Unconfigured Ad

Collapse
X
 
  • Time
  • Show
Clear All
new posts
  • crang
    Junior Member
    • Apr 2013
    • 5

    #1

    rare variant missing in the vcf file

    Hi all,

    I'm new to next-gen, so I'm sorry if my question sounds lame.

    I have genotype data for large group of samples (all from one population and same ethnicity) in vcf format. For some samples I see only AA and AB genotypes, but not the rare BB, and for the rest of the samples i have AB. My question is: is it possible the BB variant to be missing because of filtering somewhere in the variant calling steps? Or there is another explanation?
  • crang
    Junior Member
    • Apr 2013
    • 5

    #2
    Maybe I should clarify more: I do not see any BB genotypes for the reference allele in the vcf file, i.e. I have always 1/1 or 0/1, but never 0/0. The variant calls are done with samtools|bcftools

    Comment

    • swbarnes2
      Senior Member
      • May 2008
      • 910

      #3
      And you have empirical evidence that your vcf should have BB?

      Is it possible that the genotype is in a repetitive region, which might make it hard to get accurate genotypes?

      For instance, do all the AB genotypes look to be 50/50 splits read-wise? Or is it possible that most are 75/25, with only a few 50/50?

      Comment

      • vivek_
        PhD Student
        • Jul 2012
        • 164

        #4
        Originally posted by crang View Post
        Maybe I should clarify more: I do not see any BB genotypes for the reference allele in the vcf file, i.e. I have always 1/1 or 0/1, but never 0/0. The variant calls are done with samtools|bcftools
        Well unless you are asking samtools to output the consensus you will not find the 0/0 sites since there is no evidence of a variant allele present.

        Comment

        • ymc
          Senior Member
          • Mar 2010
          • 496

          #5
          You should run your bams through IGV. Then you might find the reason for your problem.

          Comment

          • JeremyCarroll
            Junior Member
            • Mar 2013
            • 1

            #6
            I think vivek has it. VCF is generally used for recording variant calls, not reference calls. A 0/0 is a reference call, and so may be omitted by some tool in your tool chain. I suspect it is not rare, but actually the most frequent genotype.

            Comment

            • swbarnes2
              Senior Member
              • May 2008
              • 910

              #7
              If you are making the mpileup with multiple .bams, which is a pretty good idea, you should see plenty of 0/0 calls.

              But yes, if you are only putting one .bam at a time in, you will not see 0/0 calls. The file would be enormous if they were all in there.

              Comment

              • crang
                Junior Member
                • Apr 2013
                • 5

                #8
                Originally posted by JeremyCarroll View Post
                I think vivek has it. VCF is generally used for recording variant calls, not reference calls. A 0/0 is a reference call, and so may be omitted by some tool in your tool chain. I suspect it is not rare, but actually the most frequent genotype.
                Yeah, I checked - its the most common and is a ref call. Its my bad that I stated it wrong in the beginning. So if I have 1/1 and/or 0/1 I'll know which samples are possible 0/0 and fill the gaps. But here comes the question how to check if the sample has been genotyped for that variant or not. If not, it will be missing and not 0/0. First that comes in my mind is to check for any good coverage in those positions. Correct me if I'm wrong.

                Comment

                • swbarnes2
                  Senior Member
                  • May 2008
                  • 910

                  #9
                  What you could do is make a vcf at every single point, and then filter it by position so it only shows you the loci of interest.

                  Comment

                  • vivek_
                    PhD Student
                    • Jul 2012
                    • 164

                    #10
                    Originally posted by crang View Post
                    Yeah, I checked - its the most common and is a ref call. Its my bad that I stated it wrong in the beginning. So if I have 1/1 and/or 0/1 I'll know which samples are possible 0/0 and fill the gaps. But here comes the question how to check if the sample has been genotyped for that variant or not. If not, it will be missing and not 0/0. First that comes in my mind is to check for any good coverage in those positions. Correct me if I'm wrong.
                    If you are genotyping multiple samples you can either

                    a) Make variants calls together for all sample bams, this is called multi-sample variant calling and actually improves the accuracy of the genotyper

                    or

                    b) Call each sample bam separately, collect an intersection set of variant locations across all samples into a bed file and re-genotype all the samples for those regions

                    Comment

                    • crang
                      Junior Member
                      • Apr 2013
                      • 5

                      #11
                      Thanks a lot for all suggestions. Will see what can be done.

                      Comment

                      Latest Articles

                      Collapse

                      • SEQadmin2
                        Beyond CRISPR/Cas9: Understand, Choose, and Use the Right Genome Editing Tool
                        by SEQadmin2



                        CRISPR/Cas9 sparked the gene editing revolution for both research and therapeutics.1 But this system still showed severe issues that limited its applications. The most prominent were the heavy reliance on PAM sequences, delivery limitations, double-stranded breaks that prompt unintended edits and cell death, and editing inefficiency (both in targeting and in knock-in reliability).

                        Despite this, “CRISPR helped turn genome editing from a specialized technique into
                        ...
                        07-31-2026, 11:01 AM
                      • SEQadmin2
                        Proteomic Platforms: How to Choose the Right Analytical Strategy to Improve Detection and Clinical Applications
                        by SEQadmin2


                        Proteomics platforms are evolving rapidly, with advances in mass spectrometry and affinity-based approaches expanding what researchers can detect and at what scale. As the field moves toward deeper proteome coverage and clinical applications, scientists face an increasingly complex landscape of tools. This article will explore how researchers are navigating these choices to find the right platform for their work.

                        The systematic characterization of the human proteome has
                        ...
                        07-20-2026, 11:48 AM

                      ad_right_rmr

                      Collapse

                      News

                      Collapse

                      Topics Statistics Last Post
                      Started by SEQadmin2, Yesterday, 10:35 AM
                      0 responses
                      7 views
                      0 reactions
                      Last Post SEQadmin2  
                      Started by SEQadmin2, 08-06-2026, 07:41 AM
                      0 responses
                      25 views
                      0 reactions
                      Last Post SEQadmin2  
                      Started by SEQadmin2, 08-03-2026, 10:13 AM
                      0 responses
                      44 views
                      0 reactions
                      Last Post SEQadmin2  
                      Started by SEQadmin2, 07-31-2026, 02:55 AM
                      0 responses
                      48 views
                      0 reactions
                      Last Post SEQadmin2  
                      Working...