Warning: preg_match(): Compilation failed: nothing to repeat at offset 14 in /home/xfoklmxg/public_html/plugins/fabrique_auto/humanpath/humanpath_fonctions.php on line 61

Warning: preg_match(): Compilation failed: nothing to repeat at offset 37 in /home/xfoklmxg/public_html/plugins/fabrique_auto/humanpath/humanpath_fonctions.php on line 61

Warning: preg_match(): Compilation failed: missing ) at offset 56 in /home/xfoklmxg/public_html/plugins/fabrique_auto/humanpath/humanpath_fonctions.php on line 61
small population - Humpath.com - Human pathology

Home > Technical section > Biology > Molecular biology > Population genetics > small population

small population

Tuesday 15 May 2007

Small populations are dominated by unique patterns of variance, largely characterized by rapid drift of allele frequencies.

Although the variance components of genetic datasets have long been recognized, most population genetic studies still treat all sampling locations equally despite differences in sampling and effective population sizes.

Because excluding the effects of variance can lead to significant biases in historical reconstruction, variance components should be incorporated explicitly into population genetic analyses.

The possible magnitude of variance effects in small populations is illustrated here via a case study of Y-chromosome haplogroup diversity in the Vanuatu Archipelago.

Deme-based modelling is used to simulate allele frequencies through time, and conservative confidence bounds are placed on the accumulation of stochastic variance effects, including diachronic genetic drift and contemporary sampling error.

When the information content of the dataset has been ascertained, demographic models with parameters falling outside the confidence bounds of the variance components can then be accepted with some statistical confidence.


- Cox M. Extreme Patterns of Variance in Small Populations: Placing Limits on Human Y-Chromosome Diversity through Time in the Vanuatu Archipelago. Ann Hum Genet. 2007 May;71(Pt 3):390-406. PMID: 17147694