Maximum-Likelihood Estimation of Allelic Dropout and False Allele Error Rates From Microsatellite Genotypes in the Absence of Reference Data

Abstract

The importance of quantifying and accounting for stochastic genotyping errors when analyzing microsatellite data is increasingly being recognized. This awareness is motivating the development of data analysis methods that not only take errors into consideration but also recognize the difference between two distinct classes of error, allelic dropout and false alleles. Currently methods to estimate rates of allelic dropout and false alleles depend upon the availability of error-free reference genotypes or reliable pedigree data, which are often not available. We have developed a maximum-likelihood-based method for estimating these error rates from a single replication of a sample of genotypes. Simulations show it to be both accurate and robust to modest violations of its underlying assumptions. We have applied the method to estimating error rates in two microsatellite data sets. It is implemented in a computer program, Pedant, which estimates allelic dropout and false allele error rates with 95% confidence regions from microsatellite genotype data and performs power analysis. Pedant is freely available at http:/ /www.stats.gla.ac. uk/$paulj/pedant.html.

Estimation of population structure in coastal Douglas-fir [Pseudotsuga menziesii (Mirb.) Franco var. menziesii] using allozyme and microsatellite markers

“…If the segregation ratio deviates much from 1:1 in a pooled individual sample, then a prevalent allele can over-compete an underrepresented allele during PCR, especially if a former one represents a shorter DNA fragment (aka "short allele dominance"). Several solutions were offered recently to deal with this problem (see Dewoody et al 2006 for review), but most of them require multiple repeats or at least duplicate microsatellite genotypes (e.g., Miller et al 2002;Johnson and Haydon 2007). We used the MICRO-CHECKER software that proved to be very efficient to minimize the problem.…”

QTL Mapping for Reproductive Maturity in Lowland Switchgrass Populations

“…A high collinearity between the hybrid population map and the published selfed population maps was observed, excepting some discrepancies described as following: inversions in marker order between our hybrid map and the published selfed population maps. The local rearrangements are common in plant genome mapping (Paterson et al 1996), and genotyping errors could also be a reason (Johnson and Haydon 2007). In addition, compared with the established linkage map of the selfed population (Liu et al 2013), seven markers were mapped to their homeologous linkage groups in the hybrid population map, from LG 1b to 1a, 2a to 2b, 3b to 3a, 5b to 5a, 6a to 6b.…”

Noninvasive genetic sampling allows estimation of capercaillie numbers and population structure in the Bohemian Forest

“…We used the program PEDANT ver. 1.0 (Johnson and Haydon 2007) to estimate the rate of genotyping error per allele due to allelic dropout and false alleles.…”