Isobel Claire Gormley

Summary: Mortality data are relevant to demography, public health and actuarial science; such data are high‐dimensional, making broad patterns difficult to identify. Clustering is increasingly used to reveal such patterns, yet a review of its application to all‐cause mortality with a focus on country‐level data is lacking. This review therefore summarises recent work and addresses key questions: Why clustering is used, which mortality data are analysed, which clustering methods are most common and what main findings emerge. To address these questions, we examine studies applying clustering to country‐level all‐cause mortality, focusing on mortality indices, data sources and methodological choices, and we replicate some approaches using Human Mortality Database (HMD) data. Our review reveals that clustering is mainly motivated by forecasting and by studying convergence and inequality. Most studies use HMD data from developed countries and rely on k‐means, hierarchical or functional clustering. Main findings include a persistent East–West European division across applications, with clustering generally improving forecast accuracy over single‐country models. Overall, this review highlights the methodological range in the literature, summarises clustering results and identifies gaps, such as the limited evaluation of clustering quality and the underuse of data from countries outside the high‐income world.

Age-specific probabilities of death provide a snapshot of population mortality at the country level at a given point in time. Due to the high dimensionality of the data, summarizing mortality information is essential for various analyses, such as visualization and clustering. We propose the use of beta latent variable (BLV) models to summarize mortality information without data transformation. A time-dependent version of the BLV model is developed by incorporating an autoregressive prior for the latent effects. This model aims to represent mortality data with a small set of K latent effects while accounting for time dependence between these effects. Inference is performed using Bayesian methods, with posterior samples generated via Hamiltonian Monte Carlo. The BLV model is applied to probabilities of death from the Human Mortality Database, covering 41 countries and 23 age-specific probabilities of death over several periods. The time-dependent BLV model with K=6 latent effects accurately reconstructs observed mortality data, and the model parameters have intuitive and insightful interpretations. The time-dependent BLV outperforms the standard Gaussian factor analysis model applied to logit probability of death, and demonstrates that BLV models can effectively summarize mortality data.

Variations in individuals’ metabolic profiles are the result of their genetic makeup and environmental and lifestyle factors. To address the challenge of identifying these intra-individual variations at the individual level, we introduce “MetaboVariation 2.0”, a multivariate Bayesian generalised linear model designed to flag individuals with intra-individual variations in metabolite levels across repeated measurements. MetaboVariation 2.0 builds upon the previous univariate MetaboVariation approach by incorporating dependencies between metabolites, offering a more comprehensive assessment of individual metabolic variations. While simultaneously considering all metabolites, MetaboVariation 2.0 flags an individual when their observed metabolite levels deviate from their individual-level posterior predictive interval at a time point. A series of simulation studies were conducted to evaluate the performance of MetaboVariation 2.0. In addition it was applied to a metabolomics data set. The efficacy of this approach was validated through a series of simulation studies. These simulations demonstrated that the multivariate model outperformed its predecessor, particularly in scenarios where the dependencies between the metabolites were positive. The model showed lower mean absolute differences between correlation matrices of metabolite levels from replicate datasets and the original simulated data, indicating improved accuracy in capturing the metabolic dependencies. In addition, analysis of plasma metabolite levels from 164 individuals with 20 metabolites measured across four time points was performed to detect individuals with intra-individual variations. MetaboVariation 2.0 revealed intra-individual variations in 15.2% of the individuals, with 20% or more of their metabolites showing variations beyond their 97.5% posterior predictive intervals in at least one time point. In conclusion, MetaboVariation 2.0 accounts for the inherent dependencies between different metabolites, offering a full view of an individual’s metabolic profile which is an important advancement for assessment of individual-level metabolite variation. A software implementation of this approach is freely available through the “MetaboVariation” R package, promoting its accessibility and use in broader metabolomics research.

Mortality data are relevant to demography, public health, and actuarial science. Whilst clustering is increasingly used to explore patterns in such data, no study has reviewed its application to country-level all-cause mortality. This review therefore summarises recent work and addresses key questions: why clustering is used, which mortality data are analysed, which methods are most common, and what main findings emerge. To address these questions, we examine studies applying clustering to country-level all-cause mortality, focusing on mortality indices, data sources, and methodological choices, and we replicate some approaches using Human Mortality Database (HMD) data. Our analysis reveals that clustering is mainly motivated by forecasting and by studying convergence and inequality. Most studies use HMD data from developed countries and rely on k-means, hierarchical, or functional clustering. Main findings include a persistent East-West European division across applications, with clustering generally improving forecast accuracy over single-country models. Overall, this review highlights the methodological range in the literature, summarises clustering results, and identifies gaps, such as the limited evaluation of clustering quality and the underuse of data from countries outside the high-income world.

Societal and economic pressure to reduce nitrogen use in the dairy cow sector is increasing. One potential contributing solution is to breed more nitrogen-efficient dairy cows that excrete less nitrogen. Although genetic variability for nitrogen utilization has been documented in dairy cows, how the phenotypic variance is partitioned into its causal components across lactation has not been previously explored in dairy cows. The objective, therefore, of the present study was to use 4,600 nitrogen utilization phenotypes across 2,255 lactations from 1,296 grazing dairy cows to explore whether the genetic variation in nitrogen utilization changes throughout lactation. Nitrogen utilization in the present study was evaluated using 2 metrics: nitrogen use efficiency (NUE), the ratio of nitrogen used and stored relative to the nitrogen available, and nitrogen balance (Nbal), calculated as nitrogen available minus the nitrogen used and stored. A random regression model was used to explore the variability of each studied trait over the lactation as well as the within-trait covariances across the lactation. The genetic variability in both NUE and Nbal did indeed change throughout lactation. The genetic SD of NUE decreased rapidly from 0.018 (SE = 0.003) at 6 DIM to 0.007 (SE = 0.001) at 82 DIM, then remained relatively constant until 191 DIM before increasing thereafter to 0.016 (SE = 0.002) at 268 DIM. Mirroring the change in the genetic variability across the lactation, the heritability of NUE decreased from 0.35 (SE = 0.09) at 6 DIM to 0.07 (SE = 0.03) at 80 DIM, before it increased up to 0.48 (SE = 0.10) by the end of the lactation. The genetic SD of Nbal increased from 11.87 (SE = 0.05) g/d of N at 6 DIM to 29.92 (SE = 3.61) g/d of N at 268 DIM, which was reflected in a gradual increase in heritability from 0.05 (SE = 0.04) at 6 DIM to 0.23 (SE = 0.05) at 268 DIM. Moreover, the within-trait genetic correlations between different DIM for NUE ranged from strongly positive to -0.25 (SE = 0.16), which was between 6 and 148 DIM. The within-trait genetic correlations for Nbal between different DIM were all strongly positive, with the weakest value being 0.61 (SE = 0.19), which was between 6 and 268 DIM. This profile in genetic correlations, combined with the sign of the eigenfunctions of the genetic covariance matrix for both nitrogen efficiency traits across lactation, suggest a greater potential to modify the shape of the lactation profile for NUE through genetic selection compared with Nbal. Nevertheless, there is still a good opportunity to genetically modify the height of the Nbal profile throughout lactation. Finally, given the limited evidence of a nonzero genetic correlation between NUE and Nbal, should both traits be deemed important, then both should be considered in a breeding objective to improve the nitrogen utilization metrics of dairy cows.

Bovine tuberculosis (bTB) remains recalcitrant to eradication in many endemic countries where current diagnostics are suboptimal. Mycobacterium bovis causes bTB and is closely related to Mycobacterium tuberculosis , which causes human tuberculosis (hTB). Although blood-based mRNA biomarkers identified through machine learning can discriminate hTB-positive from hTB-negative individuals, similar approaches have not been explored for bTB. Here, we use RNA-seq and machine learning to investigate the utility of blood mRNA as a host-response biomarker for bTB. We identify a 30-gene signature and a 273-gene elastic net classifier that differentiate bTB-positive from bTB-negative cattle, achieving area under the curve (AUC) values of 0.986/0.900 for the former and 0.968/0.938 for the latter in training and testing, respectively. Additionally, we show that these classifiers distinguish bTB-positive cattle from cattle infected with other microbial pathogens (AUC ≥ 0.819). These mRNA-based classifiers represent a promising tool for augmenting current diagnostics to advance global bTB eradication efforts.

The functional impact of genomic regulatory variation during active TB disease and anti-TB treatment (ATT) remains poorly understood. Here, using 240 paired whole-blood RNA-seq samples from n = 48 active TB patients who underwent ATT, we call and impute 1,506,948 genome-wide variants from these transcriptomes to map cis-expression quantitative trait loci (eQTL), response-eQTL, and cell type interaction eQTL (ieQTL) by computationally deconvolving the bulk RNA-seq data. We show that the whole-blood transcriptome is substantially perturbed during ATT by identifying 7,246 differentially expressed genes. We characterise 5,129 cis-eQTL and 587 response-eQTL and show that genes associated with reQTL are significantly enriched in pathways impacting the host response to mycobacterial challenge and xenobiotic metabolism. We highlight significant changes in cell type proportions due to ATT, notably for NK cells and classical monocytes. We characterise 921 ieQTL, including an active-TB classical monocyte-specific ieQTL for the gene NOD2. Our work sheds light on the immunogenetics of TB disease and treatment, while providing a framework for integrative genomics studies using only RNA-seq data.

Lameness is one of the major production diseases affecting dairy cattle. It is associated with negative welfare in affected cattle, economic losses at the farm level, and adverse effects on sustainability. Prompt identification of lameness is necessary to facilitate early treatment, enhance animal welfare, and mitigate short and long-term production impacts associated with the disease. In recent years, automated detection systems have emerged as a potential solution for identifying early signs of lameness. Among these systems, accelerometers have been widely adopted, as they continuously capture data on animal movement. Analyzing accelerometer data is challenging due to its wide, high-dimensional structure as it has many features and typically much fewer animals or samples, reducing the utility of many machine learning (ML) models and increasing the risk of overfitting. To handle this, researchers often summarize accelerometer data into indices like step counts, which simplifies analysis but may sacrifice important details needed for accurate prediction of lameness. Dimension reduction techniques, such as principal component analysis (PCA) and functional principal component analysis (fPCA), offer solutions by reducing the dimensionality of the data while retaining key information and allowing for the application of a broader set of ML approaches. Using data containing 20 thousand recordings from 383 dairy cows in 11 dairy herds, this study evaluated the effectiveness of ML methods in detecting foot lesions in dairy cows using accelerometer data, with a focus on dimensionality reduction approaches and cross-validation strategies. Our study offers practical insights for the dairy industry by highlighting the potential benefits of combining dimensionality reduction with cross-validation strategies to improve the performance of ML methods applied to wide accelerometer data. In addition, our study highlights the impact and importance of using data from independent farms. A by-farm approach to cross-validation will likely give a more robust, realistic estimate of general model performance.

Summary: Understanding the functional impact of genomic sequence variants is critical for evaluating the role of genetic variation in the host response during tuberculosis (TB) disease and anti-TB treatment (ATT). Hitherto, there have been no genome-wide in vivo response expression quantitative trait loci (reQTL) studies conducted for active TB and ATT. Here, using longitudinal peripheral blood RNA-seq data from n = 48 patients with active TB who underwent ATT, we call sequence variants directly from these transcriptomes and impute them with a multi-ancestry reference panel. Associating our variants with the expression of nearby genes, we characterise thousands of cis -eQTL and hundreds of reQTL. We further show significant changes in cell type proportions during ATT through deconvolution of the bulk RNA-seq data and identify the putative cell type specific nature of cis -eQTL. Our work sheds light on the immunogenetics of TB disease and treatment, while providing a framework for studies using only RNA-seq data.