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Related Concept Videos

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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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.
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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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

Using endophenotypes for pathway clusters to map complex disease genes.

Wen-Harn Pan1, Ke-Shiuan Lynn, Chun-Houh Chen

  • 1Institute of Biomedical Sciences, Academia Sinica, No. 128 Section 2 Academia Road, Taipei, Taiwan 11529. pan@ibms.sinica.edu.tw

Genetic Epidemiology
|January 27, 2006
PubMed
Summary
This summary is machine-generated.

Discovering complex disease genes is challenging. This study proposes using molecular phenotypes like transcript levels and gene product activities to improve gene mapping, especially for diseases with unclear causes.

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Area of Science:

  • Genetics and Genomics
  • Systems Biology
  • Biostatistics

Background:

  • Identifying genes for monogenic diseases is established, but complex disease gene mapping remains difficult.
  • Current approaches using phenotypic homogeneity for complex diseases often fail due to underlying genotypic heterogeneity.
  • The inherent complexity of disease etiology necessitates novel strategies for gene discovery.

Purpose of the Study:

  • To advocate for a shift in complex disease gene mapping strategies.
  • To propose the use of molecular phenotypes for enhanced statistical power in gene discovery.
  • To highlight the potential of transcriptomic and proteomic data in understanding diseases with unclear etiology.

Main Methods:

  • Utilizing well-measured molecular phenotypes, including transcript levels and gene/protein product activities.
  • Focusing on relatively small pathway clusters and oligogenic traits for precise dissection.
  • Employing data-mining tools for dimension reduction to identify novel molecular endophenotypes.

Main Results:

  • Well-measured molecular phenotypes can enhance statistical power for gene mapping.
  • High-throughput expression data can significantly aid gene finding, particularly for diseases with complex or unknown causes.
  • Data-mining facilitates the discovery of new molecular endophenotypes crucial for understanding disease mechanisms.

Conclusions:

  • A paradigm shift towards molecular phenotypes is crucial for advancing complex disease gene discovery.
  • Overcoming challenges like cost, reproducibility, and data accessibility requires large-scale, integrated prospective studies.
  • Future research should focus on integrating phenotype characterization, high-throughput techniques, and advanced data analysis.