Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

Santantonio, Nicholas; Jannink, Jean‐Luc; Sorrells, Mark E.

doi:10.1534/genetics.118.301851

Cited by 12 publications

(14 citation statements)

References 103 publications

(117 reference statements)

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“…By simultaneously accounting for 530 variation in all ten fatty acids, we were able to detect SNPs that were otherwise not detectable in 531 our population. The improved sensitivity of multivariate GWAS may be particularly advantageous 532 for polyploid species like oat, where the effect of a single locus may be buffered by the activity of 533 its homeologs (Santantonio et al 2019). On the other hand, the poorly characterized structural 534 variation within oat haplotypes in combination with long-range LD currently presents a significant separated by a colon (for example, palmitic acid is denoted by 16:0).…”

Section: Integration Of Gwas Results 405mentioning

confidence: 99%

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Carlson

Montilla-Bascón

Hoekenga

et al. 2019

Preprint

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34Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful 35 unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to 36 identify loci associated with variation in fatty acid composition, defined as the types and quantities 37 of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and 38 measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. 39Measurements of individual fatty acids were highly correlated across samples, consistent with fatty 40 acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations 41 in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we 42 fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for 43 population structure and relatedness among cultivars. In the second, we performed a univariate 44 association test for each principal component (PC) derived from a singular value decomposition 45 of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we 46 also conducted univariate association tests for each trait. The multivariate mixed model approach 47 revealed the numerous beneficial effects of oat consumption on human health, from reduction in 58 cardiovascular diseases risk (Grundy et al. 2018) to cancer prevention (Meydani 2009). These 59 positive health effects are likely due to oat's unique nutritional profile, which differs markedly 60 from that of other cereals, notably in the complement of essential amino acids, fatty acids, β-61 glucan, and phenolic compounds (Butt et al. 2008). In particular, lipids account for as much as 62 18% of the oat grain (Halima et al. 2015). Moreover, these lipids are predominantly composed of 63 unsaturated fatty acids, rendering oats a healthful energy source in human and animal diets. In 64 response to the growing awareness of oat's health-promoting properties, nutritional quality has 65 become a key target for oat breeders. Maintaining and/or optimizing lipid composition is an 66 important component of these efforts (Valentine et al. 2011). 67 To date, researchers have primarily investigated the genetic basis of variation in fatty acid 68 composition by mapping quantitative trait loci (QTL) in biparental populations (Hizbai et al. 69 2012). However, the use of such QTL in marker-assisted selection (MAS) is likely only effective 70 if the parents in the mapping population exhibit trait variation and are closely related to lines in 71 the relevant breeding populations (Snowdon and Friedt 2004). A genome-wide association study 72 (GWAS) can mediate this limitation if the population in which the GWAS is conducted captures 73 the genetic variation present in the target breeding population(s) (Lipka et al. 2015). Indeed, a 74 GWAS can identify allelic diversity associated with trait variation in complex plant pedigrees 75 when both genotypic and phenoty...

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Section: Integration Of Gwas Results 405mentioning

confidence: 99%

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Carlson

Montilla-Bascón

Hoekenga

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…By analyzing all ten fatty acids simultaneously with multi-trait GWAS methods, we were able to detect SNPs that were not detected by traditional, univariate GWAS. The increased sensitivity of multivariate GWAS may be particularly advantageous for polyploid species like oat, where the effect of a single locus may be buffered by the activity of its homeologs (Santantonio et al 2019). On the other hand, the poorly characterized structural variation within oat haplotypes, in combination with long-range LD, currently presents a significant challenge for resolving GWAS signals to individual genes.…”

Section: Discussionmentioning

confidence: 99%

Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Carlson

Montilla-Bascón

Hoekenga

et al. 2019

G3 Genes|Genomes|Genetics

Self Cite

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Oat ( Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.

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“…There may be an expectation of finding more epistasis as a cause of heterosis, and in allopolyploid wheat, this was thought to be the case. However, in allohexaploid wheat, (Santantonio et al ., 2019) found although homoeologous interactions explain a portion of the non‐additive genetic signal, the contribution is less than other sources of epistasis.…”

Section: Heterosis At Other Ploidy Levelsmentioning

confidence: 99%

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding

Mackay

Cockram

Howell

et al. 2020

Plant Biotechnology Journal

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Transgressive segregation and heterosis are the reasons that plant breeding works. Molecular explanations for both phenomena have been suggested and play a contributing role. However, it is often overlooked by molecular genetic researchers that transgressive segregation and heterosis are most simply explained by dispersion of favorable alleles. Therefore, advances in molecular biology will deliver the most impact on plant breeding when integrated with sources of heritable trait variationand this will be best achieved within a quantitative genetics framework. An example of the power of quantitative approaches is the implementation of genomic selection, which has recently revolutionized animal breeding. Genomic selection is now being applied to both hybrid and inbred crops and is likely to be the major source of improvement in plant breeding practice over the next decade. Breeders' ability to efficiently apply genomic selection methodologies is due to recent technology advances in genotyping and sequencing. Furthermore, targeted integration of additional molecular data (such as gene expression, gene copy number and methylation status) into genomic prediction models may increase their performance. In this review, we discuss and contextualize a suite of established quantitative genetics themes relating to hybrid vigour, transgressive segregation and their central relevance to plant breeding, with the aim of informing crop researchers outside of the quantitative genetics discipline of their relevance and importance to crop improvement. Better understanding between molecular and quantitative disciplines will increase the potential for further improvements in plant breeding methodologies and so help underpin future food security.

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Homeologous Epistasis in Wheat: The Search for an Immortal Hybrid

Cited by 12 publications

References 103 publications

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Multivariate Genome-wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding

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