Hello,
I have a question concerning the difference between the linkage disequilibrium measures D' and r-squared. I know the formal definitions. But I have problems understanding the different concepts behind D' and r-squared? And what does it mean if D' is low and r-squared is high (and vice versa).
Thanks wim
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written
by
wkreinen •
3 answers
D' : A scaled version of D
- Ranges between -1 and +1
- ±1 implies at least one of the observed haplotypes was not observed
- If allele frequencies are similar, high D' means the markers are good surrogates for each other
- D' estimates inflated in small samples (cons)
- D' estimates inflated when one allele is rare (cons)
r2: Ranges between 0 and 1. It is the measure preferred by population geneticists
- 1 when the two markers provide identical information.
- 0 when they are in perfect equilibrium.
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written
by
Felix Francis
Yes, I don't think this is the right place to ask those questions. However here is my answer.
D' and r2 have a big difference in that a high value of D' does not mean that one locus can predict the other with high accuracy, which in the case of say imputing SNPs could be a major issue. On the other hand, an r2 of 1 implies perfect predictability; if we know the allele at one locus we can predict perfectly the allele at the second locus and vice-versa.
A new and very good book in my opinion is The Fundamentals of Modern Statistical Genetics from Laird and Lange. You can find more info there.
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written
by
GabrielMontenegro
This isn't a bioinformatics question (and might be a homework question), but everything you want to know, including derivations and follow-through links for more in-depth explanation, is here.
Log in to answer this question.
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Yes, sorry. It is not a classical bioinformatic question. Thanks for the wikipedia link. Yes, I know that stuff. Unfortunately I did not understand it and obviously I could not make it clear what I did not understand ...
In my understanding of bioinformatics it is not a fault if one tries to explain some basic conceptual differences that make a difference in the end of the day. My impression is that D' and r-squared are used (quite often) arbitrarily.
I try to be more detailed in asking my question ...
D' and r-squared are different (and popular besinde others) approaches to normalise D. D' uses the theorectical maximum of D to do the normalisation, r-squared uses correlation coefficient. So far so good ... But I have problems to understand which conditions have an influence on choosing D' or r-squared as a parameter of ld. What are the important criteria to choose D' or r-squared? Maybe, this is the wrong forum to ask a question like this. If this is the case I wish to apologise. Anyway, I would appreciate if somebody could give me a productive hint.
All the best
Wim
"In my understanding of bioinformatics it is not a fault if one tries to explain some basic conceptual differences that make a difference in the end of the day."
The problem is that the question isn't about bioinformatics at all. It's about population genetics. This group is for bioinformatics questions - issues specifically related to data handling, usage, and interpretation of biological datasets, not necessarily introductory population genetic theory outside the scope of a program. Also, you should consider posting a follow-up as a comment and not an answer to your question.