Deciphering heterogeneity in pig genome assembly Sscrofa9 by isochore and isochore-like region analyses

Genome sequencing and analysis of Mangalica, a fatty local pig of Hungary

BACKGROUND

Mangalicas are fatty type local/rare pig breeds with an increasing presence in the niche pork market in Hungary and in other countries. To explore their genetic resources, we have analysed data from next-generation sequencing of an individual male from each of three Mangalica breeds along with a local male Duroc pig. Structural variations, such as SNPs, INDELs and CNVs, were identified and particular genes with SNP variations were analysed with special emphasis on functions related to fat metabolism in pigs.

RESULTS

More than 60 Gb of sequence data were generated for each of the sequenced individuals, resulting in 11× to 19× autosomal median coverage. After stringent filtering, around six million SNPs, of which approximately 10% are novel compared to the dbSNP138 database, were identified in each animal. Several hundred thousands of INDELs and about 1,000 CNV gains were also identified. The functional annotation of genes with exonic, non-synonymous SNPs, which are common in all three Mangalicas but are absent in either the reference genome or the sequenced Duroc of this study, highlighted 52 genes in lipid metabolism processes. Further analysis revealed that 41 of these genes are associated with lipid metabolic or regulatory pathways, 49 are in fat-metabolism and fatness-phenotype QTLs and, with the exception of ACACA, ANKRD23, GM2A, KIT, MOGAT2, MTTP, FASN, SGMS1, SLC27A6 and RETSAT, have not previously been associated with fat-related phenotypes.

CONCLUSIONS

Genome analysis of Mangalica breeds revealed that local/rare breeds could be a rich source of sequence variations not present in cosmopolitan/industrial breeds. The identified Mangalica variations may, therefore, be a very useful resource for future studies of agronomically important traits in pigs.

A Brief Review: The Z-curve Theory and its Application in Genome Analysis

In theoretical physics, there exist two basic mathematical approaches, algebraic and geometrical methods, which, in most cases, are complementary. In the area of genome sequence analysis, however, algebraic approaches have been widely used, while geometrical approaches have been less explored for a long time. The Z-curve theory is a geometrical approach to genome analysis. The Z-curve is a three-dimensional curve that represents a given DNA sequence in the sense that each can be uniquely reconstructed given the other. The Z-curve, therefore, contains all the information that the corresponding DNA sequence carries. The analysis of a DNA sequence can then be performed through studying the corresponding Z-curve. The Z-curve method has found applications in a wide range of areas in the past two decades, including the identifications of protein-coding genes, replication origins, horizontally-transferred genomic islands, promoters, translational start sides and isochores, as well as studies on phylogenetics, genome visualization and comparative genomics. Here, we review the progress of Z-curve studies from aspects of both theory and applications in genome analysis.

Large homogeneous genome regions (isochores) in soybean [glycine max (L.) merr]

The landscape of plant genomes, while slowly being characterized and defined, is still composed primarily of regions of undefined function. Many eukaryotic genomes contain isochore regions, mosaics of homogeneous GC content that can abruptly change from one neighboring isochore to the next. Isochores are broken into families that are characterized by their GC levels. We identified 4,339 compositionally distinct domains and 331 of these were identified as long homogeneous genome regions (LHGRs). We assigned these to four families based on finite mixture models of GC content. We then characterized each family with respect to exon length, gene content, and transposable elements. The LHGR pattern of soybeans is unique in that while the majority of the genes within LHGRs are found within a single LHGR family with a narrow GC range (Family B), that family is not the highest in GC content as seen in vertebrates and invertebrates. Instead Family B has a mean GC content of 35%. The range of GC content for all LHGRs is 16–59% GC which is a larger range than what is typical of vertebrates. This is the first study in which LHGRs have been identified in soybeans and the functions of the genes within the LHGRs have been analyzed.