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

Design of Experiments for Screening

The aim of this paper is to review methods of designing screening experiments, ranging from designs originally developed for physical experiments to those especially tailored to experiments on numerical models. The strengths and weaknesses of the various designs for screening variables in numerical models are discussed. First, classes of factorial designs for experiments to estimate main effects and interactions through a linear statistical model are described, specifically regular and nonregular fractional factorial designs, supersaturated designs and systematic fractional replicate designs. Generic issues of aliasing, bias and cancellation of factorial effects are discussed. Second, group screening experiments are considered including factorial group screening and sequential bifurcation. Third, random sampling plans are discussed including Latin hypercube sampling and sampling plans to estimate elementary effects. Fourth, a variety of modelling methods commonly employed with screening designs are briefly described. Finally, a novel study demonstrates six screening methods on two frequently-used exemplars, and their performances are compared

Ion implantation in nanodiamonds: Size effect and energy dependence

Nanoparticles are ubiquitous in nature and are increasingly important for technology. They are subject to bombardment by ionizing radiation in a diverse range of environments. In particular, nanodiamonds represent a variety of nanoparticles of significant fundamental and applied interest. Here we present a combined experimental and computational study of the behaviour of nanodiamonds under irradiation by xenon ions. Unexpectedly, we observed a pronounced size effect on the radiation resistance of the nanodiamonds: particles larger than 8 nm behave similarly to macroscopic diamond (i.e. characterized by high radiation resistance) whereas smaller particles can be completely destroyed by a single impact from an ion in a defined energy range. This latter observation is explained by extreme heating of the nanodiamonds by the penetrating ion. The obtained results are not limited to nanodiamonds, making them of interest for several fields, putting constraints on processes for the controlled modification of nanodiamonds, on the survival of dust in astrophysical environments, and on the behaviour of actinides released from nuclear waste into the environment

Traits associated with innate and adaptive immunity in pigs: heritability and associations with performance under different health status conditions

There is a need for genetic markers or biomarkers that can predict resistance towards a wide range of infectious diseases, especially within a health environment typical of commercial farms. Such markers also need to be heritable under these conditions and ideally correlate with commercial performance traits. In this study, we estimated the heritabilities of a wide range of immune traits, as potential biomarkers, and measured their relationship with performance within both specific pathogen-free (SPF) and non-SPF environments. Immune traits were measured in 674 SPF pigs and 606 non-SPF pigs, which were subsets of the populations for which we had performance measurements (average daily gain), viz. 1549 SPF pigs and 1093 non-SPF pigs. Immune traits measured included total and differential white blood cell counts, peripheral blood mononuclear leucocyte (PBML) subsets (CD4+ cells, total CD8α+ cells, classical CD8αβ+ cells, CD11R1+ cells (CD8α+ and CD8α-), B cells, monocytes and CD16+ cells) and acute phase proteins (alpha-1 acid glycoprotein (AGP), haptoglobin, C-reactive protein (CRP) and transthyretin). Nearly all traits tested were heritable regardless of health status, although the heritability estimate for average daily gain was lower under non-SPF conditions. There were also negative genetic correlations between performance and the following immune traits: CD11R1+ cells, monocytes and the acute phase protein AGP. The strength of the association between performance and AGP was not affected by health status. However, negative genetic correlations were only apparent between performance and monocytes under SPF conditions and between performance and CD11R1+ cells under non-SPF conditions. Although we cannot infer causality in these relationships, these results suggest a role for using some immune traits, particularly CD11R1+ cells or AGP concentrations, as predictors of pig performance under the lower health status conditions associated with commercial farms

A study to explore if dentists’ anxiety affects their clinical decision-making

Aims To develop a measure of dentists’ anxiety in clinical situations; to establish if dentists’ anxiety in clinical situations affected their self-reported clinical decision-making; to establish if occupational stress, as demonstrated by burnout, is associated with anxiety in clinical situations and clinical decision-making; and to explore the relationship between decision-making style and the clinical decisions which are influenced by anxiety. Design Cross-sectional study. Setting Primary Dental Care. Subjects and methods A questionnaire battery [Maslach Burnout Inventory, measuring burnout; Melbourne Decision Making Questionnaire, measuring decision-making style; Dealing with Uncertainty Questionnaire (DUQ), measuring coping with diagnostic uncertainty; and a newly designed Dentists’ Anxieties in Clinical Situations Scale, measuring dentists’ anxiety (DACSS-R) and change of treatment (DACSS-C)] was distributed to dentists practicing in Nottinghamshire and Lincolnshire. Demographic data were collected and dentists gave examples of anxiety-provoking situations and their responses to them. Main outcome measure Respondents’ self-reported anxiety in various clinical situations on a 11-point Likert Scale (DACSS-R) and self-reported changes in clinical procedures (Yes/No; DACSS-C). The DACSS was validated using multiple t-tests and a principal component analysis. Differences in DACSS-R ratings and burnout, decision-making and dealing with uncertainty were explored using Pearson correlations and multiple regression analysis. Qualitative data was subject to a thematic analysis. Results The DACSS-R revealed a four-factor structure and had high internal reliability (Cronbach’s α = 0.94). Those with higher DACSS-R scores of anxiety were more likely to report changes in clinical procedures (DACSS-C scores). DACSS-R scores were associated with decision-making self-esteem and style as measured by the MDMQ and all burnout subscales, though not with scores on the DUQ scale. Conclusion Dentists’ anxiety in clinical situations does affect the way that dentists work clinically, as assessed using the newly designed and validated DACSS. This anxiety is associated with measures of burnout and decision-making style with implications for training packages for dentists

Congruence of additive and non-additive effects on gene expression estimated from pedigree and SNP data

There is increasing evidence that heritable variation in gene expression underlies genetic variation in susceptibility to disease. Therefore, a comprehensive understanding of the similarity between relatives for transcript variation is warranted-in particular, dissection of phenotypic variation into additive and non-additive genetic factors and shared environmental effects. We conducted a gene expression study in blood samples of 862 individuals from 312 nuclear families containing MZ or DZ twin pairs using both pedigree and genotype information. From a pedigree analysis we show that the vast majority of genetic variation across 17,994 probes is additive, although non-additive genetic variation is identified for 960 transcripts. For 180 of the 960 transcripts with non-additive genetic variation, we identify expression quantitative trait loci (eQTL) with dominance effects in a sample of 339 unrelated individuals and replicate 31% of these associations in an independent sample of 139 unrelated individuals. Over-dominance was detected and replicated for a trans association between rs12313805 and ETV6, located 4MB apart on chromosome 12. Surprisingly, only 17 probes exhibit significant levels of common environmental effects, suggesting that environmental and lifestyle factors common to a family do not affect expression variation for most transcripts, at least those measured in blood. Consistent with the genetic architecture of common diseases, gene expression is predominantly additive, but a minority of transcripts display non-additive effects

Genomic Selection for Fruit Quality Traits in Apple (Malus×domestica Borkh.)

The genome sequence of apple (Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range (r2 = 0.33 and 0.19 at 100 kb and 1,000 kb respectively), contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits

Genetic architecture of gene expression in ovine skeletal muscle

In livestock populations the genetic contribution to muscling is intensively monitored in the progeny of industry sires and used as a tool in selective breeding programs. The genes and pathways conferring this genetic merit are largely undefined. Genetic variation within a population has potential, amongst other mechanisms, to alter gene expression via cis- or trans-acting mechanisms in a manner that impacts the functional activities of specific pathways that contribute to muscling traits. By integrating sire-based genetic merit information for a muscling trait with progeny-based gene expression data we directly tested the hypothesis that there is genetic structure in the gene expression program in ovine skeletal muscle. Results The genetic performance of six sires for a well defined muscling trait, longissimus lumborum muscle depth, was measured using extensive progeny testing and expressed as an Estimated Breeding Value by comparison with contemporary sires. Microarray gene expression data were obtained for longissimus lumborum samples taken from forty progeny of the six sires (4-8 progeny/sire). Initial unsupervised hierarchical clustering analysis revealed strong genetic architecture to the gene expression data, which also discriminated the sire-based Estimated Breeding Value for the trait. An integrated systems biology approach was then used to identify the major functional pathways contributing to the genetics of enhanced muscling by using both Estimated Breeding Value weighted gene co-expression network analysis and a differential gene co-expression network analysis. The modules of genes revealed by these analyses were enriched for a number of functional terms summarised as muscle sarcomere organisation and development, protein catabolism (proteosome), RNA processing, mitochondrial function and transcriptional regulation. Conclusions This study has revealed strong genetic structure in the gene expression program within ovine longissimus lumborum muscle. The balance between muscle protein synthesis, at the levels of both transcription and translation control, and protein catabolism mediated by regulated proteolysis is likely to be the primary determinant of the genetic merit for the muscling trait in this sheep population. There is also evidence that high genetic merit for muscling is associated with a fibre type shift toward fast glycolytic fibres. This study provides insight into mechanisms, presumably subject to strong artificial selection, that underpin enhanced muscling in sheep populations

Estimated breeding values and association mapping for persistency and total milk yield using natural cubic smoothing splines

BackgroundFor dairy producers, a reliable description of lactation curves is a valuable tool for management and selection. From a breeding and production viewpoint, milk yield persistency and total milk yield are important traits. Understanding the genetic drivers for the phenotypic variation of both these traits could provide a means for improving these traits in commercial production.MethodsIt has been shown that Natural Cubic Smoothing Splines (NCSS) can model the features of lactation curves with greater flexibility than the traditional parametric methods. NCSS were used to model the sire effect on the lactation curves of cows. The sire solutions for persistency and total milk yield were derived using NCSS and a whole-genome approach based on a hierarchical model was developed for a large association study using single nucleotide polymorphisms (SNP).ResultsEstimated sire breeding values (EBV) for persistency and milk yield were calculated using NCSS. Persistency EBV were correlated with peak yield but not with total milk yield. Several SNP were found to be associated with both traits and these were used to identify candidate genes for further investigation.ConclusionNCSS can be used to estimate EBV for lactation persistency and total milk yield, which in turn can be used in whole-genome association studies.Klara L. Verbyla and Arunas P. Verbyl