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GCTA: a tool for Genome-‐wide Complex Trait Analysis Overview

1. the SNPs cojo cond cond snplist Perform association analysis of the included SNPs conditional on the given list of SNPs Results will be saved in a cma Input file format cond snplist rs1001 rs1002 cojo p 5e 8 Threshold p value to declare a genome wide significant hit The default value is 5e 8 if not specified This option is only valid in conjunction with the option cojo sict NOTE it will be extremely time consuming if you set a very low significance level e g 5e 3 cojo wind 10000 24 Specify a distance d in Kb units It is assumed that SNPs more than d Kb away from each other are in complete linkage equilibrium The default value is 10000 Kb i e 10 Mb if not specified cojo collinear 0 9 During the model selection procedure the program will check the collinearity between the SNPs that have already been selected and a SNP to be tested The testing SNP will not be selected if its multiple regression R on the selected SNPs is greater than the cutoff value By default the cutoff value is 0 9 if not specified COjO gC If this option is specified p values will be adjusted by the genomic control method By default the genomic inflation factor will be calculated from the summary level statistics of all the SNPs unless you specify a value e g cojo gc 1 05 cojo actual geno If the individual level genotype data of the discovery set are available e g a single cohort GWAS you can use
2. bed Example Convert MACH or Minimac dosage data to PLINK binary PED format gcta64 dosage mach test mldose gz test mlinfo gz make bed out test 3 GCTA GRM estimating the genetic relationships between individuals using SNP data make grm or make grm bin Estimate the genetic relationship matrix GRM between pairs of individuals from a set of SNPs and save the lower triangle elements of the GRM to binary files e g test grm bin test grm N bin test grm id Output files test grm bin it is a binary file which contains the lower triangle elements of the GRM test grm N bin it is a binary file which contains the number of SNPs used to calculate the GRM test grm id no header line columns are family ID and individual ID see above You can not open test grm bin or test grm N bin by a text editor but you can use the following R script to read them in R R script to read the GRM binary file ReadGRMBin function prefix AlIN F size 4 sum_i function i return sum 1 i BinFileName paste prefix grm bin sep NFileName paste prefix grm N bin sep IDFileName paste prefix grm id sep id read table IDFileName n dim id 1 BinFile file BinFileName rb grm readBin BinFile n n n 1 2 what numeric 0 size size NFile file NFileName rb if AIIN T N readBin NFile n n n 1 2 what numeric 0 size size else N readBin NFile n 1 what numeric 0 size size i sapply 1 n
3. see PLINK user manual for details dosage mach test midose test mlinfo Input files in MACH output format uncompressed e g test mldose and test mlinfo see MACH user manual for details dosage mach gz_ test midose gz_ test mlinfo gz Input files in MACH output format compressed e g test mldose gz and test mlinfo gz out test Specify output root filename 2 Data management keep test indi list Specify a list of individuals to be included in the analysis remove test indi list Specify a list of individuals to be excluded from the analysis chr 1 Include SNPs on a specific chromosome in the analysis e g chromosome 1 autosome num 22 Specify the number of autosomes for a species other than human For example if you specify the number of autosomes to be 19 then chromosomes 1 to 19 will be recognized as autosomes and chromosome 20 will be recognized as the X chromosome The default number is 22 if this option not specified autosome Include SNPs on all of the autosomes in the analysis extract test snplist Specify a list of SNPs to be included in the analysis Input file format test snplist rs103645 rs175292 exclude_ test snplist Specify a list of SNPs to be excluded from the analysis extract snp rs123678 Specify a SNP to be included in the analysis exclude snp rs123678 Specify a single SNP to be excluded from the analysis maf 0 01 Exclude SNPs w
4. sum_i return list diag grm i off grm i id id N N make grm gz Estimate the GRM save the lower triangle elements to a compressed text file e g test grm gz and save the IDs in a plain text file e g test grm id Output file format test grm gz no header line columns are indices of pairs of individuals row numbers of the test grm id number of non missing SNPs and the estimate of genetic relatedness 1 1 1000 1 0021 2 1 998 0 0231 2 2 999 0 9998 3 1 1000 0 0031 test grm id no header line columns are family ID and individual ID 011 0101 012 0102 013 0103 make grm xchr Estimate the GRM from SNPs on the X chromosome The GRM will be saved in the same binary format as above grm bin grm N bin and grm id Due to the speciality of the GRM for the X chromosome it is not recommended to manipulate the matrix by grm cutoff or grm adj or merge it with the GRMs for autosomes see below for the options of manipulating the GRM make grm xchr gz Same as make grm xchr but the GRM will be in compressed text files see make grm gz for the format of the output files ibc Estimate the inbreeding coefficient from the SNPs by 3 different methods see the software paper for details Output file format test ibc one header line columns are family ID individual ID number of nonmissing SNPs estimator 1 estimator 2 and estimator 3 FID IID NOMISS Fhat1 Fhat2 Fhat3 011 0101 999 0 0
5. this will significantly reduce computational efficiency Examples MLM based association analysis If you have already computed the GRM gcta64 mlma bfile test grm test pheno test phen out test thread num 10 MLM based association analysis including the candidate SNP MLMi gcta64 mlma bfile test pheno test phen out test thread num 10 MLM leaving one chromosome out LOCO analysis gcta64 mlma loco bfile test pheno test phen out test thread num 10 Output file format 30 test mima or test loco mlma columns are chromosome SNP physical position reference allele the coded effect allele the other allele frequency of the reference allele SNP effect standard error and p value Chr SNP bp A1 A2 Freq b se p 1 qtl2_1 1001 L H 0 366 0 0143857 0 0411682 0 726761 1 qtl2_2 1002 H L 0 326 0 0240756 0 0421248 0 56764 1 qtl2_3 1003 H L 0 146 0 0921772 0 0565541 0 103124 31
6. 0 0237 0 0042 1 38e 08 116314 0 589 0 0237 0 0042 1 67e 08 0 0 Columns are chromosome SNP physical position frequency of the effect allele in the original data the effect allele effect size standard error and p value from the original GWAS or meta analysis estimated effective sample size frequency of the effect allele in the reference sample effect size standard error and p value from a joint analysis of all the selected SNPs LD correlation between the SNP i and SNP i 1 for the SNPs on the list test jma ldr generate by the option cojo slct or cojo joint SNP rs2001 rs2002 rs2003 rs2001 1 0 0525 0 0672 rs2002 0 0525 1 0 0045 rs2003 0 0672 0 0045 1 LD correlation matrix between all pairwise SNPs listed in test jma 26 test cma generate by the option cojo slct or cojo cond Chr SNP bp freq refA b se p on freq geno bC bC_se pC 1 rs2001 172585028 0 6105 A 0 0377 0 0042 6 38e 19 121056 0 614 0 0379 0 0042 1 74e 19 1 rs2002 174763990 0 4294 C 0 0287 0 0041 3 65e 12 124061 0 418 0 0289 0 0041 1 58e 12 1 rs2003 196696685 0 5863 T 0 0237 0 0042 1 38e 08 116314 0 589 0 0237 0 0042 1 67e 08 Columns are chromosome SNP physical position frequency of the effect allele in the original data the effect allele effect size standard error and p value from the original GWAS or meta analysis estimated effective sample size frequency of the effect allele in the reference sample effect size standard error and p value from con
7. make grm gz Example This option is very useful to deal with large dataset You can firstly run the jobs split one job into 22 pieces gcta64 bfile test chr 1 make grm out test_chr1 gcta64 bfile test chr 2 make grm out test_chr2 gcta64 bfile test chr 22 make grm out test_chr22 To estimate the GRMs from the SNPs on each chromosome then merge them by the command gcta64 mgrm multi_grm txt make grm out test grm cutoff 0 025 Remove one of a pair of individuals with estimated relatedness larger than the specified cut off value e g 0 025 GCTA selectively removes individuals to maximize the remaining sample size rather than doing it at random NOTE When merging multiple GRMs this option does not apply to each single GRM but to the final merged GRM grm adj 0 When using the SNPs to predict the genetic relationship at causal loci we have to adjust the prediction errors due to imperfect LD because of two reasons 1 the use of only a finite number of SNPs 2 causal loci tend to have lower MAF than the genotyped SNPs input 0 if you assume that the causal loci have similar distribution of allele frequencies as the genotyped SNPs see Yang et al 2010 Nat Genet for details dc 1 11 By default the GRM especially for the X chromosome is parameterized under the assumption of equal variance for males and females unless the option dc is specified 1 and 0 for full and no dosage compensat
8. 0210 0 00198 0 00229 012 0102 1000 0 0033 0 0029 0 0031 013 0103 988 0 00120 0 00118 0 00134 Examples Estimate the GRM from all the autosomal SNPs gcta64 bfile test autosome make grm out test Estimate the GRM from the SNPs on the X chromosome gcta64 bfile test make grm xchr out test_xchr Estimate the GRM from the SNPs on chromosome 1 with MAF from 0 1 to 0 4 gcta64 bfile test chr 1 maf 0 1 max maf 0 4 make grm out test Estimate the GRM using a subset of individuals and a subset of autosomal SNPs with MAF lt 0 01 gcta64 bfile test keep test indi list extract test snp list autosome maf 0 01 make grm out test Estimate the GRM from the imputed dosage scores for the SNPs with MAF gt 0 01 and imputation R gt 0 3 gcta64 dosage mach test mldose gz test mlinfo gz imput rsq 0 3 maf 0 01 make grm out test Estimate the GRM from the imputed dosage scores for a subset of individuals and a subset of SNPs gcta64 dosage mach test mldose gz test mlinfo gz keep test indi list extract test snp list make grm out test Estimate the inbreeding coefficient from all the autosomal SNPs gcta64 bfile test autosome ibc out test 4 Manipulation of the genetic relationship matrix grm_ test or grm bin test Input the GRM generated by make grm option This option actually tells GCTA to read three files e g test grm bin test grm N bin and test grm id See the option
9. AM Content HumanOmni1 Quad_v1 0_B bpm Num SNPs 1140419 TotalSNPs 1140419 Num Samples 1000 Total Samples 1000 File 62 of 1000 Data SNP Name Sample ID Sample Group GC Score Allele1 Forward Allele2 Forward Allele1 Top Allele2 Top Allele1 Design Allele2 Design Allele1 AB Allele2 AB Theta R X Y XRaw YRaw BAllele Freq Log R Ratio 200006 000001 000001 0 8203T T A A A A A A 0 018 1 901 1 848 0 053 19622 2436 0 0000 0 2777 200052 000002 000001 0 8789T T T T A A B B 0 958 0 881 0 054 0 827 2667 19381 0 9767 0 0438 200053 000003 000002 0 6387T T A A T T A A 0 105 1 396 1 196 0 200 12889 5067 0 0000 0 0175 200070 000004 000002 0 9221G C C G G A B 0 603 0 545 0 228 0 317 2767 3402 0 5133 0 0125 200078 000005 000002 0 67799C C G G 6G G B B 0 973 2 048 0 084 1 964 3114 37363 1 0000 0 0710 22 Allele1 Top and Allele2 Top are taken as the genotypes for the SNPs raw summary SNP_summary_table txt Input a file providing the summary information of the SNPs one row per SNP The headers are necessary but they are not keywords and will be ignored by the program Note the program actually only read the first four columns of this file Index Name Chr Position ChiTest100 Het Excess AA Freq AB Freq BB Freq Call Freq Minor Freq Aux P CErrors P P C Errors Rep Errors 10 GC 50 GC SNP Calls no calls Plus Minus Strand HumanOmni1 Quad_v1 0_B bpm Address HumanOmnil Quad_v1 0_B bpm GenTrain Score HumanOmni1 Quad_v1 0_B bpm Or
10. GCTA a tool for Genome wide Complex Trait Analysis Version 1 24 28 July 2014 Overview GCTA Genome wide Complex Trait Analysis was originally designed to estimate the proportion of phenotypic variance explained by genome or chromosome wide SNPs for complex traits the GREML method and has subsequently extended for many other analyses to better understand the genetic architecture of complex traits GCTA was developed by Jian Yang Hong Lee Mike Goddard and Peter Visscher and is maintained in Peter Visscher s lab at the University of Queensland GCTA currently supports the following functionalities Estimate the genetic relationship from genome wide SNPs Estimate the inbreeding coefficient from genome wide SNPs Estimate the variance explained by all the autosomal SNPs Partition the genetic variance onto individual chromosomes Estimate the genetic variance associated with the X chromosome Test the effect of dosage compensation on genetic variance on the X chromosome Predict the genome wide additive genetic effects for individual subjects and for individual SNPs Estimate the LD structure encompassing a list of target SNPs Simulate GWAS data based upon the observed genotype data Convert Illumina raw genotype data into PLINK format Conditional amp joint analysis of GWAS summary statistics without individual level genotype data Estimating the genetic correlation between two trait
11. ce rates of the two diseases in the general population so that GCTA will transform the estimate of variance explained by the SNPs from the observed 0 1 scale to that on the underlying scale for both diseases Examples With residual covariance gcta64 reml bivar grm test pheno test phen out test Without residual covariance gcta64 reml bivar reml bivar nocove grm test pheno test phen out test To test for genetic correlation 0 or 1 gcta64 reml bivar reml bivar nocove grm test pheno test phen reml bivar Irt rg O out test gcta64 reml bivar reml bivar nocove grm test pheno test phen reml bivar Irt rg 1 out test Case control data for two diseases the residual covariance will be automatically dropped from the model if there are not too many samples affected by both diseases gcta64 reml bivar grm test_CC pheno test_CC phen reml bivar prevalence 0 1 0 05 out test_CC Output file format test hsq rows are header line genetic variance for trait 1 estimate and standard error SE genetic variance for trait 2 estimate and SE genetic covariance between traits 1 and 2 estimate and SE residual variance for trait 1 estimate and SE residual variance for trait 2 estimate and SE residual covariance between traits 1 and 2 estimate and SE proportion of variance explained by all SNPs for trait 1 estimate and SE proportion of variance explained by all SNPs for trait 2 estima
12. d test map 10 GCTA COJO conditional and joint genome wide association analysis cojo file test ma Input the summary level statistics from a meta analysis GWAS or a single GWAS Input file format test ma SNP A1 A2 freq b se p N rs1001 A G 0 8493 0 0024 0 0055 0 6653 129850 rs1002 C G 0 0306 0 0034 0 0115 0 7659 129799 rs1003 A C 0 5128 0 0045 0 0038 0 2319 129830 23 Columns are SNP the effect allele the other allele frequency of the effect allele effect size standard error p value and sample size The headers are not keywords and will be omitted by the program Important A1 must be the effect allele with A2 being the other allele and freq should be the frequency of A1 NOTE 1 For a case control study the effect size should be log odds ratio with its corresponding standard error 2 Please always input the summary statistics of all the SNPs even if your analysis only focuses on a subset of SNPs because the program needs the summary data of all SNPs to calculate the phenotypic variance cojo sict Perform a stepwise model selection procedure to select independently associated SNPs Results will be saved in a jma file with additional file jma ldr showing the LD correlations between the SNPs cojo joint Fit all the included SNPs to estimate their joint effects without model selection Results will be saved in a jma file with additional file jma ldr showing the LD correlations between
13. ditional analyses 11 GCTA Bivariate GREML analysis These options are designed to perform a bivariate REML analysis of two quantitative traits continuous from population based studies two disease traits binary from case control studies or one quantitative trait and one binary disease trait to estimate the genetic variance of each trait and that genetic covariance between two traits that can be captured by all SNPs reml bivar 1 2 By default GCTA will take the first two traits in the phenotype file for analysis The phenotype file is specified by the option pheno as described in univariate REML analysis All the options for univariate REML analysis are still valid here except mpheno gxe prevalence reml Irt reml no Irt and blup snp All the input files are in the same format as in univariate REML analysis reml bivar nocove By default GCTA will model the residual covariance between two traits However if the traits were measured on different individuals e g two diseases the residual covariance will be automatically dropped from the model You could also specify this option to exclude the residual covariance at all time reml bivar Irt rg 0 To test for the hypothesis of fixing the genetic correlation at a particular value e g fixing genetic correlation at 1 O and 1 27 reml bivar prevalence 0 1 0 05 For a bivariate analysis of two disease traits you can specify the prevalen
14. e equal variances of all the components as the starting values if this option is not specified reml alg 0 Specify the algorithm to do REML iterations 0 for average information Al 1 for Fisher scoring and 2 for EM The default option is 0 i e AI REML if this option is not specified reml no constrain 13 By default if an estimate of variance component escapes from the parameter space i e negative value it will be set to be a small positive value that is Vp x10 with Vp being the phenotypic variance If the estimate keeps on escaping from the parameter space the estimate will be constrained to be Vp x10 If the option reml no constrain is specified the program will allow an estimate of variance component to be negative which may result in the estimate of proportion variance explained by all the SNPs gt 1 reml maxit 100 Specify the maximum number of iterations The default number is 100 if this option is not specified pheno test phen Input phenotype data from a plain text file e g test phen If the phenotypic value is coded as 0 or 1 then it will be recognized as a case control study 0 for controls and 1 for cases Missing value should be represented by 9 or NA Input file format test phen no header line columns are family ID individual ID and phenotypes 011 0101 0 98 012 0102 0 76 013 0103 0 06 mpheno 2 If the phenotype file contains more than one trait by default GCTA takes
15. e g reml Irt 2 assuming there are a least two genetic variance components included in the analysis You can also test multiple components simultaneously e g reml Irt 1 2 4 See FAQ 1 for more details reml no Irt Turn off the LRT prevalence 0 01 Specify the disease prevalence for a case control study Once this option is specified GCTA will transform the estimate of variance explained V 1 Vp on the observed scale to that on the underlying scale V 1 Vp_L The prevalence should be estimated from a general population in literatures rather than that estimated from the sample NOTE 1 You do not have to have exactly the same individuals in these files GTCA will find the individuals in common in the files and sort the order of the individuals 2 Please be aware that if the GRM is estimated from the imputed SNPs either best guess or dosage score the estimate of variance explained by the SNPs will depend on the imputation R cutoff used to select SNPs because the imputation R is correlated with MAF so that selection on imputation R will affect the MAF spectrum and thus affect the estimate of variance explained by the SNPs 16 3 For a case control study the phenotypic values of cases and controls should be specified as 1 and 0 or 2 and 1 compatible with PLINK respectively 4 Any missing value either phenotype or covariate should be represented by 9 or NA 5 The summary resu
16. e test simu cc 500 500 simu causal loci causal snplist simu hsq 0 5 simu k 0 1 simu rep 3 out test Output file format test par one header line columns are the name of the causal variant reference allele frequency of the reference allele effect size QTL RefAllele Frequency Effect rs13626255 C 0 136 0 0837 rs779725 G 0 204 0 0677 21 test phen no header line columns are family ID individual ID and the simulated phenotypes For the simulation of a case control study all the individuals involved in the simulation will be outputted in the file and the phenotypes for the indivdiuals neither sampled as cases nor as controls are treated as missing i e 9 011 0101 dL 48 al 012 0102 2 2 013 0103 ab aly al 9 Converting illumina raw genotype data into PLINK PED format We provide a function to convert the raw genotype data text files generated by GenomeStudio software into PLINK PED format NOTE this option is under developing Please contact to us if you have any suggestion raw files raw_geno_filenames txt Input a file which lists the filenames of the raw genotype data files one data file per individual Input file format raw_geno_filenames txt full paths can be specified if the raw genotype data files are in different directories raw_geno_file1 raw_geno_file2 raw_geno_file1000 The format of the raw genotype data looks like Header GSGT Version 1 6 3 Processing Date 7 7 2010 9 35
17. e with each row corresponding to each target SNP The columns are target SNP length of LD block two flanking SNPs of the LD block total number of SNPs within the LD block 19 mean r median r maximum 7 SNP in highest LD with the target SNP 2 test r Id the correlations r between the target SNP and all the SNPs in the LD block 3 test snp Id the names of all the SNPs in the LD with the target SNP Note LD block is defined as a region where SNPs outside this region are not in significant LD with the target SNP According to this definition the length of LD block depends on user specified window size and significance level 8 GCTA Simu simulating a GWAS based on real genotype data The phenotypes are simulated based on a set of real genotype data and a simple additive genetic model y 2 wj u j where wj x 2p sqrt 2p 1 p with x being the number of reference alleles for the i th causal variant of the j th individual and p being the allele frequency of the i th causal variant u is the allelic effect of the i th causal variant and amp j is the residual effect generated from a normal distribution with mean of 0 and variance of va 2 wj u 1 1 h For a case control study under the assumption of threshold model cases are sampled from the individuals with disease liabilities y exceeding the threshold of normal distribution truncating the proportion of K disease prevalence and controls are sampled f
18. header line columns are family ID individual ID an intermediate variable the total genetic effect another intermediate variable and the residual effect If there are multiple GRMs fitted in the model each GRM will insert additional two columns i e an intermediate variable and a total genetic effect in front of the last two columns 01 0101 0 012 0 014 0 010 0 035 02 0203 0 021 0 031 0 027 0 031 03 0305 0 097 0 102 0 026 0 041 blup snp_ test indi blp Calculate the BLUP solutions for the SNP effects you have to specify the option bfile to read the genotype data This option takes the output of the option reml pred rand as input indi blp file and transforms the BLUP solutions for individuals to the BLUP solutions for the SNPs which can subsequently be used to predict the total genetic effect of individuals in an independent sample by PLINK score option Output file format test snp blp columns are SNP ID reference allele and BLUP of SNP effect if there are multiple GRMs fitted in the model each GRM will add an additional column to the file rs103645 A 0 00312 rs175292 G 0 00021 Examples Without GRM fitting the model under the null hypothesis that the additive genetic variance is zero gcta64 reml pheno test phen out test_null gcta64 reml pheno test phen keep test indi list out test_null One GRM quantitative traits gcta64 reml grm test pheno test phen reml pred rand qco
19. ig Score HumanOmnii Quad_v1 0_B bpm Edited HumanOmni1 Quad_v1 0_B bpm Cluster Sep HumanOmnii Quad_v1 0_B bpm AAT Mean HumanOmnil Quad_v1 0_B bpm AAT Dev HumanOmnii Quad_v1 0_B bpm AB T Mean HumanOmnii1 Quad_v1 0_B bpm AB T Dev HumanOmnii1 Quad_v1 0_B bpm BB T Mean HumanOmnii1 Quad_v1 0_B bpm BB T Dev HumanOmnii Quad_v1 0_B bpm AA R Mean HumanOmnii1 Quad_v1 0_B bpm AA R Dev HumanOmnii1 Quad_v1 0_B bpm AB R Mean HumanOmnit1 Quad_v1 0_B bpm AB R Dev HumanOmni1 Quad_v1 0_B bpm BB R Mean HumanOmnii Quad_v1 0_B bpm BB R Dev HumanOmnii1 Quad_v1 0_B bpm Address2 HumanOmnil Quad_v1 0_B bpm Norm ID 1 2000069 139046223 0 6913772 0 03969868 0 124057 0 4819782 0 3939648 1 0 3650461 0 0 0 0O 0 8203169 0 8203169 A G 1193 0 60702346 0 8030853 0 8030853 0 1 0 02950359 0 009121547 0 4321907 0 01578533 0 9878551 0 005570452 2 313316 0 2726709 2 638608 0 3402262 1 769039 0 1879732 0 3 2 200052 2 219783037 0 9122009 0 01102628 0 00 0 02181208 0 9781879 0 9991618 0 01090604 0 0 0 0 0 8789128 0 8789128 T A 1192 1 37712495 0 8901258 0 8901258 0 0 7359893 0 02316774 0 02236068 0 4633549 0 03744823 0 9825876 0 009741872 1 041702 0 1 1 228919 0 1265495 0 8926759 0 1 35794467 201 gencall 0 7 Specify a cutoff value of GenCall score The default value is 0 7 if this option is not specified Example gcta64 raw files raw_geno_filenames txt raw summary SNP_summary_table txt out test The data will be saved in two files in PLINK PED format i e test ped an
20. ion respectively You need to use the option update sex to read sex information of the individuals from a file see the update sex option above NOTE you can add the option make grm or make grm gz afterwards to save the modified GRM You can also use the option keep and or remove in combination with these five commands It is also possible to use these five commands in the REML analysis see the section below Examples Prune the GRM by a cutoff of 0 025 and adjust for prediction errors assuming the causal variants have similar distribution of allele frequencies as the genotyped SNPs gcta64 grm test grm adj O grm cutoff 0 025 make grm out test_adj Use keep or remove option gcta64 grm test keep test indi list grm cutoff 0 025 make grm out test_adj gcta64 grm test remove test indi list grm adj O make grm out test_adj Assume full and no dosage compensation for the X chromosome gcta64 grm test_xchr dosage compen 1 update sex test indi sex list make grm out test_xchr_fdc gcta64 grm test_xchr dosage compen 0 update sex test indi sex list make grm out test_xchr_ndc 5 Principal component analysis pca 20 Input the GRM and output the first n n 20 by default eigenvalues saved as eigenval plain text file and eigenvectors saved as eigenvec plain text file which are equivalent to those calcuated by the progrom EIGENSTRAT The only purpose of this optio
21. ith minor allele frequency MAF less than a specified value e g 0 01 max maf 0 1 Include SNPs with MAF less than a specified value e g 0 1 update sex test indi sex list Update sex information of the individuals from a file Input file format test indi sex list no header line columns are family ID individual ID and sex Sex coding 1 or M for male and 2 or F for female 011 0101 1 012 0102 2 013 0103 1 update ref allele test_reference_allele txt Assign a list of alleles to be the reference alleles for the SNPs included in the analysis By default the first allele listed in the bim file the 5 coloumn or mlinfo gz file the a conlumn is assigned to be the reference allele NOTE This option is invalid for the imputed dosage data only Input file format test_reference_allele txt no header line columns are SNP ID and reference allele rs103645 A rs175292 G imput rsq 0 3 Include SNPs with imputation R squared correlation between imputed and true genotypes larger than a specified value e g 0 3 update imput rsq test imput rsq Update imputation R from a file For the imputed dosage data you do not have to use this option because GCTA can read the imputation R from the mlinfo gz file unless you want to write them For the best guess data usually in PLINK format if you want to use a R cut off to filter SNPs you need to use this option to read the imputatio
22. lt of REML analysis will be saved in a plain text file hsq Output file format test hsq rows are header line name of genetic variance estimate and standard error SE residual variance estimate and SE phenotypic variance estimate and SE ratio of genetic variance to phenotypic variance estimate and SE log likelihood sample size If there are multiple GRMs included in the REML analysis there will be multiple rows for the genetic variance as well as their ratios to phenotypic variance with the names of V 1 V 2 Source Variance SE V 1 0 389350 0 161719 V e 0 582633 0 160044 Vp 0 971984 0 031341 V 1 Vp 0 400573 0 164937 The estimate of variance explained on the observed scale is transformed to that on the underlying scale Proportion of cases in the sample 0 5 User specified disease prevalence 0 1 V 1 Vp_L 0 657621 0 189123 logL 945 65 logLO 940 12 LRT 11 06 Pval 4 41e 4 n 2000 reml est fix Output the estimates of fixed effects on the screen reml pred rand Predict the random effects by the BLUP best linear unbiased prediction method This option is actually to predict the total genetic effect called breeding value in animal 17 genetics of each individual attributed by the aggregative effect of the SNPs used to estimate the GRM The total genetic effects of all the individuals will be saved in a plain ext file indi blp Output file format test indi blp no
23. make grm GCTA automatically adds suffix grm bin grm N bin or grm id to the specified root filename If the test grm N bin file which contains the number of SNPs used to calculate GRM is missing the program will still be running because all the analysis except grm do not actually need the the number of SNPs used to calculate the GRM grm gz test To be compatible with the previous version of GCTA Same as grm but read the GRM files in compressed text format generated by make grm gz option This option actually tells GCTA to read two files e g test grm gz and test grm id See the option make grm gz GCTA automatically adds suffix grm gz and grm id to the specified root filename Examples converting the two formats from each other From grm gz to grm bin gcta64 grm gz test make grm out test From grm bin to grm gz gcta64 grm test make grm gz out test mgrm multi_grm txt or mgrm bin multi_grm txt Input multiple GRMs in binary format See the option make grm The root filenames of multiple GRMs are given in a file e g multi_grm txt Input file format multi_grm txt full paths can be specified if the GRM files are in different directories 10 test_chr1 test_chr2 test_chr3 test_chr22 mgrm gz multi_grm txt To be compatible with the previous version of GCTA Same as mgrm but read the GRM files in compressed text format generated by
24. n R values from the specified file Input file format test imput rsq no header line columns are SNP ID and imputation R rs103645 0 976 rs175292 1 000 freq Output allele frequencies of the SNPs included in the analysis in plain text format e g Output file format test freq no header line columns are SNP ID reference allele and its frequency rs103645 A 0 312 rs175292 G 0 602 update freq test freq Update allele frequencies of the SNPs from a file rather than calculating from the data The format of the input file is the same as the output format for the option freq recode Output the SNP genotypes in additive coding in compressed text format e g test xmat gz recode nomiss Output the SNP genotypes in additive coding and fill the missing genotype by its expected value i e 2p where p is the frequency of the reference allele Output file format test xmat gz The first two lines are header lines The first line contains headers of family ID individual ID and names of SNPs The second line contains two nonsense words Reference Allele and the reference alleles of the SNPs Any missing genotype is represented by NA unless the option recode nomiss is specified for which the missing genotype will be assigned by 2p FID IID rs103645 rs175292 Reference Allele A G 011 0101 1 0 012 0102 2 NA 013 0103 0 1 make bed Save the genotype data in PLINK binary PED files fam bim and
25. n is to calcuate the first m eigenvectors and subsquently include them as covariates in the model when estimating the variance explained by all the SNPs see below for the option of estimating the variance explained by genome wide SNPs Please find the EIGENSTRAT software if you need more sophisticated principal component analysis of the population structure Output file format test eigenval no header line the first m eigenvalues 12 20 436 7 1293 6 7267 test eigenvec no header line the first m eigenvectors columns are family ID individual ID and the first m eigenvectors 011 0101 0 00466824 0 000947 0 00467529 0 00923534 012 0102 0 00139304 0 00686406 0 0129945 0 00681755 013 0103 0 00457615 0 00287646 0 00420995 0 0169046 Examples Input the GRM file and output the first 20 eigenvectors for a subset of individuals gcta64 grm test keep test indi list pca 20 out test 6 GCTA GERML estimating of the phenotypic variance explained by the SNPs reml Perform REML restricted maximum likelihood analysis This option is usually followed by the option grm one GRM or mgrm multiple GRMs to estimate the variance explained by the SNPs that were used to estimate the genetic relationship matrix reml priors 0 45 0 55 Specify the starting values for REML iterations The number of starting values specified should NOT be less than the number of variance components in the model By default GCTA will us
26. nelis MC Weir BS Goddard ME Visscher PM Genome partitioning of genetic variation for complex traits using common SNPs Nat Genet 2011 Jun 43 6 519 525 PubMed ID 21552263 Method for conditional and joint analysis using summary statistics from GWAS with its application to the GIANT meta analysis data for height and BMI Yang J Ferreira T Morris AP Medland SE Genetic Investigation of ANthropometric Traits GIANT Consortium DlAbetes Genetics Replication And Meta analysis DIAGRAM Consortium Madden PA Heath AC Martin NG Montgomery GW Weedon MN Loos RJ Frayling TM McCarthy MI Hirschhorn JN Goddard ME Visscher PM 2012 Conditional and joint multiple SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits Nat Genet Mar 18 44 4 369 375 PubMed ID 22426310 Bivariate GREML method Lee SH Yang J Goddard ME Visscher PM Wray NR 2012 Estimation of pleiotropy between complex diseases using SNP derived genomic relationships and restricted maximum likelihood Bioinformatics 2012 Oct 28 19 2540 2542 PubMed ID 22843982 Mixed linear model based associaiton analysis Yang J Zaitlen NA Goddard ME Visscher PM and Price AL 2013 Mixed model association methods advantages and pitfalls Nat Genet 2014 Feb 46 2 100 6 Pubmed ID 24473328 OPTIONS case sensitive 1 Input and output bfile test Input PLINK binary PED files e g test fam test bim and test bed
27. r Each discrete covariate is recognized as a categorical factor with several levels The levels of each factor can be represented by a single character word or numerical number NOTE the design matrix of the mean in the model which is a vector of all ones is always a linear combination of the design matrix of a discrete covariate so that not all the effects of the levels or classes e g male and female of a discrete covariate are estimable GCTA will always constrain the effect of the first level to be zero and the effect of any other level represents its difference in effect compared to the first level Input file format test covar no header line columns are family ID individual ID and discrete covariates 01 0101 F Adult 0 02 0203 M Adult 0 03 0305 F Adolescent 1 qcovar test qcovar Input quantitative covariates from a plain text file e g test qcovar Each quantitative covariate is recognized as a continuous variable 15 Input file format test qcovar no header line columns are family ID individual ID and quantitative covariates 01 0101 0 024 0 012 02 0203 0 032 0 106 03 0305 0 143 0 056 reml Irt 1 Calculate the log likelihood of a reduce model with one or multiple genetic variance components dropped from the full model and calculate the LRT and p value By default GCTA will always calculate and report the LRT for the first genetic variance component i e reml Irt 1 unless you re specify this option
28. rom the remaining individuals simu qt Simulate a quantitative trait simu cc 100 200 Simulate a case control study Specify the number of cases and the number of controls e g 100 cases and 200 controls Since the simulation is based on the actual genotype data the maximum numbers of cases and controls are restricted to be n K and n 1 K respectively where n is the sample size of the genotype data simu causal loci causal snplist Assign a list of SNPs as causal variants If the effect sizes are not specified in the file they will be generated from a standard normal distribution 20 Input file format causal snplist columns are SNP ID and effect size rs113645 0 025 rs185292 0 021 simu hsq 0 8 Specify the heritability or heritability of liability e g 0 8 The default value is 0 1 if this option is not specified simu k 0 01 Specify the disease prevalence e g 0 01 The default value is 0 1 if this option is not specified simu rep 100 Number of simulation replicates The default value is 1 if this option is not specified Examples Simulate a quantitative trait with the heritability of 0 5 for a subset of individuals for 3 times gcta64 bfile test simu qt simu causal loci causal snplist simu hsq 0 5 simu rep 3 keep test indi list out test Simulate 500 cases and 500 controls with the heritability of liability of 0 5 and disease prevalence of 0 1 for 3 times gcta64 bfil
29. s diseases using SNP data Mixed linear model association analysis Questions and Help Requests If you have any bug reports or questions please send an email to Jian Yang at jian yang uq edu au Citations Software tool Yang J Lee SH Goddard ME and Visscher PM GCTA a tool for Genome wide Complex Trait Analysis Am J Hum Genet 2011 Jan 88 1 76 82 PubMed ID 21167468 Method for estimating the variance explained by all SNPs GREML method with its application in human height Yang J Benyamin B McEvoy BP Gordon S Henders AK Nyholt DR Madden PA Heath AC Martin NG Montgomery GW Goddard ME Visscher PM Common SNPs explain a large proportion of the heritability for human height Nat Genet 2010 Jul 42 7 565 9 PubMed ID 20562875 GREML method being extended for case control design with its application to the WTCCC data Lee SH Wray NR Goddard ME and Visscher PM Estimating Missing Heritability for Disease from Genome wide Association Studies Am J Hum Genet 2011 Mar 88 3 294 305 PubMed ID 21376301 GREML method being extended for partitioning the genetic variance into the components of chromosomes and genomic segments with its applications in height BMI vWF and QT interval Yang J Manolio TA Pasquale LR Boerwinkle E Caporaso N Cunningham JM de Andrade M Feenstra B Feingold E Hayes MG Hill WG Landi MT Alonso A Lettre G Lin P Ling H Lowe W Mathias RA Melbye M Pugh E Cor
30. software tools such as EMMAX FaST LMM and GEMMA The results will be saved in the mlma file mlma loco 29 This option will implement an MLM based association analysis with the chromosome on which the candidate SNP is located excluded from calculating the GRM We call it MLM leaving one chromosome out LOCO analysis The model is y atbx gte where g is the accumulated effect of all SNPs except those on the chromosome where the candidate SNP is located The var g will be re estimated each time when a chromosome is excluded from calculating the GRM The MLM LOCO analysis is computationally less efficient but more powerful as compared with the MLM analysis including the candidate mima The results will be saved in the loco mlma file mlma no adj covar If there are covariates included in the analysis the covariates will be fitted in the null model a model including the mean term fixed effect covariates fixed effects polygenic effects random effects and residuals random effects By default in order to improve computational efficiency the phenotype will be adjusted by the mean and covariates and the adjusted phenotype will subsequently be used for testing SNP association However if SNPs are correlated with the covariates pre adjusting the phenotype by the covariates will probably cause loss of power If this option is specified the covariates will be fitted together with the SNP for association test However
31. te and SE genetic correlation sample size Source Variance SE V G _tr1 0 479647 0 179078 V G _tr2 0 286330 0 181329 C G _tr12 0 230828 0 147958 28 V e _tr1 0 524264 0 176650 V e _tr2 0 734654 0 181146 C e _tr12 0 404298 0 146863 Vp_tr1 1 003911 0 033202 Vp_tr2 1 020984 0 033800 V G Vp_tr1 0 477779 0 176457 V G Vp_tr2 0 280445 0 176928 rG 0 622864 0 217458 n 3669 12 GCTA MLMA mixed linear model based association analysis The following options are designed to perform a MLM based association analysis Previous data management options such as keep extract and maf REML analysis options such as reml priors reml maxit and reml no constrain and multi threading option thread num are still valid for this analysis mlma This option will initiate an MLM based association analysis including the candidate SNP y atbx gte where y is the phenotype a is the mean term b is the additive effect fixed effect of the candidate SNP to be tested for association x is the SNP genotype indicator variable coded as 0 1 or 2 g is the polygenic effect random effect i e the accumulated effect of all SNPs as captured by the GRM calculated using all SNPs and e is the residual For the ease of computation the genetic variance var g is estimated based on the null modeli e y a g e and then fixed while testing for the association between each SNP and the trait This analysis would be similar as that implemented in other
32. the discovery set as the reference sample In this case the analysis will be equivalent to a multiple regression analysis with the actual genotype and phenotype data Once this option is specified GCTA will take all pairwise LD correlations between all SNPs into account which overrides the massoc wind option This option also allows GCTA to calculate the variance taken out from the residual variance by all the significant SNPs in the model otherwise the residual variance will be fixed constant at the same level of the phenotypic variance Examples Individual level genotype data of the discovery set is NOT available Robust and recommended Select multiple associated SNPs through a stepwise selection procedure gcta64 bfile test chr 1 maf 0 01 cojo file test ma cojo slct out test_chr1 Estimate the joint effects of a subset of SNPs given in the file test snplist without model selection gcta64 bfile test chr 1 extract test snplist cojo file test ma cojo joint out test_chr1 25 Perform single SNP association analyses conditional on a set of SNPs given in the file cond snplist without model selection gcta64 bfile test chr 1 maf 0 01 cojo file test ma cojo cond cond snplist out test_chr1 It should be more efficient to separate the analysis onto individual chromosomes or even some particular genomic regions Please refer to the Data management section for some other options e g including or e
33. the first trait for analysis the third column of the file unless this option is specified For example mpheno 2 tells GCTA to take the second trait for analysis the fourth column of the file gxe test gxe Input an environmental factor from a plain text file e g test gxe Apart from estimating the genetic variance this command tells GCTA to estimate the variance of genotype environment GE interaction You can fit multiple environmental factors simultaneously The main effects of an environmental factor will be included in the model as fixed effects 14 and the GE interaction effects will be treated as random effects NOTE the design matrix of the overall mean in the model which is a vector of all ones is always a linear combination of the design matrix of a discrete environmental factor so that not all the main effects fixed effects are estimable GCTA will always constrain the main effect of the first level to be zero and the main effect of any other level represents its difference in effect compared to the first level For example if you fit sex as an environmental factor GCTA will fit only one main effect in the model i e the mean difference between males and females Input file format test gxe no header line columns are family ID individual ID and environmental factors 01 0101 F smoker 02 0203 M nonsmoker 03 0305 F smoker covar test covar Input discrete covariates from a plain text file e g test cova
34. var test_10PCs txt out test gcta64 reml grm test pheno test phen grm adj O grm cutoff 0 05 out test gcta64 reml grm test pheno test phen keep test indi list grm adj O out test One GRM case control studies 18 gcta64 reml grm test pheno test_cc phen prevalence 0 01 out test_cc gcta64 reml grm test pheno test_cc phen prevalence 0 01 qcovar test_10PCs txt out test_cc GxE interaction LRT test for the significance of GxE gcta64 reml grm test pheno test phen gxe test gxe reml Irt 2 out test Multiple GRMs gcta64 reml mgrm multi_grm txt pheno test phen reml no Irt out test_mgrm gcta64 reml mgrm multi_grm txt pheno test phen keep test indi list reml no Irt out test_mgrm BLUP solutions for the SNP effects gcta64 bfile test blup snp test indi blp out test 7 Estimation of the LD structure in the genomic regions specified by a list of SNPs For each target SNP GCTA uses simple regression to search for SNPs that are in significant LD with the target SNP ld_ Id snplist Specify a list of SNPs l d wind 5000 Search for SNPs in LD with a target SNP within d Kb e g 5000 Kb region in either direction by simple regression test ld sig 0 05 Threshold p value for regression test e g 0 05 Example gcta64 bfile test ld Id snplist ld wind 5000 Id sig 0 05 out test Output files 1 test rsq Id summary of LD structur
35. xcluding a list of SNPs and individuals or filtering SNPs based on the imputation quality score Examples Individual level genotype data of the discovery set is available Select multiple associated SNPs through a stepwise selection procedure gcta64 bfile test maf 0 01 cojo file test ma cojo slct cojo actual geno out test In this case it is recommended to perform the analysis using the data of all the genome wide SNPs rather than separate the analysis onto individual chromosomes because GCTA needs to calculate the variance taken out from the residual variance by all the significant SNPs in the model which could give you a bit more power Estimate the joint effects of a subset of SNPs given in the file test snplist without model selection gcta64 bfile test extract test snplist cojo file test ma cojo actual geno cojo joint out test Perform single SNP association analyses conditional on a set of SNPs given in the file cond snplist without model selection gcta64 bfile test maf 0 01 cojo file test ma cojo actual geno cojo cond cond snplist out test Output file format test jma generate by the option cojo slct or cojo joint Chr SNP bp freq refA b se p on freq_geno bJ bJ se pJ LD r 1 rs2001 172585028 0 6105 A 0 0377 0 0042 6 38e 19 121056 0 614 0 0379 0 0042 1 74e 19 0 345 1 rs2002 174763990 0 4294 C 0 0287 0 0041 3 65e 12 124061 0 418 0 0289 0 0041 1 58e 12 0 012 1 rs2003 196696685 0 5863 T

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