scholarly journals Mitonuclear epistasis, genotype-by-environment interactions, and personalized genomics of complex traits inDrosophila

IUBMB Life ◽  
2018 ◽  
Vol 70 (12) ◽  
pp. 1275-1288 ◽  
Author(s):  
David M. Rand ◽  
Jim A. Mossman ◽  
Lei Zhu ◽  
Leann M. Biancani ◽  
Jennifer Y. Ge
Keyword(s):  
Complex Traits ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment

scholarly journals Dissecting the Genetic Regulation of Yeast Growth Plasticity in Response to Environmental Changes

Genes ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1279
Author(s):  
Yanjun Zan ◽  
Örjan Carlborg
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Environmental Changes ◽  
Genetic Regulation ◽  
Growth Responses ◽  
Yeast Growth ◽  
Heritable Variation ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Phenotype Plasticity

Variable individual responses to environmental changes, such as phenotype plasticity, are heritable, with some genotypes being robust and others plastic. This variation for plasticity contributes to variance in complex traits as genotype-by-environment interactions (G × E). However, the genetic basis of this variability in responses to the same external stimuli is still largely unknown. In an earlier study of a large haploid segregant yeast population, genotype-by-genotype-by-environment interactions were found to make important contributions to the release of genetic variation in growth responses to alterations of the growth medium. Here, we explore the genetic basis for heritable variation of different measures of phenotype plasticity in the same dataset. We found that the central loci in the environmentally dependent epistatic networks were associated with overall measures of plasticity, while the specific measures of plasticity identified a more diverse set of loci. Based on this, a rapid one-dimensional genome-wide association (GWA) approach to overall plasticity is proposed as a strategy to efficiently identify key epistatic loci contributing to the phenotype plasticity. The study thus provided both analytical strategies and a deeper understanding of the complex genetic regulation of phenotype plasticity in yeast growth.

Genotype by environment interactions and combining ability for strawberry families grown in diverse environments

Euphytica ◽  
2017 ◽  
Vol 213 (5) ◽  
Author(s):  
Megan M. Mathey ◽  
Sonali Mookerjee ◽  
Lise L. Mahoney ◽  
Kazim Gündüz ◽  
Umesh Rosyara ◽  
...  
Keyword(s):  
Combining Ability ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment

Genetic Parameters and Genotype-by-Environment Interactions in Regional Progeny Tests of Pinus taeda L. in the Southern USA

2020 ◽  
Author(s):  
Edwin Lauer ◽  
Andrew Sims ◽  
Steven McKeand ◽  
Fikret Isik
Keyword(s):  
Genetic Variation ◽  
Pinus Taeda ◽  
Genetic Parameters ◽  
Genetic Correlations ◽  
Breeding Program ◽  
Model Fit ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Progeny Tests ◽  
Pinus Taeda L

Abstract Genetic parameters were estimated using a five-series multienvironment trial of Pinus taeda L. in the southern USA. There were 324 half-sib families planted in five test series across 37 locations. A set of six variance/covariance matrices for the genotype-by-environment (G × E) effect for tree height and diameter were compared on the basis of model fit. In single-series analysis, extended factor analytical models provided generally superior model fit to simpler models for both traits; however, in the combined-series analysis, diameter was optimally modeled using simpler variance/covariance structures. A three-way compound term for modeling G × E interactions among and within series yielded substantial improvements in terms of model fit and standard errors of predictions. Heritability of family means ranged between 0.63 and 0.90 for both height and diameter. Average additive genetic correlations among sites were 0.70 and 0.61 for height and diameter, respectively, suggesting the presence of some G × E interaction. Pairs of sites with the lowest additive genetic correlations were located at opposite ends of the latitude range. Latent factor regression revealed a small number of parents with large factor scores that changed ranks significantly between southern and northern environments. Study Implications Multienvironmental progeny tests of loblolly pine (Pinus taeda L.) were established over 10 years in the southern United States to understand the genetic variation for the traits of economic importance. There was substantial genetic variation between open-pollinated families, suggesting that family selection would be efficient in the breeding program. Genotype-by-environment interactions were negligible among sites in the deployment region but became larger between sites at the extremes of the distribution. The data from these trials are invaluable in informing the breeding program about the genetic merit of selection candidates and their potential interaction with the environment. These results can be used to guide deployment decisions in the southern USA, helping landowners match germplasm with geography to achieve optimal financial returns and conservation outcomes.

scholarly journals Genome scan identifies flowering-independent effects of barley HsDry2.2 locus on yield traits under water deficit

2017 ◽  
Author(s):  
Lianne Merchuk-Ovnat ◽  
Roi Silberman ◽  
Efrat Laiba ◽  
Andreas Maurer ◽  
Klaus Pillen ◽  
...  
Keyword(s):  
Flowering Time ◽  
Water Deficit ◽  
Genome Wide Association Study ◽  
Grain Filling ◽  
Genotype By Environment Interactions ◽  
Shoot Morphology ◽  
Genotype By Environment ◽  
A Genome ◽  
Wild Allele ◽  
Novel Alleles

AbstractIncreasing crop productivity under climate change requires the identification, selection and utilization of novel alleles for breeding. We analyzed the genotype and field phenotype of the barley HEB-25 multi-parent mapping population under well-watered and water-limited (WW and WL) environments for two years. A genome-wide association study (GWAS) for genotype by-environment interactions was performed for ten traits including flowering time (HEA), plant grain yield (PGY). Comparison of the GWAS for traits per-se to that for QTL-by-environment interactions (QxE), indicates the prevalence of QxE mostly for reproductive traits. One QxE locus on chromosome 2, Hordeum spontaneum Dry2.2 (HsDry2.2), showed a positive and conditional effect on PGY and grain number (GN). The wild allele significantly reduced HEA, however this earliness was not conditioned by water deficit. Furthermore, BC2F1 lines segregating for the HsDry2.2 showed the wild allele confers an advantage over the cultivated in PGY, GN and harvest index as well as modified shoot morphology, longer grain filling period and reduced senescence (only under drought), therefore suggesting adaptation mechanism against water deficit other than escape. This study highlights the value of evaluating wild relatives in search of novel alleles and clues to resilience mechanism underlying crop adaptation to abiotic stress.HighlightA flowering-time independent reproductive advantage of wild over cultivated allele under drought identified in a barley GWAS for genotype-by-environment interactions, with modified shoot morphology, reduced senescence and longer grain filling

scholarly journals The theory on and software simulating large-scale genomic data for genotype-by-environment interactions

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xiujin Li ◽  
Hailiang Song ◽  
Zhe Zhang ◽  
Yunmao Huang ◽  
Qin Zhang ◽  
...  
Keyword(s):  
Large Scale ◽  
Simulated Data ◽  
Genomic Data ◽  
Efficient Tool ◽  
Phenotypic Data ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Threshold Trait ◽  
Genome Wide ◽  
Increasing Demand

Abstract Background With the emphasis on analysing genotype-by-environment interactions within the framework of genomic selection and genome-wide association analysis, there is an increasing demand for reliable tools that can be used to simulate large-scale genomic data in order to assess related approaches. Results We proposed a theory to simulate large-scale genomic data on genotype-by-environment interactions and added this new function to our developed tool GPOPSIM. Additionally, a simulated threshold trait with large-scale genomic data was also added. The validation of the simulated data indicated that GPOSPIM2.0 is an efficient tool for mimicking the phenotypic data of quantitative traits, threshold traits, and genetically correlated traits with large-scale genomic data while taking genotype-by-environment interactions into account. Conclusions This tool is useful for assessing genotype-by-environment interactions and threshold traits methods.

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