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RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural

Shuo Shi1, Qiheng Qian1, Shuhuan Yu1

  • 1National Genomics Data Center of Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China.

Briefings in Bioinformatics
|August 17, 2021
PubMed
Summary

RefRGim improves genotype imputation accuracy by reconstructing a study-specific reference panel using convolutional neural networks (CNNs). This method enhances performance, particularly for low-frequency and rare genetic variants.

Keywords:
deep learninggenome-wide association studygenotype imputationreference reconstruction

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genotype imputation is crucial for analyzing genomic data, but existing methods struggle with low-frequency and rare variants.
  • Population similarity between study and reference panels significantly impacts imputation accuracy.
  • Developing accurate imputation methods is essential for large-scale genetic studies.

Purpose of the Study:

  • To develop a novel genotype imputation method that improves accuracy, especially for low-frequency and rare variants.
  • To create a study-specific reference panel reconstruction tool using deep learning.
  • To enhance the utility of existing reference panels for diverse populations.

Main Methods:

  • Developed RefRGim, a reference panel reconstruction method utilizing convolutional neural networks (CNNs).
  • Pretrained CNNs with single nucleotide polymorphism (SNP) data from the 1000 Genomes Project.
  • Generated study-specific reference panels based on genetic similarity between study and reference individuals.

Main Results:

  • RefRGim achieved higher genotype imputation accuracies compared to using original reference panels.
  • Significant accuracy improvements were observed for low-frequency and rare genetic variants.
  • The method effectively reconstructs personalized reference panels for improved imputation.

Conclusions:

  • RefRGim offers an efficient and accurate approach for genotype imputation by reconstructing tailored reference panels.
  • The method addresses limitations of existing imputation techniques for low-frequency and rare variants.
  • RefRGim provides a valuable tool for genomic research, enhancing data analysis across diverse populations.