A flexible estimating equations approach for mapping function-valued traits

Hao Xiong, Evan H. Goulding, Elaine J. Carlson, Laurence H. Tecott, Charles E. McCulloch, Śaunak Sen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


In genetic studies, many interesting traits, including growth curves and skeletal shape, have temporal or spatial structure. They are better treated as curves or function-valued traits. Identification of genetic loci contributing to such traits is facilitated by specialized methods that explicitly address the function-valued nature of the data. Current methods for mapping function-valued traits are mostly likelihood-based, requiring specification of the distribution and error structure. However, such specification is difficult or impractical in many scenarios. We propose a general functional regression approach based on estimating equations that is robust to misspecification of the covariance structure. Estimation is based on a two-step least-squares algorithm, which is fast and applicable even when the number of time points exceeds the number of samples. It is also flexible due to a general linear functional model; changing the number of covariates does not necessitate a new set of formulas and programs. In addition, many meaningful extensions are straightforward. For example, we can accommodate incomplete genotype data, and the algorithm can be trivially parallelized. The framework is an attractive alternative to likelihood-based methods when the covariance structure of the data is not known. It provides a good compromise between model simplicity, statistical efficiency, and computational speed. We illustrate our method and its advantages using circadian mouse behavioral data.

Original languageEnglish (US)
Pages (from-to)305-316
Number of pages12
Issue number1
StatePublished - Sep 2011

ASJC Scopus subject areas

  • Genetics

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