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Identification of genomic regions for grain yield and yield stability and their epistatic interactions.

ABSTRACT: The task of identifying genomic regions conferring yield stability is challenging in any crop and requires large experimental data sets in conjunction with complex analytical approaches. We report findings of a first attempt to identify genomic regions with stable expression and their individual epistatic interactions for grain yield and yield stability in a large elite panel of wheat under multiple environments via a genome wide association mapping (GWAM) approach. Seven hundred and twenty lines were genotyped using genotyping-by-sequencing technology and phenotyped for grain yield and phenological traits. High gene diversity (0.250) and a moderate genetic structure (five groups) in the panel provided an excellent base for GWAM. The mixed linear model and multi-locus mixed model analyses identified key genomic regions on chromosomes 2B, 3A, 4A, 5B, 7A and 7B. Further, significant epistatic interactions were observed among loci with and without main effects that contributed to additional variation of up to 10%. Simple stepwise regression provided the most significant main effect and epistatic markers resulting in up to 20% variation for yield stability and up to 17% gain in yield with the best allelic combination.

SUBMITTER: Sehgal D

PROVIDER: S-EPMC5286416 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Identification of genomic regions for grain yield and yield stability and their epistatic interactions.

Sehgal Deepmala D Autrique Enrique E Singh Ravi R Ellis Marc M Singh Sukhwinder S Dreisigacker Susanne S

Scientific reports 20170201

The task of identifying genomic regions conferring yield stability is challenging in any crop and requires large experimental data sets in conjunction with complex analytical approaches. We report findings of a first attempt to identify genomic regions with stable expression and their individual epistatic interactions for grain yield and yield stability in a large elite panel of wheat under multiple environments via a genome wide association mapping (GWAM) approach. Seven hundred and twenty line ...[more]

PMID: 28145508

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Project description:AimsThe aim of the present study was to evaluate the performance of 'high'-'low' yielding pyramided lines (PLs), having the same combinations of qDTYs in Samba Mahsuri, MR219 and IR64-Sub1 genetic backgrounds, and to understand the genetic interactions among QTL and/with genetic background affecting grain yield.BackgroundEpistasis regulates the expression of traits governed by several major/minor genes/QTL. Multiple pyramided lines (PLs) with the same grain yield QTL (qDTYs) combinations but possessing grain yield variability under different levels of reproductive stage drought stress were identified in different rice genetic backgrounds at International Rice Research Institute (IRRI).ObjectivesThe objectives of the present study were to evaluate the performance pyramided lines (PLs) with drought QTL in the backgrounds of Samba Mahsuri, MR219 and IR64-Sub1 under reproductive stage drought stress (RS) and NS (non-stress) conditions, to understand the effect of epistatic interactions among qDTYs and with genetic background on GY under the differential level of stress and to identify the promising drought-tolerant lines with high yield under drought and higher background recovery in different genetic backgrounds.MethodsThe experiments were conducted in 2015 DS (dry season), 2015 WS (wet season) and 2017 DS at IRRI, Los Baños, Philippines, in a transplanted lowland ecosystem under lowland severe stress (LSS), lowland moderate stress (LMS) and lowland non-stress (LNS). The experiments were laid out in alpha lattice design with two replications.ResultsSeveral digenic interactions were found in different genetic backgrounds, 13 interactions in Samba Mahsuri, 11 in MR219 and 20 in IR64-Sub1 backgrounds. Among all digenic interactions, one QTL × QTL interaction, 17 QTL × background and 26 background × background interactions resulted in GY reduction in low yielding PLs in different genetic backgrounds under LSS or LMS. Negative interaction of qDTY3.1 , qDTY4.1 and qDTY9.1 with background markers and background × background interactions caused up to 15% GY reduction compared to the high yielding PLs under LMS in the Samba Mahsuri PLs. In MR219 PLs, the negative interaction of qDTY2.2 , qDTY3.2 , qDTY4.1 and qDTY12.1 with the background marker interval RM314-RM539, RM273-RM349 and RM445-RM346, RM473D-RM16, respectively resulted 52% GY reduction compared to the high yielding PLs under LSS. In IR64-Sub1 PLs, qDTY6.1 interacted with background loci at RM16-RM135, RM228-RM333, RM202-RM287 and RM415-RM558A marker interval under LSS and at RM475-RM525 marker interval under LMS, causing GY reduction to 58% compared to the high yielding PLs.ConclusionHigh yielding PLs in Samba Mahsuri (IR 99734:1-33-69-1-22-6), MR219 (IR 99784-156-87-2-4-1) and IR64-Sub1 (IR 102784:2-89-632-2-1-2) backgrounds without any negative interactions were identified. The identified selected promising PLs may be used as potential drought-tolerant donors or may be released as varieties for drought-prone ecosystems in different countries.

Project description:Modern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1-8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1-23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information.In this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands.We observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.

Dataset Information

Identification of genomic regions for grain yield and yield stability and their epistatic interactions.

Publications

Identification of genomic regions for grain yield and yield stability and their epistatic interactions.

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