BOLT-LMM v2.2 User Manual
Contents
1. 24 lt 4 3 en E SS a 10 6 2 BOLT LMM mixed model association options 0004 11 6 2 1 Reference LD score tables ue Ned See Dee hee Dee ees 11 6 2 2 Restricting SNPs used in the mixed model 11 6 3 Standard linear regression ios ae be he EOE ek EO da 11 7 Variance components analysis BOLT REML 12 7 1 Multiple variance components 20 0 0002 eee eee 12 2 Multiple ais Sad SP RES Ra ES ee A eS Bees Ee Se le ae 12 7 3 Initial variance parameter guesses 2 ce ca ee ds ave eee ee 12 7 4 Trading a little accuracy for speed ee Pegi hee A ee 12 8 Output 13 8 1 BOLFLMM association test statistics 2 2 2 13 8 2 BOLT REML output and logging e sia a Mie ae ale Ee aS 13 9 Website and contact info 14 10 License 14 1 Overview The BOLT LMM software package currently consists of two main algorithms the BOLT LMM algorithm for mixed model association testing and the BOLT REML algorithm for variance com ponents analysis 1 e partitioning of SNP heritability and estimation of genetic correlations We recommend BOLT LMM for analyses for human genetic data sets containing more than 5 000 samples The algorithms used in BOLT LMM rely on approximations that hold only at large sample sizes and have been tested only in human data sets For analyses of fewer than 5 000 samples we recommend the GCTA software 1 1 BOLT LMM mixed model association testing The BOLT LMM algorithm com2. maxMissingPerIndiv Note that filtering is not performed based on minor allele frequency or deviation from Hardy Weinberg equilibrium Allele frequency and missingness of each SNP are included in the BOLT LMM association test output however and we recommend checking these values and Hardy Weinberg p values which are easily computed using PLINK2 hardy when following up on significant associations 5 6 User specified filters Individuals to remove from the analysis may be specified in one or more remove files listing FID and IIDs one individual per line Similarly SNPs to exclude from the analysis may be specified in one ore more exclude files listing SNP IDs typically rs numbers 6 Association analysis BOLT LMM 6 1 Mixed model association tests BOLTLMM computes two association statistics Xx Borrmm and x2BoLr1mm inf described in detail in our manuscript 1 e BOLT LMM Association test on residuals from Bayesian modeling using a mixture of normals prior on SNP effect sizes This approach can fit non infinitesimal traits with loci having moderate to large effects allowing increased association power e BOLT LMM inf Standard infinitesimal mixed model association This statistic ap proximates the standard approach used by existing software 10 6 2 BOLT LMM mixed model association options The BOLI LMM software offers the following options for mixed model analysis e 1mm Perfor
3. 71 2013 Lippert C et al The benefits of selecting phenotype specific variants for applications of mixed models in genomics Scientific Reports 3 2013 Zhou X amp Stephens M Genome wide efficient mixed model analysis for association studies Nature Genetics 44 821 824 2012 Svishcheva G R Axenovich T I Belonogova N M van Duijn C M amp Aulchenko Y S Rapid variance components based method for whole genome association analysis Nature Genetics 44 1166 1170 2012 Yang J Zaitlen N A Goddard M E Visscher P M amp Price A L Advantages and pitfalls in the application of mixed model association methods Nature Genetics 46 100 106 2014 Yang J Lee S H Goddard M E amp Visscher P M GCTA a tool for genome wide complex trait analysis American Journal of Human Genetics 88 76 82 2011 Loh P R ef al Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance components analysis Nature Genetics 2015 Johnson S G The NL opt nonlinear optimization package URL http ab initio mit edu nlopt Purcell S ef al PLINK a tool set for whole genome association and population based linkage analyses American Journal of Human Genetics 81 559 575 2007 Chang C C et al Second generation PLINK rising to the challenge of larger and richer datasets GigaScience 4 1 16 2015 Howie B N Donnelly P amp Marc
4. BOLT LMM v2 2 User Manual Po Ru Loh November 13 2015 Contents 1 Overview 1 1 BOLT LMM mixed model association testing 002 1 2 BOLT REML variance components analysis o e 2 Download and installation Zale Changos e sr e la e e Sow te SL ee A RR 2 2 e A A ara R EN ey hoe Gerd as Car a A A A ce 2 3 Running BOLT LMM and BOLT REML 0 2 EXILE 25 E Bove Gir ate A a Ses a ae eee i A MACNN eri tc da e O E E Sat epee ee al Nee Be 3 Computing requirements Jl Operaune yt a See a A ae eS 3 2a a A II E psig E A A A 3 3 Running II ae Re Ree SER ee eee 3 3 1 Multi threading 03 4 4 6 5 404 5 4 3 4 45 5 a a 4 Input output file naming conventions 4 1 Automatic gzip de Jcompression 2 0 0 2 0 0 002 e 4 2 Arrays of input files and covariates 2 a 5 Input Del GENOTYPES La A Oy Sek we Pm Wk se ee es eee EAS a 5 1 1 Reference genetic Maps costera Oe See RRA Ax 5 1 2 Imputed NP dosages 26 eh eee tee bee ee ee ey eG 5 2 PENN PES lt a Gs Ge Seed eh Sed hee Be rk A tack Peleg tea ie oaks deci S 5 3 MOON AIT ATES vee SS eee Se uh yc hi hype sk ha pc al See oe beh omg e Aaa ts Batons E 5 4 Missing data treatment de de a e id ee ee ee ee 30 Genotype D ue it rd es ae eee ae amp AS Bae eS Ba ete ee JO Userspecined Alters aura Terea uE Pewee ced Weak Aer A 6 Association analysis BOLT LMM 10 6 1 Mixed model association tests
5. LEO second allele in bim file used as the reference allele e A1FREO frequency of first allele e F_MISS fraction of individuals with missing genotype at this SNP e BETA effect size from BOLI LMM approximation to infinitesimal mixed model e SE standard error of effect size e P_BOLT_LMM_INF infinitesimal mixed model association test p value e P_BOLT_LMM non infinitesimal mixed model association test p value Optional additional output To output chi square statistics for all association tests set the verboseStats flag 13 8 2 BOLT REML output and logging BOLT REML output i e variance parameter estimates and standard errors is simply printed to the terminal st dout when analysis finishes Both BOLT LMM and BOLT REML write output to stdout and stderr as analysis proceeds we recommend saving this output If you wish to save this output while simultaneously viewing it on the command line you may do so using bolt list of options 2 gt amp 1 tee oubput Log 9 Website and contact info Software updates will be posted at the following permanent website http www hsph harvard edu alkes price software If you have comments or questions about the BOLT LMM software please contact Po Ru Loh loh hsph harvard edu 10 License e The BOLT LMM source code is free software under the GNU General Public License v3 0 GPLv3 e The BOLT LMM executable is freely available but not open source because
6. ait modeling to estimate correlations BOLI REML applies a Monte Carlo algorithm that is much faster than standard methods for variance components analysis e g GCTA at large sample sizes BOLT REML is described in ref 11 Loh P R Bhatia G Gusev A Finucane HK Bulik Sullivan BK Pollack SJ PGC SCZ Working Group de Candia TR Lee SH Wray NR Kendler KS O Donovan MC Neale BM Patterson N and Price AL Contrasting genetic architectures of schizophre nia and other complex diseases using fast variance components analysis Nature Ge netics 2015 2 Download and installation You can download the latest version of the BOLT LMM software at http data broadinstitute org alkesgroup BOLT LMM downloads Previous versions are also available in the 01d subdirectory 2 1 Change log e Version 2 2 Nov 13 2015 Added support for testing imputed SNPs in BGEN format Added option to look up LD scores by base pair coordinates rather than SNP name LDscoresMatchBp Fixed bug in hg19 genetic map interpolation Fixed bug in QC filter for per sample missing rate Improved error checking e Version 2 1 Apr 29 2015 Improved handling of IMPUTE2 files large speedup INFO output column instead of F_MISS MAF filtering Fixed bug in per sample missingness filter maxMissingPerIndiv which was being ignored Implemented minor changes to input parameters removed
7. el x N Missing i e uncalled dosages can be specified with 9 You will also need to provide one additional dosageFidlidFile specifying the PLINK FIDs and IIDs of samples that the dosages correspond to See the example subdirectory for an example Imputed SNPs in IMPUTE2 format You may also specify imputed SNPs as output by the IMPUTE2 software 15 The IMPUTE2 genotype file format is as follows snpID rsID pos allelel allele0 p 11 p 10 p 00 x N BOLT LMM ignores the snpID field Here instead of dosages each genotype entry contains in dividual probabilities of the individual being homozygous for allele1 heterozygous and homozy gous for allele0 The three probabilities need not sum to 1 allowing for genotype uncertainty if the sum of the probabilities is less than the impute2CallThresh parameter BOLT LMM treats the genotype as missing To compute association statistics at a list of files containing IMPUTE2 SNPs you may list the files within a impute2FileList file Each line of this file should contain two entries a chromosome number followed by an IMPUTE2 genotype file containing SNPs from that chro mosome You will also need to provide one additional impute2FidlidFile specifying the PLINK FIDs and IDs of samples that the IMPUTE2 genotypes correspond to See the example subdirectory for an example Imputed SNPs in 2 dosage format You may also specify imputed SNPs as output by the Ri copil
8. hini J A flexible and accurate genotype imputation method for the next generation of genome wide association studies PLOS Genetics 5 e1000529 2009 15 16 Bulik Sullivan B et al LD Score regression distinguishes confounding from polygenicity in genome wide association studies Nature Genetics 47 291 295 2015 17 Galinsky K J et al Fast principal components analysis reveals independent evolution of ADHIB gene in Europe and East Asia bioRxiv 018143 2015 16
9. i pipeline and plink2 dosage format 2 This file format consists of file pairs 1 PLINK map files containing information about SNP locations and 2 genotype probability files in the 2 dosage format which consists of a header line SNP Al A2 FID IID x N followed by one line per SNP in the format rsID allelel allele0 p 11 p 10 x N 8 The third genotype probability for each entry is assumed to be p 00 1 p 11 p 10 unlike with the IMPUTE2 format To compute association statistics at SNPs in a list of 2 dosage files you may list the files within a dosage2FileList file Each line of this file should contain two entries a PLINK map file followed by the corresponding genotype file containing probabilities for those SNPs As usual 1f either file ends with gz it is automatically unzipped otherwise it is assumed to be plain text See the example subdirectory for an example Imputed SNPs in BGEN format To compute association statistics at SNPs in a single BGEN data file specify the bgen file with bgenFile and the corresponding sample file with sampleFile The bgenMinMAF option allows limiting output to SNPs passing a mini mum allele frequency threshold Note that the bgenFile argument currently takes only one BGEN file unlike the input format for the other imputed SNP file types We recommend parallelizing analyses of BGEN data across chromosomes for computational convenience using the full bfile of di
10. iance components analysis does not support dosage input This approach re quires only a trivial amount of additional computation and no additional RAM as it simply applies a genome scan as in GRAMMAR Gamma 8 of real valued dosage SNPs against the residual phenotypes that BOLT LMM already computes We expect that using a mixed model built on only a subset of 500K hard called genotypes should sacrifice almost no power while retaining the computational efficiency of BOLT LMM If you have only imputed SNP data on hand you will need to pre process your data set to create a subset of hard called SNPs in PLINK format for BOLT LMM We suggest the following procedure 1 Determine a high confidence set of SNPs e g based on IMPUTE2 INFO score at which to create an initial hard call set 2 Create hard called genotypes at these SNPs in PLINK format 3 Use PLINK to LD prune to SOOK SNPs via indep pairwise 50 5 r2thresh for an appropriate r2thresh 4 Run BOLT LMM using the final hard called SNPs as the bfile or bed bim fam argument specifying the imputed SNPs as additional association test SNPs using one of the formats below Imputed SNPs in dosage format This input format consists of one or more dosageFile parameters specifying files that contain real valued genotype expectations at imputed SNPs Each line of a dosageFile should be formatted as follows rsID chr pos allelel allele0 dosag E allel
11. impute2CallThresh which had no effect added impute2MinMAF and h2gGuess e Version 2 0 Mar 13 2015 Added BOLT REML algorithm for estimating heritability pa rameters Fixed parameter initialization bug that prevented BOLT LMM from running on some systems Implemented various minor improvements to parameter checking e Dec 8 2014 Licensed source code under GPLv3 e Version 1 2 Nov 4 2014 Added support for testing imputed SNPs in 2 dosage format Ricopili plink2 format 2 Fixed bug causing nan heritability estimates e Version 1 1 Oct 17 2014 Added support for testing imputed SNPs with probabilistic dosages e Version 1 0 Aug 8 2014 Initial release 2 2 Installation The BOLT LMM_vX X tar gz download package contains a standalone 1 e statically linked 64 bit Linux executable bolt which we have tested on several Linux systems We strongly recommend using this static executable because it is well optimized and no further installation is required If you wish to compile your own version of the BOLT LMM software from the source code in the src subdirectory provided in a separate download licensed under GNU GPLv3 you will need to ensure that library dependencies are fulfilled and will need to make appropriate modifica tions to the Makefile e Library dependencies BLAS LAPACK numerical libraries The speed of the BOLT LMM software depends critically on the efficiency of the BLAS LAPACK implementation
12. it is linked against We recommend the Intel Math Kernel Library MKL if available except on AMD processors otherwise ATLAS may be a good alternative Boost C libraries BOLT LMM links against the Boost program_options and iostreams libraries which need to be installed after downloading and unzipping Boost NLopt numerical optimization library 12 e Makefile Paths to libraries need to be modified appropriately Note that the released ver sion of the Makefile does not set the flag DUSE_MKL_MALLOC This flag turns on the Intel MKL s fast memory manager replacing calls to_mm_malloc with mk1_malloc which may improve memory performance but we have observed crashes on some systems when using mk 1_malloc For reference the provided bolt executable was created on the Harvard Medical School Orches tra research computing cluster using Intel Parallel Studio XE 2016 Composer MKL 11 3 and the Boost 1 58 and NLopt 2 4 2 libraries by invoking make linking static except glibc 2 3 Running BOLT LMM and BOLT REML To run the bolt executable simply invoke bolt on the Linux command line within the BOLT LMM install directory with parameters in the format opt ionName optionValue 2 4 Examples The example subdirectory contains a bash script run_example sh that demonstrates basic use of BOLT LMM on a small example data set Likewise run_example_rem12 sh demon strates BOLT REML e A minimal BOLT LMM invocation lo
13. it was built with the proprietary Intel Math Kernel Library under a non commercial license Specific license terms are as follows Copyright 2014 Harvard University All rights reserved This software is sup plied without any warranty or guaranteed support whatsoever Harvard University cannot be responsible for its use misuse or functionality The software may be freely copied for non commercial purposes provided this copyright notice is re tained Starting from v2 0 the BOLT REML component of the software also uses routines from the NLopt library written by Steven G Johnson and distributed under the MIT License Copy right 2007 2011 Massachusetts Institute of Technology 14 References 1 10 11 12 13 14 15 Loh P R et al Efficient Bayesian mixed model analysis increases association power in large cohorts Nature Genetics 47 284 290 2015 Kang H M et al Variance component model to account for sample structure in genome wide association studies Nature Genetics 42 348 354 2010 Lippert C et al FaST linear mixed models for genome wide association studies Nature Methods 8 833 835 2011 Listgarten J et al Improved linear mixed models for genome wide association studies Nature Methods 9 525 326 2012 Listgarten J Lippert C amp Heckerman D FaST LMM Select for addressing confounding from spatial structure and rare variants Nature Genetics 45 470 4
14. lly normalize the phenotype accordingly For multi trait analysis of D traits the remlGuessSt r needs to specify both guesses of D variance proportions and D D 1 2 pairwise correlations per variance component Viewing these values as entries of an upper triangular matrix with variance proportions on the diagonal and correlations above the diagonal you should specify these D D 1 2 values after each variance component name by reading them off left to right top to bottom 12 7 4 Trading a little accuracy for speed BOLI REML uses a Monte Carlo algorithm to increase REML optimization speed 11 By de fault BOLT REML performs an initial optimization using 15 Monte Carlo trials and then refines parameter estimates using 100 Monte Carlo trials If computational cost is a concern or to per form exploratory analyses you can skip the refinement step using the remlNoRefine flag in addition to the reml flag This option typically gives 2 3x speedup at the cost of 1 03x higher standard errors 8 Output 8 1 BOLT LMM association test statistics BOLI LMM association statistics are output in a tab delimited statsFile file with the fol lowing fields one line per SNP e SNP rs number or ID string e CHR chromosome e BP physical base pair position e GENPOS genetic position either from bim file or interpolated from genetic map e ALLELE1 first allele in bim file usually the minor allele used as the effect allele e ALLE
15. ms default BOLT LMM analysis which consists of 1a estimating heritabil ity parameters 1b computing the BOLT LMM inf statistic 2a estimating Gaussian mix ture parameters and 2b computing the BOLT LMM statistic only if an increase in power is expected If BOLT LMM determines based on cross validation that the non infinitesimal model is likely to yield no increase in power the BOLT LMM Bayesian mixed model statistic is not computed e lmmInfOnly Computes only infinitesimal mixed model association statistics 1 e steps la and 1b e lmmForceNonInf Computes both the BOLT LMM inf and BOLT LMM statistics re gardless of whether or not an increase in power is expected from the latter 6 2 1 Reference LD score tables A table of reference LD scores 16 is needed to calibrate the BOLT LMM statistic Reference LD scores appropriate for analyses of European ancestry samples are provided in the tables subdirectory and can be specified using the option H DscoresFile tables LDSCORE 1000G_EUR tab gz For analyses of non European data we recommend computing LD scores using the LDSC software on an ancestry matched subset of the 1000 Genomes samples By default LD scores in the table are matched to SNPs in the PLINK data by rsID The LDscoresMatchBp option allows matching SNPs by base pair coordinate 6 2 2 Restricting SNPs used in the mixed model If millions of SNPs are available from imputation we
16. ng times that scale roughly with MN Our largest analyses of real data M 600K SNPs N 60K individuals took 1 day using a single computational core We have also tested BOLT LMM on simulated data sets containing up to N 480K individuals for more details please see the BOLT LMM manuscript 1 3 3 1 Multi threading On multi core machines running time can be reduced by invoking multi threading using the numThreads option 4 Input output file naming conventions 4 1 Automatic gzip de compression The BOLT LMM software assumes that input files ending in gz are gzip compressed and auto matically decompresses them on the fly i e without creating a temporary file Similarly BOLT LMM writes gzip compressed output to any output file ending in gz 4 2 Arrays of input files and covariates Arrays of sequentially numbered input files and covariates can be specified by the shorthand 1 3 For example data chr 1 22 bim is interpreted as the list of files data chrl bim data chr2 bim data chr22 bim 6 5 Input 5 1 Genotypes The BOLT LMM software takes genotype input in PLINK 13 binary format bed bim fam For file conversion and data manipulation in general we highly recommend the PLINK2 soft ware 14 which is providing a comprehensive much more efficient update to PLINK If all genotypes are contained in a single bed bim fam file triple with the same file prefix you may simply use the command line
17. oks like bolt bfile geno phenoFile pheno txt phenoCol phenoName lmm LDscoresFile tables LDSCORE 1000G_EUR tab gz statsFile stats tab e A minimal BOLT REML invocation looks like bolt bfile geno phenoFile pheno txt phenoCol phenoName reml model Snps modelSnps txt To perform multi trait BOLT REML i e estimate genetic correlations provide multiple phenoCol phenoName arguments 2 5 Help To get a list of basic options run bolt h To get a complete list of basic and advanced options run bolt helpFull 3 Computing requirements 3 1 Operating system At the current time we have only compiled and tested BOLT LMM on Linux computing envi ronments however the source code is available if you wish to try compiling BOLT LMM for a different operating system 3 2 Memory For typical data sets M N exceeding 10 000 BOLT LMM and BOLT REML use approximately MN 4 bytes of memory where M is the number of SNPs and N is the number of individuals More precisely e M of SNPs in bin file s that satisfy all of the conditions not listed in any exclude file passed QC filter for missingness listed in modelSnps file s if specified e N of individuals in fam file and not listed in any remove file but pre QC i e N includes individuals filtered due to missing genotypes or covariates 3 3 Running time In practice BOLT LMM and BOLT REML have runni
18. option bfile prefix Genotypes may also be split into multiple bed and bim files containing consecutive sets of SNPs e g one bed bim file pair per chromosome either by using multiple bed and bim invocations or by using the file array shorthand described above e g bim data chr 1 22 bim 5 1 1 Reference genetic maps The BOLT LMM package includes reference maps that you can use to interpolate genetic map coordinates from SNP physical base pair positions in the event that your PLINK bin file does not contain genetic coordinates in units of morgans The BOLT LMM association testing algo rithm uses genetic positions to prevent proximal contamination BOLT REML does not use this information To use a reference map use the option geneticMapFile tables genetic_map_hg txt gz selecting the build hg17 hg18 or hg19 corresponding to the physical coordinates of your bim file You may use the geneticMapFile option even if your PLINK bim file does contain genetic coordinates in this case the genetic coordinates in the bim file will be ignored and interpolated coordinates will be used instead 5 1 2 Imputed SNP dosages As of version 1 1 the BOLT LMM association testing algorithm supports computation of mixed model association statistics at an arbitrary number of imputed SNPs with real valued dosages rather than hard called genotypes using a mixed model built on a subset of hard called genotypes BOLT REML var
19. ovarF ile with the same format as the alternate phenotype file described above The same file may be used for both phenotypes and covari ates Each covariate to be used must be specified using either a covarCol for categorical covariates or a qCovarCol for quantitative covariates option Categorical covariate values are allowed to be any text strings not containing whitespace each unique text string in a column corresponds to a category Quantitative covariate values must be numeric with the exception of NA In either case values of 9 and NA are interpreted as missing data If groups of covariates of the same type are numbered sequentially they may be specified using array shorthand e g qCovarCol PC 1 10 for columns PC1 PC2 PC10 5 4 Missing data treatment Individuals with missing phenotypes are ignored By default individuals with any missing co variates are also ignored this approach is commonly used and referred to as complete case analysis As an alternative we have also implemented the missing indicator method via the covarUseMissingIndic option which adds indicator variables demarcating missing sta tus as additional covariates Missing genotypes are replaced with per SNP averages 5 5 Genotype QC BOLI LMM and BOLT REML automatically filter SNPs and individuals with missing rates ex ceeding thresholds of 0 1 These thresholds may be modified using maxMissingPerSnp and
20. putes statistics for testing association between phenotype and genotypes using a linear mixed model LMM 1 By default BOLT LMM assumes a Bayesian mixture of normals prior for the random effect attributed to SNPs other than the one being tested This model generalizes the standard infinitesimal mixed model used by existing mixed model as sociation methods e g EMMAX 2 FaST LMM 3 6 GEMMA 7 GRAMMAR Gamma 8 GCTA LOCO 9 providing an opportunity for increased power to detect associations while con trolling false positives Additionally BOLT LMM applies algorithmic advances to compute mixed model association statistics much faster than existing methods both when using the Bayesian mix ture model and when specialized to standard mixed model association BOLT LMM is described in ref 1 Loh P R Tucker G Bulik Sullivan BK Vilhj lmsson BJ Finucane HK Salem RM Chasman DI Ridker PM Neale BM Berger B Patterson N and Price AL Efficient Bayesian mixed model analysis increases association power in large cohorts Nature Genetics 2015 1 2 BOLT REML variance components analysis The BOLT REML algorithm estimates heritability explained by genotyped SNPs and genetic cor relations among multiple traits measured on the same set of individuals Like the GCTA soft ware 10 BOLT REML applies variance components analysis to perform these tasks supporting both multi component modeling to partition SNP heritability and multi tr
21. rectly genotyped PLINK data from all chromosomes in each job WARNING The BGEN format comprises a few sub formats we have only implemented sup port for the version used by UK Biobank 5 2 Phenotypes Phenotypes may be specified in either of two ways e phenoUseFam This option tells BOLELMM and BOLT REML to use the last 6th column of the fam file as the phenotypes This column must be numeric so case control phenotypes should be 0 1 coded and missing values should be indicated with 9 e phenoFile and phenoCol Alternatively phenotypes may be provided in a sep arate whitespace delimited file specified with phenoFi le with the first line contain ing column headers and subsequent lines containing records one per individual The first two columns must be FID and 11D the PLINK identifiers of an individual Any number of columns may follow the column containing the phenotype to analyze is specified with phenoCol Values of 9 and NA are interpreted as missing data All other values in the column should be numeric The records in lines following the header line need not be in sorted order and need not match the individuals in the genotype data i e fam file BOLT LMM and BOLT REML will analyze only the individuals in the intersection of the genotype and phenotype files and will output a warning if these sets do not match 5 3 Covariates Covariate data may be specified in a file c
22. specify a modelSnps file in which each whitespace delimited line contains a SNP ID typically an rs number followed by the name of the variance component to which it belongs 7 2 Multiple traits To perform multi trait variance components analysis specify multiple phenoCol parameter value flags corresponding to different columns in the same phenoF ile BOLT REML cur rently only supports multi trait analysis of traits phenotyped on a single set of individuals so any individuals with at least one missing phenotype will be ignored For D traits BOLT REML es timates D heritability parameters per variance component and D D 1 2 correlations per variance component including the residual variance component 7 3 Initial variance parameter guesses To specify a set of variance parameters at which to start REML iteration which may save time compared to the default procedure used by BOLT REML if you have good initial guesses use remlGuessStr string with the following format For each variance component start ing with the residual term which is automatically named env noise specify the name of the variance component followed by the initial guess For instance a model with two non residual variance components named ve and vc2 in the modelSnps file could have variance param eter guesses specified by remlGuessStr env noise 0 5 vel 0 2 vc2 0 3 Note that the sum of the estimates must equal 1 BOLT REML will automatica
23. suggest including at most 1 million SNPs at a time in the mixed model using the modelSnps option when performing association analysis Using an LD pruned set of at most 1 million SNPs should achieve near optimal power and correction for confounding while reducing computational cost and improving convergence Note that even when a file of model1Snps is specified all SNPs in the genotype data are still tested for association only the random effects in the mixed model are restricted to the modelSnps Also note that BOLT LMM automatically performs leave one chromosome out LOCO analysis leaving out SNPs from the chromosome containing the SNP being tested in order to avoid proximal contamination 4 9 6 3 Standard linear regression Setting the verboseStats flag will output standard linear regression chi square statistics and p values in additional output columns CHISQ_LINREG and P_LINREG Note that unlike mixed 11 model association linear regression is susceptible to population stratification so you may wish to include principal components computed using other software e g PLINK2 or FastPCA 17 in EIGENSOFT v6 0 as covariates when performing linear regression 7 Variance components analysis BOLT REML Using the rem1 option invokes the BOLT REML algorithm for estimating heritability parame ters and genetic correlations 7 1 Multiple variance components To assign SNPs to different variance components
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