text: user4322: I have a vcf file with individual level genetic data, that I read using the R package vcfR 
  gen <- read.vcfR('test.vcf')
  
  Scanning file to determine attributes.
  File attributes:
    meta lines: 9
    header_line: 10
    variant count: 152
    column count: 3798
  Meta line 9 read in.
  All meta lines processed.
  gt matrix initialized.
  Character matrix gt created.
    Character matrix gt rows: 152
    Character matrix gt cols: 3798
    skip: 0
    nrows: 153
    row_num: 0
  Processed variant: 153
  All variants processed
  
  How do I convert this file to a data frame that I can use for a GWAS or Mendelian Randomization study?
  I was told that this should be a data frame where each column is a genetic variant and it take values 0, 1, or 2, depending on the number of "mutations".
  I never worked with genetic data before and do not know much about genetics.
  
  user4306: If you want to do GWAS or mendelian randomisation, you can do it with Plink (v2) which should be faster than R.  
  
original_text: 4322: <p>I have a vcf file with individual level genetic data, that I read using the R package <code>vcfR</code> </p>
  
  <pre><code>gen &lt;- read.vcfR('test.vcf')
  
  Scanning file to determine attributes.
  File attributes:
    meta lines: 9
    header_line: 10
    variant count: 152
    column count: 3798
  Meta line 9 read in.
  All meta lines processed.
  gt matrix initialized.
  Character matrix gt created.
    Character matrix gt rows: 152
    Character matrix gt cols: 3798
    skip: 0
    nrows: 153
    row_num: 0
  Processed variant: 153
  All variants processed
  </code></pre>
  
  <p>How do I convert this file to a data frame that I can use for a GWAS or Mendelian Randomization study?</p>
  
  <p>I was told that this should be a data frame where each column is a genetic variant and it take values 0, 1, or 2, depending on the number of "mutations".</p>
  
  <p>I never worked with genetic data before and do not know much about genetics.</p>
   4306: <p>If you want to do GWAS or mendelian randomisation, you can do it with <a href="https://www.cog-genomics.org/plink/2.0/" rel="nofollow noreferrer">Plink</a> (v2) which should be faster than R.  </p>
  