Mixed model approaches for the identification of QTLs within a maize hybrid breeding program

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance

Neonatal umbilical cord blood transplantation halts skeletal disease progression in the murine model of MPS-I

Umbilical cord blood (UCB) is a promising source of stem cells to use in early haematopoietic stem cell transplantation (HSCT) approaches for several genetic diseases that can be diagnosed at birth. Mucopolysaccharidosis type I (MPS-I) is a progressive multi-system disorder caused by deficiency of lysosomal enzyme α-L-iduronidase, and patients treated with allogeneic HSCT at the onset have improved outcome, suggesting to administer such therapy as early as possible. Given that the best characterized MPS-I murine model is an immunocompetent mouse, we here developed a transplantation system based on murine UCB. With the final aim of testing the therapeutic efficacy of UCB in MPS-I mice transplanted at birth, we first defined the features of murine UCB cells and demonstrated that they are capable of multi-lineage haematopoietic repopulation of myeloablated adult mice similarly to bone marrow cells. We then assessed the effectiveness of murine UCB cells transplantation in busulfan-conditioned newborn MPS-I mice. Twenty weeks after treatment, iduronidase activity was increased in visceral organs of MPS-I animals, glycosaminoglycans storage was reduced, and skeletal phenotype was ameliorated. This study explores a potential therapy for MPS-I at a very early stage in life and represents a novel model to test UCB-based transplantation approaches for various diseases

Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers

Information about the genetic diversity and population structure in elite breeding material is of fundamental importance for the improvement of crops. The objectives of our study were to (a) examine the population structure and the genetic diversity in elite maize germplasm based on simple sequence repeat (SSR) markers, (b) compare these results with those obtained from single nucleotide polymorphism (SNP) markers, and (c) compare the coancestry coefficient calculated from pedigree records with genetic distance estimates calculated from SSR and SNP markers. Our study was based on 1,537 elite maize inbred lines genotyped with 359 SSR and 8,244 SNP markers. The average number of alleles per locus, of group specific alleles, and the gene diversity (D) were higher for SSRs than for SNPs. Modified Roger’s distance (MRD) estimates and membership probabilities of the STRUCTURE matrices were higher for SSR than for SNP markers but the germplasm organization in four heterotic pools was consistent with STRUCTURE results based on SSRs and SNPs. MRD estimates calculated for the two marker systems were highly correlated (0.87). Our results suggested that the same conclusions regarding the structure and the diversity of heterotic pools could be drawn from both markers types. Furthermore, although our results suggested that the ratio of the number of SSRs and SNPs required to obtain MRD or D estimates with similar precision is not constant across the various precision levels, we propose that between 7 and 11 times more SNPs than SSRs should be used for analyzing population structure and genetic diversity

Spatiotemporal expansion of primary progenitor zones in the developing human cerebellum

We present histological and molecular analyses of the developing human cerebellum from 30 days after conception to 9 months after birth. Differences in developmental patterns between humans and mice include spatiotemporal expansion of both ventricular and rhombic lip primary progenitor zones to include subventricular zones containing basal progenitors. The human rhombic lip persists longer through cerebellar development than in the mouse and undergoes morphological changes to form a progenitor pool in the posterior lobule, which is not seen in other organisms, not even in the nonhuman primate the macaque. Disruptions in human rhombic lip development are associated with posterior cerebellar vermis hypoplasia and Dandy-Walker malformation. The presence of these species-specific neural progenitor populations refines our insight into human cerebellar developmental disorders

Computer vision and machine learning for robust phenotyping in genome-wide studies

Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic studies of abiotic stress iron deficiency chlorosis (IDC) of soybean is reported. IDC classification and severity for an association panel of 461 diverse plant-introduction accessions was evaluated using an end-to-end phenotyping workflow. The workflow consisted of a multi-stage procedure including: (1) optimized protocols for consistent image capture across plant canopies, (2) canopy identification and registration from cluttered backgrounds, (3) extraction of domain expert informed features from the processed images to accurately represent IDC expression, and (4) supervised ML-based classifiers that linked the automatically extracted features with expert-rating equivalent IDC scores. ML-generated phenotypic data were subsequently utilized for the genome-wide association study and genomic prediction. The results illustrate the reliability and advantage of ML-enabled image-phenotyping pipeline by identifying previously reported locus and a novel locus harboring a gene homolog involved in iron acquisition. This study demonstrates a promising path for integrating the phenotyping pipeline into genomic prediction, and provides a systematic framework enabling robust and quicker phenotyping through ground-based systems

Self-assembled hydrogel fibers for sensing the multi-compartment intracellular milieu

Targeted delivery of drugs and sensors into cells is an attractive technology with both medical and scientific applications. Existing delivery vehicles are generally limited by the complexity of their design, dependence on active transport, and inability to function within cellular compartments. Here, we developed self-assembled nanofibrous hydrogel fibers using a biologically inert, low-molecular-weight amphiphile. Self-assembled nanofibrous hydrogels offer unique physical/mechanical properties and can easily be loaded with a diverse range of payloads. Unlike commercially available E. coli membrane particles covalently bound to the pH reporting dye pHrodo, pHrodo encapsulated in self-assembled hydrogel-fibers internalizes into macrophages at both physiologic (37°C) and sub-physiologic (4°C) temperatures through an energy-independent, passive process. Unlike dye alone or pHrodo complexed to E. coli, pHrodo-SAFs report pH in both the cytoplasm and phagosomes, as well the nucleus. This new class of materials should be useful for next-generation sensing of the intracellular milieu

Heterosis as Investigated in Terms of Polyploidy and Genetic Diversity Using Designed Brassica juncea Amphiploid and Its Progenitor Diploid Species

Fixed heterosis resulting from favorable interactions between the genes on their homoeologous genomes in an allopolyploid is considered analogous to classical heterosis accruing from interactions between homologous chromosomes in heterozygous plants of a diploid species. It has been hypothesized that fixed heterosis may be one of the causes of low classical heterosis in allopolyploids. We used Indian mustard (Brassica juncea, 2n = 36; AABB) as a model system to analyze this hypothesis due to ease of its resynthesis from its diploid progenitors, B. rapa (2n = 20; AA) and B. nigra (2n = 16; BB). Both forms of heterosis were investigated in terms of ploidy level, gene action and genetic diversity. To facilitate this, eleven B. juncea genotypes were resynthesized by hybridizing ten near inbred lines of B. rapa and nine of B. nigra. Three half diallel combinations involving resynthesized B. juncea (11×11) and the corresponding progenitor genotypes of B. rapa (10×10) and B. nigra (9×9) were evaluated. Genetic diversity was estimated based on DNA polymorphism generated by SSR primers. Heterosis and genetic diversity in parental diploid species appeared not to predict heterosis and genetic diversity at alloploid level. There was also no association between combining ability, genetic diversity and heterosis across ploidy. Though a large proportion (0.47) of combinations showed positive values, the average fixed heterosis was low for seed yield but high for biomass yield. The genetic diversity was a significant contributor to fixed heterosis for biomass yield, due possibly to adaptive advantage it may confer on de novo alloploids during evolution. Good general/specific combiners at diploid level did not necessarily produce good general/specific combiners at amphiploid level. It was also concluded that polyploidy impacts classical heterosis indirectly due to the negative association between fixed heterosis and classical heterosis

Applying Bayesian model averaging for uncertainty estimation of input data in energy modelling

Background Energy scenarios that are used for policy advice have ecological and social impact on society. Policy measures that are based on modelling exercises may lead to far reaching financial and ecological consequences. The purpose of this study is to raise awareness that energy modelling results are accompanied with uncertainties that should be addressed explicitly. Methods With view to existing approaches of uncertainty assessment in energy economics and climate science, relevant requirements for an uncertainty assessment are defined. An uncertainty assessment should be explicit, independent of the assessor’s expertise, applicable to different models, including subjective quantitative and statistical quantitative aspects, intuitively understandable and be reproducible. Bayesian model averaging for input variables of energy models is discussed as method that satisfies these requirements. A definition of uncertainty based on posterior model probabilities of input variables to energy models is presented. Results The main findings are that (1) expert elicitation as predominant assessment method does not satisfy all requirements, (2) Bayesian model averaging for input variable modelling meets the requirements and allows evaluating a vast amount of potentially relevant influences on input variables and (3) posterior model probabilities of input variable models can be translated in uncertainty associated with the input variable. Conclusions An uncertainty assessment of energy scenarios is relevant if policy measures are (partially) based on modelling exercises. Potential implications of these findings include that energy scenarios could be associated with uncertainty that is presently neither assessed explicitly nor communicated adequately

In Silico Simulation of Corticosteroids Effect on an NFkB- Dependent Physicochemical Model of Systemic Inflammation

During the onset of an inflammatory response signaling pathways are activated for "translating" extracellular signals into intracellular responses converging to the activation of nuclear factor (NF)-kB, a central transcription factor in driving the inflammatory response. An inadequate control of its transcriptional activity is associated with the culmination of a hyper-inflammatory response making it a desired therapeutic target. Predicated upon the nature of the response, a systems level analysis might provide rational leads for the development of strategies that promote the resolution of the response.A physicochemical host response model is proposed to integrate biological information in the form of kinetic rules and signaling cascades with pharmacokinetic models of drug action for the modulation of the response. The unifying hypothesis is that the response is triggered by the activation of the NFkB signaling module and corticosteroids serve as a template for assessing anti-inflammatory strategies. The proposed in silico model is evaluated through its ability to predict and modulate uncontrolled responses. The pre-exposure of the system to hypercortisolemia, i.e. 6 hr before or simultaneously with the infectious challenge "reprograms" the dynamics of the host towards a balanced inflammatory response. However, if such an intervention occurs long before the inflammatory insult a symptomatic effect is observed instead of a protective relief while a steroid infusion after inducing inflammation requires much higher drug doses.We propose a reversed engineered inflammation model that seeks to describe how the system responds to a multitude of external signals. Timing of intervention and dosage regimes appears to be key determinants for the protective or symptomatic effect of exogenous corticosteroids. Such results lie in qualitative agreement with in vivo human studies exposed both to LPS and corticosteroids under various time intervals thus improving our understanding of how interacting modules generate a behavior