The objectives of this study were to evaluate the prediction performance of the single-step genomic BLUP method using a multi-trait random regression model in genomic evaluation for milk production traits of Chinese Holsteins, and investigate how parameters w, τ, and ω used in the construction of the combined relationship matrix (H) affected prediction accuracy and bias. A total of 2.8 million test-day records from 0.2 million cows were available for milk, protein, and fat yields. Pedigree information included 0.3 million animals and 7,577 of them were genotyped with medium-density single nucleotide polymorphism marker panels. Genotypes were imputed into Geneseek Genomic Profiler HD (GeneSeek, Lincoln, NE) including 77K markers. A reduced data set for evaluating models was extracted from the full data set by removing testday records from the last 4 yr. Bull and cow validation populations were constructed for each trait. We evaluated the prediction performance of the multiple-trait multiple-lactation random regression single-step genomic BLUP (RR-ssGBLUP) models with different values of parameters w, τ, and ω in the H matrix, taking consideration of inbreeding. We compared RR-ssG-BLUP with the multiple-trait multiple-lactation random regression model based on pedigree and genomic BLUP. De-regressed proofs for 305-d milk, protein, and fat yields averaged over 3 lactations, which were calculated from the full data set, were used for posteriori validations. The results showed that RR-ssGBLUP was feasible for implementation in breeding practice, and its prediction performance was superior to the other 2 methods in the comparison, including prediction accuracy and unbiasedness. For bulls, RR-ssGBLUP models with w 0 05 2 0 1 0. .

The protease inhibitors (PIs) in plants are involved primarily in defense against pathogens and pests and in response to abiotic stresses. However, information about the PI gene families in tomato (Solanum lycopersicum), one of the most important model plant for crop species, is limited. In this study, in silico analysis identified 55 PI genes and their conserved domains, phylogenetic relationships, and chromosome locations were characterized. According to genetic structure and evolutionary relationships, the PI gene families were divided into seven families. Genome-wide microarray transcription analysis indicated that the expression of SlPI genes can be induced by abiotic (heat, drought, and salt) and biotic (Botrytis cinerea and tomato spotted wilt virus (TSWV)) stresses. In addition, expression analysis using RNA-seq in various tissues and developmental stages revealed that some SlPI genes were highly or preferentially expressed, showing tissue-and developmental stage-specific expression profiles. The expressions of four representative SlPI genes in response to abscisic acid (ABA), salicylic acid (SA), ethylene (Eth), gibberellic acid (GA). and methyl viologen (MV) were determined. Our findings indicated that PI genes may mediate the response of tomato plants to environmental stresses to balance hormone signals. The data obtained here will improve the understanding of the potential function of PI gene and lay a foundation for tomato breeding and transgenic resistance to stresses.