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/.

Estimation of population allele frequencies from next-generation sequencing data: pool-versus individual-based genotyping

2013 ◽  
Vol 22 (14) ◽  
pp. 3766-3779 ◽  
Author(s):  
Mathieu Gautier ◽  
Julien Foucaud ◽  
Karim Gharbi ◽  
Timothée Cézard ◽  
Maxime Galan ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Allele Frequencies ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

scholarly journals Identifikasi SNP genom pada populasi Elaeis guineensis x Elaeis oleifera

2021 ◽  
Vol 29 (2) ◽  
pp. 81-96
Author(s):  
Sri Wening ◽  
Heri Adriwan Siregar ◽  
Edy Suprianto ◽  
Dani Setyawan ◽  
Hernawan Y Rahmadi ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Next Generation Sequencing ◽  
Elaeis Guineensis ◽  
Principal Component ◽  
Genotyping By Sequencing ◽  
Component Analysis ◽  
Next Generation ◽  
Elaeis Oleifera ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Usaha pencarian marka DNA yang berhubungan dengan sifat yang diinginkan pada Elaeis oleifera guna introgresi sifat tersebut ke genome Elaeis guineensis memerlukan marka DNA yang polimorfik. Untuk menghasilkan marka DNA yang polimorfik dengan jumlah banyak, identifikasi SNP genom dilakukan melalui pengurutan kembali (resequencing) 12 individu contoh populasi hibrida E. guineensis x E. oleifera (hibrida OxG), yaitu E. oleifera tipe liar, F1 hibrida interspesifik, pseudo-backcross dan material maju E. guineensis, menggunakan next generation sequencing (NGS). Read (urutan basa yang “dibaca”/merupakan keluaran mesin NGS) dari 12 contoh memiliki mutu yang baik dan 96% total read yang disaring dapat dilakukan demultipleks dan ditentukan pada contoh yang sesuai. Setelah proses penyaringan dan pemotongan, 84% read dapat digunakan untuk pemetaan genom dan menghasilkan 5,7X hingga 10,42X cakupan genom. Dari 34.410.224 SNP yang teridentifikasi, 98,7% diantaranya adalah varian non-coding, dan berdasarkan lokasi, 69,1% total SNP adalah SNP intergenic. Sebanyak 5.618 SNP dari total SNP yang dihasilkan dibuktikan menggunakan targeted genotyping by sequencing pada 500 individu contoh. Sebanyak 74% SNP yang digunakan bermutu tinggi yang dibaca pada setidaknya 95% contoh. Principal component analysis menggunakan SNP tersebut mampu mengidentifikasi setiap latar belakang genetik contoh. Pembuktian tersebut menyimpulkan bahwa identifikasi SNP yang dilakukan melalui pengurutan kembali menghasilkan SNP bermutu tinggi yang dapat digunakan untuk pengembangan marka DNA yang dapat diperbantukan pada seleksi populasi pemuliaan E. guineensis x E. oleifera.

scholarly journals NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

2021 ◽  
Author(s):  
Anne Krogh Nøhr ◽  
Kristian Hanghøj ◽  
Genis Garcia Erill ◽  
Zilong Li ◽  
Ida Moltke ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Research ◽  
Likelihood Estimation ◽  
Software Tool ◽  
Estimation Methods ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

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