Abstract
Pleiotropy arises when a locus influences multiple traits. Rich GWAS findings of various traits in the past decade reveal many examples of this phenomenon, suggesting the wide existence of pleiotropic effects. What underlies this phenomenon is the biological connection among seemingly unrelated traits/diseases. Characterizing the molecular mechanisms of pleiotropy not only helps to explain the relationship between diseases, but may also contribute to novel insights concerning the pathological mechanism of each specific disease, leading to better disease prevention, diagnosis and treatment. However, most pleiotropic effects remain elusive because their functional roles have not been systematically examined. A systematic investigation requires availability of qualified measurements at multilayered biological processes (e.g., transcription and translation). The rise of Big Data in biomedicine, such as high-quality multi-omics data, biomedical imaging data and electronic medical records of patients, offers us an unprecedented opportunity to investigate pleiotropy. There will be a great need of computationally efficient and statistically rigorous methods for integrative analysis of these Big Data in biomedicine. In this review, we outline many opportunities and challenges in methodology developments for systematic analysis of pleiotropy, and highlight its implications on disease prevention, diagnosis and treatment.
Highlights
IntroductionGenome-wide association studies (GWAS) have been conducted to study the genetic basis for thousands of phenotypes (Hindorff et al, 2009; Eicher et al, 2015), including diseases (e.g., the seven diseases from WTCCC, The Wellcome Trust Case Control Consortium, 2007), clinical traits (e.g., cholesterol levels), anthropometric traits (e.g., height, Wood et al, 2014), brain structures (Hibar et al, 2015) and social behaviors (e.g., educational attainment, Rietveld et al, 2013; marriage, Domingue et al, 2014)
In the past decade, genome-wide association studies (GWAS) have been conducted to study the genetic basis for thousands of phenotypes (Hindorff et al, 2009; Eicher et al, 2015), including diseases, clinical traits, anthropometric traits, brain structures (Hibar et al, 2015) and social behaviors
Accumulating evidence suggests that pleiotropy widely exists among complex traits, such as psychiatric disorders (CrossDisorder Group of the Psychiatric Genomics Consortium, 2013a,b), metabolic syndrome traits (Vattikuti et al, 2012) and cancers (Sakoda et al, 2013)
Summary
Genome-wide association studies (GWAS) have been conducted to study the genetic basis for thousands of phenotypes (Hindorff et al, 2009; Eicher et al, 2015), including diseases (e.g., the seven diseases from WTCCC, The Wellcome Trust Case Control Consortium, 2007), clinical traits (e.g., cholesterol levels), anthropometric traits (e.g., height, Wood et al, 2014), brain structures (Hibar et al, 2015) and social behaviors (e.g., educational attainment, Rietveld et al, 2013; marriage, Domingue et al, 2014). Implications of pleiotropy these fruitful findings from GWAS, recent progress has suggested that a single locus may influence multiple seemingly different phenotypes (Solovieff et al, 2013). This phenomenon, termed “Pleiotropy,” was formally introduced into the scientific literature by the German geneticist Ludwig Plate in 1910 (Stearns, 2010). A recent update (Eicher et al, 2015) on GRASP has provided even more comprehensive GWAS results—about 8.87 million SNP-phenotype associations in 2082 studies with p ≤ 0.05 Such a rich data resource allows characterizing the molecular mechanisms of PE on diverse phenotypes. It will greatly deepen our understanding of the genetic architecture that underlies complex human phenotypes, and have clinically important implications
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