Jing Hou

The paper uses high-content imaging and machine learning to study how perturbing essential genes affects subcellular organization in yeast, revealing cascade effects and a threshold for cell death.

The paper develops a barcoded yeast collection to map genotype-phenotype relationships across genetic diversity, revealing genetic variants linked to chemical responses.

The paper analyzes the pan-transcriptome of yeast isolates to show that accessory genes significantly contribute to gene expression variation across populations.

The paper quantifies transcriptomes and proteomes across 942 yeast isolates, showing weak correlation between mRNA and protein levels and distinct genetic control mechanisms.

The paper uses a diallel panel of yeast hybrids to show low-frequency SNPs have a larger impact on gene expression than common SNPs or SVs.

The paper estimates heritability of gene expression traits in yeast hybrids and finds non-additive components account for 36% of phenotypic variance.

The paper records proteomes for non-essential gene knock-outs in yeast to uncover principles of protein expression and annotate gene function.

The paper identifies background-dependent fitness genes in yeast by analyzing loss-of-function mutations across natural isolates, finding most show continuous variation while some are specific to genetic backgrounds.

The paper shows low-frequency genetic variants contribute significantly to phenotypic variation in a yeast population using a diallel cross experiment and GWAS.

The paper examines meiotic recombination differences between Saccharomyces cerevisiae and Lachancea kluyveri, finding significant variations in recombination rates, crossover patterns, and hotspot conservation across evolutionary distances.

The paper investigates Mendelian traits in yeast, finding that most are caused by common variants with stable inheritance, while a rare variant shows complex, context-dependent expression.