scholarly journals CREBBP and WDR 24 Affects Quantitative Variation in Red Colouration in the Chicken

2018 ◽  
Author(s):  
J. Fogelholm ◽  
R. Henriksen ◽  
A. Höglund ◽  
N. Huq ◽  
M. Johnsson ◽  
...  
Keyword(s):  
Qtl Mapping ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Species Recognition ◽  
Major Effect ◽  
Quantitative Variation ◽  
Wild Populations ◽  
Effect Modifier ◽  
Colour Variation ◽  
Plumage Colouration

AbstractPlumage colouration in birds is important for a plethora of reasons, ranging from camouflage, sexual signaling, and species recognition. The genes underlying colour variation have been vital in understanding how genes can affect a phenotype. Multiple genes have been identified that affect plumage variation, but research has principally focused on major-effect genes (such as those causing albinism, barring, and the like), rather than the smaller effect modifier loci that more subtly influence colour. By utilizing a domestic x wild advanced intercross with a combination of classical QTL mapping of red colouration as a quantitative trait and a targeted genetical genomics approach, we have identified five separate candidate genes (CREBBP, WDR24, ARL8A, PHLDA3, LAD1) that putatively influence quantitative variation in red colouration in chickens. Such small effect loci are potentially far more prevalent in wild populations, and can therefore potentially be highly relevant to colour evolution.

scholarly journals CREBBP and WDR 24 Identified as Candidate Genes for Quantitative Variation in Red-Brown Plumage Colouration in the Chicken

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
J. Fogelholm ◽  
R. Henriksen ◽  
A. Höglund ◽  
N. Huq ◽  
M. Johnsson ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Species Recognition ◽  
Major Effect ◽  
Quantitative Variation ◽  
Wild Populations ◽  
Effect Modifier ◽  
Qualitative Trait ◽  
Colour Variation ◽  
Plumage Colouration ◽  
Sexual Signalling

AbstractPlumage colouration in birds is important for a plethora of reasons, ranging from camouflage, sexual signalling, and species recognition. The genes underlying colour variation have been vital in understanding how genes can affect a phenotype. Multiple genes have been identified that affect plumage variation, but research has principally focused on major-effect genes (such as those causing albinism, barring, and the like), rather than the smaller effect modifier loci that more subtly influence colour. By utilising a domestic × wild advanced intercross with a combination of classical QTL mapping of red colouration as a quantitative trait and a targeted genetical genomics approach, we have identified five separate candidate genes (CREBBP, WDR24, ARL8A, PHLDA3, LAD1) that putatively influence quantitative variation in red-brown colouration in chickens. By treating colour as a quantitative rather than qualitative trait, we have identified both QTL and genes of small effect. Such small effect loci are potentially far more prevalent in wild populations, and can therefore potentially be highly relevant to colour evolution.

scholarly journals Quantitative Trait Loci for Floral Morphology in Arabidopsis thaliana

Genetics ◽  
2000 ◽  
Vol 156 (3) ◽  
pp. 1379-1392 ◽  
Author(s):  
Thomas Juenger ◽  
Michael Purugganan ◽  
Trudy F C Mackay
Keyword(s):  
Arabidopsis Thaliana ◽  
Genetic Variation ◽  
Qtl Mapping ◽  
Candidate Genes ◽  
Inbred Line ◽  
Recombinant Inbred Line Population ◽  
Quantitative Trait ◽  
Recombinant Inbred ◽  
Genetic Basis ◽  
Genome Wide

Abstract A central question in biology is how genes control the expression of quantitative variation. We used statistical methods to estimate genetic variation in eight Arabidopsis thaliana floral characters (fresh flower mass, petal length, petal width, sepal length, sepal width, long stamen length, short stamen length, and pistil length) in a cosmopolitan sample of 15 ecotypes. In addition, we used genome-wide quantitative trait locus (QTL) mapping to evaluate the genetic basis of variation in these same traits in the Landsberg erecta × Columbia recombinant inbred line population. There was significant genetic variation for all traits in both the sample of naturally occurring ecotypes and in the Ler × Col recombinant inbred line population. In addition, broad-sense genetic correlations among the traits were positive and high. A composite interval mapping (CIM) analysis detected 18 significant QTL affecting at least one floral character. Eleven QTL were associated with several floral traits, supporting either pleiotropy or tight linkage as major determinants of flower morphological integration. We propose several candidate genes that may underlie these QTL on the basis of positional information and functional arguments. Genome-wide QTL mapping is a promising tool for the discovery of candidate genes controlling morphological development, the detection of novel phenotypic effects for known genes, and in generating a more complete understanding of the genetic basis of floral development.

scholarly journals Identification of blossom-end rot loci using joint QTL-seq and linkage-based QTL mapping in tomato

2021 ◽  
Author(s):  
Yasin Topcu ◽  
Manoj Sapkota ◽  
Eudald Illa-Berenguer ◽  
Savithri U. Nambeesan ◽  
Esther van der Knaap
Keyword(s):  
Cytochrome P450 ◽  
Environmental Factors ◽  
Qtl Mapping ◽  
Candidate Genes ◽  
Quantitative Trait ◽  
Genetic Basis ◽  
Fruit Weight ◽  
Physiological Disorder ◽  
Support Interval ◽  
Blossom End Rot

Abstract Key message Blossom-End Rot is Quantitatively Inherited and Maps to Four Loci in Tomato. Abstract Blossom-end rot (BER) is a devastating physiological disorder that affects tomato and other vegetables, resulting in significant crop losses. To date, most studies on BER have focused on the environmental factors that affect calcium translocation to the fruit; however, the genetic basis of this disorder remains unknown. To investigate the genetic basis of BER, two F2 and F3:4 populations along with a BC1 population that segregated for BER occurrence were evaluated in the greenhouse. Using the QTL-seq approach, quantitative trait loci (QTL) associated with BER Incidence were identified at the bottom of chromosome (ch) 3 and ch11. Additionally, linkage-based QTL mapping detected another QTL, BER3.1, on ch3 and BER4.1 on ch4. To fine map the QTLs identified by QTL-seq, recombinant screening was performed. BER3.2, the major BER QTL on ch3, was narrowed down from 5.68 to 1.58 Mbp with a 1.5-LOD support interval (SI) corresponding to 209 candidate genes. BER3.2 colocalizes with the fruit weight gene FW3.2/SlKLUH, an ortholog of cytochrome P450 KLUH in Arabidopsis. Further, BER11.1, the major BER QTL on ch11, was narrowed down from 3.99 to 1.13 Mbp with a 1.5-LOD SI interval comprising of 141 candidate genes. Taken together, our results identified and fine mapped the first loci for BER resistance in tomato that will facilitate marker-assistant breeding not only in tomato but also in many other vegetables suffering for BER.

scholarly journals On the Differences Between Maximum Likelihood and Regression Interval Mapping in the Analysis of Quantitative Trait Loci

Genetics ◽  
2000 ◽  
Vol 156 (2) ◽  
pp. 855-865 ◽  
Author(s):  
Chen-Hung Kao
Keyword(s):  
Quantitative Trait Loci ◽  
Maximum Likelihood ◽  
Qtl Mapping ◽  
Quantitative Trait ◽  
Mean Squared Error ◽  
Interval Mapping ◽  
Size Difference ◽  
Ratio Test ◽  
Trait Loci ◽  
Variance Explained

AbstractThe differences between maximum-likelihood (ML) and regression (REG) interval mapping in the analysis of quantitative trait loci (QTL) are investigated analytically and numerically by simulation. The analytical investigation is based on the comparison of the solution sets of the ML and REG methods in the estimation of QTL parameters. Their differences are found to relate to the similarity between the conditional posterior and conditional probabilities of QTL genotypes and depend on several factors, such as the proportion of variance explained by QTL, relative QTL position in an interval, interval size, difference between the sizes of QTL, epistasis, and linkage between QTL. The differences in mean squared error (MSE) of the estimates, likelihood-ratio test (LRT) statistics in testing parameters, and power of QTL detection between the two methods become larger as (1) the proportion of variance explained by QTL becomes higher, (2) the QTL locations are positioned toward the middle of intervals, (3) the QTL are located in wider marker intervals, (4) epistasis between QTL is stronger, (5) the difference between QTL effects becomes larger, and (6) the positions of QTL get closer in QTL mapping. The REG method is biased in the estimation of the proportion of variance explained by QTL, and it may have a serious problem in detecting closely linked QTL when compared to the ML method. In general, the differences between the two methods may be minor, but can be significant when QTL interact or are closely linked. The ML method tends to be more powerful and to give estimates with smaller MSEs and larger LRT statistics. This implies that ML interval mapping can be more accurate, precise, and powerful than REG interval mapping. The REG method is faster in computation, especially when the number of QTL considered in the model is large. Recognizing the factors affecting the differences between REG and ML interval mapping can help an efficient strategy, using both methods in QTL mapping to be outlined.

scholarly journals Transcriptomic profiling of wheat near-isogenic lines reveals candidate genes on chromosome 3A for pre-harvest sprouting resistance

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xingyi Wang ◽  
Hui Liu ◽  
Kadambot H. M. Siddique ◽  
Guijun Yan
Keyword(s):  
Candidate Genes ◽  
Quantitative Trait ◽  
Genomic Region ◽  
Nucleotide Polymorphisms ◽  
Near Isogenic Lines ◽  
Gene Effects ◽  
Isogenic Lines ◽  
Trait Locus ◽  
Grain Yield And Quality ◽  
Sprouting Resistance

Abstract Background Pre-harvest sprouting (PHS) in wheat can cause severe damage to both grain yield and quality. Resistance to PHS is a quantitative trait controlled by many genes located across all 21 wheat chromosomes. The study targeted a large-effect quantitative trait locus (QTL) QPhs.ccsu-3A.1 for PHS resistance using several sets previously developed near-isogenic lines (NILs). Two pairs of NILs with highly significant phenotypic differences between the isolines were examined by RNA sequencing for their transcriptomic profiles on developing seeds at 15, 25 and 35 days after pollination (DAP) to identify candidate genes underlying the QTL and elucidate gene effects on PHS resistance. At each DAP, differentially expressed genes (DEGs) between the isolines were investigated. Results Gene ontology and KEGG pathway enrichment analyses of key DEGs suggested that six candidate genes underlie QPhs.ccsu-3A.1 responsible for PHS resistance in wheat. Candidate gene expression was further validated by quantitative RT-PCR. Within the targeted QTL interval, 16 genetic variants including five single nucleotide polymorphisms (SNPs) and 11 indels showed consistent polymorphism between resistant and susceptible isolines. Conclusions The targeted QTL is confirmed to harbor core genes related to hormone signaling pathways that can be exploited as a key genomic region for marker-assisted selection. The candidate genes and SNP/indel markers detected in this study are valuable resources for understanding the mechanism of PHS resistance and for marker-assisted breeding of the trait in wheat.

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