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High-throughput dissection of the genetics underlying complex traits

Final Report Summary - COMPLEX_TRAITS (High-throughput dissection of the genetics underlying complex traits)

Many biological traits are complex and result from specific interactions between an organism’s genotype and the environment. The objective of this study was to develop novel approaches for dissecting complex traits, and to apply these approaches to the yeast Saccharomyces cerevisiae. Yeast is an attractive model organism to study complex traits, as its genome is compact, well described and easy to manipulate by genetic engineering.

We first developed a method called reciprocal hemizygosity scanning (RHS) to assess the genome-wide effects of allelic variation in a single experiment. We evaluated RHS in comparison to bulk segregant analysis (BSA) and individual segregant analysis (ISA) and determined the strengths and weaknesses of each individual method. By combining all three approaches we detected several QTLs associated with different phenotypes, including non-fermentative growth.

During our pilot experiment, we observed that confounding factors limit the large-scale applicability of RHS. We therefore developed an alternative method based on the CRISPR/Cas9 system that allows querying the effects of thousands of variants genome-wide by multiplexed, plasmid-based genome engineering and simultaneous genomic barcoding. We show the genome-wide application of this method by individually introducing thousands of SNPs and small insertion/deletion polymorphisms, representing natural variants from the vineyard isolate RM11, into the laboratory strain S288c. Thereby, we become able to fine-map complex traits down to the individual nucleotide level.

To study the genetic architecture underlying complex traits we have performed gene-environment and multi QTL mapping using gene expression data previously generated in our lab. Our results underline the importance of genetic and environmental context for understanding variants’ effects on phenotype. We show that intermediate molecular layers, e.g. gene expression or chromatin state, assist in disentangling causative molecular pathways from correlative effects and that intermediate molecular layers are correlated by unanticipated means, as in the case of co-translational RNA degradation. As such general principles are likely conserved in higher eukaryotes, these findings have direct implications for understanding how human genetic variation impacts disease susceptibility.

We have made substantial progress in developing methods that allow querying the molecular consequences of genetic perturbations dynamically, at low cost and at large scale. 5PSeq, a drug-free method to detect RNA degradation intermediates, enables easy measurement of ribosome dynamics and translation regulation. The development of tag-based RNA-seq methods (5PSeq, 3’ T-Fill) and homemade enzymes for sequencing library preparation additionally provides new approaches to perform genomic studies at low cost and high-throughput.

Finally, building on recent developments in the single cell field, we have developed a targeted transcriptomics method that allows interrogating the transcriptional impact of thousands of genetic perturbations on hundreds of targets in one experiment. As a proof-of-principle we modulate enhancer activity in human cells by CRISPR and study the impact on putative targets, which allows reconstructing gene regulatory networks. Ultimately, a better understanding of these intermediate molecular layers will not only identify pathways through which variants mediate their effects on phenotype but may inform attempts to dynamically model phenotypic outcomes.

Taken together, the insights gained in this project substantially advance our technical capabilities to dissect complex traits while at the same time advancing our understanding of the genetic architecture of complex traits in yeast. Applying our novel methods to complex phenotypes will enable us to gain principally novel insights into the process of natural evolution and the function of cellular networks. As most of our methods can be adapted to other organisms or biological questions, they have the potential to significantly impact a wide range of fields beyond yeast complex trait biology.