Phenotypic variation of floral organs in Malus using frequency distribution functions

BACKGROUND

Phenotypic diversity of floral organs plays an important role in plant systematic taxonomy and genetic variation studies. Previous research have focused on the direction of variation but disregarded its degree. Phenotypic variation (including directions and degrees) of 17 floral traits from wild to cultivated crabapples were explored by comparing their distributions and deviations in three different dimensions: floral organ number, size, and the shape.

RESULTS

Except for petal number, petal length / petal width, and sepal length / sepal width, the analyzed floral traits of cultivated crabapples all showed downward distributed box bodies in box plot analysis and left deviations of fitted curves in frequency distribution function analysis when compared to the wild, which revealed consistent variation directions of petaloid conversion (pistils or stamens → petals), size miniaturization (large → small), and shape narrowness (petal shape: circular → elliptic; sepal shape: triangular → lanceolate). However, only seven floral traits exhibited significant differences in box plot analysis, while all of the traits in frequency distribution function analysis were obviously offset. The variation degrees were quantitatively characterized by sizing traits > shaping traits > numbering traits and by horizontal dimensions > radial dimensions.

CONCLUSIONS

Frequency distribution function analysis was more sensitive than the box plot analysis, which constructed a theoretical basis for Malus flower type breeding and would provide a new quantitative method for future evaluation of floral variation among different groups of angiosperms at large.

A Comparative Study of Five Association Tests Based on CpG Set for Epigenome-Wide Association Studies

An epigenome-wide association study (EWAS) is a large-scale study of human disease-associated epigenetic variation, specifically variation in DNA methylation. High throughput technologies enable simultaneous epigenetic profiling of DNA methylation at hundreds of thousands of CpGs across the genome. The clustering of correlated DNA methylation at CpGs is reportedly similar to that of linkage-disequilibrium (LD) correlation in genetic single nucleotide polymorphisms (SNP) variation. However, current analysis methods, such as the t-test and rank-sum test, may be underpowered to detect differentially methylated markers. We propose to test the association between the outcome (e.g case or control) and a set of CpG sites jointly. Here, we compared the performance of five CpG set analysis approaches: principal component analysis (PCA), supervised principal component analysis (SPCA), kernel principal component analysis (KPCA), sequence kernel association test (SKAT), and sliced inverse regression (SIR) with Hotelling's T2 test and t-test using Bonferroni correction. The simulation results revealed that the first six methods can control the type I error at the significance level, while the t-test is conservative. SPCA and SKAT performed better than other approaches when the correlation among CpG sites was strong. For illustration, these methods were also applied to a real methylation dataset.