The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data
Open accessAbstract
Genome-wide association studies (GWAS) for quantitative traits and disease in humans and other species have shown that there are many loci that contribute to the observed resemblance between relatives. GWAS to date have mostly focussed on discovery of genes or regulatory regions habouring causative polymorphisms, using single SNP analyses and setting stringent type-I error rates. Genome-wide marker data can also be used to predict genetic values and therefore predict phenotypes. Here, we propose a Bayesian method that utilises all marker data simultaneously to predict phenotypes. We apply the method to three traits: coat colour, %CD8 cells, and mean cell haemoglobin, measured in a heterogeneous stock mouse population. We find that a model that contains both additive and dominance effects, estimated from genome-wide marker data, is successful in predicting unobserved phenotypes and is significantly better than a prediction based upon the phenotypes of close relatives. Correlations between predicted and actual phenotypes were in the range of 0.4 to 0.9 when half of the number of families was used to estimate effects and the other half for prediction. Posterior probabilities of SNPs being associated with coat colour were high for regions that are known to contain loci for this trait. The prediction of phenotypes using large samples, high-density SNP data, and appropriate statistical methodology is feasible and can be applied in human medicine, forensics, or artificial selection programs.
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Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
Saxena, R., Voight, B. F., Lyssenko, V., Burtt, N. P., de Bakker, P. I. W., Chen, H., Roix, J. J., Kathiresan, S., Hirschhorn, J. N., Daly, M. J., et al. (2007) · Science · doi:10.1126/science.1142358 · Cited by 2,843
A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants
Scott, L. J., Mohlke, K. L., Bonnycastle, L. L., Willer, C. J., Li, Y., Duren, W. L., Erdos, M. R., Stringham, H. M., Chines, P. S., Jackson, A. U., et al. (2007) · Science · doi:10.1126/science.1142382 · Cited by 2,753
Heritability in the genomics era — concepts and misconceptions
Visscher, P. M., Hill, W. G., & Wray, N. R. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2322 · Cited by 1,921 · cited 1×
“between additive genetic value and phenotype, and is a key parameter in artificial selection programs [30].”
BackgroundSelf-citationRioux, J. D., Xavier, R. J., Taylor, K. D., Silverberg, M. S., Goyette, P., Huett, A., Green, T., Kuballa, P., Barmada, M. M., Datta, L. W., et al. (2007) · Nature Genetics · doi:10.1038/ng2032 · Cited by 1,769
Scuteri, A., Sanna, S., Chen, W.-M., Uda, M., Albai, G., Strait, J., Najjar, S., Nagaraja, R., Orrú, M., Usala, G., et al. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030115 · Cited by 1,724
Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits
Hill, W. G., Goddard, M. E., & Visscher, P. M. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000008 · Cited by 1,039
Self-citationGenome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24
Zanke, B. W., Greenwood, C. M., Rangrej, J., Kustra, R., Tenesa, A., Farrington, S. M., Prendergast, J., Olschwang, S., Chiang, T., Crowdy, E., et al. (2007) · Nature Genetics · doi:10.1038/ng2089 · Cited by 724
Simultaneous Analysis of All SNPs in Genome-Wide and Re-Sequencing Association Studies
Hoggart, C. J., Whittaker, J. C., De Iorio, M., & Balding, D. J. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000130 · Cited by 321 · cited 10×
“To perform a comparison with a ‘standard’ GWAS analysis, multiple SNP models were compared to single SNP analyses.”
MethodInfluential
Cited by
Finding the missing heritability of complex diseases
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336
Self-citationGenome-wide efficient mixed-model analysis for association studies
Zhou, X., & Stephens, M. (2012) · Nature Genetics · doi:10.1038/ng.2310 · Cited by 3,627
“…the computational tricks employed here also apply to other settings, including the combined “variable selection plus random effects” model that has been widely studied for phenotype and breeding value prediction 16 , but which, without the trick used here, is computationally challenging to fit.”
MethodMatrix eQTL: ultra fast eQTL analysis via large matrix operations
Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694
Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924
Disease-Associated Mutations That Alter the RNA Structural Ensemble
Halvorsen, M., Martin, J. S., Broadaway, S., & Laederach, A. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001074 · Cited by 325
Metadata
- Authors
- Lee, S. H., van der Werf, J. H. J., Hayes, B. J., Goddard, M. E., & Visscher, P. M.
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2008-10-24
- Volume
- 4
- Issue
- 10
- Pages
- e1000231
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- OpenAlex
- W1991415175
- Cited by
- 198
- References
- 10
- FWCI
- 10.90
- Type
- journal article
- Language
- en
- Open access
- gold
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