Analysis of multilocus fingerprinting data sets containing missing data

Abstract

Missing data are commonly encountered using multilocus, fragment‐based (dominant) fingerprinting methods, such as random amplified polymorphic DNA (RAPD) or amplified fragment length polymorphism (AFLP). Data sets containing missing data have been analysed by eliminating those bands or samples with missing data, assigning values to missing data or ignoring the problem. Here, we present a method that uses random assignments of band presence–absence to the missing data, implemented by the computer program famd (available from http://homepage.univie.ac.at/philipp.maria.schlueter/famd.html), for analyses based on pairwise similarity and Shannon's index. When missing values group in a data set, sample or band elimination is likely to be the most appropriate action. However, when missing values are scattered across the data set, minimum, maximum and average similarity coefficients are a simple means of visualizing the effects of missing data on tree structure. Our approach indicates the range of values that a data set containing missing data points might generate, and forces the investigator to consider the effects of missing values on data interpretation.

Genetic diversity among Polish landraces of common oat (Avena sativa L.)

“…The Polymorphic Information Content (PIC) for each marker was calculated using the formula described by Roldan-Ruiz et al (2000). Genetic similarity for each pair of accessions was estimated according to Dice (1945), Jaccard (1908) and simple matching coefficients in FAMD 1.25 (Schlüter and Harris 2006). For the resulting matrices the Pearson's correlation analysis was performed.…”

Population structure and diversity of the pathogenic fungus Aspergillus fumigatus isolated from different sources and geographic origins

“…Additionally, expected heterozygosity per population and average heterozygosity were calculated using allele frequencies according to Zhivotovsky's Bayesian method (Zhivotovsky 1999). Analysis of molecular variance (AMOVA) using FAMD v 1.1β (Schlüter & Harris 2006) was performed to partition the molecular variance into different hierarchical levels. AM-OVA analysis was carried out to test differences among isolations, among isolations within countries and between isolation sources (clinical and environmental).…”

Genetic diversity in Prunus spinosa L. – challenges in the use of autochthonous sources

“…Neutrality of the used markers was tested with Fst outlier methods implemented in LOSITAN (Antao et al 2008) and Tajima's D pairwise distance method implemented in Arlequin (Excoffier et al 2005) using 1000 simulations. FAMD (Schlüter and Harris 2006) was used to compile descriptive statistics and to compute genetic distances for pairwise Fst values used in AMOVA and PCoA. Mantel test was conducted using the Isolation By Distance Web Service (Jensen et al 2005).…”