Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize

Guo, Zhigang; Magwire, Michael M.; Basten, Christopher J.; Xu, Zhanyou; Wang, Daolong

doi:10.1007/s00122-016-2780-5

Cited by 86 publications

(153 citation statements)

References 52 publications

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“…85 86 A complementary way in which multiple "omics" can be used is for complex trait prediction, in 87 a similar manner to prediction using genomic data. Different layers of data may provide 88 (partially) non-redundant information about phenotypes (Guo et al 2016). For example, gene 89 expression levels may also capture some environmental effects, i.e., those that affect levels of 90 expression.…”

Section: Introduction 15mentioning

confidence: 99%

“…101 However, when the researchers turned to a clinical dataset that included individuals with both 102 DNA and RNA data and tried to predict a quantitative trait, the combined model predicted worse 103 than the best single component model (Wheeler et al 2014). 104 105 Guo et al (2016) used inbred lines of maize with genotype (G), gene expression level (T) and 106 metabolite level (M) information to predict several complex traits using BLUP methodology. In 107 general, MBLUP yielded lower accuracy than all the other models.…”

Section: Introduction 15mentioning

confidence: 99%

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Leveraging multiple layers of data to predict Drosophila complex traits

Morgante

Huang

Sørensen

et al. 2019

Preprint

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1 An important challenge in genetics is to be able to predict complex traits accurately. Despite 2 recent advances, prediction accuracy for most complex traits remains low. Here, we used the 3 Drosophila Genetic Reference Panel (DGRP), a collection of 200 lines with whole-genome 4 sequences and deep RNA sequencing data, to evaluate the usefulness of using high-quality gene 5 expression levels compared to relying on genotypes for predicting three complex traits. We 6 found that expression levels provided higher accuracy than genotypes for starvation resistance, 7 similar accuracy for chill coma recovery, and lower accuracy for startle response. Models 8 including both genotype and expressions levels did not outperform the best single component 9 model. However, accuracy increased considerably for all the three traits when we included 10 another layer of information, i.e., gene ontology (GO). We found that a limited number of GO 11 terms, some of which had a clear biological interpretation, were strongly predictive of the traits. 12In summary, this study shows that integrating different sources of information can improve 13 prediction accuracy, especially when large samples are not available. 14 2006;Hayes et al. 2009). 32 33 GS has been applied to many agricultural species, the first being livestock. Despite the fact that 34 GS has revolutionized animal breeding, the results have varied greatly between species. In dairy 35 cattle, which has the largest and highest quality reference populations, the accuracy of genomic 36 estimated breeding values (gEBVs) has been ~0.7-0.8 for many traits. This high accuracy 37combined with the reduced generation interval has doubled the rate of genetic gain since the 38 implementation of GS in breeding programs (Meuwissen et al. 2016). However, in beef cattle 39 and pigs, for example, the reference populations are much smaller within each breed, resulting in 40 lower accuracy. To overcome this issue, multi-breed reference populations have been used. 41

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Section: Introduction 15mentioning

confidence: 99%

Section: Introduction 15mentioning

confidence: 99%

Leveraging multiple layers of data to predict Drosophila complex traits

Morgante

Huang

Sørensen

et al. 2019

Preprint

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“…Values of VIP for Component 1 in the PLS‐DA result were used to filter TGW‐unrelated or low contribution analytes. Predictabilities of TGW for hybrids from the population in 2015 varied from low ( r = 0.47, P = 3.36E‐4) to high ( r = 0.72, P = 8.52E‐10) and then back to low ( r = 0.45, P = 8.14E‐4) with the increasing numbers of metabolic markers, which is similar to results with SNPs, transcripts or metabolites as predictive markers (Guo et al ., ; Slavov et al ., ). At last, after further removing low contribution analytes and additional PLS regression analysis, three hundred was the most appropriate number of analytes to predict grain weight for rice hybrids across environments, which indicated that a certain number of predictive analytes is necessary for high predictability.…”

Section: Discussionmentioning

confidence: 98%

“…Hence, environmental effects should be considered in employing markerbased prediction. In addition, other factors including population structures and numbers of predictive variables also have large effects on predictabilities (Guo et al, 2016;Slavov et al, 2014;Technow et al, 2014;Windhausen et al, 2012;Xu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Dan

Chen

et al. 2018

Plant Biotechnology Journal

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Summary Marker‐based prediction holds great promise for improving current plant and animal breeding efficiencies. However, the predictabilities of complex traits are always severely affected by negative factors, including distant relatedness, environmental discrepancies, unknown population structures, and indeterminate numbers of predictive variables. In this study, we utilised two independent F 1 hybrid populations in the years 2012 and 2015 to predict rice thousand grain weight ( TGW ) using parental untargeted metabolite profiles with a partial least squares regression method. A stable predictive model for TGW was built based on hybrids from the population in 2012 ( r = 0.75) but failed to properly predict TGW for hybrids from the population in 2015 ( r = 0.27). After integrating hybrids from both populations into the training set, the TGW of hybrids could be predicted but was largely dependent on population structures. Then, core hybrids from each population were determined by principal component analysis and the TGW of hybrids in both environments were successfully predicted ( r > 0.60). Moreover, adjusting the population structures and numbers of predictive analytes increased TGW predictability for hybrids in 2015 ( r = 0.72). Our study demonstrates that the TGW of F 1 hybrids across environments can be accurately predicted based on parental untargeted metabolite profiles with a core hybridisation strategy in rice. Metabolic biomarkers identified from early developmental stage tissues, which are grown under experimental conditions, may represent a workable approach towards the robust prediction of major agronomic traits for climate‐adaptive varieties.

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“…The last decade has seen tremendous advances in genome-scale data analysis, which was possible due to high-throughput DNA sequencing. In this way, single nucleotide polymorphism (SNPs), representing various regions of all chromosomes, are obtained to be applied in genomic studies (Guo et al 2016). …”

Section: Introductionmentioning

confidence: 99%

Influence of multi-trait modeling, dominance, and population structure in genomic prediction of maize hybrids

Lyra¹

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Piracicaba 2017 3 RESUMO Influência da modelagem multi-trait, dominância, e estruturação populacional na predição genômica em híbridos de milhoPredição genômica de híbridos simples é uma promissora ferramenta no melhoramento de milho, pois permite aumentar os ganhos genéticos por unidade de tempo, principalmente por reduzir o tempo de seleção. Uma estratégia que pode aumentar a acurácia das predições genômicas é realizar esta para múltiplos caracteres considerando os mesmos simultâneamente, ou utilizar índices de seleção, os quais captam a performance dos genótipos tanto em condições ótimas como em condições de estresse. Além disso, fatores como dominância, variantes estruturais, e estruturação populacional podem influenciar a acurácia de estimativas dos valores genéticos genômicos (VGG). Portanto, os objetivos foram aplicar predição genômica em híbridos de milho (i) incluindo modelos multi-trait, (ii) incorporando desvios de dominância e efeitos da variação no número de cópias, e (iii) controlando a estruturação populacional. Para isto, dois conjuntos de milho (HELIX e USP) foram utilizados, consistindo de 452 e 906 híbridos simples. Os caracteres avaliados foram produtividade de grãos, altura de planta e espiga, senescência, e quatro índices de seleção. A partir das análises multi-trait dos modelos GBLUP e GK, pôde-se concluir que a combinação dos índices é uma alternativa viável, aumentando a acurácia seletiva. Além disso, os resultados sugerem que o melhor método é a predição de híbridos incluindo desvios de dominância, principalmente para caracteres complexos. Observou-se também que incluir efeitos relacionados a variação no número de cópias indica ser adequado, devido ao aumento da acurácia e redução do viés nos modelos de predição genômica. Por outro lado, a acurácia de predição não aumentou quando se adicionou quatro diferentes conjuntos de estruturação como covariáveis fixas no modelo GBLUP. No entanto, usando o escalonamento multidimensional não métrico e o agrupamento do fineSTRUCTURE aumentaram a confiabilidade de estimação do VGG para produtividade de grãos e altura de plantas, respectivamente. Genomic prediction of single-crosses is a promising tool in maize breeding, increasing genetics gains and reducing selection time. A strategy that can increase accuracy is applying multiple-trait genomic prediction using selection indices, which take into account the performance under optimal and stress conditions. Moreover, factors such as dominance, structural variants, and population structure can influence the accuracy of estimates of genomic breeding values (GEBV). Therefore, the objectives were to apply genomic prediction (i) including multi-trait models, (ii) incorporating dominance deviation and copy number variation effects, and (iii) controlling population structure in maize hybrids. Hence, we used two maize datasets (HELIX and USP), consisting of 452 and 906 maize single-crosses. The traits evaluated were grain yield, plant and ear height, stay green, and four selection indices. From multi-trait GB...

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Evaluation of the utility of gene expression and metabolic information for genomic prediction in maize

Cited by 86 publications

References 52 publications

Leveraging multiple layers of data to predict Drosophila complex traits

Leveraging multiple layers of data to predict Drosophila complex traits

A metabolome‐based core hybridisation strategy for the prediction of rice grain weight across environments

Influence of multi-trait modeling, dominance, and population structure in genomic prediction of maize hybrids

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