scholarly journals A Major and Stable Quantitative Trait Locus qSS2 for Seed Size and Shape Traits in a Soybean RIL Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Giriraj Kumawat ◽  
Donghe Xu
Keyword(s):  
Quantitative Trait Locus ◽  
Seed Size ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Seed Shape ◽  
Significant Qtls ◽  
Size And Shape ◽  
Growing Seasons ◽  
Ril Population ◽  
Trait Locus

Seed size and shape traits are important determinants of seed yield and appearance quality in soybean [Glycine max (L.) Merr.]. Understanding the genetic architecture of these traits is important to enable their genetic improvement through efficient and targeted selection in soybean breeding, and for the identification of underlying causal genes. To map seed size and shape traits in soybean, a recombinant inbred line (RIL) population developed from K099 (small seed size) × Fendou 16 (large seed size), was phenotyped in three growing seasons. A genetic map of the RIL population was developed using 1,485 genotyping by random amplicon sequencing-direct (GRAS-Di) and 177 SSR markers. Quantitative trait locus (QTL) mapping was conducted by inclusive composite interval mapping. As a result, 53 significant QTLs for seed size traits and 27 significant QTLs for seed shape traits were identified. Six of these QTLs (qSW8.1, qSW16.1, qSLW2.1, qSLT2.1, qSWT1.2, and qSWT4.3) were identified with LOD scores of 3.80–14.0 and R2 of 2.36%–39.49% in at least two growing seasons. Among the above significant QTLs, 24 QTLs were grouped into 11 QTL clusters, such as, three major QTLs (qSL2.3, qSLW2.1, and qSLT2.1) were clustered into a major QTL on Chr.02, named as qSS2. The effect of qSS2 was validated in a pair of near isogenic lines, and its candidate genes (Glyma.02G269400, Glyma.02G272100, Glyma.02G274900, Glyma.02G277200, and Glyma.02G277600) were mined. The results of this study will assist in the breeding programs aiming at improvement of seed size and shape traits in soybean.

scholarly journals Genetic Mapping with Background Control for Quantitative Trait Locus (QTL) in 8-Parental Pure-Line Populations

2019 ◽  
Vol 110 (7) ◽  
pp. 880-891 ◽  
Author(s):  
Jinhui Shi ◽  
Jiankang Wang ◽  
Luyan Zhang
Keyword(s):  
Quantitative Trait Locus ◽  
Genetic Variation ◽  
Linear Model ◽  
Quantitative Trait ◽  
Genetic Model ◽  
Pure Line ◽  
Interval Mapping ◽  
Unbiased Estimation ◽  
Trait Locus ◽  
Marker Variables

Abstract Multiparental advanced generation intercross (MAGIC) populations provide abundant genetic variation for use in plant genetics and breeding. In this study, we developed a method for quantitative trait locus (QTL) detection in pure-line populations derived from 8-way crosses, based on the principles of inclusive composite interval mapping (ICIM). We considered 8 parents carrying different alleles with different effects. To estimate the 8 genotypic effects, 1-locus genetic model was first built. Then, an orthogonal linear model of phenotypes against marker variables was established to explain genetic effects of the locus. The linear model was estimated by stepwise regression and finally used for phenotype adjustment and background genetic variation control in QTL mapping. Simulation studies using 3 genetic models demonstrated that the proposed method had higher detection power, lower false discovery rate (FDR), and unbiased estimation of QTL locations compared with other methods. Marginal bias was observed in the estimation of QTL effects. An 8-parental recombinant inbred line (RIL) population previously reported in cowpea and analyzed by interval mapping (IM) was reanalyzed by ICIM and genome-wide association mapping implemented in software FarmCPU. The results indicated that ICIM identified more QTLs explaining more phenotypic variation than did IM; ICIM provided more information on the detected QTL than did FarmCPU; and most QTLs identified by IM and FarmCPU were also detected by ICIM.

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 Analysis of morphine responses in mice reveals a QTL on Chromosome 7

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2156 ◽  
Author(s):  
Wim E. Crusio ◽  
Esha Dhawan ◽  
Elissa J. Chesler ◽  
Anna Delprato
Keyword(s):  
Quantitative Trait Locus ◽  
Locomotor Activity ◽  
Quantitative Trait ◽  
Interval Mapping ◽  
Chromosome 7 ◽  
Defensive Response ◽  
Morphine Treatment ◽  
Positional Candidate ◽  
Neural Processes ◽  
Trait Locus

In this study we identified a quantitative trait locus (QTL) on mouse Chromosome 7 associated with locomotor activity and rearing post morphine treatment. This QTL was revealed after correcting for the effects of another QTL peak on Chromosome 10 using composite interval mapping. The positional candidate genes are Syt9 and Ppfibp2. Several other genes within the interval are linked to neural processes, locomotor activity, and the defensive response to harmful stimuli.

scholarly journals Verification and dissection of one quantitative trait locus for grain size and weight on chromosome 1 in rice

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yi-chen Cheng ◽  
Guan Li ◽  
Man Yin ◽  
Tosin Victor Adegoke ◽  
Yi-feng Wang ◽  
...  
Keyword(s):  
Grain Size ◽  
Quantitative Trait ◽  
Genetic Background ◽  
Molecular Design ◽  
Chromosome 1 ◽  
Recurrent Parent ◽  
Isogenic Line ◽  
Growing Seasons ◽  
Ril Population ◽  
Trait Locus

AbstractGrain size and weight are the key traits determining rice quality and yield and are mainly controlled by quantitative trait loci (QTL). In this study, one minor QTL that was previously mapped in the marker interval of JD1009-JD1019 using the Huanghuazhan/Jizi1560 (HHZ/JZ1560) recombinant inbred line (RIL) population, qTGW1-2, was validated to regulate grain size and weight across four rice-growing seasons using twenty-one near isogenic line (NIL)-F2 populations. The twenty-one populations were in two types of genetic background that were derived from the same parents HHZ and JZ1560. Twelve F9, F10 or F11 NIL-F2 populations with the sequential residual heterozygous regions covering JD1009-RM6840 were developed from one residual heterozygote (RH) in the HHZ/JZ1560 RIL population, and the remaining nine BC3F3, BC3F4 or BC3F5 NIL-F2 populations with the sequential residual heterozygous regions covering JD1009-RM6840 were constructed through consecutive backcrosses to the recurrent parent HHZ followed with marker assistant selection in each generation. Based on the QTL analysis of these genetic populations, qTGW1-2 was successfully confirmed to control grain length, width and weight and further dissected into two QTLs, qTGW1-2a and qTGW1-2b, which were respectively narrowed down to the marker intervals of JD1139-JD1127 (~ 978.2-kb) and JD1121-JD1102 (~ 54.8-kb). Furthermore, the two types of NIL-F2 populations were proved to be able to decrease the genetic background noise and increase the detection power of minor QTL. These results provided an important basis for further map-based cloning and molecular design breeding with the two QTLs in rice.

scholarly journals Genetic Mapping by Bulk Segregant Analysis in Drosophila: Experimental Design and Simulation-Based Inference

2016 ◽  
Author(s):  
John E Pool
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Bulk Segregant Analysis ◽  
Experimental Population ◽  
Modern Biology ◽  
Significant Qtls ◽  
Trait Mapping ◽  
Wide Range ◽  
Simulation Based ◽  
Trait Locus

AbstractIdentifying the genomic regions that underlie complex phenotypic variation is a key challenge in modern biology. Many approaches to quantitative trait locus mapping in animal and plant species suffer from limited power and genomic resolution. Here, I investigate whether bulk segregant analysis (BSA), which has been successfully applied for yeast, may have utility in the genomic era for trait mapping in Drosophila (and other organisms that can be experimentally bred in similar numbers). I perform simulations to investigate the statistical signal of a quantitative trait locus (QTL) in a wide range of BSA and introgression mapping (IM) experiments. BSA consistently provides more accurate mapping signals than IM (in addition to allowing the mapping of multiple traits from the same experimental population). The performance of BSA and IM is maximized by having multiple independent crosses, more generations of interbreeding, larger numbers of breeding individuals, and greater genotyping effort, but is less affected by the proportion of individuals selected for phenotypic extreme pools. I also introduce a prototype analysis method for Simulation-based Inference for BSA Mapping (SIBSAM). This method identifies significant QTLs and estimates their genomic confidence intervals and relative effect sizes. Importantly, it also tests whether overlapping peaks should be considered as two distinct QTLs. This approach will facilitate improved trait mapping in Drosophila and other species for which hundreds or thousands of offspring (but not millions) can be studied.

Quantitative Trait Loci on Chromosomes 10 and 11 Influencing Mandible Size of SMXA RI Mouse Strains

2002 ◽  
Vol 81 (7) ◽  
pp. 501-504 ◽  
Author(s):  
A. Dohmoto ◽  
K. Shimizu ◽  
Y. Asada ◽  
T. Maeda
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Major Gene ◽  
Recombinant Inbred Strain ◽  
Mouse Strains ◽  
Chromosome 11 ◽  
Significant Qtls ◽  
Mandible Length ◽  
Trait Locus ◽  
Mandible Size

Predicting the mandible size before the termination of growth of the maxillofacial bones is essential in pedodontics as well as for the predictions needed for genetic analysis. Here, Quantitative Trait Locus (QTL) analysis was used to detect the chromosomal regions responsible for the mandible length between the menton and gonion in an SMXA recombinant inbred strain of mice. Around the region 60 cM from the centromere in chromosome 10, the logarithm of the odds score showed a higher than suggestive level. Around the regions 13 cM and 16 cM in chromosome 11, two significant QTLs were detected. Analysis of genotypes from loci corresponding to those QTLs revealed a large mandible when the region between the markers Hba and D11Mit163 and D10Mit70 and D10Mit136 indicated the genotype from the A/J and SM/J alleles, respectively. These results suggest that the major gene(s) responsible for mandible length are located in these regions.

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