Search

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.

Simple Summary: This study utilized data from 936 pigs for genome-wide association studies (GWAS) of four semen traits (sperm motility, sperm progressive motility, sperm abnormality rate, and total sperm count), as well as data from 5457 pigs from FarmGTEx for transcriptome analysis. The results revealed 16, 9, and 12 significant single nucleotide polymorphisms (SNPs) associated with semen traits in Duroc, Landrace, and Yorkshire pigs, with 7, 5, and 7 candidate genes identified in these breeds, potentially linked to mammalian spermatogenesis, testicular function, and male fertility. This research deepened the understanding of semen trait genetics and provided insights for enhancing semen quality in these pig breeds. High-quality semen is an essential factor for the success of artificial insemination, and revealing the genetic structure of pig semen traits helps improve semen quality. This study aimed to identify candidate genes associated with semen traits in three pig breeds (Duroc, Landrace, and Yorkshire) through weighted GWAS and multi-tissue transcriptome analysis. In this study, to identify candidate genes associated with semen traits in Duroc, Landrace, and Yorkshire, we performed weighted GWAS in four traits (sperm motility, sperm progressive motility, sperm abnormality rate, and total sperm count) using 936 pigs and multi-tissue transcriptome analysis using 34 tissues RNA-seq data of 5457 pigs from FarmGTEx. It was found that 16, 9, and 12 significant SNPs associated with semen traits were identified in Duroc, Landrace, and Yorkshire, with corresponding 7, 5, and 7 candidate genes in these three breeds, respectively, which may be involved in mammal spermatogenesis, testicular function, and male fertility. Moreover, we not only found the same candidate gene DNAI2 as in previous studies but also found two new candidate genes PNLDC1 and RSPH3, which were identified simultaneously in both Landrace and Yorkshire. By integrating the GWAS and multi-tissue transcriptome analysis results, we found that candidate genes associated with semen traits of three pig breeds were highly expressed in the testis tissue. The three genotypes of rs320928244 had significant effects on the expression of the DYNLT1 gene in the testis tissue of Landrace. These results together showed that these candidate genes were mainly related to sperm motility defects. This study helps deepen the understanding of the genetic basis of semen traits and provides a theoretical foundation for improving the semen quality of Duroc, Landrace, and Yorkshire breeds. [ABSTRACT FROM AUTHOR]

We investigate the spatial propagation in a delayed spruce budworm diffusive model ∂w(t,x)∂t=dΔw(t,x)−δw(t,x)−w2(t,x)1+w2(t,x)+pw(t−τ1,x)e−aw(t−τ2,x),$$ \frac{\partial w\left(t,x\right)}{\partial t}=d\Delta w\left(t,x\right)-\delta w\left(t,x\right)-\frac{w^2\left(t,x\right)}{1+{w}^2\left(t,x\right)}+ pw\left(t-{\tau}_1,x\right){e}^{- aw\left(t-{\tau}_2,x\right)}, $$ where τ1$$ {\tau}_1 $$ and τ2$$ {\tau}_2 $$ represent, respectively, the incubation and the maturation delays for the spruce budworm. We find the minimal wave speed c∗$$ {c}^{\ast } $$ to determine the existence of traveling wave fronts of the model. More specifically, the model admits traveling wave fronts when c≥c∗$$ c\ge {c}^{\ast } $$; the model has no traveling wave solutions when c∈(0,c∗)$$ c\in \left(0,{c}^{\ast}\right) $$. The proofs are based on combining the upper and lower solutions with the approach of Wu and Zou's theorems, the limit arguments, and Laplace transform. The obtained results help us to understand the spreading patterns and the spreading speed of spruce budworm population. [ABSTRACT FROM AUTHOR]

[Objectives] The traditional spectral fatigue analysis method only considers the effect of linear wave loads on fatigue life. For ships with large flare,how to reckon in the effect of nonlinear slamming loads is to be a difficulty in fatigue life calculation.[Methods] In this paper,a three dimensional nonlinear time domain method was used to analyze the time history distribution of global bending moment for a ship with big flare. Then the distribution of fatigue stress range of nodes of the hull were calculated by using the rain-flow counting method and the fatigue damage considering the effect of nonlinear slamming loads was obtained. After comparing this damage with those induced by linear wave loads, the amplification coefficient of nonlinear slamming bending moment for a ship with big flare was calculated.[Results] After calculation,within the effect of slamming loads,the fatigue damage is enlarged by about 40%.[Conclusions] The method described in this paper is of practical significance to the fatigue life calculation for the ship with serious slamming loads.

[Objectives] Complex contact takes place between a deck and the undercarriage of equipment. The undercarriage load cannot simply be regarded as a uniform load. In order to investigate the pressure distribution between a deck and undercarriage, [Methods] the nonlinear contact calculation of the undercarriage of a large piece of equipment is made using the finite element method, and the pressure distribution characteristics of the deck are analyzed. The distribution of the contact force is then verified through experiment. On this basis, a calculation method for replacing the undercarriage load with the equivalent wheel load is proposed which converts complex nonlinear contact calculation into linear elasticity calculation. [Results] It is found that the contact force is mainly distributed on the web beam and the front of the undercarriage, and the latter exerts great stress on the deck. The contact force of the front end of the undercarriage is about 25% that of the total load. The experimental results are in good agreement with the numerical results. [Conclusions] This simplified calculation method is practicable in deck design and strength analysis for similar kinds of ship.

In livestock, genome‐wide association studies (GWAS) are usually conducted in a single population (single‐GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta‐analysis of GWAS (meta‐GWAS) and mega‐analysis of GWAS (mega‐GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single‐GWAS, meta‐GWAS, and mega‐GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p‐values (pC). In Yorkshire, with the largest sample size, mega‐GWAS, meta‐GWAS and single‐GWAS detected 149, 38 and 20 significant SNPs (pC < 1E‐5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, pC of SNPs from mega‐GWAS was the lowest, followed by meta‐GWAS and single‐GWAS. The correlation of pC among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta‐GWAS and mega‐GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega‐GWAS versus single‐GWAS. [ABSTRACT FROM AUTHOR]