GVCHAP: A Computing Pipeline for Genomic Prediction and Variance Component Estimation Using Haplotypes and SNP Markers

Prakapenka, Dzianis; Wang, Chunkao; Liang, Zuoxiang; Bian, Cheng; Tan, Cheng; Da, Yang

doi:10.3389/fgene.2020.00282

Cited by 16 publications

(40 citation statements)

References 21 publications

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“…Assumptions for the first and second moments are:

, and

, where

= variance of haplotype additive effects,

= residual variance,

identity matrix, and

identity matrix. The GBLUP of additive values were calculated using the GVCHAP computer package ( Prakapenka et al 2020 ; https://animalgene.umn.edu ).…”

Section: Methodsmentioning

confidence: 99%

Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

Sallam¹,

Conley

Prakapenka³

et al. 2020

G3 Genes|Genomes|Genetics

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The use of haplotypes may improve the accuracy of genomic prediction over single SNPs because haplotypes can better capture linkage disequilibrium and genomic similarity in different lines and may capture local high-order allelic interactions. Additionally, prediction accuracy could be improved by portraying population structure in the calibration set. A set of 383 advanced lines and cultivars that represent the diversity of the University of Minnesota wheat breeding program was phenotyped for yield, test weight, and protein content and genotyped using the Illumina 90K SNP Assay. Population structure was confirmed using single SNPs. Haplotype blocks of 5, 10, 15, and 20 adjacent markers were constructed for all chromosomes. A multi-allelic haplotype prediction algorithm was implemented and compared with single SNPs using both k-fold cross validation and stratified sampling optimization. After confirming population structure, the stratified sampling improved the predictive ability compared with k-fold cross validation for yield and protein content, but reduced the predictive ability for test weight. In all cases, haplotype predictions outperformed single SNPs. Haplotypes of 15 adjacent markers showed the best improvement in accuracy for all traits; however, this was more pronounced in yield and protein content. The combined use of haplotypes of 15 adjacent markers and training population optimization significantly improved the predictive ability for yield and protein content by 14.3 (four percentage points) and 16.8% (seven percentage points), respectively, compared with using single SNPs and k-fold cross validation. These results emphasize the effectiveness of using haplotypes in genomic selection to increase genetic gain in self-fertilized crops.

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“…Assumptions for the first and second moments are:

, and

, where

= variance of haplotype additive effects,

= residual variance,

identity matrix, and

identity matrix. The GBLUP of additive values were calculated using the GVCHAP computer package ( Prakapenka et al 2020 ; https://animalgene.umn.edu ).…”

Section: Methodsmentioning

confidence: 99%

Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

Sallam¹,

Conley

Prakapenka³

et al. 2020

G3 Genes|Genomes|Genetics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Haplotype blocks were defined using structural and functional genomic information. Each haplotype block was treated as a "locus" and each haplotype within the haplotype block was treated as an "allele" in the analysis using GVCHAP (Prakapenka et al, 2020). Haplotype blocking was based on two types of structural genomic information and four types of functional genomic information.…”

Section: Construction Of Haplotype Blocksmentioning

confidence: 99%

“…Genomic best linear unbiased prediction (GBLUP) of genetic values and genomic restricted maximum likelihood (GREML) estimation of variance components and heritabilities were calculated using the GVCHAP program (Prakapenka et al, 2020) that implements a multi-allelic mixed model treating each haplotype block as a "locus" and each haplotype within the haplotype block as an "allele." The mixed model starts with the quantitative genetics model resulting from genetic partition for SNPs (Da et al, 2014) and for multi-allelic loci (haplotype blocks) (Da, 2015), and implements genomic prediction and variance component estimation using a reparameterized and equivalent model resulting from the use of genomic relationship matrices of SNPs and haplotypes.…”

Section: Mixed Model For Gblup and Gremlmentioning

confidence: 99%

“…The mixed model starts with the quantitative genetics model resulting from genetic partition for SNPs (Da et al, 2014) and for multi-allelic loci (haplotype blocks) (Da, 2015), and implements genomic prediction and variance component estimation using a reparameterized and equivalent model resulting from the use of genomic relationship matrices of SNPs and haplotypes. To avoid repeating a large quantity of notations involving the relationship between the original model and the reparameterized and equivalent model (Da, 2019;Prakapenka et al, 2020), the following description only summarizes the starting quantitative genetics model with SNPs and haplotypes, and the variance-covariance matrices using genomic relationship matrices of the reparametrized and equivalent model. The mixed model with SNP and haplotype effects is:…”

Section: Mixed Model For Gblup and Gremlmentioning

confidence: 99%

“…However, estimates of haplotype epistasis were unavailable and the reason for haplotype loss was unknown. A multi-allelic haplotype method for genomic prediction and estimation (Da, 2015) and a computing pipeline named GVCHAP implementing this method (Prakapenka et al, 2020) are helpful for estimating haplotype epistasis heritability and for investigating the reason of haplotype loss. The multiallelic method is derived from the quantitative genetics theory of genetic partition of genotypic values.…”

Section: Introductionmentioning

confidence: 99%

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Haplotype Analysis of Genomic Prediction Using Structural and Functional Genomic Information for Seven Human Phenotypes

et al. 2020

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Genomic prediction using multi-allelic haplotype models improved the prediction accuracy for all seven human phenotypes, the normality transformed high density lipoproteins, low density lipoproteins, total cholesterol, triglycerides, weight, and the original height and body mass index without normality transformation. Eight SNP sets with 40,941-380,705 SNPs were evaluated. The increase in prediction accuracy due to haplotypes was 1.86-8.12%. Haplotypes using fixed chromosome distances had the best prediction accuracy for four phenotypes, fixed number of SNPs for two phenotypes, and gene-based haplotypes for high density lipoproteins and height (tied for best). Haplotypes of coding genes were more accurate than haplotypes of all autosome genes that included both coding and noncoding genes for triglycerides and weight, and nearly the same as haplotypes of all autosome genes for the other phenotypes. Haplotypes of noncoding genes (mostly lncRNAs) only improved the prediction accuracy over the SNP models for high density lipoproteins, total cholesterol, and height. ChIP-seq haplotypes had better prediction accuracy than gene-based haplotypes for total cholesterol, body mass index and low density lipoproteins. The accuracy of ChIP-seq haplotypes was most striking for low density lipoproteins, where all four haplotype models with ChIP-seq haplotypes had similarly high prediction accuracy over the best prediction model with gene-based haplotypes. Haplotype epistasis was shown to be the reason for the increased accuracy due to haplotypes. Low density lipoproteins had the largest haplotype epistasis heritability that explained 14.70% of the phenotypic variance and was 31.27% of the SNP additive heritability, and the largest increase in prediction accuracy relative to the best SNP model (8.12%). Relative to the SNP additive heritability of the same regions, noncoding genes had the highest haplotype epistasis heritability, followed by coding genes and ChIP-seq for the seven phenotypes. SNP and haplotype heritability profiles showed that the integration of SNP and haplotype additive values compensated the weakness of haplotypes in estimating SNP heritabilities for four phenotypes, whereas models with haplotype additive values fully accounted for SNP additive values for three phenotypes. These results showed that haplotype analysis can be a method to utilize functional and structural genomic information to improve the accuracy of genomic prediction.

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Genomic Selection for Phenotype Prediction in Rice

Muthazhagu Kuppuraj,

Ramadoss,

Adhimoolam

et al. 2024

Climate-Smart Rice Breeding

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GVCHAP: A Computing Pipeline for Genomic Prediction and Variance Component Estimation Using Haplotypes and SNP Markers

Cited by 16 publications

References 21 publications

Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

Improving Prediction Accuracy Using Multi-allelic Haplotype Prediction and Training Population Optimization in Wheat

Haplotype Analysis of Genomic Prediction Using Structural and Functional Genomic Information for Seven Human Phenotypes

Genomic Selection for Phenotype Prediction in Rice

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