scholarly journals Next Generation Sequencing Identifies Population Structure and Signatures of Local Adaptation in Red Alder (Alnus rubra Bong.)

2021 ◽  
Author(s):  
◽  
Jacob Loveless
Keyword(s):  
Population Structure ◽  
Next Generation Sequencing ◽  
Local Adaptation ◽  
Alnus Rubra ◽  
Next Generation ◽  
Red Alder ◽  
Generation Sequencing

scholarly journals Fine-scale population structure and the era of next-generation sequencing

2010 ◽  
Vol 19 (R2) ◽  
pp. R221-R226 ◽  
Author(s):  
B. M. Henn ◽  
S. Gravel ◽  
A. Moreno-Estrada ◽  
S. Acevedo-Acevedo ◽  
C. D. Bustamante
Keyword(s):  
Population Structure ◽  
Next Generation Sequencing ◽  
Fine Scale ◽  
Next Generation ◽  
Scale Population ◽  
Generation Sequencing

scholarly journals Inferring Population Structure and Admixture Proportions in Low Depth NGS Data

2018 ◽  
Author(s):  
Jonas Meisner ◽  
Anders Albrechtsen
Keyword(s):  
Principal Component Analysis ◽  
Population Structure ◽  
Next Generation Sequencing ◽  
Principal Component ◽  
Component Analysis ◽  
Allele Frequencies ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

ABSTRACTWe here present two methods for inferring population structure and admixture proportions in low depth next generation sequencing data. Inference of population structure is essential in both population genetics and association studies and is often performed using principal component analysis or clustering-based approaches. Next-generation sequencing methods provide large amounts of genetic data but are associated with statistical uncertainty for especially low depth sequencing data. Models can account for this uncertainty by working directly on genotype likelihoods of the unobserved genotypes. We propose a method for inferring population structure through principal component analysis in an iterative approach of estimating individual allele frequencies, where we demonstrate improved accuracy in samples with low and variable sequencing depth for both simulated and real datasets. We also use the estimated individual allele frequencies in a fast non-negative matrix factorization method to estimate admixture proportions. Both methods have been implemented in the PCAngsd framework available at http://www.popgen.dk/software/.

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