# Configuration Files¶

Two default configuration files are available to run the proposed protocol. Before using them, be sure to follow the Preprocessing Steps described in the Proposed Protocol section.

Note that lines starting with a # are comments, and are not used by the pipeline. The default parameters were commented out, and could be uncommented to change their values.

## First Configuration File¶

This file should be use with the original dataset as input. Only change the loop-assoc file name in the plate_bias section ([8]) and the reference population files (ceu-bfile, yri-bfile and jpt-chb-bfile in the check_ethnicity section ([10]). Those last three datasets are provided and can be downloaded at http://www.statgen.org.

If you want to generate the gender and BAF and LRR plots, you will require to provide the intensities (sex-chr-intensities and lrr-baf-raw-dir in the sex_check section ([7]) after uncommenting the required options).

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 # This is the first part of example configuration files for performing efficient # data clean up. All commented out parameters are those that are used by # default. [1] # ############################################################################## # Checks sample contamination using the bafRegress tool # (http://genome.sph.umich.edu/wiki/BAFRegress). Field name can be modify using # options (as describe below). # ############################################################################## script = contamination raw-dir = /PATH/TO/DIRECTORY/CONTAINING/INTENSITIES.txt # colsample = Sample Name # colmarker = SNP Name # colbaf = B Allele Freq # colab1 = Allele1 - AB # colab2 = Allele2 - AB # sge # sge-walltime = WRITE WALLTIME ONLY IF REQUIRED # sge-nodes = WRITE NB NODES AND NB PROCESSOR PER NODE ONLY IF REQUIRED # sample-per-run-for-sge = 30 [2] # ############################################################################## # Checks missing rate and pairwise concordance of duplicated samples. Duplicated # samples should have same family and individual identification numbers. The # names can be modified directly in the transposed pedfile. # ############################################################################## script = duplicated_samples # sample-completion-threshold = 0.9 # sample-concordance-threshold = 0.97 [3] # ############################################################################## # Checks missing rate and pairwise concordance of duplicated markers. Duplicated # markers are found by looking at their chromosomal position. No modification of # the transposed bedfile is required. # ############################################################################## script = duplicated_snps # snp-completion-threshold = 0.9 # snp-concordance-threshold = 0.98 # frequency_difference = 0.05 [4] # ############################################################################## # Finds and removes markers which have a missing rate of 100% or markers (not # located on mitochondrial chromosome) that have a heterozygosity rate of 0%. # ############################################################################## script = noCall_hetero_snps [5] # ############################################################################## # Removes sample with a missing rate higher than a user defined threshold. For # this step, we recommend using a threshold of 10% missing rate as samples with # a missing rate of 2% will be later removed. # ############################################################################## script = sample_missingness # mind = 0.1 [6] # ############################################################################## # Removes markers with a missing rate higher than a user defined threshold. For # this step, we recommend using a threshold of 2% missing rate. # ############################################################################## script = snp_missingness # geno = 0.02 [7] # ############################################################################## # Removes sample with a missing rate higher than a user defined threshold. For # this step, we recommend using a threshold of 2% missing rate. # ############################################################################## script = sample_missingness mind = 0.02 [8] # ############################################################################## # Using PLINK, finds samples with gender issues, according to heterozygosity # rate on the X chromosome. If you want to produce a gender plot, you need to # uncomment the "gender-plot" option and provide a file containing marker # intensities on the X and Y chromosomes. If you want to produce a BAF and LRR # plot, you need to uncomment the "lrr-baf" option and provide a directory # containing the BAF and LRR values of each marker on the X and Y chromosomes # (one file per sample). # ############################################################################## script = sex_check # femaleF = 0.3 # maleF = 0.7 # nbChr23 = 50 # gender-plot # sex-chr-intensities = /PATH/TO/FILE/CONTAINING/INTENSITIES_FILE.txt # gender-plot-format = png # lrr-baf # lrr-baf-raw-dir = /PATH/TO/DIRECTORY/CONTAINING/BAF_LRR_FILES.txt # lrr-baf-format = png # lrr-baf-dpi = 300 [9] # ############################################################################## # Using PLINK, performs a plate bias analysis, using a p value threshold of # 1.0e-7. # ############################################################################## script = plate_bias loop-assoc = /PATH/TO/FILE/CONTAINING/PLATE_INFORMATION.txt # pfilter = 1.0e-07 [10] # ############################################################################## # Checks for related individual and randomly keeps one of each related group. If # you have a server with a DRMAA-compliant distributed resource management # system, you can uncomment the "sge" and the "line-per-file-for-sge" options, # to run this step in parallel. # ############################################################################## script = find_related_samples # min-nb-snp = 10000 # indep-pairwise = 50 5 0.1 # maf = 0.05 # ibs2-ratio = 0.8 # sge # line-per-file-for-sge = 100 # sge-walltime = WRITE WALLTIME ONLY IF REQUIRED # sge-nodes = WRITE NB NODES AND NB PROCESSOR PER NODE ONLY IF REQUIRED [11] # ############################################################################## # Using PLINK, computes the MDS value of each sample, and using three reference # populations (CEU, YRI and JPT-CHB), finds outliers of one of those three # reference population. You might want to skip the reference population using # the "skip-ref-pops" option. You might need to change the "multiplier" option # to be more or less stringent, according to you dataset. If you have a server # with a DRMAA-compliant distributed resource management system, you can # uncomment the "sge" and the "line-per-file-for-sge" options, to run this step # in parallel. # ############################################################################## script = check_ethnicity ceu-bfile = /PATH/TO/PLINK/BINARY/FILE/FOR/CEU_population yri-bfile = /PATH/TO/PLINK/BINARY/FILE/FOR/YRI_population jpt-chb-bfile = /PATH/TO/PLINK/BINARY/FILE/FOR/JPT-CHB_population # skip-ref-pops # min-nb-snp = 8000 # indep-pairwise = 50 5 0.1 # maf = 0.05 # sge # line-per-file-for-sge = 100 # nb-components = 10 # outliers-of = CEU # multiplier = 1.9 # xaxis = C1 # yaxis = C2 # format = png # title = "C2 in function of C1 - MDS" # xlabel = C1 # ylabel = C2 # create-scree-plot # scree-plot-title "TITLE OF THE PLOT" # sge-walltime = WRITE WALLTIME ONLY IF REQUIRED # sge-nodes = WRITE NB NODES AND NB PROCESSOR PER NODE ONLY IF REQUIRED # ibs-sge-walltime = WRITE WALLTIME ONLY IF REQUIRED # ibs-sge-nodes = WRITE NB NODES AND NB PROCESSOR PER NODE ONLY IF REQUIRED 

## Second Configuration File¶

This configuration file should be run after the First Configuration File and with the output of the second sample missingness section ([6] in the First Configuration File).

A file containing the samples and markers to be removed should be created using the output of the sex_check, find_related_samples, check_ethnicity and plate_bias sections of the First Configuration File.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 # This is the second part of example configuration files for performing # efficient data clean up. All commented out parameters are those that are used # by default. # The input file should be the output file of the second sample missigness step # (which is the one that has been used by any of these scripts): # - sex_check # - find_related_samples # - check_ethnicity # - plate_bias # Note that the final usable dataset is the one located in the directory where # "remove_heterozygous_haploid" was run (which is the one that has been used by # any of these scripts): # - flag_maf_zero # - flag_hw # Hence, if you want to remove the flagged markers, you should use # pyGenClean_subset_data on markers in the "flag_maf_zero" and "flag_hw" # directories using the PLINK's binary file located in # "remove_heterozygous_haploid". [12] # ############################################################################## # After manually checking that everything went fine in the previous steps, you # need to create a list of samples to remove from steps [7] to [10] and a list # of markers to exclude from steps [6]. Just create a file containing family and # individual identification numbers for all those samples to remove. Note that # the two options 'reason-marker' and 'reason-sample' are for the automatic # report generated after the analysis. # ############################################################################## script = subset reason-marker = reason for marker exclusion reason-sample = reason for sample exclusion remove = /PATH/TO/FILE/CONTAINING/ALL_SAMPLES_FROM_PREVIOUS_STEPS_TO_REMOVE.txt exclude = /PATH/TO/FILE/CONTAINING/ALL_MARKERS_FROM_PREVIOUS_STEPS_TO_EXCLUDE.txt [13] # ############################################################################## # Removes heterozygous haploid genotypes from the dataset. # ############################################################################## script = remove_heterozygous_haploid [14] # ############################################################################## # Flags uninformative markers (with a MAF of 0). This step only flag markers. # You might want to exclude them later on. # ############################################################################## script = flag_maf_zero [15] # ############################################################################## # Flags markers that fail HWE test for a p value of 1e-4 and after Bonferroni # correction. This step only flag markers. You might want to exclude them later # on. # ############################################################################## script = flag_hw # hwe = 1e-4