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Related Concept Videos

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...
Polygenic Traits01:18

Polygenic Traits

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...
Polygenic Traits01:18

Polygenic Traits

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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Multiple Allele Traits

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Genetic Screens02:46

Genetic Screens

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.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Epistasis Analysis01:09

Epistasis Analysis

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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

GUESS-ing polygenic associations with multiple phenotypes using a GPU-based evolutionary stochastic search algorithm.

Leonardo Bottolo1, Marc Chadeau-Hyam, David I Hastie

  • 1Department of Mathematics, Imperial College London, London, United Kingdom. l.bottolo@imperial.ac.uk

Plos Genetics
|August 17, 2013
PubMed
Summary
This summary is machine-generated.

We developed GUESS, an efficient algorithm for pinpointing causal genetic variants in complex trait studies. GUESS improves multi-phenotype analysis, identifying novel associations in lipid metabolism not found by larger studies.

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) identify genetic variants for complex traits but struggle to pinpoint causal variants.
  • Multi-phenotype GWAS face challenges due to complex linkage disequilibrium and trait correlations, complicating SNP-trait association interpretation.

Purpose of the Study:

  • To develop a computationally efficient algorithm (GUESS) for exploring complex genetic association models and maximizing genetic variant detection.
  • To integrate GUESS with a Bayesian strategy for multi-phenotype analysis to identify SNP contributions to trait combinations and study lipid metabolism genetics.

Main Methods:

  • Developed GUESS, a computationally efficient algorithm for genetic association analysis.
  • Integrated GUESS with a Bayesian strategy for multi-phenotype analysis.
  • Utilized a Graphics Processing Unit (GPU) for parallel implementation.

Main Results:

  • GUESS recovered most major associations and refined multi-trait associations better than alternative methods in the Gutenberg Health Study (GHS).
  • Identified novel associations of SORT1 with TG-APOB and LIPC with TG-HDL, replicated in independent cohorts.
  • Demonstrated increased power of GUESS over alternative multi-phenotype approaches in simulations.

Conclusions:

  • GUESS is a powerful and efficient tool for multi-phenotype GWAS, enhancing genetic variant detection and interpretation.
  • The algorithm's flexible hierarchical prior structure improves power in identifying associated variants, adaptable to complex predictor dependencies.
  • GUESS offers a significant advancement for analyzing diverse genomic data, including gene expression and exome sequencing.