Genome-wide association studies have identified many novel loci for hundreds of traits. Interestingly, numerous genetic loci have been associated with multiple seemingly distinct traits. These cross-phenotype (CP) associations highlight the relevance of pleiotropy in human disease.
There is substantial evidence for CP associations in contemporary gene-mapping studies.
Different types of pleiotropy (biological, mediated and spurious pleiotropy) can underlie a CP association.
Various analytical approaches have been devised for detecting CP associations, especially methods that are based on summary statistics as opposed to individual-level data. Different methods have relative advantages and disadvantages and are distinguished by their underlying algorithms and by the types of phenotype data that they handle.
Study design considerations are crucial for minimizing the identification of spurious CP associations.
CP associations can highlight shared biological pathways and, when associated with different diseases, have clinical implications for diagnosis, counselling and treatment.
Genome-wide association studies have identified many variants that each affects multiple traits, particularly across autoimmune diseases, cancers and neuropsychiatric disorders, suggesting that pleiotropic effects on human complex traits may be widespread. However, systematic detection of such effects is challenging and requires new methodologies and frameworks for interpreting cross-phenotype results. In this Review, we discuss the evidence for pleiotropy in contemporary genetic mapping studies, new and established analytical approaches to identifying pleiotropic effects, sources of spurious cross-phenotype effects and study design considerations. We also outline the molecular and clinical implications of such findings and discuss future directions of research.
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This work was supported in part by the US National Institute of Mental Health (NIMH) grants R01-MH079799 and K24MH094614 (both to J.W.S.).
The authors declare no competing financial interests.
- Genome-wide association studies
(GWASs). Studies in which hundreds of thousands (or millions) of genetic markers are tested for association with a phenotypic trait; they are an unbiased approach to survey the entire genome for disease-associated regions using common variation.
A term describing the statistical significance threshold that accounts for multiple testing in GWASs.
- Complex traits
Traits controlled by a combination of many genes and environmental factors.
A gene or genetic variant that affects more than one phenotypic trait.
The proportion of phenotypic variance attributed to genetic differences among individuals in a population.
Different genetic variants in high linkage disequilibrium located in the same gene that affect different phenotypes.
- Single-nucleotide polymorphisms
Single-nucleotides in the genome that vary across individuals in the population.
- Linkage disequilibrium
(LD). The correlation between genetic markers owing to limited recombination.
- Copy number variants
Regions of the genome in which the copy number is polymorphic (for example, deletions and duplications) across individuals.
Controlled by many genes.
- Population stratification
A source of bias in genome-wide association studies that occurs when a phenotype and the allele frequency of a single-nucleotide polymorphism vary owing to ancestral differences.
- Batch effect
Systematic biases in the data that arise from differences in sample handling.
- Genotype imputation
Inference of missing genotypes or untyped single-nucleotide polymorphisms using statistical techniques.
- Ascertainment bias
A consequence of collecting a nonrandom subsample with a systematic bias so that results based on the subsample are not representative of the entire sample.
- Tag SNPs
Single-nucleotide polymorphisms (SNPs) chosen to represent a region of the genome owing to strong linkage disequilibrium.
- Multivariate analyses
The simultaneous inclusion of two or more phenotypes in one analysis when testing the association with a genetic variant.
- Univariate analyses
Tests of association between one phenotype and a genetic variant.
- Polygenic scoring
A score that aggregates the number of risk alleles a subject carries weighted by the effect size of the allele for a particular trait. The risk allele and effect size for each single-nucleotide polymorphism is generally taken from a genome-wide association study of an independent study.
- Linear mixed-effect model
A linear model that contains both fixed and random effects. This type of model can be used to estimate genetic correlation between traits using a genome-wide set of single-nucleotide polymorphisms.
- Cohort studies
Observational studies in which defined groups of people (the cohorts) are followed over time and outcomes are compared in subsets of the cohort who were exposed to different levels of factors of interest. These studies can either be prospectively or retrospectively carried out from historical records.
- Cross-sectional studies
Studies in which data are collected on subjects at one specific point in time and subjects are not selected for a particular trait or exposure.
- Case–control study
Compares cases (that is, a selected group of individuals: for example, those diagnosed with a disorder) with controls (that is, a comparison group of individuals: for example, those who are not diagnosed with the disorder). Genome-wide association case–control studies test whether genetic marker allele frequencies differ between cases and controls.
- Generalized estimating equations
A statistical technique used to estimate regression parameters that does not require the joint distribution of the variables to be fully specified.
- Log-linear model
A statistical model that captures the dependence among a set of categorical variables.
- Bayesian network
A network that captures relationships between variables or nodes of interest (for example, phenotypes and SNPs). Bayesian networks can incorporate prior information in establishing relationships between variables.
- Ordinal regression
A regression model in which the outcome variable is ordinal.
- Non-parametric approach
A statistical analysis method that does not rely on specific distributional assumptions (for example, normality) for the variables being analysed.
- Principal components analysis
A statistical method used to simplify data sets by transforming a series of correlated variables into a smaller number of uncorrelated factors. It is also commonly used to infer continuous axes of variation in genetic data, often representing genetic ancestry.
- Summary statistics
A statistic that summarizes a set of observations. In the context of genome-wide association studies, meta-analyses can be carried out solely by using summary statistics and typically include estimates of the effect size (for example, odds ratio) and standard error.
- Effect heterogeneity
Different effect sizes across phenotypes.
- Expression quantitative trait loci
Loci at which genetic allelic variation is associated with variation in gene expression.
- Fine mapping
Extensively genotyping or sequencing a region of the genome that was identified in genome-wide association studies to identify the causal variant.
- Confounding factor
A variable (for example, batch effects or population structure) that is associated with both the genotype and the phenotype of interest and can give rise to a spurious association.
- Genetic architecture
A genetic model (that is, the number of single-nucleotide polymorphisms, effect sizes, allele frequency, and so on) underlying a phenotypic trait.
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Solovieff, N., Cotsapas, C., Lee, P. et al. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet 14, 483–495 (2013). https://doi.org/10.1038/nrg3461
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