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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.
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Related Experiment Video

Updated: May 31, 2026

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Implication of next-generation sequencing on association studies.

Hoicheong Siu1, Yun Zhu, Li Jin

  • 1MOE Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433, China.

BMC Genomics
|June 21, 2011
PubMed
Summary

Genome-wide association studies (GWAS) using SNP arrays are not ideal for detecting low-frequency genetic variants. Further research is needed to improve methods for analyzing rare and low-frequency variants in genetic association studies.

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Area of Science:

  • Genomics
  • Population Genetics
  • Statistical Genetics

Background:

  • Next-generation sequencing enables comprehensive genomic variation detection.
  • Current genome-wide association studies (GWAS) primarily focus on common variants (MAF > 5%).
  • Association study designs for low-frequency (0.5% < MAF ≤ 5%) and rare (MAF ≤ 0.5%) variants require thorough investigation.

Purpose of the Study:

  • To explore strategies and study designs for future GWAS, particularly for low-frequency and rare variants.
  • To evaluate the effectiveness of SNP arrays and imputation methods for capturing low-frequency variation.
  • To assess the power of different approaches in mapping causal variants.

Main Methods:

  • Investigated linkage disequilibrium (LD) patterns among common, low-frequency, and rare single nucleotide polymorphisms (SNPs).
  • Examined various SNP tagging designs with and without statistical imputation.
  • Utilized low-coverage pilot data (~14 million SNPs) from the 1000 Genomes Project as a hypothetical genotype-array platform.

Main Results:

  • Linkage disequilibrium is significantly weaker between low-frequency alleles and common alleles compared to common-common allele LD.
  • Even with imputation, 45.4% of low-frequency SNPs remained untaggable.
  • The hypothetical 14M SNP array platform covered only 67.7% of low-frequency variation.

Conclusions:

  • SNP array-based GWAS are poorly suited for association studies involving low-frequency genetic variation.
  • Current SNP array technology and imputation methods have limitations in comprehensively capturing low-frequency variants.
  • Further development of methods and study designs is crucial for effectively analyzing low-frequency and rare variants in GWAS.