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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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...
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Related Experiment Video

Updated: Jan 11, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Genotype imputation from low-coverage WGS using haplotype reference panels in cultivated strawberry.

Tim Koorevaar1,2,3, Johan H Willemsen4, Richard G F Visser5

  • 1Fresh Forward Breeding B.V., Huissen, The Netherlands. tim.koorevaar@wur.nl.

BMC Genomics
|November 19, 2025
PubMed
Summary

Constructing a large, diverse haplotype panel improves strawberry genotype imputation from low-coverage sequencing. This cost-effective method enhances whole genome sequencing-based genotyping for crop breeding programs.

Keywords:
Genetic diversityHaplotype reference panelImputationLow-coveragePhasingQHN50SHAPEIT5StrawberryWGSWhole genome sequencing

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Area of Science:

  • Genomics
  • Plant Breeding
  • Bioinformatics

Background:

  • High-throughput sequencing-based genotyping is crucial for strawberry (Fragaria × ananassa) breeding programs.
  • Whole genome sequencing (WGS) offers high SNP density but is costly for large-scale applications.
  • Genotype dosage imputation from low-coverage data using a reference panel is a cost-effective alternative, but requires optimization for allo-octoploid strawberry.

Purpose of the Study:

  • To construct a haplotype reference panel for strawberry.
  • To explore the utility of this panel for genotype dosage imputation of low-coverage (1×) sequencing data.
  • To optimize imputation strategies for allo-octoploid strawberry.

Main Methods:

  • Combined high sequencing depth (>15×) with variant filtering (AAB, LD, MER) to reduce genotyping errors.
  • Employed statistical phasing (SHAPEIT5) achieving a mean switch error rate of 0.9%.
  • Utilized GLIMPSE2 for imputation of downsampled (1×) samples using reference panels of varying size and composition.

Main Results:

  • Achieved accurate statistical phasing with 50% of the genome in haplotype blocks of at least 654 kb.
  • Imputation accuracy varied with reference panel size and genetic diversity, yielding concordance rates from 0.87 to 0.98.
  • Demonstrated that panel size and genetic diversity significantly influence imputation accuracy.

Conclusions:

  • A large, genetically diverse haplotype reference panel enhances genotype dosage imputation from low-coverage sequencing data.
  • High accuracy imputation is achievable with limited resources, offering a cost-efficient alternative to SNP arrays for WGS-based genotyping.
  • The developed strategy is applicable to other crops needing dense genotyping with limited resources, suggesting ~70 samples at ≥25× depth suffice for panel construction.