Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat

Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement

Older adults and mobile technology: Factors that enhance and inhibit utilization in the context of behavioral health

While numbers and proportions of older adults with behavioral health issues are expected to substantially increase, there is also a widening gap in available services for older adults. Mobile health interventions (mhealth) are a way to address existing barriers to treatment, provide frontline assessment and increase access to services for older adults. Due to perpetuated stereotypes, many assume that older adults do not utilize mobile technology nor will they accept a mHealth intervention. The purpose of this paper is to synthesize contemporary literature from information technology and healthcare regarding: (1) current mobile technology utilization by older adults, particularly in regards to health; (2) factors affecting older adult motivation to engage with mobile technology; and (3) older adult preferences for interacting with mobile technology. Findings reveal that significant proportions of older adults: already utilize mobile technology; are willing to engage in existing mobile interventions for health reasons; and have positive attitudes overall towards mobile technology. Finally, recommendations for optimizing mobile interventions to better suit older adults with behavioral health problems are reviewed

Maximizing the potential of multi-parental crop populations.

Most agriculturally significant crop traits are quantitatively inherited which limits the ease and efficiency of trait dissection. Multi-parent populations overcome the limitations of traditional trait mapping and offer new potential to accurately define the genetic basis of complex crop traits. The increasing popularity and use of nested association mapping (NAM) and multi-parent advanced generation intercross (MAGIC) populations raises questions about the optimal design and allocation of resources in their creation. In this paper we review strategies for the creation of multi-parent populations and describe two complementary in silico studies addressing the design and construction of NAM and MAGIC populations. The first simulates the selection of diverse founder parents and the second the influence of multi-parent crossing schemes (and number of founders) on haplotype creation and diversity. We present and apply two open software resources to simulate alternate strategies for the development of multi-parent populations

Maintenance of UK bread baking quality: Trends in wheat quality traits over 50 years of breeding and potential for future application of genomic-assisted selection

Improved selection of wheat varieties with high end-use quality contributes to sustainable food systems by ensuring productive crops are suitable for human consumption end-uses. Here, we investigated the genetic control and genomic prediction of milling and baking quality traits in a panel of 379 historic and elite, high-quality UK bread wheat (Triticum eastivum L.) varieties and breeding lines. Analysis of the panel showed that genetic diversity has not declined over recent decades of selective breeding while phenotypic analysis found a clear trend of increased loaf baking quality of modern milling wheats despite declining grain protein content. Genome-wide association analysis identified 24 quantitative trait loci (QTL) across all quality traits, many of which had pleiotropic effects. Changes in the frequency of positive alleles of QTL over recent decades reflected trends in trait variation and reveal where progress has historically been made for improved baking quality traits. It also demonstrates opportunities for marker-assisted selection for traits such as Hagberg falling number and specific weight that do not appear to have been improved by recent decades of phenotypic selection. We demonstrate that applying genomic prediction in a commercial wheat breeding program for expensive late-stage loaf baking quality traits outperforms phenotypic selection based on early-stage predictive quality traits. Finally, trait-assisted genomic prediction combining both phenotypic and genomic selection enabled slightly higher prediction accuracy, but genomic prediction alone was the most cost-effective selection strategy considering genotyping and phenotyping costs per sample

Effectiveness of a structured, framework-based approach to implementation: the Researching Effective Approaches to Cleaning in Hospitals (REACH) Trial

BACKGROUND: Implementing sustainable practice change in hospital cleaning has proven to be an ongoing challenge in reducing healthcare associated infections. The purpose of this study was to develop a reliable framework-based approach to implement and quantitatively evaluate the implementation of evidence-based practice change in hospital cleaning. DESIGN/METHODS: The Researching Effective Approaches to Cleaning in Hospitals (REACH) trial was a pragmatic, stepped-wedge randomised trial of an environmental cleaning bundle implemented in 11 Australian hospitals from 2016 to 2017. Using a structured multi-step approach, we adapted the integrated Promoting Action on Research Implementation in Health Services (i-PARIHS) framework to support rigorous and tailored implementation of the cleaning bundle intervention in eleven diverse and complex settings. To evaluate the effectiveness of this strategy we examined post-intervention cleaning bundle alignment calculated as a score (an implementation measure) and cleaning performance audit data collected using ultraviolet (UV) gel markers (an outcome measure). RESULTS: We successfully implemented the bundle and observed improvements in cleaning practice and performance, regardless of hospital size, intervention duration and contextual issues such as staff and organisational readiness at baseline. There was a positive association between bundle alignment scores and cleaning performance at baseline. This diminished over the duration of the intervention, as hospitals with lower baseline scores were able to implement practice change successfully. CONCLUSION: Using a structured framework-based approach allows for pragmatic and successful implementation of clinical trials across diverse settings, and assists with quantitative evaluation of practice change. TRIAL REGISTRATION: Australia New Zealand Clinical Trial Registry ACTRN12615000325505, registered on 4 September 2015

Limited haplotype diversity underlies polygenic trait architecture across 70 years of wheat breeding

Background Selection has dramatically shaped genetic and phenotypic variation in bread wheat. We can assess the genomic basis of historical phenotypic changes, and the potential for future improvement, using experimental populations that attempt to undo selection through the randomizing effects of recombination. Results We bred the NIAB Diverse MAGIC multi-parent population comprising over 500 recombinant inbred lines, descended from sixteen historical UK bread wheat varieties released between 1935 and 2004. We sequence the founders’ genes and promoters by capture, and the MAGIC population by low-coverage whole-genome sequencing. We impute 1.1 M high-quality SNPs that are over 99% concordant with array genotypes. Imputation accuracy only marginally improves when including the founders’ genomes as a haplotype reference panel. Despite capturing 73% of global wheat genetic polymorphism, 83% of genes cluster into no more than three haplotypes. We phenotype 47 agronomic traits over 2 years and map 136 genome-wide significant associations, concentrated at 42 genetic loci with large and often pleiotropic effects. Around half of these overlap known quantitative trait loci. Most traits exhibit extensive polygenicity, as revealed by multi-locus shrinkage modelling. Conclusions Our results are consistent with a gene pool of low haplotypic diversity, containing few novel loci of large effect. Most past, and projected future, phenotypic changes arising from existing variation involve fine-scale shuffling of a few haplotypes to recombine dozens of polygenic alleles of small effect. Moreover, extensive pleiotropy means selection on one trait will have unintended consequences, exemplified by the negative trade-off between yield and protein content, unless selection and recombination can break unfavorable trait-trait associations

The Microevolution and Epidemiology of Staphylococcus aureus Colonization during Atopic Eczema Disease Flare.

Staphylococcus aureus is an opportunistic pathogen and variable component of the human microbiota. A characteristic of atopic eczema (AE) is colonization by S. aureus, with exacerbations associated with an increased bacterial burden of the organism. Despite this, the origins and genetic diversity of S. aureus colonizing individual patients during AE disease flares is poorly understood. To examine the microevolution of S. aureus colonization, we deep sequenced S. aureus populations from nine children with moderate to severe AE and 18 non-atopic children asymptomatically carrying S. aureus nasally. Colonization by clonal S. aureus populations was observed in both AE patients and control participants, with all but one of the individuals carrying colonies belonging to a single sequence type. Phylogenetic analysis showed that disease flares were associated with the clonal expansion of the S. aureus population, occurring over a period of weeks to months. There was a significant difference in the genetic backgrounds of S. aureus colonizing AE cases versus controls (Fisher exact test, P = 0.03). Examination of intra-host genetic heterogeneity of the colonizing S. aureus populations identified evidence of within-host selection in the AE patients, with AE variants being potentially selectively advantageous for intracellular persistence and treatment resistance.CPH was supported by Wellcome Trust (grant number 104241/z/14/z). MTGH, KAP, and KO were supported by the Scottish Infection Research Network and Chief Scientist Office through the Scottish Healthcare Associated Infection Prevention Institute consortium funding (CSO reference: SIRN10). Bioinformatics and computational biology analyses were supported by the University of St Andrews Bioinformatics Unit that is funded by a Wellcome Trust ISSF award (grant 097831/Z/11/Z). JP and MTGH were supported by Wellcome Trust grant 098051. AEM is supported by Biotechnology and Biological Sciences Research Council grant BB/M014088/1. SJB is supported by a Wellcome Trust Senior Research Fellowship in Clinical Science (106865/Z/15/Z)

The Epoch of Disk Settling: Z Approximately Equal to 1 to Now

We present evidence from a sample of 544 galaxies from the DEEP2 Survey for evolution of the internal kinematics of blue galaxies over 0.2 < z < 1.2. DEEP2 provides a large sample of high resolution galaxy spectra and dual-band Hubble imaging from which we measure emission-line kinematics and galaxy inclinations, respectively. Our large sample allows us to overcome scatter intrinsic to galaxy properties, in order to examine trends. At a fixed stellar mass, galaxies systematically decrease in disturbed motions and increase in rotation velocity and potential well depth with time. The most massive galaxies are the most well-ordered at all times, with higher rotation velocities and less disturbed motions compared to less massive galaxies. We quantify disturbed motions with an integrated gas velocity dispersion (sigma(sub g)), which is unlike the typical pressure-supported velocity dispersion measured for early type galaxies and galaxy bulges. Due to finite slit width and seeing, sigma(sub g) integrates over unresolved velocity gradients which can correspond to non-ordered gas kinematics such as small-scale velocity gradients, gas motions due to star-formation, or super-imposed clumps along the line-of-sight. We compile surveys of galaxy kinematics over 1.2 < z < 3.8 and do not find any trends with redshift, likely because these studies are biased toward the most highly star-forming systems. In summary, over the last approx 8 billion years since z = 1.2, blue galaxies evolve from disturbed to ordered systems as they settle to become the rotation-dominated disk galaxies observed in the Universe today, with the most massive galaxies always being the most evolved at any time

Going back to school – an opportunity for lifelong learning for people with dementia in Denmark (Innovative practice)

The provision of lifelong learning for older people is often promoted as a way of engaging socially and maintaining cognitive function. The concept is also used with people with dementia, but is often limited to short-term programmes. Innovative practice from Denmark takes this concept further, offering people with early stage dementia the opportunity to return to school to attend classes in cognitive training, music, art and woodcraft. A pilot study conducted by the school of teaching and communication (VUK), offers evidence for the benefits of prolonged educational programmes for people with dementia in maintaining decision making, cognitive function and social interactions, with limited evidence of the impact on memory. Further evidence is required to understand the impact of a person with dementia attending school as a student and to understand if this concept is transferrable to a different cultural setting

Multi-trait ensemble genomic prediction and simulations of recurrent selection highlight importance of complex trait genetic architecture for long-term genetic gains in wheat

Cereal crop breeders have achieved considerable genetic gain in genetically complex traits, such as grain yield, while maintaining genetic diversity. However, focus on selection for yield has negatively impacted other important traits. To better understand multi-trait selection within a breeding context, and how it might be optimized, we analysed genotypic and phenotypic data from a genetically diverse, 16-founder wheat multi-parent advanced generation inter-cross population. Compared to single-trait models, multi-trait ensemble genomic prediction models increased prediction accuracy for almost 90 % of traits, improving grain yield prediction accuracy by 3–52 %. For complex traits, non-parametric models (Random Forest) also outperformed simplified, additive models (LASSO), increasing grain yield prediction accuracy by 10–36 %. Simulations of recurrent genomic selection then showed that sustained greater forward prediction accuracy optimized long-term genetic gains. Simulations of selection on grain yield found indirect responses in related traits, involving optimized antagonistic trait relationships. We found multi-trait selection indices could effectively optimize undesirable relationships, such as the trade-off between grain yield and protein content, or combine traits of interest, such as yield and weed competitive ability. Simulations of phenotypic selection found that including Random Forest rather than LASSO genetic models, and multi-trait rather than single-trait models as the true genetic model accelerated and extended long-term genetic gain whilst maintaining genetic diversity. These results (i) suggest important roles of pleiotropy and epistasis in the wider context of wheat breeding programmes, and (ii) provide insights into mechanisms for continued genetic gain in a limited genepool and optimization of multiple traits for crop improvement