Computing approximate standard errors for genetic parameters derived from random regression models fitted by average information REML

Approximate standard errors (ASE) of variance components for random regression coefficients are calculated from the average information matrix obtained in a residual maximum likelihood procedure. Linear combinations of those coefficients define variance components for the additive genetic variance at given points of the trajectory. Therefore, ASE of these components and heritabilities derived from them can be calculated. In our example, the ASE were larger near the ends of the trajectory

Flexible, High-Speed, Small Satellite Production

Planet’s first mission is to image the entire land mass of the Earth every day in an effort to make global change visible, accessible, and actionable. To do this, Planet designs and builds highly capable Earth-imaging satellites and today operates the largest Earth-imaging fleet in history. To support this mission, Planet had to develop an adaptable concurrent product development cycle associated with a unique assembly and manufacturing line to support the quick production and delivery of satellites. This paper introduces how Planet achieved that objective by building multiple spacecraft design iterations concurrently and how Planet orchestrates a production line for speed, flexibility, and high throughput of satellite delivery in just over a few weeks

Estimation in a multiplicative mixed model involving a genetic relationship matrix

Genetic models partitioning additive and non-additive genetic effects for populations tested in replicated multi-environment trials (METs) in a plant breeding program have recently been presented in the literature. For these data, the variance model involves the direct product of a large numerator relationship matrix A, and a complex structure for the genotype by environment interaction effects, generally of a factor analytic (FA) form. With MET data, we expect a high correlation in genotype rankings between environments, leading to non-positive definite covariance matrices. Estimation methods for reduced rank models have been derived for the FA formulation with independent genotypes, and we employ these estimation methods for the more complex case involving the numerator relationship matrix. We examine the performance of differing genetic models for MET data with an embedded pedigree structure, and consider the magnitude of the non-additive variance. The capacity of existing software packages to fit these complex models is largely due to the use of the sparse matrix methodology and the average information algorithm. Here, we present an extension to the standard formulation necessary for estimation with a factor analytic structure across multiple environments

Wool and meat genetics - the joint possibilities

Wool and meat contribute to profit in sheep enterprises and both need to be considered in breeding programs. The relative responses expected from selection for a range of traits are presented and the realised responses that have been achieved in Merinos and variation in maternal breeds are illustrated. Knowledge of genetic parameters is required for the development of complex breeding objectives and selection indexes, comprehensive genetic evaluation of animals and the design of effective breeding programs. A review of world literature has highlighted the lack of accurate estimates of genetic parameters, especially for genetic correlations between trait groups. Analyses of a combined dataset from seven Australian Merino resource flocks comprising over 2000 sires and up to 100,000 records for each of various traits have provided accurate estimates of parameters to fill these gaps in current knowledge. The results show that there are no major genetic antagonisms between wool and meat traits and that improvement of both can be achieved by using appropriate selection indexes. Sheep Genetics Australia now provides a common system for genetic evaluation of Australian sheep, including across-flock estimated breeding values for a comprehensive range of traits and several standard indexes for various wool and meat breeding objectives

Sheep Updates 2006 - part 2

This session covers six papers from different authors: GENETICS 1. Novel selection traits - what are the possible side effects?, Darryl Smith, Kathryn Kemper, South Australian Research and Development Institute, David Rutley, University of Adelaide. 2. Genetic Changes in the Australian Merino since 1900, Sheep Genetics Australia Technical Committee, R.R. Woolaston Pullenvale, Queensland, D.J. Brown, Animal Genetics and Breeding Unit*, University of New England, K.D. Atkins, A.E. Casey, NSW Department of Primary Industries, A.J. Ball, Meat and Livestock Australia, University of New England 3. Influence of Sire Growth Estimated Breeding Value (EBV0 on Progeny Growth, David Hopkins, David Stanley, Leonie Martin, NSW Department Primary Industries, Centre for Sheep Meat Development, Arthur Gilmour, Remy van de Ven, NSW Department Primary Industries, Orange Agricultural Institute FINISHING 4. Predicting Input Sensitivity on Lamb Feedlot Profitability by Using Feedlot Calculator, David Stanley, NSW Department Primary Industries, Centre for Sheep Meat Development, Geoff Duddy, NSW Department Primary Industries, Yanco Agricultural Institute, Steve Semple, NSW Department Primary Industries, Orange Agricultural Institute, David Hopkins, NSW Department Primary Industries, Centre for Sheep Meat Development 5. Annual ryegrass toxicity (ARGT) in WA - 2006, David Kessell, Meat & Livestock Australia ARGT Project, Northam, WA 6. Poor ewe nutrition during pregnancy increases fatness of their progeny, Andrew Thompson, Department of Primary Industries, Victori

Mitogen- and Stress-Activated Kinase 1 (MSK1) Regulates Cigarette Smoke-Induced Histone Modifications on NF-κB-dependent Genes

Cigarette smoke (CS) causes sustained lung inflammation, which is an important event in the pathogenesis of chronic obstructive pulmonary disease (COPD). We have previously reported that IKKα (I kappaB kinase alpha) plays a key role in CS-induced pro-inflammatory gene transcription by chromatin modifications; however, the underlying role of downstream signaling kinase is not known. Mitogen- and stress-activated kinase 1 (MSK1) serves as a specific downstream NF-κB RelA/p65 kinase, mediating transcriptional activation of NF-κB-dependent pro-inflammatory genes. The role of MSK1 in nuclear signaling and chromatin modifications is not known, particularly in response to environmental stimuli. We hypothesized that MSK1 regulates chromatin modifications of pro-inflammatory gene promoters in response to CS. Here, we report that CS extract activates MSK1 in human lung epithelial (H292 and BEAS-2B) cell lines, human primary small airway epithelial cells (SAEC), and in mouse lung, resulting in phosphorylation of nuclear MSK1 (Thr581), phospho-acetylation of RelA/p65 at Ser276 and Lys310 respectively. This event was associated with phospho-acetylation of histone H3 (Ser10/Lys9) and acetylation of histone H4 (Lys12). MSK1 N- and C-terminal kinase-dead mutants, MSK1 siRNA-mediated knock-down in transiently transfected H292 cells, and MSK1 stable knock-down mouse embryonic fibroblasts significantly reduced CS extract-induced MSK1, NF-κB RelA/p65 activation, and posttranslational modifications of histones. CS extract/CS promotes the direct interaction of MSK1 with RelA/p65 and p300 in epithelial cells and in mouse lung. Furthermore, CS-mediated recruitment of MSK1 and its substrates to the promoters of NF-κB-dependent pro-inflammatory genes leads to transcriptional activation, as determined by chromatin immunoprecipitation. Thus, MSK1 is an important downstream kinase involved in CS-induced NF-κB activation and chromatin modifications, which have implications in pathogenesis of COPD

Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals

peer-reviewedH.D.D., A.J.C., P.J.B. and B.J.H. would like to acknowledge the Dairy Futures Cooperative Research Centre for funding. H.P. and R.F. acknowledge funding from the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr ‘Synbreed—Synergistic Plant and Animal Breeding’ (grant 0315527B). H.P., R.F., R.E. and K.-U.G. acknowledge the Arbeitsgemeinschaft Süddeutscher Rinderzüchter, the Arbeitsgemeinschaft Österreichischer Fleckviehzüchter and ZuchtData EDV Dienstleistungen for providing genotype data. A. Bagnato acknowledges the European Union (EU) Collaborative Project LowInputBreeds (grant agreement 222623) for providing Brown Swiss genotypes. Braunvieh Schweiz is acknowledged for providing Brown Swiss phenotypes. H.P. and R.F. acknowledge the German Holstein Association (DHV) and the Confederación de Asociaciones de Frisona Española (CONCAFE) for sharing genotype data. H.P. was financially supported by a postdoctoral fellowship from the Deutsche Forschungsgemeinschaft (DFG) (grant PA 2789/1-1). D.B. and D.C.P. acknowledge funding from the Research Stimulus Fund (11/S/112) and Science Foundation Ireland (14/IA/2576). M.S. and F.S.S. acknowledge the Canadian Dairy Network (CDN) for providing the Holstein genotypes. P.S. acknowledges funding from the Genome Canada project entitled ‘Whole Genome Selection through Genome Wide Imputation in Beef Cattle’ and acknowledges WestGrid and Compute/Calcul Canada for providing computing resources. J.F.T. was supported by the National Institute of Food and Agriculture, US Department of Agriculture, under awards 2013-68004-20364 and 2015-67015-23183. A. Bagnato, F.P., M.D. and J.W. acknowledge EU Collaborative Project Quantomics (grant 516 agreement 222664) for providing Brown Swiss and Finnish Ayrshire sequences and genotypes. A.C.B. and R.F.V. acknowledge funding from the public–private partnership ‘Breed4Food’ (code BO-22.04-011- 001-ASG-LR) and EU FP7 IRSES SEQSEL (grant 317697). A.C.B. and R.F.V. acknowledge CRV (Arnhem, the Netherlands) for providing data on Dutch and New Zealand Holstein and Jersey bulls.Stature is affected by many polymorphisms of small effect in humans1. In contrast, variation in dogs, even within breeds, has been suggested to be largely due to variants in a small number of genes2,3. Here we use data from cattle to compare the genetic architecture of stature to those in humans and dogs. We conducted a meta-analysis for stature using 58,265 cattle from 17 populations with 25.4 million imputed whole-genome sequence variants. Results showed that the genetic architecture of stature in cattle is similar to that in humans, as the lead variants in 163 significantly associated genomic regions (P < 5 × 10−8) explained at most 13.8% of the phenotypic variance. Most of these variants were noncoding, including variants that were also expression quantitative trait loci (eQTLs) and in ChIP–seq peaks. There was significant overlap in loci for stature with humans and dogs, suggesting that a set of common genes regulates body size in mammals

Proxy decision making and dementia: Using Construal Level Theory to analyse the thoughts of decision makers

Aims: This study explored the feasibility of using Construal Level Theory to analyse proxy decision maker thinking about a hypothetical ethical dilemma, relating to a person who has dementia. Background: Proxy decision makers make decisions on behalf of individuals who are living with dementia when dementia affects that individual's decision making ability. Ethical dilemmas arise because there is a need to balance the individual's past and contemporary values and views. Understanding of how proxy decision makers respond is incomplete. Construal Level Theory contends that individuals imagine reactions and make predications about the future by crossing psychological distance. This involves abstract thinking, giving meaning to decisions. There is no empirical evidence of Construal Level Theory being used to analyse proxy decision maker thinking. Exploring the feasibility of using Construal Level Theory to understand dementia carer thinking regarding proxy decisions may provide insights which inform the support given. Design: Descriptive qualitative research with semi‐structured interviews. Methods: Seven participants were interviewed using a hypothetical dementia care scenario in February 2016. Interview transcripts were analysed for themes. Construal Level Theory was applied to analyse participant responses within themes using the Linguistic Category Model. Results: Participants travelled across psychological distance, using abstract thinking to clarify goals and provide a basis for decisions. When thinking concretely participants established boundaries regarding the ethical dilemma. Conclusion: Construal Level Theory gives insight into proxy decision maker thinking and the levels of abstraction used. Understanding what dementia carers think about when making proxy decisions may help nurses to understand their perspectives and to provide appropriate support