337 results on '"Goddard, M. E."'
Search Results
2. Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle
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van den Berg, I., Hayes, B. J., Chamberlain, A. J., and Goddard, M. E.
- Published
- 2019
- Full Text
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3. Improving accuracy and stability of genetic predictions for dairy cow survival
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Khansefid, M., primary, Pryce, J. E., additional, Shahinfar, S., additional, Axford, M., additional, Goddard, M. E., additional, and Haile-Mariam, M., additional
- Published
- 2023
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- View/download PDF
4. How old are quantitative trait loci and how widely do they segregate?
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Kemper, K. E., Hayes, B. J., Daetwyler, H. D., and Goddard, M. E.
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- 2015
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5. Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses
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Visscher, P M, Goddard, M E, Derks, E M, and Wray, N R
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- 2012
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6. Genome-wide association studies establish that human intelligence is highly heritable and polygenic
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Davies, G, Tenesa, A, Payton, A, Yang, J, Harris, S E, Liewald, D, Ke, X, Le Hellard, S, Christoforou, A, Luciano, M, McGhee, K, Lopez, L, Gow, A J, Corley, J, Redmond, P, Fox, H C, Haggarty, P, Whalley, L J, McNeill, G, Goddard, M E, Espeseth, T, Lundervold, A J, Reinvang, I, Pickles, A, Steen, V M, Ollier, W, Porteous, D J, Horan, M, Starr, J M, Pendleton, N, Visscher, P M, and Deary, I J
- Published
- 2011
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7. Reliability of genomic predictions across multiple populations
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de Roos, A. P. W., Hayes, B. J., and Goddard, M. E.
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Cattle -- Physiological aspects ,Cattle -- Genetic aspects ,Gene expression -- Research ,Linkage (Genetics) -- Research ,Population genetics -- Research ,Biological sciences - Published
- 2009
8. Evidence for pleiotropism and recent selection in the PLAG1 region in Australian Beef cattle
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Fortes, M. R. S., Kemper, K., Sasazaki, S., Reverter, A., Pryce, J. E., Barendse, W., Bunch, R., McCulloch, R., Harrison, B., Bolormaa, S., Zhang, Y. D., Hawken, R. J., Goddard, M. E., and Lehnert, S. A.
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- 2013
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9. Inferring the recent ancestry of myostatin alleles affecting muscle mass in cattle
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OʼRourke, B. A., Greenwood, P. L., Arthur, P. F., and Goddard, M. E.
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- 2013
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10. Estimation of pleiotropy between complex diseases using single-nucleotide polymorphism-derived genomic relationships and restricted maximum likelihood
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Lee, S. H., Yang, J., Goddard, M. E., Visscher, P. M., and Wray, N. R.
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- 2012
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11. Using the genomic relationship matrix to predict the accuracy of genomic selection
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Goddard, M. E., Hayes, B. J., and Meuwissen, T. H.E.
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- 2011
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12. A novel predictor of multilocus haplotype homozygosity: comparison with existing predictors
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MACLEOD, I. M., MEUWISSEN, T. H. E., HAYES, B. J., and GODDARD, M. E.
- Published
- 2009
13. A genome map of divergent artificial selection between Bos taurus dairy cattle and Bos taurus beef cattle
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Hayes, B. J., Chamberlain, A. J., Maceachern, S., Savin, K., McPartlan, H., MacLeod, I., Sethuraman, L., and Goddard, M. E.
- Published
- 2009
- Full Text
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14. Increased accuracy of artificial selection by using the realized relationship matrix
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HAYES, B. J., VISSCHER, P. M., and GODDARD, M. E.
- Published
- 2009
15. Accuracy of marker-assisted selection with single markers and marker haplotypes in cattle
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HAYES, B. J., CHAMBERLAIN, A. J., McPARTLAN, H., MACLEOD, I., SETHURAMAN, L., and GODDARD, M. E.
- Published
- 2007
16. A mixed model for analyses of data on multiple genetic markers
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Goddard, M. E.
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- 1992
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17. Heterosis in crosses between geographically separated populations of Drosophila melanogaster
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Ehiobu, N. G. and Goddard, M. E.
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- 1990
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18. Prediction of heterosis in crosses between inbred lines of Drosophila melanogaster
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Ehiobu, N. G., Goddard, M. E., and Taylor, J. F.
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- 1990
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19. Gene expression analysis of blood, liver, and muscle in cattle divergently selected for high and low residual feed intake1
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Khansefid, M., primary, Millen, C. A., additional, Chen, Y., additional, Pryce, J. E., additional, Chamberlain, A. J., additional, Vander Jagt, C. J., additional, Gondro, C., additional, and Goddard, M. E., additional
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- 2017
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20. Including nonadditive genetic effects in mating programs to maximize dairy farm profitability
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Aliloo, H., Pryce, J. E., González Recio, Oscar, Cocks, B. G., Goddard, M. E., Hayes, B. J., Aliloo, H., Pryce, J. E., González Recio, Oscar, Cocks, B. G., Goddard, M. E., and Hayes, B. J.
- Abstract
We compared the outcome of mating programs based on different evaluation models that included nonadditive genetic effects (dominance and heterozygosity) in addition to additive effects. The additive and dominance marker effects and the values of regression on average heterozygosity were estimated using 632,003 single nucleotide polymorphisms from 7,902 and 7,510 Holstein cows with calving interval and production (milk, fat, and protein yields) records, respectively. Expected progeny values were computed based on the estimated genetic effects and genotype probabilities of hypothetical progeny from matings between the available genotyped cows and the top 50 young genomic bulls. An index combining the traits based on their economic values was developed and used to evaluate the performance of different mating scenarios in terms of dollar profit. We observed that mating programs with nonadditive genetic effects performed better than a model with only additive effects. Mating programs with dominance and heterozygosity effects increased milk, fat, and protein yields by up to 38, 1.57, and 1.21 kg, respectively. The inclusion of dominance and heterozygosity effects decreased calving interval by up to 0.70 d compared with random mating. The average reduction in progeny inbreeding by the inclusion of nonadditive genetic effects in matings compared with random mating was between 0.25 to 1.57 and 0.64 to 1.57 percentage points for calving interval and production traits, respectively. The reduction in inbreeding was accompanied by an average of A$8.42 (Australian dollars) more profit per mating for a model with additive, dominance, and heterozygosity effects compared with random mating. Mate allocations that benefit from nonadditive genetic effects can improve progeny performance only in the generation where it is being implemented, and the gain from specific combining abilities cannot be accumulated over generations. Continuous updating of genomic predictions and mate allocatio
- Published
- 2017
21. Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps
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Meuwissen, T. H. E., Hayes, B. J., and Goddard, M. E.
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Genomes -- Analysis ,Haplotypes -- Genetic aspects ,Bayesian statistical decision theory -- Usage ,Genetic markers -- Analysis ,Biological sciences - Abstract
Recent advances in molecular genetic techniques will make dense marker maps available and genotyping many individuals for these markers feasible. Here we attempted to estimate the effects of ~50,000 marker haplotypes simultaneously from a limited number of phenotypic records. A genome of 1000 cM was simulated with a marker spacing of 1 cM. The markers surrounding every l-cM region were combined into marker haplotypes. Due to finite population size ([N.sub.e] = 100), the marker haplotypes were in linkage disequilibrium with the QTL located between the markers. Using least squares, all haplotype effects could not be estimated simultaneously. When only the biggest effects were included, they were overestimated and the accuracy of predicting genetic values of the offspring of the recorded animals was only 0.32. Best linear unbiased prediction of haplotype effects assumed equal variances associated to each l-cM chromosomal segment, which yielded an accuracy of 0.73, although this assumption was far from true. Bayesian methods that assumed a prior distribution of the variance associated with each chromosome segment increased this accuracy to 0.85, even when the prior was not correct. It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
- Published
- 2001
22. Fine Mapping of Quantitative Trait Loci Using Linkage Disefxquilibria With Closely Linked Marker Loci
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Meuwissen, T. H. E. and Goddard, M. E.
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Biological sciences - Abstract
A multimarker linkage disequilibrium mapping method was developed for the fine mapping of quantitative trait loci (QTL) using a dense marker map. The method compares the expected covariances between haplotype effects given a postulated QTL position to the covariances that are found in the data. The expected covariances between the haplotype effects are proportional to the probability that the QTL position is identical by descent (IBD) given the marker haplotype information, which is calculated using the genedropping method. Simulation results showed that a QTL was correctly positioned within a region of 3, 1.5, or 0.75 cM in 70, 62, and 68%, respectively, of the replicates using markers spaced at intervals of 1, 0.5, and 0.25 cM, respectively. These results were rather insensitive to the number of generations since the QTL occurred and to the effective population size, except that 10 generations yielded rather poor estimates of the QTL position. The position estimates of this multimarker disequilibrium mapping method were more accurate than those from a single marker transmission disequilibrium test. A general approach for identifying QTL is suggested, where several stages of disequilibrium mapping are used with increasingly dense marker spacing.
- Published
- 2000
23. Leveraging genetically simple traits to identify small-effect variants for complex phenotypes
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Kemper, K. E., primary, Littlejohn, M. D., additional, Lopdell, T., additional, Hayes, B. J., additional, Bennett, L. E., additional, Williams, R. P., additional, Xu, X. Q., additional, Visscher, P. M., additional, Carrick, M. J., additional, and Goddard, M. E., additional
- Published
- 2016
- Full Text
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24. 0415 Improving genomic selection across breeds and across generations with functional annotation
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Hayes, B., primary, Chamberlain, A. J., additional, Daetwyler, H., additional, Vander Jagt, C. J., additional, and Goddard, M. E., additional
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- 2016
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25. P3003 The extent of cis-regulation of gene expression and its influence on complex trait variation in cattle
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Chamberlain, A. J., primary, Khansefid, M., additional, Jagt, C. J. Vander, additional, Hayes, B. J., additional, Marett, L. C., additional, Chen, Y., additional, Bolormaa, S., additional, Millen, C. A., additional, Nguyen, T. T., additional, and Goddard, M. E., additional
- Published
- 2016
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26. S0104 Improving genomic selection across breeds and across generations with functional annotation
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Hayes, B., primary, Chamberlain, A. J., additional, Daetwyler, H., additional, Jagt, C. J. Vander, additional, and Goddard, M. E., additional
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- 2016
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27. Exploiting biological priors and sequence variants enhances QTL discovery and genomic prediction of complex traits
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MacLeod, I. M., primary, Bowman, P. J., additional, Vander Jagt, C. J., additional, Haile-Mariam, M., additional, Kemper, K. E., additional, Chamberlain, A. J., additional, Schrooten, C., additional, Hayes, B. J., additional, and Goddard, M. E., additional
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- 2016
- Full Text
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28. Estimation of genomic breeding values for residual feed intake in a multi-breed cattle population
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Pryce, J. E., Wang, Z., Bolormaa, S., Khansefid, M., Li, C., Goddard, M. E., and Miller, S. P.
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- 2014
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29. A marker-derived gene network reveals the regulatory role of PPARGC1A, HNF4G, and FOXP3 in intramuscular fat deposition of beef cattle
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RAMAYO CALDAS, Yuliaxis, Fortes, M. R. S., Hudson, N. J., Porto-Neto, L. R., Bolormaa, S., Barendse, W., Kelly, M., Moore, S. S., Goddard, M. E., Lehnert, S. A., Reverter, A., Génétique Animale et Biologie Intégrative (GABI), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Food Futures Flagship - Animal Food and Health Sciences, Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO), Facultat de Veterinària, Departament de Ciencia Animal i dels Aliments, Universitat Autònoma de Barcelona [Barcelona] (UAB), Queensland Alliance for Agriculture and Food Innovation, Center for Animal Science, University of Queensland [Brisbane], Victorian Department of Environment and Primary Industries, Univ Melbourne, Sch Land & Environm, Parkville, Vic 3010, Australia, Université Paris Diderot - Paris 7 (UPD7), Victorian Dept Environm & Primary Ind, Bundoora, Vic 3083, Australia, Spanish Ministerio de Educacion [AP2008-01450], European Union [267196], European Project: 267196, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and Universitat Autònoma de Barcelona (UAB)
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[SDV]Life Sciences [q-bio] ,association weight matrix ,fat deposition ,beef quality ,genomewide association study ,marbling - Abstract
High intramuscular fat (IMF) awards price premiums to beef producers and is associated with meat quality and flavor. Studying gene interactions and pathways that affect IMF might unveil causative physiological mechanisms and inform genomic selection, leading to increased accuracy of predictions of breeding value. To study gene interactions and pathways, a gene network was derived from genetic markers associated with direct measures of IMF, other fat phenotypes, feedlot performance, and a number of meat quality traits relating to body conformation, development, and metabolism that might be plausibly expected to interact with IMF biology. Marker associations were inferred from genomewide association studies (GWAS) based on high density genotypes and 29 traits measured on 10,181 beef cattle animals from 3 breed types. For the network inference, SNP pairs were assessed according to the strength of the correlation between their additive association effects across the 29 traits. The co-association inferred network was formed by 2,434 genes connected by 28,283 edges. Topological network parameters suggested a highly cohesive network, in which the genes are strongly functionally interconnected. Pathway and network analyses pointed towards a trio of transcription factors (TF) as key regulators of carcass IMF: PPARG-C1A, HNF4G, and FOXP3. Importantly, none of these genes would have been deemed as significantly associated with IMF from the GWAS. Instead, a total of 313 network genes show significant co-association with the 3 TF. These genes belong to a wide variety of biological functions, canonical pathways, and genetic networks linked to IMF-related phenotypes. In summary, our GWAS and network predictions are supported by the current literature and suggest a cooperative role for the 3 TF and other interacting genes including CAPN6, STC2, MAP2K4, EYA1, COPS5, XKR4, NR2E1, TOX, ATF1, ASPH, TGS1, and TTPA as modulators of carcass and meat quality traits in beef cattle.
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- 2014
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30. A multivariate analysis of the genetics of fearfulness in potential guide dogs
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Goddard, M. E. and Beilharz, R. G.
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- 1985
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31. Heterosis in crosses between lines of Drosophila melanogaster selected for adaptation to different environments
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Ehiobu, N. G. and Goddard, M. E.
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- 1989
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32. Assessment of heat stress in dairy cattle in papua new guinea
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Lemerle, C. and Goddard, M. E.
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- 1986
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33. Selection indices for non-linear profit functions
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Goddard, M. E.
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- 1983
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34. Genetic and environmental factors affecting the suitability of dogs as Guide Dogs for the Blind
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Goddard, M. E. and Beilharz, R. G.
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- 1982
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35. Effect of rate of inbreeding on inbreeding depression in Drosophila melanogaster
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Ehiobu, N. G., Goddard, M. E., and Taylor, J. F.
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- 1989
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36. Effect of prior distributions on accuracy of genomic breeding values for two dairy traits
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Nicolazzi, E. L, Negrini, Riccardo, Chamberlain, A. J, Ajmone Marsan, Paolo, and Goddard, M. E.
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Settore AGR/17 - ZOOTECNICA GENERALE E MIGLIORAMENTO GENETICO ,genomic breeding ,distributions - Published
- 2013
37. Gene expression in the mammary gland of the tammar wallaby during the lactation cycle reveals conserved mechanisms regulating mammalian lactation
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Vander Jagt, C. J., primary, Whitley, J. C., additional, Cocks, B. G., additional, and Goddard, M. E., additional
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- 2016
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38. Genetic studies of body mass index yield new insights for obesity biology
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Locke, A, Kahali, B, Berndt, S, Justice, A, Pers, T, Day, F, Powell, C, Vedantam, S, Buchkovich, M, Yang, J, Croteau-Chonka, D, Esko, T, Fall, T, Ferreira, T, Gustafsson, S, Kutalik, Z, Luan, J, Magi, R, Randall, J, Winkler, T, Wood, A, Workalemahu, T, Faul, J, Smith, J, Zhao, J, Zhao, W, Chen, J, Fehrmann, R, Hedman, A, Karjalainen, J, Schmidt, E, Absher, D, Amin, N, Anderson, D, Beekman, M, Bolton, J, Bragg-Gresham, J, Buyske, S, Demirkan, A, Deng, G, Ehret, G, Feenstra, B, Feitosa, M, Fischer, K, Goel, A, Gong, J, Jackson, A, Kanoni, S, Kleber, M, Kristiansson, K, Lim, U, Lotay, V, Mangino, M, Leach, I, Medina-Gomez, C, Medland, S, Nalls, M, Palmer, C, Pasko, D, Pechlivanis, S, Peters, M, Prokopenko, I, Shungin, D, Stancakova, A, Strawbridge, R, Sung, Y, Tanaka, T, Teumer, A, Trompet, S, van der Laan, S, van Setten, J, Van Vliet-Ostaptchouk, J, Wang, Z, Yengo, L, Zhang, W, Isaacs, A, Albrecht, E, Arnlov, J, Arscott, G, Attwood, A, Bandinelli, S, Barrett, A, Bas, I, Bellis, C, Bennett, A, Berne, C, Blagieva, R, Bluher, M, Bohringer, S, Bonnycastle, L, Bottcher, Y, Boyd, H, Bruinenberg, M, Caspersen, I, Chen, Y, Clarke, R, Daw, E, de Craen, A, Delgado, G, Dimitriou, M, Doney, A, Eklund, N, Estrada, K, Eury, E, Folkersen, L, Fraser, R, Garcia, M, Geller, F, Giedraitis, V, Gigante, B, Go, A, Golay, A, Goodall, A, Gordon, S, Gorski, M, Grabe, H, Grallert, H, Grammer, T, Grassler, J, Gronberg, H, Groves, C, Gusto, G, Haessler, J, Hall, P, Haller, T, Hallmans, G, Hartman, C, Hassinen, M, Hayward, C, Heard-Costa, N, Helmer, Q, Hengstenberg, C, Holmen, O, Hottenga, J, James, A, Jeff, J, Johansson, A, Jolley, J, Juliusdottir, T, Kinnunen, L, Koenig, W, Koskenvuo, M, Kratzer, W, Laitinen, J, Lamina, C, Leander, K, Lee, N, Lichtner, P, Lind, L, Lindstrom, J, Lo, K, Lobbens, S, Lorbeer, R, Lu, Y, Mach, F, Magnusson, P, Mahajan, A, Mcardle, W, Mclachlan, S, Menni, C, Merger, S, Mihailov, E, Milani, L, Moayyeri, A, Monda, K, Morken, M, Mulas, A, Muller, G, Muller-Nurasyid, M, Musk, A, Nagaraja, R, Nothen, M, Nolte, I, Pilz, S, Rayner, N, Renstrom, F, Rettig, R, Ried, J, Ripke, S, Robertson, N, Rose, L, Sanna, S, Scharnagl, H, Scholtens, S, Schumacher, F, Scott, W, Seufferlein, T, Shi, J, Smith, A, Smolonska, J, Stanton, A, Steinthorsdottir, V, Stirrups, K, Stringham, H, Sundstrom, J, Swertz, M, Swift, A, Syvanen, A, Tan, S, Tayo, B, Thorand, B, Thorleifsson, G, Tyrer, J, Uh, H, Vandenput, L, Verhulst, F, Vermeulen, S, Verweij, N, Vonk, J, Waite, L, Warren, H, Waterworth, D, Weedon, M, Wilkens, L, Willenborg, C, Wilsgaard, T, Wojczynski, M, Wong, A, Wright, A, Zhang, Q, Brennan, E, Choi, M, Dastani, Z, Drong, A, Eriksson, P, Franco-Cereceda, A, Gadin, J, Gharavi, A, Goddard, M, Handsaker, R, Huang, J, Karpe, F, Kathiresan, S, Keildson, S, Kiryluk, K, Kubo, M, Lee, J, Liang, L, Lifton, R, Ma, B, Mccarroll, S, Mcknight, A, Min, J, Moffatt, M, Montgomery, G, Murabito, J, Nicholson, G, Nyholt, D, Okada, Y, Perry, J, Dorajoo, R, Reinmaa, E, Salem, R, Sandholm, N, Scott, R, Stolk, L, Takahashi, A, Van't Hooft, F, Vinkhuyzen, A, Westra, H, Zheng, W, Zondervan, K, Heath, A, Arveiler, D, Bakker, S, Beilby, J, Bergman, R, Blangero, J, Bovet, P, Campbell, H, Caulfield, M, Cesana, G, Chakravarti, A, Chasman, D, Chines, P, Collins, F, Crawford, D, Cupples, L, Cusi, D, Danesh, J, de Faire, U, Den Ruijter, H, Dominiczak, A, Erbel, R, Erdmann, J, Eriksson, J, Farrall, M, Felix, S, Ferrannini, E, Ferrieres, J, Ford, I, Forouhi, N, Forrester, T, Franco, O, Gansevoort, R, Gejman, P, Gieger, C, Gottesman, O, Gudnason, V, Gyllensten, U, Hall, A, Harris, T, Hattersley, A, Hicks, A, Hindorff, L, Hingorani, A, Hofman, A, Homuth, G, Hovingh, G, Humphries, S, Hunt, S, Hypponen, E, Illig, T, Jacobs, K, Jarvelin, M, Jockel, K, Johansen, B, Jousilahti, P, Jukema, J, Jula, A, Kaprio, J, Kastelein, J, Keinanen-Kiukaanniemi, S, Kiemeney, L, Knekt, P, Kooner, J, Kooperberg, C, Kovacs, P, Kraja, A, Kumari, M, Kuusisto, J, Lakka, T, Langenberg, C, Marchand, L, Lehtimaki, T, Lyssenko, V, Mannisto, S, Marette, A, Matise, T, Mckenzie, C, Mcknight, B, Moll, F, Morris, A, Murray, J, Nelis, M, Ohlsson, C, Oldehinkel, A, Ong, K, Madden, P, Pasterkamp, G, Peden, J, Peters, A, Postma, D, Pramstaller, P, Price, J, Qi, L, Raitakari, O, Rankinen, T, Rao, D, Rice, T, Ridker, P, Rioux, J, Ritchie, M, Rudan, I, Salomaa, V, Samani, N, Saramies, J, Sarzynski, M, Schunkert, H, Schwarz, P, Sever, P, Shuldiner, A, Sinisalo, J, Stolk, R, Strauch, K, Tonjes, A, Tregouet, D, Tremblay, A, Tremoli, E, Virtamo, J, Vohl, M, Volker, U, Waeber, G, Willemsen, G, Witteman, J, Zillikens, M, Adair, L, Amouyel, P, Asselbergs, F, Assimes, T, Bochud, M, Boehm, B, Boerwinkle, E, Bornstein, S, Bottinger, E, Bouchard, C, Cauchi, S, Chambers, J, Chanock, S, Cooper, R, de Bakker, P, Dedoussis, G, Ferrucci, L, Franks, P, Froguel, P, Groop, L, Haiman, C, Hamsten, A, Hui, J, Hunter, D, Hveem, K, Kaplan, R, Kivimaki, M, Kuh, D, Laakso, M, Liu, Y, Martin, N, Marz, W, Melbye, M, Metspalu, A, Moebus, S, Munroe, P, Njolstad, I, Oostra, B, Pedersen, N, Perola, M, Perusse, L, Peters, U, Power, C, Quertermous, T, Rauramaa, R, Rivadeneira, F, Saaristo, T, Saleheen, D, Sattar, N, Schadt, E, Schlessinger, D, Slagboom, P, Snieder, H, Spector, T, Thorsteinsdottir, U, Stumvoll, M, Tuomilehto, J, Uitterlinden, A, Uusitupa, M, van der Harst, P, Walker, M, Wallaschofski, H, Wareham, N, Watkins, H, Weir, D, Wichmann, H, Wilson, J, Zanen, P, Borecki, I, Deloukas, P, Fox, C, Heid, I, O'Connell, J, Strachan, D, Stefansson, K, van Duijn, C, Abecasis, G, Franke, L, Frayling, T, Mccarthy, M, Visscher, P, Scherag, A, Willer, C, Boehnke, M, Mohlke, K, Lindgren, C, Beckmann, J, Barroso, I, North, K, Ingelsson, E, Hirschhorn, J, Loos, R, Speliotes, E, Thompson, J, Goldstein, B, Konig, I, Cazier, J, Grundberg, E, Havulinna, A, Ho, W, Hopewell, J, Eriksson, N, Lundmark, P, Lyytikainen, L, Rafelt, S, Tikkanen, E, Van Zuydam, N, Voight, B, Ziegler, A, Altshuler, D, Balmforth, 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P., Swan E. J., Boright A. P., Ahlqvist E., Keller B. J., Huang H., Ahola A., Fagerholm E., Gordin D., Harjutsalo V., He B., Heikkila O., Hietala K., Kyto J., Lahermo P., Lehto M., Osterholm A. M., Parkkonen M., Pitkaniemi J., Rosengard-Barlund M., Saraheimo M., Sarti C., Soderlund J., Soro-Paavonen A., Syreeni A., Thorn L. M., Tikkanen H., Tolonen N., Tryggvason K., Waden J., Gill G. V., Prior S., Guiducci C., Mirel D. B., Taylor A., Hosseini M., Parving H. H., Rossing P., Tarnow L., Ladenvall C., Alhenc-Gelas F., Lefebvre P., Rigalleau V., Roussel R., Maestroni A., Maestroni S., Falhammar H., Gu T., Mollsten A., Cimponeriu D., Mihai I., Mota M., Mota E., Serafinceanu C., Stavarachi M., Hanson R. L., Nelson R. G., Kretzler M., Panduru N. M., Gu H. F., Brismar K., Zerbini G., Hadjadj S., Marre M., Lajer M., Waggott D., Savage D. A., Bain S. C., Martin F., Godson C., Groop P. H., Maxwell A. P., Sengupta S., Peloso G. M., Ganna A., Mora S., Chang H. Y., Den Hertog H. M., Donnelly L. A., Freitag D. F., Gurdasani D., Heikkila K., Johnson T., Kaakinen M., Kettunen J., Li X., Montasser M. E., Petersen A. K., Saxena R., Service S. K., Sidore C., Surakka I., Teslovich T. M., Van den Herik E. G., Volcik K. A., Wu Y., Asiki G., Been L. F., Burnett M. S., Elliott P., Eyjolfsson G. I., Goodarzi M. O., Gravito M. L., Hartikainen A. L., Hung Y. J., Jones M. R., Kaleebu P., Khaw K. T., Kim E., Komulainen P., Lin S. Y., Narisu N., Nieminen T. V., Nsubuga R. N., Olafsson I., Palotie A., Papamarkou T., Pomilla C., Pouta A., Ruokonen A., Seeley J., Silander K., Tiret L., van Pelt L., Wainwright N., Wijmenga C., Young E. H., Bennett F., Boomsma D. I., Burnier M., Chen Y. D., Feranil A. B., Freimer N. B., Hsiung C. A., Kesaniemi A., Koudstaal P. J., Krauss R. M., Kyvik K. O., Meneton P., Moilanen L., Sanghera D. K., Sheu W. H., Whitfield J. B., Wolffenbuttel B. H., Ordovas J. M., Rich S. S., Johnson A., Johnson L., Larson M., Levy D., Newton-Cheh C., O'reilly P., Palmas W., Rice K., Smith A., Snider H., Tobin M., Verwoert G., Rice K. M., Verwoert G. C., Pihur V., Heath S., Sober S., Arora P., Zhang F., Lucas G., Milaneschi Y., Parker A. N., Fava C., Fox E. R., Go M. J., Sjogren M., Vinay D., Alexander M., Tabara Y., Shaw-Hawkins S., Whincup P. H., Shi G., Seielstad M., Sim X., Nguyen K. D., Matullo G., Gaunt T. R., Onland-Moret N. C., Cooper M. N., Platou C. G., Org E., Hardy R., Dahgam S., Palmen J., Kuznetsova T., Uiterwaal C. S., Adeyemo A., Ludwig B., Tomaszewski M., Tzoulaki I., Palmer N. D., Chang Y. P., Steinle N. I., Grobbee D. E., Morrison A. C., Najjar S., Hadley D., Brown M. J., Connell J. M., Day I. N., Lawlor D. A., Lawrence R. W., Ongen H., Li Y., Young J. H., Bis J. C., Bolton J. A., Chaturvedi N., Islam M., Jafar T. H., Kulkarni S. R., Howard P., Guarrera S., Ricceri F., Emilsson V., Plump A., Weder A. B., Sun Y. V., Scott L. J., Peltonen L., Vartiainen E., Brand S. M., Staessen J. A., Wang T. J., Burton P. R., Artigas M. S., Dong Y., Wang X., Zhu H., Rudock M. E., Heckbert S. R., Smith N. L., Wiggins K. L., Doumatey A., Shriner D., Veldre G., Viigimaa M., Kinra S., Prabhakaran D., Tripathy V., Langefeld C. D., Rosengren A., Thelle D. S., Corsi A. M., Singleton A., Hilton G., Salako T., Iwai N., Kita Y., Ogihara T., Ohkubo T., Okamura T., Ueshima H., Umemura S., Eyheramendy S., Meitinger T., Cho Y. S., Kim H. L., Scott J., Sehmi J. S., Hedblad B., Nilsson P., Smith G. D., Raffel L. J., Yao J., Schwartz S. M., Ikram M., W L., Mosley T. H., Seshadri S., Shrine N. R., Wain L. V., Zitting P., Cooper J. A., van Gilst W. H., Janipalli C. S., Mani K., Yajnik C. S., Mattace-Raso F. U., Lakatta E. G., Orru M., Scuteri A., Ala-Korpela M., Kangas A. J., Soininen P., Tukiainen T., Wurtz P., Ong R. T., Dorr M., Galan P., Hercberg S., Lathrop M., Zelenika D., Zhai G., Meschia J. F., Sharma P., Terzic J., Kumar M., Denniff M., Zukowska-Szczechowska E., Wagenknecht L. E., Fowkes F., Charchar F. J., Guo X., Rotimi C., Bots M. L., Brand E., Talmud P. J., Nyberg F., Laan M., Palmer L. J., van der Schouw Y. T., Casas J. P., Vineis P., Ganesh S. K., Wong T. Y., Tai E. S., Morris R. W., Marmot M. G., Miki T., Chandak G. R., Zhu X., Elosua R., Soranzo N., Sijbrands E. J., Uda M., Vasan R. S., Alizadeh B. Z., de Boer R. A., Boezen H. M., Hillege H. L., van der Klauw M. M., Ormel J., Rosmalen J. G., Slaets J. P., Lagou V., Welch R. P., Wheeler E., Rehnberg E., Rasmussen-Torvik L. J., Lecoeur C., Johnson P. C., Sennblad B., Salo P., Timpson N. J., Evans D. M., St Pourcain B., Bielak L. F., Horikoshi M., Navarro P., Raychaudhuri S., Chen H., Rybin D., Willems S. M., Song K., An P., Marullo L., Jansen H., Pankow J. S., Edkins S., Varga T. V., Oksa H., Antonella M., Kong A., Herder C., Antti J., Small K., Miljkovic I., Atalay M., Kiess W., Smit J. H., Campbell S., Fowkes G. R., Rathmann W., Maerz W., Watanabe R. M., de Geus E. J., Penninx B. W., Toenjes A., Peyser P. A., Korner A., Dupuis J., Cucca F., Balkau B., Bouatia-Naji N., Purcell S., Musunuru K., Ardissino D., Mannucci P. M., Anand S., Engert J. C., Morgan T., Spertus J. A., Stoll M., Girelli D., McKeown P. P., Patterson C. C., Merlini P. A., Berzuini C., Bernardinelli L., Peyvandi F., Tubaro M., Celli P., Fetiveau R., Marziliano N., Casari G., Galli M., Ribichini F., Rossi M., Bernardi F., Zonzin P., Piazza A., Yee J., Friedlander Y., Marrugat J., Subirana I., Sala J., Ramos R., Williams G., Nathan D. M., Macrae C. A., Berglund G., Asselta R., Duga S., Spreafico M., Daly M. J., Nemesh J., Korn J. M., Surti A., Gianniny L., Parkin M., Burtt N., Gabriel S. B., Wright B. J., Ball S. G., Schunkert I., Linsel-Nitschke P., Lieb W., Fischer M., Grosshennig A., Preuss M., Scholz M., Chen Z., Wilensky R., Matthai W., Qasim A., Hakonarson H. H., Devaney J., Pichard A. D., Kent K. M., Satler L., Lindsay J. 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- Abstract
Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P < 5 × 10-8), 56 of which are novel. Five loci demonstrate clear evidence of several independent association signals, and many loci have significant effects on other metabolic phenotypes. The 97 loci account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for >20 % of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.
- Published
- 2015
39. Estimation of genomic breeding values for residual feed intake in a multibreed cattle population1
- Author
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Khansefid, M., primary, Pryce, J. E., additional, Bolormaa, S., additional, Miller, S. P., additional, Wang, Z., additional, Li, C., additional, and Goddard, M. E., additional
- Published
- 2014
- Full Text
- View/download PDF
40. Title: Estimation of genomic breeding values for residual feed intake in a multi-breed cattle population
- Author
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Khansefid, M., primary, Pryce, J. E., additional, Bolormaa, S., additional, Miller, S. P., additional, Wang, Z., additional, Li, C., additional, and Goddard, M. E., additional
- Published
- 2014
- Full Text
- View/download PDF
41. Evidence for pleiotropism and recent selection in thePLAG1region in Australian Beef cattle
- Author
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Fortes, M. R. S., primary, Kemper, K., additional, Sasazaki, S., additional, Reverter, A., additional, Pryce, J. E., additional, Barendse, W., additional, Bunch, R., additional, McCulloch, R., additional, Harrison, B., additional, Bolormaa, S., additional, Zhang, Y. D., additional, Hawken, R. J., additional, Goddard, M. E., additional, and Lehnert, S. A., additional
- Published
- 2013
- Full Text
- View/download PDF
42. M1 macrophages are an early feature of shear stress modulated vulnerable atherosclerotic plaques
- Author
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Seneviratne, A. N., primary, Cole, J. E., additional, Goddard, M. E., additional, Udalova, I., additional, Krams, R., additional, and Monaco, C., additional
- Published
- 2013
- Full Text
- View/download PDF
43. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle1
- Author
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Bolormaa, S., primary, Pryce, J. E., additional, Kemper, K., additional, Savin, K., additional, Hayes, B. J., additional, Barendse, W., additional, Zhang, Y., additional, Reich, C. M., additional, Mason, B. A., additional, Bunch, R. J., additional, Harrison, B. E., additional, Reverter, A., additional, Herd, R. M., additional, Tier, B., additional, Graser, H.-U., additional, and Goddard, M. E., additional
- Published
- 2013
- Full Text
- View/download PDF
44. Gene expression in the mammary gland of the tammar wallaby during the lactation cycle reveals conserved mechanisms regulating mammalian lactation.
- Author
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Jagt, C. J. Vander, Whitley, J. C., Cocks, B. G., and Goddard, M. E.
- Subjects
LACTATION ,MAMMARY glands ,GENE expression in mammals ,MESSENGER RNA ,MILK proteins ,MACROPUS eugenii ,GENE ontology - Abstract
The tammar wallaby (Macropus eugenii), an Australian marsupial, has evolved a different lactation strategy compared with eutherian mammals, making it a valuable comparative model for lactation studies. The tammar mammary gland was investigated for changes in gene expression during key stages of the lactation cycle using microarrays. Differentially regulated genes were identified, annotated and subsequent gene ontologies, pathways and molecular networks analysed. Major milk-protein gene expression changes during lactation were in accord with changes in milkprotein secretion. However, other gene expression changes included changes in genes affecting mRNA stability, hormone and cytokine signalling and genes for transport and metabolism of amino acids and lipids. Some genes with large changes in expression have poorly known roles in lactation. For instance, SIM2 was upregulated at lactation initiation and may inhibit proliferation and involution of mammary epithelial cells, while FUT8 was upregulated in Phase 3 of lactation and may support the large increase in milk volume that occurs at this point in the lactation cycle. This pattern of regulation has not previously been reported and suggests that these genes may play a crucial regulatory role in marsupial milk production and are likely to play a related role in other mammals. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
45. Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models.
- Author
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de Maturana, E. López, Picornell, A., Masson-Lecomte, A., Kogevinas, M., Márquez, M., Carrato, A., Tardón, A., Lloreta, J., García-Closas, M., Silverman, D., Rothman, N., Chanock, S., Real, F. X., Goddard, M. E., Malats, N., López de Maturana, E, and SBC/EPICURO Study Investigators
- Subjects
BLADDER tumors ,CANCER relapse ,GENETIC polymorphisms ,PHARMACOKINETICS ,PROBABILITY theory ,PROGNOSIS ,RESEARCH funding ,PREDICTIVE tests ,RECEIVER operating characteristic curves ,DISEASE progression ,TRANSITIONAL cell carcinoma ,GENOTYPES - Abstract
Background: We adapted Bayesian statistical learning strategies to the prognosis field to investigate if genome-wide common SNP improve the prediction ability of clinico-pathological prognosticators and applied it to non-muscle invasive bladder cancer (NMIBC) patients.Methods: Adapted Bayesian sequential threshold models in combination with LASSO were applied to consider the time-to-event and the censoring nature of data. We studied 822 NMIBC patients followed-up >10 years. The study outcomes were time-to-first-recurrence and time-to-progression. The predictive ability of the models including up to 171,304 SNP and/or 6 clinico-pathological prognosticators was evaluated using AUC-ROC and determination coefficient.Results: Clinico-pathological prognosticators explained a larger proportion of the time-to-first-recurrence (3.1 %) and time-to-progression (5.4 %) phenotypic variances than SNPs (1 and 0.01 %, respectively). Adding SNPs to the clinico-pathological-parameters model slightly improved the prediction of time-to-first-recurrence (up to 4 %). The prediction of time-to-progression using both clinico-pathological prognosticators and SNP did not improve. Heritability (ĥ (2)) of both outcomes was <1 % in NMIBC.Conclusions: We adapted a Bayesian statistical learning method to deal with a large number of parameters in prognostic studies. Common SNPs showed a limited role in predicting NMIBC outcomes yielding a very low heritability for both outcomes. We report for the first time a heritability estimate for a disease outcome. Our method can be extended to other disease models. [ABSTRACT FROM AUTHOR]- Published
- 2016
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46. Technical note: Equivalent genomic models with a residual polygenic effect.
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Liu, Z., Goddard, M. E., Hayes, B. J., Reinhardt, F., and Reents, R.
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SINGLE nucleotide polymorphisms , *GENETIC polymorphisms , *MONOGENIC & polygenic inheritance (Genetics) , *PHENOTYPES , *DAIRY cattle - Abstract
Routine genomic evaluations in animal breeding are usually based on either a BLUP with genomic relationship matrix (GBLUP) or single nucleotide polymorphism (SNP) BLUP model. For a multi-step genomic evaluation, these 2 alternative genomic models were proven to give equivalent predictions for genomic reference animals. The model equivalence was verified also for young genotyped animals without phenotypes. Due to incomplete linkage disequilibrium of SNP markers to genes or causal mutations responsible for genetic inheritance of quantitative traits, SNP markers cannot explain all the genetic variance. A residual polygenic effect is normally fitted in the genomic model to account for the incomplete linkage disequilibrium. In this study, we start by showing the proof that the multi-step GBLUP and SNP BLUP models are equivalent for the reference animals, when they have a residual polygenic effect included. Second, the equivalence of both multistep genomic models with a residual polygenic effect was also verified for young genotyped animals without phenotypes. Additionally, we derived formulas to convert genomic estimated breeding values of the GBLUP model to its components, direct genomic values and residual polygenic effect. Third, we made a proof that the equivalence of these 2 genomic models with a residual polygenic effect holds also for single-step genomic evaluation. Both the single-step GBLUP and SNP BLUP models lead to equal prediction for genotyped animals with phenotypes (e.g., reference animals), as well as for (young) genotyped animals without phenotypes. Finally, these 2 single-step genomic models with a residual polygenic effect were proven to be equivalent for estimation of SNP effects, too. [ABSTRACT FROM AUTHOR]
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- 2016
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47. Understanding and predicting complex traits: knowledge from cattle
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Kemper, K. E., primary and Goddard, M. E., additional
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- 2012
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48. Genome‐Wide Association Studies and Linkage Disequilibrium in Cattle
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Goddard, M. E., primary and Hayes, B. J., additional
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- 2012
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49. Inferring the recent ancestry of myostatin alleles affecting muscle mass in cattle
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O'Rourke, B. A., primary, Greenwood, P. L., additional, Arthur, P. F., additional, and Goddard, M. E., additional
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- 2012
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50. Oral abstract presentations & Young Investigators Competition
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Leone, A., primary, Aquila, I., additional, Vicinanza, C., additional, Iaconetti, C., additional, Bochicchio, A., additional, Ottolenghi, S., additional, Indolfi, C., additional, Nadal-Ginard, B., additional, Ellison, G. M., additional, Torella, D., additional, Mias, C., additional, Genet, G., additional, Guilbeau-Frugier, C., additional, Pathak, A., additional, Senard, J. M., additional, Gales, C., additional, Egorova, A. D., additional, Khedoe, P. S. J., additional, Goumans, M. T. H., additional, Nauli, S. M., additional, Ten Dijke, P., additional, Poelmann, R. E., additional, Hierck, B. P., additional, Miragoli, M., additional, Lab, M. J., additional, Singh, A., additional, Sikkel, M., additional, Lyon, A., additional, Gorelik, J., additional, Cheung, C., additional, Bernardo, A. S., additional, Trotter, M. W., additional, Pedersen, R. A., additional, Sinha, S., additional, Mioulane, M., additional, Foldes, G., additional, Harding, S. E., additional, Reglin, B., additional, Secomb, T. W., additional, Pries, A. R., additional, Buckingham, M., additional, Lescroart, F., additional, Meilhac, S., additional, Le Garrec, J.-F., additional, Rozmaritsa, N., additional, Christ, T., additional, Wettwer, E., additional, Knaut, M., additional, Ravens, U., additional, Tokar, S., additional, Schobesberger, S., additional, Wright, P. T., additional, Lyon, A. R., additional, Van Mil, A., additional, Grundmann, S., additional, Goumans, M.-J., additional, Jaksani, S., additional, Doevendans, P. A., additional, Sluijter, J. P., additional, Tijsen, A. J., additional, Amin, A. S., additional, Giudicessi, J. R., additional, Tanck, M. W., additional, Bezzina, C. R., additional, Creemers, E. E., additional, Wilde, A. M., additional, Ackerman, M. J., additional, Pinto, Y. M., additional, Gedicke-Hornung, C., additional, Behrens-Gawlik, V., additional, Khajetoorians, D., additional, Mearini, G., additional, Reischmann, S., additional, Geertz, B., additional, Voit, T., additional, Dreyfus, P., additional, Eschenhagen, T., additional, Carrier, L., additional, Duerr, G. D., additional, Heinemann, J. C., additional, Wenzel, D., additional, Ghanem, A., additional, Alferink, J. C., additional, Zimmer, A., additional, Lutz, B., additional, Welz, A., additional, Fleischmann, B. K., additional, Dewald, O., additional, Sbroggio', M., additional, Bertero, A., additional, Giuliano, L., additional, Brancaccio, M., additional, Tarone, G., additional, Meiser, M., additional, Kohlhaas, M., additional, Chen, Y., additional, Csordas, G., additional, Dorn, G., additional, Maack, C., additional, Stapel, B., additional, Hoch, M., additional, Haghikia, A., additional, Fischer, P., additional, Hilfiker-Kleiner, D., additional, Schroen, B., additional, Corsten, M., additional, Verhesen, W., additional, De Windt, L., additional, Zacchigna, S., additional, Thum, T., additional, Carmeliet, P., additional, Papageorgiou, A., additional, Heymans, S., additional, Lunde, I. G., additional, Finsen, A. V., additional, Florholmen, G., additional, Skrbic, B., additional, Kvaloy, H., additional, Jarstadmarken, H. O., additional, Sjaastad, I., additional, Tonnessen, T., additional, Carlson, C. R., additional, Christensen, G., additional, Paavola, J., additional, Schliffke, S., additional, Rossetti, S., additional, Kuo, I., additional, Yuan, S., additional, Sun, Z., additional, Harris, P., additional, Torres, V., additional, Ehrlich, B., additional, Robinson, P., additional, Adams, K., additional, Zhang, Y.-H., additional, Casadei, B., additional, Watkins, H., additional, Redwood, C., additional, Seneviratne, A. N., additional, Cole, J. E., additional, Goddard, M. E., additional, Mohri, Z., additional, Cross, A. J., additional, Krams, R., additional, Monaco, C., additional, Everaert, B. R., additional, Van Laere, S. J., additional, Hoymans, V. Y., additional, Timmermans, J. P., additional, and Vrints, C. J., additional
- Published
- 2012
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