@article { ISI:000361373800008,
	title = {Functional Data Model for Genetically Related Individuals With Application to Cow Growth},
	journal = {JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS},
	volume = {24},
	number = {3},
	year = {2015},
	month = {JUL 3},
	pages = {756-770},
	publisher = {AMER STATISTICAL ASSOC},
	type = {Article},
	address = {732 N WASHINGTON ST, ALEXANDRIA, VA 22314-1943 USA},
	abstract = {We propose a new version of functional data model for analyzing familial related individuals, where the within-subject correlation depends smoothly on a covariate such as age and the between-subject correlation follows family-wise genetic association. Our motivating example concerns measurements of weight as a function of age in sibling cows from independent families. Observations are sparsely sampled from trajectories of a phenotype contaminated with measurement error, where the phenotypic trajectory consists of a genetic component and an environmental component. By combining information across individuals, the genetic and environmental covariances are estimated via smoothing techniques. We study the genetic and environmental effects using principal component analysis, taking into account the genetic correlation to enhance the subject-level signal extraction. We show via the real data and simulations that incorporating the correlation structure improves predictions of individual phenotypic trajectories.},
	keywords = {Functional principal components, Genetic relationship, Smoothing, Sparse functional data},
	issn = {1061-8600},
	doi = {10.1080/10618600.2014.948180},
	author = {Lei, Edwin and Yao, Fang and Heckman, Nancy and Meyer, Karin}
}
