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Jean-Luc Jannink

Adjunct Professor

258 Emerson Hall

Jean-Luc Jannink's primary focus is on developing statistical methods to use DNA markers in public sector small grains breeding.  To make the research relevant to small grains, it should emphasize low cost markers to the extent possible because small grains have relatively low value. To make the research relevant to the public sector, it should be applicable to many relatively little programs that seek to leverage their joint efforts into something greater.
Currently in this area he is particularly interested in using association genetics within breeding.  Common associations across multiple small programs can indeed leverage each programs phenotyping efforts into more accurate estimates of marker effects. Further, common associations across years within a program can allow current evaluations to draw more benefit from past efforts and historical data, leading to decreased phenotyping costs.

He is exploring genome-wide analysis methods that avoid marker selection by fitting all markers into a linear model simultaneously. These methods improve the ability to predict phenotypes for complex traits (e.g., agronomic performance) on the basis of marker data only. Their strengths for mapping causal polymorphisms are not documented.  He is also interested in using multi-marker segments (haplotypes), rather than single markers, for QTL identification and for predicting breeding values of experimental lines.  Association studies entail large data sets and consequently the potential for many missing and erroneous marker data points, stringent quality control notwithstanding. They are investigating marker imputation methods using local LD structure developed for human genetics that show promise to help tackle these problems in crops. This research is mostly theoretical and will be pursued by simulations, usually starting from existing marker data that capture the linkage disequilibrium state of populations of crops of interest. He also has a grant to apply these methods to oat to identify loci affecting beta-glucan content and to select for higher beta-glucan content. Beta-glucan is a soluble fiber presumed responsible for many of the health benefits attributed to oat.