Thesis Defence Department of Plant Physiology

Franziska Bandau


Title: Importance of tannins for responses of aspen to anthropogenic nitrogen enrichment

Faculty Examiner: Lisbeth Jonsson, professor, Stockholm University,  Department of Ecology, Environment and Plant Sciences

Supervisor: Benedicte Albrectsen.

Room: Lilla hörsalen, KB3A9

Speaker:
Paul C. Rogers

Director, Western Aspen Alliance
Department of Wildland Resources
Utah State University

http://www.western-aspen-alliance.org/

Title: North American Quaking Aspen: functional effects of elk, fire, and climate on long-term resilience

Host: Lars Edenius (VFM, SLU)
Contact: This email address is being protected from spambots. You need JavaScript enabled to view it.

Place: Skogis

NOTE CHANGED DAY: WEDNESDAY

UPSC Cutting Edge Seminar

Speaker:
Daniel Cosgrove
Department of Biology

The Pennsylvania State University, University Park, PA, USA

Title: Rethinking the architecture and mechanics of growing plant cell walls and mechanisms of cell wall loosening. Insights from biomechanics, atomic force microscopy and the actions of wall-loosening enzymes.



Host: Stephanie Robert

Place: Lilla Hörsalen, KB3A9

UPSC Seminar

Speaker:
Mikko Sillanpää
Helsinki University, Finland

Title:
An efficient genome-wide multilocus epistasis search

Room: Lilla hörsalen, KB3A9

Host: Rosario Garcia Gil


Abstract

High-throughput laboratory techniques are producing vast amount of genomic marker data – discrete predictors to association studies. Linear regression model is often considered to link study phenotypes and these marker measurements to each other. Number of predictors in multi-marker regression models can easily be much larger than number of observations. Therefore, one needs application of variable selection to find small subset of important predictors out of large number of candidates. Such models can occasionally include also all pairwise locus-by-locus (epistasis) interactions which increases dimensionality of the model very rapidly.

We consider variable selection problem of linear model containing large amount of predictors and all of their pairwise interactions in the model jointly. Our suggested approach (Kärkkäinen et al. 2015) use sure-independence-screening to first drop dimension of the problem by considering marginal importance of each interaction term within the huge loop. Subsequent estimation step then consider Bayesian variable selection approach (Extended Bayesian LASSO – Mutshinda and Sillanpää 2010). We also show that it is important to separate search of main and interaction effects in the algorithm to control number of false positives. Examples illustrates superior performance of our method over PLINK in terms of computation time and empirical power. Our successful examples consider even problem of originally of order of 280,000,000 interactions within a reasonable time frame.

REFERENCES

Mutshinda CM, Sillanpää MJ (2010) Extended Bayesian LASSO for multiple quantitative trait loci mapping and unobserved phenotype prediction. Genetics 186: 1067-1075.

Kärkkäinen HP, Li Z, Sillanpää MJ (2015) An efficient genome-wide multilocus epistasis search. Genetics 201: 865-870.