Accurate genetic data are important prerequisite of performing genetic linkage test or association test. Currently, most analytical methods assume that the observed genotypes are correct. However, due to the constraint at the technical level, most of the genetic data that people used so far contain errors. In this paper, we considered the problem of QTL mapping based on biological data with genotyping errors. By analysing all possible genotypes of each individual in framework of multipleinterval mapping, we proposed an algorithm of inferring all model parameters through the expectation-maximization (EM) algorithm and discussed the hypothesis testing of the existence of QTL. We carried out extensive simulation studies to assess the proposed method. Simulation results showed that the new method outperforms the method that does not take the genotyping errors into account, and therefore it can decrease the impact of genotyping errors on QTL mapping. The proposed method was also applied to analyse a real barley dataset.

The analysis of quantitative trait loci (QTLs) aims at mapping and estimating the positions and effects of the genes that may affect the quantitative trait, and evaluating the relationship between the gene variation and the phenotype. In existing studies, most methods mainly focus on the association/linkage between multiple gene loci and one trait, in which some useful joint information of multiple traits may be ignored. In this paper, we proposed a method of simultaneously estimating all QTL parameters in the framework of multiple-trait multiple-interval mapping. Simulation results show that in accuracy aspect, the proposed method outperforms an existing method for mapping multiple traits. A real example is also provided to validate the performance of the new method.

Statistical gene detection plays an important role in biostatistics and bioinformatics. So far, many gene loci associated with human complex disease have been found by statistical methods. However, it is difficult to find all the mutation genes that are associated with a certain disease. Researchers need to detect more associated genes aiming at a disease so that human will conquer the disease one day. In this paper, we considered a real and big data set and study the detection problem of genes associated with the PIK3C2B gene on lung disease. 168 significant genes associated with the PIK3C2B gene were detected at nominal significance level 0.001 by using statistical multiple testing method. The detected genes will provide some reference to further study the function of the PIK3C2B gene to lung disease for biologists and medical scientists.

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With the development of genome-wide association analysis and sequencing techniques, lots of rare and common variants associated with complex traits or diseases have been detected. Besides, in recent years, the research based on family data has attracted wide attention, but most of the research only consider the data of unrelated individuals and siblings, and rarely consider the data of distant relatives like grandparent-grandchild pairs, uncle-nephew pairs, cousin pairs, and so on. In this paper, we propose an effective method for generating affected grandparent-grandchild pair data (called GAGP). Based on association analysis, we use a large number of simulation experiments to evaluate the effectiveness and application of the new sampling method. The simulation results show that in all cases, the new method is valid and obtains good test results compared with other methods, which indicates that the method has better performance. The new method is implemented by the software R.