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Related Concept Videos

Pedigree Analysis01:35

Pedigree Analysis

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Pedigree Analysis01:35

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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Association test between haplotypes and longitudinal traits in complex pedigrees.

Q Wang1, D Yu, Y Pan

  • 1School of Agriculture and Biology, Shanghai Jiao Tong University, China.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie
|September 13, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to analyze genetic associations with complex traits over time using haplotypes. The approach enhances the detection of genetic effects on traits like litter size in pigs.

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Area of Science:

  • Quantitative genetics
  • Statistical genomics
  • Animal breeding

Background:

  • Complex traits are influenced by multiple genes and environmental factors.
  • Haplotypes, combinations of single nucleotide polymorphisms (SNPs), offer richer genetic information than individual SNPs for association studies.
  • Analyzing longitudinal data is crucial for understanding the genetic basis of traits that change over time.

Purpose of the Study:

  • To extend random regression models for analyzing haplotype effects in longitudinal data.
  • To develop a hierarchical Bayesian method for estimating haplotype effects in a longitudinal framework.
  • To assess the performance, validity, and power of the proposed haplotype-based longitudinal analysis method.

Main Methods:

  • Developed an extended random regression model incorporating haplotype data for longitudinal traits.
  • Employed a hierarchical Bayesian approach for parameter estimation.
  • Validated the method using simulations and a real-world dataset of Meishan pigs.

Main Results:

  • The proposed method demonstrated validity and power through simulations.
  • Successfully identified a haplotype influencing total litter size in Meishan pigs, which was missed by single SNP analysis.
  • The model framework is flexible and can be extended to include gene-gene and gene-environment interactions.

Conclusions:

  • Haplotype-based analysis within a longitudinal framework provides greater power for detecting genetic associations with complex traits.
  • The developed method offers a robust tool for genetic studies of traits exhibiting temporal variation.
  • This approach has significant implications for genetic improvement programs in livestock and other species.