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Zheng M, Liao J, Li Z, Xu Z, Jiang Z, Tan L, Fu R, Xu H, Li Z, Zhang X, Nie Q. Evaluation of the selection of key individuals for genotype imputation in Chinese yellow-feathered chicken. Poult Sci 2023; 102:102901. [PMID: 37499612 PMCID: PMC10393784 DOI: 10.1016/j.psj.2023.102901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 06/02/2023] [Accepted: 06/24/2023] [Indexed: 07/29/2023] Open
Abstract
Genotype imputation is a powerful technique employed by next-generation sequencing (NGS) and genotyping arrays, which can significantly enhance the cost-effectiveness and efficiency of genomic selection. The accuracy of imputation is largely determined by the choice of reference panel, with previous studies generally demonstrating that a closely related population as a reference panel leads to greater accuracy than a more distantly related population. Various strategies have been proposed for selecting desirable individuals via targeted resequencing, but their efficiencies need further improvement. In this study, we present a practical broiler selection methodology for a local Chinese chicken line that integrates established methods based on pedigree, genomics, and random sampling, and leverages genotype and pedigree information from the yellow-plumage dwarf chicken line. The efficacy of these selection strategies was assessed by evaluating their ability to accurately impute masked genotypes from data obtained using a 600K chip. Our findings reveal that the pedigree-based method yields superior accuracy in genotype imputation, whereas the haplotype-based method exhibits greater stability. Nonetheless, the impact of these targeted methods for selecting key individuals is slightly different when initiating a new sequencing project in a production context. Overall, this study highlights the advantages of using the pedigree-based approach as the preferred method for optimizing genotype imputation in broiler chickens.
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Affiliation(s)
- Ming Zheng
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Jiahao Liao
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Zhuohang Li
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Zhenqiang Xu
- Guangdong Wens Nanfang Poultry Breeding Co., Ltd., Xinxing 527439, China
| | - Ziqin Jiang
- Guangdong Wens Nanfang Poultry Breeding Co., Ltd., Xinxing 527439, China
| | - Liangtian Tan
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Rong Fu
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Haiping Xu
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Zhenhui Li
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Xiquan Zhang
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China
| | - Qinghua Nie
- Lingnan Guangdong Laboratory of Modern Agriculture, College of Animal Science, South China Agricultural University, Guangzhou 510642, Guangdong, China; Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding, and Key Laboratory of Chicken Genetics, Breeding and Reproduction, Ministry of Agriculture, Guangzhou 510642, Guangdong, China; State Key Laboratory of Livestock and Poultry Breeding, South China Agricultural University, Guangzhou 510642, Guangdong, China.
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Zhao C, Teng J, Zhang X, Wang D, Zhang X, Li S, Jiang X, Li H, Ning C, Zhang Q. Towards a Cost-Effective Implementation of Genomic Prediction Based on Low Coverage Whole Genome Sequencing in Dezhou Donkey. Front Genet 2021; 12:728764. [PMID: 34804115 PMCID: PMC8595392 DOI: 10.3389/fgene.2021.728764] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/20/2021] [Indexed: 11/25/2022] Open
Abstract
Low-coverage whole genome sequencing is a low-cost genotyping technology. Combined with genotype imputation approaches, it is likely to become a critical component of cost-effective genomic selection programs in agricultural livestock. Here, we used the low-coverage sequence data of 617 Dezhou donkeys to investigate the performance of genotype imputation for low-coverage whole genome sequence data and genomic prediction based on the imputed genotype data. The specific aims were as follows: 1) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, minor allele frequency (MAF), and imputation pipelines and 2) to assess the accuracy of genomic prediction under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single-vs. multi-trait models. We found that a high imputation accuracy (>0.95) can be achieved for sequence data with a sequencing depth as low as 1x and the number of sequenced individuals ≥400. For genomic prediction, the best performance was obtained by using a marker density of 410K and a G matrix constructed using expected marker dosages. Multi-trait genomic best linear unbiased prediction (GBLUP) performed better than single-trait GBLUP. Our study demonstrates that low-coverage whole genome sequencing would be a cost-effective approach for genomic prediction in Dezhou donkey.
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Affiliation(s)
- Changheng Zhao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
| | - Jun Teng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
| | - Xinhao Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China.,National Engineering Research Center for Gelatin-based TCM, Dong-E E-Jiao Co., Ltd., Dong'e County, China
| | - Dan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
| | - Xinyi Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
| | - Shiyin Li
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
| | - Xin Jiang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
| | - Haijing Li
- National Engineering Research Center for Gelatin-based TCM, Dong-E E-Jiao Co., Ltd., Dong'e County, China
| | - Chao Ning
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
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Nato AQ, Chapman NH, Sohi HK, Nguyen HD, Brkanac Z, Wijsman EM. PBAP: a pipeline for file processing and quality control of pedigree data with dense genetic markers. Bioinformatics 2015; 31:3790-8. [PMID: 26231429 PMCID: PMC4668752 DOI: 10.1093/bioinformatics/btv444] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2015] [Revised: 07/07/2015] [Accepted: 07/25/2015] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Huge genetic datasets with dense marker panels are now common. With the availability of sequence data and recognition of importance of rare variants, smaller studies based on pedigrees are again also common. Pedigree-based samples often start with a dense marker panel, a subset of which may be used for linkage analysis to reduce computational burden and to limit linkage disequilibrium between single-nucleotide polymorphisms (SNPs). Programs attempting to select markers for linkage panels exist but lack flexibility. RESULTS We developed a pedigree-based analysis pipeline (PBAP) suite of programs geared towards SNPs and sequence data. PBAP performs quality control, marker selection and file preparation. PBAP sets up files for MORGAN, which can handle analyses for small and large pedigrees, typically human, and results can be used with other programs and for downstream analyses. We evaluate and illustrate its features with two real datasets. AVAILABILITY AND IMPLEMENTATION PBAP scripts may be downloaded from http://faculty.washington.edu/wijsman/software.shtml. CONTACT wijsman@uw.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | - Hiep D Nguyen
- Division of Medical Genetics, Department of Medicine
| | | | - Ellen M Wijsman
- Division of Medical Genetics, Department of Medicine, Department of Biostatistics and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
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Li W, Xu W, Fu G, Ma L, Richards J, Rao W, Bythwood T, Guo S, Song Q. High-accuracy haplotype imputation using unphased genotype data as the references. Gene 2015; 572:279-84. [PMID: 26232609 PMCID: PMC5373555 DOI: 10.1016/j.gene.2015.07.082] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2015] [Revised: 06/24/2015] [Accepted: 07/28/2015] [Indexed: 12/19/2022]
Abstract
Enormously growing genomic datasets present a new challenge on missing data imputation, a notoriously resource-demanding task. Haplotype imputation requires ethnicity-matched references. However, to date, haplotype references are not available for the majority of populations in the world. We explored to use existing unphased genotype datasets as references; if it succeeds, it will cover almost all of the populations in the world. The results showed that our HiFi software successfully yields 99.43% accuracy with unphased genotype references. Our method provides a cost-effective solution to breakthrough the bottleneck of limited reference availability for haplotype imputation in the big data era.
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Affiliation(s)
- Wenzhi Li
- Department of Neurosurgery, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, Shaanxi, China; Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA, USA
| | - Wei Xu
- Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA, USA
| | | | - Li Ma
- Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA, USA; 4DGenome Inc, Atlanta, GA, USA
| | - Jendai Richards
- Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA, USA
| | | | - Tameka Bythwood
- Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA, USA
| | - Shiwen Guo
- Department of Neurosurgery, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
| | - Qing Song
- Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, GA, USA; 4DGenome Inc, Atlanta, GA, USA; First Affiliated Hospital of Medical School, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
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Choosing Subsamples for Sequencing Studies by Minimizing the Average Distance to the Closest Leaf. Genetics 2015; 201:499-511. [PMID: 26307072 PMCID: PMC4596665 DOI: 10.1534/genetics.115.176909] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 08/14/2015] [Indexed: 12/29/2022] Open
Abstract
Imputation of genotypes in a study sample can make use of sequenced or densely genotyped external reference panels consisting of individuals that are not from the study sample. It also can employ internal reference panels, incorporating a subset of individuals from the study sample itself. Internal panels offer an advantage over external panels because they can reduce imputation errors arising from genetic dissimilarity between a population of interest and a second, distinct population from which the external reference panel has been constructed. As the cost of next-generation sequencing decreases, internal reference panel selection is becoming increasingly feasible. However, it is not clear how best to select individuals to include in such panels. We introduce a new method for selecting an internal reference panel—minimizing the average distance to the closest leaf (ADCL)—and compare its performance relative to an earlier algorithm: maximizing phylogenetic diversity (PD). Employing both simulated data and sequences from the 1000 Genomes Project, we show that ADCL provides a significant improvement in imputation accuracy, especially for imputation of sites with low-frequency alleles. This improvement in imputation accuracy is robust to changes in reference panel size, marker density, and length of the imputation target region.
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Peil B, Kabisch M, Fischer C, Hamann U, Bermejo JL. Tailored selection of study individuals to be sequenced in order to improve the accuracy of genotype imputation. Genet Epidemiol 2014; 39:114-21. [PMID: 25537753 DOI: 10.1002/gepi.21873] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 08/28/2014] [Accepted: 11/05/2014] [Indexed: 01/21/2023]
Abstract
The addition of sequence data from own-study individuals to genotypes from external data repositories, for example, the HapMap, has been shown to improve the accuracy of imputed genotypes. Early approaches for reference panel selection favored individuals who best reflect recombination patterns in the study population. By contrast, a maximization of genetic diversity in the reference panel has been recently proposed. We investigate here a novel strategy to select individuals for sequencing that relies on the characterization of the ancestral kernel of the study population. The simulated study scenarios consisted of several combinations of subpopulations from HapMap. HapMap individuals who did not belong to the study population constituted an external reference panel which was complemented with the sequences of study individuals selected according to different strategies. In addition to a random choice, individuals with the largest statistical depth according to the first genetic principal components were selected. In all simulated scenarios the integration of sequences from own-study individuals increased imputation accuracy. The selection of individuals based on the statistical depth resulted in the highest imputation accuracy for European and Asian study scenarios, whereas random selection performed best for an African-study scenario. Present findings indicate that there is no universal 'best strategy' to select individuals for sequencing. We propose to use the methodology described in the manuscript to assess the advantage of focusing on the ancestral kernel under own study characteristics (study size, genetic diversity, availability and properties of external reference panels, frequency of imputed variants…).
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Affiliation(s)
- Barbara Peil
- Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
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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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Assessing accuracy of genotype imputation in American Indians. PLoS One 2014; 9:e102544. [PMID: 25014012 PMCID: PMC4094523 DOI: 10.1371/journal.pone.0102544] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 06/20/2014] [Indexed: 12/04/2022] Open
Abstract
Background Genotype imputation is commonly used in genetic association studies to test untyped variants using information on linkage disequilibrium (LD) with typed markers. Imputing genotypes requires a suitable reference population in which the LD pattern is known, most often one selected from HapMap. However, some populations, such as American Indians, are not represented in HapMap. In the present study, we assessed accuracy of imputation using HapMap reference populations in a genome-wide association study in Pima Indians. Results Data from six randomly selected chromosomes were used. Genotypes in the study population were masked (either 1% or 20% of SNPs available for a given chromosome). The masked genotypes were then imputed using the software Markov Chain Haplotyping Algorithm. Using four HapMap reference populations, average genotype error rates ranged from 7.86% for Mexican Americans to 22.30% for Yoruba. In contrast, use of the original Pima Indian data as a reference resulted in an average error rate of 1.73%. Conclusions Our results suggest that the use of HapMap reference populations results in substantial inaccuracy in the imputation of genotypes in American Indians. A possible solution would be to densely genotype or sequence a reference American Indian population.
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Duan Q, Liu EY, Auer PL, Zhang G, Lange EM, Jun G, Bizon C, Jiao S, Buyske S, Franceschini N, Carlson CS, Hsu L, Reiner AP, Peters U, Haessler J, Curtis K, Wassel CL, Robinson JG, Martin LW, Haiman CA, Le Marchand L, Matise TC, Hindorff LA, Crawford DC, Assimes TL, Kang HM, Heiss G, Jackson RD, Kooperberg C, Wilson JG, Abecasis GR, North KE, Nickerson DA, Lange LA, Li Y. Imputation of coding variants in African Americans: better performance using data from the exome sequencing project. Bioinformatics 2013; 29:2744-9. [PMID: 23956302 DOI: 10.1093/bioinformatics/btt477] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
SUMMARY Although the 1000 Genomes haplotypes are the most commonly used reference panel for imputation, medical sequencing projects are generating large alternate sets of sequenced samples. Imputation in African Americans using 3384 haplotypes from the Exome Sequencing Project, compared with 2184 haplotypes from 1000 Genomes Project, increased effective sample size by 8.3-11.4% for coding variants with minor allele frequency <1%. No loss of imputation quality was observed using a panel built from phenotypic extremes. We recommend using haplotypes from Exome Sequencing Project alone or concatenation of the two panels over quality score-based post-imputation selection or IMPUTE2's two-panel combination. CONTACT yunli@med.unc.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qing Duan
- Department of Genetics and Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Biostatistics and Center for Statistical Genetics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA, Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Statistics and Department of Genetics, Rutgers University, Piscataway, NJ 08854, USA, Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599, USA, Department of Epidemiology, University of Washington, Seattle, WA 98195, USA, Division of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA 15261, USA, Department of Epidemiology and Medicine, University of Iowa, Iowa City, IA 52242, Division of Cardiology, George Washington University School of Medicine and Health Sciences, Washington, DC 20037, USA, Department of Preventive Medicine, Keck School of Medicine, University of Southern California/Norris Comprehensive Cancer Center, Los Angeles, CA 90033, USA, Epidemiology Program, University of Hawaii Cancer Center, HI 96813, USA, Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA, Department of Molecular Physiology and Biophysics, Center for Human Genetics Research, Vanderbilt University, Nashville, TN 37232, USA, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA, Division of Endocrinology, Diabetes and Metabolism, Ohio State University, Columbus, OH 43210, USA, Department of Physiology and Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
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Abstract
The recent dramatic cost reduction of next-generation sequencing technology enables investigators to assess most variants in the human genome to identify risk variants for complex diseases. However, sequencing large samples remains very expensive. For a study sample with existing genotype data, such as array data from genome-wide association studies, a cost-effective approach is to sequence a subset of the study sample and then to impute the rest of the study sample, using the sequenced subset as a reference panel. The use of such an internal reference panel identifies population-specific variants and avoids the problem of a substantial mismatch in ancestry background between the study population and the reference population. To efficiently select an internal panel, we introduce an idea of phylogenetic diversity from mathematical phylogenetics and comparative genomics. We propose the “most diverse reference panel”, defined as the subset with the maximal “phylogenetic diversity”, thereby incorporating individuals that span a diverse range of genotypes within the sample. Using data both from simulations and from the 1000 Genomes Project, we show that the most diverse reference panel can substantially improve the imputation accuracy compared to randomly selected reference panels, especially for the imputation of rare variants. The improvement in imputation accuracy holds across different marker densities, reference panel sizes, and lengths for the imputed segments. We thus propose a novel strategy for planning sequencing studies on samples with existing genotype data.
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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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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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Genotype imputation in a coalescent model with infinitely-many-sites mutation. Theor Popul Biol 2012; 87:62-74. [PMID: 23079542 DOI: 10.1016/j.tpb.2012.09.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2012] [Revised: 09/09/2012] [Accepted: 09/28/2012] [Indexed: 11/20/2022]
Abstract
Empirical studies have identified population-genetic factors as important determinants of the properties of genotype-imputation accuracy in imputation-based disease association studies. Here, we develop a simple coalescent model of three sequences that we use to explore the theoretical basis for the influence of these factors on genotype-imputation accuracy, under the assumption of infinitely-many-sites mutation. Employing a demographic model in which two populations diverged at a given time in the past, we derive the approximate expectation and variance of imputation accuracy in a study sequence sampled from one of the two populations, choosing between two reference sequences, one sampled from the same population as the study sequence and the other sampled from the other population. We show that, under this model, imputation accuracy-as measured by the proportion of polymorphic sites that are imputed correctly in the study sequence-increases in expectation with the mutation rate, the proportion of the markers in a chromosomal region that are genotyped, and the time to divergence between the study and reference populations. Each of these effects derives largely from an increase in information available for determining the reference sequence that is genetically most similar to the sequence targeted for imputation. We analyze as a function of divergence time the expected gain in imputation accuracy in the target using a reference sequence from the same population as the target rather than from the other population. Together with a growing body of empirical investigations of genotype imputation in diverse human populations, our modeling framework lays a foundation for extending imputation techniques to novel populations that have not yet been extensively examined.
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Liu EY, Li M, Wang W, Li Y. MaCH-admix: genotype imputation for admixed populations. Genet Epidemiol 2012; 37:25-37. [PMID: 23074066 DOI: 10.1002/gepi.21690] [Citation(s) in RCA: 95] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2012] [Revised: 09/02/2012] [Accepted: 09/10/2012] [Indexed: 12/23/2022]
Abstract
Imputation in admixed populations is an important problem but challenging due to the complex linkage disequilibrium (LD) pattern. The emergence of large reference panels such as that from the 1,000 Genomes Project enables more accurate imputation in general, and in particular for admixed populations and for uncommon variants. To efficiently benefit from these large reference panels, one key issue to consider in modern genotype imputation framework is the selection of effective reference panels. In this work, we consider a number of methods for effective reference panel construction inside a hidden Markov model and specific to each target individual. These methods fall into two categories: identity-by-state (IBS) based and ancestry-weighted approach. We evaluated the performance on individuals from recently admixed populations. Our target samples include 8,421 African Americans and 3,587 Hispanic Americans from the Women' Health Initiative, which allow assessment of imputation quality for uncommon variants. Our experiments include both large and small reference panels; large, medium, and small target samples; and in genome regions of varying levels of LD. We also include BEAGLE and IMPUTE2 for comparison. Experiment results with large reference panel suggest that our novel piecewise IBS method yields consistently higher imputation quality than other methods/software. The advantage is particularly noteworthy among uncommon variants where we observe up to 5.1% information gain with the difference being highly significant (Wilcoxon signed rank test P-value < 0.0001). Our work is the first that considers various sensible approaches for imputation in admixed populations and presents a comprehensive comparison.
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Affiliation(s)
- Eric Yi Liu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
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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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16
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Sung YJ, Gu CC, Tiwari HK, Arnett DK, Broeckel U, Rao DC. Genotype imputation for African Americans using data from HapMap phase II versus 1000 genomes projects. Genet Epidemiol 2012; 36:508-16. [PMID: 22644746 DOI: 10.1002/gepi.21647] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2011] [Revised: 04/06/2012] [Accepted: 04/26/2012] [Indexed: 11/08/2022]
Abstract
Genotype imputation provides imputation of untyped single nucleotide polymorphisms (SNPs) that are present on a reference panel such as those from the HapMap Project. It is popular for increasing statistical power and comparing results across studies using different platforms. Imputation for African American populations is challenging because their linkage disequilibrium blocks are shorter and also because no ideal reference panel is available due to admixture. In this paper, we evaluated three imputation strategies for African Americans. The intersection strategy used a combined panel consisting of SNPs polymorphic in both CEU and YRI. The union strategy used a panel consisting of SNPs polymorphic in either CEU or YRI. The merge strategy merged results from two separate imputations, one using CEU and the other using YRI. Because recent investigators are increasingly using the data from the 1000 Genomes (1KG) Project for genotype imputation, we evaluated both 1KG-based imputations and HapMap-based imputations. We used 23,707 SNPs from chromosomes 21 and 22 on Affymetrix SNP Array 6.0 genotyped for 1,075 HyperGEN African Americans. We found that 1KG-based imputations provided a substantially larger number of variants than HapMap-based imputations, about three times as many common variants and eight times as many rare and low-frequency variants. This higher yield is expected because the 1KG panel includes more SNPs. Accuracy rates using 1KG data were slightly lower than those using HapMap data before filtering, but slightly higher after filtering. The union strategy provided the highest imputation yield with next highest accuracy. The intersection strategy provided the lowest imputation yield but the highest accuracy. The merge strategy provided the lowest imputation accuracy. We observed that SNPs polymorphic only in CEU had much lower accuracy, reducing the accuracy of the union strategy. Our findings suggest that 1KG-based imputations can facilitate discovery of significant associations for SNPs across the whole MAF spectrum. Because the 1KG Project is still under way, we expect that later versions will provide better imputation performance.
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Affiliation(s)
- Yun J Sung
- Division of Biostatistics, Washington University in St. Louis, School of Medicine, St. Louis, Missouri 63110-1093, USA.
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17
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Sampson JN, Jacobs K, Wang Z, Yeager M, Chanock S, Chatterjee N. A two-platform design for next generation genome-wide association studies. Genet Epidemiol 2012; 36:400-8. [PMID: 22508365 DOI: 10.1002/gepi.21634] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2011] [Revised: 02/16/2012] [Accepted: 03/01/2012] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies (GWAS) have been successful in their search for common genetic variants associated with complex traits and diseases. With new advances in array technologies together with available genetic reference sets, the next generation of GWAS will extend the search for associations with uncommon SNPs (1% ≤ MAF ≤ 10%). Two possible approaches are genotyping all participants, a prohibitively expensive option for large GWAS, or using a combination of genotyping and imputation. Here, we consider a two platform method that genotypes all participants on a standard genotyping array, designed to identify common variants, and then supplements that data by genotyping only a small proportion of the participants on a platform that has higher coverage for uncommon SNPs. This subset of the study population is then included as part of the imputation reference set. To demonstrate the use of this two-platform design, we evaluate its potential efficiency using a newly available dataset containing 756 individuals genotyped on both the Illumina Human OmniExpress and Omni2.5 Quad. Although genotyping all individuals on the denser array would be ideal, we find that genotyping only 100 individuals on this array, in combination with imputation, leads to only a modest loss of power for detecting associations. However, the loss of power due to imputation can be more substantial if the relative risks for rare variants are significantly larger than those previously observed for common variants.
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Affiliation(s)
- Joshua N Sampson
- Biostatistics Branch, DCEG, National Cancer Institute, Rockville, Maryland 20852, USA.
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Chen Z, Craiu RV, Bull SB. Two-Phase Stratified Sampling Designs for Regional Sequencing. Genet Epidemiol 2012; 36:320-32. [DOI: 10.1002/gepi.21624] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2011] [Revised: 01/16/2012] [Accepted: 01/17/2012] [Indexed: 12/12/2022]
Affiliation(s)
- Zhijian Chen
- Samuel Lunenfeld Research Institute of Mount Sinai Hospital; Toronto ON; Canada
| | - Radu V. Craiu
- Department of Statistics; University of Toronto; Toronto ON; Canada
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Sung YJ, Wang L, Rankinen T, Bouchard C, Rao DC. Performance of genotype imputations using data from the 1000 Genomes Project. Hum Hered 2011; 73:18-25. [PMID: 22212296 DOI: 10.1159/000334084] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2011] [Accepted: 09/24/2011] [Indexed: 02/03/2023] Open
Abstract
Genotype imputations based on 1000 Genomes (1KG) Project data have the advantage of imputing many more SNPs than imputations based on HapMap data. It also provides an opportunity to discover associations with relatively rare variants. Recent investigations are increasingly using 1KG data for genotype imputations, but only limited evaluations of the performance of this approach are available. In this paper, we empirically evaluated imputation performance using 1KG data by comparing imputation results to those using the HapMap Phase II data that have been widely used. We used three reference panels: the CEU panel consisting of 120 haplotypes from HapMap II and 1KG data (June 2010 release) and the EUR panel consisting of 566 haplotypes also from 1KG data (August 2010 release). We used Illumina 324,607 autosomal SNPs genotyped in 501 individuals of European ancestry. Our most important finding was that both 1KG reference panels provided much higher imputation yield than the HapMap II panel. There were more than twice as many successfully imputed SNPs as there were using the HapMap II panel (6.7 million vs. 2.5 million). Our second most important finding was that accuracy using both 1KG panels was high and almost identical to accuracy using the HapMap II panel. Furthermore, after removing SNPs with MACH Rsq <0.3, accuracy for both rare and low frequency SNPs was very high and almost identical to accuracy for common SNPs. We found that imputation using the 1KG-EUR panel had advantages in successfully imputing rare, low frequency and common variants. Our findings suggest that 1KG-based imputation can increase the opportunity to discover significant associations for SNPs across the allele frequency spectrum. Because the 1KG Project is still underway, we expect that later versions will provide even better imputation performance.
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Affiliation(s)
- Yun Ju Sung
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, Mo. 63110, USA.
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