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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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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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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.
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Behavioral Genetics and Its Designs01:23

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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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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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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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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS.

Anna Hutchinson1, James Liley2,3, Chris Wallace4,5,6

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

BMC Bioinformatics
|July 30, 2022
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Summary
This summary is machine-generated.

This study introduces Binary cFDR, a new method to boost the power of genome-wide association studies (GWAS) using binary functional genomic data. The Binary cFDR framework enhances genetic discovery while maintaining false discovery rate control.

Keywords:
FDRFunctional genomicsGWASMultiple testingPower

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWAS) are limited by stringent significance thresholds, hindering the detection of genetic associations.
  • Existing methods for leveraging functional genomic data in GWAS are restricted to continuous auxiliary data.
  • This limitation excludes valuable binary functional genomic information, such as SNP type (synonymous/non-synonymous) or genomic region activity states.

Purpose of the Study:

  • To extend the conditional false discovery rate (cFDR) framework to accommodate binary auxiliary data for GWAS.
  • To develop a method that increases statistical power in GWAS by integrating binary functional genomic information.
  • To provide a user-friendly R package for applying the enhanced cFDR method.

Main Methods:

  • Developed an extension to the cFDR framework, termed "Binary cFDR", specifically for binary auxiliary data.
  • Conducted detailed simulations to demonstrate the statistical validity and performance of Binary cFDR.
  • Created a comprehensive CRAN R package named 'fcfdr' for practical application.

Main Results:

  • Binary cFDR effectively controls the false discovery rate (FDR).
  • The method demonstrates superior sensitivity and FDR control compared to existing comparator methods in simulations.
  • Application to type 1 diabetes data identified additional genetic associations, showcasing the practical utility of Binary cFDR.

Conclusions:

  • The 'fcfdr' R package provides a unified toolkit for integrating GWAS and functional genomic data.
  • This approach significantly enhances statistical power for detecting genetic associations.
  • Binary cFDR enables the effective use of binary functional genomic data, broadening the scope of genetic discovery.