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| Registros recuperados : 8 | |
| 2. |  | NOYES, N.; WEINROTH, M.; LAKIN, S.; DOSTER, E.; RAYMOND, R.; ROVIRA, P.J.; ABDO, Z.; RUIZ, J.; MARTIN, J.; BOUCHER, C.; JONES, K.; BELK, K.E. Comparing the resistome of poultry, swine, cattle and salmon production and nearby human waste water treatment plants. [Abstract]. In: Conference of Research Workers in Animal Diseases, 96th, 2015,Chicago (USA): CRWAD, 2015. p. 127.| Biblioteca(s): INIA Treinta y Tres. |
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| 3. | | LAKIN, S.M.; DEAN, C.J.; DETTENWANGER, A.; ROSS, A.; DOSTER, E.; ROVIRA, P.J.; ABDO, Z.; JONES, K.L.; BELK, K.E.; MORLEY, P.S.; BOUCHER, C. MEGaRES and AmrPlusPlus, a comprehensive database of antimicrobial resistance genes and user-friendly pipeline for analysis of high-throughput sequencing data.[Abstract]. In: PROCEEDINGS OF THE 96TH ANNUAL CONFERENCE OF RESEARCH WORKERS IN ANIMAL DEISEASES, CHICAGO, USA. 2016. Session Ecology and Management of Foodborne Agents. 065.| Biblioteca(s): INIA Treinta y Tres. |
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| 4. | | ROVIRA, P.J.; MCALLISTER, T.; LAKIN, S.M.; COOK, S.R.; DOSTER, E.; NOYES, N. R.; WEINROTH, M.D.; YANG, X.; PARKER, J. K.; BOUCHER, C.; BOOKER, C. W.; WOENER, D. R.; BELK, K. E.; MORLEY, P. S. Characterization of the microbial resistome in conventional and "raised without antibiotics" beef and dairy production systems. Frontier in Microbiology, September 2019. v. 10, article 1980, 11 p. OPEN ACCESS. Received 18 March 2019 // Accepted 12 August 2019 // Published 4 September 2019.| Biblioteca(s): INIA Treinta y Tres. |
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| 5. | | MORLEY, P.; BELK, K.; DOSTER, E.; LAKIN, S.; DEAN, C.; MUGGLI, M.; NOYES, N.; ROVIRA, P.J.; WEINROTH, M.; YANG, X.; ABDO, Z.; BOUCHER, C.; RUIZ, J.; SCOTT, H.M.; VAN METRE, D.C.; WOERNER, D.E. Metagnomic investigations of antimicrobial resitance in food animal populations. In: USDA NIFA Antimicrobial Resistance Program Project. 2017, Florida, USA. p. 14-15| Biblioteca(s): INIA Treinta y Tres. |
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| 6. | | LAKIN, S.M.; DEAN, C.; NOYES, N.R.; DETTENWANGER, A.; ROSS, A. S.; DOSTER, E.; ROVIRA, P.J.; ABDO, Z.; JONES, K.L.; RUIZ, J.; BELK, K.E.; MORLEY, P.S.; BOUCHER, C. MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Research, 2017 v.45 p.574-580. Article History: Published online 2016 Nov 24.
DOI: https://doi.org/10.1093/nar/gkw1009| Biblioteca(s): INIA Treinta y Tres. |
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| 7. | | DOSTER, E.; ROVIRA, P.J.; NOYES, N.R.; BURGESS, B. A.; YANG, X.; WEINROTH, M.D.; LAKIN, S.M.; DEAN, C.J.; LINKE, L.; MAGNUSON, R.; JONES, K.I.; BOUCHER, C.; RUIZ, J.; BELK, K.E.; MORLEY, P.S. Investigating effects of tulathromycin metaphylaxis on the fecal resistome and microbiome of commercial feedlot cattle early in the feeding period. Frontier in Microbiology, 2018, 9:1715. 14 p. Article history: Received: 14 April 2018; Accepted: 09 July 2018; Published: 30 July 2018.
Open Access journal.
https://doi.org/10.3389/fmicb.2018.01715| Biblioteca(s): INIA Treinta y Tres. |
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| 8. | | NOYES, N.R.; WEINROTH, M.E.; PARKER, J.K.; DEAN, C.J.; LAKIN, S.M.; RAYMOND, R.A.; ROVIRA, P.J.; DOSTER, E.; ABDO, Z.; MARTIN, J.N.; JONES, K.L.; RUIZ, J.; BOUCHER, C.A.; BELK, K.E.; MORLEY, P.S. Enrichment allows identification of diverse, rate elements in metagenomic resistome-virulome sequencing. Microbiome, 2017, 5, p. 142 13 p. Article History: Received: 29 May 2017, Accepted: 5 October 2017, Published: 17 October 2017| Biblioteca(s): INIA Treinta y Tres. |
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| Registros recuperados : 8 | |
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 | Acceso al texto completo restringido a Biblioteca INIA Las Brujas. Por información adicional contacte bibliolb@inia.org.uy. |
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Registro completo
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Biblioteca (s) : |
INIA Las Brujas. |
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Fecha actual : |
04/03/2020 |
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Actualizado : |
29/05/2020 |
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Tipo de producción científica : |
Artículos en Revistas Indexadas Internacionales |
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Circulación / Nivel : |
Internacional - -- |
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Autor : |
LEGARRA, A.; AGUILAR, I.; COLLEAU, J.J. |
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Afiliación : |
A. LEGARRA, UMR GenPhySE, INRA, France; IGNACIO AGUILAR GARCIA, INIA (Instituto Nacional de Investigación Agropecuaria), Uruguay; J. J. COLLEAU, UMR GABI, INRA, AgroParisTech, Université Paris-Saclay, France. |
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Título : |
Short communication: Methods to compute genomic inbreeding for ungenotyped individuals. |
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Fecha de publicación : |
2020 |
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Fuente / Imprenta : |
Journal of Dairy Science, April 2020, Volume 103, Issue 4, Pages 3363-3367. Doi: https://doi.org/10.3168/jds.2019-17750 |
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ISSN : |
0022-0302 |
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DOI : |
10.3168/jds.2019-17750 |
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Idioma : |
Inglés |
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Notas : |
Article history: Received October 15, 2019. / Accepted December 18, 2019.
Corresponding author: A. Legarra - email: andres.legarra@inra.fr
This study was partially funded by the INRA (Paris, France) SELGEN funding metaprogram (Project GDivSelGen) and FEDER (Poctefa funds, project ARDI). This project has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement no. 772787-SMARTER. |
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Contenido : |
ABSTRACT.
The genomic measure of inbreeding is closer to the actual inbreeding than the pedigree-based measure. However, it cannot be computed for ungenotyped animals. An estimate of genomic inbreeding comes from the diagonal of matrix H used in single-step methods. This matrix projects genomic relationships to all ungenotyped members of the pedigree. The diagonal element of H−1 gives an estimate of the genomic inbreeding coefficient. However, so far no computational methods are available to compute the diagonal of H. Here we propose 3 exact methods to compute this diagonal. The first uses an already-existing algorithm to compute, for each ungenotyped individual, products of the form Hx to obtain the corresponding diagonal element of H. The second method computes, for each ungenotyped individual, a term that can be written as a quadratic form involving pedigree and genomic relationships. For both methods, the computational burden is linear in the number of ungenotyped animals. The last method reorders the computations of the second method so that they become linear in the number of genotyped animals, which is usually much smaller. We tested the methods in 3 small data sets (with ~2,000 genotyped animals and 30,000?500,000 animals in pedigree) and in a large simulated population (with 1,220,000 animals in pedigree and 36,000 genotyped animals). Tests resulted in satisfactory computing times (<10 min in the largest example using 10 parallel threads). Computing times were much shorter for the third method, as expected. Using these methods, estimates of genomic inbreeding in ungenotyped animals can be obtained on a regular basis.
© 2020 American Dairy Science Association MenosABSTRACT.
The genomic measure of inbreeding is closer to the actual inbreeding than the pedigree-based measure. However, it cannot be computed for ungenotyped animals. An estimate of genomic inbreeding comes from the diagonal of matrix H used in single-step methods. This matrix projects genomic relationships to all ungenotyped members of the pedigree. The diagonal element of H−1 gives an estimate of the genomic inbreeding coefficient. However, so far no computational methods are available to compute the diagonal of H. Here we propose 3 exact methods to compute this diagonal. The first uses an already-existing algorithm to compute, for each ungenotyped individual, products of the form Hx to obtain the corresponding diagonal element of H. The second method computes, for each ungenotyped individual, a term that can be written as a quadratic form involving pedigree and genomic relationships. For both methods, the computational burden is linear in the number of ungenotyped animals. The last method reorders the computations of the second method so that they become linear in the number of genotyped animals, which is usually much smaller. We tested the methods in 3 small data sets (with ~2,000 genotyped animals and 30,000?500,000 animals in pedigree) and in a large simulated population (with 1,220,000 animals in pedigree and 36,000 genotyped animals). Tests resulted in satisfactory computing times (<10 min in the largest example using 10 parallel threads). Computing times were... Presentar Todo |
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Palabras claves : |
Genetic variability; Genomic selection; Inbreeding; Single step. |
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Asunto categoría : |
L10 Genética y mejoramiento animal |
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Marc : |
LEADER 02859naa a2200229 a 4500
001 1060890
005 2020-05-29
008 2020 bl uuuu u00u1 u #d
022 $a0022-0302
024 7 $a10.3168/jds.2019-17750$2DOI
100 1 $aLEGARRA, A.
245 $aShort communication$bMethods to compute genomic inbreeding for ungenotyped individuals.$h[electronic resource]
260 $c2020
500 $aArticle history: Received October 15, 2019. / Accepted December 18, 2019. Corresponding author: A. Legarra - email: andres.legarra@inra.fr This study was partially funded by the INRA (Paris, France) SELGEN funding metaprogram (Project GDivSelGen) and FEDER (Poctefa funds, project ARDI). This project has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement no. 772787-SMARTER.
520 $aABSTRACT. The genomic measure of inbreeding is closer to the actual inbreeding than the pedigree-based measure. However, it cannot be computed for ungenotyped animals. An estimate of genomic inbreeding comes from the diagonal of matrix H used in single-step methods. This matrix projects genomic relationships to all ungenotyped members of the pedigree. The diagonal element of H−1 gives an estimate of the genomic inbreeding coefficient. However, so far no computational methods are available to compute the diagonal of H. Here we propose 3 exact methods to compute this diagonal. The first uses an already-existing algorithm to compute, for each ungenotyped individual, products of the form Hx to obtain the corresponding diagonal element of H. The second method computes, for each ungenotyped individual, a term that can be written as a quadratic form involving pedigree and genomic relationships. For both methods, the computational burden is linear in the number of ungenotyped animals. The last method reorders the computations of the second method so that they become linear in the number of genotyped animals, which is usually much smaller. We tested the methods in 3 small data sets (with ~2,000 genotyped animals and 30,000?500,000 animals in pedigree) and in a large simulated population (with 1,220,000 animals in pedigree and 36,000 genotyped animals). Tests resulted in satisfactory computing times (<10 min in the largest example using 10 parallel threads). Computing times were much shorter for the third method, as expected. Using these methods, estimates of genomic inbreeding in ungenotyped animals can be obtained on a regular basis. © 2020 American Dairy Science Association
653 $aGenetic variability
653 $aGenomic selection
653 $aInbreeding
653 $aSingle step
700 1 $aAGUILAR, I.
700 1 $aCOLLEAU, J.J.
773 $tJournal of Dairy Science, April 2020, Volume 103, Issue 4, Pages 3363-3367. Doi: https://doi.org/10.3168/jds.2019-17750
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