Significant Single Nucleotide Polymorphisms Research Articles

Unraveling the genetic architecture of blood unfolded p-53 among non-demented elderlies: novel candidate genes for early Alzheimer's disease

BackgroundAlzheimer's disease (AD) is a heritable neurodegenerative disease whose long asymptomatic phase makes the early diagnosis of it pivotal. Blood U-p53 has recently emerged as a superior predictive biomarker for AD in the early stages. We hypothesized that genetic variants associated with blood U-p53 could reveal novel loci and pathways involved in the early stages of AD.ResultsWe performed a blood U-p53 Genome-wide association study (GWAS) on 484 healthy and mild cognitively impaired subjects from the ADNI cohort using 612,843 Single nucleotide polymorphisms (SNPs). We performed a pathway analysis and prioritized candidate genes using an AD single-cell gene program. We fine-mapped the intergenic SNPs by leveraging a cell-type-specific enhancer-to-gene linking strategy using a brain single-cell multimodal dataset. We validated the candidate genes in an independent brain single-cell RNA-seq and the ADNI blood transcriptome datasets. The rs279686 between AASS and FEZF1 genes was the most significant SNP (p-value = 4.82 × 10–7). Suggestive pathways were related to the immune and nervous systems. Twenty-three candidate genes were prioritized at 27 suggestive loci. Fine-mapping of 5 intergenic loci yielded nine cell-specific candidate genes. Finally, 15 genes were validated in the independent single-cell RNA-seq dataset, and five were validated in the ADNI blood transcriptome dataset.ConclusionsWe underlined the importance of performing a GWAS on an early-stage biomarker of AD and leveraging functional omics datasets for pinpointing causal genes in AD. Our study prioritized nine genes (SORCS1, KIF5C, TMEFF2, TMEM63C, HLA-E, ATAT1, TUBB, ARID1B, and RUNX1) strongly implicated in the early stages of AD.

Unraveling genetic causality between metformin and myocardial infarction on the basis of Mendelian randomization

BackgroundIn recent years, several studies have explored the effect of metformin on myocardial infarction (MI), but whether metformin has an improvement effect in patients with MI is controversial. This study was aimed to investigate the causal relationship between metformin and MI using Mendelian randomization (MR) analysis.MethodsThe genome-wide significant (P<5×10-8) single-nucleotide polymorphisms (SNPs) in patients with metformin and patients with MI were screened from the Open genome-wide association study (GWAS) project as instrumental variables (IVs). The study outcomes mainly included MI, old MI, acute MI, acute transmural MI of inferior wall, and acute transmural MI of anterior wall. The inverse variance weighted (IVW) method was applied to assess the main causal effect, and weighted median, simple mode, weighted mode methods, and MR-Egger regression were auxiliary applied for supplementary proof. The causal relationship between metformin and MI was assessed using odds ratios (OR) and 95% confidence intervals (95% CI). A leave-one-out method was used to explore the effect of individual SNPs on the results of IVW analyses, and a funnel plot was used to analyze the potential bias of the study results, thus ensuring the robustness of the results.ResultsIn total, 16, 84, 39, 26, and 34 SNPs were selected as IVs to assess the genetic association between metformin and outcomes of MI, old MI, acute MI, acute transmural MI of inferior wall, and acute transmural MI of anterior wall, respectively. Treatment with metformin does not affect the risk of acute transmural MI of anterior wall at the genetic level (P>0.05; OR for inverse variance weighted was 1.010). In the cases of MI, old MI, acute MI, and acute transmural MI of inferior wall, metformin may even be a risk factor for patients (P<0.05; ORs for inverse variance weighted were 1.078, 1.026, 1.022 and 1.018 respectively). There was no horizontal pleiotropy or heterogeneity among IVs. The results were stable when removing the SNPs one by one.ConclusionMetformin is not protective against the risk of myocardial infarction in patients and may even be a risk factor for MI, old MI, acute MI, and acute transmural MI of inferior wall.

Genetic Diversity and Genome-Wide Association in Cowpeas (Vigna unguiculata L. Walp)

Cowpea is one of the most popular dry-land legumes cultivated for food and forage in arid and semi-arid areas. Genetic diversity for global germplasm can be organized into core collections providing optimum resources to serve breeding requirements. Here, we present diversity analysis and genome-wide association study (GWAS) results for part of the cowpea core collection of the United States Department of Agriculture (USDA) along with breeding line controls. Included in the analysis were a total of 373 accessions analyzed with 6880 Single Nucleotide Polymorphism (SNP) markers from Genotyping by Sequencing (GBS). Population structure differentiated accessions into two groups irrespective of geographical origin and formed three clusters based on taxa upon phylogenetic analysis. A total of 56 SNPs were significantly associated to nine traits including pod length (25 Quantitative Trait Nucleotides, QTNs), seed anti-oxidant content (7 QTNs), dry pod color (7 QTNs), plant maturity (5 QTNs), flower color (5 QTNs), seed weight (4 QTNs), tolerance to low phosphate (1 QTN), growth habit (1 QTN), and response to rock phosphate (1 QTN) using Bayesian-information, Linkage-disequilibrium Iteratively Nested Keyway (BLINK), and Fixed and random model Circulating Probability Unification (FarmCPU) association models. Key genes related to all significant SNPs were identified based on annotations of the cowpea reference genome, including a flavonoid gene controlling flower color (Vigun08g040200.1), a root nodulation regulator for tolerance to low phosphate (Vigun11g168000.1), and numerous genes involved in signaling, biosynthesis, metabolite transport, and abiotic stress. Our results highlight the importance of maintaining public phenotyping databases at USDA and strengthening collaborations for data collection in cowpea to maximize research impacts.

An Explainable Deep Learning Classifier of Bovine Mastitis Based on Whole-Genome Sequence Data—Circumventing the p >> n Problem

The serious drawback underlying the biological annotation of whole-genome sequence data is the p >> n problem, which means that the number of polymorphic variants (p) is much larger than the number of available phenotypic records (n). We propose a way to circumvent the problem by combining a LASSO logistic regression with deep learning to classify cows as susceptible or resistant to mastitis, based on single nucleotide polymorphism (SNP) genotypes. Among several architectures, the one with 204,642 SNPs was selected as the best. This architecture was composed of two layers with, respectively, 7 and 46 units per layer implementing respective drop-out rates of 0.210 and 0.358. The classification of the test data resulted in AUC = 0.750, accuracy = 0.650, sensitivity = 0.600, and specificity = 0.700. Significant SNPs were selected based on the SHapley Additive exPlanation (SHAP). As a final result, one GO term related to the biological process and thirteen GO terms related to molecular function were significantly enriched in the gene set that corresponded to the significant SNPs. Our findings revealed that the optimal approach can correctly predict susceptibility or resistance status for approximately 65% of cows. Genes marked by the most significant SNPs are related to the immune response and protein synthesis.

Causal association of metformin treatment with diverse cardiovascular diseases: a Mendelian randomization analysis.

The cardiovascular effects of metformin continue to be a subject of debate within the medical community. The Mendelian randomization (MR) study used data from genome-wide association studies (GWAS) to explore the causal association with six diseases that are associated with bimatoprost treatment and myocardial infarction, chronic heart failure, atrial fibrillation, hypertrophic and dilated cardiomyopathy, and valvular disease. Genome-wide significant single nucleotide polymorphisms (SNPs), that are associated with metformin use were selected as the instrumental variables. To determine the causal relationship between metformin use and various cardiovascular diseases, MR analysis was conducted, employing methods such as Instrumental Variable Weighting (IVW). The IVW analysis demonstrated a positive association between metformin treatment and the risk of myocardial infarction (OR = 22.67, 95% CI 3.22-34.01; P = 0.002). Conversely, metformin treatment exhibited a negative association with the risk of developing valvular disease (OR = 0.98, 95% CI 0.95-1.00; P = 0.046) and hypertrophic cardiomyopathy (OR = 0.01, 95% CI 0.00-0.22; P = 0.016). Multiple test correction found that metformin treatment was causally associated with the risk of both hypertrophic cardiomyopathy (PFDR = 0.048) and myocardial infarction (PFDR = 0.012). The analysis revealed limited heterogeneity in the individual results, absence of pleiotropy evidence, and indications of stability in the findings. The MR study discovered from a genetic standpoint that metformin may lower the risk of hypertrophic cardiomyopathy and valvular heart disease, yet it could elevate the risk of myocardial infarction.

PM2.5 associates with blood pressure: a Mendelian randomization analysis

ABSTRACT The relationship between fine particulate matter (PM2.5) and blood pressure (BP) is a controversial issue. We conducted a two-sample Mendelian randomization (MR) analysis and identified 58 genome-wide significant single-nucleotide polymorphisms associated with PM2.5 as instrument variables. Inverse-variance weighted (IVW) was used as the primary analysis approach. MR-Egger, weighted median, simple model, and weighted model methods were selected for quality control. We found a significant negative causal association of higher genetically predicted PM2.5 levels with lower systolic BP (SBP), while no causal relationship was identified between PM2.5 and diastolic BP (DBP). For each 1 standard deviation increase in genetically predicted PM2.5 levels, the beta value (95% CI) of SBP was -0.14 (-0.25, -0.03) for IVW (p=0.02), and -0.13 (-0.22, -0.04) for weighted median (p=0.005). Increased PM2.5 concentrations can lead to decreased SBP levels. Our findings provided novel insights into the controversial topic on the causal relationship between PM2.5 and BP.

GWAS determined genetic loci associated with callus induction in oil palm tissue culture.

GWAS identified six loci at 25kb downstream of WAK2, a crucial gene for cell wall and callus formation, enabling development of a SNP marker for enhanced callus induction potential. Efficient callus induction is vital for successful oil palm tissue culture, yet identifying genomic loci and markers for early detection of genotypes with high potential of callus induction remains unclear. In this study, immature male inflorescences from 198 oil palm accessions (dura, tenera and pisifera) were used as explants for tissue culture. Callus induction rates were collected at one-, two- and three-months after inoculation (C1, C2 and C3) as phenotypes. Resequencing generated 11,475,258 high quality single nucleotide polymorphisms (SNPs) as genotypes. GWAS was then performed, and correlation analysis revealed a positive association of C1 with both C2 (R = 0.81) and C3 (R = 0.50), indicating that C1 could be used as the major phenotype for callus induction rate. Therefore, only significant SNPs (P ≤ 0.05) in C1 were identified to develop markers for screening individuals with high potential of callus induction. Among 21 significant SNPs in C1, LD block analysis revealed six SNPs on chromosome 12 (Chr12) potentially linked to callus formation. Subsequently, 13 SNP markers were identified from these loci and electrophoresis results showed that marker C-12 at locus Chr12_12704856 can be used effectively to distinguish the GG allele, which showed the highest probability (69%) of callus induction. Furthermore, a rapid SNP variant detection method without electrophoresis was established via qPCR-based melting curve analysis. Our findings facilitated marker-assisted selection for specific palms with high potential of callus induction using immature male inflorescence as explant, aiding ortet palm selection in oil palm tissue culture.

Genome-wide association analysis for drought tolerance and component traits in groundnut gene pool

The potential production and productivity of groundnuts are limited due to severe drought stress associated with climate change. The current study aimed to identify genomic regions and candidate genes associated with drought tolerance and component traits for gene introgression and to guide marker-assisted breeding of groundnut varieties. Ninety-nine genetically diverse groundnut genotypes were phenotyped under drought-stressed and non-stressed field conditions in 2018/19 and 2019/20, and using the LeasyScan platform under non-stressed conditions in 2019/20 at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)/India. The samples were genotyped using 48 K single nucleotide polymorphisms (SNPs) markers at the University of Georgia/USA. Phenotypic data was collected on 17 agronomic traits and subjected to statistical analyses. The SNP data were computed, and population structure was inferred using a Bayesian clustering method in Structure version 2.3.4, while linkage disequilibrium was calculated using the GAPIT program in R software. Marker-trait associations were deduced using Tassel 5.2.86. Significant phenotypic variations were recorded for drought tolerance and the assessed agronomic traits. GWAS analysis using PCA + K and Q + K models identified significant SNPs associated with leaf area (1 SNP), leaf area Index (1 SNP), specific leaf area (1 SNP), leaf relative water content (43 SNPs), number of primary branches (1 SNP) and hundred seed weight (1 SNP). Forty-seven and one marker-trait associations were detected under drought-stressed and non-stressed conditions, respectively. The candidate genes and markers identified in the current study are useful for accelerated groundnut breeding targeting drought tolerance and market-preferred traits.

Genome-wide association study identified five quantitative trait loci and two candidate genes for digestive traits in Suhuai pigs.

This work aimed to identify markers and candidate genes underlying porcine digestive traits. In total, 331 pigs were genotyped by 80 K Chip data or 50 K Chip data. For apparent neutral detergent fiber digestibility, a total of 19 and 21 candidate single nucleotide polymorphisms (SNP) were respectively identified using a genome-wide efficient mixed-model association algorithm and linkage-disequilibrium adjusted kinship. Among them, three quantitative trait locus (QTL) regions were identified. For apparent acid detergent fiber digestibility, a total of 16 and 17 SNPs were identified by these two methods, respectively. Of these, three QTL regions were also identified. Moreover, two candidate genes (MST1 and LATS1), which are functionally related to intestinal homeostasis and health, were detected near these significant SNPs. Taken together, our results could provide a basis for deeper research on digestive traits in pigs.

Genome-wide association study identifies a novel candidate gene for egg production traits in chickens.

Qingyuan partridge chicken is a renowned indigenous yellow broiler breed in China. Egg production traits are important economic traits for chickens. With the decreasing cost of whole genome resequencing, identifying candidate genes with more precision has become possible. In order to identify molecular markers and candidate genes associated with egg production traits, we conducted genome-wide association studies based on the resequencing data of 287 female Qingyuan partridge chickens. For each hen, age at first egg and egg laying rate were recorded and calculated, respectively. With a univariate linear mixed model, we detected one genome-wide significant single nucleotide polymorphism (SNP) and three chromosome-wide significant SNPs associated with egg laying rate. MTA2 is highly likely to be a functional gene for egg laying rate. Our study identifies MTA2 as the first time to be associated with egg laying rate. Findings in our study will advance our understanding of the genetic basis of egg production and have the potential to improve the efficiency of genomic selection in chickens.

A Genome-Wide Association Study of Seed Morphology-Related Traits in Sorghum Mini-Core and Senegalese Lines

Sorghum (Sorghum bicolor L.) ranks fifth as the most crucial cereal crop globally, yet its seed morphology remains relatively unexplored. This study investigated seed morphology in sorghum based on 115 mini-core and 130 Senegalese germplasms. Eight seed morphology traits encompassing size, shape, and color parameters were assessed. Statistical analyses explored potential associations between these traits and resistance to three major sorghum diseases: anthracnose, head smut, and downy mildew. Furthermore, genome-wide association studies (GWAS) were conducted using phenotypic data from over 24,000 seeds and over 290,000 publicly available single nucleotide polymorphisms (SNPs) through the Genome Association and Prediction Integrated Tool (GAPIT) R package. Significant SNPs associated with various seed morphology traits were identified and mapped onto the reference sorghum genome to identify novel candidate defense genes.

Genome-Wide Association Study of Body Conformation Traits in Tashi Goats (Capra hircus).

Identifying genetic markers of economically valuable traits has practical benefits for the meat goat industry. To better understand the genomic variations influencing body conformation traits, a genome-wide association study was performed on Tashi goats, an indigenous Chinese goat breed. A total of 155 Tashi goats were phenotyped for eight body conformation traits: body height, body length, chest depth, chest width, chest girth, rump width, rump height, and cannon bone circumference. Then, 100 Tashi goats were randomly selected for whole-genome sequencing and genotyped. We obtained 1676.4 Gb of raw data with an average sequencing depth of 6.2X. Clean reads were aligned to the ARS1.2 reference genome, and 11,257,923 single nucleotide polymorphisms (SNPs) were identified. The structure analysis showed that these Tashi goats were almost not genetically related. The 109, 20, 52, 14, 62, 51, 70, and 7 SNPs were significantly associated with body height, body length, chest depth, chest width, chest girth, rump width, rump height, and cannon bone circumference. Within the ±500 kb region of significant SNPs, 183 genes were annotated. The most significantly enriched KEGG pathway was "olfactory transduction", and the most significantly enriched gene ontology (GO) terms were "cellular process", "cellular anatomical entity", and "molecular transducer activity". Interestingly, we found several SNPs on chromosomes 10 and 11 that have been identified multiple times for all eight body conformation traits located in two fragments (114 kb and 1.03 Mb). In chr.10:25988403-26102739, the six SNPs were tightly linked, the TACTAG genotype was the highest at 91.8%, and the FNTB (Farnesyltransferase, CAAX Box Beta) and CHURC1 (Churchill Domain Containing 1) genes were located. In chr.11:88216493-89250659, ten SNPs were identified with several dependent linkage disequilibrium (LD) blocks, and seven related genes were annotated, but no significant SNP was located in them. Our results provide valuable biological information for improving growth performance with practical applications for genomic selection in goats.

Genetic dissection of ten photosynthesis-related traits based on InDel- and SNP-GWAS in soybean.

A total of 416 InDels and 112 SNPs were significantly associated with soybean photosynthesis-related traits. GmIWS1 and GmCDC48 might be related to chlorophyll fluorescence and gas-exchange parameters, respectively. Photosynthesis is one of the main factors determining crop yield. A better understanding of the genetic architecture for photosynthesis is of great significance for soybean yield improvement. Our previous studies identified 5,410,112 single nucleotide polymorphisms (SNPs) from the resequencing data of 219 natural soybean accessions. Here, we identified 634,106 insertions and deletions (InDels) from these 219 accessions and used these InDel variations to perform principal component and linkage disequilibrium analysis of this population. The genome-wide association study (GWAS) were conducted on six chlorophyll fluorescence parameters (chlorophyll content, light energy absorbed per reaction center, quantum yield for electron transport, probability that a trapped exciton moves an electron into the electron transport chain beyond primary quinone acceptor, maximum quantum yield of photosystem II primary photochemistry in the dark-adapted state, performance index on absorption basis) and four gas-exchange parameters (intercellular carbon dioxide concentration, stomatal conductance, net photosynthesis rate, transpiration rate) and revealed 416 significant InDels and 112 significant SNPs. Based on GWAS results, GmIWS1 (encoding a transcription elongation factor) and GmCDC48 (encoding a cell division cycle protein) with the highest expression in the mapping region were determined as the candidate genes responsible for chlorophyll fluorescence and gas-exchange parameters, respectively. Further identification of favorable haplotypes with higher photosynthesis, seed weight and seed yield were carried out for GmIWS1 and GmCDC48. Overall, this study revealed the natural variations and candidate genes underlying the photosynthesis-related traits based on abundant phenotypic and genetic data, providing valuable insights into the genetic mechanisms controlling photosynthesis and yield in soybean.

Genome-wide association study and development of molecular markers for yield and quality traits in peanut (Arachis hypogaea L.)

BackgroundThis study aims to decipher the genetic basis governing yield components and quality attributes of peanuts, a critical aspect for advancing molecular breeding techniques. Integrating genotype re-sequencing and phenotypic evaluations of seven yield components and two grain quality traits across four distinct environments allowed for the execution of a genome-wide association study (GWAS).ResultsThe nine phenotypic traits were all continuous and followed a normal distribution. The broad heritability ranged from 88.09 to 98.08%, and the genotype-environment interaction effects were all significant. There was a highly significant negative correlation between protein content (PC) and oil content (OC). The 10× genome re-sequencing of 199 peanut accessions yielded a total of 631,988 high-quality single nucleotide polymorphisms (SNPs), with 374 significant SNP loci identified in association with the nine traits of interest. Notably, 66 of these pertinent SNPs were detected in multiple environments, and 48 of them were linked to multiple traits of interest. Five loci situated on chromosome 16 (Chr16) exhibited pleiotropic effects on yield traits, accounting for 17.64–32.61% of the observed phenotypic variation. Two loci on Chr08 were found to be strongly associated with protein and oil contents, accounting for 12.86% and 14.06% of their respective phenotypic variations, respectively. Linkage disequilibrium (LD) block analysis of these seven loci unraveled five nonsynonymous variants, leading to the identification of one yield-related candidate gene and two quality-related candidate genes. The correlation between phenotypic variation and SNP loci in these candidate genes was validated by Kompetitive allele-specific PCR (KASP) marker analysis.ConclusionsOverall, molecular markers were developed for genetic loci associated with yield and quality traits through a GWAS investigation of 199 peanut accessions across four distinct environments. These molecular tools can aid in the development of desirable peanut germplasm with an equilibrium of yield and quality through marker-assisted breeding.

Novel SNP markers and other stress-related genomic regions associated with nitrogen use efficiency in cassava.

Cassava productivity is constrained by low soil nitrogen, which is predominant in most cassava-growing regions in the tropics and subtropical agroecology. Improving the low nitrogen tolerance of cassava has become an important breeding objective. The current study aimed to develop cassava varieties with improved nitrogen use efficiency by identifying genomic regions and candidate genes linked to nitrogen use efficiency in cassava. A genome-wide association study (GWAS) was performed using the Genome Association and Prediction Integrated Tool (GAPIT). A panel of 265 diverse cassava genotypes was phenotyped for 10 physiological and agronomic traits under optimum and low-nitrogen regimes. Whole-genome genotyping of these cassava cloneswas performed using the Diversity Arrays Technology (DArTseq) sequencing platform. A total of 68,814 single nucleotide polymorphisms (SNPs) were identified, which were spread across the entire 18 chromosomes of the cassava genome, of which 52 SNPs at various densities were found to be associated with nitrogen use efficiency in cassava and other yield-related traits. The putative genes identified through GWAS, especially those with significant associated SNP markers for NUE and related traits have the potential, if deployed appropriately, to develop cassava varieties with improved nitrogen use efficiency, which would translate to a reduction in the economic and environmental cost of cassava production.

Integration of genome-wide association and genomic prediction for dissecting seed protein and amino acid in foxtail millet

ContextThe exploitation and utilization of genetic variation in crop germplasm benefit genomics-informed breeding programs. The integration of genome-wide association study (GWAS) and genomic prediction (GP) holds promise for illustrating genetic basis of phenotypic variation. Currently, the use of this integrative approach for dissecting grain protein and amino acid traits remains limited in crop species. ObjectiveThe research objectives were to identify significant single nucleotide polymorphisms (SNPs) associated with seed protein and amino acids and to assess the genomic predictive ability for nutritional traits in natural population of foxtail millet. MethodsIn this study, the concentrations of seed protein and 17 amino acids were assessed in a training population consisting of 238 diverse accessions of foxtail millet. The significant SNPs associated with these phenotypes were identified by GWAS with a mix-linear model. Extensive cross-validation was performed to evaluate the accuracy of genomic prediction of seed nutritional traits. Furthermore, the genomic prediction model was optimized and externally validated in 211 untested materials with the most significant SNPs obtained from GWAS. ResultsA significant genotype variance was detected for seed protein and amino acids and broad-sense heritability (h2) ranged from 0.45 to 0.87 (P < 0.05). The GWAS identified 39 significant SNPs associated with 15 amino acids (p < 1e-06), explaining an average of 12.2% phenotypic variation per locus. The number of significant SNPs ranged from 1 (Asp, Ile, Glu) to 23 for Phe amino acid, including six SNPs associated with one or multiple amino acids. In the training population, the prediction accuracies of protein and amino acids, tested using the 10-fold cross-validation model with whole genome-wide SNPs, ranged from 0.23 (Glu) to 0.60 (Lys) with an average value of 0.44%. Population structure had little effect on the prediction accuracy. In an empirical validation experiment involving 211 untested accessions, the averaged prediction accuracy increased by 12.3% across all traits when using the 1000 top SNPs obtained from GWAS, compared with the same number of randomly selected SNPs from the entire genome. ConclusionsLarge variations of seed protein and amino acids were found in a natural population of foxtail millet. GWAS identified 39 significant SNPs associated with 15 amino acids, and six SNPs were linked to more than one trait. The 10-fold cross-validation model produced the relatively higher prediction accuracies in seed quality traits of diverse accessions. By applying the significant SNPs from GWAS to the untested accessions, the use of 1000 GWAS-top SNPs resulted in more powerful predictions for complex nutritional traits than those using the same number of randomly selected SNPs across the genome. SignificanceThe research highlights the importance and effectiveness of integrative GWAS and genomic prediction in revealing the genetic architecture of seed protein and amino acid variation in foxtail millet. The results provide valuable insights into assessment and improvement of germplasm resources for advancing precision crop phenotyping and enhancing breeding programs for crop nutritional traits.

Genome-wide Association Studies of Novel Resilience Traits Identify Important Immune QTL Regions and Candidate Genes in Duroc Pigs

Resilience traits in pig populations allow animals to deal better with infectious disease and suboptimal production environments. The data on daily weight, feed intake and feed behaviors in pigs are collected in test period by automated feeding stations, which facilitate to evaluate the resilience traits. In this study, we adopted the mean square error roots (RMSE) of ordinary least squares (OLS) and the negative residuals of quantile regression (QR) to generate four different novel resilience traits using daily records of feed intake and feed duration between 90 and 180 days of age in a population of commercial Duroc pigs. The genome-wide association studies (GWAS) based on single- and two- trait mixed models were carried out on 550 pigs using 48,603 single nucleotide polymorphisms (SNPs) to identify genomic regions associated with resilience traits in growing pigs. We further focused on the GWAS signals to conduct gene annotation, colocalization with multi-tissue eQTL summary statistics of PigGTEx project and identification of enhancers and promoters using the publicly available data. The genomic heritabilities of four novel resilience traits ranged from 0.09 to 0.41. The pairwise genetic and phenotypic correlations ranged from 0.16 to 0.95 and from 0.05 to 0.36, respectively. Twenty-seven SNPs were identified to be significantly associated with these resilience traits. They were distributed on nine chromosomes (SSC1, SSC2, SSC6, SSC7, SSC8, SSC12, SSC14, SSC16, and SSC17). After annotation, 39 QTLs and 49 candidate genes were identified. Several of these are functionally relevant candidate genes including OTUD4, TIFA and CARD14, which are involved in the host immune response, disease susceptibility and signal transduction. Eight unique SNPs were found to be causal in both GWAS and eQTL analyses across 15 tissues. Notably, one SNP (rs80794541) was associated with eQTLs identified concurrently across seven tissues/cell types, including the macrophage cell type. Furthermore, four significant SNPs (rs81467127, rs81356029, rs80794541, and rs81305085) were linked to the function of the primed enhancer, active element, and poised promoter in five pig tissues. Using the porcine fibroblast HiC dataset, SNP (rs81356029) on SSC2 regulates the CARNS1 and SSH3, while SNP (rs80794541) on SSC7 regulates the H2AC6. In this study, we generated four novel resilience traits and identified SNPs significantly associated with these resilience traits in a Duroc pig population. GWAS signals were associated with candidate genes involving in the immune traits, and were linked to the crucial regulatory elements as well. Our findings will contribute to elucidating the genetic mechanism that can enhance genome-enabled breeding and inform further research on resilience in domestic pigs.

Effects of genetically predicted posttraumatic stress disorder on autoimmune phenotypes

Observational studies suggest that posttraumatic stress disorder (PTSD) increases risk for various autoimmune diseases. Insights into shared biology and causal relationships between these diseases may inform intervention approaches to PTSD and co-morbid autoimmune conditions. We investigated the shared genetic contributions and causal relationships between PTSD, 18 autoimmune diseases, and 3 immune/inflammatory biomarkers. Univariate MiXeR was used to contrast the genetic architectures of phenotypes. Genetic correlations were estimated using linkage disequilibrium score regression. Bi-directional, two-sample Mendelian randomization (MR) was performed using independent, genome-wide significant single nucleotide polymorphisms; inverse variance weighted and weighted median MR estimates were evaluated. Sensitivity analyses for uncorrelated (MR PRESSO) and correlated horizontal pleiotropy (CAUSE) were also performed. PTSD was considerably more polygenic (10,863 influential variants) than autoimmune diseases (median 255 influential variants). However, PTSD evidenced significant genetic correlation with nine autoimmune diseases and three inflammatory biomarkers. PTSD had putative causal effects on autoimmune thyroid disease (p = 0.00009) and C-reactive protein (CRP) (p = 4.3 × 10−7). Inferences were not substantially altered by sensitivity analyses. Additionally, the PTSD-autoimmune thyroid disease association remained significant in multivariable MR analysis adjusted for genetically predicted inflammatory biomarkers as potential mechanistic pathway variables. No autoimmune disease had a significant causal effect on PTSD (all p values > 0.05). Although causal effect models were supported for associations of PTSD with CRP, shared pleiotropy was adequate to explain a putative causal effect of CRP on PTSD (p = 0.18). In summary, our results suggest a significant genetic overlap between PTSD, autoimmune diseases, and biomarkers of inflammation. PTSD has a putative causal effect on autoimmune thyroid disease, consistent with existing epidemiologic evidence. A previously reported causal effect of CRP on PTSD is potentially confounded by shared genetics. Together, results highlight the nuanced links between PTSD, autoimmune disorders, and associated inflammatory signatures, and suggest the importance of targeting related pathways to protect against disease and disability.

Genome-wide association studies of egg production traits by whole genome sequencing of Laiwu Black chicken

Compared to high-yield commercial laying hens, Chinese indigenous chicken breeds have poor egg laying capacity due to the lack of intensive selection. However, as these breeds have not undergone systematic selection, it is possible that there is a greater abundance of genetic variations related to egg laying traits. In this study, we assessed 5 egg number (EN) traits at different stages of the egg-laying period: EN1 (from the first egg to 23 wk), EN2 (from 23 to 35 wk), EN3 (from 35 to 48 wk), EN4 (from the first egg to 35 wk), and EN5 (from the first egg to 48 wk). To investigate the molecular mechanisms underlying egg number traits in a Chinese local chicken breed, we conducted a genome-wide association study (GWAS) using data from whole-genome sequencing (WGS) of 399 Laiwu Black chickens. We obtained a total of 3.01 Tb of raw data with an average depth of 7.07 × per individual. A total of 86 genome-wide suggestive or significant single-nucleotide polymorphisms (SNP) contained within a set of 45 corresponding candidate genes were identified and found to be associated with stages EN1-EN5. The genes vitellogenin 2 (VTG2), lipase maturation factor 1 (LMF1), calcium voltage-gated channel auxiliary subunit alpha2delta 3 (CACNA2D3), poly(A) binding protein cytoplasmic 1 (PABPC1), programmed cell death 11 (PDCD11) and family with sequence similarity 213 member A (FAM213A) can be considered as the candidate genes associated with egg number traits, due to their reported association with animal reproduction traits. Noteworthy, results suggests that VTG2 and PDCD11 are not only involved in the regulation of EN3, but also in the regulation of EN5, implies that VTG2 and PDCD11 have a significant influence on egg production traits. Our study offers valuable genomic insights into the molecular genetic mechanisms that govern egg number traits in a Chinese indigenous egg-laying chicken breed. These findings have the potential to enhance the egg-laying performance of chickens.

Unveiling the population genetic structure of Iranian horses breeds by whole-genome resequencing analysis.

Preserving genetic diversity is pivotal for enhancing genetic improvement and facilitating adaptive responses to selection. This study focuses on identifying key genetic variants, including single nucleotide polymorphisms (SNPs), insertion/deletion polymorphisms (INDELs), and copy number variants (CNVs), while exploring the genomic evolutionary connectedness among seven Iranian horses representing five indigenous breeds: Caspian, Turkemen, DareShuri, Kurdish, and Asil. Using whole-genome resequencing, we generated 2.7Gb of sequence data, with raw reads ranging from 1.2Gb for Caspian horses to 0.38Gb for Turkoman horses. Post-filtering, approximately 1.9Gb of reads remained, with ~ 1.5Gb successfully mapped to the horse reference genome (EquCab3.0), achieving mapping rates between 76.4% (Caspian) and 98.35% (Turkoman). We identified 2,909,816 SNPs in Caspian horses, constituting around 0.1% of the genome. Notably, 71% of these SNPs were situated in intergenic regions, while 8.5 and 6.8% were located upstream and downstream, respectively. A comparative analysis of SNPs between Iranian and non-Iranian horse breeds showed that Caspian horses had the lowest number of shared SNPs with Turkoman horses. Instead, they showed a closer genetic relationship with DareShuri, Quarter, Arabian, Standardbred, and Asil breeds. Hierarchical clustering highlighted Caspian horses as a distinct cluster, underscoring their distinctive genomic signature. Caspian horses exhibit a unique genetic profile marked by an enrichment of private mutations in neurological genes, influencing sensory perception and awareness. This distinct genetic makeup shapes mating preferences and signifies a separate evolutionary trajectory. Additionally, significant non-synonymous single nucleotide polymorphisms (nsSNPs) in reproductive genes offer intervention opportunities for managing Caspian horses. These findings reveal the population genetic structure of Iranian horse breeds, contributing to the advancement of knowledge in areas such as conservation, performance traits, climate adaptation, reproduction, and resistance to diseases in equine science.