genomic selection and reproductive efficiency in dairy...

GenomicSelectionandReproductiveEfficiencyinDairyCattleThomasE.Spencer1,PeterJ.Hansen2,JohnB.Cole3,JosephDalton4,andHollyNeibergs1

1DepartmentofAnimalSciences,WashingtonStateUniversity,Pullman,WA991642DepartmentofAnimalSciences,UniversityofFlorida,Gainesville,FL32611

3USDA‐ARS,AnimalGenomicsandImprovementLaboratory,Beltsville,MD207054DepartmentofAnimalandVeterinaryScience,UniversityofIdaho,Caldwell,ID83605

[email protected]

IntroductionAveragemilk yield per lactation nearly dou‐bledinU.S.Holsteincowsbetween1950and2000 (Figure 1). More than half of this pro‐gressresultedfromgeneticselectionformilkyield (Dekkers and Hospital, 2002), becausemilk yield and composition have moderateheritability (20 to 40%) (Hayes et al., 2010;KemperandGoddard,2012).Duringthesameperiod,cowfertilitydeclinedinUSdairycattle(reviewedbyWashburnetal.,2002;Diskinetal., 2006;Diskin andMorris, 2008b;Hansen,2011).ReproductivestatusofUSHolsteinandJersey cattle between 1996 and 2006 wasevaluatedin2009(Normanetal.,2009).Dur‐ingthat10‐yearperiod,conceptionrate(CR),definedastheprobabilityofasuccessfulout‐comeof individualbreedingservices (Averilletal.,2004),declinedby3%and4%forallser‐vicesinHolsteinsandJerseys,respectively.By2006, overall CR was 30% in Holsteins and35%inJerseys.ThenumberofbreedingsperlactationincreasedinbothHolsteinsandJer‐seys,andthedaysfromcalvingtolastbreed‐ing increased10or11days inHolsteinsandJerseys, respectively. Conception rate de‐creased inbothHolstein and Jersey cattle asmorebreedingstookplaceduringlactationorasthenumberofcalvingsincreased.Primipa‐rousHolsteinshadaCRof34%afterthefirstbreeding,whichdeclined to26%by the fifthbreeding.Incontrast,meanCRforUSHolsteinandJerseyreplacementheiferswas56.3%and52.2%, respectively (Kuhn et al., 2006). In2010,reproductivemanagementdatafrom85Holsteindairiesin4regionsoftheUSwerean‐alyzedandfoundthat21‐daypregnancyrates(PR)toartificialinsemination(AI)weresimi‐lar among regions and averaged only 18.5%(Moeller et al., 2010).Thus,dairy cattle con‐

tinuetohaveproblemswithfertilityorrepro‐ductiveefficiencythataffectsthesustainabil‐ityandprofitabilityofUSdairyproductionen‐terprises.Given the decline in important reproductivetraits, more selection emphasis has beenplacedon fertility in recentyears.Forexam‐ple,daysopen(DO),whichisusedtocalculatePR for genetic evaluations, increased by 37days forHolsteins from 1960 through 2000;approximately75%ofthatincreaseinDOwasattributedtogeneticsasaconsequenceofse‐lectionforgreatermilkyieldtraits.Anegativegenetic correlation between milk yield andfertilityexistsindairycattle(VanRadenetal.,2004;Pritchardetal.,2013).Althoughfertilitytraitshavelowheritability(1to10%;(Sunetal.,2010),thelargeimprovementinmilkyieldwas accompanied by a decline in fertility(Washburn et al., 2002; Hare et al., 2006;Normanetal.,2009).Thegeneticcorrelationbetweenmilkyieldandintervalbetweencalv‐ing and first insemination is0.43 (Sunet al.,2010).Thegeneticcorrelationofcowfertility,asmeasuredbyDO,withmilk yield is about0.35 (VanRadenet al., 2004).Thus, selectiononmilkyieldtraitswithoutconcomitantselec‐tion for fertility is proposed to be themajorcauseof thedecline in cow fertility between1960and2000inspiteofrelativelylowherit‐abilities for reproductive traits (VeerkampandBeerda,2007).Forexample,theheritabil‐ity for daughter pregnancy rate (DPR), thedairy fertility traitmostwidelymeasured intheUnited States, has been estimated at 4%(VanRadenetal.,2004).Fortunately,historicaltrendsin fertilityhavestartedtorecoverbe‐causeoftheincorporationofgeneticmeritforproductivelife(PL)in1994andDPRin2003as well as the change in breeding strategies

16

The Dairy Cattle Reproduction Council does not support one product over another and any mention herein is meant as an example, not an endorsement

2014 Dairy Cattle Reproduction Conference Salt Lake City, UT

fromnaturalservicetoovulationsynchroniza‐tionprogramscoupledwithAI thatdecreaseDO.Poorfertilityofdairycattleinvolvesamyriadofdifferentaspectsincludinganovulation,in‐adequate expression of estrus, irregular es‐trouscycles,andpregnancylossafterinsemi‐nation (Lucy, 2007). The majority of preg‐nancy lossoccursduringthefirstmonthandembryonicperiodofgestation(Hansen,2011).Themainfactorsimplicatedinpregnancysuc‐cess (or loss) in dairy cows are those of ge‐netic, physiological, endocrine, environmen‐tal,anddiseaseorigin.Thereaderisreferredtoseveralrecentreviewsonthecontributionsof oocyte quality, progesterone insufficiency,spermdamage,oviductalanduterineenviron‐ment, disease, negative energy balance, andmaternalmetabolism to fertilityproblems inlactatingdairycattle(Lucy,2007;DiskinandMorris,2008a;Hansen,2011;,LeBlanc,2014;Wiltbank et al., 2014). Although amyriad ofgene expression profiling studies have beenconductedindairyandbeefcattle,westilldonotreallyunderstandthegenenetworksandbiologicalpathwaysthatregulatethephysio‐logicalprocessesthatcontrolmaternal fertil‐ity(i.e.,estrousbehavior,hypothalamic‐pitui‐taryfunction,ovarianfunction[oogenesisandovulation]), oviductal and uterine function,conceptusdevelopment,andmaternalmetab‐olism(BauersachsandWolf,2012lLonerganandForde,2014).Newmanagementtoolsareneeded incattle todecreaseembryonicmor‐talityandpregnancy loss, thereby increasingoverall herd fertility, reproductive and pro‐ductive longevity, and sustainability of dairyenterprises. Sufficient genetic variability ex‐istswithinmajorbreedsforfertilitytraitsthatarecomplexandpolygenicwithlowheritabil‐ity.Selectionforthosefertilitytraits,however,reliesongenomicselectionstrategiesthatre‐quiremanydifferentmarkersdevelopedfromanalysisofcarefullyphenotypedanimalpopu‐lations. Fortunately, advent of high through‐put“nextgeneration”DNAsequencing(NGS)andothergenomictechnologies,suchassinglenucleotide polymorphism (SNP) genotyping,

is useful to identify genes to improve repro‐ductiveefficiencyindairycattlewithoutnega‐tively affecting desirable production traits,suchasmilkyield.Geneticselectionforrepro‐ductionandhealthtraitsaswellasnewrepro‐ductiveandgenomictechnologiesisexpectedtocontinueimprovingdairycowfertilityandproductivity(Lucy,2007;SchefersandWeigel,2012).Theobjectiveofthisreviewistohigh‐lighthowgeneticsandgenomicselectionmaybe used to increase reproductive efficiencyandfertilityindairycattle.

GeneticsofFertility

Improvements in fertilityofbothheifersandlactatingcowswouldbeofgreatbenefittotheU.S.dairyindustry.Despitethelowheritabilityforcommonfertilitytraits,substantialgeneticeffectshavebeenreportedandsignificantge‐netic variation for fertility exists in lactatingcows (Veerkamp and Beerda, 2007). A ge‐nome‐wideanalysis(GWAS)of31production,health, reproduction and body conformationtraitsincontemporaryHolsteincowswascon‐ductedrecently,andtheresultssupportedthehypothesisthatmosttraitsarepolygenic,eachgene having a small effect on the phenotype(Coleetal.,2011).Geneticcausesinfluencingfertilityincludechromosomaldefects,individ‐ualgenesandgeneticinteractions(VanRadenandMiller,2006).TheadventofnewgenomictechnologiesandimprovementsinNGSallowsforthediscoveryofnewgeneticmarkersuse‐fultoimprovefertilityindairycattleusingge‐nomicselectionstrategies.Sequencingandanalysisoftheapproximately2.87 gigabase pairsBos taurus genome indi‐catesthatitcontainsmorethan22,000genes.Availabilityofthecattlegenomesequencehasalloweddevelopmentofcommerciallyavaila‐blegenome‐wideSNPassaysthatcanbeusedtoidentifygenomicregionsandpathwaysas‐sociatedwithcomplexpolygenictraitssuchasdisease resistance and susceptibility(Matukumallietal.,2009;Settlesetal.,2009).TheSNPsareestimatedtoaccountfor84%ofthevariationingeneexpressioninanimals.Ifpresentinthecodingregionofgenes,SNPscanaffect protein production or function. The

17



SNPsnotinthecodingregionmaystillaffectproteinproductionbymodulatinggenesplic‐ing, transcription factor binding, and mRNAdegradationofnoncodingRNAs.Forcomplextraits,individualSNPsusuallyworkincoordi‐nationwithotherSNPs.ThenextgenerationofcattleSNPassayscurrentlyavailableconsistsof 2 different high density cattle SNP panelsavailablefromIllumina(777K)andAffymetrix(640K)foridentifyingandprecisemappingoflociassociatedwithdesirabletraits.Itisnowrecognizedinhumansthatstructuralgenetic variation involves more DNA se‐quences than the sequences represented bySNP(Conradetal.,2010).Thecattlesequenceand recent NGS studies revealedmore com‐plexmutationssuchassegmentalduplicationsthatmakeupapproximately3%ofthecattlegenomewiththemajority(76%)ofthosecor‐respondingtocompleteorpartialgenedupli‐cations(Bickhartetal.,2012).Theduplicatedgenestypicallyencodeproteinswithfunctionsthatinvolveinteractionswiththeexternalen‐vironment;however,themajorityofsegmen‐talduplicationswithin thecattlegenomearestructuralDNAvariantsknownascopynum‐bervariants (CNV).TheCNVsaredefinedasDNA fragments 1,000 nucleotides or longerthatvaryinnumberwhenthetestedgenomeiscomparedwithareferencegenome(Feuketal.,2006)andmayinvolveDNAduplications,deletions,inversions,andtranslocations.Theyareestimatedtoaccountfor18%ofthevaria‐tioningeneexpressioninanimals.TheCNVsmaybeinheritedoroccurdenovoduringgam‐etogenesis,eveninarelativelyinbredpopula‐tion(Eganetal.,2007).Incattle,over850CNVloci have been identified in 538 cattle genesthatcover22megabasepairs(Mbp)ofthege‐nome (Bae et al., 2010; Fadista et al., 2010).TwentypercentoftheCNVidentifiedincattleare associated with segmental duplicationsand30%withgenes.Itisnotknowniffertilitytraits are associatedwith CNVs, but an esti‐mated84%and18%ofthevariationingeneexpressioninhumansisassociatedwithSNPsandCNV,respectively(Strangeretal.,2007).Thus,interrogationofbothformsofvariationis important for a comprehensive evaluation

ofthegenomicregionsassociatedwithpheno‐types.Availabilityofgenome‐wideSNPassaysmakeassociation analysis the most powerful ap‐proach for identifying loci associated withcomplextraits(RischandMerikangas,1996).This method of analysis relies upon linkagedisequilibriumorthenon‐randomassociationof allelesat2ormore loci todetect associa‐tionsbetweenneutralmarkersandcasualloci.Associationstudiesdonotrequirestructuredbreedcrossesorpedigreedpopulationsofcat‐tle, but instead use a sampling of the inde‐pendentmeiosispresentinacattlepopulation.Thestudiesalsoutilizehigh‐throughputhigh‐density SNPgenotyping assays thatdramati‐cally reducecostsand increasestudypower.UseofahighdensitySNPassayisveryefficienttoidentifylocioflargeeffectsthatareassoci‐atedwithproductiontraitsincattle(Settlesetal.,2009;Zanellaetal.,2011).DaughterPregnancyRate(DPR)Geneticimprovementoffemalefertilitycanbeachievedby indirect selection forproductivelifeorbodyconditionscoreanddirectselec‐tionforDPR,theonlymeasureoffemalefertil‐ityusedforgeneticevaluationofdairysires.IntheU.S.,introductionofgeneticevaluationsforPLinthemid‐1990sprovidedthefirstoppor‐tunitytoimprovefemalefertilitythroughge‐netic selection (VanRaden and Klaaskate,1993).Althoughindirectselectiontoolscanbehelpful,directselectionforimprovedfertilityis more desirable. Thus, genetic evaluationsforDPRwereintroducedin2003(VanRadenet al., 2004). The DPR involves use of DO,which is computed from reported breedingdatesforcurrentcowsandfromcalvinginter‐val for historical cows, and these are subse‐quentlytransformedinto21‐dayPRthat isacommon,timelymeasureofreproductiveeffi‐ciency on dairy farms. The best and worstavailableHolsteinsiresdifferby7.2%inDPR.Becausea1%differenceinPRcorrespondstoapproximately4DO(VanRadenetal.,2004),daughtersofthebestandworstDPRsiresdif‐ferbyroughly29DOperlactation.MeanDPRbetweensiresinthetopandbottomdecilesis

18



4.9%,whichcorrespondstoadifferenceof20DO per lactation. During the past 10 years,DPRhasbeenincorporatedintoallmajorse‐lectionindicesusedbyU.S.dairyfarmerswitharelativeweightof6.9to12%ofthetotaleco‐nomic value. The lifetime net merit index(http://aipl.arsusda.gov/refer‐ence/nmcalc.htm)willbeupdated inDecem‐ber2014withtheproposedweightonDPRde‐creasingfrom11%to7%.However,heiferandcowCRwillbeaddedtotheindexwithweightsof 2% each,whichwillmaintain 11% of theoverallemphasisonfemalefertility.TheDPRisstronglycorrelatedwithanumberoffertil‐itytraitsincludingdaystofirstservice,CRandPR.Thus, producers can expectdaughters ofbetter DPR bulls to have improved fertilityacrossavarietyofmanagementsystems.Be‐causeofrelativelylowreliabilities,somebullswithbetterDPRwillhaveover‐estimatedDPR.Selection forDPRhasresulted ingenetic im‐provement(oratleaststabilization)offemalefertility (Figure 2) and could be used to in‐creasePRbyasmuchas7%withinaherd.Ad‐ditional knowledge of genes and their prod‐ucts,however,isexpectedtomakeasubstan‐tialcontributiontounderstandingandeventu‐ally improving fertility and other desirabletraitsindairycattle(Dawson,2006).Geneticestimatesoffertilitycanbeimprovedbygenome‐wideSNParrays.UtilizationoftheBovineSNP50chipfromIllumina(SanDiego,CA) improved reliability forDPR (Cole et al.,2011;Wiggansetal.,2011),butthepoorher‐itabilityandpolygenicnatureof thetraithasmeant that improvements in reliabilitiesachieved by incorporation of genomic infor‐mationwaslessthanforothertraits.Thus,alt‐hough incorporationof information fromtheSNP50 chip increased reliability of DPR by17%inHolsteins, this improvementwasoneoftheleastofthe12traitsexamined(Wiggansetal.,2011).Onepossiblewaytoimproveac‐curacyofgenomicestimatesoffertilityistoin‐corporateSNPsforspecificgenesinvolvedinreproduction into genomic selection panels.The bovine genome contains over 20,000genes,andover14,000ofthosedonotcontaina single SNP on the Bovine SNP50 chip

(Michelizzietal.,2011).Incorporationofcan‐didategeneSNPsintogenomictestsforrepro‐duction would allow selection of causativeSNPsorSNPsphysicallymoreclosetocausa‐tiveSNPs.Suchanapproachhasbeensuccess‐fulforimprovingabilitytodetectgenomicas‐sociations with disease (Amos et al., 2011).Manygeneshavebeenassociatedwithrepro‐duction in the dairy cow. Among these areSNPsrelatedto invitro fertilizationordevel‐opment, such as DNAJC27, FGF2, GHR, PGR,SPP1,STAT3,andSTAT5A(Khatibetal.,2008a;Khatibetal.,2008b;Driveretal.,2009;Khatibet al., 2009b;Wanget al., 2009;Zhanget al.,2011),DPR(CAST;Garciaetal.,2006),sireCRincludingFGF2, ITGB5 andSTAT5A (Feugangetal.,2009;Khatibetal.,2010),andcalvingin‐terval(GHR;Watersetal.,2011),superovula‐tionresponse(FSHR;Yangetal.,2010),twin‐ning rate (IGF1; Kim et al., 2009), and inci‐denceofstillbirth(NLRP9andLEP;Ponsuksiliet al., 2006; Brickell et al., 2010). Theprevi‐ouslymentionedSNPsonlyrepresentasmallportionofthegenesinvolvedinreproductiveprocesses.Recentstudieshaverevealedgeneswhose expression in tissues or cells of im‐portancetoreproductionvarywithreproduc‐tivestatus;thesegenesarecandidatesforcon‐tainingSNPsthatimpactfertility.Forexample,geneswereidentifiedthatweredifferentiallyregulated in the brain of cows displayingstrongestruscomparedwiththosedisplayingweakestrus(Kommadathetal.,2011),intheendometriumofheiferswhichproducedvia‐bleembryoscomparedwiththosewhichpro‐duced non‐viable embryos (Beltman et al.,2010),andinbiopsiesfromembryosthatre‐sulted in livecalvescomparedwithembryosthatdiedfollowingembryotransfer(El‐Sayedetal.,2006).Geneticvariantsinthegenesdif‐ferentially expressed in the aforementionedstudiesandothersmayberesponsiblefordif‐ferencesinfertilityamonganimals.In order to evaluate effectiveness of SNPs incandidategenes forexplaininggeneticvaria‐tion inDPR,semenwasgenotyped from550HolsteinbullsofhighorlowDPRfor434can‐didate SNPs (Cochran et al., 2013a). Three

19



typesofSNPswereevaluated:(1)SNPsprevi‐ously reported to be associated with repro‐ductive traits or physically close to geneticmarkers for reproduction; (2) SNPs in geneswell known to be involved in reproductiveprocesses;and(3)SNPsingenesdifferentiallyexpressedbetweenphysiologicalconditionsinavarietyoftissuesassociatedwithreproduc‐tivefunction.Atotalof40SNPswereassoci‐atedwith DPR. Among thesewere genes in‐volvedintheendocrinesystem,cellsignaling,immunefunction,andinhibitionofapoptosis.Atotalof10geneswereregulatedbyestradiol.In addition, 22 SNPs were associated withheiferCR,33withcowCR,36withproductivelife,34withnetmerit,23withmilkyield,19withmilk fatyield,13withmilk fatpercent‐age,19withmilkproteinyield,22withmilkproteinpercentage, and13with somatic cellscore.AllelesubstitutioneffectforSNPsasso‐ciated with heifer CR, cow CR, PL, and netmeritwere in thesamedirectionas forDPR.AllelesubstitutioneffectsforseveralSNPsas‐sociated with production traits were corre‐lated negatively with DPR. Nonetheless, 29SNPsassociatedwithDPRwerenotnegativelyassociatedwithproductiontraits.TheseSNPsarenowbeingincorporatedintogenomictestsof reproduction and other traits such as theGeneSeek®GenomicProfiler™(NeogenAgri‐genomics,Lincoln,NE).GenesassociatedwithDPRalsoarelikelyimportantforunderstand‐ing reproduction. Given the large number ofSNPsassociatedwithDPRthatwerenotnega‐tively associated with production traits, itshouldbepossible to select forDPRwithoutcompromisingproduction.The results of that study (Cochran et al.,2013a)supporttheideathatacandidategeneapproachcouldbeasuccessfulmethodofde‐terminingmarkersforDPR.Itwasanticipatedthat, because the SNPs used for genotypingwerespecifically chosen for their function inreproductiveprocesses,alargerproportionofthemwouldbe associatedwith reproductivetraitsthanforproductiontraits.Sucharesultwasobtained.Ofthe98genesthatmetanaly‐sis criteria, 42 genes were associated withDPR, but only 23were associatedwithmilk

yield.Moreover, allof the significantSNPef‐fects for DPRwere between 5 and 25 timesgreaterthanthelargestmarkereffectfromtheBovineSNP50chip(Coleetal.,2011),proba‐blybecauseofdifferencesinSNPselectionbe‐tweenthe2methods.ThemajorityofSNPsonthe Bovine SNP50 chip are between genes(63%)withover14,000genesrepresentedbya SNP in close proximity, but notwithin thegene itself on the Bovine SNP50 chip(Michelizzietal.,2011).Inthecurrentstudy,almostalloftheSNPsexaminedwerelocatedin the coding regionof the gene, and the re‐mainderclosephysicallytothecodingregion.Moreover,SNPswerechosentomaximizetheprobability that a changemight occur in thecharacteristic of the protein encoded for thegene.Thus, it is likely thatmanyof theSNPsthathavelargeeffectsonDPRdosobecausetheyarecausativeSNPsresultinginchangesinprotein function. The remainder may repre‐sent linkages to causative SNPs. The SNPsidentified in this studymay be closer to thecausative SNPs than the SNPs on theBovineSNP50chip.FertilizationandEmbryoDevelopmentFertilizationanddevelopmentof thepre‐im‐plantation embryo is under genetic control.Existenceofgenescontrollingfertilizationandembryonic development indicates that somehistoricaldeclineinfertilityindairycattlemaybetheresultofincreasedfrequencyofallelesthat inhibit conceptus development (Lucy,2001,Hansen,2011).ThereareSNPsincattleforwhichthebeneficialalleleformilkproduc‐tion is the lessdesirable allele for reproduc‐tion (Pimentel et al., 2011; Cochran et al.,2013a). Therefore, selection for productiontraitsmayhave reduced the frequencyof al‐leles that benefit fertility. In cattle, severalSNPshave been associatedwith these traits.For example, SNPs in DNAJC27, FGF2, GHR,PGR,SPP1,STAT3,andSTAT5Ahavebeenas‐sociatedwith in vitro fertilization (Khatib etal., 2008b; Driver et al., 2009; Khatib et al.,2009a; Khatib et al., 2009b; Khatib et al.,2009c; Wang et al., 2009; Cochran et al.,2013b).Furthermore,SNPsinDNAJC15,FGF2,GHR,PGR,PRLR,SERPINA15,STAT3,STAT5A,

20



and SPP1 have been associatedwith in vitrodevelopment of embryos to the blastocyststage(Khatibetal.,2008a;Khatibetal.,2008b;Driveretal.,2009;Khatibetal.,2009b;Wangetal.,2009;Zhangetal.,2011).Thereisare‐portthatallelesforFGF2andSTAT5Aalsoareassociated with bull fertility (Khatib et al.,2010),butanothergroupfailedtofindanef‐fectofallelicvariationinFGF2orSTAT5Aonfemale fertility in vivo (Oikonomou et al.,2011).Incontrast,(AlNaibetal.(2011)foundthatbullswithincreasedCRinvivoalsohadin‐creasedcleavageratesinvitro.AllelicSNPvar‐iantsrelatedtoinvitrodevelopmentalsomayberelatedtoDPRandfertilityinvivo.Recently,Cochranetal.(2013b)determinedifany of the 434 SNPs previously studied(Cochranet al., 2013a)wereassociatedwithgeneticvariationinfertilizationandearlyem‐bryonic development. They produced em‐bryosfrom93bullsusinginvitroproceduresandrelatedcleavagerateanddevelopmentofcleavedembryostotheblastocyststagetothegenotypeforeachSNP.Bullswereselectedtohave either large or small estimates for pre‐dictedtransmittedabilityforDPR,anestimateoffuturedaughters’fertility.Therepeatabilitywas0.84forcleavagerateand0.55forblasto‐cyst development. No significant correlationwasdetectedbetweenDPRandeither cleav‐agerateorblastocystdevelopment.Atotalof100SNPshadaminorallele frequencysuffi‐cientlylarge(>5%)toallowassociationanal‐ysis. Nine genes with SNPs were associatedwith cleavage rate (AVP, DEPP, EPAS1,HSD17B6, NT5E, SERPINE2, SLC18A2,TBC1D24, and a noncharacterized gene) and12geneswithSNPsassociatedwithblastocystdevelopment (C1QB, FAM5C, HSPA1A, IRF9,MON1B, PARM1, PCCB, PMM2, SLC18A2,TBC1D24,TTLL3,andWBP1).Resultsindicatethatinvitrocleavagerateandblastocystdevel‐opment are under genetic control and high‐lightthepotential importanceofsomeprevi‐ouslyunknowngenes in theseprocesses.Se‐lectionofcattlebasedonthegenotypeatoneormoreof these19 locimayproveuseful inconjunction with other genetic markers forimprovingfertility.Indeed,failureoffertiliza‐tionandembryocleavagetoformablastocyst

isacauseofpregnancylossinhighmilk‐pro‐ducingdairycattle(Hansen,2011).Genome‐WideAssociationStudies(GWAS)Candidate gene studies have often producedinconclusiveresults,duelargelytosystematicunder‐powering and lack of replication(Pasche and Yi, 2010).With thewidespreadavailabilityoflow‐cost,high‐densitySNPpan‐els,candidategenestudiesarebeingcomple‐mentedbyGWAS,whichcanprovidevaluableinsights intothegeneticarchitectureofcom‐plex processes. A study of high‐density (>250K SNP) human panels (Wilkening et al.,2009)concludedthatthereissufficientcover‐ageofindividualgenesandpathwaysthatcan‐didategenestudiesarenotnecessary.Incon‐trast, most dairy cattle currently are geno‐typedusinga50KSNPpanel,whichmaynotadequatelycoverraregenevariantsormark‐erswithonlymoderateeffectsonphenotypes.The latterpoint isparticularly importantbe‐causeassociations thatarenotdetectedmaybe valuable for understanding the biologicalbasis of complex phenotypes (Cantor et al.,2010).Measuresof fertility,suchasDPR,arethe phenotypic expression of complex geno‐typesaffectedbymanyloci(Coleetal.,2009).Fertility measurements have low heritabili‐ties, and few lociwith large effects on thosetraitshavebeen identified (Cole et al., 2009;2011).NoGWASemployingthehigh‐densitybovineSNPpanels(>500KSNP)havebeenreportedwithafocusonfertilityindairycows.Usingalow‐densitySNPpanel,Sugimotoetal.(2013)investigatedgeneticeffectsonCRinHolsteincows.TheDNAwascollectedfrom4,362Hol‐steincowsandusedtoevaluatetheestimatedbreeding value (EBV) for CR (Averill et al.,2004). The EBV is a genetic component ob‐tainedbysubtractinganenvironmentalcom‐ponent fromaphenotype.ThemeanEBVforCRwas45.3±3.5%.Next, the sampleswerestratified and grouped by CR with 192 lowsamples(CR<41.1%)and192highsamples(CR > 51%) and then genotyped for 54,001SNPs using the Illumina Bovine SNP50 v1panel. This analysis found 6 loci associated

21



withCRonchromosomes3,5,13,18,and28.Theassociatedregionssurrounding2signifi‐cant SNPs, ARS‐BFGL‐NGS‐72055 (chromo‐some3)andBTB‐01171634(chromosome28),didnotincludeanyknowngenes.Theother4loci harbored 2 gap junction‐related genes,plakophilin 2 (PKP2) and cortactin‐bindingprotein2N‐terminallike(CTTNBP2NL),and2neuroendocrine‐related genes, SET domaincontaining 6 (SETD6) and calcium channel,voltage‐dependent, beta 2 subunit (CACNB2).KnockdownofPkp2oroverexpressionofCTT‐NBP2NL inhibited embryo implantation inmice. Additional experiments found thatSETD6isinvolvedinthetranscriptionalregu‐lation of GnRH, whereas CACNB2 controlledthesecretionofFSHincattle.Thetotalallelesubstitution effect of these genes on CRwas3.5%.Moreover,thesegeneshadfavorableef‐fectsonthedurationofDO,whichwasreducedby8.5daysasthetotalallelesubstitutionef‐fect.Daysopenisanimportantfertilitytraitincattle,anditsadditivegeneticvarianceis90.5days. Therefore, genetic variants related togap junctions and neuroendocrinology influ‐enceCRaswellasDO.DeleteriousRecessiveLethalAllelesWith the recent advent of genomic tools forcattle, several recessive conditions affectingfertility have been identified and selectedagainst, such as deficiency of uridine mono‐phosphate synthase, complex vertebral mal‐formation, and brachyspina. In the Holsteinbreed,2majorrecessivedefectsaffectingem‐bryoorfetalsurvivalhavebeendetected.De‐ficiency of uridine monophosphate synthase(DUMPS; Robinson et al., 1984), a homozy‐gous recessive condition, causes fetal deathbetween40and50ofgestation (ShanksandRobinson, 1989). Testing of AI sires forDUMPS and selecting against it has reducedthefrequencyofheterozygoussiresandofho‐mozygousrecessiveembryos,andhasnowal‐mosteliminatedDUMPSasacauseofinfertil‐ity(VanRadenandMiller,2006).Complexver‐tebralmalformationisalethalrecessivecon‐ditionthatcauseslatefetaldeathincattle.Thisdefectwasdisseminatedbywidespreaduseofthe Holstein sire, Carlin‐M Ivanhoe Bell

(Thomsenetal.,2006).Availabilityofgenome‐wide,high‐densitySNPpanels,combinedwiththetypicalstructureoflivestockpopulations,markedly accelerates the identification ofgenesandmutationsthatcauseinheritedde‐fects (Charlier et al., 2008). This approach,combinedwithNGS, identifieda3.3Kbdele‐tionintheFANCIgenecausingthebrachyspinasyndrome, a rare recessive genetic defect inHolsteincattle(Charlieretal.,2012).Despitethevery small incidenceofbrachyspina (<1per100,000),carrierfrequencywasashighas7.4%intheHolsteinbreed.VanRadenetal.(2011)recentlyreportedthediscoveryof5haplotypeswithdeleteriousef‐fectsonfertilityin3breedsofdairycattle,in‐cluding1recessiveinAyrshirecattle,2reces‐sivesinBrownSwisscattle,3inHolsteins,and1inJerseys(VanRadenetal.,2011;Cooperetal.,2014).Similarmethodologyalsohasbeenusedtoidentifynovelrecessivesinothercattlepopulations (Fritz et al., 2013). The Jerseyhaplotype (JH1) was localized to the regionbetween11and16MbponBos taurus chro‐mosome15,andwasdeterminedtohaveasig‐nificantnegativeeffectonfertility.Despitetherelatively high carrier frequency (23.4%) inthepopulation,nohomozygousanimalshavebeen identified in the population, leading tothe conclusion that JH1 is associated withearlyembryonicloss.The5‐Mbregionidenti‐fied using SNP genotypes was therefore anideal candidate for resequencing to identifythecausalvariantassociatedwiththispheno‐type.TheDNAsequencevariationassociatedwith the lethal haplotype JH1was identifiedusing targeted resequencingofheterozygousanimals and the putative biological mecha‐nisms underlying the JH1 phenotype(Sonstegard et al., 2013). By combining SNPanalysis ofwhole‐genome sequences alignedtotheJH1intervalandsubsequentSNPvalida‐tion,anonsensemutationinCWC15wasiden‐tifiedasthelikelycausativemutationunderly‐ingthefertilityphenotype.Nohomozygousre‐cessive individualswere found in 749 geno‐typedanimals,whereasallknowncarriersandcarrier haplotypes possessed 1 copy of themutantallele.Thisnewlyidentifiedlethalhas

22



beenresponsibleforasubstantialnumberofspontaneous abortions in Jersey dairy cattlethroughout the past half‐century and origi‐nated with 1 highly influential Jersey bull.Withthemutationidentified,selectionagainstthedeleteriousalleleinbreedingschemeswillreduceincidenceofthisdefectinthepopula‐tion.Whole‐genome resequencingusingNGSproved to be a powerful strategy to identifyrapidlyapreviouslymappeddeleteriousmu‐tationinaknowncarrierofarecessivelethalallele.Althoughthelethaleffectsdiscoveredtodatemanifestthemselvesduringlatergestationorcause stillbirths, it is tempting to speculatethatchromosomalabnormalitiesandspecificlethal genes may cause failure of embryocleavage to form a blastocyst or conceptuselongation that results in early embryonicmortalitydenotedbylargeorslightlydelayed21‐dayreturntoestrusrates.CopyNumberVariation(CNV)InadditiontorecessivelethalallelesandSNPs,CNVmayalsobeasourceofgeneticvariationthataffectspregnancylossindairycattle.Are‐centstudyidentifieda660‐kbdeletionencom‐passing4genesasacausativevariantforama‐jor fertility quantitative trait locus (QTL) inNordicRedCattle(Kadrietal.,2014).Ofnote,the deletion causes embryonic lethality be‐cause of the loss ofRNASEH2B,which elicitsembryonicdeath inmice lackingbothallelesor null mice. Despite causing problemswithfertility resulting from embryonic lethality,carrierfrequencyforthevariantis13%,23%,and32%ofDanish,SwedishandFinnishRedCattle,respectively,becausethedeletionhasastrong positive effect on milk yield in thosedairycattle.Thisstudysupportstheideathatthatembryoniclethalmutationsaccountforanon‐negligiblefractionofthedeclineinfertil‐ityofdairycattleandthatpositiveeffectsonmilkyieldmayaccountforpartofthenegativegeneticcorrelation.Thisworkaddstotheevi‐dence that embryonic lethals are at least, inpart, responsible for the increase in fertilityproblemsandconception failureobserved inhighly selected cattle populations. In man,

largely deleterious alleles are typically rare,andhencehomozygosity for such variants isexceptionalinabsenceofconsanguinity.Indo‐mesticanimals,however,andasaresultofin‐tenseselectionandreductionineffectivepop‐ulationsizes,ayetunidentifiednumberofem‐bryonic lethalsmaybe segregatingat low tomoderate frequencies in most dairy cattlepopulations.

GenomicSelectionGenomicselectioncanbedefinedasthestudyof differences between individual animals inthe bovine genome sequence (SNP or CNV)that canbeused topredicteconomically im‐portant traits, such asmilk production,milkcomposition, health, fertility, or longevity(Meuwissenetal.,2001).Geneticinformationforagivencalf,heifer,cow,orbulliscomparedwiththatofareferencepopulationofolderan‐imalsofthesamebreed.Thisreferencepopu‐lation is composed of animals with knownphenotypes thathavebeen genotypedprevi‐ously,andtheirphenotypicandgenomicinfor‐mationarestoredinanextensivedatabaseattheUSDA‐ARSAnimalGenomicsandImprove‐mentLaboratory(AGIL,Beltsville,MD).Dairycattleareparticularlywell‐suitedforgenomicselection, because individual animals withhigh EBV have sufficient value to offset thecosts of genomic testing, and because largereferencepopulationsofbullswithhighrelia‐bilitypredictionsofgeneticmeritexistforthepurposeofestimatingSNPeffectsorcalculat‐ing genomic predicted transmitting abilities(GPTA).Morethan350,000dairybulls,cows,heifers, and calves have genomic data in theAGIL database, and genomic predictions areavailableforHolstein,Jersey,andBrownSwisscattle. Current cost of genomic testing is ap‐proximately $45 per animalwith a low‐den‐sity(9Kor12K)chip,whereasthecostofme‐dium‐density (54K) or high‐density chip(648K or 777K), genotyping is 2‐ to 4‐foldgreater(GeneSeek,Lincoln,NE).In North America, as inmost countrieswithwell‐developed genomic evaluation systemsfor dairy cattle, genotype information has

23



beenincorporatedintogeneticevaluationsys‐temsinanearlyseamlessmanner(Wiggansetal., 2011). Approximately 45,000 SNPs areused in routinegenomicevaluations,and foranimals that havebeen genotypedwith low‐densitychipstheremainingSNPsareimputedwith 90 to 99% accuracy based on the me‐dium‐andhigh‐densitygenotypesofreferenceanimals of the same breed (Boichard et al.,2012;VanRadenetal.,2013).Inthismanner,inexpensive low‐density genotyping of cows,heifers,andcalvesoncommercialdairyfarmsis possible, and after genotype imputation,theirGPTAvaluesareofsufficientaccuracyforselectionandcullingdecisions (Weigel et al.,2010;Dassonnevilleetal.,2011).InHolsteins,theaveragegainsinreliabilityforproductiontraitsare29,31,and23%formilk,fat, and protein, respectively, whereas gainsforfitnesstraitsare22,27,and22%forDPR,somaticcellscore,anddurationofproductivelife,respectively.Forproteinyield,whichhasa heritability of approximately 30%, theamount of information provided by a youngcalf’spedigree is equivalent to about7milk‐recordedoffspring,whereastheamountofin‐formation provided by the calf’s genotype isequivalent to about 34 additional daughters.Incontrast,forDPR,whichhasaheritabilityofabout4%,theamountofinformationprovidedby the calf’s genotype is equivalent to about131 additional daughters. Historically, theweak link in dairy cattle improvement pro‐gramshasbeenthe“damstoproducedaugh‐ters”selectionpathway,becauseofpooraccu‐racyand lowselection intensity (VanTassellandVanVleck,1991).Reliabilityoftraditionalpedigree‐basedPTAvaluesforcowsoncom‐mercialfarmshastendedtobesmall,andele‐vatedratesofcullingresultingfromillness,in‐jury, or infertility have typically preventedculling of genetically inferior replacementheifers. Culling rates, however, on modern,well‐managedfree‐stalloperationstendtobelow, and widespread usage of gender‐en‐hanced (sexed) semen has generated an ex‐cess of replacementheifers. Thus, dairypro‐ducershaveanopportunitytoimprovethege‐neticpotentialoftheirherdsbycullinginferior

femalesatayoungageand,moreimportantly,theycansignificantlyreducefeedcostsassoci‐atedwithrearingheifers thatareunlikely toperformataprofitable leveloncetheyreachlactatingage.The discovery process for genomic selectionand implementation into breeding strategiesrequiresa2‐stageapproachinwhichanimalswith known phenotypes and genotypes areused in a training analysis to statistically es‐tablish the relationships between individualSNPsandtraitvariationandthentheinferredequationsthatpredictbreedingvaluearevali‐dated in independent populations. All dairybreedingpopulationsintheU.S.relyonusingthemost superior sires in the population toproducethenextgenerationofsons,leadingtohighpedigreerelationshipsbetween trainingand target populations. Simulation studieshaveshownthatgenomicselectionimprovesthe accuracy of selecting juvenile animalscomparedwithtraditionalbreedingmethodsand compared with selection using infor‐mationfromafewgenesorquantitativetraitloci(VeerkampandBeerda,2007).Improvingdairycowfertilitybymeansofgeneticselec‐tion is becoming increasingly important, be‐cause improved management alone cannotsolvefertilityproblems.

FuturePossibilitiesandConclusionsUseofgenetictechnologiesandgenomicselec‐tion in livestock management will undoubt‐edlyincreasedairycowfertilitywithoutsacri‐ficingmilk yield, thereby improving the eco‐nomicandsustainabilityaspectsofproductionenterprises. It isclearthatgeneticvariabilityexistswithin the Holstein and Jersey breedsforimportanttraitsrelatingtofertility(Berryetal.,2007).Marker‐assistedselectionwillin‐creasetherateofgeneticprogressforrepro‐ductivetraitsbyincreasingaccuracyofselec‐tionandpermittingselectioninbothgendersatayoungerage(Rohrer,2004).Thecurrentgenomicselectionstrategiesforfertilitytraitsrelymostly on sires, butmarkers developedfordamfertilitytraitsaresorelyneeded.Re‐production‐enhancingtechnologiessuchasAI,embryo transferandovumpick‐upwillhave

24



animportantroleinfullyexploitingthepoten‐tialofmarker‐assistedselection(GeorgesandMassey,1991;Humblotetal.,2010).Selectivebreedinghaslongbeenpracticedtoenrichfordesirable DNA variation that influences live‐stock traits.Adventofnewgeneticengineer‐ingtechnologiesallowsforgeneticvariantstobe directly introgressed into livestock ge‐nomesusingmodifiedmeganucleasesystems(Pennisi, 2013; Tan et al., 2013; Hsu et al.,2014).Transientexposureoflivestockcellstosequence‐targeted editors stimulates homol‐ogy‐directed repair to levels that eliminateneed for transgene‐dependent selection. Useof oligonucleotide template enables efficientsinglenucleotidechanges to thegenomeandpermitsthetransmissionofbothnaturalandnovelDNAsequencevariants intonaïve live‐stock breeds. Indeed, gene editing offers apowerfulmethodforacceleratingthegeneticimprovementoflivestock.Insummary,incor‐porationofgeneticselectionforreproductionandhealthtraitsaswellasnewtechnologiesisexpectedtoimprovedairycowfertility,repro‐ductiveefficiency,productivity,andsustaina‐bility.

AcknowledgmentsThisproject is supportedbyAgriculture andFood Research Initiative Competitive Grantno. 2013‐68004‐20365 from the USDA Na‐tionalInstituteofFoodandAgriculture.

LiteratureCitedAlNaib,A.,J.P.Hanrahan,P.Lonergan,andS.

Fair. 2011. In vitro assessment of spermfrom bulls of high and low field fertility.Theriogenology76:161‐167.

Amos,W.,E.Driscoll,andJ.I.Hoffman.2011.Candidate genes versus genome‐wideassociations: which are better fordetecting genetic susceptibility toinfectious disease? Proc. Biol. Sci.‐TheRoyalSoc.278:1183‐1188.

Averill,T.A.,R.Rekaya,andK.Weigel.2004.Genetic analysis of male and femalefertility using longitudinal binary data.J.DairySci.87:3947‐3952.

Bae,J.S.,H.S.Cheong,L.H.Kim,S.NamGung,T.J.Park,J.Y.Chun,J.Y.Kim,C.F.Pasaje,J.

S.Lee,andH.D.Shin.2010.Identificationof copy number variations and commondeletion polymorphisms in cattle. BMCGenomics11:232.

Bauersachs, S. and E. Wolf. 2012.Transcriptomeanalysesofbovine,porcineandequineendometriumduringthepre‐implantation phase. Anim. Reprod. Sci.134:84‐94.

Beltman,M.E.,N.Forde,P.Furney,F.Carter,J.F. Roche, P. Lonergan, and M. A. Crowe.2010. Characterisation of endometrialgene expression and metabolicparametersinbeefheifersyieldingviableor non‐viable embryos on Day 7 afterinsemination.Reprod.Fertil.Dev.22:987‐999.

Bickhart, D. M., Y. Hou, S. G. Schroeder, C.Alkan,M.F.Cardone,L.K.Matukumalli,J.Song, R. D. Schnabel, M. Ventura, J. F.Taylor, J.F.Garcia,C.P.VanTassell,T.S.Sonstegard, E. E. Eichler, and G. E. Liu.2012.Copynumbervariationofindividualcattle genomes using next‐generationsequencing.GenomeRes.22:778‐790.

Brickell, J.S.,G.E.Pollott,A.M.Clempson,N.Otter, and D. C. Wathes. 2010.Polymorphisms in thebovine leptingeneassociated with perinatal mortality inHolstein‐Friesian heifers. J. Dairy Sci.93:340‐347.

Cantor, R.M., K. Lange, and J. S. Sinsheimer.2010.PrioritizingGWASresults:Areviewof statistical methods andrecommendations for their application.Am.J.HumanGenet.86:6‐22.

Charlier, C., J. S. Agerholm,W. Coppieters, P.Karlskov‐Mortensen,W. Li, G. de Jong, C.Fasquelle, L. Karim, S. Cirera, N.Cambisano, N. Ahariz, E. Mullaart, M.Georges, and M. Fredholm. 2012. Adeletion in the bovine FANCI genecompromises fertility by causing fetaldeath and brachyspina. PLoS One7:e43085.

Charlier, C., W. Coppieters, F. Rollin, D.Desmecht,J.S.Agerholm,N.Cambisano,E.Carta,S.Dardano,M.Dive,C.Fasquelle,J.C.Frennet,R.Hanset,X.Hubin,C.Jorgensen,L.Karim,M.Kent,K.Harvey,B.R.Pearce,

25



P.Simon,N.Tama,H.Nie,S.Vandeputte,S.Lien,M.Longeri,M.Fredholm,R.J.Harvey,and M. Georges. 2008. Highly effectiveSNP‐based association mapping andmanagement of recessive defects inlivestock.Nat.Genet.40:449‐454.

Cochran, S. D., J. B. Cole, D. J. Null, and P. J.Hansen. 2013a. Discovery of singlenucleotide polymorphisms in candidategenes associated with fertility andproduction traits in Holstein cattle. BMCGenet.14:49.

Cochran, S. D., J. B. Cole, D. J. Null, and P. J.Hansen. 2013b. Single nucleotidepolymorphisms in candidate genesassociatedwithfertilizingabilityofspermand subsequent embryonic developmentincattle.Biol.Reprod.89:69.

Cole,J.B.,P.M.VanRaden,J.R.O'Connell,C.P.Van Tassell, T. S. Sonstegard, R. D.Schnabel, J. F. Taylor, andG. R.Wiggans.2009.Distributionandlocationofgeneticeffects for dairy traits. J. Dairy Sci.92:2931‐2946.

Cole,J.B.,G.R.Wiggans,L.Ma,T.S.Sonstegard,T. J. Lawlor, Jr., B. A. Crooker, C. P. VanTassell,J.Yang,S.Wang,L.K.Matukumalli,andY.Da.2011.Genome‐wideassociationanalysis of thirty one production, health,reproduction and body conformationtraitsincontemporaryU.S.Holsteincows.BMCGenom.12:408.

Conrad, D. F., D. Pinto, R. Redon, L. Feuk, O.Gokcumen, Y. Zhang, J. Aerts, T. D.Andrews, C. Barnes, P. Campbell, T.Fitzgerald, M. Hu, C. H. Ihm, K.Kristiansson, D. G. Macarthur, J. R.Macdonald, I. Onyiah, A. W. Pang, S.Robson,K.Stirrups,A.Valsesia,K.Walter,J.Wei,C.Tyler‐Smith,N.P.Carter,C.Lee,S.W.Scherer,andM.E.Hurles.2010.Originsand functional impact of copy numbervariation in the human genome. Nature464:704‐712.

Cooper, T. A., G. R. Wiggans, D. J. Null, J. L.Hutchison, and J. B. Cole. 2014. Genomicevaluation, breed identification, anddiscoveryofahaplotypeaffectingfertilityfor Ayrshire dairy cattle. J. Dairy Sci.97:3878‐3882.

Dawson, K. A. 2006. Nutrigenomics: feedingthe genes for improved fertility. Anim.Reprod.Sci.96:312‐322.

Dekkers,J.C.andF.Hospital.2002.Theuseofmoleculargeneticsintheimprovementofagricultural populations. Nat. Rev. Genet.3:22‐32.

Diskin, M. G. and D. G. Morris. 2008a.Embryonicandearlyfoetallossesincattleand other ruminants. Reprod. Domest.Anim.43(Suppl.2):260‐267.

Diskin, M. G. and D. G. Morris. 2008b.Embryonicandearlyfoetallossesincattleand other ruminants. Reprod. Domest.Anim. Zuchthygiene 43 (Suppl. 2):260‐267.

Diskin,M.G., J. J.Murphy, and J.M. Sreenan.2006. Embryo survival in dairy cowsmanagedunderpastoralconditions.Anim.Reprod.Sci.96:297‐311.

Driver,A.M.,W.Huang,S.Gajic,R.L.Monson,G. J. Rosa, and H. Khatib. 2009. Shortcommunication: Effects of theprogesteronereceptorvariantsonfertilitytraitsincattle.J.DairySci.92:4082‐4085.

Egan,C.M.,S.Sridhar,M.Wigler,andI.M.Hall.2007. Recurrent DNA copy numbervariationinthelaboratorymouse.NatureGenet.39:1384‐1389.

El‐Sayed,A.,M.Hoelker,F.Rings,D.Salilew,D.Jennen, E. Tholen, M.‐A. Sirard, K.Schellander,andD.Tesfaye.2006.Large‐scale transcriptional analysis of bovineembryobiopsies inrelationtopregnancysuccess after transfer to recipients.Physiol.Genom.28:84‐96.

Fadista, J., B. Thomsen, L. E. Holm, and C.Bendixen.2010.Copynumbervariationinthebovinegenome.BMCGenom.11:284.

Feugang, J.M.,A.Kaya,G.P.Page,L.Chen,T.Mehta,K.Hirani,L.Nazareth,E.Topper,R.Gibbs, and E. Memili. 2009. Two‐stagegenome‐wideassociation study identifiesintegrinbeta5ashavingpotentialroleinbullfertility.BMCGenom.10:176.

Feuk,L.,A.R.Carson,andS.W.Scherer.2006.Structuralvariationinthehumangenome.Naturereviews.Genetics7:85‐97.

Fritz,S.,A.Capitan,A.Djari,S.C.Rodriguez,A.Barbat, A. Baur, C. Grohs, B. Weiss, M.

26



Boussaha,D.Esquerre,C.Klopp,D.Rocha,and D. Boichard. 2013. Detection ofhaplotypesassociatedwithprenataldeathin dairy cattle and identification ofdeleteriousmutationsinGART,SHBGandSLC37A2.PLoSOne8:e65550.

Garcia, M. D., J. J. Michal, C. T. Gaskins, J. J.Reeves,T.L.Ott,Y.Liu,andZ.Jiang.2006.Significant association of the calpastatingenewith fertility and longevity in dairycattle.Anim.Genet.37:304‐305.

Hansen, P. J. 2011. Challenges to fertility indairy cattle: from ovulation to the fetalstage of pregnancy. Rev. Bras, Reprod.Anim.35:229‐238.

Hare,E.,H.D.Norman,andJ.R.Wright.2006.Trends in calving ages and calvingintervals for dairy cattle breeds in theUnitedStates.J.DairySci.89:365‐370.

Hayes, B. J., J. Pryce, A. J. Chamberlain, P. J.Bowman,andM.E.Goddard.2010.Geneticarchitecture of complex traits andaccuracy of genomic prediction: coatcolour, milk‐fat percentage, and type inHolsteincattleascontrastingmodeltraits.PLoSGenet.6:e1001139.

Hsu, P. D., E. S. Lander, and F. Zhang. 2014.DevelopmentandapplicationsofCRISPR‐Cas9 for genome engineering. Cell157:1262‐1278.

Kadri,N.K.,G.Sahana,C.Charlier,T.Iso‐Touru,B.Guldbrandtsen,L.Karim,U.S.Nielsen,F.Panitz, G. P. Aamand, N. Schulman, M.Georges,J.Vilkki,M.S.Lund,andT.Druet.2014.A660‐Kbdeletionwithantagonisticeffects on fertility and milk productionsegregatesathighfrequencyinNordicRedcattle:additionalevidenceforthecommonoccurrence of balancing selection inlivestock.PLoSGenet.10:e1004049.

Kemper, K. E. and M. E. Goddard. 2012.Understanding and predicting complextraits: knowledge from cattle. Hum. Mol.Genet.21:R45‐51.

Khatib,H.,W.Huang,D.Mikheil,V.Schutzkus,andR.L.Monson.2009a.Effectsofsignaltransducer and activator of transcription(STAT)genesSTAT1andSTAT3genotypiccombinations on fertilization and

embryonic survival rates in Holsteincattle.J.DairySci.92:6186‐6191.

Khatib,H.,W.Huang,X.Wang,A.H.Tran,A.B.Bindrim,V.Schutzkus,R.L.Monson,andB.S. Yandell. 2009b. Single gene and geneinteraction effects on fertilization andembryonicsurvivalratesincattle.J.DairySci.92:2238‐2247.

Khatib, H., C. Maltecca, R. L. Monson, V.Schutzkus, and J. J. Rutledge. 2009c.Monoallelic maternal expression ofSTAT5A affects embryonic survival incattle.BMCgenetics10:13.

Khatib, H., C. Maltecca, R. L. Monson, V.Schutzkus, X. Wang, and J. J. Rutledge.2008a.Thefibroblastgrowthfactor2geneisassociatedwithembryonicmortalityincattle.J.Anim.Sci.86:2063‐2067.

Khatib,H.,R.L.Monson,W.Huang,R.Khatib,V.Schutzkus,H.Khateeb,andJ.J.Parrish.2010.Shortcommunication:Validationofin vitro fertility genes in a Holstein bullpopulation.J.DairySci.93:2244‐2249.

Khatib, H., R. L. Monson, V. Schutzkus, D.M.Kohl,G. J.Rosa,and J. J.Rutledge.2008b.Mutations in the STAT5A gene areassociated with embryonic survival andmilk composition in cattle. J. Dairy Sci.91:784‐793.

Kim,E.S.,X.Shi,O.Cobanoglu,K.Weigel,P.J.Berger, and B. W. Kirkpatrick. 2009.Refined mapping of twinning‐ratequantitative trait loci on bovinechromosome5andanalysisofinsulin‐likegrowth factor‐1asapositionalcandidategene.J.Anim.Sci.87:835‐843.

Kommadath,A.,H.Woelders,B.Beerda,H.A.Mulder,A.A.deWit,R.F.Veerkamp,M.F.te Pas, and M. A. Smits. 2011. Geneexpression patterns in four brain areasassociate with quantitative measure ofestrous behavior in dairy cows. BMCGenom.12:200.

Kuhn,M.T.,J.L.Hutchison,andG.R.Wiggans.2006. Characterization of Holstein heiferfertility in the United States. J. Dairy Sci.89:4907‐4920.

LeBlanc, S. J. 2014. Reproductive tractinflammatorydiseaseinpostpartumdairycows.Animal8(Suppl.1):54‐63.

27



Lonergan, P. and N. Forde. 2014. Maternal‐embryo interaction leading up to theinitiationof implantationofpregnancyincattle.Animal8(Suppl.1):64‐69.

Lucy, M. C. 2001. Reproductive loss in high‐producingdairycattle:wherewillitend?J.DairySci.84:1277‐1293.

Lucy, M. C. 2007. Fertility in high‐producingdairy cows: reasons for decline andcorrective strategies for sustainableimprovement. Soc. Reprod. Fertil. Suppl.64:237‐254.

Matukumalli,L.K.,C.T.Lawley,R.D.Schnabel,J. F. Taylor, M. F. Allan, M. P. Heaton, J.O'Connell, S. S. Moore, T. P. Smith, T. S.Sonstegard, and C. P. Van Tassell. 2009.Development and characterization of ahigh density SNP genotyping assay forcattle.PLoSOne4:e5350.

Meuwissen, T. H., B. J. Hayes, and M. E.Goddard.2001.Predictionoftotalgeneticvalue using genome‐wide dense markermaps.Genetics157:1819‐1829.

Michelizzi, V. N., X. Wu, M. V. Dodson, J. J.Michal, J. Zambrano‐Varon, D. J. McLean,andZ.Jiang.2011.Aglobalviewof54,001single nucleotide polymorphisms (SNPs)on the Illumina BovineSNP50 BeadChipandtheir transferability towaterbuffalo.Int.J.Biol.Sci.7:18‐27.

Moeller,L.M.,N.A.Michael,J.C.Dalton,andG.C. Lamb. 2010. Evaluating reproductiveoutcomes in the United States Holsteindairies.J.DairySci.93(ESuppl.1):408.

Norman,H.D.,J.R.Wright,S.M.Hubbard,R.H.Miller, and J. L. Hutchison. 2009.ReproductivestatusofHolsteinandJerseycows in the United States. J. Dairy Sci.92:3517‐3528.

Oikonomou, G., G. Michailidis, A.Kougioumtzis,M.Avdi,andG.Banos.2011.Effect of polymorphisms at the STAT5AandFGF2genelocionreproduction,milkyieldandlamenessofHolsteincows.Res.Vet.Sci.91:235‐239.

Pasche, B. and N. Yi. 2010. Candidate geneassociationstudies:successesandfailures.CurrentOpin.Genet.Dev.20:257‐261.

Pennisi, E. 2013. The CRISPR craze. Science341:833‐836.

Pimentel, E. C., S. Bauersachs, M. Tietze, H.Simianer,J.Tetens,G.Thaller,F.Reinhardt,E.Wolf,andS.Konig.2011.Explorationofrelationships between production andfertilitytraitsindairycattleviaassociationstudies of SNPs within candidate genesderived by expression profiling. Anim.Genet.42:251‐262.

Ponsuksili,S.,R.M.Brunner,T.Goldammer,C.Kuhn, C.Walz, S. Chomdej,D. Tesfaye,K.Schellander, K. Wimmers, and M.Schwerin. 2006. Bovine NALP5, NALP8,and NALP9 genes: assignment to a QTLregionandtheexpressioninadulttissues,oocytes, and preimplantation embryos.Biol.Reprod.74:577‐584.

Pritchard,T.,M.Coffey,R.Mrode,andE.Wall.2013.Geneticparametersforproduction,health, fertility and longevity traits indairycows.Animal7:34‐46.

Risch,N.andK.Merikangas.1996.Thefutureof genetic studies of complex humandiseases.Science273:1516‐1517.

Robinson, J. L., D. B. Dombrowski, G. W.Harpestad, and R. D. Shanks. 1984.DetectionandprevalenceofUMPsynthasedeficiency among dairy cattle. J. Hered.75:277‐280.

Schefers,J.M.andK.A.Weigel.2012.Genomicselection in dairy cattle: Integration ofDNA testing into breeding programs.Anim.Front.2:4‐9.

Settles, M., R. Zanella, S. D. McKay, R. D.Schnabel, J. F. Taylor, R. Whitlock, Y.Schukken,J.S.VanKessel,J.M.Smith,andH. Neibergs. 2009. A whole genomeassociation analysis identifies lociassociated with Mycobacterium aviumsubsp.paratuberculosisinfectionstatusinUS holstein cattle. Anim. Genet. 40:655‐662.

Shanks, R. D. and J. L. Robinson. 1989.Embryonic mortality attributed toinherited deficiency of uridinemonophosphate synthase. J. Dairy Sci.72:3035‐3039.

Sonstegard,T.S.,J.B.Cole,P.M.VanRaden,C.P.VanTassell,D.J.Null,S.G.Schroeder,D.Bickhart, and M. C. McClure. 2013.Identification of a nonsense mutation in

28



CWC15 associated with decreasedreproductive efficiency in Jersey cattle.PLoSOne8:e54872.

Stranger,B.E.,M.S.Forrest,M.Dunning,C.E.Ingle,C.Beazley,N.Thorne,R.Redon,C.P.Bird,A.deGrassi,C.Lee,C.Tyler‐Smith,N.Carter, S. W. Scherer, S. Tavare, P.Deloukas, M. E. Hurles, and E. T.Dermitzakis. 2007. Relative impact ofnucleotideandcopynumbervariationongene expression phenotypes. Science315:848‐853.

Sugimoto,M.,S.Sasaki,Y.Gotoh,Y.Nakamura,Y. Aoyagi, T. Kawahara, andY. Sugimoto.2013. Genetic variants related to gapjunctions and hormone secretioninfluence conception rates in cows.Proceedings of the National Academy ofSciences of the United States of America110(48):19495‐19500.

Sun,C.,P.Madsen,M.S.Lund,Y.Zhang,U.S.Nielsen,andG.Su.2010.Improvementingenetic evaluation of female fertility indairy cattle using multiple‐trait modelsincludingmilkproductiontraits.Journalofanimalscience88:871‐878.

Tan,W.,D.F.Carlson,C.A.Lancto,J.R.Garbe,D. A. Webster, P. B. Hackett, and S. C.Fahrenkrug. 2013. Efficient nonmeioticallele introgression in livestock usingcustom endonucleases. Proc. Natl. Acad.Sci.USA110:16526‐16531.

Thomsen,B.,P.Horn,F.Panitz,E.Bendixen,A.H.Petersen,L.E.Holm,V.H.Nielsen, J.S.Agerholm, J. Arnbjerg, and C. Bendixen.2006.Amissensemutation in thebovineSLC35A3 gene, encoding a UDP‐N‐acetylglucosamine transporter, causescomplexvertebralmalformation.GenomeRes.16:97‐105.

VanRaden, P. M. and E. J. Klaaskate. 1993.Geneticevaluationoflengthofproductivelife including predicted longevity of livecows.J.DairySci.76:2758‐2764.

VanRaden,P.M.andR.H.Miller.2006.Effectsof nonadditive genetic interactions,inbreeding, and recessive defects onembryo and fetal loss by seventy days. J.DairySci.89:2716‐2721.

VanRaden,P.M.,K.M.Olson,D.J.Null,andJ.L.Hutchison.2011.Harmfulrecessiveeffectson fertility detected by absence ofhomozygous haplotypes. J. Dairy Sci.94:6153‐6161.

VanRaden,P.M.,A.H.Sanders,M.E.Tooker,R.H.Miller,H.D.Norman,M.T.Kuhn,andG.R. Wiggans. 2004. Development of anational genetic evaluation for cowfertility.J.DairySci.87:2285‐2292.

Veerkamp,R.F.andB.Beerda.2007.Geneticsandgenomics to improve fertility inhighproducingdairycows.Theriogenology68(Suppl.1):S266‐S273.

Wang, X., V. Schutzkus,W.Huang, G. J. Rosa,and H. Khatib. 2009. Analysis ofsegregation distortion and association ofthebovineFGF2withfertilizationrateandearly embryonic survival. Anim. Genet.40:722‐728.

Washburn,S.P.,W.J.Silvia,C.H.Brown,B.T.McDaniel, and A. J. McAllister. 2002.Trends in reproductive performance inSoutheastern Holstein and Jersey DHIherds.J.DairySci.85:244‐251.

Waters, S.M.,M. S.McCabe, D. J. Howard, L.Giblin,D.A.Magee,D.E.MacHugh,andD.P. Berry. 2011. Associations betweennewly discovered polymorphisms in theBostaurusgrowthhormonereceptorgeneand performance traits in Holstein‐Friesiandairy cattle.Anim.Genet. 42:39‐49.

Wiggans, G. R., P. M. Vanraden, and T. A.Cooper. 2011. The genomic evaluationsystemintheUnitedStates:past,present,future.J.DairySci.94:3202‐3211.

Wilkening, S., B. Chen, J. L. Bermejo, and F.Canzian. 2009. Is there still a need forcandidate gene approaches in the era ofgenome‐wide association studies?Genomics93:415‐419.

Wiltbank,M.C.,A.H.Souza,P.D.Carvalho,A.P.Cunha,J.O.Giordano,P.M.Fricke,G.M.Baez,andM.G.Diskin.2014.Physiologicaland practical effects of progesterone onreproduction in dairy cattle. Animal8(Suppl.1):70‐81.

Yang,W.C.,S.J.Li,K.Q.Tang,G.H.Hua,C.Y.Zhang,J.N.Yu,L.Han,andL.G.Yang.2010.

29



Polymorphismsinthe5'upstreamregionof the FSH receptor gene, and theirassociation with superovulation traits inChineseHolsteincows.Anim.Reprod.Sci.119:172‐177.

Zanella, R., M. L. Settles, S. D. McKay, R.Schnabel, J. Taylor, R. H. Whitlock, Y.Schukken,J.S.VanKessel,J.M.Smith,andH.L.Neibergs.2011.Identificationoflociassociated with tolerance to Johne's

disease in Holstein cattle. Anim. Genet.42:28‐38.

Zhang,B.,F.Penagaricano,A.Driver,H.Chen,and H. Khatib. 2011. Differentialexpression of heat shock protein genesand their splice variants in bovinepreimplantation embryos. J. Dairy Sci.94:4174‐4182.

Figure1.Associationofmilkproductionindairycattlewithdaughterpregnancyrate.SelectionformilkyieldinU.S.dairycattlehasbeenverysuccessfultoincreasemilkproduction.Incontrast,cowfertilitydeclinedfrom1950to2003inspiteoflowheritabilitiesforreproductivetraits.Fortunately,historicaltrendsinfertilityhavestartedtorecoverbecauseoftheincorporationofgeneticmeritforproductivelife(PL)in1994andDPRin2003aswellasthechangeinbreedingstrategiesfromnaturalservicetoartificialinseminationandovulationsynchronizationprogramsthatdecreasedaysopen.

20

24

28

32

10,000

15,000

20,000

25,000

30,000

1950 1960 1970 1980 1990 2000 2010

Da

ug

hte

r P

reg

na

nc

y R

ate

(%

)

Mil

k P

rod

uct

ion

(lb

s)

Year

Milk Production Rate (lbs) Daughter Pregnancy Rate (%)

30



Figure2.Historicalchangesinestimatedbreedingvalue(BV)fordaughterpregnancyrate(DPR)inU.S.Holsteinsfrom1957to2012.DatawereobtainedfromtheUSDAAnimalGenomicsImprovementLaboratory(https://www.cdcb.us/eval/summary/trend.cfm).

31



genomic selection and reproductive efficiency in dairy...

Documents