Comparative analysis of the genetic diversity of the neutral microsatellite loci and second exon of the goat MHC-DQB1 gene.

This study compared and analyzed the genetic diversity and population structure of exon 2 of the DQB1 gene and 13 autosomal neutral microsatellite markers from 14 Chinese goat breeds to explore the potential evolutionary mechanism of the major histocompatibility complex (MHC). A total of 287 haplotypes were constructed from MHC-DQB1 exon 2 from 14 populations, and 82 nucleotide polymorphic sites (SNPs, 31.78%) and 172 heterozygous individuals (79.12%) were identified. The FST values of the microsatellites and MHC-DQB ranged between 0.01831-0.26907 and 0.00892-0.38871, respectively. Furthermore, 14 goat populations showed rich genetic diversity in the microsatellite loci and MHC-DQB1 exon 2. However, the population structure and phylogenetic relationship represented by the two markers were different. Positive selection and Tajima's D test results showed the occurrence of a diversified selection mechanism, which was primarily based on a positive and balancing selection in goat DQB. This study also found that the DQB sequences of bovines exhibited trans-species polymorphism (TSP) among species and families. In brief, this study indicated that positive and balancing selection played a major role in maintaining the genetic diversity of DQB, and TSP of MHC in bovines was common, which enhanced the understanding of the MHC evolution.

Genome-Wide Association Study Based on Random Regression Model Reveals Candidate Genes Associated with Longitudinal Data in Chinese Simmental Beef Cattle.

Body weight (BW) is an important longitudinal trait that directly described the growth gain of bovine in production. However, previous genome-wide association study (GWAS) mainly focused on the single-record traits, with less attention paid to longitudinal traits. Compared with traditional GWAS models, the association studies based on the random regression model (GWAS-RRM) have better performance in the control of the false positive rate through considering time-stage effects. In this study, the BW trait data were collected from 808 Chinese Simmental beef cattle aged 0, 6, 12, and 18 months, then we performed a GWAS-RRM to fit the time-varied SNP effect. The results showed a total of 37 significant SNPs were associated with BW. Gene functional annotation and enrichment analysis indicated FGF4, ANGPT4, PLA2G4A, and ITGA5 were promising candidate genes for BW. Moreover, these genes were significantly enriched in the signaling transduction pathway and lipid metabolism. These findings will provide prior molecular information for bovine gene-based selection, as well as facilitate the extensive application of GWAS-RRM in domestic animals.

PacBio Single-Molecule Long-Read Sequencing Provides New Light on the Complexity of Full-Length Transcripts in Cattle.

Cattle (Bos taurus) is one of the most widely distributed livestock species in the world, and provides us with high-quality milk and meat which have a huge impact on the quality of human life. Therefore, accurate and complete transcriptome and genome annotation are of great value to the research of cattle breeding. In this study, we used error-corrected PacBio single-molecule real-time (SMRT) data to perform whole-transcriptome profiling in cattle. Then, 22.5 Gb of subreads was generated, including 381,423 circular consensus sequences (CCSs), among which 276,295 full-length non-chimeric (FLNC) sequences were identified. After correction by Illumina short reads, we obtained 22,353 error-corrected isoforms. A total of 305 alternative splicing (AS) events and 3,795 alternative polyadenylation (APA) sites were detected by transcriptome structural analysis. Furthermore, we identified 457 novel genes, 120 putative transcription factors (TFs), and 569 novel long non-coding RNAs (lncRNAs). Taken together, this research improves our understanding and provides new insights into the complexity of full-length transcripts in cattle.

Transcriptome profiling analysis of muscle tissue reveals potential candidate genes affecting water holding capacity in Chinese Simmental beef cattle.

Water holding capacity (WHC) is an important sensory attribute that greatly influences meat quality. However, the molecular mechanism that regulates the beef WHC remains to be elucidated. In this study, the longissimus dorsi (LD) muscles of 49 Chinese Simmental beef cattle were measured for meat quality traits and subjected to RNA sequencing. WHC had significant correlation with 35 kg water loss (r = - 0.99, p < 0.01) and IMF content (r = 0.31, p < 0.05), but not with SF (r = - 0.20, p = 0.18) and pH (r = 0.11, p = 0.44). Eight individuals with the highest WHC (H-WHC) and the lowest WHC (L-WHC) were selected for transcriptome analysis. A total of 865 genes were identified as differentially expressed genes (DEGs) between two groups, of which 633 genes were up-regulated and 232 genes were down-regulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment revealed that DEGs were significantly enriched in 15 GO terms and 96 pathways. Additionally, based on protein-protein interaction (PPI) network, animal QTL database (QTLdb), and relevant literature, the study not only confirmed seven genes (HSPA12A, HSPA13, PPARγ, MYL2, MYPN, TPI, and ATP2A1) influenced WHC in accordance with previous studies, but also identified ATP2B4, ACTN1, ITGAV, TGFBR1, THBS1, and TEK as the most promising novel candidate genes affecting the WHC. These findings could offer important insight for exploring the molecular mechanism underlying the WHC trait and facilitate the improvement of beef quality.

KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency.

Nowadays, advances in high-throughput sequencing benefit the increasing application of genomic prediction (GP) in breeding programs. In this research, we designed a Cosine kernel-based KRR named KCRR to perform GP. This paper assessed the prediction accuracies of 12 traits with various heritability and genetic architectures from four populations using the genomic best linear unbiased prediction (GBLUP), BayesB, support vector regression (SVR), and KCRR. On the whole, KCRR performed stably for all traits of multiple species, indicating that the hypothesis of KCRR had the potential to be adapted to a wide range of genetic architectures. Moreover, we defined a modified genomic similarity matrix named Cosine similarity matrix (CS matrix). The results indicated that the accuracies between GBLUP_kinship and GBLUP_CS almost unanimously for all traits, but the computing efficiency has increased by an average of 20 times. Our research will be a significant promising strategy in future GP.

A Stacking Ensemble Learning Framework for Genomic Prediction.

Machine learning (ML) is perhaps the most useful tool for the interpretation of large genomic datasets. However, the performance of a single machine learning method in genomic selection (GS) is currently unsatisfactory. To improve the genomic predictions, we constructed a stacking ensemble learning framework (SELF), integrating three machine learning methods, to predict genomic estimated breeding values (GEBVs). The present study evaluated the prediction ability of SELF by analyzing three real datasets, with different genetic architecture; comparing the prediction accuracy of SELF, base learners, genomic best linear unbiased prediction (GBLUP) and BayesB. For each trait, SELF performed better than base learners, which included support vector regression (SVR), kernel ridge regression (KRR) and elastic net (ENET). The prediction accuracy of SELF was, on average, 7.70% higher than GBLUP in three datasets. Except for the milk fat percentage (MFP) traits, of the German Holstein dairy cattle dataset, SELF was more robust than BayesB in all remaining traits. Therefore, we believed that SEFL has the potential to be promoted to estimate GEBVs in other animals and plants.

Genome-Wide Association Analysis of Growth Curve Parameters in Chinese Simmental Beef Cattle.

The objective of the present study was to perform a genome-wide association study (GWAS) for growth curve parameters using nonlinear models that fit original weight-age records. In this study, data from 808 Chinese Simmental beef cattle that were weighed at 0, 6, 12, and 18 months of age were used to fit the growth curve. The Gompertz model showed the highest coefficient of determination (R2 = 0.954). The parameters' mature body weight (A), time-scale parameter (b), and maturity rate (K) were treated as phenotypes for single-trait GWAS and multi-trait GWAS. In total, 9, 49, and 7 significant SNPs associated with A, b, and K were identified by single-trait GWAS; 22 significant single nucleotide polymorphisms (SNPs) were identified by multi-trait GWAS. Among them, we observed several candidate genes, including PLIN3, KCNS3, TMCO1, PRKAG3, ANGPTL2, IGF-1, SHISA9, and STK3, which were previously reported to associate with growth and development. Further research for these candidate genes may be useful for exploring the full genetic architecture underlying growth and development traits in livestock.

Application of ensemble learning to genomic selection in chinese simmental beef cattle.

Genomic selection (GS) using the whole-genome molecular makers to predict genomic estimated breeding values (GEBVs) is revolutionizing the livestock and plant breeding. Seeking out novel strategies with higher prediction accuracy for GS has been the ultimate goal of breeders. With the rapid development of artificial intelligence, machine learning algorithms were applied to estimate the GEBVs increasingly. Although some machine learning methods have better performance in phenotype prediction, there is still considerable room for improvement. In this study, we applied an ensemble-learning algorithm, Adaboost.RT, which integrated support vector regression (SVR), kernel ridge regression (KRR) and random forest (RF), to predict genomic breeding values of three economic traits (carcass weight, live weight, and eye muscle area) in Chinese Simmental beef cattle. Predictive accuracy measured as the Pearson correlation between the corrected phenotypes and predicted GEBVs. Moreover, we compared the reliability of SVR, KRR, RF, Adaboost.RT and GBLUP methods. The result showed that machine learning methods outperformed GBLUP, and the average improvement of four machine learning methods over the GBLUP was 12.8%, 14.9%, 5.4% and 14.4%, respectively. Among the four machine learning methods, the reliability of Adaboost.RT was comparable to KRR with higher stability. We therefore believe that the Adaboost.RT algorithm is a reliable and efficient method for GS.

SNP of AHSA2 gene in three cattle breeds using snapshot technology.

Droughtmaster is a tropical breed of beef cattle that can survive in hot climates and easily adapt to torrid environments. These traits are important in livestock breeding. In this study, we genotyped five single-nucleotide polymorphisms (SNPs) of the AHSA2 gene from 190 cattle belonging to three different breeds (Droughtmaster, Angus and Simmental) by using snapshot technology. This work aimed to identify the valuable molecular marker of heat resistance in cattle. Results showed that Droughtmaster exhibited higher expected heterozygosity and polymorphic information content compared with the two other breeds. The AHSA2-1 locus deviated from the Hardy-Weinberg equilibrium in the Droughtmaster breed (P < 0.05). Two SNPs in Droughtmaster diverged significantly from Angus and Simmental. The SNPs were identified as AHSA2-3 and AHSA2-4, which were closely linked to the three breeds based on pair-wise FST. AHSA2-4 involved a missense mutation. In summary, the GG genotypes in AHSA2-3 and AHSA2-4 may be candidate genotypes associated with heat resistance traits and may serve as valuable genetic markers for breeding of heat-tolerant beef cattle in the future.

Identification of a Goat Intersexuality-Associated Novel Variant Through Genome-Wide Resequencing and Hi-C.

Background: Polled intersex syndrome (PIS) leads to reproductive disorders in goats and exerts a heavy influence on goat breeding. Since 2001, the core variant of an 11.7 kb deletion at ~129 Mb on chromosome 1 (CHI1) has been widely used as a genetic diagnostic criterion. In 2020, a ~0.48 Mb insertion within the PIS deletion was identified by sequencing in XX intersex goats. However, the suitability of this variation for the diagnosis of intersex goats worldwide and its further molecular genetic mechanism need to be clarified. Results: The whole-genome selective sweep of intersex goats from China was performed with whole-genome next-generation sequencing technology for large sample populations and a case-control study on interbreeds. A series of candidate genes related to the goat intersexuality phenotype were found. We further confirmed that a ~0.48 Mb duplicated fragment (including ERG and KCNJ15) downstream of the ~20 Mb PIS region was reversely inserted into the PIS locus in intersex Chinese goats and was consistent with that in European Saanen and Valais black-necked goats. High-throughput chromosome conformation capture (Hi-C) technology was then used to compare the 3D structures of the PIS variant neighborhood in CHI1 between intersex and non-intersex goats. A newly found structure was validated as an intrachromosomal rearrangement. This inserted duplication changed the original spatial structure of goat CHI1 and caused the appearance of several specific loop structures in the adjacent ~20 kb downstream region of FOXL2. Conclusions: Results suggested that the novel complex PIS variant genome was sufficient as a broad-spectrum clinical diagnostic marker of XX intersexuality in goats from Europe and China. A series of private dense loop structures caused by segment insertion into the PIS deletion might affect the expression of FOXL2 or other neighboring novel candidate genes. However, these structures require further in-depth molecular biological experimental verification. In general, this study provided new insights for future research on the molecular genetic mechanism underlying female-to-male sex reversal in goats.

Genome-wide selection signatures analysis of litter size in Dazu black goats using single-nucleotide polymorphism.

Litter size is considered to be the most important index for estimating domestic animal productivity. The number of indigenous goats in China with higher litter sizes than those of commercial breeds in other countries may be helpful for accelerating genetic improvements in goat breeding. We performed a genome-wide selective sweep analysis of 31 Dazu black goats with extreme standard deviation in litter size within the third fetus to identify significant genomic regions and candidate genes through different analyses. The analysis identified a total of 33,917,703 variants, including 32,262,179 SNPs and 1,655,524 indels. In addition, two novel candidate genes (LRP1B and GLRB), which are related to litter size, were obtained with π, Tajima's D, πA/πB, and F ST at the individual level with a 95% threshold for each parameter. These two genes were annotated in five GO terms (localization, binding, macromolecular complex, membrane part, and membrane) and two pathways (long-term depression and neuroactive ligand-receptor interaction pathway). Regarding the result of linkage disequilibrium (LD) analysis, in LRP1B and GRID2, the high-yield Dazu black goats exhibit significantly different LD patterns from low-yield goats. Litter size variability has low heritability and is related to multiple complex factors found in domestic animals. Obtaining a clear explanation and significant signal by genome-wide selective sweep analysis with a small sample size is difficult. However, we investigated some candidate genes, particularly LRP1B and GLRB, which may provide useful information for further research.

Genome-wide analysis of Chongqing native intersexual goats using next-generation sequencing.

Sex reversal has been studied extensively in vertebrate species, particularly in domestic goats, because polled intersex syndrome (PIS) has seriously affected their production efficiency. In the present study, we used histopathologically diagnosed cases of PIS to identify correlated genomic regions and variants using representative selection signatures and performed GWAS using Restriction-Site Associated Resequencing DNA. We identified 171 single-nucleotide polymorphisms (SNPs) that may have contributed to this phenotype, and 53 SNPs were determined to be located in coding regions using a general linear model. The transcriptome data sets of differentially expressed genes (DEGs) in the pituitary tissues of intersexual and nonintersexual goats were examined using high-throughput technology. A total of 10,063 DEGs and 337 long noncoding RNAs were identified. The DEGs were clustered into 56 GO categories and determined to be significantly enriched in 53 signaling pathways by KEGG analysis. In addition, according to qPCR results, PSPO2 and FSH were significantly more highly expressed in sexually mature pituitary tissues of intersexual goats compared to healthy controls (nonintersexual). These results demonstrate that certain novel potential genomic regions may be responsible for intersexual goats, and the transcriptome data indicate that the regulation of various physiological systems is involved in intersexual goat development. Therefore, these results provide helpful data for understanding the molecular mechanisms of intersex syndrome in goats.