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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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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Single Nucleotide Polymorphisms-SNPs01:05

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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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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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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
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Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Genome-wide barebones regression scan for mixed-model association analysis.

Jin Gao1, Xuefei Zhou2, Zhiyu Hao3

  • 1Wuxi Fisheries College, Nanjing Agricultural University, Wuxi, 214081, China.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|September 26, 2019
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Summary
This summary is machine-generated.

We developed Single-RunKing software to speed up genetic association analysis by simplifying mixed models. This computational efficiency allows for faster identification of significant single nucleotide polymorphism (SNP) effects in large datasets.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding the genetic basis of complex traits.
  • Mixed-model association analyses are computationally intensive, limiting their application in large-scale studies.

Purpose of the Study:

  • To improve the computational efficiency of genome-wide mixed-model association analysis.
  • To develop a novel software application for faster genetic analysis.

Main Methods:

  • Implemented a simplified linear mixed model using R/fastLmPure for rapid estimation of single nucleotide polymorphism (SNP) effects.
  • Utilized the EMMAX algorithm to identify large or highly significant SNPs.
  • Developed the Single-RunKing software to transform nonlinear mixed-model analyses into linear regression scans.

Main Results:

  • The simplified mixed model significantly improved computational efficiency.
  • Focusing on significant SNPs reduced analysis time to approximately two genome-wide regression scans.
  • Single-RunKing demonstrated substantial computational time savings compared to existing methods like FaST-LMM.

Conclusions:

  • The developed methods and Single-RunKing software offer a computationally efficient approach for genome-wide association analysis.
  • This advancement facilitates the analysis of large genomic datasets for identifying genetic associations with quantitative traits.