Whole transcriptome RNA sequencing provides novel insights into the molecular dynamics of ovarian development in mud crab, Scylla paramamosain after mating.

Ovarian development in animals is a complicated biological process, requiring the simultaneous coordination among various genes and pathways. To understand the dynamic changes and molecular regulatory mechanisms of ovarian development in mud crab (Scylla paramamosain), both histological observation and whole transcriptome sequencing of ovarian tissues at different mating stages were implemented in this study. The histological results revealed that ovarian development was delayed in unmated females (60 days after courtship behavior but not mating), who exhibited an oocyte diameter of 56.38 ± 15.17 µm. Conversely, mated females exhibited accelerated the ovarian maturation process, with females reaching ovarian stage III (proliferative stage) 23 days after mating and attained an average oocyte diameter of 132.19 ± 15.07 µm. Thus, mating process is essential in promoting the rapid ovarian development in mud crab. Based on the whole transcriptome sequencing analysis, a total of 518 mRNAs, 1502 lncRNAs, 18 circRNAs and 151 miRNAs were identified to be differentially expressed between ovarian tissues at different mating stages. Notably, six differentially expressed genes (DEGs) associated with ovarian development were identified, including ovary development-related protein, red pigment concentrating hormone receptor, G2/mitotic-specific cyclin-B3-like, lutropin-chorio gonadotropic hormone receptor, renin receptor, and SoxB2. More importantly, both DEGs and targets of differentially expressed non-coding RNAs (DEncRNAs) were enriched in renin-angiotensin system, TGF-ß signaling, cell adhesion molecules, MAPK signaling pathway, and ECM-receptor interaction, suggesting that these pathways may play significant roles in the ovarian development of mud crabs. Moreover, competition endogenous RNA (ceRNA) networks were constructed while mRNAs were differentially expressed between mating stages were involved in Gene Ontology (GO) biological processes such as developmental process, reproduction, and growth. These findings could provide solid foundations for the future development of female mud crab maturation enhancement strategy, and improve the understanding of the ovarian maturation process in crustaceans.

Evaluation of the Feasibility of Harvest Optimisation of Soft-Shell Mud Crab (Scylla paramamosain) from the Perspective of Nutritional Values.

Soft-shell crabs have attracted consumers' attention due to their unique taste and nutritional value. To evaluate the feasibility of harvest optimisation of soft-shell mud crabs, the proximate composition, mineral composition, and total carotenoid, amino acid, and fatty acid contents of edible parts of male and female soft-shell mud crabs at different moulting stages were determined and compared from a nutritional value perspective. The results showed that the sex and moulting stages could significantly affect the nutritional values of the edible portions of soft-shell crabs. The female or male soft-shell crabs in the postmoult Ⅰ stage had a much richer mineral element content than that in other moulting stages. The total carotenoid content in female soft-shell crabs was significantly higher than that in male crabs in all moulting stages, while male soft-shell crabs had better performance in amino acid nutrition than female soft-shell crabs. Moreover, it was found that soft-shell crabs in the postmoult Ⅱ stage had significantly higher contents of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), while significantly lower contents of saturated fatty acids (SFA) than those in other stages. The present study will provide a reference basis for the diversified cultivation of soft-shell crabs, and further promote the development of the mud crab industry.

Full-Length Transcriptome Reconstruction Reveals the Genetic Mechanisms of Eyestalk Displacement and Its Potential Implications on the Interspecific Hybrid Crab (Scylla serrata ♀ × S. paramamosain ♂).

The lack of high-quality juvenile crabs is the greatest impediment to the growth of the mud crab (Scylla paramamosain) industry. To obtain high-quality hybrid offspring, a novel hybrid mud crab (S. serrata ♀ × S. paramamosain ♂) was successfully produced in our previous study. Meanwhile, an interesting phenomenon was discovered, that some first-generation (F1) hybrid offspring's eyestalks were displaced during the crablet stage I. To uncover the genetic mechanism underlying eyestalk displacement and its potential implications, both single-molecule real-time (SMRT) and Illumina RNA sequencing were implemented. Using a two-step collapsing strategy, three high-quality reconstructed transcriptomes were obtained from purebred mud crabs (S. paramamosain) with normal eyestalks (SPA), hybrid crabs with normal eyestalks (NH), and hybrid crabs with displaced eyestalks (DH). In total, 37 significantly differential alternative splicing (DAS) events (17 up-regulated and 20 down-regulated) and 1475 significantly differential expressed transcripts (DETs) (492 up-regulated and 983 down-regulated) were detected in DH. The most significant DAS events and DETs were annotated as being endoplasmic reticulum chaperone BiP and leucine-rich repeat protein lrrA-like isoform X2. In addition, the top ten significant GO terms were related to the cuticle or chitin. Overall, high-quality reconstructed transcriptomes were obtained for the novel interspecific hybrid crab and provided valuable insights into the genetic mechanisms of eyestalk displacement in mud crab (Scylla spp.) crossbreeding.

Insights into the architecture of human-induced polygenic selection in Duroc pigs.

BACKGROUND: As one of the most utilized commercial composite boar lines, Duroc pigs have been introduced to China and undergone strongly human-induced selection over the past decades. However, the efficiencies and limitations of previous breeding of Chinese Duroc pigs are largely understudied. The objective of this study was to uncover directional polygenic selection in the Duroc pig genome, and investigate points overlooked in the past breeding process. RESULTS: Here, we utilized the Generation Proxy Selection Mapping (GPSM) on a dataset of 1067 Duroc pigs with 8,766,074 imputed SNPs. GPSM detected a total of 5649 putative SNPs actively under selection in the Chinese Duroc pig population, and the potential functions of the selection regions were mainly related to production, meat and carcass traits. Meanwhile, we observed that the allele frequency of variants related to teat number (NT) relevant traits was also changed, which might be influenced by genes that had pleiotropic effects. First, we identified the direction of selection on NT traits by [Formula: see text], and further pinpointed large-effect genomic regions associated with NT relevant traits by selection signature and GWAS. Combining results of NT relevant traits-specific selection signatures and GWAS, we found three common genome regions, which were overlapped with QTLs related to production, meat and carcass traits besides "teat number" QTLs. This implied that there were some pleiotropic variants underlying NT and economic traits. We further found that rs346331089 has pleiotropic effects on NT and economic traits, e.g., litter size at weaning (LSW), litter weight at weaning (LWW), days to 100 kg (D100), backfat thickness at 100 kg (B100), and loin muscle area at 100 kg (L100) traits. CONCLUSIONS: The selected loci that we identified across methods displayed the past breeding process of Chinese Duroc pigs, and our findings could be used to inform future breeding decision.

Whole-transcriptome RNA sequencing revealed the roles of chitin-related genes in the eyestalk abnormality of a novel mud crab hybrid (Scylla serrata ♀ × S. paramamosain ♂).

Chitin is a kind of insoluble structural polysaccharide and plays different roles in different species. In crustaceans, it forms the structural components in the exoskeleton. In our previous studies, novel mud crab hybrids have been produced from the interspecific hybridization of Scylla serrata ♀ × S. paramamosain ♂. Some of the hybrid crabs have been found to be morphologically (eyestalk) abnormal, but the genetic mechanism remains unknown. To address this question, we performed whole-transcriptome RNA sequencing on the control group (normal hybrids), abnormal hybrids, and S. paramamosain to uncover the genetic basis underlying this morphological abnormality. A total of 695 mRNAs, 10 miRNAs, 44 circRNAs, and 1957 lncRNAs were differentially expressed between normal and abnormal hybrids. Several differentially expressed genes (DEGs) associated with chitin and cuticle metabolism were identified, including chitin synthase, chitinase, chitin deacetylase, ß-N-acetylglucosaminidase, ß-1,4-endoglucanase, N-alpha-acetyltransferase, cuticle proprotein, early cuticle protein, and arthrodial cuticle protein. Functional analysis showed that DE miRNAs, DE circRNAs, DE lncRNAs, and lncRNA/circRNA-miRNA-mRNA network were enriched in pathways related to the amino acid, carbohydrate, and glycogen metabolism. Considering the importance of the chitin and cuticle in exoskeleton formation, it can be concluded that the changes in the chitin and cuticle biosynthesis might have caused the eyestalk abnormality in hybrid crabs. These findings can lay the solid foundation for a better understanding of the important roles of chitin and cuticle related genes and the development of hybridization techniques in crustaceans.

Polymorphisms of AMY1A gene and their association with growth, carcass traits and feed intake efficiency in chickens.

Investigations on the association between chicken traits and genetic variations can provide basic information to improve production performance in chickens. In our previous work, we genotyped 450 male chickens with a 600 K SNP array [1] and found that several SNPs in the genomic regions of the amylase alpha 1A (AMY1A) gene were significantly associated with feed intake efficiency and carcass traits. Given the lower accuracy of the SNP array, we performed direct sequencing with male and female chickens to further test chicken AMY1A polymorphisms and investigate their association with 17 traits in chickens. The results showed that 7 SNPs in the 5' flanking region, exon, intron and 3' UTR (3' untranslated region) of AMY1A, were significantly associated with daily gain (DG), average daily feed intake (ADFI), leg muscle weight (LMW) and abdominal fat (AF) (p < 0.05). Additionally, the haplotypes based on three SNPs, rs15910189, rs314354067 and rs316026696, showed significant associations with DG (p < 0.01), ADFI and AF (p < 0.05). To better understand the transcriptional regulation of AMY1A, we cloned its 5' flanking region and found that the SNPs rs316436216 and rs314213090 which might change the transcriptional regulator binding sites, were in the suppressor and enhancer regions, respectively. In addition, luciferase assays revealed that the SNP rs314613110 in the 3' UTR influenced the binding of the miRNA gga-miR-1764-3p. To validate whether there is any copy number variation in AMY1A in our population, we performed a genome-wide assessment of CNVs through whole-genome resequencing data. However, no CNV was found in AMY1A in our population, which is different from the increased copy number of AMY1A found in humans who consume a high-starch diet. Therefore, the present study provides substantial evidence for the association of AMY1A polymorphisms with growth traits and feed intake efficiency, which might contribute to chicken breeding programs.

Multi-omics-data-assisted genomic feature markers preselection improves the accuracy of genomic prediction.

BACKGROUND: Presently, multi-omics data (e.g., genomics, transcriptomics, proteomics, and metabolomics) are available to improve genomic predictors. Omics data not only offers new data layers for genomic prediction but also provides a bridge between organismal phenotypes and genome variation that cannot be readily captured at the genome sequence level. Therefore, using multi-omics data to select feature markers is a feasible strategy to improve the accuracy of genomic prediction. In this study, simultaneously using whole-genome sequencing (WGS) and gene expression level data, four strategies for single-nucleotide polymorphism (SNP) preselection were investigated for genomic predictions in the Drosophila Genetic Reference Panel. RESULTS: Using genomic best linear unbiased prediction (GBLUP) with complete WGS data, the prediction accuracies were 0.208 ± 0.020 (0.181 ± 0.022) for the startle response and 0.272 ± 0.017 (0.307 ± 0.015) for starvation resistance in the female (male) lines. Compared with GBLUP using complete WGS data, both GBLUP and the genomic feature BLUP (GFBLUP) did not improve the prediction accuracy using SNPs preselected from complete WGS data based on the results of genome-wide association studies (GWASs) or transcriptome-wide association studies (TWASs). Furthermore, by using SNPs preselected from the WGS data based on the results of the expression quantitative trait locus (eQTL) mapping of all genes, only the startle response had greater accuracy than GBLUP with the complete WGS data. The best accuracy values in the female and male lines were 0.243 ± 0.020 and 0.220 ± 0.022, respectively. Importantly, by using SNPs preselected based on the results of the eQTL mapping of significant genes from TWAS, both GBLUP and GFBLUP resulted in great accuracy and small bias of genomic prediction. Compared with the GBLUP using complete WGS data, the best accuracy values represented increases of 60.66% and 39.09% for the starvation resistance and 27.40% and 35.36% for startle response in the female and male lines, respectively. CONCLUSIONS: Overall, multi-omics data can assist genomic feature preselection and improve the performance of genomic prediction. The new knowledge gained from this study will enrich the use of multi-omics in genomic prediction.

Optimizing genomic prediction model given causal genes in a dairy cattle population.

As genotypic data are moving from SNP chip toward whole-genome sequence, the accuracy of genomic prediction (GP) exhibits a marginal gain, although all genetic variation, including causal genes, are contained in whole-genome sequence data. Meanwhile, genetic analyses on complex traits, such as genome-wide association studies, have identified an increasing number of genomic regions, including potential causal genes, which would be reliable prior knowledge for GP. Many studies have tried to improve the performance of GP by modifying the prediction model to incorporate prior knowledge. Although several plausible results have been obtained from model modification or strategy optimization, most of them were validated in a specific empirical population with a limited variety of genetic architecture for complex traits. An alternative approach is to use simulated genetic architecture with known causal genes (e.g., simulated causative SNP) to evaluate different GP models with given causal genes. Our objectives were to (1) evaluate the performance of GP under a variety of genetic architectures with a subset of known causal genes and (2) compare different GP models modified by highlighting causal genes and different strategies to weight causal genes. In this study, we simulated pseudo-phenotypes under a variety of genetic architectures based on the real genotypes and phenotypes of a dairy cattle population. Besides classical genomic best linear unbiased prediction, we evaluated 3 modified GP models that highlight causal genes as follows: (1) by treating them as fixed effects, (2) by treating them as a separate random component, and (3) by combining them into the genomic relationship matrix as random effects. Our results showed that highlighting the known causal genes, which explained a considerable proportion of genetic variance in the GP models, increased the predictive accuracy. Combining all given causal genes into the genomic relationship matrix was the optimal strategy under all the scenarios validated, and treating causal genes as a separate random component is also recommended, when more than 20% of genetic variance was explained by known causal genes. Moreover, assigning differential weights to each causal gene further improved the predictive accuracy.

New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens.

Poultry feed constitutes the largest cost in poultry production, estimated to be up to 70% of the total cost. Moreover, there is pressure on the poultry industry to increase production to meet the protein demand of humans and simultaneously reduce emissions to protect the environment. Therefore, improving feed efficiency plays an important role to improve profits and the environmental footprint in broiler production. In this study, using imputed whole-genome sequencing data, genome-wide association analysis (GWAS) was performed to identify single-nucleotide polymorphisms (SNPs) and genes associated with residual feed intake (RFI) and its component traits. Furthermore, a transcriptomic analysis between the high-RFI and the low-RFI groups was performed to validate the candidate genes from GWAS. The results showed that the heritability estimates of average daily gain (ADG), average daily feed intake (ADFI), and RFI were 0.29 (0.004), 0.37 (0.005), and 0.38 (0.004), respectively. Using imputed sequence-based GWAS, we identified seven significant SNPs and five candidate genes [MTSS I-BAR domain containing 1, folliculin, COP9 signalosome subunit 3, 5',3'-nucleotidase (mitochondrial), and gametocyte-specific factor 1] associated with RFI, 20 significant SNPs and one candidate gene (inositol polyphosphate multikinase) associated with ADG, and one significant SNP and one candidate gene (coatomer protein complex subunit alpha) associated with ADFI. After performing a transcriptomic analysis between the high-RFI and the low-RFI groups, both 38 up-regulated and 26 down-regulated genes were identified in the high-RFI group. Furthermore, integrating regional conditional GWAS and transcriptome analysis, ras-related dexamethasone induced 1 was the only overlapped gene associated with RFI, which also suggested that the region (GGA14: 4767015-4882318) is a new quantitative trait locus associated with RFI. In conclusion, using imputed sequence-based GWAS is an efficient method to identify significant SNPs and candidate genes in chicken. Our results provide valuable insights into the genetic mechanisms of RFI and its component traits, which would further improve the genetic gain of feed efficiency rapidly and cost-effectively in the context of marker-assisted breeding selection.

Using imputation-based whole-genome sequencing data to improve the accuracy of genomic prediction for combined populations in pigs.

BACKGROUND: For genomic selection in populations with a small reference population, combining populations of the same breed or populations of related breeds is an effective way to increase the size of the reference population. However, genomic predictions based on single nucleotide polymorphism (SNP)-chip genotype data using combined populations with different genetic backgrounds or from different breeds have not shown a clear advantage over using within-population or within-breed predictions. The increasing availability of whole-genome sequencing (WGS) data provides new opportunities for combined population genomic prediction. Our objective was to investigate the accuracy of genomic prediction using imputation-based WGS data from combined populations in pigs. Using 80K SNP panel genotypes, WGS genotypes, or genotypes on WGS variants that were pruned based on linkage disequilibrium (LD), three methods [genomic best linear unbiased prediction (GBLUP), single-step (ss)GBLUP, and genomic feature (GF)BLUP] were implemented with different prior information to identify the best method to improve the accuracy of genomic prediction for combined populations in pigs. RESULTS: In total, 2089 and 2043 individuals with production and reproduction phenotypes, respectively, from three Yorkshire populations with different genetic backgrounds were genotyped with the PorcineSNP80 panel. Imputation accuracy from 80K to WGS variants reached 92%. The results showed that use of the WGS data compared to the 80K SNP panel did not increase the accuracy of genomic prediction in a single population, but using WGS data with LD pruning and GFBLUP with prior information did yield higher accuracy than the 80K SNP panel. For the 80K SNP panel genotypes, using the combined population resulted in a slight improvement, no change, or even a slight decrease in accuracy in comparison with the single population for GBLUP and ssGBLUP, while accuracy increased by 1 to 2.4% when using WGS data. Notably, the GFBLUP method did not perform well for both the combined population and the single populations. CONCLUSIONS: The use of WGS data was beneficial for combined population genomic prediction. Simply increasing the number of SNPs to the WGS level did not increase accuracy for a single population, while using pruned WGS data based on LD and GFBLUP with prior information could yield higher accuracy than the 80K SNP panel.

Genome-Wide Association Study for Reproductive Traits in a Duroc Pig Population.

In the pig industry, reproductive traits constantly influence the production efficiency. To identify markers and candidate genes underlying porcine reproductive traits, a genome-wide association study (GWAS) was performed in a Duroc pig population. In total, 1067 pigs were genotyped using single-nucleotide polymorphism (SNP) chips, and four reproductive traits, including litter size at birth (LSB), litter weight at birth (LWB), litter size at weaning (LSW), and litter weight at weaning (LWW), were examined. The results showed that 20 potential SNPs reached the level of suggestive significance and were associated with these traits of interest. Several important candidate genes, including TXN2, KCNA1, ENSSSCG00000003546, ZDHHC18, MAP2K6, BICC1, FAM135B, EPHB2, SEMA4D, ST3GAL1, KCTD3, FAM110A, TMEM132D, TBX3, and FAM110A, were identified and might compose the underlying genetic architecture of porcine reproductive traits. These findings help to understand the genetic basis of porcine reproductive traits and provide important information for molecular breeding in pigs.

Strategies for Obtaining and Pruning Imputed Whole-Genome Sequence Data for Genomic Prediction.

Genomic prediction with imputed whole-genome sequencing (WGS) data is an attractive approach to improve predictive ability with low cost. However, high accuracy has not been realized using this method in livestock. In this study, we imputed 435 individuals from 600K single nucleotide polymorphism (SNP) chip data to WGS data using different reference panels. We also investigated the prediction accuracy of genomic best linear unbiased prediction (GBLUP) using imputed WGS data from different reference panels, linkage disequilibrium (LD)-based marker pruning, and pre-selected variants based on Genome-wide association society (GWAS) results. Results showed that the imputation accuracies from 600K to WGS data were 0.873 ± 0.038, 0.906 ± 0.036, and 0.979 ± 0.010 for the internal, external, and combined reference panels, respectively. In most traits of chickens, the prediction accuracy of imputed WGS data obtained from the internal reference panel was greater than or equal to that of the combined reference panel; the external reference panel had the lowest prediction accuracy. Compared with 600K chip data, GBLUP with imputed WGS data had only a small increase (1-3%) in prediction accuracy. Using only variants selected from imputed WGS data based on GWAS results resulted in almost no increase for most traits and even increased the bias of the regression coefficient. The impact of the degree of LD of selected and remaining variants on prediction accuracy was different. For average daily gain (ADG), residual feed intake (RFI), intestine length (IL), and body weight in 91 days (BW91), the accuracy of GBLUP increased as the degree of LD of selected variants decreased, but the opposite relationship occurred for the remaining variants. But for breast muscle weight (BMW) and average daily feed intake (ADFI), the accuracy of GBLUP increased as the degree of LD of selected variants increased, and the degree of LD of remaining variants had a small effect on prediction accuracy. Overall, the optimal imputation strategy to obtain WGS data for genomic prediction should consider the relationship between selected individuals and target population individuals to avoid heterogeneity of imputation. LD-based marker pruning can be used to improve the accuracy of genomic prediction using imputed WGS data.

Genome-wide association studies for the number of animals born alive and dead in duroc pigs.

Litter size is one of the most important economic traits for pig production as it is directly related to the production efficiency. As an important litter size trait in pigs, the number of piglets born alive at birth (NBA) receives widespread interests in the pig industry. However, traits of piglets born dead, including the number of stillborn piglets (NS) and the piglets mummified at birth (NM) should be noted to explain the loss of reproduction. Herein, in the present study, a total of 803 producing sows were sampled and 2807 farrowing records for NBA, NM, and NS traits were collected in a Duroc swine population. Subsequently, a genome-wide association study (GWAS) was performed for NBA, NS and NM in parity groups 1 to 5. In total, 10 putative regions were found associated with these traits. After stepwise conditional analyses around the putative regions, eight independent signals were ultimately identified for NBA, NS, and NM, and there were seven promising candidate genes related to these traits, including ARID1A, RXRG, NFATC4, ABTB2, GRAMD1B, NDRG1, and APC. Our findings contribute to the understanding of the significant genetic causes of piglets born alive and dead, and could have a positive effect on pig production efficiency and economic profits.

Genetic Diversity of Indigenous Pigs from South China Area Revealed by SNP Array.

To investigate the genetic diversity, population structure, extent of linkage disequilibrium (LD), effective population size (Ne), and selection signatures in indigenous pigs from Guangdong and Guangxi in China, 226 pigs belonging to ten diverse populations were genotyped using single nucleotide polymorphism (SNP) chips. The genetic divergence between Chinese and Western pigs was determined based on the SNP chip data. Low genetic diversity of Dahuabai (DHB), Luchuan (LC), Lantang (LT), and Meihua (MH) pigs, and introgression of Western pigs into Longlin (LL), MH, and Yuedonghei (YDH) pigs were detected. Analysis of the extent of LD showed that indigenous pigs had low LD when pairwise SNP distance was short and high LD when pairwise SNP distance was long. Effective population size analysis showed a rapid decrease for Chinese indigenous pigs, and some pig populations had a relatively small Ne. This result indicated the loss of genetic diversity in indigenous pigs, and introgression from Western commercial pigs. Selection signatures detected in this study overlapped with meat quality traits, such as drip loss, intramuscular fat content, meat color b*, and average backfat thickness. Our study deepened understanding of the conservation status and domestication of Chinese indigenous pigs.

Dynamic DNA methylation of ovaries during pubertal transition in gilts.

BACKGROUND: In female mammals, the initiation of puberty, coupling with the dramatically morphological changes in ovaries, indicates the sexual and follicular maturation. Previous studies have suggested that the disrupted DNA methylation results in the delayed puberty. However, to date, the changes in ovarian methylomes during pubertal transition have not been investigated. In this study, using gilts as a pubertal model, the genome-wide DNA methylation were profiled to explore their dynamics during pubertal transition across Pre-, In- and Post-puberty. RESULTS: During pubertal transition, the follicles underwent maturation and luteinization, coupled with the significant changes in the mRNA expression of DNMT1 and DNMT3a. DNA methylation levels of In-puberty were higher than that of Pre- and Post-puberty at the locations of genes and CpG islands (CGIs). Analysis of the DNA methylation changes identified 12,313, 20,960 and 17,694 differentially methylated CpGs (DMCs) for the comparisons of Pre- vs. In-, In vs. Post-, and Pre- vs. Post-puberty, respectively. Moreover, the CGIs, upstream and exonic regions showed a significant underrepresentation of DMCs, but the CGI shores, CGI shelves, intronic, downstream and intergenic regions showed a significant overrepresentation of DMCs. Furthermore, biological functions of these methylation changes enriched in PI3K-Akt signaling pathway, GnRH signaling pathway, and Insulin secretion, and the mRNA expressions of several genes of these signaling pathway, including MMP2, ESR1, GSK3B, FGF21, IGF1R, and TAC3, were significantly changed across Pre-, In- and Post-puberty in ovaries. CONCLUSIONS: During pubertal transition in gilts, the DNA methylation changes of ovaries were likely to affect the transcription of genes related to PI3K-Akt signaling pathway, GnRH signaling pathway, and Insulin secretion. These observations can provide new insight into the epigenetic mechanism of follicular and sexual maturation during pubertal transition in mammals.

Genome-Wide DNA Methylation Analysis of Hypothalamus During the Onset of Puberty in Gilts.

Although selection of the early age at puberty in gilts will make for a favorable effect on the reproductivity of sow, a large proportion of phenotypic variation in age at puberty of gilts cannot be explained by genetics. Previous studies have implicated hypothalamic DNA methylation in the onset of puberty in mammals. However, the underlying molecular mechanism regarding the regulation of the onset of puberty has remained largely unexplored in gilts. Herein, the genome-scale DNA methylation of hypothalamus was acquired, using the reduced representation bisulfite sequencing, to compare and describe the changes of DNA methylation across Pre-, In- and Post-pubertal gilts. In this study, the average methylation levels of CpGs and CpHs (where H = C, T, or A) in CpG islands- and gene-related regions were gradually decreased in hypothalamic methylomes during the pubertal transition. Comparisons of Pre- vs. In-, In- vs. Post-, and Pre- vs. Post-pubertal stage revealed that there were 85726, 92914, and 100421 differentially methylated CpGs and 5940, 14804, and 16893 differentially methylated CpHs (where H = C, T, or A) in the hypothalamic methylomes. The methylation changes of CpHs were more dynamic than that of CpGs, and methylation changes of CpGs and CpHs were likely to be, respectively, involved in the developmental processes of reproduction and the molecular processes of cellular communications in the hypothalamus. Moreover, methylation changes of CpHs were observed to overrepresent in the quantitative trait loci of age at puberty, and the biological function of these CpH methylation changes was enriched in the pancreas development in gilts. Furthermore, the mRNA levels of several differentially CpG or CpH methylated genes related to the transcription of RNA II polymerase, GnRH signaling pathway, Estrogen signaling pathway, PI3K-AKt signaling pathway, and Insulin signaling pathway, including MAX, MMP2, FGF11, IGF1R, FGF21, and GSK3B, were significantly changed across these pubertal stages in the hypothalamus. These results will help our understanding of how DNA methylation contributes to phenotypic variation of age at puberty.

Genome-wide DNA methylation analysis of pituitaries during the initiation of puberty in gilts.

It has been widely recognized that the early or delayed puberty appears to display harmful effects on adult health outcomes. During the timing of puberty, pituitaries responds to the hypothalamus and then introduce the following response of ovaries in hypothalamic-pituitary-gonadal axis. DNA methylation has been recently suggested to regulate the onset of puberty in female mammals. However, to date, the changes of DNA methylation in pituitaries have not been investigated during pubertal transition. In this study, using gilts as the pubertal model, the genome-scale DNA methylation of pituitaries was profiled and compared across Pre-, In- and Post-puberty by using the reduced representation bisulfite sequencing. We found that average methylation levels of each genomic feature in Post- were lower than Pre- and In-pubertal stage in CpG context, but they were higher in In- than that in Pre- and Post-pubertal stage in CpH (where H = A, T, or C) context. The methylation patterns of CpHs were more dynamic than that of CpGs at the location of high CpG content, low CpG content promoter genes, and differently genomic CGIs. Furthermore, the differently genomic CGIs were likely to show in a similar manner in CpG context but display in a stage-specific manner in the CpH context across the Pre-, In- and Post-pubertal stage. Among these pubertal stages, 5 kb upstream regions of the transcription start sites were protected from both CpG and CpH methylation changes. 12.65% of detected CpGs were identified as the differentially methylated CpGs, regarding 4301 genes which were involved in the fundamental functions of pituitaries. 0.35% of detected CpHs were identified as differentially methylated CpHs, regarding 3691 genes which were involved in the biological functions of releasing gonadotropin hormones. These observations and analyses would provide valuable insights into epigenetic mechanism of the initiation of puberty in pituitary level.

Genome wide association study on feed conversion ratio using imputed sequence data in chickens.

OBJECTIVE: Feed consumption contributes a large percentage for total production costs in the poultry industry. Detecting genes associated with feeding traits will be of benefit to improve our understanding of the molecular determinants for feed efficiency. The objective of this study was to identify candidate genes associated with feed conversion ratio (FCR) via genome-wide association study (GWAS) using sequence data imputed from single nucleotide polymorphism (SNP) panel in a Chinese indigenous chicken population. METHODS: A total of 435 Chinese indigenous chickens were phenotyped for FCR and were genotyped using a 600K SNP genotyping array. Twenty-four birds were selected for sequencing, and the 600K SNP panel data were imputed to whole sequence data with the 24 birds as the reference. The GWAS were performed with GEMMA software. RESULTS: After quality control, 8,626,020 SNPs were used for sequence based GWAS, in which ten significant genomic regions were detected to be associated with FCR. Ten candidate genes, ubiquitin specific peptidase 44, leukotriene A4 hydrolase, ETS transcription factor, R-spondin 2, inhibitor of apoptosis protein 3, sosondowah ankyrin repeat domain family member D, calmodulin regulated spectrin associated protein family member 2, zinc finger and BTB domain containing 41, potassium sodium-activated channel subfamily T member 2, and member of RAS oncogene family were annotated. Several of them were within or near the reported FCR quantitative trait loci, and others were newly reported. CONCLUSION: Results from this study provide valuable prior information on chicken genomic breeding programs, and potentially improve our understanding of the molecular mechanism for feeding traits.

Genomic Prediction of Complex Phenotypes Using Genic Similarity Based Relatedness Matrix.

In the last years, a series of methods for genomic prediction (GP) have been established, and the advantages of GP over pedigree best linear unbiased prediction (BLUP) have been reported. However, the majority of previously proposed GP models are purely based on mathematical considerations while seldom take the abundant biological knowledge into account. Prediction ability of those models largely depends on the consistency between the statistical assumptions and the underlying genetic architectures of traits of interest. In this study, gene annotation information was incorporated into GP models by constructing haplotypes with SNPs mapped to genic regions. Haplotype allele similarity between pairs of individuals was measured through different approaches at single gene level and then converted into whole genome level, which was then treated as a special kernel and used in kernel based GP models. Results shown that the gene annotation guided methods gave higher or at least comparable predictive ability in some traits, especially in the Arabidopsis dataset and the rice breeding population. Compared to SNP models and haplotype models without gene annotation, the gene annotation based models improved the predictive ability by 0.56~26.67% in the Arabidopsis and 1.62~16.53% in the rice breeding population, respectively. However, incorporating gene annotation slightly improved the predictive ability for several traits but did not show any extra gain for the rest traits in a chicken population. In conclusion, integrating gene annotation into GP models could be beneficial for some traits, species, and populations compared to SNP models and haplotype models without gene annotation. However, more studies are yet to be conducted to implicitly investigate the characteristics of these gene annotation guided models.

Genome-wide association study on chicken carcass traits using sequence data imputed from SNP array.

Chicken carcass traits are economically important for the chicken industry. Detecting which genes affect chicken carcass traits is of great benefit to the genetic improvement of this important agricultural species. To investigate the genetic mechanism of carcass traits in chickens, we carried out a genome-wide association study (GWAS). A total of 435 Chinese indigenous chickens were phenotyped for carcass weight (CW), eviscerated weight with giblets (EWG), and eviscerated weight (EW) after slaughter at 91 days and were genotyped using a 600-K single nucleotide polymorphism (SNP) genotyping array. Twenty-four birds were selected for sequencing, and the 600 K SNP panel data were imputed to sequence data with the 24 birds as the reference. Univariate GWASs were performed with GEMMA software using the whole genome sequence data imputed from SNP chip data. Finally, 3, 25, and 63 suggestively significant SNPs were identified to be associated with carcass weight (CW), eviscerated weight with giblets (EWG), and eviscerated weight (EW), respectively. Six candidate genes, RNF219, SCEL, MYCBP2, ETS1, APLP2, and PRDM10 were detected. SCEL and MYCBP2 were potentially associated with these three traits, RNF219 and APLP2 were potentially associated with EWG and EW, and ETS1 and PRDM10 were only potentially associated with EWG and EW, respectively. Compared with forefathers' research, 10 reported QTLs associated with CW were located within a 5-Mb distance near the SNPs with P value lower than 1×10-5. This study enriched the knowledge of the genetic mechanisms of chicken carcass traits.