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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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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene

Joelle Mbatchou1, Mary Sara McPeek2

  • 1Regeneron Genetics Center, Tarrytown, NY 10591, USA; Department of Statistics, The University of Chicago, Chicago, IL 60637, USA.

American Journal of Human Genetics
|July 18, 2024
PubMed
Summary

We developed JASPER, a novel method for multitrait genetic association analysis. JASPER offers increased power and speed, especially for complex genetic studies with population structure.

Keywords:
GWASbinary trait associationhigh-dimensional traitlinear mixed modelmultitrait associationpedigreepermutation testpleiotropypopulation structurerare variant association

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Joint association analysis of multiple traits and genetic variants offers insights into genetic architecture and pleiotropy.
  • High-dimensional traits (e.g., images, longitudinal data) present unique challenges for genetic association studies.
  • Assessing multitrait genetic association significance is difficult in samples with population substructure or relatedness, leading to power loss and inflated type 1 error.

Purpose of the Study:

  • To develop a fast, powerful, and robust method for assessing multitrait genetic association significance.
  • To address challenges posed by population substructure, admixture, and relatedness in genetic samples.
  • To improve the detection of genetic associations and pleiotropic effects in complex datasets.

Main Methods:

  • Developed JASPER (Joint Association Significance Testing in Population-stratified Samples), a novel statistical method.
  • Evaluated JASPER's performance using simulations, comparing it against existing methods.
  • Applied JASPER to analyze gene expression data from the Framingham Heart Study.

Main Results:

  • JASPER demonstrated higher statistical power and better type 1 error control compared to existing methods in simulations.
  • The computational speed and power advantages of JASPER increased with the number of traits analyzed.
  • Application to the Framingham Heart Study identified more significant gene expression associations, including novel pleiotropic effects.

Conclusions:

  • JASPER is a powerful and efficient tool for multitrait genetic association analysis in structured samples.
  • The method is robust to phenotype model misspecification and accommodates covariates, ascertainment, and rare variants.
  • JASPER shows significant promise for advancing genetic discovery in diverse biological and clinical applications.