Bayesian inference on major loci in related multigeneration selection lines of laying hens

A mixed inheritance model, postulating a polygenic effect and differences between the 3 genotypes of a biallelic locus, was fitted separately to the data of 2 multigeneration selection lines and a control evolving from a common base population. Inferences about the model were drawn from the application of the Gibbs sampler. Body weight at 20 and 40 wk (BW20, BW40) and average egg weight to 40 wk (EW40) were included in the analyses. Significance of differences between posterior means of parameters was established by comparing their 95% highest probability density regions. Significant (P 0.05) differences of posterior means of any parameter could be observed between lines. No significant genotypic or polygenic selection response was found for BW40. On the contrary, both genetic responses were found significant for EW40 in the selected lines, but not in the control. No differences (P > 0.05) between lines could be observed for the derived frequencies of the allele causing the higher trait value and the frequencies of one homozygote and the heterozygote genotypes at the major locus. The detection of a major locus with relatively modest effect confirmed that this type of analysis with data from selected lines was indeed powerfu

Efficiency of genomic selection using Bayesian multi-marker models for traits selected to reflect a wide range of heritabilities and frequencies of detected quantitative traits loci in mice

BACKGROUND: Genomic selection uses dense single nucleotide polymorphisms (SNP) markers to predict breeding values, as compared to conventional evaluations which estimate polygenic effects based on phenotypic records and pedigree information. The objective of this study was to compare polygenic, genomic and combined polygenic-genomic models, including mixture models (labelled according to the percentage of genotyped SNP markers considered to have a substantial effect, ranging from 2.5% to 100%). The data consisted of phenotypes and SNP genotypes (10,946 SNPs) of 2,188 mice. Various growth, behavioural and physiological traits were selected for the analysis to reflect a wide range of heritabilities (0.10 to 0.74) and numbers of detected quantitative traits loci (QTL) (1 to 20) affecting those traits. The analysis included estimation of variance components and cross-validation within and between families. RESULTS: Genomic selection showed a high predictive ability (PA) in comparison to traditional polygenic selection, especially for traits of moderate heritability and when cross-validation was between families. This occurred although the proportion of genomic variance of traits using genomic models was 22 to 33% smaller than using polygenic models. Using a 2.5% mixture genomic model, the proportion of genomic variance was 79% smaller relative to the polygenic model. Although the proportion of variance explained by the markers was reduced further when a smaller number of SNPs was assumed to have a substantial effect on the trait, PA of genomic selection for most traits was little affected. These low mixture percentages resulted in improved estimates of single SNP effects. Genomic models implemented for traits with fewer QTLs showed even lower PA than the polygenic models. CONCLUSIONS: Genomic selection generally performed better than traditional polygenic selection, especially in the context of between family cross-validation. Reducing the number of markers considered to affect the trait did not significantly change PA for most traits, particularly in the case of within family cross-validation, but increased the number of markers found to be associated with QTLs. The underlying number of QTLs affecting the trait has an effect on PA, with a smaller number of QTLs resulting in lower PA using the genomic model compared to the polygenic model

A two step Bayesian approach for genomic prediction of breeding values

<p>Abstract</p> <p>Background</p> <p>In genomic models that assign an individual variance to each marker, the contribution of one marker to the posterior distribution of the marker variance is only one degree of freedom (df), which introduces many variance parameters with only little information per variance parameter. A better alternative could be to form clusters of markers with similar effects where markers in a cluster have a common variance. Therefore, the influence of each marker group of size <it>p </it>on the posterior distribution of the marker variances will be <it>p </it>df.</p> <p>Methods</p> <p>The simulated data from the 15^thQTL-MAS workshop were analyzed such that SNP markers were ranked based on their effects and markers with similar estimated effects were grouped together. In step 1, all markers with minor allele frequency more than 0.01 were included in a SNP-BLUP prediction model. In step 2, markers were ranked based on their estimated variance on the trait in step 1 and each 150 markers were assigned to one group with a common variance. In further analyses, subsets of 1500 and 450 markers with largest effects in step 2 were kept in the prediction model.</p> <p>Results</p> <p>Grouping markers outperformed SNP-BLUP model in terms of accuracy of predicted breeding values. However, the accuracies of predicted breeding values were lower than Bayesian methods with marker specific variances.</p> <p>Conclusions</p> <p>Grouping markers is less flexible than allowing each marker to have a specific marker variance but, by grouping, the power to estimate marker variances increases. A prior knowledge of the genetic architecture of the trait is necessary for clustering markers and appropriate prior parameterization.</p

LIFE Adaptamed Layman’s Report. Action E13. LIFE14 CCA/ES/000612

Aguas de Font Vella y Lanjaró

Genomic Prediction in Tetraploid Ryegrass Using Allele Frequencies Based on Genotyping by Sequencing

Perennial ryegrass is an outbreeding forage species and is one of the most widely used forage grasses in temperate regions. The aim of this study was to investigate the possibility of implementing genomic prediction in tetraploid perennial ryegrass, to study the effects of different sequencing depth when using genotyping-by-sequencing (GBS), and to determine optimal number of single-nucleotide polymorphism (SNP) markers and sequencing depth for GBS data when applied in tetraploids. A total of 1,515 F2 tetraploid ryegrass families were included in the study and phenotypes and genotypes were scored on family-pools. The traits considered were dry matter yield (DM), rust resistance (RUST), and heading date (HD). The genomic information was obtained in the form of allele frequencies of pooled family samples using GBS. Different SNP filtering strategies were designed. The strategies included filtering out SNPs having low average depth (FILTLOW), having high average depth (FILTHIGH), and having both low average and high average depth (FILTBOTH). In addition, SNPs were kept randomly with different data sizes (RAN). The accuracy of genomic prediction was evaluated by using a “leave single F2 family out” cross validation scheme, and the predictive ability and bias were assessed by correlating phenotypes corrected for fixed effects with predicted additive breeding values. Among all the filtering scenarios, the highest estimates for genomic heritability of family means were 0.45, 0.74, and 0.73 for DM, HD and RUST, respectively. The predictive ability generally increased as the number of SNPs included in the analysis increased. The highest predictive ability for DM was 0.34 (137,191 SNPs having average depth higher than 10), for HD was 0.77 (185,297 SNPs having average depth lower than 60), and for RUST was 0.55 (188,832 SNPs having average depth higher than 1). Genomic prediction can help to optimize the breeding of tetraploid ryegrass. GBS data including about 80–100 K SNPs are needed for accurate prediction of additive breeding values in tetraploid ryegrass. Using only SNPs with sequencing depth between 10 and 20 gave highest predictive ability, and showed the potential to obtain accurate prediction from medium-low coverage GBS in tetraploids

Response Properties of Nucleus Reticularis Lateralis Neurons After Electroacupuncture Stimulation in Rats

A descending inhibitory mechanism from the periaqueductal gray (PAG) to the spinal cord through the nucleus raphe magnus (NRM) is strongly involved in endogenous analgesic system produced by acupuncture stimulation. In addition to the PAG to NRM system which descends in the medial pathway of the brain stem, the nucleus reticularis lateralis (NRL) situated in the lateral part of the brain stem is reported to play an important role in modulating centrifugal antinociceptive action. In the present study, to clarify the role of NRL in acupuncture analgesia, we investigated the response properties of NRL neurons to acupuncture stimulation. The majority of NRM-projecting NRL neurons were inhibited by electroacupuncture stimulation. This effect was antagonized by ionophoretic application of naloxone, indicating that endogenous opioids act directly onto these NRL neurons. By contrast, about half of spinal projecting NRL neurons were excited by electroacupuncture stimulation, suggesting that part of the NRL neurons may modulate pain transmission directly at the spinal level

Informe Layman Life Adaptamed. Acción E13. LIFE14 CCA/ES/000612

Aguas de Font Vella y Lanjaró

Genome-assisted prediction of a quantitative trait measured in parents and progeny: application to food conversion rate in chickens

Accuracy of prediction of yet-to-be observed phenotypes for food conversion rate (FCR) in broilers was studied in a genome-assisted selection context. Data consisted of FCR measured on the progeny of 394 sires with SNP information. A Bayesian regression model (Bayes A) and a semi-parametric approach (Reproducing kernel Hilbert Spaces regression, RKHS) using all available SNPs (p = 3481) were compared with a standard linear model in which future performance was predicted using pedigree indexes in the absence of genomic data. The RKHS regression was also tested on several sets of pre-selected SNPs (p = 400) using alternative measures of the information gain provided by the SNPs. All analyses were performed using 333 genotyped sires as training set, and predictions were made on 61 birds as testing set, which were sons of sires in the training set. Accuracy of prediction was measured as the Spearman correlation (r¯S) between observed and predicted phenotype, with its confidence interval assessed through a bootstrap approach. A large improvement of genome-assisted prediction (up to an almost 4-fold increase in accuracy) was found relative to pedigree index. Bayes A and RKHS regression were equally accurate (r¯S = 0.27) when all 3481 SNPs were included in the model. However, RKHS with 400 pre-selected informative SNPs was more accurate than Bayes A with all SNPs

Changing the culture of assessment: the dominance of the summative assessment paradigm

Background Despite growing evidence of the benefits of including assessment for learning strategies within programmes of assessment, practical implementation of these approaches is often problematical. Organisational culture change is often hindered by personal and collective beliefs which encourage adherence to the existing organisational paradigm. We aimed to explore how these beliefs influenced proposals to redesign a summative assessment culture in order to improve students’ use of assessment-related feedback. Methods Using the principles of participatory design, a mixed group comprising medical students, clinical teachers and senior faculty members was challenged to develop radical solutions to improve the use of post-assessment feedback. Follow-up interviews were conducted with individual members of the group to explore their personal beliefs about the proposed redesign. Data were analysed using a socio-cultural lens. Results Proposed changes were dominated by a shared belief in the primacy of the summative assessment paradigm, which prevented radical redesign solutions from being accepted by group members. Participants’ prior assessment experiences strongly influenced proposals for change. As participants had largely only experienced a summative assessment culture, they found it difficult to conceptualise radical change in the assessment culture. Although all group members participated, students were less successful at persuading the group to adopt their ideas. Faculty members and clinical teachers often used indirect techniques to close down discussions. The strength of individual beliefs became more apparent in the follow-up interviews. Conclusions Naïve epistemologies and prior personal experiences were influential in the assessment redesign but were usually not expressed explicitly in a group setting, perhaps because of cultural conventions of politeness. In order to successfully implement a change in assessment culture, firmly-held intuitive beliefs about summative assessment will need to be clearly understood as a first step