Siegfried Preuss

Genome-wide mapping of quantitative trait loci for fatness, fat cell characteristics and fat metabolism in three porcine F2 crosses.

BackgroundQTL affecting fat deposition related performance traits have been considered in several studies and mapped on numerous porcine chromosomes. However, activity of specific enzymes, protein content and cell structure in fat tissue probably depend on a smaller number of genes than traits related to fat content in carcass. Thus, in this work traits related to metabolic and cytological features of back fat tissue and fat related performance traits were investigated in a genome-wide QTL analysis. QTL similarities and differences were examined between three F2 crosses, and between male and female animals.MethodsA total of 966 F2 animals originating from crosses between Meishan (M), Pietrain (P) and European wild boar (W) were analysed for traits related to fat performance (11), enzymatic activity (9) and number and volume of fat cells (20). Per cross, 216 (M × P), 169 (W × P) and 195 (W × M) genome-wide distributed marker loci were genotyped. QTL mapping was performed separately for each cross in steps of 1 cM and steps were reduced when the distance between loci was shorter. The additive and dominant components of QTL positions were detected stepwise by using a multiple position model.ResultsA total of 147 genome-wide significant QTL (76 at P < 0.05 and 71 at P < 0.01) were detected for the three crosses. Most of the QTL were identified on SSC1 (between 76-78 and 87-90 cM), SSC7 (predominantly in the MHC region) and SSCX (in the vicinity of the gene CAPN6). Additional genome-wide significant QTL were found on SSC8, 12, 13, 14, 16, and 18. In many cases, the QTL are mainly additive and differ between F2 crosses. Many of the QTL profiles possess multiple peaks especially in regions with a high marker density. Sex specific analyses, performed for example on SSC6, SSC7 and SSCX, show that for some traits the positions differ between male and female animals. For the selected traits, the additive and dominant components that were analysed for QTL positions on different chromosomes, explain in combination up to 23% of the total trait variance.ConclusionsOur results reveal specific and partly new QTL positions across genetically diverse pig crosses. For some of the traits associated with specific enzymes, protein content and cell structure in fat tissue, it is the first time that they are included in a QTL analysis. They provide large-scale information to analyse causative genes and useful data for the pig industry.

Genome-wide association analysis for growth, muscularity and meat quality in Piétrain pigs

Improvement in growth and meat quality is one of the main objectives in sire line pig breeding programmes. Mapping quantitative trait loci for these traits using experimental crosses and a linkage-based approach has been performed frequently in the past. The Piétrain breed often was involved as a founder breed to establish the experimental crosses. This breed was selected for muscularity and leanness but shows relatively poor meat quality. It is frequently used as a sire line breed. With the advent of genome-wide and dense SNP chips in pig genomic research, it is possible to also conduct genome-wide association studies within the Piétrain breed. In this study, around 500 progeny-tested sires were genotyped with 60k SNPs. Data filtering showed that around 48k SNPs were useable in this sample. These SNPs were used to conduct a genome-wide association study for growth, muscularity and meat quality traits. Because it is known that a mutation in the RYR1 gene located on chromosome 6 shows a major effect on meat quality, this mutation was included in the models. Single-marker and multimarker association analyses were performed. The results revealed between zero and eight significant associations per trait with P < 5 × 10(-5) . Of special interest are SNPs located on SSC6, SSC10 and SSC15.

Mapping quantitative trait loci for metabolic and cytological fatness traits of connected F crosses in pigs

In the present study 3 connected F(2) crosses were used to map QTL for classical fat traits as well as fat-related metabolic and cytological traits in pigs. The founder breeds were Chinese Meishan, European Wild Boar, and Pietrain with to some extent the same founder animals in the different crosses. The different selection history of the breeds for fatness traits as well as the connectedness of the crosses led to a high statistical power. The total number of F(2) animals varied between 694 and 966, depending on the trait. The animals were genotyped for around 250 genetic markers, mostly microsatellites. The statistical model was a multi-allele, multi-QTL model that accounted for imprinting. The model was previously introduced from plant breeding experiments. The traits investigated were backfat depth and fat area as well as relative number of fat cells with different sizes and 2 metabolic traits (i.e., soluble protein content as an indicator for the level of metabolic turnover and NADP-malate dehydrogenase as an indicator for enzyme activity). The results revealed in total 37 significant QTL on chromosomes 1, 2, 4, 5, 6, 7, 8, 9, 14, 17, and 18, with often an overlap of confidence intervals of several traits. These confidence intervals were in some cases remarkably small, which is due to the high statistical power of the design. In total, 18 QTL showed significant imprinting effects. The small and overlapping confidence intervals for the classical fatness traits as well as for the cytological and metabolic traits enabled positional and functional candidate gene identification for several mapped QTL.

A two‐step approach to map quantitative trait loci for meat quality in connected porcine F2 crosses considering main and epistatic effects

The aim of this study was to map QTL for meat quality traits in three connected porcine F(2) crosses comprising around 1000 individuals. The three crosses were derived from the founder breeds Chinese Meishan, European Wild Boar and Pietrain. The animals were genotyped genomewide for approximately 250 genetic markers, mostly microsatellites. They were phenotyped for seven meat quality traits (pH at 45 min and 24 h after slaughter, conductivity at 45 min and 24 h after slaughter, meat colour, drip loss and rigour). QTL mapping was conducted using a two-step procedure. In the first step, the QTL were mapped using a multi-QTL multi-allele model that was tailored to analyse multiple connected F(2) crosses. It considered additive, dominance and imprinting effects. The major gene RYR1:g.1843C>T affecting the meat quality on SSC6 was included as a cofactor in the model. The mapped QTL were tested for pairwise epistatic effects in the second step. All possible epistatic effects between additive, dominant and imprinting effects were considered, leading to nine orthogonal forms of epistasis. Numerous QTL were found. The most interesting chromosome was SSC6. Not all genetic variance of meat quality was explained by RYR1:g.1843C>T. A small confidence interval was obtained, which facilitated the identification of candidate genes underlying the QTL. Epistasis was significant for the pairwise QTL on SSC12 and SSC14 for pH24 and for the QTL on SSC2 and SSC5 for rigour. Some evidence for additional pairwise epistatic effects was found, although not significant. Imprinting was involved in epistasis.

Host Genome Influence on Gut Microbial Composition and Microbial Prediction of Complex Traits in Pigs

The aim of the present study was to analyze the interplay between gastrointestinal tract (GIT) microbiota, host genetics, and complex traits in pigs using extended quantitative-genetic methods. The study design consisted of 207 pigs that were housed and slaughtered under standardized conditions, and phenotyped for daily gain, feed intake, and feed conversion rate. The pigs were genotyped with a standard 60 K SNP chip. The GIT microbiota composition was analyzed by 16S rRNA gene amplicon sequencing technology. Eight from 49 investigated bacteria genera showed a significant narrow sense host heritability, ranging from 0.32 to 0.57. Microbial mixed linear models were applied to estimate the microbiota variance for each complex trait. The fraction of phenotypic variance explained by the microbial variance was 0.28, 0.21, and 0.16 for daily gain, feed conversion, and feed intake, respectively. The SNP data and the microbiota composition were used to predict the complex traits using genomic best linear unbiased prediction (G-BLUP) and microbial best linear unbiased prediction (M-BLUP) methods, respectively. The prediction accuracies of G-BLUP were 0.35, 0.23, and 0.20 for daily gain, feed conversion, and feed intake, respectively. The corresponding prediction accuracies of M-BLUP were 0.41, 0.33, and 0.33. Thus, in addition to SNP data, microbiota abundances are an informative source of complex trait predictions. Since the pig is a well-suited animal for modeling the human digestive tract, M-BLUP, in addition to G-BLUP, might be beneficial for predicting human predispositions to some diseases, and, consequently, for preventative and personalized medicine.

Genetic parameter estimates and targeted association analyses of growth, carcass, and meat quality traits in German Merinoland and Merinoland-cross lambs1

In this study, genetic parameters of nine growth, carcass, and meat quality (MQ) traits were estimated, and targeted association studies were conducted using mixed models. Phenotypic information was collected on 1,599 lambs, including both purebred Merinoland (ML) animals and five different F1 crosses. The F1 lambs were produced by mating rams of the meat-type breeds Charollais, Ile de France, German Blackheaded Mutton (Deutsches Schwarzköpfiges Fleischschaf), Suffolk, and Texel with ML ewes. Between four and six sires were used per sire breed. In total, 29 sires and 298 purebred ML sheep were genotyped with the Illumina OvineSNP50 BeadChip. All F1 individuals were genotyped for 289 SNPs located on the chromosomes 1, 2, 3, 18, and 21. These SNPs were used to impute SNPs on five chromosomes of the Illumina Ovine chip in the F1 individuals. Several Bonferroni-corrected significant associations were identified for shoulder width. A number of additional significant associations were found for other traits. Genetic parameters were estimated and single-marker association analyses were performed with breed-specific effects. Moderate heritability estimates were found for average daily gain (0.23), kidney fat weight (0.19), carcass length (0.15), shoulder width (0.33), subcutaneous fat thickness (0.22), and cutlet area (0.36). While heritability for cooking loss was found to be low (0.07), shear force (0.17) and dressing percentage (0.20) showed moderate heritability, and thus might be candidate traits to be included in the selection index in the population. In general, low phenotypic and low or moderate genetic correlations were detected between the traits.