Related Experiment Video
Updated: Oct 9, 2025
11:11
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
Published on: August 24, 2017
17.0K
Increasing calling accuracy, coverage, and read-depth in sequence data by the use of haplotype blocks
Torsten Pook1, Adnane Nemri2, Eric Gerardo Gonzalez Segovia3
1Center for Integrated Breeding Research, Animal Breeding and Genetics Group, University of Goettingen, Goettingen, Germany.
Plos Genetics
|December 23, 2021
Summary
A new imputation pipeline, HBimpute, generates high-quality genomic data from low-coverage sequencing. This method halves imputation error rates and improves genomic prediction accuracy in maize breeding.
Area of Science:
- Plant genetics
- Genomics
- Bioinformatics
Background:
- High-throughput genotyping is crucial but challenging in plant genetics.
- Limited resources necessitate balancing data quality with the number of genotyped lines.
- Existing technologies face limitations in cost-effectiveness and data resolution.
Purpose of the Study:
- To develop a novel imputation pipeline (HBimpute) for generating high-quality genomic data from low read-depth whole-genome sequencing (WGS) data.
- To improve variant calling accuracy and enable downstream applications like copy number variation analysis.
- To assess the utility of imputed WGS data in plant breeding applications compared to traditional genotyping arrays.
Main Methods:
- Developed HBimpute pipeline utilizing haplotype blocks from HaploBlocker to identify locally similar lines.
- Applied HBimpute to low read-depth (0.5X) WGS data from 321 doubled haploid maize lines.
- Compared HBimpute's imputation error rates against state-of-the-art software (BEAGLE, STITCH).
- Evaluated imputed data performance in genome-wide association studies (GWAS) and genomic prediction (GP) against 600k genotyping array data.
Main Results:
- HBimpute reduced imputation error rates by half compared to BEAGLE and STITCH.
- Average read-depth effectively increased to 83X, enabling copy number variation calling.
- Imputed sequence data performed comparably or better than 600k array data in GWAS and GP.
- Genomic prediction accuracies were slightly higher with imputed sequence data, especially when using array-matched markers.
Conclusions:
- HBimpute offers a cost-effective solution for generating high-quality genomic data from low-coverage WGS.
- The pipeline significantly enhances the usability of WGS data for plant genetics and breeding.
- Imputed WGS data provides a valuable alternative to genotyping arrays, offering competitive or superior performance in key breeding applications.
Related Concept Videos
01:11Genome-wide Association Studies-GWAS
14.7K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
GWAS does not require the identification of the target gene involved in...
14.7K
03:00Next-generation Sequencing
93.8K
The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
93.8K