. [PMID: 27129452 DOI: 10.1007/s00335-016-9636-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 04/11/2016] [Indexed: 01/08/2023]
Abstract
Application of imputation methods to accurately predict a dense array of SNP genotypes in the dog could provide an important supplement to current analyses of array-based genotyping data. Here, we developed a reference panel of 4,885,283 SNPs in 83 dogs across 15 breeds using whole genome sequencing. We used this panel to predict the genotypes of 268 dogs across three breeds with 84,193 SNP array-derived genotypes as inputs. We then (1) performed breed clustering of the actual and imputed data; (2) evaluated several reference panel breed combinations to determine an optimal reference panel composition; and (3) compared the accuracy of two commonly used software algorithms (Beagle and IMPUTE2). Breed clustering was well preserved in the imputation process across eigenvalues representing 75 % of the variation in the imputed data. Using Beagle with a target panel from a single breed, genotype concordance was highest using a multi-breed reference panel (92.4 %) compared to a breed-specific reference panel (87.0 %) or a reference panel containing no breeds overlapping with the target panel (74.9 %). This finding was confirmed using target panels derived from two other breeds. Additionally, using the multi-breed reference panel, genotype concordance was slightly higher with IMPUTE2 (94.1 %) compared to Beagle; Pearson correlation coefficients were slightly higher for both software packages (0.946 for Beagle, 0.961 for IMPUTE2). Our findings demonstrate that genotype imputation from SNP array-derived data to whole genome-level genotypes is both feasible and accurate in the dog with appropriate breed overlap between the target and reference panels.
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Affiliation(s)
- S G Friedenberg
- Department of Clinical Sciences and Comparative Medicine Institute, North Carolina State University College of Veterinary Medicine, 1060 William Moore Drive, Raleigh, NC, 27607, USA.
| | - K M Meurs
- Department of Clinical Sciences and Comparative Medicine Institute, North Carolina State University College of Veterinary Medicine, 1060 William Moore Drive, Raleigh, NC, 27607, USA
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Hoffmann TJ, Witte JS. Strategies for Imputing and Analyzing Rare Variants in Association Studies. Trends Genet 2016; 31:556-563. [PMID: 26450338 DOI: 10.1016/j.tig.2015.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2015] [Revised: 07/28/2015] [Accepted: 07/31/2015] [Indexed: 01/22/2023]
Abstract
Rare genetic variants may be responsible for a significant amount of the uncharacterized genetic risk underlying many diseases. An efficient approach to characterizing the disease burden of rare variants may be to impute them into existing large datasets. It is well known that the ability to impute a rare variant is dependent both on the array choice and number of individuals in the reference panel carrying that variant, although it is still unclear exactly how well imputation will work for rare variants. Here, we review the additional challenges that arise when imputing rare variants, looking at studies that have been able to impute rare variants, methods behind merging reference panels, approaches for imputing rare variants, and methods for analyzing rare variants.
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Affiliation(s)
- Thomas J Hoffmann
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94143 USA.
| | - John S Witte
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94143 USA; Department of Urology, University of California San Francisco, San Francisco, CA 94158, USA; UCSF Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA 94158, USA
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16
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Winham SJ, Pirie A, Chen YA, Larson MC, Fogarty ZC, Earp MA, Anton-Culver H, Bandera EV, Cramer D, Doherty JA, Goodman MT, Gronwald J, Karlan BY, Kjaer SK, Levine DA, Menon U, Ness RB, Pearce CL, Pejovic T, Rossing MA, Wentzensen N, Bean YT, Bisogna M, Brinton LA, Carney ME, Cunningham JM, Cybulski C, deFazio A, Dicks EM, Edwards RP, Gayther SA, Gentry-Maharaj A, Gore M, Iversen ES, Jensen A, Johnatty SE, Lester J, Lin HY, Lissowska J, Lubinski J, Menkiszak J, Modugno F, Moysich KB, Orlow I, Pike MC, Ramus SJ, Song H, Terry KL, Thompson PJ, Tyrer JP, van den Berg DJ, Vierkant RA, Vitonis AF, Walsh C, Wilkens LR, Wu AH, Yang H, Ziogas A, Berchuck A, Chenevix-Trench G, Schildkraut JM, Permuth-Wey J, Phelan CM, Pharoah PDP, Fridley BL, Sellers TA, Goode EL. Investigation of Exomic Variants Associated with Overall Survival in Ovarian Cancer. Cancer Epidemiol Biomarkers Prev 2016; 25:446-54. [PMID: 26747452 PMCID: PMC4779669 DOI: 10.1158/1055-9965.epi-15-0240] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Accepted: 11/19/2015] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND While numerous susceptibility loci for epithelial ovarian cancer (EOC) have been identified, few associations have been reported with overall survival. In the absence of common prognostic genetic markers, we hypothesize that rare coding variants may be associated with overall EOC survival and assessed their contribution in two exome-based genotyping projects of the Ovarian Cancer Association Consortium (OCAC). METHODS The primary patient set (Set 1) included 14 independent EOC studies (4,293 patients) and 227,892 variants, and a secondary patient set (Set 2) included six additional EOC studies (1,744 patients) and 114,620 variants. Because power to detect rare variants individually is reduced, gene-level tests were conducted. Sets were analyzed separately at individual variants and by gene, and then combined with meta-analyses (73,203 variants and 13,163 genes overlapped). RESULTS No individual variant reached genome-wide statistical significance. A SNP previously implicated to be associated with EOC risk and, to a lesser extent, survival, rs8170, showed the strongest evidence of association with survival and similar effect size estimates across sets (Pmeta = 1.1E-6, HRSet1 = 1.17, HRSet2 = 1.14). Rare variants in ATG2B, an autophagy gene important for apoptosis, were significantly associated with survival after multiple testing correction (Pmeta = 1.1E-6; Pcorrected = 0.01). CONCLUSIONS Common variant rs8170 and rare variants in ATG2B may be associated with EOC overall survival, although further study is needed. IMPACT This study represents the first exome-wide association study of EOC survival to include rare variant analyses, and suggests that complementary single variant and gene-level analyses in large studies are needed to identify rare variants that warrant follow-up study. Cancer Epidemiol Biomarkers Prev; 25(3); 446-54. ©2016 AACR.
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Affiliation(s)
- Stacey J Winham
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Ailith Pirie
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Yian Ann Chen
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Melissa C Larson
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Zachary C Fogarty
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Madalene A Earp
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota
| | - Hoda Anton-Culver
- Department of Epidemiology, University of California Irvine, Irvine, California
| | - Elisa V Bandera
- Rutgers Cancer Institute of New Jersey and Robert Wood Johnson Medical School, New Brunswick, New Jersey
| | - Daniel Cramer
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Jennifer A Doherty
- Section of Biostatistics and Epidemiology, The Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire
| | - Marc T Goodman
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jacek Gronwald
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Beth Y Karlan
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Susanne K Kjaer
- Virus, Lifestyle, and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark. Department of Gynecology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Douglas A Levine
- Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Usha Menon
- Gynaecological Cancer Research Centre, Department of Women's Cancer, Institute for Women's Health, University College London, London, United Kingdom
| | - Roberta B Ness
- The University of Texas School of Public Health, Houston, Texas
| | - Celeste L Pearce
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Tanja Pejovic
- Department of Obstetrics and Gynecology, Oregon Health and Science University, Portland, Oregon. Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon
| | - Mary Anne Rossing
- Department of Epidemiology, University of Washington, Seattle, Washington. Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington
| | | | - Yukie T Bean
- Department of Obstetrics and Gynecology, Oregon Health and Science University, Portland, Oregon. Knight Cancer Institute, Oregon Health and Science University, Portland, Oregon
| | - Maria Bisogna
- Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Louise A Brinton
- Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland
| | - Michael E Carney
- Department of Obstectrics and Gynecology, John A. Burns School of Medicine, University of Hawaii, Honolulu, Hawaii
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Division of Experimental Pathology, Mayo Clinic, Rochester, Minnesota
| | - Cezary Cybulski
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical Academy, Szczecin, Poland
| | - Anna deFazio
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, Australia. Center for Cancer Research, University of Sydney at Westmead Millennium Institute, Sydney, Australia
| | - Ed M Dicks
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Robert P Edwards
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Division of Gynecologic Oncology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Simon A Gayther
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Aleksandra Gentry-Maharaj
- Gynaecological Cancer Research Centre, Department of Women's Cancer, Institute for Women's Health, University College London, London, United Kingdom
| | - Martin Gore
- Gynecological Oncology Unit, The Royal Marsden Hospital, London, United Kingdom
| | - Edwin S Iversen
- Department of Statistical Science, Duke University, Durham, North Carolina
| | - Allan Jensen
- Virus, Lifestyle, and Genes, Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Sharon E Johnatty
- Department of Genetics, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Jenny Lester
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Hui-Yi Lin
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie Memorial Cancer Center & Institute of Oncology, Warsaw, Poland
| | - Jan Lubinski
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland
| | - Janusz Menkiszak
- Department of Surgical Gynecology and Gynecological Oncology of Adults and Adolescents, Pomeranian Medical University, Szczecin, Poland
| | - Francesmary Modugno
- Department of Obstetrics, Gynecology, and Reproductive Sciences, Division of Gynecologic Oncology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania. Department of Epidemiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, Pennsylvania. Womens Cancer Research Program, Magee-Women's Research Institute and University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania
| | - Kirsten B Moysich
- Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, New York
| | - Irene Orlow
- Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Malcolm C Pike
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California. Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York
| | - Susan J Ramus
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Honglin Song
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Kathryn L Terry
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts
| | - Pamela J Thompson
- Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Jonathan P Tyrer
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - David J van den Berg
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Robert A Vierkant
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota
| | - Allison F Vitonis
- Obstetrics and Gynecology Epidemiology Center, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Christine Walsh
- Women's Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, California
| | - Lynne R Wilkens
- Cancer Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii
| | - Anna H Wu
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California
| | - Hannah Yang
- Division of Cancer Epidemiology and Genetics, NCI, Bethesda, Maryland
| | - Argyrios Ziogas
- Department of Epidemiology, Center for Cancer Genetics Research and Prevention, School of Medicine, University of California Irvine, Irvine, California
| | - Andrew Berchuck
- Duke Cancer Institute, Duke University Medical Center, Durham, North Carolina
| | | | | | - Jennifer Permuth-Wey
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Catherine M Phelan
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Paul D P Pharoah
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Brooke L Fridley
- Kansas IDeA Network of Biomedical Research Excellence Bioinformatics Core, University of Kansas Cancer Center, Kansas City, Kansas
| | - Thomas A Sellers
- Department of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Ellen L Goode
- Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, Minnesota.
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Kim YJ, Lee J, Kim BJ, Park T. A new strategy for enhancing imputation quality of rare variants from next-generation sequencing data via combining SNP and exome chip data. BMC Genomics 2015; 16:1109. [PMID: 26715385 PMCID: PMC4696174 DOI: 10.1186/s12864-015-2192-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2015] [Accepted: 11/03/2015] [Indexed: 02/07/2023] Open
Abstract
Background Rare variants have gathered increasing attention as a possible alternative source of missing heritability. Since next generation sequencing technology is not yet cost-effective for large-scale genomic studies, a widely used alternative approach is imputation. However, the imputation approach may be limited by the low accuracy of the imputed rare variants. To improve imputation accuracy of rare variants, various approaches have been suggested, including increasing the sample size of the reference panel, using sequencing data from study-specific samples (i.e., specific populations), and using local reference panels by genotyping or sequencing a subset of study samples. While these approaches mainly utilize reference panels, imputation accuracy of rare variants can also be increased by using exome chips containing rare variants. The exome chip contains 250 K rare variants selected from the discovered variants of about 12,000 sequenced samples. If exome chip data are available for previously genotyped samples, the combined approach using a genotype panel of merged data, including exome chips and SNP chips, should increase the imputation accuracy of rare variants. Results In this study, we describe a combined imputation which uses both exome chip and SNP chip data simultaneously as a genotype panel. The effectiveness and performance of the combined approach was demonstrated using a reference panel of 848 samples constructed using exome sequencing data from the T2D-GENES consortium and 5,349 sample genotype panels consisting of an exome chip and SNP chip. As a result, the combined approach increased imputation quality up to 11 %, and genomic coverage for rare variants up to 117.7 % (MAF < 1 %), compared to imputation using the SNP chip alone. Also, we investigated the systematic effect of reference panels on imputation quality using five reference panels and three genotype panels. The best performing approach was the combination of the study specific reference panel and the genotype panel of combined data. Conclusions Our study demonstrates that combined datasets, including SNP chips and exome chips, enhances both the imputation quality and genomic coverage of rare variants.
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Affiliation(s)
- Young Jin Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, South Korea. .,Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, 363-951, South Korea.
| | - Juyoung Lee
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, 363-951, South Korea.
| | - Bong-Jo Kim
- Division of Structural and Functional Genomics, Center for Genome Science, Korean National Institute of Health, Osong, Chungchungbuk-do, 363-951, South Korea.
| | | | - Taesung Park
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, 151-742, South Korea. .,Department of Statistics, Seoul National University, San 56-1, Shilim-dong, Kwanak-gu, Seoul, 151-742, South Korea.
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18
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Heidaritabar M, Calus MPL, Vereijken A, Groenen MAM, Bastiaansen JWM. Accuracy of imputation using the most common sires as reference population in layer chickens. BMC Genet 2015; 16:101. [PMID: 26282557 PMCID: PMC4539854 DOI: 10.1186/s12863-015-0253-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Accepted: 07/10/2015] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Genotype imputation has become a standard practice in modern genetic research to increase genome coverage and improve the accuracy of genomic selection (GS) and genome-wide association studies (GWAS). We assessed accuracies of imputing 60K genotype data from lower density single nucleotide polymorphism (SNP) panels using a small set of the most common sires in a population of 2140 white layer chickens. Several factors affecting imputation accuracy were investigated, including the size of the reference population, the level of the relationship between the reference and validation populations, and minor allele frequency (MAF) of the SNP being imputed. RESULTS The accuracy of imputation was assessed with different scenarios using 22 and 62 carefully selected reference animals (Ref(22) and Ref(62)). Animal-specific imputation accuracy corrected for gene content was moderate on average (~ 0.80) in most scenarios and low in the 3K to 60K scenario. Maximum average accuracies were 0.90 and 0.93 for the most favourable scenario for Ref(22) and Ref(62) respectively, when SNPs were masked independent of their MAF. SNPs with low MAF were more difficult to impute, and the larger reference population considerably improved the imputation accuracy for these rare SNPs. When Ref(22) was used for imputation, the average imputation accuracy decreased by 0.04 when validation population was two instead of one generation away from the reference and increased again by 0.05 when validation was three generations away. Selecting the reference animals from the most common sires, compared with random animals from the population, considerably improved imputation accuracy for low MAF SNPs, but gave only limited improvement for other MAF classes. The allelic R(2) measure from Beagle software was found to be a good predictor of imputation reliability (correlation ~ 0.8) when the density of validation panel was very low (3K) and the MAF of the SNP and the size of the reference population were not extremely small. CONCLUSIONS Even with a very small number of animals in the reference population, reasonable accuracy of imputation can be achieved. Selecting a set of the most common sires, rather than selecting random animals for the reference population, improves the imputation accuracy of rare alleles, which may be a benefit when imputing with whole genome re-sequencing data.
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Affiliation(s)
- Marzieh Heidaritabar
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
| | - Mario P L Calus
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
| | - Addie Vereijken
- Hendrix Genetics Research, Technology and Services B.V., P.O. Box 114, 5830 AC, Boxmeer, the Netherlands.
| | - Martien A M Groenen
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
| | - John W M Bastiaansen
- Animal Breeding and Genomics Centre, Wageningen University, P.O. Box 338, 6700 AH, Wageningen, the Netherlands.
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19
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Zhang QS, Browning BL, Browning SR. Genome-wide haplotypic testing in a Finnish cohort identifies a novel association with low-density lipoprotein cholesterol. Eur J Hum Genet 2015; 23:672-7. [PMID: 24896150 PMCID: PMC4402615 DOI: 10.1038/ejhg.2014.105] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2013] [Revised: 03/30/2014] [Accepted: 04/30/2014] [Indexed: 12/11/2022] Open
Abstract
We performed genome-wide tests for association between haplotype clusters and each of 9 metabolic traits in a cohort of 5402 Northern Finnish individuals genotyped for 330 000 single-nucleotide polymorphisms. The metabolic traits were body mass index, C-reactive protein, diastolic blood pressure, glucose, high-density lipoprotein (HDL), insulin, low-density lipoprotein (LDL), systolic blood pressure, and triglycerides. Haplotype clusters were determined using Beagle. There were LDL-associated clusters in the chromosome 4q13.3-q21.1 region containing the albumin (ALB) and platelet factor 4 (PF4) genes. This region has not been associated with LDL in previous genome-wide association studies. The most significant haplotype cluster in this region was associated with 0.488 mmol/l higher LDL (95% CI: 0.361-0.615 mmol/l, P-value: 6.4 × 10(-14)). We also observed three previously reported associations: Chromosome 16q13 with HDL, chromosome 1p32.3-p32.2 with LDL and chromosome 19q13.31-q13.32 with LDL. The chromosome 1 and chromosome 4 LDL associations do not reach genome-wide significance in single-marker analyses of these data, illustrating the power of haplotypic association testing.
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Affiliation(s)
- Qian S Zhang
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Brian L Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | - Sharon R Browning
- Department of Biostatistics, University of Washington, Seattle, WA, USA
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20
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Livne OE, Han L, Alkorta-Aranburu G, Wentworth-Sheilds W, Abney M, Ober C, Nicolae DL. PRIMAL: Fast and accurate pedigree-based imputation from sequence data in a founder population. PLoS Comput Biol 2015; 11:e1004139. [PMID: 25735005 PMCID: PMC4348507 DOI: 10.1371/journal.pcbi.1004139] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 01/19/2015] [Indexed: 12/31/2022] Open
Abstract
Founder populations and large pedigrees offer many well-known advantages for genetic mapping studies, including cost-efficient study designs. Here, we describe PRIMAL (PedigRee IMputation ALgorithm), a fast and accurate pedigree-based phasing and imputation algorithm for founder populations. PRIMAL incorporates both existing and original ideas, such as a novel indexing strategy of Identity-By-Descent (IBD) segments based on clique graphs. We were able to impute the genomes of 1,317 South Dakota Hutterites, who had genome-wide genotypes for ~300,000 common single nucleotide variants (SNVs), from 98 whole genome sequences. Using a combination of pedigree-based and LD-based imputation, we were able to assign 87% of genotypes with >99% accuracy over the full range of allele frequencies. Using the IBD cliques we were also able to infer the parental origin of 83% of alleles, and genotypes of deceased recent ancestors for whom no genotype information was available. This imputed data set will enable us to better study the relative contribution of rare and common variants on human phenotypes, as well as parental origin effect of disease risk alleles in >1,000 individuals at minimal cost.
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Affiliation(s)
- Oren E. Livne
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Lide Han
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Gorka Alkorta-Aranburu
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - William Wentworth-Sheilds
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Mark Abney
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Carole Ober
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
| | - Dan L. Nicolae
- Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America
- Departments of Medicine, and Statistics, The University of Chicago, Chicago, Illinois, United States of America
- * E-mail:
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21
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Hoffmann TJ, Sakoda LC, Shen L, Jorgenson E, Habel LA, Liu J, Kvale MN, Asgari MM, Banda Y, Corley D, Kushi LH, Quesenberry CP, Schaefer C, Van Den Eeden SK, Risch N, Witte JS. Imputation of the rare HOXB13 G84E mutation and cancer risk in a large population-based cohort. PLoS Genet 2015; 11:e1004930. [PMID: 25629170 PMCID: PMC4309593 DOI: 10.1371/journal.pgen.1004930] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Accepted: 12/01/2014] [Indexed: 11/25/2022] Open
Abstract
An efficient approach to characterizing the disease burden of rare genetic variants is to impute them into large well-phenotyped cohorts with existing genome-wide genotype data using large sequenced referenced panels. The success of this approach hinges on the accuracy of rare variant imputation, which remains controversial. For example, a recent study suggested that one cannot adequately impute the HOXB13 G84E mutation associated with prostate cancer risk (carrier frequency of 0.0034 in European ancestry participants in the 1000 Genomes Project). We show that by utilizing the 1000 Genomes Project data plus an enriched reference panel of mutation carriers we were able to accurately impute the G84E mutation into a large cohort of 83,285 non-Hispanic White participants from the Kaiser Permanente Research Program on Genes, Environment and Health Genetic Epidemiology Research on Adult Health and Aging cohort. Imputation authenticity was confirmed via a novel classification and regression tree method, and then empirically validated analyzing a subset of these subjects plus an additional 1,789 men from Kaiser specifically genotyped for the G84E mutation (r2 = 0.57, 95% CI = 0.37−0.77). We then show the value of this approach by using the imputed data to investigate the impact of the G84E mutation on age-specific prostate cancer risk and on risk of fourteen other cancers in the cohort. The age-specific risk of prostate cancer among G84E mutation carriers was higher than among non-carriers. Risk estimates from Kaplan-Meier curves were 36.7% versus 13.6% by age 72, and 64.2% versus 24.2% by age 80, for G84E mutation carriers and non-carriers, respectively (p = 3.4×10−12). The G84E mutation was also associated with an increase in risk for the fourteen other most common cancers considered collectively (p = 5.8×10−4) and more so in cases diagnosed with multiple cancer types, both those including and not including prostate cancer, strongly suggesting pleiotropic effects. An efficient approach to characterizing the disease burden of rare genetic variants is to impute them into existing well-phenotyped cohorts with genome-wide data by using large sequenced reference panels; however, the efficacy of this approach remains controversial. A recent study suggested that it is not possible to impute the rare HOXB13 G84E variant using neighboring SNP markers. We show that by using an enriched reference sequenced sample of 22 mutation carriers, we were able to impute this mutation into a large cohort of 83,285 non-Hispanic White individuals from the Kaiser Permanente Research Program on Genes, Environment, and Health Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. The imputation was confirmed via a novel classification and regression tree method, and then empirically validated by direct mutation genotyping of a subset of 1,673 of these individuals in addition to 1,789 other men from Kaiser. Using the same GERA cohort, we then confirmed that the G84E mutation is associated with increased risk of prostate cancer, and estimated the age-specific risk for carriers of the mutation. Finally, we obtained evidence that the mutation is associated with additional types of cancer in the GERA cohort.
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Affiliation(s)
- Thomas J. Hoffmann
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Lori C. Sakoda
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Ling Shen
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Laurel A. Habel
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Jinghua Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Mark N. Kvale
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Maryam M. Asgari
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Yambazi Banda
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
| | - Douglas Corley
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Lawrence H. Kushi
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Charles P. Quesenberry
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Catherine Schaefer
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - Stephen K. Van Den Eeden
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
| | - Neil Risch
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Division of Research, Kaiser Permanente, Northern California, Oakland, California, United States of America
| | - John S. Witte
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, United States of America
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, United States of America
- Department of Urology, University of California San Francisco, San Francisco, California, United States of America
- * E-mail:
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22
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Zheng HF, Rong JJ, Liu M, Han F, Zhang XW, Richards JB, Wang L. Performance of genotype imputation for low frequency and rare variants from the 1000 genomes. PLoS One 2015; 10:e0116487. [PMID: 25621886 PMCID: PMC4306552 DOI: 10.1371/journal.pone.0116487] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2014] [Accepted: 12/08/2014] [Indexed: 01/28/2023] Open
Abstract
Genotype imputation is now routinely applied in genome-wide association studies (GWAS) and meta-analyses. However, most of the imputations have been run using HapMap samples as reference, imputation of low frequency and rare variants (minor allele frequency (MAF) < 5%) are not systemically assessed. With the emergence of next-generation sequencing, large reference panels (such as the 1000 Genomes panel) are available to facilitate imputation of these variants. Therefore, in order to estimate the performance of low frequency and rare variants imputation, we imputed 153 individuals, each of whom had 3 different genotype array data including 317k, 610k and 1 million SNPs, to three different reference panels: the 1000 Genomes pilot March 2010 release (1KGpilot), the 1000 Genomes interim August 2010 release (1KGinterim), and the 1000 Genomes phase1 November 2010 and May 2011 release (1KGphase1) by using IMPUTE version 2. The differences between these three releases of the 1000 Genomes data are the sample size, ancestry diversity, number of variants and their frequency spectrum. We found that both reference panel and GWAS chip density affect the imputation of low frequency and rare variants. 1KGphase1 outperformed the other 2 panels, at higher concordance rate, higher proportion of well-imputed variants (info>0.4) and higher mean info score in each MAF bin. Similarly, 1M chip array outperformed 610K and 317K. However for very rare variants (MAF≤0.3%), only 0–1% of the variants were well imputed. We conclude that the imputation of low frequency and rare variants improves with larger reference panels and higher density of genome-wide genotyping arrays. Yet, despite a large reference panel size and dense genotyping density, very rare variants remain difficult to impute.
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Affiliation(s)
- Hou-Feng Zheng
- Institute of Aging Research, School of Medicine, Hangzhou Normal University, and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
- Department of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- * E-mail:
| | - Jing-Jing Rong
- Institute of Aging Research, School of Medicine, Hangzhou Normal University, and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Ming Liu
- Institute of Aging Research, School of Medicine, Hangzhou Normal University, and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Fang Han
- Department of Pulmonary, Critical Care Medicine, Peking University People’s Hospital, Beijing, China
| | - Xing-Wei Zhang
- Institute of Aging Research, School of Medicine, Hangzhou Normal University, and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - J. Brent Richards
- Department of Medicine, Human Genetics, Epidemiology and Biostatistics, Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
- Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
| | - Li Wang
- Institute of Aging Research, School of Medicine, Hangzhou Normal University, and the Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
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23
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Sepúlveda N, Manjurano A, Drakeley C, Clark TG. On the performance of multiple imputation based on chained equations in tackling missing data of the African α3.7 -globin deletion in a malaria association study. Ann Hum Genet 2015; 78:277-89. [PMID: 24942080 PMCID: PMC4140543 DOI: 10.1111/ahg.12065] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2013] [Accepted: 03/23/2014] [Indexed: 11/30/2022]
Abstract
Multiple imputation based on chained equations (MICE) is an alternative missing genotype method that can use genetic and nongenetic auxiliary data to inform the imputation process. Previously, MICE was successfully tested on strongly linked genetic data. We have now tested it on data of the HBA2 gene which, by the experimental design used in a malaria association study in Tanzania, shows a high missing data percentage and is weakly linked with the remaining genetic markers in the data set. We constructed different imputation models and studied their performance under different missing data conditions. Overall, MICE failed to accurately predict the true genotypes. However, using the best imputation model for the data, we obtained unbiased estimates for the genetic effects, and association signals of the HBA2 gene on malaria positivity. When the whole data set was analyzed with the same imputation model, the association signal increased from 0.80 to 2.70 before and after imputation, respectively. Conversely, postimputation estimates for the genetic effects remained the same in relation to the complete case analysis but showed increased precision. We argue that these postimputation estimates are reasonably unbiased, as a result of a good study design based on matching key socio-environmental factors.
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Affiliation(s)
- Nuno Sepúlveda
- London School of Hygiene and Tropical Medicine, London, UK; Centre of Statistics and Applications of University of Lisbon, Lisbon, Portugal
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24
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van Binsbergen R, Bink MC, Calus MP, van Eeuwijk FA, Hayes BJ, Hulsegge I, Veerkamp RF. Accuracy of imputation to whole-genome sequence data in Holstein Friesian cattle. Genet Sel Evol 2014; 46:41. [PMID: 25022768 PMCID: PMC4226983 DOI: 10.1186/1297-9686-46-41] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Accepted: 04/02/2014] [Indexed: 11/21/2022] Open
Abstract
Background The use of whole-genome sequence data can lead to higher accuracy in genome-wide association studies and genomic predictions. However, to benefit from whole-genome sequence data, a large dataset of sequenced individuals is needed. Imputation from SNP panels, such as the Illumina BovineSNP50 BeadChip and Illumina BovineHD BeadChip, to whole-genome sequence data is an attractive and less expensive approach to obtain whole-genome sequence genotypes for a large number of individuals than sequencing all individuals. Our objective was to investigate accuracy of imputation from lower density SNP panels to whole-genome sequence data in a typical dataset for cattle. Methods Whole-genome sequence data of chromosome 1 (1737 471 SNPs) for 114 Holstein Friesian bulls were used. Beagle software was used for imputation from the BovineSNP50 (3132 SNPs) and BovineHD (40 492 SNPs) beadchips. Accuracy was calculated as the correlation between observed and imputed genotypes and assessed by five-fold cross-validation. Three scenarios S40, S60 and S80 with respectively 40%, 60%, and 80% of the individuals as reference individuals were investigated. Results Mean accuracies of imputation per SNP from the BovineHD panel to sequence data and from the BovineSNP50 panel to sequence data for scenarios S40 and S80 ranged from 0.77 to 0.83 and from 0.37 to 0.46, respectively. Stepwise imputation from the BovineSNP50 to BovineHD panel and then to sequence data for scenario S40 improved accuracy per SNP to 0.65 but it varied considerably between SNPs. Conclusions Accuracy of imputation to whole-genome sequence data was generally high for imputation from the BovineHD beadchip, but was low from the BovineSNP50 beadchip. Stepwise imputation from the BovineSNP50 to the BovineHD beadchip and then to sequence data substantially improved accuracy of imputation. SNPs with a low minor allele frequency were more difficult to impute correctly and the reliability of imputation varied more. Linkage disequilibrium between an imputed SNP and the SNP on the lower density panel, minor allele frequency of the imputed SNP and size of the reference group affected imputation reliability.
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Affiliation(s)
- Rianne van Binsbergen
- Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, P,O, Box 338, 6700 AH Wageningen, the Netherlands.
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25
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Lee S, Abecasis G, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet 2014; 95:5-23. [PMID: 24995866 DOI: 10.1016/j.ajhg.2014.06.009] [Citation(s) in RCA: 658] [Impact Index Per Article: 65.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2014] [Indexed: 12/30/2022] Open
Abstract
Despite the extensive discovery of trait- and disease-associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants can explain additional disease risk or trait variability. An increasing number of studies are underway to identify trait- and disease-associated rare variants. In this review, we provide an overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests. We present the design and analysis pipeline of rare-variant studies and review cost-effective sequencing designs and genotyping platforms. We compare various gene- or region-based association tests, including burden tests, variance-component tests, and combined omnibus tests, in terms of their assumptions and performance. Also discussed are the related topics of meta-analysis, population-stratification adjustment, genotype imputation, follow-up studies, and heritability due to rare variants. We provide guidelines for analysis and discuss some of the challenges inherent in these studies and future research directions.
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26
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Sargolzaei M, Chesnais JP, Schenkel FS. A new approach for efficient genotype imputation using information from relatives. BMC Genomics 2014; 15:478. [PMID: 24935670 PMCID: PMC4076979 DOI: 10.1186/1471-2164-15-478] [Citation(s) in RCA: 586] [Impact Index Per Article: 58.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2013] [Accepted: 06/10/2014] [Indexed: 01/21/2023] Open
Abstract
Background Genotype imputation can help reduce genotyping costs particularly for implementation of genomic selection. In applications entailing large populations, recovering the genotypes of untyped loci using information from reference individuals that were genotyped with a higher density panel is computationally challenging. Popular imputation methods are based upon the Hidden Markov model and have computational constraints due to an intensive sampling process. A fast, deterministic approach, which makes use of both family and population information, is presented here. All individuals are related and, therefore, share haplotypes which may differ in length and frequency based on their relationships. The method starts with family imputation if pedigree information is available, and then exploits close relationships by searching for long haplotype matches in the reference group using overlapping sliding windows. The search continues as the window size is shrunk in each chromosome sweep in order to capture more distant relationships. Results The proposed method gave higher or similar imputation accuracy than Beagle and Impute2 in cattle data sets when all available information was used. When close relatives of target individuals were present in the reference group, the method resulted in higher accuracy compared to the other two methods even when the pedigree was not used. Rare variants were also imputed with higher accuracy. Finally, computing requirements were considerably lower than those of Beagle and Impute2. The presented method took 28 minutes to impute from 6 k to 50 k genotypes for 2,000 individuals with a reference size of 64,429 individuals. Conclusions The proposed method efficiently makes use of information from close and distant relatives for accurate genotype imputation. In addition to its high imputation accuracy, the method is fast, owing to its deterministic nature and, therefore, it can easily be used in large data sets where the use of other methods is impractical.
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Affiliation(s)
- Mehdi Sargolzaei
- Centre for Genetic Improvement of Livestock, Animal and Poultry Science Department, University of Guelph, 50 Stone Road East, Guelph, ON, Canada.
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27
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Moutsianas L, Morris AP. Methodology for the analysis of rare genetic variation in genome-wide association and re-sequencing studies of complex human traits. Brief Funct Genomics 2014; 13:362-70. [PMID: 24916163 PMCID: PMC4168660 DOI: 10.1093/bfgp/elu012] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Genome-wide association studies have been successful in identifying common variants that impact complex human traits and diseases. However, despite this success, the joint effects of these variants explain only a small proportion of the genetic variance in these phenotypes, leading to speculation that rare genetic variation might account for much of the ‘missing heritability’. Consequently, there has been an exciting period of research and development into the methodology for the analysis of rare genetic variants, typically by considering their joint effects on complex traits within the same functional unit or genomic region. In this review, we describe a general framework for modelling the joint effects of rare genetic variants on complex traits in association studies of unrelated individuals. We summarise a range of widely used association tests that have been developed from this model and provide an overview of the relative performance of these approaches from published simulation studies.
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28
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Deelen P, Menelaou A, van Leeuwen EM, Kanterakis A, van Dijk F, Medina-Gomez C, Francioli LC, Hottenga JJ, Karssen LC, Estrada K, Kreiner-Møller E, Rivadeneira F, van Setten J, Gutierrez-Achury J, Westra HJ, Franke L, van Enckevort D, Dijkstra M, Byelas H, van Duijn CM, de Bakker PIW, Wijmenga C, Swertz MA. Improved imputation quality of low-frequency and rare variants in European samples using the 'Genome of The Netherlands'. Eur J Hum Genet 2014; 22:1321-6. [PMID: 24896149 PMCID: PMC4200431 DOI: 10.1038/ejhg.2014.19] [Citation(s) in RCA: 74] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Revised: 11/01/2013] [Accepted: 01/16/2014] [Indexed: 12/23/2022] Open
Abstract
Although genome-wide association studies (GWAS) have identified many common variants associated with complex traits, low-frequency and rare variants have not been interrogated in a comprehensive manner. Imputation from dense reference panels, such as the 1000 Genomes Project (1000G), enables testing of ungenotyped variants for association. Here we present the results of imputation using a large, new population-specific panel: the Genome of The Netherlands (GoNL). We benchmarked the performance of the 1000G and GoNL reference sets by comparing imputation genotypes with 'true' genotypes typed on ImmunoChip in three European populations (Dutch, British, and Italian). GoNL showed significant improvement in the imputation quality for rare variants (MAF 0.05-0.5%) compared with 1000G. In Dutch samples, the mean observed Pearson correlation, r(2), increased from 0.61 to 0.71. We also saw improved imputation accuracy for other European populations (in the British samples, r(2) improved from 0.58 to 0.65, and in the Italians from 0.43 to 0.47). A combined reference set comprising 1000G and GoNL improved the imputation of rare variants even further. The Italian samples benefitted the most from this combined reference (the mean r(2) increased from 0.47 to 0.50). We conclude that the creation of a large population-specific reference is advantageous for imputing rare variants and that a combined reference panel across multiple populations yields the best imputation results.
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Affiliation(s)
- Patrick Deelen
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Androniki Menelaou
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Elisabeth M van Leeuwen
- Department of Epidemiology, Genetic Epidemiology Unit, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Alexandros Kanterakis
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Freerk van Dijk
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Carolina Medina-Gomez
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam, The Netherlands
| | - Laurent C Francioli
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Lennart C Karssen
- Department of Epidemiology, Genetic Epidemiology Unit, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Karol Estrada
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA [4] Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eskil Kreiner-Møller
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] COPSAC; Copenhagen Prospective Studies on Asthma in Childhood; Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Fernando Rivadeneira
- 1] Department of Internal Medicine, Erasmus Medical Center, Rotterdam, The Netherlands [2] Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands [3] Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam, The Netherlands
| | - Jessica van Setten
- Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Javier Gutierrez-Achury
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Harm-Jan Westra
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Lude Franke
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - David van Enckevort
- 1] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands [2] NBIC BioAssist, Netherlands Bioinformatics Center, Nijmegen, The Netherlands
| | - Martijn Dijkstra
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Heorhiy Byelas
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
| | - Cornelia M van Duijn
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | | | - Paul I W de Bakker
- 1] Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands [2] Department of Epidemiology, University Medical Center Utrecht, Utrecht, The Netherlands [3] Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA [4] Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands
| | - Morris A Swertz
- 1] University of Groningen, University Medical Center Groningen, Department of Genetics, Groningen, The Netherlands [2] University of Groningen, University Medical Center Groningen, Genomics Coordination Center, Groningen, The Netherlands
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Kinnamon DD, Martin ER. Valid Monte Carlo permutation tests for genetic case-control studies with missing genotypes. Genet Epidemiol 2014; 38:325-44. [PMID: 24723341 PMCID: PMC6391735 DOI: 10.1002/gepi.21805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2013] [Revised: 12/30/2013] [Accepted: 02/28/2014] [Indexed: 02/04/2023]
Abstract
Monte Carlo permutation tests can be formally constructed by choosing a set of permutations of individual indices and a real-valued test statistic measuring the association between genotypes and affection status. In this paper, we develop a rigorous theoretical framework for verifying the validity of these tests when there are missing genotypes. We begin by specifying a nonparametric probability model for the observed genotype data in a genetic case-control study with unrelated subjects. Under this model and some minimal assumptions about the test statistic, we establish that the resulting Monte Carlo permutation test is exact level α if (1) the chosen set of permutations of individual indices is a group under composition and (2) the distribution of the observed genotype score matrix under the null hypothesis does not change if the assignment of individuals to rows is shuffled according to an arbitrary permutation in this set. We apply these conditions to show that frequently used Monte Carlo permutation tests based on the set of all permutations of individual indices are guaranteed to be exact level α only for missing data processes satisfying a rather restrictive additional assumption. However, if the missing data process depends on covariates that are all identified and recorded, we also show that Monte Carlo permutation tests based on the set of permutations within strata of individuals with identical covariate values are exact level α. Our theoretical results are verified and supplemented by simulations for a variety of missing data processes and test statistics.
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Affiliation(s)
- Daniel D. Kinnamon
- Division of Human Genetics, Department of Internal Medicine, The
Ohio State University Wexner Medical Center, Columbus, OH, USA
- Dr. John T. Macdonald Foundation Department of Human Genetics,
University of Miami Miller School of Medicine, Miami, FL, USA
| | - Eden R. Martin
- Dr. John T. Macdonald Foundation Department of Human Genetics,
University of Miami Miller School of Medicine, Miami, FL, USA
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30
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Chen GB, Lee SH, Brion MJA, Montgomery GW, Wray NR, Radford-Smith GL, Visscher PM. Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data. Hum Mol Genet 2014; 23:4710-20. [PMID: 24728037 DOI: 10.1093/hmg/ddu174] [Citation(s) in RCA: 79] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
As custom arrays are cheaper than generic GWAS arrays, larger sample size is achievable for gene discovery. Custom arrays can tag more variants through denser genotyping of SNPs at associated loci, but at the cost of losing genome-wide coverage. Balancing this trade-off is important for maximizing experimental designs. We quantified both the gain in captured SNP-heritability at known candidate regions and the loss due to imperfect genome-wide coverage for inflammatory bowel disease using immunochip (iChip) and imputed GWAS data on 61,251 and 38.550 samples, respectively. For Crohn's disease (CD), the iChip and GWAS data explained 19 and 26% of variation in liability, respectively, and SNPs in the densely genotyped iChip regions explained 13% of the SNP-heritability for both the iChip and GWAS data. For ulcerative colitis (UC), the iChip and GWAS data explained 15 and 19% of variation in liability, respectively, and the dense iChip regions explained 10 and 9% of the SNP-heritability in the iChip and the GWAS data. From bivariate analyses, estimates of the genetic correlation in risk between CD and UC were 0.75 (SE 0.017) and 0.62 (SE 0.042) for the iChip and GWAS data, respectively. We also quantified the SNP-heritability of genomic regions that did or did not contain the previous 163 GWAS hits for CD and UC, and SNP-heritability of the overlapping loci between the densely genotyped iChip regions and the 163 GWAS hits. For both diseases, over different genomic partitioning, the densely genotyped regions on the iChip tagged at least as much variation in liability as in the corresponding regions in the GWAS data, however a certain amount of tagged SNP-heritability in the GWAS data was lost using the iChip due to the low coverage at unselected regions. These results imply that custom arrays with a GWAS backbone will facilitate more gene discovery, both at associated and novel loci.
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Affiliation(s)
| | | | | | | | | | - Graham L Radford-Smith
- School of Medicine, The University of Queensland, Brisbane, QLD, Australia Inflammatory Bowel Disease Research Group, Immunology Division, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia Department of Gastroenterology, Royal Brisbane and Women's Hospital, Brisbane, QLD, Australia
| | - Peter M Visscher
- The Queensland Brain Institute and The University of Queensland Diamantina Institute, Brisbane, QLD, Australia
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31
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Abstract
The role of rare variants has become a focus in the search for association with complex traits. Imputation is a powerful and cost-efficient tool to access variants that have not been directly typed, but there are several challenges when imputing rare variants, most notably reference panel selection. Extensions to rare variant association tests to incorporate genotype uncertainty from imputation are discussed, as well as the use of imputed low-frequency and rare variants in the study of population isolates.
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Cheung C, Thompson E, Wijsman E. GIGI: an approach to effective imputation of dense genotypes on large pedigrees. Am J Hum Genet 2013; 92:504-16. [PMID: 23561844 DOI: 10.1016/j.ajhg.2013.02.011] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2012] [Revised: 01/15/2013] [Accepted: 02/27/2013] [Indexed: 12/11/2022] Open
Abstract
Recent emergence of the common-disease-rare-variant hypothesis has renewed interest in the use of large pedigrees for identifying rare causal variants. Genotyping with modern sequencing platforms is increasingly common in the search for such variants but remains expensive and often is limited to only a few subjects per pedigree. In population-based samples, genotype imputation is widely used so that additional genotyping is not needed. We now introduce an analogous approach that enables computationally efficient imputation in large pedigrees. Our approach samples inheritance vectors (IVs) from a Markov Chain Monte Carlo sampler by conditioning on genotypes from a sparse set of framework markers. Missing genotypes are probabilistically inferred from these IVs along with observed dense genotypes that are available on a subset of subjects. We implemented our approach in the Genotype Imputation Given Inheritance (GIGI) program and evaluated the approach on both simulated and real large pedigrees. With a real pedigree, we also compared imputed results obtained from this approach with those from the population-based imputation program BEAGLE. We demonstrated that our pedigree-based approach imputes many alleles with high accuracy. It is much more accurate for calling rare alleles than is population-based imputation and does not require an outside reference sample. We also evaluated the effect of varying other parameters, including the marker type and density of the framework panel, threshold for calling genotypes, and population allele frequencies. By leveraging information from existing genotypes already assayed on large pedigrees, our approach can facilitate cost-effective use of sequence data in the pursuit of rare causal variants.
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Imputation of unordered markers and the impact on genomic selection accuracy. G3-GENES GENOMES GENETICS 2013; 3:427-39. [PMID: 23449944 PMCID: PMC3583451 DOI: 10.1534/g3.112.005363] [Citation(s) in RCA: 100] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2012] [Accepted: 12/28/2012] [Indexed: 12/24/2022]
Abstract
Genomic selection, a breeding method that promises to accelerate rates of genetic gain, requires dense, genome-wide marker data. Genotyping-by-sequencing can generate a large number of de novo markers. However, without a reference genome, these markers are unordered and typically have a large proportion of missing data. Because marker imputation algorithms were developed for species with a reference genome, algorithms suited for unordered markers have not been rigorously evaluated. Using four empirical datasets, we evaluate and characterize four such imputation methods, referred to as k-nearest neighbors, singular value decomposition, random forest regression, and expectation maximization imputation, in terms of their imputation accuracies and the factors affecting accuracy. The effect of imputation method on the genomic selection accuracy is assessed in comparison with mean imputation. The effect of excluding markers with a large proportion of missing data on the genomic selection accuracy is also examined. Our results show that imputation of unordered markers can be accurate, especially when linkage disequilibrium between markers is high and genotyped individuals are related. Of the methods evaluated, random forest regression imputation produced superior accuracy. In comparison with mean imputation, all four imputation methods we evaluated led to greater genomic selection accuracies when the level of missing data was high. Including rather than excluding markers with a large proportion of missing data nearly always led to greater GS accuracies. We conclude that high levels of missing data in dense marker sets is not a major obstacle for genomic selection, even when marker order is not known.
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Chen J, Zhang JG, Li J, Pei YF, Deng HW. On combining reference data to improve imputation accuracy. PLoS One 2013; 8:e55600. [PMID: 23383238 PMCID: PMC3559437 DOI: 10.1371/journal.pone.0055600] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2012] [Accepted: 12/27/2012] [Indexed: 12/17/2022] Open
Abstract
Genotype imputation is an important tool in human genetics studies, which uses reference sets with known genotypes and prior knowledge on linkage disequilibrium and recombination rates to infer un-typed alleles for human genetic variations at a low cost. The reference sets used by current imputation approaches are based on HapMap data, and/or based on recently available next-generation sequencing (NGS) data such as data generated by the 1000 Genomes Project. However, with different coverage and call rates for different NGS data sets, how to integrate NGS data sets of different accuracy as well as previously available reference data as references in imputation is not an easy task and has not been systematically investigated. In this study, we performed a comprehensive assessment of three strategies on using NGS data and previously available reference data in genotype imputation for both simulated data and empirical data, in order to obtain guidelines for optimal reference set construction. Briefly, we considered three strategies: strategy 1 uses one NGS data as a reference; strategy 2 imputes samples by using multiple individual data sets of different accuracy as independent references and then combines the imputed samples with samples based on the high accuracy reference selected when overlapping occurs; and strategy 3 combines multiple available data sets as a single reference after imputing each other. We used three software (MACH, IMPUTE2 and BEAGLE) for assessing the performances of these three strategies. Our results show that strategy 2 and strategy 3 have higher imputation accuracy than strategy 1. Particularly, strategy 2 is the best strategy across all the conditions that we have investigated, producing the best accuracy of imputation for rare variant. Our study is helpful in guiding application of imputation methods in next generation association analyses.
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Affiliation(s)
- Jun Chen
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Ji-Gang Zhang
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Jian Li
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Yu-Fang Pei
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America
| | - Hong-Wen Deng
- Center for Bioinformatics and Genomics, Department of Biostatistics and Bioinformatics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, United States of America
- Center of System Biomedical Sciences, University of Shanghai for Science and Technology, Shanghai, P. R. China
- College of Life Sciences and Bioengineering, Beijing Jiaotong University, Beijing, P. R. China
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35
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Empirical power of very rare variants for common traits and disease: results from sanger sequencing 1998 individuals. Eur J Hum Genet 2013; 21:1027-30. [PMID: 23321613 DOI: 10.1038/ejhg.2012.284] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2012] [Revised: 10/12/2012] [Accepted: 11/22/2013] [Indexed: 11/09/2022] Open
Abstract
The optimal study design for identifying rare variants associated with common disease is not yet clear and researchers have to decide whether to prioritize lower sequencing coverage on larger sample sizes, or higher coverage on smaller sample sizes. High-coverage sequencing affords several advantages, such as genotype accuracy and improved identification of very rare variants, but this comes at increased cost. However, the magnitude of the contribution of very rare variants to the statistical power of gene-based association tests is unknown. By using Sanger sequence data on seven genes from 1998 subjects with simulated phenotypes, we provide evidence that excluding very rare variants, in general, reduces the statistical power of rare variant association tests only modestly. However, if the probability of being causal and the effect size of the causal variants are inversely related to the minor allele frequency, then very rare variants do contribute to some power, however the absolute power remains low. As very rare variants constitute the majority of variants identified in sequencing studies, these findings suggest that careful attention need to be placed on the plausible relationship that exist between very rare variants and common disease.
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36
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Duan Q, Liu EY, Croteau-Chonka DC, Mohlke KL, Li Y. A comprehensive SNP and indel imputability database. Bioinformatics 2013; 29:528-31. [PMID: 23292738 DOI: 10.1093/bioinformatics/bts724] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
MOTIVATION Genotype imputation has become an indispensible step in genome-wide association studies (GWAS). Imputation accuracy, directly influencing downstream analysis, has shown to be improved using re-sequencing-based reference panels; however, this comes at the cost of high computational burden due to the huge number of potentially imputable markers (tens of millions) discovered through sequencing a large number of individuals. Therefore, there is an increasing need for access to imputation quality information without actually conducting imputation. To facilitate this process, we have established a publicly available SNP and indel imputability database, aiming to provide direct access to imputation accuracy information for markers identified by the 1000 Genomes Project across four major populations and covering multiple GWAS genotyping platforms. RESULTS SNP and indel imputability information can be retrieved through a user-friendly interface by providing the ID(s) of the desired variant(s) or by specifying the desired genomic region. The query results can be refined by selecting relevant GWAS genotyping platform(s). This is the first database providing variant imputability information specific to each continental group and to each genotyping platform. In Filipino individuals from the Cebu Longitudinal Health and Nutrition Survey, our database can achieve an area under the receiver-operating characteristic curve of 0.97, 0.91, 0.88 and 0.79 for markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. Specifically, by filtering out 48.6% of markers (corresponding to a reduction of up to 48.6% in computational costs for actual imputation) based on the imputability information in our database, we can remove 77%, 58%, 51% and 42% of the poorly imputed markers at the cost of only 0.3%, 0.8%, 1.5% and 4.6% of the well-imputed markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. AVAILABILITY http://www.unc.edu/∼yunmli/imputability.html
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Affiliation(s)
- Qing Duan
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
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37
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Keller MF, Saad M, Bras J, Bettella F, Nicolaou N, Simón-Sánchez J, Mittag F, Büchel F, Sharma M, Gibbs JR, Schulte C, Moskvina V, Durr A, Holmans P, Kilarski LL, Guerreiro R, Hernandez DG, Brice A, Ylikotila P, Stefánsson H, Majamaa K, Morris HR, Williams N, Gasser T, Heutink P, Wood NW, Hardy J, Martinez M, Singleton AB, Nalls MA. Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease. Hum Mol Genet 2012; 21:4996-5009. [PMID: 22892372 DOI: 10.1093/hmg/dds335] [Citation(s) in RCA: 158] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17-38, P = 8.08E - 08) phenotypic variance associated with all types of PD, 15% (95% CI -0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17-44, P = 1.34E - 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.
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Affiliation(s)
- Margaux F Keller
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892, USA
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Effect of genome-wide genotyping and reference panels on rare variants imputation. J Genet Genomics 2012; 39:545-50. [PMID: 23089364 DOI: 10.1016/j.jgg.2012.07.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2012] [Accepted: 07/03/2012] [Indexed: 11/20/2022]
Abstract
Common variants explain little of the variance of most common disease, prompting large-scale sequencing studies to understand the contribution of rare variants to these diseases. Imputation of rare variants from genome-wide genotypic arrays offers a cost-efficient strategy to achieve necessary sample sizes required for adequate statistical power. To estimate the performance of imputation of rare variants, we imputed 153 individuals, each of whom was genotyped on 3 different genotype arrays including 317k, 610k and 1 million single nucleotide polymorphisms (SNPs), to two different reference panels: HapMap2 and 1000 Genomes pilot March 2010 release (1KGpilot) by using IMPUTE version 2. We found that more than 94% and 84% of all SNPs yield acceptable accuracy (info > 0.4) in HapMap2 and 1KGpilot-based imputation, respectively. For rare variants (minor allele frequency (MAF) ≤5%), the proportion of well-imputed SNPs increased as the MAF increased from 0.3% to 5% across all 3 genome-wide association study (GWAS) datasets. The proportion of well-imputed SNPs was 69%, 60% and 49% for SNPs with a MAF from 0.3% to 5% for 1M, 610k and 317k, respectively. None of the very rare variants (MAF ≤ 0.3%) were well imputed. We conclude that the imputation accuracy of rare variants increases with higher density of genome-wide genotyping arrays when the size of the reference panel is small. Variants with lower MAF are more difficult to impute. These findings have important implications in the design and replication of large-scale sequencing studies.
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39
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Gao X, Haritunians T, Marjoram P, McKean-Cowdin R, Torres M, Taylor KD, Rotter JI, Gauderman WJ, Varma R. Genotype Imputation for Latinos Using the HapMap and 1000 Genomes Project Reference Panels. Front Genet 2012; 3:117. [PMID: 22754564 PMCID: PMC3384355 DOI: 10.3389/fgene.2012.00117] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2012] [Accepted: 06/05/2012] [Indexed: 11/13/2022] Open
Abstract
Genotype imputation is a vital tool in genome-wide association studies (GWAS) and meta-analyses of multiple GWAS results. Imputation enables researchers to increase genomic coverage and to pool data generated using different genotyping platforms. HapMap samples are often employed as the reference panel. More recently, the 1000 Genomes Project resource is becoming the primary source for reference panels. Multiple GWAS and meta-analyses are targeting Latinos, the most populous, and fastest growing minority group in the US. However, genotype imputation resources for Latinos are rather limited compared to individuals of European ancestry at present, largely because of the lack of good reference data. One choice of reference panel for Latinos is one derived from the population of Mexican individuals in Los Angeles contained in the HapMap Phase 3 project and the 1000 Genomes Project. However, a detailed evaluation of the quality of the imputed genotypes derived from the public reference panels has not yet been reported. Using simulation studies, the Illumina OmniExpress GWAS data from the Los Angles Latino Eye Study and the MACH software package, we evaluated the accuracy of genotype imputation in Latinos. Our results show that the 1000 Genomes Project AMR + CEU + YRI reference panel provides the highest imputation accuracy for Latinos, and that also including Asian samples in the panel can reduce imputation accuracy. We also provide the imputation accuracy for each autosomal chromosome using the 1000 Genomes Project panel for Latinos. Our results serve as a guide to future imputation based analysis in Latinos.
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Affiliation(s)
- Xiaoyi Gao
- Department of Ophthalmology, Keck School of Medicine, University of Southern California Los Angeles, CA, USA
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40
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Rubio JP, Topp S, Warren L, St Jean PL, Wegmann D, Kessner D, Novembre J, Shen J, Fraser D, Aponte J, Nangle K, Cardon LR, Ehm MG, Chissoe SL, Whittaker JC, Nelson MR, Mooser VE. Deep sequencing of the LRRK2 gene in 14,002 individuals reveals evidence of purifying selection and independent origin of the p.Arg1628Pro mutation in Europe. Hum Mutat 2012; 33:1087-98. [PMID: 22415848 DOI: 10.1002/humu.22075] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2011] [Accepted: 02/24/2012] [Indexed: 12/12/2022]
Abstract
Genetic variation in LRRK2 predisposes to Parkinson disease (PD), which underpins its development as a therapeutic target. Here, we aimed to identify novel genotype-phenotype associations that might support developing LRRK2 therapies for other conditions. We sequenced the 51 exons of LRRK2 in cases comprising 12 common diseases (n = 9,582), and in 4,420 population controls. We identified 739 single-nucleotide variants, 62% of which were observed in only one person, including 316 novel exonic variants. We found evidence of purifying selection for the LRRK2 gene and a trend suggesting that this is more pronounced in the central (ROC-COR-kinase) core protein domains of LRRK2 than the flanking domains. Population genetic analyses revealed that LRRK2 is not especially polymorphic or differentiated in comparison to 201 other drug target genes. Among Europeans, we identified 17 carriers (0.13%) of pathogenic LRRK2 mutations that were not significantly enriched within any disease or in those reporting a family history of PD. Analysis of pathogenic mutations within Europe reveals that the p.Arg1628Pro (c4883G>C) mutation arose independently in Europe and Asia. Taken together, these findings demonstrate how targeted deep sequencing can help to reveal fundamental characteristics of clinically important loci.
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Affiliation(s)
- Justin P Rubio
- Quantitative Sciences, Research and Development, GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire, England, United Kingdom.
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41
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Liu EY, Buyske S, Aragaki AK, Peters U, Boerwinkle E, Carlson C, Carty C, Crawford DC, Haessler J, Hindorff LA, Marchand LL, Manolio TA, Matise T, Wang W, Kooperberg C, North KE, Li Y. Genotype imputation of Metabochip SNPs using a study-specific reference panel of ~4,000 haplotypes in African Americans from the Women's Health Initiative. Genet Epidemiol 2012; 36:107-17. [PMID: 22851474 PMCID: PMC3410659 DOI: 10.1002/gepi.21603] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Genetic imputation has become standard practice in modern genetic studies. However, several important issues have not been adequately addressed including the utility of study-specific reference, performance in admixed populations, and quality for less common (minor allele frequency [MAF] 0.005-0.05) and rare (MAF < 0.005) variants. These issues only recently became addressable with genome-wide association studies (GWAS) follow-up studies using dense genotyping or sequencing in large samples of non-European individuals. In this work, we constructed a study-specific reference panel of 3,924 haplotypes using African Americans in the Women's Health Initiative (WHI) genotyped on both the Metabochip and the Affymetrix 6.0 GWAS platform. We used this reference panel to impute into 6,459 WHI SNP Health Association Resource (SHARe) study subjects with only GWAS genotypes. Our analysis confirmed the imputation quality metric Rsq (estimated r(2) , specific to each SNP) as an effective post-imputation filter. We recommend different Rsq thresholds for different MAF categories such that the average (across SNPs) Rsq is above the desired dosage r(2) (squared Pearson correlation between imputed and experimental genotypes). With a desired dosage r(2) of 80%, 99.9% (97.5%, 83.6%, 52.0%, 20.5%) of SNPs with MAF > 0.05 (0.03-0.05, 0.01-0.03, 0.005-0.01, and 0.001-0.005) passed the post-imputation filter. The average dosage r(2) for these SNPs is 94.7%, 92.1%, 89.0%, 83.1%, and 79.7%, respectively. These results suggest that for African Americans imputation of Metabochip SNPs from GWAS data, including low frequency SNPs with MAF 0.005-0.05, is feasible and worthwhile for power increase in downstream association analysis provided a sizable reference panel is available.
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Affiliation(s)
- Eric Yi Liu
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
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42
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A general framework for detecting disease associations with rare variants in sequencing studies. Am J Hum Genet 2011; 89:354-67. [PMID: 21885029 DOI: 10.1016/j.ajhg.2011.07.015] [Citation(s) in RCA: 209] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2011] [Revised: 07/21/2011] [Accepted: 07/26/2011] [Indexed: 12/19/2022] Open
Abstract
Biological and empirical evidence suggests that rare variants account for a large proportion of the genetic contributions to complex human diseases. Recent technological advances in high-throughput sequencing platforms have made it possible for researchers to generate comprehensive information on rare variants in large samples. We provide a general framework for association testing with rare variants by combining mutation information across multiple variant sites within a gene and relating the enriched genetic information to disease phenotypes through appropriate regression models. Our framework covers all major study designs (i.e., case-control, cross-sectional, cohort and family studies) and all common phenotypes (e.g., binary, quantitative, and age at onset), and it allows arbitrary covariates (e.g., environmental factors and ancestry variables). We derive theoretically optimal procedures for combining rare mutations and construct suitable test statistics for various biological scenarios. The allele-frequency threshold can be fixed or variable. The effects of the combined rare mutations on the phenotype can be in the same direction or different directions. The proposed methods are statistically more powerful and computationally more efficient than existing ones. An application to a deep-resequencing study of drug targets led to a discovery of rare variants associated with total cholesterol. The relevant software is freely available.
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