C. W. Ernst

Estimation of linkage disequilibrium in four US pig breeds

BackgroundThe success of marker assisted selection depends on the amount of linkage disequilibrium (LD) across the genome. To implement marker assisted selection in the swine breeding industry, information about extent and degree of LD is essential. The objective of this study is to estimate LD in four US breeds of pigs (Duroc, Hampshire, Landrace, and Yorkshire) and subsequently calculate persistence of phase among them using a 60 k SNP panel. In addition, we report LD when using only a fraction of the available markers, to estimate persistence of LD over distance.ResultsAverage r2 between adjacent SNP across all chromosomes was 0.36 for Landrace, 0.39 for Yorkshire, 0.44 for Hampshire and 0.46 for Duroc. For markers 1 Mb apart, r2 ranged from 0.15 for Landrace to 0.20 for Hampshire. Reducing the marker panel to 10% of its original density, average r2 ranged between 0.20 for Landrace to 0.25 for Duroc. We also estimated persistence of phase as a measure of prediction reliability of markers in one breed by those in another and found that markers less than 10 kb apart could be predicted with a maximal accuracy of 0.92 for Landrace with Yorkshire.ConclusionsOur estimates of LD, although in good agreement with previous reports, are more comprehensive and based on a larger panel of markers. Our estimates also confirmed earlier findings reporting higher LD in pigs than in American Holstein cattle, especially at increasing marker distances (> 1 Mb). High average LD (r2 > 0.4) between adjacent SNP found in this study is an important precursor for the implementation of marker assisted selection within a livestock species.Results of this study are relevant to the US purebred pig industry and critical for the design of programs of whole genome marker assisted evaluation and selection. In addition, results indicate that a more cost efficient implementation of marker assisted selection using low density panels with genotype imputation, would be feasible for these breeds.

Molecular advances in QTL discovery and application in pig breeding

Thousands of quantitative trait loci (QTL) have been identified for a wide range of economically important phenotypes in pigs. Recently, QTL analyses have begun to use high-density single nucleotide polymorphism (SNP) panels and applications have extended beyond experimental intercrosses to outbred populations by exploiting long-range linkage disequilibrium that results in higher resolution QTL mapping. Relevant phenotypes generally fall under categories of growth and body composition, carcass and meat quality, reproduction, and disease resistance. A few expression QTL (eQTL) studies have been performed that integrate transcriptional profiles with genotype data by considering expression levels as response variables in QTL analyses for identifying genes controlling important trait phenotypes. Rapidly evolving genomics technologies, including RNAseq, provide tremendous opportunities for QTL and eQTL discovery. In this review, we discuss recent progress in pig QTL and eQTL discovery, including approaches for allele-specific expression, and implications of these discoveries for pig breeding and genetics.

Generation of expressed sequence tags from a normalized porcine skeletal muscle cDNA library.

ABSTRACT Recent developments in microarray technologies permit scientists to analyze expression of thousands of genes simultaneously in diverse biological systems. In an effort to provide integrated resources for application of microarray technologies to studies of skeletal muscle growth and development in swine, we have constructed a normalized cDNA library from porcine skeletal muscle. The effectiveness of normalization was evaluated by DNA sequencing of clones randomly picked from the library before and after normalization, and also by Southern blot hybridization using probes representing abundant transcripts. Our data suggests that the normalization procedure successfully reduced the highly abundant cDNA species in the normalized library. To date, a total of 782 EST (expressed sequence tag) sequences have been generated from this normalized library (687 ESTs) and the original library (95 ESTs). The sequence information of these ESTs plus their BLAST results has been made available through a web accessible database (http://nbfgc.msu.edu). Cluster analysis of the data indicates that a total of 742 unique sequences are present in this collection. BLASTN search of the 742 EST sequences against the public database (dbEST) revealed that 139 had no significant matches (E-value > 10−15) to porcine ESTs already entered in the database, suggesting the possibility of their specific expression in porcine skeletal muscle. Generation of non-redundant ESTs from this library will allow us to construct cDNA microarrays for identification of gene expression changes that regulate muscle growth and affect meat quality in swine.

Genotype imputation accuracy in a F2 pig population using high density and low density SNP panels

BackgroundF2 resource populations have been used extensively to map QTL segregating between pig breeds. A limitation associated with the use of these resource populations for fine mapping of QTL is the reduced number of founding individuals and recombinations of founding haplotypes occurring in the population. These limitations, however, become advantageous when attempting to impute unobserved genotypes using within family segregation information. A trade-off would be to re-type F2 populations using high density SNP panels for founding individuals and low density panels (tagSNP) in F2 individuals followed by imputation. Subsequently a combined meta-analysis of several populations would provide adequate power and resolution for QTL mapping, and could be achieved at relatively low cost. Such a strategy allows the wealth of phenotypic information that has previously been obtained on experimental resource populations to be further mined for QTL identification. In this study we used experimental and simulated high density genotypes (HD-60K) from an F2 cross to estimate imputation accuracy under several genotyping scenarios.ResultsSelection of tagSNP using physical distance or linkage disequilibrium information produced similar imputation accuracies. In particular, tagSNP sets averaging 1 SNP every 2.1 Mb (1,200 SNP genome-wide) yielded imputation accuracies (IA) close to 0.97. If instead of using custom panels, the commercially available 9K chip is used in the F2, IA reaches 0.99. In order to attain such high imputation accuracy the F0 and F1 generations should be genotyped at high density. Alternatively, when only the F0 is genotyped at HD, while F1 and F2 are genotyped with a 9K panel, IA drops to 0.90.ConclusionsCombining 60K and 9K panels with imputation in F2 populations is an appealing strategy to re-genotype existing populations at a fraction of the cost.

Genome-wide linkage analysis of global gene expression in loin muscle tissue identifies candidate genes in pigs.

Background Nearly 6,000 QTL have been reported for 588 different traits in pigs, more than in any other livestock species. However, this effort has translated into only a few confirmed causative variants. A powerful strategy for revealing candidate genes involves expression QTL (eQTL) mapping, where the mRNA abundance of a set of transcripts is used as the response variable for a QTL scan. Methodology/Principal Findings We utilized a whole genome expression microarray and an F2 pig resource population to conduct a global eQTL analysis in loin muscle tissue, and compared results to previously inferred phenotypic QTL (pQTL) from the same experimental cross. We found 62 unique eQTL (FDR <10%) and identified 3 gene networks enriched with genes subject to genetic control involved in lipid metabolism, DNA replication, and cell cycle regulation. We observed strong evidence of local regulation (40 out of 59 eQTL with known genomic position) and compared these eQTL to pQTL to help identify potential candidate genes. Among the interesting associations, we found aldo-keto reductase 7A2 (AKR7A2) and thioredoxin domain containing 12 (TXNDC12) eQTL that are part of a network associated with lipid metabolism and in turn overlap with pQTL regions for marbling, % intramuscular fat (% fat) and loin muscle area on Sus scrofa (SSC) chromosome 6. Additionally, we report 13 genomic regions with overlapping eQTL and pQTL involving 14 local eQTL. Conclusions/Significance Results of this analysis provide novel candidate genes for important complex pig phenotypes.

Methods of tagSNP selection and other variables affecting imputation accuracy in swine

BackgroundGenotype imputation is a cost efficient alternative to use of high density genotypes for implementing genomic selection. The objective of this study was to investigate variables affecting imputation accuracy from low density tagSNP (average distance between tagSNP from 100kb to 1Mb) sets in swine, selected using LD information, physical location, or accuracy for genotype imputation. We compared results of imputation accuracy based on several sets of low density tagSNP of varying densities and selected using three different methods. In addition, we assessed the effect of varying size and composition of the reference panel of haplotypes used for imputation.ResultsTagSNP density of at least 1 tagSNP per 340kb (∼7000 tagSNP) selected using pairwise LD information was necessary to achieve average imputation accuracy higher than 0.95. A commercial low density (9K) tagSNP set for swine was developed concurrent to this study and an average accuracy of imputation of 0.951 based on these tagSNP was estimated. Construction of a haplotype reference panel was most efficient when these haplotypes were obtained from randomly sampled individuals. Increasing the size of the original reference haplotype panel (128 haplotypes sampled from 32 sire/dam/offspring trios phased in a previous study) led to an overall increase in imputation accuracy (IA = 0.97 with 512 haplotypes), but was especially useful in increasing imputation accuracy of SNP with MAF below 0.1 and for SNP located in the chromosomal extremes (within 5% of chromosome end).ConclusionThe new commercially available 9K tagSNP set can be used to obtain imputed genotypes with high accuracy, even when imputation is based on a comparably small panel of reference haplotypes (128 haplotypes). Average imputation accuracy can be further increased by adding haplotypes to the reference panel. In addition, our results show that randomly sampling individuals to genotype for the construction of a reference haplotype panel is more cost efficient than specifically sampling older animals or trios with no observed loss in imputation accuracy. We expect that the use of imputed genotypes in swine breeding will yield highly accurate predictions of GEBV, based on the observed accuracy and reported results in dairy cattle, where genomic evaluation of some individuals is based on genotypes imputed with the same accuracy as our Yorkshire population.

Identifying putative candidate genes and pathways involved in immune responses to porcine reproductive and respiratory syndrome virus (PRRSV) infection

Differences in gene expression were compared between RNAs from lungs of high (HR) and low (LR) porcine reproductive and respiratory syndrome virus (PRRSV) burden pigs using the swine protein-annotated long oligonucleotide microarray, the Pigoligoarray. Pathway analyses were carried out to determine biological processes, pathways and networks that differ between the LR and HR responses. Differences existed between HR and LR pigs for 16 signalling pathways [P < 0.01/-log (P-value) >1.96]. Top canonical pathways included acute phase response signalling, crosstalk between dendritic cells and natural killer cells and tight junction signalling, with numerous immune response genes that were upregulated (SOCS1, SOD2, RBP4, HLA-B, HLA-G, PPP2R1A and TAP1) or downregulated (IL18, TF, C4BPA, C1QA, C1QB and TYROBP). One mechanism, regulation of complement activation, may have been blocked in HR (PRRSV-susceptible) pigs and could account for the poor clearance of PRRSV by infected macrophages. Multiple inhibiting signals may have prevented effective immune responses in susceptible HR pigs, although some protective genes were upregulated in these pigs. It is likely that in HR pigs, expression of genes associated with protection was delayed, so that the immune response was not stimulated early; thus, PRRSV infection prevented protective immune responses.

Identification of Carcass and Meat Quality QTL in an F2 Duroc × Pietrain Pig Resource Population Using Different Least-Squares Analysis Models

A three-generation resource population was constructed by crossing pigs from the Duroc and Pietrain breeds. In this study, 954 F2 animals were used to identify quantitative trait loci (QTL) affecting carcass and meat quality traits. Based on results of the first scan analyzed with a line-cross (LC) model using 124 microsatellite markers and 510 F2 animals, 9 chromosomes were selected for genotyping of additional markers. Twenty additional markers were genotyped for 954 F2 animals and 20 markers used in the first scan were genotyped for 444 additional F2 animals. Three different Mendelian models using least-squares for QTL analysis were applied for the second scan: a LC model, a half-sib (HS) model, and a combined LC and HS model. Significance thresholds were determined by false discovery rate (FDR). In total, 50 QTL using the LC model, 38 QTL using the HS model, and 3 additional QTL using the combined LC and HS model were identified (q < 0.05). The LC and HS models revealed strong evidence for QTL regions on SSC6 for carcass traits (e.g., 10th-rib backfat; q < 0.0001) and on SSC15 for meat quality traits (e.g., tenderness, color, pH; q < 0.01), respectively. QTL for pH (SSC3), dressing percent (SSC7), marbling score and moisture percent (SSC12), CIE a* (SSC16), and carcass length and spareribs weight (SSC18) were also significant (q < 0.01). Additional marker and animal genotypes increased the statistical power for QTL detection, and applying different analysis models allowed confirmation of QTL and detection of new QTL.

Assessment of the swine protein-annotated oligonucleotide microarray

The specificity and utility of the swine protein-annotated oligonucleotide microarray, or Pigoligoarray (http://www.pigoligoarray.org), has been evaluated by profiling the expression of transcripts from four porcine tissues. Tools for comparative analyses of expression on the Pigoligoarray were developed including HGNC identities and comparative mapping alignments with human orthologs. Hybridization results based on the Pigoligoarrays sets of control, perfect match (PM) and deliberate mismatch (MM) probes provide an important means of assessing non-specific hybridization. Simple descriptive diagnostic analyses of PM/MM probe sets are introduced in this paper as useful tools for detecting non-specific hybridization. Samples of RNA from liver, brain stem, longissimus dorsi muscle and uterine endothelium from four pigs were prepared and hybridized to the arrays. Of the total 20,400 oligonucleotides on the Pigoligoarray, 12,429 transcripts were putatively differentially expressed (DE). Analyses for tissue-specific expression [over-expressed in one tissue with respect to all the remaining three tissues (q < 0.01)] identified 958 DE transcripts in liver, 726 in muscle, 286 in uterine endothelium and 1027 in brain stem. These hybridization results were confirmed by quantitative PCR (QPCR) expression patterns for a subset of genes after affirming that cDNA and amplified antisense RNA (aRNA) exhibited similar QPCR results. Comparison to human ortholog expression confirmed the value of this array for experiments of both agricultural importance and for tests using pigs as a biomedical model for human disease.

Accuracy of Estimation of Genomic Breeding Values in Pigs Using Low-Density Genotypes and Imputation

Genomic selection has the potential to increase genetic progress. Genotype imputation of high-density single-nucleotide polymorphism (SNP) genotypes can improve the cost efficiency of genomic breeding value (GEBV) prediction for pig breeding. Consequently, the objectives of this work were to: (1) estimate accuracy of genomic evaluation and GEBV for three traits in a Yorkshire population and (2) quantify the loss of accuracy of genomic evaluation and GEBV when genotypes were imputed under two scenarios: a high-cost, high-accuracy scenario in which only selection candidates were imputed from a low-density platform and a low-cost, low-accuracy scenario in which all animals were imputed using a small reference panel of haplotypes. Phenotypes and genotypes obtained with the PorcineSNP60 BeadChip were available for 983 Yorkshire boars. Genotypes of selection candidates were masked and imputed using tagSNP in the GeneSeek Genomic Profiler (10K). Imputation was performed with BEAGLE using 128 or 1800 haplotypes as reference panels. GEBV were obtained through an animal-centric ridge regression model using de-regressed breeding values as response variables. Accuracy of genomic evaluation was estimated as the correlation between estimated breeding values and GEBV in a 10-fold cross validation design. Accuracy of genomic evaluation using observed genotypes was high for all traits (0.65−0.68). Using genotypes imputed from a large reference panel (accuracy: R2 = 0.95) for genomic evaluation did not significantly decrease accuracy, whereas a scenario with genotypes imputed from a small reference panel (R2 = 0.88) did show a significant decrease in accuracy. Genomic evaluation based on imputed genotypes in selection candidates can be implemented at a fraction of the cost of a genomic evaluation using observed genotypes and still yield virtually the same accuracy. On the other side, using a very small reference panel of haplotypes to impute training animals and candidates for selection results in lower accuracy of genomic evaluation.