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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
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Updated: Sep 6, 2025

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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COMPILE: a GWAS computational pipeline for gene discovery in complex genomes.

Matthew J Hill1,2,3, Bryan W Penning4, Maureen C McCann5,6

  • 1Department of Botany and Plant Pathology, Purdue University, West Lafayette, Indiana, 47907, USA.

BMC Plant Biology
|July 1, 2022
PubMed
Summary

We developed COMPILE, a computational pipeline for Genome-Wide Association Studies (GWAS), to efficiently identify maize genes linked to complex traits. This tool accelerates the discovery of candidate genes and their homologs in other species.

Keywords:
Computational biologyEuropean corn borerFlowering timeGWASGenomeMaizeOstrinia nubilalisQTLZea maysγ-Tocopherol synthesis

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Area of Science:

  • Plant genetics
  • Bioinformatics
  • Computational biology

Background:

  • Genome-Wide Association Studies (GWAS) identify genes contributing to quantitative traits.
  • Complex genetic architectures pose computational challenges for gene discovery.
  • A streamlined computational pipeline, COMPILE, was developed to accelerate maize gene identification.

Purpose of the Study:

  • To accelerate the identification and annotation of candidate maize genes associated with quantitative traits.
  • To match maize genes to their closest rice and Arabidopsis homologs.
  • To identify candidate loci contributing to European Corn Borer resistance in maize.

Main Methods:

  • Developed COMPILE, a computational pipeline for GWAS.
  • Utilized a Mixed Linear Model incorporating population structure control.
  • Linked significant Quantitative Trait Loci (QTL) to candidate genes and RNA regulatory elements.
  • Validated COMPILE using published data for α-tocopherol biosynthesis and flowering time.

Main Results:

  • COMPILE identified known and novel candidate genes and non-coding RNAs for α-tocopherol biosynthesis and flowering time.
  • Applied to the maize Goodman Association Panel, COMPILE identified candidate loci for European Corn Borer resistance.
  • Identified candidate genes including transcriptional factors, signaling molecules, and metabolic enzymes.
  • Found a gene of unknown function, homologous to a pathogen-stress-associated gene in rice.

Conclusions:

  • COMPILE's speed enables analysis of population size and compression effects.
  • Population size and diversity, not marker density, are key constraints for trait analysis.
  • COMPILE is customizable and adaptable for other species with comprehensive genomic data.