Extension of the bayesian alphabet for genomic selection

Habier, David; Fernando, Rohan L.; Kızılkaya, Kadir; Garrick, Dorian J.

doi:10.1186/1471-2105-12-186

Cited by 1,032 publications

(1,190 citation statements)

References 22 publications

(37 reference statements)

Supporting

Mentioning

1,126

Contrasting

Unclassified

Order By: Relevance

“…Heslot et al (2012) compared the predictive performance of different linear and non-linear GS models for several traits in plants and found that, overall, linear and non-linear models performed similarly. RKHS seemingly gave the best predictive ability: for example, in 16 out of their 18 comparisons RKHS outperformed Bayes Cp (Habier et al, 2011). The two neural network models gave mixed results.…”

Section: Resultsmentioning

confidence: 99%

Genome-enabled methods for predicting litter size in pigs: a comparison

Tusell

Pérez‐Rodríguez

Forni

et al. 2013

Animal

View full text Add to dashboard Cite

Predictive ability of models for litter size in swine on the basis of different sources of genetic information was investigated. Data represented average litter size on 2598, 1604 and 1897 60K genotyped sows from two purebred and one crossbred line, respectively. The average correlation (r) between observed and predicted phenotypes in a 10-fold cross-validation was used to assess predictive ability. Models were: pedigree-based mixed-effects model (PED), Bayesian ridge regression (BRR), Bayesian LASSO (BL), genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Bayesian regularized neural networks (BRNN) and radial basis function neural networks (RBFNN). BRR and BL used the marker matrix or its principal component scores matrix (UD) as covariates; RKHS employed a Gaussian kernel with additive codes for markers whereas neural networks employed the additive genomic relationship matrix (G) or UD as inputs. The non-parametric models (RKHS, BRNN, RNFNN) gave similar predictions to the parametric counterparts (average r ranged from 0.15 to 0.23); most of the genome-based models outperformed PED (r 5 0.16). Predictive abilities of linear models and RKHS were similar over lines, but BRNN varied markedly, giving the best prediction (r 5 0.31) when G was used in crossbreds, but the worst (r 5 0.02) when the G matrix was used in one of the purebred lines. The r values for RBFNN ranged from 0.16 to 0.23. Predictive ability was better in crossbreds (0.26) than in purebreds (0.15 to 0.22). This may be related to family structure in the purebred lines.

show abstract

Section: Resultsmentioning

confidence: 99%

Genome-enabled methods for predicting litter size in pigs: a comparison

Tusell

Pérez‐Rodríguez

Forni

et al. 2013

Animal

View full text Add to dashboard Cite

show abstract

“…Comparisons involved BayesB procedure as implemented in the GenSel package (Habier et al, 2010). These procedures used the model :…”

Section: (V) Computationsmentioning

confidence: 99%

Genome-wide association mapping including phenotypes from relatives without genotypes

Wang¹,

Misztal²,

Aguilar³

et al. 2012

Genet. Res.

466

735

View full text Add to dashboard Cite

SummaryA common problem for genome-wide association analysis (GWAS) is lack of power for detection of quantitative trait loci (QTLs) and precision for fine mapping. Here, we present a statistical method, termed single-step GBLUP (ssGBLUP), which increases both power and precision without increasing genotyping costs by taking advantage of phenotypes from other related and unrelated subjects. The procedure achieves these goals by blending traditional pedigree relationships with those derived from genetic markers, and by conversion of estimated breeding values (EBVs) to marker effects and weights. Additionally, the application of mixed model approaches allow for both simple and complex analyses that involve multiple traits and confounding factors, such as environmental, epigenetic or maternal environmental effects. Efficiency of the method was examined using simulations with 15 800 subjects, of which 1500 were genotyped. Thirty QTLs were simulated across genome and assumed heritability was 0 . 5. Comparisons included ssGBLUP applied directly to phenotypes, BayesB and classical GWAS (CGWAS) with deregressed proofs. An average accuracy of prediction 0 . 89 was obtained by ssGBLUP after one iteration, which was 0 . 01 higher than by BayesB. Power and precision for GWAS applications were evaluated by the correlation between true QTL effects and the sum of m adjacent single nucleotide polymorphism (SNP) effects. The highest correlations were 0 . 82 and 0 . 74 for ssGBLUP and CGWAS with m=8, and 0 . 83 for BayesB with m=16. Standard deviations of the correlations across replicates were several times higher in BayesB than in ssGBLUP. The ssGBLUP method with marker weights is faster, more accurate and easier to implement for GWAS applications without computing pseudo-data.

show abstract

“…As proposed by Habier et al (2011), the priors of all SNP effects were assumed to have a common variance and the effect of an SNP fitted with probability (1 − π) followed a mixture of multivariate Student's t-distributions. Following the estimation of the posterior mean of π from the Bayes Cπ algorithm, the Bayes B algorithm was then invoked using the posterior mean of π.…”

Section: Genotypic Datamentioning

confidence: 99%

Genome-wide association study for calving traits in Holstein–Friesian dairy cattle

Purfield

Bradley

Kearney

et al. 2014

Animal

View full text Add to dashboard Cite

Dystocia and perinatal mortality are quantitative traits that significantly impact animal productivity and welfare. Their economic importance is reflected by their inclusion in the national breeding goals of many cattle populations. The genetic architecture that influences these traits, however, has still yet to be thoroughly defined. Regions of the bovine genome associated with calving difficulty (direct and maternal) and perinatal mortality were detected in this study using a Bayesian approach with 43 204 single nucleotide polymorphisms (SNPs) on up to 1970 Holstein-Friesian bulls. Several SNPs on chromosomes 5, 6, 11, 12, 17,18 and 28 were detected to be strongly associated with these calving performance traits. Novel genomic regions with previously reported associations with growth, stature, birth weight and bone morphology were identified in the present study as being associated with the three calving performance traits. Morphological abnormalities are a known contributor to perinatal mortality and the most significantly associated SNP for perinatal mortality in the present study was located in a region in linkage disequilibrium with the gene SLC26A7. This gene, SLC26A7, has similarities and colocalises with SLC4A2, which has previously been associated with osteoporosis and mortality in cattle populations. The HHIP gene that is known to be associated with stature in humans was strongly associated with direct calving difficulty in the present study; large calves are known to, on average, have a greater likelihood of dystocia. A stemloop microRNA, bta-mir-1256, on chromosome 12, involved in post-transcriptional regulation of gene expression was associated with maternal calving difficulty. Previously reported quantitative trait loci associated with calving performance traits in other populations were again identified in this study; with one genomic region on chromosome 18 supporting very strong evidence of an underlying causative mutation and accounting for 2.1% of the genetic variation in direct calving difficulty. Overlapping genomic regions associated with one or more of the calving traits were also detected substantiating the known genetic covariances existing between these traits. Moreover, some genomic regions were only associated with one of the calving traits implying the selective genomic breeding programs exploiting these regions could help resolve genetic antagonisms.Keywords: dystocia, mortality, calving difficulty, genomic, quantitative trait loci ImplicationsThis study contributes to the understanding of the underlying genetic architecture and aetiology of dystocia and perinatal mortality both of which are quantitative traits that have a significant economic farm impact. Moreover, the detection of quantitative trait loci associated with only one calving trait can help resolve genetic antagonisms if targeted in genomic breeding programs -for example, selecting for decreased calving difficulty without impacting maternal calving difficulty.

show abstract

Extension of the bayesian alphabet for genomic selection

Cited by 1,032 publications

References 22 publications

Genome-enabled methods for predicting litter size in pigs: a comparison

Genome-enabled methods for predicting litter size in pigs: a comparison

Genome-wide association mapping including phenotypes from relatives without genotypes

Genome-wide association study for calving traits in Holstein–Friesian dairy cattle

Contact Info

Product

Resources

About