Health Traits Research Articles

Operationalisation of post-COVID condition case definition in a comprehensive research protocol.

Post-COVID-19 condition (PCC) is a prevalent and high-burden sequela of SARS-CoV-2 infection. Because of the complexity of its manifestations, PCC case definition currently lacks standardisation and reproducibility. We aimed to devise a simple screening tool to boost reproducibility and comparability of PCC case definition across PCC studies, and to provide a framework in which to reliably identify suspected PCC cases. We also developed a comprehensive assessment protocol based on the most frequently reported PCC characteristics. Within a European multi-centre study and based on the conclusions of a previous systematic review, we devised a checklist to assess the presence of PCC-associated symptoms and their temporal relationship with COVID-19. The checklist was developed to include three symptom cores (cognitive, psychiatric, and general). For each symptom, onset is assessed within 3 months after COVID-19 resolution and persistence for at least 2 months. Any symptom fulfilling this criterion is sufficient to prompt suspicion of PCC. At least one symptom is required in the cognitive or psychiatric domains to suspect PCC with neuropsychiatric involvement. Our protocol features an extensive neuropsychological evaluation and self-administered scales for mood, anxiety, stress-related symptoms, sleep disorders, quality of life, disability, mental health, and personality traits; scales for quantitative assessment of fatigue and headache are also included. Consistent identification of PCC cases is crucial to correctly include patients in research and clinical studies. We propose a simple, reproducible, and flexible screening tool and a proposal for a comprehensive assessment that could be employed to enhance standardisation and comparability.

Soil biological health assessment based on nematode communities under maize and peanut intercropping

Abstract Background Cereal/legume intercropping can enhance crop productivity and improve soil health in dryland farming. However, little is known about soil biological health under maize/peanut intercropping. The aim of this study was to assess soil biological health based on nematode communities in a maize/peanut intercropping system. Methods We conducted a field experiment with different planting patterns, including monoculture maize (M), monoculture peanut (P), and maize intercropped with peanut (IM, intercropped maize; IP, intercropped peanut) to determine the influence on soil biological health. We measured soil physicochemical properties and nematode communities, and employed exploratory factor analysis combined with cumulative normal distribution curve scoring to identify potential soil biological health traits. Results The intercropped maize gave the highest plant parasitic nematode abundance, trophic diversity index, evenness index, and structure index. The monoculture peanut gave the highest enrichment index and least plant parasitic nematode abundance, trophic diversity index, Shannon diversity index, evenness index, structure index, and channel index. We identified four soil biological health traits, including basic nutrients and biodiversity, food web complexity, slow energy channel, and fast energy channel, mainly represented by soil ammonium nitrogen and Shannon diversity index, structural index and omnivore-predator nematode abundance, fungivorous nematode abundance and plant parasitic nematode abundance, microbial biomass carbon and bacterivorous nematode abundance, respectively. The intercropping systems improved the comprehensive score of soil biological health, especially maize intercropping soil. Intercropping maize and intercropping peanut significantly improved oil biological health traits representing the food web complexity compared with the corresponding monoculture soils. Conclusions Our results indicate that soil nematode and physicochemical indicators reflect different soil biological health traits. Among those traits, the improvement of basic nutrients and biodiversity and the complexity of the food web were the main reasons for improving soil biological health through the intercropping system.

A dominant missense variant within LMBR1 related to equine polydactyly

Polydactyly was recorded before 100 BCE and attracted widespread interest because of its relationship to limb health and ancestral traits in horses. However, the underlying reasons for the development of polydactyly remain unclear. To search for polydactyly-related genes, we utilize a paternal half-sib family and screen for variants that match the mode of inheritance. Through this screening process, 77 variants in 65 genes are filtered. A missense variant (EqCab3.0 chr4: <107353368> A > G) (rs1138485164) in the 3rd exon of LMBR1 is identified as a source of amino acid sequence variation. Gene editing confirms that the variant down-regulates LMBR1expression, increases the proliferative viability of mutant cells, and inhibits apoptosis. This study suggests that LMBR1 might play a role in the development of polydactyly and that the variant detected in this study is related to polydactyly in horses. However, further research is needed to determine whether a direct relationship exists.

Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort

Classifying individuals based on metabotypes and lifestyle phenotypes using exploratory factor analyses, cluster definition, and machine-learning algorithms is promising for precision chronic disease prevention and management. This study analyzed data from the NUTRiMDEA online cohort (baseline: n = 17332 and 62 questions) to develop a clustering tool based on 32 accessible questions using machine-learning strategies. Participants ranged from 18 to over 70 years old, with 64.1% female and 35.5% male. Five clusters were identified, combining metabolic, lifestyle, and personal data: Cluster 1 (“Westernized Millennial”, n = 967) included healthy young individuals with fair lifestyle habits; Cluster 2 (“Healthy”, n = 10616) consisted of healthy adults; Cluster 3 (“Mediterranean Young Adult”, n = 2013) represented healthy young adults with a healthy lifestyle and showed the highest adherence to the Mediterranean diet; Cluster 4 (“Pre-morbid”, n = 600) was characterized by healthy adults with declined mood; Cluster 5 (“Pro-morbid”, n = 312) comprised older individuals (47% >55 years) with poorer lifestyle habits, worse health, and a lower health-related quality of life. A computational algorithm was elicited, which allowed quick cluster assignment based on responses (“lifemetabotypes”). This machine-learning approach facilitates personalized interventions and precision lifestyle recommendations, supporting online methods for targeted health maintenance and chronic disease prevention.

MeHA: A Computational Framework in Revealing the Genetic Basis of Animal Mental Health Traits Under an Intensive Farming System-A Case Study in Pigs.

Intensively farmed animals such as pigs inevitably experience a certain degree of psychological stress, which leads to a reduction in production performance. Mental health traits are currently difficult to measure, resulting in a gap in understanding their genetic basis. To address this challenge, we propose a computational framework called mental health of animals (MeHA), capable of revealing genes related to animal mental health traits. Using MeHA, we identified 109 candidate genes associated with pig mental health and discovered their intricate connections with critical functions, such as memory, cognition, and neural development, which are essential components of mental health and cognitive performance. Importantly, our findings provide evidence of the potential impact of these genes on economically important traits, including meat quality and piglet survival. This research underscores the importance of genetic studies in enhancing our understanding of animal behavior and cognition, as well as promoting agricultural practices. By applying our approach to study the genetic basis of mental health in pigs as a case, we confirmed that our framework is an effective way to reveal genetic factors affecting animal mental health traits, which contributes to animal welfare and has potential implications for understanding human mental disorders.

Path analysis reveals cross-country differences between Czech and Chinese university students in effect of internet and smartphone addiction, mental health, and personality traits on academic achievement in the post-pandemic era

Despite extensive evidence on the impact of various mental health issues including smartphone/internet addiction, and personality traits on academic achievement, little is known about the complex interactions between multiple of these factors simultaneously, as well as cross-country differences in these nuanced relationships. In particular, understanding the role of the mentioned addictions has become increasingly important in recent years in the context of the COVID-19 pandemic. The aim of this cross-country study was to investigate, using path analysis, the complex relationships between mental health determinants (depression, anxiety, stress, resilience, and smartphone/internet addiction) and academic achievement in 1785 Czech and Chinese university students using an online battery of psychological tests. The results confirmed the previously described effect of multiple factors (anxiety, stress, resilience, smartphone/internet addiction, personality traits, and sex, Extraversion, Agreeableness, Conscientiousness) on academic achievement, overlapping in most cases for both groups of students. At the same time, however, different country-dependent patterns of interactions emerged. For the Czech students, the variables formed a complex network of interacting factors, whereas for the Chinese students, the effect of each cluster of factors was separate for individual domains of academic achievement. These cross-country differences have implications particularly for planning and targeting the most effective interventions to promote and develop academic achievement.

Back pain exercise therapy remodels human epigenetic profiles in buccal and human peripheral blood mononuclear cells: an exploratory study in young male participants.

With its high and increasing lifetime prevalence, back pain represents a contemporary challenge for patients and healthcare providers. Monitored exercise therapy is a commonly prescribed treatment to relieve pain and functional limitations. However, the benefits of exercise are often gradual, subtle, and evaluated by subjective self-reported scores. Back pain pathogenesis is interlinked with epigenetically mediated processes that modify gene expression without altering the DNA sequence. Therefore, we hypothesize that therapy effects can be objectively evaluated by measurable epigenetic histone posttranslational modifications and proteome expression. Because epigenetic modifications are dynamic and responsive to environmental exposure, lifestyle choices-such as physical activity-can alter epigenetic profiles, subsequent gene expression, and health traits. Instead of invasive sampling (e.g., muscle biopsy), we collect easily accessible buccal swabs and plasma. The plasma proteome provides a systemic understanding of a person's current health state and is an ideal snapshot of downstream, epigenetically regulated, changes upon therapy. This study investigates how molecular profiles evolve in response to standardized sport therapy and non-controlled lifestyle choices. We report that the therapy improves agility, attenuates back pain, and triggers healthier habits. We find that a subset of participants' histone methylation and acetylation profiles cluster samples according to their therapy status, before or after therapy. Integrating epigenetic reprogramming of both buccal cells and peripheral blood mononuclear cells (PBMCs) reveals that these concomitant changes are concordant with higher levels of self-rated back pain improvement and agility gain. Additionally, epigenetic changes correlate with changes in immune response plasma factors, reflecting their comparable ability to rate therapy effects at the molecular level. We also performed an exploratory analysis to confirm the usability of molecular profiles in (1) mapping lifestyle choices and (2) evaluating the distance of a given participant to an optimal health state. This pre-post cohort study highlights the potential of integrated molecular profiles to score therapy efficiency. Our findings reflect the complex interplay of an individual's background and lifestyle upon therapeutic exposure. Future studies are needed to provide mechanistic insights into back pain pathogenesis and lifestyle-based epigenetic reprogramming upon sport therapy intervention to maintain therapeutic effects in the long run.

Effect of host breeds on gut microbiome and fecal metabolome in commercial pigs

BackgroundGut microbial composition and its metabolites are crucial for livestock production performance. Metabolite profiles from autopsied biospecimens provide vital information on the basic mechanisms that affect the overall health and production traits in livestock animals. However, the role of the host breed in the gut microbiome and fecal metabolome of commercial pigs remains unclear. In this work, differences in microbiota composition among three commercial pig breeds Duroc, Yorkshire, and Landrace were measured by 16S rRNA gene sequencing. Fecal metabolite compositions of the three pig breeds were detected using untargeted metabolomics.ResultsThere were significant differences in the gut microbiomes of the three species, indicating that host breed affects the diversity and structure of gut microbiota. Several breed-associated microorganisms were identified at different taxonomic levels. Notely, most microbial taxa were annotated as Lactobacillacea, Muribaculaceae, and Oscillospiraceae. Several bacteria, including Lactobacillus, Subdoligranulum, Faecalibacterium, Oscillospira, Oscillospiraceae_UCG-002, and Christensenellaceae_R-7_group, could be considered as biomarkers for improving the backfat thickness (BF) for commercial pigs. Additionally, KEGG analysis of gut microbiota further revealed that arginine biosynthesis, pyruvate metabolism, and fatty acid biosynthesis varied greatly among pig breeds. Multiple gut bacterial metabolites (e.g., spermidine, estradiol, and palmitic acid) were identified as breed-associated. Mediation analysis ultimately revealed the cross-talk among gut microbiota, metabolites, and BF thickness, proclaiming that the microbial and metabolic biomarkers identified in this study could be used as biomarkers for improving BF phenotype.ConclusionsThis work provides vital insights into breed effects on gut microbiota and metabolite compositions of commercial pigs and uncovers potential biomarkers that are significant for pig breed improvement.

Short communication: Do veterinary diagnoses coming from electronic recording system of veterinary treatments have the potential to be used for breeding in small populations? The case of the dual-purpose Alpine Grey cattle breed

In cattle breeding, emphasis has historically been placed on productive traits, so-called primary traits, while health and fitness traits have been overlooked due to their low heritability and partly negative genetic correlations with productivity. In addition, the recording of phenotypes caused problems due to a lack of high-quality information and / or high costs of recording. This oversight has additionally led to increased incidences of fertility issues and health problems in cattle populations, resulting in economic losses and consumer concerns regarding animal welfare and food safety. This study investigates the potential use of veterinary diagnoses, collected via the Italian electronic recording system of veterinary treatments, for breeding decisions in the local dual-purpose Alpine Grey cattle breed for which the consideration of functional traits in breeding has also become imperative over the last years. The dataset comprised 97 821 records spanning from 2021 to 2023, representing 8 273 individuals and 18 110 lactations of the Alpine Grey breed. Each record documented the administration of treatments for mammary, respiratory, and urogenital pathologies. After data editing and model development, heritability estimates were obtained for each pathology using univariate linear animal models and gibbs sampling algorithms. Results indicate that mammary pathologies affected the highest number of cows (11%), followed by respiratory (4%) and urogenital (5%) pathologies. Heritability estimates ranged from 0.01 to 0.04 across all traits, confirming generally low heritability values. Respiratory pathologies exhibited the highest heritability (0.04; sd 0.04), followed by mammary (0.03; sd 0.01) and urogenital (0.01; sd 0.01) pathologies. This study, although preliminary, provides valuable insights into the health status of local dual-purpose Alpine Grey cattle and highlights the potential of considering veterinary diagnoses for improving animal health and welfare in dairy cattle breeding. However, the low heritability of these traits, which is consistent with literature data, clearly shows the current limitations of using them for breeding. Further, the diagnostic key of the Italian electronic recording system of veterinary treatments should be specified for the use of health data for breeding purposes, as it is formulated in too general manner in its current form. Nevertheless, their significance for animal welfare and economic sustainability underlines the need for their consideration in dairy cattle breeding. Therefore, further research with larger sample sizes and consideration of additional indicators, such as somatic cell counts, is recommended to enhance the accuracy of genetic evaluations and improve breeding strategies for sustainable cattle production.

A landmark federal interagency collaboration to promote data science in health care: Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now.

In 2016, the Department of Veterans Affairs (VA) and the Department of Energy (DOE) established an Interagency Agreement (IAA), the Million Veteran Program-Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP-CHAMPION) research collaboration. Oversight fell under the VA Office of Research Development (VA ORD) and DOE headquarters. An Executive Committee and 2 senior scientific liaisons work with VA and DOE leadership to optimize efforts in the service of shared scientific goals. The program supported centralized data management and genomic analysis including creation of a scalable approach to cataloging phenotypes. Cross-cutting methods including natural language processing, image processing, and reusable code were developed. The 79.6 million dollar collaboration has supported centralized data management and genomic analysis including a scalable approach to cataloging phenotypes and launched over 10 collaborative scientific projects in health conditions highly prevalent in veterans. A ground-breaking analysis on the Summit and Andes supercomputers at the Oak Ridge National Laboratory (ORNL) of the genetic underpinnings of over 2000 health conditions across 44 million genetic variants which resulted in the identification of 38270 independent genetic variants associating with one or more health traits. Of these, over 2000 identified associations were unique to non-European ancestry. Cross-cutting methods have advanced state-of-the-art artificial intelligence (AI) including large language natural language processing and a system biology study focused on opioid addiction awarded the 2018 Gordon Bell Prize for outstanding achievement in high-performance computing. The collaboration has completed work in prostate cancer, suicide prevention, and cardiovascular disease, and cross-cutting data science. Predictive models developed in these projects are being tested for application in clinical management. Eight new projects were launched in 2023, taking advantage of the momentum generated by the previous collaboration. A major challenge has been limitations in the scope of appropriated funds at DOE which cannot currently be used for health research. Extensive multidisciplinary interactions take time to establish and are essential to continued progress. New funding models for maintaining high-performance computing infrastructure at the ORNL and for supporting continued collaboration by joint VA-DOE research teams are needed.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

Progeny Selection and Genetic Diversity in a Pinus taeda Clonal Seed Orchard

The present article describes the development of an improved Pinus taeda clonal seed orchard adapted to the edaphoclimatic conditions of Uruguay. Initially, 2068 hectares distributed in nine companies were prospected, and 124 plus trees were identified based on growth, straightness, and health traits. These trees were clonally propagated via grafting to establish a clonal seed orchard. For the genetic evaluation of the orchard, two progeny tests were carried out in the Rivera and Paysandú municipalities. Quantitative genetic analyses allowed us to identify a simple genotype–environment interaction and an expected genetic gain for volumes of 17%, 13%, and 8% for selection intensities of 12%, 25%, and 50%, respectively. Moreover, the genetic diversity of the 124 clones of the orchard was assessed using 10 microsatellite markers. The fingerprinting profiles allowed us to identify a total of 224 alleles. The polymorphism information content of the different markers was in the range of 0.594 to 0.895. The combined probability of identity and probability of identity among siblings had a discrimination power of 8.26 × 10–14 and 5.91 × 10–5, respectively. Analysis of the genetic structure demonstrated that the seed orchard population was not structured by the supplier.

Insecticide and low food quality treatments reduce health and pollination services of two key pollinator taxa

Abstract Pesticides and the lack of floral resources are key drivers of insect decline in agricultural areas. Both land‐use stressors can have a variety of synergistic and sublethal effects on pollinators, affecting their health and foraging behaviour. Pollinating insects include species with vastly different life histories, giving them potentially different vulnerability and resilience to stressors. However, most research has generally focused on bees. Here, we contrast synergistic effects of nutritional stress and pesticide use on social bumblebees and solitary hoverflies. We experimentally tested the effects of the neonicotinoid acetamiprid and low‐quality food on health traits of Bombus terrestris workers and Episyrphus balteatus adults (ovarian development, body size and colony development or survival). The foraging behaviour of treated pollinators was recorded in a semi‐field setup and, for B. terrestris, we measured the pollen amount on the body surface and the pollen deposited on stigmata after a single floral visit. Both stressors affected bumblebee health. Additionally, insecticide‐treated workers showed increased flower handling times and flight durations, while low‐quality food reduced the amount of pollen on the surface of bumblebees and the pollen deposited on stigmata. Syrphids were mostly affected by low‐quality food, which decreased their survival probability and changed their foraging behaviour. Furthermore, we found an interactive effect between the two stressors, reducing E. balteatus ovary development. Synthesis and applications. Insecticide and food stress affected fitness traits of both bumblebees and syrphid flies, possibly reducing pollinator populations under natural conditions. Especially for bumblebees, long‐term exposure led to a reduction in the provision of pollination services, both by changing their behaviour and reducing pollen transfer. We found differences between the two pollinator groups, showing that results gained from single groups like bees should not be extrapolated to all pollinators. Interactive effects indicate potential buffering effects of high‐quality food against other stressors and highlight the importance of considering synergies between multiple stressors in risk assessments. This further emphasizes the relevance of floral resources such as wild flower strips in agricultural areas to conserve pollinators and pollination services.

301 Leveraging genomics to advance the breeding of Canadian livestock

Abstract Implementing breeding strategies for a more sustainable food system is one of the most pressing topics for livestock industries. Although different species face different challenges, large-scale research projects can pave the way for future opportunities in these key sectors. Herein, we present two examples (dairy and poultry) of how research projects can result in meaningful changes to commercial breeding programs. The first example is an international large-scale dairy project, which will deliver a roadmap for greenhouse gas (GHG) management using genomics and nutrition. The project aims to facilitate a 55% reduction in GHG emissions from Canadian dairy at an estimated value of $338M CAD. This systems-level approach will leverage resources developed through previous and current large-scale projects to produce accurate multi-level emission estimates and identify opportunities to mitigate enteric GHG emissions. The results of this project will provide accurate, reliable, and robust data for industry stakeholders, national policy, and GHG inventories. The second example is a research collaboration with a commercial turkey breeding company that will enable breeding strategies for long-term and sustainable genetic improvement in health traits. This project aims to reduce preslaughter mortality by 5% and condemnations due to myopathies and health issues. This reduction alone is expected to result in 7.5M kg more turkey meat at a value of $19.8M CAD. Furthermore, there is also an expected reduction of 2.3M kg of CO2 equivalent per year for each 1% livability improvement due to production efficiencies and reduced inputs wasted for more sustainable and ethical meat production. Overall, ongoing and future research initiatives will continue to deliver solutions for key Canadian livestock sectors to improve animal health and welfare as well as efficiency and sustainability. The strides made in these ongoing collaborative projects are imperative for enhancing the sustainability of animal agriculture to feed the growing population.

121 Copy number variation: From mapping to variance components in Holsteins

Abstract Copy number variations (CNVs) modify transcription levels mainly through changing genes and regulatory elements dosages. Hence, CNVs can contribute to the additive genetic variance of quantitative traits, which might not be fully captured by SNPs. Here, CNVs were mapped from 3,601 Holsteins and their contribution to complex traits was explored by i) quantitative trait loci (QTL) enrichment analysis, and ii) variance component analysis for metritis (1 = disease, 0 = no disease). The 4,113 CNVs derived from high-density (777k) genotypic information were compiled into 1,184 CNV regions (CNVRs) overlapping 7,832 QTLs reported in dairy cattle and available in the AnimalQTLdb. Fisher’s exact test was applied to perform the QTL enrichment analysis followed by False Discovery Rate (FDR) correction. Then, QTLs overlapping CNVRs were enriched for traits when the corrected P-value (PFDR) was lower than 0.05. For variance components estimation, genomic information from all CNVs loci was used to build the CNV-derived genomic relationship matrix (CNV_GRM). CNV loci showing double deletions, single deletions, and normal state in the population were coded as 0, 1, and 2, respectively, whereas CNV loci presenting normal state, single duplications, and double duplications were coded as 0, 1, and 2, respectively. Given that some CNVs presented both deletions and duplications at the same position in the population, these mixed-CNVs were treated as two distinct CNVs loci, one to represent deletions and the other to represent duplications. The variance components for metritis were estimated by fitting either only a SNP-derived GRM (SNP_GRM) or jointly SNP_GRM and CNV_GRM considering 3,272 cows in models that included the female category and farm-year-season as fixed effects. QTLs overlapped with CNVRs were enriched for milk (e.g., milk fat yield, milk yield, and milk protein percentage), reproduction (e.g., calving ease, non-return rate, and heat intensity), exterior (e.g., teat length, udder depth, and udder height), production (e.g., length of productive life, lifetime profit index, and net merit), and health traits (e.g., immune globulin G level, somatic cell, and bovine respiratory susceptibility). Regarding variance components, copy number variations were able to capture a fraction of the additive genetic variance not captured by SNPs alone, increasing the heritability estimate for metritis. In summary, our findings suggest that CNVs overlap QTLs associated with economically important traits more than only by chance and CNVs can capture additional additive genetic variance not fully captured by SNPs alone.

Genomic selection strategies for the German Merino sheep breeding programme - A simulation study.

Genomic selection is widely implemented in livestock breeding programmes across species. Its potential is also evident for sheep breeding; however, it has several limitations, particularly because of the high genetic diversity across and within sheep breeds. In Germany, the predominant sheep breed is the Merino sheep. Until now, there has been no use of genomic selection in the German Merino sheep breeding programme. In this simulation study, different genomic selection strategies were compared with a reference scenario with a breeding value estimation based on pedigree BLUP. A simplified version of the German Merino sheep breeding programme, including a health and a production trait in the breeding goal, was simulated via the R package Modular Breeding Program Simulator (MoBPS). Real genotype data were used to create a population specific simulation. The reference scenario was compared with several alternative scenarios in which selection was based on single-step GBLUP (ssGBLUP) breeding value estimation with varying genotyping strategies. In addition to scenarios in which all male and all male plus all female lambs were genotyped, scenarios with a preselection of lambs, that is only a certain proportion (top 25%, top 50%) genotyped, were simulated. The results revealed that genetic gain increased with increasing numbers of available genotypes. However, marginal gains decreased with increasing numbers of genotypes. Compared with the reference scenario, genotyping the top 25% of male lambs increased the genetic gain for the breeding ram population by 13% for both traits whereas genotyping the top 50% of male lambs or all male lambs led to increases of 18% (17%) or 26% (21%) for the health (production) trait, respectively. The potential of genotyping females in addition to male lambs was less evident on the male side with no significant differences between the scenarios with different proportions of genotyped females. The results have shown that genomic selection can be a valuable tool to increase genetic gain in the German Merino sheep population and that the genotyping of a certain proportion of animals might lead to substantial improvement over pedigree-based breeding value estimation. Nevertheless, further studies, especially economic evaluations, are needed before practical implementation.

Genetic effects of variation at ADH1B-ADH1C locus on substance use disorders and physical health traits

Genetic effects of variation at ADH1B-ADH1C locus on substance use disorders and physical health traits

DNAm scores for serum GDF15 and NT-proBNP levels associate with a range of traits affecting the body and brain

BackgroundPlasma growth differentiation factor 15 (GDF15) and N‐terminal proB‐type natriuretic peptide (NT‐proBNP) are cardiovascular biomarkers that associate with a range of diseases. Epigenetic scores (EpiScores) for GDF15 and NT-proBNP may provide new routes for risk stratification.ResultsIn the Generation Scotland cohort (N ≥ 16,963), GDF15 levels were associated with incident dementia, ischaemic stroke and type 2 diabetes, whereas NT-proBNP levels were associated with incident ischaemic heart disease, ischaemic stroke and type 2 diabetes (all PFDR < 0.05). Bayesian epigenome-wide association studies (EWAS) identified 12 and 4 DNA methylation (DNAm) CpG sites associated (Posterior Inclusion Probability [PIP] > 95%) with levels of GDF15 and NT-proBNP, respectively. EpiScores for GDF15 and NT-proBNP were trained in a subset of the population. The GDF15 EpiScore replicated protein associations with incident dementia, type 2 diabetes and ischaemic stroke in the Generation Scotland test set (hazard ratios (HR) range 1.36–1.41, PFDR < 0.05). The EpiScore for NT-proBNP replicated the protein association with type 2 diabetes, but failed to replicate an association with ischaemic stroke. EpiScores explained comparable variance in protein levels across both the Generation Scotland test set and the external LBC1936 test cohort (R2 range of 5.7–12.2%). In LBC1936, both EpiScores were associated with indicators of poorer brain health. Neither EpiScore was associated with incident dementia in the LBC1936 population.ConclusionsEpiScores for serum levels of GDF15 and Nt-proBNP associate with body and brain health traits. These EpiScores are provided as potential tools for disease risk stratification.

Did I Meet My Expectations? Exploring Whether Teen College Expectations Predict Degree Attainment and Mental Health

The purpose of this study was to investigate the predictive power of teen college expectations on future degree attainment and mental health outcomes. It also explores the role of personality traits in this process. The data are from a sample of 8,984 U.S. residents who participated in the National Longitudinal Study of Youth – 1997 Cohort (NLSY97). Teens reported on their college expectations through self-reported measures in 1997 or 2001. In subsequent surveys (2008 onward), participants reported on their degree attainment, mental health, and personality traits. Individuals with high college expectations compared to individuals with low college expectations had a more positive personality trait profile and better mental health outcomes. When comparing people who met their expectations with people who did not meet their expectations, there were few differences in mental health. There were no significant interactions between college expectations and degree attainment, controlling for ability, socioeconomic status, and gender.

Combining genomics and semen microbiome increases the accuracy of predicting bull prolificacy.

Commercial livestock producers need to prioritize genetic progress for health and efficiency traits to address productivity, welfare, and environmental concerns but face challenges due to limited pedigree information in extensive multi-sire breeding scenarios. Utilizing pooled DNA for genotyping and integrating seminal microbiome information into genomic models could enhance predictions of male fertility traits, thus addressing complexities in reproductive performance and inbreeding effects. Using the Angus Australia database comprising genotypes and pedigree data for 78,555 animals, we simulated percentage of normal sperm (PNS) and prolificacy of sires, resulting in 713 sires and 27,557 progeny in the final dataset. Publicly available microbiome data from 45 bulls was used to simulate data for the 713 sires. By incorporating both genomic and microbiome information our models were able to explain a larger proportion of phenotypic variation in both PNS (0.94) and prolificacy (0.56) compared to models using a single data source (e.g., 0.36 and 0.41, respectively, using only genomic information). Additionally, models containing both genomic and microbiome data revealed larger phenotypic differences between animals in the top and bottom quartile of predictions, indicating potential for improved productivity and sustainability in livestock farming systems. Inbreeding depression was observed to affect fertility traits, which makes the incorporation of microbiome information on the prediction of fertility traits even more actionable. Crucially, our inferences demonstrate the potential of the semen microbiome to contribute to the improvement of fertility traits in cattle and pave the way for the development of targeted microbiome interventions to improve reproductive performance in livestock.