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de Boer, C. G.

Publications and source records attributed to de Boer, C. G..

2 recordsLinked to original sources

Genome-wide discovery of lupus genetic risk variant allelic regulatory activity

Genome-wide association studies of Systemic Lupus Erythematosus (SLE) nominate 3,073 genetic variants at 91 risk loci. To systematically screen these variants for allelic transcriptional enhancer activity, we constructed a massively parallel reporter assay (MPRA) library comprising 12,396 DNA oligonucleotides containing the genomic context around every allele of each SLE variant. Transfection into the Epstein-Barr virus-transformed B cell line GM12878 revealed 482 variants with enhancer activity, with 51 variants showing genotype-dependent (allelic) enhancer activity at 27 risk loci. Comparison of MPRA results in GM12878 and Jurkat T cell lines highlights shared and unique allelic transcriptional regulatory mechanisms at SLE risk loci. In-depth analysis of allelic transcription factor (TF) binding at and around allelic variants identifies one class of TFs whose DNA-binding motif tends to be directly altered by the risk variant and a second, larger class of TFs that bind allelically without direct alteration of their motif by the variant. Collectively, our approach provides a blueprint for the discovery of allelic gene regulation at risk loci for any disease and offers insight into the transcriptional regulatory mechanisms underlying SLE.

genomics

MAUDE: Inferring expression changes in sorting-based CRISPR screens

Pooled CRISPR screens allow high-throughput interrogation of genetic elements that alter expression of a reporter gene readout. New computational methods are needed to model these data. We created MAUDE (Mean Alterations Using Discrete Expression) for quantifying the impact of guide RNAs on a target genes expression in a pooled, sorting-based expression screen. MAUDE quantifies guide-level effects by modeling the distribution of cells across sorting expression bins. It then combines guides to estimate the statistical significance and effect size of targeted genetic elements. We show that MAUDE significantly improves over previous approaches and provide experimental design guidelines to best leverage MAUDE.

bioinformatics