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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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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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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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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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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Related Experiment Video

Updated: Jul 14, 2025

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New statistical selection method for pleiotropic variants associated with both quantitative and qualitative traits.

Kipoong Kim1, Tae-Hwan Jun2, Bo-Keun Ha3

  • 1Department of Statistic, Pusan National University, 46241, Busan, Korea.

BMC Bioinformatics
|October 10, 2023
PubMed
Summary

This study introduces a novel statistical method to identify pleiotropic variants affecting multiple traits, outperforming existing methods in simulations and real data analyses for genetic discovery.

Keywords:
Genetic associationMultiple phenotypesPleiotropic variantsRegularizationSelection probability

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Identifying pleiotropic variants associated with multiple phenotypic traits is crucial for genetic association studies.
  • Existing methods often focus on quantitative traits, overlooking the challenge of analyzing both quantitative and qualitative traits simultaneously.
  • Current meta-analysis approaches integrate summary statistics but do not account for correlations among genetic variants.

Purpose of the Study:

  • To develop a novel statistical selection method for identifying pleiotropic variants associated with both quantitative and qualitative traits.
  • To address the limitations of existing methods in handling mixed trait types and genetic variant correlations.

Main Methods:

  • A new statistical selection method was developed using a unified selection score to quantify associations between genetic variants and multiple trait types.
  • The method's performance was evaluated through extensive simulation studies considering various pleiotropic effects.
  • The proposed method was applied to real-world datasets, including peanut and cowpea, with mixed quantitative and qualitative traits.

Main Results:

  • The proposed method demonstrated superior performance compared to existing meta-analysis methods in terms of true positive selection in simulations.
  • Application to peanut and cowpea datasets identified potentially pleiotropic variants that were missed by conventional methods.
  • The method successfully detected pleiotropic variants influencing both quantitative and qualitative traits in real genetic data.

Conclusions:

  • The developed statistical method effectively identifies pleiotropic variants associated with both quantitative and qualitative traits.
  • The method has been implemented in an R package named 'UNISS' for broader accessibility and application.
  • This tool offers a valuable approach for uncovering complex genetic associations in diverse datasets.