Dataset Information

Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population.

ABSTRACT: Gene-by-gene interactions, also known as epistasis, regulate many complex traits in different species. With the availability of low-cost genotyping it is now possible to study epistasis on a genome-wide scale. However, identifying genome-wide epistasis is a high-dimensional multiple regression problem and needs the application of dimensionality reduction techniques. Flowering Time (FT) in crops is a complex trait that is known to be influenced by many interacting genes and pathways in various crops. In this study, we successfully apply Sure Independence Screening (SIS) for dimensionality reduction to identify two-way and three-way epistasis for the FT trait in a Multiparent Advanced Generation Inter-Cross (MAGIC) barley population using the Bayesian multilocus model. The MAGIC barley population was generated from intercrossing among eight parental lines and thus, offered greater genetic diversity to detect higher-order epistatic interactions. Our results suggest that SIS is an efficient dimensionality reduction approach to detect high-order interactions in a Bayesian multilocus model. We also observe that many of our findings (genomic regions with main or higher-order epistatic effects) overlap with known candidate genes that have been already reported in barley and closely related species for the FT trait.

SUBMITTER: Mathew B

PROVIDER: S-EPMC5788519 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population.

Mathew Boby B Léon Jens J Sannemann Wiebke W Sillanpää Mikko J MJ

Genetics 20171218 2

Gene-by-gene interactions, also known as epistasis, regulate many complex traits in different species. With the availability of low-cost genotyping it is now possible to study epistasis on a genome-wide scale. However, identifying genome-wide epistasis is a high-dimensional multiple regression problem and needs the application of dimensionality reduction techniques. Flowering Time (FT) in crops is a complex trait that is known to be influenced by many interacting genes and pathways in various cr ...[more]

PMID: 29254994

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Project description:The control of flowering time has important impacts on crop yield. The variation in response to day length (photoperiod) and low temperature (vernalization) has been selected in barley to provide adaptation to different environments and farming practices. As a further step towards unraveling the genetic mechanisms underlying flowering time control in barley, we investigated the allelic variation of ten known or putative photoperiod and vernalization pathway genes between two genotypes, the spring barley elite cultivar 'Scarlett' (Hordeum vulgare ssp. vulgare) and the wild barley accession 'ISR42-8' (Hordeum vulgare ssp. spontaneum). The genes studied are Ppd-H1, VRN-H1, VRN-H2, VRN-H3, HvCO1, HvCO2, HvGI, HvFT2, HvFT3 and HvFT4. 'Scarlett' and 'ISR42-8' are the parents of the BC(2)DH advanced backcross population S42 and a set of wild barley introgression lines (S42ILs). The latter are derived from S42 after backcrossing and marker-assisted selection. The genotypes and phenotypes in S42 and S42ILs were utilized to determine the genetic map location of the candidate genes and to test if these genes may exert quantitative trait locus (QTL) effects on flowering time, yield and yield-related traits in the two populations studied. By sequencing the characteristic regions of the genes and genotyping with diagnostic markers, the contrasting allelic constitutions of four known flowering regulation genes were identified as ppd-H1, Vrn-H1, vrn-H2 and vrn-H3 in 'Scarlett' and as Ppd-H1, vrn-H1, Vrn-H2 and a novel allele of VRN-H3 in 'ISR42-8'. All candidate genes could be placed on a barley simple sequence repeat (SSR) map. Seven candidate genes (Ppd-H1, VRN-H2, VRN-H3, HvGI, HvFT2, HvFT3 and HvFT4) were associated with flowering time QTLs in population S42. Four exotic alleles (Ppd-H1, Vrn-H2, vrn-H3 and HvCO1) possibly exhibited significant effects on flowering time in S42ILs. In both populations, the QTL showing the strongest effect corresponded to Ppd-H1. Here, the exotic allele was associated with a reduction of number of days until flowering by 8.0 and 12.7%, respectively. Our data suggest that Ppd-H1, Vrn-H2 and Vrn-H3 may also exert pleiotropic effects on yield and yield-related traits.

Project description:Multi-parent Advanced Generation Inter-crosses (MAGIC) lines have mosaic genomes that are generated shuffling the genetic material of the founder parents following pre-defined crossing schemes. In cereal crops, these experimental populations have been extensively used to investigate the genetic bases of several traits and dissect the genetic bases of epistasis. In plants, genomic prediction models are usually fitted using either diverse panels of mostly unrelated accessions or individuals of biparental families and several empirical analyses have been conducted to evaluate the predictive ability of models fitted to these populations using different traits. In this paper, we constructed, genotyped and evaluated a barley MAGIC population of 352 individuals developed with a diverse set of eight founder parents showing contrasting phenotypes for grain yield. We combined phenotypic and genotypic information of this MAGIC population to fit several genomic prediction models which were cross-validated to conduct empirical analyses aimed at examining the predictive ability of these models varying the sizes of training populations. Moreover, several methods to optimize the composition of the training population were also applied to this MAGIC population and cross-validated to estimate the resulting predictive ability. Finally, extensive phenotypic data generated in field trials organized across an ample range of water regimes and climatic conditions in the Mediterranean were used to fit and cross-validate multi-environment genomic prediction models including G×E interaction, using both genomic best linear unbiased prediction and reproducing kernel Hilbert space along with a non-linear Gaussian Kernel. Overall, our empirical analyses showed that genomic prediction models trained with a limited number of MAGIC lines can be used to predict grain yield with values of predictive ability that vary from 0.25 to 0.60 and that beyond QTL mapping and analysis of epistatic effects, MAGIC population might be used to successfully fit genomic prediction models. We concluded that for grain yield, the single-environment genomic prediction models examined in this study are equivalent in terms of predictive ability while, in general, multi-environment models that explicitly split marker effects in main and environmental-specific effects outperform simpler multi-environment models.

Dataset Information

Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population.

Publications

Detection of Epistasis for Flowering Time Using Bayesian Multilocus Estimation in a Barley MAGIC Population.

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