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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...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
Genetic Screens02:46

Genetic Screens

Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
RACE - Rapid Amplification of cDNA Ends02:35

RACE - Rapid Amplification of cDNA Ends

Rapid Amplification of cDNA Ends, or RACE, is one of the most effective methods to obtain a full-length cDNA from an mRNA sequence between a known internal region to the unknown sequence at the 5’ or 3’ end. The unknown region is cloned in the cDNA by a gene-specific primer that binds the known end, and a hybrid primer that attaches a predefined anchor sequence to the unknown end of the cDNA. The sequence in between is amplified by PCR with an anchor primer and a gene-specific primer.
Since the...

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Related Experiment Video

Updated: May 31, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

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Conditional random fields for fast, large-scale genome-wide association studies.

Jim C Huang1, Christopher Meek, Carl Kadie

  • 1Microsoft Research, Redmond, Washington, United States of America.

Plos One
|July 19, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model for genome-wide association studies (GWAS) to accurately link single nucleotide polymorphisms (SNPs) to human diseases by accounting for confounding factors. The method offers a scalable solution with modest power loss compared to existing approaches.

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Related Experiment Videos

Last Updated: May 31, 2026

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

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Infinium Assay for Large-scale SNP Genotyping Applications
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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Area of Science:

  • Genomics
  • Statistical Genetics
  • Human Disease Research

Background:

  • Genetic variations, particularly single nucleotide polymorphisms (SNPs), are crucial for understanding human diseases.
  • Associating SNPs with phenotypes is often confounded by population structure, family relatedness, and cryptic relatedness.
  • Existing methods like linear mixed-effect models (LMMs) and principal components analysis (PCA) have limitations in power or scalability.

Purpose of the Study:

  • To develop a novel statistical model for genome-wide association studies (GWAS) that effectively addresses confounding factors.
  • To provide a computationally efficient method for analyzing large-scale genetic data.
  • To improve the accuracy of SNP-phenotype associations in human genomics.

Main Methods:

  • A new statistical model was developed for GWAS that accounts for confounding factors.
  • The model's runtime complexity scales quadratically with the number of individuals.
  • Performance was evaluated using synthetic data generated from a generalized LMM and real human genotype/phenotype data.

Main Results:

  • The proposed method demonstrated the ability to correct for confounding factors in GWAS.
  • It achieved a modest loss in statistical power compared to LMM-based and PCA-based methods.
  • The new model requires significantly less runtime than LMMs, especially for larger sample sizes.

Conclusions:

  • The developed statistical model offers an effective and scalable approach for GWAS.
  • It successfully corrects for confounding factors, leading to more reliable SNP-phenotype associations.
  • This method enhances the efficiency and accuracy of genetic studies in human disease research.