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Nonparametric Tests of Associations with Disease Based on U‐Statistics
Author(s) -
Jin Lina,
Zhu Wensheng,
Yu Yaqin,
Kou Changgui,
Meng Xiangfei,
Tao Yuchun,
Guo Jianhua
Publication year - 2014
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1111/ahg.12049
Subject(s) - nonparametric statistics , statistic , multiple comparisons problem , test statistic , genetic association , haplotype , type i and type ii errors , statistics , kernel (algebra) , statistical hypothesis testing , statistical power , association test , computer science , biology , mathematics , genetics , gene , genotype , combinatorics , single nucleotide polymorphism
Summary In case‐control studies, association analysis was designed to test whether genetic variants were associated with human diseases. To evaluate the association, analysing one genetic marker at a time suffered from weak power, because of the correction for multiple testing and possibly small genetic effects. An alternative strategy was to test simultaneous effects of multiple markers, which was believed to be more powerful. However, when the number of markers under investigation was large, they would be subjected to weak power as well, because of the greater degrees of freedom. To conquer these limitations in case‐control studies, we proposed a novel method that could test joint association of several loci (i.e. haplotype), with only a single degree of freedom. In this research, we developed a nonparametric approach, which was based on U‐statistics. We also introduced a new kernel for U‐statistic, which could combine the haplotype structure information, and was expected to enhance the power. Simulations indicated that our proposed approach offered merits in identifying the associations between diseases and haplotypes. Application of our method to a study of candidate genes for internalising disorder illustrated its virtue in utility and interpretation, and provided an excellent result in detecting the associations.