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Understanding the genomic control of tissue-specific gene expression and regulation can help to inform the application of genomic technologies in farm animal breeding programmes. The fine mapping of promoters (transcription start sites [TSS]) and enhancers (divergent amplifying segments of the genome local to TSS) in different populations of cattle across a wide diversity of tissues provides information to locate and understand the genomic drivers of breed- and tissue-specific phenotypes. To this aim we used Cap Analysis Gene Expression (CAGE) sequencing to define TSS and their co-expressed short-range enhancers (

The objectives of this study were to evaluate the performance of the SCR eSense ear tag automated activity monitor (AAM) to detect estrus behavior in Holstein heifers and to determine the optimal time from estrus alert to artificial insemination (AI) using sex-sorted or conventional semen. In total, 281 heifers were fitted with the AAM once eligible for breeding (13.5 m of age). For the first AI, estrus was synchronized using 500 μg of cloprostenol (PGF), given 14 d apart, and heifers were given estrus detection patches (Estrotect™) after the second PGF. Heifers were inseminated at randomly attributed times after high activity alert from the AAM system or if the estrus patch had ≥ 50% colour change. Most heifers received sex-sorted semen for the first AI and conventional semen for subsequent inseminations. Pregnancy diagnosis was performed at 30 d post AI and heifers had four opportunities to become pregnant. In a subset of heifers (n = 149), ovaries were scanned every 12 h from the time of AI until ovulation (OV). The system recorded a heat index (measure of estrus strength), maximum activity change, maximum rumination change and duration of high activity. The sensitivity was 91.0%, with a false positive and false negative rate of 8.0%, and the positive predictive value to detect true estrus events was 83.5%. Pregnancy per AI to first AI was 67.6% and 97.9% of heifers become pregnant after four inseminations. Most false positive estrus events had a heat index45 and a rumination change -20, while false negative events had a rumination change ≥ -20. Odds of pregnancy was not associated with any estrus characteristics measured by the system. However, pre-ovulatory follicle diameter had a weak correlation (r 0.25) with all estrus characteristics. The average (range) interval of onset of high activity, peak activity and end of high activity to OV was 28 h (16-46 h), 22 h (10-40 h) and 16 h (0-36 h), respectively. For conventional semen, each hour increase in interval from activity onset or peak activity to AI reduced the predicted probability of pregnancy by 3.8 and 4.2%, respectively. For sex-sorted semen, the relationship between activity onset or peak activity to AI and predicted probability of pregnancy was quadratic, but not significant. Overall, the SCR eSense ear tag AAM performed well and strategies to identify false positive and false negative estrus events, along with optimization of timing of AI, should further improve performance in Holstein heifers.

The 24th annual symposium of the International Isotope Society's United Kingdom Group took place at the Moller Centre, Churchill College, Cambridge, UK on Friday 6th November 2015. The meeting was attended by 77 delegates from academia and industry, the life sciences, chemical, radiochemical and scientific instrument suppliers. Delegates were welcomed by Dr Ken Lawrie (GlaxoSmithKline, UK, chair of the IIS UK group). The subsequent scientific programme consisted of oral presentations, short 'flash' presentations in association with particular posters and poster presentations. The scientific areas covered included isotopic synthesis, regulatory issues, applications of labelled compounds in imaging, isotopic separation and novel chemistry with potential implications for isotopic synthesis. Both short-lived and long-lived isotopes were represented, as were stable isotopes. The symposium was divided into a morning session chaired by Dr Rebekka Hueting (University of Oxford, UK) and afternoon sessions chaired by Dr Sofia Pascu (University of Bath, UK) and by Dr Alan Dowling (Syngenta, UK). The UK meeting concluded with remarks from Dr Ken Lawrie (GlaxoSmithKline, UK).

Summary As part of a continued system design strategy aimed at expanding its early-time data, VTEM ET (Early Time) focuses on further improving the system’s capabilities for near surface applications, such as groundwater and environmental. The VTEM ET system improvements include: a) increased receiver bandwidth, b) microsecond resolution of early time channel measurements, and c) shorter transmitter current waveform turn-off. Each of these improvements contributes to an overall increase in the system’s sensitivity to subtle changes in the near-surface geology and improves the accuracy of data models. This is initially demonstrated using 1D synthetic forward and inversion modelling of VTEM ET data compared to the standard VTEM system. To further demonstrate increased sensitivity, inversion results are presented from the Spiritwood aquifer region of North Dakota and are compared against well log lithology.

Summary The South African National Roads Agency Ltd (SANRAL) appointed AECOM SA (PTY) Ltd (AECOM) in August 2014 to investigate the viability of constructing the new proposed ring road around the town of Ermelo, in the Mpumalanga Province, South Africa. A portion of the new proposed route was affected by previously undermined areas around the north-eastern part of the town. A decision was taken to evaluate the planned route across the undermined area for old mine voids (tunnels and cavities) using airborne and ground geophysics, followed by drilling. Experience with old coal undermining areas indicated that voids can potentially be filled with Acid Mine Water (AMW). AMW’s low pH makes it conductive. The air-layer above it is resistive. VTEM helicopter TDEM and aeromagnetics were employed to survey the large planned area swiftly. This was followed by ground TDEM, electrical resistivity tomography (ERT) and gravity. The results were used to guide drilling to verify undermining targets, which was successful.

Summary Buried valley aquifers, consisting of permeable sand and gravel deposits in eroded bedrock valleys, are important sources of groundwater supply in many regions of the United States and Canada. But they have been difficult to define because they are often partially eroded, have complex lithology and are hidden within thick glacial overburden. Recent investigations of the Spiritwood Valley aquifer in Manitoba by the GSC using helicopter time-domain EM have provided the impetus for the North Dakota State Water Commission to fly a VTEM helicopter TDEM survey in the Jamestown, ND region in October, 2016. The VTEM data collected over the Spiritwood-JT block allowed for geological mapping from near surface to depth, in spite of relatively weak resistivity contrasts (

Porcine reproductive and respiratory syndrome (PRRS) is a devastating disease in the swine industry. Identification of host genetic factors that enable selection for improved performance during PRRS virus (PRRSV) infection would reduce the impact of this disease on animal welfare and production efficiency. We conducted genomewide association study (GWAS) analyses of data from 13 trials of approximately 200 commercial crossbred nursery-age piglets that were experimentally infected with 1 of 2 type 2 isolates of PRRSV (NVSL 97-7985 [NVSL] and KS2006-72109 [KS06]). Phenotypes analyzed were viral load (VL) in blood during the first 21 d after infection (dpi) and weight gain (WG) from 0 to 42 dpi. We accounted for the previously identified QTL in the region on SSC4 in our models to increase power to identify additional regions. Many regions identified by single-SNP analyses were not identified using Bayes-B, but both analyses identified the same regions on SSC3 and SSC5 to be associated with VL in the KS06 trials and on SSC6 in the NVSL trials (5 × 10); for WG, regions on SSC5 and SSC17 were associated in the NVSL trials (3 × 10). No regions were identified with either method for WG in the KS06 trials. Except for the region on SSC4, which was associated with VL for both isolates (but only with WG for NVSL), identified regions did not overlap between the 2 PRRSV isolate data sets, despite high estimates of the genetic correlation between isolates for traits based on these data. We also identified genomic regions whose associations with VL or WG interacted with either PRRSV isolate or with genotype at the SSC4 QTL. Gene ontology (GO) annotation terms for genes located near moderately associated SNP (0.003) were enriched for multiple immunologically (VL) and metabolism- (WG) related GO terms. The biological relevance of these regions suggests that, although it may increase the number of false positives, the use of single-SNP analyses and a relaxed threshold also increased the identification of true positives. In conclusion, although only the SSC4 QTL was associated with response to both PRRSV isolates, genes near associated SNP were enriched for the same GO terms across PRRSV isolates, suggesting that host responses to these 2 isolates are affected by the actions of many genes that function together in similar biological processes.

In January-February 2016 an integrated multi-parameter helicopter-borne geophysical system was flown over the Vredefort Dome Structure in South Africa. The system consisted of an on-board GT-2A gravimeter and a towed-bird ZTEM(Z-Axis Tipper Electromagnetics)with caesium magnetometer. The survey consisted of nine (9) approximately 70km long NW-SE oriented flight lines, totaling 640km, acquired at a nominal spacing of 1000m over an area of approximately 650 square kilometers. The survey data are presented and analyzed with the support of 2D and 3D inversion. The results appear to correlate well with previous geophysical survey data and the known geology. These results demonstrate that high quality gravity measurements can be made concurrently with helicopter ZTEM and magnetics, and therefore represents a potentially useful multi-parameter and deep penetrating geophysical mapping tool.

The accuracy of genomic predictions can be used to assess the utility of dense marker genotypes for genetic improvement of beef efficiency traits. This study was designed to test the impact of genomic distance between training and validation populations, training population size, statistical methods, and density of genetic markers on prediction accuracy for feed efficiency traits in multibreed and crossbred beef cattle. A total of 6,794 beef cattle data collated from various projects and research herds across Canada were used. Illumina BovineSNP50 (50K) and imputed Axiom Genome-Wide BOS 1 Array (HD) genotypes were available for all animals. The traits studied were DMI, ADG, and residual feed intake (RFI). Four validation groups of 150 animals each, including Angus (AN), Charolais (CH), Angus-Hereford crosses (ANHH), and a Charolais-based composite (TX) were created by considering the genomic distance between pairs of individuals in the validation groups. Each validation group had 7 corresponding training groups of increasing sizes ( = 1,000, 1,999, 2,999, 3,999, 4,999, 5,998, and 6,644), which also represent increasing average genomic distance between pairs of individuals in the training and validations groups. Prediction of genomic estimated breeding values (GEBV) was performed using genomic best linear unbiased prediction (GBLUP) and Bayesian method C (BayesC). The accuracy of genomic predictions was defined as the Pearson's correlation between adjusted phenotype and GEBV (), unless otherwise stated. Using 50K genotypes, the highest average achieved in purebreds (AN, CH) was 0.41 for DMI, 0.34 for ADG, and 0.35 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.21 for ADG, and 0.25 for RFI. Similarly, when imputed HD genotypes were applied in purebreds (AN, CH), the highest average was 0.14 for DMI, 0.15 for ADG, and 0.14 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.22 for ADG, and 0.24 for RFI. The of GBLUP predictions were greatly reduced with increasing genomic average distance compared to those from BayesC predictions. The results indicate that 50K genotypes, used with BayesC, are more effective for predicting GEBV in purebred cattle. Imputed HD genotypes found utility when dealing with composites and crossbreds. Formulation of a fairly large training set for genomic predictions in beef cattle should consider the genomic distance between the training and target populations.

The 24th annual symposium of the International Isotope Society's United Kingdom Group took place at the Møller Centre, Churchill College, Cambridge, UK on Friday 6th November 2015. The meeting was attended by 77 delegates from academia and industry, the life sciences, chemical, radiochemical and scientific instrument suppliers. Delegates were welcomed by Dr Ken Lawrie (GlaxoSmithKline, UK, chair of the IIS UK group). The subsequent scientific programme consisted of oral presentations, short 'flash' presentations in association with particular posters and poster presentations. The scientific areas covered included isotopic synthesis, regulatory issues, applications of labelled compounds in imaging, isotopic separation and novel chemistry with potential implications for isotopic synthesis. Both short-lived and long-lived isotopes were represented, as were stable isotopes. The symposium was divided into a morning session chaired by Dr Rebekka Hueting (University of Oxford, UK) and afternoon sessions chaired by Dr Sofia Pascu (University of Bath, UK) and by Dr Alan Dowling (Syngenta, UK). The UK meeting concluded with remarks from Dr Ken Lawrie (GlaxoSmithKline, UK).

Interest in genetic improvement of carcass and tenderness traits of beef cattle using genome-based selection (GS) and marker-assisted management programs is increasing. The success of such a program depends on the presence of linkage disequilibrium between the observed markers and the underlying QTL as well as on the relationship between the discovery, validation, and target populations. For molecular breeding values (MBV) predicted for a target population using SNP markers, reliabilities of these MBV can be obtained from validation analyses conducted in an independent population distinct from the discovery set. The objective of this study was to test MBV predicted for carcass and tenderness traits of beef cattle in a Canadian-based validation population that is largely independent of a United States-based discovery set. The discovery data set comprised of genotypes and phenotypes from2,900 multibreed beef cattle while the validation population consisted of 802 crossbred feeder heifers and steers. A bivariate animal model that fitted actual phenotype and MBV was used for validation analyses. The reliability of MBV was defined as square of the genetic correlation (R(2) g) that represents the proportion of the additive genetic variance explained by the SNP markers. Several scenarios involving different starting marker panels (384, 3K, 7K, and 50K) and different sets of SNP selected to compute MBV (50, 100, 200, 375, 400, 600, and 800) were investigated. Validation results showed that the most reliable MBV (R(2) g) were 0.34 for HCW, 0.36 for back fat thickness, 0.28 for rib eye area, 0.30 for marbling score, 0.25 for yield grade, and 0.38 for Warner-Bratzler shear force across the different scenarios explored. The results indicate that smaller SNP panels can be developed for use in genetic improvement of beef carcass and tenderness traits to exploit GS benefits.

Meat quality is becoming increasingly important as a trait for selection in animal breeding, whether for fresh meat or processed products. However, it can only be measured postmortem, such that it is an ideal trait for deoxyribonucleic acid-assisted selection or the application of genomics. This article reviews some of the results obtained to date and introduces some of the techniques used to find genes influencing the quality of meat.

A genome-wide association study using the Illumina 50K BeadChip included 38,745 SNP on 29 BTA analyzed on 751 animals, including 33 purebreds and 718 crossbred cattle. Genotypes and 6 production traits: birth weight (BWT), weaning weight (WWT), ADG, DMI, midtest metabolic BW (MMWT), and residual feed intake (RFI), were used to estimate effects of individual SNP on the traits. At the genome-wide level false discovery rate (FDR10%), 41 and 5 SNP were found significantly associated with BWT and WWT, respectively. Thirty-three of them were located on BTA6. At a less stringent significance level (P0.001), 277 and 27 SNP were in association with single traits and multiple traits, respectively. Seventy-three SNP on BTA6 and were mostly associated with BW-related traits, and heavily located around 30 to 50Mb. Markers that significantly affected multiple traits appeared to impact them in same direction. In terms of the size of SNP effect, the significant SNP (P0.001) explained between 0.26 and 8.06% of the phenotypic variation in the traits. Pairs of traits with low genetic correlation, such as ADG vs. RFI or DMI vs. BWT, appeared to be controlled by 2 groups of SNP; 1 of them affected the traits in same direction, the other worked in opposite direction. This study provides useful information to further assist the identification of chromosome regions and subsequently genes affecting growth and feed efficiency traits in beef cattle.

Characterisation of genetic diversity in a large number of European pig populations has been undertaken with EC support. The populations sampled included local (rare) breeds, national varieties of the major international breeds, commercial lines and the Chinese Meishan breed. A second phase of the project will sample a further 50 Chinese breeds. Neutral genetic markers (AFLP and microsatellites), with individual or bulk typing, were used and compared.DNA from 59 European pig populations was extracted on samples of about 50 individuals per population. Individuals were typed for 50 microsatellites and for 148 AFLP bands. A subset of 25 populations was typed for 20 microsatellites on pools of DNA. Allele frequencies were estimated by direct allele counting for the co-dominant markers. Frequencies of AFLP negative alleles (absent bands) were obtained by taking the square root of absent band frequencies. Within-breed variability was summarised using standard statistics: expected and observed heterozygosity, mean observed and effective numbers of alleles, and F statistics. Between-breed diversity analysis was based on a bootstrapped Neighbor-Joining (NJ) tree derived from Reynolds distances (DR). The standard distance of Nei (DS) was also calculated.

There is concern in the beef industry that selecting bulls for feed efficiency based on residual feed intake (RFI) may have a negative impact on bull reproductive performance and fertility. Here we investigated the impact of selection of bulls for low RFI on breeding soundness evaluation (BSE), reproductive performance, and fertility of bulls under natural service in multisire mating groups on pasture. Of the 412 RFI-tested bulls available, 98 (23.8%) were culled for performance, type, temperament, or other reasons, and 88 (21.4%) were culled for failing BSE, for an overall cull rate of 45.1%. From among the 314 bulls subjected to BSE, 32 (10.2%), 20 (6.4%), and 36 (11.4%) were culled for poor feet and legs, scrotal circumference, and semen quality, respectively. The BSE traits were not different (P0.10) between bulls categorized as either inefficient (+RFI) or efficient (-RFI), but the proportion of bulls that failed to meet the 60% minimum sperm motility requirement tended (P = 0.07) to be greater in the -RFI group than in the +RFI group (10.2% vs. 4.4%, respectively). In a subpopulation of 115 bulls, individual progressive sperm motility was greater (P0.05) in +RFI (85%) than -RFI (80%) bulls. A multisire natural mating experiment was conducted during 2 consecutive breeding seasons (2006 to 2007 and 2007 to 2008) using 18 +RFI and 18 -RFI bulls. The overall calving rate (calves born/cows exposed) was 72.9%. Mean number of progeny per sire was significantly greater (P0.01) in -RFI bulls (18.3) than in +RFI bulls (11.8). Selection for feed efficiency based on RFI appears to have no detrimental impact on reproductive performance and fertility in beef bulls bred in multisire groups on pasture. However, the decreased sperm motility and the greater number of progeny per sire associated with -RFI status need further investigation.

The chicken major histocompatibility complex (MHC-B locus) has a strong association with resistance and susceptibility to numerous diseases. We have found a B haplotype designated WLA that associated with the regression of tumours caused by Rous sarcoma virus J strain (RSV-J). Haplotype WLA was identical to the regressive B6 haplotype when partial genotyping was performed (Poultry Science, 89, 2010, 651). We then constructed a bacterial artificial chromosome (BAC) library from a WLA homozygote chicken to evaluate the structure of this regression haplotype and compared it to those of the B6 haplotype. Comparison between WLA and B6 above 59 kb within the 167 kb, including 14 genes from BG1 to BF2, revealed 75 SNPs and 14 indels. However, several genes were identical between WLA and B6, including the BF1 and BF2 genes, which encode a class I molecule previously suggested to be related to the regression phenotype. The BLB2 gene encoding the MHC class II beta chain showed the greatest diversity, with 19 non-synonymous SNPs. A comparison of WLA and B6 haplotpyes that are associated with tumour regression and RIRa and B24 haplotypes associated with tumour progression suggests that DMA1, DMA2, BRD2, TAPBP and BLB2 genes are not involved in the intensity of RSV J tumour regression.

The objective of this study was to identify single-nucleotide polymorphisms using a bovine chromosome 14 high-density SNP panel after accounting for the effect of DGAT1. Linkage disequilibrium information and sire heterozygosity were used to select markers for linkage analysis on bovine chromosome 14 for milk production traits in 321 Holstein animals. Results show putative milk peaks at 42 and 61 cM, both at p0.10, a fat yield peak at 42 and 63 cM, both at p0.05; a protein yield peak at 42 (p0.01) and 84 cM (p0.05); fat per cent peaks at 3 (p0.01) and 29 cM (p0.05), and a protein per cent peak at 4 cM (p0.05). Once quantitative trait loci positions were established, allele substitution effects for all markers were evaluated using the same statistical model. Overlaying information between quantitative trait loci (QTL) and allele effect analysis enabled the identification (p0.01) of 20 SNPs under the milk yield QTL, 2 under both of the fat yield peaks, 8 and 9 under the protein yield peaks, 2 and 6 for the fat per cent peaks and 5 for the protein per cent peak. One SNP in particular, ss61514555:AC, showed association with 3 of the 5 traits: milk (p=1.59E-04), fat (p=6.88E-05) and protein yields (p=5.76E-05). Overall, combining information from linkage disequilibrium, sire heterozygosity and genetic knowledge of traits enabled the characterization of additional markers with significant associations with milk production traits.

Because of the moderate heritability and the expense associated with collecting feed intake data, effective selection for residual feed intake would be enhanced if marker-assisted evaluation were used for accurate estimation of genetic merit. In this study, a suite of genetic markers predictive of residual feed intake, DMI, and ADG were preselected using single-marker regression analysis, and the top 100 SNP were analyzed further to provide prediction equations for the traits. The data used consisted of 728 spring-born beef steers, offspring of a cross between a composite dam line and Angus, Charolais, or University of Alberta hybrid bulls. Feed intake data were collected over a 5-yr period, with 2 groups (fall-winter and winter-spring) tested every year. Training and validation data sets were obtained by splitting the data into 2 distinct sets, by randomly splitting the data into training and testing sets based on sire family (split 1) in 5 replicates or by retaining all animals with no known pedigree relationships as the validation set (split 2). A total of 37,959 SNP were analyzed by single-marker regression, of which only the top 100 that corresponded to a P-value0.002 were retained. The 100 SNP were then analyzed using random regression BLUP, and only SNP that were jointly significant (P0.05) were included in the final marker panels. The marker effects from the selected panels were used to derive the molecular breeding values, which were calculated as a weighted sum of the number of copies of the more frequent allele at each SNP locus, with the weights being the allele substitution effects. The correlation between molecular breeding value and phenotype represented the accuracy of prediction. For all traits evaluated, accuracy across breeds was low, ranging between 0.007 and 0.414. Accuracy was least in data split 2, where the validation individuals had no pedigree relationship with animals in the training data. Given the low predictive ability observed, a large number of individuals may be needed for prediction when using such an admixed population. Further, these results suggest that breed composition of the target population in which the marker panels are likely to be used should be an important consideration when developing prediction equations across breeds, especially where an admixed population is used as the training data set.

The benefit of using genomic breeding values (GEBV) in predicting ADG, DMI, and residual feed intake for an admixed population was investigated. Phenotypic data consisting of individual daily feed intake measurements for 721 beef cattle steers tested over 5 yr was available for analysis. The animals used were an admixed population of spring-born steers, progeny of a cross between 3 sire breeds and a composite dam line. Training and validation data sets were defined by randomly splitting the data into training and testing data sets based on sire family so that there was no overlap of sires in the 2 sets. The random split was replicated to obtain 5 separate data sets. Two methods (BayesB and random regression BLUP) were used to estimate marker effects and to define marker panels and ultimately the GEBV. The accuracy of prediction (the correlation between the phenotypes and GEBV) was compared between SNP panels. Accuracy for all traits was low, ranging from 0.223 to 0.479 for marker panels with 200 SNP, and 0.114 to 0.246 for marker panels with 37,959 SNP, depending on the genomic selection method used. This was less than accuracies observed for polygenic EBV accuracies, which ranged from 0.504 to 0.602. The results obtained from this study demonstrate that the utility of genetic markers for genomic prediction of residual feed intake in beef cattle may be suboptimal. Differences in accuracy were observed between sire breeds when the random regression BLUP method was used, which may imply that the correlations obtained by this method were confounded by the ability of the selected SNP to trace breed differences. This may also suggest that prediction equations derived from such an admixed population may be useful only in populations of similar composition. Given the sample size used in this study, there is a need for increased feed intake testing if substantially greater accuracies are to be achieved.

The objective of this study was to examine the genetic parameters and genetic correlations of feed efficiency traits in steers (n = 490) fed grower or finisher diets in 2 feeding periods. A bivariate model was used to estimate phenotypic and genetic parameters using steers that received the grower and finisher diets in successive feeding periods, whereas a repeated animal model was used to estimate the permanent environmental effects. Genetic correlations between the grower-fed and finisher-fed regimens were 0.50 ± 0.48 and 0.78 ± 0.43 for residual feed intake (RFI) and G:F, respectively. The moderate genetic correlation between the 2 feeding regimens may indicate the presence of a genotype × environment interaction for RFI. Permanent environmental effects (expressed in percentage of phenotypic variance) were detected in the grower-fed steers for ADG (38%), DMI (30%), RFI (18%), and G:F (40%) and also in the finisher-fed steers for ADG (28%), DMI (35%), metabolic mid-weight (23%), and RFI (10%). Heritability estimates were 0.08 ± 0.10 and 0.14 ± 0.15 for the grower-fed steers and 0.42 ± 0.16 and 0.40 ± 17 for the finisher-fed steers for RFI and G:F, respectively. The dependency of the RFI on the feeding regimen may have serious implications when selecting animals in the beef industry. Because of the higher cost of grains, feed efficiency in the feedlot might be overemphasized, whereas efficiency in the cow herd and the backgrounding segments may have less emphasis. These results may also favor the retention (for subsequent breeding) of cows whose steers were efficient in the feedlot sector. Therefore, comprehensive feeding trials may be necessary to provide more insight into the mechanisms surrounding genotype × environment interaction in steers.

A number of studies have mapped QTL regulating porcine fatness and growth traits to the region of the major histocompatibility complex (MHC) on porcine chromosome 7 using various experimental crosses. The QTL results from crosses using the Chinese Meishan (MS) (slow growing and fat) are particularly interesting because the MS alleles have been found to be associated with increased growth rate and reduced backfat depth. We investigated these QTL further in a composite population derived previously over eight generations by intercrossing Meishan and the European Large White breeds. Genotype information from 32 markers in a 15cM target region was used in linkage and association analyses. A two-step variance component analysis identified QTL for three growth-related traits, explaining 19 ∼ 24% of the phenotypic variance with a confidence interval of 4 cM in the target region. SNP association analyses found that ss181128966 and ss181128924 within the QTL interval were strongly associated with the growth traits. Only weak signals for an effect on backfat depth were found in the association and linkage analyses, possibly because of past directional selection in the composite population.

The first International Symposium on Animal Genomics for Animal Health, held at the World Organisation for Animal Health (OIE) Headquarter, 23-25 October, 2007, Paris, France, assembled more than 250

In commercial livestock populations, QTL detection methods often use existing half-sib family structures and ignore additional relationships within and between families. We reanalyzed the data from a large QTL confirmation experiment with 10 pig lines and 10 chromosome regions using identity-by-descent (IBD) scores and variance component analyses. The IBD scores were obtained using a Monte Carlo Markov Chain method, as implemented in the LOKI software, and were used to model a putative QTL in a mixed animal model. The analyses revealed 61 QTL at a nominal 5% level (out of 650 tests). Twenty-seven QTL mapped to areas where QTL have been reported, and eight of these exceeded the threshold to claim confirmed linkage (P0.01). Forty-two of the putative QTL were detected previously using half-sib analyses, whereas 46 QTL previously identified by half-sib analyses could not be confirmed using the variance component approach. Some of the differences could be traced back to the underlying assumptions between the two methods. Using a deterministic approach to estimate IBD scores on a subset of the data gave very similar results to LOKI. We have demonstrated the feasibility of applying variance component QTL analysis to a large amount of data, equivalent to a genome scan. In many situations, the deterministic IBD approach offers a fast alternative to LOKI.

We have constructed a radiation hybrid (RH) map of the porcine genome using an RH panel generated by an irradiation dose of 5000-rad (Sus scrofa radiation hybrid map, SSRH map). Normal porcine aortic endothelial cells were irradiated and fused with a thymidine kinase-deficient mouse cell line, L-M (TK-). A total of 110 cell lines were selected and used for further analysis. Among 1091 microsatellite (MS) markers selected for mapping, 842 markers (77%) could be typed on the panel. The framework map comprised 342 MS markers and an additional 247 MS markers were then added to generate the whole-genome map. The average retention frequency for the data set was 30.6%. The total map length was 5596.2 centiRay (cR). Using an estimated physical length of 2718 Mbp, the average ratio between cR and physical distance over the porcine genome was estimated to be 0.49 Mb/cR.