Jurg Ott1, Zhe Liu, Yuanyuan Shen. 1. Key Laboratory of Mental Health, Institute of Psychology, CAS, Beijing, China. ottjurg@psych.ac.cn
Abstract
BACKGROUND/AIMS: We consider the situation that multiple genetic variants are underlying a heritable trait and assume that each contributes to the trait only to a small degree. The aim is to develop a statistical test for disease association of these multiple variants. METHODS: We expect that p values resulting from a genome-wide case-control association analysis will fall into two classes: those reflecting true association and those occurring randomly in the interval from 0 to 1. We develop a partition test to find the set of smallest p values deviating most from the number of p values expected under randomness. RESULTS: Power calculations demonstrate the superiority of our partition test over conventional SNP-by-SNP analyses. Applications of the partition test to six published datasets show that our test is particularly suitable when multiple SNPs appear to contribute to a trait, and furnished more significant results than a well-known procedure to estimate the false discovery rate. CONCLUSIONS: Our partition test also furnishes an estimate of the number of functional SNPs underlying disease and can be highly significant, while single-locus tests may be far from significant.
BACKGROUND/AIMS: We consider the situation that multiple genetic variants are underlying a heritable trait and assume that each contributes to the trait only to a small degree. The aim is to develop a statistical test for disease association of these multiple variants. METHODS: We expect that p values resulting from a genome-wide case-control association analysis will fall into two classes: those reflecting true association and those occurring randomly in the interval from 0 to 1. We develop a partition test to find the set of smallest p values deviating most from the number of p values expected under randomness. RESULTS: Power calculations demonstrate the superiority of our partition test over conventional SNP-by-SNP analyses. Applications of the partition test to six published datasets show that our test is particularly suitable when multiple SNPs appear to contribute to a trait, and furnished more significant results than a well-known procedure to estimate the false discovery rate. CONCLUSIONS: Our partition test also furnishes an estimate of the number of functional SNPs underlying disease and can be highly significant, while single-locus tests may be far from significant.
Authors: Teodor T Postolache; Laura Del Bosque-Plata; Serge Jabbour; Michael Vergare; Rongling Wu; Claudia Gragnoli Journal: Am J Med Genet B Neuropsychiatr Genet Date: 2019-02-06 Impact factor: 3.568
Authors: Yong Zhang; Brandan Mayer-Blackwell; Stefan D Schlussman; Matthew Randesi; Eduardo R Butelman; Ann Ho; Jurg Ott; Mary Jeanne Kreek Journal: Psychopharmacology (Berl) Date: 2013-11-13 Impact factor: 4.530