Use of Genetic Data to Infer Population-Specific Ecological and Phenotypic Traits from Mixed Aggregations

Abstract

Many applications in ecological genetics involve sampling individuals from a mixture of multiple biological populations and subsequently associating those individuals with the populations from which they arose. Analytical methods that assign individuals to their putative population of origin have utility in both basic and applied research, providing information about population-specific life history and habitat use, ecotoxins, pathogen and parasite loads, and many other non-genetic ecological, or phenotypic traits. Although the question is initially directed at the origin of individuals, in most cases the ultimate desire is to investigate the distribution of some trait among populations. Current practice is to assign individuals to a population of origin and study properties of the trait among individuals within population strata as if they constituted independent samples. It seemed that approach might bias population-specific trait inference. In this study we made trait inferences directly through modeling, bypassing individual assignment. We extended a Bayesian model for population mixture analysis to incorporate parameters for the phenotypic trait and compared its performance to that of individual assignment with a minimum probability threshold for assignment. The Bayesian mixture model outperformed individual assignment under some trait inference conditions. However, by discarding individuals whose origins are most uncertain, the individual assignment method provided a less complex analytical technique whose performance may be adequate for some common trait inference problems. Our results provide specific guidance for method selection under various genetic relationships among populations with different trait distributions.

Polyphyletic ancestry of expanding Patagonian Chinook salmon populations

“…This is relevant, for example, to studies that assume a probability threshold (e.g., 0.8) for accurate assignment, and especially to those with incomplete baseline datasets. While a probability threshold such as 0.8 is clearly useful in conventional genetic mixture applications 28 , in a mixed-origin founding application, it is almost certainly the case that some high-probability individual assignments are attributable to the misassignment phenomenon that we demonstrated through simulation. Apparently, introgression between donor populations results in unique genotypes that assign arbitrarily to other, unrelated populations.…”

“…; 27 Mostly 1+ year class and older since most escapes were from marine net-pens . ; 28 Follow fragmentary records of ova imported (OI) by the Chilean aquaculture industry in 1987-2000 (Aedo 2011). Some information of suppliers was available for 60% of the imports; we report specific lineages and origins of livestock whenever possible, and ova suppliers and/or geographic origin of shipments otherwise.…”

“…The monophyletic ancestry and known history of introduction in New Pacific Salmonids (GAPS) consortium assembled a comprehensive genetic baseline for coast-wide fishery management of mixed-stock fisheries 25 . The accuracy and precision of mixture analysis using the GAPS baseline is substantial with either conditional or unconditional Bayesian methods [25][26][27][28] , and provides a potentially useful approach for inferring the ancestry of Patagonian Chinook salmon. CML mixture analysis was used in two recent studies of the ancestry of Patagonian Chinook salmon 29,30 .…”

Genetic assignment to stock of stranded common bottlenose dolphins in southeastern Louisiana after the Deepwater Horizon oil spill

“…Use of the MSA option in ON COR should help minimize some of this bias, however. Use of mixture models integrates all the information available in the baseline as well as in the mixture sample, and, since they do not require estimating assignments for each individual in the mixture, they do not carry with them the uncertainty and error inherent in each of those assignments, resulting in improved performance over classical assignment tests (Koljonen et al 2005, Manel et al 2005, Moran et al 2014. Moreover, they are less prone to over estimating assignment power (Anderson et al 2008, Anderson 2010.…”

Wild Estonian and Russian sea trout (Salmo trutta) in Finnish coastal sea trout catches: results of genetic mixed-stock analysis

“…The Bayesian estimation was chosen for the estimation of the final results, as it has been shown in previous studies to out‐perform the maximum likelihood‐based methods ONCOR and SPAM, e.g. in tests where mixed samples of known origin were available (Griffiths et al , Moran et al ).…”