Seedling root system adaptation to water availability during maize domestication and global expansion.

The maize root system has been reshaped by indirect selection during global adaptation to new agricultural environments. In this study, we characterized the root systems of more than 9,000 global maize accessions and its wild relatives, defining the geographical signature and genomic basis of variation in seminal root number. We demonstrate that seminal root number has increased during maize domestication followed by a decrease in response to limited water availability in locally adapted varieties. By combining environmental and phenotypic association analyses with linkage mapping, we identified genes linking environmental variation and seminal root number. Functional characterization of the transcription factor ZmHb77 and in silico root modeling provides evidence that reshaping root system architecture by reducing the number of seminal roots and promoting lateral root density is beneficial for the resilience of maize seedlings to drought.

Genomic prediction within and across maize landrace derived populations using haplotypes.

Genomic prediction (GP) using haplotypes is considered advantageous compared to GP solely reliant on single nucleotide polymorphisms (SNPs), owing to haplotypes' enhanced ability to capture ancestral information and their higher linkage disequilibrium with quantitative trait loci (QTL). Many empirical studies supported the advantages of haplotype-based GP over SNP-based approaches. Nevertheless, the performance of haplotype-based GP can vary significantly depending on multiple factors, including the traits being studied, the genetic structure of the population under investigation, and the particular method employed for haplotype construction. In this study, we compared haplotype and SNP based prediction accuracies in four populations derived from European maize landraces. Populations comprised either doubled haploid lines (DH) derived directly from landraces, or gamete capture lines (GC) derived from crosses of the landraces with an inbred line. For two different landraces, both types of populations were generated, genotyped with 600k SNPs and phenotyped as lines per se for five traits. Our study explores three prediction scenarios: (i) within each of the four populations, (ii) across DH and GC populations from the same landrace, and (iii) across landraces using either DH or GC populations. Three haplotype construction methods were evaluated: 1. fixed-window blocks (FixedHB), 2. LD-based blocks (HaploView), and 3. IBD-based blocks (HaploBlocker). In within population predictions, FixedHB and HaploView methods performed as well as or slightly better than SNPs for all traits. HaploBlocker improved accuracy for certain traits but exhibited inferior performance for others. In prediction across populations, the parameter setting from HaploBlocker which controls the construction of shared haplotypes between populations played a crucial role for obtaining optimal results. When predicting across landraces, accuracies were low for both, SNP and haplotype approaches, but for specific traits substantial improvement was observed with HaploBlocker. This study provides recommendations for optimal haplotype construction and identifies relevant parameters for constructing haplotypes in the context of genomic prediction.

Genomic prediction using information across years with epistatic models and dimension reduction via haplotype blocks.

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this regard, epistasis models have shown to be capable of increasing the prediction accuracy while their high computational load is challenging. In this study, we investigated the predictive ability obtained in additive and epistasis models when utilizing haplotype blocks versus pruned sets of SNPs by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The prediction accuracies of bivariate GBLUP, ERRBLUP and sERRBLUP were assessed with eight phenotypic traits for 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot. The results indicate that the obtained prediction accuracies are similar when utilizing a pruned set of SNPs or haplotype blocks, while utilizing haplotype blocks reduces the computational load significantly compared to the pruned sets of SNPs. The number of interactions considered in the model was reduced from 323.5/456.4 million for the pruned SNP panel to 4.4/5.5 million in the haplotype block dataset for Kemater and Petkuser landraces, respectively. Since the computational load scales linearly with the number of parameters in the model, this leads to a reduction in computational time of 98.9% from 13.5 hours for the pruned set of markers to 9 minutes for the haplotype block dataset. We further investigated the impact of genomic correlation, phenotypic correlation and trait heritability as factors affecting the bivariate models' prediction accuracy, identifying the genomic correlation between years as the most influential one. As computational load is substantially reduced, while the accuracy of genomic prediction is unchanged, the here proposed framework to use haplotype blocks in sERRBLUP provided a solution for the practical implementation of sERRBLUP in real breeding programs. Furthermore, our results indicate that sERRBLUP is not only suitable for prediction across different locations, but also for the prediction across growing seasons.

Genetic diversity of European maize landraces: Dataset on the molecular and phenotypic variation of derived doubled-haploid populations.

Genetic variation is the basis of selection, evolution and breeding. Maize landraces represent a rich source of allelic diversity, but their efficient utilization in breeding and research has been hampered by their heterogeneous and heterozygous nature and insufficient information about most accessions. While molecular inventories of germplasm repositories are growing steadily, linking these data to meaningful phenotypes for quantitative traits is challenging. Here, we present comprehensive molecular and phenotypic data for ∼1,000 doubled-haploid (DH) lines derived from three pre-selected European maize landraces. Due to their full homozygosity, the DH lines can be multiplied ad libitum and represent a powerful biological resource available to the community. The DH lines allow high-precision phenotyping in repeated experiments and reveal the full additive genetic variance of the population. The DH lines were evaluated for nine agronomically important, quantitative traits in multi-environment field trials comprising seven locations and two years. The DH populations revealed high genetic variance and high heritability for the analysed traits. The DH lines were genotyped with 600k SNP markers. After stringent quality filtering 500k markers remained for further analyses. This is the largest resource of landrace derived DH material in maize, unprecedented in its structure and dimension. The presented data are ideal for linking molecular variation to meaningful phenotypes. They can be used for genome-wide association studies, genomic prediction, and population genetic analyses as well as for developing and testing statistical methods. All plant material is available to the community for conducting additional experiments, extending the panel of traits and environments, and for testing the landrace-derived lines in combination with other genetic material.

Theoretical and experimental assessment of genome-based prediction in landraces of allogamous crops.

Discovery and enrichment of favorable alleles in landraces are key to making them accessible for crop improvement. Here, we present two fundamentally different concepts for genome-based selection in landrace-derived maize populations, one based on doubled-haploid (DH) lines derived directly from individual landrace plants and the other based on crossing landrace plants to a capture line. For both types of populations, we show theoretically how allele frequencies of the ancestral landrace and the capture line translate into expectations for molecular and genetic variances. We show that the DH approach has clear advantages over gamete capture with generally higher prediction accuracies and no risk of masking valuable variation of the landrace. Prediction accuracies as high as 0.58 for dry matter yield in the DH population indicate high potential of genome-based selection. Based on a comparison among traits, we show that the genetic makeup of the capture line has great influence on the success of genome-based selection and that confounding effects between the alleles of the landrace and the capture line are best controlled for traits for which the capture line does not outperform the ancestral population per se or in testcrosses. Our results will guide the optimization of genome-enabled prebreeding schemes.

[Project report on fostering scientific competencies in pain medicine in the context of student education]. / Projektbericht zur Förderung wissenschaftlicher Kompetenzen im Fach Schmerzmedizin im Rahmen der curricularen Lehre.

AIM: The training of scientific skills and competencies is an essential part of academic medical studies. As part of the MaReCuM model study program at Heidelberg University's Mannheim Medical School, a fifth-year rotation on scientific skills in the field of pain medicine was implemented. This paper describes this competence-oriented rotation as well as the investigation of the educational effect. METHOD: A total of 114 fifth-year medical students participated in the survey (response rate: 83%). The control group completed the fifth year prior to the implementation of the rotation. The experimental group was required to participate in the rotation and the real healthcare research study "Case management program: low back pain". A survey of both groups was conducted on the first day of the rotation and at the end of the module. RESULTS: The innovative and competency-based learning unit was successfully implemented as part of the MaReCuM model study program and carried out with partners in general practice as well as the Mannheim Institute of Public Health. The participating students accepted the rotation well. There was no measurable effect on the subjective learning success of the rotation in the evaluation. DISCUSSION: To the authors' knowledge, this educational approach has never been tested before in a German study program. The presented rotation offers an additional option for the training of scientific competencies as part of medical studies. The missing of a measurable effect could be due to the extensive experience of the medical students as well as the limitations on participation in a real healthcare study. An additional learning opportunity could be created by connecting the preexisting lectures to a longitudinal module on scholarly competencies. The implementation of the program also offers a unique opportunity for educational research on the acquisition of scientific competencies in medical students.

Using phosphate amendments to reduce bioaccessible Pb in contaminated soils: A meta-analysis.

Measuring the reduction of in vitro bioaccessible (IVBA) Pb from the addition of phosphate amendments has been researched for more than 20 years. A range of effects have been observed from increases in IVBA Pb to almost 100% reduction. This study determined the mean change in IVBA Pb as a fraction of total Pb (AC) and relative to the IVBA Pb of the control soil (RC) with a random effects meta-analysis. Forty-four studies that investigated the ability of inorganic phosphate amendments to reduce IVBA Pb were identified through 5 databases. These studies were split into 3 groups: primary, secondary, and EPA Method 1340 based on selection criteria, with the primary group being utilized for subgroup analysis and meta-regression. The mean AC was approximately -12% and mean RC was approximately -25% for the primary and secondary groups. For the EPA Method 1340 group, the mean AC was -5% and mean RC was -8%. The results of subgroup analysis identified the phosphorous amendment applied and contamination source as having a significant effect on the AC and RC. Soluble amendments reduce bioaccessible Pb more than insoluble amendments and phosphoric acid is more effective than other phosphate amendments. Urban Pb contamination associated with legacy Pb-paint and tetraethyl Pb from gasoline showed lower reductions than other sources such as shooting ranges and smelting operations. Meta-regression identified high IVBA Pb in the control, low incubated soil pH, and high total Pb with the greater reductions in AC and RC. In order to facilitate comparisons across future remediation research, a set of minimum reported data should be included in published studies and researchers should use standardized in vitro bioaccessibility methods developed for P-treated soils. Additionally, a shared data repository should be created for soil remediation research to enhance available soil property information and better identify unique materials.

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments.

KEY MESSAGE: The accuracy of genomic prediction of phenotypes can be increased by including the top-ranked pairwise SNP interactions into the prediction model. We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from two European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested at six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings for predictions within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Across all studied traits, locations and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from -0.3 to + 27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP, with an average increase of 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for "sparse testing" approaches in which only a subset of the lines/hybrids of interest is observed at each location.

Calibration and validation of predicted genomic breeding values in an advanced cycle maize population.

KEY MESSAGE: Model training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets. The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.

Generalized gametic relationships for flexible analyses of parent-of-origin effects.

A class of epigenetic inheritance patterns known as genomic imprinting allows alleles to influence the phenotype in a parent-of-origin-specific manner. Various pedigree-based parent-of-origin analyses of quantitative traits have attempted to determine the share of genetic variance that is attributable to imprinted loci. In general, these methods require four random gametic effects per pedigree member to account for all possible types of imprinting in a mixed model. As a result, the system of equations may become excessively large to solve using all available data. If only the offspring have records, which is frequently the case for complex pedigrees, only two averaged gametic effects (transmitting abilities) per parent are required (reduced model). However, the parents may have records in some cases. Therefore, in this study, we explain how employing single gametic effects solely for informative individuals (i.e., phenotyped individuals), and only average gametic effects otherwise, significantly reduces the complexity compared with classical gametic models. A generalized gametic relationship matrix is the covariance of this mixture of effects. The matrix can also make the reduced model much more flexible by including observations from parents. Worked examples are present to illustrate the theory and a realistic body mass data set in mice is used to demonstrate its utility. We show how to set up the inverse of the generalized gametic relationship matrix directly from a pedigree. An open-source program is used to implement the rules. The application of the same principles to phased marker data leads to a genomic version of the generalized gametic relationships.

Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools.

KEY MESSAGE: High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, "Kemater Landmais Gelb" (KE) and "Petkuser Ferdinand Rot" (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r = - 0.27 to 0.20; [Formula: see text]= - 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low [Formula: see text] that could be improved by using fixed marker effects in the GS model.

Discovery of beneficial haplotypes for complex traits in maize landraces.

Genetic variation is of crucial importance for crop improvement. Landraces are valuable sources of diversity, but for quantitative traits efficient strategies for their targeted utilization are lacking. Here, we map haplotype-trait associations at high resolution in ~1000 doubled-haploid lines derived from three maize landraces to make their native diversity for early development traits accessible for elite germplasm improvement. A comparative genomic analysis of the discovered haplotypes in the landrace-derived lines and a panel of 65 breeding lines, both genotyped with 600k SNPs, points to untapped beneficial variation for target traits in the landraces. The superior phenotypic performance of lines carrying favorable landrace haplotypes as compared to breeding lines with alternative haplotypes confirms these findings. Stability of haplotype effects across populations and environments as well as their limited effects on undesired traits indicate that our strategy has high potential for harnessing beneficial haplotype variation for quantitative traits from genetic resources.

Genomic imprinting analyses identify maternal effects as a cause of phenotypic variability in type 1 diabetes and rheumatoid arthritis.

Imprinted genes, giving rise to parent-of-origin effects (POEs), have been hypothesised to affect type 1 diabetes (T1D) and rheumatoid arthritis (RA). However, maternal effects may also play a role. By using a mixed model that is able to simultaneously consider all kinds of POEs, the importance of POEs for the development of T1D and RA was investigated in a variance components analysis. The analysis was based on Swedish population-scale pedigree data. With P = 0.18 (T1D) and P = 0.26 (RA) imprinting variances were not significant. Explaining up to 19.00% (± 2.00%) and 15.00% (± 6.00%) of the phenotypic variance, the maternal environmental variance was significant for T1D (P = 1.60 × 10-24) and for RA (P = 0.02). For the first time, the existence of maternal genetic effects on RA was indicated, contributing up to 16.00% (± 3.00%) of the total variance. Environmental factors such as the social economic index, the number of offspring, birth year as well as their interactions with sex showed large effects.

Improving Imputation Quality in BEAGLE for Crop and Livestock Data.

Imputation is one of the key steps in the preprocessing and quality control protocol of any genetic study. Most imputation algorithms were originally developed for the use in human genetics and thus are optimized for a high level of genetic diversity. Different versions of BEAGLE were evaluated on genetic datasets of doubled haploids of two European maize landraces, a commercial breeding line and a diversity panel in chicken, respectively, with different levels of genetic diversity and structure which can be taken into account in BEAGLE by parameter tuning. Especially for phasing BEAGLE 5.0 outperformed the newest version (5.1) which in turn also lead to improved imputation. Earlier versions were far more dependent on the adaption of parameters in all our tests. For all versions, the parameter ne (effective population size) had a major effect on the error rate for imputation of ungenotyped markers, reducing error rates by up to 98.5%. Further improvement was obtained by tuning of the parameters affecting the structure of the haplotype cluster that is used to initialize the underlying Hidden Markov Model of BEAGLE. The number of markers with extremely high error rates for the maize datasets were more than halved by the use of a flint reference genome (F7, PE0075 etc.) instead of the commonly used B73. On average, error rates for imputation of ungenotyped markers were reduced by 8.5% by excluding genetically distant individuals from the reference panel for the chicken diversity panel. To optimize imputation accuracy one has to find a balance between representing as much of the genetic diversity as possible while avoiding the introduction of noise by including genetically distant individuals.

European maize landraces made accessible for plant breeding and genome-based studies.

KEY MESSAGE: Doubled-haploid libraries from landraces capture native genetic diversity for a multitude of quantitative traits and make it accessible for breeding and genome-based studies. Maize landraces comprise large allelic diversity. We created doubled-haploid (DH) libraries from three European flint maize landraces and characterized them with respect to their molecular diversity, population structure, trait means, variances, and trait correlations. In total, 899 DH lines were evaluated using high-quality genotypic and multi-environment phenotypic data from up to 11 environments. The DH lines covered 95% of the molecular variation present in 35 landraces of an earlier study and represent the original three landrace populations in an unbiased manner. A comprehensive analysis of the target trait plant development at early growth stages as well as other important agronomic traits revealed large genetic variation for line per se and testcross performance. The majority of the 378 DH lines evaluated as testcrosses outperformed the commercial hybrids for early development. For total biomass yield, we observed a yield gap of 15% between mean testcross yield of the commercial hybrids and mean testcross yield of the DH lines. The DH lines also exhibited genetic variation for undesirable traits like root lodging and tillering, but correlations with target traits early development and yield were low or nonsignificant. The presented diversity atlas is a valuable, publicly available resource for genome-based studies to identify novel trait variation and evaluate the prospects of genomic prediction in landrace-derived material.

Correction: Care management intervention to strengthen self-care of multimorbid patients with type 2 diabetes in a German primary care network: A randomized controlled trial.

[This corrects the article DOI: 10.1371/journal.pone.0214056.].

Care management intervention to strengthen self-care of multimorbid patients with type 2 diabetes in a German primary care network: A randomized controlled trial.

PURPOSE: This study aimed to assess the effectiveness of a care management intervention in improving self-management behavior in multimorbid patients with type 2 diabetes; care was delivered by medical assistants in the context of a primary care network (PCN) in Germany. METHODS: This study is an 18-month, multi-center, two-armed, open-label, patient-randomized parallel-group superiority trial (ISRCTN 83908315). The intervention group received the care management intervention in addition to the usual care. The control group received usual care only. The primary outcome was the change in self-care behavior at month 9 compared to baseline. The self-care behavior was measured with the German version of the Summary of Diabetes Self-Care Activities Measure (SDSCA-G). A multilevel regression analysis was applied. RESULTS: We assigned 495 patients to intervention (n = 252) and control (n = 243). At baseline, the mean age was 68 ±11 years, 47.8% of the patients were female and the mean HbA1c was 7.1±1.2%. The primary analysis showed no statistically significant effect, but a positive trend was observed (p = 0.206; 95%-CI = -0.084; 0.384). The descriptive analysis revealed a significantly increased sum score of the SDSCA-G in the intervention group over time (P = 0.012) but not in the control group (p = 0.1973). CONCLUSION: The sum score for self-care behavior markedly improved in the intervention group over time. However, the results of our primary analysis showed no statistically significant effect. Possible reasons are the high baseline performance in our sample and the low intervention fidelity. The implementation of this care management intervention in PCNs has the potential to improve self-care behavior of multimorbid patients with type 2 diabetes.

HaploBlocker: Creation of Subgroup-Specific Haplotype Blocks and Libraries.

The concept of haplotype blocks has been shown to be useful in genetics. Fields of application range from the detection of regions under positive selection to statistical methods that make use of dimension reduction. We propose a novel approach ("HaploBlocker") for defining and inferring haplotype blocks that focuses on linkage instead of the commonly used population-wide measures of linkage disequilibrium. We define a haplotype block as a sequence of genetic markers that has a predefined minimum frequency in the population, and only haplotypes with a similar sequence of markers are considered to carry that block, effectively screening a dataset for group-wise identity-by-descent. From these haplotype blocks, we construct a haplotype library that represents a large proportion of genetic variability with a limited number of blocks. Our method is implemented in the associated R-package HaploBlocker, and provides flexibility not only to optimize the structure of the obtained haplotype library for subsequent analyses, but also to handle datasets of different marker density and genetic diversity. By using haplotype blocks instead of single nucleotide polymorphisms (SNPs), local epistatic interactions can be naturally modeled, and the reduced number of parameters enables a wide variety of new methods for further genomic analyses such as genomic prediction and the detection of selection signatures. We illustrate our methodology with a dataset comprising 501 doubled haploid lines in a European maize landrace genotyped at 501,124 SNPs. With the suggested approach, we identified 2991 haplotype blocks with an average length of 2685 SNPs that together represent 94% of the dataset.

Meeting the needs of multimorbid patients with Type 2 diabetes mellitus - A randomized controlled trial to assess the impact of a care management intervention aiming to improve self-care.

AIMS: This study explored the impact of a care management intervention aiming to improve self-care behavior in multimorbid individuals with Type 2 diabetes mellitus on health-related quality of life (HRQoL). METHODS: A patient-level randomized parallel-group superiority trial with 32 primary care practice teams, 11 care managers and 495 patients was conducted. The intervention was delivered as add-on to an already implemented disease management program and embedded in a network of primary care practices. Hierarchical linear modeling was used to analyze impacts of the care management approach on HRQoL. RESULTS: Small improvements of HRQoL in the intervention arm were found after nine months (r = 0.024; 95%CI = [0.000, 0.047]). However, compared to standard care no significant differences of HRQoL changes were observed (r = 0.022; 95%CI = [-0.011, 0.054]). Subgroup analyses showed effects for female participants favoring the intervention arm (r = 0.059; 95%CI = [0.010, 0.108]). No significant differences between intervention and control arm for several other subgroups were observed, including subgroups defined by comorbidities. CONCLUSION: Additional care management did not influence HRQoL over and above standard disease management. Improving diabetes patients' self-care behavior in the context of structured disease management programs may be difficult to achieve. Women might benefit from additional care management, but this finding needs to be confirmed in future research.

Testcross performance of doubled haploid lines from European flint maize landraces is promising for broadening the genetic base of elite germplasm.

KEY MESSAGE: Selected doubled haploid lines averaged similar testcross performance as their original landraces, and the best of them approached the yields of elite inbreds, demonstrating their potential to broaden the narrow genetic diversity of the flint germplasm pool. Maize landraces represent a rich source of genetic diversity that remains largely idle because the high genetic load and performance gap to elite germplasm hamper their use in modern breeding programs. Production of doubled haploid (DH) lines can mitigate problems associated with the use of landraces in pre-breeding. Our objective was to assess in comparison with modern materials the testcross performance (TP) of the best 89 out of 389 DH lines developed from six landraces and evaluated in previous studies for line per se performance (LP). TP with a dent tester was evaluated for the six original landraces, ~ 15 DH lines from each landrace selected for LP, and six elite flint inbreds together with nine commercial hybrids for grain and silage traits. Mean TP of the DH lines rarely differed significantly from TP of their corresponding landrace, which averaged in comparison with the mean TP of the elite flint inbreds ~ 20% lower grain yield and ~ 10% lower dry matter and methane yield. Trait correlations of DH lines closely agreed with the literature; correlation of TP with LP was zero for grain yield, underpinning the need to evaluate TP in addition to LP. For all traits, we observed substantial variation for TP among the DH lines and the best showed similar TP yields as the elite inbreds. Our results demonstrate the high potential of landraces for broadening the narrow genetic base of the flint heterotic pool and the usefulness of the DH technology for exploiting idle genetic resources from gene banks.