sHe {biotools} | R Documentation |

## Spatial Analysis of Gene Diversity

### Description

Estimate spatial gene diversity (expected heterozygozity - *He*) through the individual-centred approach by Manel et al. (2007).
`sHe()`

calculates the unbiased estimate of *He* based on the information of allele frequency obtained from codominant or
dominant markers in individuals within a circular moving windows of known radius over the sampling area.

### Usage

```
sHe(x, coord.cols = 1:2, marker.cols = 3:4,
marker.type = c("codominant", "dominant"),
grid = NULL, latlong2km = TRUE, radius, nmin = NULL)
```

### Arguments

`x` |
a data frame or numeric matrix containing columns with coordinates of individuals and marker genotyping |

`coord.cols` |
a vector of integer giving the columns of coordinates in |

`marker.cols` |
a vector of integer giving the columns of markers in |

`marker.type` |
a character; the type of molecular marker |

`grid` |
optional; a two-column matrix containing coordinates over which to predict |

`latlong2km` |
logical; should coordinates be converted from lat/long format into kilometer-grid based? |

`radius` |
the radius of the moving window. It must be in the same format as sampling coordinates |

`nmin` |
optional; a numeric value indicating the minimum number of individuals used to calculate |

### Details

The unbiased estimate of expected heterogygozity (Nei, 1978) is given by:

` He = (1 - \sum_{i=1}^{n} p_{i}^{2}) \frac{2n}{2n - 1} `

where `p_{i}`

is the frequency of the i-th allele per locus considering the `n`

individuals in a certain location.

### Value

A list of

`diversity` |
a data frame with the following columns: |

`mHe` |
a matrix containing the estimates of |

`locations` |
a numeric matrix containing the sampling coordinates, as provides as input. |

### Warning

Depending on the dimension of `x`

and/or `grid`

, `sHe()`

can be time demanding.

### Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

Ivandilson Pessoa Pinto de Menezes <ivan.menezes@ifgoiano.edu.br>

### References

da Silva, A.R.; Malafaia, G.; Menezes, I.P.P. (2017) biotools: an R function to predict
spatial gene diversity via an individual-based approach. *Genetics and Molecular Research*,
**16**: gmr16029655.

Manel, S., Berthoud, F., Bellemain, E., Gaudeul, M., Luikart, G., Swenson, J.E., Waits, L.P.,
Taberlet, P.; Intrabiodiv Consortium. (2007) A new individual-based spatial approach for
identifying genetic discontinuities in natural populations. *Molecular Ecology*, **16**:2031-2043.

Nei, M. (1978) Estimation of average heterozygozity and genetic distance from a small number of individuals.
*Genetics*, **89**: 583-590.

### See Also

### Examples

```
data(moco)
data(brazil)
# check points
plot(brazil, cex = 0.1, col = "gray")
points(Lat ~ Lon, data = moco, col = "blue", pch = 20)
# using a retangular grid (not passed as input!)
# ex <- sHe(x = moco, coord.cols = 1:2,
# marker.cols = 3:20, marker.type = "codominant",
# grid = NULL, radius = 150)
#ex
# plot(ex, xlab = "Lon", ylab = "Lat")
# A FANCIER PLOT...
# using Brazil's coordinates as prediction grid
# ex2 <- sHe(x = moco, coord.cols = 1:2,
# marker.cols = 3:20, marker.type = "codominant",
# grid = brazil, radius = 150)
# ex2
#
# library(maps)
# borders <- data.frame(x = map("world", "brazil")$x,
# y = map("world", "brazil")$y)
#
# library(latticeExtra)
# plot(ex2, xlab = "Lon", ylab = "Lat",
# xlim = c(-75, -30), ylim = c(-35, 10), aspect = "iso") +
# latticeExtra::as.layer(xyplot(y ~ x, data = borders, type = "l")) +
# latticeExtra::as.layer(xyplot(Lat ~ Lon, data = moco))
# End (not run)
```

*biotools*version 4.2 Index]