Insight into the carbon turnover in soil aggregates and density fractions is essential for reducing the uncertainty in estimating carbon pools on the Tibetan Plateau, and how they vary with land use type is unclear.  In this study, the effect of land use type on carbon storage and fractionation was quantified based on organic carbon and its ¹³C abundance at the microscale of soil aggregates and density fractions in Tibetan alpine ecosystems.  The sequence of soil aggregate destruction in the land use types of plantation (13.1%)<shrubland (32.7%)<grassland (47.9%)<farmland (61.8%) shows that plantations strengthen the soil structure.  Plantation land had a greater contribution of light fraction organic carbon (28.3%) but a lower contribution of mineral-associated organic carbon (40.6%) to the carbon stock compared to farmland (13.5 and 70.3%).  Interestingly, plantation land enhanced the aggregational differentiation of organic carbon and ¹³C in each density fraction, whereas no such phenomenon existed in the soil organic carbon.  Carbon isotope analyses revealed that carbon transfer in the plantation land occurred from the light fraction in macroaggregates (–24.9‰) to the mineral-associated fraction in microaggregates (–19.9‰).  When compared to the other three land use types, the low transferability of carbon in aggregates and density fractions in plantation land provides a stable carbon pool for the Tibetan Plateau.  This study shows that plantations can mitigate global climate change by slowing carbon transfer and increasing carbon storage at the microscale of aggregates and density fractions in alpine regions.

Cattle carcass traits are economically important in the beef industry.  In the present study, we identified 184 significant genes and 822 alternative genes for 7 carcass traits using genome-wide association studies (GWAS) in 1,566 Huaxi beef cattle.  We then identified 5,860 unique cis-genes and 734 trans-genes in 227 longissimus dorsi muscle (LDM) samples to better understand the genetic regulation of gene expression.  Our integration study of the GWAS and cis-eQTL analysis detected 13 variants regulating 12 identical genes, in which one variant was also detected in fine-mapping analysis.  Moreover, using a transcriptome-wide association study (TWAS), we identified 4 genes (TTC30B, HMGA1, PRKD3 and FXN) that were significantly related to carcass chest depth (CCD), carcass length (CL), carcass weight (CW) and dressing percentage (DP).  This study identified variants and genes that may be useful for understanding the molecular mechanism of carcass traits in beef cattle.

Presently, integrating multi-omics information into a prediction model has become a ameliorate strategy for genomic selection to improve genomic prediction accuracy.  Here, we set the genomic and transcriptomic data as the training population data, using BSLMM, TWAS, and eQTL mapping to prescreen features according to | ^β_b|>0, top 1% of phenotypic variation explained (PVE), expression-associated single nucleotide polymorphisms (eSNPs), and egenes (false discovery rate (FDR)<0.01), where these loci were set as extra fixed effects (named GBLUP-Fix) and random effects (GFBLUP) to improve the prediction accuracy in the validation population, respectively.  The results suggested that both GBLUP-Fix and GFBLUP models could improve the accuracy of longissimus dorsi muscle (LDM), water holding capacity (WHC), shear force (SF), and pH in Huaxi cattle on average from 2.14 to 8.69%, especially the improvement of GFBLUP-TWAS over GBLUP was 13.66% for SF.  These methods also captured more genetic variance than GBLUP.  Our study confirmed that multi-omics-assisted large-effects loci prescreening could improve the accuracy of genomic prediction.