Integrating large-scale meta-analysis of genome-wide association studies improve the genomic prediction accuracy for combined pig populations.

The strategy of combining reference populations has been widely recognized as an effective way to enhance the accuracy of genomic prediction (GP). This study investigated the efficiency of genomic prediction using prior information and combined reference population. In total, prior information considering trait-associated single nucleotide polymorphisms (SNPs) obtained from meta-analysis of genome-wide association studies (GWAS meta-analysis) was incorporated into three models to assess the performance of GP using combined reference populations. Two different Yorkshire populations with imputed whole genome sequence (WGS) data (9,741,620 SNPs), named as P1 (1259 individuals) and P2 (1018 individuals), were used to predict genomic estimated breeding values for three live carcass traits, including backfat thickness, loin muscle area, and loin muscle depth. A 10 × 5 fold cross-validation was used to evaluate the prediction accuracy of 203 randomly selected candidate pigs from the P2 population and the reference population consisted of the remaining pigs from P2 and the stepwise added pigs from P1. By integrating SNPs with different p-value thresholds from GWAS meta-analysis downloaded from PigGTEx Project, the prediction accuracy of GBLUP, genomic feature BLUP (GFBLUP) and GBLUP given genetic architecture (BLUP|GA) were compared. Moreover, we explored effects of reference population size and heritability enrichment of genomic features on the prediction accuracy improvement of GFBLUP and BLUP|GA relative to GBLUP. The prediction accuracy of GBLUP using all WGS markers showed average improvement of 4.380% using the P1 + P2 reference population compared with the P2 reference population. Using the combined reference population, GFBLUP and BLUP|GA yielded 6.179% and 5.525% higher accuracies than GBLUP using all SNPs based on the single reference population, respectively. Positive regression coefficients were estimated in relation to the improvement in prediction accuracy (between GFBLUP/BLUP|GA and GBLUP) and the size of the reference as well as the heritability enrichment of genomic features. Compared to the classic GBLUP model, GFBLUP and BLUP|GA models integrating GWAS meta-analysis information increase the prediction accuracy and using combined populations with enlarged reference population size further enhances prediction accuracy of the two approaches. The heritability enrichment of genomic features can be used as an indicator to reflect weather prior information is accurately presented.

Metallogels self-assembled from linear rod-like platinum complexes: influence of the linkage.

Two linear rod-like platinum complexes, which only differed in the linkage, were prepared. They both self-assemble into metallogels in nonpolar solvents; however, a very big contrast was observed. Unexpectedly, a much weaker gel was acquired upon replacing the ester linkage by an amide group. The intermolecular hydrogen bonding offered by the amide motif leads to a different stacking fashion and mechanism. The results demonstrated herein contribute to the rational design of metallogels as well as other functional supramolecular materials.

Robust Ordered Bundles of Porous Helical Nanotubes Assembled from Fully Rigid Ionic Benzene-1,3,5-tricarboxamides.

Size-controlled and ordered assemblies of artificial nanotubes are promising for practical applications; however, the supramolecular assembly of such systems remains challenging. A novel strategy is proposed that can be used to reinforce intermolecular noncovalent interactions to construct hierarchical supramolecular structures with fixed sizes and long-range ordering by introducing ionic terminals and fully rigid arms into benzene-1,3,5-tricarboxamide (BTA) molecules. A series of similar BTA molecules with distinct terminal groups and arm lengths are synthesized; all form hexagonal bundles of helical rosette nanotubes spontaneously in water. Despite differences in molecular packing, the dimensions and bundling of the supramolecular nanotubes show almost identical concentration dependence for all molecules. The similarities of the hierarchical assemblies, which tolerate certain molecular irregularities, can extend to properties such as the void ratio of the nanotubular wall. This is a rational strategy that can be used to achieve supramolecular nanotubes in aqueous environments with precise size and ordering at the same time as allowing molecular modifications for functionality.

Supramolecular polymers self-assembled from trans-bis(pyridine) dichloropalladium(II) and platinum(II) complexes.

Two structurally similar trans-bis(pyridine) dichloropalladium(II)- and platinum(II)-type complexes were synthesized and characterized. They both self-assemble in n-hexane to form viscous fluids at lower concentrations, but form metallogels at sufficient concentrations. The viscous solutions were studied by capillary viscosity measurements and UV/Vis absorption spectra monitored during the disassembly process indicated that a metallophilic interaction was involved in the supramolecular polymerization process. For the two supramolecular assemblies, uncommon continuous porous networks were observed by using SEM and TEM revealed that they were built from nanofibers that fused and crosslinked with the increase of concentration. The xerogels of the palladium and platinum complexes were carefully studied by using synchrotron radiation WAXD and EXAFS. The WAXD data show close stacking distances driven by π-π and metal-metal interactions and an evident dimer structure for the platinum complex was found. The coordination bond lengths were extracted from fitting of the EXAFS data. Moreover, close Pt(II) -Pt(II) (Pd(II) -Pd(II) ) and PtCl (PdCl) interactions proposed from DFT calculations in the reported oligo(phenylene ethynylene) (OPE)-based palladium(II) pyridyl supramolecular polymers were also confirmed by using EXAFS. The Pt(II) -Pt(II) interaction is more feasible for supramolecular interaction than the Pd(II) -Pd(II) interaction in our simple case.

Integration of non-additive genome-wide association study with a multi-tissue transcriptome analysis of growth and carcass traits in Duroc pigs.

Growth and carcass traits are of economic importance in the pig production, which affect pork quality and profitability of finishing pig production. This study used whole-genome and transcriptome sequencing technologies to identify potential candidate genes affecting growth and carcass traits in Duroc pigs. The medium (50-60 k) single nucleotide polymorphism (SNP) arrays of 4 154 Duroc pigs from three populations were imputed to whole-genome sequence data, yielding 10 463 227 markers on 18 autosomes. The dominance heritabilities estimated for growth and carcass traits ranged from 0.000 ± 0.041 to 0.161 ± 0.054. Using non-additive genome-wide association study (GWAS), we identified 80 dominance quantitative trait loci for growth and carcass traits at genome-wide significance (false discovery rate < 5%), 15 of which were also detected in our additive GWAS. After fine mapping, 31 candidate genes for dominance GWAS were annotated, and 8 of them were highlighted that have been previously reported to be associated with growth and development (e.g. SNX14, RELN and ENPP2), autosomal recessive diseases (e.g. AMPH, SNX14, RELN and CACNB4) and immune response (e.g. UNC93B1 and PPM1D). By integrating the lead SNPs with RNA-seq data of 34 pig tissues from the Pig Genotype-Tissue Expression project (https://piggtex.farmgtex.org/), we found that the rs691128548, rs333063869, and rs1110730611 have significantly dominant effects for the expression of SNX14, AMPH and UNC93B1 genes in tissues related to growth and development for pig, respectively. Finally, the identified candidate genes were significantly enriched for biological processes involved in the cell and organ development, lipids catabolic process and phosphatidylinositol 3-kinase signalling (P < 0.05). These results provide new molecular markers for meat production and quality selection of pig as well as basis for deciphering the genetic mechanisms of growth and carcass traits.

Preselecting Variants from Large-Scale Genome-Wide Association Study Meta-Analyses Increases the Genomic Prediction Accuracy of Growth and Carcass Traits in Large White Pigs.

Preselected variants associated with the trait of interest from genome-wide association studies (GWASs) are available to improve genomic prediction in pigs. The objectives of this study were to use preselected variants from a large GWAS meta-analysis to assess the impact of single-nucleotide polymorphism (SNP) preselection strategies on genome prediction of growth and carcass traits in pigs. We genotyped 1018 Large White pigs using medium (50k) SNP arrays and then imputed SNPs to sequence level by utilizing a reference panel of 1602 whole-genome sequencing samples. We tested the effects of different proportions of selected top SNPs across different SNP preselection strategies on genomic prediction. Finally, we compared the prediction accuracies by employing genomic best linear unbiased prediction (GBLUP), genomic feature BLUP and three weighted GBLUP models. SNP preselection strategies showed an average improvement in accuracy ranging from 0.3 to 2% in comparison to the SNP chip data. The accuracy of genomic prediction exhibited a pattern of initial increase followed by decrease, or continuous decrease across various SNP preselection strategies, as the proportion of selected top SNPs increased. The highest level of prediction accuracy was observed when utilizing 1 or 5% of top SNPs. Compared with the GBLUP model, the utilization of estimated marker effects from a GWAS meta-analysis as SNP weights in the BLUP|GA model improved the accuracy of genomic prediction in different SNP preselection strategies. The new SNP preselection strategies gained from this study bring opportunities for genomic prediction in limited-size populations in pigs.