Functional Mapping of Multiple Dynamic Traits

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• 作者：Jiguo Cao ; Liangliang Wang ; Zhongwen Huang…
• 关键词：B ; spline ; Functional mapping ; Multivariate model ; Quantitative trait loci ; Soybean
• 刊名：Journal of Agricultural, Biological and Environmental Statistics
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：22
• 期：1
• 页码：60-75
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics for Life Sciences, Medicine, Health Sciences; Agriculture; Monitoring/Environmental Analysis; Biostatistics;
• 出版者：Springer US
• ISSN：1537-2693
• 卷排序：22

文摘

Many biological phenomena undergo developmental changes in time and space. Functional mapping, which is aimed at mapping genes that affect developmental patterns, is instrumental for studying the genetic architecture of biological changes. Often biological processes are mediated by a network of developmental and physiological components and, therefore, are better described by multiple phenotypes. In this article, we develop a multivariate model for functional mapping that can detect and characterize quantitative trait loci (QTLs) that simultaneously control multiple dynamic traits. Because the true genotypes of QTLs are unknown, the measurements for the multiple dynamic traits are modeled using a mixture distribution. The functional means of the multiple dynamic traits are estimated using the nonparametric regression method, which avoids any parametric assumption on the functional means. We propose the profile likelihood method to estimate the mixture model. A likelihood ratio test is exploited to test for the existence of pleiotropic effects on distinct but developmentally correlated traits. A simulation study is implemented to illustrate the finite sample performance of our proposed method. We also demonstrate our method by identifying QTLs that simultaneously control three dynamic traits of soybeans. The three dynamic traits are the time-course biomass of the leaf, the stem, and the root of the whole soybean. The genetic linkage map is constructed with 950 microsatellite markers. The new model can aid in our comprehension of the genetic control mechanisms of complex dynamic traits over time.