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Genome-wide Association Studies-GWAS

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Related Experiment Video

Updated: Oct 12, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Bayesian model comparison for rare-variant association studies.

Guhan Ram Venkataraman¹, Christopher DeBoever¹, Yosuke Tanigawa¹

¹Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.

American Journal of Human Genetics

|November 25, 2021

Summary

This summary is machine-generated.

We developed a new Bayesian method, MRP, for analyzing rare genetic variants and multiple traits simultaneously using summary statistics. This approach enhances gene-trait association discovery in large-scale exome sequencing studies.

Keywords:

GWAS aggregation techniques gene-based analysis rare variants

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Last Updated: Oct 12, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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Published on: August 20, 2019

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Large-scale whole-genome sequencing and phenotyping present analytical challenges.
Traditional single-variant, single-phenotype studies miss complex genetic architectures.

Purpose of the Study:

Introduce MRP (Multiple Rare variants and Phenotypes), a Bayesian model comparison approach.
Enable simultaneous analysis of multiple rare variants, phenotypes, and studies using summary statistics.
Enhance discovery in rare-variant association studies.

Main Methods:

Bayesian model comparison framework (MRP).
Handles correlation, scale, and direction of genetic effects.
Requires only summary statistic data.

Main Results:

Applied MRP to UK Biobank exome data (n=184,698, 2019 traits).
Recovered known associations (e.g., PCSK9 and LDL cholesterol).
Identified novel associations (e.g., MC1R, IL17RA, IQGAP2).
Demonstrated power gains in multi-phenotype analysis (e.g., TNFRSF13B).

Conclusions:

MRP improves upon existing meta-analysis methods for rare-variant association studies.
Effectively prioritizes genetic variants influencing disease risk.
Facilitates discovery in complex genetic architectures.