Table 2.

Univariate approaches for detecting CP associations

	Input	Explicit test of CP association	Allows effect heterogeneity	Types of phenotype (such as continuous or categorical)	Accommodates overlapping subjects	Combine data across multiple studies	Identify subset of associated phenotypes	Genetic variant versus region	Refs
Fisher	P value	No	Yes	Any	No	Yes	No	Variant	56
CPMA	P value	Yes	Yes	Any	No	Yes	No	Variant	14
Fixed effects meta-analysis	Effect estimate	No	No	Same type; need to standardize continuous phenotypes	No	Yes	No	Variant	54,57,58‖
Random effects meta-analysis	Effect estimate	No	Moderate level; not opposite effects	Same type; need to standardize continuous phenotypes	No	Yes	No	Variant	54,57,58‖
Subset-based meta-analysis	Effect estimate	No	Yes	Same type; need to standardize continuous phenotypes	No; offer extension to account for some overlap	Yes	Yes	Variant	59
Extensions to O’Brien	Effect estimate	No	Yes	Any	Yes; all subjects overlap*	No§	No	Variant	61,62
TATES	P value	No	Yes	Any	Yes; all subjects overlap‡	No§	No	Variant	63
PRIMe	P value	No	Yes	Any	Yes	Yes	No	Region	64

CP, cross-phenotype; CPMA, cross-phenotype meta-analysis; PRIMe, Pleiotropy Regional Identification Method; TATES, Trait-based Association Test that uses Extended Simes.

^*Can accommodate values missing completely at random.

^‡Can accommodate values missing completely at random and blockwise missingness.

^§Can combine across multiple studies if all subjects have non-missing values for all phenotypes; TATES can accommodate situations in which a subset of studies have missing values for a subset of the phenotypes.

^‖References are given for meta-analytical methods typically used in genome-wide association studies.