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Published equation contexts

N^e≈13 (r^2−1S)\hat{N}_e \approx \frac{1}{3\,(\hat{r}^2 - \tfrac{1}{S})}

Why this formula appears here

The relationship Waples and Do build their estimator around, in the form most commonly used for the single-sample method: N^e≈13 (r^2−1S)\hat{N}_e \approx \frac{1}{3\,(\hat{r}^2 - \tfrac{1}{S})}. where r^2\hat{r}^2 is the mean squared correlation of allele frequencies across pairs of loci observed in the sample, and S is the sample size, with the 1/S term correcting for the sampling bias that a finite number of genotyped individuals introduces into r^2\hat{r}^2 even when the true population NeN_e is large. The practical consequence practitioners have to internalize: r^2\hat{r}^2 is estimated with noise from a finite sample, and that noise itself creates apparent linkage disequilibrium indistinguishable from drift-generated LD unless it is explicitly…

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3 (r^2−1S)3\,(\hat{r}^2 - \tfrac{1}{S})

Denominator: 3(hatr^2 - tfrac1S)

The complete quantity below the fraction bar; it must be nonzero for this division.

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How to interpret it

With a fixed numerator, increasing a nonzero denominator reduces the fraction. Its accuracy depends on the assumptions and range of use described in the article.

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Published contexts (1)

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N^e≈13 (r^2−1S)\hat{N}_e \approx \frac{1}{3\,(\hat{r}^2 - \tfrac{1}{S})}

Equation 21 · Biology

Evolutionary Biology and Ecology in Practice: An Advanced Technical Guide

This equation gives an approximation: it relates the quantities while allowing an approximation.

The relationship Waples and Do build their estimator around, in the form most commonly used for the single-sample method: N^e≈13 (r^2−1S)\hat{N}_e \approx \frac{1}{3\,(\hat{r}^2 - \tfrac{1}{S})}. where r^2\hat{r}^2 is the mean squared correlation of allele frequencies across pairs of loci observed in the sample, and S is the sample size, with the 1/S term correcting for the sampling bias that a finite number of genotyped individuals introduces into r^2\hat{r}^2 even when the true population NeN_e is large. The practical consequence practitioners have to internalize: r^2\hat{r}^2 is estimated with noise from a finite sample, and that noise itself creates apparent linkage disequilibrium indistinguishable from drift-generated LD unless it is explicitly…

Meanings in this article

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