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Following the Dynamics of Structural Variants in Experimentally Evolved Populations

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Quantitative trait analysis in sequencing studies under trait-dependent sampling.

Dan-Yu Lin¹, Donglin Zeng, Zheng-Zheng Tang

¹Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA. lin@bios.unc.edu

Proceedings of the National Academy of Sciences of the United States of America

|July 13, 2013

Summary

This summary is machine-generated.

Sequencing extreme trait values in large cohorts is cost-effective. New statistical methods accurately analyze this trait-dependent sampling for robust genetic association studies.

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Targeted DNA Methylation Analysis by Next-generation Sequencing

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Published on: February 24, 2015

Area of Science:

Genetics
Biostatistics
Bioinformatics

Background:

Whole-exome sequencing in large cohorts is expensive.
Trait-dependent sampling, selecting subjects with extreme trait values (e.g., BMI, LDL, blood pressure), offers a cost-effective alternative.
Ignoring trait-dependent sampling in quantitative trait analysis can lead to inflated Type I errors and reduced statistical power.

Purpose of the Study:

To develop valid and efficient statistical methods for association analysis of sequencing data under trait-dependent sampling.
To enable accurate gene-based analysis of rare variants.
To allow quantitative trait analysis for both the selection trait and other measured traits.

Main Methods:

Development of statistical methods for association analysis accounting for trait-dependent sampling.
Application to gene-based analysis, particularly for rare variants.
Meta-analysis framework to combine results from multiple studies with varying selection criteria.

Main Results:

Proposed methods provide valid and powerful quantitative trait association analysis under trait-dependent sampling.
Meta-analysis of trait-dependent sampling data is significantly more powerful than single-study analysis.
Standard meta-analysis ignoring trait-dependent sampling can be less powerful than single-study analysis.

Conclusions:

The developed statistical methods are crucial for accurate genetic association studies using trait-dependent sampling.
These methods enhance the power and validity of meta-analyses, especially for rare variant detection.
The approach is broadly applicable to various genetic and non-genetic association studies.