QTLMap 0.8 User's guide
Contents
1. in_paramsimul param_ sim optim ANALYSIS PARAMETERS analysis step in Morgan minimum 0 000001 opt step 0 1 minimal number of progeny by dams opt_ndmin 20 chromosome to analyse opt_chromosome 7 HHHEHH OUTPUT out_output OUTPUT result out_summary OUTPUT summary out_maxlrt OUTPUTSIM simul Texte 16 Example of a parameter file to design a new protocol QTLMap 0 7 29 44 10 Output files A set of files is proposed to the user as the result of an analysis or a simulation The main output analyse report simulation report A summary Additional files optional in analysis case Likelihood ratio test profile per Sire per Dam global e QTL effect estimation at each tested position Sire and dam Parental phases report e Alleles frequencies informations e Haplotypes assigned from parents Grand parental segment transmission marginal probabilities Grand parental segment transmission joint probabilities Specifics files e Coefficients of the discriminant analysis among the linkage group Additional file optional in a simulation permutation case Maximum likelihood Ratio Test and optimal positions reached for each simulations permutations 10 1 Analysis report The first part describes the data as given by the user The name of the corresponding file is given by the user with the key out_output in the parameter file Configuration defined by the user The list of op2. The heritability h2 phenotypics and genotype correlation between traits classical traits gt A filter list of traits be kept in the analysis This line is optional If absent all traits described above will be analysed 3 Number of traits 11 Number of fixed effects and covariables sexe poids Names of the fixed effects and covariables malade r 1 1 O Ist trait nature real value model malcor r 0 O 1 2nd trait nature real value model third r 0 0 0 3nd trait nature real value model correlation matrix 0 35 0 28 0 29 0 20 0 32 0 28 0 20 0 20 0 33 Texte 6 Example 1 of a model file This model file describes the performance file where one fixed effect one covariate and three performances are referenced for each animals The model for each performance is malade u sexe B poids malcor u QTL x sexe third u The correlation matrix are given according the following rules gt The heritability h2 are defined in the diagonal gt Phenotype correlations the upper triangle matrix gt Genotype correlations the lower triangle matrix The following example gives a model file with a filter on the trait names third and malcor QTLMap 0 7 10 44 5 Number of traits 1 1 Number of fixed effects and covariables sexe poids Names of the fixed effects and covariables I I I malade r110 lst trait nature real value model malcor r 0 0 1 2nd trait nature re
3. lt string gt string code for missing value QTLMap 0 7 12 44 main output file out_output lt path file gt Full information about the results output analysis files keys out_summary lt path file gt Short information about the results out_Irtsires lt path file gt Sire family likelihood ratio test file out_Irtdams lt path file gt Dam family likelihood ratio test file out_pateff lt path file gt Sire QTL effect estimations file under Hypothesis H1 out_mateff lt path file gt Dam QTL effect estimations file out_phases lt path file gt Parental phases informations out_freqall lt path file gt Alleles frequencies informations out_grid2qtl lt path file gt Sire QTL effect estimations file under Hypothesis H2 out_pded lt path file gt Grand parental segment transmission marginal probabilities out_pdedjoin lt path file gt Grand parental segment transmission joint probabilities out_haplotypes lt path file gt out_coeffda lt path file gt input simulation file in_paramsimul lt path file gt output simulation file out_maxlrt lt path file gt QTLMap 0 7 13 44 qtlmap help panalyse for more information USER FILES in_map carte in_genealogy genea in_genotype typage in_traits perf in_model model ANALYSIS PARAMETERS analysis step in Morgan opt step 0 1 minimal number of progeny by dams opt_ndmin 20 Minimal paternal phase probability opt_minsirephasep
4. 0 123 0 092 0 132 0 324 sign sign sign 0 439 0 540 0 072 0 408 sign sign ns 0 010 0 042 0 145 0 351 ns ns sign 60 097 0 151 0 083 0 319 sign sign sign Texte 23 Summary with qtl 3 option QTLMap 0 7 36 44 10 4 The family likelihood The user have to define the following key to obtains the likelihood ratio test among the linkage group under hypothesis one out_lrtsires out_Irtdam and or the grid of the likelihood ratio test under hypothesis two out_grid2qtl LRT Sires files For each tested position the file contains Chromosome Position global LRT Sire 1 LRT Sire 2 LRT Chr Pos GlobalLRT 910001 910045 910081 910088 1 0 010 8 63 4 93 0 91 2 47 0 33 1 0 9029 8 62 4 82 1 03 2 47 0 30 1 0 030 8 56 4 66 1 14 2 45 0 31 1 0 040 8 45 4 47 1 23 2 41 0 35 1 0 050 8 29 4 24 1 28 2 34 0 42 1 0 060 8 35 4 21 os Zao 0 48 Chr1 Chr2 Fasi Pos2 GlobalLRT 910001 910045 910081 910088 il 1 0 02 0 65 3 78 oJ Oil iloildl 2 32 1 1 0 02 0 66 4 70 3 05 0 12 0 38 Gy 1 1 0 02 0 67 5 38 3 31 0 40 0 26 1 41 il il 0 02 0 68 5 80 Sol 0 70 0 79 0 80 1 1 0 02 0 69 5 96 3 65 1 01 1 19 0 11 il il 0 02 0 70 5 86 Sho VL 132 1 46 0 63 Texte 24 Sire likelihood file LRT Dams file For each tested position the file contains Chromosome Position Dam 1 LRT Dam 2 LRT Note when the offspring size of a dam is below the threshold for the search of the phase the LRT is fixed at 0 000 see opt_ndmin option L
5. 0 190633612968503 0 344837877148359 0 154406432653772 0 328663903209088 4 0 151093991655429 0 10964888434473 0 15832262904679 0 284848089326391 0 0808434990010986 0 306550168430082 0 00906573426897184 0 10731093171816 0 390146267506709 0 0562950676047775 Texte 5 Example of a expression quantitative trait values file In this previous example the animal 6380 have a missing data for the gene 3 6 Descriprtion of the dataset performan 95001 1 0 2 23 2 95002 1 0 3 65 5 95003 2 0 4 21 5 95004 2 0 6 52 2 p_analysis in_map map in_genealogy genealogy in_genotype genotype in_traits performance in_model model opt_step 0 01 opt_chromosome 1 2 3 Illustration 1 set of needed files for the analysis QTLMap 0 7 9 44 6 1 The model file In this file the model analysis of each trait is described Number of traits Number of fixed effetcs nf Number of covariables nc Names of the fixed effects and covariables Name of the Ist trait nature of trait r for real value i discrete ordered data and c categorial data model for this trait symbolized by 0 1 indicators for each fixed effects nf first indicators each covariables nc following and each interactions beetwen the QTL and the fixed effects nf last indicators A fixed effect covariable or interaction will be included in the analysis if its indicator is 1 will not be if it is 0 gt Name of the 2nd trait VVV V gt Optional
6. 2 QTL Position 2 H0 H2 H1 H2 std dev 1940 General Mean Sire QTL effects 1 Sire QTL effects 2 Sire polygenic effects note 0 0 mean not estimable 1 1 000 1 130 1 000 1 430 4 933 2 632 0 125 0 105 0 084 0 071 0 000 2 1 000 1 530 1 000 1 730 1 104 0 451 0 114 0 113 0 030 0 026 0 000 3 1 000 0 930 1 000 1 030 9 842 5 396 0 148 0 142 0 371 0 365 0 000 4 1 000 1 030 1 000 1 330 2 963 0 715 0 094 0 174 0 019 0 037 0 000 5 1 000 1 530 1 000 1 730 1 095 0 848 0 133 0 146 0 032 0 034 0 000 6 1 000 0 830 1 000 1 530 2 245 0 237 0 253 0 057 0 029 0 045 0 000 Texte 22 EQTL report under hypothesis 2 10 3 Analyse summary In the file SUMMARY parameter file key out_summary several chapters are given summarising the analysis under all hypothesis For each hypothesis HO 0 qtl H1 1 qtl H2 2qtl for each analysed variable by lines Number of genotyped progeny with phenotypes for the trait Maximum likelihood ratio e QTL most likely positions e for each sire e Estimations of the QTL effect e Within sire family standard deviation e Significance of the QTL effect based on a Student test sign significant ns not significant na not available SE AK KOK KK AK KK KC CA KK llo EA CE EAE CA CAC EA lalola lolo lok ak Summary QTL versus 1 QTL Variable N Max Lik Pos M Sire 910001 910045 910081 910088 0 1QTL Chr 1 Pos1 eff1 SD sigl eff1 SD sigl eff1 SD sigl eff1 SD sigl bardiere
7. 236 45 2 1 0 7 0 089 0 511 sign 0 118 0 560 sign 0 162 0 572 sign 0 167 0 598 sign imf 236 43 7 1 0 7 0 156 0 338 sign 0 187 0 426 sign 0 133 0 355 sign 0 051 0 339 ns AA dd dd dd od od od od 2 od o 2 2 od 2 o od od od E E od od od 2 E 2 od 2 E 2k dd E 2 dl dd dd dd dl dl a dd dd a a ok ok k k Summary QTL versus 2 QTL 1 QTL versus 2 QTL Variable N Max Lik Pos M Sire 910001 910045 910081 910088 0 2QTL 1 2QTL Chr 1 Posi Cht 2 Pos2 eff1 eff2 SD sigl sig2 eff1 eff2 SD sigl sig2 eff1 eff2 SD sigl sig2 eff1 eff2 SD sigl sig2 bardiere 236 57 0 11 9 1 1 0 7 1 1 0 148 0 082 0 481 sign sign 0 226 0 160 0 543 sign sign 0 182 0 030 0 570 sign ns 0 239 0 122 0 589 sign sign imf 236 49 3 5 6 1 1 0 9 19 0 405 0 245 0 335 sign sign 0 415 0 227 0 427 sign sign 0 348 0 227 0 351 sign sign 0 265 0 214 0 329 sign sign SRE EEEE ESEESE ESEESE EEES lll lll EE EEEE E E E E E E K K K k k k k kk kk Summary 0 QTL versus 3 QTL 1 QTL versus 3 QTL 2 QTL versus 3 QTL Variable N Max Lik Pos M Sire 910001 910045 910081 910088 0 3QTL 1 3QTL 2 3QTL Chr 1 Posi Chita 2 Pos2 Chr 3 Pos3 eff1 eff2 eff3 SD sigl sig2 sig3 eff1 eff2 eff3 SD sigl sig2 sig3 eff1 eff2 eff3 SD sigl sig2 sig3 eff1 eff2 eff3 SD sigl sig2 sig3 bardiere 236 63 9 18 8 6 9 1 1 1 0 7 0 8 dls dk 0 340 0 266 0 006 0 480 sign sign ns 0 211 0 528 0 271 0 533 sign sign sign 0 701 0 676 0 145 0 561 sign sign sign 0 838 0 819 0 133 0 575 sign sign sign imf 236 60 6 16 9 11 3 1 1 1 0 1 0 3 0 7
8. 61 28 64 10 11 Report simulations result This file give the maximum LRT reached with its associated position and the linkage group under the N hypothesis for each simulation permutation For each analysed variable e aheader to explain the following line to the user e for each simulation The Maximum likelihood ratio test e Position and linkage group of the first QTL e Position and linkage group of the second QTL Trait traitsimul1 LRTMAX HO H1 Position CHR Position DX 12 7928 il 0 4100 18 5180 1 0 1100 17 0331 1 1 2100 Trait traitsimul2 LRTMAX HO H1 Position CHR Position DX 8 9628 il 0 7100 9 3228 1 1 0000 16 6090 1 0 7100 Texte 30 The simulation report file H1 QTLMap 0 7 41 44 Position2 CHR Positionl DX2 12 7928 1 0 4100 9 6459 18 5180 1 0 1100 14 2922 17 0331 1 1 2100 15 4039 Trait traitsimul2 LRTMAX HO H1 Position2 CHR Positionl DX2 8 9628 1 0 7100 12 8711 9 3228 il 1 0000 8 4281 16 6090 1 0 7100 9 5829 Trait traitsimul1 LRTMAX HO H1 Position CHR Position DX LRTMAX H1 H2 Positionl CHR Position1 DX T 0 4100 1 1 2100 1 0 1100 1 1 0100 al 0 3100 1 1 2100 Position CHR Position 1 1 5100 1 1 6100 1 0 0100 1 0 3100 dl 0 3100 1 0 4100 Texte 31 The simulation report file H2 DX LRTMAX H1 H2 Positionl CHR Position1 DX 11 Reference Legarra A Fernando RL 2009 Linear models for joint association and linkage QTL mapping Genet Sel Ev
9. Input genealogy file in_genotype Input genotype file in_traits Input traits file in_model Input model description of traits in_paramsimul Input simulation parameters opt_step Chromosomic segment 0 05 exploration steps in Morgan opt_ndmin Minimal number of progeny by dam opt_minsirephaseproba Minimal sire phase probability 0 90 opt_mindamphaseproba Minimal dam phase probability 0 10 opt_unknown_char Unknown genotype value 0 opt_eps_cholesky coeff cholesky decomposition 0 5 QTLMap 0 7 42 44 opt_chromosome Linkage group out_output Main report file out_summary Output summary file out_Irtsires Output file paternal effects out_lrtdams Output file maternal effects out_pded Grand parental segment transmission marginal probabilities out_pdedjoin Grand parental segment transmission joint probabilities out_phases Parental phases file out_fregall Allele frequency file out_haplotypes Haplotype file out_pateff Sire QTL effect estimations out_mateff Dam QTL effect estimations out_maxlrt Simulation report Position and max LRT opt_eps_confusion Threshold to test confusion 0 70 betwwen level inside a contingence matrix opt_eps_hwe Threshold to check the 0 001 equilibrium of marker transmission within each family opt_eps_linear_heteroscedastic Threshold for convergence in 0 5 the linear mode heteroscedastic opt_max_iteration_linear_heter Maximum ite
10. QTL and H2 2 QTL gt grand parental segment transmission marginal and joint probabilities gt fixed options gt chromosomes explored gt step length of the scan gt minimum size of a full sib above which the dam effects QTL and polygenic are estimated gt minimal paternal and maternal phase probability gt missing genotype value The parameter file use the format lt key gt lt value gt None of the characters after the character are interpreted useful to add comments several key may be defined input file keys in_map lt path file gt the map file in_genealogy lt path file gt the genalogy file in_genotype lt path file gt the genotype file in_traits lt path file gt the traits file in_model lt path file gt the model files describing the performances optionals keys opt_step lt real gt step length of the scan Morgan opt_ndmin lt real gt Minimal number of progeny by dam offspring size above which the polygenic and QTL effects of the dam are estimated opt_mindamphaseproba lt real gt Minimal maternal phase probability threshold above which the probable maternal phases will be considered in the analysis opt_minsirephaseproba lt real gt Minimal paternal phase probability the analysis is interrupted if for a sire none of its phases reach this threshold opt_chromosome lt string string gt chromosomes to be analysed as denoted in the marker map file opt_unknown_char
11. alleles must be given the missing value code as given in the parametrisation of the analysis see 6 2 markl mark2 mark3 911714 253 1 4 13 912892 8 265 4 13 924758 2561125 922961 2 2 3 1 12 13 944547 2513 12 4 944985 2815 12 4 961924 2500 13 4 961925 0 0 13 4 961926 250000 963187 2800 12 4 963188 223113 4 963189 221112 4 963190 281512 4 Texte 3 Example of a marker genotypes file means that amongst the 5 grand parents 3 were genotyped 911714 912892 et 924758 For instance grand dam 911714 is heterozygous 2 5 at marker SW552 the individual 961925 has no genotype at marker mark etc QTLMap 0 7 7 44 Quantitative trait values file This file gives the phenotypes of the traits to be analysed The progeny performances only are considered in the analysis and must be given in the file For each animal its ID identical to the ID given in the pedigree file is followed by information about nuisance effects fixed effect levels covariable value and then by three information for each trait the performance an 0 1 variable IP which indicates if IP 1 or not IP 0 the trait was measured for this animal and must be included in the analysis and 0 1 variable IC which indicates if IC 0 it was censored or not IC 1 this IC information being needed for survival analysis by default IC 1 961924 1 10 43 7 8 11 77 6 11 961925 2 5 34 0 0 01 90 11 961926 1 12 34 11 3 11 103 11 963187 2 9 45 12
12. be sets of half sib families or mixture of full and half sib families The computations of Phase and Transmission probabilities are optimized to be rapid and as exact as possible QTLMap is able to deal with large numbers of markers SNP and traits eQTL The aim of QTLMap developers is to propose various genetic models depending on 1 the number of QTL alleles segregating biallelic in crosses between monomorphic breeds biallelic without hypothesis on the origin multiallelic haplotype identity 2 the number of QTL segregating one two linked several unlinked 3 the number of traits under the QTL influence The trait determinism may vary depending on 1 the trait distribution gaussian trait survival trait or threshold distribution 2 the interactions between the QTL and fixed effects or other loci 3 the residual variance structure homo or heteroskedasticity for half sib families Due to differences with the asymptotical conditions from the chi2 theory the test statistic significance are evaluated either through numerical approximations or through empirical calculations obtained from permutations or simulations under the null hypothesis QTLmap is written in fortran and either uses the NAG or SLATEC libraries Up to now the following functionnalities have been implemented e QTL detection in half sib families or mixture of full and half sib families e One or two linked QTL segregating in the population e Single trait or mul
13. discriminante amalySis cccceeeseceesseceesseceeseeceesneceeaeceeaecessaeeeeeeeeeeeaeees 17 7 7 Single survey trait with the cox model with a model descriptiON ooconnnocccnnnccconncnonancnonnnnnnnos 17 RE 17 AMA iia 17 Haplotyp E 18 OPUS OM A least 18 Console ouput modei is 19 Report o utput Mode tidad 19 Number of qtl detection available it di Saa Riiie ate ek 19 EOT lA id 20 8 Estimation of the test statistic rejection thresholdS ooonocnnncninccnoncconannnnncnoncnonccnnn conc ccoo n crono nnnccnnnnos 20 8 1 Estimation of the test statistic rejection thresholds with missing data s es 20 Format of the simulation parameter le i 21 Addition keys in the parameter fle cisssccses sedencascssaaceandscedesncsaaevased catavaceedeasepeieasadeassianddaacdaens 23 8 2 PermUtationS sissien n iiA Eaa d Pabe abes 24 Information about the permutation process ccceeecceesseceesseceesteceeseeeesaeceseeeeeaeeeeesseeeneeeeeees 24 8 3 Estimate of the test statistic rejection thresholds without missing data oooocccinncccnnncccnnocnncnnn 24 QTLMap 0 7 2 44 9 Simulate and design a new protocol ista is 27 A O OS 29 POLA a e ad 29 Contretiration defined by th Uso 29 DESCHPUON OF th genealogy iia lila 29 DES CrEption or the Markets sti iaa 29 D scription of the traits aisi e a E ng A vies E eeu Daas 30 Parental o e td de E 30 CENAMES CM a A A A alee 30 10 2 BOLLO A ee ee E EEE 33 TTS SPAT VS SSRI A O ESS 34 1
14. in the bin directory QTLMAP_DIR bin gt make install OpenMP support supports multi platform shared memory parallel programming To define the number of threads gt export OMP_NUM THREADS 8 5 Input files To carry on an analysis you need 4 data files Marker map QTLMap 0 7 5 44 Pedigree Marker genotypes Quantitative traits values 1 file describing the performance Model 5 1 Dataset format Pedigree file The file contains pedigree information for the 2 last generations of a design which comprises 3 generations 1 e parents and progeny It must not contain the grand parental pedigree information Each line is made of an alphanumeric ID triplet individual sire dam A fourth information gives the generation number 1 for the parental generation 2 for the progeny generation An animal missing one or both parents ID has not to be included in the file The missing value code given in the parameterization of the analyses see 6 2 cannot be used in the pedigree file The file must be sorted by generation sire ID and dam ID 922961 911287 902206 944547 924758 911714 944985 924758 912892 961924 922961 944547 961925 922961 944547 961926 922961 944547 963187 922961 944985 963188 922961 944985 963189 922961 944985 963190 922961 944985 Texte 1 Example of a pedigree file NNNNNNNFFF means that the pedigree includes 7 progeny born from 1 sire and 2 dams Sire 922961 is the son of si
15. the cox model 1 Real with censored data 8 LD for a single data with a model description 1 Real 9 LDLA for a single data with a model description 1 Real 25 LD for a single data with a model description likelihood In Real linearised homoscedatic 26 LD for a single data with a model description likelihood In Real linearised heteroscedastic 27 LDLA for a single data with a model description In Real likelihood linearised homoscedatic 28 LDLA for a single data with a model description likelihood In Real linearised heteroscedastic 23 LA for a set of traits with a model description In Real 7 2 Single real trait with pre corrected data 7 3 Single real or discrete trait with a model description 7 4 Single real trait with a model description and a complete linearised likelihood 7 5 Set of real traits with a multivariate analysis based on a multi normal penetrance function QTLMap 0 7 16 44 7 6 Set of traits with a discriminante analysis 7 7 Single survey trait with the cox model with a model description 7 8 Runtime options Analyse The calcul runtime option allows the choice between different types of modelling D 2 3 4 5 6 7 8 9 Analysis of a single real trait with pre corrected data gt QTLMAP PATH qtlmap p analyse calcul 1 Analysis a single real or discrete trait with a model description gt QTLMAP PATH qtlmap p anal
16. x x x x x x x and estimation of the main effects polygenic QTL LRT for the nuisance x x x x effects Risk Factor estimation x Precision of the x x x parameter estimation General Mean x x x estimation Nuisances effects x x x x estimations Interactions between x x x x QTL and fixed effects Traits residual X correlations Tableau 1 Output availables according to the analysis Confusion between QTL effects and all other effects As the design may be poorly balanced leading to strong colinearity between QTL and some other effects in the model a warning is provided if this situation occurs The confusion is measured by the correlation between the columns of the incidence matrix in an equivalent fully linear model at the starting position of the scan a warning is edited if this correlation exceeds opt_eps_confusion A second test of confusion between the QTL and other estimable effects finally kept in the model is edited Variances and estimation of main effects Within sire residual variance estimations are printed under all tested hypotheses no QTL one QTL two QTL MThe maximum likelihood solutions for the parameters are given with an indication about their precision available only for calcul 2 3 4 estimated by the diagonal element of the incidence matrix in an equivalent fully linear model the lower the better e global mean e sire QTL effects QTLMa
17. 0 4 The family likelihood vinci ai puabanevend ceeds atan EEEE 35 ERT Sires files ios id E Sa eee end ead Grae eee 35 ERT Dams Hevia tall cl diia leida 35 ERT Sid Z O Ti cada 36 10 5 QTL effects estimations Ml iii ii iia ibvass 36 OTs Paternal ec masas 36 OTL Maternal efect aula dd ls ds 37 MIA Parents phase tepoto eona oaadecynae Gauci teau soaps saree esau ges naadasanda deus tence eased 37 10 7 Haplotypes assigned from Parents li 37 10 8 Grand parental segment transmission marginal probabiliti8S ooonnncnnnnnnnnnnocononnconanancnnnn noo 38 10 9 Grand parental segment transmission joint probabilities oonnconnnnninncnocnoncnnnannnnncnonacnnno noo 38 10 10 Simu lation TP aiii reos 39 1011 Report SIMULATIONS Tesla Id ART o A od ls 39 A ss eae a ese a dane ahs Auth a ee ieste a Magus aaee Set enso iais 40 A nisen e E A aa a o a ddaa 40 121 Parameter fle Option KEY Sierre oi 40 QTLMap 0 7 3 44 1 Introduction QTLMap is a software dedicated to the detection of QTL from experimental designs in outbred population QTLMap software is developed at INRA French National Institute for Agronomical Research The statistical techniques used are linkage analysis LA and linkage disequilibrium linkage analysis LDLA using interval mapping Different versions of the LA are proposed from a quasi Maximum Likelihood approach to a fully linear regression model The LDLA is a regression approach Legarra and Fernando 2009 The population may
18. 1 yes 0 129 0 068 Dam 910074 Sire 910088 yes 0 221 0 075 NOTE known allelic origin means QTL effect maternal paternal allele effects Texte 18 Estimation of variances general mean and polygenic effect under hypothesis null with the calcul 2 Interactions between QTL and fixed effects When interactions between the QTL and m fixed effects are considered in the model the dam and sire qtl effects are estimated for each level of the composite interacting fixed effect if n n2 Nm are the number of levels for effect 1 2 m a total of n n nn qtl effects are estimated for each parents Testing nuisances effects For each of the nuisance effect a LRT is reported with the value and significance of the likelihood ratio when comparing a model with or without this effect The significance is the probability for the LRT to be higher than the observed value under HO no effect When this probability exceeds the standard threshold corresponding to the 5 1 or 0 1 Pent level the effect should be removed from the model SRE RAR RR RK RK lolo 2k ak ok test of the effets of the model Tested effect df Likelihood p value ratio itll direct effect 23 100 823 1 000 f2 direct effect 10 121 576 1 000 sex direct effect 2 11 146 1 000 Texte 19 Test of the nuisances effects Risks factor estimation QTLMap 0 7 34 44 Traits residual correlations 10 2 EQTL analysis report A special format presents th
19. 4217 944220 952658 952659 Dam 910002 955654 955655 955656 955657 955658 957204 957205 You may overload the option opt_ndmin and consider all families as half sib using the runtime option family 1 Minimal paternal and maternal phases probability In the current release QTLMap considers only one phase for the sire When the runtime option haplotype 1 2 3 is used the probabilities of all possible sire and dam phases are computed If none of those probabilities for the sire exceed a given threshold opt_minsirephaseproba in the parameter file the process is aborted As the dams generally have a lower offspring size all phases the probability of which exceeds a given threshold opt_mindamphaseproba in the parameter file are considered in the analysis 7 Analyses 7 1 Available analysis Calcul Description QTL Type data 1 LA for a single trait with pre corrected data 1 2 Real 2 LA for a single data with a model description 1 Real Discrete 3 LA for a single data with a model description likelihood Ln Real linearised homoscedatic 4 LA for a single data with a model description likelihood Ln Real linearised heteroscedastic QTLMap 0 7 15 44 5 LA for a set of traits with a multivariate analysis based on a 1 Real multi normal penetrance function 6 LA for a set of traits without missing data with a 1 Real discriminante analysis 7 LA for a single survey trait with
20. 7 11 98 1 1 963188 1 11 10 13 5 11 0 0 01 963189 2 10 11 10 11 94 8 11 963190 1 9 98 14 2 11 98 3 11 Texte 4 Example of a quantitative trait values file This file describes 2 traits For progeny 961924 the recorded information are sexe 1 fixed effect body weight 10 43 covariable backfat thickness 7 8mm trait 1 and fatening period of 77 6 days trait 2 etc Expression quantitative trait values file This file gives the phenotypes expression traits to be analysed The header line is the list of animals phenotyped The following line are the fixed effects covariates and finally the phenotype The format of the nuisances effects and phenotype line is lt IDANIMAL gt lt VALUE _ANIMAL1 gt lt VALUE ANIMAL2 gt For missing data insert a character string which is not interpretable as a numeric e g n a QTLMap 0 7 8 44 4112 4114 6380 6553 4142 4120 6388 6537 6548 6536 sexe 1111111111 covl 0 3 0 4 0 3 0 5 0 5 0 6 0 3 0 2 0 6 0 9 1 0 0184170490684831 0 143560443113406 0 118137020630747 0 06666521254513 0 0642879011796014 0 255460347400393 0 189477060869665 0 25462868498086 0 00530461929594204 0 254172485884001 2 0 127806826817031 0 163876647400758 0 0184043832497863 0 296146098377366 0 112715209230912 0 0684375510992924 0 180990247175303 0 182892021501701 0 063104337861525 0 0334596435779586 3 0 259405679027549 0 365184085691961 n a 0 104403755609133 0 154653751085067 0 213511162284327
21. 8 0 001 4 910001 910014 1 944217 0 001 0 001 0 998 0 001 5 910001 910014 1 944217 0 000 0 001 0 999 0 000 6 910001 910014 1 944217 0 001 0 001 0 941 0 056 Ye 910001 910014 al 944217 0 003 0 001 0 884 0 112 Texte 29 Grand parental segment transmission marginal probabilities file QTLMap 0 7 40 44 10 10 Simulation report xe ees eine ee eee eee Variable traitsimull Siete ves eo cee os etn en sere cee eee esc ets eet Test Ovs1Q Fe ere erie o e E e E Test statistic distribution Number of simulations 100 Mean 14 24685 Standard deviation i 4 07168 Skewness E 0 70693 Kurtosis 1 05302 Minimum 8 6 62047 Maximum 8 28 64581 A AA chromosome genome Threshold level HR Ae eye ee 0 1000 19 39 0 0500 21 39 0 0100 chrom level 27 40 0 0050 28 18 0 0027 nb chrom 28 44 0 0010 28 58 0 0005 28 61 0 0001 28 64 a E tm E ep eae ebe E eee See For each analysed variable a single line gives the empirical thresholds at 5 1 and 0 1 at the chromosome and the genome level The genome level corresponds to a genome scan of 18 autosomes in pigs For any other species the genome level is obtained easily multiplying the chromosome level by the number of chromosomes In such cases see the RESULT file for low chromosome wide quantile estimations Ovs1Q p_value at Trait chromosome level genome level 5 1 0 1 5 1 0 1 traitsim 21 39 27 40 28 58 28 44 28
22. QTLMap 0 8 User s guide 08 11 10 QTLMap 0 7 1 44 Table des mati res LO A T AE E E ne al a 4 2 COOMEUDULOUS A NO 4 DS SUPPOL tasan n n n a a dienes a a a eae OER 5 A Setting Up OT LM ia ea 5 4 1 Runtime environment with GNU software COMpONeNLoooocccnnoccconocononoccnononononononnnnncnnnanocnnnnoncnnnns 5 O TO 5 Compralo 5 MOTI SU PO die USE E o E iS RAS 5 S np t O OR 5 DAS OU a E 6 A A OO 6 A E e 6 The Marker perno peste naaa 7 Quantitative trait values fil6 oooooononnnnconnonnananononoononnnnann nono noonononnnno nono a ea a ee SEa nn o naar nnnnos 7 Expression quantitative trait values file escocia idoneidad dd 8 6 Descripriion OF the dataset nas aorta 9 Gul The modi A A A A nae oda aes 9 6 2 Tie el A A an tem ane we ages tana E esis sas MM au oe wa ees 11 A O 14 Mixture of half sib and full sib families di id BA ee A a Rs 14 Minimal paternal and maternal phases probability ooooonnnccnoncniononocaconacconocann nana nonanncnnnannncnnnns 15 TAME SEA A as 15 TA Available anal iS ide 15 7 2 Single real trait with pre Corrected data iio sario ictericia ade 16 7 3 Single real or discrete trait with a model descCriptiOM ooonncccnnnococonocononoccnonancnnnnconnncnonnnnnnnnnnnos 16 7 4 Single real trait with a model description and a complete linearised likelihood 16 7 5 Set of real traits with a multivariate analysis based on a multi normal penetrance function 16 7 6 Set of traits with a
23. RT grid 2 QTL The file presents two tables The first part of the output concerns the comparison between the and 2 QTL hypotheses The fist line gives possible 1 QTL position The following lines give a possible 2 QTL position followed by the LRT 1 vs 2 QTL for each couple of positions The second part of the output concerns the comparison between the 0 and 2 QTL hypotheses The fist line gives possible 1 QTL position The following lines give a possible 2 QTL position followed by the LRT 0 vs 2 QTL for each couple of positions QTLMap 0 7 37 44 TEST 1QTL 2QTL t H 01 02 03 04 05 06 01 00 3 67 8 42 10 30 11 66 12 80 02 00 00 3 74 8 43 10 30 11 68 03 00 00 00 3 81 8 43 10 31 04 00 00 00 00 3 87 8 44 05 00 00 00 00 00 Beal tttttttttt tt t TEST OQTL 2QTL 4 4 4 4 4 4 4 4 4 4 4 4 4 44 44 01 02 03 04 195 06 01 00 27 46 32 21 34 09 35 45 36 59 02 00 00 27 53 32 22 34 09 35 47 03 00 00 00 27 60 32 22 34 10 04 00 00 00 00 27 66 32 23 05 00 00 00 00 00 27 70 Texte 25 Likelihood Grid 2 QTL file 10 5 QTL effects estimations files The user have to define the following key to obtains the QTL estimations among the linkage group under hypothesis one out_pateff out_mateff QTL Paternal effects For each tested position the file contains Chromosome Position Sire 1 QTL effect estimation Sire 2 QTL effect es
24. al value model third r000 3nd trait nature real value model correlation matrix 0 35 0 28 0 29 0 20 0 32 0 28 0 20 0 20 0 33 third malcor Texte 7 Example 2 of a model file The key word all allows the use of the same model for all the traits useful for eQTL detection 10000 Number of traits 11 Number of fixed effects and covariables sexe covl Names of the fixed effects and covariables all r 110 all is a word key the model will be applied for all the 10000 expression trait Texte 8 Example 3 of a model file To apply a filter with the key word all the user have to give an index trait list referenced in the phenotype file Trait one gt index 1 Trait two gt 2 10000 Number of traits 11 Number of fixed effects and covariables sexe covl Names of the fixed effects and covariables allri110 3 45 6 45 46 Texte 9 Example 4 of a model file 6 2 The parameter file All information needed by an analysis is the parameter file p_analyse gt name of the dataset files genealogy map genotypes and performances gt name of the model file describing the performances gt paths and names of the ouput files QTLMap 0 7 11 44 full information analysis result file summary of the analysis sire and dam family likelihood ratio test LRT along the linkage group V VV WV sire and dam QTL effect estimations along the linkage group under hypothesis H1 1
25. ap 0 7 27 44 9 Simulate and design a new protocol p_analysis 1 01 opt _chromosome 1 2 3 in i i QTLMap offers you the possibilty of simulating all the data markers genealogy traits in order to plan a new experiment You will get in the output file named by the out_maxlrt OUTPUTSIM simul option in the following example the value of the LRT resulting from the simulation allowing an estimation of designs power To perform those simulations two specific section must be created in the param_sim file The first with the head section MARKERS must give on a single line Marker density M number alleles marker map size Morgan The second with the head section GENEALOGY followed by the key word F2 BC or OUT BRED depending on the type of population and a line giving the number of sires of dam sire and of progeny dam MARKERS lt real gt lt integer gt lt integer gt lt integer gt lt character gt GENEALOGY lt F2 BC OUTBRED gt lt integer gt lt integer gt lt integer gt QTL lt integer gt Position lt real gt lt real gt chromosome lt integer gt lt integer gt frequency lt real gt lt real gt SIMULTRAITS lt integer gt lt IDNAME gt r lt real gt lt IDNAME DISCR DATA gt i lt real gt lt int gt lt real gt lt real gt correlation a bc def 0 1 qtleffect lt real gt lt real gt 0 1 QTLMap 0 7 28 44
26. c output are produced To get this situation the runtime option data transcriptomic must be indicated gt QTLMAP_ PATH qtlmap p analyse calcul 1 qtl 1 data transcriptomic 8 Estimation of the test statistic rejection thresholds 8 1 Estimation of the test statistic rejection thresholds with missing data performance 95001 1 0 2 23 2 2 2 p_analysis in_map map in_genealogy genealogy in_genotype genotype in_traits performance in_model model opt_step 0 01 opt_chromosome 1 2 3 in_paramsim param_sim A specific file opt_paramsimul param_sim must be provided by the user This file contains the needed information about the simulation gt QTLs informations gt Number of QTLs N gt N QTL positions in Morgan gt Nchromosomes where are localised QTLs QTLMap 0 7 20 44 gt N QTL allele frequencies in the grand sire population gt Traits informations gt Number of traits M gt List of traits M lines corresponding to the model file gt N QTLs effects for each M traits If the simulations are made under the null hypothesis No QTL on the linkage group the user has only to give the second part Trait of the simulation parameter file In the case of simulations made under the hypothesis of N QTL N0 this case occurs when the aim is to get rejection thresholds for the test of H1 only 1 QTL vs H2 2 QTLs segregating the QTL is supposed to be biallelic Q1 Q2 and the genoty
27. e obtained with permutations on performances This option is available with the runtime option permute gt QTLMAP_ PATH qtlmap p analyse calcul 1 nsim 100 permute Information about the permutation process The permutation option concerns the phenotypes and all nuisances effects attached to the phenotypes The performances are permuted within the full sib family However if the number of progeny for a dam is less than the minimum between opt_ndmin key value building full sib family and 10 this figure was chosen by the developers of QTLMap and will be controlled by advanced users soon the permutation is realized within half sib family In multi trait analysis multi variate or discriminant only phenotyped animals are permuted In successive uni trait analysis animal without any phenotype are not included in the permutation 8 3 Estimate of the test statistic rejection thresholds without missing data QTLMap 0 7 25 44 p_analysis in_map map in_genealogy genealogy in_genotype genotype opt_step 0 01 opt_chromosome 1 2 3 in_paramsim param_sim The user have the possibility to estimate thresholds rejections for dummy traits assuming there is no missing data In this case the parameter file does not need the keys in_model nor in_trait The parameter simulation file will have a specific head section for simulation trait SIMULTRAITS This section is identical to the TRAIT section but an additi
28. e report analysis for each gene expression depends the dynamic flag data transcriptomic Only calculus 1 2 3 4 manage this format single trait analysis For each hypothesis the report gives The header of the following array e Array with e first column gene name e others column estimation of each parameters given in the header note The values 0 0 means that the parameter is not estimable Hypothesis 0 Given parameters are respectively Gene position on the array std dev 1940 General Mean Sire polygenic effects note 0 0 mean not estimable 1 0 132 0 106 0 000 2 0 116 0 114 0 000 3 0 165 0 140 0 000 4 0 097 0 174 0 000 5 0 135 0 147 0 000 6 0 259 0 059 0 000 Texte 20 EQTL report under hypothesis O Hypothesis 1 Given parameters are respectively Gene position on the array Chromosome 1 QTL Position 1 H0 H1 std dev 1940 General Mean Sire QTL effects 1 Sire polygenic effects note 0 0 mean not estimable 1 1 000 0 930 2 301 0 128 0 106 0 033 0 000 2 1 000 0 830 0 653 0 115 0 114 0 017 0 000 3 1 000 1 430 4 446 0 157 0 139 0 055 0 000 4 1 000 1 430 2 248 0 095 0 174 0 023 0 000 5 1 000 1 230 0 247 0 134 0 147 0 010 0 000 6 1 000 1 430 2 007 0 254 0 057 0 059 0 000 Texte 21 EQTL report under hypothesis 1 QTLMap 0 7 35 44 Hypothesis 2 Given parameters are respectively Gene position on the array Chromosome 1 QTL Position 1 Chromosome
29. lation file is given in the parameter analyse file with the key in_paramsimul A second key optional out_maxlrt specifies the name of a file reporting the maximum likelihood ratio test values found in the simulations QTLMap 0 7 23 44 qtlmap help panalyse for more information USER FILES in_map carte in_genealogy genea in_genotype typage in_traits perf in_model model in_paramsimul param_sim simul ANALYSIS PARAMETERS analysis step in Morgan minimum 0 000001 opt step 0 1 minimal number of progeny by dams opt_ndmin 20 Minimal paternal phase probability opt minsirephaseproba 0 80 overload opt_minsirephaseproba 0 90 Minimal maternal phase probability opt_mindamphaseproba 0 10 chromosome to analyse opt_chromosome 7 for several chromosomes opt_chromosome 7 8 Y missing phenotype marker value opt_unknown_char 0 HHHEHH OUTPUT out_output OUTPUT result out_summary OUTPUT summary out_maxlrt OUTPUTSIM simul Texte 14 Example of a parameter file to estimate the rejections thresholds with missing data QTLMap 0 7 24 44 8 2 Permutations Model 4 JOA Sexe Poid Trait 100 performance 95001 1 0 2 23 2 95002 1 0 3 65 5 95003 2 0 4 21 5 95004 2 0 6 52 2 p_analysis in_map map in_genealogy genealogy in_genotype genotype in_traits performance in_model model opt_step 0 01 opt_chromosome 1 2 3 The rejection thresholds may b
30. ol 41 43 Elsen JM Filangi O Gilbert H Le Roy P Moreno C 2009 A fast algorithm for estimating transmission probabilities in QTL detection designs with dense maps Genet Sel Evol 41 50 Gilbert H Le Roy P Moreno C Robelin D Elsen J M 2008 QTLMAP a software for QTL detection in outbred population Annals of Human Genetics 72 5 694 Gilbert H Le Roy P 2007 Methods for the detection of multiple linked QTL applied to a mixture of full and half sib families Genet Sel Evol 39 2 139 58 Moreno C R Elsen J M Le Roy P Ducrocq V 2005 Interval mapping methods for detecting QTL affecting survival and time to event phenotypes Genet Res Camb 85 139 149 Goffinet B Le Roy P Boichard D Elsen JM Mangin B 1999 Alternative models for QTL detection in livestock III Heteroskedastic model and models corresponding to several distributions of the QTL effect Genet Sel Evol 31 341 350 Mangin B Goffinet B Le Roy P Boichard D Elsen JM 1999 Alternative models for QTL detection in livestock II Likelihood approximations and sire marker genotype estimations Genet Sel Evol 31 225 237 Elsen JM Mangin B Goffinet B Boichard D Le Roy P 1999 Alternative models for QTL detection in livestock I General introduction Genet Sel Evol 31 213 224 12 Appendix 12 1 Parameter file Option Keys Key Description Default in_map Input map file in_genealogy
31. on opt_optim_tolg stopping criteria lower bound of the gradient opt_optim_h_precision precision to obtain the gradient gt To get the maximum information during the process add v or verbose to the command gt QTLMAP PATH qtlmap p analyse calcul 1 v gt When debuging the software add d or debug to the command gt QTLMAP PATH qtlmap p analyse calcul 1 d gt To avoid outpout add q or quiet to the command gt QTLMAP PATH qtlmap p analyse calcul 1 q Report output mode When performing eQTL analysis using data transcriptomic command or simulation the output is minimised To force the classical reporting format use the runtime option print all Example gt QTLMAP_ PATH qtlmap p analyse calcul 1 data transcriptomic print all Number of qtl detection available For most of the analyses controlled by the runtime option calcul only 1 QTL is considered in the model However this number may be increased to 2 if calcul 1 to 2 or more if calcul 3 or 4 The number of QTL is given by the qtl runtime option Analysis calcul QTL test detection qtl 1 1 2 2 7 8 9 10 1 3 4 25 26 27 28 gt 1 5 6 1 QTLMap 0 7 19 44 Example gt QTLMAP PATH qtlmap p analyse calcul 1 qtl 1 EQTL analysis When looking for eQTL the number of traits to be analysed becomes very large In this case specific routines are needed and ad ho
32. onal information abouit the nature of the trait as described for the model file This information is given next the IDNAME of trait gt r for real data gt i for integer ordered discrete data QTL lt integer gt Position lt real gt lt real gt chromosome lt integer gt lt integer gt frequency lt real gt lt real gt SIMULTRAITS lt integer gt lt IDNAME gt r lt real gt lt IDNAME DISCR DATA gt i lt real gt lt int gt lt real gt lt real gt correlation a bc def 0 1 qtleffect lt real gt lt real gt 0 1 QTLMap 0 7 26 44 qtlmap help panalyse for more information USER FILES in_map carte in_genealogy genea in genotype typage in_paramsimul param_sim simul ANALYSIS PARAMETERS analysis step in Morgan minimum 0 000001 opt step 0 1 minimal number of progeny by dams opt_ndmin 20 Minimal paternal phase probability opt minsirephaseproba 0 80 overload opt_minsirephaseproba 0 90 Minimal maternal phase probability opt_mindamphaseproba 0 10 chromosome to analyse opt _chromosome 7 for several chromosomes opt_chromosome 7 8 Y missing phenotype marker value opt_unknown_char 0 HHHHH OUTPUT out_output OUTPUT result out_summary OUTPUT summary out_maxlrt OUTPUTSIM simul Texte 15 Example of a parameter file to estimate the rejections thresholds without missing data QTLM
33. or SNP gt QTLMAP PATH qtlmap p analyse calcul 1 snp haplotype Description 1 Classical approach by enumeration All possible phases are considered in turn and their probability computed Transmission probabilities are computed using all available information Recommended for small number of markers Optimised approach for sparse maps All possible phases are considered in turn and their probability computed Transmission probabilities are computed using local information Approximate phasing based on closest marker information Exact transmission probability minimising the computation Recommended for dense maps Optimisation The optim runtime option allows a control of the optimisation procedure The following table describes the available methods optim Description DEPENDANCES 1 EQ4JYF NAG routine quasi Newton NAGG 2 L BFGS routine the Broyden Fletcher no Goldfarb Shanno quasi Newton E M l LUKSAN optimisation no QTLMap 0 7 18 44 12 47 NLOPT Optimisation GCC methods may be parametrized with the following options gt opt_optim_maxeval maximum number of objective function gt opt_optim_maxtime maximum time to find the solution of the objective function Y VV WV Console ouput mode opt_optim_tolx tolerance lower bound of a step opt_optim_tolf stopping criteria lower bound of the objective functi
34. p 0 7 32 44 e dam QTL effects e sire polygenic effects e dam polygenic effects e covariables e fixed effects The two following example give difference report according to the calcul option Trait bardiere sire 910001 s d sire 910045 s d sire 910081 s d sire 910088 s d parameter Mean Sire Sire 910001 Sire 910045 Sire 910081 Sire 910088 Mean dam Dam 910014 Sire 910001 Dam 910002 Sire 910081 Dam 910010 Sire 910081 Dam 910074 Sire 910088 Within sire standard deviation 0 551 0 578 0 659 0 663 estimable yes yes yes yes yes yes yes yes ocooo value 902 091 220 441 040 000 000 000 oooo oooo precision 000 000 000 000 000 000 000 000 Texte 17 Estimation of variances and polygenic effect under hypothesis null with the calcul 1 Note that with calcul 1 the precision is not computed and is arbitrary given the vaue 0 0 QTLMap 0 7 33 44 Within sire standard deviation Trait bardiere sire 910001 s d 0 550 sire 910045 s d 0 579 sire 910081 s d 0 658 sire 910088 s d 0 654 parameter estimable value precision General Mean yes 7 539 0 033 Sire polygenic effects Sire 910001 yes 0 666 0 067 Sire 910045 yes 0 448 0 058 Sire 910081 yes 0 264 0 065 Sire 910088 no Dam polygenic effects Dam 910014 Sire 910001 yes 0 061 0 069 Dam 910002 Sire 910081 yes 0 052 0 073 Dam 910010 Sire 91008
35. pes frequencies in the parental population are Q1Q1 f1 1 f1 Q1Q2 f1 f1 1 f1 1 f1 Q2Q2 1 f1 f1 where fl is the frequency of the first allele if the grand sire population the second allele in the grand dam population To get for instance all parents heterozygous the frequency f1 must be given the value 1 or 0 Format of the simulation parameter file OTL lt integer gt The specific QTL Label on the first line followed by and the number of QTLs to be simulated Position lt real gt lt real gt chromosome lt integer gt lt integer gt frequency lt real gt lt real gt The user defined for each QTL gt its position gt the chromosome where it is located gt the frequency in grand sire population P1 TRAITS lt integer gt The specific TRAITS Label on a first line then the number of traits to be simulated lt IDNAME gt For continuously distributed traits the name of one of the traits as referenced in the model file lt IDNAME DISCR DATA gt lt int gt lt real gt lt real gt For discrete traits the name of one of the discrete traits as referenced in the model file with QTLMap 0 7 21 44 gt its heritability gt the number of modalities gt the frequency of each modality qtleffect lt real gt lt real gt Only if one or more QTL is defined gt QTL 1 Effect on trait 1 QTL 1 Effect on t
36. rait 2 QTL 2 Effect on trait 1 QTL 2 Effect on trait 2 On the whole the opt_paramsimul is the following The entirely format QTL lt integer gt Position lt real gt lt real gt chromosome lt integer gt lt integer gt frequency lt real gt lt real gt TRAITS lt integer gt lt IDNAME gt lt IDNAME_DISCR_DATA gt lt int gt lt real gt lt real gt qtleffect lt real gt lt real gt 0 1 The qtleffect line is defined if at least one QTL are simulated Example of a parameter file for the estimation of the rejection thresholds for the test There are one qtl on the linkage group against there are no QTL TRAITS 2 imf bardiere Texte 11 Parameter simulation file 2 0 0 nofix nocov imf r000 bardiere r000 Texte 12 Model file Example of a parameter file for the estimation of the rejection thresholds for the test There are two qtl on the linkage group against there are one QTL at the position 0 6 Morgan on the first chromosome on the linkage group QTLMap 0 7 22 44 In this example the QTL simulated have an effect 0 4 on the first trait and 0 5 on the second traits The QTL have a frequence of 100 QTL 1 position 0 6 chromosome 1 frequency 1 0 TRAITS 2 imf bardiere qtleffect 0 4 0 5 Texte 13 Parameter simulation file Addition keys in the parameter file The parameters simu
37. ration in the linear 5 oscedastic mode heteroscedastic to avoid infinity loop opt_eps_recomb 0 5 opt_nb_haplo_prior 200 opt_pro_haplo_min opt_long_min_ibs opt_longhap opt_optim_maxeval opt_optim_maxtime opt_optim_tolx opt_optim_tolf opt_optim_tolg QTLMap 0 7 43 44 opt_optim_h_precision QTLMap 0 7 44 44
38. re 911287 and dam 902206 etc constraint The file must be sorted by generation sire ID and dam ID Marker map file This file gives the locations of the markers on the chromosome s Each line corresponds to a single marker and gives order to be followed gt marker name alphanumerique gt name of the chromosome carrying the marker alphanumerique gt marker position of the marker on the average map in Morgan gt marker position of the marker on the male map in Morgan QTLMap 0 7 6 44 gt marker position of the marker on the female map in Morgan gt inclusion key 1 if the marker has to be included in the analysis 0 if not SW552 1 0 08 0 05 0 09 1 SW64 1 0 24 0 24 0 25 0 CGA 1 0 49 0 45 0 55 1 S0088 15 0 50 0 37 0 59 1 SWR1002 15 0 58 0 49 0 63 1 Texte 2 Example of a marker map file means that marker SW552 is on chromosome 1 at position 0 08 on the average map 0 05 on male map and 0 09 on the female map and will be included in the analysis of chromosome 1 etc The marker genotypes file This file contains the animals phenotypes at the markers The first line gives the marker names the markers must belong to the marker map file For each animal a line gives its ID as decribed in the pedigree file followed by the markers phenotypes ranked following in the first line order Each phenotype is made of 2 alleles unordered When an animal has no phenotype for a marker both
39. roba 0 80 overload opt minsirephaseproba 0 90 Minimal maternal phase probability opt mindamphaseproba 0 10 chromosome to analyse opt chromosome 7 for several chromosomes opt_chromosome 7 8 Y missing phenotype marker value opt_unknown_char 0 HHHEHH OUTPUT out_output OUTPUT result out_summary OUTPUT summary out _lrtsires 0UTPUT sires out_lrtdams 0OUTPUT dams out _pded OUTPUT pded out pdedjoin OUTPUT pdedjoin out pateff OUTPUT pateff out mateff OUTPUT mateff out_phases OUTPUT phases out haplotypes OUTPUT haplotypes Texte 10 Example of a parameter analyse file 6 3 Principes Mixture of half sib and full sib families The maximul likelihood methods implemented in QTLMap considers the population as being a mixture of half sib and full sib families The sires and the dams are supposed unrelated A sire resp a dam may be mated to more than one dam resp sire Thus two animals of the second generation may be unrelated half sibs or full sibs A polygenic and a QTL effect are estimated for each parent having a large enough family To avoid numerical difficulties these effects are not estimated for dams having too small offspring In this case the dam progeny are considered as sire half sibs only A control of the structure is allowed through the option number of progeny opt_ndmin which is given in the parameter file QTLMap 0 7 14 44 Sire 910014 Progenies Dam 910001 910014 94
40. timation FEES COCOA AK llo 4K This file is unvalide if interaction qtl case 2K 2 2 i k i k i k i k K K K K K K 2 K 2 FK 2K K K K K K K K K K K K K oi oo K oo K K Chr Pos 910001 910045 910081 910088 1 0 010 0 24 0 14 0 13 0 0 1 0 020 0 24 0 15 0 14 0 01 1 0 030 0 24 0 15 0 14 0 01 1 0 040 0 23 0 16 0 15 0 03 1 0 050 0 22 0 16 0 15 0 05 1 0 060 0 23 0 16 0 15 0 06 1 0 070 0 23 0 17 0 16 0 08 1 0 080 0 23 0 17 0 16 6 09 Chr1 Chr2 Pos1 Pos2 910001 0t1 1 910001 Qt1 2 910045 0t1 1 910045 0t1 2 910081 0t1 1 910081 0t1 2 910088 0t1 1 910088 0t1 2 il il 0 01 0 020 0 57 0 04 0 57 0 04 0 57 0 04 0 57 0 04 1 1 0 010 0 030 0 24 0 04 0 24 0 04 0 24 0 04 0 24 0 04 il il 0 010 0 040 0 17 0 04 0 17 0 04 0 17 0 04 0 17 0 04 1 1 0 010 0 050 0 14 0 04 0 14 0 04 0 14 0 04 0 14 0 04 il il 0 010 0 060 0 14 0 04 0 14 0 04 0 14 0 04 0 14 0 04 il il 0 010 0 070 0 14 0 03 0 14 0 03 0 14 0 03 0 14 0 03 1 1 0 010 0 080 0 13 0 03 0 13 0 03 0 13 0 03 0 13 0 03 il 1 0 010 0 090 0 12 0 02 0 12 0 02 0 12 0 02 0 12 0 02 Texte 26 Paternal qtl effect file QTL Maternal effect For each position the file contains Chromosome Position Dam 1 QTL effect estimation Dam 2 QTL effect estimation Note the QTL effect are given only for dams the offspring size of which is over the threshold given by opt_ndmin QTLMap 0 7 38 44 10 6 10 7 Parents phase report Haplotypes assigned from parents T
41. tion keys used by the application runtime environment is given All keys are described at the end of this document Description of the genealogy Number of parents grand parents and progenies Description of the markers Number of animal genotyped Number and names of the genetic markers of alleles by marker and allele frequencies QTLMap 0 7 30 44 Warning about the equilibrium of marker transmission within each family Description of the traits Names of the quantitative traits for each trait number of animals measured number of animals measured for both performance traits and marker genotypes mean variance minimum and maximum Names of fixed effect if any with the list of levels Names of the covariates if any with their mean variance minimum and maximum The second part describes the result of the phase building Parental phases A part of the most probable phases of the reproducers built from available marker and pedigree information are listed The full information is found in the specific file A control is given to the user with the keys opt_minsirephaseproba and opt_mindamphaseproba Minimal sire and dam phase probability In the third part results of the genome scan are given for each traits Details depends on tests and models Genome scan QTLMap 0 7 31 44 Section calcul 1 2 3 4 5 6 7 8 Possible confusions x X x between QTL and other effects Residual variances
42. tiple trait analyses e Nuisance parameters e g sex batch weight and their interactions with QTL can be included in the analysis e Gaussian discrete or survival Cox model data Familial heterogeneity of variances heteroscedasticity e Can handle eQTL analyses e Computation of transmission and phase probabilities adapted to high throughput genotyping SNP e Empirical thresholds are estimated using simulations under the null hypothesis or permutations of trait values e Computation of power and accuracy of your design or any simulated design 2 Contributors Pascale Le Roy UMR GARen Rennes France QTLMap 0 7 4 44 Jean Michel Elsen SAGA INRA Toulouse France H l ne Gilbert GABI INRA Jouy en Josas France Carole Moreno SAGA INRA Toulouse France Andres Legarra SAGA INRA Toulouse France Olivier Filangi UMR GARen Rennes 3 Support Subsribe and post any message question to the qtlmap users list mailto gtlmap users listes inra fr 4 Setting up QTLMap 4 1 Runtime environment with GNU software component Pre requisites gt The GNU compiler collection gfortran 4 4 gcc gt Cmake 2 6 4 cross platform open source build system Compilation gt cd QTLMAP_DIR gt mkdir build gt cd build gt cmake DCMAKE BUILD TYPE Release gt cmake DCMAKE Fortran _COMPILER gfortran gt make The binary qtlmap is created in the QTLMAP_DIR build src directory To install the qtlmap binary
43. ult file at position x given the dam phase QTLMap 0 7 39 44 Position Sire Dam Dam Phase Animal p 2nd sire allele p 2nd dam allele 1 000 ils 910001 910014 1 944217 0 000 2 910001 910014 al 944217 0 999 0 001 BE 910001 910014 1 944217 0 999 0 001 4 910001 910014 1 944217 0 999 0 001 5 910001 910014 il 944217 0 999 0 001 Texte 28 Grand parental segment transmission marginal probabilities file 10 9 Grand parental segment transmission joint probabilities Each line gives for a tested QTL position x e Position e Sire ID e Dam ID Dam phase number in the order of the main results file e Progeny ID e Probability that the progeny inherited the 1 sire and 1 dam alleles in the order of the main result file at position x given the dam phase The probability that the progeny inherited the 1 sire and 2 dam alleles in the order of the main result file at position x given the dam phase e Probability that the progeny inherited the 2 sire and 1 dam alleles in the order of the main result file at position x given the dam phase e Probability that the progeny inherited the 2 sire and 2 dam alleles in the order of the main result file at position x given the dam phase Position Sire Dam Dam Phase Animal p Hs1 Hd1 p Hs1 Hd2 p Hs2 Hd1 p Hs2 Hd2 944217 000 000 1 000 il 910001 910014 1 0 0 0 000 2 910001 910014 al 944217 0 001 0 000 0 999 0 001 3 910001 910014 1 944217 0 001 0 000 0 99
44. wo lines are edited for each progeny The first contains Progeny ID followed by an s indicator for sire origin The list of marker alleles transmitted by the sire to the progeny e origin as a separator The list of sire grand parental origin of the haplotypes transmitted by the sire 1 for grand sire 2 for grand dam and un for unknown assuming the most probable sire phase The second contains e Progeny ID followed by an d indicator The list of marker alleles transmitted by the dam to the progeny e origin as a separator The list of dam grand parental origin of the haplotypes transmitted by the dam 1 for grand sire 2 for grand dam and un for unknown assuming the most probable dam phase 91104 91104 91105 91105 1 1 un s P 2 un 1 un il 1 d origin origin un un al un un 19 origin 3 ST cote ieee i 2 5 19 origin Ne eN PR eN e e NN ON Ne e R bp o w s d un 2 1 Texte 27 haplotypes file 10 8 Grand parental segment transmission marginal probabilities Each line gives for a tested QTL position x The sire ID The dam ID The dam phase number in the order of the main results file The progeny ID The probability that the progeny inherited the 2 sire allele in the order of the main result file at position x given the dam phase The probability that the progeny inherited the 2 dam allele in the order of the main res
45. yse calcul 2 Analysis a single real trait with a model description and a complete linearised likelihood homoscedastic and heteroscedastic gt QTLMAP PATH qtlmap p analyse calcul 3 gt QTLMAP PATH qtlmap p analyse calcul 4 Analysis a set of real traits without missing data with a multivariate analysis based on a multi normal penetrance function gt QTLMAP PATH qtlmap p analyse calcul 5 Analysis a set of traits without missing data with a discriminant analysis gt QTLMAP PATH qtlmap p analyse calcul 6 Analyse a single survey trait with the cox model gt QTLMAP PATH qtlmap p analyse calcul 7 Analyse a single survey trait with the LD gt QTLMAP PATH qtlmap p analyse calcul 8 Analyse a single survey trait with the LDLA gt QTLMAP PATH qtlmap p analyse calcul 9 Analysis a single real trait with a model description and a complete linearised likelihood homoscedastic and heteroscedastic with the LD gt QTLMAP PATH qtlmap p analyse calcul 25 gt QTLMAP PATH qtlmap p analyse calcul 26 10 Analysis a single real trait with a model description and a complete linearised likelihood homoscedastic and heteroscedastic with the LDLA QTLMap 0 7 17 44 gt QTLMAP PATH qtlmap p analyse calcul 27 gt QTLMAP PATH qtlmap p analyse calcul 28 Haplotype Changing the calculus of the parental phases and for all progeny the grand parental segment transmission adapted f
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