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Fast heritability estimation based on MINQUE and batch training.

Mingsheng Tang1, Tingting Hou2, Xiaoran Tong2

  • 1Division of Health Statistics, School of Public Health, Shanxi Medical University, No.56 Xin jian South Road, 030001 Shanxi, China.

Briefings in Bioinformatics
|April 6, 2022
PubMed
Summary
This summary is machine-generated.

We developed a faster method, LMM.MNQ.BCH, for estimating heritability from large genetic datasets. This approach significantly speeds up genetic architecture studies compared to existing methods.

Keywords:
MINQUESNP heritabilitybatch traininghuman genomekernel

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Heritability quantifies the genetic contribution to human diseases using genome-wide single nucleotide polymorphisms (SNPs).
  • Linear mixed models (LMMs) with restricted maximum likelihood (REML) are standard for SNP heritability estimation but are computationally intensive for large datasets like the UK Biobank.

Purpose of the Study:

  • To develop a computationally efficient method for estimating SNP heritability in large-scale genetic datasets.
  • To accelerate the analysis of genetic architecture in human diseases.

Main Methods:

  • Developed LMM.MNQ.BCH, utilizing the minimum norm quadratic unbiased estimator (MINQUE) with batch training for variance component estimation in LMMs.
  • MINQUE provides a closed-form solution, ensuring fast computation and avoiding convergence issues inherent in iterative methods like REML.
  • Batch training further accelerates computation for large datasets.

Main Results:

  • LMM.MNQ.BCH demonstrates significantly faster computation times compared to established methods such as GCTA and BOLT-REML.
  • Simulations and real data analyses confirm the speed advantage of LMM.MNQ.BCH.

Conclusions:

  • LMM.MNQ.BCH offers a computationally efficient alternative for SNP heritability estimation in large genetic datasets.
  • This method facilitates large-scale genetic architecture studies, potentially improving our understanding of human diseases.