Mapping short tandem repeats for liver gene expression traits helps prioritize potential causal variants for complex traits in pigs

Background Short tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds. Results We identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively. Conclusions This study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.

Inflammatory Cytokines and Risk of Ischemic Stroke: A Mendelian Randomization Study

Background: Observational studies have revealed the association between some inflammatory cytokines and the occurrence of ischemic stroke, but the causal relationships remain unclear.Methods: We conducted a two-sample Mendelian randomization (MR) analysis to assess the causal effects of thirty inflammatory cytokines and the risk of ischemic stroke. For exposure data, we collected genetic variants associated with inflammatory cytokines as instrumental variables (IVs) from a genome-wide association study (GWAS) meta-analysis from Finland (sample size up to 8,293). For the outcome data, we collected summary data of ischemic stroke from a large-scale GWAS meta-analysis involved 17 studies (34,217 cases and 406,111 controls). We further performed a series of sensitivity analyses as validation of primary MR results.Results: According to the primary MR estimations and further sensitivity analyses, we established one robust association after Bonferroni correction: the odds ratio (95% CI) per unit change in genetically increased IL-4 was 0.84 (0.89–0.95) for ischemic stroke. The chemokine MCP3 showed a nominally significant association with ischemic stroke risk (OR: 0.93, 95% CI: 0.88–0.99, unadjusted p &lt; 0.05). There was no evidence of a causal effect of other inflammatory cytokines and the risk of ischemic stroke.Conclusions: Our study suggested that genetically increased IL-4 levels showed a protective effect on the risk of ischemic stroke, which provides important new insights into the potential therapeutic target for preventing ischemic stroke.

Development and Validation of an Insulin Resistance Predicting Model Using a Machine-Learning Approach in a Population-Based Cohort in Korea

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

Hao1 Is Not a Pathogenic Factor for Ectopic Ossifications but Functions to Regulate the TCA Cycle In Vivo

Ossification of the posterior longitudinal ligament (OPLL), a disease characterized by the ectopic ossification of a spinal ligament, promotes neurological disorders associated with spinal canal stenosis. While blocking ectopic ossification is mandatory to prevent OPLL development and progression, the mechanisms underlying the condition remain unknown. Here we show that expression of hydroxyacid oxidase 1 (Hao1), a gene identified in a previous genome-wide association study (GWAS) as an OPLL-associated candidate gene, specifically and significantly decreased in fibroblasts during osteoblast differentiation. We then newly established Hao1-deficient mice by generating Hao1-flox mice and crossing them with CAG-Cre mice to yield global Hao1-knockout (CAG-Cre/Hao1flox/flox; Hao1 KO) animals. Hao1 KO mice were born normally and exhibited no obvious phenotypes, including growth retardation. Moreover, Hao1 KO mice did not exhibit ectopic ossification or calcification. However, urinary levels of some metabolites of the tricarboxylic acid (TCA) cycle were significantly lower in Hao1 KO compared to control mice based on comprehensive metabolomic analysis. Our data indicate that Hao1 loss does not promote ectopic ossification, but rather that Hao1 functions to regulate the TCA cycle in vivo.

A genome-wide meta-analysis identifies 53 sequence variants associating with carpal tunnel syndrome

Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy and has a largely unknown underlying biology. In a genome-wide association study of CTS (Ncases = 48,843, Ncontrols = 1,190,837), we found 53 sequence variants at 50 loci that associate with the syndrome. The most significant association is with a missense variant (p.Glu366Lys) in SERPINA1 that protects against CTS (P = 2.9 × 10−24, OR = 0.76). Through various functional analyses, we conclude that at least 22 genes mediate CTS risk and highlight the role of 19 CTS variants in the biology of the extracellular matrix. We show that the genetic component to the risk is higher in recurrent/persistent cases than nonrecurrent/nonresistant cases. Anthropometric traits including height and BMI are genetically correlated with CTS, in addition to early hormonal-replacement therapy, osteoarthritis, and restlessness. Our findings suggest that the components of the extracellular matrix play a key role in the pathogenesis of CTS.

Genome-wide association mapping for high temperature tolerance in wheat through 90k SNP array using physiological and yield traits

Dissecting the genetic basis of physiological and yield traits against tolerance to heat stress is an essential in wheat breeding programs to boost up the wheat yield for sustainable food security. Herein, a genome-wide association study (GWAS) was performed to reveal the genetic basis of heat tolerance using high-density Illumina 90K Infinium SNPs array through physiological and yield indices. These indices were phenotyped on a diverse panel of foreign and domestic genotypes of Pakistan, grown in normal and heat-stressed environments. Based on STRUCTURE analysis, the studied germplasm clustered into four sub-population. Highly significant variations with a range of moderate (58.3%) to high (77.8%) heritability was observed under both conditions. Strong positive correlation existed among physiological and yield related attributes. A total of 320 significant (-log10 P ≥ 3) marker-trait associations (MTAs) were identified for the observed characters. Out of them 169 and 151 MTAs were recorded in normal and heat stress environments, respectively. Among the MTA loci, three (RAC875_c103017_302, Tdurum_contig42087_1199, and Tdurum_contig46877_488 on chromosomes 4B, 6B, and 7B respectively), two (BobWhite_c836_422 and BS00010616_51) and three (Kukri_rep_c87210_361, D_GA8KES401BNLTU_253 and Tdurum_contig1015_131) on chromosomes 5A, 1B, and 3D at the positions 243.59cM, 77.82cM and 292.51cM) showed pleiotropic effects in studied traits under normal, heat-stressed and both conditions respectively. The present study not only authenticated the numerous previously reported MTAs for examined attributes but also revealed novel MTAs under heat-stressed conditions. Identified SNPs will be beneficial in determining the novel genes in wheat to develop the heat tolerant and best yielded genotypes to fulfill the wheat requirement for the growing population.

Genetic and genomic characterization followed by single-step genomic evaluation of withers height in German Warmblood horses

Reliability of genomic predictions is influenced by the size and genetic composition of the reference population. For German Warmblood horses, compilation of a reference population has been enabled through the cooperation of five German breeding associations. In this study, preliminary data from this joint reference population were used to genetically and genomically characterize withers height and to apply single-step methodology for estimating genomic breeding values for withers height. Using data on 2113 mares and their genomic information considering about 62,000 single nucleotide polymorphisms (SNPs), analysis of the genomic relationship revealed substructures reflecting breed origin and different breeding goals of the contributing breeding associations. A genome-wide association study confirmed a known quantitative trait locus (QTL) for withers height on equine chromosome (ECA) 3 close to LCORL and identified a further significant peak on ECA 1. Using a single-step approach with a combined relationship matrix, the estimated heritability for withers height was 0.31 (SE = 0.08) and the corresponding genomic breeding values ranged from − 2.94 to 2.96 cm. A mean reliability of 0.38 was realized for these breeding values. The analyses of withers height showed that compiling a reference population across breeds is a suitable strategy for German Warmblood horses. The single-step method is an appealing approach for practical genomic prediction in horses, because not many genotypes are available yet and animals without genotypes can by this way directly contribute to the estimation system.

Simultaneous Dissection of Grain Carotenoid Levels and Kernel Color in Biparental Maize Populations with Yellow-to-Orange Grain

ABSTRACT Maize enriched in provitamin A carotenoids could be key in combatting vitamin A deficiency in human populations relying on maize as a food staple. Consumer studies indicate that orange maize may be regarded as novel and preferred. This study identifies genes of relevance for grain carotenoid concentrations and kernel color, through simultaneous dissection of these traits in 10 families of the U.S. maize nested association mapping panel that have yellow to orange grain. Quantitative trait loci (QTL) were identified via joint-linkage analysis, with phenotypic variation explained for individual kernel color QTL ranging from 2.4 to 17.5%. These QTL were cross-analyzed with significant marker-trait associations in a genome-wide association study that utilized ∼27 million variants. Nine genes were identified: four encoding activities upstream of the core carotenoid pathway, one at the pathway branchpoint, three within the α- or β-pathway branches, and one encoding a carotenoid cleavage dioxygenase. Of these, three exhibited significant pleiotropy between kernel color and one or more carotenoid traits. Kernel color exhibited moderate positive correlations with β-branch and total carotenoids and negligible correlations with α-branch carotenoids. These findings can be leveraged to simultaneously achieve desirable kernel color phenotypes and increase concentrations of provitamin A and other priority carotenoids.