scholarly journals Local ancestry inference gets faster and better

2013 ◽  
Vol 14 (9) ◽  
pp. 599-599
Keyword(s):  
Local Ancestry ◽  
Ancestry Inference ◽  
Local Ancestry Inference

scholarly journals Simulation-Based Evaluation of Three Methods for Local Ancestry Deconvolution of Non-model Crop Species Genomes

2019 ◽  
Vol 10 (2) ◽  
pp. 569-579
Author(s):  
Aurélien Cottin ◽  
Benjamin Penaud ◽  
Jean-Christophe Glaszmann ◽  
Nabila Yahiaoui ◽  
Mathieu Gautier
Keyword(s):  
R Package ◽  
Source Population ◽  
Crop Species ◽  
Data Set ◽  
Reproduction Mode ◽  
Local Ancestry ◽  
Ancestry Inference ◽  
Source Populations ◽  
Inference Methods ◽  
Local Ancestry Inference

Hybridizations between species and subspecies represented major steps in the history of many crop species. Such events generally lead to genomes with mosaic patterns of chromosomal segments of various origins that may be assessed by local ancestry inference methods. However, these methods have mainly been developed in the context of human population genetics with implicit assumptions that may not always fit plant models. The purpose of this study was to evaluate the suitability of three state-of-the-art inference methods (SABER, ELAI and WINPOP) for local ancestry inference under scenarios that can be encountered in plant species. For this, we developed an R package to simulate genotyping data under such scenarios. The tested inference methods performed similarly well as far as representatives of source populations were available. As expected, the higher the level of differentiation between ancestral source populations and the lower the number of generations since admixture, the more accurate were the results. Interestingly, the accuracy of the methods was only marginally affected by i) the number of ancestries (up to six tested); ii) the sample design (i.e., unbalanced representation of source populations); and iii) the reproduction mode (e.g., selfing, vegetative propagation). If a source population was not represented in the data set, no bias was observed in inference accuracy for regions originating from represented sources and regions from the missing source were assigned differently depending on the methods. Overall, the selected ancestry inference methods may be used for crop plant analysis if all ancestral sources are known.

scholarly journals RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference

2013 ◽  
Vol 93 (2) ◽  
pp. 278-288 ◽  
Author(s):  
Brian K. Maples ◽  
Simon Gravel ◽  
Eimear E. Kenny ◽  
Carlos D. Bustamante
Keyword(s):  
Local Ancestry ◽  
Modeling Approach ◽  
Ancestry Inference ◽  
Local Ancestry Inference

scholarly journals Putting RFMix and ADMIXTURE to the test in a complex admixed population

2019 ◽  
Author(s):  
Caitlin Uren ◽  
Eileen G. Hoal ◽  
Marlo Möller
Keyword(s):  
Association Studies ◽  
Structured Populations ◽  
Human Populations ◽  
Computational Tools ◽  
Local Ancestry ◽  
Population Structure Analysis ◽  
Admixed Population ◽  
Ancestry Inference ◽  
Global And Local ◽  
Local Ancestry Inference

AbstractGlobal and local ancestry inference in admixed human populations can be performed using computational tools implementing distinct algorithms, such as RFMix and ADMIXTURE. The accuracy of these tools has been tested largely on populations with relatively straightforward admixture histories but little is known about how well they perform in more complex admixture scenarios. Using simulations, we show that RFMix outperforms ADMIXTURE in determining global ancestry proportions in a complex 5-way admixed population. In addition, RFMix correctly assigns local ancestry with an accuracy of 89%. The increase in reported local ancestry inference accuracy in this population (as compared to previous studies) can largely be attributed to the recent availability of large-scale genotyping data for more representative reference populations. The ability of RFMix to determine global and local ancestry to a high degree of accuracy, allows for more reliable population structure analysis, scans for natural selection, admixture mapping and case-control association studies. This study highlights the utility of the extension of computational tools to become more relevant to genetically structured populations, as seen with RFMix. This is particularly noteworthy as modern-day societies are becoming increasingly genetically complex and some genetic tools are therefore less appropriate. We therefore suggest that RFMix be used for both global and local ancestry estimation in complex admixture scenarios.

scholarly journals LAIT: a local ancestry inference toolkit

BMC Genetics ◽  
2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Daniel Hui ◽  
Zhou Fang ◽  
Jerome Lin ◽  
Qing Duan ◽  
Yun Li ◽  
...  
Keyword(s):  
Local Ancestry ◽  
Ancestry Inference ◽  
Local Ancestry Inference

scholarly journals Local Ancestry Inference in Large Pedigrees

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Heming Wang ◽  
Tamar Sofer ◽  
Xiang Zhang ◽  
Robert C. Elston ◽  
Susan Redline ◽  
...  
Keyword(s):  
Local Ancestry ◽  
Ancestry Inference ◽  
Local Ancestry Inference

scholarly journals Accurate Local-Ancestry Inference in Exome-Sequenced Admixed Individuals via Off-Target Sequence Reads

2013 ◽  
Vol 93 (5) ◽  
pp. 891-899 ◽  
Author(s):  
Youna Hu ◽  
Cristen Willer ◽  
Xiaowei Zhan ◽  
Hyun Min Kang ◽  
Gonçalo R. Abecasis
Keyword(s):  
Target Sequence ◽  
Local Ancestry ◽  
Ancestry Inference ◽  
Local Ancestry Inference

scholarly journals Versatile simulations of admixture and accurate local ancestry inference with mixnmatch and ancestryinfer

2020 ◽  
Vol 20 (4) ◽  
pp. 1141-1151 ◽  
Author(s):  
Molly Schumer ◽  
Daniel L. Powell ◽  
Russ Corbett‐Detig
Keyword(s):  
Local Ancestry ◽  
Ancestry Inference ◽  
Local Ancestry Inference
Sign in / Sign up

Export Citation Format

Share Document