Bayesian inference from the conditional genetic stock identification model

Moran, Ben; Anderson, Eric C.

doi:10.1139/cjfas-2018-0016

Cited by 91 publications

(113 citation statements)

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“…Genotypes from potential source species were used to estimate allele frequencies that compose a reference against which data from individuals of unknown species were assessed (Clemento, Crandall, Garza, & Anderson, 2014;Pella & Milner, 1987), with maximum-likelihood or Bayesian methods (Pella & Masuda, 2001;Smouse, Waples, & Tworek, 1990). Here, genotypes from adults of these four species (kelp, copper, black-and-yellow and gopher rockfishes) were included in the genetic stock identification (GSI) baseline and each adult rockfish genotype was assigned a likelihood of being from each of these species using the self-assignment function in the software gsi _ sim (Anderson, Waples, & Kalinowski, 2008;Moran & Anderson, 2018). Adult rockfishes are typically identifiable by colour and morphology, and the gsi _ sim assignments confirmed field identifications and detected any misidentified fish.…”

Section: Discussionmentioning

confidence: 99%

Dispersal of a nearshore marine fish connects marine reserves and adjacent fished areas along an open coast

et al. 2019

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Marine species with pelagic larvae typically exhibit little population structure, suggesting long‐distance dispersal and high gene flow. Directly quantifying dispersal of marine fishes is challenging but important, particularly for the design of marine protected areas (MPAs). Here, we studied kelp rockfish (Sebastes atrovirens) sampled along ~25 km of coastline in a boundary current‐dominated ecosystem and used genetic parentage analysis to identify dispersal events and characterize them, because the distance between sedentary parents and their settled offspring is the lifetime dispersal distance. Large sample sizes and intensive sampling are critical for increasing the likelihood of detecting parent–offspring matches in such systems and we sampled more than 6,000 kelp rockfish and analysed them with a powerful set of 96 microhaplotype markers. We identified eight parent–offspring pairs with high confidence, including two juvenile fish that were born inside MPAs and dispersed to areas outside MPAs, and four fish born in MPAs that dispersed to nearby MPAs. Additionally, we identified 25 full‐sibling pairs, which occurred throughout the sampling area and included all possible combinations of inferred dispersal trajectories. Intriguingly, these included two pairs of young‐of‐the‐year siblings with one member each sampled in consecutive years. These sibling pairs suggest monogamy, either intentional or accidental, which has not been previously demonstrated in rockfishes. This study provides the first direct observation of larval dispersal events in a current‐dominated ecosystem and direct evidence that larvae produced within MPAs are exported both to neighbouring MPAs and to proximate areas where harvest is allowed.

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Section: Discussionmentioning

confidence: 99%

Dispersal of a nearshore marine fish connects marine reserves and adjacent fished areas along an open coast

et al. 2019

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“…In particular, we exploit known ancestral populations in a supervised learning model in ADMIXTURE (Alexander & Lange, ) and use the EM algorithm for estimating the ancestry coefficient matrix Q . In addition, we compare our method with two recent population assignment techniques, RUBIAS (Moran & Anderson, ) and AssignPOP (Chen et al, ). These methods are implemented in R packages radmixture, RUBIAS and AssignPOP respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Next, we compare BONE with three population assignment methods: commonly used ADMIXTURE (Alexander, Novembre, & Lange, ), along with two recently published methods ‘RUBIAS’ (Moran & Anderson, ) and ‘AssignPOP’ (Chen et al, ) using both simulated and real data. ADMIXTURE, RUBIAS and AssignPOP are implemented in R packages radmixture, RUBIAS and AssignPOP, respectively, and are publicly available at CRAN (The Comprehensive R Archive Network).…”

Section: Introductionmentioning

confidence: 99%

Genetic assignment of individuals to source populations using network estimation tools

et al. 2019

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1. Dispersal, the movement of individuals between populations, is crucial in many ecological and genetic processes. However, direct identification of dispersing individuals is difficult or impossible in natural populations. By using genetic assignment methods, individuals with unknown genetic origin can be assigned to source populations. This knowledge is necessary in studying many key questions in ecology, evolution and conservation. We introduce a network-based tool BONE (Baseline Oriented Network Estimation)for genetic population assignment, which borrows concepts from undirected graph inference. In particular, we use sparse multinomial Least Absolute Shrinkage and Selection Operator (LASSO) regression to estimate probability of the origin of all mixture individuals and their mixture proportions without tedious selection of the LASSO tuning parameter. We compare BONE with three genetic assignment methods implemented in R packages radmixture, assignPOP and RUBIAS. 3. Probability of the origin and mixture proportion estimates of both simulated and real data (an insular house sparrow metapopulation and Chinook salmon populations) given by BONE are competitive or superior compared to other assignment methods. Our examples illustrate how the network estimation method adapts to population assignment, combining the efficiency and attractive properties of sparse network representation and model selection properties of the L 1 regularization. As far as we know, this is the first approach showing how one can use network tools for genetic identification of individuals' source populations.4. BONE is aimed at any researcher performing genetic assignment and trying to infer the genetic population structure. Compared to other methods, our approach also identifies outlying mixture individuals that could originate outside of the baseline populations. BONE is a freely available R package under the GPL licence and can be downloaded at GitHub. In addition to the R package, a tutorial for BONE is available at https ://github.com/markk ukuis min/BONE/. K E Y W O R D Sassignment analysis, dispersal, genetic assignment methods, genetic processes, genetic stock identification, LASSO, networks, SNP

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“…We decided to use Oncor for this study based on two main criteria; (a) It is/has been widely used in GSI studies and (b) is user‐friendly and has several built‐in simulation tests (e.g., 100% simulation and self‐assignment‐test) very commonly used in published GSI studies. Several other computer programs developed for GSI analysis exists, e.g., cBayes (Neaves, Wallace, Candy, & Beacham, ) , GeneClass2 (Piry et al, ) , Rubias (Moran & Anderson, ) and Spam (Debevec et al, ), but a comparison of outcomes from different software was beyond the scope of this study. Furthermore, when evaluated in other studies, Oncor has been shown to perform equal to several of those alternative software (Debevec et al, ; Griffiths et al, ; Ikediashi, Billington, & Stevens, ; Vaha et al, ).…”

Section: Methodsmentioning

confidence: 99%

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

Palm

Gilbey

Dannewitz

2020

Molecular Ecology Resources

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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.

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Bayesian inference from the conditional genetic stock identification model

Cited by 91 publications

References 33 publications

Dispersal of a nearshore marine fish connects marine reserves and adjacent fished areas along an open coast

Dispersal of a nearshore marine fish connects marine reserves and adjacent fished areas along an open coast

Genetic assignment of individuals to source populations using network estimation tools

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

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