<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>

CC BY-SA 4.0
2019-01-29T11:11:04.683Z
Furthermore, it also looks like N_MISS + N_DATA = 2*68
winni2k
CC BY-SA 4.0
2019-01-29T14:48:36.703Z
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.
spvensko
CC BY-SA 4.0
2019-01-29T15:10:35.957Z
Right. That is surprising. What could cause that?
winni2k
CC BY-SA 4.0
2019-01-29T21:50:05.393Z
@spvensko Great catch.  My data are structural variants, maybe that is why?  Thanks.
Death Metal
CC BY-SA 4.0
2019-01-28T20:45:33.503Z
2019-01-29T02:22:37.567Z
2019-01-29T02:22:37.567Z
spvensko
<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>

<variant-calling><variation><filtering><vcftools>
Output from vcftools missingness
# 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**: @spvensko Great catch.  My data are structural variants, maybe that is why?  Thanks.  

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