Yumei Yang

Genotyping by Genome Reducing and Sequencing for Outbred Animals

Next-generation sequencing (NGS) approaches are widely used in genome-wide genetic marker discovery and genotyping. However, current NGS approaches are not easy to apply to general outbred populations (human and some major farm animals) for SNP identification because of the high level of heterogeneity and phase ambiguity in the haplotype. Here, we reported a new method for SNP genotyping, called genotyping by genome reducing and sequencing (GGRS) to genotype outbred species. Through an improved procedure for library preparation and a marker discovery and genotyping pipeline, the GGRS approach can genotype outbred species cost-effectively and high-reproducibly. We also evaluated the efficiency and accuracy of our approach for high-density SNP discovery and genotyping in a large genome pig species (2.8 Gb), for which more than 70,000 single nucleotide polymorphisms (SNPs) can be identified for an expenditure of only

A New Genotype Imputation Method with Tolerance to High Missing Rate and Rare Variants

80 (USD)/sample.

Genetic diversity and population structure of six Chinese indigenous pig breeds in the Taihu Lake region revealed by sequencing data.

We report a novel algorithm, iBLUP, to impute missing genotypes by simultaneously and comprehensively using identity by descent and linkage disequilibrium information. The simulation studies showed that the algorithm exhibited drastically tolerance to high missing rate, especially for rare variants than other common imputation methods, e.g. BEAGLE and fastPHASE. At a missing rate of 70%, the accuracy of BEAGLE and fastPHASE dropped to 0.82 and 0.74 respectively while iBLUP retained an accuracy of 0.95. For minor allele, the accuracy of BEAGLE and fastPHASE decreased to −0.1 and 0.03, while iBLUP still had an accuracy of 0.61.We implemented the algorithm in a publicly available software package also named iBLUP. The application of iBLUP for processing real sequencing data in an outbred pig population was demonstrated.

A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARα Knockout by Gene Set Enrichment Analysis

Summary The Chinese indigenous pig breeds in the Taihu Lake region are the most prolific pig breeds in the world. In this study, we investigated the genetic diversity and population structure of six breeds, including Meishan, Erhualian, Mi, Fengjing, Shawutou and Jiaxing Black, in this region using whole‐genome SNP data. A high SNP with proportions of polymorphic markers ranging from 0.925 to 0.995 was exhibited by the Chinese indigenous pigs in the Taihu Lake region. The allelic richness and expected heterozygosity also were calculated and indicated that the genetic diversity of the Meishan breed was the greatest, whereas that of the Fengjing breed was the lowest. The genetic differentiation, as indicated by the fixation index, exhibited an overall mean of 0.149. Both neighbor‐joining tree and principal components analysis were able to distinguish the breeds from each other, but structure analysis indicated that the Mi and Erhualian breeds exhibited similar major signals of admixture. With this genome‐wide comprehensive survey of the genetic diversity and population structure of the indigenous Chinese pigs in the Taihu Lake region, we confirmed the rationality of the current breed classification of the pigs in this region.

A genome-wide scan for selection signatures in Yorkshire and Landrace pigs based on sequencing data.

Gene expression profiling of PPARα has been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPARα activation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPARα related respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPARα activation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pathways. Several pathways were cancer-related such as pancreatic cancer and small cell lung cancer, which indicated that PPARα may have an important role in prevention of cancer development. 12 PPARα dependent pathways and 4 PPARα independent pathways were identified highly common in both liver and intestine of mice. Most of them were metabolism related, such as fatty acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPARα regulation but gluconeogenesis and propanoate metabolism independent of PPARα regulation. Keratan sulfate biosynthesis, the pathway of regulation of actin cytoskeleton, the pathways associated with prostate cancer and small cell lung cancer were not identified as hepatic PPARα independent but as WY14643 dependent ones in intestinal study. We also provided some novel hepatic tissue-specific marker genes.

Identification of pleiotropic genes and gene sets underlying growth and immunity traits: a case study on Meishan pigs

Pigs have experienced dramatic selection due to domestication, which has led to many different phenotypes when compared to their wild counterparts, especially in the last several decades. Currently, genome-wide scans in both cattle and humans showing positive selection footprints have been investigated. However, few studies have focused on porcine selection footprints, particularly on a genome-wide scale. Surveying for selection footprints across porcine genomes can be quite valuable for revealing the genetic mechanisms of phenotypic diversity. Here, we employed a medium sequencing depth (5–20x/site per individual, on average) approach called genotyping by genome reducing and sequencing (GGRS) to detect genome-wide selection signatures of two domestic pig breeds (Yorkshire and Landrace) that have been under intensive selection for traits of muscle development, growth and behavior. The relative extended haplotype homozygosity test, which identifies selection signatures by measuring the characteristics of haplotypes’ frequency distribution within a single population, was also applied to identify potential positively selected regions. As a result, signatures of positive selection were found in each breed. However, most selection signatures were population specific and related to genomic regions containing genes for biological categories including brain development, metabolism, growth and olfaction. Furthermore, the result of the gene set enrichment analysis indicated that selected regions of the two breeds presented a different over-representation of genes in the Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes pathways. Our results revealed a genome-wide map of selection footprints in pigs and may help us better understand the mechanisms of selection in pig breeding.

SNPpath: characterizing cattle SNPs by enriched pathway terms.

Both growth and immune capacity are important traits in animal breeding. The animal quantitative trait loci (QTL) database is a valuable resource and can be used for interpreting the genetic mechanisms that underlie growth and immune traits. However, QTL intervals often involve too many candidate genes to find the true causal genes. Therefore, the aim of this study was to provide an effective annotation pipeline that can make full use of the information of Gene Ontology terms annotation, linkage gene blocks and pathways to further identify pleiotropic genes and gene sets in the overlapping intervals of growth-related and immunity-related QTLs. In total, 55 non-redundant QTL overlapping intervals were identified, 1893 growth-related genes and 713 immunity-related genes were further classified into overlapping intervals and 405 pleiotropic genes shared by the two gene sets were determined. In addition, 19 pleiotropic gene linkage blocks and 67 pathways related to immunity and growth traits were discovered. A total of 343 growth-related genes and 144 immunity-related genes involved in pleiotropic pathways were also identified, respectively. We also sequenced and genotyped 284 individuals from Chinese Meishan pigs and European pigs and mapped the single nucleotide polymorphisms (SNPs) to the pleiotropic genes and gene sets that we identified. A total of 971 high-confidence SNPs were mapped to the pleiotropic genes and gene sets that we identified, and among them 743 SNPs were statistically significant in allele frequency between Meishan and European pigs. This study explores the relationship between growth and immunity traits from the view of QTL overlapping intervals and can be generalized to explore the relationships between other traits.

The prediction of the porcine pre-microRNAs in genome-wide based on support vector machine (SVM) and homology searching

High-density single nucleotide polymorphism (SNP) microarrays have made large-scale genome-wide association studies (GWAS) and genomic selection (GS) feasible. Valuable insight into the genetic basis underlying complex polygenic traits will likely be gained by considering functionally related sets of genes simultaneously. SNPpath, a suite of computer-generated imagery-based web servers has been developed to automatically annotate and characterize cattle SNPs by enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway terms. The SNPpath allows users to navigate and analysis large SNP sets and is the only web server currently providing pathway annotations of cattle SNPs in National Center for Biotechnology Informations dbSNP database and three commercial platforms. Hence, we describe SNPpath and provide details of the query options, as well as biological examples of use. The SNPpath may be favorable for the analysis of combining SNP association analysis with pathway-driven gene set enrichment analysis and is freely available at http://klab.sjtu.edu.cn/SNPpath.

Uncovering the transcriptional circuitry in skeletal muscle regeneration

BackgroundMicroRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene expression by targeting mRNAs for translation repression or mRNA degradation. Although many miRNAs have been discovered and studied in human and mouse, few studies focused on porcine miRNAs, especially in genome wide.ResultsHere, we adopted computational approaches including support vector machine (SVM) and homology searching to make a global scanning on the pre-miRNAs of pigs. In our study, we built the SVM-based porcine pre-miRNAs classifier with a sensitivity of 100%, a specificity of 91.2% and a total prediction accuracy of 95.6%, respectively. Moreover, 2204 novel porcine pre-miRNA candidates were found by using SVM-based pre-miRNAs classifier. Besides, 116 porcine pre-miRNA candidates were detected by homology searching.ConclusionsWe identified the porcine pre-miRNA in genome-wide through computational approaches by utilizing the data sets of pigs and set up the porcine pre-miRNAs library which may provide us a global scanning on the pre-miRNAs of pigs in genome level and would benefit subsequent experimental research on porcine miRNA functional and expression analysis.