Bayesian genomic prediction of junctional epidermolysis bullosa in sheep
Bayesian genomic prediction of junctional epidermolysis bullosa in sheep
Junctional epidermolysis bullosa (JEP) is a heritable skin and mucosa disorder in association with mendelian mutations in sheep. The purpose of this investigation is to explore the relationship between different priors, linkage disequilibrium, and single nucleotide polymorphism (SNP) selection methods and accuracy of Bayesian GP of JEP in sheep. Ninety-two Spanish Churra sheep breed genotyped by 40668 SNP markers. Bayes Cπ was shown to have slightly higher predicted accuracy [0.724 (0.113)] by unselected data. Prediction performance of the Bayesian GP models was found to be similar after correction for LD. There was a significant difference between predicted accuracies due to the SNP selection by ranked p values of whole and training only dataset using linear model. The relevance of genetic architecture in conjugate to the prior distributions was clearly supported by the unselected data. The most obvious finding of this study is that preselection of SNPs referring to genetic architecture of the phenotype may lower the needs of computational load.
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- 1. VanRaden PM. Symposium review: How to implement genomic selection. Journal of dairy science 2020; 103.6: 5291- 5301.
- 2. Gutierrez-Reinoso MA, Aponte PM, Garcia-Herreros M. Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review. Animals 2021; 11.3: 599.
- 3. Meuwissen T, Hayes B, Goddard M. Genomic selection: A paradigm shift in animal breeding. Animal frontiers 2016; 6.1: 6-14.
- 4. Baker LA, Momen M, Chan K, Bollig N, Lopes FB et al. Bayesian and machine learning models for genomic prediction of anterior cruciate ligament rupture in the canine model. G3: Genes, genomes, genetics 2020; 10.8: 2619-2628.
- 5. Grinberg NF, Orhobor OI, King RD. An evaluation of machinelearning for predicting phenotype: studies in yeast, rice, and wheat. Machine Learning 2020; 109.2: 251-277.
- 6. Shi S, Li X, Fang L, Liu A, Su G et al. Genomic Prediction Using Bayesian Regression Models With Global–Local Prior. Frontiers in Genetics 2021; 12: 426.
- 7. Gianola D. Priors in whole-genome regression: the Bayesian alphabet returns. Genetics 2013; 194.3: 573-596.
- 8. Suárez-Vega A, Gutiérrez-Gil B, Benavides J, Perez V, TosserKlopp G et al. Combining GWAS and RNA-Seq approaches for detection of the causal mutation for hereditary junctional epidermolysis bullosa in sheep. PLoS One 2015; 10.5: e0126416.
- 9. Mömke S, Kerkmann A, Wöhlke A, Ostmeier M, HewickerTrautwein M et al. A frameshift mutation within LAMC2 is responsible for Herlitz type junctional epidermolysis bullosa (HJEB) in black headed mutton sheep. PloS one 2011; 6.5: e18943.
- 10. Sartelet A, Harland C, Tamma N, Karim L, Bayrou C et al. A stop‐gain in the laminin, alpha 3 gene causes recessive junctional epidermolysis bullosa in Belgian Blue cattle. Animal genetics 2015; 46.5: 566-570.
- 11. Kerkmann A, Ganter M, Frase R, Ostmeier M, HewickerTrautwein M et al. Epidermolysis bullosa in German black headed mutton sheep. Berliner und Munchener Tierarztliche Wochenschrift 2010; 123.9-10: 413-421.
- 12. Ostmeier M, Kerkmann A, Frase R, Ganter M, Distl O et al. Inherited junctional epidermolysis bullosa (Herlitz type) in German black-headed mutton sheep. Journal of comparative pathology 2012; 146.4: 338-347.
- 13. Milenkovic D, Chaffaux S, Taourit S, Guérin G. A mutation in the LAMC2 gene causes the Herlitz junctional epidermolysis bullosa (H-JEB) in two French draft horse breeds. Genetics Selection Evolution 2003; 35.2: 249-256.
- 14. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American journal of human genetics 2007; 81.3: 559-575.
- 15. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics 2001; 157.4: 1819-1829.
- 16. Park T, Casella G. The bayesian lasso. Journal of the American Statistical Association 2008; 103.482: 681-686.
- 17. Moser G, Lee SH, Hayes BJ, Goddard ME, Wray NR, Visscher PM. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS genetics 2015; 11.4: e1004969.
- 18. Pérez P, de Los Campos G. Genome-wide regression and prediction with the BGLR statistical package. Genetics 2014; 198.2: 483-495.
- 19. Schulz-Streeck T, Ogutu JO, Piepho HP. Pre-selection of markers for genomic selection. In BMC proceedings 2011; 5.3: 1-4. BioMed Central.
- 20. Zhou X, Stephens M. Genome-wide efficient mixed-model analysis for association studies. Nature genetics 2012; 44.7: 821-824.
- 21. Calus MPL, De Haas Y, Veerkamp RF. Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies. Journal of Dairy Science 2013; 96.10: 6703-6715.
- 22. Fanny M, Andrea R, Pascal C. Evaluating the Interpretability of SNP Effect Size Classes in Bayesian Genomic Prediction Models. In Human Heredıty 2021; 85.2: 86-86.
- 23. Bang SJ, Kim YG, Park T. Joint selection of SNPs for improving prediction in genome-wide association studies. In 2012 IEEE International Conference on Bioinformatics and Biomedicine Workshops 2012: 852-858.
- 24. Xiang R, Breen E, Prowse-Wilkins C, Chamberlain A, Goddard, M. Bayesian genome-wide analysis of cattle traits using variants with functional and evolutionary significance. bioRxiv 2021.
- 25. Meuwissen T, van den Berg I, Goddard M. On the use of wholegenome sequence data for across-breed genomic prediction and fine-scale mapping of QTL. Genetics Selection Evolution 2021; 53.1: 1-15.
- 26. Guo P, Zhu B, Niu H, Wang Z, Liang Y et al. Fast genomic prediction of breeding values using parallel Markov chain Monte Carlo with convergence diagnosis. BMC bioinformatics 2018; 19.1: 1-11.
- 27. Calus MP. Right-hand-side updating for fast computing of genomic breeding values. Genetics Selection Evolution 2014; 46.1: 1-11.