A nearest neighbour approach by genetic distance to the assignment of individual trees to geographic origin

Abstract

During the past decade, the use of DNA for forensic applications has been extensively implemented for plant and animal species, as well as in humans. Tracing back the geographical origin of an individual usually requires genetic assignment analysis. These approaches are based on reference samples that are grouped into populations or other aggregates and intend to identify the most likely group of origin. Often this grouping does not have a biological but rather a historical or political justification, such as "country of origin". In this paper, we present a new nearest neighbour approach to individual assignment or classification within a given but potentially imperfect grouping of reference samples. This method, which is based on the genetic distance between individuals, functions better in many cases than commonly used methods. We demonstrate the operation of our assignment method using two data sets. One set is simulated for a large number of trees distributed in a 120km by 120km landscape with individual genotypes at 150 SNPs, and the other set comprises experimental data of 1221 individuals of the African tropical tree species Entandrophragma cylindricum (Sapelli) genotyped at 61 SNPs. Judging by the level of correct self-assignment, our approach outperformed the commonly used frequency and Bayesian approaches by 15% for the simulated data set and by 5-7% for the Sapelli data set. Our new approach is less sensitive to overlapping sources of genetic differentiation, such as genetic differences among closely-related species, phylogeographic lineages and isolation by distance, and thus operates better even for suboptimal grouping of individuals.

Predicting the geographic origin of Spanish Cedar (Cedrela odorata L.) based on DNA variation

“…Methods have also been developed for estimating the origin of genotypes using continuous assignment (Wasser et al 2004;Yang et al 2012;Rañola et al 2014;Guillot et al 2016). These methods assume Hardy-Weinberg and linkage equilibrium and only use genetic data, but these limitations are balanced by the potential power of providing a precise geospatial source for a sample, rather than a categorical assignment (Degen et al 2017;Chen et al 2017). Continuous assignment with methods like SPASIBA are particularly relevant for questions involving wood legality because harvest locations are frequently not included in genetic reference populations, especially for geographically widespread species.…”

New perspectives on multilocus ancestry informativeness

“…2 in [Witherspoon et al, 2007], with data from low MAF sites ("rare" alleles, M AF < 0.1). Nevertheless, other treatments have ignored this issue, e.g., [Degen et al, 2017] who use a k-Nearest Neighbor (k-NN) algorithm for classification to one of a collection of source populations based on simple pairwise individual allele-sharing distance.…”

“…Whereas a Bayes classifier is based on the probability of observing a given genotype in a target population, distance methods assign a genotype to the "closest" population ( [Liao et al, 2009], [Degen et al, 2017]). In particular, we will require a genetic distance metric adopted as a measure between an individual and a population, or rather, the population genetic centroid.…”

Tracing the Geographic Origin of Merbau (Intsia palembanica Miq.) in Century Old Planting Trials

“…The simulation algorithm generated population samples of the same size as the reference population sample. Furthermore, a test with exclusion probability was also carried out using the GeoAssign software [32], where the nearest-neighbor approach was utilized for individual assignment.…”