Answers.Body: <p>Humans are diploid, so you can expect to see up to 2*N (2*68=136) alleles, so N_DATA is the number of observances of that allele.</p>
  
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Answers.Text: Furthermore, it also looks like N_MISS + N_DATA = 2*68
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Answers.Text: My initial answer mentioned that, but that doesn't seem to hold true for all rows. Consider the following rows from their table:
  
  `22      16849476        132     0       6       0.0454545`  
  `22      16851734        126     0       36      0.285714`  
  
  
  The N_DATA and N_MISS instead sum to 138 and 162, respectively.
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Answers.Text: Right. That is surprising. What could cause that?
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Answers.Text: @spvensko Great catch.  My data are structural variants, maybe that is why?  Thanks.
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Body: <p>I'm new to data filtering on vcf data and vcftools.<br>
  I performed variant calling on my dataset, CHR22, homo sapiens. I'd like to remove sites that are missing in more than 5% of individuals.  </p>
  
  <pre><code>vcftools --missing-site --vcf updated_ids68.vcf
  </code></pre>
  
  <h1> </h1>
  
  <p>This gives me a file with out.lmiss. That is missingness per locus.<br>
  I had 68 individuals in my input vcf file.</p>
  
  <pre><code>CHR     POS     N_DATA  N_GENOTYPE_FILTERED     N_MISS  F_MISS  
  22      16848278        106     0       30      0.283019  
  22      16848492        68      0       68      1  
  22      16849180        69      0       67      0.971014  
  22      16849229        68      0       68      1  
  22      16849376        133     0       3       0.0225564  
  22      16849476        132     0       6       0.0454545  
  22      16851734        126     0       36      0.285714  
  22      16852588        123     0       13      0.105691
  </code></pre>
  
  <h1> </h1>
  
  <p>I'm unable to understand the output here. What does third column - <em>N_DATA</em> tell?<br>
  133, 126 doesn't tell that this is number of individuals if my input had 68 individuals only.  </p>
  
  <p>VCFtools (0.1.15)<br>
  fileformat=VCFv4.1</p>
  
Tags: <variant-calling><variation><filtering><vcftools>
Title: Output from vcftools missingness
ThreadText: # Output from vcftools missingness
  
  (Asked by: username_0 on 2019-01-28)
  
  I'm new to data filtering on vcf data and vcftools.  
  I performed variant calling on my dataset, CHR22, homo sapiens. I'd like to remove sites that are missing in more than 5% of individuals.
  
  ```
  vcftools --missing-site --vcf updated_ids68.vcf
  
  ```
  
  # 
  
  This gives me a file with out.lmiss. That is missingness per locus.  
  I had 68 individuals in my input vcf file.
  
  ```
  CHR     POS     N_DATA  N_GENOTYPE_FILTERED     N_MISS  F_MISS  
  22      16848278        106     0       30      0.283019  
  22      16848492        68      0       68      1  
  22      16849180        69      0       67      0.971014  
  22      16849229        68      0       68      1  
  22      16849376        133     0       3       0.0225564  
  22      16849476        132     0       6       0.0454545  
  22      16851734        126     0       36      0.285714  
  22      16852588        123     0       13      0.105691
  
  ```
  
  # 
  
  I'm unable to understand the output here. What does third column - *N\_DATA* tell?  
  133, 126 doesn't tell that this is number of individuals if my input had 68 individuals only.
  
  VCFtools (0.1.15)  
  fileformat=VCFv4.1
  
  **Tags:** variant-calling, variation, filtering, vcftools
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-01-28. Score: 5 | Accepted answer
  
  Humans are diploid, so you can expect to see up to 2\*N (2\*68=136) alleles, so N\_DATA is the number of observances of that allele.
  
  > **Comments:**
  > **username_2**: Furthermore, it also looks like N_MISS + N_DATA = 2*68  
  > **username_1**: My initial answer mentioned that, but that doesn't seem to hold true for all rows. Consider the following rows from their table:  `22      16849476        132     0       6       0.0454545`   `22      16851734        126     0       36      0.285714`     The N_DATA and N_MISS instead sum to 138 and 162, respectively.  
  > **username_2**: Right. That is surprising. What could cause that?  
  > **username_0**: @username_1 Great catch.  My data are structural variants, maybe that is why?  Thanks.  
  
  ---
  
  