scholarly journals Quantitative Trait Loci (QTL)-Guided Metabolic Engineering of a Complex Trait

2016 ◽  
Vol 6 (3) ◽  
pp. 566-581 ◽  
Author(s):  
Matthew J. Maurer ◽  
Lawrence Sutardja ◽  
Dominic Pinel ◽  
Stefan Bauer ◽  
Amanda L. Muehlbauer ◽  
...  
Genetics ◽  
2009 ◽  
Vol 182 (3) ◽  
pp. 851-861 ◽  
Author(s):  
Jiaqin Shi ◽  
Ruiyuan Li ◽  
Dan Qiu ◽  
Congcong Jiang ◽  
Yan Long ◽  
...  

2021 ◽  
Vol 12 ◽  
Author(s):  
Jia Zhao ◽  
Yongqi He ◽  
Shuilai Huang ◽  
Zhoufei Wang

Seed vigor is a complex trait, including the seed germination, seedling emergence, and growth, as well as seed storability and stress tolerance, which is important for direct seeding in rice. Seed vigor is established during seed development, and its level is decreased during seed storage. Seed vigor is influenced by genetic and environmental factors during seed development, storage, and germination stages. A lot of factors, such as nutrient reserves, seed dying, seed dormancy, seed deterioration, stress conditions, and seed treatments, will influence seed vigor during seed development to germination stages. This review highlights the current advances on the identification of quantitative trait loci (QTLs) and regulatory genes involved in seed vigor at seed development, storage, and germination stages in rice. These identified QTLs and regulatory genes will contribute to the improvement of seed vigor by breeding, biotechnological, and treatment approaches.


Plant Disease ◽  
2014 ◽  
Vol 98 (7) ◽  
pp. 957-964 ◽  
Author(s):  
S. M. Zuo ◽  
Y. J. Zhu ◽  
Y. J. Yin ◽  
H. Wang ◽  
Y. F. Zhang ◽  
...  

Sheath blight (SB), caused by Rhizoctonia solani, is one of the worst rice (Orzya sativa) diseases worldwide. Resistance to the SB disease in rice is a complex trait controlled by quantitative trait loci (QTLs). Through map integration, we found several previously identified SB resistance (SBR) QTLs reported in inconsistent regions on the long arm of chromosome 9. Five of them were detected on ‘Jasmine 85’ (J85), ‘Minghui 63’ (MH63), and ‘Lemont’ (LMNT) rice and were designated qSB-9J85-1, qSB-9J85-2, qSB-9MH63-1, qSB-9MH63-2, and qSB-9LMNT, respectively, in the present study. To further verify and physically map the five potential SBR QTLs, we introduced these SBR QTLs into a common susceptible variety (LMNT) and developed a few chromosomal segment substitution lines through marker-assisted selection. After artificial inoculation with the SB fungus, we were able to validate qSB-9J85-2 but not the other four SBR QTLs; whereas, on MH63, an SBR QTL designated qSB-9MH63-3 was confirmed in the region defined by markers Y83 and Y91.8 that included qSB-9J85-2, covering approximately 1,235 kb. Both qSB-9J85-2 and qSB-9MH63-3 appeared to be dominant resistance genes and contributed to similar levels to SB resistance, reducing SB disease severity by approximately 1.0 on a 0-to-9 SB disease rating system. After comparing with another confirmed SBR QTL (qSB-9TQ) from ‘Teqing’ rice (TQ), we conclude that qSB-9J85-2, qSB-9MH63-3, and qSB-9TQ are probably controlled by the same allelic resistance genes. These results will accelerate the utilization of this major SBR QTL and its map-based cloning.


2003 ◽  
Vol 5 (3-4) ◽  
pp. 227-234 ◽  
Author(s):  
Yun Zhu ◽  
Wei Hou ◽  
Rongling Wu

The dynamic change of human immunodeficiency virus type-1 (HIV-1) particles that cause AIDS displays considerable variation from patients to patients. It is likely that such variation in HIV-1 pathogenesis is correlated with the genetic architecture of hosts. Traditional genetic analysis of HIV-1 infection is based on various biochemical approaches, but it has been little successful because HIV-1 dynamics, as a complex trait, is under polygenic control and sensitive to environmental changes. Here, we present a novel model for integrating mathematical functions for HIV-1 dynamics that have been well constructed into a multivariate mixture model for genetic mapping. This integrative mapping model on the foundation of linkage disequilibrium (LD)-based haplotype block analysis provides unique power to precisely detect human quantitative trait loci (QTL) determining HIV-1 dynamics and facilitates positional cloning of target QTL. The model allows for a number of hypothesis tests for the effects of the dynamic QTL on the virion clearance rate, the infected cell life-span and the average viral generation timein vivo, all of which provide theoretical principles to guide the development of efficient gene therapy strategies.


Author(s):  
Emilien Peltier ◽  
Sabrina Bibi-Triki ◽  
Fabien Dutreux ◽  
Claudia Caradec ◽  
Anne Friedrich ◽  
...  

Abstract Dissecting the genetic basis of complex trait remains a real challenge. The budding yeast Saccharomyces cerevisiae has become a model organism for studying quantitative traits, successfully increasing our knowledge in many aspects. However, the exploration of the genotype-phenotype relationship in non-model yeast species could provide a deeper insight into the genetic basis of complex traits. Here, we have studied this relationship in the Lachancea waltii species which diverged from the S. cerevisiae lineage prior to the whole-genome duplication. By performing linkage mapping analyses in this species, we identified 86 quantitative trait loci (QTL) impacting the growth in a large number of conditions. The distribution of these loci across the genome has revealed two major QTL hotspots. A first hotspot corresponds to a general growth QTL, impacting a wide range of conditions. By contrast, the second hotspot highlighted a trade-off with a disadvantageous allele for drug-free conditions which proved to be advantageous in the presence of several drugs. Finally, a comparison of the detected QTL in L. waltii with those which had been previously identified for the same trait in a closely related species, Lachancea kluyveri was performed. This analysis clearly showed the absence of shared QTL across these species. Altogether, our results represent a first step toward the exploration of the genetic architecture of quantitative trait across different yeast species.


2012 ◽  
Vol 50 (08) ◽  
Author(s):  
R Hall ◽  
R Müllenbach ◽  
S Huss ◽  
R Alberts ◽  
K Schughart ◽  
...  

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