Hybrid peeling for fast and accurate calling, phasing, and imputation with sequence data of any coverage in pedigrees

Whalen, Andrew; Ros‐Freixedes, Roger; Wilson, David L.; Hickey, John M.

doi:10.1101/228999

Cited by 25 publications

(46 citation statements)

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“…We performed imputation using hybrid peeling, as implemented in AlphaPeel 345 [1], with the default settings. Hybrid peeling extends the methods of Kerr and 346 Kinghorn [36] for single-locus iterative peeling and of Meuwissen and Goddard [37] 347 for multi-locus iterative peeling to efficiently call, phase and impute whole-genome 348 sequence data in complex multi-generational pedigrees.…”

Section: Hybrid Peeling Imputationmentioning

confidence: 99%

“…The lower imputation accuracy in the first generations can be explained by 443 insufficient information from immediate ancestors to accurately estimate the 444 segregation probabilities on which hybrid peeling relies [1,28] (i.e., their parents and 445 grandparents were unknown or did not have any marker array data). In light of these 446 results, the populations with simulated pedigrees with 2 or 5 generations were not 447 considered further.…”

Section: Imputation Accuracy Across Generations 431mentioning

confidence: 99%

“…The coupling of appropriate sequencing strategies and powerful imputation 53 methods enables the generation of large datasets of sequenced individuals at a low 54 cost. In this paper we assess the impact that the sequencing strategy and the level of 55 investment in sequencing have on the imputation of whole-genome sequence data for 56 entire livestock populations with a pedigree-based imputation method that does not 57 require haplotype reference panels, such as hybrid peeling [1]. 58…”

Section: Introductionmentioning

confidence: 99%

“…We have recently proposed the use of 'hybrid peeling' [1], a fast and accurate 92 imputation method explicitly designed for jointly calling, phasing and imputing 93 whole-genome sequence data in large and complex multi-generational pedigreed 94 populations where individuals can be sequenced at variable coverage or not 95 sequenced at all. Hybrid peeling is a two-step process.…”

Section: Introductionmentioning

confidence: 99%

“…for the rest of the population. This may seem counterintuitive at first but is explained 633 by the fact that the accuracy of the segregation probabilities for these individuals, 634 which are used in hybrid peeling, was low due to insufficient information from their 635 immediate ancestors (i.e., their parents and grandparents were unknown or did not 636 have any marker array data) [1,28]. The poor accuracy of their segregation 637…”

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confidence: 99%

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Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

Ros‐Freixedes

Whalen

Mileham

et al. 2019

Preprint

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AbstractBackgroundFor assembling large whole-genome sequence datasets to be used routinely in research and breeding, the sequencing strategy should be adapted to the methods that will later be used for variant discovery and imputation. In this study we used simulation to explore the impact that the sequencing strategy and level of sequencing investment have on the overall accuracy of imputation using hybrid peeling, a pedigree-based imputation method well-suited for large livestock populations.MethodsWe simulated marker array and whole-genome sequence data for fifteen populations with simulated or real pedigrees that had different structures. In these populations we evaluated the effect on imputation accuracy of seven methods for selecting which individuals to sequence, the generation of the pedigree to which the sequenced individuals belonged, the use of variable or uniform coverage, and the trade-off between the number of sequenced individuals and their sequencing coverage. For each population we considered four levels of investment in sequencing that were proportional to the size of the population.ResultsImputation accuracy largely depended on pedigree depth. The distribution of the sequenced individuals across the generations of the pedigree underlay the performance of the different methods used to select individuals to sequence. Additionally, it was critical to balance high imputation accuracy in early generations as well as in late generations. Imputation accuracy was highest with a uniform coverage across the sequenced individuals of around 2x rather than variable coverage. An investment equivalent to the cost of sequencing 2% of the population at 2x provided high imputation accuracy. The gain in imputation accuracy from additional investment diminished with larger populations and larger levels of investment. However, to achieve the same imputation accuracy, a proportionally greater investment must be used in the smaller populations compared to the larger ones.ConclusionsSuitable sequencing strategies for subsequent imputation with hybrid peeling involve sequencing around 2% of the population at a uniform coverage around 2x, distributed preferably from the third generation of the pedigree onwards. Such sequencing strategies are beneficial for generating whole-genome sequence data in populations with deep pedigrees of closely related individuals.

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Section: Hybrid Peeling Imputationmentioning

confidence: 99%

Section: Imputation Accuracy Across Generations 431mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

Ros‐Freixedes

Whalen

Mileham

et al. 2019

Preprint

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AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data

Whalen

Hickey

2020

Bioinformatics

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Summary AlphaFamImpute is an imputation package for calling, phasing, and imputing genome-wide genotypes in outbred full-sib families from single nucleotide polymorphism (SNP) array and genotype-by-sequencing (GBS) data. GBS data is increasingly being used to genotype individuals, especially when SNP arrays do not exist for a population of interest. Low-coverage GBS produces data with a large number of missing or incorrect naïve genotype calls, which can be improved by identifying shared haplotype segments between full-sib individuals. Here we present AlphaFamImpute, an algorithm specifically designed to exploit the genetic structure of full-sib families. It performs imputation using a two-step approach. In the first step it phases and imputes parental genotypes based on the segregation states of their offspring (that is, which pair of parental haplotypes the offspring inherited). In the second step it phases and imputes the offspring genotypes by detecting which haplotype segments the offspring inherited from their parents. With a series of simulations we find that AlphaFamImpute obtains high accuracy genotypes, even when the parents are not genotyped and individuals are sequenced at less than 1x coverage. Availability and implementation AlphaFamImpute is available as a Python package from the AlphaGenes website, http://www.AlphaGenes.roslin.ed.ac.uk/AlphaFamImpute. Supplementary information A complete description of the methods is available in the supplementary information.

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Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

Phuc

Oanh

et al. 2021

G3 Genes|Genomes|Genetics

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Assessments of genomic prediction accuracies using artificial intelligence (AI) algorithms (i.e.,, machine and deep learning methods) are currently not available or very limited in aquaculture species. The principal aim of this study was to examine the predictive performance of these new methods for disease resistance to Edwardsiella ictaluri in a population of striped catfish Pangasianodon hypophthalmus and to make comparisons with four common methods, i.e.,, pedigree-based best linear unbiased prediction (PBLUP), genomic-based best linear unbiased prediction (GBLUP), single-step GBLUP (ssGBLUP) and a non-linear Bayesian approach (notably BayesR). Our analyses using machine learning (i.e.,, ML-KAML) and deep learning (i.e.,, DL-MLP and DL-CNN) together with the four common methods (PBLUP, GBLUP, ssGBLUP and BayesR) were conducted for two main disease resistance traits (i.e.,, survival status coded as 0 and 1 and survival time, i.e.,, days that the animals were still alive after the challenge test) in a pedigree consisting of 560 individual animals (490 offspring and 70 parents) genotyped for 14,154 Single Nucleotide Polymorphism (SNPs). The results using 6,470 SNPs after quality control showed that machine learning methods outperformed PBLUP, GBLUP and ssGBLUP, with the increases in the prediction accuracies for both traits by 9.1–15.4%. However, the prediction accuracies obtained from machine learning methods were comparable to those estimated using BayesR. Imputation of missing genotypes using AlphaFamImpute increased the prediction accuracies by 5.3–19.2% in all the methods and data used. On the other hand, there were insignificant decreases (0.3–5.6%) in the prediction accuracies for both survival status and survival time when multivariate models were used in comparison to univariate analyses. Interestingly, the genomic prediction accuracies based on only highly significant SNPs (P < 0.00001, 318 - 400 SNPs for survival status and 1,362–1,589 SNPs for survival time) were somewhat lower (0.3 to 15.6%) than those obtained from the whole set of 6,470 SNPs. In most of our analyses, the accuracies of genomic prediction were somewhat higher for survival time than survival status (0/1 data). It is concluded that although there are prospects for the application of genomic selection to increase disease resistance to Edwardsiella ictaluri in striped catfish breeding programs, further evaluation of these methods should be made in independent families/populations when more data are accumulated in future generations to avoid possible biases in the genetic parameters estimates and prediction accuracies for the disease resistant traits studied in this population of striped catfish P. hypophthalmus.

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Hybrid peeling for fast and accurate calling, phasing, and imputation with sequence data of any coverage in pedigrees

Cited by 25 publications

References 46 publications

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

Evaluation of sequencing strategies for whole-genome imputation with hybrid peeling

AlphaFamImpute: high-accuracy imputation in full-sib families from genotype-by-sequencing data

Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms

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