Meta - analysis of genome - wide association from genomic prediction models
Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.
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| Subjects: | GBLUP, GENOME - WIDE ASSOCIATION STUDIES, MULTIPLE POPULATIONS, |
| Online Access: | http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46294 http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= |
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KOHA-OAI-AGRO:462942022-03-18T10:43:07Zhttp://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46294http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=AAGMeta - analysis of genome - wide association from genomic prediction modelsBernal Rubio, Yeni LilianaGualdrón Duarte, José LuisBates, R. O.Ernst, C. W.Nonneman, D.Rohrer, G. A.King, A.Shackelford, S. D.Wheeler, T. L.Cantet, Rodolfo Juan CarlosSteibel, Juan Pedrotextengapplication/pdfGenome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits.GBLUPGENOME - WIDE ASSOCIATION STUDIESMULTIPLE POPULATIONSAnimal Genetics |
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GBLUP GENOME - WIDE ASSOCIATION STUDIES MULTIPLE POPULATIONS GBLUP GENOME - WIDE ASSOCIATION STUDIES MULTIPLE POPULATIONS |
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GBLUP GENOME - WIDE ASSOCIATION STUDIES MULTIPLE POPULATIONS GBLUP GENOME - WIDE ASSOCIATION STUDIES MULTIPLE POPULATIONS Bernal Rubio, Yeni Liliana Gualdrón Duarte, José Luis Bates, R. O. Ernst, C. W. Nonneman, D. Rohrer, G. A. King, A. Shackelford, S. D. Wheeler, T. L. Cantet, Rodolfo Juan Carlos Steibel, Juan Pedro Meta - analysis of genome - wide association from genomic prediction models |
| description |
Genome-wide association (GWA) studies based on GBLUP models are a common practice in animal breeding. However, effect sizes of GWA tests are small, requiring larger sample sizes to enhance power of detection of rare variants. Because of difficulties in increasing sample size in animal populations, one alternative is to implement a meta-analysis (MA), combining information and results from independent GWA studies. Although this methodology has been used widely in human genetics, implementation in animal breeding has been limited. Thus, we present methods to implement a MA of GWA, describing the proper approach to compute weights derived from multiple genomic evaluations based on animal-centric GBLUP models. Application to real datasets shows that MA increases power of detection of associations in comparison with population-level GWA, allowing for population structure and heterogeneity of variance components across populations to be accounted for. Another advantage of MA is that it does not require access to genotype data that is required for a joint analysis. Scripts related to the implementation of this approach, which consider the strength of association as well as the sign, are distributed and thus account for heterogeneity in association phase between QTL and SNPs. Thus, MA of GWA is an attractive alternative to summarizing results from multiple genomic studies, avoiding restrictions with genotype data sharing, definition of fixed effects and different scales of measurement of evaluated traits. |
| format |
Texto |
| topic_facet |
GBLUP GENOME - WIDE ASSOCIATION STUDIES MULTIPLE POPULATIONS |
| author |
Bernal Rubio, Yeni Liliana Gualdrón Duarte, José Luis Bates, R. O. Ernst, C. W. Nonneman, D. Rohrer, G. A. King, A. Shackelford, S. D. Wheeler, T. L. Cantet, Rodolfo Juan Carlos Steibel, Juan Pedro |
| author_facet |
Bernal Rubio, Yeni Liliana Gualdrón Duarte, José Luis Bates, R. O. Ernst, C. W. Nonneman, D. Rohrer, G. A. King, A. Shackelford, S. D. Wheeler, T. L. Cantet, Rodolfo Juan Carlos Steibel, Juan Pedro |
| author_sort |
Bernal Rubio, Yeni Liliana |
| title |
Meta - analysis of genome - wide association from genomic prediction models |
| title_short |
Meta - analysis of genome - wide association from genomic prediction models |
| title_full |
Meta - analysis of genome - wide association from genomic prediction models |
| title_fullStr |
Meta - analysis of genome - wide association from genomic prediction models |
| title_full_unstemmed |
Meta - analysis of genome - wide association from genomic prediction models |
| title_sort |
meta - analysis of genome - wide association from genomic prediction models |
| url |
http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46294 http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= |
| work_keys_str_mv |
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1756046625604632576 |