Answers.Body: <p>You can use <a href="https://bedops.readthedocs.io/en/latest/content/reference/set-operations/closest-features.html" rel="nofollow noreferrer">BEDOPS' closest-features</a> tool. You only need to have the SNPs coordinates in .bed format and a .bed file with gene locations. BEDOPS will look for the closest genes upstream and downstream of your SNPs. The command will look something like this: </p>
  
  <pre><code>closest-features yourSNPs.bed yourAnnotation.bed
  </code></pre>
  
  <p>Then, you can obtain your functional annotations through different tools such as biomart. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-07T08:46:07.073Z
Answers.Text: OK thanks very much for the help. Will try this one out.
Answers.UserDisplayName: antonymaodzeka
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-08-07T08:38:19.983Z
Answers.LastActivityDate: 2019-08-07T08:38:19.983Z
Answers.UserDisplayName: jxx_fa
Body: <p>I have performed GWAS and got some significant SNPs. How can I get candidates genes in the flanking regions of those SNPs and also their functional annotations (eg. KEGG). </p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-07T10:49:19.463Z
Comments.Text: Hi and welcome to BioinfoSE! The amount of information in the post is far too low to answer it. What API you are using? What packages are you familiar with? Any information on biological system is also very helpful. Could also clarify the question, it infers that you already have the genes for the SNPs and annotation of the gene?
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-07T11:10:34.157Z
Comments.Text: hi. I used TASSEL and the R package GAPIT to perform GWAS and got a list of significant SNPs  with info such as chromosome, location (bp), p value. From these SNPs now I need to identify candidate genes from flanking regions of these SNPs and also need to annotate these genes according to their function which will make it easy to identify the potential candidates. So far information from @plat has been helpful and any further help is welcome.
Comments.UserDisplayName: antonymaodzeka
Tags: <snp><gwas>
Title: significant SNPs to annotated candidate genes
ThreadText: # significant SNPs to annotated candidate genes
  
  (Asked by: username_0 on 2019-08-07)
  
  I have performed GWAS and got some significant SNPs. How can I get candidates genes in the flanking regions of those SNPs and also their functional annotations (eg. KEGG).
  
  **Tags:** snp, gwas
  
  > **Comments:**
  > **username_1**: Hi and welcome to BioinfoSE! The amount of information in the post is far too low to answer it. What API you are using? What packages are you familiar with? Any information on biological system is also very helpful. Could also clarify the question, it infers that you already have the genes for the SNPs and annotation of the gene?  
  > **username_0**: hi. I used TASSEL and the R package GAPIT to perform GWAS and got a list of significant SNPs  with info such as chromosome, location (bp), p value. From these SNPs now I need to identify candidate genes from flanking regions of these SNPs and also need to annotate these genes according to their function which will make it easy to identify the potential candidates. So far information from @plat has been helpful and any further help is welcome.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2019-08-07. Score: 2
  
  You can use [BEDOPS' closest-features](https://bedops.readthedocs.io/en/latest/content/reference/set-operations/closest-features.html) tool. You only need to have the SNPs coordinates in .bed format and a .bed file with gene locations. BEDOPS will look for the closest genes upstream and downstream of your SNPs. The command will look something like this:
  
  ```
  closest-features yourSNPs.bed yourAnnotation.bed
  
  ```
  
  Then, you can obtain your functional annotations through different tools such as biomart.
  
  > **Comments:**
  > **username_0**: OK thanks very much for the help. Will try this one out.  
  
  ---
  
  