Purging putative siblings from population genetic data sets: a cautionary view

Abstract

Interest has surged recently in removing siblings from population genetic data sets before conducting downstream analyses. However, even if the pedigree is inferred correctly, this has the potential to do more harm than good. We used computer simulations and empirical samples of coho salmon to evaluate strategies for adjusting samples to account for family structure. We compared performance in full samples and siblingreduced samples of estimators of allele frequency (P), population differentiation (F ST ) and effective population size (N e ). Results: (i) unless simulated samples included large family groups together with a component of unrelated individuals, removing siblings generally reduced precision ofP andF ST ; (ii)N e based on the linkage disequilibrium method was largely unbiased using full random samples but became increasingly upwardly biased under aggressive purging of siblings. Under nonrandom sampling (some families over-represented), N e using full samples was downwardly biased; removing just the right 'Goldilocks' fraction of siblings could produce an unbiased estimate, but this sweet spot varied widely among scenarios; (iii) weighting individuals based on the inferred pedigree (to produce a best linear unbiased estimator, BLUE) maximized precision of P when the inferred pedigree was correct but performed poorly when the pedigree was wrong; (iv) a variant of sibling removal that leaves intact small sibling groups appears to be more robust to errors in inferences about family structure. Our results illustrate the complex challenges posed by presence of family structure, suggest that no single optimal solution exists and argue for caution in adjusting population genetic data sets for the presence of putative siblings without fully understanding the consequences.

Recent advances in conservation and population genomics data analysis

“…Many researchers have concluded that it is important to remove putative siblings from population genetics datasets before conducting downstream analyses (Corlett, 2017; Johnson et al., 2016), but there are several good reasons why this can create more problems than it solves (Waples & Anderson, 2017). First, siblings occur naturally in all natural populations, at frequencies that are inversely related to effective population size; therefore, removing siblings erases signals characteristic of small populations and makes the populations appear to be larger.…”

“…As shown by Waples and Anderson (2017), however, performance of the BLUE also depends on having accurate pedigree information. When sample identification is not reliable, the use of the full dataset outperforms BLUE.…”

Historical Processes and Contemporary Anthropogenic Activities Influence Genetic Population Dynamics of Nassau Grouper (Epinephelus striatus) within The Bahamas

“…Variability in N e estimates has been linked to differences in reproductive success and/or the methods used in their computation (Hare et al, 2011;Waples et al, 2013Waples et al, , 2014Coscia et al, 2016;Waples and Anderson, 2017). For example, Waples et al (2014) demonstrated that N e estimates based on a variety of age-structured samples are likely to be underestimated due to Wahlund effects.…”

Genetic and genomic monitoring with minimally invasive sampling methods

“…For many population genetic parameters (category II monitoring in Schwartz et al, 2007) sampling should be designed to be random with respect to kin (this can also be addressed by post hoc data pruning, but see Waples & Anderson, 2017). …”