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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Analysis and optimal design for association studies using next-generation sequencing with case-control pools.

Wei E Liang1, Duncan C Thomas, David V Conti

  • 1Department of Preventive Medicine, University of Southern California, Los Angeles, California.

Genetic Epidemiology
|September 14, 2012
PubMed
Summary

Sequencing pooled samples in genetic association studies significantly reduces costs. A new hierarchical Bayesian model optimizes study design for maximum power, considering factors like pool number and size.

Keywords:
genetic association studiesrare variantssequencing

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Next-generation sequencing (NGS) offers vast genetic variation discovery for association studies.
  • High costs of individual sequencing in large populations limit its application.
  • Pooled sequencing offers a cost-effective alternative, sacrificing individual-level data for reduced expense.

Purpose of the Study:

  • To develop a statistical model for genetic association studies using pooled sequencing data.
  • To optimize study design parameters for maximizing statistical power under cost constraints.
  • To account for technical variations like read depth and sequencing errors in pooled samples.

Main Methods:

  • Proposed a hierarchical Bayesian model to estimate variant associations from case and control pools.
  • Performed extensive simulations to evaluate the model's performance under varying conditions (pool number, size, coverage, effect size, MAF, error rate).
  • Developed an R package (hiPOD) for optimal study design selection based on user-defined cost functions and constraints.

Main Results:

  • The number of pools and individuals per pool significantly impact statistical power.
  • Total sequencing coverage per pool has a moderate effect on power.
  • The proposed model effectively estimates associations while accounting for sequencing variability.

Conclusions:

  • Pooled sequencing with a hierarchical Bayesian approach is a powerful and cost-effective strategy for genetic association studies.
  • Study design parameters, particularly the number and size of pools, are critical for maximizing power.
  • The hiPOD R package facilitates the selection of optimal study designs balancing cost and power.