Estimating pollen flow using SSR markers and paternity exclusion: accounting for mistyping

Abstract

Highly informative genetic markers, such as simple sequence repeats (SSRs), can be used to directly measure pollen flow by parentage analysis. However, mistyping (i.e. false inference of genotypes caused by the occurrence of null alleles, mutations, and detection errors) can lead to substantial biases in the estimates obtained. Using computer simulations, we evaluated a direct method for estimating pollen immigration using SSR markers and a paternity exclusion approach. This method accounts for mistyping and does not rely on assumptions about the distribution of male reproductive success. If ignored, even minor rates of mistyping (1.5%) resulted in overestimating pollen immigration by up to 150%. When we required at least two mismatching loci before excluding candidate fathers from paternity, the resulting pollen immigration estimates had small biases for rates of mistyping up to 4.5%. Requiring at least three mismatches for exclusion was needed to minimize the upward biases of pollen immigration caused by rates of mistyping up to 10.5%. The minimum number of highly variable SSR loci needed to minimize cryptic gene flow and obtain reliable estimates of pollen immigration varied from five to seven for a sampling scheme applicable to most conifers (i.e. when paternal haplotypes can be unambiguously determined). Between five and nine highly variable SSR loci were needed for a more general sampling scheme that is applicable to all diploid seed plants. With moderately variable SSR markers, consistently accurate estimates of pollen immigration could be obtained only for rates of mistyping up to 4.5%. We developed the POLLEN FLOW (PFL) computer program which can be used to obtain unbiased and precise estimates of pollen immigration under a wide range of conditions, including population sizes as large as 600 parents and mistyping rates as high as 10.5%.

Paternity analysis reveals significant isolation and near neighbor pollen dispersal in small Cariniana legalis Mart. Kuntze populations in the Brazilian Atlantic Forest

“…The standard error of mean selfing was estimated assuming a binomial distribution, as $SEfalse(sfalse)=\sqrt{sfalse(1-sfalse)/m_{st}}$, where m st is the number of sampled seed trees in each fragment (Slavov et al. 2005). To estimate the rate of mating among relatives ( t r ), we calculated the pairwise coancestry coefficient ( θ ij ) among seed trees and assigned pollen donors by paternity analysis, using also the Spagedi 1.3 program (Hardy and Vekemans 2002).…”

“…The standard error of pollen immigration [ SE ( m )] was estimated as $SEfalse(mfalse)=m\sqrt{(1-b)/(bn)}$ (Slavov et al. 2005), where n is the number of seeds sampled from each seed tree. We also calculated the number of pollen donors that mated with each seed tree ( N ep ) and from this parameter the paternity correlation within progenies, r p  = 2/ N ep (Ritland 1989).…”

Comparison of pollen gene flow among four European beech (Fagus sylvatica L.) populations characterized by different management regimes

“…Different analytical approaches are currently available to study contemporary gene flow. They differ in rationale, assumptions on the genetic variability of the background population and ways to handle possible errors due to low discriminatory power of the marker set or the presence of genotyping errors (Burczyk and Chybicki, 2004;Slavov et al, 2005;Jones et al, 2010). Recent methodological improvements allow to estimate the statistical precision of a parentage/paternity assignment for a given sample of a reproductive population (Gerber et al, 2003;Burczyk and Chybicki, 2004).…”

Estimating gametic introgression rates in a risk assessment context: a case study with Scots pine relicts

“…Genetic parentage exclusion methods have been developed for assessing immigration into small populations, such as seed orchards (Adams et al, 1997;Plomion et al, 2001;Stoehr and Newton, 2002;Slavov et al, 2005), but these methods are not feasible for large-scale experiments, because they require exhaustive genotyping of all potential parents within the recipient population. Genetic assignment methods, on the other hand, have been designed primarily to ascertain population membership of individuals and admixture proportions, and are not suitable for addressing the statistical question of obtaining accurate migration rates exclusively (Manel et al, 2005).…”