scholarly journals Linkage mapping of quantitative trait loci for fiber yield and its related traits in the population derived from cultivated ramie and wild B. nivea var. tenacissima

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Zheng Zeng ◽  
Yanzhou Wang ◽  
Chan Liu ◽  
Xiufeng Yang ◽  
Hengyun Wang ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Linkage Mapping ◽  
Genetic Basis ◽  
Natural Fiber ◽  
Stem Bark ◽  
High Yield ◽  
Fiber Yield ◽  
Wild Allele ◽  
Trait Locus ◽  
Genomic Regions

AbstractRamie is an important natural fiber crop, and the fiber yield and its related traits are the most valuable traits in ramie production. However, the genetic basis for these traits is still poorly understood, which has dramatically hindered the breeding of high yield in this fiber crop. Herein, a high-density genetic map with 6,433 markers spanning 2476.5 cM was constructed using a population derived from two parents, cultivated ramie Zhongsizhu 1 (ZSZ1) and its wild progenitor B. nivea var. tenacissima (BNT). The fiber yield (FY) and its four related traits—stem diameter (SD) and length (SL), stem bark weight (BW) and thickness (BT)—were performed for quantitative trait locus (QTL) analysis, resulting in a total of 47 QTLs identified. Forty QTLs were mapped into 12 genomic regions, thus forming 12 QTL clusters. Among 47 QTLs, there were 14 QTLs whose wild allele from BNT was beneficial. Interestingly, all QTLs in Cluster 10 displayed overdominance, indicating that the region of this cluster was likely heterotic loci. In addition, four fiber yield-related genes underwent positive selection were found either to fall into the FY-related QTL regions or to be near to the identified QTLs. The dissection of FY and FY-related traits not only improved our understanding to the genetic basis of these traits, but also provided new insights into the domestication of FY in ramie. The identification of many QTLs and the discovery of beneficial alleles from wild species provided a basis for the improvement of yield traits in ramie breeding.

scholarly journals Epigenome-Wide Study Identified Methylation Sites Associated with the Risk of Obesity

Nutrients ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1984
Author(s):  
Majid Nikpay ◽  
Sepehr Ravati ◽  
Robert Dent ◽  
Ruth McPherson
Keyword(s):  
Quantitative Trait Locus ◽  
Quantitative Trait ◽  
Rare Variants ◽  
Genome Wide ◽  
A Genome ◽  
Genome Wide Search ◽  
Risk Snps ◽  
Trait Locus ◽  
Genomic Regions ◽  
Eqtl Data

Here, we performed a genome-wide search for methylation sites that contribute to the risk of obesity. We integrated methylation quantitative trait locus (mQTL) data with BMI GWAS information through a SNP-based multiomics approach to identify genomic regions where mQTLs for a methylation site co-localize with obesity risk SNPs. We then tested whether the identified site contributed to BMI through Mendelian randomization. We identified multiple methylation sites causally contributing to the risk of obesity. We validated these findings through a replication stage. By integrating expression quantitative trait locus (eQTL) data, we noted that lower methylation at cg21178254 site upstream of CCNL1 contributes to obesity by increasing the expression of this gene. Higher methylation at cg02814054 increases the risk of obesity by lowering the expression of MAST3, whereas lower methylation at cg06028605 contributes to obesity by decreasing the expression of SLC5A11. Finally, we noted that rare variants within 2p23.3 impact obesity by making the cg01884057 site more susceptible to methylation, which consequently lowers the expression of POMC, ADCY3 and DNAJC27. In this study, we identify methylation sites associated with the risk of obesity and reveal the mechanism whereby a number of these sites exert their effects. This study provides a framework to perform an omics-wide association study for a phenotype and to understand the mechanism whereby a rare variant causes a disease.

Genetic dissection of partial resistance to race 6 of Venturia inaequalis in apple

Genome ◽  
2003 ◽  
Vol 46 (2) ◽  
pp. 224-234 ◽  
Author(s):  
C E Durel ◽  
L Parisi ◽  
F Laurens ◽  
W E Van de Weg ◽  
R Liebhard ◽  
...  
Keyword(s):  
Resistance Gene ◽  
Malus Domestica ◽  
Quantitative Trait ◽  
Partial Resistance ◽  
Venturia Inaequalis ◽  
Genetic Dissection ◽  
Resistance Breakdown ◽  
Trait Locus ◽  
New Races ◽  
Genomic Regions

Scab, caused by the fungus Venturia inaequalis, is one of the most important diseases of apple (Malus × domestica). The major resistance gene, Vf, has been widely used in apple breeding programs, but two new races of the fungus (races 6 and 7) are able to overcome this gene. A mapped F1 progeny derived from a cross between the cultivars Prima and Fiesta has been inoculated with two monoconidial strains of race 6. These strains originated from sporulating leaves of 'Prima' and a descendant of 'Prima' that were grown in an orchard in northern Germany. 'Prima' carries the Vf resistance gene, whereas 'Fiesta' lacks Vf. A large variation in resistance and (or) susceptibility was observed among the individuals of the progeny. Several quantitative trait loci (QTLs) for resistance were identified that mapped on four genomic regions. One of them was located in the very close vicinity of the Vf resistance gene on linkage group LG-1 of the 'Prima' genetic map. This QTL is isolate specific because it was only detected with one of the two isolates. Two out of the three other genomic regions were identified with both isolates (LG-11 and LG-17). On LG-11, a QTL effect was detected in both parents. The genetic dissection of this QTL indicated a favourable intra-locus interaction between some parental alleles.Key words: Malus × domestica, partial resistance, Venturia inaequalis, resistance breakdown, quantitative trait locus.

scholarly journals Relationship between Yield Components and Partial Resistance to Lecanicillium fungicola in the Button Mushroom, Agaricus bisporus, Assessed by Quantitative Trait Locus Mapping

2012 ◽  
Vol 78 (7) ◽  
pp. 2435-2442 ◽  
Author(s):  
Marie Foulongne-Oriol ◽  
Anne Rodier ◽  
Jean-Michel Savoie
Keyword(s):  
Quantitative Trait ◽  
Wild Strain ◽  
Genomic Region ◽  
Yield Component ◽  
Phenotypic Variance ◽  
Button Mushroom ◽  
Content Type ◽  
Dry Bubble ◽  
Trait Locus ◽  
Genomic Regions

ABSTRACTDry bubble, caused byLecanicillium fungicola, is one of the most detrimental diseases affecting button mushroom cultivation. In a previous study, we demonstrated that breeding for resistance to this pathogen is quite challenging due to its quantitative inheritance. A second-generation hybrid progeny derived from an intervarietal cross between a wild strain and a commercial cultivar was characterized forL. fungicolaresistance under artificial inoculation in three independent experiments. Analysis of quantitative trait loci (QTL) was used to determine the locations, numbers, and effects of genomic regions associated with dry-bubble resistance. Four traits related to resistance were analyzed. Two to four QTL were detected per trait, depending on the experiment. Two genomic regions, on linkage group X (LGX) and LGVIII, were consistently detected in the three experiments. The genomic region on LGX was detected for three of the four variables studied. The total phenotypic variance accounted for by all QTL ranged from 19.3% to 42.1% over all traits in all experiments. For most of the QTL, the favorable allele for resistance came from the wild parent, but for some QTL, the allele that contributed to a higher level of resistance was carried by the cultivar. Comparative mapping with QTL for yield-related traits revealed five colocations between resistance and yield component loci, suggesting that the resistance results from both genetic factors and fitness expression. The consequences for mushroom breeding programs are discussed.

scholarly journals QTG-Finder: a machine-learning based algorithm to prioritize causal genes of quantitative trait loci

2018 ◽  
Author(s):  
Fan Lin ◽  
Jue Fan ◽  
Seung Y. Rhee
Keyword(s):  
Machine Learning ◽  
Quantitative Trait ◽  
Linkage Mapping ◽  
Stop Codon ◽  
Premature Stop Codon ◽  
Computational Method ◽  
Public Data ◽  
Causal Genes ◽  
Mapping Process ◽  
Trait Locus

AbstractLinkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. With an independent validation analysis, we demonstrate the models can correctly prioritize about 65% and 60% of Arabidopsis and rice causal genes when the top 20% ranked genes were considered. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.One sentence summaryWe systematically analyzed the genomic characteristics of causal genes in QTLs and developed a novel computational tool to prioritize causal genes.

scholarly journals QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice

2019 ◽  
Vol 9 (10) ◽  
pp. 3129-3138 ◽  
Author(s):  
Fan Lin ◽  
Jue Fan ◽  
Seung Y. Rhee
Keyword(s):  
Machine Learning ◽  
Quantitative Trait ◽  
Linkage Mapping ◽  
Stop Codon ◽  
Premature Stop Codon ◽  
Computational Method ◽  
Public Data ◽  
Causal Genes ◽  
Mapping Process ◽  
Trait Locus

Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. An independent validation analysis showed that the models could recall about 64% of Arabidopsis and 79% of rice causal genes when the top 20% ranked genes were considered. The top 20% ranked genes can range from 10 to 100 genes, depending on the size of a QTL. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.

scholarly journals Mapping Quantitative Trait Loci for 1000-Grain Weight in a Double Haploid Population of Common Wheat

2020 ◽  
Vol 21 (11) ◽  
pp. 3960 ◽  
Author(s):  
Tao Liu ◽  
Lijun Wu ◽  
Xiaolong Gan ◽  
Wenjie Chen ◽  
Baolong Liu ◽  
...  
Keyword(s):  
Quantitative Trait ◽  
Genetic Distances ◽  
Snp Markers ◽  
Grain Weight ◽  
High Yield ◽  
Doubled Haploid Population ◽  
Phenotypic Variance ◽  
Yield Trait ◽  
Haploid Population ◽  
Trait Locus

Thousand-grain weight (TGW) is a very important yield trait of crops. In the present study, we performed quantitative trait locus (QTL) analysis of TGW in a doubled haploid population obtained from a cross between the bread wheat cultivar “Superb” and the breeding line “M321” using the wheat 55-k single-nucleotide polymorphism (SNP) genotyping assay. A genetic map containing 15,001 SNP markers spanning 2209.64 cM was constructed, and 9 QTLs were mapped to chromosomes 1A, 2D, 4B, 4D, 5A, 5D, 6A, and 6D based on analyses conducted in six experimental environments during 2015–2017. The effects of the QTLs qTgw.nwipb-4DS and qTgw.nwipb-6AL were shown to be strong and stable in different environments, explaining 15.31–32.43% and 21.34–29.46% of the observed phenotypic variance, and they were mapped within genetic distances of 2.609 cM and 5.256 cM, respectively. These novel QTLs may be used in marker-assisted selection in wheat high-yield breeding.

scholarly journals Genetic analysis of albuminuria in a cross between C57BL/6J and DBA/2J mice

2007 ◽  
Vol 293 (5) ◽  
pp. F1649-F1656 ◽  
Author(s):  
Susan Sheehan ◽  
Shirng-Wern Tsaih ◽  
Benjamin L. King ◽  
Caitlin Stanton ◽  
Gary A. Churchill ◽  
...  
Keyword(s):  
Kidney Disease ◽  
Genetic Basis ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Medical Problem ◽  
Progression Of Kidney Disease ◽  
Trait Locus ◽  
Stage Renal Disease ◽  
End Stage ◽  
Genomic Regions

Chronic kidney disease (CKD) is a growing medical problem and a significant risk factor for the development of end-stage renal disease, cardiovascular disease, and cardiovascular mortality. The genetic basis of CKD is recognized, but knowledge of the specific genes that contribute to the onset and progression of kidney disease is limited, mainly because of the difficulty and expense of identifying genes underlying CKD in humans. Results from genetic studies of CKD in rodents often correspond to findings in humans; therefore, we used quantitative trait locus (QTL) analysis to detect genomic regions affecting albuminuria in a cross between C57BL/6J and DBA/2J mice, strains resistant and susceptible to CKD, respectively. We identified several independent and interacting loci affecting albuminuria, including one QTL on mouse chromosome (Chr) 2 that is concordant with QTL influencing urinary albumin excretion on rat Chr 3 and diabetic nephropathy on human Chr 20p. Because this QTL was identified in multiple mouse crosses, as well as in rats and in humans, we used comparative genomics, haplotype analysis, and expression profiling to narrow the initial QTL interval from 386 genes to 10 genes with known coding sequence polymorphisms or expression differences between the strains. These results support the continued use of multiple cross-mapping and cross-species comparisons to further our understanding of the genetic basis of kidney disease.

scholarly journals Explaining the heritability of an ecologically significant trait in terms of individual quantitative trait loci

2011 ◽  
Vol 7 (6) ◽  
pp. 896-898 ◽  
Author(s):  
Alison G. Scoville ◽  
Young Wha Lee ◽  
John H. Willis ◽  
John K. Kelly
Keyword(s):  
Qtl Mapping ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Genetic Variance ◽  
Balancing Selection ◽  
Natural Populations ◽  
Population Based ◽  
Population Variation ◽  
Trait Locus ◽  
Genomic Regions

Most natural populations display substantial genetic variation in behaviour, morphology, physiology, life history and the susceptibility to disease. A major challenge is to determine the contributions of individual loci to variation in complex traits. Quantitative trait locus (QTL) mapping has identified genomic regions affecting ecologically significant traits of many species. In nearly all cases, however, the importance of these QTLs to population variation remains unclear. In this paper, we apply a novel experimental method to parse the genetic variance of floral traits of the annual plant Mimulus guttatus into contributions of individual QTLs. We first use QTL-mapping to identify nine loci and then conduct a population-based breeding experiment to estimate V Q , the genetic variance attributable to each QTL. We find that three QTLs with moderate effects explain up to one-third of the genetic variance in the natural population. Variation at these loci is probably maintained by some form of balancing selection. Notably, the largest effect QTLs were relatively minor in their contribution to heritability.

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