A multilevel analysis was performed to identify and quantify risk factors associated with mortality and bruises occurring between catching and slaughter of broiler flocks. The effect of each factor in the final model was expressed as an odds ratio (OR). Data included 1,907 Dutch and German broiler flocks slaughtered in 2000 and 2001 at a Dutch processing plant. The mean dead on arrival (DOA) percentage was 0.46. Percentage of bruises was corrected for economic value. The mean corrected bruises percentage was 2.20. Factors associated with corrected bruises percentage were season, moment of transport, and ambient temperature. Unfortunately, these factors are quite difficult to manipulate. Factors associated with DOA percentage were ambient temperature, moment of transport, catching company, breed, flock size, mean BW, mean compartment stocking density, transport time, lairage time, and the interaction term transport time x ambient temperature. The most important factors that influence DOA percentage, and which can be reduced relatively easily, were compartment stocking density (OR = 1.09 for each additional bird in a compartment), transport time (OR = 1.06 for each additional 15 min), and lairage time (OR = 1.03 for each additional 15 min). In particular, reduction of transport and lairage times might have a major influence due to their large variations. Reducing or removing these factors will reduce DOA percentage. Consequently, profitability and animal welfare will increase.

Background Haplotyping in polyploids is a very relevant but challenging task. Most methods based on short-read technologies require haplotype assembly, which is error-prone and computationally intensive. However, most (auto)polyploids have high SNP densities. Therefore, haplotyping could already be done in regions of a size that can be entirely covered by reads generated by current short-read sequencing techniques. Here, we used a method that enables massively parallel amplicon sequencing in order to generate such micro-haplotypes in autotetraploid potato and autohexaploid chrysanthemum. Results For potato, we generated 79 million reads to sequence 412 regions in 96 samples. The average region size was 154 base-pairs. On average, we found 2.4 haplotypes per locus per individual. For chrysanthemum, we generated a similar amount of reads to sequence 940 regions in 92 samples. The average region size here was 171 base-pairs. On average, 2.7 haplotypes per locus per individual were found. Concordance with dosage data based on SNP-arrays was up to 96.8% in potato and 94.2% in chrysanthemum. Conclusions Interpretation of genotyping data generated with next-generation sequencing can be challenging in polyploids, because estimation of allele dosages is often a requisite. Taking advantage of high SNP densities in many autopolyploids we show that massively parallel amplicon sequencing can generate high-quality data with high information content. This has a broad range of applications, including linkage mapping, diversity studies, and genomic selection.

Genetic diversity is crucial for the success of plant breeding programs and core collections that capture this diversity are an important resource to exploit this. In this study, we present a method for constructing a core collection that integrates both genomic and pedigree information to capture maximum genetic variation in a minimum subset of genotypes within a strawberry breeding program, while also being future proof. Our stepwise approach starts with selecting the most important crossing parents of advanced selections and genotypes included for specific traits, to represent also future genetic variation. We then use pedigree-genomic-based relationship coefficients combined with the ‘accession to nearest entry’ criterion to complement the core collection and maximize its representativeness of the current breeding program. Combined pedigree-genomic-based relationship coefficients allow for accurate relationship estimation without the need to genotype every individual in the breeding program. The resulting core collection can for example be used to develop a haplotype reference panel for imputation-based genotyping methods.

Linkage mapping is an approach to order markers based on recombination events. Mapping algorithms cannot easily handle genotyping errors, which are common in high-throughput genotyping data. To solve this issue, strategies have been developed, aimed mostly at identifying and eliminating these errors. One such strategy is SMOOTH (van Os et al. 2005), an iterative algorithm to detect genotyping errors. Unlike other approaches, SMOOTH can also be used to impute the most probable alternative genotypes, but its application is limited to diploid species and to markers heterozygous in only one of the parents. In this study we adapted SMOOTH to expand its use to any marker type and to autopolyploids with the use of identity-by-descent probabilities, naming the updated algorithm Smooth Descent (SD). We applied SD to real and simulated data, showing that in the presence of genotyping errors this method produces better genetic maps in terms of marker order and map length. SD is particularly useful for error rates between 5% and 20% and when error rates are not homogeneous among markers or individuals. With a starting error rate of 10%, SD reduced it to ~ 5% in diploids, ~ 7% in tetraploids and ~ 8.5% in hexaploids. Conversely, the correlation between true and estimated genetic maps increased by 0.03 in tetraploids and by 0.2 in hexaploids, while worsening slightly in diploids (~ 0.0015). We also show that the combination of genotype curation and map re-estimation allowed us to obtain better genetic maps while correcting wrong genotypes. We have implemented this algorithm in the R package SmoothDescent.

Marker genotypes are generally called as discrete values: homozygous versus heterozygous in the case of diploids, or an integer allele dosage in the case of polyploids. Software for linkage map construction and/or QTL analysis usually relies on such discrete genotypes. However, it may not always be possible, or desirable, to assign definite values to genotype observations in the presence of uncertainty in the genotype calling. Here, we present an approach that uses probabilistic marker dosages for linkage map construction in polyploids. We compare our method to an approach based on discrete dosages, using simulated SNP array and sequence reads data with varying levels of data quality. We validate our approach using experimental data from a potato (Solanum tuberosum L.) SNP array applied to an F1 mapping population. In comparison to the approach based on discrete dosages, we mapped an additional 562 markers. All but three of these were mapped to the expected chromosome and marker position. For the remaining three markers, no physical position was known. The use of dosage probabilities is of particular relevance for map construction in polyploids using sequencing data, as these often result in a higher level of uncertainty regarding allele dosage.