QTL作图的基本原理和完备区间作图方法

1、第六届9TI，作图和育种模拟研讨会”，2()1。年4月21日，湖北武汉QTL作图的基本原理和完备区间作图方法李慧慧InstituteofCropScience,ChineseAcademyofAgriculturalSciences HYPERLINK mailto: OUTLINESQTL作图所需数据标记数据和连锁图谱构建QTL作图的基本原理数量性状基因的完备区间作图方法(ICIM)ICIM在实际作图群体中的应用WHATISQTLMAPPING?Theproceduretomapindividualgeneticfactorswithsmalleffectsonthequantitative

2、traits,tospecificchromosomalsegmentsinthegenomeiscalledQTLmapping.ThekeyquestionsinQTLmappingstudiesare:HowmanyQTLaiethere?Wherearetheyinthemarkermap?Howlargeaninfluencedoeseachofthemhaveonthetraitofinterest?DATASETOFQTLMAPPINGMappingpopulationLinkagemapMarkergenotypePhenotypicdataQTL作图群体F2群体（张鲁燕专门讲

3、解）回交(BC,backcross)群体加倍单倍体(DH,doubledhaploids)群体重组近交家系(RIL,recombinationinbredlines)群体导入系（染色体片断置换系）自然群体作图群体的分类按基因型是否纯合分暂时群体(Temporalpopulation)永久群体(Permanentpopulation)自然群体(Naturalpopulation)按群体间的亲缘关系初级作图群体(Primarymappingpopulation)次级作图群体(Secondarymappingpopulation)交群体和DH群体中的期望基因型频率RGBC2DH群体观测次数理论须率M

4、imiMzmi/S=!（l-r）M1*1二小2Mtiihnuni-inEhhl小f2fr=rMiDiiMiMsnijniiXfni*h金*MiiniMnmzmiiniiihm：niimimnin?/t=Ul-r）重组率的极大似然估计X建立似然函数X建立对数似然函数InL=InC+（/I】十n4）ln（l-r）+（nz十n3）lnrX求解重组率的极大似然估计A_刃2+_斤彳丹3T二n+n十/?彳nM求信息量.rd2nL.小叫+见+,.n1=钛討=詡冷-计I=乔町X应用估计公式求重组率的估计值和它的方差片=*=厂（17）实例x某回交试验中+P1和P2的基因型分别为AABB和azbbx回交BC1世代

5、中4种基因型的植株数+AABB：162；AABb：40；+AaBB：41；AaBb：158耳=理二1=4.02x103个标记间的重组率+金_2（1_叭扇嫩当/二0（即两个区间上的交换是独立的）时有（103）=（1-12）0-创）+巾尸23或巾二金（1尸23十（I一厂12）3二勺2+勺3一2斤2厂23帝当时5=1（即完全干涉,一个区间上的交换完全阻止另外一个区间上的交换），有E=住+尸23作图函数距(Mappingdistance)X距的单位：摩尔根(M,Morgan)或厘摩(cM,centi-Morgan),1M=100cM3C图距m是交换率啲函数，即:rn=/(r),称f为作图函数(Mapp

6、ingfunction)Q常见作图函数Morgan作图函数“以1为单位/w=r(M)/以cM为单位m=rX100(cM)Haldane作图函数没有考虑干涉的情况下，即间的交换和M2-M3间的交换相互独立“丿以M为单位m=/(r)=-|ln(l-2r)r=|(l-e2m)/以cM为单位/(r)=-5()ln(1-2r)r=l(-M)Kosambi作图函数考虑干涉的情况下，即M1-M2间的交换和M2-M3间的交换不独立，干涉招数应重组率的函数/以、1为单位w=/(r)=iln|/以1为单位吩2弘存三种作图函数的比较不同物种的遗传图距和物理图距间的关系物种单借体基因组大小(kb)遗传懈阳皱(cM)破

7、基对(kb)/cM酵母(Yeast)2.2X1037006Ncurospora4.2X1050080Arabidopsis7.0X105001402.0X10;290700两红柿(Tomato)7.2X10I4(X)510人类(Human)3.OX1O627101110小麦(Whe/)1.6X10257562U水稻(Rice)4.4X101575279Hi米(Com)3.OX1O51400214()EXAMPLE:10RILSOFRICE(LINKAGEMAPOFCHR.5)MarkerC263RM0R31&6XMpb3S7口569R1553Ici23IC14O2XNpbSIC2462953C

8、1447IGoinIwidih1(mm)Position側)0.0358.519.532066.674.178.681$91.992796.8RIL11111111111112.33RIL22222211112221.9$RIL31222222222222.24RIL4111111222222L94RIL51111122111112.76RIL61112222222222.32RIL71111111111112.32RIL62212211112222-05RIL91111221111112.24RIL101111221111112.45QTL作图的基本原理一个标记位点上3种基因型的性状平均Pl

9、；MMQQXP2；MMQQ回交群体中标记位点M与数量性状基因位点Q的基因型及其频率和基因型值BGBQ羞因型底因型频率堆凶型值基隔型基闪型频率基囲唱值MMQQ乂1一)maMmQqm十dMMQqirrn-K/Mvnqq4r/MWMmQQmmQqMmQq4(1-r)mmqqi(l-Dmoi单标记基因型均值差异分析原理两种标记基因型：=(I-r)(rn+a)+)二加+(1-r)a+rd“Mrn=厂“MmQQ+U厂)“MmQq=r(tn+)4(1-r)(m+d=m4ra4-(l-r)c/两种标记基因型的平均值差异心一如厂(1_2尸)()单标记分析中的假设测验亚群体(Sul)-卩opukilioiisj)

10、f统计畳的计笄J_dfXS；+血乂；$df+爲。2和期分别代表苇一个亚群体的均方和自由盛J?刊“人分别代表第二个亚舜体的均方和自由度，f测验的自由度为町=dj+df2OINTERVALMAPPING(IM)(LANDERANDBOTSTEIN1989)线性模型(j=E2,，n)另二心+尸尤：+勺/T表示QTL的效应，X；为取值()和1的指示变量区间测验(Intervaltest)似然曲线(Likelihoodprofile)区的标记型标记炉玄侧标记F右侧标记141样本量X1十十12-取1的概率为lp：取0餉概率为P3-+x収1的概率为严収0的概率为1卩4-40回交群体区间作图方法中指示变量的取

11、值交群体中的区间标记型和QTL基因型M.MjOmqMitiMqrn!区何标记英埜2htOMlmvwQMii莎何标记类513AdditivegeneticmodelandthederivedstatisticalmodelformappingadditiveQTL沏JiTheexpectationofthegenotypicvalueGconditionalonknownmarkertypescanbewrittenasaIinearfunctiQnofmarkervariableswrm.I以G|X）二工陽（久內+p內J几where片入知b切(/=2,.fm).andjPz詁andptaref

12、unctionsofthethreerecombinationfractionsbetweenthe/thmarkerandthejthQTL,betweenthe/thQTLand命T)Vimarker,andbetweentheRhand(/+1)thmarkers.Linearregressionmodel表型对标记线性回归模型的性质假定不同QTL间的效应是可加的，偏回归系:只依赖于两个相邻标记所标定区间上的QT4模型中加入非连锁标记，能有效控制剩余遗传方差,从而降低统计量的抽样方差，提高QTL的检测功效.模型中的连锁标记可以降低连锁QTL对检验统计量的影响.模型中的两个标记的偏回归系数

13、是不相关的。CompositeInteivalMapping(CIM;Zeng1994)Likelihoodfunctionunderthenullhypothesis:Par苹mete$estimation：=(KX)VY叭=(、xBWi)i/Likelihoodratiotest:恥-2碍)=刊)fl1Linearregressionmodel:y,=优+b*+瓦Eg+eiHypotheses：Hu；b=Qv$.H、;b+QLikelihoodfunctionundertheallernalivehypothesis:厶flfp/iJAO*p,(o)r/o)iParametersestim

14、ation;b=VB=PF)7J=(Y-XBrMultipleintervalmapping(M1M;Kaoctal.1999)G严“十a內+/=72mMIMisthestatc-of-thc-artgenenuippingprocedure.Blit;/morecomplexanddifficulttoimplenient“muchlaigtrsamplesize/timeconsumingBayesianmodel(SillanpaiiandCorander2002)/complexityofcomputation$lackofuser-ftiendlysoftware./timecons

15、uniing$noparameterestimationasmarkersarcdenselydistributed(127markers,145samplesize;DII(XuandJia2007)StatisticalMethodsinQTLMappingFrequencystatisticsANOVAMixedmodel(REML,BLURMINQUE)Rgr-ession(foiard,bsKkward,skpwise)Maximumlikelihood(EMsilgorithm)BayesianstatisticsHierarchical(SSSICIC,reversiblejum

16、pMCAIC,shrink狛唱qestimation)Noto:XuandClMmayincreasethesam卩linginimndcomparedtoIMimdthu/Differentbackgroundmarkerselectionmethodsmaygiveverydifferentmappingresultsundwhichmodelselectionmethodsshouldbeusedisnotclear.Ok79wePROBLEMSWHENUSINGCIMBrnxlo-carrj-史WJ,on.famQltfiA”nxCIMisnotextendedtocpistasism

17、apping(Zengetal.1999)ProblemswiththealgorithminCIMWefoundthatinZengsalgorithm,bothQTLeffectatthecurrenttestingpositionandregressioncoefficientsofthemarkervariablesusedtocontrolgeneticbackgroundwereestimatedsimultaneouslyinanexpectationandmaximization(EM)algorithm.Thus,thisalgorithmcouldnotcompletely

18、ensurethattheeffectofQTLatcurrenttestingintervalwasnotabsorbedbythebackgroundmarkervariablesandthereforemayresultinbiasedestimationoftheQTLeffect.AmodifiedalgorithmfortheimprovementofCIM-ICIM(InclusiveCIM)Usingallmarkersinlinearregressionmodel”】A冃Adjustingtheobservationvaluesby5=片一牙？3note:Theadjuste

19、dobservationdoesnotchangeuntilthetestingposrtionmovesintoanewinterval.Onedimensionalscanning厶亍10f(如？匕S叫上p)J(纲州Q)十pf心)J+1*1巧ny.Injpf(Ayz;jM,)+(1-/?)/(AypPj.cr2)J+工iff|+SIMULATIONSTUDYInthecontextofQTLexperiments,theideaistosimulateasetofQTLwithknowngeneticlocationsandeffectsinasegregatingpopulationan

20、dthenevaluateiftheQTLcanbeconsistentlyidentifiedamongindependentsamplesfromthepopulation.ComparisonofICIMandCIMMeanperformanceaciDSs100simulationrunsTheSimulatedgggPWcofisitecloff)elgmomg、euchof15()PoweranalysisPowerwascalculatedastheproportionofrunsthatdetectedthepresenceofQTLforeachofthe90interval

21、sdefinedby96markersevenlydistributedcm6chromosomes.C-WICB4PowerwascalculatedastheproportionofrunsthatdetectedQILwithintheintensildefinedas5cMfromeachsideofthepredefinedQTL.The10putativeQTLswererearrangedintheascendingorderbythepercentageofvarianceoxpliine-dbyeachQTL(xiononortwonozonComparisonofICIMa

22、ndBayesianModelMappingresultsfromICIMundtochsisticsearchvariabkselection(SSVS;(ieorgeandMeMuiloch1995;Yietal.2003)Thesimulatedgenomectmsistedofthreechrnrnosornes.eachwith100cMinlengthand11evenlydistributedmarkers(Yietat.2003).ComparisonofICIMandBayesianModelMappingresultsfromKIMandanempiricalBayesia

23、nmodel(Xu2007)ICWAnumUfcrndcim.rrroo.A-thA-”Afugk卸視m六介石：亠亠3人一丄4-.厶IX.1亍丄.丄A亠倉丄十、,-丄百各十廿丄.11A:專人人丄亠A一一，丄7人人人4字$丰一-A-一-A一-A人一人亠一.4A.丄R亠丄丄人备、4丄丄4-4*-a*II;,(m?近藝？纯绽辽蓟氓負漳藝龔出处典肥莊狀箕代些轶呎童典转彳吿釜廉裁竦u2：ft*J:?I涎址泛婪汀契田無鬃诞E纠秦廷送旦蛀共鏗涎典栽黎呎匪料警吿苦焉警吿ICIMforepistasismappingLODaLODaaBIIIII：IIIIIIlli电Ei人JU*ComparisonofICIM

24、withMIMA,B0In?04ICIM定位加性不明显的QTL间的互作Thesimulatedgenome(GenomeII)consistedofthreechromosomes,eachwith100cMinlengthand11evenlydistributedmarkers.Forth虽genome,weconsideredtwogeneticmodelscorrespondingtoSetICVA=0375,VI=0375andH=()6;leftcolumn)andSet-定ni(A=O,VI=OJ75andH=0.3;rightcolumn)ofBoeretaL(2002),re

25、spectivelyAPPLICATIONSOFICIMApplicationI:ThefamousbarleyDHpopulation丁hisbarleypopulationwasderivedfrom1hvo-rowbarley(ffordeunrvulgareL.)cross,HurringtonXTR306,andconsistsof145Dfllines(Tinkerctal.1996)Markers:127markerswasusedtobuildlinkagemapwiththeaveragedensity10.62cM.Phenotype:Phenotypicdakiforse

26、venagronomic(ruitswerK*iW.fiAxxsxzrxxExxxzzxxxxxrzxxxxxxxxxxszizxxxxxxzxxxxxxxzxIUiluycmkiio*iiiiioiNineadditiveQTLidentifledbyICIM(PIN=0e01,POUT=0.02)QTLnmnePosition(cM)LODAdditiveeffectscore(mg)PVES34.6033qKVT2H21407.230.515-34qKWT2H32015.593.77qKWTSH-114.39-0.393-04qKVmH-2227.4-15.33qKWT4H125412-

27、0.372.73534.28-1.3738.37qKVT7H1斗827-0.556.07gVT7H29519.81-0.9217.20Toialvariatinnexplained(%)80.76KWTforHarringtonis3&7mg,whileTR30645.0mg.DIGEN1CINTERACTIONS/Scanningstep5cMApplicationII:AmaizeRILpopulationAmaizepopulationof236recombinantinbredlines(RILs)wasdevelopedbycrossingthedrought-toleranttro

28、picalmaizelineCML444withthedrought-susceptibletropicalmaizelineSC-Malawi.Markers:Tliegeneticmapwithatotallengthof2250cMconsistedoftheallelicinformationof160molecularmarkerlociSSRsand79RFLPs)withanaveragedensityof17cM(Frachcboud2002).Phenotype：TheRILsweregrownandphenotypedinatotalofelevenfieldexperim

29、entsinMexicoandZimbabweeitherunderdroughtstressatfloweringorunderadequatewatersupplyintherain-fedexperiments.Femaleflowcring(FFLW)asthenumberofdaysfromsowingtothefirstvisiblesilkwasusedasthephenotypicdataAdditiveMappingResultsFromICIMfOAfCihi.0O1多-00】.丿/V.亠-人Drl_-.ex/yCCIMPNOOS一一ZEZZZCGCEgHfTheorApplGenet(2006)112:1258-127066CSSLs;116markers;grainlengthPlantCellReport(2009)28:247-256139CSSLs;117markers;matureseedcudurabilityCn)卩Science(2008)48:1799-180671RIL;375mark