Traits Of Economic Importance Research Articles

Cloning and functional verification of a porcine adipose tissue-specific promoter

BackgroundFat deposition is an important economic trait in pigs. In the past decades, many genes regulating porcine fat deposition were identified by Omics technology and verified by cell biology studies. Using genetically modified pigs to investigate the function of these genes in vivo is necessary before applying in breeding. However, lack of tissue-specific promoters of pigs hinders the generation of adipose tissue-specific genetically modified pigs.ResultsIn order to identify a porcine adipose tissue-specific promoter, we used the software Digital Differential Display (DDD) to screen 99 genes highly expressed in porcine adipose tissue. GO and KEGG enrichment analysis indicated that the 99 genes were mainly related to lipid metabolism. Q-PCR proved that LGALS12 was an adipose tissue-specific gene. Five truncated fragments of the LGALS12 promoter were cloned and the 4 kb fragment (L-4 kb) exhibited a high level of promoter activity in adipocytes and no promoter activity in non-adipocytes. Following co-transfection with adipogenic transcription factors, the promoter activity of L-4 kb was enhanced by PPARγ, C/EBPβ, and KLF15, whereas it was suppressed by KLF4. Finally, we demonstrated that L-4 kb can drive APOR gene expression to exert its function in adipocytes.ConclusionsThis study demonstrates that porcine LGALS12 is an adipose tissue-specific gene, and identified the 4 kb fragment of LGALS12 promoter that exhibited adipocyte-specific promoter activity. These results provide new evidence for understanding porcine fat deposition and a promoter element for adipose tissue-specific genetic modification in pigs.HighlightsIdentified porcine LGALS12 as an adipose tissue-specific gene.Truncated LGALS12 promoter (L-4 kb) showed adipose tissue-specific promoter activity.Identified transcription factors involved in the regulation of L-4 kb promoter activity.

Generation of Heritable Prominent Double Muscle Buttock Rabbits via Novel Site Editing of Myostatin Gene Using CRISPR/Cas9 System

Rabbits have been domesticated for meat, wool, and fur production, and have also been cherished as a companion, artistic inspiration, and an experimental model to study many human diseases. In the present study, the muscle mass negative regulator gene myostatin (MSTN) was knocked out in rabbits at two novel sites in exon3, and the function of these mutations was determined in subsequent generations. The prominent double muscle phenotype with hyperplasia or hypertrophy of muscle fiber was observed in the MSTN-KO rabbits, and a similar phenotype was confirmed in the F1 generation. Moreover, the average weight of 80-day-old MSTN-KO rabbits (2,452 ± 63 g) was higher than that of wild-type rabbits (2,393.2 ± 106.88 g), and also the bodyweight of MSTN-KO rabbits (3,708 ± 43.06g) was significantly higher (P < 0.001) at the age of 180 days than wild-type (WT) rabbits (3,224 ± 48.64g). In MSTN-KO rabbits, fourteen rabbit pups from the F1 generation and thirteen from the F2 generation stably inherited the induced MSTN gene mutations. Totally, 194 pups were produced in the F1 generation of which 49 were MSTN-KO rabbits, while 47 pups were produced in the F2 generation of which 20 were edited rabbits, and the ratio of edited to wild-type rabbits in the F2 generation was approximately 1:1. Thus, we successfully generated a heritable double muscle buttocks rabbits via myostatin mutation with CRISPR/Cas9 system, which could be valuable in rabbit's meat production and also a useful animal model to study the development of muscles among livestock species and improve their important economic traits as well as the human muscle development-related diseases.

DNA methylation may affect beef tenderness through signal transduction in Bos indicus

BackgroundBeef tenderness is a complex trait of economic importance for the beef industry. Understanding the epigenetic mechanisms underlying this trait may help improve the accuracy of breeding programs. However, little is known about epigenetic effects on Bos taurus muscle and their implications in tenderness, and no studies have been conducted in Bos indicus.ResultsComparing methylation profile of Bos indicus skeletal muscle with contrasting beef tenderness at 14 days after slaughter, we identified differentially methylated cytosines and regions associated with this trait. Interestingly, muscle that became tender beef had higher levels of hypermethylation compared to the tough group. Enrichment analysis of predicted target genes suggested that differences in methylation between tender and tough beef may affect signal transduction pathways, among which G protein signaling was a key pathway. In addition, different methylation levels were found associated with expression levels of GNAS, PDE4B, EPCAM and EBF3 genes. The differentially methylated elements correlated with EBF3 and GNAS genes overlapped CpG islands and regulatory elements. GNAS, a complex imprinted gene, has a key role on G protein signaling pathways. Moreover, both G protein signaling pathway and the EBF3 gene regulate muscle homeostasis, relaxation, and muscle cell-specificity.ConclusionsWe present differentially methylated loci that may be of interest to decipher the epigenetic mechanisms affecting tenderness. Supported by the previous knowledge about regulatory elements and gene function, the methylation data suggests EBF3 and GNAS as potential candidate genes and G protein signaling as potential candidate pathway associated with beef tenderness via methylation.

Gene-set enrichment analysis of selective sweeps reveals phenotypic traits in Nguni cattle

Adaptation of animals to different environments is typically associated with structural and functional genomic variations. High throughput SNP genotyping and next-generation sequencing (NGS) have made it possible to study positive selection footprints and adaptation traits. Nguni is a small frame-size breed, mostly horned, and well known for being adapted to diverse South African environmental conditions. This study used previously identified selective sweeps to perform functional analysis of genes related to phenotypic characteristics in Nguni. Two hundred and sixty-four candidate selective sweeps were used for gene-set enrichment analysis in molecular functional categories (KEGG pathways) using the database for annotation, visualization, and integrated discovery (DAVID). In total, 107 genes were identified across all the chromosomes with 74 genes associated with eight phenotype queries, including fat content, milk production, walking ability, heat tolerance, meat production, reproduction, and bone and muscle development. Gene CRHR2 was associated with meat quality (juiciness and flavour). The IRAK3 gene was associated with decreased body size, feed intake and fatness in cattle, and CARD15 with disease resistance. Gene annotation using phenotype queries identified four genes (SPI, YWHAZ, RGS4, and RGS5) that were associated with myometrial relaxation in cattle. Genes such as NOD2 and IL21R were associated with inflammatory bowel diseases in cattle, whereas CPLS gene was associated with fat content. These genes are important to the phenotypic and adaptive characteristics present in South African Nguni cattle and hold potential for selection for traits of economic importance.

Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods

Genomic selection or genomic prediction (GP) has increasingly become an important molecular breeding technology for crop improvement. GP aims to utilise genome-wide marker data to predict genomic breeding value for traits of economic importance. Though GP studies have been widely conducted in various crop species such as wheat and maize, its application in cotton, an essential renewable textile fibre crop, is still significantly underdeveloped. We aim to develop a new GP-based breeding system that can improve the efficiency of our cotton breeding program. This article presents a GP study on cotton fibre quality and yield traits using 1385 breeding lines from the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia) cotton breeding program which were genotyped using a high-density SNP chip that generated 12,296 informative SNPs. The aim of this study was twofold: (1) to identify the models and data sources (i.e. genomic and pedigree) that produce the highest prediction accuracies; and (2) to assess the effectiveness of GP as a selection tool in the CSIRO cotton breeding program. The prediction analyses were conducted under various scenarios using different Bayesian predictive models. Results highlighted that the model combining genomic and pedigree information resulted in the best cross validated prediction accuracies: 0.76 for fibre length, 0.65 for fibre strength, and 0.64 for lint yield. Overall, this work represents the largest scale genomic selection studies based on cotton breeding trial data. Prediction accuracies reported in our study indicate the potential of GP as a breeding tool for cotton. The study highlighted the importance of incorporating pedigree and environmental factors in GP models to optimise the prediction performance.

Selection Signature Analyses Revealed Genes Associated With Adaptation, Production, and Reproduction in Selected Goat Breeds in Kenya.

Artificial and natural selection in livestock is expected to leave unique footprints on their genomes. Goat breeds in Kenya have evolved for survival, breeding, and production in various harsh ecological areas, and their genomes are likely to have acquired unique alleles for adaptation to such diverse production environments and other traits of economic importance. To investigate signals of selection for some selected goat breeds in Kenya, Alpine (n = 29), Galla (n = 12), Saanen (n = 24), and Toggenburg (n = 31) were considered. A total of 53,347 single-nucleotide polymorphisms (SNPs) generated using the Illumina GoatSNP50 BeadChip were analyzed. After quality control, 47,663 autosomal single-nucleotide polymorphisms remained for downstream analyses. Several complementary approaches were applied for the following analyses: integrated Haplotype Score (iHS), cross-population-extended haplotype homozygosity (XP-EHH), hapFLK, and FLK. A total of 404 top genomic regions were identified across all the four breeds, based on the four complementary analyses. Out of the 16 identified putative selection signature regions by the intersection of multiple-selective signal analyses, most of the putative regions were found to overlap significantly with the iHS and XP-EHH analyses on chromosomes 3, 4, 10, 15, 22, and 26. These regions were enriched with some genes involved in pathways associated directly or indirectly with environmental adaptation regulating immune responses (e.g., HYAL1 and HYAL3), milk production (e.g., LEPR and PDE4B), and adaptability (e.g., MST1 and PCK). The results revealed few intersect between breeds in genomic selection signature regions. In general, this did not present the typical classic selection signatures as predicted due to the complex nature of the traits. The results support that some various selection pressures (e.g., environmental challenges, artificial selection, and genome admixture challenges) have molded the genome of goat breeds in Kenya. Therefore, the research provides new knowledge on the conservation and utilization of these goat genetic resources in Kenya. In-depth research is needed to detect precise genes connected with adaptation and production in goat breeds in Kenya.

Transcriptomic analyses of the HPG axis-related tissues reveals potential candidate genes and regulatory pathways associated with egg production in ducks

BackgroundEgg production is one of the most important economic traits in the poultry industry. The hypothalamic-pituitary–gonadal (HPG) axis plays an essential role in regulating reproductive activities. However, the key genes and regulatory pathways within the HPG axis dominating egg production performance remain largely unknown in ducks.ResultsIn this study, we compared the transcriptomic profiles of the HPG-related tissues between ducks with high egg production (HEP) and low egg production (LEP) to reveal candidate genes and regulatory pathways dominating egg production. We identified 543, 759, 670, and 181 differentially expressed genes (DEGs) in the hypothalamus, pituitary, ovary stroma, and F5 follicle membrane, respectively. Gene Ontology (GO) analysis revealed that DEGs from four HPG axis-related tissues were enriched in the "cellular component" category. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that the neuroactive ligand-receptor interaction pathway was significantly enriched based on DEGs commonly identified in all four HPG axis-related tissues. Gene expression profiles and Protein–Protein Interaction (PPI) network were performed to show the regulatory relationships of the DEGs identified. Five DEGs encoding secreted proteins in the hypothalamus and pituitary have interaction with DEGs encoding targeted proteins in the ovary stroma and F5 follicle membrane, implying that they were these DEGs might play similar roles in the regulation of egg production.ConclusionsOur results revealed that neuroactive ligand-receptor interaction pathway and five key genes(VEGFC, SPARC, BMP2, THBS1, and ADAMTS15) were identified as the key signaling pathways and candidate genes within the HPG axis responsible for different egg production performance between HEP and LEP. This is the first study comparing the transcriptomic profiles of all HPG axis-related tissues in HEP and LEP using RNA-seq in ducks to the best of our knowledge. These data are helpful to enrich our understanding of the classical HPG axis regulating the egg production performance and identify candidate genes that can be used for genetic selection in ducks.

Multivariate genomic prediction for commercial traits of economic importance in Banana shrimp Fenneropenaeus merguiensis

Advantages of multi-trait machine and deep learning genomic prediction models for quantitative complex traits have not been documented or very limited in aquaculture species. Thus, the present study sought to understand effects of the multi-trait single-step genomic best linear unbiased prediction (ssGBLUP), Bayesian (BayesCpi), random forest (RF) and multilayer perceptron (MLP) models on genomic prediction accuracies for traits of commercial importance in banana white shrimp (Fenneropenaeus merguiensis). Our analyses were conducted in a breeding shrimp population comprising 562 individuals (offspring of 48 parental pairs) genotyped for 9472 single nucleotide polymorphisms (SNPs) and the animals had full phenotype records for five important traits (i.e., body weight, abdominal width, tail weight, raw colour of live shrimp and resistance to hepatopancreatic parvovirus). In both univariate and multi-trait analyses, machine (RF) and deep learning (MLP) models outperformed ssGBLUP for all traits studied. However, they had similar predictive performance to BayesCpi. The benefits of the multivariate relative to univariate models were trait- and method-specific. Multi-trait BayesCpi increased the prediction accuracies for growth (weight and width), carcass (tail weight) and HPV resistance by 9.3 to 17.8%. However, the multi-trait random forest models improved the predictive power for only abdominal width (14.3%) and disease resistance to hepatopancreatic parvovirus (10.0%). When the multi-trait MLP was used, the improvements in the prediction accuracies were observed for abdominal width and raw colour (4.9 and 6.0%, respectively). There were almost no differences in the predictive power between univariate and multi-trait ssGBLUP. Among the multi-trait models used, BayesCpi outperformed other methods (ssGBLUP, RF and MLP). It is concluded that either BayesCpi or machine and deep learning-based multi-trait genomic prediction models should be employed in large-scale genetic enhancement programs for banana shrimp. These approaches show enormous potential to enhance genetic progress made in this population of banana shrimp and potentially for other aquaculture species.

Transcriptome Analysis Reveals miR-302a-3p Affects Granulosa Cell Proliferation by Targeting DRD1 in Chickens.

Egg production is an important economic trait in laying chickens as higher yields bring higher profits. Small yellow follicle (SYFL) development is a key determinant of chicken reproductive performance; however, the majority of SYFLs are not selected during the process of chicken reproduction and thus, atresia occurs. Although there have been numerous omic studies focused on egg production, the molecular mechanisms involved are still not well-understood. In this study, we used high-throughput technology to analyze the differences between the SYFL mRNA transcriptomes of high– (H) and low–egg-yielding (L) Taihang layer hens, with the aim of identifying the potential candidate genes involved in controlling the rate of egg production. We constructed six cDNA libraries, three from H and three from L Taihang hens and then performed high-throughput sequencing. Comparison of the H and L groups showed 415 differentially expressed genes (DEGs). In the high-yield group, 226 were upregulated and 189 were downregulated. Differentially enriched biological functions and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database analysis. Ten of the candidate DEGs we identified (DRD1, MC5R, PCK1, CTSA, TGFBR3, AGO4, SLIT2, RGS1, SCNN1B, and ZP3) have been identified in previous studies as being involved in the development of small yellow follicles. DRD1 was significantly enriched in the gap junction pathway, which is an important pathway in chicken granulosa cells (GCs) to pass nutrition to an oocyte. Homology analysis showed that DRD1 was highly conserved in numerous species, indicating that it may be a productive target for improving egg production. Evidence from bioinformatics analysis revealed that gga-miR-302a-3p putatively targets the 3′UTR region of DRD1. We then identified the functions of gga-miR-302a-3p in follicular granulosa cell proliferation by targeting DRD1. RT-qPCR analysis showed that DRD1 and miR-302a-3p expression were inversely related in the SYLs of high and low egg-yielding chickens. Luciferase assays showed that miR-302a-3p targets the 3′UTR of DRD1, and overexpression of miR-302a-3p significantly inhibits the expression of DRD1 in chicken GCs (p < 0.01). Functional experiments revealed that by targeting DRD1, miR-302a-3p acts as an inhibitor of GC proliferation. Taken together, we concluded that miR-302a-3p affects chicken GC proliferation by targeting DRD1. Our data expanded the knowledge base of genes whose functions are important in egg production and the molecular mechanisms of high-yield egg production in chicken small yellow follicles.

Impact of Pre-Anthesis Drought Stress on Physiology, Yield-Related Traits, and Drought-Responsive Genes in Green Super Rice.

Optimum soil water availability is vital for maximum yield production in rice which is challenged by increasing spells of drought. The reproductive stage drought is among the main limiting factors leading to the drastic reduction in grain yield. The objective of this study was to investigate the molecular and morphophysiological responses of pre-anthesis stage drought stress in green super rice. The study assessed the performance of 26 rice lines under irrigated and drought conditions. Irrigated treatment was allowed to grow normally, while drought stress was imposed for 30 days at the pre-anthesis stage. Three important physiological traits including pollen fertility percentage (PFP), cell membrane stability (CMS), and normalized difference vegetative index (NDVI) were recorded at anthesis stage during the last week of drought stress. Agronomic traits of economic importance including grain yield were recorded at maturity stage. The analysis of variance demonstrated significant variation among the genotypes for most of the studied traits. Correlation and principal component analyses demonstrated highly significant associations of particular agronomic traits with grain yield, and genetic diversity among genotypes, respectively. Our study demonstrated a higher drought tolerance potential of GSR lines compared with local cultivars, mainly by higher pollen viability, plant biomass, CMS, and harvest index under drought. In addition, the molecular basis of drought tolerance in GSR lines was related to upregulation of certain drought-responsive genes including OsSADRI, OsDSM1, OsDT11, but not the DREB genes. Our study identified novel drought-responsive genes (LOC_Os11g36190, LOC_Os12g04500, LOC_Os12g26290, and LOC_Os02g11960) that could be further characterized using reverse genetics to be utilized in molecular breeding for drought tolerance.

Accuracy of Genomic Selection for Important Economic Traits of Cashmere and Meat Goats Assessed by Simulation Study.

Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

Predicted genetic gain for carcass yield in rainbow trout from indirect and genomic selection

Carcass and fillet yields are traits of great economic importance in aquaculture species, including rainbow trout. Headless gutted carcass percentage (HC) is a convenient selection criterion to improve carcass yield given that it is highly genetically correlated with the latter and also with fillet yield. However, HC is a sib trait that cannot be recorded on selection candidates. Consequently, the within-family component of the genetic variance cannot be exploited with traditional pedigree-based BLUP selection. Two alternatives to exploit this component would be to select directly on an indicator trait genetically correlated with HC that can be recorded on live candidates or to apply genomic selection. The objective of this simulation study was to predict the phenotypic gains for HC in rainbow trout breeding programs when four alternative selection strategies are used: i) sib selection for HC, ii) indirect selection for a morphological indicator recorded in vivo; iii) genomic selection for HC; and iv) genomic selection for the indicator. Also, the four strategies were compared in a multitrait selection scenario where body weight was also included in the breeding objective. The different scenarios were compared at the same selection intensity and number of records (2000) for HC (on sibs) and IHC (on candidates). Two different heritabilities for HC (0.55 and 0.25) were considered. For the highest heritability, the phenotypic gain for HC was higher with sib than with indirect selection for both BLUP (1.03 versus 0.98) and genomic selection (1.22 versus 1.04). However, for the lowest heritability, the phenotypic gain for HC was lower with sib than with indirect selection for both BLUP (0.60 versus 0.64) and genomic selection (0.70 versus 0.71). In any case, the differences in phenotypic gains for HC between sib and indirect selection were not large. Therefore, given that sib selection implies extra costs associated with maintaining and genotyping sibs and indirect selection allows higher selection intensity for a given number of fish, indirect selection appears to be a more cost-effective option. The patterns found for single trait selection were maintained when the yield trait (HC or indicator) was selected simultaneously with body weight. We conclude that the optimum approach to improve carcass yield is a combination of genomic and indirect selection in both single and multitrait selection scenarios.

Application of nonlinear predictive models for egg production of quail receiving diets supplemented with silica+ or betaine in a tropical environment

Introduction Egg production is the most important economic trait in laying quail. In a single production cycle, Japanese quails start producing eggs around 35–42 days of age. Subsequently, egg production increases rapidly until reaching a peak

The Candidate Chromosomal Regions Responsible for Milk Yield of Cow: A GWAS Meta-Analysis.

Simple SummaryMilk production is one of the most important economic traits in dairy cattle. Therefore, determining the genomic regions influencing this trait can improve milk yield. In this study, we collected data from 16 articles associated with milk yield genome-wide association studies (GWAS) on different cattle breeds. Based on the information from the analysis and level of significance (p-value < 2.5 × 10−6), we identified different genomic regions on chromosomes with the highest marker density, markers with the highest effect, genes within or near these regions, chromosomes with the greatest effects on milk yield.Milk yield (MY) is highly heritable and an economically important trait in dairy livestock species. To increase power to detect candidate genomic regions for this trait, we carried out a meta-analysis of genome-wide association studies (GWAS). In the present study, we identified 19 studies in PubMed for the meta-analysis. After review of the studies, 16 studies passed the filters for meta-analysis, and the number of chromosomes, detected markers and their positions, number of animals, and p-values were extracted from these studies and recorded. The final data set based on 16 GWAS studies had 353,698 cows and 3950 markers and was analyzed using METAL software. Our findings revealed 1712 significant (p-value < 2.5 × 10−6) genomic loci related to MY, with markers associated with MY found on all autosomes and sex chromosomes and the majority of them found on chromosome 14. Furthermore, gene ontology (GO) annotation was used to explore biological functions of the genes associated with MY; therefore, different regions of this chromosome may be suitable as genomic regions for further research into gene expression.

Heritability and genetic correlation for residual feed intake of Pacific abalone Haliotis discus hannai

Pacific abalone (Haliotis discus hannai) is the dominant farmed abalone species in China. Feed efficiency is an important economic trait for abalone, but little attention has been paid to this trait in breeding programs of Pacific abalone. In this study, the growth and feed efficiency traits of 648 abalone were measured during two time periods (days 1–36 and days 37–72), while an individual-based accurate measurement method of feed efficiency for abalone was established. The residual feed intake (RFI) was first evaluated in abalone; this was significantly correlated between the two periods, while the feed efficiency ratio (FER) was not significantly correlated between the two periods. Genetic analysis showed that RFI exhibited moderate or high heritability, with respective scores of 0.32 and 0.40 in the two periods, and there was a strong positive genetic correlation between the two periods (0.892 ± 0.042). RFI showed weak negative genetic correlations with average daily gain (ADG) during the two growth periods (−0.144 and − 0.008), and a positive genetic correlation with daily feed intake (DFI) (0.901 and 0.794). In addition, a group-feeding experiment were carried out on 12 families. It was found that the body growth and feed intake of abalone were highly correlated with the results of individual-feeding experiment. The results demonstrated that RFI could be used as a suitable trait for indicating the feed efficiency of abalone in future breeding programs.

Functionally prioritised whole-genome sequence variants improve the accuracy of genomic prediction for heat tolerance

BackgroundHeat tolerance is a trait of economic importance in the context of warm climates and the effects of global warming on livestock production, reproduction, health, and well-being. This study investigated the improvement in prediction accuracy for heat tolerance when selected sets of sequence variants from a large genome-wide association study (GWAS) were combined with a standard 50k single nucleotide polymorphism (SNP) panel used by the dairy industry.MethodsOver 40,000 dairy cattle with genotype and phenotype data were analysed. The phenotypes used to measure an individual’s heat tolerance were defined as the rate of decline in milk production traits with rising temperature and humidity. We used Holstein and Jersey cows to select sequence variants linked to heat tolerance. The prioritised sequence variants were the most significant SNPs passing a GWAS p-value threshold selected based on sliding 100-kb windows along each chromosome. We used a bull reference set to develop the genomic prediction equations, which were then validated in an independent set of Holstein, Jersey, and crossbred cows. Prediction analyses were performed using the BayesR, BayesRC, and GBLUP methods.ResultsThe accuracy of genomic prediction for heat tolerance improved by up to 0.07, 0.05, and 0.10 units in Holstein, Jersey, and crossbred cows, respectively, when sets of selected sequence markers from Holstein cows were added to the 50k SNP panel. However, in some scenarios, the prediction accuracy decreased unexpectedly with the largest drop of − 0.10 units for the heat tolerance fat yield trait observed in Jersey cows when 50k plus pre-selected SNPs from Holstein cows were used. Using pre-selected SNPs discovered on a combined set of Holstein and Jersey cows generally improved the accuracy, especially in the Jersey validation. In addition, combining Holstein and Jersey bulls in the reference set generally improved prediction accuracy in most scenarios compared to using only Holstein bulls as the reference set.ConclusionsInformative sequence markers can be prioritised to improve the genomic prediction of heat tolerance in different breeds. In addition to providing biological insight, these variants could also have a direct application for developing customized SNP arrays or can be used via imputation in current industry SNP panels.

Comprehensive Analysis of Endogenous Volatile Compounds, Transcriptome, and Enzyme Activity Reveals PmCAD1 Involved in Cinnamyl Alcohol Synthesis in Prunus mume.

Floral scent is an important economic and ornamental trait of Prunus mume. The floral volatiles from most cultivars of P. mume in composition exist significant differences. Cinnamyl alcohol was one of the main floral volatile compounds with distinct abundances in different cultivars, namely, ‘Zaohua Lve,’ ‘Zao Yudie,’ ‘Fenpi Gongfen,’ ‘Jiangsha Gongfen,’ and ‘Fenhong Zhusha.’ Based on the determination of endogenous volatiles of full-blooming flowers, vital enzyme activity and transcriptomes were comprehensively analyzed to screen the key potential genes involved in cinnamyl alcohol synthesis. Transcriptome combining with enzyme activity level analysis suggested that the expression levels of three PmCADs were highly correlated with the cinnamyl alcohol dehydrogenase (CAD) enzyme activities in six cultivars. Furthermore, phylogenetic tree and transcriptome analysis suggested that PmCAD1 and PmCAD2 might contribute to the cinnamyl alcohol synthesis. Relative expression analyses and enzyme activity assays showed that PmCAD1 played an important role in cinnamyl alcohol biosynthesis in vitro. Overall, this research lays a theoretical foundation for clarifying comprehensively the molecular biosynthesis mechanism of floral volatiles in P. mume.

Estimates of (co)variance components and phenotypic and genetic parameters of growth traits and wool traits in Alpine Merino sheep.

The (co)variance components and corresponding phenotypic and genetic parameters for growth traits and wool traits of economic importance were estimated in the Alpine Merino sheep population maintained at Gansu Provincial Sheep Breeding Technology Extension Station in northwestern China. Data from a maximum of 49,474 animals sired by 526 rams and born from 22,531 ewes over 20years from 2000 to 2019 were used in this study. Birth type, age of dam, birth year, sex and/or management group, and age at measurement were initially fitted as fixed effects in an animal model with various random effects. Genetic groups were defined for all animals by the sire breed and breed genotype interacted with dam-strain flocks and were fitted as one of the random effects. Analyses were conducted using a residual maximum likelihood procedure (ASReml). Seven different animal models were fitted for all traits, and the most appropriate model with relevant random effects was selected through log-likelihood ratio testing. After identifying the appropriate model through single-trait analysis, bivariate analyses were used to obtain the phenotypic and genetic correlations among the traits. The estimates of additive direct heritability for birth weight (BWT), weaning weight (WWT), preweaning growth rate (prwADG), postweaning growth rate (powADG), yearling body weight (YWT), average fibre diameter (AFD), greasy fleece weight (GFW), clean fleece weight (CFW), yield (YLD), yearling wool staple length (YSL), coefficient of variation of average fibre diameter (FDcv) and wool visual fineness counts (VFC) were 0.30, 0.18, 0.18, 0.20, 0.29, 0.20, 0.19, 0.20, 0.35, 0.19, 0.16 and 0.13, respectively, with standard errors ranging from 0.02 to 0.05. The corresponding ratios of genetic group variance to additive genetic variance were significant and, respectively, 0.35, 0.80, 0.62, 0.26, 0.13, 1.06, 0.38, 0.64, 0.09, 0.12, 0.06 and 0.58. These results suggest for these traits that there is potential to exploit both the additive genetic variation and between genetic group variation although for most traits the between group variation was smaller than the variation within groups. Favourable genetic correlations were found among the growth traits, and between growth traits and fleece production traits, and among wool traits GFW, CFW, YSL and YLD. This study provides the required estimates of genetic parameters of both growth and wool traits of the new breed for the design of more effective breeding programmes.

Transcriptome analysis reveals the potential roles of long non-coding RNAs in feed efficiency of chicken

Feed efficiency is an important economic trait and reduces the production costs per unit of animal product. Up to now, few studies have conducted transcriptome profiling of liver tissue in feed efficiency-divergent chickens (Ross vs native breeds). Also, molecular mechanisms contributing to differences in feed efficiency are not fully understood, especially in terms of long non-coding RNAs (lncRNAs). Hence, transcriptome profiles of liver tissue in commercial and native chicken breeds were analyzed. RNA-Seq data along with bioinformatics approaches were applied and a series of lncRNAs and target genes were identified. Furthermore, protein–protein interaction network construction, co-expression analysis, co-localization analysis of QTLs and functional enrichment analysis were used to functionally annotate the identified lncRNAs. In total, 2,290 lncRNAs were found (including 1,110 annotated, 593 known and 587 novel), of which 53 (including 39 known and 14 novel), were identified as differentially expressed genes between two breeds. The expression profile of lncRNAs was validated by RT-qPCR. The identified novel lncRNAs showed a number of characteristics similar to those of known lncRNAs. Target prediction analysis showed that these lncRNAs have the potential to act in cis or trans mode. Functional enrichment analysis of the predicted target genes revealed that they might affect the differences in feed efficiency of chicken by modulating genes associated with lipid metabolism, carbohydrate metabolism, growth, energy homeostasis and glucose metabolism. Some gene members of significant modules in the constructed co-expression networks were reported as important genes related to feed efficiency. Co-localization analysis of QTLs related to feed efficiency and the identified lncRNAs suggested several candidates to be involved in residual feed intake. The findings of this study provided valuable resources to further clarify the genetic basis of regulation of feed efficiency in chicken from the perspective of lncRNAs.

Polymorphisms and association of GRM1, GNAQ and HCRTR1 genes with seasonal reproduction and litter size in three sheep breeds

Litter size is one of the important economic traits of livestock. Seasonal oestrus, ovulation and lambing of sheep have severely restricted the development of sheep farming in Xinjiang, China. The purpose of this study was to investigate the polymorphisms and genetic correlation between GRM1, GNAQ and HCRTR1 genes and the seasonal reproduction and litter size in three sheep breeds. The DNA mixed pool sequencing and PCR-SSCP methods were used to detect single nucleotide polymorphisms (SNPs) of GRM1, GNAQ and HCRTR1 genes in seasonal oestrous (Kazakh and Chinese Merino [Xinjiang Junken type]) and perennial oestrous (Hu) sheep breeds. The association between genetic polymorphism and litter size was also analysed. The results showed that T945C in exon 2 of GRM1 gene, C589T in exon 2 of HCRTR1 gene and A191G in exon 2 of GNAQ gene were identified by Sanger sequencing, and three genotypes were existed in each SNP site, which all belonged to the synonymous mutation. GRM1 (CC), GNAQ (GA) and HCRTR1 (TC) were the dominant genotypes of seasonal reproduction and litter size in Kazakh sheep and Chinese Merino sheep, respectively, while, in perennial oestrous Hu sheep populations, the dominant genotypes were GRM1 (TC), GNAQ (GA) and HCRTR1 (TC), respectively, and association analysis also confirmed the results. The above results implied that GRM1, GNAQ and HCRTR1 genes are significantly associated with lambing traits in Kazakh, Chinese Merino and Hu sheep. Among them, the locus of GRM1 (T945C), GNAQ (A191G) and HCRTR1 (C589T) might be considered as a potential molecular marker, which controls seasonal reproduction and litter size in sheep.