Unbiased Relatedness Estimation in Structured Populations

Abstract

Knowledge of the genetic relatedness between individuals is important in many research areas in quantitative genetics, conservation genetics, forensics, evolution, and ecology. In the absence of pedigree records, relatedness can be estimated from genetic marker data using a number of estimators. These estimators, however, make the critical assumption of a large random mating population without genetic structures. The assumption is frequently violated in the real world where geographic/social structures or nonrandom mating usually lead to genetic structures. In this study, I investigated two approaches to the estimation of relatedness between a pair of individuals from a subpopulation due to recent common ancestors (i.e., relatedness is defined and measured with the current focal subpopulation as reference). The indirect approach uses the allele frequencies of the entire population with and without accounting for the population structure, and the direct approach uses the allele frequencies of the current focal subpopulation. I found by simulations that currently widely applied relatedness estimators are upwardly biased under the indirect approach, but can be modified to become unbiased and more accurate by using Wright's F st to account for population structures. However, the modified unbiased estimators under the indirect approach are clearly inferior to the unmodified original estimators under the direct approach, even when small samples are used in estimating both allele frequencies and relatedness.K NOWING the degree of relatedness between individuals is essential in many research areas in quantitative genetics, conservation genetics, forensics, evolution, and ecology (Ritland 1996;Lynch and Ritland 1999;Weir et al. 2006). The expected value of relatedness between two individuals (e.g., 0.5 for parent-offspring in a large random mating population) can be easily calculated from their pedigree records. When pedigree is unavailable, incomplete, or unreliable, genetic marker information can be used instead to obtain an estimate of the realized value of relatedness, using a number of estimators developed for this purpose (e.g

Genetic and genomic monitoring with minimally invasive sampling methods

“…Metrics generally used to measure relatedness between two individuals estimate either a summary statistic (such as coancestry coefficient it its equivalents), which would correspond to the relatedness between two individuals, or the probability that two individuals are linked with a particular relationship (parent-offspring, first cousins, self-outbred sibs…) given the data (Wang, 2011). In some cases, the reliability of relatedness estimates can be limited, especially when the population under study exhibits low genetic variation for the marker set; therefore a priori simulations should be performed to select the most appropriate estimator and assess its accuracy (Glaubitz et al 2003;Taylor, 2015).…”

Clonal structure and high genetic diversity at peripheral populations of Sorbus torminalis (L.) Crantz.

“…The coordinates of a genet were determined as the arithmetic mean of coordinates of all individuals belonging to a given genet. The kinship coefficients (FijLoiselle et al 1995) between pairs of individuals (or genets) were computed relative to local (within population) allele frequencies as suggested by Wang (2011), and they were regressed on the logarithm of spatial distance to estimate the logarithmic regression slope blog. The significance of blog was tested based on 10,000 permutations of spatial positions of individuals within populations.…”

Higher reproductive success for chimeras than solitary individuals in the kelp Lessonia spicata but no benefit for individual genotypes

“…Yet a high variance was observed among chimeric thalli of L. spicata and, in most grids, average intragroup relatedness fell within the range of background population R-values. Relatedness estimates are calculated in relation to the allele frequencies of the population, assuming a large population size and random mating (Wang 2011). When these assumptions are not met, relatedness coefficients tend to be biased (Wang 2011) and the detection of a further structure due to group formation is less likely, unless it is a very strong pattern.…”

“…Relatedness estimates are calculated in relation to the allele frequencies of the population, assuming a large population size and random mating (Wang 2011). When these assumptions are not met, relatedness coefficients tend to be biased (Wang 2011) and the detection of a further structure due to group formation is less likely, unless it is a very strong pattern. Strong spatial structure has been reported for L. spicata, with population genetic differentiation detected at scales of a few km (Tellier et al 2009) and down to 25 m within sampling sites for L. berteroana (Faugeron et al 2005).…”