<p>LD Clumping with <span class="math-container">$r^2$</span> filtering seems like a good start, but bear in mind that population structure has a significant effect on association statistics.</p>
<p>I recommend carrying out separate PCAs (or some other dimensional reduction) on your variable groups (e.g. risk/non-risk, or high/medium/low) to make sure that their genomic population structure is similar. Alternatively / equivalently, carry out a whole-group reduction, and confirm that the coordinate centres for each variable group are similar.</p>
<p>If the population structures are <em>not</em> similar (which is very likely), then it's important to [also] exclude structure-specific SNPs from the analysis, as these will confound the results of your analysis. Use a clustering method to identify people with similar genetic structure, and treat the different clusters as different trait groupings for identifying those SNPs.</p>

CC BY-SA 4.0
2022-03-16T23:05:05.233Z
2022-03-16T23:05:05.233Z
gringer
<p>I am working with some GWAS summary statistics. My ultimate goal is to extract variants to use as instrument variables for Mendelian Randomization analysis, which requires me to find SNPs that are independently associated with the exposure variable of interest.</p>
<p>My current workflow involves extracting SNPs from summary statistics with P-value &lt; 5E-08, then performing LD Clumping with a stringent R^2 cutoff (R^2&lt;0.05) within a locus. Is this sufficient to ensure that my variants are independently associated, or should I also plan on running conditional analysis(e.g. GCTA-COJO) in addition to/in lieu of clumping for my variants?</p>

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2022-03-16T09:26:58.817Z
Please clarify your specific problem or provide additional details to highlight exactly what you need. Input/output examples are *really* helpful. As it's currently written, it's hard to tell exactly what you're asking.
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<gwas>
LD Clumping vs. Conditional Analysis for independent associations
# LD Clumping vs. Conditional Analysis for independent associations

(Asked by: username_0 on 2022-03-16)

I am working with some GWAS summary statistics. My ultimate goal is to extract variants to use as instrument variables for Mendelian Randomization analysis, which requires me to find SNPs that are independently associated with the exposure variable of interest.

My current workflow involves extracting SNPs from summary statistics with P-value < 5E-08, then performing LD Clumping with a stringent R^2 cutoff (R^2<0.05) within a locus. Is this sufficient to ensure that my variants are independently associated, or should I also plan on running conditional analysis(e.g. GCTA-COJO) in addition to/in lieu of clumping for my variants?

**Tags:** gwas
> **Comments:**
> **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. Input/output examples are *really* helpful. As it's currently written, it's hard to tell exactly what you're asking.  

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## 1 answers

### Answer by username_2 on 2022-03-16. Score: 1

LD Clumping with $r^2$ filtering seems like a good start, but bear in mind that population structure has a significant effect on association statistics.

I recommend carrying out separate PCAs (or some other dimensional reduction) on your variable groups (e.g. risk/non-risk, or high/medium/low) to make sure that their genomic population structure is similar. Alternatively / equivalently, carry out a whole-group reduction, and confirm that the coordinate centres for each variable group are similar.

If the population structures are *not* similar (which is very likely), then it's important to [also] exclude structure-specific SNPs from the analysis, as these will confound the results of your analysis. Use a clustering method to identify people with similar genetic structure, and treat the different clusters as different trait groupings for identifying those SNPs.


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