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Genome-wide Association Studies-GWAS

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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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Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Accurate haplotype imputation with individualized ancestry-adjusted reference panels.

Qing Song¹, Wei Xu², Wenzhi Li²

¹Center of Big Data and Bioinformatics, First Affiliated Hospital of Medical School, Xi'an Jiaotong University, No. 277 Yanta Xi Street, Xi'an, Shaanxi 710061, China; Cardiovascular Research Institute, Department of Medicine, Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA 30310, USA; 4DGENOME, 2360 Elon Way, Decatur, GA 30033, USA; Shapiro Cardiovascular Center, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St., Boston MA02115, USA.

|December 5, 2017

Summary

Mosaic reference panels improve genomic data imputation accuracy for admixed populations. This novel strategy creates personalized panels, outperforming single-ethnicity approaches for accurate missing data imputation in the big data era.

Keywords:

African-American Big data Imputation Minority population Reference

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Area of Science:

Genomics
Bioinformatics
Population Genetics

Background:

Accurate data imputation relies on ethnicity-matched reference panels.
Recent human genome admixture results in mosaic genomes with varied ethnic segments.
Single-ethnicity panels are insufficient for imputing data in admixed individuals.

Purpose of the Study:

To develop and evaluate a novel imputation strategy using individualized mosaic reference panels.
To assess the imputation accuracy of mosaic reference panels across diverse populations.

Main Methods:

Creation of individualized mosaic reference panels tailored to each person's ethnic background at specific genomic loci.
Evaluation of imputation performance on datasets with high missing values (70% for haplotypes, 50% for genotypes).

Main Results:

Imputation using mosaic reference panels consistently achieved high accuracy across various populations.
Specific imputation accuracies: 98.8% (CEU), 98.7% (YRI), 98.5% (CHB), 98.6% (ASW), 97.3% (MKK), and 98.2% (MXL).
The mosaic reference strategy outperformed other imputation methods.

Conclusions:

Individualized mosaic reference panels offer a robust solution for accurate data imputation in admixed populations.
This approach is a promising option for future missing value imputation in large-scale genomic datasets.