Beyond genomic selection: The animal model strikes back (one generation)
Summary Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary wit...
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| Veröffentlicht in: | Journal of animal breeding and genetics (1986) 2017-06, Vol.134 (3), p.224-231 |
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| container_title | Journal of animal breeding and genetics (1986) |
| container_volume | 134 |
| creator | Cantet, R.J.C. García‐Baccino, C.A. Rogberg‐Muñoz, A. Forneris, N.S. Munilla, S. |
| description | Summary
Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix. |
| doi_str_mv | 10.1111/jbg.12271 |
| format | Article |
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Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix.</description><identifier>ISSN: 0931-2668</identifier><identifier>EISSN: 1439-0388</identifier><identifier>DOI: 10.1111/jbg.12271</identifier><identifier>PMID: 28508480</identifier><language>eng</language><publisher>Germany: Blackwell Publishing Ltd</publisher><subject>Algorithms ; Animals ; Breeding ; breeding value ; causal inference ; Computational Biology - methods ; Covariance ; Deoxyribonucleic acid ; DNA ; Gaussian Markov density ; Gene loci ; Genetic Markers ; Genetics, Population ; Genomes ; genomic data ; Genomics - methods ; Heredity ; Inbreeding ; Markov processes ; Mathematical models ; Models, Animal ; Models, Genetic ; Parents ; Pedigree ; Population genetics ; segmental inheritance ; Selection, Genetic ; Simultaneous equations ; Strikes</subject><ispartof>Journal of animal breeding and genetics (1986), 2017-06, Vol.134 (3), p.224-231</ispartof><rights>2017 Blackwell Verlag GmbH</rights><rights>2017 Blackwell Verlag GmbH.</rights><rights>Copyright © 2017 Blackwell Verlag GmbH</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3881-106dbfd77c450e8a16e01e69abc70667570528718e78547b6a93de977a3646b63</citedby><cites>FETCH-LOGICAL-c3881-106dbfd77c450e8a16e01e69abc70667570528718e78547b6a93de977a3646b63</cites><orcidid>0000-0001-6282-146X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fjbg.12271$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fjbg.12271$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,27923,27924,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28508480$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cantet, R.J.C.</creatorcontrib><creatorcontrib>García‐Baccino, C.A.</creatorcontrib><creatorcontrib>Rogberg‐Muñoz, A.</creatorcontrib><creatorcontrib>Forneris, N.S.</creatorcontrib><creatorcontrib>Munilla, S.</creatorcontrib><title>Beyond genomic selection: The animal model strikes back (one generation)</title><title>Journal of animal breeding and genetics (1986)</title><addtitle>J Anim Breed Genet</addtitle><description>Summary
Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Breeding</subject><subject>breeding value</subject><subject>causal inference</subject><subject>Computational Biology - methods</subject><subject>Covariance</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>Gaussian Markov density</subject><subject>Gene loci</subject><subject>Genetic Markers</subject><subject>Genetics, Population</subject><subject>Genomes</subject><subject>genomic data</subject><subject>Genomics - methods</subject><subject>Heredity</subject><subject>Inbreeding</subject><subject>Markov processes</subject><subject>Mathematical models</subject><subject>Models, Animal</subject><subject>Models, Genetic</subject><subject>Parents</subject><subject>Pedigree</subject><subject>Population genetics</subject><subject>segmental inheritance</subject><subject>Selection, Genetic</subject><subject>Simultaneous equations</subject><subject>Strikes</subject><issn>0931-2668</issn><issn>1439-0388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp10E1PwjAYB_DGaATRg1_ANPECh0m7l754E6KgIfGC56bbHnCwrdiyGL69nUMPJvbSpPn1n-f5I3RNyR31Z7xJ13c0DDk9QX0aRzIgkRCnqE9kRIOQMdFDF85tCPHvXJ6jXigSImJB-mg-gYOpc7yG2lRFhh2UkO0LU9_j5TtgXReVLnFlciix29tiCw6nOtvioamh_QVWt3x0ic5WunRwdbwH6O3pcTmdB4vX2fP0YRFkfiYaUMLydJVznsUJAaEpA0KBSZ1mnDDGE06SUHAqgIsk5inTMspBcq4jFrOURQM07HJ31nw04PaqKlwGZalrMI1TVEgZk0Sy0NPbP3RjGlv76VolBGGCEa9Gncqscc7CSu2sX9oeFCWqrVf5etV3vd7eHBObtIL8V_706cG4A59FCYf_k9TLZNZFfgH2Z4D5</recordid><startdate>201706</startdate><enddate>201706</enddate><creator>Cantet, R.J.C.</creator><creator>García‐Baccino, C.A.</creator><creator>Rogberg‐Muñoz, A.</creator><creator>Forneris, N.S.</creator><creator>Munilla, S.</creator><general>Blackwell Publishing Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-6282-146X</orcidid></search><sort><creationdate>201706</creationdate><title>Beyond genomic selection: The animal model strikes back (one generation)</title><author>Cantet, R.J.C. ; García‐Baccino, C.A. ; Rogberg‐Muñoz, A. ; Forneris, N.S. ; Munilla, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3881-106dbfd77c450e8a16e01e69abc70667570528718e78547b6a93de977a3646b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Animals</topic><topic>Breeding</topic><topic>breeding value</topic><topic>causal inference</topic><topic>Computational Biology - methods</topic><topic>Covariance</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>Gaussian Markov density</topic><topic>Gene loci</topic><topic>Genetic Markers</topic><topic>Genetics, Population</topic><topic>Genomes</topic><topic>genomic data</topic><topic>Genomics - methods</topic><topic>Heredity</topic><topic>Inbreeding</topic><topic>Markov processes</topic><topic>Mathematical models</topic><topic>Models, Animal</topic><topic>Models, Genetic</topic><topic>Parents</topic><topic>Pedigree</topic><topic>Population genetics</topic><topic>segmental inheritance</topic><topic>Selection, Genetic</topic><topic>Simultaneous equations</topic><topic>Strikes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cantet, R.J.C.</creatorcontrib><creatorcontrib>García‐Baccino, C.A.</creatorcontrib><creatorcontrib>Rogberg‐Muñoz, A.</creatorcontrib><creatorcontrib>Forneris, N.S.</creatorcontrib><creatorcontrib>Munilla, S.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of animal breeding and genetics (1986)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cantet, R.J.C.</au><au>García‐Baccino, C.A.</au><au>Rogberg‐Muñoz, A.</au><au>Forneris, N.S.</au><au>Munilla, S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Beyond genomic selection: The animal model strikes back (one generation)</atitle><jtitle>Journal of animal breeding and genetics (1986)</jtitle><addtitle>J Anim Breed Genet</addtitle><date>2017-06</date><risdate>2017</risdate><volume>134</volume><issue>3</issue><spage>224</spage><epage>231</epage><pages>224-231</pages><issn>0931-2668</issn><eissn>1439-0388</eissn><abstract>Summary
Genome inheritance is by segments of DNA rather than by independent loci. We introduce the ancestral regression (AR) as a recursive system of simultaneous equations, with grandparental path coefficients as novel parameters. The information given by the pedigree in the AR is complementary with that provided by a dense set of genomic markers, such that the resulting linear function of grandparental BV is uncorrelated to the average of parental BV in the absence of inbreeding. AR is then connected to segmental inheritance by a causal multivariate Gaussian density for BV. The resulting covariance structure (Σ) is Markovian, meaning that conditional on the BV of parents and grandparents, the BV of the animal is independent of everything else. Thus, an algorithm is presented to invert the resulting covariance structure, with a computing effort that is linear in the number of animals as in the case of the inverse additive relationship matrix.</abstract><cop>Germany</cop><pub>Blackwell Publishing Ltd</pub><pmid>28508480</pmid><doi>10.1111/jbg.12271</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-6282-146X</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; Wiley Online Library Journals Frontfile Complete |
| subjects | Algorithms Animals Breeding breeding value causal inference Computational Biology - methods Covariance Deoxyribonucleic acid DNA Gaussian Markov density Gene loci Genetic Markers Genetics, Population Genomes genomic data Genomics - methods Heredity Inbreeding Markov processes Mathematical models Models, Animal Models, Genetic Parents Pedigree Population genetics segmental inheritance Selection, Genetic Simultaneous equations Strikes |
| title | Beyond genomic selection: The animal model strikes back (one generation) |
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