scholarly journals Rapid, Reference-Free Human Genotype Imputation with Denoising Autoencoders

2021 ◽  
Author(s):  
Raquel Dias ◽  
Doug Evans ◽  
Shang-Fu Chen ◽  
Kai-Yu Chen ◽  
Leslie Chan ◽  
...  
Keyword(s):  
Imputation Accuracy ◽  
Study Data ◽  
Genotype Imputation ◽  
Chromosome 22 ◽  
Privacy Risk ◽  
Inference Models ◽  
Allele Frequency Spectrum ◽  
Barriers To Access ◽  
Computing Environments ◽  
Statistical Approaches

AbstractGenotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium.Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly-used reference panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least 4-fold faster inference run time relative to standard imputation tools.

Assessing single nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

2021 ◽  
Author(s):  
Simon F Lashmar ◽  
Donagh P Berry ◽  
Rian Pierneef ◽  
Farai C Muchadeyi ◽  
Carina Visser
Keyword(s):  
South African ◽  
Clustering Algorithm ◽  
Imputation Accuracy ◽  
Developed Countries ◽  
Genotype Imputation ◽  
Selection Strategy ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Single Nucleotide Polymorphism Selection ◽  
The Mean

Abstract A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen 1) at random, 2) with even genomic dispersion, 3) by maximizing the mean minor allele frequency (MAF), 4) using a combined score of MAF and linkage disequilibrium (LD), 5) using a partitioning-around-medoids (PAM) algorithm, and finally 6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen versus a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) versus 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01<MAF≤0.1) versus high MAF (0.4<MAF≤0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the South African Drakensberger. Based on the results, a genotyping panel consisting of approximately 10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a less than 3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.

scholarly journals EagleImp: Fast and Accurate Genome-wide Phasing and Imputation in a Single Tool

2022 ◽  
Author(s):  
Lars Wienbrandt ◽  
David Ellinghaus
Keyword(s):  
Memory Management ◽  
Imputation Accuracy ◽  
Simulated Data ◽  
Genotype Imputation ◽  
Whole Genome Sequencing Data ◽  
Common Variants ◽  
Sequencing Data ◽  
1000 Genomes ◽  
Genome Wide ◽  
Reference Genomes

Background: Reference-based phasing and genotype imputation algorithms have been developed with sublinear theoretical runtime behaviour, but runtimes are still high in practice when large genome-wide reference datasets are used. Methods: We developed EagleImp, a software with algorithmic and technical improvements and new features for accurate and accelerated phasing and imputation in a single tool. Results: We compared accuracy and runtime of EagleImp with Eagle2, PBWT and prominent imputation servers using whole-genome sequencing data from the 1000 Genomes Project, the Haplotype Reference Consortium and simulated data with more than 1 million reference genomes. EagleImp is 2 to 10 times faster (depending on the single or multiprocessor configuration selected) than Eagle2/PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical GWAS studies, EagleImp provides same or higher imputation accuracy than the Sanger Imputation Service, Michigan Imputation Server and the newly developed TOPMed Imputation Server, despite larger (not publicly available) reference panels. It has many new features, including automated chromosome splitting and memory management at runtime to avoid job aborts, fast reading and writing of large files, and various user-configurable algorithm and output options. Conclusions: Due to the technical optimisations, EagleImp can perform fast and accurate reference-based phasing and imputation for future very large reference panels with more than 1 million genomes. EagleImp is freely available for download from https://github.com/ikmb/eagleimp.

scholarly journals Genotype-Imputation Accuracy across Worldwide Human Populations

2009 ◽  
Vol 84 (2) ◽  
pp. 235-250 ◽  
Author(s):  
Lucy Huang ◽  
Yun Li ◽  
Andrew B. Singleton ◽  
John A. Hardy ◽  
Gonçalo Abecasis ◽  
...  
Keyword(s):  
Imputation Accuracy ◽  
Genotype Imputation ◽  
Human Populations

scholarly journals Genotype Imputation in Case-Only Studies of Gene-Environment Interaction: Validity and Power

2021 ◽  
Author(s):  
Milda Aleknonytė-Resch ◽  
Silke Szymczak ◽  
Sandra Freitag-Wolf ◽  
Astrid Dempfle ◽  
Michael Krawczak
Keyword(s):  
Statistical Power ◽  
Disease Risk ◽  
Imputation Accuracy ◽  
Genotype Imputation ◽  
Type I ◽  
Environment Interaction ◽  
Genetic Main Effect ◽  
Gene Environment ◽  
Main Effects ◽  
Different Levels

Abstract Case-only (CO) studies are a powerful means to uncover gene-environment (G×E) interactions for complex human diseases. Moreover, such studies may in principle also draw upon genotype imputation to increase statistical power even further. However, genotype imputation usually employs healthy controls such as the Haplotype Reference Consortium (HRC) data as an imputation base, which may systematically perturb CO studies in genomic regions with main effects upon disease risk. Using genotype data from 719 German Crohn Disease (CD) patients, we investigated the level of imputation accuracy achievable for single nucleotide polymorphisms (SNPs) with or without a genetic main effect, and with varying minor allele frequency (MAF). Genotypes were imputed from neighbouring SNPs at different levels of linkage disequilibrium (LD) to the target SNP using the HRC data as an imputation base. Comparison of the true and imputed genotypes revealed lower imputation accuracy for SNPs with strong main effects. We also simulated different levels of G×E interaction to evaluate the potential loss of statistical validity and power incurred by the use of imputed genotypes. Simulations under the null hypothesis revealed that genotype imputation does not inflate the type I error rate of CO studies of G×E. However, the statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased. Our study thus highlights that genotype imputation should be employed with great care in CO studies of G×E interaction.

scholarly journals Evaluating genotype imputation pipeline for ultra-low coverage ancient genomes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Ruoyun Hui ◽  
Eugenia D’Atanasio ◽  
Lara M. Cassidy ◽  
Christiana L. Scheib ◽  
Toomas Kivisild
Keyword(s):  
Ancient Dna ◽  
Imputation Accuracy ◽  
Single Step ◽  
Genotype Imputation ◽  
Common Variants ◽  
High Coverage ◽  
Input Mode ◽  
Missing Genotypes ◽  
Low Coverage ◽  
Reference Bias

Abstract Although ancient DNA data have become increasingly more important in studies about past populations, it is often not feasible or practical to obtain high coverage genomes from poorly preserved samples. While methods of accurate genotype imputation from > 1 × coverage data have recently become a routine, a large proportion of ancient samples remain unusable for downstream analyses due to their low coverage. Here, we evaluate a two-step pipeline for the imputation of common variants in ancient genomes at 0.05–1 × coverage. We use the genotype likelihood input mode in Beagle and filter for confident genotypes as the input to impute missing genotypes. This procedure, when tested on ancient genomes, outperforms a single-step imputation from genotype likelihoods, suggesting that current genotype callers do not fully account for errors in ancient sequences and additional quality controls can be beneficial. We compared the effect of various genotype likelihood calling methods, post-calling, pre-imputation and post-imputation filters, different reference panels, as well as different imputation tools. In a Neolithic Hungarian genome, we obtain ~ 90% imputation accuracy for heterozygous common variants at coverage 0.05 × and > 97% accuracy at coverage 0.5 ×. We show that imputation can mitigate, though not eliminate reference bias in ultra-low coverage ancient genomes.

scholarly journals Optimizing Low-Cost Genotyping and Imputation Strategies for Genomic Selection in Atlantic Salmon

2019 ◽  
Vol 10 (2) ◽  
pp. 581-590 ◽  
Author(s):  
Smaragda Tsairidou ◽  
Alastair Hamilton ◽  
Diego Robledo ◽  
James E. Bron ◽  
Ross D. Houston
Keyword(s):  
Atlantic Salmon ◽  
Genomic Selection ◽  
Environmental Sustainability ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Imputation Accuracy ◽  
Cost Effective ◽  
High Density ◽  
Genotype Imputation ◽  
Breeding Programs

Genomic selection enables cumulative genetic gains in key production traits such as disease resistance, playing an important role in the economic and environmental sustainability of aquaculture production. However, it requires genome-wide genetic marker data on large populations, which can be prohibitively expensive. Genotype imputation is a cost-effective method for obtaining high-density genotypes, but its value in aquaculture breeding programs which are characterized by large full-sibling families has yet to be fully assessed. The aim of this study was to optimize the use of low-density genotypes and evaluate genotype imputation strategies for cost-effective genomic prediction. Phenotypes and genotypes (78,362 SNPs) were obtained for 610 individuals from a Scottish Atlantic salmon breeding program population (Landcatch, UK) challenged with sea lice, Lepeophtheirus salmonis. The genomic prediction accuracy of genomic selection was calculated using GBLUP approaches and compared across SNP panels of varying densities and composition, with and without imputation. Imputation was tested when parents were genotyped for the optimal SNP panel, and offspring were genotyped for a range of lower density imputation panels. Reducing SNP density had little impact on prediction accuracy until 5,000 SNPs, below which the accuracy dropped. Imputation accuracy increased with increasing imputation panel density. Genomic prediction accuracy when offspring were genotyped for just 200 SNPs, and parents for 5,000 SNPs, was 0.53. This accuracy was similar to the full high density and optimal density dataset, and markedly higher than using 200 SNPs without imputation. These results suggest that imputation from very low to medium density can be a cost-effective tool for genomic selection in Atlantic salmon breeding programs.

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

2019 ◽  
Vol 21 (5) ◽  
pp. 1806-1817 ◽  
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).

scholarly journals Kinpute: using identity by descent to improve genotype imputation

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.

scholarly journals Optimizing Genomic Selection in Dezhou Donkey Using Low Coverage Whole Genome Sequencing

2021 ◽  
Author(s):  
Changheng Zhao ◽  
Jun Teng ◽  
Xinhao Zhang ◽  
Dan Wang ◽  
Xinyi Zhang ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genomic Selection ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Low Cost ◽  
Imputation Accuracy ◽  
Genotype Imputation ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Low Coverage

Abstract Background Low coverage whole genome sequencing is a low-cost genotyping technology. Combining with genotype imputation approaches, it is likely to become a critical component of cost-efficient 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 selection based on the imputed genotype data. The specific aims were: (i) to measure the accuracy of genotype imputation under different sequencing depths, sample sizes, MAFs, and imputation pipelines; and (ii) to assess the accuracy of genomic selection under different marker densities derived from the imputed sequence data, different strategies for constructing the genomic relationship matrixes, and single- vs multi-trait models. Results We found that a high imputation accuracy (> 0.95) can be achieved for sequence data with sequencing depth as low as 1x and the number of sequenced individuals equal to 400. For genomic selection, the best performance was obtained by using a marker density of 410K and a G matrix constructed using marker dosage information. Multi-trait GBLUP performed better than single-trait GBLUP. Conclusions Our study demonstrates that low coverage whole genome sequencing would be a cost-effective method for genomic selection in Dezhou Donkey.

Sign in / Sign up

Export Citation Format

Share Document