BOLT-LMM v1.0 User Manual
Contents
1. SNP IDs typically rs numbers 6 Association analysis 6 1 Mixed model association tests BOLTI LMM computes two association statistics Y BOLTLMM and X BOLTLMM 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 6 2 BOLT LMM mixed model association options The BOLT LMM software offers the following options for mixed model analysis e 1mm Performs 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 i e steps la and 1b e lmmForceNonInf Computes both the BOLT LMM inf and BOLT LMM statistics re gardless of whether or not an increase2. in power is expected from the latter 6 2 1 Reference LD Score tables A table of reference LD Scores is needed to calibrate the BOLT LMM statistic this table is pro vided with the BOLT LMM package and can be specified using the option H DscoresFile tables LDSCORE 1000G_EUR tab gz 6 2 2 Restricting SNPs used in the mixed model If millions of SNPs are available from imputation we suggest including lt 1 million SNPs at a time in the mixed model using the modelSnps option when performing association analy sis Using an LD pruned set of lt 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 modelSnps is specified all SNPs in the genotype data are still tested for as sociation 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 leav ing 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 CH SQ_L NREG and P_L NREG Note that unlike mixed model association linear regression is susceptible to population stratification so you may wish to inclu
3. 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 BOLI LMM assumes that input files ending in gz are gzip compressed and automatically de compresses 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 i j For example data chr 1 22 bim is interpreted as the list of files data chrl bim data chr2 bim data chr22 bim 5 Input 5 1 Genotypes BOLI LMM takes genotype input is in PLINK 10 binary format bed bim fam For file conversion and data manipulation in general we highly recommend the PLINK 2 project which is providing a comprehensive much more efficient update to PLINK 11 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 option bfile prefix Genotypes may also be split into multiple bed and bim files containing consecutive sets of SNPs e g one bed binm file pair per chromosome either by using multiple bed and bim invocations or by using the file array shorthand described above e g b
4. BOLT LMM v1 0 User Manual Po Ru Loh August 8 2014 1 Overview The BOLT LMM software package computes statistics for 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 asso ciation statistics much faster than existing methods both when using the Bayesian mixture model and when specialized to standard mixed model association 1 1 Citing BOLT LMM BOLT LMM is described in ref 1 Loh P R Tucker G Bulik Sullivan BK Vilhj lmsson BJ Finucane HK Chasman DI Ridker PM Neale BM Berger B Patterson N and Price AL Efficient Bayesian mixed model analysis increases association power in large cohorts Submitted and available on bioRxiv 2 Installation We provide a standalone i 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 co
5. 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 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 Purcell S et al PLINK a tool set for whole genome association and population based linkage analyses The American Journal of Human Genetics 81 559 575 2007 Purcell S amp Chang C PLINK 1 9 URL https www cog genomics org plink2
6. de principal components computed using other software as covariates when performing linear regression 7 Output Association statistics are output in a tab delimited file with the following 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 ALLELEL1 first allele in bim file usually the minor allele used as the effect allele e ALLELEO second allele in bim file used as the reference allele e A1FREQ 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 7 1 Optional additional output P_BOLT_LMM_INF infinitesimal mixed model association test p value P_BOLT_LMM non infinitesimal mixed model association test p value To output chi square statistics for all association tests set the verboseStats flag 7 2 Diagnostics and logging As BOLT LMM proceeds output is written to the terminal stdout and stderr If you wish to save this output while simultaneously viewing it on the command line you may do so using bolt vai list of options 2261 tee outputs log 8 Change log Version 1 0 Initial release 9 Website and contact info Software updates will be posted at the following website http www h
7. ile with the first line contain ing column headers and subsequent lines containing records one per individual The first two columns must be FID and IID 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 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 covarFile 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
8. im 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 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 dosages The current BOLT LMM release does not support imputed dosage data but we plan to include support for imputed dosages in a future release Please contact us if you are interested in using this functionality we will try to provide support for the most popular dosage data format 5 2 Phenotypes Phenotypes may be specified in either of two ways e phenoUseFam This option tells BOLT LMM 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 phenoF
9. 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 BOLT LMM also offers 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 QC BOLI LMM automatically filters out SNPs and individuals with missing rates exceeding thresh olds of 10 These thresholds may be modified using the options maxMissingPerSnp and maxMissingPerIndiv Note that BOLT LMM does not perform automatic filtering based on minor allele frequency or deviation from Hardy Weinberg equilibrium Allele frequency and missingness of each SNP are included in the BOLT LMM output however and we recommend checking these values and Hardy Weinberg p values which are easily computed using PLINK 2 hardy when following up on significant associations 5 6 User specified filters Individual 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
10. mpile your own version of the BOLT LMM software from the source code in the src subdirectory you will need to ensure that library dependencies are fulfilled and make appropriate modifications 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 it is linked against We recommend the Intel Math Kernel Library MKL if available 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 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 mkl_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 Composer XE 12 0 5 with Intel MKL 10 3 5 and the Boost C libraries 1 42 by invoking make cluster orchestra linking static 2 1 Running BOLT LMM To run the bolt executable simply invoke bolt on the Linux command line within the BOLT LMM install directo
11. ry with parameters in the format opt ionName optionValue 2 2 Example The example subdirectory contains a bash script use of BOLT LMM on a small example data set 2 3 Help To get a list of basic options run bolt h To get a complete list of basic and advanced options bolt helpFull 3 Computing requirements 3 1 Operating system run_example sh that demonstrates basic run 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 gt 10 000 BOLI LMM uses 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 has a running time that scales 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
12. sph 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 Software copyright notice agreement This software and its documentation are copyright 2014 by Harvard University and The Broad Institute All rights are reserved This software is supplied without any warranty or guaranteed support whatsoever Neither Harvard University nor The Broad Institute can be responsible for its use misuse or functionality The software may be freely copied for non commercial purposes provided this copyright notice is retained References 1 10 11 Loh P R et al Efficient bayesian mixed model analysis increases association power in large cohorts bioRxiv 2014 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 9 525 526 2012 Listgarten J Lippert C amp Heckerman D FaST LMM Select for addressing confounding from spatial structure and rare variants Nature Genetics 45 470 471 2013 Lippert C et al The benefits of selecting phenotype specific variants for applications of mixed models in genomics
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