Predicting genetic variance in bi-parental breeding populations is more accurate when explicitly modeling the segregation of informative genomewide markers

Tiede, Tyler; Kumar, Leticia; Mohammadi, Mohsen; Smith, Kevin P.

doi:10.1007/s11032-015-0390-6

Cited by 32 publications

(49 citation statements)

References 33 publications

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“…For example, in PHM81 ´ A588, the observed plant height was 134 cm, whereas the predicted plant height was 117 cm. The downward bias in the predicted V G for plant height (−59%) and ear height (−30%) in the current study was consistent with the downward bias of −28 to −60% found by Lian et al (2015) for V G for grain yield, moisture, and test weight but was larger than the minimal downward bias (−4%) reported by Tiede et al (2015). The correlation between the midparent phenotypic value and the observed mean of the population was 0.77 for ear height, 0.80 for silking Fig.…”

Section: Accurate Prediction Of the Population Mean But Not V Gsupporting

confidence: 88%

“…When compared with the results of previous studies (Lian et al, 2015;Tiede et al, 2015), our results suggested that virtual populations have erratic effectiveness for predicting V G . The results from this study do not support the use of genomewide marker effects, estimated from diverse inbreds, for predicting V G in maize biparental crosses.…”

Section: Discussionmentioning

confidence: 50%

“…The correlation between the midparent phenotypic value and the observed mean of the population was 0.77 for ear height, 0.80 for silking Fig. The Lian et al (2015) and Tiede et al (2015) studies used data routinely generated in a breeding program, and different biparental populations were evaluated in different sets of locations and years. Relationship between marker-predicted and observed means of the best 10% of progeny (U 0.1 ) among eight maize biparental crosses: (1) PHM81 ´ A588, (2) PHM81 ´ A661, (3) PHM81 ´ PHG50, (4) A588 ´ PHG50, (5) A641 ´ A661, (6) A641 ´ PHG50, (7) PHJ75 ´ A668, and (8) A668 ´ PHG50.…”

Section: Accurate Prediction Of the Population Mean But Not V Gmentioning

confidence: 99%

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Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Adeyemo

Bernardo

2019

Crop Science

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Predicting the genetic variance (VG) in a biparental population has been difficult. Our objective was to determine whether the population mean, VG, and mean of the top 10% of progeny in a cross can be predicted effectively from genomewide marker effects estimated from a diverse panel of inbreds. Eight maize (Zea mays L.) crosses that differed in their predicted mean and VG were evaluated for plant and ear height and growing degree days to silking. Each cross was represented by 120 to 144 random F3 lines that were evaluated in balanced experiments at three locations in Minnesota in 2017. Correlations between the observed and predicted means of each breeding population were significant (r ≥ 0.80, P = 0.05) for all three traits. However, correlations between the observed and predicted VG were nonsignificant (−0.24 to 0.14) for the three traits. Correlations between the observed and predicted mean of the top 10% of progeny in each cross were significant (P = 0.05) for plant height (0.72), but not for ear height and silking date. These results for predicting the mean of the top 10% of progeny reflected the ability to predict the mean but not VG. We concluded that the mean, but not the VG, of biparental crosses can be effectively predicted from genomewide marker effects estimated from a diverse panel of inbreds.

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Section: Accurate Prediction Of the Population Mean But Not V Gsupporting

confidence: 88%

Section: Discussionmentioning

confidence: 50%

Section: Accurate Prediction Of the Population Mean But Not V Gmentioning

confidence: 99%

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Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Adeyemo

Bernardo

2019

Crop Science

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“…Nevertheless, most previous studies used genome-wide molecular marker data, which lack specificity and accuracy. Tiede et al (2015) showed that segregating informative genome-wide markers could improve the accuracy of predicting genetic variance across 40 barley parents. Wegary et al (2013) suggested that the genetic distance calculated using selected markers associated with quantitative trait loci (QTL) may better predict heterosis.…”

Section: Discussionmentioning

confidence: 99%

Combining ability, heterosis, genetic distance and their intercorrelations for waterlogging tolerance traits in chrysanthemum

Zhang

Yang

et al. 2017

Euphytica

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Waterlogging tolerance (WT) is a major objective in chrysanthemum breeding programs, and although certain genotypes with different tolerance levels have been identified, their value as parents for WT breeding is unknown. Here, twelve F 1 crosses derived from an incomplete diallel mating scheme were conducted to investigate combining ability and heterosis for WT and their relationships with parental genetic distance. The results showed that the membership function value of waterlogging (MFVW) was controlled by additive and non-additive gene effects, whereas other growth and biomass traits were mainly controlled by non-additive gene effects. The estimated broad and narrow sense heritabilities of the MFVW were 97.5 and 51.5%, respectively. Combining ability analyses indicated that 'Nannong Xuefeng' showing the largest general combining ability (GCA) effect for the MFVW was the best combiner, and identified several best cross combinations with high positive specific combining ability (SCA) effects for most WT-related traits. Mid-and high-parent heterosis occurred widely. The three distance measures, based on phenotypic traits (PD), molecular markers (GD) and markers linked with quantitative trait loci (QTL-GD), presented a nonsignificant correlation with combining ability except for the GD with GCA for the relative root fresh weight. The correlations between the QTL-GD and heterosis were significant for certain traits and generally higher than that of the PD or GD and heterosis. The SCA effects were positively correlated with heterosis for most of the WT traits (0.51 B r B 0.80). The findings benefit understanding the inheritance mode and then achieving desirable improvement for WT in chrysanthemum.

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“…The number of replications was informed from previously published uses of the PopVar package Tiede et al, 2015). The number of replications was informed from previously published uses of the PopVar package Tiede et al, 2015).…”

Section: Training Populationmentioning

confidence: 99%

Validating Genomewide Predictions of Genetic Variance in a Contemporary Breeding Program

Neyhart

Smith

2019

Crop Science

Self Cite

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Predicting the genetic variance among progeny from a cross—prior to making said cross—would be a valuable metric for plant breeders to discriminate among possible parent combinations. The use of genomewide markers and simulated populations is one proposed method for making such predictions. Our objective was to assess the predictive ability of this method for three relevant quantitative traits within a breeding program regularly using genomewide selection. Using a training population of two‐row barley (Hordeum vulgare L.) lines, we predicted the mean (μ), genetic variance (VG), and superior progeny mean (μSP, mean of the best 10% of lines) of 330,078 possible parent combinations for Fusarium head blight (FHB) severity, heading date, and plant height. Twenty‐seven of these combinations were chosen to develop biparental populations, which were subsequently phenotyped for the same traits. We found that the predictive abilities (rMP) for μ and μSP were moderate to high (rMP = 0.46–0.69), whereas those for VG were lower (rMP = 0.01–0.48). Unsurprisingly, predictive ability was likely a function of trait heritability, as rMP estimates for heading date (the most heritable trait) were highest, and rMP estimates for FHB severity (the least heritable trait) were lowest. We observed strong negative bias when predicting VG (on average −83 to −96%), but the relative consistency of this bias across validation families indicates that it may have little impact when selecting crosses. We concluded that accurate predictions of VG and μSP are feasible, but as with any implementation of genomewide selection, reliable phenotypic data are critical.

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Predicting genetic variance in bi-parental breeding populations is more accurate when explicitly modeling the segregation of informative genomewide markers

Cited by 32 publications

References 33 publications

Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Predicting Genetic Variance from Genomewide Marker Effects Estimated from a Diverse Panel of Maize Inbreds

Combining ability, heterosis, genetic distance and their intercorrelations for waterlogging tolerance traits in chrysanthemum

Validating Genomewide Predictions of Genetic Variance in a Contemporary Breeding Program

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