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Amplification, Next-generation Sequencing, and Genomic DNA Mapping of Retroviral Integration Sites

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Published on: March 22, 2016

Biotools: an R function to predict spatial gene diversity via an individual-based approach.

A R da Silva¹, G Malafaia², I P P Menezes³

¹Laboratório de Estatística Aplicada, Instituto Federal Goiano, Urutaí, GO, Brasil anderson.silva@ifgoiano.edu.br.

Genetics and Molecular Research : GMR

|April 14, 2017

Summary

This study introduces an R function to estimate gene diversity (expected heterozygosity) across landscapes. This method aids in conservation strategies and detecting genetic boundaries for population management.

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Area of Science:

Population Genetics
Conservation Biology
Bioinformatics

Background:

Gene diversity, measured as expected heterozygosity (H_E), quantifies genetic variability within populations.
Understanding the spatial distribution of H_E is crucial for effective conservation and sampling strategies.
Spatial genetic data can reveal genetic boundaries within a landscape.

Purpose of the Study:

To develop and present a novel method for estimating expected heterozygosity (H_E) across a prediction grid.
To provide an accessible R function for spatial analysis of genetic diversity.
To facilitate the detection of genetic boundaries and inform conservation efforts.

Main Methods:

Adaptation of a Wombling method using assignment tests within a circular moving window.
Estimation of H_E at grid points through spatial prediction.
Implementation of the `sHe()` function in the R package `biotools`.

Main Results:

The `sHe()` function in `biotools` provides a flexible R implementation for estimating spatial H_E.
The method allows for the estimation of genetic diversity across a defined prediction grid.
The approach integrates geographical and genotyping data for spatial genetic analysis.

Conclusions:

The `sHe()` function offers a user-friendly tool for spatial genetic diversity analysis.
This method supports informed decision-making in conservation and population sampling.
The approach effectively maps genetic variability and boundaries in landscapes.