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Ancestry estimation and control of population stratification for sequence-based association studies
Chaolong Wang1, Xiaowei Zhan2, Jennifer Bragg-Gresham3
11] Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA. [2] Department of Biostatistics, Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan, USA. [3].
Nature Genetics
|March 18, 2014
Summary
We developed a new method to accurately estimate individual genetic ancestry using off-target sequence reads. This approach improves genetic association studies by reducing false positives from population structure.
Area of Science:
- Genetics
- Bioinformatics
Background:
- Accurate estimation of individual genetic ancestry is crucial for genetic association studies.
- Population structure can introduce false positive signals, complicating analysis.
- Current methods struggle with targeted sequence data.
Purpose of the Study:
- To propose a novel method for precise individual genetic ancestry estimation.
- To implement this method in the LASER software.
- To validate its performance across various data types and scales.
Main Methods:
- Analysis of off-target sequence reads for ancestry inference.
- Development and application of the LASER software.
- Validation using simulated and empirical datasets with varying sequencing coverage.
Main Results:
- Accurate inference of worldwide continental ancestry at low sequencing coverage (0.001×).
- Effective fine-scale ancestry estimation within Europe at 0.1× coverage.
- Improved discrimination of individuals from different Finnish provinces using exome data.
Conclusions:
- The LASER method accurately estimates individual genetic ancestry from sequence data.
- It enhances case-control matching and mitigates spurious findings in genetic association studies.
- The method is robust across different scales of genetic ancestry and sequencing depths.
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