JointGenotyping on multiple samples is less accurate vs. one at a time. Pooled sequencing.

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Mads Nielsen

• October 25, 2024 08:10

Dear GATK community,

I am benchmarking a range of variant callers for analyzing pooled sequencing samples, and I found that HaplotypeCaller works well for analyzing one sample at a time. I benchmark the output against each pooled sample individually sequenced by WGS. 

However, I thought that performing joint genotyping on multiple samples would increase the accuracy, with the benefit of allowing variant filtering using VQSR, but the opposite happens. Analyzing with HaplotypeCaller with "-ERC GVCF" and genotyping these files with GenotypeGVCF on each GVCF file gives a sensible output while collecting the files with GenomicsDB and genotyping all at the same time with GenotypeGVCF reduces the number of variants called to a fraction.

I use GATK v. 4.6.0.0.

This is my workflow:

HaplotypeCaller

gatk --java-options "-Xmx8g -XX:-UsePerfData" HaplotypeCaller \

-R ${reference} \

-I ${bam_file} \

-L ${bedfile} \

-O "${sample_id}.GATK.g.vcf.gz" \

-ploidy ${params.ploidy} \

-ERC GVCF \

-G StandardAnnotation \

-G AS_StandardAnnotation \

-G StandardHCAnnotation \

--disable-read-filter NotDuplicateReadFilter \

--max-alternate-alleles 3 \

--max-num-haplotypes-in-population 1000 \

--max-reads-per-alignment-start 0 \

--create-output-variant-index \

--tmp-dir .

GenomicsDB

gatk --java-options "-Xmx8g -XX:-UsePerfData" \

GenomicsDBImport \

$inpu_files_command \

--genomicsdb-workspace-path genomicsdb \

--intervals ${bedfile} \

--tmp-dir .

 The input_files adds all input gcvf files with the argument "--variant" in front.

GenotypeGVCFs

gatk --java-options "-Xmx8g -XX:-UsePerfData" \

GenotypeGVCFs \

-R ${db} \

-V gendb://genomicsdb \

-O "joint.GATK.vcf.gz" \

-G StandardAnnotation \

-G AS_StandardAnnotation \

-G StandardHCAnnotation \

--tmp-dir .

All samples are included in the output file, so it is not because something fails in creating of the genomicsDB. Furthermore, I tested GenotypeGVCF with the "--use-new-qual-calculator" and reducing the call confidence to zero with  "-stand-call-conf 0". It has close to no effect.

The log of GenotypeGVCF with the combined samples is basically just an infinite list of:

Sample/Callset XX ( TileDB row idx 7) at Chromosome chr10 position 87863181 (TileDB column 1762746809) has too many genotypes in the combined VCF record : 53130 : current limit : 1024 (num_alleles, ploidy) = (6, 20). Fields, such as  PL, with length equal to the number of genotypes will NOT be added
     for this sample for this location.

Is this the cause of the error? or did I miss something else in the documentation? I have seen this error in years-old posts, but has there been any development here?

Thanks for your help!

Mads

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  Gökalp Çelik

  • October 28, 2024 06:59

  Hi Mads Nielsen

  Have you tried adding the ploidy parameter to the GenotypeGVCFs step? GenotypeGVCFs assumes a diploid sample as default. 

  --sample-ploidy,-ploidy <Integer>
                                Ploidy (number of chromosomes) per sample. For pooled data, set to (Number of samples in
                                each pool * Sample Ploidy).  Default value: 2. 

  Regards.

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  Mads Nielsen

  • October 28, 2024 07:39

  Thanks for your reply!

  Yes, I tried both with and without the "-ploidy" parameter - same result - and it outputs the correct number of alleles.

  Best

  Mads

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  Gökalp Çelik

  • October 28, 2024 08:27

  Hi again.

  Looking at the region that you provided in your log, indicates that it is a region of high GC content and repeats. Maximum genotype count of 1024 is calculated based on ploidy and number of samples per locus and based on that ploidy of 20 with number of alleles 6 for that locus. Observing 50000 genotypes in that region is highly unlikely therefore what is observed in that locus is mostly read errors, PCR errors and/or mapping errors. 

  I hope this helps. 

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  Mads Nielsen

  • October 28, 2024 08:36

  Hi again,

  Sorry, maybe my question was not super clear - but the problem is not the specific region I attached, it is that GenotypeGVCF only outputs a tiny fraction of the expected number of variants across all the regions, and that the output is a long list of lines like the one I attached. When I run one gvcf at a time, it gives the correct output, but when I do joint genotyping with all the files at once it is completely off. I benchmark against a "truth" set based on WGS, and single sample genotyping has a sensitivity of 0.95, while joint genotyping has a sensitivity of 0.14.

  I worry that GenotypeGVCF simply cannot handle multiple pooled samples, or that it has a build-in filter, that I have overlooked.

  Thanks for your help!

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  Gökalp Çelik

  • October 28, 2024 08:46

  Did your GenotypeGVCFs command reached a successful end with exit code 0?

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  Mads Nielsen

  • October 28, 2024 08:52

  Yes - there were no errors.

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  Gökalp Çelik

  • October 28, 2024 09:19

  Can you elaborate more on your single samples vs pooled samples?

  What are the depths for single unpooled samples and pooled samples? Have you tried running CollectWgsMetrics tool to check for the theoretical sensitivity for calls? 

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  Mads Nielsen

  • October 28, 2024 10:00

  Yes. I am benchmarking different tools for calling variants in samples of pooled DNA. I have two data sets. One is sequenced pools of DNA from 10 individuals and the other is the 10 individuals sequenced individually. Variant calling of the individuals is just done using GATK best practices, and the pooled samples are called using a range of different tools. I benchmark the calls of pooled samples against the individually called samples. The median coverage of the individual samples are in the range of ~30-50x and the pooled samples are 2-3000x. I have in total of twenty pooled samples (100 individuals in all) and the problem arises when I take all the pooled sample GVCFs, combine them using GenomicsDB and call variants using GenotypeGVCF. It runs without errors, outputs a single VCF with all the samples and the correct number of alleles, but a completely off number of variants. The sensitivity for it is 0.14 compared to >0.90 for all other tools. The strange thing is, that if I just take one pooled sample GVCF at a time, and run GenotypeGVCF on it, the combined result is a sensitivity of 0.96.

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  Gökalp Çelik

  • October 28, 2024 10:03

  Oh. So if each pool contains 100 (assuming diploid) individuals then your ploidy should be around 200. But the message you showed me indicates only 20. Could that be the reason why your call sensitivity is dropped significantly?

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  Mads Nielsen

  • October 28, 2024 10:10

  Each of the pools only contain 10 individuals. I have 100 individuals pooled twice in pools of 10. Sorry for the confusing set-up. It is overlapped pooled sequencing.

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  Gökalp Çelik

  • October 28, 2024 10:50

  Do your pooled samples all have different Sample IDs and Sample Names attached to them? 

  Again going back to my original questions.

  Did you observe any different genotype assignments when GenotypeGVCFs are run with --ploidy 6 vs no ploidy parameter? Alleles won't matter as they are already in the file. Important thing is the assigned genotypes to them. 

  Also have you looked at Theoretical Sensitivity numbers calculated by CollectWgsMetrics tool?Important allele fractions should be around 0.05 and the sensitivity of those fractions would matter. Heterozygous sensitivity for 30-50X coverage should already be above 0.95 but even with 3000X depth capturing 0.05 allele fraction may have quite reduced sensitivity. 

   

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  Mads Nielsen

  • October 29, 2024 11:42

  Thanks for all your inputs!

  All files input to GenomicsDB have different sample ids. The VCF resulting from running genotypeGVCF on the genomicsDB also has all the expected sample columns. I just re-tested and running genotypeGVCF with different ploidies has no effect. Which is also what the documentation states. I just ran CollectWgsMetrics with default settings on a couple of the pooled samples, and it returns a single theoretical HET SNP sensitivity above 0.999 for all of them. I am not sure where you sensitivities for specific fractions?

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  Gökalp Çelik

  • October 29, 2024 12:18

  CollectWgsMetrics has a parameter called

  --THEORETICAL_SENSITIVITY_OUTPUT <File>
                                Output for Theoretical Sensitivity metrics.  Default value: null.

  This file collects theoretical sensitivity results for different allele fractions. Heterozygous calls are made with close to 50% allele fraction estimations however in your case your ploidy value is 20 and single allele present within a pool will cause allele fractions to go as low as 0.01 to 0.05. 

  ALLELE_FRACTION    THEORETICAL_SENSITIVITY    THEORETICAL_SENSITIVITY_Q    LOG_ODDS_THRESHOLD    SAMPLE_SIZE
  0.001    0    0    6.2    10000
  0.005    0.000007    0    6.2    10000
  0.01    0.000487    0    6.2    10000
  0.02    0.020443    0    6.2    10000
  0.05    0.255898    1    6.2    10000
  0.1    0.714858    5    6.2    10000
  0.2    0.954775    13    6.2    10000
  0.3    0.978113    17    6.2    10000
  0.5    0.985147    18    6.2    10000

  This is from a sample of 50X coverage PCR Free whole genome sequencing data. This sample has HET sensitivity around 0.99. However numbers do not linearly increase as your depth goes up. Sensitivity for the allele fraction 0.01 less than 0.1% and even your depth goes as high as 2000X this value may not become 0.99 therefore there will be a higher probability for false negative calls in pooled sequencing. 

  I hope this helps. 

  Regards. 

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  Mads Nielsen

  • October 30, 2024 07:20

  These are the results for one of the pooled samples:

  ALLELE_FRACTION THEORETICAL_SENSITIVITY THEORETICAL_SENSITIVITY_Q LOG_ODDS_THRESHOLD SAMPLE_SIZE
  0.001 0 0 6.2 10000
  0.005 0.000897 0 6.2 10000
  0.01 0.054652 0 6.2 10000
  0.02 0.4605 3 6.2 10000
  0.05 0.977045 16 6.2 10000
  0.1 0.99729 26 6.2 10000
  0.2 0.99729 26 6.2 10000
  0.3 0.99729 26 6.2 10000
  0.5 0.99729 26 6.2 10000

  This looks sensible right?

  And again, GenotypeGVCF gives the correct results if I just give it a single pool, but gives results that are completely off when I want it to do joint genotyping on multiple pools via GenomicsDB.

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  Gökalp Çelik

  • October 30, 2024 07:41
  • Edited

  Does joint genotyping also produce complete HOMREF calls in some of your pools where there is only a single pool has the variant site? Also based on that do you remove those sites before you compare it to your single sample comparisons?

  Also do you observe any false negative calls in your joint genotyped pools and  can you share some example variant contexts to indicate that here?

  If there are no false negatives but just extra HOMREF sites due to joint genotyping those sites may not be evident in the single sample analysis therefore GenotypeConcordance may fall off. 

  Can you provide examples of FP and FN sites according to your GenotypeConcordance results?

   

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  Mads Nielsen

  • October 30, 2024 08:24

  Yes, many calls are missing in the jointly genotyped VCF, but for the once that are there, you see complete HOMREF calls for all other pools, than the ones having the variants. 

  This is an example of a correct site, in the jointly called VCF, with two pools having a variant:

  chr17   7676148 .       G       A       2300.79 .       AC=3;AF=7.500e-03;AN=400;AS_BaseQRankSum=0.250;AS_FS=1.059;AS_MQ=60.00;AS_MQRankSum=0.000;AS_QD=1.07;AS_ReadPosRankSum=-0.050;AS_SOR=0.531;BaseQRankSum=1.18;DP=19429;FS=1.059;MLEAC=2;MLEAF=5.000e-03;MQ=60.00;MQRankSum=0.00;QD=1.07;ReadPosRankSum=0.415;SOR=0.531     GT:AD:DP:GQ:PL  0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:607,0:607:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:957,0:957:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/1/1:992,85:1077:6:1284,6,0,103,261,459,689,951,1246,1576,1946,2362,2834,3375,4006,4757,5682,6878,8567,11435,32425        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:937,0:937:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1010,0:1010:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800      0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1189,0:1189:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800      0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1328,0:1328:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800      0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:916,0:916:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1237,0:1237:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800      0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:748,0:748:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:920,0:920:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/1:987,79:1066:19:1039,0,19,136,303,506,741,1007,1304,1635,2006,2423,2894,3434,4063,4812,5733,6923,8600,11445,30792     0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:807,0:807:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1026,0:1026:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800      0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:970,0:970:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:814,0:814:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1007,0:1007:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800      0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:900,0:900:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:855,0:855:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800        0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1007,0:1007:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800

  (This is overlapped pooled sequencing, so all variants should appear in two pools).

  This is an example of a variant that should have been in the jointly called VCF:

  As it appear in the GVCF:

  chr13   32339012        .       A       G,<NON_REF>     1447.43 .       AS_RAW_BaseQRankSum=|0.3,1|NaN;AS_RAW_MQ=6991200.00|403200.00|0.00;AS_RAW_MQRankSum=|0.0,1|NaN;AS_RAW_ReadPosRankSum=|0.3,1|NaN;AS_SB_TABLE=895,1047|53,59|0,0;BaseQRankSum=0.388;DP=2131;MLEAC=1,0;MLEAF=0.050,0.00;MQRankSum=0.000;RAW_MQandDP=7671600,2131;ReadPosRankSum=0.342  GT:AD:DP:GQ:PL:SB       0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/1:1942,112,0:2054:99:1449,0,131,420,792,1227,1719,2266,2874,3546,4294,5130,6074,7152,8403,9889,11713,14068,17378,22974,55642,1880,454,612,929,1334,1806,2340,2938,3603,4345,5176,6116,7190,8439,9922,11745,14097,17406,23000,55667,2334,934,1121,1471,1913,2428,3012,3667,4402,5227,6162,7232,8477,9958,11778,14128,17435,23028,55693,2815,1444,1663,2050,2535,3099,3741,4466,5284,6213,7278,8519,9997,11814,14162,17466,23057,55721,3324,1986,2242,2672,3206,3829,4540,5348,6270,7329,8565,10039,11852,14197,17500,23088,55750,3866,2565,2864,3343,3935,4627,5422,6334,7386,8616,10085,11894,14236,17536,23122,55781,4445,3186,3535,4073,4734,5510,6408,7450,8673,10136,11940,14278,17574,23157,55815,5067,3858,4264,4871,5616,6495,7524,8737,10192,11991,14324,17616,23196,55850,5738,4587,5063,5754,6602,7612,8811,10257,12048,14375,17662,23238,55889,6468,5386,5945,6739,7718,8899,10331,12112,14432,17713,23284,55932,7266,6268,6931,7855,9005,10418,12186,14496,17770,23335,55977,8148,7254,8047,9142,10525,12274,14570,17834,23392,56028,9134,8370,9334,10662,12380,14657,17908,23456,56085,10250,9657,10854,12518,14764,17995,23530,56149,11537,11177,12709,14901,18102,23617,56223,13057,13032,15093,18239,23724,56310,14912,15416,18431,23861,56417,17296,18754,24053,56554,20634,24376,56746,26256,57069,58949:895,1047,53,59

  As it appear in the VCF from GenotypeGVCF on the single pool:

  chr13   32339012        .       A       G       1447.43 .       AC=1;AF=0.050;AN=20;ASSEMBLED_HAPS=416;AS_BaseQRankSum=0.300;AS_FS=0.000;AS_MQ=60.00;AS_MQRankSum=0.000;AS_QD=0.70;AS_ReadPosRankSum=0.300;AS_SOR=0.643;BQHIST=0;BaseQRankSum=0.388;ClippingRankSum=0.423;DP=2131;FILTERED_HAPS=0;FS=0.000;GQ_MEAN=131.00;HAPCOMP=2;HAPDOM=0.267;HEC=1854;LikelihoodRankSum=-3.452e+00;MLEAC=1;MLEAF=0.050;MQ=60.00;MQ0=0;MQRankSum=0.00;NCC=0;NCount=0;QD=0.70;ReadPosRankSum=0.342;SOR=0.643;X_GCC=0.400;X_HIL=0    GT:AD:AF:DD:DF:DP:GQ:PL   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/1:1942,112:0.055:1899.08,100.25:0.9489,0.0506:2054:99:1449,0,131,420,792,1227,1719,2266,2874,3546,4294,5130,6074,7152,8403,9889,11713,14068,17378,22974,55642

  Not sure if this helps. I remove the complete HOMREF calls when comparing to the single samples. This shouldn't affect the sensitivity though. The problem is that most variants are completely missing from the joint VCF.

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  Gökalp Çelik

  • October 30, 2024 08:53
  • Edited

  OK these are TP ones. Can you show me a single site that is present in single sample, present in single pool and not present when it is jointly called? You might want to put the GVCF entry in the joint call input file. If there is an entry in the GVCF but not in the genotyped VCF after joint genotyping it calls for a bug report. If the site gets lost after import or combinegvcf step that may also call for a bug report.

  I am trying to understand the root cause of the problem because this is not expected.

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  Mads Nielsen

  • October 30, 2024 08:53
  • Edited

  The one on chr17 is TP. The one on chr13 is not present in the joint called VCF. The two lines given for the site on chr13 are in the initial GVCF and in the VCF output when not jointly called, but called by giving GenotypeGVCF only a single pool.

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  Gökalp Çelik

  • October 30, 2024 08:56
  • Edited

  Can you check the GVCF entry in the combined file for the variant in chr13. I am curious about what happened to it. 

  You can dump GenomicsDB into a GVCF file by using SelectVariants tool.

  gatk SelectVariants -V gendb://gdbimport -O GVCFdump.g.vcf.gz -R reference.fasta

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  Mads Nielsen

  • October 30, 2024 09:01

  Thanks - I will do that now!

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  Mads Nielsen

  • October 30, 2024 09:17

  This is the site in the GVCF:

  chr13   32339012        .       A       G,T,<NON_REF>   .       .       AS_RAW_BaseQRankSum=|-0.600,1,0.300,1|-2.000,1|;AS_RAW_MQ=14731200.000|828000.000|3600.000|0.000;AS_RAW_MQRankSum=|0.000,2|0.000,1|;AS_RAW_ReadPosRankSum=|0.300,1,1.900,1|0.100,1|;AS_SB_TABLE=1874,2218|112,118|0,1|0,0;BaseQRankSum=0.388;DP=39331;MQRankSum=0;RAW_MQandDP=16254000,4515;ReadPosRankSum=2.007  GT:AD:GQ:MIN_DP:SB:DP ./././././././././././././././././././.:.:9:1445:.:1628 ./././././././././././././././././././.:2150,118,1,0:99:.:979,1171,59,60:2269        ./././././././././././././././././././.:.:9:1867:.:2079 ./././././././././././././././././././.:.:9:1927:.:2216 ./././././././././././././././././././.:.:9:2056:.:2328      ./././././././././././././././././././.:.:9:2611:.:2992 ./././././././././././././././././././.:.:9:2547:.:2857      ./././././././././././././././././././.:.:9:1836:.:2175 ./././././././././././././././././././.:.:9:2334:.:2681      ./././././././././././././././././././.:.:9:1723:.:1966 ./././././././././././././././././././.:.:9:1790:.:2010      ./././././././././././././././././././.:1942,112,0,0:99:.:895,1047,53,59:2054   ./././././././././././././././././././.:.:9:1743:.:2058      ./././././././././././././././././././.:.:9:1937:.:2283 ./././././././././././././././././././.:.:9:1918:.:2244 ./././././././././././././././././././.:.:9:1743:.:1964      ./././././././././././././././././././.:.:9:2032:.:2290 ./././././././././././././././././././.:.:9:1748:.:1953      ./././././././././././././././././././.:.:9:1748:.:2037 ./././././././././././././././././././.:.:9:1811:.:2017

   

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  Gökalp Çelik

  • October 30, 2024 09:22

  OK so this site is certainly lost in the import process but not in the GenotypeGVCFs step. Can you try CombineGVCFs tool to see if it will collect the data properly from all the pooled GVCFs into a single GVCF?

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  Mads Nielsen

  • October 30, 2024 13:40

  This was more tricky than I thought. CombineGVCFs kept using up all memory on the HPC. I ended up only combining chr13. This is the site:

  chr13 32339012 . A G,T,<NON_REF> . . BaseQRankSum=0.388;DP=39331;MQRankSum=0.00;RAW_MQandDP=16254000,4515;ReadPosRankSum=2.01 GT:AD:DP:GQ:MIN_DP:PL:SB ./././././././././././././././././././.:.:1628:9:1445:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,330,400,520,1800,120,139,159,182,209,241,280,330,400,520,1800,139,159,182,209,241,280,330,400,520,1800,159,182,209,241,280,330,400,520,1800,182,209,241,280,330,400,520,1800,209,241,280,330,400,520,1800,241,280,330,400,520,1800,280,330,400,520,1800,330,400,520,1800,400,520,1800,520,1800,1800,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,330,400,520,1800,120,139,159,182,209,241,280,330,400,520,1800,139,159,182,209,241,280,330,400,520,1800,159,182,209,241,280,330,400,520,1800,182,209,241,280,330,400,520,1800,209,241,280,330,400,520,1800,241,280,330,400,520,1800,280,330,400,520,1800,330,400,520,1800,400,520,1800,520,1800,1800,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,330,400,520,1800,120,139,159,182,209,241,280,330,400,520,1800,139,159,182,209,241,280,330,400,520,1800,159,182,209,241,280,330,400,520,1800,182,209,241,280,330,400,520,1800,209,241,280,330,400,520,1800,241,280,330,400,520,1800,280,330,400,520,1800,330,400,520,1800,400,520,1800,520,1800,1800,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,330,400,520,1800,120,139,159,182,209,241,280,330,400,520,1800,139,159,182,209,241,280,330,400,520,1800,159,182,209,241,280,330,400,520,1800,182,209,241,280,330,400,520,1800,209,241,280,330,400,520,1800,241,280,330,400,520,1800,280,330,400,520,1800,330,400,520,1800,400,520,1800,520,1800,1800,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,330,400,520,1800,120,139,159,182,209,241,280,330,400,520,1800,139,159,182,209,241,280,330,400,520,1800,159,182,209,241,280,330,400,520,1800,182,209,241,280,330,400,520,1800,209,241,280,330,400,520,1800,241,280,330,400,520,1800,280,330,400,520,1800,330,400,520,1800,400,520,1800,520,1800,1800,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,330,400,520,1800,120,139,159,182,209,241,280,330,400,520,1800,139,159,182,209,241,280,330,400,520,1800,159,182,209,241,280,330,400,520,1800,182,209,241,280,330,400,520,1800,209,241,280,330,400,520,1800,241,280,330,400,520,1800,280,330,400,520,1800,330,400,520,1800,400,520,1800,520,1800,1800,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,330,400,520,1800,120,139,159,182,209,241,280,330,400,520,1800,139,159,182,209,241,280,330,400,520,1800,159,182,209,241,280,330,400,520,1800,182,209,241,280,330,400,520,1800,209,241,280,330,400,520,1800,241,280,330,400,520,1800,280,330,400,520,1800,330,400,520,1800,400,520,1800,520,1800,1800,75,89,104,120,139,159,182,209,241,280,330,400,520,1800,89,104,120,139,159,182,209,241,280,330,400,520,1800,104,120,139,159,182,209,241,280,33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  Mads Nielsen

  • October 30, 2024 13:50

  My previous post is pending approval. I guess there was too much info in the code block. It seems like the site is also lost here.

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  Gökalp Çelik

  • October 30, 2024 14:18
  • Edited

  I approved that one. So this is much better. Now it looks like there are 3 alleles present in the region and also PL field is present therefore it will be possible for GenotypeGVCFs to provide a GT assignment to the correct pool. Can you also try genotyping this GVCF to see if missing sites appear as they should?

  Additionally I was wondering if you could send us snippets (after anonymizing your sample names of course) of your pooled GVCFs around this particular region, preferably 100KB up and downstream so that we may try recreating this issue and see if this is a GenomicsDBImport bug (looks like it) that we should pursue immediately. 

  You may use the directions in the article below for sending us those snippets if possible. 

  https://gatk.broadinstitute.org/hc/en-us/articles/360035889671-How-do-I-submit-a-detailed-bug-report 

  Thank you for your patience. 

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  Mads Nielsen

  • October 30, 2024 14:54

  Yes, this the site in the resulting VCF after genotyping the combined GVCF:

  chr13   32339012        .       A       G       2920.02 .       AC=2;AF=5.000e-03;AN=400;AS_QD=0.68;BaseQRankSum=0.388;DP=39331;FS=0.000;MLEAC=2;MLEAF=5.000e-03;MQ=60.00;MQRankSum=0.00;QD=0.68;ReadPosRankSum=2.01;SOR=0.591    GT:AD:DP:GQ:PL  0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1445,0:1445:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/1:2150,118:2269:99:1489,0,166,496,916,1403,1952,2562,3237,3985,4814,5742,6787,7981,9366,11010,13027,15629,19286,25464,62283   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1867,0:1867:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1927,0:1927:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:2056,0:2056:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:2611,0:2611:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:2547,0:2547:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1836,0:1836:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:2334,0:2334:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1723,0:1723:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1790,0:1790:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/1:1942,112:2054:99:1449,0,131,420,792,1227,1719,2266,2874,3546,4294,5130,6074,7152,8403,9889,11713,14068,17378,22974,55642  0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1743,0:1743:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1937,0:1937:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1918,0:1918:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1743,0:1743:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:2032,0:2032:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1748,0:1748:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1748,0:1748:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800   0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0/0:1811,0:1811:9:0,9,18,28,39,50,62,75,89,104,120,139,159,182,209,241,280,330,400,520,1800

  Thanks so much for your help! Can you elaborate on how you could see that the site was lost in the GVCF coming from genomicsDB?

  I would really like to help with creating the snippet for you, but I would have to check if I am allowed to.

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• 

  Gökalp Çelik

  • October 30, 2024 15:42

  So there are some technical differences between how GenomicsDBImport and CombineGVCFs work in terms of generating large PL and Genotype arrays. Details are not too important but the key is 2 different methods have different caps for number of possible genotypes per locus given ploidy and sample number. GenomicsDBImport is slightly lower capped compared to CombineGVCFs therefore it fails to keep the large PL array. 

  Our germline methods are more conservative against these exponentially expanding PL arrays when too many possible genotypes are possible. After a certain number of genotypes algorithm simply refuses to generate any PL arrays therefore your genotypes get lost in translation. 

  TL:DR. 

  Pooled sequencing and extremely high ploidy sample sequencing is not handled as the regular single diploid samples in joint genotyping by HaplotypeCaller/GenotypeGVCFs due to the large number of array elements. We recommend using Mutect2 to simplify pooled sequencing samples and there is a somatic mode present in GenotypeGVCFs tool that can handle joint calling of pooled samples. In your case using Mutect2 may generate a better result in terms of data simplicity. 

  In case you want to try with Mutect2 GVCF mode I can recommend you to ignore Germline Filter of the FilterMutectCalls tool to avoid removing high frequency alleles in the pool to get filtered out. FilterMutectCalls can remove some of the sequencing artifacts based on evidence from reads however you may wish to check options to enable and disable appropriate filters for your use case. You certainly don't need to use any germline resource to mark any high fraction variants as GERMLINE, however contamination filter can help you remove any cross pool contaminants to avoid calling variants that should not be in a particular pool. 

  I hope this helps.

  Regards. 

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• 

  Mads Nielsen

  • October 31, 2024 12:10
  • Edited

  Thanks for the explanation! I will definitely also try running Mutect2. Do I understand you correctly, that the workflow can reduce/remove cross-contamination between pools? This would be very helpful.

  Would this be a good adjusted workflow for pooled sequencing:

  1. Mutect2

  gatk Mutect2 \
          -R ref.fasta \
          -I pool.bam \
          --f1r2-tar-gz f1r2.tar.gz \
          -O unfiltered.vcf

  This should be without germline resource and PON table right? and would it be necessary to do force calling or adding --genotype-germline-sites?

   

  2. LearnReadOrientationModel

  gatk LearnReadOrientationModel \
          -I f1r2.tar.gz \
          -O read-orientation-model.tar.gz

   

  3. GetPileupSummaries

  gatk GetPileupSummaries \
      -I pool.bam \
      -V common_variants_within_regions.vcf.gz \
      -L common_variants_within_regions.vcf.gz \
      -O pileupsummaries.table

   

  4. CalculateContamination

  gatk CalculateContamination \
          -I pileupsummaries.table \
          -O contamination.table

  Without tumor segmentation right?

   

  5. FilterMutectCalls

  gatk FilterMutectCalls \
          -V unfiltered.vcf \
          --contamination-table contamination.table \
          --ob-priors read-orientation-model.tar.gz \
          -O filtered.vcf

   

  I am not sure how it will filter cross pool contamination, since each sample is handled individually. Multi-sample mode in mutect2 is meant for multiple samples from the same individual right? Thanks so much for all your help!

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• 

  Gökalp Çelik

  • November 01, 2024 18:16

  Hi again.

  Workflow looks good. CalculateContamination uses allelic imbalance of homozygous sites and determines if the sample has any possible contamination due to experimental setup. It will help FilterMutectCalls to mark such sites as contamination and it is up to you do decide if it is really a contamination or not since you are not working with tumor samples. Mutect2 considers all reads belong to the same sample unless it is indicated as normal. Since you don't have any normal present it will emit all variants as a part of the same sample. 

  For the joint calling of different pools we suggest using the GVCF mode of Mutect2 and using somatic input mode of GenotypeGVCFs tool. 

  I hope this helps. 

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