Max F. Rothschild

Design of a high density SNP genotyping assay in the pig using SNPs identified and characterized by next generation sequencing technology

Background The dissection of complex traits of economic importance to the pig industry requires the availability of a significant number of genetic markers, such as single nucleotide polymorphisms (SNPs). This study was conducted to discover several hundreds of thousands of porcine SNPs using next generation sequencing technologies and use these SNPs, as well as others from different public sources, to design a high-density SNP genotyping assay. Methodology/Principal Findings A total of 19 reduced representation libraries derived from four swine breeds (Duroc, Landrace, Large White, Pietrain) and a Wild Boar population and three restriction enzymes (AluI, HaeIII and MspI) were sequenced using Illuminas Genome Analyzer (GA). The SNP discovery effort resulted in the de novo identification of over 372K SNPs. More than 549K SNPs were used to design the Illumina Porcine 60K+SNP iSelect Beadchip, now commercially available as the PorcineSNP60. A total of 64,232 SNPs were included on the Beadchip. Results from genotyping the 158 individuals used for sequencing showed a high overall SNP call rate (97.5%). Of the 62,621 loci that could be reliably scored, 58,994 were polymorphic yielding a SNP conversion success rate of 94%. The average minor allele frequency (MAF) for all scorable SNPs was 0.274. Conclusions/Significance Overall, the results of this study indicate the utility of using next generation sequencing technologies to identify large numbers of reliable SNPs. In addition, the validation of the PorcineSNP60 Beadchip demonstrated that the assay is an excellent tool that will likely be used in a variety of future studies in pigs.

THE PIGMAP CONSORTIUM LINKAGE MAP OF THE PIG (SUS SCROFA).

A linkage map of the porcine genome has been developed by segregation analysis of 239 genetic markers. Eighty-one of these markers correspond to known genes. Linkage groups have been assigned to all 18 autosomes plus the X Chromosome (Chr). As 69 of the markers on the linkage map have also been mapped physically (by others), there is significant integration of linkage and physical map data. Six informative markers failed to show linkage to these maps. As in other species, the genetic map of the heterogametic sex (male) was significantly shorter (∼16.5 Morgans) than the genetic map of the homogametic sex (female) (∼21.5 Morgans). The sex-averaged genetic map of the pig was estimated to be ∼18 Morgans in length. Mapping information for 61 Type I loci (genes) enhances the contribution of the pig gene map to comparative gene mapping. Because the linkage map incorporates both highly polymorphic Type II loci, predominantly microsatellites, and Type I loci, it will be useful both for large experiments to map quantitative trait loci and for the subsequent isolation of trait genes following a comparative and candidate gene approach.

A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. II. Meat and muscle composition

Genome scans can be employed to identify chromosomal regions and eventually genes (quantitative trait loci or QTL) that control quantitative traits of economic importance. A three-generation resource family was developed by using two Berkshire grand sires and nine Yorkshire grand dams to detect QTL for growth and body composition traits in pigs. A total of 525 F2 progeny were produced from 65 matings. All F2 animals were phenotyped for birth weight, 16-day weight, growth rate, carcass weight, carcass length, back fat thickness, and loin eye area. Animals were genotyped for 125 microsatellite markers covering the genome. Least squares regression interval mapping was used for QTL detection. All carcass traits were adjusted for live weight at slaughter. A total of 16 significant QTL, as determined by a permutation test, were detected at the 5% chromosome-wise level for growth traits on Chromosomes (Chrs) 1, 2, 3, 4, 6, 7, 8, 9, 11, 13, 14, and X, of which two were significant at the 5% genome-wise level and two at the 1% genome-wise level (on Chrs 1, 2, and 4). For composition traits, 20 QTL were significant at the 5% chromosome-wise level (on Chrs 1, 4, 5, 6, 7, 12, 13, 14, 18), of which one was significant at the 5% genome-wise level and three were significant at the 1% genome-wise level (on Chrs 1, 5, and 7). For several QTL the favorable allele originated from the breed with the lower trait mean.

A missense variant of the porcine melanocortin-4 receptor (MC4R) gene is associated with fatness, growth, and feed intake traits.

Abstract. Our knowledge of the genetic factors affecting obesity is increasing, but information about the individual gene effects remains limited in humans as well as in animal models. The melanocortin-4 receptor gene (MC4R) has been implicated in the regulation of feeding behavior and body weight in humans and mice. We have studied MC4R as a candidate gene for the control of economically important growth and performance traits in the pig. A missense mutation was identified in a region highly conserved among melanocortin receptor (MCR) genes. To determine whether there was an association of this MC4R polymorphism with phenotypic variation, we tested the mutation in a large number of individual animals from several different pig lines. Analyses of growth and performance test records showed significant associations of MC4R genotypes with backfat and growth rate in a number of lines as well as feed intake overall. It is probable that the variant amino acid residue of the MC4R mutation (or a closely linked mutation) causes a significant change of the MC4R function. These results support the functional significance of a pig MC4R missense mutation and suggest that comparative genomics based on model species may be equally important for application to farm animals as they are for human medicine.

The genetics of the pig.

Systematics and evolution of the pig genetic aspects of domestication , common breeds and their origin genetics of colour variation genetics of morphological traits and inherited disorders biochemical genetics molecular genetics immunogenetics cytogenetics and physical chromosome maps genetic linkage maps genetics of behaviour biology and genetics of reproduction transgenics and modern reproductive technologies developmental genetics genetic resources and the global programme for their management genetics of performance traits genetics of meat and carcass traits genetic improvement of the pig standard nomenclature and pig genetic glossary. Appendices: the comparative linkage maps of pig full list of identified loci in the pig.

Controlling the proportion of false positives in multiple dependent tests.

Genome scan mapping experiments involve multiple tests of significance. Thus, controlling the error rate in such experiments is important. Simple extension of classical concepts results in attempts to control the genomewise error rate (GWER), i.e., the probability of even a single false positive among all tests. This results in very stringent comparisonwise error rates (CWER) and, consequently, low experimental power. We here present an approach based on controlling the proportion of false positives (PFP) among all positive test results. The CWER needed to attain a desired PFP level does not depend on the correlation among the tests or on the number of tests as in other approaches. To estimate the PFP it is necessary to estimate the proportion of true null hypotheses. Here we show how this can be estimated directly from experimental results. The PFP approach is similar to the false discovery rate (FDR) and positive false discovery rate (pFDR) approaches. For a fixed CWER, we have estimated PFP, FDR, pFDR, and GWER through simulation under a variety of models to illustrate practical and philosophical similarities and differences among the methods.

Genome-Wide Association Study Identifies Loci for Body Composition and Structural Soundness Traits in Pigs

Background The recent completion of the swine genome sequencing project and development of a high density porcine SNP array has made genome-wide association (GWA) studies feasible in pigs. Methodology/Principal Findings Using Illuminas PorcineSNP60 BeadChip, we performed a pilot GWA study in 820 commercial female pigs phenotyped for backfat, loin muscle area, body conformation in addition to feet and leg (FL) structural soundness traits. A total of 51,385 SNPs were jointly fitted using Bayesian techniques as random effects in a mixture model that assumed a known large proportion (99.5%) of SNPs had zero effect. SNP annotations were implemented through the Sus scrofa Build 9 available from pig Ensembl. We discovered a number of candidate chromosomal regions, and some of them corresponded to QTL regions previously reported. We not only have identified some well-known candidate genes for the traits of interest, such as MC4R (for backfat) and IGF2 (for loin muscle area), but also obtained novel promising genes, including CHCHD3 (for backfat), BMP2 (for loin muscle area, body size and several FL structure traits), and some HOXA family genes (for overall leg action). The candidate regions responsible for body conformation and FL structure soundness did not overlap greatly which implied that these traits were controlled by different genes. Functional clustering analyses classified the genes into categories related to bone and cartilage development, muscle growth and development or the insulin pathway suggesting the traits are regulated by common pathways or gene networks that exert roles at different spatial and temporal stages. Conclusions/Significance This study is one of the earliest GWA reports on important quantitative traits in pigs, and the findings will contribute to the further biological function analysis of the identified candidate genes and potential utilization of them in marker assisted selection.

A QTL resource and comparison tool for pigs: PigQTLDB

During the past decade, efforts to map quantitative trait loci (QTL) in pigs have resulted in hundreds of QTL being reported for growth, meat quality, reproduction, disease resistance, and other traits. It is a challenge to locate, interpret, and compare QTL results from different studies. We have developed a pig QTL database (PigQTLdb) that integrates available pig QTL data in the public domain, thus, facilitating the use of this QTL data in future studies. We also developed a pig trait classification system to standardize names of traits and to simplify organization and searching of the trait data. These steps made it possible to compare primary data from diverse sources and methods. We used existing pig map databases and other publicly available data resources (such as PubMed) to avoid redundant developmental work. The PigQTLdb was also designed to include data representing major genes and markers associated with a large effect on economically important traits. To date, over 790 QTL from 73 publications have been curated into the database. Those QTL cover more than 300 different traits. The data have been submitted to the Entrez Gene and the Map Viewer resources at NCBI, where the information about markers was matched to marker records in NCBI’s UniSTS database. Having these data in a public resource like NCBI allows regularly updated automatic matching of markers to public sequence data by e-PCR. The submitted data, and the results of these calculations, are retrievable from NCBI via Entrez Gene, Map Viewer, and UniSTS. Efforts were undertaken to improve the integrated functional genomics resources for pigs.

Pig genome sequence - analysis and publication strategy

BackgroundThe pig genome is being sequenced and characterised under the auspices of the Swine Genome Sequencing Consortium. The sequencing strategy followed a hybrid approach combining hierarchical shotgun sequencing of BAC clones and whole genome shotgun sequencing.ResultsAssemblies of the BAC clone derived genome sequence have been annotated using the Pre-Ensembl and Ensembl automated pipelines and made accessible through the Pre-Ensembl/Ensembl browsers. The current annotated genome assembly (Sscrofa9) was released with Ensembl 56 in September 2009. A revised assembly (Sscrofa10) is under construction and will incorporate whole genome shotgun sequence (WGS) data providing > 30× genome coverage. The WGS sequence, most of which comprise short Illumina/Solexa reads, were generated from DNA from the same single Duroc sow as the source of the BAC library from which clones were preferentially selected for sequencing. In accordance with the Bermuda and Fort Lauderdale agreements and the more recent Toronto Statement the data have been released into public sequence repositories (Genbank/EMBL, NCBI/Ensembl trace repositories) in a timely manner and in advance of publication.ConclusionsIn this marker paper, the Swine Genome Sequencing Consortium (SGSC) sets outs its plans for analysis of the pig genome sequence, for the application and publication of the results.

MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans

BackgroundMicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them.ResultsWe have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions.ConclusionThe MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at http://www.bioinformatics.org/mirfinder/.