Regression-based approach for testing the association between multi-region haplotype configuration and complex trait.
BACKGROUND
It is quite common that the genetic architecture of complex traits involves many genes and their interactions. Therefore, dealing with multiple unlinked genomic regions simultaneously is desirable.


RESULTS
In this paper we develop a regression-based approach to assess the interactions of haplotypes that belong to different unlinked regions, and we use score statistics to test the null hypothesis of non-genetic association. Additionally, multiple marker combinations at each unlinked region are considered. The multiple tests are settled via the minP approach. The P value of the "best" multi-region multi-marker configuration is corrected via Monte-Carlo simulations. Through simulation studies, we assess the performance of the proposed approach and demonstrate its validity and power in testing for haplotype interaction association.


CONCLUSIONS
Our simulations showed that, for binary trait without covariates, our proposed methods prove to be equal and even more powerful than htr and hapcc which are part of the FAMHAP program. Additionally, our model can be applied to a wider variety of traits and allow adjustment for other covariates. To test the validity, our methods are applied to analyze the association between four unlinked candidate genes and pig meat quality.
Hu
Yanling
Y
School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, PR China. ylhu0323@sjtu.edu.cn
Jason
Sinnwell
S
Wang
Qishan
Q
Pan
Yuchun
Y
Zhang
Xiangzhe
X
Zhao
Hongbo
H
Li
Changlong
C
Sun
Libin
L
D000818:Animals; D003198:Computer Simulation; D005787:Gene Frequency; D005819:Genetic Markers; D006239:Haplotypes; D015810:Linkage Disequilibrium; D008460:Meat / Q000592:standards; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D019655:Quantitative Trait, Heritable; D012044:Regression Analysis; D013552:Swine / Q000235:genetics
D005819:Genetic Markers