Salt tolerance QTLs of an endemic rice landrace, \u3ci\u3eHorkuch\u3c/i\u3e at seedling and reproductive stages

Salinity has a significant negative impact on production of rice. To cope with the increased soil salinity due to climate change, we need to develop salt tolerant rice varieties that can maintain their high yield. Rice landraces indigenous to coastal Bangladesh can be a great resource to study the genetic basis of salt adaptation. In this study, we implemented a QTL analysis framework with a reciprocal mapping population developed from a salt tolerant landrace Horkuch and a high yielding rice variety IR29. Our aim was to detect genetic loci that contributes to the salt adaptive responses of the two different developmental stages of rice which are very sensitive to salinity stress. We identified 14 QTLs for 9 traits and found that most are unique to specific developmental stages. In addition, we detected a significant effect of the cytoplasmic genome on the QTL model for some traits such as leaf total potassium and filled grain weight. This underscores the importance of considering cytoplasm-nuclear interaction for breeding programs. Finally, we identified QTLs co-localization for multiple traits that highlights the possible constraint of multiple QTL selection for breeding programs due to different contributions of a donor allele for different traits

A review of population-based metaheuristics for large-scale black-box global optimization: Part A

Scalability of optimization algorithms is a major challenge in coping with the ever growing size of optimization problems in a wide range of application areas from high-dimensional machine learning to complex large-scale engineering problems. The field of large-scale global optimization is concerned with improving the scalability of global optimization algorithms, particularly population-based metaheuristics. Such metaheuristics have been successfully applied to continuous, discrete, or combinatorial problems ranging from several thousand dimensions to billions of decision variables. In this two-part survey, we review recent studies in the field of large-scale black-box global optimization to help researchers and practitioners gain a bird’s-eye view of the field, learn about its major trends, and the state-of-the-art algorithms. Part of the series covers two major algorithmic approaches to large-scale global optimization: problem decomposition and memetic algorithms. Part of the series covers a range of other algorithmic approaches to large-scale global optimization, describes a wide range of problem areas, and finally touches upon the pitfalls and challenges of current research and identifies several potential areas for future research

Plasticity, allelic diversity, and genetic architecture of industrial hemp (Cannabis sativa L.)

2019 Summer.Includes bibliographical references.The first time in United States history that hemp was legally distinguished from high-THC Cannabis (marijuana) was in 2014 when the Farm Bill was passed. Although the two crops had been distinguished by their usage for thousands of years, their monospecific nature led to both psychoactive and non-psychoactive forms being legislated in tandem from the time that Americans began regulating Cannabis cultivation and usage. A simple statement in the 2014 Farm Bill distinguished hemp as Cannabis sativa L. with a tetrahydrocannabinol (THC) content of 0.3% or less. A second sentence enabled research into the crop and production within pilot programs in states where it is legal. This minor change in legality, followed by subsequent relaxation of laws surrounding hemp in the 2018 Farm Bill, has allowed a burgeoning hemp industry to form in the United States and enabled the return of a relict crop. Due to the long period of prohibition, hemp did not undergo the same type of crops research as other staple American crops. Consequently, little is known about the genetic mechanisms that control many of the key traits in hemp production. Understanding basic information about how traits are affected by environmental factors is highly important when regulation of the crop is based on a stringent and arbitrarily set threshold for chemical content. In 2016, we performed field trials of a diverse set of industrial hemp cultivars in multiple growing environments and assessed a wide range of traits. Expression of some traits, like days to maturity and THC content, were strongly influenced by genotype. Other traits, such as grain yield and plant height, exhibited large proportions of variance due to environmental factors and genotype-by-environment interactions. There were also varying ranges of plasticity exhibited between cultivars, underscoring the importance of selecting the right cultivar for target production environments. This highlights the importance of thoroughly characterizing genotype-by-environment interactions when breeding locally adapted hemp cultivars. Understanding genetic control of important traits and their range of plasticity enables the development of locally adapted cultivars for a wide range of end uses. Another aspect of Cannabis that is understudied is the genetic basis for differentiating hemp and high-THC Cannabis. Since the legal distinction is based on a strict threshold placed on a quantitative trait and not any known geographic or biological reproductive barriers, it is unclear whether or not there is genetic evidence to support the distinction or if the two groups are simply divergent phenotypes. A joint-site frequency and FST analysis show that individuals of the two groups mainly share common polymorphisms, with a small number of loci where differentiation occurs. These loci serve as the basis for distinguishing the two groups, but more study is needed to determine if alleles in these regions were driven to fixation via genetic drift and selection on unrelated traits, or if there is an evolutionary basis for the observed differences. When heterozygosity was assessed in these samples, the hemp group had higher overall heterozygosity levels, but the high-THC Cannabis group had more outliers which lead to a wider distribution with more extreme minimum and maximum values. Although it is clear that there are genetic differences distinguishing the two groups, extensive human vectoring and admixture between the groups, both historically and currently, makes it difficult to differentiate causes for the differences. A lack of centralized germplasm makes large-scale genomic studies of the species difficult, but, as more samples are surveyed over time, a more detailed picture of the genomic variation will emerge. These types of studies will be able to provide a more nuanced picture of the evolutionary history and current state of allelic variation within the species. In addition to plasticity and allelic diversity, genetic architecture of traits has also largely been ignored until recently. The first QTL study in Cannabis was performed in 2015 and was limited by legal restraints. Since understanding how economically relevant traits function is important to breeding improved hemp cultivars, we developed a genetic mapping population that captured variation for a wide range of traits. Utilizing whole-genome sequencing and phenotype data from a replicated field trial, we were able to detect 121 QTL associated with 38 agronomic and biochemical traits. Some traits, like days to maturity, had single loci of large effect accounting for the majority of trait variance, while other traits, like α-Pinene production, exhibited more complex polygenic architecture with epistatic interactions. Colocalization of QTL and significant trait correlations showed that there were positive relationships within both agronomic and biochemical trait groups. Although this study was limited by assessment of the population in a single environment, detecting these putative QTL serves as a substantial step forward in characterizing many relevant production traits

Genetic mapping in polyploids

Many of our most important crop species are polyploid – an unusual phenomenon whereby each chromosome is present in multiple copies (more than the usual two copies). The most common such arrangement is tetraploidy, where each chromosome is present four times. Plant species can tolerate this condition quite well (the same cannot be said of animals or humans). In fact, polyploidy can confer certain advantages such as larger fruits and flowers, seedless fruits (useful for fruit growers) or improved tolerance to environmental stresses. However, carrying multiple copies of each chromosome complicates things, particularly when crop breeders would like to use DNA information to help inform selection decisions. This PhD project looked at how DNA information of polyploids should be best analysed, developing methods and new software tools to achieve this. We analysed DNA information from polyploid crops such as potato, rose and chrysanthemum, yielding many novel insights and important results.</p

Genetic architecture of trait divergence in Panicum hallii ecotypes

Environmental heterogeneity across a species range can drive functional trait variation and lead to the formation of locally adapted ecotypes. Plant ecotypes are often differentiated by suites of correlated root and shoot traits that share common genetic, developmental, and physiological relationships. This divergence requires coordination between multiple plant organ systems. This research predominantly examines the genetic architecture underlying root-shoot trait relationships and their interaction with the environment in order to develops a more complete picture of the adaptive differences that arise between ecotypes. We used a recombinant inbred line population derived from upland and lowland ecotypes of the diploid C4 perennial bunch grass Panicum hallii to examine the following: 1. The quantitative genetics of root and shoot trait coordination. 2. The quantitative genetics of the impact of plant root microbiomes collected from natural environments on plant root and shoot traits. 3. How plant host genetics shape root microbiomes. Utilizing extensive phenotyping of plant traits and a quantitative genetic approach, we identified several genomic ‘hotspots’ which control suites of correlated root and shoot traits, thus indicating genetic coordination between plant organ systems in the process of ecotypic divergence. In addition, we found that genomic regions of colocalized quantitative trait loci (QTL) for the majority of shoot and root growth related traits were independent of colocalized QTL for shoot and root resource acquisition traits. The allelic effects of individual QTL underscore ecological specialization for drought adaptation between ecotypes and reveal possible hybrid breakdown through epistatic interactions. We show that the growth and development of ecotypes and their trait divergence depends on soil microbiomes and find that broad-sense heritability is modified by soil microbiomes, revealing important plant genotype-by-microbiome interactions for quantitative traits. We detected a number QTL interacting with the soil microbiome, including epistatic interactions dependent on soil microbiome context. We also show that microbial inocula habitat of origin changes the heritability for individual microbes (ASVs) and that different plant genomic regions are associated with abundance of individual microbes and community level structure. Our results highlight the genetic architecture underlying trait divergence and the importance of microbial interactions in C4 perennial grasses.Plant Biolog

TSPmap, a tool making use of traveling salesperson problem solvers in the efficient and accurate construction of high-density genetic linkage maps

BackgroundRecent advances in nucleic acid sequencing technologies have led to a dramatic increase in the number of markers available to generate genetic linkage maps. This increased marker density can be used to improve genome assemblies as well as add much needed resolution for loci controlling variation in ecologically and agriculturally important traits. However, traditional genetic map construction methods from these large marker datasets can be computationally prohibitive and highly error prone.ResultsWe present TSPmap, a method which implements both approximate and exact Traveling Salesperson Problem solvers to generate linkage maps. We demonstrate that for datasets with large numbers of genomic markers (e.g. 10,000) and in multiple population types generated from inbred parents, TSPmap can rapidly produce high quality linkage maps with low sensitivity to missing and erroneous genotyping data compared to two other benchmark methods, JoinMap and MSTmap. TSPmap is open source and freely available as an R package.ConclusionsWith the advancement of low cost sequencing technologies, the number of markers used in the generation of genetic maps is expected to continue to rise. TSPmap will be a useful tool to handle such large datasets into the future, quickly producing high quality maps using a large number of genomic markers

Additional file 4: Figure S2. of TSPmap, a tool making use of traveling salesperson problem solvers in the efficient and accurate construction of high-density genetic linkage maps

Linkage maps generated by TSPmap using marker datasets from A. Arabidopsis thaliana [26] and B. & C. rice [27] compared to those generated by JoinMap. (DOCX 213 kb