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Mikko J. Sillanpää

Researcher at University of Oulu

Publications -  164
Citations -  7381

Mikko J. Sillanpää is an academic researcher from University of Oulu. The author has contributed to research in topics: Population & Quantitative trait locus. The author has an hindex of 33, co-authored 153 publications receiving 6742 citations. Previous affiliations of Mikko J. Sillanpää include Imperial College London & University of Helsinki.

Papers
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The Collaborative Cross, a community resource for the genetic analysis of complex traits

Gary A. Churchill, +113 more
- 01 Nov 2004 - 
TL;DR: The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way the authors approach human health and disease.
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Bayesian analysis of genetic differentiation between populations.

TL;DR: A Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design is introduced, suggesting that this method is capable of estimating a population subst structure, while not artificially enforcing a substructure when it does not exist.
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A review of Bayesian variable selection methods: what, how and which

TL;DR: The results suggest that SSVS, reversible jump MCMC and adaptive shrinkage methods can all work well, but the choice of which method is better will depend on the priors that are used, and also on how they are implemented.
Book

Micronutrients and the Nutrient Status of Soils: A Global Study

TL;DR: Micronutrients and the nutrient status of soils is studied in detail in a global study on behalf of the World Health Organization.
Journal ArticleDOI

BAPS 2: enhanced possibilities for the analysis of genetic population structure

TL;DR: A Markov chain Monte Carlo algorithm for characterizing genetically divergent groups based on molecular markers and geographical sampling design of the dataset is modified to support multiple parallel MCMC chains, with enhanced features that enable considerably faster and more reliable estimation compared to the earlier version of the algorithm.