Several of these chromosomes also showed significant associations with this study, but the verified region on 4B was not among them. Similar to our results, individual QTL were almost always exclusive to each population. Kabir et al. identified QTLs for root number on 1B, 2A, 3A, 4A, 4D, and 7A and no QTL was consistent across the two populations. These observations suggest two explanations: either seminal root number is sensitive to environmental effects and many statistically significant associations detected in all studies are spurious, or this trait is controlled by a large number of genes, in different combinations in each parental line. No single locus appears to have a large dominant effect, perhaps with the exceptions of the loci on chromosomes 2DS and 4BL in our study. Unfortunately the two studies of Zhang et al and Kabir et al did not investigate seminal root angle so we have no insight into how that trait behaved in both cases. Within our populations, the expression of seminal root angle QTLs were also highly dependent upon year and population, however, five QTLs were consistent across both years . One of those QTL, QRA.ucr-2D, rolling bench was also verified within two of the three populations . This QTL accounted for the largest proportion of the phenotypic variation of all verified QTLs. This could potentially be due to the greater phenotypic difference between the two parents in the SC and SF populations which allow for greater detection of QTL .
Using relative genetic map distances this QTL appears to be the same QTL as identified by Bektas with a large effect upon other root traits such as deep root weight. Bai et al. also reported a QTL on chromosome 2D for seminal root biomass. In other cases such as QRA.ucr-6A and QRA.ucr-7B the QTLs consistently appeared in the SC population in both years; however, they do not appear in other populations. Given the crossing pattern used in the development of the populations, and even with an assumption that the QTL donor carries the same allele as Foisy, segregation should have been observed in the CF population, but it was not. Perhaps this is because this QTL explains a small percentage of the total phenotypic variation and its effect is overshadowed, hence undetectable, by segregation of different allelic combinations within the SF population. In the CF population qRA.ucr-5B and qRA.ucr-7A varied more from one year to the next than any other QTL, and no association with common markers were detected, even though on the consensus map of Wang et al. all these associated markers fall within 10-20cM of one another. Because the effect of this specific genome region was reproducible it is deserving of further study. These shifts of QTL positions are often associated with changes in the total amount of variation explained by the QTL between years. For example, qRA.ucr-5B in the CF population explained 9.40 % of the phenotypic variation in 2014 but 19.50% in 2015. These QTL appear verified as they produce significant effects in both years, however, their effects were not detected in the other two populations.
This may be an effect of considerable plasticity of the characters measured, illustrating technical difficulties in precise phenotyping. On the other hand, this may hint at the existence of closely linked loci within the same family, each with a minor effect on the total expression of the character, and minor variation within the environment from one year to the next may cause shifts in the locus/loci responsible, thus changing marker associations in the region. These examples could potentially be shedding light on the plasticity of QTL for seminal root angle in light of environmental cues. New techniques such as the clear pot method proposed by Richard et al. may provide less variability by reducing the experimental error. Unraveling the genetics of seminal root angle in wheat may prove to be a longer road than in other crops like rice. Uga et al. identified DEEPER ROOTING 1as a gene controlling the gravitropic response of roots and thus the angle of root growth. Higher expression of DRO1 caused roots to grow more downward and when introduced into a shallow rooted cultivar it improved grain yield under drought by enabling access to water deeper in the soil profile. It is likely simpler to study quantitative traits like seminal root angle within the smaller diploid genome of rice. Although there is synteny between rice and wheat within the region where DRO1 was identified, QTL in that region were not identified. Until recently rice was the closest relative of wheat that had information about seminal root angle genetics. However, researchers interested in barley have now begun to study seminal root traits as well . Using the clear pot method demonstrated by Richard et al. they were able to identify seven QTLs for seminal root angle and number . Using cross species analysis they were able to identify 10 common genes underlying root trait QTLs in barley, wheat, and sorghum.
Perhaps as seminal root angle is unraveled in barley, a closer relative to wheat than rice, it will provide insights which may aid in our understanding of wheat seminal root trait genetics. Seminal root angle and number appear to be interrelated and both appear to be related to seed weight . In the SC population root number and angle are negatively correlated so that seeds with more seminal roots have narrower angles and vice versa. This correlation explained 22% and 30% of the variation seen in 2014 and 2015 respectively. In the SF population seminal root number and seed weight were positively correlated so that heavier seeds tended towards a higher number of seminal roots. That correlation explained 36% and 45% of the variation in 2014 and 2015 respectively. In the CF population all these characters are correlated where seminal root number is positively correlated with seed weight and seminal root angle is negatively correlated with number and weight. The correlation between root number and seed weight explained 46% of the variation in 2014 and 2015, seminal root number and angle explained 24% and 33% in 2014 and 2015 respectively,grow table hydroponic and the correlation of seminal root angle and seed weight explained 23% and 35% of the variation. These results show that in the CF population a significant amount of the variation can be explained by these interactions. This is interesting in that those two parents have more similar seminal root angles . Since seed weight explained a significant amount of the variation for root number and angle it could mean loci for seminal root angle or number in CF are actually seed weight QTLs. The only way to ascertain which character is actually monitored is to map QTLs for seed weight and test their associations with those found for seminal root angle and number. In the SC and SF population no QTL for seed weight was similar to that mapped for root angle and number. However, in the CF population two QTLs for seed weight were in similar positions to QTLs for root angle and number . The QTL for seed weight on chromosome 1B clearly overlaps with the QTL for root number on 1B, each sharing common markers in both years. Of the QTLs mapped for root number in CF this QTL on 1B was the only one observed in both years. For the QTL on 5B there are not any overlapping markers for the root angle QTL and seed weight QTL, however, the QTLs for root angle on 5B shift from one year to the next making this region suspect and deserving of further inquiry. Of the QTLs for root angle in the CF population the QTL on 5B explained the greater portion of variation seen in the population over two years. Since so much variation is explained by the interaction of seed weight with angle and number it is not a major leap to assume this region could be associated with seed weight and inadvertently associated with root angle. Given those results, coupled with the correlation analysis, it seems that seed weight is a major factor, if not the major factor, in the CF population giving rise to most differences in seminal root traits. These interactions between seed weight, seminal root angle and seminal root number indicate the high complexity of root traits.
The nature of these interactions has not been tested but it appears plausible that when five seminal roots are initiated they occupy greater space at the developing point of the embryo than when only three roots are initiated. This may force the inner pair of roots more downward, thus reducing the angle between them and explain why more seminal roots is correlated with narrower angles of growth and why those with less seminal roots have a tendency toward wider angles. Additionally, heavier seeds are correlated with higher seminal root numbers which then may influence the association of seed weight with seminal root angle. Perhaps this argument is overly simplistic, and it does not begin to explain why these three characters are correlated only in some populations, and why the levels of interaction change from one population to the next. Another explanation could be linkage of loci for individual traits which could make them difficult to tease apart. In any case, these correlations underscore the complexity of these traits and call for further dissection of each trait and their interactions, so that actual genetic effects are studied. Those interactions could lend new dimensions of complexity when considering the inheritance of seminal root angle and number. These findings also provide new information for considerations when designing future projects centered on these traits. Another point to be made is that QTLs in other studies should be further verified and looked at again through this perspective. As far as we know, other studies did not map QTLs for seed weight when an interaction was observed with seminal root traits. Since the green revolution, semi-dwarf high yielding wheat cultivars have become a standard in commercial production. The semi-dwarf character of wheat lead to a threefold increase in grain yield and provided food security for developing countries . These green revolution wheats were selected for under high-input farming practices which led to a decrease in root biomass . A greater understanding of root traits and how those traits relate to whole plant strategies may enable breeders to increase yields under drought conditions . This understanding can only come by actively studying the root system in a controlled environment and until the relationship of root and shoot traits is better understood we cannot determine how to improve a plants ability to be productive in a fluctuating environment. Roots absorb water and nutrients while also anchoring the plant to the soil. The shoots utilize those resources for photosynthesis and are the site of sexual reproduction. All these functions must work together in coordination for the plant to thrive within its environment. In general plants maintain a fairly strict harmony between shoot and root biomass partitioning . However, during different growth and developmental stages the partitioning of biomass does fluctuate. In the early stages of growth resource allocation and biomass accumulation is focused towards the roots but that shifts considerably as the plant reaches flowering with the major part of photosynthates directed to the shoots . These general principles were supported by Frageria who demonstrated that the root-to-shoot ratio in wheat, as well as other crops, decreased as plants advanced in age. For these reasons it is essential to understand what effect any changes to these general principals may have upon yield within wheat and other crops as well. Increased root biomass increases grain yield under limited or rain-fed environments . This is likely due to the ability of a larger root system to absorb water and nitrogen from the soil; an added benefit is reduced leaching and agricultural run-off . What remains unclear is if increasing root biomass will continue to increase grain yields. This issue has been touched upon in wheat by Maheepala et al. leaving plenty of room for further inquiry and testing.