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Pleiotropy01:33

Pleiotropy

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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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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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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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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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Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Improving genetic risk prediction by leveraging pleiotropy.

Cong Li1, Can Yang, Joel Gelernter

  • 1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA, cong.li@yale.edu.

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Summary

Integrating genetically correlated human traits improves disease risk prediction. Analyzing GWAS data for related disorders like bipolar disorder and schizophrenia, or Crohn's disease and ulcerative colitis, substantially increased prediction accuracy.

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Area of Science:

  • Human genetics
  • Complex disease risk prediction
  • Statistical genetics

Background:

  • Genome-wide association studies (GWAS) have limited success in clinical risk prediction due to small effect sizes of variants.
  • Predictive accuracy is often bottlenecked by training sample size.
  • Different human traits may share common genetic underpinnings (pleiotropy).

Purpose of the Study:

  • To explore the utility of integrating genetically correlated phenotypes for improving disease risk prediction.
  • To assess if combining GWAS data from related disorders enhances predictive accuracy.

Main Methods:

  • Bivariate ridge regression analysis of GWAS data.
  • Joint prediction of bipolar disorder and schizophrenia.
  • Joint analysis of Crohn's disease and ulcerative colitis.
  • Comprehensive simulation studies to validate findings.

Main Results:

  • Jointly predicting bipolar disorder and schizophrenia substantially increased prediction accuracy (measured by AUC).
  • Similar accuracy improvements were observed for Crohn's disease and ulcerative colitis.
  • Simulation studies confirmed that combining phenotypes with high genetic correlations improves prediction accuracy.

Conclusions:

  • Integrating genetically correlated phenotypes is a valuable strategy to enhance genetic risk prediction.
  • Leveraging pleiotropy through data integration offers a new opportunity for improving clinical utility of GWAS data.
  • This approach can overcome limitations of small effect sizes and small training sample sizes in human genetics studies.