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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

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Estimating effect sizes in genome-wide association studies.

József Bukszár¹, Edwin J C G van den Oord

¹Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia, Virginia Commonwealth University, P.O. Box 980533, Richmond, VA 23298-0533, USA. ejvandenoord@vcu.edu

Behavior Genetics

|January 7, 2010

Summary

This study introduces a new method to estimate the proportion of non-effect markers (p(0)) and their effect sizes in large genetic studies. The procedure precisely estimates individual effects, providing a more interpretable p(0) and aiding in study design.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

Published on: August 21, 2016

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Accurate estimation of the proportion of non-effect markers (p(0)) is crucial for large-scale genetic studies.
Existing p(0) estimators may not account for the full range of effect sizes in high-dimensional data.
Understanding effect size distribution is vital for controlling false discoveries and designing replication studies.

Purpose of the Study:

To develop a novel estimation procedure for p(0) and effect sizes applicable across various scenarios.
To address the uncertainty regarding whether p(0) estimates reflect the entire spectrum of effect sizes or only large effects.
To provide an interpretable estimate of p(0) and detailed effect size estimations for all markers.

Main Methods:

Developed an iterative estimation procedure starting with the largest effect sizes.
The procedure estimates individual marker effect sizes sequentially until precision limits are reached.
Applicable when the test statistic distribution under the alternative can be parameterized (e.g., non-central chi-square).

Main Results:

The proposed method provides interpretable estimates of p(0) and individual effect sizes.
Simulations indicate precise estimation of effect sizes with minimal upward bias.
The R code for the estimation procedure is publicly available.

Conclusions:

The new method offers a robust approach to estimating p(0) and effect sizes in large genetic datasets.
This enhances the understanding of genetic study data properties and improves downstream applications.
The availability of the R code facilitates wider adoption and application in genetic research.