Genomic Prediction and QTL Mapping Using Bayesian Methods

Several genomic selection methods were applied to a data set that was simulated for the 2010 QTLMAS workshop to predict the genomic breeding values (GEBV) of the offspring generation and to map the QTL. The GEBV had an accuracy of 0.894 with very small bias. QTL were detected based on the variance of 10 SNP windows. Using a threshold chosen for a 10% chromosome-wise type-I error rate, most of the large QTL were successfully detected with few false positives. Results for both prediction of breeding values and detection of QTL were among the best among all analyses of this data set by groups across the globe. Genomic selection method BayesCπ was identified to be appropriate for the 2010 QTLMAS dataset and also applicable to real cases with similar settings

Extension of the bayesian alphabet for genomic selection

<p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability <it>π </it>that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.</p> <p>Results</p> <p>Estimates of <it>π </it>from BayesC<it>π</it>, in contrast to BayesD<it>π</it>, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesC<it>π </it>than for BayesD<it>π</it>, and longest for our implementation of BayesA.</p> <p>Conclusions</p> <p>Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesC<it>π </it>has merit for routine applications.</p

Persistence of accuracy of genomic estimated breeding values over generations in layer chickens

<p>Abstract</p> <p>Background</p> <p>The predictive ability of genomic estimated breeding values (GEBV) originates both from associations between high-density markers and QTL (Quantitative Trait Loci) and from pedigree information. Thus, GEBV are expected to provide more persistent accuracy over successive generations than breeding values estimated using pedigree-based methods. The objective of this study was to evaluate the accuracy of GEBV in a closed population of layer chickens and to quantify their persistence over five successive generations using marker or pedigree information.</p> <p>Methods</p> <p>The training data consisted of 16 traits and 777 genotyped animals from two generations of a brown-egg layer breeding line, 295 of which had individual phenotype records, while others had phenotypes on 2,738 non-genotyped relatives, or similar data accumulated over up to five generations. Validation data included phenotyped and genotyped birds from five subsequent generations (on average 306 birds/generation). Birds were genotyped for 23,356 segregating SNP. Animal models using genomic or pedigree relationship matrices and Bayesian model averaging methods were used for training analyses. Accuracy was evaluated as the correlation between EBV and phenotype in validation divided by the square root of trait heritability.</p> <p>Results</p> <p>Pedigree relationships in outbred populations are reduced by 50% at each meiosis, therefore accuracy is expected to decrease by the square root of 0.5 every generation, as observed for pedigree-based EBV (Estimated Breeding Values). In contrast the GEBV accuracy was more persistent, although the drop in accuracy was substantial in the first generation. Traits that were considered to be influenced by fewer QTL and to have a higher heritability maintained a higher GEBV accuracy over generations. In conclusion, GEBV capture information beyond pedigree relationships, but retraining every generation is recommended for genomic selection in closed breeding populations.</p

Accuracy of Genomic EBV Using an Evenly Spaced, Low-density SNP Panel in Broiler Chickens

Whole-genome or genomic selection is based on associations of large number of markers across the genome with phenotype but will require use of small SNP panels to be cost effective in chickens. The potential loss of accuracy of genotyping selection candidates with an evenly-spaced low-density marker panel and imputation of high-density SNP genotypes was evaluated in a commercial broiler chicken line. Several methods were used to estimate marker effects. The loss in accuracy was less than 5% for different methods and traits. Thus, genomic selection using evenly-spaced low-density marker panels is a cost-effective choice for implementation of genomic selection

Breeding value prediction for production traits in layer chickens using pedigree or genomic relationships in a reduced animal model

<p>Abstract</p> <p>Background</p> <p>Genomic selection involves breeding value estimation of selection candidates based on high-density SNP genotypes. To quantify the potential benefit of genomic selection, accuracies of estimated breeding values (EBV) obtained with different methods using pedigree or high-density SNP genotypes were evaluated and compared in a commercial layer chicken breeding line.</p> <p>Methods</p> <p>The following traits were analyzed: egg production, egg weight, egg color, shell strength, age at sexual maturity, body weight, albumen height, and yolk weight. Predictions appropriate for early or late selection were compared. A total of 2,708 birds were genotyped for 23,356 segregating SNP, including 1,563 females with records. Phenotypes on relatives without genotypes were incorporated in the analysis (in total 13,049 production records).</p> <p>The data were analyzed with a Reduced Animal Model using a relationship matrix based on pedigree data or on marker genotypes and with a Bayesian method using model averaging. Using a validation set that consisted of individuals from the generation following training, these methods were compared by correlating EBV with phenotypes corrected for fixed effects, selecting the top 30 individuals based on EBV and evaluating their mean phenotype, and by regressing phenotypes on EBV.</p> <p>Results</p> <p>Using high-density SNP genotypes increased accuracies of EBV up to two-fold for selection at an early age and by up to 88% for selection at a later age. Accuracy increases at an early age can be mostly attributed to improved estimates of parental EBV for shell quality and egg production, while for other egg quality traits it is mostly due to improved estimates of Mendelian sampling effects. A relatively small number of markers was sufficient to explain most of the genetic variation for egg weight and body weight.</p

Extent and consistency of linkage disequilibrium and identification of DNA markers for production and egg quality traits in commercial layer chicken populations

A 3,072 single nucleotide polymorphism (SNP) panel was used to identify genetic markers linked to quantitative trait loci (QTL). Two association methods were used to search for QTL, SNP-wise and genome-wise models. The QTL associated with SNPs, found using both of these methods, can be applied to breeding programs in marker assisted selection (MAS). The extent and consistency of linkage disequilibrium (LD) was measured in two lines of commercial egg laying chickens by analysis of SNPs. Correlations were drawn between measurements of two consecutive years to determine consistency. At short distances, LD is retained which allows for markers at high LD with a trait to be effectively applied in MAS

The impact of genetic relationship information on genomic breeding values in German Holstein cattle

<p>Abstract</p> <p>Background</p> <p>The impact of additive-genetic relationships captured by single nucleotide polymorphisms (SNPs) on the accuracy of genomic breeding values (GEBVs) has been demonstrated, but recent studies on data obtained from Holstein populations have ignored this fact. However, this impact and the accuracy of GEBVs due to linkage disequilibrium (LD), which is fairly persistent over generations, must be known to implement future breeding programs.</p> <p>Materials and methods</p> <p>The data set used to investigate these questions consisted of 3,863 German Holstein bulls genotyped for 54,001 SNPs, their pedigree and daughter yield deviations for milk yield, fat yield, protein yield and somatic cell score. A cross-validation methodology was applied, where the maximum additive-genetic relationship (<it>a</it>_<it>max</it>) between bulls in training and validation was controlled. GEBVs were estimated by a Bayesian model averaging approach (BayesB) and an animal model using the genomic relationship matrix (G-BLUP). The accuracy of GEBVs due to LD was estimated by a regression approach using accuracy of GEBVs and accuracy of pedigree-based BLUP-EBVs.</p> <p>Results</p> <p>Accuracy of GEBVs obtained by both BayesB and G-BLUP decreased with decreasing <it>a</it>_{<it>max </it>}for all traits analyzed. The decay of accuracy tended to be larger for G-BLUP and with smaller training size. Differences between BayesB and G-BLUP became evident for the accuracy due to LD, where BayesB clearly outperformed G-BLUP with increasing training size.</p> <p>Conclusions</p> <p>GEBV accuracy of current selection candidates varies due to different additive-genetic relationships relative to the training data. Accuracy of future candidates can be lower than reported in previous studies because information from close relatives will not be available when selection on GEBVs is applied. A Bayesian model averaging approach exploits LD information considerably better than G-BLUP and thus is the most promising method. Cross-validations should account for family structure in the data to allow for long-lasting genomic based breeding plans in animal and plant breeding.</p

Breeding Value Prediction for Production Traits in Layers Using High-density SNP Markers

Accuracy of breeding values estimated by different methods using pedigree and high-density SNP genotypes in predicting the next generation in a commercial layer breeding line was evaluated. Early and late selection was considered. Use of markers increased accuracies up to two-fold for early selection and by up to 88% for late selection

The RNA Complement of Outer Membrane Vesicles From Salmonella enterica Serovar Typhimurium Under Distinct Culture Conditions

Bacterial outer membrane vesicles (OMVs), as well as OMV-associated small RNAs, have been demonstrated to play a role in host–pathogen interactions. The presence of larger RNA transcripts in OMVs has been less studied and their potential role in host–pathogen interactions remains largely unknown. Here we analyze RNA from OMVs secreted by Salmonella enterica serovar Typhimurium (S. Typhimurium) cultured under different conditions, which mimic host–pathogen interactions. S. Typhimurium was grown to exponential and stationary growth phases in minimal growth control medium (phosphate-carbon-nitrogen, PCN), as well as in acidic and phosphate-depleted PCN, comparable to the macrophage environment and inducing therefore the expression of Salmonella pathogenicity island 2 (SPI-2) genes. Moreover, Salmonella pathogenicity island 1 (SPI-1), which is required for virulence during the intestinal phase of infection, was induced by culturing S. Typhimurium to the stationary phase in Lysogeny Broth (LB). For each condition, we identified OMV-associated RNAs that are enriched in the extracellular environment relative to the intracellular space. All RNA classes could be observed, but a vast majority of rRNA was exported in all conditions in variable proportions with a notable decrease in LB SPI-1 inducing media. Several mRNAs and ncRNAs were specifically enriched in/on OMVs dependent on the growth conditions. Important to note is that some RNAs showed identical read coverage profiles intracellularly and extracellularly, whereas distinct coverage patterns were observed for other transcripts, suggesting a specific processing or degradation. Moreover, PCR experiments confirmed that distinct RNAs were present in or on OMVs as full-length transcripts (IsrB-1/2; IsrA; ffs; SsrS; CsrC; pSLT035; 10Sa; rnpB; STM0277; sseB; STM0972; STM2606), whereas others seemed to be rather present in a processed or degraded form. Finally, we show by a digestion protection assay that OMVs are able to prevent enzymatic degradation of given full-length transcripts (SsrS, CsrC, 10Sa, and rnpB). In summary, we show that OMV-associated RNA is clearly different in distinct culture conditions and that at least a fraction of the extracellular RNA is associated as a full-length transcripts with OMVs, indicating that some RNAs are protected by OMVs and thereby leaving open the possibility that those might be functionally active