Gene

Null hypothesis

Likelihood-ratio test

Statistical power

Biology

Statistics

Null (SQL)

Data mining

Computer science

Computational biology

Single-nucleotide polymorphism

Genetics

Genome-wide association study

Mathematics

Statistical hypothesis testing

Null distribution

Genetic association

Genotype

Test statistic

Type I and type II errors

False discovery rate

Alternative hypothesis

Quantitative trait locus

Pleiotropy

Phenotype

PLoS Genetics

There is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a genome-wide association study (GWAS). The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits. We propose testing the null hypothesis based on the product of the Z-statistics of the genetic variants across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of variants to be associated with none or only one of the traits. We borrow approaches from the statistical literature on mediation analysis that allow asymptotic approximation of the null distribution avoiding estimation of nuisance parameters related to mixture proportions and variance components. Simulation studies demonstrate that the proposed method can maintain type I error and can achieve major power gain over alternative simpler methods that are typically used for testing pleiotropy. PLACO allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. Application of PLACO to publicly available summary data from two large case-control GWAS of Type 2 Diabetes and of Prostate Cancer implicated a number of novel shared genetic regions: 3q23 ( ZBTB38 ), 6q25.3 ( RGS17 ), 9p22.1 ( HAUS6 ), 9p13.3 ( UBAP2 ), 11p11.2 ( RAPSN ), 14q12 ( AKAP6 ), 15q15 ( KNL1 ) and 18q23 ( ZNF236 ).

A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer