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

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

The importance of identity-by-state information for the accuracy of genomic selection

<p>Abstract</p> <p>Background</p> <p>It is commonly assumed that prediction of genome-wide breeding values in genomic selection is achieved by capitalizing on linkage disequilibrium between markers and QTL but also on genetic relationships. Here, we investigated the reliability of predicting genome-wide breeding values based on population-wide linkage disequilibrium information, based on identity-by-descent relationships within the known pedigree, and to what extent linkage disequilibrium information improves predictions based on identity-by-descent genomic relationship information.</p> <p>Methods</p> <p>The study was performed on milk, fat, and protein yield, using genotype data on 35 706 SNP and deregressed proofs of 1086 Italian Brown Swiss bulls. Genome-wide breeding values were predicted using a genomic identity-by-state relationship matrix and a genomic identity-by-descent relationship matrix (averaged over all marker loci). The identity-by-descent matrix was calculated by linkage analysis using one to five generations of pedigree data.</p> <p>Results</p> <p>We showed that genome-wide breeding values prediction based only on identity-by-descent genomic relationships within the known pedigree was as or more reliable than that based on identity-by-state, which implicitly also accounts for genomic relationships that occurred before the known pedigree. Furthermore, combining the two matrices did not improve the prediction compared to using identity-by-descent alone. Including different numbers of generations in the pedigree showed that most of the information in genome-wide breeding values prediction comes from animals with known common ancestors less than four generations back in the pedigree.</p> <p>Conclusions</p> <p>Our results show that, in pedigreed breeding populations, the accuracy of genome-wide breeding values obtained by identity-by-descent relationships was not improved by identity-by-state information. Although, in principle, genomic selection based on identity-by-state does not require pedigree data, it does use the available pedigree structure. Our findings may explain why the prediction equations derived for one breed may not predict accurate genome-wide breeding values when applied to other breeds, since family structures differ among breeds.</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

A Genome-Wide Association Study Reveals Loci Influencing Height and Other Conformation Traits in Horses

The molecular analysis of genes influencing human height has been notoriously difficult. Genome-wide association studies (GWAS) for height in humans based on tens of thousands to hundreds of thousands of samples so far revealed ∼200 loci for human height explaining only 20% of the heritability. In domestic animals isolated populations with a greatly reduced genetic heterogeneity facilitate a more efficient analysis of complex traits. We performed a genome-wide association study on 1,077 Franches-Montagnes (FM) horses using ∼40,000 SNPs. Our study revealed two QTL for height at withers on chromosomes 3 and 9. The association signal on chromosome 3 is close to the LCORL/NCAPG genes. The association signal on chromosome 9 is close to the ZFAT gene. Both loci have already been shown to influence height in humans. Interestingly, there are very large intergenic regions at the association signals. The two detected QTL together explain ∼18.2% of the heritable variation of height in horses. However, another large fraction of the variance for height in horses results from ECA 1 (11.0%), although the association analysis did not reveal significantly associated SNPs on this chromosome. The QTL region on ECA 3 associated with height at withers was also significantly associated with wither height, conformation of legs, ventral border of mandible, correctness of gaits, and expression of the head. The region on ECA 9 associated with height at withers was also associated with wither height, length of croup and length of back. In addition to these two QTL regions on ECA 3 and ECA 9 we detected another QTL on ECA 6 for correctness of gaits. Our study highlights the value of domestic animal populations for the genetic analysis of complex traits

Deregressed EBV as the response variable yield more reliable genomic predictions than traditional EBV in pure-bred pigs

<p>Abstract</p> <p>Background</p> <p>Genomic selection can be implemented by a multi-step procedure, which requires a response variable and a statistical method. For pure-bred pigs, it was hypothesised that deregressed estimated breeding values (EBV) with the parent average removed as the response variable generate higher reliabilities of genomic breeding values than EBV, and that the normal, thick-tailed and mixture-distribution models yield similar reliabilities.</p> <p>Methods</p> <p>Reliabilities of genomic breeding values were estimated with EBV and deregressed EBV as response variables and under the three statistical methods, genomic BLUP, Bayesian Lasso and MIXTURE. The methods were examined by splitting data into a reference data set of 1375 genotyped animals that were performance tested before October 2008, and 536 genotyped validation animals that were performance tested after October 2008. The traits examined were daily gain and feed conversion ratio.</p> <p>Results</p> <p>Using deregressed EBV as the response variable yielded 18 to 39% higher reliabilities of the genomic breeding values than using EBV as the response variable. For daily gain, the increase in reliability due to deregression was significant and approximately 35%, whereas for feed conversion ratio it ranged between 18 and 39% and was significant only when MIXTURE was used. Genomic BLUP, Bayesian Lasso and MIXTURE had similar reliabilities.</p> <p>Conclusions</p> <p>Deregressed EBV is the preferred response variable, whereas the choice of statistical method is less critical for pure-bred pigs. The increase of 18 to 39% in reliability is worthwhile, since the reliabilities of the genomic breeding values directly affect the returns from genomic selection.</p

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

Genomic Selection for Fruit Quality Traits in Apple (Malus×domestica Borkh.)

The genome sequence of apple (Malus×domestica Borkh.) was published more than a year ago, which helped develop an 8K SNP chip to assist in implementing genomic selection (GS). In apple breeding programmes, GS can be used to obtain genomic breeding values (GEBV) for choosing next-generation parents or selections for further testing as potential commercial cultivars at a very early stage. Thus GS has the potential to accelerate breeding efficiency significantly because of decreased generation interval or increased selection intensity. We evaluated the accuracy of GS in a population of 1120 seedlings generated from a factorial mating design of four females and two male parents. All seedlings were genotyped using an Illumina Infinium chip comprising 8,000 single nucleotide polymorphisms (SNPs), and were phenotyped for various fruit quality traits. Random-regression best liner unbiased prediction (RR-BLUP) and the Bayesian LASSO method were used to obtain GEBV, and compared using a cross-validation approach for their accuracy to predict unobserved BLUP-BV. Accuracies were very similar for both methods, varying from 0.70 to 0.90 for various fruit quality traits. The selection response per unit time using GS compared with the traditional BLUP-based selection were very high (>100%) especially for low-heritability traits. Genome-wide average estimated linkage disequilibrium (LD) between adjacent SNPs was 0.32, with a relatively slow decay of LD in the long range (r2 = 0.33 and 0.19 at 100 kb and 1,000 kb respectively), contributing to the higher accuracy of GS. Distribution of estimated SNP effects revealed involvement of large effect genes with likely pleiotropic effects. These results demonstrated that genomic selection is a credible alternative to conventional selection for fruit quality traits

Lack of Cul4b, an E3 Ubiquitin Ligase Component, Leads to Embryonic Lethality and Abnormal Placental Development

Cullin-RING ligases (CRLs) complexes participate in the regulation of diverse cellular processes, including cell cycle progression, transcription, signal transduction and development. Serving as the scaffold protein, cullins are crucial for the assembly of ligase complexes, which recognize and target various substrates for proteosomal degradation. Mutations in human CUL4B, one of the eight members in cullin family, are one of the major causes of X-linked mental retardation. We here report the generation and characterization of Cul4b knockout mice, in which exons 3 to 5 were deleted. In contrast to the survival to adulthood of human hemizygous males with CUL4B null mutation, Cul4b null mouse embryos show severe developmental arrest and usually die before embryonic day 9.5 (E9.5). Accumulation of cyclin E, a CRL (CUL4B) substrate, was observed in Cul4b null embryos. Cul4b heterozygotes were recovered at a reduced ratio and exhibited a severe developmental delay. The placentas in Cul4b heterozygotes were disorganized and were impaired in vascularization, which may contribute to the developmental delay. As in human CUL4B heterozygotes, Cul4b null cells were selected against in Cul4b heterozygotes, leading to various degrees of skewed X-inactivation in different tissues. Together, our results showed that CUL4B is indispensable for embryonic development in the mouse