Recessive/dominant model: Alternative choice in case-control-based genome-wide association studies.

An additive genetic model is usually employed in case-control-based genome-wide association studies. The model usually encodes "AA", "Aa" and "aa" ("a" represents the minor allele) as three different numbers, implying the contribution of genotype "Aa" to the phenotype is different from "AA" and "aa". From the perspective of biological phenomena, the coding is reasonable since the phenotypes of lives are not "black and white". A case-control based study, however, has only two phenotypes, case and control, which means that the phenotypes are "black and white". It suggests that a recessive/dominant model may be an alternative to the additive model. In order to investigate whether the alternative is feasible, we conducted comparative experiments on several models used in those studies through chi-square test and logistic regression. Our simulation experiments demonstrate that a recessive model is better than the additive model. The area under the curve of the former has increased by 5% compared with the latter, the discrimination of identifying risk single nucleotide polymorphisms has been improved by 61%, and the precision has also reached 1.10 times that of the latter. Furthermore, the real data experiments show that the precision and area under the curve of the former are 16% and 20% higher than the latter respectively, and the area under the curve of dominant model of the former is 13% higher than the latter. The results indicate a recessive/dominant model may be an alternative to the additive model and suggest a new route for case-control-based studies.

coPLINK: A complementary tool to PLINK.

In genome-wide association studies (GWAS), a wide variety of analysis tools have been designed, leading to various formats of GWAS data. How to convert a dataset in non-PLINK format into PLINK format to use its powerful analysis performance, or to convert a dataset in PLINK format into the format of other analysis tools, is a problem that needs to be faced and solved. To address this issue, we developed a tool called coPLINK, a complementary tool to PLINK, to cooperate with PLINK to implement the conversions of GWAS data formats and to provide some additional functions, such as data files comparison. The tool can implement mutual conversions not only between an existing data format and PLINK PED/BED, but also between a user-defined data format and PLINK PED. The usage and performance of the tool are similar to PLINK. The characteristics of the conversions of existing data formats and user-defined formats make it be a good assistant to PLINK or other tools and, have good potential for GWAS studies or other works.

Density distribution of gene expression profiles and evaluation of using maximal information coefficient to identify differentially expressed genes.

The hypothesis of data probability density distributions has many effects on the design of a new statistical method. Based on the analysis of a group of real gene expression profiles, this study reveal that the primary density distributions of the real profiles are normal/log-normal and t distributions, accounting for 80% and 19% respectively. According to these distributions, we generated a series of simulation data to make a more comprehensive assessment for a novel statistical method, maximal information coefficient (MIC). The results show that MIC is not only in the top tier in the overall performance of identifying differentially expressed genes, but also exhibits a better adaptability and an excellent noise immunity in comparison with the existing methods.