<p>Your approach is correct - it's not the only one permitted under their conditions, but it would be the standard way given only that information.</p>

<p>F-test compares a "full model" (predicting the phenotype using genotype and an intercept) versus a "reduced model" (predicting the phenotype using only the intercept, i.e. the mean value of phenotype). The p-value tells whether the full model predicts better, i.e. whether adding the genotype variable significantly improves the prediction. In this single-variable case, it should be approximately equal to the p-value for the beta coefficient of the genotype, if that's more familiar.</p>

<p>Anyway, this isn't specific to genetics, so you can check out other online sources - e.g. <a href="https://onlinecourses.science.psu.edu/stat501/node/295" rel="nofollow noreferrer">https://onlinecourses.science.psu.edu/stat501/node/295</a> for a longer, but accessible explanation of the F-test, and <a href="https://stats.stackexchange.com/">https://stats.stackexchange.com/</a> for many answers to similar questions.</p>

CC BY-SA 3.0
2018-02-12T09:23:59.950Z
2018-02-12T09:23:59.950Z
juod
<p>So if I attempt this question using their method I put the data of genotype and phenotype into a data frame in R then use <code>lm()</code> to do linear regression and <code>summary()</code> to get an F statistic with p value 0.54. I'm not connecting what the F statistic means in this case to the answer.</p>

<p>Question 2: Assuming that we have the following SNP and phenotype data, is the SNP significantly associated with the phenotype? Here, we represent each SNP site as the number of minor alleles on that locus, so 0 and 2 are for major and minor homozygous sites, respectively, and 1 is for the heterozygous sites. We also assume that minor alleles contribute to the phenotype and the effect is additive. In other words, the effect from a minor homozygous site should be twice as large as that from a heterozygous site. You may use any test methods introduced in the chapter. How about permutation tests?</p>

<blockquote>
  <p>GENOTYPE: 1, 2, 2, 1, 1, 0, 2, 0, 1, 0</p>
  
  <p>PHENOTYPE: 0.53, 0.78, 0.81, -0.23, -0.73, 0.81, 0.27, 2.59, 1.84, 0.03</p>
</blockquote>

<p>Answer: Take the linear regression with F-test as an example, the p-value without the permutation test is 0.54 and is 0.55 after 1000 permutation (this number could be different with your answer because of the randomness of the permutation test).</p>

<p>Link to where I'm getting this: <a href="http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002828#pcbi.1002828-Balding1" rel="nofollow noreferrer">http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002828#pcbi.1002828-Balding1</a> </p>

CC BY-SA 3.0
2018-02-11T23:27:37.813Z
What part do you not understand? What did you tried? Did you look at the help pages of the test or any other resource of statistics?
llrs
CC BY-SA 3.0
2018-02-11T23:31:22.303Z
Does the edit make it more clear? Sorry, the first question was poorly written
user2332
CC BY-SA 3.0
2018-02-11T23:41:07.073Z
Yes, much better
llrs
<snp><gwas>
Assuming that we have the following SNP and phenotype data, is the SNP significantly associated with the phenotype?
# Assuming that we have the following SNP and phenotype data, is the SNP significantly associated with the phenotype?

(Asked by: username_0 on 2018-02-11)

So if I attempt this question using their method I put the data of genotype and phenotype into a data frame in R then use `lm()` to do linear regression and `summary()` to get an F statistic with p value 0.54. I'm not connecting what the F statistic means in this case to the answer.

Question 2: Assuming that we have the following SNP and phenotype data, is the SNP significantly associated with the phenotype? Here, we represent each SNP site as the number of minor alleles on that locus, so 0 and 2 are for major and minor homozygous sites, respectively, and 1 is for the heterozygous sites. We also assume that minor alleles contribute to the phenotype and the effect is additive. In other words, the effect from a minor homozygous site should be twice as large as that from a heterozygous site. You may use any test methods introduced in the chapter. How about permutation tests?

> GENOTYPE: 1, 2, 2, 1, 1, 0, 2, 0, 1, 0
>
> PHENOTYPE: 0.53, 0.78, 0.81, -0.23, -0.73, 0.81, 0.27, 2.59, 1.84, 0.03

Answer: Take the linear regression with F-test as an example, the p-value without the permutation test is 0.54 and is 0.55 after 1000 permutation (this number could be different with your answer because of the randomness of the permutation test).

Link to where I'm getting this: <http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002828#pcbi.1002828-Balding1>

**Tags:** snp, gwas
> **Comments:**
> **username_1**: What part do you not understand? What did you tried? Did you look at the help pages of the test or any other resource of statistics?  
> **username_0**: Does the edit make it more clear? Sorry, the first question was poorly written  
> **username_1**: Yes, much better  

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## 1 answers

### Answer by username_2 on 2018-02-12. Score: 2 | Accepted answer

Your approach is correct - it's not the only one permitted under their conditions, but it would be the standard way given only that information.

F-test compares a "full model" (predicting the phenotype using genotype and an intercept) versus a "reduced model" (predicting the phenotype using only the intercept, i.e. the mean value of phenotype). The p-value tells whether the full model predicts better, i.e. whether adding the genotype variable significantly improves the prediction. In this single-variable case, it should be approximately equal to the p-value for the beta coefficient of the genotype, if that's more familiar.

Anyway, this isn't specific to genetics, so you can check out other online sources - e.g. <https://onlinecourses.science.psu.edu/stat501/node/295> for a longer, but accessible explanation of the F-test, and <https://stats.stackexchange.com/> for many answers to similar questions.


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