A simple regression-based method to map quantitative trait loci underlying function-valued phenotypesopen access
- Authors
- Kwak, I.-Y.; Moore, C.R.; Spalding, E.P.; Broman, K.W.
- Issue Date
- 2014
- Publisher
- Genetics
- Keywords
- Function-valued trait; Growth curves; Model selection; QTL
- Citation
- Genetics, v.197, no.4, pp 1409 - 1416
- Pages
- 8
- Journal Title
- Genetics
- Volume
- 197
- Number
- 4
- Start Page
- 1409
- End Page
- 1416
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/64765
- DOI
- 10.1534/genetics.114.166306
- ISSN
- 0016-6731
- Abstract
- Most statistical methods for quantitative trait loci (QTL) mapping focus on a single phenotype. However, multiple phenotypes are commonly measured, and recent technological advances have greatly simplified the automated acquisition of numerous phenotypes, including function-valued phenotypes, such as growth measured over time. While methods exist for QTL mapping with function-valued phenotypes, they are generally computationally intensive and focus on single-QTL models. We propose two simple, fast methods that maintain high power and precision and are amenable to extensions with multiple-QTL models using a penalized likelihood approach. After identifying multiple QTL by these approaches, we can view the function-valued QTL effects to provide a deeper understanding of the underlying processes. Our methods have been implemented as a package for R, funqtl. © 2014 by the Genetics Society of America.
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Collections - College of Business & Economics > Department of Applied Statistics > 1. Journal Articles
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