scholarly journals A major quantitative trait locus for cold-responsive gene expression is linked to frost-resistance gene Fr-A2 in common wheat

2013 ◽  
Vol 63 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Yoichi Motomura ◽  
Fuminori Kobayashi ◽  
Julio C. M. Iehisa ◽  
Shigeo Takumi
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Resistance Gene ◽  
Common Wheat ◽  
Quantitative Trait ◽  
Frost Resistance ◽  
Major Quantitative Trait Locus ◽  
Responsive Gene ◽  
Trait Locus

Further genetic analysis of a major quantitative trait locus controlling root length and related traits in common wheat

2014 ◽  
Vol 33 (4) ◽  
pp. 975-985 ◽  
Author(s):  
Pei Cao ◽  
Yongzhe Ren ◽  
Kunpu Zhang ◽  
Wan Teng ◽  
Xueqiang Zhao ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Genetic Analysis ◽  
Common Wheat ◽  
Quantitative Trait ◽  
Root Length ◽  
Major Quantitative Trait Locus ◽  
Trait Locus

scholarly journals Inheritance and Identification of a Major Quantitative Trait Locus (QTL) that Confers Resistance to Meloidogyne incognita and a Novel QTL for Plant Height in Sweet Sorghum

2015 ◽  
Vol 105 (12) ◽  
pp. 1522-1528 ◽  
Author(s):  
Karen R. Harris-Shultz ◽  
Richard F. Davis ◽  
Joseph E. Knoll ◽  
William Anderson ◽  
Hongliang Wang
Keyword(s):  
Quantitative Trait Locus ◽  
Resistance Gene ◽  
Meloidogyne Incognita ◽  
Quantitative Trait ◽  
Sweet Sorghum ◽  
Major Quantitative Trait Locus ◽  
Interval Mapping ◽  
Chromosome 3 ◽  
Trait Locus ◽  
Stalk Weight

Southern root-knot nematodes (Meloidogyne incognita) are a pest on many economically important row crop and vegetable species and management relies on chemicals, plant resistance, and cultural practices such as crop rotation. Little is known about the inheritance of resistance to M. incognita or the genomic regions associated with resistance in sorghum (Sorghum bicolor). In this study, an F2 population (n = 130) was developed between the resistant sweet sorghum cultivar ‘Honey Drip’ and the susceptible sweet cultivar ‘Collier’. Each F2 plant was phenotyped for stalk weight, height, juice Brix, root weight, total eggs, and eggs per gram of root. Strong correlations were observed between eggs per gram of root and total eggs, height and stalk weight, and between two measurements of Brix. Genotyping-by-sequencing was used to generate single nucleotide polymorphism markers. The G-Model, single marker analysis, interval mapping, and composite interval mapping were used to identify a major quantitative trait locus (QTL) on chromosome 3 for total eggs and eggs per gram of root. Furthermore, a new QTL for plant height was also discovered on chromosome 3. Simple sequence repeat markers were developed in the total eggs and eggs per gram of root QTL region and the markers flanking the resistance gene are 4.7 and 2.4 cM away. These markers can be utilized to move the southern root-knot nematode resistance gene from Honey Drip to any sorghum line.

scholarly journals Optimizing expression quantitative trait locus mapping workflows for single-cell studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Anna S. E. Cuomo ◽  
Giordano Alvari ◽  
Christina B. Azodi ◽  
Davis J. McCarthy ◽  
Marc Jan Bonder ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait Locus ◽  
Single Cell ◽  
Quantitative Trait ◽  
Cell Types ◽  
Expression Quantitative Trait Locus ◽  
Eqtl Mapping ◽  
Trait Locus ◽  
The Impact

Abstract Background Single-cell RNA sequencing (scRNA-seq) has enabled the unbiased, high-throughput quantification of gene expression specific to cell types and states. With the cost of scRNA-seq decreasing and techniques for sample multiplexing improving, population-scale scRNA-seq, and thus single-cell expression quantitative trait locus (sc-eQTL) mapping, is increasingly feasible. Mapping of sc-eQTL provides additional resolution to study the regulatory role of common genetic variants on gene expression across a plethora of cell types and states and promises to improve our understanding of genetic regulation across tissues in both health and disease. Results While previously established methods for bulk eQTL mapping can, in principle, be applied to sc-eQTL mapping, there are a number of open questions about how best to process scRNA-seq data and adapt bulk methods to optimize sc-eQTL mapping. Here, we evaluate the role of different normalization and aggregation strategies, covariate adjustment techniques, and multiple testing correction methods to establish best practice guidelines. We use both real and simulated datasets across single-cell technologies to systematically assess the impact of these different statistical approaches. Conclusion We provide recommendations for future single-cell eQTL studies that can yield up to twice as many eQTL discoveries as default approaches ported from bulk studies.

scholarly journals Identification of A Major Quantitative Trait Locus for Grain Weight In Rice Using Microsatellite Marker

2017 ◽  
Vol 21 (4) ◽  
Author(s):  
Mariana Susilowati ◽  
Hajrial Aswidinnoor ◽  
Wening Enggarini ◽  
Kurniawan Rudi Trijatmiko
Keyword(s):  
Quantitative Trait Locus ◽  
Microsatellite Marker ◽  
Quantitative Trait ◽  
Major Quantitative Trait Locus ◽  
Grain Weight ◽  
Trait Locus

Mapping and validation of a major quantitative trait locus qRN5a associated with increasing root number under low potassium in rice

2020 ◽  
Vol 90 (3) ◽  
pp. 519-528 ◽  
Author(s):  
Anowerul Islam ◽  
Yingxin Zhang ◽  
Galal Anis ◽  
M. H. Rani ◽  
Workie Anley ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Major Quantitative Trait Locus ◽  
Root Number ◽  
Low Potassium ◽  
Trait Locus

A major quantitative trait locus determining serum leptin levels and fat mass is located on human chromosome 2

1997 ◽  
Vol 15 (3) ◽  
pp. 273-276 ◽  
Author(s):  
Anthony G. Comuzzie ◽  
James E. Hixson ◽  
Laura Almasy ◽  
Braxton D. Mitchell ◽  
Michael C. Mahaney ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Human Chromosome ◽  
Fat Mass ◽  
Quantitative Trait ◽  
Major Quantitative Trait Locus ◽  
Chromosome 2 ◽  
Serum Leptin ◽  
Trait Locus
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