scholarly journals Quantitative Trait Locus Mapping Combined with RNA Sequencing Reveals the Molecular Basis of Seed Germination in Oilseed Rape

Biomolecules ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1780
Author(s):  
Kunjiang Yu ◽  
Yuqi He ◽  
Yuanhong Li ◽  
Zhenhua Li ◽  
Jiefu Zhang ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Seed Germination ◽  
Oilseed Rape ◽  
Quantitative Trait ◽  
Molecular Basis ◽  
Germination Rate ◽  
Tca Cycle ◽  
Germination Percentage ◽  
Epistatic Interactions ◽  
Trait Locus

Rapid and uniform seed germination improves mechanized oilseed rape production in modern agricultural cultivation practices. However, the molecular basis of seed germination is still unclear in Brassica napus. A population of recombined inbred lines of B. napus from a cross between the lower germination rate variety ‘APL01’ and the higher germination rate variety ‘Holly’ was used to study the genetics of seed germination using quantitative trait locus (QTL) mapping. A total of five QTLs for germination energy (GE) and six QTLs for germination percentage (GP) were detected across three seed lots, respectively. In addition, six epistatic interactions between the QTLs for GE and nine epistatic interactions between the QTLs for GP were detected. qGE.C3 for GE and qGP.C3 for GP were co-mapped to the 28.5–30.5 cM interval on C3, which was considered to be a novel major QTL regulating seed germination. Transcriptome analysis revealed that the differences in sugar, protein, lipid, amino acid, and DNA metabolism and the TCA cycle, electron transfer, and signal transduction potentially determined the higher germination rate of ‘Holly’ seeds. These results contribute to our knowledge about the molecular basis of seed germination in rapeseed.

scholarly journals A molecular quantitative trait locus map for osteoarthritis

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Julia Steinberg ◽  
Lorraine Southam ◽  
Theodoros I. Roumeliotis ◽  
Matthew J. Clark ◽  
Raveen L. Jayasuriya ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Molecular Basis ◽  
Functional Disability ◽  
Cell Types ◽  
Molecular Signature ◽  
Tissue Type ◽  
Effector Genes ◽  
Clinical Challenge ◽  
Trait Locus

AbstractOsteoarthritis causes pain and functional disability for over 500 million people worldwide. To develop disease-stratifying tools and modifying therapies, we need a better understanding of the molecular basis of the disease in relevant tissue and cell types. Here, we study primary cartilage and synovium from 115 patients with osteoarthritis to construct a deep molecular signature map of the disease. By integrating genetics with transcriptomics and proteomics, we discover molecular trait loci in each tissue type and omics level, identify likely effector genes for osteoarthritis-associated genetic signals and highlight high-value targets for drug development and repurposing. These findings provide insights into disease aetiopathology, and offer translational opportunities in response to the global clinical challenge of osteoarthritis.

scholarly journals A quantitative trait locus, qSE 3 , promotes seed germination and seedling establishment under salinity stress in rice

2019 ◽  
Vol 97 (6) ◽  
pp. 1089-1104 ◽  
Author(s):  
Yongqi He ◽  
Bin Yang ◽  
Ying He ◽  
Chengfang Zhan ◽  
Yanhao Cheng ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Seed Germination ◽  
Salinity Stress ◽  
Seedling Establishment ◽  
Quantitative Trait ◽  
Trait Locus

Quantitative Trait Locus (QTL) Mapping Using Different Testers and Independent Population Samples in Maize Reveals Low Power of QTL Detection and Large Bias in Estimates of QTL Effects

Genetics ◽  
1998 ◽  
Vol 149 (1) ◽  
pp. 383-403 ◽  
Author(s):  
Albrecht E Melchinger ◽  
H Friedrich Utz ◽  
Chris C Schön
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Unbiased Estimation ◽  
Rflp Markers ◽  
Qtl Detection ◽  
Epistatic Interactions ◽  
Independent Population ◽  
Calibration Sample ◽  
Trait Locus ◽  
Qtl Effects

AbstractThe efficiency of marker-assisted selection (MAS) depends on the power of quantitative trait locus (QTL) detection and unbiased estimation of QTL effects. Two independent samples (N = 344 and 107) of F2 plants were genotyped for 89 RFLP markers. For each sample, testcross (TC) progenies of the corresponding F3 lines with two testers were evaluated in four environments. QTL for grain yield and other agronomically important traits were mapped in both samples. QTL effects were estimated from the same data as used for detection and mapping of QTL (calibration) and, based on QTL positions from calibration, from the second, independent sample (validation). For all traits and both testers we detected a total of 107 QTL with N = 344, and 39 QTL with N = 107, of which only 20 were in common. Consistency of QTL effects across testers was in agreement with corresponding genotypic correlations between the two TC series. Most QTL displayed no significant QTL × environment nor epistatic interactions. Estimates of the proportion of the phenotypic and genetic variance explained by QTL were considerably reduced when derived from the independent validation sample as opposed to estimates from the calibration sample. We conclude that, unless QTL effects are estimated from an independent sample, they can be inflated, resulting in an overly optimistic assessment of the efficiency of MAS.

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