<p>Visualise your problem and solution.</p>
<p>6 SNPs is not a particularly large search space (64 possible combinations assuming a dominant or recessive model with dimorphisms, 729 if heterozygous variants are taken into account). I would graph all those haplotypes out in a heatmap (but only the ones present in your samples, so there'd be a maximum of 80 different ones) and make sure that there is a clear pattern.</p>
<p>If population stratification is a concern, then you should also show the PCA, with separate colouring for the different phenotypes. If the two groups have similar spread in the PCA (which would be expected for a trait that is only affected by a small number of genes), then PCA correction doesn't make sense (and may lead to false associations).</p>

CC BY-SA 4.0
2024-04-07T12:21:48.950Z
2024-04-07T12:21:48.950Z
gringer
<p>On 80 WGS samples, I'm dissecting SNP signatures linked to milk production in a scarcely studied animal. Post-variant calling and QC association analysis have been tricky. I'm here to tap into our collective wisdom and refine my approach.
Study Snapshot:</p>
<p>Objective: Identify SNPs that differentiate between high (&gt;5 L/day) and low (&lt;5 L/day) milk producers.
Data: 80 WGS samples.
Methodology:</p>
<p>Phenotypes were normalized (high = 1, low = 0).
PCs from PCA are used to correct for population stratification, with phenotype information as a covariate.
Initial approach: linear models, then FDR and Bonferroni for multiple comparison correction. A significant SNP threshold was set at &lt;1e-5, yielding four significant SNPs.
Expanded to Linear Mixed Models (LMM), we found six significant SNPs at 1e-5 and 99 at 1e-4, focusing on positive associations with high milk production.</p>
<p>Seeking Guidance:</p>
<p>Feasibility: Can 80 WGS samples provide a reliable genetic signature for milk production?
Methodology Recommendations: Given my data and initial results, what methodologies or modifications could improve the analysis?</p>

<ngs><r><snp><wgs>
SNP Signatures with Limited WGS Data:
# SNP Signatures with Limited WGS Data:

(Asked by: username_0 on 2024-04-06)

On 80 WGS samples, I'm dissecting SNP signatures linked to milk production in a scarcely studied animal. Post-variant calling and QC association analysis have been tricky. I'm here to tap into our collective wisdom and refine my approach.
Study Snapshot:

Objective: Identify SNPs that differentiate between high (>5 L/day) and low (<5 L/day) milk producers.
Data: 80 WGS samples.
Methodology:

Phenotypes were normalized (high = 1, low = 0).
PCs from PCA are used to correct for population stratification, with phenotype information as a covariate.
Initial approach: linear models, then FDR and Bonferroni for multiple comparison correction. A significant SNP threshold was set at <1e-5, yielding four significant SNPs.
Expanded to Linear Mixed Models (LMM), we found six significant SNPs at 1e-5 and 99 at 1e-4, focusing on positive associations with high milk production.

Seeking Guidance:

Feasibility: Can 80 WGS samples provide a reliable genetic signature for milk production?
Methodology Recommendations: Given my data and initial results, what methodologies or modifications could improve the analysis?

**Tags:** ngs, r, snp, wgs

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## 1 answers

### Answer by username_1 on 2024-04-07. Score: 2 | Accepted answer

Visualise your problem and solution.

6 SNPs is not a particularly large search space (64 possible combinations assuming a dominant or recessive model with dimorphisms, 729 if heterozygous variants are taken into account). I would graph all those haplotypes out in a heatmap (but only the ones present in your samples, so there'd be a maximum of 80 different ones) and make sure that there is a clear pattern.

If population stratification is a concern, then you should also show the PCA, with separate colouring for the different phenotypes. If the two groups have similar spread in the PCA (which would be expected for a trait that is only affected by a small number of genes), then PCA correction doesn't make sense (and may lead to false associations).


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