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A flexible estimating equations approach for mapping function-valued traits.

Hao Xiong1, Evan H Goulding, Elaine J Carlson

  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94143-0560, USA.

Genetics
|June 28, 2011
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Summary
This summary is machine-generated.

We developed a robust functional regression method for genetic studies of complex traits. This approach accurately maps genetic loci for function-valued traits without needing to know the data's covariance structure.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Functional Data Analysis

Background:

  • Genetic studies often involve traits with temporal or spatial structures, such as growth curves and skeletal shape.
  • These traits are best analyzed as function-valued data, requiring specialized methods for genetic locus identification.
  • Existing likelihood-based methods for mapping function-valued traits are sensitive to the accurate specification of data distribution and error structures, which is often challenging.

Purpose of the Study:

  • To propose a novel, robust functional regression approach for mapping genetic loci contributing to function-valued traits.
  • To overcome the limitations of likelihood-based methods by not requiring precise specification of covariance structures.
  • To provide a flexible and computationally efficient alternative for genetic analysis of complex traits.

Main Methods:

  • Developed a general functional regression framework using estimating equations, robust to covariance structure misspecification.
  • Employed a two-step least-squares algorithm for estimation, suitable even when time points exceed sample size.
  • The method utilizes a general linear functional model, allowing flexibility with covariates and straightforward extensions like handling incomplete genotype data.

Main Results:

  • The proposed method demonstrates robustness against misspecification of the covariance structure.
  • The two-step least-squares algorithm ensures computational efficiency and applicability across various data scenarios.
  • The framework is flexible, easily accommodates extensions, and can be parallelized for large datasets.
  • Demonstrated the method's utility and advantages using circadian mouse behavioral data.

Conclusions:

  • The functional regression approach based on estimating equations offers a powerful and flexible alternative to likelihood-based methods for genetic studies of function-valued traits.
  • It provides a favorable balance of model simplicity, statistical efficiency, and computational speed, especially when covariance structures are unknown.
  • This method enhances the ability to identify genetic loci underlying complex, structured traits in biological research.