Efficiency of genomic prediction of non-assessed single crosses

Viana, José Marcelo Soriano; Pereira, Helcio Duarte; Mundim, Gabriel Borges; Piepho, Hans‐Peter; Silva, Fabyano Fonseca e

doi:10.1101/141440

Cited by 4 publications

(6 citation statements)

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“…The genotyping and phenotyping simulation processes used REALbreeding software (available by request). This software has been recently used in studies on genomic selection (Viana, Pereira, Mundim, Piepho, & Silva, 2018), genome‐wide association study (Viana, Mundim, Pereira, Andrade, & Silva, 2017a), QTL mapping (Viana, Silva, Mundim, Azevedo, & Jan, 2017b), population structure (Viana, Valente, Silva, Mundim, & Paes, 2013), and linkage disequilibrium (Andrade, Viana, Pereira, Pinto, & Fonseca, 2019). The software simulates individual genotypes for genes and molecular markers and phenotypes in three steps using user inputs.…”

Section: Methodsmentioning

confidence: 99%

Efficiency of Bayesian quantitative trait loci mapping with full‐sib progeny

et al. 2020

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Quantitative trait loci (QTL) mapping with perennial crops is based on one or more full‐sib progeny because homozygous genotypes are difficult to obtain. The objective of this study was to assess the efficiency of Bayesian QTL mapping with full‐sib progeny. The analyses used two simulated data sets, one assuming genotyping for 292 simple sequence repeats (SSR) loci (density of 10 cM) and the other assuming genotyping for 2969 single nucleotide polymorphisms (SNPs) (density of 1 cM). Each data set consisted of 50 replications of 40 full‐sib progeny of size 400. We assumed a broad sense heritability of 60%, genetic control by 10 QTLs and 90 minor genes, and positive dominance. The QTL heritability values ranged from 1 to 12%. The scenarios included one and multiple progeny. In the best scenarios for the low (four progeny of size 400) and high marker density (one progeny of size 400), the average power of detection was 52 and 67% for the low heritability QTLs, 83 and 95% for the average heritability QTLs, and 95 and 94% for the high heritability QTLs, respectively. The observed false discovery rate (FDR) was 15 and 9% with low and high density, respectively. The Bayesian QTL mapping provides a precise localization of candidate genes with a bias in the QTL position of approximately 4–6 cM. The polygenic effect is important to control the false discovery rate (FDR) and to provide a higher power of QTL detection with multiple progeny.

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Section: Methodsmentioning

confidence: 99%

Efficiency of Bayesian quantitative trait loci mapping with full‐sib progeny

et al. 2020

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“…The datasets of DHs, testers, and testcrosses were simulated using the software REALbreeding (developed in Xojo, Version 2017 Release 3; available by request). This program has been used for studies on population structure (Viana et al, 2013), quantitative trait locus (QTL) mapping (Viana et al, 2017b), genomic selection (Viana et al, 2018), and genome‐wide association studies (Viana et al, 2017a). The genotyping and phenotyping processes begin with the positioning of molecular markers and QTLs and minor genes on up to 10 chromosomes of up to 300 cM in length.…”

Section: Methodsmentioning

confidence: 99%

“…The quantitative genomics theory developed by Viana et al (2018) for genomic prediction of single crosses is adequate for testcrosses involving n 1 (thousands) DH or inbred lines and n 2 (few) elite DH or inbred lines. In the case of one elite DH or inbred line, the SNP genotypic values of the testcrosses are, assuming a QTL (B/b) in LD with a SNP (C/c) in the group of DH or inbred lines, and regardless of the tester SNP genotype,

\begin{matrix} left G_{C C 1} & left = M_{normalH} + q_{c 1} ({normalΔ}_{b c} / p_{c 1} q_{c 1}) [a_{b} + (q_{b 2} - p_{b 2}) d_{b}] \\ left & left = M_{normalH} + q_{c 1} {normalκ}_{b c 1} {normalα}_{b 2} = M_{normalH} + q_{c 1} {normalα}_{SNP 1} = M_{normalH} + {GCA}_{C C 1} \end{matrix}

G_{c c 1} = M_{normalH} - p_{c 1} {normalα}_{SNP1} = M_{normalH} + {GCA}_{c c 1}

where the indices 1 and 2 refers to DH or inbred lines and testers, respectively, M H = m b + ( p b 1 p b 2 − q b 1 q b 2 ) a b + ( p b 1 q b 2 − q b 1 p b 2 ) d b is the testcross mean, α b 2 is the QTL effect of substitution in the elite DH or inbred lines, αSNP1 is the SNP effect of allelic substitution in the hybrid population relative to a SNP derived from the DH or inbred lines, and GCA is the general combining ability effect for a SNP locus.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, for the analysis of testcrosses involving the best tester, we could fit the SNP effects of allelic substitution using ridge regression BLUP (note that the elements of the incidence matrix are the SNP allele frequencies) or the DH GCA effects using GBLUP (note that the elements of the incidence matrix are 1 and 0). Because the two models are equivalent (Viana et al, 2018), we processed only the GBLUP model using the package “sommer” (Covarrubias‐Pazaran, 2016). We also processed pedigree‐based BLUP.…”

Section: Methodsmentioning

confidence: 99%

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Efficiency of Genomic Prediction of Nonassessed Testcrosses

et al. 2019

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In plant breeding, genomic selection has been mainly used to predict untested single crosses and testcrosses. The objectives of this study were to assess the efficiency of prediction of untested testcrosses and the significance of the factors affecting the prediction. We simulated 20,000 testcrosses from two groups of 5000 related doubled haploid lines (DHs) and two unrelated elite inbred lines. The DHs were genotyped for 20,000 single nucleotide polymorphisms (SNPs) and the testcrosses were phenotyped for grain yield. The average SNP density was 0.1 cM. We assumed genetic control by 400 quantitative trait loci (QTLs) and heritability of 25, 50, and 75% for the assessed testcrosses. The training set sizes were 10 and 30% of the available DHs. The process of random sampling of the field assessed and predicted testcrosses were replicated 50 times. We computed the prediction accuracy and the coincidence index, a measure of the selection efficacy of the nonassessed testcrosses. The results evidenced that genomic selection is an efficient process for selecting superior nonassessed testcrosses, if there is sufficient relatedness between the available DHs, linkage disequilibrium in the DHs and genotypic variance between testcrosses, a training set size of at least 10% of the available DHs, and a SNP density of at least 1 cM. The efficacy of selecting the superior nonassessed testcrosses ranged between 0.1 and 0.9, proportional to the heritability of the assessed testcrosses (0.25–1.00). It is important to highlight that the parametric coincidence ranged from 0.1 to 0.8. Furthermore, genomic prediction is much more efficient than pedigree‐based best linear unbiased prediction.

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“…To evaluate if the LD maps allow inference on the overall degree of LD by chromosome in the populations we also processed a simulated data set, generated with REALbreeding software (available by request). This software has been recently used in studies of population structure [17], QTL mapping [18], genomic selection [19], and genome-wide association studies [20]. We simulated the genotyping of 200 individuals in a population (generation 0) and 200 individuals in the same population after 10 generations of random crossings (generation 10), for 287 SNPs covering 298 cM (density of 1 cM) of a single chromosome.…”

Section: Ld and Haplotype Block Analysesmentioning

confidence: 99%

Linkage disequilibrium and haplotype block patterns in popcorn populations

Andrade

Viana

Pereira

et al. 2019

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Linkage disequilibrium (LD) analysis provides information on evolutionary aspects of the 9 populations and allows selecting populations and single nucleotide polymorphisms (SNPs) for 10 association studies. Recently, haplotype blocks have been used to increase the power of quantitative 11 trait loci detection in genome-wide association studies and the prediction accuracy with genomic 12 selection. The objectives of this study were to compare the degree of LD, the LD decay, the LD 13 decay extent, and the number and length of haplotype blocks in the populations and to elaborate the 14 first LD map for maize, for elucidating if the maize chromosomes also had a pattern of interspaced 15 regions of high and low rates of recombination. We used a biparental temperate population, a 16 tropical synthetic, and a tropical breeding population, genotyped for approximately 75,000 SNPs. 17The level of LD expressed by the r 2 values is surprisingly low (0.02, 0.04, and 0.04), but 18 comparable to some non-isolated human populations. The general evidence is that the synthetic is 19 the population with higher LD. It is not expected a significant advantage of haplotype-based 20 association study and along generations genomic selection due to the reduced number of SNPs in 21 the haplotype blocks (2 to 3). The results concerning LD decay (rapid decay after 5-10 kb) and LD 22 decay extent (along up to 300 kb) are in the range observed with maize inbred line panels. Our most 23 important result is that maize chromosomes had a pattern of regions of extensive LD interspaced 24 with regions of low LD. However, our simple simulated LD map provides evidence that this pattern 25 can reflect regions with differences of allele frequencies and LD level (expressed by D') and not 26 regions with high and low rates of recombination. 27 Keywords: gametic phase disequilibrium; LD map; haplotype blocks; intragenic LD. Introduction Linkage disequilibrium (LD) analysis is important to humans, other animal species, and plants because the results can be used for positional cloning, provide information on rate of recombination, gene conversion, and evolutionary aspects of the populations, including recombination history, mutation, selection, genetic drift, and admixture, and allows selecting populations and single nucleotide polymorphisms (SNPs) for association studies [1]. The most common LD measures are D' and r 2 . The statistic D' is the ratio between D (the difference between products of haplotypes, D = P(AB).P(ab)  P(Ab).P(aB)) and the deviation of the actual gametic frequency from linkage equilibrium [2]. The statistic r 2 is the square of the correlation between the values of alleles at two loci in the same gamete, where D is the covariance [3]. Additional information on historical recombination is provided by the analysis of the haplotype blocks pattern in populations. A haplotype block is a chromosome region in which there are few haplotypes (combinations of alleles of multiple SNPs within a haplotype block) (2-4 per block), and for which the ...

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Efficiency of genomic prediction of non-assessed single crosses

Abstract: An important application of genomic selection in plant breeding is the prediction of

Cited by 4 publications

References 33 publications

Efficiency of Bayesian quantitative trait loci mapping with full‐sib progeny

Efficiency of Bayesian quantitative trait loci mapping with full‐sib progeny

Efficiency of Genomic Prediction of Nonassessed Testcrosses

Linkage disequilibrium and haplotype block patterns in popcorn populations

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