Huimin Kang

Identification of Genome-Wide Copy Number Variations among Diverse Pig Breeds Using SNP Genotyping Arrays

Copy number variations (CNVs) are important forms of genetic variation complementary to SNPs, and can be considered as promising markers for some phenotypic and economically important traits or diseases susceptibility in domestic animals. In the present study, we performed a genome-wide CNV identification in 14 individuals selected from diverse populations, including six types of Chinese indigenous breeds, one Asian wild boar population, as well as three modern commercial foreign breeds. We identified 63 CNVRs in total, which covered 9.98 Mb of polymorphic sequence and corresponded to 0.36% of the genome sequence. The length of these CNVRs ranged from 3.20 to 827.21 kb, with an average of 158.37 kb and a median of 97.85 kb. Functional annotation revealed these identified CNVR have important molecular function, and may play an important role in exploring the genetic basis of phenotypic variability and disease susceptibility among pigs. Additionally, to confirm these potential CNVRs, we performed qPCR for 12 randomly selected CNVRs and 8 of them (66.67%) were confirmed successfully. CNVs detected in diverse populations herein are essential complementary to the CNV map in the pig genome, which provide an important resource for studies of genomic variation and the association between various economically important traits and CNVs.

Enhancing genome-wide copy number variation identification by high density array CGH using diverse resources of pig breeds.

Copy number variations (CNVs) are important forms of genomic variation, and have attracted extensive attentions in humans as well as domestic animals. In the study, using a custom-designed 2.1 M array comparative genomic hybridization (aCGH), genome-wide CNVs were identified among 12 individuals from diverse pig breeds, including one Asian wild population, six Chinese indigenous breeds and two modern commercial breeds (Yorkshire and Landrace), with one individual of the other modern commercial breed, Duroc, as the reference. A total of 1,344 CNV regions (CNVRs) were identified, covering 47.79 Mb (∼1.70%) of the pig genome. The length of these CNVRs ranged from 3.37 Kb to 1,319.0 Kb with a mean of 35.56 Kb and a median of 11.11 Kb. Compared with similar studies reported, most of the CNVRs (74.18%) were firstly identified in present study. In order to confirm these CNVRs, 21 CNVRs were randomly chosen to be validated by quantitative real time PCR (qPCR) and a high rate (85.71%) of confirmation was obtained. Functional annotation of CNVRs suggested that the identified CNVRs have important function, and may play an important role in phenotypic and production traits difference among various breeds. Our results are essential complementary to the CNV map in the pig genome, which will provide abundant genetic markers to investigate association studies between various phenotypes and CNVs in pigs.

Global copy number analyses by next generation sequencing provide insight into pig genome variation

BackgroundCopy number variations (CNVs) confer significant effects on genetic innovation and phenotypic variation. Previous CNV studies in swine seldom focused on in-depth characterization of global CNVs.ResultsUsing whole-genome assembly comparison (WGAC) and whole-genome shotgun sequence detection (WSSD) approaches by next generation sequencing (NGS), we probed formation signatures of both segmental duplications (SDs) and individualized CNVs in an integrated fashion, building the finest resolution CNV and SD maps of pigs so far. We obtained copy number estimates of all protein-coding genes with copy number variation carried by individuals, and further confirmed two genes with high copy numbers in Meishan pigs through an enlarged population. We determined genome-wide CNV hotspots, which were significantly enriched in SD regions, suggesting evolution of CNV hotspots may be affected by ancestral SDs. Through systematically enrichment analyses based on simulations and bioinformatics analyses, we revealed CNV-related genes undergo a different selective constraint from those CNV-unrelated regions, and CNVs may be associated with or affect pig health and production performance under recent selection.ConclusionsOur studies lay out one way for characterization of CNVs in the pig genome, provide insight into the pig genome variation and prompt CNV mechanisms studies when using pigs as biomedical models for human diseases.

Structural variant detection by large-scale sequencing reveals new evolutionary evidence on breed divergence between Chinese and European pigs

In this study, we performed a genome-wide SV detection among the genomes of thirteen pigs from diverse Chinese and European originated breeds by next genetation sequencing, and constrcuted a single-nucleotide resolution map involving 56,930 putative SVs. We firstly identified a SV hotspot spanning 35 Mb region on the X chromosome specifically in the genomes of Chinese originated individuals. Further scrutinizing this region by large-scale sequencing data of extra 111 individuals, we obtained the confirmatory evidence on our initial finding. Moreover, thirty five SV-related genes within the hotspot region, being of importance for reproduction ability, rendered significant different evolution rates between Chinese and European originated breeds. The SV hotspot identified herein offers a novel evidence for assessing phylogenetic relationships, as well as likely explains the genetic difference of corresponding phenotypes and features, among Chinese and European pig breeds. Furthermore, we employed various SVs to infer genetic structure of individuls surveyed. We found SVs can clearly detect the difference of genetic background among individuals. This clues us that genome-wide SVs can capture majority of geneic variation and be applied into cladistic analyses. Characterizing whole genome SVs demonstrated that SVs are significantly enriched/depleted with various genomic features.

Improved Detection and Characterization of Copy Number Variations Among Diverse Pig Breeds by Array CGH.

As a major component of genomic variation, copy number variations (CNVs) are considered as promising markers for some phenotypic and economically important traits in domestic animals. Using a custom-designed 1M array CGH (aCGH), we performed CNV discovery in 12 pig samples from one Asian wild boar population, six Chinese indigenous breeds, and two European commercial breeds. In total, we identified 758 CNV regions (CNVRs), covering 47.43 Mb of the pig genome sequence. Of the total porcine genes, 1295 genes were completely or partially overlapped with the identified CNVRs, which enriched in the terms related to sensory perception of the environment, neurodevelopmental processes, response to external stimuli, and immunity. Further probing the potential functions of these genes, we also found a suite of genes related important traits, which make them a promising resource for exploring the genetic basis of phenotype differences among diverse pig breeds. Compared with previous relevant studies, the current study highlights that different platforms can complement each other, and the combined implementation of different platforms is beneficial to achieve the most comprehensive CNV calls. CNVs detected in diverse populations herein are essentially complementary to the CNV map in the pig genome, which would be helpful for understanding the pig genome variants and investigating the associations between various phenotypes and CNVs.

Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait.

In prediction of genomic values, the single-step method has been demonstrated to outperform multi-step methods. In statistical analyses of longitudinal traits, the random regression test-day model (RR-TDM) has clear advantages over other models. Our goal in this study was to evaluate the performance of a model that integrates both single-step and RR-TDM prediction methods, called the single-step random regression test-day model (SS RR-TDM), in comparison with the pedigree-based RR-TDM and genomic best linear unbiased prediction (GBLUP) model. We performed extensive simulations to exploit the potential advantages of SS RR-TDM over the other two models under various scenarios with different levels of heritability, number of quantitative trait loci, as well as selection scheme. SS RR-TDM was found to achieve the highest accuracy and unbiasedness under all scenarios, exhibiting robust prediction ability in longitudinal trait analyses. Moreover, SS RR-TDM showed better persistency of accuracy over generations than the GBLUP model. In addition, we also found that the SS RR-TDM had advantages over RR-TDM and GBLUP in terms of its being a real data set of humans contributed by the Genetic Analysis Workshop 18. The findings of our study demonstrated the feasibility and advantages of SS RR-TDM, thus enhancing the strategies for genomic prediction of longitudinal traits in the future.

Profiling long noncoding RNA of multi-tissue transcriptome enhances porcine noncoding genome annotation

AIM To construct a comprehensive pig noncoding transcriptome and further enhance porcine noncoding genome annotation. MATERIALS & METHODS We performed a tissue-based long noncoding RNA (lncRNA) profiling via exploiting 32,212 nonredundant lncRNA isoforms corresponding to 18,676 lncRNA loci across 34 normal pig tissues using high-throughput sequencing. Furthermore, the potential relationship between our identified lncRNAs and known protein-coding genes were globally assessed via a comprehensive computation-based strategy, developing a genome-wide lncRNA-targeted genome draft for further functional studies on noncoding genes. RESULTS & CONCLUSION Among these lncRNAs, ubiquitously expressed lncRNA appeared at a higher level compared with tissue-specific one. Findings herein provide insight into comprehensive knowledge of porcine noncoding RNAs and further enhance pig noncoding annotation. For ease of accessing the information of the identified lncRNAs, we deposited those with high confidence in the publicly available NONCODE database, providing a valuable resource for facilitating pig noncoding genomic studies.

Performance Gains in Genome-Wide Association Studies for Longitudinal Traits via Modeling Time-varied effects

Complex traits with multiple phenotypic values changing over time are called longitudinal traits. In traditional genome-wide association studies (GWAS) for longitudinal traits, a combined/averaged estimated breeding value (EBV) or deregressed proof (DRP) instead of multiple phenotypic measurements per se for each individual was frequently treated as response variable in statistical model. This can result in power losses or even inflate false positive rates (FPRs) in the detection due to failure of exploring time-dependent relationship among measurements. Aiming at overcoming such limitation, we developed two random regression-based models for functional GWAS on longitudinal traits, which could directly use original time-dependent records as response variable and fit the time-varied Quantitative Trait Nucleotide (QTN) effect. Simulation studies showed that our methods could control the FPRs and increase statistical powers in detecting QTN in comparison with traditional methods where EBVs, DRPs or estimated residuals were considered as response variables. Besides, our proposed models also achieved reliable powers in gene detection when implementing into two real datasets, a Chinese Holstein Cattle data and the Genetic Analysis Workshop 18 data. Our study herein offers an optimal way to enhance the power of gene detection and further understand genetic control of developmental processes for complex longitudinal traits.

Resequencing diverse Chinese indigenous breeds to enrich the map of genomic variations in swine

To enrich the map of genomic variations in swine, we randomly sequenced 13 domestic and wild individuals from China and Europe. We detected approximately 28.1 million single nucleotide variants (SNVs) and 3.6 million short insertions and deletions (INDELs), of which 2,530,248 SNVs and 3,456,626 INDELs were firstly identified compared with dbSNP 143. Moreover, 208,687 SNVs and 24,161 INDELs were uniquely observed in Chinese pigs, potentially accounting for phenotypic differences between Chinese and European pigs. Furthermore, significantly high correlation between SNV and INDEL was witnessed, which indicated that these two distinct variants may share similar etiologies. We also predicted loss of function genes and found that they were under weaker evolutionary constraints. This study gives interesting insights into the genomic features of the Chinese pig breeds. These data would be useful in the establishment of high-density SNP map and would lay a foundation for facilitating pig functional genomics study.

Mining unknown porcine protein isoforms by tissue-based map of proteome enhances the pig genome annotation

A lack of the complete pig proteome has left a gap in our knowledge of the pig genome and has restricted the feasibility of using pigs as a biomedical model. We developed the tissue-based proteome maps using 34 major normal pig tissues. A total of 7,319 unknown protein isoforms were identified and systematically characterized, including 3,703 novel protein isoforms, 669 protein isoforms from 460 genes symbolized beginning with LOC, and 2,947 protein isoforms without clear NCBI annotation in current pig reference genome. These newly identified protein isoforms were functionally annotated through profiling the pig transcriptome with high-throughput RNA sequencing (RNA-seq) of the same pig tissues, further improving the genome annotation of corresponding protein coding genes. Combining the well-annotated genes that having parallel expression pattern and subcellular witness, we predicted the tissue related subcellular components and potential function for these unknown proteins. Finally, we mined 3,656 orthologous genes for 49.95% of unknown protein isoforms across multiple species, referring to 65 KEGG pathways and 25 disease signaling pathways. These findings provided valuable insights and a rich resource for enhancing studies of pig genomics and biology as well as biomedical model application to human medicine.