The overall mean phenotypic value of each line across the environments was taken as the input phenotype for that line representing the genotypic value of that line. All QTL detected would represent those showing consistent effects across environments. There were many statistical approaches to identify and estimate QTL effects such as linear regression model, interval mapping, maximum likelihood method, and Bayesian statistics. Also, there were a number of software, such as MAPMAKER/QTL and Cartographer to map QTL for different phenotypic traits in plants as well as in animals . The E-BAYES method used in this study was chosen for its advantages over other commonly available software and methods. E-BAYES outperformed all other Bayesian methods, stochastic search variable selection , penalized likelihood , and least absolute shrinkage selection operator , in terms of minimizing the error variance. It made shrinkage very selective by providing optimal estimates of variance components, with unshrinking of large effects while small effects are shrunk to zero . There were reports of an empirical Bayes method developed by other groups but they did not shrink each regression coefficient by its own prior and also they were not as easy to understand as E-BAYES developed by Xu . In this study, E-Bayesian approach was applied for the first time in wheat to dissect QTL for root traits. The mapping population was novel and different from conventional breeding populations. It is analogous to near-isogenic lines where two lines differ for a genomic segment in an otherwise homogeneous background,nft system the distinction here is cross genome differences.

In a recent study , a comparison between mapping power of the RIL and NIL populations revealed that population size of RIL is more important than that of replication number, where due to Beavis effect , the explained variances are overestimated in smaller populations. On the other hand, mapping power of a NIL population relies more on replication number than population size . The lower localization resolution of NILs can be increased by generating chromosome or region specific mapping populations. The population in this study was a set of single chromosome arm recombinant lines. In such sets, the two parents differ by a single chromosome arm, the remaining chromosome arm pairs being as identical as ca. ten back crosses can achieve. This population has statistical advantage in detecting QTL with relatively small phenotypic effects in fewer progeny than by analyzing a large segregating population . It is due to the absence of phenotypic noise of segregating unlinked QTL in usual mapping populations with heterogeneous background. Thus, an undetectable effect in a large segregating population can be converted to a detectible effect even in fewer progeny in a chromosome specific population . Nadaeu et al. proved this concept, explaining a sample of 28 mice specific for a single chromosome, was sufficient for the strong detection of a specific trait locus which they could detect with weak evidence in an inter cross progeny of 300 individuals. The present study was a step further in using a chromosome arm-specific population. Here, the parent lines differed by the presence/absence of the 1RS.1BL wheat-rye translocation. A set of 1RS-1BS recombinant chromosomes was generated , each one originating from a different crossover event and therefore differing from all other recombinants in the proportions of rye and wheat chromatin present. This near-isogenic line approach in essence eliminates the question of population size as a factor in QTL detection. If a QTL is present on the studied arm, it will manifest itself in any properly conducted experiment.

The number of lines/recombinants used only affects mapping resolution, that is, the size of the segment to which a trait can be assigned. Thus, 29 lines used in this study, each originating from a single crossover, produce an average resolution of ca. 1.7 cM, a feat not frequently achieved in mapping populations. Most of the additive and epistatic effects detected for variation in different root traits shared common QTL regions. Markers Sr31 and Pm8 shared the QTL with additive effects for the number of roots >30 cm and total root length. One of these markers, Pm8, showed inter-genomic epistatic interaction with Xucr_2 and this epistatic effect was again common for number of roots >30 cm and total root length. Number of roots >30 cm was affected by four QTL involving almost all markers within the distal 15% of the 1RS-1BS genetic map. Three QTL effects were involved in the expression of total root length and all three shared the same region as for number of roots >30 cm. All the three characters for root biomass viz; shallow root weight, deep root weight, and total root weight, showed two epistatic effects each, and both epistatic effects were common. Pairs of loci involved were NOR & Xucr_4 for inter-genomic epistatic effect and Pm8 & Gli-1,Glu-3 for the other intra-genomic epistatic effect. Pm8 and Gli-1,Glu-3 also showed significant interactions with other markers for other root traits. This repetitive detection of the same chromosome regions or the associations of the same markers with QTL effects further consolidates our approach to map QTL for root traits in wheat. In conventional QTL mapping, the focus is on detecting the QTL with main effects and then applying an epistatic model to examine the epistatic effect between the QTLs with main effects. In nature, there are loci with small genetic effects which sometimes go undetected in a phenotype but their interaction with other similar loci may have a significant effect. It would be a disadvantage not to include them in the genetic model. Here, we proved the superiority of our method in detecting epistatic effects between two pairs of loci and none of those two pairs of loci had main effects as against other methods where epistatic effects were estimated for loci with main effects only .

This has clearly been shown in the intra-genomic epistatic effects between pairs of loci, Xucr_8 and Gli-1,Glu-3 for longest root length. Similar intra- and inter-genomic epistatic effects were also explained for shallow root weight, deep root weight, and total root weight. All these three characters for root biomass showed two pairs of loci involved in two different epistatic interactions and none of these loci had detectable additive effect for these characters. We studied seven shoot characters for the estimation of QTL effects. Interestingly,nft hydroponic we did not find any significant main or epistatic effect for any of the shoot characters except shoot biomass. Variation in shoot biomass was explained by two intragenomic epistatic effects; Xucr_4 with the Sr31 region and Xucr_8 with Gli-1, Glu-3 . The second epistatic effect was also detected for “longest root length”. It is not unexpected that shoot biomass shares QTL with some of the root characters as they are involved in the growth and development of the same plant. Recently, a QTL study was conducted for only three shoot characters; plant height, heading date, and grain yield, in durum wheat . They found five QTLs for plant height and one out of the five was on chromosome 1BS, and this QTL was detected only in 7 out of 16 environments. Similar to these results, we also found a very weak QTL effects for number of tillers and plant height. They fell short of our LOD threshold value, but QTL for number of tillers shared the same location with shoot biomass in our study which was expected. Four regions were identified that carry almost all the QTL with both additive and epistatic effects . In Figure 2, they have been marked as I, II, III, and IV as we move from the centromere towards the telomere. Region I was involved in five of the six root traits and covered 0.7 cM, which is the highest resolution we could obtain. Region II involved only one marker, Sec-1, showing a single additive effect . Region III covered 0.7 cM and was involved in two main QTL effects for number of roots >30 cm and total root length along with one epistatic effect for shoot biomass. Region IV covered 3.7 cM with three markers. This region indicated presence of QTL effects for all the root traits and also for shoot biomass. Three out of four QTL regions were located in the satellite region of chromosome 1RS. Two of them were located in the distal most 10% of the 1RS region which is in agreement with previous studies . All higher plants have roots and the root fraction of the plant’s total mass varies widely, even within the same species. Although, roots encounter many fluctuations in their external environments that affect their growth, it is their tendency to accommodate and survive these as a whole system that makes them strongly homoeostatic . Knowledge of these modifications in the root system at a morphological and anatomical level, whether due to environmental changes or genetic control, is of importance . In response to the external environment, root morphological traits and root growth have been studied widely in a number of crop species. Drought stressed Lolium perenne plants have increased number and growth of lateral roots . In barley, potassium deficiency is associated with reduced length of laterals and larger root diameter compared to phosphorus deficiency . In wheat, temperature has a profound effect on dry weight and root length . Besides root morphology, anatomical traits are also influenced greatly by the surrounding environment and have been widely studied in different crop species such as maize , rice , and other cereals . In winter and spring wheat, chilling and high temperature decreased the diameter of the central metaxylem vessel .There are reports which suggest specific morphological and anatomical root traits help stress tolerant plants survive a particular stress condition.

In rice, traits such as deep root to shoot ratio and deep specific root length were found to contribute to drought avoidance in the field .This anatomical adaptation of the tolerant wheat genotypes proved to be an advantage for survival and higher grain yield under water stress . The genetic control of root characteristics is poorly understood especially in bread wheat. Robertson et al. characterized genetic variability for seedling root number within the genus Triticum to examine its value in a breeding program and found this trait to be positively correlated with seed weight. In bread wheat, xylem vessel diameter was found to have greater genetic variation and higher heritability with significant response to selection . In a wheat backcross breeding program, lines selected for reduced xylem vessel diameter yielded 3-11% more in driest environments than unselected controls, depending on genetic background . Besides these findings, there is still no report in bread wheat of the chromosomal localization of genes that affect root anatomy. Weaver compared the root systems of rye and bread wheat under natural conditions and reported rye had deeper seminal roots. The spontaneous translocation of the short arm of chromosome 1 of rye  to the long arm of chromosome 1B in bread wheat was first identified in the late 1930s . Over the past few decades, there have been several reports of better performance of 1RS translocated wheats for grain yield over other commonly grown wheat genotypes . In other studies, increase in grain yield among 1RS wheats was found to be positively correlated with higher root biomass while there were no significant differences found for shoot traits . Roots of 1RS.1BL translocation wheats were thinner and there was a higher root length density when grown in acid soils and this likely enhanced the root surface area . Lukaszewski reconstructed the complete chromosomes of 1B and 1R from 1RS.1BL translocation and later, produced three new centric translocations viz., 1RS.1Al, 1RS.1BL, and 1RS.1DL in ‘Pavon 76’ . Each of three translocations had the same 1RS arm but in different location in the genome and each was mitotically stable. All three translocations performed better for grain yield under field conditions and had greater root biomass. These translocation lines were ranked for root biomass as Pavon 1RS.1AL > Pavon 1RS.1DL > Pavon 1RS.1BL for root biomass . Recently, a genetic map was generated using 1RS-1BS recombinant breakpoints in wheat and their genetic analysis indicated the distal 15% of the physical length of chromosome 1RS may carry the gene for better rooting ability and root morphological traits.