Close relatives in population samples: Evaluation of the consequences for genetic stock identification

Abstract

Determining the origin of individuals in mixed population samples is key in many ecological, conservation and management contexts. Genetic data can be analyzed using genetic stock identification (GSI), where the origin of single individuals is determined using Individual Assignment (IA) and population proportions are estimated with Mixed Stock Analysis (MSA). In such analyses, allele frequencies in a reference baseline are required. Unknown individuals or mixture proportions are assigned to source populations based on the likelihood that their multilocus genotypes occur in a particular baseline sample. Representative sampling of populations included in a baseline is important when designing and performing GSI. Here, we investigate the effects of family sampling on GSI, using both simulated and empirical genotypes for Atlantic salmon (Salmo salar). We show that nonrepresentative sampling leading to inclusion of close relatives in a reference baseline may introduce bias in estimated proportions of contributing populations in a mixed sample, and increases the amount of incorrectly assigned individual fish. Simulated data further show that the induced bias increases with increasing family structure, but that it can be partly mitigated by increased baseline population sample sizes. Results from standard accuracy tests of GSI (using only a reference baseline and/or self‐assignment) gave a false and elevated indication of the baseline power and accuracy to identify stock proportions and individuals. These findings suggest that family structure in baseline population samples should be quantified and its consequences evaluated, before carrying out GSI.

Dispersal in a house sparrow metapopulation: An integrative case study of genetic assignment calibrated with ecological data and pedigree information

“…This clearly indicates that more realistic representations of the genetic variation and relatedness structure of a population will cause fewer assignment errors in network based genetic assignment analysis such as BONE, and hence increase the accuracy of the genetic assignments in such situations. However, one should note that this may not be the case for the assignment methods utilizing a Bayesian framework: it has in fact been shown that inclusion of close relatives in baseline populations can decrease the accuracy of genetic assignments using methods such as ONCOR (Östergren et al, 2019).…”

A century of genetic homogenization in Baltic salmon—evidence from archival DNA

“…To counteract potential bias on genetic parameters due to nonrepresentative sampling caused by family structure [43][44][45][46] we screened the data for the existence of full siblings using the maximum-likelihood approach implemented in COLONY 2.0.4.4 [47,48] We subsequently kept a maximum of two randomly selected individuals from each inferred full-sibling family.…”

A large wild salmon stock shows genetic and life history differentiation within, but not between, rivers

“…In order to avoid potentially biased allele frequency estimates due to family sampling of juvenile salmon (Hansen et al 1997;Östergren et al 2020), Colony 2.0.6.5 (Wang 2004; Jones and Wang 2010) was used to identify full siblings within the parr dataset. A total of 16 analyses (corresponding to the 16 sample sites) were performed with Colony, assuming polygamous mating patterns in both sexes as is typical for salmonids (Fleming 1996).…”