OBJECTIVES: Host genetics have been recently reported to affect human microbiome composition. We previously developed a statistical framework, microbiomeGWAS, to identify host genetic variants associated with microbiome composition by testing a distance matrix. However, statistical power depends on the choice of a microbiome distance matrix. To achieve more robust statistical power, we aim to extend microbiomeGWAS to test the association with many distance matrices, which are defined based on multilevel taxa abundances and phylogenetic information. METHODS: The main challenge is to accurately and rapidly evaluate the significance for millions of SNPs. We propose methods for approximating p values by correcting for the multiple testing introduced by testing many distance matrices and by correcting for the skewness and kurtosis of score statistics. RESULTS: The accuracy of p value approximation was verified by simulations. We applied our method to a set of 147 lung cancer patients with 16S rRNA microbiome profiles from nonmalignant lung tissues. We show that correcting for skewness and kurtosis eliminated dramatic deviations in the quantile-quantile plot. CONCLUSION: We developed computationally efficient methods for identifying host genetic variants associated with microbiome composition by testing many distance matrices. The algorithms are implemented in the package microbiomeGWAS (https://github.com/lsncibb/microbiomeGWAS).
OBJECTIVES: Host genetics have been recently reported to affect human microbiome composition. We previously developed a statistical framework, microbiomeGWAS, to identify host genetic variants associated with microbiome composition by testing a distance matrix. However, statistical power depends on the choice of a microbiome distance matrix. To achieve more robust statistical power, we aim to extend microbiomeGWAS to test the association with many distance matrices, which are defined based on multilevel taxa abundances and phylogenetic information. METHODS: The main challenge is to accurately and rapidly evaluate the significance for millions of SNPs. We propose methods for approximating p values by correcting for the multiple testing introduced by testing many distance matrices and by correcting for the skewness and kurtosis of score statistics. RESULTS: The accuracy of p value approximation was verified by simulations. We applied our method to a set of 147 lung cancerpatients with 16S rRNA microbiome profiles from nonmalignant lung tissues. We show that correcting for skewness and kurtosis eliminated dramatic deviations in the quantile-quantile plot. CONCLUSION: We developed computationally efficient methods for identifying host genetic variants associated with microbiome composition by testing many distance matrices. The algorithms are implemented in the package microbiomeGWAS (https://github.com/lsncibb/microbiomeGWAS).
Authors: Nandan P Deshpande; Stephen M Riordan; Natalia Castaño-Rodríguez; Marc R Wilkins; Nadeem O Kaakoush Journal: Microbiome Date: 2018-12-17 Impact factor: 14.650
Authors: Denis Awany; Imane Allali; Shareefa Dalvie; Sian Hemmings; Kilaza S Mwaikono; Nicholas E Thomford; Andres Gomez; Nicola Mulder; Emile R Chimusa Journal: Front Genet Date: 2019-01-22 Impact factor: 4.599