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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.
GWAS does not require the identification of the target gene involved in...
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Hardy-Weinberg Principle01:49

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Comparing Copy Number Variations and SNPs02:26

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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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What is Population Genetics?01:25

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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
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Genetic Drift03:33

Genetic Drift

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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Related Experiment Video

Updated: Oct 18, 2025

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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False discovery rate control in genome-wide association studies with population structure.

Matteo Sesia1, Stephen Bates2,3, Emmanuel Candès4,5

  • 1Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089; candes@stanford.edu sesia@marshall.usc.edu.

Proceedings of the National Academy of Sciences of the United States of America
|September 28, 2021
PubMed
Summary

This study introduces a new statistical framework for analyzing genome-wide association studies (GWAS) of polygenic traits. The method uses knockoffs for robust genetic analysis, improving discovery power and controlling false discoveries in large datasets.

Keywords:
false discovery rategenome-wide association studieshidden Markov modelsknockoffspopulation structure

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding polygenic traits.
  • Standard GWAS methods often struggle with complex genetic architectures and population structures.
  • Controlling the false discovery rate (FDR) is essential for reliable genetic discoveries.

Purpose of the Study:

  • To develop a comprehensive statistical framework for analyzing GWAS data of polygenic traits.
  • To produce interpretable findings while effectively controlling the false discovery rate.
  • To offer a powerful alternative to standard GWAS approaches without making parametric assumptions.

Main Methods:

  • A novel statistical framework utilizing multivariate algorithms.
  • Generation of 'knockoffs' (imperfect copies) of genetic variables as negative controls.
  • Correction for linkage disequilibrium and unknown population structure (ancestry, relatedness).

Main Results:

  • The method demonstrates validity and effectiveness through extensive simulations.
  • Application to UK Biobank data shows high power compared to state-of-the-art methods.
  • Most discoveries made by the method are validated by comparisons with existing studies.

Conclusions:

  • The proposed framework provides a powerful and flexible approach for GWAS of polygenic traits.
  • It effectively controls the false discovery rate and accounts for complex genetic factors.
  • Fast, publicly available software enables analysis of Biobank-scale genetic datasets.