Genetic Correlations Research Articles

Genetic correlations between enteric methane and traits of economic importance in a beef finishing system.

With the pressing global challenge of climate change, the potential to breed cattle that produce less lifetime methane offers a transformative solution that is both sustainable and impactful. The objective of this study was to determine the genetic correlations between enteric methane emissions and economically important traits included in the current Terminal Index used to breed animals for meat in Ireland. This Terminal Index is typical of terminal-type indexes used globally, constituting traits associated with calving performance, carcass merit, and efficiency traits such as feed intake and age at finish, as well as some ancillary traits such as docility. Methane and carbon dioxide flux measurements recorded from 2018 to 2024 using ten GreenFeed Emission Monitoring systems in a progeny performance test centre on 1,835 beef animals and a more expansive dataset from commercial farmers with phenotypic performance data on calving performance, carcass quality, and efficiency traits were available on up to 402,039 animals for analyses. Five trait definitions for methane and carbon dioxide emissions were derived: individual spot measures, 1-day, 5-day, and 10-day averages of spot measures, and a full test average per animal, where all emission measurements were averaged across the test period. (Co)variance components between all trait definitions and phenotypic performance traits were estimated using animal linear mixed models. Methane emissions were strongly correlated with feed intake ranging from 0.49 (SE = 0.119) to 0.76 (SE = 0.057) and carcass weight ranging from 0.44 (SE = 0.050) to 0.50 (SE = 0.060) across trait definitions, suggesting that selection for reduced methane emissions could adversely impact growth and performance. An antagonistic correlation was found between methane and age at finish ranging -0.27 (SE = 0.063) to -0.18 (SE =0.084), which suggests that animals who have an earlier finishing age produce more methane per day. Carcass conformation was positively weakly correlated with methane (0.09 to 0.12), thus suggesting there is a potential to select for improved carcass conformation with minimal impact on enteric methane emissions. Overall, these findings emphasize the need for breeding strategies that capture the trade-offs between reducing methane emissions and preserving economically valuable traits such as feed intake, carcass weight, and conformation in beef finishing systems.

Lactation Curve Modelling and Genetic Parameters Estimation in Murciano-Granadina Goats.

The present study aims to determine the best non-linear model for describing lactation curves and estimating genetic parameters for the lactation curve traits in the Murciano-Granadina goats in Iran. We compared five mathematical models including the Cappio-Borlino (CB), Cobby and Le Du (CD), Narushin-Takma (NT), Wilmink (WL), and Wood (WD) to characterise the lactation curve in the first and second lactations of Murciano-Granadina does. The dataset consisted of 36,958 and 23,319 milk yield test-day records from 4964 first-parity and 3335 s-parity Murciano-Granadina does, respectively. These records were collected from 2017 to 2024 in a private dairy farm, located in Ghale-Ganj city, Kerman province, southern area of Iran. In both lactation periods, the WD model showed the lowest values for root mean squares of prediction error (RMSE) and Akaike's information criterion (AIC), as well as the highest adjusted coefficient of determination ( ) among the evaluated models. Additionally, positive autocorrelations were observed among the residuals for all the models considered, with the lowest positive autocorrelation obtained under the WD model. Therefore, WD was identified as the best model to characterise the lactation curve of the Murciano-Granadina does in the first and second lactation periods. Consequently, we computed the individual lactation curve traits for does in the ith parity (where i = 1 for the first parity and i = 2 for the second parity), including peak time (PTi), peak milk yield (PYi), and lactation persistency (LPi), using the parameters derived from the WD model. A multivariate animal model utilising a Bayesian approach was employed to estimate the genetic parameters of the lactation curve traits. The posterior means for heritability estimates were 0.07, 0.13, 0.05, 0.05, 0.11, and 0.08 for PT1, PY1, LP1, PT2, PY2, and LP2, respectively. In the first parity, genetic correlations among the lactation curve traits were positive estimates of 0.28, 0.96, and 0.25 for PT1-PY1, PT1-LP1, and PY1-LP1, respectively. In the second parity, the corresponding genetic correlation estimates were 0.88, 0.89, and 0.59 for PT2-PY2, PT2-LP2 and PY2-LP2, respectively. It can be concluded that the low heritability estimates for the investigated lactation curve traits suggest these traits are mainly affected by non-additive genetic and environmental effects. Consequently, direct genetic selection may not effectively modify the shape of the Murciano-Granadina lactation curve. The positive genetic correlation estimates among the traits examined within each parity, as well as among the same traits across the parities, suggest that selecting one trait will also enhance the other traits.

Transdiagnostic Neurocognitive Endophenotypes for Schizophrenia, Bipolar I Disorder and a Broad Psychosis/Bipolar I Disorder Phenotype: A Mega-Analysis of Twin and Sibling Data.

Psychiatric research is increasingly embracing a paradigm shift from categorical diagnoses to neurobiologically meaningful dimensions that cross current diagnostic boundaries. This transposition calls for redefining endophenotypes to accommodate transdiagnostic vulnerabilities. We sought to identify shared and disorder-specific neurocognitive endophenotypes for schizophrenia, bipolar I disorder (BD-I) and a broad psychosis/BD-I phenotype in a mega-analysis of twin/sibling data. We performed genetic model fitting to intelligence (IQ) and computerised neurocognitive data derived from 1050 twins/siblings from three research centres in the UK, Denmark and the Netherlands, affected (n = 257) or unaffected (n = 793) by schizophrenia, other primary psychoses and BD-I. We examined the endophenotypic status of IQ, spatial working memory (SWM), visual recognition, sustained attention/rapid visual processing (RVP), mental flexibility, and spatial planning/problem solving (all validated as endophenotypes for schizophrenia in previous studies) in relation to schizophrenia, BD-I and the broad phenotype. After covarying for age, gender, education and research centre, IQ and SWM emerged as transdiagnostic endophenotypes, showing statistically significant heritabilities (h2 67-75% and 28-30%, respectively), phenotypic correlations (rph |0.14|-|0.25|) and genetic correlations (rg |0.18|-|0.42|) with all diagnostic phenotypes. Additionally, all remaining cognitive domains received validation as endophenotypes for the broad phenotype, and all, but RVP, for schizophrenia. IQ and SWM tap into transdiagnostic elements of the genetic vulnerabilities to psychosis and BD-I. Our findings add to emergent evidence which spurs cautious optimism that a psychiatric nosology based on aetiology rather than phenotypical classifications may be feasible in the future, enabling biotyping and novel approaches to treatment.

Identification of Shared Genetic Loci Associated With Inflammatory Bowel Disease, Ischemic Heart Disease, and Atrial Fibrillation and Flutter.

The occurrence of ischemic heart disease (IHD), atrial fibrillation, and flutter demonstrates certain associations with inflammatory bowel disease (IBD), warranting further exploration at the genetic architecture level. This study focused on genome-wide association study (GWAS) data of IHD, atrial fibrillation and flutter, and IBD, analyzing from two dimensions: genetic correlation and shared locus identification. Initially, linkage disequilibrium score regression and genetic covariance analyzer were utilized to assess the overall genetic correlations. Subsequently, the association patterns of local genomic regions were determined using Local Ancestry Variance Association (LAVA) analysis. Mendelian randomization (MR) was employed to assess causal effects. The genetic overlap among different traits was analyzed based on the statistical framework of conditional/conjunctional false discovery rate (cond/conjFDR). Finally, shared loci across these traits were identified by integrating conjFDR analysis with GWAS multi-trait analysis (MTAG). At the genomic level, significant overall correlations were observed among IHD, atrial fibrillation and flutter, and IBD and Crohn's disease (CD), while associations with ulcerative colitis appeared less pronounced. At the local level, IHD and IBD (including subtypes) showed significant associations in multiple regions. However, atrial fibrillation and flutter exhibited local associations only in the context of CD. Through conjFDR analysis, the genetic overlap across these diseases was validated. Additionally, several shared genetic loci were identified by integrating conjFDR and MTAG analyses, with genes confirmed in both IHD and IBD (including subtypes), such as SMAD3, PLCG2, ZNF831, PTPN22, RP11-136O12.2, and RP11-449I17.5. Moreover, six common genes were identified in the analysis between atrial fibrillation and flutter and IBD (including subtypes), such as ZMIZ1, MTHFS, ERAP2, GNA12, and RP1-15D23.2. This study offers empirical evidence of the genetic association between IHD, atrial fibrillation and flutter, and IBD comorbidity, providing new insights for cases where IBD co-occurs with IHD or atrial fibrillation and flutter.

Investigating the shared genetic architecture between obesity and depression: a large-scale genomewide cross-trait analysis

IntroductionIncreasing evidence suggests that individuals with obesity are at a higher risk of developing depression, and conversely, depression can contribute to the onset of obesity, creating a detrimental cycle. This study aims to investigate the potential shared biological pathways between obesity and depression by examining genetic correlations, identifying common polymorphisms, and conducting cross-trait genetic analyses.MethodsWe assessed the genetic correlation between obesity and depression using linkage disequilibrium score regression and high-density lipoprotein levels. We combined two different sources of obesity data using METAL and employed bidirectional Mendelian randomization to determine the causal relationship between obesity and depression. Additionally, we conducted multivariate trait analysis using the MTAG method to improve statistical robustness and identify novel genetic associations. Furthermore, we performed a thorough investigation of independent risk loci using GCTA-COJO, PLACO, MAGMA, POPS, and SMR, integrating different QTL information and methods to further identify risk genes and proteins.ResultsOur analysis revealed genetic correlations and bidirectional positive causal relationships between obesity and depression, highlighting shared risk SNP (rs10789340). We identified RPL31P12, NEGR1, and DCC as common risk genes for obesity and depression. Using the BLISS method, we identified SCG3 and FLRT2 as potential drug targets.LimitationMost of our data sources are from Europe, which may limit the generalization of our findings to other ethnic populations.ConclusionThis study demonstrates the genetic causal relationship and common risk SNPs, genes, proteins, and pathways between obesity and depression. These findings contribute to a deeper understanding of their pathogenesis and the identification of potential therapeutic targets.

Genomic Correlations, Shared Loci, and Drug Targets between Polycystic Ovary Syndrome and Asthma: Insights from Genome-wide Association Analysis.

Observational studies have shown association between polycystic ovary syndrome (PCOS) and asthma-related traits. However, whether this association is genetically driven or arises from observational biases remains unclear. This study integrated data from 10,074 PCOS cases and asthma-related traits obtained from UK Biobank and FinnGen cohorts. Global and local genetic architectures were examined using pleiotropic analysis under the composite null hypothesis, Functional Mapping and Annotation of Genetic Associations, and fine-mapping credible set analysis. Drug database mining was employed to identify pleiotropic genes as potential therapeutic targets. Tissue and cell enrichment analyses were conducted to uncover shared biological mechanisms. We identified 3 novel significant genetic loci for asthma subtypes (2 for allergic asthma and 1 for childhood asthma). A positive overall genetic correlation between PCOS and asthma-related traits was observed. We discovered 5 pleiotropic causal regions encompassing 13 genes, with ERBB3 emerging as a potential central gene contributing to the shared pathophysiology of PCOS and asthma-related traits. Additionally, drug repositioning analysis suggested anakinra and artenimol as potential therapeutic candidates for PCOS and asthma comorbidity. Linkage disequilibrium score for the specific expression of genes analysis, along with transcriptome-wide association study, further identified gene expression patterns at the tissue/cell level in hypothalamo-pituitary, exocrine/endocrine, respiratory, and urogenital systems. Our findings provide novel insights into the genetic basis and biological processes underlying the association between PCOS and asthma-related traits, warranting evaluation of whether PCOS-specific asthma risk assessment could improve clinical outcomes.

Integrating HiTOP and RDoC frameworks Part I: Genetic architecture of externalizing and internalizing psychopathology.

There is considerable comorbidity between externalizing (EXT) and internalizing (INT) psychopathology. Understanding the shared genetic underpinnings of these spectra is crucial for advancing knowledge of their biological bases and informing empirical models like the Research Domain Criteria (RDoC) and Hierarchical Taxonomy of Psychopathology (HiTOP). We applied genomic structural equation modeling to summary statistics from 16 EXT and INT traits in individuals genetically similar to European reference panels (EUR-like; n=16,400 to 1,074,629). Traits included clinical (e.g. major depressive disorder, alcohol use disorder) and subclinical measures (e.g. risk tolerance, irritability). We tested five confirmatory factor models to identify the best fitting and most parsimonious genetic architecture and then conducted multivariate genome-wide association studies (GWAS) of the resulting latent factors. A two-factor correlated model, representing EXT and INT spectra, provided the best fit to the data. There was a moderate genetic correlation between EXT and INT (r=0.37, SE=0.02), with bivariate causal mixture models showing extensive overlap in causal variants across the two spectra (94.64%, SE=3.27). Multivariate GWAS identified 409 lead genetic variants for EXT, 85 for INT, and 256 for the shared traits. The shared genetic liabilities for EXT and INT identified here help to characterize the genetic architecture underlying these frequently comorbid forms of psychopathology. The findings provide a framework for future research aimed at understanding the shared and distinct biological mechanisms underlying psychopathology, which will help to refine psychiatric classification systems and potentially inform treatment approaches.

Mapping the genetic landscape of immune-mediated disorders: potential implications for classification and therapeutic strategies

ObjectivesBased on clinical, biomarker, and genetic data, McGonagle and McDermott suggested that autoimmune and autoinflammatory disorders can be classified as a disease continuum from purely autoimmune to autoinflammatory with mixed diseases in between. However, the genetic architecture of this spectrum has not been systematically described. Here, we investigate the continuum of polygenic immune-mediated disorders using genome-wide association studies (GWAS) and statistical genetics methods.MethodsWe mapped the genetic landscape of 15 immune-mediated disorders using GWAS summary statistics and methods including genomic structural equation modeling (genomic SEM), linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and Gaussian causal mixture modeling (MiXeR). We performed enrichment analyses of tissues and biological gene sets using MAGMA.ResultsGenomic SEM suggested a continuum structure with four underlying latent factors from autoimmune diseases at one end to autoinflammatory on the opposite end. Across disorders, we observed a balanced mixture of negative and positive local genetic correlations within the major histocompatibility complex, while outside this region, local genetic correlations were predominantly positive. MiXeR analysis showed large genetic overlap in accordance with the continuum landscape. MAGMA analysis implicated genes associated with known monogenic immune diseases for prominent autoimmune and autoinflammatory component.ConclusionsOur findings support a polygenic continuum across immune-mediated disorders, with four genetic clusters. The “polygenic autoimmune” and “polygenic autoinflammatory” clusters reside on margins of this continuum. These findings provide insights and lead us to hypothesize that the identified clusters could inform future therapeutical strategies, with patients in the same clusters potentially responding similarly to specific therapies.

Transdiagnostic and Disorder-Level GWAS Enhance Precision of Substance Use and Psychiatric Genetic Risk Profiles in African and European Ancestries.

Transdiagnostic and Disorder-Level GWAS Enhance Precision of Substance Use and Psychiatric Genetic Risk Profiles in African and European Ancestries.

Relationship Between Problematic Alcohol Use and Various Psychiatric Disorders: A Genetically Informed Study.

Problematic alcohol use (PAU) adversely affects the clinical course of psychiatric disorders. Genetic studies have suggested that genetic factors underlie the co-occurrence of PAU with psychiatric disorders. This study aimed to elucidate shared genetic architectures, prioritizing genes that disorders may have in common. Using genome-wide association data of PAU including 435,563 samples from people of European ancestry, this study investigated the genetic relationship between PAU and 11 psychiatric disorders using a bivariate causal mixture model (MiXeR). Local genetic correlation and colocalization analyses were conducted to identify the genomic regions significantly associated with PAU and each psychiatric disorder. Postanalysis included the false discovery rate (FDR) and transcriptome-wide association studies (TWASs), as well as summary-data-based Mendelian randomization to prioritize shared genes by integrating brain transcriptome data. MiXeR analysis revealed a substantial polygenic overlap (39%-73%) between PAU and psychiatric disorders. Four bivariate genomic regions with high correlations suggest shared causal variants of PAU with major depression and schizophrenia. Within these regions, four and six genes for the PAU-major depression and PAU-schizophrenia pairs, respectively, were mapped by conjunctional FDR analysis. Furthermore, TTC12 and ANKK1 were identified as potential causal genes for PAU and these disorders. The findings were replicated in multi-ancestry analyses of colocalization and TWASs. Despite the varying degrees of genetic overlap and directions of shared genetic effect correlations, these results imply the presence of shared genetic factors influencing the comorbidity of PAU and psychiatric disorders. Additionally, TTC12 and ANKK1, located near DRD2, may be causally associated with comorbid conditions.

Genetic etiology and clinical features of non-syndromic pediatric obesity in the Chinese population: a large cohort study

BackgroundThis study aimed to investigate the genetic etiology and clinical features of non-syndromic pediatric obesity in a large Chinese cohort, providing insights into the genetic profile and its correlation with clinical phenotypes.MethodsWe enrolled 391 children, aged 7–14 years, diagnosed with non-syndromic pediatric obesity at Jiangxi Provincial Children’s Hospital from January 2020 to June 2022. Whole-exome sequencing was employed to identify potential genetic causes, focusing on 79 candidate genes associated with obesity. Multivariate logistic regression analysis was performed on the clinical data of the non-syndromic obesity gene-positive group and the gene-negative group.ResultsAmong the 391 patients, 32 (8.2%) carried 18 non-syndromic obesity genes, with UCP3 and MC4R being the most common. Seven cases (1.8%) were rated as likely pathogenic by the American College of Medical Genetics and Genomics (ACMG). Clinical phenotype and genetic correlation analysis revealed that urinary microalbumin, fT4, GGT, uric acid, serum phosphorus, paternal weight, family history, impaired glucose tolerance (IGT), non-HDL cholesterol (non-HDL-C), and metabolic syndrome (MetS) showed significant statistical differences (P < 0.05). Serum phosphorus is an independent risk factor associated with genetic predispositions to obesity in children and adolescents (P < 0.05).ConclusionOur findings highlight the genetic heterogeneity of non-syndromic pediatric obesity and identify UCP3 and MC4R as potential hotspot genes in the Chinese population. The study underscores the potential of genetic testing for early diagnosis and personalized management of pediatric obesity.

The genetic overlap and causal relationship between attention deficit hyperactivity disorder and obstructive sleep apnea: a large-scale genomewide cross-trait analysis

BackgroundAttention deficit hyperactivity disorder (ADHD) and Obstructive sleep apnea (OSA) are highly clinically co-occurring, but the mechanisms behind this remain unclear, so this article analyzes the reasons for the co-morbidities from a genetic perspective.MethodsWe examined the genetic architecture of ADHD and OSA based on the large genome-wide association studies (GWAS). The global genetic relationship between OSA and ADHD was explored. Cross-trait analysis from single nucleotide polymorphism (SNP) and gene level was performed subsequently to detect the crucial genomic regions. Finally, we revealed the anatomical change on which genetic overlap relies and further explored whether genetic factors exert a causal effect.ResultsAfter using both linkage disequilibrium score regression (LDSC) and High-definition likelihood inference (HDL) methods, we identified a significant genetic correlation between OSA and ADHD (PLDSC = 2.45E-28, PHDL = 1.09E-25), demonstrating a consistent direction. Furthermore, through the application of various cross-trait methods, we pinpointed 5 loci and 57 genes involved in regulating the co-occurrence of these disorders. These genetic regions were thought to be associated with the prefrontal lobes (P = 3.07E-06) and the nucleus accumbens basal ganglia (P = 2.85E-06). Lastly, utilizing Mendelian randomization (MR), we established a link indicating that individuals with ADHD were at an elevated risk of developing OSA (PIVM = 0.02, OR (95%CI):1.09 (1.01–1.17)).ConclusionsThis study reveals a strong genetic correlation between ADHD and OSA. It offers insights for future drug target development and sleep management in ADHD.

Genetic variability, association and principal component analysis for agronomical traits in mungbean (Vigna radiata L. Wilczek)

Aim: This study aimed to assess the extent of genetic variability in 205 diverse green gram genotypes for agronomical traits and to identify the most effective traits for consideration in development of high yielding cultivars in mungbean. Methodology: The 205 diverse genotype including five checks of mungbean were evaluated in augmented block design-I with eight blocks. In each block, the 30 genotypes (25 genotypes + 5 checks) were grown in paired rows of 4 m length with 45 x 10 cm spacing. The mean data from selected plants across all the genotypes and checks were analyzed for genetic variability parameters (PCV, GCV, h2bs, GAM), Correlation, Path Analysis and Principal Component Analysis (PCA). Results: The high estimates of genotypic coefficient of variation, phenotypic coefficient of variation, heritability (h2bs) along with high genetic advance over mean were observed for number of branches per plant, number of clusters per plant, number of pods per cluster,number of pods per plant and seed yield per plant. The present study indicated that seed yield per plant had significant and positive correlation with harvest index, biological yield per plant, number of pods per plant and number of pods per cluster, and also highly influenced by these traits both directly and indirectly. PCA analysis revealed that out of thirteen principal components, five Pcs (PC1 to PC5) had eigenvalue of &gt;1.0 explaining 19.20%, 16.10%, 11.90%, 10.70% and 8.10%, respectively, accounted for 66% of total variation indicated the strong association of PCs and traits studied. PCA suggested that traits such as vegetative period, number of clusters per plant, number of pods per cluster, number of pods per plant and harvest index were the principal discriminatory traits. Interpretation: It is suggested that selection based on harvest index, biological yield per plant, number of pods per plant and number of pods per cluster may result in improvement of seed yield in mungbean. Key words: Correlation analysis, Genetic variability, Mungbean, Path analysis, Principal component analysis

Circulating Inflammatory Proteins and Age-related Macular Degeneration: New Insights from Mendelian Randomization.

Inflammation is a key mechanism underlying age-related macular degeneration (AMD); however, the specific circulating inflammatory proteins involved remain unclear. This study investigated the causal relationship between 91 circulating inflammatory proteins and AMD using a two-sample Mendelian randomization (MR) approach. We conducted a two-sample magnetic resonance imaging MR analysis using genomewide association study (GWAS) data. Five MR methodologies were applied, with inverse variance weighting (IVW) as the primary approach. We applied false discovery rate (FDR) correction to mitigate false positives. Sensitivity analyses were performed to assess horizontal pleiotropy and heterogeneity. Additionally, Steiger's test, reverse MR analysis, and linkage disequilibrium score regression (LDSC) were used to validate the results. Five inflammatory proteins demonstrated significant associations with overall AMD, including three associated with wet AMD and one associated with dry AMD. LDSC analysis indicated that, except for fibroblast growth factor-19, no genetic correlation confounded the causal relationships. Additionally, the expression of the identified proteins was unaffected by the genetic prediction of AMD. This study highlights the causal relationship between specific inflammatory proteins and AMD, emphasizing their potential roles in AMD pathogenesis and potential therapeutic targets.

Impact of genomic selection for growth and carcass traits on foot structure in Angus cattle.

Genomic evaluation improves accuracy and enables shorter generation intervals, accelerating genetic changes, possibly strengthening the antagonism between performance and less-selected traits. Our objective was to evaluate the impact of genomic selection for performance on foot structure in Angus cattle. Variance component estimation was done under the Bayesian approach (VCE) with partial or no genotypes, and with a new method based on predictivity (PRED) using all genotypes, this to examine changes in genetic parameters over time. The performance trait groups were growth (GT), carcass (CT), ultrasound carcass (uCT), and marbling (MT). Foot structure traits (FT) were foot angle (FA) and claw set (CS). Genetic parameters through VCE over 5-year intervals and using genotypes were obtained. From 2011-2015 to 2019-2022, changes in heritability were observed for CS (0.12 ± 0.01 to 0.16 ±, FA (0.18 ± 0.02 to 0.14 ± 0.01), carcass weight (0.30 ± 0.03 to 0.35 ± 0.04), marbling (0.43 ± 0.02 to 0.60 ± 0.04), and ultrasound backfat thickness (0.32 ± 0.01 to 0.38 ± 0.01). Changes in genetic correlations were found for CS-carcass weight (0.25 ± 0.15 to -0.04 ± 0.08), CS-rib eye area (0.20 ± 0.11 to -0.12 ± 0.08), and CS-weight at ultrasound scanning (0.12 ± 0.06 to 0.0 ± 0.03). For PRED, estimates from two 2-year slices showed that most GT and uCT heritabilities were lower than those from the last VCE interval (e.g., birth weight: 0.34 vs. 0.26). In comparison, FT heritabilities were higher (e.g., CS: 0.16 vs. 0.29). In general, all genetic correlations from PRED ranged from -0.15 to 0.10, whereas the values were between -0.15 and 0.05 in the last interval based on VCE. The predictivity method provides updated genetic parameters for young animals, whereas VCE estimates refer to the base population. Including genotypes had a strong impact on some estimates. Our results indicate that heritability estimates in recent generations for strongly selected traits have decreased compared to older generations. However, genetic correlations between foot structure and performance traits have consistently remained close to zero, likely due to the differences in selection intensity between these traits. While no strong antagonistic correlations were found, selecting multiple traits is crucial to maintain conformation while improving performance. Since the population structure changes due to genetic or environmental factors, updating the genetic parameters is vital for achieving expected genetic gains.

Effects of INSL3 and WNT2B gene polymorphisms on seasonal reproductive traits of Xinjiang Qira black sheep, Kazakh sheep and Duolang sheep

The purpose of this study was to investigate the polymorphism and genetic correlation of INSL3 and WNT2B genes with seasonal estrus and litter size in three different Xinjiang sheep breeds. The genetic diversity of INSL3 and WNT2B genes were analyzed, and their association with litter size and estrous traits were analyzed. The results showed that two SNPs (SNP1, SNP2) were detected in INSL3 gene and there were three genotypes in SNP2 (INSL3 (A100T)), named of AA, AT and TT, A was the dominant allele. Additionally, five SNPs (SNP3, SNP4, SNP5, SNP6, SNP7) were detected in the WNT2B gene and there were three genotypes in SNP4 (WNT2B (G126T)), named GG, GT and TT, G was dominant allele. SNP2 was in Hardy-Weinberg equilibrium in three sheep breeds (P > 0.05). SNP4 was deviated from Hardy-Weinberg equilibrium in three sheep breeds (P < 0.05). Further, AT genotype of SNP2 (INSL3 (A100T)) could significantly affect the estrus trait in Duolang sheep and Qira black sheep, and related to the litter size in Duolang sheep. The WNT2B significantly affected the estrus and litter size of Duolang sheep and Qira black sheep. INSL3 (A100T) and WNT2B (G126T) may be potential molecular markers for controlling seasonal reproductive trait in sheep.

Pleiotropic Genes Affecting Milk Production, Fertility, and Health in Thai-Holstein Crossbred Dairy Cattle: A GWAS Approach

Understanding the genetic basis of economically important traits is essential for enhancing the productivity, fertility, and health of dairy cattle. This study aimed to identify the pleiotropic genes associated with the 305-day milk yield (MY305), days open (DO), and milk fat-to-protein ratio (FPR) in Thai-Holstein crossbred dairy cattle using a genome-wide association study (GWAS) approach. The dataset included 18,843 records of MY305 and milk FPR, as well as 48,274 records of DO, collected from first-lactation Thai-Holstein crossbred dairy cattle. A total of 868 genotyped animals and 43,284 informative SNPs out of 50,905 were used for the analysis. The single-nucleotide polymorphism (SNP) effects were evaluated using a weighted single-step GWAS (wssGWAS), which estimated these effects based on genomic breeding values (GEBVs) through a multi-trait animal model with single-step genomic BLUP (ssGBLUP). Genomic regions explaining at least 5% of the total genetic variance were selected for candidate gene analysis. Single-step genomic REML (ssGREML) with a multi-trait animal model was used to estimate components of (co)variance. The heritability estimates from additive genetic variance were 0.262 for MY305, 0.029 for DO, and 0.102 for milk FPR, indicating a moderate genetic influence on milk yield and a lower genetic impact on fertility and milk FPR. The genetic correlations were 0.559 (MY305 and DO), −0.306 (MY305 and milk FPR), and −0.501 (DO and milk FPR), indicating potential compromises in genetic selection. wssGBLUP showed a higher accuracy than ssGBLUP, although the improvement was modest. A total of 24, 46, and 33 candidate genes were identified for MY305, DO, and milk FPR, respectively. Pleiotropic effects, identified by SNPs showing significant influence with more than trait, were observed in 14 genes shared among all three traits, 17 genes common between MY305 and DO, 14 genes common between MY305 and milk FPR, and 26 genes common between DO and milk FPR. Overall, wssGBLUP is a promising approach for improving the genomic prediction of economic traits in multi-trait analyses, outperforming ssGBLUP. This presents a viable alternative for genetic evaluation in dairy cattle breeding programs in Thailand. However, further studies are needed to validate these candidate genes and refine marker selection for production, fertility, and health traits in dairy cattle.

Calbindin 2 as a Novel Biomarker and Therapeutic Target for Abdominal Aortic Aneurysm: Integrative Analysis of Human Proteomes and Genetics.

Abdominal aortic aneurysm (AAA) is a clinical life-threatening issue. No pharmacological treatments are currently approved for the prevention and treatment of AAA. Therefore, identifying novel biomarkers and therapeutic targets is crucial for improving AAA management and outcomes. To identify plasma proteins with potential causal effects on AAA, we integrated genetic evidence from proteome-wide Mendelian randomization, genetic correlation, and colocalization analysis. The role of identified proteins in AAA was further explored through the phenome-wide association study and mediation analysis. Multiomics data analysis, including bulk RNA sequencing, single-cell/single-nucleus RNA sequencing, and spatial transcriptomics, was employed to characterize the expression patterns of these proteins. Experimental validation was performed using an AAA model in apolipoprotein E-deficient mice infused with angiotensin II. Druggability analysis was conducted to identify drug candidates, which were tested in preclinical mouse models. CALB2 (calbindin 2) was identified as having a causal effect on AAA and may influence the progression of AAA through the regulation of lipid metabolism. Multiomics analysis revealed that CALB2 is predominantly expressed in the mesothelial cells of adipose tissues. Inhibition of CALB2 in an AAA mouse model alleviated AAA progression. Druggability analysis identified lenalidomide and genistein as potential therapeutic candidates, and experiments confirmed their efficacy in preventing AAA development. This study identifies CALB2 as being associated with an increased risk of AAA and suggests that i might be a novel biomarker and therapeutic molecule for AAA management. Lenalidomide and genistein hold promising potential as treatments for patients with AAA.

The role of the brain-bone axis in skeletal degenerative diseases and psychiatric disorders, A genome-wide pleiotropic analysis.

The role of the brain-bone axis in skeletal degenerative diseases and psychiatric disorders, A genome-wide pleiotropic analysis.

Trans-eQTL hotspots shape complex traits by modulating cellular states.

Regulatory genetic variation shapes gene expression, providing an important mechanism connecting DNA variation and complex traits. The causal relationships between gene expression and complex traits remain poorly understood. Here, we integrated transcriptomes and 46 genetically complex growth traits in a large cross between two strains of the yeast Saccharomyces cerevisiae. We discovered thousands of genetic correlations between gene expression and growth, suggesting potential functional connections. Local regulatory variation was a minor source of these genetic correlations. Instead, genetic correlations tended to arise from multiple independent trans-acting regulatory loci. Trans-acting hotspots that affect the expression of numerous genes accounted for particularly large fractions of genetic growth variation and of genetic correlations between gene expression and growth. Genes with genetic correlations were enriched for similar biological processes across traits but with heterogeneous direction of effect. Our results reveal how trans-acting regulatory hotspots shape complex traits by altering cellular states.