Assessment of Imputation Quality: Comparison of Phasing and Imputation Algorithms in Real Data

Despite the widespread use of genotype imputation tools and the availability of different approaches, late developments of currently used programs have not been compared comprehensively. We therefore assessed the performance of 35 combinations of phasing and imputation programs, including versions of SHAPEIT, Eagle, Beagle, minimac, PBWT, and IMPUTE, for genetic imputation of completely missing SNPs with a HRC reference panel regarding quality and speed. We used a data set comprising 1,149 fully sequenced individuals from the German population, subsetting the SNPs to approximate the Illumina Infinium-Omni5 array. Five hundred fifty-three thousand two hundred and thirty-four SNPs across two selected chromosomes were utilized for comparison between imputed and sequenced genotypes. We found that all tested programs with the exception of PBWT impute genotypes with very high accuracy (mean error rate < 0.005). PBTW hardly ever imputes the less frequent allele correctly (mean concordance for genotypes including the minor allele <0.0002). For all programs, imputation accuracy drops for rare alleles with a frequency <0.05. Even though overall concordance is high, concordance drops with genotype probability, indicating that low genotype probabilities are rare. The mean concordance of SNPs with a genotype probability <95% drops below 0.9, at which point disregarding imputed genotypes might prove favorable. For fast and accurate imputation, a combination of Eagle2.4.1 using a reference panel for phasing and Beagle5.1 for imputation performs best. Replacing Beagle5.1 with minimac3, minimac4, Beagle4.1, or IMPUTE4 results in a small gain in accuracy at a high cost of speed.

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ModStore:Genotype Imputationasa Service Powered by SG10K Reference Panel

Current Bioinformatics ◽

10.2174/1574893615999200831112522 ◽

2020 ◽

Vol 15 ◽

Author(s):

Weiwen Zhang ◽

Long Wang ◽

Theint Theint Aye

Keyword(s):

Association Study ◽

High Performance ◽

Genome Wide Association Study ◽

Imputation Accuracy ◽

Genotype Imputation ◽

Reference Panel ◽

Genome Wide Association ◽

Specific Reference ◽

Data Set ◽

Genome Wide

Background: Asia is the largest continent in the world with a large group of populations. However, we are still in lack of an imputation server with an Asian-specific reference panel to estimate genotypes for genome wide association study in Asia. Currently, two well-known imputation servers are available, i.e., Michigan imputation server in the US and Sanger in the UK. However, the quality of imputation for Southeast Asia's populations is not satisfying by using their genotype imputation services and reference panels. Objective: In this paper, we develop ModStore imputation server with a specially designed reference panel to offer genotype imputation as a service, aiming to increase the power of genome wide association study of Singapore in the context of National Precision Medicine. Method: We present the implementation and customization of ModStore imputation server on high performance computing infrastructure. Meanwhile, we construct a reference panel based on whole-genome sequencing of Singaporeans, referred to as the SG10K reference panel, for improving the imputation accuracy of Southeast Asia's populations. Results: Experiment results show that by using the SG10K reference panel, over 79% improvement of mean Rsq can be achieved for the imputation of three Singapore ethnic populations data set, i.e., Malay, Chinese, and Indian, under MAF<0.005 compared to the 1000 Genome reference panel. Conclusion: With ModStore imputation server, genotype imputation can be performed more accurately for data derived from array-based pharmacogenomics and pre-existing Southeast Asia's population-scale genetic.

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Improved imputation of summary statistics for admixed populations

10.1101/203927 ◽

2017 ◽

Cited By ~ 3

Author(s):

Sina Rüeger ◽

Aaron McDaid ◽

Zoltán Kutalik

Keyword(s):

Meta Analysis ◽

Country Of Origin ◽

Real Data ◽

Genotype Imputation ◽

Reference Panel ◽

Summary Statistics ◽

Panel Size ◽

Noticeable Improvement

AbstractMotivationSummary statistics imputation can be used to infer association summary statistics of an already conducted, genotype-based meta-analysis to higher ge-nomic resolution. This is typically needed when genotype imputation is not feasible for some cohorts. Oftentimes, cohorts of such a meta-analysis are variable in terms of (country of) origin or ancestry. This violates the assumption of current methods that an external LD matrix and the covariance of the Z-statistics are identical.ResultsTo address this issue, we present variance matching, an extention to the existing summary statistics imputation method, which manipulates the LD matrix needed for summary statistics imputation. Based on simulations using real data we find that accounting for ancestry admixture yields noticeable improvement only when the total reference panel size is > 1000. We show that for population specific variants this effect is more pronounced with increasing FST.

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Genotype imputation and reference panel: a systematic evaluation on haplotype size and diversity

Briefings in Bioinformatics ◽

10.1093/bib/bbz108 ◽

2019 ◽

Vol 21 (5) ◽

pp. 1806-1817 ◽

Cited By ~ 1

Author(s):

Wei-Yang Bai ◽

Xiao-Wei Zhu ◽

Pei-Kuan Cong ◽

Xue-Jun Zhang ◽

J Brent Richards ◽

...

Keyword(s):

Chinese Population ◽

Large Scale ◽

Imputation Accuracy ◽

European Population ◽

Han Chinese ◽

Population Diversity ◽

Genotype Imputation ◽

Reference Panel ◽

Systematic Evaluation ◽

Han Chinese Population

Abstract Here, 622 imputations were conducted with 394 customized reference panels for Han Chinese and European populations. Besides validating the fact that imputation accuracy could always benefit from the increased panel size when the reference panel was population specific, the results brought two new thoughts. First, when the haplotype size of the reference panel was fixed, the imputation accuracy of common and low-frequency variants (Minor Allele Frequency (MAF) > 0.5%) decreased while the population diversity of the reference panel increased, but for rare variants (MAF < 0.5%), a small fraction of diversity in panel could improve imputation accuracy. Second, when the haplotype size of the reference panel was increased with extra population-diverse samples, the imputation accuracy of common variants (MAF > 5%) for the European population could always benefit from the expanding sample size. However, for the Han Chinese population, the accuracy of all imputed variants reached the highest when reference panel contained a fraction of an extra diverse sample (8–21%). In addition, we evaluated the imputation performances in the existing reference panels, such as the Haplotype Reference Consortium (HRC), 1000 Genomes Project Phase 3 and the China, Oxford and Virginia Commonwealth University Experimental Research on Genetic Epidemiology (CONVERGE). For the European population, the HRC panel showed the best performance in our analysis. For the Han Chinese population, we proposed an optimum imputation reference panel constituent ratio if researchers would like to customize their own sequenced reference panel, but a high-quality and large-scale Chinese reference panel was still needed. Our findings could be generalized to the other populations with conservative genome; a tool was provided to investigate other populations of interest (https://github.com/Abyss-bai/reference-panel-reconstruction).

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Kinpute: using identity by descent to improve genotype imputation

Bioinformatics ◽

10.1093/bioinformatics/btz221 ◽

2019 ◽

Vol 35 (21) ◽

pp. 4321-4326

Author(s):

Mark Abney ◽

Aisha ElSherbiny

Keyword(s):

Sequence Data ◽

Imputation Accuracy ◽

Genotype Imputation ◽

Supplementary Information ◽

Specific Reference ◽

Imputation Methods ◽

Identical By Descent ◽

Novel Method ◽

Optimal Set ◽

Genotype Probabilities

Abstract Motivation Genotype imputation, though generally accurate, often results in many genotypes being poorly imputed, particularly in studies where the individuals are not well represented by standard reference panels. When individuals in the study share regions of the genome identical by descent (IBD), it is possible to use this information in combination with a study-specific reference panel (SSRP) to improve the imputation results. Kinpute uses IBD information—due to recent, familial relatedness or distant, unknown ancestors—in conjunction with the output from linkage disequilibrium (LD) based imputation methods to compute more accurate genotype probabilities. Kinpute uses a novel method for IBD imputation, which works even in the absence of a pedigree, and results in substantially improved imputation quality. Results Given initial estimates of average IBD between subjects in the study sample, Kinpute uses a novel algorithm to select an optimal set of individuals to sequence and use as an SSRP. Kinpute is designed to use as input both this SSRP and the genotype probabilities output from other LD-based imputation software, and uses a new method to combine the LD imputed genotype probabilities with IBD configurations to substantially improve imputation. We tested Kinpute on a human population isolate where 98 individuals have been sequenced. In half of this sample, whose sequence data was masked, we used Impute2 to perform LD-based imputation and Kinpute was used to obtain higher accuracy genotype probabilities. Measures of imputation accuracy improved significantly, particularly for those genotypes that Impute2 imputed with low certainty. Availability and implementation Kinpute is an open-source and freely available C++ software package that can be downloaded from. Supplementary information Supplementary data are available at Bioinformatics online.

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Whole-genome reference panel of 1,781 Northeast Asians improves imputation accuracy of rare and low-frequency variants

10.1101/600353 ◽

2019 ◽

Cited By ~ 1

Author(s):

Seong-Keun Yoo ◽

Chang-Uk Kim ◽

Hie Lim Kim ◽

Sungjae Kim ◽

Jong-Yeon Shin ◽

...

Keyword(s):

Imputation Accuracy ◽

Northeast Asia ◽

Low Frequency ◽

Genotype Imputation ◽

Reference Panel ◽

Whole Genome Sequencing Data ◽

Reference Database ◽

Whole Genome ◽

Sequencing Data ◽

Northeast Asian

AbstractGenotype imputation using the reference panel is a cost-effective strategy to fill millions of missing genotypes for the purpose of various genetic analyses. Here, we present the Northeast Asian Reference Database (NARD), including whole-genome sequencing data of 1,781 individuals from Korea, Mongolia, Japan, China, and Hong Kong. NARD provides the genetic diversities of Korean (n=850) and Mongolian (n=386) ancestries that were not present in the 1000 Genomes Project Phase 3 (1KGP3). We combined and re-phased the genotypes from NARD and 1KGP3 to construct a union set of haplotypes. This approach established a robust imputation reference panel for the Northeast Asian populations, which yields the greatest imputation accuracy of rare and low-frequency variants compared with the existing panels. Also, we illustrate that NARD can potentially improve disease variant discovery by reducing pathogenic candidates. Overall, this study provides a decent reference panel for the genetic studies in Northeast Asia.

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A multi-breed reference panel and additional rare variants maximize imputation accuracy in cattle

Genetics Selection Evolution ◽

10.1186/s12711-019-0519-x ◽

2019 ◽

Vol 51 (1) ◽

Cited By ~ 8

Author(s):

Troy N. Rowan ◽

Jesse L. Hoff ◽

Tamar E. Crum ◽

Jeremy F. Taylor ◽

Robert D. Schnabel ◽

...

Keyword(s):

Rare Variants ◽

Imputation Accuracy ◽

High Density ◽

Genotype Imputation ◽

Reference Panel ◽

Low Density ◽

Single Nucleotide ◽

Functional Variants ◽

Causal Variants ◽

Downstream Analysis

Abstract Background During the last decade, the use of common-variant array-based single nucleotide polymorphism (SNP) genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data. Although low-density assays work well in the context of genomic prediction, they are less useful for detecting and mapping causal variants and the effects of rare variants are not captured. The objective of this project was to maximize the accuracies of genotype imputation from medium- and low-density assays to the marker set obtained by combining two high-density research assays (~ 850,000 SNPs), the Illumina BovineHD and the GGP-F250 assays, which contains a large proportion of rare and potentially functional variants and for which the assay design is described here. This 850 K SNP set is useful for both imputation to sequence-level genotypes and direct downstream analysis. Results We found that a large multi-breed composite imputation reference panel that includes 36,131 samples with either BovineHD and/or GGP-F250 genotypes significantly increased imputation accuracy compared with a within-breed reference panel, particularly at variants with low minor allele frequencies. Individual animal imputation accuracies were maximized when more genetically similar animals were represented in the composite reference panel, particularly with complete 850 K genotypes. The addition of rare variants from the GGP-F250 assay to our composite reference panel significantly increased the imputation accuracy of rare variants that are exclusively present on the BovineHD assay. In addition, we show that an assay marker density of 50 K SNPs balances cost and accuracy for imputation to 850 K. Conclusions Using high-density genotypes on all available individuals in a multi-breed reference panel maximized imputation accuracy for tested cattle populations. Admixed animals or those from breeds with a limited representation in the composite reference panel were still imputed at high accuracy, which is expected to further increase as the reference panel expands. We anticipate that the addition of rare variants from the GGP-F250 assay will increase the accuracy of imputation to sequence level.

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Evaluating the quality of the 1000 Genomes Project data

10.1101/383950 ◽

2018 ◽

Cited By ~ 2

Author(s):

Saurabh Belsare ◽

Michal Sakin-Levy ◽

Yulia Mostovoy ◽

Steffen Durinck ◽

Subhra Chaudhry ◽

...

Keyword(s):

Imputation Accuracy ◽

Genomic Analysis ◽

Error Rates ◽

Reference Panel ◽

Specific Reference ◽

1000 Genomes Project ◽

Data Set ◽

1000 Genomes ◽

Project Data

ABSTRACTData from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotype, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform. We observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account.

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36 A large multi-breed reference panel and maximizes genotype imputation accuracy in cattle

Journal of Animal Science ◽

10.1093/jas/skz122.033 ◽

2019 ◽

Vol 97 (Supplement_2) ◽

pp. 18-18

Author(s):

Troy Rowan ◽

Jesse L Hoff ◽

Tamar Crum ◽

Jerry F Taylor ◽

Robert Schnabel ◽

...

Keyword(s):

Rare Variants ◽

Positive Impact ◽

Imputation Accuracy ◽

Causal Variant ◽

Genotype Imputation ◽

Reference Panel ◽

Pedigree Information ◽

Low Density ◽

Functional Variants ◽

Downstream Analysis

Abstract The use of array-based SNP genotyping in the beef and dairy industries has produced an astounding amount of medium-to-low density genomic data in the last decade. While low-density assays work exceptionally well in the context of genomic prediction, they are less useful in mapping and causal variant discovery. This project focuses on maximizing imputation accuracies to the marker set of two high-density research assays, the Illumina Bovine HD, and the GGP-F250 which contains a large proportion of rare and potentially functional variants (~850,000 total SNPs). This 850K SNP set is well-suited for both imputation to sequence-level genotypes and direct downstream analysis. For testing, 310 animals from multiple breeds, all with observed HD and F250 genotypes, were downsampled to various commercial chip densities ranging from 8K–130K markers. We use both well-established and novel measures of imputation accuracy to categorize precisely where, why, and how imputation errors are made. These metrics provide insights into downstream interpretation and identify situations where caution should be exercised when analyzing imputed variants. We find that a large multi-breed composite imputation reference comprised of 36,131 samples with either HD and F250 genotypes significantly increases imputation accuracy compared to a standard within-breed reference panel, particularly at low minor allele frequencies. Breed composition information for each animal in our testing panel allowed us to identify how a breed’s representation in the reference panel affects the imputation accuracy of both purebred and admixed animals. Starting chip density also impacts imputation accuracy, but gains appear to plateau at around 50,000 markers. The addition of the F250’s rare variation to the reference panel increased the imputation accuracy of rare variants from the HD assay by an average of 4.32%. We expect this low MAF content from the F250 to have a similar positive impact on rare variant imputation at the sequence level. Early work using 850K imputed data in genomic predictions has shown substantial increases in both chip heritability and prediction accuracies. Using a large multi-breed reference and the best practices identified through this work will maximize imputation accuracies in virtually all cattle populations, particularly ones that are highly admixed with little or no available pedigree information.

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Kinpute: Using identity by descent to improve genotype imputation

10.1101/399147 ◽

2018 ◽

Author(s):

Mark Abney ◽

Aisha El Sherbiny

Keyword(s):

Sequence Data ◽

Imputation Accuracy ◽

Genotype Imputation ◽

Specific Reference ◽

Identity By Descent ◽

Imputation Methods ◽

Identical By Descent ◽

Novel Method ◽

Optimal Set ◽

Genotype Probabilities

1AbstractMotivationGenotype imputation, though generally accurate, often results in many genotypes being poorly imputed, particularly in studies where the individuals are not well represented by standard reference panels. When individuals in the study share regions of the genome identical by descent (IBD), it is possible to use this information in combination with a study specific reference panel (SSRP) to improve the imputation results. Kinpute uses IBD information—due to either recent, familial relatedness or distant, unknown ancestors— in conjunction with the output from linkage disequilibrium (LD) based imputation methods to compute more accurate genotype probabilities. Kinpute uses a novel method for IBD imputation, which works even in the absence of a pedigree, and results in substantially improved imputation quality.ResultsGiven initial estimates of average IBD between subjects in the study sample, Kinpute uses a novel algorithm to select an optimal set of individuals to sequence and use as an SSRP. Kinpute is designed to use as input both this SSRP and the genotype probabilities output from other LD based imputation software, and uses a new method to combine the LD imputed genotype probabilities with IBD configurations to substantially improve imputation. We tested Kinpute on a human population isolate where 98 individuals have been sequenced. In half of this sample, whose sequence data was masked, we used Impute2 to perform LD based imputation and Kinpute was used to obtain higher accuracy genotype probabilities. Measures of imputation accuracy improved significantly, particularly for those genotypes that Impute2 imputed with low certainty.AvailabilityKinpute is an open-source and freely available C++ software package that can be downloaded from https://github.com/markabney/Kinpute/releases.

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EXPONENTIATED HALF-LOGISTIC LOMAX DISTRIBUTION WITH PROPERTIES AND APPLICATION

NED University Journal of Research ◽

10.35453/nedjr-ascn-2018-0033 ◽

2019 ◽

Vol XVI (2) ◽

pp. 1-11

Author(s):

Farrukh Jamal ◽

Hesham Mohammed Reyad ◽

Soha Othman Ahmed ◽

Muhammad Akbar Ali Shah ◽

Emrah Altun

Keyword(s):

Real Data ◽

Continuous Model ◽

Model Parameters ◽

Data Set ◽

Lomax Distribution ◽

Mathematical Properties ◽

Proposed Model ◽

Probability Weighted Moment ◽

Record Statistics ◽

Maximum Likelihood Criterion

A new three-parameter continuous model called the exponentiated half-logistic Lomax distribution is introduced in this paper. Basic mathematical properties for the proposed model were investigated which include raw and incomplete moments, skewness, kurtosis, generating functions, Rényi entropy, Lorenz, Bonferroni and Zenga curves, probability weighted moment, stress strength model, order statistics, and record statistics. The model parameters were estimated by using the maximum likelihood criterion and the behaviours of these estimates were examined by conducting a simulation study. The applicability of the new model is illustrated by applying it on a real data set.

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