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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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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Related Experiment Video

Updated: Jul 13, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Sequence-based GWAS meta-analyses for beef production traits.

Marie-Pierre Sanchez1, Thierry Tribout2, Naveen K Kadri3

  • 1Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France. marie-pierre.sanchez@inrae.fr.

Genetics, Selection, Evolution : GSE
|October 12, 2023
PubMed
Summary
This summary is machine-generated.

This study used sequence-level meta-analysis (MA) to identify genetic variants for beef production traits in cattle. The approach successfully pinpointed candidate genes and potential causal variants, offering insights into the genetic architecture of these complex traits.

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Area of Science:

  • Animal Genetics
  • Genomics
  • Quantitative Genetics

Background:

  • Genome-wide association studies (GWAS) are powerful for identifying variants linked to complex traits.
  • Meta-analysis (MA) of sequence-level GWAS enhances accuracy and power.
  • The H2020 BovReg project focused on beef production traits.

Conclusions:

  • Meta-analysis is a potent tool for identifying candidate genes and causal variants in beef cattle.
  • This approach provides deeper insights into the genetic architecture of complex beef production traits.
  • MA complements within-population GWAS, enhancing the understanding of genetic mechanisms.