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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Structured Pooling Improves Detection of Rare Regulatory Mutations in Population-Scale Reporter Assays.

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    Biorxiv : the Preprint Server for Biology
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    This study introduces a large-scale STARR-seq method to identify genetic variants affecting gene expression and disease risk. The innovative pooled design enhances accuracy for detecting functional regulatory variants, especially rare ones.

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    Area of Science:

    • Genomic Medicine
    • Molecular Biology
    • Computational Biology

    Background:

    • Identifying noncoding genetic variants impacting gene expression and disease risk is challenging.
    • Reporter assays like STARR-seq and MPRA enable high-throughput testing of millions of variants.
    • Previous efforts scaled these assays to whole genomes or populations, but not concurrently.

    Purpose of the Study:

    • To report the first whole-genome population-scale STARR-seq experiment.
    • To introduce a novel experimental design for increased scale and accuracy.
    • To develop a robust Bayesian model for variant effect size estimation.

    Main Methods:

    • Performed the first whole-genome population-scale STARR-seq experiment on 100 individuals.
    • Developed a sample pooling strategy to increase allele frequencies and signal-to-noise ratio.
    • Applied a Bayesian model for robust estimation of variant effect sizes with posterior distributions.

    Main Results:

    • The novel pooled design yielded more accurate estimates of variant effect sizes.
    • The Bayesian model provided robust estimations and confidence assessments.
    • Methodological innovations enabled higher accuracy and scale in detecting functional regulatory variants, including rare variants.

    Conclusions:

    • The developed approach significantly improves the detection of functional regulatory variants at unprecedented scale.
    • This method facilitates accurate functional annotation of quantitative trait loci (eQTLs, caQTLs).
    • The findings show concordance with transcription factor binding profiles, validating the approach.