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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Optimal Estimation of Genetic Relatedness in High-dimensional Linear Models.

Zijian Guo1, Wanjie Wang2, T Tony Cai3

  • 1Department of Statistics and Biostatistics, Rutgers University.

Journal of the American Statistical Association
|March 4, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces new methods to estimate genetic relatedness between traits using genome-wide association data. Functional de-biased estimators (FDEs) improve accuracy for genetic covariance and correlation, aiding complex trait analysis.

Keywords:
Genetic correlationsgenome-wide association studiesinner productminimax rate of convergencequadratic functional

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Estimating genetic relatedness between traits is crucial for understanding complex genetic architectures.
  • Genome-wide association studies (GWAS) generate large datasets for such analyses.
  • High-dimensional linear models provide a framework for analyzing complex genetic data.

Purpose of the Study:

  • To introduce novel measures of genetic relatedness: genetic covariance and genetic correlation.
  • To develop optimal and statistically robust estimators for these genetic relatedness measures.
  • To provide methods for estimating heritability of individual traits.

Main Methods:

  • Development of functional de-biased estimators (FDEs) for genetic covariance and correlation.
  • Utilizing a two-step approach: initial estimation with scaled Lasso, followed by bias correction.
  • Estimation of quadratic functionals of regression vectors for heritability assessment.

Main Results:

  • The proposed FDEs are demonstrated to be minimax rate-optimal.
  • Efficient implementation strategies for the developed estimators are presented.
  • Simulations confirm FDEs outperform simple plug-in estimates in accuracy.

Conclusions:

  • FDEs offer a significant improvement for estimating genetic relatedness from GWAS data.
  • The methods are applicable to multi-trait analyses, as shown in a yeast dataset.
  • This work advances the statistical toolkit for genetic architecture studies.