Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p &lt; 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.

The Qinghai-Tibet Plateau, characterized by its extreme environmental conditions, presents significant challenges to life, making it an ideal region for studying adaptation and evolution. Tibetan pigs, known for their high genetic diversity and exceptional adaptability to high altitudes, serve as excellent models for investigating high-altitude adaptation. While previous studies have extensively identified genetic determinants associated with high-altitude adaptation, the molecular mechanisms, particularly cis-regulatory patterns, remain poorly understood. Here, we conducted a selective sweep analysis using 484 genomes from Chinese and Western pig breeds across various altitudes, revealing 38.56 Mb of genomic regions under selection in Tibetan pigs. Enrichment analysis identified the lung as the primary functional tissue involved in high-altitude adaptation, supported by tissue-specific transcriptional and regulatory patterns observed between Tibetan and Meishan pigs (low altitude). By integrating genomic, RNA-seq, ATAC-seq, and H3K27ac HiChIP data, we constructed comprehensive enhancer-promoter regulatory maps of candidate genes and pinpointed promising genetic determinants associated with high-altitude adaptation, including SNPs in EPAS1, KLF13, SPRED1, and CFD. These loci were predicted to influence chromatin accessibility and the interactions of regulatory elements, with altered binding strength of relevant transcription factors. Further in vitro experiments confirmed that these loci function as allele-specific enhancers, modulating the expression of target genes. Our findings elucidate the regulatory basis of high-altitude adaptation in Tibetan pigs and provide valuable insights for exploring hypoxia-related diseases in livestock and humans.

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.

In the Suidae family, warthogs show significant survival adaptability and trait specificity. This study offers a comparative genomic analysis between the warthog and other Suidae species, including the Luchuan pig, Duroc pig, and Red River hog. By integrating the four genomes with sequences from the other four species, we identified 8868 single-copy orthologous genes. Based on 8868 orthologous protein sequences, phylogenetic assessments highlighted divergence timelines and unique evolutionary branches within suid species. Warthogs exist on different evolutionary branches compared to DRCs and LCs, with a divergence time preceding that of DRC and LC. Contraction and expansion analyses of warthog gene families have been conducted to elucidate the mechanisms of their evolutionary adaptations. Using GO, KEGG, and MGI databases, warthogs showed a preference for expansion in sensory genes and contraction in metabolic genes, underscoring phenotypic diversity and adaptive evolution direction. Associating genes with the QTLdb-pigSS11 database revealed links between gene families and immunity traits. The overlap of olfactory genes in immune-related QTL regions highlighted their importance in evolutionary adaptations. This work highlights the unique evolutionary strategies and adaptive mechanisms of warthogs, guiding future research into the distinct adaptability and disease resistance in pigs, particularly focusing on traits such as resistance to African Swine Fever Virus.

Direct seeding has been widely adopted as an economical and labor-saving technique in rice production, though problems such as low seedling emergence rate, emergence irregularity and poor lodging resistance are existing. These problems are currently partially overcome by increasing seeding rate, however it is not acceptable for hybrid rice due to the high seed cost. Improving direct seeding by breeding is seen as the ultimate solution to these problems. For hybrid breeding, identifying superior hybrids among a massive number of hybrids from crossings between male and female parental populations by phenotypic evaluation is tedious and costly. Contrastingly, genomic selection/prediction (GS/GP) could efficiently detect the superior hybrids capitalizing on genomic data, which holds a great potential in plant hybrids breeding. In this study, we utilized 402 rice inbred varieties and 401 hybrids to investigate the effectiveness of GS on rice mesocotyl length, a representative indicative trait of direct seeding suitability. Several GP methods and training set designs were studied to seek the optimal scenario of hybrid prediction. It was shown that using half-sib hybrids as training set with the phenotypes of all parental lines being fitted as a covariate could optimally predict mesocotyl length. Partitioning the molecular markers into trait-associated and -unassociated groups based on genome-wide association study using all parental lines and hybrids could further improve the prediction accuracy. This study indicates that GS could be an effective and efficient method for hybrid breeding for rice direct seeding.

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a partial phenotyping strategy that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the partial genomic phenotyping strategy in a wheat dataset. Whether the partial phenotyping strategy resulted in more selection response depended on the correlations of phenotypes between environments. The partial phenotyping strategy consistently showed statistically significant higher simulated responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. Our results indicate that genomics-based partial phenotyping can improve selection response at middle stages of crop breeding programs.