Generalized genetical genomics. Advanced methods and applications

PhD ceremony: Ms. Y. Li, 14.45 uur, Academiegebouw, Broerstraat 5, Groningen

Thesis: Generalized genetical genomics. Advanced methods and applications

Promotor(s): prof. R.C. Jansen

Faculty: Mathematics and Natural Sciences

Generalized genetical genomics (GGG) is a systems genetics approach that combines the analysis of genetic variation with population-wide assessment of variation in molecular traits in multiple environments to identify genotype-by-environment interactions. Yang Li presents a newly developed software, designGG for designing optimal GGG experiments.

Next, two important statistical issues relevant to GGG studies were addressed by her. She discussed the critical concerns on causal inference with genetic data. In addition, she examined the permutation method used for determining the significance of quantitative trait loci (QTL) hotspots in linkage and association studies.

Furthermore, Li applied the GGG strategy to three pilot studies: In the first of these, she showed that heritable differences in the plastic responses of gene expression are largely regulated in ‘trans’. In the second pilot study, she demonstrated that heritable differences in transcript abundance are highly sensitive to cellular differentiation stage. In the third study, she found that the alternative splicing machinery exhibits a general genetic robustness in C. elegans and that only a minor fraction of genes shows heritable variation in splicing forms and relative abundance

Finally, Li concludes by discussing various fundamental issues involved in data preprocessing, QTL mapping, result interpretation and network reconstruction and suggesting future directions yet to be explored in order to expand the reach of systems genetics.

By comparing variations in the genome of individuals with variations in gene activity, this strategy has the potential to identify which pieces of the genome exerted an influence on the activity of which genes and how this effect changes across different conditions. With the software and recommended methods, biologists will have an optimal design of their genetic experiments and have more reliable interpretation of the genetic data.