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

Whalen, Andrew; Hickey, John M.

doi:10.1093/bioinformatics/btaa499

Cited by 16 publications

(19 citation statements)

References 18 publications

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“…, Beagle ( Browning and Browning 2009 ) or AlphaPeel ( Whalen et al 2018 ). With the use of offspring-parent information such as AlphaFamImpute ( Whalen et al 2020 ) or FImpute ( Sargolzaei et al 2014 ), the imputation likely increased accuracy of G and H matrices used in GBLUP and single-step methods ( i.e. , ssGBLUP) ( Hickey et al 2012 ).…”

Section: Discussionmentioning

confidence: 99%

“…The missing genotypes were about 10.0% in this study. They were imputed using AlphaFamImpute ( Whalen et al 2020 ). The imputation was based on offspring-parent ( i.e.…”

Section: Methodsmentioning

confidence: 99%

“…, pedigree) information. Specifically, this method used parental genotypes to impute the missing values of offspring genotypes and then, the offspring genotypes were used to fulfill the missing values of their parents ( Whalen et al 2020 ). The pedigree used in our analysis was traced back to the base population, including four generations.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

“…The missing genotypes were about 10.0% in this study. They were imputed using AlphaFamImpute ( Whalen et al 2020 ). The imputation was based on offspring-parent ( i.e.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Parental genotypes from one dataset were substituted when a sample by the same name was found to be inconsistent in another. Genotypes were phased and imputed using AlphaFamImpute 69 v0.1 and parent-averaged linkage maps ( Supplementary Data 3 ) constructed in JoinMap 70,71 v4.1 with the maximum-likelihood mapping function for cross-pollinated populations, which were then integrated into a composite map using LPmerge 72 v1.7. Further detail regarding genetic linkage mapping can be found in Supplementary Note 2 .…”

Section: Methodsmentioning

confidence: 99%

Chromosome evolution and the genetic basis of agronomically important traits in greater yam

et al. 2021

Preprint

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The nutrient-rich tubers of the greater yam Dioscorea alata L. provide food and income security for millions of people around the world. Despite its global importance, however, greater yam remains an "orphan crop." Here we address this resource gap by presenting a highly-contiguous chromosome-scale genome assembly of greater yam combined with a dense genetic map derived from African breeding populations. The genome sequence reveals an ancient lineage-specific genome duplication, followed by extensive genome-wide reorganization. Using our new genomic tools we find quantitative trait loci for susceptibility to anthracnose, a damaging fungal pathogen of yam, and several tuber quality traits. Genomic analysis of breeding lines reveals both extensive inbreeding as well as regions of extensive heterozygosity that may represent interspecific introgression during domestication. These tools and insights will enable yam breeders to unlock the potential of this staple crop and take full advantage of its adaptability to varied environments.

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“…The high-density individuals can then be used as a reference to impute the low-density individuals to high-density (Gorjanc, Battagin, et al, 2017; Gorjanc, Dumasy, et al, 2017; Jacobson et al, 2015; Thorn et al, 2021). Many imputation methods for plant and animal breeding have been developed over the years such as Fimpute (Sargolzaei et al, 2014), AlphaPlantImpute (Gonen et al, 2018), AlphaFamImpute (Whalen et al, 2020), AlphaImpute2 (Whalen & Hickey, 2020), OutcrossSeq (Chen et al, 2021), AlphaPlantImpute2 (Thorn et al, 2021), HBImpute (Pook et al, 2021). The majority of these methods are designed for genotyping only a fraction of the population at high-density.…”

Section: Introductionmentioning

confidence: 99%

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

Niehoff

Pook

Gholami

et al. 2022

Preprint

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Low-density genotyping followed by imputation reduces genotyping costs while still providing high-density marker information. An increased marker density has the potential to improve the outcome of all applications that are based on genomic data. This study investigates techniques for 1k to 20k genomic marker imputation for plant breeding programs with sugar beet as an example crop, where these are realistic marker numbers for modern breeding applications. The generally accepted "gold standard" for imputation, Beagle 5.1, was compared to the recently developed software AlphaPlantImpute2 which is designed specifically for plant breeding. For Beagle 5.1 and AlphaPlantImpute2, the imputation strategy as well as the imputation parameters were optimized in this study. We found that the imputation accuracy of Beagle could be tremendously improved (0.22 to 0.67) by tuning parameters, mainly by lowering the values for the parameter for the effective population size and increasing the number of iterations performed. Separating the phasing and imputation steps also improved accuracies when optimized parameters were used (0.67 to 0.82). We also found that the imputation accuracy of Beagle decreased when more low-density lines were included for imputation. AlphaPlantImpute2 produced very high accuracies without optimization (0.89) and was generally less responsive to optimization. Overall, AlphaPlantImpute2 performed relatively better for imputation while Beagle was better for phasing. Combining both tools yielded the highest accuracies.

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

Cited by 16 publications

References 18 publications

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

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

Chromosome evolution and the genetic basis of agronomically important traits in greater yam

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

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