A bivariate quantitative genetic model for a threshold trait and a survival trait

Many of the functional traits considered in animal breeding can be analyzed as threshold traits or survival traits with examples including disease traits, conformation scores, calving difficulty and longevity. In this paper we derive and implement a bivariate quantitative genetic model for a threshold character and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted in which model parameters were augmented with unobserved liabilities associated with the threshold trait. The fully conditional posterior distributions associated with parameters of the threshold trait reduced to well known distributions. For the survival trait the two baseline Weibull parameters were updated jointly by a Metropolis-Hastings step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. The Gibbs sampler was tested in a simulation study and illustrated in a joint analysis of calving difficulty and longevity of dairy cattle. The simulation study showed that the estimated marginal posterior distributions covered well and placed high density to the true values used in the simulation of data. The data analysis of calving difficulty and longevity showed that genetic variation exists for both traits. The additive genetic correlation was moderately favorable with marginal posterior mean equal to 0.37 and 95% central posterior credibility interval ranging between 0.11 and 0.61. Therefore, this study suggests that selection for improving one of the two traits will be beneficial for the other trait as well

A bivariate quantitative genetic model for a linear Gaussian trait and a survival trait

With the increasing use of survival models in animal breeding to address the genetic aspects of mainly longevity of livestock but also disease traits, the need for methods to infer genetic correlations and to do multivariate evaluations of survival traits and other types of traits has become increasingly important. In this study we derived and implemented a bivariate quantitative genetic model for a linear Gaussian and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted. Model parameters were inferred from their marginal posterior distributions. The required fully conditional posterior distributions were derived and issues on implementation are discussed. The two Weibull baseline parameters were updated jointly using a Metropolis-Hasting step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. Simulation results showed that the estimated marginal posterior distributions covered well and placed high density to the true parameter values used in the simulation of data. In conclusion, the proposed method allows inferring additive genetic and environmental correlations, and doing multivariate genetic evaluation of a linear Gaussian trait and a survival trait

Analysis of rabbit doe longevity using a semiparametric log-Normal animal frailty model with time-dependent covariates

Data on doe longevity in a rabbit population were analysed using a semiparametric log-Normal animal frailty model. Longevity was defined as the time from the first positive pregnancy test to death or culling due to pathological problems. Does culled for other reasons had right censored records of longevity. The model included time dependent covariates associated with year by season, the interaction between physiological state and the number of young born alive, and between order of positive pregnancy test and physiological state. The model also included an additive genetic effect and a residual in log frailty. Properties of marginal posterior distributions of specific parameters were inferred from a full Bayesian analysis using Gibbs sampling. All of the fully conditional posterior distributions defining a Gibbs sampler were easy to sample from, either directly or using adaptive rejection sampling. The marginal posterior mean estimates of the additive genetic variance and of the residual variance in log frailty were 0.247 and 0.690

Loss of C/EBPα cell cycle control increases myeloid progenitor proliferation and transforms the neutrophil granulocyte lineage

CCAAT/enhancer binding protein (C/EBP)α is a myeloid-specific transcription factor that couples lineage commitment to terminal differentiation and cell cycle arrest, and is found mutated in 9% of patients who have acute myeloid leukemia (AML). We previously showed that mutations which dissociate the ability of C/EBPα to block cell cycle progression through E2F inhibition from its function as a transcriptional activator impair the in vivo development of the neutrophil granulocyte and adipose lineages. We now show that such mutations increase the capacity of bone marrow (BM) myeloid progenitors to proliferate, and predispose mice to a granulocytic myeloproliferative disorder and transformation of the myeloid compartment of the BM. Both of these phenotypes were transplantable into lethally irradiated recipients. BM transformation was characterized by a block in granulocyte differentiation, accumulation of myeloblasts and promyelocytes, and expansion of myeloid progenitor populations—all characteristics of AML. Circulating myeloblasts and hepatic leukocyte infiltration were observed, but thrombocytopenia, anemia, and elevated leukocyte count—normally associated with AML—were absent. These results show that disrupting the cell cycle regulatory function of C/EBPα is sufficient to initiate AML-like transformation of the granulocytic lineage, but only partially the peripheral pathology of AML

Mammalian tissues defective in nonsense-mediated mRNA decay display highly aberrant splicing patterns

BACKGROUND: Nonsense-mediated mRNA decay (NMD) affects the outcome of alternative splicing by degrading mRNA isoforms with premature termination codons. Splicing regulators constitute important NMD targets; however, the extent to which loss of NMD causes extensive deregulation of alternative splicing has not previously been assayed in a global, unbiased manner. Here, we combine mouse genetics and RNA-seq to provide the first in vivo analysis of the global impact of NMD on splicing patterns in two primary mouse tissues ablated for the NMD factor UPF2. RESULTS: We developed a bioinformatic pipeline that maps RNA-seq data to a combinatorial exon database, predicts NMD-susceptibility for mRNA isoforms and calculates the distribution of major splice isoform classes. We present a catalog of NMD-regulated alternative splicing events, showing that isoforms of 30% of all expressed genes are upregulated in NMD-deficient cells and that NMD targets all major splicing classes. Importantly, NMD-dependent effects are not restricted to premature termination codon+ isoforms but also involve an abundance of splicing events that do not generate premature termination codons. Supporting their functional importance, the latter events are associated with high intronic conservation. CONCLUSIONS: Our data demonstrate that NMD regulates alternative splicing outcomes through an intricate web of splicing regulators and that its loss leads to the deregulation of a panoply of splicing events, providing novel insights into its role in core- and tissue-specific regulation of gene expression. Thus, our study extends the importance of NMD from an mRNA quality pathway to a regulator of several layers of gene expression

The Danish Atrial Fibrillation Registry:A Multidisciplinary National Pragmatic Initiative for Monitoring and Supporting Quality of Care Based on Data Retrieved from Administrative Registries

AIM: The Danish Atrial Fibrillation (AF) Registry monitors and supports improvement of quality of care for all AF patients in Denmark. This report describes the registry's administrative and organizational structure, data sources, data flow, data analyses, annual reporting, and feedback between the registry, clinicians, and the administrative system. We also report the selection process of the quality indicators and the temporal trends in results from 2017-2021.METHODS AND RESULTS: The Danish AF Registry aims for complete registration and monitoring of care for all patients diagnosed with AF in Denmark. Administrative registries provide data on contacts to general practice, contacts to private cardiology practice, hospital contacts, medication prescriptions, updated vital status information, and biochemical test results. The Danish Stroke Registry provides information on stroke events. From 2017 to 2021, the proportion with a reported echocardiography among incident AF patients increased from 39.9% (95% CI: 39.3-40.6) to 82.6% (95% CI: 82.1-83.1). The initiation of oral anticoagulant therapy among patients with incident AF and a CHA2DS2-VASc score of ≥1 in men and ≥2 in women increased from 85.3% (95% CI: 84.6-85.9) to 90.4% (95% CI: 89.9-91.0). The 1-year and 2-year persistence increased from 85.2% (95% CI: 84.5-85.9) to 88.7% (95% CI: 88.0-89.3), and from 85.4% (95% CI: 84.7-86.2) to 88.2% (95% CI: 87.5-88.8), respectively. The 1-year risk of ischemic stroke among prevalent patients with AF decreased from 0.88% (95% CI: 0.83-0.93) to 0.71% (95% CI: 0.66-0.75). Variation in clinical performance between the five administrative Danish regions was reduced.CONCLUSION: Continuous nationwide monitoring of quality indicators for AF originating from administrative registries is feasible and supportive of improvements of quality of care.</p

Population genomics of the Viking world.

The maritime expansion of Scandinavian populations during the Viking Age (about AD 750-1050) was a far-flung transformation in world history1,2. Here we sequenced the genomes of 442 humans from archaeological sites across Europe and Greenland (to a median depth of about 1×) to understand the global influence of this expansion. We find the Viking period involved gene flow into Scandinavia from the south and east. We observe genetic structure within Scandinavia, with diversity hotspots in the south and restricted gene flow within Scandinavia. We find evidence for a major influx of Danish ancestry into England; a Swedish influx into the Baltic; and Norwegian influx into Ireland, Iceland and Greenland. Additionally, we see substantial ancestry from elsewhere in Europe entering Scandinavia during the Viking Age. Our ancient DNA analysis also revealed that a Viking expedition included close family members. By comparing with modern populations, we find that pigmentation-associated loci have undergone strong population differentiation during the past millennium, and trace positively selected loci-including the lactase-persistence allele of LCT and alleles of ANKA that are associated with the immune response-in detail. We conclude that the Viking diaspora was characterized by substantial transregional engagement: distinct populations influenced the genomic makeup of different regions of Europe, and Scandinavia experienced increased contact with the rest of the continent

A bivariate quantitative genetic model for a threshold trait and a survival trait

Many of the functional traits considered in animal breeding can be analyzed as threshold traits or survival traits with examples including disease traits, conformation scores, calving difficulty and longevity. In this paper we derive and implement a bivariate quantitative genetic model for a threshold character and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted in which model parameters were augmented with unobserved liabilities associated with the threshold trait. The fully conditional posterior distributions associated with parameters of the threshold trait reduced to well known distributions. For the survival trait the two baseline Weibull parameters were updated jointly by a Metropolis-Hastings step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. The Gibbs sampler was tested in a simulation study and illustrated in a joint analysis of calving difficulty and longevity of dairy cattle. The simulation study showed that the estimated marginal posterior distributions covered well and placed high density to the true values used in the simulation of data. The data analysis of calving difficulty and longevity showed that genetic variation exists for both traits. The additive genetic correlation was moderately favorable with marginal posterior mean equal to 0.37 and 95% central posterior credibility interval ranging between 0.11 and 0.61. Therefore, this study suggests that selection for improving one of the two traits will be beneficial for the other trait as well

A bivariate quantitative genetic model for a linear Gaussian trait and a survival trait

With the increasing use of survival models in animal breeding to address the genetic aspects of mainly longevity of livestock but also disease traits, the need for methods to infer genetic correlations and to do multivariate evaluations of survival traits and other types of traits has become increasingly important. In this study we derived and implemented a bivariate quantitative genetic model for a linear Gaussian and a survival trait that are genetically and environmentally correlated. For the survival trait, we considered the Weibull log-normal animal frailty model. A Bayesian approach using Gibbs sampling was adopted. Model parameters were inferred from their marginal posterior distributions. The required fully conditional posterior distributions were derived and issues on implementation are discussed. The two Weibull baseline parameters were updated jointly using a Metropolis-Hasting step. The remaining model parameters with non-normalized fully conditional distributions were updated univariately using adaptive rejection sampling. Simulation results showed that the estimated marginal posterior distributions covered well and placed high density to the true parameter values used in the simulation of data. In conclusion, the proposed method allows inferring additive genetic and environmental correlations, and doing multivariate genetic evaluation of a linear Gaussian trait and a survival trait