The Multiple Environments in Which We Grow Old

1984 ◽  
Vol 29 (3) ◽  
pp. 230-231
Author(s):  
Frances M. Carp
Keyword(s):  
Multiple Environments

Reversible Writing/Re‐Writing Polymeric Paper in Multiple Environments

2021 ◽  
pp. 2104784
Author(s):  
Chi Chen ◽  
Xinkun Zhao ◽  
Yujie Chen ◽  
Xinyue Wang ◽  
Zhen Chen ◽  
...  
Keyword(s):  
Multiple Environments

scholarly journals Uncovering genomic regions controlling plant architectural traits in hexaploid wheat using different GWAS models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ali Muhammad ◽  
Jianguo Li ◽  
Weichen Hu ◽  
Jinsheng Yu ◽  
Shahid Ullah Khan ◽  
...  
Keyword(s):  
Plant Height ◽  
Genome Wide Association Study ◽  
Flag Leaf ◽  
Significant Snps ◽  
Complex Trait ◽  
Leaf Length ◽  
Candidate Snps ◽  
A Genome ◽  
Multiple Environments ◽  
Architectural Traits

AbstractWheat is a major food crop worldwide. The plant architecture is a complex trait mostly influenced by plant height, tiller number, and leaf morphology. Plant height plays a crucial role in lodging and thus affects yield and grain quality. In this study, a wheat population was genotyped by using Illumina iSelect 90K single nucleotide polymorphism (SNP) assay and finally 22,905 high-quality SNPs were used to perform a genome-wide association study (GWAS) for plant architectural traits employing four multi-locus GWAS (ML-GWAS) and three single-locus GWAS (SL-GWAS) models. As a result, 174 and 97 significant SNPs controlling plant architectural traits were detected by ML-GWAS and SL-GWAS methods, respectively. Among these SNP makers, 43 SNPs were consistently detected, including seven across multiple environments and 36 across multiple methods. Interestingly, five SNPs (Kukri_c34553_89, RAC875_c8121_1490, wsnp_Ex_rep_c66315_64480362, Ku_c5191_340, and tplb0049a09_1302) consistently detected across multiple environments and methods, played a role in modulating both plant height and flag leaf length. Furthermore, candidate SNPs (BS00068592_51, Kukri_c4750_452 and BS00022127_51) constantly repeated in different years and methods associated with flag leaf width and number of tillers. We also detected several SNPs (Jagger_c6772_80, RAC875_c8121_1490, BS00089954_51, Excalibur_01167_1207, and Ku_c5191_340) having common associations with more than one trait across multiple environments. By further appraising these GWAS methods, the pLARmEB and FarmCPU models outperformed in SNP detection compared to the other ML-GWAS and SL-GWAS methods, respectively. Totally, 152 candidate genes were found to be likely involved in plant growth and development. These finding will be helpful for better understanding of the genetic mechanism of architectural traits in wheat.

Gains in Grain Yield of Early Maize Cultivars Developed During Three Breeding Eras under Multiple Environments

Crop Science ◽  
2015 ◽  
Vol 55 (2) ◽  
pp. 527-539 ◽  
Author(s):  
B. Badu-Apraku ◽  
M. A. B. Fakorede ◽  
M. Oyekunle ◽  
G. C. Yallou ◽  
K. Obeng-Antwi ◽  
...  
Keyword(s):  
Grain Yield ◽  
Maize Cultivars ◽  
Multiple Environments

scholarly journals Stability of fruit quality traits in diverse watermelon cultivars tested in multiple environments

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Mahendra Dia ◽  
Todd C Wehner ◽  
Penelope Perkins-Veazie ◽  
Richard Hassell ◽  
Daniel S Price ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Quality Traits ◽  
Fruit Quality Traits ◽  
Multiple Environments

scholarly journals The dynamic effect of regulatory genetic variation on the in vivo ER stress transcriptional response

2021 ◽  
Author(s):  
Nikki D. Russell ◽  
Clement Y. Chow
Keyword(s):  
Genetic Variation ◽  
Stress Response ◽  
Er Stress ◽  
Transcriptional Response ◽  
Mouse Strains ◽  
Transcriptional Responses ◽  
Er Stress Response ◽  
Multiple Environments ◽  
Context Specific

AbstractGenotype x Environment (GxE) interactions occur when environmental conditions drastically change the effect of a genetic variant. In order to truly understand the effect of genetic variation, we need to incorporate multiple environments into our analyses. Many variants, under steady state conditions, may be silent or even have the opposite effect under stress conditions. This study uses an in vivo mouse model to investigate how the effect of genetic variation changes with tissue type and cellular stress. Endoplasmic reticulum (ER) stress occurs when misfolded proteins accumulate in the ER. This triggers the unfolded protein response (UPR), a large transcriptional response which attempts to return the cell to homeostasis. This transcriptional response, despite being a well conserved, basic cellular process, is highly variable across different genetic backgrounds, making it an ideal system to study GxE effects. In this study, we sought to better understand how genetic variation alters expression across tissues, in the presence and absence of ER stress. The use of different mouse strains and their F1s allow us to also identify context specific cis- and trans-regulatory mechanisms underlying variable transcriptional responses. We found hundreds of genes that respond to ER stress in a tissue- and/or genotype-dependent manner. Genotype-dependent ER stress-responsive genes are enriched for processes such as protein folding, apoptosis, and protein transport, indicating that some of the variability occurs in canonical ER stress factors. The majority of regulatory mechanisms underlying these variable transcriptional responses derive from cis-regulatory variation and are unique to a given tissue or ER stress state. This study demonstrates the need for incorporating multiple environments in future studies to better elucidate the effect of any particular genetic factor in basic biological pathways, like the ER stress response.Author SummaryThe effect of genetic variation is dependent on environmental context. Here we use genetically diverse mouse strains to understand how genetic variation interacts with stress state to produce variable transcriptional profiles. In this study, we take advantage of the endoplasmic reticulum (ER) stress response which is a large transcriptional response to misfolded proteins. Using this system, we uncovered tissue- and ER stress-specific effects of genetic variation on gene expression. Genes with genotype-dependent variable expression levels in response to ER stress were enriched for canonical ER stress functions, such as protein folding and transport. These variable effects of genetic variation are driven by unique sets of regulatory variation that are only active under context-specific circumstances. The results of this study highlight the importance of including multiple environments and genetic backgrounds when studying the ER stress response and other cellular pathways.

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