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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Using a very low-density SNP panel for genomic selection in a breeding program for sheep</title><author><name sortKey="Raoul, Jerome" sort="Raoul, Jerome" uniqKey="Raoul J" first="Jérôme" last="Raoul">Jérôme Raoul</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2199 2457</institution-id><institution-id institution-id-type="GRID">grid.425193.8</institution-id><institution>Institut de l’Elevage,</institution></institution-wrap>Castanet-Tolosan, France</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2169 1988</institution-id><institution-id institution-id-type="GRID">grid.414548.8</institution-id><institution>GenPhySE,</institution><institution>INRA,</institution></institution-wrap>Castanet-Tolosan, France</nlm:aff></affiliation></author><author><name sortKey="Swan, Andrew A" sort="Swan, Andrew A" uniqKey="Swan A" first="Andrew A." last="Swan">Andrew A. Swan</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1936 7371</institution-id><institution-id institution-id-type="GRID">grid.1020.3</institution-id><institution>Animal Genetics and Breeding Unit,</institution><institution>University of New England,</institution></institution-wrap>Armidale, Australia</nlm:aff></affiliation></author><author><name sortKey="Elsen, Jean Michel" sort="Elsen, Jean Michel" uniqKey="Elsen J" first="Jean-Michel" last="Elsen">Jean-Michel Elsen</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2169 1988</institution-id><institution-id institution-id-type="GRID">grid.414548.8</institution-id><institution>GenPhySE,</institution><institution>INRA,</institution></institution-wrap>Castanet-Tolosan, France</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">29065868</idno><idno type="pmc">5655911</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5655911</idno><idno type="RBID">PMC:5655911</idno><idno type="doi">10.1186/s12711-017-0351-0</idno><date when="2017">2017</date><idno type="wicri:Area/Pmc/Corpus">000D07</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000D07</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Using a very low-density SNP panel for genomic selection in a breeding program for sheep</title><author><name sortKey="Raoul, Jerome" sort="Raoul, Jerome" uniqKey="Raoul J" first="Jérôme" last="Raoul">Jérôme Raoul</name><affiliation><nlm:aff id="Aff1"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2199 2457</institution-id><institution-id institution-id-type="GRID">grid.425193.8</institution-id><institution>Institut de l’Elevage,</institution></institution-wrap>Castanet-Tolosan, France</nlm:aff></affiliation><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2169 1988</institution-id><institution-id institution-id-type="GRID">grid.414548.8</institution-id><institution>GenPhySE,</institution><institution>INRA,</institution></institution-wrap>Castanet-Tolosan, France</nlm:aff></affiliation></author><author><name sortKey="Swan, Andrew A" sort="Swan, Andrew A" uniqKey="Swan A" first="Andrew A." last="Swan">Andrew A. Swan</name><affiliation><nlm:aff id="Aff3"><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1936 7371</institution-id><institution-id institution-id-type="GRID">grid.1020.3</institution-id><institution>Animal Genetics and Breeding Unit,</institution><institution>University of New England,</institution></institution-wrap>Armidale, Australia</nlm:aff></affiliation></author><author><name sortKey="Elsen, Jean Michel" sort="Elsen, Jean Michel" uniqKey="Elsen J" first="Jean-Michel" last="Elsen">Jean-Michel Elsen</name><affiliation><nlm:aff id="Aff2"><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2169 1988</institution-id><institution-id institution-id-type="GRID">grid.414548.8</institution-id><institution>GenPhySE,</institution><institution>INRA,</institution></institution-wrap>Castanet-Tolosan, France</nlm:aff></affiliation></author></analytic><series><title level="j">Genetics, Selection, Evolution : GSE</title><idno type="ISSN">0999-193X</idno><idno type="eISSN">1297-9686</idno><imprint><date when="2017">2017</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p id="Par1">Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs).</p></sec><sec><title>Methods</title><p id="Par2">A population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated.</p></sec><sec><title>Results</title><p id="Par3">The genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from − 20 to − 34%) for the genomic design than for the classical design regardless of the genomic scenario.</p></sec><sec><title>Conclusions</title><p id="Par4">We show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Shumbusho, F" uniqKey="Shumbusho F">F Shumbusho</name></author><author><name sortKey="Raoul, J" uniqKey="Raoul J">J Raoul</name></author><author><name sortKey="Astruc, Jm" uniqKey="Astruc J">JM Astruc</name></author><author><name sortKey="Palhiere, I" uniqKey="Palhiere I">I Palhière</name></author><author><name sortKey="Elsen, Jm" uniqKey="Elsen J">JM Elsen</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Casellas, J" uniqKey="Casellas J">J Casellas</name></author><author><name sortKey="Piedrafita, J" uniqKey="Piedrafita J">J Piedrafita</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Granleese, T" uniqKey="Granleese T">T Granleese</name></author><author><name sortKey="Clark, Sa" uniqKey="Clark S">SA Clark</name></author><author><name sortKey="Swan, Aa" uniqKey="Swan A">AA Swan</name></author><author><name sortKey="Van Der Werf, Jhj" uniqKey="Van Der Werf J">JHJ van der Werf</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Shumbusho, F" uniqKey="Shumbusho F">F Shumbusho</name></author><author><name sortKey="Raoul, J" uniqKey="Raoul J">J Raoul</name></author><author><name sortKey="Astruc, Jm" uniqKey="Astruc J">JM Astruc</name></author><author><name sortKey="Palhiere, I" uniqKey="Palhiere I">I Palhiere</name></author><author><name sortKey="Lemarie, S" uniqKey="Lemarie S">S Lemarié</name></author><author><name sortKey="Fugeray Scarbel, A" uniqKey="Fugeray Scarbel A">A Fugeray-Scarbel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rupp, R" uniqKey="Rupp R">R Rupp</name></author><author><name sortKey="Mucha, S" uniqKey="Mucha S">S Mucha</name></author><author><name sortKey="Larroque, H" uniqKey="Larroque H">H Larroque</name></author><author><name sortKey="Mcewan, J" uniqKey="Mcewan J">J McEwan</name></author><author><name sortKey="Conington, J" uniqKey="Conington J">J Conington</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hayes, Bj" uniqKey="Hayes B">BJ Hayes</name></author><author><name sortKey="Bowman, Pj" uniqKey="Bowman P">PJ Bowman</name></author><author><name sortKey="Chamberlain, Aj" uniqKey="Chamberlain A">AJ Chamberlain</name></author><author><name sortKey="Goddard, Me" uniqKey="Goddard M">ME Goddard</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hall, Sjg" uniqKey="Hall S">SJG Hall</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Santos, Bfs" uniqKey="Santos B">BFS Santos</name></author><author><name sortKey="Van Der Werf, Jhj" uniqKey="Van Der Werf J">JHJ van der Werf</name></author><author><name sortKey="Gibson, Jp" uniqKey="Gibson J">JP Gibson</name></author><author><name sortKey="Byrne, Tj" uniqKey="Byrne T">TJ Byrne</name></author><author><name sortKey="Amer, Pr" uniqKey="Amer P">PR Amer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Moghaddar, N" uniqKey="Moghaddar N">N Moghaddar</name></author><author><name sortKey="Swan, Aa" uniqKey="Swan A">AA Swan</name></author><author><name sortKey="Van Der Werf, Jh" uniqKey="Van Der Werf J">JH van der Werf</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Mchugh, N" uniqKey="Mchugh N">N McHugh</name></author><author><name sortKey="Meuwissen, The" uniqKey="Meuwissen T">THE Meuwissen</name></author><author><name sortKey="Cromie, Ar" uniqKey="Cromie A">AR Cromie</name></author><author><name sortKey="Sonesson, Ak" uniqKey="Sonesson A">AK Sonesson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Buch, Lh" uniqKey="Buch L">LH Buch</name></author><author><name sortKey="Kargo, M" uniqKey="Kargo M">M Kargo</name></author><author><name sortKey="Berg, P" uniqKey="Berg P">P Berg</name></author><author><name sortKey="Lassen, J" uniqKey="Lassen J">J Lassen</name></author><author><name sortKey="S Rensen, Ac" uniqKey="S Rensen A">AC Sørensen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gonzalez Recio, O" uniqKey="Gonzalez Recio O">O Gonzalez-Recio</name></author><author><name sortKey="Coffey, Mp" uniqKey="Coffey M">MP Coffey</name></author><author><name sortKey="Pryce, Je" uniqKey="Pryce J">JE Pryce</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Koivula, M" uniqKey="Koivula M">M Koivula</name></author><author><name sortKey="Stranden, I" uniqKey="Stranden I">I Strandén</name></author><author><name sortKey="Aamand, Gp" uniqKey="Aamand G">GP Aamand</name></author><author><name sortKey="M Ntysaari, Ea" uniqKey="M Ntysaari E">EA Mäntysaari</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Plieschke, L" uniqKey="Plieschke L">L Plieschke</name></author><author><name sortKey="Edel, C" uniqKey="Edel C">C Edel</name></author><author><name sortKey="Pimentel, Ecg" uniqKey="Pimentel E">ECG Pimentel</name></author><author><name sortKey="Emmerling, R" uniqKey="Emmerling R">R Emmerling</name></author><author><name sortKey="Bennewitz, J" uniqKey="Bennewitz J">J Bennewitz</name></author><author><name sortKey="Gotz, Ku" uniqKey="Gotz K">KU Götz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Der Werf, J" uniqKey="Van Der Werf J">J van der Werf</name></author><author><name sortKey="Banks, R" uniqKey="Banks R">R Banks</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Pryce, Je" uniqKey="Pryce J">JE Pryce</name></author><author><name sortKey="Daetwyler, Hd" uniqKey="Daetwyler H">HD Daetwyler</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Clarke, Sm" uniqKey="Clarke S">SM Clarke</name></author><author><name sortKey="Henry, Hm" uniqKey="Henry H">HM Henry</name></author><author><name sortKey="Dodds, Kg" uniqKey="Dodds K">KG Dodds</name></author><author><name sortKey="Jowett, Twd" uniqKey="Jowett T">TWD Jowett</name></author><author><name sortKey="Manley, Tr" uniqKey="Manley T">TR Manley</name></author><author><name sortKey="Anderson, Rm" uniqKey="Anderson R">RM Anderson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Heaton, Mp" uniqKey="Heaton M">MP Heaton</name></author><author><name sortKey="Leymaster, Ka" uniqKey="Leymaster K">KA Leymaster</name></author><author><name sortKey="Kalbfleisch, Ts" uniqKey="Kalbfleisch T">TS Kalbfleisch</name></author><author><name sortKey="Kijas, Jw" uniqKey="Kijas J">JW Kijas</name></author><author><name sortKey="Clarke, Sm" uniqKey="Clarke S">SM Clarke</name></author><author><name sortKey="Mcewan, J" uniqKey="Mcewan J">J McEwan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Tortereau, F" uniqKey="Tortereau F">F Tortereau</name></author><author><name sortKey="Moreno, Cr" uniqKey="Moreno C">CR Moreno</name></author><author><name sortKey="Tosser Klopp, G" uniqKey="Tosser Klopp G">G Tosser-Klopp</name></author><author><name sortKey="Servin, B" uniqKey="Servin B">B Servin</name></author><author><name sortKey="Raoul, J" uniqKey="Raoul J">J Raoul</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Habier, D" uniqKey="Habier D">D Habier</name></author><author><name sortKey="Fernando, Rl" uniqKey="Fernando R">RL Fernando</name></author><author><name sortKey="Dekkers, Jcm" uniqKey="Dekkers J">JCM Dekkers</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Zhang, Z" uniqKey="Zhang Z">Z Zhang</name></author><author><name sortKey="Druet, T" uniqKey="Druet T">T Druet</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hayes, Bj" uniqKey="Hayes B">BJ Hayes</name></author><author><name sortKey="Bowman, Pj" uniqKey="Bowman P">PJ Bowman</name></author><author><name sortKey="Daetwyler, Hd" uniqKey="Daetwyler H">HD Daetwyler</name></author><author><name sortKey="Kijas, Jw" uniqKey="Kijas J">JW Kijas</name></author><author><name sortKey="Van Der Werf, Jhj" uniqKey="Van Der Werf J">JHJ van der Werf</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Huang, Y" uniqKey="Huang Y">Y Huang</name></author><author><name sortKey="Hickey, Jm" uniqKey="Hickey J">JM Hickey</name></author><author><name sortKey="Cleveland, Ma" uniqKey="Cleveland M">MA Cleveland</name></author><author><name sortKey="Maltecca, C" uniqKey="Maltecca C">C Maltecca</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wang, C" uniqKey="Wang C">C Wang</name></author><author><name sortKey="Habier, D" uniqKey="Habier D">D Habier</name></author><author><name sortKey="Peiris, Bl" uniqKey="Peiris B">BL Peiris</name></author><author><name sortKey="Wolc, A" uniqKey="Wolc A">A Wolc</name></author><author><name sortKey="Kranis, A" uniqKey="Kranis A">A Kranis</name></author><author><name sortKey="Watson, Ka" uniqKey="Watson K">KA Watson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Cleveland, Ma" uniqKey="Cleveland M">MA Cleveland</name></author><author><name sortKey="Hickey, Jm" uniqKey="Hickey J">JM Hickey</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Wellmann, R" uniqKey="Wellmann R">R Wellmann</name></author><author><name sortKey="Preu, S" uniqKey="Preu S">S Preuß</name></author><author><name sortKey="Tholen, E" uniqKey="Tholen E">E Tholen</name></author><author><name sortKey="Heinkel, J" uniqKey="Heinkel J">J Heinkel</name></author><author><name sortKey="Wimmers, K" uniqKey="Wimmers K">K Wimmers</name></author><author><name sortKey="Bennewitz, J" uniqKey="Bennewitz J">J Bennewitz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hoze, C" uniqKey="Hoze C">C Hozé</name></author><author><name sortKey="Fouilloux, Mn" uniqKey="Fouilloux M">MN Fouilloux</name></author><author><name sortKey="Venot, E" uniqKey="Venot E">E Venot</name></author><author><name sortKey="Guillaume, F" uniqKey="Guillaume F">F Guillaume</name></author><author><name sortKey="Dassonneville, R" uniqKey="Dassonneville R">R Dassonneville</name></author><author><name sortKey="Fritz, S" uniqKey="Fritz S">S Fritz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="He, S" uniqKey="He S">S He</name></author><author><name sortKey="Wang, S" uniqKey="Wang S">S Wang</name></author><author><name sortKey="Fu, W" uniqKey="Fu W">W Fu</name></author><author><name sortKey="Ding, X" uniqKey="Ding X">X Ding</name></author><author><name sortKey="Zhang, Q" uniqKey="Zhang Q">Q Zhang</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Bolormaa, S" uniqKey="Bolormaa S">S Bolormaa</name></author><author><name sortKey="Gore, K" uniqKey="Gore K">K Gore</name></author><author><name sortKey="Van Der Werf, Jhj" uniqKey="Van Der Werf J">JHJ van der Werf</name></author><author><name sortKey="Hayes, Bj" uniqKey="Hayes B">BJ Hayes</name></author><author><name sortKey="Daetwyler, Hd" uniqKey="Daetwyler H">HD Daetwyler</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Moghaddar, N" uniqKey="Moghaddar N">N Moghaddar</name></author><author><name sortKey="Gore, Kp" uniqKey="Gore K">KP Gore</name></author><author><name sortKey="Daetwyler, Hd" uniqKey="Daetwyler H">HD Daetwyler</name></author><author><name sortKey="Hayes, Bj" uniqKey="Hayes B">BJ Hayes</name></author><author><name sortKey="Van Der Werf, Jhj" uniqKey="Van Der Werf J">JHJ van der Werf</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Xiang, T" uniqKey="Xiang T">T Xiang</name></author><author><name sortKey="Ma, P" uniqKey="Ma P">P Ma</name></author><author><name sortKey="Ostersen, T" uniqKey="Ostersen T">T Ostersen</name></author><author><name sortKey="Legarra, A" uniqKey="Legarra A">A Legarra</name></author><author><name sortKey="Christensen, Of" uniqKey="Christensen O">OF Christensen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Ventura, Rv" uniqKey="Ventura R">RV Ventura</name></author><author><name sortKey="Lee, Ma" uniqKey="Lee M">MA Lee</name></author><author><name sortKey="Miller, Sp" uniqKey="Miller S">SP Miller</name></author><author><name sortKey="Clarke, Sm" uniqKey="Clarke S">SM Clarke</name></author><author><name sortKey="Mcewan, Jc" uniqKey="Mcewan J">JC McEwan</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Toghiani, S" uniqKey="Toghiani S">S Toghiani</name></author><author><name sortKey="Aggrey, Se" uniqKey="Aggrey S">SE Aggrey</name></author><author><name sortKey="Rekaya, R" uniqKey="Rekaya R">R Rekaya</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Badke, Ym" uniqKey="Badke Y">YM Badke</name></author><author><name sortKey="Bates, Ro" uniqKey="Bates R">RO Bates</name></author><author><name sortKey="Ernst, Cw" uniqKey="Ernst C">CW Ernst</name></author><author><name sortKey="Fix, J" uniqKey="Fix J">J Fix</name></author><author><name sortKey="Steibel, Jp" uniqKey="Steibel J">JP Steibel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Kijas, Jw" uniqKey="Kijas J">JW Kijas</name></author><author><name sortKey="Porto Neto, L" uniqKey="Porto Neto L">L Porto-Neto</name></author><author><name sortKey="Dominik, S" uniqKey="Dominik S">S Dominik</name></author><author><name sortKey="Reverter, A" uniqKey="Reverter A">A Reverter</name></author><author><name sortKey="Bunch, R" uniqKey="Bunch R">R Bunch</name></author><author><name sortKey="Mcculloch, R" uniqKey="Mcculloch R">R McCulloch</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sargolzaei, M" uniqKey="Sargolzaei M">M Sargolzaei</name></author><author><name sortKey="Chesnais, Jp" uniqKey="Chesnais J">JP Chesnais</name></author><author><name sortKey="Schenkel, Fs" uniqKey="Schenkel F">FS Schenkel</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Clark, Sa" uniqKey="Clark S">SA Clark</name></author><author><name sortKey="Hickey, Jm" uniqKey="Hickey J">JM Hickey</name></author><author><name sortKey="Daetwyler, Hd" uniqKey="Daetwyler H">HD Daetwyler</name></author><author><name sortKey="Van Der Werf, Jh" uniqKey="Van Der Werf J">JH van der Werf</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hayes, Bj" uniqKey="Hayes B">BJ Hayes</name></author><author><name sortKey="Goddard, Me" uniqKey="Goddard M">ME Goddard</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Falconer, Ds" uniqKey="Falconer D">DS Falconer</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Henderson, Cr" uniqKey="Henderson C">CR Henderson</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Aguilar, I" uniqKey="Aguilar I">I Aguilar</name></author><author><name sortKey="Misztal, I" uniqKey="Misztal I">I Misztal</name></author><author><name sortKey="Johnson, Dl" uniqKey="Johnson D">DL Johnson</name></author><author><name sortKey="Legarra, A" uniqKey="Legarra A">A Legarra</name></author><author><name sortKey="Tsuruta, S" uniqKey="Tsuruta S">S Tsuruta</name></author><author><name sortKey="Lawlor, Tj" uniqKey="Lawlor T">TJ Lawlor</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Misztal, I" uniqKey="Misztal I">I Misztal</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Van Raden, Pm" uniqKey="Van Raden P">PM Van Raden</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Aguilar, I" uniqKey="Aguilar I">I Aguilar</name></author><author><name sortKey="Misztal, I" uniqKey="Misztal I">I Misztal</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Hill, Wg" uniqKey="Hill W">WG Hill</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Daetwyler, Hd" uniqKey="Daetwyler H">HD Daetwyler</name></author><author><name sortKey="Villanueva, B" uniqKey="Villanueva B">B Villanueva</name></author><author><name sortKey="Woolliams, Ja" uniqKey="Woolliams J">JA Woolliams</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Thomasen, Jr" uniqKey="Thomasen J">JR Thomasen</name></author><author><name sortKey="S Rensen, Ac" uniqKey="S Rensen A">AC Sørensen</name></author><author><name sortKey="Lund, Ms" uniqKey="Lund M">MS Lund</name></author><author><name sortKey="Guldbrandtsen, B" uniqKey="Guldbrandtsen B">B Guldbrandtsen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Su, G" uniqKey="Su G">G Su</name></author><author><name sortKey="Ma, P" uniqKey="Ma P">P Ma</name></author><author><name sortKey="Nielsen, Us" uniqKey="Nielsen U">US Nielsen</name></author><author><name sortKey="Aamand, Gp" uniqKey="Aamand G">GP Aamand</name></author><author><name sortKey="Wiggans, G" uniqKey="Wiggans G">G Wiggans</name></author><author><name sortKey="Guldbrandtsen, B" uniqKey="Guldbrandtsen B">B Guldbrandtsen</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Gao, H" uniqKey="Gao H">H Gao</name></author><author><name sortKey="Madsen, P" uniqKey="Madsen P">P Madsen</name></author><author><name sortKey="Nielsen, Us" uniqKey="Nielsen U">US Nielsen</name></author><author><name sortKey="Aamand, Gp" uniqKey="Aamand G">GP Aamand</name></author><author><name sortKey="Su, G" uniqKey="Su G">G Su</name></author><author><name sortKey="Byskov, K" uniqKey="Byskov K">K Byskov</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Calus, Mpl" uniqKey="Calus M">MPL Calus</name></author><author><name sortKey="De Haas, Y" uniqKey="De Haas Y">Y de Haas</name></author><author><name sortKey="Veerkamp, Rf" uniqKey="Veerkamp R">RF Veerkamp</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dassonneville, R" uniqKey="Dassonneville R">R Dassonneville</name></author><author><name sortKey="Baur, A" uniqKey="Baur A">A Baur</name></author><author><name sortKey="Fritz, S" uniqKey="Fritz S">S Fritz</name></author><author><name sortKey="Boichard, D" uniqKey="Boichard D">D Boichard</name></author><author><name sortKey="Ducrocq, V" uniqKey="Ducrocq V">V Ducrocq</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Edel, C" uniqKey="Edel C">C Edel</name></author><author><name sortKey="Pimentel, Ecg" uniqKey="Pimentel E">ECG Pimentel</name></author><author><name sortKey="Plieschke, L" uniqKey="Plieschke L">L Plieschke</name></author><author><name sortKey="Emmerling, R" uniqKey="Emmerling R">R Emmerling</name></author><author><name sortKey="Gotz, Ku" uniqKey="Gotz K">KU Götz</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lee, Sh" uniqKey="Lee S">SH Lee</name></author><author><name sortKey="Weerasinghe, Wmsp" uniqKey="Weerasinghe W">WMSP Weerasinghe</name></author><author><name sortKey="Wray, Nr" uniqKey="Wray N">NR Wray</name></author><author><name sortKey="Goddard, Me" uniqKey="Goddard M">ME Goddard</name></author><author><name sortKey="Van Der Werf, Jhj" uniqKey="Van Der Werf J">JHJ van der Werf</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Habier, D" uniqKey="Habier D">D Habier</name></author><author><name sortKey="Fernando, Rl" uniqKey="Fernando R">RL Fernando</name></author><author><name sortKey="Garrick, Dj" uniqKey="Garrick D">DJ Garrick</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Sun, X" uniqKey="Sun X">X Sun</name></author><author><name sortKey="Fernando, R" uniqKey="Fernando R">R Fernando</name></author><author><name sortKey="Dekkers, J" uniqKey="Dekkers J">J Dekkers</name></author></analytic></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Genet Sel Evol</journal-id><journal-id journal-id-type="iso-abbrev">Genet. Sel. Evol</journal-id><journal-title-group><journal-title>Genetics, Selection, Evolution : GSE</journal-title></journal-title-group><issn pub-type="ppub">0999-193X</issn><issn pub-type="epub">1297-9686</issn><publisher><publisher-name>BioMed Central</publisher-name><publisher-loc>London</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">29065868</article-id><article-id pub-id-type="pmc">5655911</article-id><article-id pub-id-type="publisher-id">351</article-id><article-id pub-id-type="doi">10.1186/s12711-017-0351-0</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Using a very low-density SNP panel for genomic selection in a breeding program for sheep</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-3140-4433</contrib-id><name><surname>Raoul</surname><given-names>Jérôme</given-names></name><address><email>Jerome.raoul@inra.fr</email></address><xref ref-type="aff" rid="Aff1">1</xref><xref ref-type="aff" rid="Aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>Swan</surname><given-names>Andrew A.</given-names></name><address><email>Andrew.Swan@une.edu.au</email></address><xref ref-type="aff" rid="Aff3">3</xref></contrib><contrib contrib-type="author"><name><surname>Elsen</surname><given-names>Jean-Michel</given-names></name><address><email>jean-michel.elsen@inra.fr</email></address><xref ref-type="aff" rid="Aff2">2</xref></contrib><aff id="Aff1"><label>1</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2199 2457</institution-id><institution-id institution-id-type="GRID">grid.425193.8</institution-id><institution>Institut de l’Elevage,</institution></institution-wrap>Castanet-Tolosan, France</aff><aff id="Aff2"><label>2</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0001 2169 1988</institution-id><institution-id institution-id-type="GRID">grid.414548.8</institution-id><institution>GenPhySE,</institution><institution>INRA,</institution></institution-wrap>Castanet-Tolosan, France</aff><aff id="Aff3"><label>3</label><institution-wrap><institution-id institution-id-type="ISNI">0000 0004 1936 7371</institution-id><institution-id institution-id-type="GRID">grid.1020.3</institution-id><institution>Animal Genetics and Breeding Unit,</institution><institution>University of New England,</institution></institution-wrap>Armidale, Australia</aff></contrib-group><pub-date pub-type="epub"><day>24</day><month>10</month><year>2017</year></pub-date><pub-date pub-type="pmc-release"><day>24</day><month>10</month><year>2017</year></pub-date><pub-date pub-type="collection"><year>2017</year></pub-date><volume>49</volume><elocation-id>76</elocation-id><history><date date-type="received"><day>19</day><month>5</month><year>2017</year></date><date date-type="accepted"><day>17</day><month>10</month><year>2017</year></date></history><permissions><copyright-statement>© The Author(s) 2017</copyright-statement><license license-type="OpenAccess"><license-p><bold>Open Access</bold>This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/publicdomain/zero/1.0/">http://creativecommons.org/publicdomain/zero/1.0/</ext-link>) applies to the data made available in this article, unless otherwise stated.</license-p></license></permissions><abstract id="Abs1"><sec><title>Background</title><p id="Par1">Building an efficient reference population for genomic selection is an issue when the recorded population is small and phenotypes are poorly informed, which is often the case in sheep breeding programs. Using stochastic simulation, we evaluated a genomic design based on a reference population with medium-density genotypes [around 45 K single nucleotide polymorphisms (SNPs)] of dams that were imputed from very low-density genotypes (≤ 1000 SNPs).</p></sec><sec><title>Methods</title><p id="Par2">A population under selection for a maternal trait was simulated using real genotypes. Genetic gains realized from classical selection and genomic selection designs were compared. Genomic selection scenarios that differed in reference population structure (whether or not dams were included in the reference) and genotype quality (medium-density or imputed to medium-density from very low-density) were evaluated.</p></sec><sec><title>Results</title><p id="Par3">The genomic design increased genetic gain by 26% when the reference population was based on sire medium-density genotypes and by 54% when the reference population included both sire and dam medium-density genotypes. When medium-density genotypes of male candidates and dams were replaced by imputed genotypes from very low-density SNP genotypes (1000 SNPs), the increase in gain was 22% for the sire reference population and 42% for the sire and dam reference population. The rate of increase in inbreeding was lower (from − 20 to − 34%) for the genomic design than for the classical design regardless of the genomic scenario.</p></sec><sec><title>Conclusions</title><p id="Par4">We show that very low-density genotypes of male candidates and dams combined with an imputation process result in a substantial increase in genetic gain for small sheep breeding programs.</p></sec></abstract><funding-group><award-group><funding-source><institution>Institut de l’Elevage</institution></funding-source></award-group><award-group><funding-source><institution>INRA (Department of Animal Genetic and metaprogram SELGEN)</institution></funding-source></award-group><award-group><funding-source><institution>Apis-gène</institution></funding-source></award-group><award-group><funding-source><institution>AGBU</institution></funding-source></award-group></funding-group><custom-meta-group><custom-meta><meta-name>issue-copyright-statement</meta-name><meta-value>© The Author(s) 2017</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="Sec1"><title>Background</title><p id="Par5">Although technical and economic interests of genomic breeding programs in sheep have been positively assessed by various authors [<xref ref-type="bibr" rid="CR1">1</xref>–<xref ref-type="bibr" rid="CR5">5</xref>], only a few countries deliver genomic breeding values for meat and dairy sheep [<xref ref-type="bibr" rid="CR6">6</xref>]. For many sheep breeds, the remaining main obstacle is to build an efficient reference population based on medium-density (MD ~ 50 K) genotypes and estimated breeding values (EBV) of sires to estimate the effects of single nucleotide polymorphisms (SNPs). Genomic prediction accuracy depends mainly on the number of animals included in the reference population, the accuracy of their EBV, their relationship with target animals (mostly candidates without phenotypes), and the effective size of the population [<xref ref-type="bibr" rid="CR7">7</xref>]. In sheep, compared to dairy cattle, the effective population size is generally larger [<xref ref-type="bibr" rid="CR8">8</xref>], and especially for maternal traits, EBV of both artificial insemination (AI) and naturally-mated sires are less accurate due to smaller numbers of progeny per sire. An increase in reference population size could counterbalance these unfavorable factors. However, this increase is limited when the population itself is small and the reference population is based on sires only. Including records and genotypes from lower tiers of the population is promising [<xref ref-type="bibr" rid="CR9">9</xref>], whereas, so far, multi-breed approaches have not led to the expected increases in genomic prediction accuracy [<xref ref-type="bibr" rid="CR10">10</xref>]. Another way to increase the reference population would be to include females. Studies on dairy cattle breeding programs show that this strategy is efficient, especially when the reference population is based on a limited number of sires and/or records [<xref ref-type="bibr" rid="CR11">11</xref>–<xref ref-type="bibr" rid="CR15">15</xref>]. The impact of including females in the reference population on a sheep breeding program has never been assessed.</p><p id="Par6">In sheep, the profitability at the nucleus level remains a critical factor for the design of breeding programs [<xref ref-type="bibr" rid="CR16">16</xref>]. Implementation of genomic selection is mostly likely only if the cost is similar to that of the current design. In a genomic design, AI sires are no longer progeny-tested but both the animals in the reference population and the selection candidates must be genotyped. Since genotyping costs are quite high in sheep relative to the economic value of breeding animals, the number of genotypes has a large influence on the profitability of the design. Including dams in the reference population would increase the genotyping costs. Besides, the magnitude of the selection differential on genomic estimated breeding values (GEBV) of male candidates is a critical factor that determines the additional benefit of genomic selection breeding programs [<xref ref-type="bibr" rid="CR17">17</xref>]. Increasing the selection differential requires additional candidate genotypes resulting in additional genotyping costs as well.</p><p id="Par7">Very low-density (VLD) genotypes in association with imputation techniques would reduce the genotyping costs of a genomic design. VLD panels based on a few hundred SNPs are available at lower cost than low-density genotypes (~ 15 K) and are mainly used for parentage assignment [<xref ref-type="bibr" rid="CR18">18</xref>–<xref ref-type="bibr" rid="CR20">20</xref>]. Since candidate SNPs for parentage assignment are widely available across breeds (e.g. 9269 SNPs had a minor allele frequency higher than 0.30 in at least 20 French sheep populations [<xref ref-type="bibr" rid="CR20">20</xref>]), new panels under development will probably be close to 1000 SNPs. Imputation techniques based on common SNPs that are present on both MD and VLD panels can be used to infer missing MD genotypes of male candidates and dams, thus to exploit MD genotypes of selected sires as a reference population. Population-based methods use the linkage disequilibrium (LD) between SNPs and haplotype frequencies only, whereas population- and family-based methods also include co-segregation information based on pedigree. The factors that affect imputation accuracy [<xref ref-type="bibr" rid="CR21">21</xref>–<xref ref-type="bibr" rid="CR35">35</xref>] and the relation between imputation accuracy and genomic prediction quality [<xref ref-type="bibr" rid="CR21">21</xref>, <xref ref-type="bibr" rid="CR25">25</xref>–<xref ref-type="bibr" rid="CR27">27</xref>, <xref ref-type="bibr" rid="CR30">30</xref>, <xref ref-type="bibr" rid="CR31">31</xref>, <xref ref-type="bibr" rid="CR34">34</xref>, <xref ref-type="bibr" rid="CR35">35</xref>] are well documented. In sheep, Moghaddar et al. [<xref ref-type="bibr" rid="CR31">31</xref>] found a correlation close to 1 between GEBV computed from real versus imputed genotypes with an average imputation accuracy of 0.96. The imputation accuracy depends on: (1) the characteristics of the low-density panel with respect to the minor allele frequency, the number, spacing and localization of SNPs [<xref ref-type="bibr" rid="CR21">21</xref>–<xref ref-type="bibr" rid="CR27">27</xref>, <xref ref-type="bibr" rid="CR30">30</xref>, <xref ref-type="bibr" rid="CR33">33</xref>, <xref ref-type="bibr" rid="CR34">34</xref>]; (2) the linkage between adjacent SNPs [<xref ref-type="bibr" rid="CR22">22</xref>, <xref ref-type="bibr" rid="CR24">24</xref>]; and (3) the characteristics of the reference population including size, single or multi-breed population and relationship with imputed animals [<xref ref-type="bibr" rid="CR21">21</xref>, <xref ref-type="bibr" rid="CR24">24</xref>, <xref ref-type="bibr" rid="CR26">26</xref>, <xref ref-type="bibr" rid="CR27">27</xref>, <xref ref-type="bibr" rid="CR29">29</xref>–<xref ref-type="bibr" rid="CR31">31</xref>, <xref ref-type="bibr" rid="CR34">34</xref>]. As observed with genomic prediction accuracy, imputation accuracy increases as close relatives are included in the reference population and pedigree information is used [<xref ref-type="bibr" rid="CR21">21</xref>, <xref ref-type="bibr" rid="CR24">24</xref>, <xref ref-type="bibr" rid="CR26">26</xref>, <xref ref-type="bibr" rid="CR29">29</xref>, <xref ref-type="bibr" rid="CR30">30</xref>, <xref ref-type="bibr" rid="CR34">34</xref>].</p><p id="Par8">Focusing on a breeding program applied to a small population of purebred sheep in which both AI and natural mating sires are used, the objectives of this study were to quantify the impact of (1) increasing the reference population size with female genotypes and (2) imputing genotypes of male candidates and females from very low- to medium-density SNP panels. The presence of all sires and grand-sires of dams and male candidates in the reference population for imputation is expected to limit the detrimental effect of using a VLD panel on imputation accuracy. Various designs were compared, including a classical selection scheme based on progeny testing of AI sires as a baseline scenario, and several genomic selection schemes. Five scenarios were assessed for genomic selection by varying the reference population component (with or without dams), and genotype information (MD or imputed MD genotypes for male candidates and dams).</p></sec><sec id="Sec2"><title>Methods</title><p id="Par9">To compare different designs of breeding programs, we developed a stochastic simulation model where individuals, including their genome, were simulated. Stochastic events were simulated using the NAG FORTRAN library [the Numerical Algorithms Group (NAG), Oxford, UK]. Parents were randomly mated and replacements randomly selected during 20 reproductive cycles to establish a founder population. Ten years of selection for a maternal trait were then simulated by applying a classical progeny test design. At this time (Time = 10), the average LD (r<sup>2</sup>) between SNPs was equal to 0.13 at 50 kb, 0.07 at 200 kb and 0.05 at 1000 kb. These values of r<sup>2</sup> were within the range of estimated r<sup>2</sup> and Ne reported by Kijas et al. [<xref ref-type="bibr" rid="CR36">36</xref>] for the Merino breed (r<sup>2</sup> ~ 0.10 at 50 kb, Ne = 900), the Suffolk breed (r<sup>2</sup> ~ 0.13, Ne = 569) and the Poll Dorset breed (r<sup>2</sup> ~ 0.19 at 50 kb, Ne = 318). The next 15 years were simulated by applying either a classical or a genomic design. The main steps of the simulation are described in Fig. <xref rid="Fig1" ref-type="fig">1</xref>.<fig id="Fig1"><label>Fig. 1</label><caption><p>General overview of the simulation steps. <sup>1</sup>SNP, single nucleotide polymorphism; <sup>2</sup>QTL: quantitative trait locus; <sup>3</sup>MAF: minor allele frequency; <sup>4</sup>VLD: very low density; <sup>5</sup>BLUP: best linear unbiased prediction; <sup>6</sup>ssGBLUP: single step genomic BLUP</p></caption><graphic xlink:href="12711_2017_351_Fig1_HTML" id="MO1"></graphic></fig></p><sec id="Sec3"><title>Founder population</title><p id="Par10">A total of 11,816 real genotypes of 54,241 bi-allelic SNPs (Illumina OvineSNP50 BeadChip-Illumina ©) from progeny-tested sires of the dairy Lacaune sheep breed were used to initialize population genomes (initial population). The genotypes were cleaned (call frequency ≥ 0.95, Hardy–Weinberg equilibrium, Mendelian inheritance compatibilities) and only autosomal SNPs with a known position on the genome were retained: 47,706 SNPs were used for the genome simulations. Using genealogical information, phased chromosomes were obtained from Fimpute [<xref ref-type="bibr" rid="CR37">37</xref>]. At Time -20, the phased genome of the 11,816 individuals and a randomly assigned sex were used as a starting point for each simulation run.</p><p id="Par11">Accuracy of genomic predictions depends on the relationships between individuals of the reference population and candidate animals [<xref ref-type="bibr" rid="CR31">31</xref>, <xref ref-type="bibr" rid="CR38">38</xref>]. To obtain the genomes of a small population of females mated to a limited number of males, random mating cycles were performed using numbers of males and females close to the replacement rates used in selection in the subsequent cycles. The number of random mating cycles has a limited effect on LD (differences in LD between various numbers of cycles were lower than 0.01 at 50 kb, 200 kb and 1000 kb within 10 to 30 cycles). To obtain the desired demographic structure, 20 random mating cycles were chosen. At each cycle, 1200 females were randomly selected from females that were born in the previous two cycles and randomly mated to 100 males that were selected among males born in the previous cycle. Given the number of progeny per dam allocated according to a prior distribution (single = 0.4, twin = 0.5, triplet = 0.1), selection from two cycles was required to generate a sufficient number of female replacements. The parental gametes were generated to produce new individual genomes: the number of crossovers was simulated following a Poisson distribution <inline-formula id="IEq1"><alternatives><tex-math id="M1">\documentclass[12pt]{minimal}
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\begin{document}$$\frac{1 - rep}{{h^{2} }}$$\end{document}</tex-math><mml:math id="M6"><mml:mfrac><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>p</mml:mi></mml:mrow><mml:msup><mml:mi>h</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mfrac></mml:math><inline-graphic xlink:href="12711_2017_351_Article_IEq3.gif"></inline-graphic></alternatives></inline-formula>. The <italic>k</italic>th phenotype <italic>y</italic><sub><italic>jk</italic></sub> of a female <italic>j</italic> was simulated as <italic>y</italic><sub><italic>jk</italic></sub> = <italic>TBV</italic><sub><italic>j</italic></sub> + <italic>pe</italic><sub><italic>j</italic></sub> + <italic>e</italic><sub><italic>jk</italic></sub> with <italic>TBV</italic><sub><italic>j</italic></sub> the true breeding value depending on the effects of all QTL and the female genotype.</p></sec><sec id="Sec5"><title>Estimation of breeding values</title><p id="Par13">The estimation of breeding values was based on an animal best linear unbiased prediction (BLUP) model [<xref ref-type="bibr" rid="CR41">41</xref>] for the classical design and genomic evaluation on an animal single-step genomic (G)BLUP [<xref ref-type="bibr" rid="CR42">42</xref>] for the genomic design. Both types of evaluation were performed using the Blupf90 software [<xref ref-type="bibr" rid="CR43">43</xref>] developed by Misztal et al. The genomic relationship matrix was built following Van Raden [<xref ref-type="bibr" rid="CR44">44</xref>]. Marker inconsistencies between parents and progeny were due to imputation errors. Errors detected by Blupf90 resulted in the removal of the corresponding progeny genotype before the evaluation. Coefficients of inbreeding based on pedigree information were computed using the methodology developed by Aguilar and Misztal [<xref ref-type="bibr" rid="CR45">45</xref>] and implemented in the Blupf90 software.</p></sec><sec id="Sec6"><title>Very low-density and medium-density panels</title><p id="Par14">SNPs with a minor allele frequency higher or equal to 0.25 and not selected as a QTL were considered as candidates for inclusion in the VLD panel. To select <italic>n</italic><sub><italic>vld</italic></sub> evenly spaced SNPs (<italic>n</italic><sub><italic>vld</italic></sub> = 250, 500 and 1000), the genome was divided into <italic>n</italic><sub><italic>vld</italic></sub> windows. Each window was subdivided into three parts and a SNP was randomly selected from candidate SNPs located in the central part. If no candidate was available, a SNP located within the window or in adjacent windows was selected. The MD panel (46,706 SNPs) included all SNPs except those selected as QTL.</p></sec><sec id="Sec7"><title>Population and breeding designs</title><p id="Par15">Around 5000 females divided into 10 flocks were recorded each year for the trait under selection. According to Hill [<xref ref-type="bibr" rid="CR46">46</xref>], and neglecting the correlation between progeny sizes of sires to breed sires and sires to breed dams and between progeny sizes of dams to breed sires and dams to breed dams, the effective population size was around 180. At each reproductive cycle (length = 1 year), half of the breeding females were selected on EBV for mating by AI. Assuming a fertility rate of 55% on induced estrus, females that did not conceive to AI and females not selected for AI were then randomly mated to natural mating sires allocated to their flock assuming a fertility rate of 90%. The average number of females per naturally-mated sire was equal to 35. To prevent inbreeding, a male (AI or natural mating) could not be mated to a female belonging to its dam’s flock. The number of progeny per dam depended on the reproduction mode (induced or natural estrus) and parity (first versus second and more). At each cycle, some dams were randomly culled with a proportion varying from 0.10 after parity 1 to 0.50 on average after parity 6. The maximum parity was 7 and around 24% of dams were replaced per cycle. Replacement females, allocated to their dam’s flock, were first randomly chosen among females that were born from an AI sire and then among females that were born from a natural mating sire. There is no difference between the classical and genomic designs, presented in Fig. <xref rid="Fig2" ref-type="fig">2</xref>, regarding how females were selected.<fig id="Fig2"><label>Fig. 2</label><caption><p>General overview of the classical and genomic designs. Sel. on PA EBV: truncation selection on parent average estimated breeding values; Sel. on EBV: truncation selection on estimated breeding values; Sel. on PA GEBV: truncation selection on parent average genomic estimated breeding values; Sel. on GEBV: truncation selection on genomic estimated breeding values; prog. test: males in progeny testing using artificial insemination (AI); Proven sires: AI sires selected on progeny testing; NM sires: natural mating sires; AI sires: artificial insemination sires</p></caption><graphic xlink:href="12711_2017_351_Fig2_HTML" id="MO2"></graphic></fig></p><p id="Par16">About 600 male candidates were preselected among newborn males (~ 3500) based on the parental mean EBV in the classical design and on the parental mean GEBV in the genomic design. In practice, all candidates were born from proven AI sires (classical design) and genomic AI sires (genomic design) given their genetic superiority. About 10% of male candidates died within their first year. At 1 year of age, 50% of the male candidates were culled for non-genetic factors. In the classical design, the ten candidates with the highest parental mean EBV (out of 270 candidates) were selected and mated across flocks by AI to 1100 females (110 per male on average) for progeny-testing. Two cycles later, the five AI sires with the highest EBV (including progeny records) were selected as proven AI sires and used at most for four cycles (from 4 to 7 years old). At each cycle, 1400 females were mated to proven AI sires. In the genomic design, MD or imputed MD genotypes of candidates were available. The ten candidates with the highest GEBV were selected as AI males and mated across flocks to 1100 females. Genomic AI sires were used at most for another cycle and mated to 1400 females. To mimic the variation in the AI dose production per male, the inseminated dams were randomly allocated to one AI sire with a maximum number of AI per male equal to 140 for 2-year old sires and to 250 for older sires. About 50 naturally-mated male replacements were selected among 260 candidates not selected as AI sires, based on their parental mean EBV in the classical design or on their GEBV in the genomic design. These naturally-mated sires were used at most for four cycles (from 2 to 5 years old).</p><p id="Par17">Five genomic scenarios (Table <xref rid="Tab1" ref-type="table">1</xref>) were assessed. Some scenarios included an imputation step to reconstruct the MD genotypes of individuals genotyped with the VLD panel. The imputation was performed using the software Fimpute developed by Sargolzaei et al. [<xref ref-type="bibr" rid="CR37">37</xref>]. The reference population for imputation was based first on the historical population of AI and natural mating sires at Time 10: about 50 progeny tested sires (each with ~ 30 daughters), 50 proven sires (~ 125 daughters) and 400 sires (~ 9 daughters). From Time 10 to 25, new selected sires were added, i.e. about 10 AI and 50 natural mating sires per year. The scenarios differed in the choice of genotyped animals (including dams or not), the SNP panel used for the genotyping of male candidates and dams (MD or VLD followed by imputation to MD) and the information used for imputation (including pedigree or not). In the GSsc scenario, sires and male candidates are genotyped using the MD panel. In the GSs_Ic scenario, sires are genotyped using the MD panel and male candidates using the VLD panel. In the GSscd scenario, sires, male candidates and dams are genotyped using the MD panel. In the GSs_Icd scenario, sires are genotyped using the MD panel and male candidates and dams using the VLD panel. To assess the importance of pedigree information in the genomic design, the last scenario, GSs_Icd_pop, was similar to GSs_Icd but an imputation method that ignored pedigree information (population based) was used.<table-wrap id="Tab1"><label>Table 1</label><caption><p>Information taken into account for (genomic) breeding value estimation and imputation step according to scenarios</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left">Scenarios<sup>a</sup></th><th align="left">CS<sup>b</sup></th><th align="left">GSsc<sup>c</sup></th><th align="left">GSs_Ic<sup>c</sup></th><th align="left">GSscd<sup>c</sup></th><th align="left">GSs_Icd<sup>c</sup></th><th align="left">GSs_Icd_pop<sup>c</sup></th></tr></thead><tbody><tr><td align="left">Genotypes (G<sup>d</sup>) or imputed genotype (IG<sup>e</sup>)</td><td align="left"></td><td align="left"></td><td align="left"></td><td align="left"></td><td align="left"></td><td align="left"></td></tr><tr><td align="left"> Sires</td><td align="left"></td><td align="left">G</td><td align="left">G</td><td align="left">G</td><td align="left">G</td><td align="left">G</td></tr><tr><td align="left"> Male candidates</td><td align="left"></td><td align="left">G</td><td align="left">IG</td><td align="left">G</td><td align="left">IG</td><td align="left">IG</td></tr><tr><td align="left"> Dams</td><td align="left"></td><td align="left"></td><td align="left"></td><td align="left">G</td><td align="left">IG</td><td align="left">IG</td></tr><tr><td align="left">Imputation methodology: population (P)/familial (F)</td><td align="left"></td><td align="left"></td><td align="left">P + F</td><td align="left"></td><td align="left">P + F</td><td align="left">P</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>Phenotypes and pedigree were included in all scenarios</p><p><sup>b</sup>CS = classical selection design</p><p><sup>c</sup>GS = genomic selection design; GSsc, sires and candidates had medium-density genotypes; GSs_Ic, sires had medium-density genotypes and candidates had medium-density genotypes imputed from very low-density genotypes; GSscd, sires, candidates and dams had medium-density genotypes; GSs_Icd, sires had medium-density genotypes and candidates and dams had medium-density genotypes imputed from very low-density genotypes; GSs_Icd_pop, sires had medium-density genotypes and candidates and dams had medium-density genotypes imputed from very low-density genotypes without using the pedigree information</p><p><sup>d</sup>Medium-density genotypes (46 K)</p><p><sup>e</sup>Medium-density genotypes (46 K) imputed from very low-density genotypes (≤ 1000)</p></table-wrap-foot></table-wrap></p></sec><sec id="Sec8"><title>Simulation outputs</title><p id="Par18">At each cycle and for each scenario, the average TBV and inbreeding coefficient of females in their first parity were computed. The annual genetic gain and annual rate of inbreeding were estimated as the regression slopes of the average TBV and average inbreeding coefficient of first parity females on time between cycles 10 and 25. The means and standard deviations presented are based on 50 replicates. At time 25, we computed the average Pearson correlation between the TBV and the (G)EBV for dams and male candidates as well as the average concordance rate of imputed genotypes (the number of correctly imputed alleles divided by the number of imputed alleles) of dam and male imputed genotypes.</p></sec></sec><sec id="Sec9"><title>Results</title><p id="Par19">Table <xref rid="Tab2" ref-type="table">2</xref> includes the annual genetic gain, the annual increase in inbreeding, (G)EBV accuracies, and concordance rates of imputed dams and male candidates for six designs. Computed at time 25, we assumed that GEBV accuracies and concordance rates were close to the upper bounds of values obtained over the simulation time. We used a VLD panel of 1000 SNPs for designs that included an imputation step to the MD panel. Table <xref rid="Tab3" ref-type="table">3</xref> provides the same information for designs based on female imputed genotypes using panels of 1000, 500 or 250 SNPs.<table-wrap id="Tab2"><label>Table 2</label><caption><p>Genetic gain, inbreeding rate, (G)EBV accuracies, and imputation concordance rates for six scenarios<sup>a</sup> with VLD panels of 1000 SNPs (standard deviations for 50 replicates shown in brackets)</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left">Scenarios<sup>a</sup></th><th align="left">CS<sup>b</sup></th><th align="left">GSsc<sup>c</sup></th><th align="left">GSs_Ic<sup>c</sup></th><th align="left">GSscd<sup>c</sup></th><th align="left">GSs_Icd<sup>c</sup></th><th align="left">GSs_Icd_pop<sup>c</sup></th></tr></thead><tbody><tr><td align="left">Genetic gain<sup>d</sup> (σ<sub>a</sub>/year)</td><td char="(" align="char">0.162 (0.015)</td><td char="(" align="char">0.205 (0.019)</td><td char="(" align="char">0.197 (0.021)</td><td char="(" align="char">0.249 (0.016)</td><td char="(" align="char">0.230 (0.014)</td><td char="(" align="char">0.179 (0.020)</td></tr><tr><td align="left">Inbreeding rate/year<sup>e</sup></td><td char="(" align="char">0.0043 (0.0011)</td><td char="(" align="char">0.0034 (0.0006)</td><td char="(" align="char">0.0033 (0.0006)</td><td char="(" align="char">0.0028 (0.0005)</td><td char="(" align="char">0.0031 (0.0006)</td><td char="(" align="char">0.0040 (0.0008)</td></tr><tr><td align="left" colspan="7">(G)EBV accuracy<sup>f</sup></td></tr><tr><td align="left"> Dams<sup>g</sup></td><td char="(" align="char">0.71 (0.02)</td><td char="(" align="char">0.76 (0.02)</td><td char="(" align="char">0.75 (0.02)</td><td char="(" align="char">0.87 (0.01)</td><td char="(" align="char">0.83 (0.02)</td><td char="(" align="char">0.74 (0.03)</td></tr><tr><td align="left"> Male candidates</td><td char="(" align="char">0.36 (0.07)</td><td char="(" align="char">0.53 (0.06)</td><td char="(" align="char">0.51 (0.06)</td><td char="(" align="char">0.71 (0.05)</td><td char="(" align="char">0.63 (0.05)</td><td char="(" align="char">0.43 (0.07)</td></tr><tr><td align="left" colspan="7">Imputation concordance rate<sup>h</sup></td></tr><tr><td align="left"> Dams<sup>g</sup></td><td align="left"></td><td align="left"></td><td align="left"></td><td align="left"></td><td char="(" align="char">96.1 (0.1)</td><td char="(" align="char">91.1 (0.2)</td></tr><tr><td align="left"> Old females<sup>i</sup></td><td align="left"></td><td align="left"></td><td align="left"></td><td align="left"></td><td char="(" align="char">93.3 (0.1)</td><td char="(" align="char">88.3 (0.2)</td></tr><tr><td align="left"> Male candidates</td><td align="left"></td><td align="left"></td><td char="(" align="char">94.7 (0.2)</td><td align="left"></td><td char="(" align="char">96.5 (0.1)</td><td char="(" align="char">92.3 (0.2)</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>Scenarios based on imputation were performed with a very low-density 1000-SNP panel</p><p><sup>b</sup>CS = classical selection design</p><p><sup>c</sup>GS = genomic selection design; GSsc, sires and candidates had medium-density genotypes; GSs_Ic, sires had medium-density genotypes and candidates had medium-density genotypes imputed from very low-density genotypes; GSscd, sires, candidates and dams had medium-density genotypes; GSs_Icd, sires had medium-density genotypes and candidates and dams had medium-density genotypes imputed from very low-density genotypes; GSs_Icd_pop, sires had medium-density genotypes and candidates and dams had medium-density genotypes imputed from very low-density genotypes without using the pedigree information</p><p><sup>d</sup>Computed as the slope of the average true breeding value of females in first parity between time10 and time25</p><p><sup>e</sup>Computed as the slope of the average inbreeding coefficient of females in first parity between time10 and time25</p><p><sup>f</sup>Computed as the average Pearson correlation between the true breeding value and (genomic) estimated breeding values of animals at time25</p><p><sup>g</sup>Dams mated at time 25</p><p><sup>h</sup>Computed as the average of number of correctly imputed SNP divided by the number of imputed SNP obtained for the imputation realized at time 25</p><p><sup>i</sup>Dams not present anymore at time 25</p></table-wrap-foot></table-wrap><table-wrap id="Tab3"><label>Table 3</label><caption><p>Genetic gain, inbreeding increase, (G)EBV accuracies, and imputation concordance rates for the GSs_Icd genomic design using VLD densities of 250, 500, and 1000 SNPs (standard deviations for 50 replicates shown in brackets)</p></caption><table frame="hsides" rules="groups"><thead><tr><th align="left">Scenarios</th><th align="left">GSs Icd<sup>a</sup></th><th align="left">GSs Icd<sup>a</sup></th><th align="left">GSs_Icd<sup>a</sup></th></tr></thead><tbody><tr><td align="left">Number of SNPs</td><td align="left">1000</td><td align="left">500</td><td align="left">250</td></tr><tr><td align="left">Genetic gain<sup>b</sup> (σ<sub>a</sub>/year)</td><td align="left">0.230 (0.014)</td><td align="left">0.183 (0.016)</td><td align="left">0.175 (0.015)</td></tr><tr><td align="left">Inbreeding<sup>c</sup></td><td align="left">0.0031 (0.0006)</td><td align="left">0.0037 (0.0007)</td><td align="left">0.0040 (0.0010)</td></tr><tr><td align="left" colspan="4">GEBV accuracy<sup>d</sup></td></tr><tr><td align="left"> Dams<sup>e</sup></td><td align="left">0.83 (0.02)</td><td align="left">0.74 (0.02)</td><td align="left">0.73 (0.03)</td></tr><tr><td align="left"> Male candidates</td><td align="left">0.63 (0.05)</td><td align="left">0.45 (0.07)</td><td align="left">0.38 (0.02)</td></tr><tr><td align="left" colspan="4">Imputation concordance rate<sup>f</sup></td></tr><tr><td align="left"> Dams<sup>e</sup></td><td align="left">96.1 (0.1)</td><td align="left">91.8 (0.3)</td><td align="left">87.3 (0.4)</td></tr><tr><td align="left"> Old females<sup>g</sup></td><td align="left">93.3 (0.1)</td><td align="left">88.7 (0.2)</td><td align="left">84.5 (0.3)</td></tr><tr><td align="left"> Male candidates</td><td align="left">96.5 (0.1)</td><td align="left">92.4 (0.3)</td><td align="left">88.0 (0.4)</td></tr></tbody></table><table-wrap-foot><p><sup>a</sup>GSs_Icd = genomic selection design, sires had medium-density genotypes and candidates and dams had medium-density genotypes imputed from very low-density genotypes</p><p><sup>b</sup>Computed as the slope of the average true breeding value of females in first parity between time10 and time25</p><p><sup>c</sup>Computed as the slope of the average inbreeding coefficient of females in first parity between time10 and time25</p><p><sup>d</sup>Computed as the average Pearson correlation between the true breeding value and (genomic) estimated breeding values of animals at time 25</p><p><sup>e</sup>Dams mated at time 25</p><p><sup>f</sup>Computed as the average of number of correctly imputed SNP divided by the number of imputed SNP obtained for the imputation realized at time 25</p><p><sup>g</sup>Dams not present anymore at time 25</p></table-wrap-foot></table-wrap></p><sec id="Sec10"><title>Annual genetic gain</title><p id="Par20">Annual genetic gains indicated in Tables <xref rid="Tab2" ref-type="table">2</xref> and <xref rid="Tab3" ref-type="table">3</xref> correspond to the slope of the average TBV of females in parity 1 from year 10 to 25 for each design. Results show that the genetic gain for a genomic design based on a sire reference population (GSsc: 0.205<inline-formula id="IEq4"><alternatives><tex-math id="M7">\documentclass[12pt]{minimal}
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\begin{document}$$\upsigma_{\text{a}}$$\end{document}</tex-math><mml:math id="M20"><mml:msub><mml:mi mathvariant="normal">σ</mml:mi><mml:mtext>a</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="12711_2017_351_Article_IEq10.gif"></inline-graphic></alternatives></inline-formula>) was close to the gain achieved in the classical design.</p></sec><sec id="Sec11"><title>Inbreeding</title><p id="Par21">The rates of inbreeding shown in Tables <xref rid="Tab2" ref-type="table">2</xref> and <xref rid="Tab3" ref-type="table">3</xref> correspond to the slope of the average inbreeding coefficient of females in parity 1 from year 10 to 25 for each design. Apart from when the imputation method did not take pedigree information into account, the increase in inbreeding was significantly slower for the genomic scenarios (from 0.0028 to 0.0034 per year) than for the classical design (0.0043 per year). Within genomic scenarios, the increase in inbreeding per year was lower as the number and quality of genotypes increased.</p></sec><sec id="Sec12"><title>GEBV accuracies and imputation concordance rate</title><p id="Par22">The EBV accuracies shown in Tables <xref rid="Tab2" ref-type="table">2</xref> and <xref rid="Tab3" ref-type="table">3</xref> were calculated as the average Pearson correlation between TBV and (G)EBV of females in parity 1–7 (dams) and young male candidates at time 25. Accuracies for dams were slightly higher in the genomic design based on a sire reference population (GSsc: 0.76) than in the classical design (CS: 0.71). Including dams in the reference population resulted in a substantial increase in accuracy either when they were genotyped with MD (GSscd: 0.87) or genotyped with VLD and imputed to MD using population and family information (GSs_Icd: 0.87). When pedigree information was not used for imputation, the GEBV accuracy for dams was lower (GSs_Icd_pop: 0.74) than in the genomic design without dams in the reference population. Accuracy for male candidates was lower in the classical design (CS: 0.36), since they were selected on mid-parent EBV, compared to the genomic designs where their own genomic information was included. For these males, accuracies increased from 0.43 to 0.71 as the quality of their own genomic information (imputed genotypes based either on population or population and family information, MD genotypes) increased and by including dams in the reference population.</p><p id="Par23">The concordance rates of imputation reported in Tables <xref rid="Tab2" ref-type="table">2</xref> and <xref rid="Tab3" ref-type="table">3</xref> correspond to the number of correctly imputed alleles divided by the number of imputed alleles at Time 25. Results show that dams and young male candidates are imputed with higher accuracy than old females (+ 3%). Adding pedigree information (GSs_Icd) resulted in a higher imputation accuracy of + 4.2% for male candidates and + 5.0% for females compared to ignoring the pedigree in the imputation process (GSs_Icd_pop). Adding female VLD genotypes resulted in a gain of + 1.8% for male candidates.</p></sec><sec id="Sec13"><title>Effect of the number of SNPs in the VLD panel on the GSs_Icd design</title><p id="Par24">Table <xref rid="Tab3" ref-type="table">3</xref> shows that the genetic gain was much affected by the number of VLD SNPs used for imputation. The additional gain obtained for 1000 SNPs (0.230<inline-formula id="IEq11"><alternatives><tex-math id="M21">\documentclass[12pt]{minimal}
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\begin{document}$$\upsigma_{\text{a}}$$\end{document}</tex-math><mml:math id="M24"><mml:msub><mml:mi mathvariant="normal">σ</mml:mi><mml:mtext>a</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="12711_2017_351_Article_IEq12.gif"></inline-graphic></alternatives></inline-formula>) than, the genetic gain obtained for the classical design (0.162<inline-formula id="IEq13"><alternatives><tex-math id="M25">\documentclass[12pt]{minimal}
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\begin{document}$$\upsigma_{\text{a}}$$\end{document}</tex-math><mml:math id="M26"><mml:msub><mml:mi mathvariant="normal">σ</mml:mi><mml:mtext>a</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="12711_2017_351_Article_IEq13.gif"></inline-graphic></alternatives></inline-formula>). Compared to the use of 1000 SNPs, the decrease in accuracy was approximately 0.1 for dams irrespective of the number of SNPs, 500 or 250. These accuracies were close to the accuracy obtained when dams were not included in the reference population (GSsc, GSs_Ic). The same comparison for male candidates shows that the decrease was larger when 250 SNPs (− 0.25) were used than when 500 SNPs (− 0.18) were used. Across all numbers of SNPs, the magnitude of the decrease in concordance rate was similar regardless of the category considered: from − 4.1 to − 4.6 for 500 SNPs and from − 8.5 to − 8.8 for 250 SNPs compared to 1000 SNPs.</p></sec></sec><sec id="Sec14"><title>Discussion</title><p id="Par25">In this study, our reference scenario the classical design produced a rate of genetic gain of 0.162<inline-formula id="IEq14"><alternatives><tex-math id="M27">\documentclass[12pt]{minimal}
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\begin{document}$$\upsigma_{\text{a}}$$\end{document}</tex-math><mml:math id="M28"><mml:msub><mml:mi mathvariant="normal">σ</mml:mi><mml:mtext>a</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="12711_2017_351_Article_IEq14.gif"></inline-graphic></alternatives></inline-formula>. This result is close to 0.173<inline-formula id="IEq15"><alternatives><tex-math id="M29">\documentclass[12pt]{minimal}
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\begin{document}$$\upsigma_{\text{a}}$$\end{document}</tex-math><mml:math id="M30"><mml:msub><mml:mi mathvariant="normal">σ</mml:mi><mml:mtext>a</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="12711_2017_351_Article_IEq15.gif"></inline-graphic></alternatives></inline-formula>, which is the annual genetic gain from 1986 to 1999 estimated from real data for the Red Faced Manech breed [<xref ref-type="bibr" rid="CR47">47</xref>], a breeding program similar to the simulation design (single trait selection until 2003). Compared to the classical design, genomic designs generated more annual genetic gain and limited the increase in inbreeding. Sensitivity to a lower heritability (h<sup>2</sup> = 0.10 instead of 0.25) and a higher efficiency of the classical design (proportion selected of 0.33 instead of 0.5 to select proven AI sires) were also assessed. Based on ten replicates (results not shown), differences in genetic gain were slightly smaller for a low heritability or a higher efficiency of the classical design, but were of a similar magnitude to the results obtained with the classical design, GSscd and GSs_Icd in this study.</p><p id="Par26">The rate of increase in inbreeding was lower in genomic scenarios for three main reasons: first, the average contribution of natural mating sires was identical across designs whereas the average contribution of AI sires was smaller in the genomic design (genomic AI sires were systematically culled after they had been used for two cycles); second, the selection intensity applied on parent average genetic value to select male candidates in the genomic design was lower than that to select AI and natural mating sires in the classical design; and third, genomic information was expected to reduce the relative importance of pedigree information in breeding value estimation through improved estimation of the Mendelian sampling term [<xref ref-type="bibr" rid="CR17">17</xref>]. The latter might explain why within genomic scenarios, the increase in the rate of inbreeding was lower as the quality and quantity of the genomic information improved.</p><sec id="Sec15"><title>Performance of a genomic selection design based on a reference population of related sires</title><p id="Par27">Compared to the classical design, a genomic design based on a reference population of related sires resulted in an increase in genetic gain of 26.3% per year when candidates were genotyped with the MD panel (GSsc). This increase is inferior to the increase in gain of 50% calculated by Shumbusho et al. [<xref ref-type="bibr" rid="CR1">1</xref>] for the Red Faced Manech breed using a deterministic model. Although the designs were similar, Shumbusho et al. [<xref ref-type="bibr" rid="CR1">1</xref>] computed the accuracy of GEBV based on a reference population of 2000 animals with a constant number of records using the formula of Daetwyler et al. [<xref ref-type="bibr" rid="CR48">48</xref>], whereas in our study, the cumulated number of genotyped sires (included in the single-step GBLUP) increased from around 500 (Time 10) to 1250 (Time 25), and the number of daughters per sire was highly variable due to both use of AI and natural mating. For the GSsc scenario, the increase in accuracy of GEBV was moderate for dams (7%) but considerable for young males (47%). When candidate male genotypes were imputed from genotypes based on 1000 SNPs (GSs_Ic), the increase in gain (+ 21.6%) compared to that obtained with the classical design was lower than with GSsc. The average concordance rate, equal to 94.7%, is of the same order as results obtained by Wang et al. [<xref ref-type="bibr" rid="CR25">25</xref>] and corroborates that an acceptable imputation quality based on a VLD panel could be achieved provided that sires are included in the training population. The GEBV accuracies for male candidates were slightly lower for GSs_Ic than for GSsc (<italic>P</italic> = 0.068) in agreement with results obtained in sheep by Moghaddar et al. [<xref ref-type="bibr" rid="CR31">31</xref>] for a similar range of imputation accuracies.</p><p id="Par28">Sires of male candidates were selected among young males in the genomic design and progeny-tested males in the classical design. Although the accuracy of GEBV of young males was lower than that of progeny-tested males (0.71—results not shown), higher selection intensities and a shorter average generation interval resulted in a higher genetic gain. Regardless of the design, male candidates were preselected based on parent average genetic values. The genomic AI sires were selected on their own genomic value before 2 years of age with a proportion selected of 1/27 versus 1/2 at 4 years old after progeny-testing for AI sires in the classical design. Since genomic AI sires were used for a maximum of 2 years, the average generation interval was reduced (3 years instead of 4). The natural sires were used and selected at the same age with a proportion selected close to 1/5 but on their own genomic value in the genomic design or on parent average genetic value in the classical design.</p></sec><sec id="Sec16"><title>Value of female VLD genotypes</title><p id="Par29">The comparison between scenarios with (GSscd) or without females (GSsc) included in the reference population shows that adding dam genotypes, along with their phenotypes, resulted in a doubling of the increase in genetic gain of the genomic design. Genotyping dams led to an increase in the average accuracy of GEBV for both dams (14%) and male candidates (34%). The effect of including cow genotypes in the reference population on accuracy and bias of genomic prediction has been widely reported in dairy cattle [<xref ref-type="bibr" rid="CR11">11</xref>–<xref ref-type="bibr" rid="CR15">15</xref>, <xref ref-type="bibr" rid="CR49">49</xref>–<xref ref-type="bibr" rid="CR55">55</xref>]. Genetic gain varied depending on the population and reference population structure. For example, Koivula [<xref ref-type="bibr" rid="CR14">14</xref>] reported a small additional gain (increase in accuracy of + 2 to 4%) when the reference population contained about 4400 sires, whereas McNugh [<xref ref-type="bibr" rid="CR11">11</xref>] reported that including a large female population in the reference population increased the annual genetic gain by a factor of 3. In our study, the increase in gain was also large. Buch et al. [<xref ref-type="bibr" rid="CR12">12</xref>] and Gonzalez-Recio et al. [<xref ref-type="bibr" rid="CR13">13</xref>] showed that the increase in genomic prediction accuracy due to female information was most important when phenotypes and sizes of sire progeny groups were limited. Thus, this result was expected given the structure of our reference population: the number of sires was limited and most sire progeny groups were generated by natural mating, resulting in small numbers of daughters and thus inaccurate phenotypes.</p><p id="Par30">When male candidates and dams were genotyped with a 1000-SNP panel and imputed genotypes were included for genomic evaluation (GSs_Icd), the additional genetic gain was reduced but it remained higher (+ 41%) than that obtained with the classical design. With a concordance rate of imputed animals of about 96%, the accuracy of GEBV of male candidates and dams decreased compared to that in GSscd (respectively − 11.3 and − 4.6%) but remained higher than accuracies obtained with a reference population based on sires (respectively + 15.9% and + 8.4% pts). Compared to the classical design, the increase in accuracy with a 1000-SNP panel was + 41% but this decreased to + 8% with a 250-SNP panel. Lower imputation accuracies obtained with lower density SNP panels and larger numbers of genotypes discarded from the genomic evaluation due to parent-progeny mismatches (results not shown), resulted in no increase in GEBV accuracy of dams when they were genotyped with the 250- or 500-SNP panel: both panels gave the same accuracies as the GSsc scenario in which dams were not included in the reference population. For male candidates, the accuracy of GEBV was lower for 500 (− 40.0%) and 250 (− 65.8%) SNPs than the accuracy obtained with 1000 SNPs but the increases in gain for genomic scenarios based on 250 and 500 SNPs, compared to the classical design, were highly significant (<italic>P</italic> < 0.001). Comparing GSs_Icd based on 250 SNPs with the classical design, the differences in accuracy of GEBV for dams and male candidates were small (+ 0.02) but highly significant (<italic>P</italic> < 0.001) for dams and moderate for male candidates (<italic>P</italic> = 0.0565).</p></sec><sec id="Sec17"><title>Importance of using pedigree information in the imputation of MD genotypes from VLD genotypes</title><p id="Par31">In GSs_Icd_pop, we removed pedigree information in the imputation of male candidate and dam genotypes. The comparison of GSs_Icd_pop with GSs_Icd shows that removing pedigree information in the imputation step decreased the concordance rate by 5 points and 4.2 points, respectively for dams and male candidates. A high proportion of genotypes (around 80%) were removed in GSs_Icd_pop, due to mismatches between parents and progeny (results not shown). In GSs_Icd_pop, the annual genetic gain remained higher (+ 9.5%) than in the classical design but was substantially lower than in GSs_Icd (+ 45%). The contribution to the analysis of co-segregation between VLD and MD SNPs has already been reported in previous studies which highlight the beneficial effect of including relatives in the reference population for imputation [<xref ref-type="bibr" rid="CR21">21</xref>, <xref ref-type="bibr" rid="CR24">24</xref>, <xref ref-type="bibr" rid="CR26">26</xref>, <xref ref-type="bibr" rid="CR27">27</xref>, <xref ref-type="bibr" rid="CR29">29</xref>–<xref ref-type="bibr" rid="CR31">31</xref>, <xref ref-type="bibr" rid="CR34">34</xref>], especially sires and grandsires [<xref ref-type="bibr" rid="CR24">24</xref>]. The effect of including close relatives in the reference population on genomic prediction accuracy was also pointed out by previous studies on simulated and real data [<xref ref-type="bibr" rid="CR38">38</xref>, <xref ref-type="bibr" rid="CR56">56</xref>]. Habier et al. [<xref ref-type="bibr" rid="CR57">57</xref>] showed that co-segregation, as well as LD and additive genetic relationships, all contribute to the capture of QTL effects, whereas Sun et al. [<xref ref-type="bibr" rid="CR58">58</xref>] showed that explicitly modelling the co-segregation results in higher accuracy of genomic prediction when the recent effective population is small. Such results suggest that a small reference population that contains all dams and sires of candidates can result in substantial additional genetic gain based on a better genomic prediction accuracy.</p></sec><sec id="Sec18"><title>Feasibility of a genomic design including imputed genotypes on dams in the reference population</title><p id="Par32">Higher gain was obtained with a genomic design when including dam medium-density genotypes in the reference population. The economic value of such a scenario should be assessed over the whole population, including both the nucleus and commercial levels. However, as discussed for Australian sheep and beef cattle industries by van der Werf and Banks [<xref ref-type="bibr" rid="CR16">16</xref>], for an individual breeder or nucleus, the main objective is to achieve the maximum economic return within the company. Regarding the value of breeding stock, a lower genetic gain but cheaper design can be relevant for sheep breeding programs. For a French small ruminant breeding program, the cost of AI males including their progeny testing is assumed to be at least 400 euros per male per year [<xref ref-type="bibr" rid="CR59">59</xref>]. In a genomic selection design, due to AI sires being used earlier and over a shorter period, the corresponding cost would be reduced, but genotyping costs have to be considered for building the reference population and identifying candidates. Implementing a GSscd design would require a major investment compared to the current classical design since all dams have to be genotyped. The investment for implementing a GSs_Icd design would be the same as that for the classical design, for example if VLD genotypes of dams were supported by breeders for parentage assignment purposes. However, the current price of the very low-density panel available for sheep (around 20 euros) limits its use for parentage assignment by breeders. A combined use of a VLD panel for both parentage and genomic selection might be cost-effective.</p><p id="Par33">Plieschke et al. [<xref ref-type="bibr" rid="CR15">15</xref>] noted that including female genotypes led to increased accuracy of GEBV without bias as long as the females chosen for genotyping were randomly sampled. In our study, all dams were genotyped. The main purpose was to assess the usefulness for genomic selection of VLD genotypes used for parentage assignment. From a practical point of view, one can imagine that only a certain proportion of flocks will use parentage assignment based on SNPs. In that case, there could be variation across flocks, and potentially within a flock, of the average dam GEBV accuracy according to the VLD genotyping status of dams. This could result in a bias since male candidates are selected on parent mean GEBV.</p></sec></sec><sec id="Sec19"><title>Conclusions</title><p id="Par34">Using a stochastic model, we compared classical and genomic selection designs. Five genomic scenarios were assessed by varying the structure of the reference population (with or without genotyping females) and the genotyping panels that were used (medium density, or imputed to medium from very low density). Compared to the classical design with progeny testing, genomic scenarios generated more genetic gain and limited the increase in inbreeding. This superiority was based on higher selection differentials that are applied to male candidates to select sires for both AI and natural mating. The combined use of very low-density genotypes for male candidates and dams together with imputation resulted in lower genetic gain than scenarios based on medium-density genotypes. However, the increase in gain was substantial compared to that in the classical design. Using very low-density SNP panels might be more profitable at the nucleus level given its lower cost relative to medium- or low-density SNP panels and multipurpose use (parentage assignment and genomic selection).</p></sec></body><back><ack><title>Authors’ contributions</title><p>JR and JME designed the study. JME established the methodology to create the founder population and genome simulation. JR wrote the simulation program, performed analysis and drafted the manuscript. JR, JME and AS contributed in interpreting the results and revising the manuscript. All authors read and approved the final manuscript.</p><sec id="FPar1"><title>Acknowledgements</title><p id="Par35">We gratefully acknowledge Misztal et al. and Sargolzaei et al. for respectively making the Blupf90 software and the Fimpute software available.</p></sec><sec id="FPar2"><title>Competing interests</title><p id="Par36">The authors declare that they have no competing interests.</p></sec><sec id="FPar3"><title>Availability of data and materials</title><p id="Par37">The datasets generated during and/or analysed during this study are available from the corresponding author on reasonable request.</p></sec><sec id="FPar4"><title>Consent for publication</title><p id="Par38">Not applicable.</p></sec><sec id="FPar5"><title>Ethics approval and consent to participate</title><p id="Par39">Not applicable.</p></sec><sec id="FPar6"><title>Funding</title><p id="Par40">This study was funded by Institut de l’Elevage, INRA (Department of Animal Genetic and metaprogram SELGEN), Apis-gène and AGBU (Animal Genetic and Breeding Unit). AGBU is a joint unit between the University of New England and the Australian Department of Primary Industry. We gratefully acknowledge the funding.</p></sec><sec id="FPar7"><title>Publisher’s Note</title><p id="Par41">Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></sec></ack><ref-list id="Bib1"><title>References</title><ref id="CR1"><label>1.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Shumbusho</surname><given-names>F</given-names></name><name><surname>Raoul</surname><given-names>J</given-names></name><name><surname>Astruc</surname><given-names>JM</given-names></name><name><surname>Palhière</surname><given-names>I</given-names></name><name><surname>Elsen</surname><given-names>JM</given-names></name></person-group><article-title>Potential benefits of genomic selection on genetic gain of small ruminant breeding programs</article-title><source>J Anim Sci</source><year>2013</year><volume>91</volume><fpage>3644</fpage><lpage>3657</lpage><pub-id pub-id-type="doi">10.2527/jas.2012-6205</pub-id><pub-id pub-id-type="pmid">23736059</pub-id></element-citation></ref><ref id="CR2"><label>2.</label><mixed-citation publication-type="other">van der Werf JHJ, Banks RG, Clark SA, Lee SJ, Daetwyler HD, Hayes BJ, et al. Genomic selection in sheep breeding programs. In: Proceedings of the 10th world congress on genetics applied to livestock production, 17–22 August 2014; Vancouver. 2014. <ext-link ext-link-type="uri" xlink:href="http://agbu.une.edu.au/publications/10wcgalp/351_paper_10381_manuscript_1648_0.pdf">http://agbu.une.edu.au/publications/10wcgalp/351_paper_10381_manuscript_1648_0.pdf</ext-link>. Accessed 06 April 2017.</mixed-citation></ref><ref id="CR3"><label>3.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Casellas</surname><given-names>J</given-names></name><name><surname>Piedrafita</surname><given-names>J</given-names></name></person-group><article-title>Accuracy and expected genetic gain under genetic or genomic evaluation in sheep flocks with different amounts of pedigree, genomic and phenotypic data</article-title><source>Livest Sci</source><year>2015</year><volume>182</volume><fpage>58</fpage><lpage>63</lpage><pub-id pub-id-type="doi">10.1016/j.livsci.2015.10.014</pub-id></element-citation></ref><ref id="CR4"><label>4.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Granleese</surname><given-names>T</given-names></name><name><surname>Clark</surname><given-names>SA</given-names></name><name><surname>Swan</surname><given-names>AA</given-names></name><name><surname>van der Werf</surname><given-names>JHJ</given-names></name></person-group><article-title>Increased genetic gains in sheep, beef and dairy breeding programs from using female reproductive technologies combined with optimal contribution selection and genomic breeding values</article-title><source>Genet Sel Evol</source><year>2015</year><volume>47</volume><fpage>70</fpage><pub-id pub-id-type="doi">10.1186/s12711-015-0151-3</pub-id><pub-id pub-id-type="pmid">26370143</pub-id></element-citation></ref><ref id="CR5"><label>5.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Shumbusho</surname><given-names>F</given-names></name><name><surname>Raoul</surname><given-names>J</given-names></name><name><surname>Astruc</surname><given-names>JM</given-names></name><name><surname>Palhiere</surname><given-names>I</given-names></name><name><surname>Lemarié</surname><given-names>S</given-names></name><name><surname>Fugeray-Scarbel</surname><given-names>A</given-names></name><etal></etal></person-group><article-title>Economic evaluation of genomic selection in small ruminants: a sheep meat breeding program</article-title><source>Animal</source><year>2016</year><volume>10</volume><fpage>1033</fpage><lpage>1041</lpage><pub-id pub-id-type="doi">10.1017/S1751731115002049</pub-id><pub-id pub-id-type="pmid">26446712</pub-id></element-citation></ref><ref id="CR6"><label>6.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Rupp</surname><given-names>R</given-names></name><name><surname>Mucha</surname><given-names>S</given-names></name><name><surname>Larroque</surname><given-names>H</given-names></name><name><surname>McEwan</surname><given-names>J</given-names></name><name><surname>Conington</surname><given-names>J</given-names></name></person-group><article-title>Genomic application in sheep and goat breeding</article-title><source>Anim Frontiers</source><year>2016</year><volume>6</volume><fpage>39</fpage><lpage>44</lpage><pub-id pub-id-type="doi">10.2527/af.2016-0006</pub-id></element-citation></ref><ref id="CR7"><label>7.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hayes</surname><given-names>BJ</given-names></name><name><surname>Bowman</surname><given-names>PJ</given-names></name><name><surname>Chamberlain</surname><given-names>AJ</given-names></name><name><surname>Goddard</surname><given-names>ME</given-names></name></person-group><article-title>Invited review: genomic selection in dairy cattle: progress and challenges</article-title><source>J Dairy Sci</source><year>2009</year><volume>92</volume><fpage>433</fpage><lpage>443</lpage><pub-id pub-id-type="doi">10.3168/jds.2008-1646</pub-id><pub-id pub-id-type="pmid">19164653</pub-id></element-citation></ref><ref id="CR8"><label>8.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hall</surname><given-names>SJG</given-names></name></person-group><article-title>Effective population sizes in cattle, sheep, horses, pigs and goats estimated from census and herdbook data</article-title><source>Animal</source><year>2016</year><volume>10</volume><fpage>1778</fpage><lpage>1785</lpage><pub-id pub-id-type="doi">10.1017/S1751731116000914</pub-id><pub-id pub-id-type="pmid">27160794</pub-id></element-citation></ref><ref id="CR9"><label>9.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Santos</surname><given-names>BFS</given-names></name><name><surname>van der Werf</surname><given-names>JHJ</given-names></name><name><surname>Gibson</surname><given-names>JP</given-names></name><name><surname>Byrne</surname><given-names>TJ</given-names></name><name><surname>Amer</surname><given-names>PR</given-names></name></person-group><article-title>Genetic and economic benefits of selection based on performance recording and genotyping in lower tiers of multi-tiered sheep breeding schemes</article-title><source>Genet Sel Evol</source><year>2017</year><volume>49</volume><fpage>10</fpage><pub-id pub-id-type="doi">10.1186/s12711-016-0281-2</pub-id><pub-id pub-id-type="pmid">28095776</pub-id></element-citation></ref><ref id="CR10"><label>10.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Moghaddar</surname><given-names>N</given-names></name><name><surname>Swan</surname><given-names>AA</given-names></name><name><surname>van der Werf</surname><given-names>JH</given-names></name></person-group><article-title>Comparing genomic prediction accuracy from purebred, crossbred and combined purebred and crossbred reference populations in sheep</article-title><source>Genet Sel Evol</source><year>2014</year><volume>46</volume><fpage>58</fpage><pub-id pub-id-type="doi">10.1186/s12711-014-0058-4</pub-id><pub-id pub-id-type="pmid">25927315</pub-id></element-citation></ref><ref id="CR11"><label>11.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>McHugh</surname><given-names>N</given-names></name><name><surname>Meuwissen</surname><given-names>THE</given-names></name><name><surname>Cromie</surname><given-names>AR</given-names></name><name><surname>Sonesson</surname><given-names>AK</given-names></name></person-group><article-title>Use of female information in dairy cattle genomic breeding programs</article-title><source>J Dairy Sci</source><year>2011</year><volume>94</volume><fpage>4109</fpage><lpage>4118</lpage><pub-id pub-id-type="doi">10.3168/jds.2010-4016</pub-id><pub-id pub-id-type="pmid">21787946</pub-id></element-citation></ref><ref id="CR12"><label>12.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Buch</surname><given-names>LH</given-names></name><name><surname>Kargo</surname><given-names>M</given-names></name><name><surname>Berg</surname><given-names>P</given-names></name><name><surname>Lassen</surname><given-names>J</given-names></name><name><surname>Sørensen</surname><given-names>AC</given-names></name></person-group><article-title>The value of cows in reference populations for genomic selection of new functional traits</article-title><source>Animal</source><year>2012</year><volume>6</volume><fpage>880</fpage><lpage>886</lpage><pub-id pub-id-type="doi">10.1017/S1751731111002205</pub-id><pub-id pub-id-type="pmid">22558957</pub-id></element-citation></ref><ref id="CR13"><label>13.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gonzalez-Recio</surname><given-names>O</given-names></name><name><surname>Coffey</surname><given-names>MP</given-names></name><name><surname>Pryce</surname><given-names>JE</given-names></name></person-group><article-title>On the value of the phenotypes in the genomic era</article-title><source>J Dairy Sci</source><year>2014</year><volume>97</volume><fpage>7905</fpage><lpage>7915</lpage><pub-id pub-id-type="doi">10.3168/jds.2014-8125</pub-id><pub-id pub-id-type="pmid">25453600</pub-id></element-citation></ref><ref id="CR14"><label>14.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Koivula</surname><given-names>M</given-names></name><name><surname>Strandén</surname><given-names>I</given-names></name><name><surname>Aamand</surname><given-names>GP</given-names></name><name><surname>Mäntysaari</surname><given-names>EA</given-names></name></person-group><article-title>Effect of cow reference group on validation reliability of genomic evaluation</article-title><source>Animal</source><year>2016</year><volume>10</volume><fpage>1061</fpage><lpage>1066</lpage><pub-id pub-id-type="doi">10.1017/S1751731115002864</pub-id><pub-id pub-id-type="pmid">27075712</pub-id></element-citation></ref><ref id="CR15"><label>15.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Plieschke</surname><given-names>L</given-names></name><name><surname>Edel</surname><given-names>C</given-names></name><name><surname>Pimentel</surname><given-names>ECG</given-names></name><name><surname>Emmerling</surname><given-names>R</given-names></name><name><surname>Bennewitz</surname><given-names>J</given-names></name><name><surname>Götz</surname><given-names>KU</given-names></name></person-group><article-title>Systematic genotyping of groups of cows to improve genomic estimated breeding values of selection candidates</article-title><source>Genet Sel Evol</source><year>2016</year><volume>48</volume><fpage>73</fpage><pub-id pub-id-type="doi">10.1186/s12711-016-0250-9</pub-id><pub-id pub-id-type="pmid">27677439</pub-id></element-citation></ref><ref id="CR16"><label>16.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>van der Werf</surname><given-names>J</given-names></name><name><surname>Banks</surname><given-names>R</given-names></name></person-group><article-title>Effective breeding programmes down under</article-title><source>J Anim Breed Genet</source><year>2017</year><volume>134</volume><fpage>1</fpage><lpage>2</lpage><pub-id pub-id-type="doi">10.1111/jbg.12252</pub-id><pub-id pub-id-type="pmid">28070967</pub-id></element-citation></ref><ref id="CR17"><label>17.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Pryce</surname><given-names>JE</given-names></name><name><surname>Daetwyler</surname><given-names>HD</given-names></name></person-group><article-title>Designing dairy cattle breeding schemes under genomic selection: a review of international research</article-title><source>Anim Prod Sci.</source><year>2012</year><volume>52</volume><fpage>107</fpage><pub-id pub-id-type="doi">10.1071/AN11098</pub-id></element-citation></ref><ref id="CR18"><label>18.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Clarke</surname><given-names>SM</given-names></name><name><surname>Henry</surname><given-names>HM</given-names></name><name><surname>Dodds</surname><given-names>KG</given-names></name><name><surname>Jowett</surname><given-names>TWD</given-names></name><name><surname>Manley</surname><given-names>TR</given-names></name><name><surname>Anderson</surname><given-names>RM</given-names></name><etal></etal></person-group><article-title>A high throughput single nucleotide polymorphism multiplex assay for parentage assignment in New Zealand sheep</article-title><source>PLoS One</source><year>2014</year><volume>9</volume><fpage>e93392</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0093392</pub-id><pub-id pub-id-type="pmid">24740141</pub-id></element-citation></ref><ref id="CR19"><label>19.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Heaton</surname><given-names>MP</given-names></name><name><surname>Leymaster</surname><given-names>KA</given-names></name><name><surname>Kalbfleisch</surname><given-names>TS</given-names></name><name><surname>Kijas</surname><given-names>JW</given-names></name><name><surname>Clarke</surname><given-names>SM</given-names></name><name><surname>McEwan</surname><given-names>J</given-names></name><etal></etal></person-group><article-title>SNPs for parentage testing and traceability in globally diverse breeds of sheep</article-title><source>PLoS One</source><year>2014</year><volume>9</volume><fpage>e94851</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0094851</pub-id><pub-id pub-id-type="pmid">24740156</pub-id></element-citation></ref><ref id="CR20"><label>20.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Tortereau</surname><given-names>F</given-names></name><name><surname>Moreno</surname><given-names>CR</given-names></name><name><surname>Tosser-Klopp</surname><given-names>G</given-names></name><name><surname>Servin</surname><given-names>B</given-names></name><name><surname>Raoul</surname><given-names>J</given-names></name></person-group><article-title>Development of a SNP panel dedicated to parentage assignment in French sheep populations</article-title><source>BMC Genet</source><year>2017</year><volume>18</volume><fpage>50</fpage><pub-id pub-id-type="doi">10.1186/s12863-017-0518-2</pub-id><pub-id pub-id-type="pmid">28549462</pub-id></element-citation></ref><ref id="CR21"><label>21.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Habier</surname><given-names>D</given-names></name><name><surname>Fernando</surname><given-names>RL</given-names></name><name><surname>Dekkers</surname><given-names>JCM</given-names></name></person-group><article-title>Genomic selection using low-density marker panels</article-title><source>Genetics</source><year>2009</year><volume>182</volume><fpage>343</fpage><lpage>353</lpage><pub-id pub-id-type="doi">10.1534/genetics.108.100289</pub-id><pub-id pub-id-type="pmid">19299339</pub-id></element-citation></ref><ref id="CR22"><label>22.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Zhang</surname><given-names>Z</given-names></name><name><surname>Druet</surname><given-names>T</given-names></name></person-group><article-title>Marker imputation with low-density marker panels in Dutch Holstein cattle</article-title><source>J Dairy Sci</source><year>2010</year><volume>93</volume><fpage>5487</fpage><lpage>5494</lpage><pub-id pub-id-type="doi">10.3168/jds.2010-3501</pub-id><pub-id pub-id-type="pmid">20965364</pub-id></element-citation></ref><ref id="CR23"><label>23.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hayes</surname><given-names>BJ</given-names></name><name><surname>Bowman</surname><given-names>PJ</given-names></name><name><surname>Daetwyler</surname><given-names>HD</given-names></name><name><surname>Kijas</surname><given-names>JW</given-names></name><name><surname>van der Werf</surname><given-names>JHJ</given-names></name></person-group><article-title>Accuracy of genotype imputation in sheep breeds</article-title><source>Anim Genet</source><year>2012</year><volume>43</volume><fpage>72</fpage><lpage>80</lpage><pub-id pub-id-type="doi">10.1111/j.1365-2052.2011.02208.x</pub-id><pub-id pub-id-type="pmid">22221027</pub-id></element-citation></ref><ref id="CR24"><label>24.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Huang</surname><given-names>Y</given-names></name><name><surname>Hickey</surname><given-names>JM</given-names></name><name><surname>Cleveland</surname><given-names>MA</given-names></name><name><surname>Maltecca</surname><given-names>C</given-names></name></person-group><article-title>Assessment of alternative genotyping strategies to maximize imputation accuracy at minimal cost</article-title><source>Genet Sel Evol</source><year>2012</year><volume>44</volume><fpage>25</fpage><pub-id pub-id-type="doi">10.1186/1297-9686-44-25</pub-id><pub-id pub-id-type="pmid">22849718</pub-id></element-citation></ref><ref id="CR25"><label>25.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wang</surname><given-names>C</given-names></name><name><surname>Habier</surname><given-names>D</given-names></name><name><surname>Peiris</surname><given-names>BL</given-names></name><name><surname>Wolc</surname><given-names>A</given-names></name><name><surname>Kranis</surname><given-names>A</given-names></name><name><surname>Watson</surname><given-names>KA</given-names></name><etal></etal></person-group><article-title>Accuracy of genomic prediction using an evenly spaced, low-density single nucleotide polymorphism panel in broiler chickens</article-title><source>Poult Sci</source><year>2013</year><volume>92</volume><fpage>1712</fpage><lpage>1723</lpage><pub-id pub-id-type="doi">10.3382/ps.2012-02941</pub-id><pub-id pub-id-type="pmid">23776257</pub-id></element-citation></ref><ref id="CR26"><label>26.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Cleveland</surname><given-names>MA</given-names></name><name><surname>Hickey</surname><given-names>JM</given-names></name></person-group><article-title>Practical implementation of cost-effective genomic selection in commercial pig breeding using imputation</article-title><source>J Anim Sci</source><year>2013</year><volume>91</volume><fpage>3583</fpage><lpage>3592</lpage><pub-id pub-id-type="doi">10.2527/jas.2013-6270</pub-id><pub-id pub-id-type="pmid">23736050</pub-id></element-citation></ref><ref id="CR27"><label>27.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Wellmann</surname><given-names>R</given-names></name><name><surname>Preuß</surname><given-names>S</given-names></name><name><surname>Tholen</surname><given-names>E</given-names></name><name><surname>Heinkel</surname><given-names>J</given-names></name><name><surname>Wimmers</surname><given-names>K</given-names></name><name><surname>Bennewitz</surname><given-names>J</given-names></name></person-group><article-title>Genomic selection using low density marker panels with application to a sire line in pigs</article-title><source>Genet Sel Evol</source><year>2013</year><volume>45</volume><fpage>28</fpage><pub-id pub-id-type="doi">10.1186/1297-9686-45-28</pub-id><pub-id pub-id-type="pmid">23895218</pub-id></element-citation></ref><ref id="CR28"><label>28.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hozé</surname><given-names>C</given-names></name><name><surname>Fouilloux</surname><given-names>MN</given-names></name><name><surname>Venot</surname><given-names>E</given-names></name><name><surname>Guillaume</surname><given-names>F</given-names></name><name><surname>Dassonneville</surname><given-names>R</given-names></name><name><surname>Fritz</surname><given-names>S</given-names></name><etal></etal></person-group><article-title>High-density marker imputation accuracy in sixteen French cattle breeds</article-title><source>Genet Sel Evol</source><year>2013</year><volume>45</volume><fpage>33</fpage><pub-id pub-id-type="doi">10.1186/1297-9686-45-33</pub-id><pub-id pub-id-type="pmid">24004563</pub-id></element-citation></ref><ref id="CR29"><label>29.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>He</surname><given-names>S</given-names></name><name><surname>Wang</surname><given-names>S</given-names></name><name><surname>Fu</surname><given-names>W</given-names></name><name><surname>Ding</surname><given-names>X</given-names></name><name><surname>Zhang</surname><given-names>Q</given-names></name></person-group><article-title>Imputation of missing genotypes from low- to high-density SNP panel in different population designs</article-title><source>Anim Genet</source><year>2015</year><volume>46</volume><fpage>1</fpage><lpage>7</lpage><pub-id pub-id-type="doi">10.1111/age.12236</pub-id><pub-id pub-id-type="pmid">25431355</pub-id></element-citation></ref><ref id="CR30"><label>30.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Bolormaa</surname><given-names>S</given-names></name><name><surname>Gore</surname><given-names>K</given-names></name><name><surname>van der Werf</surname><given-names>JHJ</given-names></name><name><surname>Hayes</surname><given-names>BJ</given-names></name><name><surname>Daetwyler</surname><given-names>HD</given-names></name></person-group><article-title>Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy</article-title><source>Anim Genet</source><year>2015</year><volume>46</volume><fpage>544</fpage><lpage>556</lpage><pub-id pub-id-type="doi">10.1111/age.12340</pub-id><pub-id pub-id-type="pmid">26360638</pub-id></element-citation></ref><ref id="CR31"><label>31.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Moghaddar</surname><given-names>N</given-names></name><name><surname>Gore</surname><given-names>KP</given-names></name><name><surname>Daetwyler</surname><given-names>HD</given-names></name><name><surname>Hayes</surname><given-names>BJ</given-names></name><name><surname>van der Werf</surname><given-names>JHJ</given-names></name></person-group><article-title>Accuracy of genotype imputation based on random and selected reference sets in purebred and crossbred sheep populations and its effect on accuracy of genomic prediction</article-title><source>Genet Sel Evol</source><year>2015</year><volume>47</volume><fpage>97</fpage><pub-id pub-id-type="doi">10.1186/s12711-015-0175-8</pub-id><pub-id pub-id-type="pmid">26694131</pub-id></element-citation></ref><ref id="CR32"><label>32.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Xiang</surname><given-names>T</given-names></name><name><surname>Ma</surname><given-names>P</given-names></name><name><surname>Ostersen</surname><given-names>T</given-names></name><name><surname>Legarra</surname><given-names>A</given-names></name><name><surname>Christensen</surname><given-names>OF</given-names></name></person-group><article-title>Imputation of genotypes in Danish purebred and two-way crossbred pigs using low-density panels</article-title><source>Genet Sel Evol</source><year>2015</year><volume>47</volume><fpage>54</fpage><pub-id pub-id-type="doi">10.1186/s12711-015-0134-4</pub-id><pub-id pub-id-type="pmid">26122927</pub-id></element-citation></ref><ref id="CR33"><label>33.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Ventura</surname><given-names>RV</given-names></name><name><surname>Lee</surname><given-names>MA</given-names></name><name><surname>Miller</surname><given-names>SP</given-names></name><name><surname>Clarke</surname><given-names>SM</given-names></name><name><surname>McEwan</surname><given-names>JC</given-names></name></person-group><article-title>Assessing imputation accuracy using a 15 K low density panel in a multi-breed New Zealand sheep population</article-title><source>Proc Assoc Advmt Anim Breed Genet.</source><year>2015</year><volume>21</volume><fpage>302</fpage><lpage>305</lpage></element-citation></ref><ref id="CR34"><label>34.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Toghiani</surname><given-names>S</given-names></name><name><surname>Aggrey</surname><given-names>SE</given-names></name><name><surname>Rekaya</surname><given-names>R</given-names></name></person-group><article-title>Multi-generational imputation of single nucleotide polymorphism marker genotypes and accuracy of genomic selection</article-title><source>Animal</source><year>2016</year><volume>10</volume><fpage>1077</fpage><lpage>1085</lpage><pub-id pub-id-type="doi">10.1017/S1751731115002906</pub-id><pub-id pub-id-type="pmid">27076192</pub-id></element-citation></ref><ref id="CR35"><label>35.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Badke</surname><given-names>YM</given-names></name><name><surname>Bates</surname><given-names>RO</given-names></name><name><surname>Ernst</surname><given-names>CW</given-names></name><name><surname>Fix</surname><given-names>J</given-names></name><name><surname>Steibel</surname><given-names>JP</given-names></name></person-group><article-title>Accuracy of estimation of genomic breeding values in pigs using low-density genotypes and imputation</article-title><source>G3 (Bethesda)</source><year>2014</year><volume>4</volume><fpage>623</fpage><lpage>631</lpage><pub-id pub-id-type="doi">10.1534/g3.114.010504</pub-id><pub-id pub-id-type="pmid">24531728</pub-id></element-citation></ref><ref id="CR36"><label>36.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Kijas</surname><given-names>JW</given-names></name><name><surname>Porto-Neto</surname><given-names>L</given-names></name><name><surname>Dominik</surname><given-names>S</given-names></name><name><surname>Reverter</surname><given-names>A</given-names></name><name><surname>Bunch</surname><given-names>R</given-names></name><name><surname>McCulloch</surname><given-names>R</given-names></name><etal></etal></person-group><article-title>Linkage disequilibrium over short physical distances measured in sheep using a high-density SNP chip</article-title><source>Anim Genet</source><year>2014</year><volume>45</volume><fpage>754</fpage><lpage>757</lpage><pub-id pub-id-type="doi">10.1111/age.12197</pub-id><pub-id pub-id-type="pmid">25040320</pub-id></element-citation></ref><ref id="CR37"><label>37.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sargolzaei</surname><given-names>M</given-names></name><name><surname>Chesnais</surname><given-names>JP</given-names></name><name><surname>Schenkel</surname><given-names>FS</given-names></name></person-group><article-title>A new approach for efficient genotype imputation using information from relatives</article-title><source>BMC Genomics</source><year>2014</year><volume>15</volume><fpage>478</fpage><pub-id pub-id-type="doi">10.1186/1471-2164-15-478</pub-id><pub-id pub-id-type="pmid">24935670</pub-id></element-citation></ref><ref id="CR38"><label>38.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Clark</surname><given-names>SA</given-names></name><name><surname>Hickey</surname><given-names>JM</given-names></name><name><surname>Daetwyler</surname><given-names>HD</given-names></name><name><surname>van der Werf</surname><given-names>JH</given-names></name></person-group><article-title>The importance of information on relatives for the prediction of genomic breeding values and the implications for the makeup of reference data sets in livestock breeding schemes</article-title><source>Genet Sel Evol</source><year>2012</year><volume>44</volume><fpage>4</fpage><pub-id pub-id-type="doi">10.1186/1297-9686-44-4</pub-id><pub-id pub-id-type="pmid">22321529</pub-id></element-citation></ref><ref id="CR39"><label>39.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hayes</surname><given-names>BJ</given-names></name><name><surname>Goddard</surname><given-names>ME</given-names></name></person-group><article-title>The distribution of the effects of genes affecting quantitative traits in livestock</article-title><source>Genet Select Evol.</source><year>2001</year><volume>33</volume><fpage>209</fpage><lpage>229</lpage><pub-id pub-id-type="doi">10.1186/1297-9686-33-3-209</pub-id></element-citation></ref><ref id="CR40"><label>40.</label><element-citation publication-type="book"><person-group person-group-type="author"><name><surname>Falconer</surname><given-names>DS</given-names></name></person-group><source>Introduction to quantitative genetics</source><year>1960</year><publisher-loc>London</publisher-loc><publisher-name>Oliver and Boyd</publisher-name></element-citation></ref><ref id="CR41"><label>41.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Henderson</surname><given-names>CR</given-names></name></person-group><article-title>Best linear unbiased estimation and prediction under a selection model</article-title><source>Biometrics</source><year>1975</year><volume>31</volume><fpage>423</fpage><lpage>447</lpage><pub-id pub-id-type="doi">10.2307/2529430</pub-id><pub-id pub-id-type="pmid">1174616</pub-id></element-citation></ref><ref id="CR42"><label>42.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Aguilar</surname><given-names>I</given-names></name><name><surname>Misztal</surname><given-names>I</given-names></name><name><surname>Johnson</surname><given-names>DL</given-names></name><name><surname>Legarra</surname><given-names>A</given-names></name><name><surname>Tsuruta</surname><given-names>S</given-names></name><name><surname>Lawlor</surname><given-names>TJ</given-names></name></person-group><article-title>Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score1</article-title><source>J Dairy Sci</source><year>2010</year><volume>93</volume><fpage>743</fpage><lpage>752</lpage><pub-id pub-id-type="doi">10.3168/jds.2009-2730</pub-id><pub-id pub-id-type="pmid">20105546</pub-id></element-citation></ref><ref id="CR43"><label>43.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Misztal</surname><given-names>I</given-names></name></person-group><article-title>Complex models, more data: simpler programming</article-title><source>Interbull Bull.</source><year>1999</year><volume>20</volume><fpage>33</fpage><lpage>42</lpage></element-citation></ref><ref id="CR44"><label>44.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Van Raden</surname><given-names>PM</given-names></name></person-group><article-title>Efficient methods to compute genomic predictions</article-title><source>J Dairy Sci</source><year>2008</year><volume>91</volume><fpage>4414</fpage><lpage>4423</lpage><pub-id pub-id-type="doi">10.3168/jds.2007-0980</pub-id><pub-id pub-id-type="pmid">18946147</pub-id></element-citation></ref><ref id="CR45"><label>45.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Aguilar</surname><given-names>I</given-names></name><name><surname>Misztal</surname><given-names>I</given-names></name></person-group><article-title>Technical note: recursive algorithm for inbreeding coefficients assuming nonzero inbreeding of unknown parents</article-title><source>J Dairy Sci</source><year>2008</year><volume>91</volume><fpage>1669</fpage><lpage>1672</lpage><pub-id pub-id-type="doi">10.3168/jds.2007-0575</pub-id><pub-id pub-id-type="pmid">18349260</pub-id></element-citation></ref><ref id="CR46"><label>46.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Hill</surname><given-names>WG</given-names></name></person-group><article-title>A note on effective population size with overlapping generations</article-title><source>Genetics</source><year>1979</year><volume>92</volume><fpage>317</fpage><lpage>322</lpage><pub-id pub-id-type="pmid">17248921</pub-id></element-citation></ref><ref id="CR47"><label>47.</label><mixed-citation publication-type="other">Astruc JM, Barillet F, Barbat A, Clement V, Boichard D. Genetic evaluation of dairy sheep in France. In Proceedings of the 7th world congress on genetics applied to livestock production, 19–23 August 2002; Montpellier. 2002.</mixed-citation></ref><ref id="CR48"><label>48.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Daetwyler</surname><given-names>HD</given-names></name><name><surname>Villanueva</surname><given-names>B</given-names></name><name><surname>Woolliams</surname><given-names>JA</given-names></name></person-group><article-title>Accuracy of predicting the genetic risk of disease using a genome-wide approach</article-title><source>PLoS One</source><year>2008</year><volume>2008</volume><issue>3</issue><fpage>e3395</fpage><pub-id pub-id-type="doi">10.1371/journal.pone.0003395</pub-id></element-citation></ref><ref id="CR49"><label>49.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Thomasen</surname><given-names>JR</given-names></name><name><surname>Sørensen</surname><given-names>AC</given-names></name><name><surname>Lund</surname><given-names>MS</given-names></name><name><surname>Guldbrandtsen</surname><given-names>B</given-names></name></person-group><article-title>Adding cows to the reference population makes a small dairy population competitive</article-title><source>J Dairy Sci</source><year>2014</year><volume>97</volume><fpage>5822</fpage><lpage>5832</lpage><pub-id pub-id-type="doi">10.3168/jds.2014-7906</pub-id><pub-id pub-id-type="pmid">24996280</pub-id></element-citation></ref><ref id="CR50"><label>50.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Su</surname><given-names>G</given-names></name><name><surname>Ma</surname><given-names>P</given-names></name><name><surname>Nielsen</surname><given-names>US</given-names></name><name><surname>Aamand</surname><given-names>GP</given-names></name><name><surname>Wiggans</surname><given-names>G</given-names></name><name><surname>Guldbrandtsen</surname><given-names>B</given-names></name><etal></etal></person-group><article-title>Sharing reference data and including cows in the reference population improve genomic predictions in Danish Jersey</article-title><source>Animal</source><year>2016</year><volume>10</volume><fpage>1067</fpage><lpage>1075</lpage><pub-id pub-id-type="doi">10.1017/S1751731115001792</pub-id><pub-id pub-id-type="pmid">26330119</pub-id></element-citation></ref><ref id="CR51"><label>51.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Gao</surname><given-names>H</given-names></name><name><surname>Madsen</surname><given-names>P</given-names></name><name><surname>Nielsen</surname><given-names>US</given-names></name><name><surname>Aamand</surname><given-names>GP</given-names></name><name><surname>Su</surname><given-names>G</given-names></name><name><surname>Byskov</surname><given-names>K</given-names></name><etal></etal></person-group><article-title>Including different groups of genotyped females for genomic prediction in a Nordic Jersey population</article-title><source>J Dairy Sci</source><year>2015</year><volume>98</volume><fpage>9051</fpage><lpage>9059</lpage><pub-id pub-id-type="doi">10.3168/jds.2015-9947</pub-id><pub-id pub-id-type="pmid">26433419</pub-id></element-citation></ref><ref id="CR52"><label>52.</label><mixed-citation publication-type="other">Pryce JE, Hayes BJ, Goddard ME. Genotyping dairy females can improve the reliability of genomic selection for young bulls and heifers and provide farmers with new management tools. In: Proceedings of the international committee for animal recording 38th annual meeting, 28 May–1 June 2012; Cork; 2012.</mixed-citation></ref><ref id="CR53"><label>53.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Calus</surname><given-names>MPL</given-names></name><name><surname>de Haas</surname><given-names>Y</given-names></name><name><surname>Veerkamp</surname><given-names>RF</given-names></name></person-group><article-title>Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies</article-title><source>J Dairy Sci</source><year>2013</year><volume>96</volume><fpage>6703</fpage><lpage>6715</lpage><pub-id pub-id-type="doi">10.3168/jds.2012-6013</pub-id><pub-id pub-id-type="pmid">23891299</pub-id></element-citation></ref><ref id="CR54"><label>54.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Dassonneville</surname><given-names>R</given-names></name><name><surname>Baur</surname><given-names>A</given-names></name><name><surname>Fritz</surname><given-names>S</given-names></name><name><surname>Boichard</surname><given-names>D</given-names></name><name><surname>Ducrocq</surname><given-names>V</given-names></name></person-group><article-title>Inclusion of cow records in genomic evaluations and impact on bias due to preferential treatment</article-title><source>Genet Sel Evol</source><year>2012</year><volume>44</volume><fpage>40</fpage><pub-id pub-id-type="doi">10.1186/1297-9686-44-40</pub-id><pub-id pub-id-type="pmid">23270502</pub-id></element-citation></ref><ref id="CR55"><label>55.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Edel</surname><given-names>C</given-names></name><name><surname>Pimentel</surname><given-names>ECG</given-names></name><name><surname>Plieschke</surname><given-names>L</given-names></name><name><surname>Emmerling</surname><given-names>R</given-names></name><name><surname>Götz</surname><given-names>KU</given-names></name></person-group><article-title>Short communication: the effect of genotyping cows to improve the reliability of genomic predictions for selection candidates</article-title><source>J Dairy Sci</source><year>2016</year><volume>99</volume><fpage>1999</fpage><lpage>2004</lpage><pub-id pub-id-type="doi">10.3168/jds.2015-10246</pub-id><pub-id pub-id-type="pmid">26723131</pub-id></element-citation></ref><ref id="CR56"><label>56.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Lee</surname><given-names>SH</given-names></name><name><surname>Weerasinghe</surname><given-names>WMSP</given-names></name><name><surname>Wray</surname><given-names>NR</given-names></name><name><surname>Goddard</surname><given-names>ME</given-names></name><name><surname>van der Werf</surname><given-names>JHJ</given-names></name></person-group><article-title>Using information of relatives in genomic prediction to apply effective stratified medicine</article-title><source>Sci Rep</source><year>2017</year><volume>7</volume><fpage>42091</fpage><pub-id pub-id-type="doi">10.1038/srep42091</pub-id><pub-id pub-id-type="pmid">28181587</pub-id></element-citation></ref><ref id="CR57"><label>57.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Habier</surname><given-names>D</given-names></name><name><surname>Fernando</surname><given-names>RL</given-names></name><name><surname>Garrick</surname><given-names>DJ</given-names></name></person-group><article-title>Genomic BLUP decoded: a look into the black box of genomic prediction</article-title><source>Genetics</source><year>2013</year><volume>194</volume><fpage>597</fpage><lpage>607</lpage><pub-id pub-id-type="doi">10.1534/genetics.113.152207</pub-id><pub-id pub-id-type="pmid">23640517</pub-id></element-citation></ref><ref id="CR58"><label>58.</label><element-citation publication-type="journal"><person-group person-group-type="author"><name><surname>Sun</surname><given-names>X</given-names></name><name><surname>Fernando</surname><given-names>R</given-names></name><name><surname>Dekkers</surname><given-names>J</given-names></name></person-group><article-title>Contributions of linkage disequilibrium and co-segregation information to the accuracy of genomic prediction</article-title><source>Genet Sel Evol</source><year>2016</year><volume>48</volume><fpage>77</fpage><pub-id pub-id-type="doi">10.1186/s12711-016-0255-4</pub-id><pub-id pub-id-type="pmid">27729012</pub-id></element-citation></ref><ref id="CR59"><label>59.</label><mixed-citation publication-type="other">Buisson D. Sélection génomique des races ovines laitières françaises: analyse des schémas actuels, première modélisation de scénarios génomiques et bilan technico- économique. 2012. <ext-link ext-link-type="uri" xlink:href="https://dumas.ccsd.cnrs.fr/dumas-00801194/document">https://dumas.ccsd.cnrs.fr/dumas-00801194/document</ext-link>. Accessed 25 Apr 2017.</mixed-citation></ref></ref-list></back></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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