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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.
GWAS does not require the identification of the target gene involved in...
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Pleiotropy01:33

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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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Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Polygenic Traits01:18

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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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Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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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Genetic Screens02:46

Genetic Screens

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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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Updated: Oct 7, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Leveraging pleiotropic association using sparse group variable selection in genomics data.

Matthew Sutton1, Pierre-Emmanuel Sugier2,3, Therese Truong3

  • 1Queensland University of Technology Centre for Data Science, Brisbane, Australia. matt.sutton.stat@gmail.com.

BMC Medical Research Methodology
|January 8, 2022
PubMed
Summary
This summary is machine-generated.

We developed new statistical methods to identify genetic risk variants affecting multiple diseases by leveraging pleiotropy and gene pathway knowledge. This approach enhances statistical power and biological interpretation for complex trait analysis.

Keywords:
Genetic epidemiologyHigh dimensional dataLasso penalizationOncologyPathway analysisPleiotropySparse methodsVariable selection

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Area of Science:

  • Genetics
  • Statistical genomics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genetic variants linked to complex diseases.
  • Pleiotropy, where variants affect multiple traits, offers opportunities for integrated analysis.
  • Gene pathway knowledge can enhance statistical efficiency and biological interpretation.

Purpose of the Study:

  • To propose statistical methods integrating gene pathway and pleiotropy knowledge.
  • To increase statistical power for identifying risk variants affecting multiple traits.
  • To improve biological interpretation of genetic associations.

Main Methods:

  • Novel feature selection methods for group variable selection in multi-task regression.
  • Penalised likelihood methods with structured sparsity at gene/pathway and SNP levels.
  • Alternating direction method of multipliers (ADMM) algorithm implementation.

Main Results:

  • Application to joint analysis of thyroid and breast cancers identified 11 pleiotropic SNPs and 6 pathways.
  • Simulation studies demonstrated higher true signal detection compared to a competing method.
  • Maintained a similar false discovery rate as the competing method.

Conclusions:

  • Developed feature selection methods for jointly analyzing multiple logistic regression tasks with prior grouping knowledge.
  • Methods performed well in simulation studies and real-world cancer data analysis.
  • Successfully identified potential pleiotropic SNPs and pathways.