Quantitative technologies for allele frequency estimation of SNPs in DNA pools

2002 ◽  
Vol 16 (6) ◽  
pp. 429-434 ◽  
Author(s):  
Sagiv Shifman ◽  
Anne Pisanté-Shalom ◽  
Benjamin Yakir ◽  
Ariel Darvasi
Keyword(s):  
Allele Frequency ◽  
Frequency Estimation ◽  
Allele Frequency Estimation ◽  
Dna Pools

Pyrosequencing?-based SNP allele frequency estimation in DNA pools

2003 ◽  
Vol 23 (1) ◽  
pp. 92-97 ◽  
Author(s):  
Catharina Lavebratt ◽  
Selim Sengul ◽  
Marten Jansson ◽  
Martin Schalling
Keyword(s):  
Allele Frequency ◽  
Frequency Estimation ◽  
Allele Frequency Estimation ◽  
Dna Pools

Cheap, accurate and rapid allele frequency estimation of single nucleotide polymorphisms by primer extension and DHPLC in DNA pools

2000 ◽  
Vol 107 (5) ◽  
pp. 488-493 ◽  
Author(s):  
Bastiaan Hoogendoorn ◽  
Nadine Norton ◽  
George Kirov ◽  
Nigel Williams ◽  
Marian Hamshere ◽  
...  
Keyword(s):  
Single Nucleotide Polymorphisms ◽  
Allele Frequency ◽  
Frequency Estimation ◽  
Primer Extension ◽  
Nucleotide Polymorphisms ◽  
Single Nucleotide ◽  
Allele Frequency Estimation ◽  
Dna Pools

scholarly journals SNP allele frequency estimation in DNA pools and variance components analysis

BioTechniques ◽  
2004 ◽  
Vol 36 (5) ◽  
pp. 840-845 ◽  
Author(s):  
Kate Downes ◽  
Bryan J. Barratt ◽  
Pelin Akan ◽  
Sue J. Bumpstead ◽  
Stacey D. Taylor ◽  
...  
Keyword(s):  
Allele Frequency ◽  
Variance Components ◽  
Frequency Estimation ◽  
Allele Frequency Estimation ◽  
Variance Components Analysis ◽  
Dna Pools ◽  
Components Analysis

scholarly journals Polymorphism discovery and allele frequency estimation using high-throughput DNA sequencing of target-enriched pooled DNA samples

BMC Genomics ◽  
2012 ◽  
Vol 13 (1) ◽  
pp. 16 ◽  
Author(s):  
Michael P Mullen ◽  
Christopher J Creevey ◽  
Donagh P Berry ◽  
Matt S McCabe ◽  
David A Magee ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
Allele Frequency ◽  
High Throughput ◽  
Frequency Estimation ◽  
Allele Frequency Estimation ◽  
Polymorphism Discovery ◽  
Pooled Dna ◽  
High Throughput Dna Sequencing

High throughput crop genome genotyping by a combination of pool next generation sequencing and haplotype-based data processing

2021 ◽  
Author(s):  
Michael Schneider ◽  
Asis Shrestha ◽  
Agim Ballvora ◽  
Jens Leon
Keyword(s):  
Next Generation Sequencing ◽  
Allele Frequency ◽  
Frequency Estimation ◽  
Whole Genome ◽  
Next Generation ◽  
Conservation Genomics ◽  
High Coverage ◽  
Allele Frequency Estimation ◽  
Low Coverage ◽  
Generation Sequencing

Abstract BackgroundThe identification of environmentally specific alleles and the observation of evolutional processes is a goal of conservation genomics. By generational changes of allele frequencies in populations, questions regarding effective population size, gene flow, drift, and selection can be addressed. The observation of such effects often is a trade-off of costs and resolution, when a decent sample of genotypes should be genotyped for many loci. Pool genotyping approaches can derive a high resolution and precision in allele frequency estimation, when high coverage sequencing is utilized. Still, pool high coverage pool sequencing of big genomes comes along with high costs.ResultsHere we present a reliable method to estimate a barley population’s allele frequency at low coverage sequencing. Three hundred genotypes were sampled from a barley backcross population to estimate the entire population’s allele frequency. The allele frequency estimation accuracy and yield were compared for three next generation sequencing methods. To reveal accurate allele frequency estimates on a low coverage sequencing level, a haplotyping approach was performed. Low coverage allele frequency of positional connected single polymorphisms were aggregated to a single haplotype allele frequency, resulting in two to 271 times higher depth and increased precision. We compared different haplotyping tactics, showing that gene and chip marker-based haplotypes perform on par or better than simple contig haplotype windows. The comparison of multiple pool samples and the referencing against an individual sequencing approach revealed whole genome pool resequencing having the highest correlation to individual genotyping (up to 0.97), while transcriptomics and genotyping by sequencing indicated higher error rates and lower correlations.ConclusionUsing the proposed method allows to identify the allele frequency of populations with high accuracy at low cost. This is particularly interesting for conservation genomics in species with big genomes, like barley or wheat. Whole genome low coverage resequencing at 10x coverage can deliver a highly accurate estimation of the allele frequency, when a loci-based haplotyping approach is applied. Using annotated haplotypes allows to capitalize from biological background and statistical robustness.

scholarly journals On a Unifying ‘Reverse’ Regression for Robust Association Studies and Allele Frequency Estimation with Related Individuals

2018 ◽  
Author(s):  
Lin Zhang ◽  
Lei Sun
Keyword(s):  
Allele Frequency ◽  
Frequency Estimation ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Linear Mixed Effect Model ◽  
Supporting Evidence ◽  
Mixed Effect ◽  
Allele Frequency Estimation ◽  
Reverse Regression ◽  
Related Individuals

AbstractFor genetic association studies with related individuals, standard linear mixed-effect model is the most popular approach. The model treats a complex trait (phenotype) as the response variable while a genetic variant (genotype) as a covariate. An alternative approach is to reverse the roles of phenotype and genotype. This class of tests includes quasi-likelihood based score tests. In this work, after reviewing these existing methods, we propose a general, unifying ‘reverse’ regression framework. We then show that the proposed method can also explicitly adjust for potential departure from Hardy–Weinberg equilibrium. Lastly, we demonstrate the additional flexibility of the proposed model on allele frequency estimation, as well as its connection with earlier work of best linear unbiased allele-frequency estimator. We conclude the paper with supporting evidence from simulation and application studies.

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