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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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One-Way ANOVA: Equal Sample Sizes01:15

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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Test for Homogeneity01:23

Test for Homogeneity

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The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Wilcoxon Signed-Ranks Test for Median of Single Population01:14

Wilcoxon Signed-Ranks Test for Median of Single Population

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The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...
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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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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Related Experiment Video

Updated: Jul 16, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics.

Camille M Williams1,2, Holly Poore3, Peter T Tanksley4

  • 1Department of Psychology, University of Texas at Austin, Austin, TX, USA. williams.m.camille@gmail.com.

Behavior Genetics
|September 15, 2023
PubMed
Summary

Down-sampling genetic data for genome-wide association studies (GWASs) may reduce statistical power but maintains robust findings for multivariate analyses like Genomic SEM, supporting open science data sharing practices.

Keywords:
Data removalDown-sampleGenome-wide association studyGenomic SEMGenomicsLeave-one-outMeta-analysisSummary statistics

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Area of Science:

  • Genetics
  • Psychiatric Genomics
  • Statistical Genetics

Background:

  • Proprietary genetic datasets enhance genome-wide association study (GWAS) power but limit public sharing of summary statistics.
  • Down-sampling datasets to exclude restricted data can reduce statistical power and potentially alter genetic etiology.
  • Multivariate GWAS methods, such as Genomic SEM, further complicate data sharing due to modeling genetic correlations across traits.

Purpose of the Study:

  • To propose and validate a systematic approach for assessing the comparability of GWAS summary statistics derived from complete versus down-sampled datasets.
  • To evaluate the impact of down-sampling on univariate GWAS, multivariate Genomic SEM, and downstream genetic analyses.

Main Methods:

  • Developed a systematic approach to compare GWAS summary statistics from complete and down-sampled datasets.
  • Applied the approach to a multivariate GWAS of an externalizing factor.
  • Assessed impacts on univariate GWAS signal strength, Genomic SEM factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses.

Main Results:

  • Down-sampling led to a reduction in genetic signal and the number of genome-wide significant loci in univariate GWAS.
  • Multivariate Genomic SEM factor loadings, model fit, gene-property analyses, genetic correlations, and polygenic score analyses remained robust despite down-sampling.
  • The proposed approach confirmed the comparability of key findings between complete and down-sampled datasets for the externalizing factor GWAS.

Conclusions:

  • Down-sampling proprietary genetic data for GWAS is a viable strategy that preserves the robustness of multivariate analyses and genetic correlations.
  • Researchers sharing down-sampled summary statistics should provide accompanying documentation detailing these comparability analyses.
  • This facilitates data sharing and promotes open science principles while acknowledging potential power limitations.