Functional Data Model for Genetically Related Individuals With Application to Cow Growth

Subscribe to email list

Please select the email list(s) to which you wish to subscribe.

You are here

Functional Data Model for Genetically Related Individuals With Application to Cow Growth

TitleFunctional Data Model for Genetically Related Individuals With Application to Cow Growth
Publication TypeJournal Article
Year of Publication2015
AuthorsLei, E, Yao, F, Heckman, N, Meyer, K
JournalJOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS
Volume24
Pagination756-770
Date PublishedJUL 3
Type of ArticleArticle
ISSN1061-8600
KeywordsFunctional principal components, Genetic relationship, Smoothing, Sparse functional data
AbstractWe 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.
DOI10.1080/10618600.2014.948180