Nonparametric Evaluation of Quantitative Traits in Population-Based Association Studies when the Genetic Model is Unknown

Statistical association between a single nucleotide polymorphism (SNP) genotype and a quantitative trait in genome-wide association studies is usually assessed using a linear regression model, or, in the case of non-normally distributed trait values, using the Kruskal-Wallis test. While linear regression models assume an additive mode of inheritance via equi-distant genotype scores, Kruskal-Wallis test merely tests global differences in trait values associated with the three genotype groups. Both approaches thus exhibit suboptimal power when the underlying inheritance mode is dominant or recessive. Furthermore, these tests do not perform well in the common situations when only a few trait values are available in a rare genotype category (disbalance), or when the values associated with the three genotype categories exhibit unequal variance (variance heterogeneity). We propose a maximum test based on Marcus-type multiple contrast test for relative effect sizes. This test allows model-specific testing of either dominant, additive or recessive mode of inheritance, and it is robust against variance heterogeneity. We show how to obtain mode-specific simultaneous confidence intervals for the relative effect sizes to aid in interpreting the biological relevance of the results. Further, we discuss the use of a related all-pairwise comparisons contrast test with range preserving confidence intervals as an alternative to Kruskal-Wallis heterogeneity test. We applied the proposed maximum test to the Bogalusa Heart Study dataset, and gained a remarkable increase in the power to detect association, particularly for rare genotypes. Our simulation study also demonstrated that the proposed non-parametric tests control family-wise error rate in the presence of non-normality and variance heterogeneity contrary to the standard parametric approaches. We provide a publicly available R library nparcomp that can be used to estimate simultaneous confidence intervals or compatible multiplicity-adjusted p-values associated with the proposed maximum test.

Identification of superior reference genes for data normalisation of expression studies via quantitative PCR in hybrid roses (Rosa hybrida)

Background: Gene expression studies are a prerequisite for understanding the biological function of genes. Because of its high sensitivity and easy use, quantitative PCR (qPCR) has become the gold standard for gene expression quantification. To normalise qPCR measurements between samples, the most prominent technique is the use of stably expressed endogenous control genes, the so called reference genes. However, recent studies show there is no universal reference gene for all biological questions. Roses are important ornamental plants for which there has been no evaluation of useful reference genes for gene expression studies. Results: We used three different algorithms (BestKeeper, geNorm and NormFinder) to validate the expression stability of nine candidate reference genes in different rose tissues from three different genotypes of Rosa hybrida and in leaves treated with various stress factors. The candidate genes comprised the classical "housekeeping genes" (Actin, EF-1α, GAPDH, Tubulin and Ubiquitin), and genes showing stable expression in studies in Arabidopsis (PP2A, SAND, TIP and UBC). The programs identified no single gene that showed stable expression under all of the conditions tested, and the individual rankings of the genes differed between the algorithms. Nevertheless the new candidate genes, specifically, PP2A and UBC, were ranked higher as compared to the other traditional reference genes. In general, Tubulin showed the most variable expression and should be avoided as a reference gene. Conclusions: Reference genes evaluated as suitable in experiments with Arabidopsis thaliana were stably expressed in roses under various experimental conditions. In most cases, these genes outperformed conventional reference genes, such as EF1-α and Tubulin. We identified PP2A, SAND and UBC as suitable reference genes, which in different combinations may be used for normalisation in expression analyses via qPCR for different rose tissues and stress treatments. However, the vast genetic variation found within the genus Rosa, including differences in ploidy levels, might also influence expression stability of reference genes, so that future research should also consider different genotypes and ploidy levels.