Opinion Exploiting genomics to improve the benefits of wheat: Prospects and limitations

Conventional breeding has been immensely successful in increasing crop production to meet the demands of the growing global population, particularly for wheat where production has increased by over threefold over the last 60 years without a significant increase in the area of land used. However, the pace of improvement by conventional breeding is slow and limited by the range of variation present in wheat and species with which it can be crossed. Genomics can be defined as “an interdisciplinary field of biology focusing on the structure, function, evolution, mapping, and editing of genomes” (Wikipedia). As such it has the potential to revolutionise crop improvement, by accelerating the rate of progress and increasing the range of variation that is available. Despite this potential, progress in the application of biotechnology to improve wheat has been slow, particularly when applied to the quality of the grain for processing and nutrition. We will therefore consider the reasons for this and identify priorities for future research

Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform

Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5–8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1–4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops

Natural selection towards wild-type in composite cross populations of winter wheat

Most of our crops are grown in monoculture with single genotypes grown over wide acreage. An alternative approach, where segregating populations are used as crops, is an exciting possibility, but outcomes of natural selection upon this type of crop are not well understood. We tracked allelic frequency changes in evolving composite cross populations of wheat grown over ten generations under organic and conventional farming. At three generations, each population was genotyped with 19 SSR and 8 SNP markers. The latter were diagnostic for major functional genes. Gene diversity was constant at SSR markers but decreased over time for SNP markers. Population differentiation between the four locations could not be detected, suggesting that organic vs. non-organic crop management did not drive allele frequency changes. However, we did see changes for genes controlling plant height and phenology in all populations independently and consistently. We interpret these changes as the result of a consistent natural selection towards wild-type. Independent selection for alleles that are associated with plant height suggests that competition for light was central, resulting in the predominance of stronger intraspecific competitors, and highlighting a potential trade-off between individual and population performanc

Exploring the Relationship between Social Class and Quality of Life: the Mediating Role of Power and Status

Funder: Universität zu Köln (1017)AbstractWhy does social class affect Quality of Life? We simultaneously investigated two novel possible explanations: Because a high social class is associated with increased control over resources (i.e., power) or because a high social class is associated with higher respect and esteem in the eyes of others (i.e., status). To test these explanations, we collected data from 384 US-based individuals. We measured their social class, power, status, and four facets of Quality of Life (physical, mental, social, and environmental). For each facet, we calculated the correlation with social class. Next, we tested whether the relationship between social class and the specific facet was mediated by power, status, or both. Social class correlated significantly with all facets of Quality of Life (physical, mental, social, and environmental). Using parallel mediation models, we found that this positive relationship was mediated by status, but not by power. For some facets of Quality of Life (physical, environmental), power even had a negative indirect effect. These results suggest that upper-class individuals indeed have a higher Quality of Life. However, this seems to be mostly due to the increased status of upper-class individuals, whereas power was less important or even had detrimental effects on Quality of Life. Researchers and policymakers aiming to address class-based Quality of Life inequality could thus benefit from focusing on status as an important mediator. Moreover, our work demonstrates the importance of considering power and status as distinct constructs, in order to fully unravel the relationship between social class and Quality of Life.</jats:p

Nitrogen uptake and remobilization from pre‑ and post‑anthesis stages contribute towards grain yield and grain protein concentration in wheat grown in limited nitrogen conditions

Background In wheat, nitrogen (N) remobilization from vegetative tissues to developing grains largely depends on genetic and environmental factors. The evaluation of genetic potential of crops under limited resource inputs such as limited N supply would provide an opportunity to identify N-efficient lines with improved N utilisation efficiency and yield potential. We assessed the genetic variation in wheat recombinant inbred lines (RILs) for uptake, partitioning, and remobilization of N towards grain, its association with grain protein concentration (GPC) and grain yield. Methods We used the nested association mapping (NAM) population (195 lines) derived by crossing Paragon (P) with CIMMYT core germplasm (P × Cim), Baj (P × Baj), Watkins (P × Wat), and Wyalkatchem (P × Wya). These lines were evaluated in the field for two seasons under limited N supply. The plant sampling was done at anthesis and physiological maturity stages. Various physiological traits were recorded and total N uptake and other N related indices were calculated. The grain protein deviation (GPD) was calculated from the regression of grain yield on GPC. These lines were grouped into different clusters by hierarchical cluster analysis based on grain yield and N-remobilization efficiency (NRE). Results The genetic variation in accumulation of biomass at both pre- and post-anthesis stages were correlated with grain-yield. The NRE significantly correlated with aboveground N uptake at anthesis (AGNa) and grain yield but negatively associated with AGN at post-anthesis (AGNpa) suggesting higher N uptake till anthesis favours high N remobilization during grain filling. Hierarchical cluster analysis of these RILs based on NRE and yield resulted in four clusters, efficient (31), moderately efficient (59), moderately inefficient (58), and inefficient (47). In the N-efficient lines, AGNa contributed to 77% of total N accumulated in grains, while it was 63% in N-inefficient lines. Several N-efficient lines also exhibited positive grain protein deviation (GPD), combining high grain yield and GPC. Among crosses, the P × Cim were superior and N-efficient, while P × Wya responded poorly to low N input