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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

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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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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,...
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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Related Experiment Video

Updated: Feb 23, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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A Weighted SNP Correlation Network Method for Estimating Polygenic Risk Scores.

Morgan E Levine1,2, Peter Langfelder3, Steve Horvath4,5

  • 1Department of Human Genetics, University of California, Box 708822, 695 Charles E. Young Drive South, Los Angeles, CA, 90095, USA. melevine@mednet.ucla.edu.

Methods in Molecular Biology (Clifton, N.J.)
|August 30, 2017
PubMed
Summary
This summary is machine-generated.

Weighted SNP correlation network analysis (WSCNA) offers a novel approach to polygenic scores. This network-based method better captures complex biology and explains more variance in human height than traditional methods.

Keywords:
GWASHeightPolygenic scoreWeighted network

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Area of Science:

  • Genetics
  • Bioinformatics
  • Complex Trait Analysis

Background:

  • Traditional polygenic scores sum weighted allele counts, potentially oversimplifying complex genetic architectures.
  • Existing methods may not fully capture the intricate biological relationships underlying complex traits.

Purpose of the Study:

  • To introduce weighted SNP correlation network analysis (WSCNA) as an advanced method for generating polygenic scores.
  • To demonstrate WSCNA's utility in identifying genetic networks, creating network-specific scores, and uncovering biological insights from GWAS data.

Main Methods:

  • Developed and applied weighted SNP correlation network analysis (WSCNA) to identify SNP networks from Genome-Wide Association Study (GWAS) data.
  • Generated network-specific polygenic scores and analyzed network topology to identify hub single nucleotide polymorphisms (SNPs).
  • Utilized data from a US population of non-Hispanic whites, focusing on human height as a model complex trait.

Main Results:

  • WSCNA identified SNP networks and hub SNPs associated with human height.
  • Network-specific polygenic scores derived from WSCNA explained a greater proportion of variance in human height compared to traditional methods.
  • Identified key genes and pathways previously implicated in human height regulation.

Conclusions:

  • WSCNA provides a more biologically meaningful approach to polygenic score development than traditional methods.
  • This network-based method enhances the understanding of complex traits by revealing underlying genetic architectures.
  • WSCNA holds potential for applications in predicting genetic susceptibility, understanding pleiotropy, and exploring gene-environment interactions for various health-related traits.