Weight of the evidence of genetic investigations of ancestry informative markers

Tvedebrink, Torben; Eriksen, Poul Svante; Mogensen, Helle Smidt; Morling, Niels

doi:10.1016/j.tpb.2017.12.004

Cited by 27 publications

(34 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The assignment of individuals to a population of origin was assessed using the GenoGeographer software (Tvedebrink et al, 2018;Mogensen et al, 2020). In this analysis, the z-score was computed for each individual, considering AFR, EUR, NAM, and Rio de Janeiro as reference populations.…”

Section: Population Assignment Of Individualsmentioning

confidence: 99%

“…A z-score test was applied to the 214 unrelated individuals and to each offspring of the 65 sibling pairs from Rio de Janeiro, to assess whether one (or more) of the four reference populations was accepted as a potential population of origin of each AIM profile. This test was performed using the approach described in Tvedebrink et al (2018).…”

Section: Inferences On Biogeographical Ancestry (Bga)mentioning

confidence: 99%

“…The same AIM sets developed for human population genetics have also been used to investigate forensic cases. In this context, however, these sets are not usually utilized to question the continental ancestry of a sample contributor, but rather, the most likely population of origin of the DNA profile, i.e., the Biogeographical Ancestry (BGA) of a sample donor (Phillips et al, 2007;Rajeevan et al, 2012Rajeevan et al, , 2020Tvedebrink et al, 2017Tvedebrink et al, , 2018Mogensen et al, 2020). However, inferring the most likely population of origin of an individual does not always provide direct information about its ancestry profile (and vice versa), namely in recently admixed populations.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the Precision of Ancestry Inferences in South American Admixed Populations

et al. 2020

Self Cite

View full text Add to dashboard Cite

Ancestry informative markers (AIMs) are used in forensic genetics to infer biogeographical ancestry (BGA) of individuals and may also have a prominent role in future police and identification investigations. In the last few years, many studies have been published reporting new AIM sets. These sets include markers (usually around 100 or less) selected with different purposes and different population resolutions. Regardless of the ability of these sets to separate populations from different continents or regions, the uncertainty associated with the estimates provided by these panels and their capacity to accurately report the different ancestral contributions in individuals of admixed populations has rarely been investigated. This issue is addressed in this study by evaluating different AIM sets. Ancestry inference was carried out in admixed South American populations, both at population and individual levels. The results of ancestry inferences using AIM sets with different numbers of markers among admixed reference populations were compared. To evaluate the performance of the different ancestry panels at the individual level, expected and observed estimates among families and their offspring were compared, considering that (1) the apportionment of ancestry in the offspring should be closer to the average ancestry of the parents, and (2) full siblings should present similar ancestry values. The results obtained illustrate the importance of having a good balance/compromise between not only the number of markers and their ability to differentiate ancestral populations, but also a balanced differentiation among reference groups, to obtain more precise values of genetic ancestry. This work also highlights the importance of estimating errors associated with the use of a limited number of markers. We demonstrate that although these errors have a moderate effect at the population level, they may have an important impact at the individual level. Considering that many AIM-sets are being described for inferences at the individual level and not at the population level, e.g., in association studies or the determination of a suspect’s BGA, the results of this work point to the need of a more careful evaluation of the uncertainty associated with the ancestry estimates in admixed populations, when small AIM-sets are used.

show abstract

Section: Population Assignment Of Individualsmentioning

confidence: 99%

Section: Inferences On Biogeographical Ancestry (Bga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the Precision of Ancestry Inferences in South American Admixed Populations

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The short distance between SNPs within a microhap implies that recombination among them rarely occurs. Hence, the methodology of T. Tvedebrink, Eriksen, Mogensen, & Morling (2018) can not be used as this assumes mutual independence of the SNPs within a population (corresponding to the null graph with no edges).…”

Section: A Use Case In Forensic Sciencementioning

confidence: 99%

molic: An R package for multivariate outlier detection in contingency tables

Lindskou¹

2019

JOSS

View full text Add to dashboard Cite

Outlier detection is an important task in statistical analyses. An outlier is a case-specific unit since it may be interpreted as natural extreme noise in some applications, whereas in other applications it may be the most interesting observation. The molic package has been written to facilitate the novel outlier detection method in high-dimensional contingency tables (Lindskou, Eriksen, & Tvedebrink, 2019). In other words, the method works for data sets in which all variables are categorical, implying that they can only take on a finite set of values (also called levels).The software uses decomposable graphical models (DGMs), where the probability mass function can be associated with an interaction graph, from which conditional independences among the variables can be inferred. This gives a way to investigate the underlying nature of outliers. This is also called understandability in the literature. Outlier detection has many applications including areas such as • Fraud detection • Medical and public health • Anomaly detection in text data • Fault detection (on critical systems) • Forensic science The MethodThe method can be described by the outlier test procedure below. Assume we are interested in whether or not a new observation z is an outlier in some data set D. First an interaction graph G is fitted to the variables in D; a decomposable undirected graph that describes the association structure between variables in D. If the assumption that z belongs to D is true, z should be included in D. Denote by D z the new data set including z. Finally the outlier model M is constructed using G and D z from which we can query the p-value, p, for the test about z belonging to D. If p is less than some chosen threshold (significance level), say 0.05, z is declared an outlier in D.

show abstract

“…The methodology for evaluating p-values and estimating confidence intervals [4] have been implemented in R and made available online at http://genogeographer.org using the Shiny R-package.…”

Section: Genogeographerorgmentioning

confidence: 99%

GenoGeographer – A tool for genogeographic inference

Tvedebrink

Eriksen

Mogensen

et al. 2017

Forensic Science International: Genetics Supplement Series

Self Cite

View full text Add to dashboard Cite

Ancestry-informative markers (AIMs) are genetic markers that give information about the genogeographic ancestry of individuals. They are used for predicting the genogeographic origin of the investigated individual in crime and identification cases. When exploring the genogeographic origin of an AIMs profile, the likelihoods of the AIMs profile in various populations may be calculated. However, there might not be an apt population in the database of reference populations. The fact that the likelihood ratios (LR) of one population compared to other populations are large does not imply that any of the populations are relevant. This is because that even though the populations might be exclusive, they are not exhaustive in the sense that they cover all possible human populations.To handle this phenomena, we derive a likelihood ratio test (LRT), by which we can judge whether there is at least one population in our reference database that is "sufficiently close" to a profile's "true" genogeographic population. The LRT is a measure of absolute concordance between a profile and a population, rather than a relative measure of the profile's likelihood in two populations (the LR).The LRT is similar to a Fisher's exact test, which means that the varying sample sizes of the reference populations in the database is explicitly included in the calculations, and makes fewer assumptions than for LR calculations. The methodology has been implemented in an free open source interactive platform, GenoGeographer, that enables the forensic geneticist to make explorative analyses, produce various graphical outputs together with evidential weight computations.

show abstract

Weight of the evidence of genetic investigations of ancestry informative markers

Cited by 27 publications

References 26 publications

Evaluation of the Precision of Ancestry Inferences in South American Admixed Populations

Evaluation of the Precision of Ancestry Inferences in South American Admixed Populations

molic: An R package for multivariate outlier detection in contingency tables

GenoGeographer – A tool for genogeographic inference

Contact Info

Product

Resources

About