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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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.
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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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ACID: Association Correction for Imbalanced Data in GWAS.

Feng Bao, Yue Deng, Qionghai Dai

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |January 24, 2017
    PubMed
    Summary
    This summary is machine-generated.

    We developed a computational framework, Association Correction for Imbalanced Data (ACID), to address biases in genome-wide association studies (GWAS) caused by imbalanced case-control sample sizes. ACID improves the power of traditional GWAS methods for identifying disease-associated genetic markers.

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    Area of Science:

    • Genetics
    • Computational Biology
    • Statistical Genetics

    Background:

    • Genome-wide association studies (GWAS) are crucial for identifying disease-associated genetic loci.
    • Real-world GWAS often face data imbalance, with limited cases and abundant controls, leading to biased results and reduced power to detect true causal markers.

    Purpose of the Study:

    • To develop a computational framework, Association Correction for Imbalanced Data (ACID), to address data imbalance in GWAS.
    • To improve the performance and accuracy of GWAS in identifying significant genetic markers from imbalanced datasets.

    Main Methods:

    • ACID is a novel computational framework inspired by imbalance learning theory, specifically adapted for sequential genomic data analysis.
    • The framework was evaluated using simulation studies with severe data imbalances.
    • ACID was applied to two real-world imbalanced datasets for gastric and bladder cancer genome-wide association analysis.

    Main Results:

    • Simulation studies showed ACID significantly improves the power of traditional GWAS methods on imbalanced datasets.
    • Experimental results on gastric and bladder cancer datasets demonstrated ACID's superior ability in identifying suspicious loci compared to the regression approach.
    • The findings from ACID were consistent with existing genetic discoveries.

    Conclusions:

    • ACID effectively corrects for data imbalance in GWAS, enhancing the detection of true causal genetic markers.
    • The proposed framework offers a robust solution for analyzing imbalanced genomic data, improving the reliability of GWAS findings in complex diseases.