In experimental research a scientific conclusion is always drawn from the statistical testing of hypothesis, in which an acceptable cutoff of probability, such as 0.05 or 0.01, is used for decision-making. However, the probability of committing false statistical inferences would considerably increase when more than one hypothesis is simultaneously tested (namely the multiple comparisons), which therefore requires proper adjustment. Although the adjustment for multiple comparisons is proposed to be mandatory in some journals, it still remains difficult to select a proper method suitable for the various experimental properties and study purposes, especially for researchers without good background in statistics. In the present paper, we provide a brief review on mathematical framework, general concepts and common methods of adjustment for multiple comparisons, which is expected to facilitate the understanding and selection of adjustment methods.

It is widely acknowledged that transcriptional diversity largely contributes to biological regulation in eukaryotes. Since the advent of second-generation sequencing technologies, a large number of RNA sequencing studies have considerably improved our understanding of transcriptome complexity. However, it still remains a huge challenge for obtaining full-length transcripts because of difficulties in the short read-based assembly. In the present study we employ PacBio single-molecule long-read sequencing technology for whole-transcriptome profiling in rabbit (Oryctolagus cuniculus). We totally obtain 36,186 high-confidence transcripts from 14,474 genic loci, among which more than 23% of genic loci and 66% of isoforms have not been annotated yet within the current reference genome. Furthermore, about 17% of transcripts are computationally revealed to be non-coding RNAs. Up to 24,797 alternative splicing (AS) and 11,184 alternative polyadenylation (APA) events are detected within this de novo constructed transcriptome, respectively. The results provide a comprehensive set of reference transcripts and hence contribute to the improved annotation of rabbit genome.

The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from -0.48 to -0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (-0.58 ± 0.02) and MFAIL and FRM (-0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.

Milking speed (MS) and temperament (MT) are 2 workability traits of great importance in dairy cattle production and breeding. This is mainly due to an increased intensification of the worldwide production systems and greater adoption of precision technologies with less human-cattle interaction. Both MS and MT are heritable traits and thus, genomic selection is a promising tool to expedite their genetic progress. However, the genetic architecture and biological mechanisms underlying the phenotypic expression of these traits remain underexplored. In this study, we investigated the association of >5.7 million imputed whole-genome sequence variants with MT and MS in 4,381 and 4,219 North American Holstein cattle, respectively. The statistical analyses were performed using a mixed linear model fitting a polygenic effect. We detected 40 and 35 significant SNPs independently associated with MT and MS, respectively, which were distributed across 26 chromosomes. Eight candidate genes (GRIN3A, KCNJ3, BOSTAUV1R417, BOSTAUV1R419, MAP2K5, KCTD3, GAP43, and LSAMP) were suggested to play an important role in MT as they are involved in biologically relevant pathways, such as glutamatergic synapse, vomeronasal receptor and oxytocin signaling. Within their coding and upstream sequences, we used an independent data set to further detect or validate significantly differentiated SNP between cattle breeds with known differences in MT. There were fewer candidate genes potentially implicated in MS, but immunity-related genes (e.g., BOLA-NC1 and LOC512672), also identified in other populations, were validated in this study. The significant SNP and novel candidate genes identified contribute to a better understanding of the biological mechanisms underlying both traits in dairy cattle. This information will also be useful for the optimization of prediction of genomic breeding values by giving greater weights to SNP located in the genomic regions identified.

Shi-Yi Chen*, Feilong Deng, Ming Zhang, Xianbo Jia, and Song-Jia Lai*. J. Microbiol. Biotechnol. 2020;30:804-10. https://doi.org/10.4014/jmb.2002.02010