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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
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Determining Which Phenotypes Underlie a Pleiotropic Signal.

Arunabha Majumdar1, Tanushree Haldar1, John S Witte1

  • 1Department of Epidemiology and Biostatistics, University of California, San Francisco, United States of America.

Genetic Epidemiology
|May 31, 2016
PubMed
Summary

Identifying pleiotropic genetic loci is crucial for understanding complex traits. A modified Benjamini-Hochberg procedure effectively identifies specific traits associated with pleiotropy, outperforming other methods in simulations and real-world genetic studies.

Keywords:
multiple phenotypesmultivariate associationnonnull traitspleiotropyselection

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Pleiotropy, where one gene influences multiple traits, is key to understanding complex diseases.
  • Current methods often assess overall genetic association with multiple phenotypes, not specific trait contributions.
  • Distinguishing specific pleiotropic effects is vital for biological insight.

Purpose of the Study:

  • To evaluate and compare three distinct strategies for identifying pleiotropic genetic variants and their associated traits.
  • To determine the most effective method for pinpointing specific phenotypes influenced by pleiotropic loci.
  • To assess method performance across varying degrees of phenotype correlation.

Main Methods:

  • Inverse regression-based model selection (e.g., MultiPhen).
  • Subset-based meta-analysis (ASSET).
  • A modified Benjamini-Hochberg (B-H) procedure for controlling false discovery rates in phenome-wide association studies.

Main Results:

  • MultiPhen showed higher power than ASSET for overall pleiotropy detection, except when all phenotypes had same-direction genetic effects.
  • The modified B-H procedure consistently outperformed other methods in identifying specific pleiotropic traits.
  • Inverse regression methods were competitive with modified B-H only for weakly correlated phenotypes.

Conclusions:

  • The modified Benjamini-Hochberg procedure is a robust and optimal approach for identifying specific traits underlying pleiotropic signals.
  • Method performance varies with phenotype correlation, highlighting the need for tailored analytical strategies.
  • This work advances the understanding and detection of pleiotropy in genetic association studies.