Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare

Hansen, Pernille Bjarup; Ruud, Anja Karine; Campos, Gustavo de los; Malinowska, Marta; Nagy, István; Svane, Simon Fiil; Thorup‐Kristensen, Kristian; Jensen, J.; Krusell, Lene; Asp, Torben

doi:10.3390/plants11172190

Cited by 3 publications

(3 citation statements)

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“…Many studies in crop breeding have focused on investigating above-ground traits with a primary focus on grain yield (Sallam et al, 2015;Nielsen et al, 2016;Thorwarth et al, 2017), and fewer studies have investigated root traits (Den Herder et al, 2010;Robinson et al, 2018;Jia et al, 2019;Guo et al, 2020;Hansen et al, 2022;Malinowska et al, 2022). The complexity and di culty of root phenotyping under eld conditions are some of the major reasons for the scarcity of root studies.…”

Section: Introductionmentioning

confidence: 99%

Genomic prediction for root and yield traits of barley under a water availability gradient. A case study comparing different spatial adjustments

Tessema,

Raffo,

Guo

et al. 2023

Preprint

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Background In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors. Results The estimated heritabilities (${\widehat{h}}^{2}$) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ($GCV$) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest $GCV$ observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2. Conclusions The significant $GCV$ and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

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Section: Introductionmentioning

confidence: 99%

Genomic prediction for root and yield traits of barley under a water availability gradient. A case study comparing different spatial adjustments

Tessema,

Raffo,

Guo

et al. 2023

Preprint

Self Cite

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“…This has already been observed in some plant species, where CG and CHG methylation patterns best explained the geographical origin of individuals rather than the treatments individuals were exposed to (Ibañez et al, 2023;Peña-Ponton et al, 2022). (Bossdorf et al, 2010;Cubas et al, 1999;Ehrenhofer-Murray, 2021;Entrambasaguas et al, 2021;Hansen et al, 2022;Herman & Sultan, 2011;Herrera & Bazaga, 2013;van Dooren et al, 2020).…”

Section: Transgenerational Inheritance Of Dna Methylation In All Plan...mentioning

confidence: 61%

“…epiGBS calls nucleotide-specific DNA methylation levels after constructing a de novo consensus reference sequence for the genomic regions targeted by the RRBS method from the bisulfite sequencing reads produced in the experiment van Gurp et al, 2016). epiGBS created new opportunities to study DNA methylation in non-model species, and has been applied to a range of different model and non-model species (Boquete et al, 2022a(Boquete et al, , 2022bHansen et al, 2022;Johnson et al, , 2022Krick et al, 2021;Malinowska et al, 2020;McNew et al, 2021;Meröndun et al, 2019;Mouginot et al, 2021;Mounger et al, 2021;Prudencio et al, 2018;van Antro et al, 2022;van Moorsel et al, 2019). Nevertheless, the technique still required advanced bioinformatic skills and was difficult to implement.…”

Section: Introductionmentioning

confidence: 99%

DNA methylation dynamics in response to stress : An insight into stress-induced and stably inherited DNA methylation variants in asexual and sexual plants

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The term Epigenetics was first used by the developmental biologist Conrad Waddington in 1940, who while studying the fruit fly Drosophila, discovered that exposing developing individuals to heat stress induced changes in wing structure, which was in turn inherited over several generations (Waddington, 1940). He integrated the term 'epigenesis' with 'genetics' to describe this complex interaction between the developmental environment and the genome he observed in Drosophila. Epigenetic literally means 'epi' (greek for 'on top of') genetics. Over the years, as our understanding of molecular mechanisms involved in gene expression improved, the definition of 'epigenetics' underwent numerous substantial changes. Within the scope of this thesis, I refer to epigenetics as heritable (mitotically and/or meiotically), stable molecular mechanisms that regulate genome activity without modifying the underlying DNA sequence (E. J. Richards, 2006).

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Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments

Tessema,

Raffo,

Guo

et al. 2024

Plant Methods

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Background In drought periods, water use efficiency depends on the capacity of roots to extract water from deep soil. A semi-field phenotyping facility (RadiMax) was used to investigate above-ground and root traits in spring barley when grown under a water availability gradient. Above-ground traits included grain yield, grain protein concentration, grain nitrogen removal, and thousand kernel weight. Root traits were obtained through digital images measuring the root length at different depths. Two nearest-neighbor adjustments (M1 and M2) to model spatial variation were used for genetic parameter estimation and genomic prediction (GP). M1 and M2 used (co)variance structures and differed in the distance function to calculate between-neighbor correlations. M2 was the most developed adjustment, as accounted by the Euclidean distance between neighbors. Results The estimated heritabilities ($${\widehat{h}}^{2}$$ h ^ 2 ) ranged from low to medium for root and above-ground traits. The genetic coefficient of variation ($$GCV$$ GCV ) ranged from 3.2 to 7.0% for above-ground and 4.7 to 10.4% for root traits, indicating good breeding potential for the measured traits. The highest $$GCV$$ GCV observed for root traits revealed that significant genetic change in root development can be achieved through selection. We studied the genotype-by-water availability interaction, but no relevant interaction effects were detected. GP was assessed using leave-one-line-out (LOO) cross-validation. The predictive ability (PA) estimated as the correlation between phenotypes corrected by fixed effects and genomic estimated breeding values ranged from 0.33 to 0.49 for above-ground and 0.15 to 0.27 for root traits, and no substantial variance inflation in predicted genetic effects was observed. Significant differences in PA were observed in favor of M2. Conclusions The significant $$GCV$$ GCV and the accurate prediction of breeding values for above-ground and root traits revealed that developing genetically superior barley lines with improved root systems is possible. In addition, we found significant spatial variation in the experiment, highlighting the relevance of correctly accounting for spatial effects in statistical models. In this sense, the proposed nearest-neighbor adjustments are flexible approaches in terms of assumptions that can be useful for semi-field or field experiments.

show abstract

Integration of DNA Methylation and Transcriptome Data Improves Complex Trait Prediction in Hordeum vulgare

Cited by 3 publications

References 88 publications

Genomic prediction for root and yield traits of barley under a water availability gradient. A case study comparing different spatial adjustments

Genomic prediction for root and yield traits of barley under a water availability gradient. A case study comparing different spatial adjustments

DNA methylation dynamics in response to stress : An insight into stress-induced and stably inherited DNA methylation variants in asexual and sexual plants

Genomic prediction for root and yield traits of barley under a water availability gradient: a case study comparing different spatial adjustments

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