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Related Concept Videos

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.
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...
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Incomplete Dominance01:43

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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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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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SuSiE 2.0: improved methods and implementations for genetic fine-mapping and phenotype prediction.

Alexander McCreight, Yanghyeon Cho, Ruixi Li

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    |December 15, 2025
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    Summary

    We introduce SuSiE 2.0, an improved genetic fine-mapping tool with a modular design for better performance and extensibility. A new method, SuSiE-ash, enhances calibration for complex genetic scenarios, reducing false discoveries.

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    Area of Science:

    • Genetics
    • Statistical Genetics
    • Bioinformatics

    Background:

    • Sum of Single Effects regression (SuSiE) is a popular method for genetic fine-mapping.
    • The original SuSiE implementation has limitations in extensibility and performance.

    Purpose of the Study:

    • To present SuSiE 2.0, a redesigned and enhanced version of the SuSiE algorithm.
    • To introduce SuSiE-ash, a novel method for improved calibration in genetic fine-mapping.
    • To evaluate the performance and utility of SuSiE 2.0 and SuSiE-ash.

    Main Methods:

    • Modular redesign of the SuSiE algorithm for improved extensibility.
    • Implementation of speed enhancements for summary statistics applications.
    • Development and application of the SuSiE-ash method for handling complex genetic signals.
    • Simulations and real data benchmarks across diverse genetic architectures.

    Main Results:

    • SuSiE 2.0 offers up to 5x speed improvements.
    • SuSiE-ash demonstrates improved calibration, achieving 1.5-3x FDR reduction in complex polygenic settings while maintaining power.
    • SuSiE-based methods show effectiveness for TWAS (Transcriptome-Wide Association Studies) prediction.

    Conclusions:

    • SuSiE 2.0 provides a more extensible and performant framework for genetic fine-mapping.
    • SuSiE-ash significantly improves fine-mapping calibration under complex genetic architectures.
    • SuSiE methods are valuable tools for genetic fine-mapping and TWAS prediction.