Articles written in Journal of Genetics

    • Association analysis of multiple traits by an approach of combining P values


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      Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region,we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutralvariants and the different directions of effects of causal variants.

    • An adaptive combination method for Cauchy variable based on optimal threshold


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      In sequence study, set-based analysis has been developed as a popular tool for analysing the association of a group rare variant with disease. However, most of the methods are sensitive to the genetic architecture. Besides, by directly combining the association signals of multiple markers within a genomic region can inevitably include a large proportion of noises. To address this issue, we extend the aggregated Cauchy association test (ACAT; Liu et al. 2019) and propose an adaptive Cauchy-variable combination method (ACC). Our proposed method combines Cauchy-variables, which are transformed from variant-level P-values in a given genomic region and adaptively truncate noises by choosing an optimal truncation threshold of the variant-level P-value that is determined by the data. Besides, the ACC method can use summary statistics obtained from open access database when the original genotype and phenotype data are unavailable. Extensive simulation studies and Genetic Analysis Workshop 19 real data analysis show that ACC is more powerful than the other comparative methods when only a small proportion of variants are causal, and ACC is robust to the varied genetic architecture.

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