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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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Speciation is the evolutionary process resulting in the formation of new, distinct species—groups of reproductively isolated populations.
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Related Experiment Video

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Genetic Variant Detection in the CALR gene using High Resolution Melting Analysis
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PCA-based GRS analysis enhances the effectiveness for genetic correlation detection.

Yan Zhao1, Yujie Ning1,2, Feng Zhang1

  • 1School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, P. R. China.

Briefings in Bioinformatics
|September 1, 2018
PubMed
Summary

Principal component analysis (PCA)-based genetic risk score (GRS) analysis improves statistical power for complex diseases with multiple phenotypes. This novel approach enhances the exploration of genetic architectures and trait relationships compared to traditional GRS methods.

Keywords:
bioinformaticscomplex diseasescorrelation analysisgenetic risk scoreprincipal component analysis

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Area of Science:

  • Genetics
  • Biostatistics
  • Computational Biology

Background:

  • Genetic risk score (GRS) analysis is vital for understanding complex disease genetics.
  • Complex diseases often involve multiple correlated phenotypes, posing challenges for individual analysis.
  • Traditional GRS analysis can suffer from reduced statistical power due to multiple testing corrections.

Purpose of the Study:

  • To introduce a principal component analysis (PCA)-based GRS analysis approach.
  • To enhance statistical power when analyzing multiple correlated disease phenotypes.
  • To improve the exploration of genetic architectures and relationships of complex traits.

Main Methods:

  • Developed a novel principal component analysis (PCA)-based GRS analysis method.
  • Conducted extensive simulation studies comparing PCA-based GRS with traditional GRS.
  • Applied both methods to real Caucasian genome-wide association study (GWAS) data for bone geometry.

Main Results:

  • PCA-based GRS analysis demonstrated significantly improved performance over traditional GRS in simulations.
  • Real data analysis on bone geometry GWAS data confirmed the superior performance of the PCA-based approach.
  • The proposed method effectively handles multiple correlated phenotypes, increasing statistical power.

Conclusions:

  • The PCA-based GRS analysis offers a powerful alternative for studying complex diseases.
  • This approach enhances the ability to uncover genetic architectures and relationships.
  • It provides a valuable tool for researchers utilizing the growing volume of GWAS data.