Predicting the purebred-crossbred genetic correlation from phenotype and genotype data of parental lines pigs

In previous work, we theoretically derived expressions for an upper and lower bound of the correlation between purebred and crossbred performance (r pc), using only variance components of the parental purebred lines. In the current study, we validated these expressions in real data of pigs by comparing predicted bounds of r pc with the estimated r pc. We compared three methods to approximate the required variance components. The results suggest that the most useful method is to use ordinary REML estimates. If confirmed in other datasets, this approach may help breeders to predict the value of r pc based only on parental line information, or to determine the relative contributions of genotype by genotype and genotype by environment interactions to the value of r pc. We therefore advise studies estimating r pc with genotype data to also estimate and report genetic variance components within and between the parental lines, estimated as described in this study

Using convolutional neural networks for image-based genomic prediction in mice

Convolutional neural networks (CNN) are well suited image recognition tools for their ability to recognize latent patterns from images. Here, we investigated whether CNN can be used for genomic prediction. We created genomic images from genotype data and used them to predict phenotypes in mice. This approach was compared with traditional GBLUP and gradient boosting machine (GBM) models. For the two traits analysed, CNN was competitive in terms of predictive performance. The resolution of genomic images impacted model performance where, for this dataset, optimum results were obtained with 100×100 pixels. These first results demonstrate the potential of genomic images for genomic prediction using CNNs and merit investigation on adding layers of information to further increase accuracy of prediction

Proactive palliative care for patients with COPD (PROLONG): a pragmatic cluster controlled trial

Contains fulltext : 177904.pdf (publisher's version ) (Open Access)BACKGROUND AND AIM: Patients with advanced chronic obstructive pulmonary disease (COPD) have poor quality of life. The aim of this study was to assess the effects of proactive palliative care on the well-being of these patients. TRIAL REGISTRATION: This trial is registered with the Netherlands Trial Register, NTR4037. PATIENTS AND METHODS: A pragmatic cluster controlled trial (quasi-experimental design) was performed with hospitals as cluster (three intervention and three control) and a pretrial assessment was performed. Hospitals were selected for the intervention group based on the presence of a specialized palliative care team (SPCT). To control for confounders, a pretrial assessment was performed in which hospitals were compared on baseline characteristics. Patients with COPD with poor prognosis were recruited during hospitalization for acute exacerbation. All patients received usual care while patients in the intervention group received additional proactive palliative care in monthly meetings with an SPCT. Our primary outcome was change in quality of life score after 3 months, which was measured using the St George Respiratory Questionnaire (SGRQ). Secondary outcomes were, among others, quality of life at 6, 9 and 12 months; readmissions: survival; and having made advance care planning (ACP) choices. All analyses were performed following the principle of intention to treat. RESULTS: During the year 2014, 228 patients (90 intervention and 138 control) were recruited and at 3 months, 163 patients (67 intervention and 96 control) completed the SGRQ. There was no significant difference in change scores of the SGRQ total at 3 months between groups (-0.79 [95% CI, -4.61 to 3.34], p=0.70). However, patients who received proactive palliative care experienced less impact of their COPD (SGRQ impact subscale) at 6 months (-6.22 [-11.73 to -0.71], p=0.04) and had more often made ACP choices (adjusted odds ratio 3.26 [1.49-7.14], p=0.003). Other secondary outcomes were not significantly different. CONCLUSION: Proactive palliative care did not improve the overall quality of life of patients with COPD. However, patients more often made ACP choices which may lead to better quality of care toward the end of life

The Impact of Non-additive Effects on the Genetic Correlation Between Populations

Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations ([Formula: see text]). This [Formula: see text] can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of [Formula: see text] remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of [Formula: see text] In addition, we found that with epistasis, [Formula: see text] decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, [Formula: see text] had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true [Formula: see text] values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, [Formula: see text] dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations

Accuracy of genomic estimated breeding values for crossbred performance in broilers using a purebred or crossbred reference population

In breeding programs of pigs and poultry, selection is typically applied at the level of the purebred (PB) animals using PB data. This may result in a suboptimal response at the crossbred (CB) level when the genetic correlation between PB and CB performance () is lower than one. The objectives of this study, therefore, were to (1) estimate the , and (2) compare the accuracy of genomic estimated breeding values (GEBVs) for CB body weight in broilers using a PB reference population (RP) or a CB RP. Phenotype and genotype data were available for 5,274 PB and 10,395 CB offspring, and genotype data for their 163 PB sires. For comparison of cross-validation (CV) GEBV accuracies, the size of the CB RP was reduced to approximately match the size of the PB RP. Because the number of sires waslimited, validation was also performed on GEBVs of CB offspring of the sires. The estimated was 0.94 (0.04). The results show that the accuracy of sire GEBVs was slightly higher with a CB RP (0.43) compared to a PB RP (0.40). Similarly, the accuracy of CB offspring GEBVs was higher with a CB RP (0.47) compared to a PB RP (0.25). These results are likely due to (1) the that is lower than one, and (2) differences in genomic relationships between reference and validation animals. In finalizing this work, we will develop an equation that uses the accuracy of CB GEBVs for CB performance to predict the accuracy of PB GEBVs for CBperformance

The Impact of Non-additive Effects on the Genetic Correlation Between Populations

Average effects of alleles can show considerable differences between populations. The magnitude of these differences can be measured by the additive genetic correlation between populations ([Formula: see text]). This [Formula: see text] can be lower than one due to the presence of non-additive genetic effects together with differences in allele frequencies between populations. However, the relationship between the nature of non-additive effects, differences in allele frequencies, and the value of [Formula: see text] remains unclear, and was therefore the focus of this study. We simulated genotype data of two populations that have diverged under drift only, or under drift and selection, and we simulated traits where the genetic model and magnitude of non-additive effects were varied. Results showed that larger differences in allele frequencies and larger non-additive effects resulted in lower values of [Formula: see text] In addition, we found that with epistasis, [Formula: see text] decreases with an increase of the number of interactions per locus. For both dominance and epistasis, we found that, when non-additive effects became extremely large, [Formula: see text] had a lower bound that was determined by the type of inter-allelic interaction, and the difference in allele frequencies between populations. Given that dominance variance is usually small, our results show that it is unlikely that true [Formula: see text] values lower than 0.80 are due to dominance effects alone. With realistic levels of epistasis, [Formula: see text] dropped as low as 0.45. These results may contribute to the understanding of differences in genetic expression of complex traits between populations, and may help in explaining the inefficiency of genomic trait prediction across populations

Accuracy of genomic estimated breeding values for crossbred performance in broilers using a purebred or crossbred reference population

In breeding programs of pigs and poultry, selection is typically applied at the level of the purebred (PB) animals using PB data. This may result in a suboptimal response at the crossbred (CB) level when the genetic correlation between PB and CB performance () is lower than one. The objectives of this study, therefore, were to (1) estimate the , and (2) compare the accuracy of genomic estimated breeding values (GEBVs) for CB body weight in broilers using a PB reference population (RP) or a CB RP. Phenotype and genotype data were available for 5,274 PB and 10,395 CB offspring, and genotype data for their 163 PB sires. For comparison of cross-validation (CV) GEBV accuracies, the size of the CB RP was reduced to approximately match the size of the PB RP. Because the number of sires waslimited, validation was also performed on GEBVs of CB offspring of the sires. The estimated was 0.94 (0.04). The results show that the accuracy of sire GEBVs was slightly higher with a CB RP (0.43) compared to a PB RP (0.40). Similarly, the accuracy of CB offspring GEBVs was higher with a CB RP (0.47) compared to a PB RP (0.25). These results are likely due to (1) the that is lower than one, and (2) differences in genomic relationships between reference and validation animals. In finalizing this work, we will develop an equation that uses the accuracy of CB GEBVs for CB performance to predict the accuracy of PB GEBVs for CBperformance