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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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Association Areas of the Cortex01:21

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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Correlations02:20

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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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The somatosensory cortex in the parietal lobes is crucial for interpreting sensory data such as touch, temperature, and proprioception. The somatosensory cortex, situated in the parietal lobes, plays a vital role in interpreting sensory information like touch, temperature, and proprioception—awareness of body position. This specialized brain region features an organized structure wherein neurons at the top primarily process sensations originating from the lower body. In contrast, those at...
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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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Related Experiment Video

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A small-sample kernel association test for correlated data with application to microbiome association studies.

Xiang Zhan1, Lingzhou Xue2, Haotian Zheng3

  • 1Department of Public Health Sciences, Pennsylvania State University, Hershey, Pennsylvania.

Genetic Epidemiology
|September 16, 2018
PubMed
Summary

New Correlated Sequence Kernel Association Test (CSKAT) methods accurately analyze microbiome data from related individuals. This approach improves microbiome association studies by accounting for outcome correlations, enhancing accuracy and preventing biased conclusions.

Keywords:
SKATcorrelated outcomeslinear mixed modelmicrobiome association analysissmall sample

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Area of Science:

  • Microbiome Research
  • Statistical Genetics
  • Human Health and Disease

Background:

  • The human microbiome plays a crucial role in health and disease.
  • Current microbiome association analyses often overlook sample correlations in longitudinal and family studies.
  • Ignoring correlations can lead to suboptimal results and biased conclusions in microbiome research.

Purpose of the Study:

  • To develop novel statistical methods for microbiome association studies with correlated outcome data.
  • To address the limitations of existing methods in analyzing complex study designs like longitudinal and pedigree studies.
  • To introduce a robust method that accounts for correlations in microbiome data analysis.

Main Methods:

  • Proposal of the Correlated Sequence Kernel Association Test (CSKAT).
  • Utilizes linear mixed models with random effects to account for outcome correlations.
  • Employs a variance component test to assess microbiome effects and includes a correction procedure for small sample sizes typical in microbiome studies.

Main Results:

  • CSKAT demonstrates validity and efficiency in comprehensive simulation studies.
  • Achieves higher statistical power compared to existing methods while maintaining correct Type I error rates.
  • Successfully applied to a UK twin study dataset, showcasing practical utility.

Conclusions:

  • CSKAT provides a statistically sound approach for analyzing microbiome data from correlated samples.
  • The method enhances the accuracy of microbiome association studies, particularly in longitudinal and family-based designs.
  • Offers a valuable tool for researchers investigating the microbiome's role in health and disease, with an R implementation available.