snpSTARRseq

snpSTARRseq evaluates the functional effects of non-coding single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) on enhancer activity to link GWAS-identified variants to gene regulatory mechanisms.

Key Features:

• High-Throughput Capability: Enables the analysis of hundreds to thousands of non-coding SNPs in parallel to increase experimental throughput.
• Enhanced Signal-to-Noise Ratio: Employs a novel sequencing and bioinformatic approach that increases insert size and the number of variants tested per locus to improve signal clarity and reliability.
• Functional Impact Assessment: Specifically evaluates the impact of non-coding SNPs on enhancer activity, including effects on transcription factor binding and gene expression modulation.
• Integration with Chromosomal Looping Data: Combines functional measurements with chromosomal looping information to identify interacting genes and elucidate mechanisms of action at disease-associated loci.
• Correlation with In Vivo Allelic-Imbalance Studies: Demonstrates strong correlation with in vivo experimental allelic-imbalance data, providing empirical evidence of variant impact that complements in silico predictions.

Scientific Applications:

• Prostate cancer risk-locus analysis: Applied to prostate cancer (PCa) risk-associated loci, revealing that 35% of these loci contain SNPs that significantly alter enhancer activity and enabling identification of functional variants within GWAS regions.
• Variant-to-gene mapping: Links functional variant effects to putative target genes through integration with chromosomal looping data to propose mechanisms by which variants contribute to disease risk.

Methodology:

Performs high-throughput screening of non-coding SNPs followed by sequencing that increases insert size and variant coverage per locus, with bioinformatic analysis that integrates sequencing results with chromosomal looping information and correlates findings with in vivo allelic-imbalance data.