Dissecting Quantitative Trait Loci for Agronomic Traits Responding to Iron Deficeincy in Mungbean [Vigna Radiata (L.) Wilczek]

Calcareous soil is prevalent in many areas of the world agricultural land causing substantial yield loss of crops. We previously identified two quantitative trait locus (QTL) qIDC3.1 and qIDC2.1 controlling leaf chlorosis in mungbean grown in calcareous soil in two years (2010 and 2011) using visual score and SPAD measurement in a RIL population derived from KPS2 (susceptible) and NM10-12-1 (resistant). The two QTLs together accounted for 50% of the total leaf chlorosis variation and only qIDC3.1 was confirmed, although heritability estimated for the traits was as high as 91.96%. In this study, we detected QTLs associated with days to flowering , plant height, number of pods per plants, number of seeds per pods, and seed yield per plants in the same population grown under the same environment with the aim to identify additional QTLs controlling resistance to calcareous soil in mungbean. Single marker analysis revealed 18 simple sequence repeat markers, while composite interval mapping identified 33 QTLs on six linkage groups (1A, 2, 3, 4, 5 and 9) controlling the five agronomic traits. QTL cluster on LG 3 coincided with the position of qIDC3.1, while QTL cluster on LG 2 was not far from qIDC2.1. The results confirmed the importance of qIDC3.1 and qIDC2.1 and revealed four new QTLs for the resistance to calcareous soil

A Latent Variable Approach to Multivariate Quantitative Trait Loci

A novel approach based on latent variable modelling is presented for the analysis of multivariate quantitative and qualitative trait loci. The approach is general in the sense that it enables the joint analysis of many kinds of quantitative and qualitative traits (including count data and censored traits) in a single modelling framework. In the framework, the observations are modelled as functions of latent variables, which are then affected by quantitative trait loci. Separating the analysis in this way means that measurement errors in the phenotypic observations can be included easily in the model, providing robust inferences. The performance of the method is illustrated using two real multivariate datasets, from barley and Scots pine

Detection and Mapping of Quantitative Trait Loci that Determine Responsiveness

Exposure to 70% N2O evokes a robust antinociceptive effect in C57BL/6 (B6) but not in DBA/2 (D2) inbred mice. This study was conducted to identify quantitative trait loci (QTL) in the mouse genome that might determine responsiveness to N2O. Offspring from the F2 generation bred from B6 and D2 progenitors exhibited a broad range of responsiveness to N2O antinociception as determined by the acetic acid-induced abdominal constriction test. QTL analysis was then used to dissect this continuous trait distribution into component loci, and to map them to broad chromosomal regions. To this end, 24 spleens were collected from each of the following four groups: male and female F2 mice responding to 70% N2O in oxygen with 100% response (high-responders); and male and female F2 mice responding with 0% response (low-responders). Genomic DNA was extracted from the spleens and genotyped with simple sequence length polymorphism MapPairs markers. Findings were combined with findings from the earlier QTL analysis from BXD recombinant inbred mice [Brain Res 725 (1996) 23]. Combined results revealed two significant QTL that influence responsiveness to nitrous oxide on proximal chromosome 2 and distal chromosome 5, and one suggestive QTL on midchromosome 18. The chromosome 2 QTL was evident only in males. A significant interaction was found between a locus on chromosome 6 and another on chromosome 13 with a substantial effect on N2O antinociception

Analysis of Quantitative Trait Loci for Protein Content in Soybean Seeds Using Recombinant Inbred Lines

Protein content in the seed is quantitatively inherited and controlled by polygene. The quality of seed protein content has been studied extensively, however, information on its quantity is still limited. In order to analyze the genetic basis of these traits, recombinant inbred lines (RILs) derived from a cross between Glycine max (L.) Merrill variety Misuzudaizu and variety Moshidou Gong 503 were planted in two environments and evaluated for seed protein content. The broad sense heritability of the traits ranged from 0.74 to 0.79 in our RIL population. Single-factor analysis of variance, interval mapping and composite interval mapping were used to detect significant associations between traits and genetic markers. A total of 10 QTLs, which were significant in at least one environment were identified. Each QTL explained the total phenotypic variation for protein content in the range from 3.4% to 29.7%. Among all the detected QTLs, three of them were detected in both environments. QTLs identified in this study were mapped in the soybean linkage map. The results obtained in our study may serve as a base for analyzing the genetic control of protein content and may eventually enable to change the seed constituents

Expression quantitative trait loci are highly sensitive to cellular differentiation state

Blood cell development from multipotent hematopoietic stem cells to specialized blood cells is accompanied by drastic changes in gene expression for which the triggers remain mostly unknown. Genetical genomics is an approach linking natural genetic variation to gene expression variation, thereby allowing the identification of genomic loci containing gene expression modulators (eQTLs). In this paper, we used a genetical genomics approach to analyze gene expression across four developmentally close blood cell types collected from a large number of genetically different but related mouse strains. We found that, while a significant number of eQTLs (365) had a consistent “static” regulatory effect on gene expression, an even larger number were found to be very sensitive to cell stage. As many as 1,283 eQTLs exhibited a “dynamic” behavior across cell types. By looking more closely at these dynamic eQTLs, we show that the sensitivity of eQTLs to cell stage is largely associated with gene expression changes in target genes. These results stress the importance of studying gene expression variation in well-defined cell populations. Only such studies will be able to reveal the important differences in gene regulation between different ce

Targeted Methods for Finding Quantitative Trait Loci

Conventional genetic mapping methods typically assume parametric models with Gaussian errors, and obtain parameter estimates through maximum likelihood estimation. We propose a general semiparametric model to map quantitative trait loci (QTL) in experimental crosses. In contrast with widely-used interval mapping (IM) derived methods, our model requires fewer assumptions and also accommodates various machine learning algorithms. Estimation using both targeted maximum likelihood and collaborative targeted maximum likelihood methods is compared to a composite interval mapping (CIM) approach. We demonstrate with simulations and real data analyses that, on average, our semiparametric targeted learning approach produces less biased QTL effect estimates than those from parametric models