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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.
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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...
Polygenic Traits01:18

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Published on: July 27, 2021

Score statistics for mapping quantitative trait loci.

Myron N Chang1, Rongling Wu, Samuel S Wu

  • 1University of Florida, USA. mchang@cog.ufl.edu

Statistical Applications in Genetics and Molecular Biology
|February 19, 2009
PubMed
Summary
This summary is machine-generated.

We developed a simpler score test for detecting quantitative trait loci (QTL) in backcross populations. This method provides accurate critical thresholds, matching the complex likelihood ratio test (LRT) for robust genetic mapping.

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Detecting quantitative trait loci (QTL) is crucial for understanding genetic contributions to complex traits.
  • Existing methods like the Likelihood Ratio Test (LRT) can be computationally intensive and face challenges with asymptotic distributions under null hypotheses.
  • The unidentifiable nature of QTL location parameters under the null hypothesis complicates standard statistical approaches.

Purpose of the Study:

  • To propose a computationally simpler score test for detecting QTL in backcross populations.
  • To derive and compute the asymptotic null distribution for the score test statistics.
  • To establish the asymptotic equivalence between the score test and LRT statistics for QTL mapping.

Main Methods:

  • Utilized a score test approach, focusing on Maximum Likelihood Estimates (MLEs) under the null hypothesis for computational efficiency.
  • Developed numerical methods to compute the asymptotic null distribution of the maximum of squared score test statistics.
  • Demonstrated the application using a simple backcross design for QTL mapping.

Main Results:

  • The score test offers a computationally simpler alternative to the LRT for QTL detection.
  • The asymptotic null distribution of the maximum of squared score test statistics was successfully derived.
  • The maximum of LR test statistics and the maximum of squared score statistics were shown to be asymptotically equivalent.

Conclusions:

  • The proposed score test provides a computationally efficient and statistically valid method for QTL detection in backcross populations.
  • The derived critical thresholds for the score test are applicable to the LRT, simplifying QTL mapping procedures.
  • This method enhances the feasibility of QTL analysis, particularly in large-scale genetic studies.