A nonparametric regression method for multiple longitudinal phenotypes using multivariate adaptive splines

被引:0
|
作者
Wensheng Zhu
Heping Zhang
机构
[1] Northeast Normal University,Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics
[2] Yale University School of Medicine,Department of Epidemiology and Public Health
来源
Frontiers of Mathematics in China | 2013年 / 8卷
关键词
Multivariate phenotypes; longitudinal data analysis; genetic association test; multivariate adaptive regression splines; 62G08; 62G10; 62P10;
D O I
暂无
中图分类号
学科分类号
摘要
In genetic studies of complex diseases, particularly mental illnesses, and behavior disorders, two distinct characteristics have emerged in some data sets. First, genetic data sets are collected with a large number of phenotypes that are potentially related to the complex disease under study. Second, each phenotype is collected from the same subject repeatedly over time. In this study, we present a nonparametric regression approach to study multivariate and time-repeated phenotypes together by using the technique of the multivariate adaptive regression splines for analysis of longitudinal data (MASAL), which makes it possible to identify genes, gene-gene and gene-environment, including time, interactions associated with the phenotypes of interest. Furthermore, we propose a permutation test to assess the associations between the phenotypes and selected markers. Through simulation, we demonstrate that our proposed approach has advantages over the existing methods that examine each longitudinal phenotype separately or analyze the summarized values of phenotypes by compressing them into one-time-point phenotypes. Application of the proposed method to the Framingham Heart Study illustrates that the use of multivariate longitudinal phenotypes enhanced the significance of the association test.
引用
收藏
页码:731 / 743
页数:12
相关论文
共 50 条
  • [31] Sequential adaptive nonparametric regression via H-splines
    Dias, R
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 1999, 28 (02) : 501 - 515
  • [32] Settlement prediction of the piles socketed into rock using multivariate adaptive regression splines
    Zuo, QiLi
    JOURNAL OF APPLIED SCIENCE AND ENGINEERING, 2023, 26 (01): : 111 - 119
  • [33] Seismic Stability Assessment of Rock Slopes Using Multivariate Adaptive Regression Splines
    Keawsawasvong, Suraparb
    Kounlavong, Khamnoy
    Duong, Nhat Tan
    Lai, Van Qui
    Khatri, Vishwas Nandkishor
    Eskandarinejad, Alireza
    TRANSPORTATION INFRASTRUCTURE GEOTECHNOLOGY, 2024, 11 (04) : 2296 - 2318
  • [34] Using Multivariate Adaptive Regression Splines to Estimate the Body Weight of Savanna Goats
    Rashijane, Lebo Trudy
    Mokoena, Kwena
    Tyasi, Thobela Louis
    ANIMALS, 2023, 13 (07):
  • [35] Prediction of energy dissipation on the stepped spillway using the multivariate adaptive regression splines
    Parsaie A.
    Haghiabi A.H.
    Saneie M.
    Torabi H.
    ISH Journal of Hydraulic Engineering, 2016, 22 (03) : 281 - 292
  • [36] Predictors of Anemia after Bariatric Surgery using Multivariate Adaptive Regression Splines
    Lee, Yi-Chih
    Lee, Tian-Shyug
    Lee, Wei-Jei
    Lin, Yang-Chu
    Lee, Chia-Ko
    Liew, Phui-Ly
    HEPATO-GASTROENTEROLOGY, 2012, 59 (117) : 1378 - 1380
  • [37] ESTIMATING STRENGTH OF RUBBERIZED CONCRETE USING EVOLUTIONARY MULTIVARIATE ADAPTIVE REGRESSION SPLINES
    Cheng, Min-Yuan
    Cao, Minh-Tu
    JOURNAL OF CIVIL ENGINEERING AND MANAGEMENT, 2016, 22 (05) : 711 - 720
  • [38] Prediction of mechanical property of steel strips using multivariate adaptive regression splines
    Mukhopadhyay, A.
    Iqbal, A.
    JOURNAL OF APPLIED STATISTICS, 2009, 36 (01) : 1 - 9
  • [39] Prediction of gastro-intestinal absorption using multivariate adaptive regression splines
    Deconinck, E
    Xu, QS
    Put, R
    Coomans, D
    Massart, DL
    Heyden, YV
    JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS, 2005, 39 (05) : 1021 - 1030