Statistical methods to detect pleiotropy in human complex traits

Abstract

In recent years pleiotropy, the phenomenon of one genetic locus influencing several traits, has become a widely researched field in human genetics. With the increasing availability of genome-wide association study summary statistics, as well as the establishment of deeply phenotyped sample collections, it is now possible to systematically assess the genetic overlap between multiple traits and diseases. In addition to increasing power to detect associated variants, multi-trait methods can also aid our understanding of how different disorders are aetiologically linked by highlighting relevant biological pathways. A plethora of available tools to perform such analyses exists, each with their own advantages and limitations. In this review, we outline some of the currently available methods to conduct multi-trait analyses. First, we briefly introduce the concept of pleiotropy and outline the current landscape of pleiotropy research in human genetics; second, we describe analytical considerations and analysis methods; finally, we discuss future directions for the field.

Keywords: genome-wide association study; pleiotropy; statistical methods.

Figure 1.

Schematic representation of different scenarios for cross-phenotype associations. Such effects might arise due to biological pleiotropy, whereby causal variants for two traits colocalize in the same locus (a,b), due to mediated pleiotropy, whereby a variant exerts an effect on one trait through another one (c), or due to spurious pleiotropy, whereby causal variants for two traits fall into distinct loci but are in LD with a variant associated with both traits (d).

Figure 2.

Directed acyclic graph of the Mendelian randomization model. IV, instrumental variable.