A fast non-parametric test of association for multiple traits

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Large-scale genotyped cohorts generate multidimensional phenotypic data.
• Identifying genetic effects on multiple traits requires efficient analytical methods.
• Permutational multivariate analysis of variance (PERMANOVA) is a powerful non-parametric tool but computationally intensive for large datasets due to its reliance on permutations.

Purpose of the Study:

• To develop a computationally efficient method for analyzing genetic effects on multiple traits in large cohorts.
• To derive the limiting null distribution of the PERMANOVA test statistic for fast asymptotic p-value computation.
• To provide a robust statistical framework for quantitative trait loci (QTL) mapping and genome-wide association studies (GWAS).

Main Methods:

• Derivation of the limiting null distribution of the PERMANOVA test statistic.
• Development of an asymptotic test for significance assessment.
• Evaluation of the asymptotic test's performance in terms of type I error control and statistical power.
• Application of the method to QTL mapping and GWAS.

Main Results:

• The derived asymptotic test enables fast computation of p values for PERMANOVA.
• The asymptotic test demonstrates controlled type I error rates.
• The proposed method exhibits high statistical power, frequently outperforming traditional parametric approaches.
• The framework is successfully applied to analyze genetic associations in large-scale datasets.

Conclusions:

• The asymptotic PERMANOVA test provides an efficient and powerful alternative for analyzing genetic effects on multiple traits in large cohorts.
• This method overcomes the computational limitations of permutation-based approaches.
• The framework enhances the analysis of complex genetic architectures in genomics research, including QTL mapping and GWAS.