Answers.Body: <p>This is a good idea - low pass sequencing has shown to be better than genotype arrays for things such as GWAS or QTL mapping.</p>
  <p>The primary way of processing low-pass sequencing data is to use imputation - partciularly methods such as GLIMPSE or QUILT and a large reference panel such as the HRC or TopMed in order to refine the genotype calls.</p>
  <p>This is a figure from <a href="https://www.nature.com/articles/s41588-020-00756-0.pdf?proof=t%C2%A0" rel="nofollow noreferrer">the GLIMPSE paper</a> showing how you can retain variants of different frequencies at different levels of sequencing depth.</p>
  <p><a href="https://i.sstatic.net/CBbN0.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/CBbN0.png" alt="enter image description here" /></a></p>
  <p>And a similar image from <a href="https://www.nature.com/articles/s41588-021-00877-0.pdf" rel="nofollow noreferrer">the QUILT paper</a>:</p>
  <p><a href="https://i.sstatic.net/6yTqT.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/6yTqT.png" alt="enter image description here" /></a></p>
  <p>You might also want to check out some of the methodology in this paper <a href="http://m.genome.cshlp.org/content/31/4/529" rel="nofollow noreferrer">http://m.genome.cshlp.org/content/31/4/529</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-07-17T11:35:27.690Z
Answers.LastActivityDate: 2021-07-17T14:32:38.147Z
Answers.LastEditDate: 2021-07-17T14:32:38.147Z
Answers.UserDisplayName: user438383
Body: <p>I would like to use <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3276614/" rel="nofollow noreferrer">low pass sequencing</a> to replace a <a href="https://www.frontiersin.org/articles/10.3389/fpls.2019.00544/full" rel="nofollow noreferrer">genotyping chip</a> to be able to detect <a href="https://en.wikipedia.org/wiki/Variant_(biology)" rel="nofollow noreferrer">variants</a> up to 0.1 % <a href="https://en.wikipedia.org/wiki/Allele" rel="nofollow noreferrer">allele</a> frequency in available population data. What is the minimum <a href="https://www.nature.com/articles/nrg3642" rel="nofollow noreferrer">depth</a> I can opt for to retain the <a href="https://pubmed.ncbi.nlm.nih.gov/8188283/" rel="nofollow noreferrer">confidence</a> of <a href="https://en.wikipedia.org/wiki/Base_calling" rel="nofollow noreferrer">base calls</a>? Can <a href="https://gatk.broadinstitute.org/hc/en-us" rel="nofollow noreferrer">GATK</a> be used to analyse this data?</p>
  
Tags: <ngs><gwas><gatk><coverage>
Title: Low pass sequencing has been reported to detect common variants. How low can one go and get reliable data? Is 2X pass sequencing analysis possible?
ThreadText: # Low pass sequencing has been reported to detect common variants. How low can one go and get reliable data? Is 2X pass sequencing analysis possible?
  
  (Asked by: username_0 on 2021-07-17)
  
  I would like to use [low pass sequencing](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3276614/) to replace a [genotyping chip](https://www.frontiersin.org/articles/10.3389/fpls.2019.00544/full) to be able to detect [variants](https://en.wikipedia.org/wiki/Variant_(biology)) up to 0.1 % [allele](https://en.wikipedia.org/wiki/Allele) frequency in available population data. What is the minimum [depth](https://www.nature.com/articles/nrg3642) I can opt for to retain the [confidence](https://pubmed.ncbi.nlm.nih.gov/8188283/) of [base calls](https://en.wikipedia.org/wiki/Base_calling)? Can [GATK](https://gatk.broadinstitute.org/hc/en-us) be used to analyse this data?
  
  **Tags:** ngs, gwas, gatk, coverage
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2021-07-17. Score: 2
  
  This is a good idea - low pass sequencing has shown to be better than genotype arrays for things such as GWAS or QTL mapping.
  
  The primary way of processing low-pass sequencing data is to use imputation - partciularly methods such as GLIMPSE or QUILT and a large reference panel such as the HRC or TopMed in order to refine the genotype calls.
  
  This is a figure from [the GLIMPSE paper](https://www.nature.com/articles/s41588-020-00756-0.pdf?proof=t%C2%A0) showing how you can retain variants of different frequencies at different levels of sequencing depth.
  
  [![enter image description here](https://i.sstatic.net/CBbN0.png)](https://i.sstatic.net/CBbN0.png)
  
  And a similar image from [the QUILT paper](https://www.nature.com/articles/s41588-021-00877-0.pdf):
  
  [![enter image description here](https://i.sstatic.net/6yTqT.png)](https://i.sstatic.net/6yTqT.png)
  
  You might also want to check out some of the methodology in this paper <http://m.genome.cshlp.org/content/31/4/529>
  
  
  ---
  
  