Joshua S. Bloom

This study uses genome-scale CRISPR base editing in yeast to assess the functional effects of 308,461 variants, identifying 18,009 with significant growth impacts.

This paper introduces a 96-well liquid format for high-throughput QTL mapping in yeast, enabling efficient analysis of multiple crosses and environmental conditions.

The paper develops a pooled selection platform to map modifiers of RNAi gene knockdown effects in C. elegans, identifying two classes of QTL influencing RNAi responses.

This study identifies context-specific eQTLs and their dynamic effects on gene expression in yeast under acute environmental stress, linking genetic variation to physiological adaptation.

This study applies high-resolution deep mutational scanning to the melanocortin-4 receptor to identify functionally significant variants and guide drug development.

The paper maps eQTLs in yeast using single-cell RNA sequencing, revealing tradeoffs between growth and reproduction through allele-specific effects on gene expression and cell-cycle progression.

The paper introduces a library of synthetic promoters and demonstrates their ability to respond to various stimuli with precise transcriptional output.

The paper identifies a new TA system in C. elegans with divergent toxin alleles suppressed by distinct mechanisms, including piRNA-mediated silencing in the N2 haplotype.

The paper uses long-read sequencing to identify pangenomic genes contributing to yeast phenotypic variation by resolving QTLs that are missed by short-read methods.

The paper reanalyzes data from Shen et al. to argue that most synonymous mutations in yeast genes are not deleterious, challenging their original conclusions about fitness effects.

The paper uses a CRISPR base editor screen in yeast to identify regulators of protein abundance, revealing both specific and broad regulatory relationships.

The paper identifies KTD1 as a novel defense factor in Saccharomyces cerevisiae that confers resistance to the K28 killer toxin.

The paper identifies a mutation in leopard geckos causing white coloration and tumors, linking it to a tumor suppressor gene.

The paper identifies chromosome 1 in Schmidtea mediterranea as a sex-primed autosome through genomic analysis showing restricted recombination and sex-specific gene expression.

The paper maps eQTLs at cellular resolution in C. elegans using single-cell RNA sequencing, revealing cell-type-specific and single-cell-specific eQTL effects.

The paper uses pooled radiation hybrid analysis to identify loci affecting growth, drug response, and mitochondrial abundance in mammalian cells, finding high-resolution genetic effects and new insights into centromere function.

The paper develops a statistical test to detect polygenic epistasis by analyzing genotype-by-ancestry interactions in mice and yeast, finding significant associations across multiple phenotypes.

The paper introduces V-seq, a cost-effective method for viral genome sequencing that uses multiplexed RT primers to improve coverage and reduce costs.

This study shows rare DNA variants have a larger impact on quantitative traits in yeast compared to common variants, due to their greater effect sizes and recent evolutionary origins.

This study identifies genetic loci contributing to height variation in a Jewish population cohort, explaining 6% of the variance through rare variants.

This study identifies genetic variants affecting mutation rates in yeast by analyzing natural isolates and segregant progeny, finding heritability and specific genes involved in mutation rate variation.

The paper identifies QTL underlying toxin response variation in C. elegans and validates their contributions through experimental assays.

This study identifies eQTLs in yeast to show that most gene expression variation is driven by distant, trans-acting eQTLs clustered at hotspots.

The paper uses CRISPR/Cas9 in yeast to engineer and screen variants, finding most premature termination codons are deleterious except near the C-terminal end.

The paper shows that accounting for genetic interactions improves modeling of quantitative trait phenotypes in yeast by considering epistatic networks and allele frequencies.