Genetic Correlations Research Articles

Causal relationships between epilepsy and the microstructure of the white matter: A Mendelian randomization study.

To examine the causal bidirectional relationships between epilepsy and microstructural changes in the white matter (WM). A genome-wide association study meta-analysis of the International League Against Epilepsy Consortium on Epilepsy and 360 WM imaging-derived phenotypes (IDPs) from the UK Biobank was used for the analysis. Genetic correlation analyses were conducted based on summary statistics of various "IDP-epilepsy" pairs for 2-sample Mendelian randomization (MR) analysis to explore the causal relationships. We used the inverse variance weighted (IVW) method as the primary MR analysis approach, and conducted sensitivity analyses for pleiotropy and heterogeneity. Forward MR analysis revealed that alterations in the 16 WM IDPs increased the risk of epilepsy (q value < 0.05). Changes in the 38 WM IDPs were associated with a decreased risk of epilepsy (q value < 0.05). In the reverse analysis, seizures from all epilepsy types changed 5 WM IDPs, whereas seizures from juvenile myoclonic epilepsy altered 11 WM IDPs (q value < 0.05). This study revealed causal associations between changes in the WM microstructure and epilepsy subtypes. These findings offer new directions for early prevention and treatment of epilepsy.

Predation affects the evolution of sex-specific longevity.

Predation, a major cause of natural selection, is classically thought to target the weak and sick. However, predators can target animals with condition-dependent sexual traits, and therefore, high-quality individuals can also be the focus of predation. Thus, it is not always clear which individuals are the foci of predators or how this affects trait evolution. Here, we tested for evolutionary effects of sex-specific predation on male and female longevity using replicate populations of the broad-horned flour beetle Gnatocerus cornutus. We found that male-limited predation resulted in the evolution of reduced male and increased female longevity, while female-limited predation had no effects on the longevity of either sex. We also document the costs of reproduction. Coupled with other findings, our results suggest that predation impacts high-quality males and, because of negative intersexual genetic correlations, this increases female longevity.

Investigating genotype by environment interaction for beef cattle fertility traits in commercial herds in northern Australia with multi-trait analysis

BackgroundGenotype by environment interactions (GxE) affect a range of production traits in beef cattle. Quantifying the effect of GxE in commercial and multi-breed herds is challenging due to unknown genetic linkage between animals across environment levels. The primary aim of this study was to use multi-trait models to investigate GxE for three heifer fertility traits, corpus luteum (CL) presence, first pregnancy and second pregnancy, in a large tropical beef multibreed dataset (n = 21,037). Environmental levels were defined by two different descriptors, burden of heat load (temperature humidity index, THI) and nutritional availability (based on mean average daily gain for the herd, ADWG). To separate the effects of genetic linkage and real GxE across the environments, 1000 replicates of a simulated phenotype were generated by simulating QTL effects with no GxE onto real marker genotypes from the population, to determine the genetic correlations that could be expected across environments due to the existing genetic linkage only. Correlations from the real phenotypes were then compared to the empirical distribution under the null hypothesis from the simulated data. By adopting this approach, this study attempted to establish if low genetic correlations between environmental levels were due to GxE or insufficient genetic linkage between animals in each environmental level.ResultsThe correlations (being less than <0.8) for the real phenotypes were indicative of GxE for CL presence between ADWG environmental levels and in pregnancy traits. However, none of the correlations for CL presence or first pregnancy between ADWG levels were below the 5th percentile value for the empirical distribution under the null hypothesis from the simulated data. Only one statistically significant (P < 0.05) indication of GxE for first pregnancy was found between THI environmental levels, where rg = 0.28 and 5th percentile value = 0.29, and this result was marginal.ConclusionsOnly one case of statistically significant GxE for fertility traits was detected for first pregnancy between THI environmental levels 2 and 3. Other initial indications of GxE that were observed from the real phenotypes did not prove significant when compared to an empirical null distribution from simulated phenotypes. The lack of compelling evidence of GxE indicates that direct selection for fertility traits can be made accurately, using a single evaluation, regardless of environment.

A finite mixture distribution to model genetic architecture of image‐based oat grain morphology

AbstractThe multi‐floral oat (Avena sativa L.) inflorescence influences grain size and shape distributions, affecting the physical attributes of grain quality such as plumpness, size, and uniformity. While the grain size and shape distribution has been characterized as multi‐modal, very little is known about the genetic determinants of those distributions and their properties. The goal of this study was to model grain size and shape distribution using a finite mixture distribution approach and propose new distributional traits (i.e., emerging distributional traits) to characterize genotypes. We evaluated 47 oat genotypes in four highly replicated field experiments. Grains of three panicles per plot were individually threshed and scanned. Grain area, length, width, and roundness were obtained from each grain‐based image, while emerging distributional traits were evaluated using a finite mixture distribution approach. Finally, grain size distributions from hand‐threshed panicles (representing the full biological distribution) were compared with the grain size distributions of grains harvested with a combine harvester representing commercial harvest where small grains may be blown out. The heritability of all grain traits was high (0.89–0.94), and trait distributions differed among genotypes. Grain area and length show bi‐ and trimodal distributions, while grain width and roundness are uni‐ and bimodal. Although the full biological distribution of grains differed from the combine‐harvested grains, their genetic correlations were high, suggesting the combine‐harvested distributions can be used as a proxy for full biological distributions. This study proposes a straightforward methodological approach to model grain attributes that can aid in quality evaluations for genetic studies, breeding decisions, and industry characterization.

Risk of Alzheimer’s disease and genetically predicted levels of 1400 plasma metabolites: a Mendelian randomization study

Alzheimer’s disease (AD) is a metabolic disorder. Discovering the metabolic products involved in the development of AD may help not only in the early detection and prevention of AD but also in understanding its pathogenesis and treatment. This study investigated the causal association between the latest large-scale plasma metabolites (1091 metabolites and 309 metabolite ratios) and AD. Through the application of Mendelian randomization analysis methods such as inverse-variance weighted (IVW), MR-Egger, and weighted median models, 66 metabolites and metabolite ratios were identified as potentially having a causal association with AD, with 13 showing significant causal associations. During the replication validation phase, six metabolites and metabolite ratios were confirmed for their roles in AD: N-lactoyl tyrosine, argininate, and the adenosine 5’-monophosphate to flavin adenine dinucleotide ratio were found to exhibit protective effects against AD. In contrast, ergothioneine, piperine, and 1,7-dimethyluric acid were identified as contributing to an increased risk of AD. Among them, argininate showed a significant effect against AD. Replication and sensitivity analyses confirmed the robustness of these findings. Metabolic pathway analysis linked “Vitamin B6 metabolism” to AD risk. No genetic correlations were found, but colocalization analysis indicated potential AD risk elevation through top SNPs in APOE and PSEN2 genes. This provides novel insights into AD’s etiology from a metabolomic viewpoint, suggesting both protective and risk metabolites.

Heritability and genetic contribution analysis of structural-functional coupling in human brain

Abstract The flow of functional connectivity (FC) is thought to be supported by white matter structural connectivity (SC). While research on the correlations between SC and FC (SC-FC coupling) has progressed, the genetic implications of SC-FC coupling have not been thoroughly examined. Traditionally, SC-FC coupling investigations utilize predefined atlases. Here, we adopted an atlas-free SC-FC coupling built on the high-resolution white surface (the interface of white matter and gray matter) to uncover common genetic variations. Leveraging data from the Human Connectome Project, we demonstrated considerable heritability in areas within the early and intermediate visual cortex and across dorsal-attention, language, and somatomotor functional networks. We detected 334 genetic loci (spanning 234 cytogenetic bands) linked to SC-FC coupling (P &amp;lt; 1.26 × 10−11), notably in cingulo-opercular, somatomotor, and default mode networks. Using an external dataset from the Adolescent Brain Cognitive Development study, we confirmed 187 cytogenetic bands associated with SC-FC coupling across 22 brain regions (P &amp;lt; 1 × 10-5). Genetic correlation analyses revealed high genetic interrelatedness for SC-FC coupling in neighboring regions. Furthermore, it showed genetic correlations with a spectrum of complex traits, encompassing various neurological and psychiatric conditions. In essence, our study paves the way towards deciphering the genetic interplay between structural and functional connectivity of the brain.

Genetic Variability and Correlation Studies in Red Rice (Oryza sativa L.) for Yield Related Traits and Grain Zinc, Iron and Protein Content

Aims: Nutrient deficiencies, particularly zinc (Zn), iron (Fe) and protein, pose severe health risks especially in children and lactating women. Red rice is significantly rich in iron, zinc and protein compared to white rice. The present investigation was undertaken in this context to elucidate information on variability and character association in red rice genotypes to identify effective selection criteria for grain yield and quality improvement of red rice genotypes. Study Design: Randomized complete block design with two replications. Place and Duration of Study: Zonal Agricultural and Horticultural Research Station, Brahmavar, Udupi during Kharif 2023. Methodology: A total of 108 red rice genotypes were used in the study and observations were recorded for ten characters. Protein was estimated by Kjeldhal method, zinc and iron content by Atomic absorption spectrophotometer (AAS) method. The data collected was subjected to statistical procedures using R software. Results: Analysis of variance revealed significant variation among the genotypes for all the characters studied. Grain Zn (11.4-44.7ppm), Fe (13.11-37.12ppm) and protein (5.0-11.43%) showed wide range of variability. High genotypic and phenotypic coefficient of variation was observed for Zn, number of productive tillers per plant, Fe, grain yield per plant, number of grains per panicle and plant height. Correlation studies revealed that grain yield per plant exhibited significant positive association with yield component traits. Protein and Fe content had negative association with grain yield. Conclusion: The traits that have high PCV, GCV, heritability and genetic advance as per cent mean are controlled by additive gene action which is very valuable for selection. The traits that are positively correlated with grain yield can be used for indirect selection which aids crop improvement. Karthika, Bilihallga and Shahara genotypes have high protein, iron and zinc respectively, which can be used in the further breeding program to develop nutritionally rich varieties.

Genetic Estimates of Correlation and Causality Between Keratoconus and Osteoarthritis.

The relationship between keratoconus (KC) and osteoarthritis (OA) has been hypothesized, yet the causal link remained obscure. We aimed to evaluate the genetic correlation and causality between KC and OA through a genetic perspective. Linkage Disequilibrium Score Regression and bidirectional 2-sample Mendelian randomization (MR) analysis were conducted. Data were used from genome-wide association study on KC (4669 cases and 116,547 controls), OA (encompassing 24,955 patients with knee OA, 15,704 patients with hip OA, 39,427 patients with knee or hip OA, and 378,169 control participants), and KC data in the FinnGen consortium for replication and meta-analysis. Simple model, weighted model, inverse-variance weighted, weighted median, and MR-Egger regression were employed to assess the causal effects. MR pleiotropy residual sum and outlier method, MR-Egger method, and leave-one-out analysis were also applied to detect pleiotropy. No statistically significant genetic correlation between KC and OA (all P > 0.05) were observed. MR estimates obtained from all 5 methods after meta-analysis revealed nonsignificant odds ratio values of KC-related traits to knee OA, hip OA, and OA (all adjusted P > 0.05). No evidence of causal relationships of knee and hip OA with KC-related traits was detected after meta-analysis of all 5 MR methods (all adjusted P > 0.05). The large MR analysis indicated that KC may not be causally associated with a risk of OA.

Genetic overlap between inflammatory bowel disease and iridocyclitis: insights from a genome-wide association study in a European population.

Inflammatory bowel disease (IBD) is occasionally associated with ophthalmic diseases, including iridocyclitis (IC). The co-occurrence of IBD and IC has been increasingly observed, possibly due to shared genetic structures. A three-part analysis was executed utilizing genome-wide association study (GWAS) data on IBD and IC. First, the overall genetic correlation between the two traits was observed using linkage disequilibrium score regression (LDSC). Subsequent to this, a local genetic correlation analysis was conducted utilizing the heritability estimation from summary statistics (HESS) methodology. Finally, the conditional/conjunctional false discovery rate (cond/conjFDR) statistical framework was utilized to ascertain the degree of genetic overlap between the two traits. Positive overall correlations were observed among IBD, ulcerative colitis (UC), and IC, encompassing both acute/subacute and chronic IC presentations. While a significant correlation was identified between Crohn's disease (CD) and IC, it was not evident for acute/subacute or chronic IC (P > 0.05). Notably, IBD (encompassing CD and UC) demonstrated local genetic correlations with IC and acute/subacute IC, with pronounced enrichment notably on chromosomes 1 and 6, though such correlations were not observed with chronic IC. The conjFDR analysis confirmed the genetic overlap between the two diseases. The shared genes overlapping between IBD (encompassing CD and UC) and IC were IL23R, GPR35, and ERAP1. For acute/subacute IC and chronic IC, there were six overlapping genes (GPR35, RPL23AP12, IL23R, SNAPC4, ERAP1, and INAVA) and one overlapping gene (INAVA), respectively. This study confirms the existence of a shared genetic structure between IBD and IC, providing a biological basis for their comorbidity. Additionally, this finding has significant implications for preventing and treating these two diseases.

Reconstruction of a matrix of genotypic correlations between variants within a gene for joint analysis of imputed and sequenced data

When combining imputed and sequenced data in a single gene-based association analysis, the problem of reconstructing genetic correlation matrices arises. It is related to the fact that for a gene, we know the correlations between genotypes of all imputed variants and the correlations between genotypes of all sequenced variants, but we do not know the correlations between genotypes of variants, one of which is imputed and the other is sequenced. To recover these correlations, we propose an efficient method based on maximising the determinant of the matrix. This method has a number of useful properties and has an analytical solution for our task. Approbation of the proposed method was performed by comparing reconstructed and real correlation matrices constructed on individual genotypes from the UK biobank. Comparison of the results of gene-based association analysis performed by the SKAT, BT and PCA methods on reconstructed and real matrices, using modelled summary statistics and calculated summary statistics on real phenotypes, showed high quality of reconstruction and robustness of the method to different gene structures.

Multi-modality deep learning prediction of heart age: insights from UK-Biobank

Abstract Background Identification of apparently healthy individuals at high cardiovascular risk who may benefit from targeted preventive strategies remains challenging. Predicted heart age, the chronological age of individuals with similar absolute cardiovascular disease (CVD) risk but with no risk factors, has been proposed to appropriately describe the absolute CVD risk over conventional prediction tools. Purpose To develop multi-modality based deep learning predictors for heart age in UK-Biobank individuals. Methods We used deep learning to develop a heart age predictor based on videos from heart magnetic resonance imaging (MRI) (anatomical dimension), electrocardiograms (ECG) (electrical dimension) or both from 45000 individuals of the UK Biobank cohort (age range 45-81 years) (Figure). We estimated the heritability of heart aging and identified single-nucleotide polymorphisms associated with accelerated heart aging. We performed X-wide association study to identify non-genetic factors potentially associated with accelerated heart aging. Results We predicted age with a mean absolute error (MAE) of 2.5±0.03 years (R2=85.6±0.6%) and found that accelerated heart aging is heritable at more than 35%. MRI-based anatomical features predicted age better than ECG-based electro-physiological features (MAE=2.29±0.03 years versus 4.90±.0.08 years), and heart anatomical and electrical aging were weakly correlated (Pearson correlation=0.249±0.002). Our attention maps highlighted the aorta, the mitral valve, and the interventricular septum as key anatomical features driving heart age prediction in videos that capture the entire heartbeat cycle. We found accelerated anatomical and electrical heart aging to be genetically correlated (Pearson correlation=0.508±0.089). The most significant locus was in TTN (Titin) and it was associated with heart anatomical aging. Cardiovascular diseases, general health status, and mental health disorders were highly associated with accelerated heart aging. Conclusions A multi-modality based deep learning approach was highly predictive of heart age. Accelerated heart age has a complex biological basis with genetic and environmental correlates. Leveraging of the described pipeline to predict survival and the onset of age-related cardiovascular diseases may shed light on heart aging role in health outcomes and effectively guide personalized preventive strategies.

Mapping renal impairment and cardiac structure and function: observation and genetic insights from 30,000 cardiac magnetic resonance images

Abstract Background The elevated risk of heart failure in kidney dysfunction is well-identified. However, the early alteration of renal impairment on cardiac structure and function, as well as genetic susceptibility associations have not been well reported. We sought to map the early effects of kidney dysfunction on cardiac remodeling with the cardiac magnetic resonance images (MRI) and identify share genetic correlation in community population. Methods Of 29,592 subjects underwent MRI test were recruited from the population-based UK Biobank study from 2006-2010. Renal function was assessed by historical identified chronic kidney disease (CKD), log-transfrom estimated glomerular filtration rate (eGFR) based on cystatin C (eGFRCysC) or serum creatine (eGFRSCr) and urine albumin-to-creatinine ratio (uACR). Multivariable generalized linear regression was used to analyse the association of 4 different kidney function indexes with six cardiac MRI-derived traits: left atria and ventricle, right atria and ventricle, and ascending and descending aortas. Genetic correlation between renal function decline and cardiac traits were evaluated by Mendelian randomization (MR). Results The mean (SD) age of subjects was 54.9 (7.5) years, and 51.1% were women. Observational and MR analysis showed a reliable relationship of lower level of eGFRCysC with reduced biventricular end systolic volume, biventricular ventricular end diastolic volume and left ventricular mass, and lower level of eGFRSCr with reduced left ventricular cardiac output. Conclusions Our study indicates the observational and genetic correlation between kidney function decline and cardiac measurements. These findings provide insights into the potential mechanisms behind their comorbidity and the future development of therapeutics.Renal function decline &amp; Cardiac Imaging

Causal relationships between cortical brain structural alterations and migraine subtypes: a bidirectional Mendelian randomization study of 2,347 neuroimaging phenotypes

BackgroundPrevious studies have shown that migraines are associated with brain structural changes. However, the causal relationships between these changes and migraine, as well as its subtypes, migraine with aura (MA) and migraine without aura (MO), remain largely unclear.MethodsWe utilized genome-wide association study (GWAS) summary statistics from European cohorts for 2,347 cortical structural magnetic resonance imaging (MRI) phenotypes, derived from both T1-weighted and diffusion tensor imaging scans (n = 36,663), with migraine and its subtypes (n = 147,970–375,752). Cortical phenotypes included both macrostructural (e.g., cortical thickness, surface area) and microstructural (e.g., fractional anisotropy, mean diffusivity) features. Genetic correlations were first assessed to identify significant associations, followed by bidirectional Mendelian randomization (MR) analyses to determine causal relationships between these brain phenotypes and migraine, as well as its subtypes (MA and MO). Sensitivity analyses were applied to ensure the robustness of the results.ResultsGenetic correlation analysis identified 510 significant associations between cortical structural phenotypes and migraine across 401 distinct traits. Forward MR analysis revealed nine significant causal effects of cortical structural changes on migraine risk. Specifically, increased cortical thickness and local gyrification index in specific cortical regions were associated with a decreased risk of overall migraine, MA, and MO, while intracellular volume fraction and orientation diffusion index in specific regions increased the risk of MA and MO. Reverse MR analysis demonstrated that MA causally increased mean diffusivity in the insular and frontal opercular cortex. Sensitivity analyses confirmed the robustness of these findings, with no evidence of horizontal pleiotropy or heterogeneity.ConclusionThis study identifies causal relationships between cortical neuroimaging phenotypes and migraine, highlighting potential biomarkers for migraine diagnosis, treatment, and prevention.

Genetics of Some Wool Traits in Sheep: A Review

Greasy fleece yield, staple length and fibre diameter are complex quantitative traits influenced by various genetic and environmental factors. Among non-genetic factors year of birth, gender of lamb, centre of rearing, parity and type of birth are known to influence wool traits in different sheep genetic resources. To develop effective breeding objectives and accurately evaluate genetic potential, more precise analysis of genetic parameters and correlations between growth traits is essential. However, inconsistent results were observed across breeds and studies, highlighting the need for breed-specific optimization strategies. Greasy fleece yield, fibre diameter and staple length are critical determinants of wool quality and quantity. Wool traits are moderately (20–50%) to highly heritable (≥50%), so improving quality and quantity of a wool clip can happen even after one generation. The significant variations observed among breeds underscore the need for breed-specific optimization strategies. By integrating genetic and environmental factors, sheep breeders and producers can develop effective breeding programs, improve wool production, and enhance profitability. Wool traits are moderately to highly heritable, however, very low and very high estimates have also been reported in different sheep genetic resources. The heritability estimates correspond to the genetic variation in a trait. Gene mapping has identified chromosomal regions influencing fibre quality and production, enabling more efficient selection of superior wool production. Recent studies have employed various techniques to investigate the impact of fibre-related genes, including the candidate gene approach, transcriptome analysis and genome-wide association studies. It is concluded that wool traits being quantitative in nature are governed by different genes and are influenced by genetic and different environmental factors.

Common Genetic Influence on the Relationship Between Gaming Addiction and Attention Deficit Hyperactivity Disorder in Young Adults: A Twin Study.

Although the relationship between gaming addiction (GA) and attention deficit hyperactivity disorder (ADHD) is well established, the causal mechanism of this relationship remains ambiguous. We aimed to investigate whether common genetic and/or environmental factors explain the GA-ADHD relationship. We recruited 1413 South Korean adult twins (837 monozygotic [MZ], 326 same-sex dizygotic [DZ], and 250 opposite-sex DZ twins; mean age = 23.1 ± 2.8 years) who completed an online survey on GA and related traits. Correlational analysis and bivariate model-fitting analysis were conducted. Phenotypic correlation between GA and ADHD in the present sample was 0.55 (95% CI [0.51, 0.59]). Bivariate model-fitting analysis revealed that genetic variances were 69% (95% CI [64%, 73%]) and 68% (95% CI [63%, 72%]) for ADHD and GA respectively. The remaining variances (ADHD: 31%; GA: 32%) were associated with nonshared environmental variances, including measurement error. Genetic and nonshared environmental correlations between ADHD and GA were 0.68 (95% CI [0.62, 0.74]) and 0.22 (95% CI [0.13, 0.30]) respectively, which indicates that shared genes can explain 82% of the phenotypic correlation between ADHD and GA. Our study demonstrated that the ADHD-GA association was largely due to shared genetic vulnerability.

Whole-Genome Sequencing Analysis of Antimicrobial Resistance, Virulence Factors, and Genetic Diversity of Salmonella from Wenzhou, China.

Salmonella species are important foodborne pathogens worldwide. Salmonella pathogenicity is associated with multiple virulence factors and enhanced antimicrobial resistance. To determine the molecular characteristics and genetic correlations of Salmonella, 24 strains of Salmonella isolated from different sources (raw poultry, human stool, and food) in the Wenzhou area were investigated to determine the distribution of antimicrobial resistance and virulence determinants using whole-genome sequencing (WGS). Aminoglycoside resistance genes were detected in all samples. Over half of the samples found antimicrobial resistance genes (ARGs) and point mutations for several clinically frequently used antibiotic, beta-lactams, tetracyclines, and quinolones. Of these strains, 62.5% were predicted to be multidrug-resistant (MDR). The quinolone-modifying enzyme gene aac(6')-Ib-cr, detected in five samples (S1-S4 and S10), was located on integrons. The analysis of Salmonella pathogenicity island (SPI) profiles suggests that serotypes with close genetic relationships share the same distribution of virulence factors, revealing a link between genotype and SPI profiles. cgMLST analysis indicated that five isolates S14-S18 were closely related to strains originating from the United Kingdom, suggesting that they may share a common origin. Data from this study may enrich the molecular traceability database for Salmonella and provide a basis for effective public health policies.

Estimates of genetic parameters and genetic trends for growth traits in Menz sheep under community-based breeding programs

The main objectives of this study were to estimate genetic and phenotypic parameters for growth traits in Menz sheep under community-based breeding programs (CBBPs). Data used in the study were collected from five Menz CBBPs villages during 15-year period (2009–2023). Studied growth traits were birth weight (BW), weaning weight (WW), six-month weight (6 MW), yearling weight (YW), Average daily gain from birth to weaning (ADG1), average daily gain from weaning to six months (ADG2) and average daily gain from six months to yearling (ADG3). Fixed effects were estimated using the GLM procedure of SAS 9.4. Genetic parameters were estimated fitting different univariate animal models using Average Information Restricted Maximum Likelihood (AI-REML) procedure with WOMBAT software. The most appropriate model for each trait was determined applying likelihood ratio tests. Model including only direct additive genetic effect was the most appropriate model for BW, YW, ADG2 and ADG3, whereas model including direct additive and maternal additive genetic effect without taking into account covariance between them was the most appropriate for WW, 6 MW and ADG1. Based on the most appropriate models, direct heritability of BW, WW, 6 MW, YW, ADG1, ADG2 and ADG3 were 0.29 ± 0.01, 0.45 ± 0.01, 0.46 ± 0.02, 0.42 ± 0.02, 0.44 ± 0.01, 0.75 ± 0.02 and 0.21 ± 0.02, respectively. The estimated maternal heritability were 0.04 ± 0.01, 0.02 ± 0.01 and 0.02 ± 0.01 for WW, 6 MW and ADG1, respectively. Additive coefficient of variance were ranged from 9.53 % for BW to 33.02 % for ADG2. Moderate to higher estimates of direct heritability and additive coefficient of variation (CVA) in the current study indicates higher potential for genetic improvement of studied traits through direct selection. The genetic trend for the Menz sheep's six-month weight in Molale CBBPs village reveals a promising annual growth rate of 0.20 kg. Optimizing the ongoing breeding program into specializing-sire producer groups and applying intensive selection on the sub-nucleus flock could improve the efficiency of the breeding program. Genetic correlations among the studied traits were ranged from −0.50 for ADG-ADG2 to 0.98 for WW-ADG1. Genetic correlation between 6 MW and other studied traits was positive and moderate in magnitude. To achieve optimum genetic gain, the implemented selection criteria for Menz sheep (6 MW) should be continued.

Genetic determinism of sensitivity to environmental challenges using daily feed intake records in three lines of pigs.

In pig breeding, environmental challenges can affect the welfare and productivity of animals. Resilient animals have the capacity to be minimally affected by these environmental challenges. Understanding the genetic basis of sensitivity to these environmental challenges is crucial for selecting more resilient animals, thereby enhancing welfare and productivity. The aims of this study were to: (1) estimate the probability of the occurrence of an unrecorded environmental challenge at a given day using daily feed intake (DFI) data, and (2) evaluate the genetic determinism of environmental sensitivity in three pig lines bred in real selection conditions. Data comprised of 100,799, 186,247, and 304,826 DFI records from 1,618, 2,517, and 3,788 Landrace (LA), Large White (LW) and Piétrain (PI) male pigs, respectively. The pedigree included 3,730, 5,649, and 9,293 animals for LA, LW, and PI, respectively. The probabilities of the occurrence of an unrecorded environmental challenge at a given day were estimated via a mixture model. The probabilities (p) of being "high CV days" were then taken as reference and used in genetic analysis as an environmental descriptor to describe the environment. DFI records were analysed using two linear models: a linear reaction norm animal model (RNAM) and the animal model. (Co)variance components were estimated using average-information restricted maximum likelihood (AI-REML). The means of the probabilities of the occurrence of an environmental challenge for LA, LW, and PI were 0.24, 0.10, and 0.22, respectively, indicating that the probability of an environmental challenge was low for most of the days. The genetic correlations between the intercept and the slope obtained from the RNAM for LA, LW, PI were -0.52, 0.06, and -0.36, respectively. These findings suggest that selecting hypothetically for decreased DFI in non-stressful conditions would result in pigs with increased DFI in stressful conditions in the LA and PI lines, whereas it would have a minor impact on the environmental sensitivity of LW. The proportion of resilient animals for LA, LW, and PI was 75.0, 74.2, and 72.2%, respectively, implying that most of the animals were resilient. The study demonstrated that the slope of DFI is heritable and can effectively be used as an indicator of sensitivity to environmental challenges. These results are valuable in improving the resilience of livestock species to environmental challenges through genetic selection.

Gestational diabetes and future cardiovascular diseases: associations by sex-specific genetic data.

Observational studies have highlighted that gestational diabetes mellitus is associated with a higher risk of cardiovascular diseases, but the causality remains unclear. Herein, the causality between genetic predisposition to gestational diabetes mellitus and the risk of cardiovascular diseases was investigated using sex-specific Mendelian randomization analysis. Linkage disequilibrium score regression analysis and two-sample Mendelian randomization analysis were applied to infer the genetic correlation and causality, respectively. Mediation analysis was conducted using a two-step Mendelian randomization approach. Sensitivity analyses were performed to differentiate causality from pleiotropy. The genome-wide association study summary statistics for gestational diabetes mellitus were obtained from FinnGen consortium, while for cardiovascular diseases were generated based on individual-level genetic data from the UK Biobank. Linkage disequilibrium score regression analyses revealed that gestational diabetes mellitus had a significant genetic correlation with coronary artery disease and myocardial infarction after Benjamini-Hochberg correction in ever-pregnant women. In Mendelian randomization analyses, odds ratios (95% confidence interval) for coronary artery disease and myocardial infarction were 1.09 (1.01-1.17) and 1.12 (.96-1.31) per unit increase in the log-odds of genetic predisposition to gestational diabetes mellitus in ever-pregnant women, respectively. Further, Type 2 diabetes and hypertension were identified as mediators for the causality of genetic predisposition to gestational diabetes mellitus on coronary artery disease. In sensitivity analyses, the direction of odds ratio for the association between instrumental variables with gestational diabetes mellitus-predominant effects and the risk of coronary artery disease was consistent with the primary results in ever-pregnant women, although not statistically significant. This study demonstrated a suggestive causal relationship between genetic predisposition to gestational diabetes mellitus and the risk of coronary artery disease, which was mainly mediated by Type 2 diabetes and hypertension. These findings highlight targeting modifiable cardiometabolic risk factors may reduce the risk of coronary artery disease in women with a history of gestational diabetes mellitus.

Genetic insights into the association between inflammatory bowel disease and Alzheimer's disease.

Myeloid cells, including monocytes, macrophages, microglia, dendritic cells and neutrophils are a part of innate immune system, playing a major role in orchestrating innate and adaptive immune responses. Both Alzheimer's disease (AD) and inflammatory bowel disease (IBD) susceptibility loci are enriched for genes expressed in myeloid cells, but it is not clear whether these myeloid risk factors are shared between the two diseases. Leveraging results of genome-wide association studies, we investigated the causal effect of IBD (including ulcerative colitis (UC) and Crohn's disease (CD)) variants on AD and its endophenotypes. Microglia and monocyte expression Quantitative Trait Locus (eQTLs) were used to examine the functional consequences of IBD and AD variants. Our results revealed distinct sets of genes and pathways of AD and IBD susceptibility loci. Specifically, AD loci are enriched for microglial eQTLs, while IBD loci are enriched for monocyte eQTLs. However, we also found that genetically determined IBD is associated with a protective effect against AD (p<0.03). Yet, a genetic propensity for the CD subtype is associated with increased amyloid accumulation (beta=7.14, p-value=0.02) and susceptibility to AD. Susceptibility to UC was associated with increased deposition of TDP-43 (beta=7.58, p-value=6.11×10-4). The relation of these gastrointestinal inflammatory disease to AD is therefore complex; while the different subsets of susceptibility variants preferentially affect different myeloid cell subtypes, there do appear to be certain shared pathways and the possible protective effect of IBD susceptibility on the risk of AD which may provide therapeutic insights.