A department chair, Mathematical Sciences RU

University of Oulu;

email: mikko.sillanpaa [at] oulu.fi

ORCID : https://orcid.org/0000-0003-2808-2768

The research group of Prof Mikko J. Sillanpää is developing new computationally efficient and practical statistical methods and their applications in biology, medicine and other fields. Bayesian analysis and data analytic methods using hierarchical models and Markov Chain Monte Carlo sampling methods are preferred in the group. In many of the problems we are working with, important parts of the solution are formed by the efficient handling of the big-data, use of data analytic tools having close connections to the machine learning theory, and algorithmic view of computational methods. Specifically, the present research interest address statistical variable selection methods having sparsity-inducing mechanism. From the Bayesian viewpoint, this mechanism can be called as a shrinkage prior and it can be called as a penalty or regularization function from the classic statistical viewpoint. Examples of these are different LASSO-related priors.

From biology point of view, the group studies different problems in QTL mapping and genomic prediction. Especially, the properties and use of Bayesian multi-locus models (i.e., Bayesian whole-genome regression or Bayesian alphabets) in identifying genetic determinants (including epistasis) and prediction of individual's genetic value (merit / risk) to the quantitative, qualitative and function-valued human, plant and animal traits based on genome-wide sets of molecular markers. Also the group have a specific interest towards use of Bayesian mixed models such as Bayesian G-BLUP and other mixed models in estimating genomic parameters, genomic heritability, or genomic breeding values.

Newer interests are different variance and precision matrix inference methods having sparsity-inducing mechanism. The conditional independence structure between variables can be inferred by studying which of the precision matrix elements are zero and non-zero. The inferring conditional independence structure between variables is same as estimating topology of the graph or estimating network structure. Thus, we are developing new statistical sparsity-inducing estimation tools for variance and precision matrices as well as estimating sparse network structures from data sets. Biologically this is closely connected to different problems in systems biology.

Sillanpaa has 125 journal publications and his research has been cited in 4739 times; with h-index 28 (Web of Science). His most cited publication has 734 citations and one of his papers is so called highly cited paper (Web of Science, Sept 2019) which received enough citations to place it in the top 1% of the academic field of Mathematics based on a highly cited threshold for the field and publication year.

2012 picture

Academy of Finland consortium on Molecular Regulatory Networks of Life (R'Life) 2019-2023: Forest Tree Evolution Via Expression Regulation (FOREVER)

Academy of Finland funded research project 'Future Methods for Identifying Interacting Genes using Dynamic Gene Network Inference' for years 2023-2026.

Academy of Finland funded research project '6G Wireless Communications via Enhanced Channel Modeling and Estimation, Channel Morphing and Machine Learning for mmWave Bands' for years 2023-2025 (Mikko SillanpÃ¤Ã¤). This is the collaborative project together with Prof. Markku Juntti (Oulu) and Prof. B.R. Rao (San Diego, US).

Perimä kertoo männyn sopeutumiskyvystä

Speed and security of future 5G applications enhanced with the help of artificial intelligence

(C source codes) MM.tar.Z.

(reference manual in PS) MMmanual.ps.Z.

(reference manual in PDF) MMmanual.pdf.

(simulated test data set) MMtestdata.tar.Z.

(simulated test data set with missing values) MMtest30.cro.

Multimapper (Version 1.0) with environmental covariates

(info) MM_info.txt.

(updated codes) metropolis.c. data_structure.h.

This sofware is made for Linux but can also be used under Windows through Cygwin (which provides linux-like environment for Windows).

MULTIMAPPER was presented in the papers :

Sillanpää, M. J. and E. Arjas (1998) Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data. Genetics 148: 1373-1388.

Martinez, V., G. Thorgaard, B. Robison, and M. J. Sillanpää (2005) An application of Bayesian QTL mapping to early development in double haploid lines of rainbow trout including environmental effects. Genetical Research 86: 209-221.

Multimapper/OUTBRED

Bayesian QTL mapping software for analysing backcross and F2 data from designed crossing experiments of outbred lines (Version 1.1)

(C source codes for MM/OUTBRED) MM_outbred.tar.Z.

(C source codes for preprocessor) MMo_prepro.tar.Z.

(reference manual in PS) MMo_manual.ps.Z.

(reference manual in PDF) MMo_manual.pdf.

(simulated test data set) o_testdata.tar.Z.

This sofware is made for Linux but can also be used under Windows through Cygwin (which provides linux-like environment for Windows).

MULTIMAPPER/OUTBRED was presented in the paper :

Sillanpää, M. J. and E. Arjas (1999) Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data. Genetics 151: 1605-1619.

BAPS / Windows software for Bayesian analysis of population structure

winzip-file containing executable program, dll-files, manual, and test data set can be found in BAPS

The population structure estimation work that we initially started as methodological project to correct for unobserved counfounders in association analysis (Sillanpää et al. 2001; Ripatti et al. 2001) later on culminated in development of BAPS (Corander et al. 2003, 2004). Our new development of population structure estimation work is represented by Gasbarra et al. (2007a,b) where the unknown relatedness structure (pedigree and geneflow) are simultaneously estimated and different degrees of relationships are allowed among the study individuals.

BAPS versions are implemented using MATLAB and are then converted to C-language, where only executable programs are distributed. For user information and installation details, see the manual. Details of the method are presented in the papers :

Corander, J., P. Waldmann, and M. J. Sillanpää (2003) Bayesian analysis of genetic differentiation between populations. Genetics 163: 367-374.

Corander, J., P. Waldmann, P. Marttinen, and M. J. Sillanpää (2004) BAPS 2: enhanced possibilities for the analysis of the genetic population structure. Bioinformatics 20: 2363-2469.

BAMA / Software for Bayesian analysis of multilocus association (initial version)

(C source codes) bama.c.Z.

(reference manual in PS) BAMAmanual.ps.

(reference manual in PDF) BAMAmanual.pdf.

(simulated test data sets) BAMAtestdata.tar.Z.

This sofware is made for Linux but can also be used under Windows through Cygwin (which provides linux-like environment for Windows).

BAMA was presented in the paper :

Kilpikari, R. and M. J. Sillanpää (2003) Bayesian analysis of multilocus association in quantitative and qualitative traits. Genetic Epidemiology 25: 122-135.

For epistasis and interaction searches using BAMA, see :

Sillanpää, M. J. (2009) Detecting interactions in association studies by using simple allele recoding. Human Heredity 67: 69-75.

Bayesian association-based fine mapping in small chromosomal segments

(WinBUGS model specification code in ASCII) Assoc_Map_WinBUGS_1.txt

(Tiny help: How to read genetic data into the WinBUGS)

(WinBUGS code for predicting complex binary trait) Bhattacharjee_code.doc

The method for association mapping is presented in the paper :

Sillanpää, M. J. and M. Bhattacharjee (2005) Bayesian association-based fine mapping in small chromosomal segments. Genetics 169: 427-439.

Hierarchical modeling of clinical and expression QTLs

(Instructions) eQTLcQTL.pdf

(OpenBUGS model specification codes) eQTL.tar

The method is presented in the paper :

Sillanpää, M. J., and N. Noykova (2008) Hierarchical modeling of clinical and expression quantitative trait loci. Heredity 101: 271-284.

QTL mapping using hierarchical modeling of functional traits

Explanations of notations in WinBUGS code) code_and_notation.pdf

(WinBUGS model specification code) functional_code.txt

The method is presented in the paper :

Sillanpää, M. J., P. Pikkuhookana, S. Abrahamsson, T. Knürr, A. Fries, E. Lerceteau, P. Waldmann, and M. R. Garcia-Gil (2012) Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling. Heredity 108: 135-146.

BMRV / R-package for Hierarchical Bayesian Multiple Regression model for rare variant association test and for detecting rare variant interaction effect in twin study

(BMRV 1.31. R-package - Linux version) BMRV_1.31.tar.gz.

(BMRV 1.31. R-package - Windows version) BMRV_1.31.zip.

(reference manual in PDF) BMRV-manual.pdf.

The methods implemented in the BMRV 1.31. R-package are presented in the papers :

He, L., M. J. Sillanpää, S. Ripatti, and J. Pitkäniemi (2014) Bayesian latent variable collapsing model for detecting rare variant interaction effect in twin study. Genetic Epidemiology 38: 310-324.

He, L., J. Pitkäniemi, A-P. Sarin, V. Salomaa, M. J. Sillanpää, and S. Ripatti (2015) Hierarchical Bayesian model for rare variant association analysis integrating genotype uncertainty in human sequence data. Genetic Epidemiology 39: 89-100.

Fraimout, A., Z. Li, M. J. Sillanpaa, and J. Merila (2022) Age-dependent genetic architecture across ontogeny of body size in sticklebacks. Proceedings of the Royal Society B: Biological Sciences 289: 20220352.

Ross, S., A. Arjas, I. I. Virtanen, M. J. Sillanpaa, L. Roininen, and A. Hauptmann (2022) Hierarchical deconvolution for incoherent scatter radar data. Atmospheric Measurement Techniques 15: 3843-3857.

Arjas, A., E. J. Alles, E. Maneas, S. Arridge, A. Desjardins, M. J. Sillanpaa, and A. Hauptmann (2022) Neural Network Kalman filtering for 3D object tracking from linear array ultrasound data. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 69: 1691-1702.

Mathew, B., A. Hauptmann, J. Leon, and M. J. Sillanpaa (2022) NeuralLasso: Neural networks meet lasso in genomic prediction. Frontiers in Plant Science 13: 800161.

Kulathinal, S., J. Peltonen, and M. J. Sillanpaa (2022) Editorial. Scandinavian Journal of Statistics 49: 1-3.

Kuismin, M., F. Dodangeh, and M. J. Sillanpaa (2022) Gap-com: General model selection criterion for sparse undirected gene networks with nontrivial community structure. G3: Genes, Genomes, Genetics 12: jkab437.

Lahderanta, T., J. Salonen, J. Mottonen, and M. J. Sillanpaa (2022) Modelling old-age retirement: An adaptive multi-outcome LAD-lasso regression approach. International Social Security Review 75: 3-29.

Kuismin, M., and M. J. Sillanpaa (2021) MCPeSe: Monte Carlo penalty selection for graphical lasso. Bioinformatics 37: 726-727.

Kontio, J., T. Pyhajarvi, and M. J. Sillanpaa (2021) Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways. PLOS Computational Biology 17: e1008960.

Lahderanta, T. , T. Leppanen, L. Ruha, L. Loven, E. Harjula, M. Ylianttila, J. Riekki, and M. J. Sillanpaa (2021) Edge computing server placement with capacitated location allocation. Journal of Parallel and Distributed Computing 153: 130-149.

Mathew, B., J. Leon, S. Dadshani, K. Pillen, M. J. Sllanpaa, and A. A. Naz (2021) Importance of correcting genomic relationships in single-locus QTL mapping model with an Advanced Backcross population. G3: Genes, Genomes, Genetics 11: jkab105.

Loven, L., T. Lahderanta, L. Ruha, E. Peltonen, I. Launonen, M. J. Sillanpaa, J. Riekki, and S. Pirttikangas (2021) EDISON: An edge-native method and architecture for distributed interpolation. Sensors 21: 2279.

Kontio J.A.J., M. Rinta-aho, and M. J. Sillanpaa (2020) Estimating linear and nonlinear gene coexpression networks by semiparametric neighborhood selection. Genetics 215: 597-607.

Arjas, A., A. Hauptmann, and M. J. Sillanpaa (2020) Estimation of dynamic SNP-heritability with Bayesian Gaussian process models. Bioinformatics 36: 3795-3802.

Sarviaho R., O. Hakosalo, K. Tiiira, S. Sulkama, J.E. Niskanen, M.K. Hytonen, M. J. Sillanpaa, and H. Lohi (2020) A novel genomic region on chromosome 11 associated with fearfulness in dogs. Translational Psychiatry 10: 169.

Huotari, T., J. Rusanen, T. Keistinen, T. Lahderanta, L. Ruha, M. J. Sillanpaa, and H. Antikainen (2020) Effect of centralization on geographic accessibility of maternity hospitals in Finland. BMC Health Services Research 20: 337.

Kuismin, M., D. Saatoglu, A. Niskanen, H. Jensen, and M. J. Sillanpaa (2020) Genetic assignment of individuals to source populations using network estimation tools. Methods in Ecology and Evolution 11: 333-344.

Mutshinda, C. M., A. J. Irwin, and M. J. Sillanpaa (2020) A Bayesian framework for robust quantitative trait locus mapping and outlier detection. International Journal of Biostatistics 16: 20190038.

Baison, J., A. Vidalis, L. Zhou, Z. Chen, Z. Li, M. J. Sillanpaa, C. Bernhardsson, D. Scofield, N. Forsberg, T. Grahn, L. Olsson, B. Karlsson, H. Wu, P. K. Ingvarsson, S-O. Lundqvist, T. Niittyla, and R. Garcia Gil (2019) Genome-Wide Association Study (GWAS) identified candidate loci affecting wood formation in Norway spruce. The Plant Journal 100: 83-100.

Vanhatalo, J., Z. Li, and M. J. Sillanpaa (2019) A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data. Bioinformatics 35: 3684-3692.

Loven, L., V. Karsisto, H. Jarvinen , M. J. Sillanpaa, T. Leppanen, E. Peltonen, S. Pirttikangas, and J. Riekki (2019) Mobile road weather sensor calibration by sensor fusion and linear mixed models. PLoS ONE 14: e0211702.

Sarviaho, R., O. Hakosalo, K. Tiira, S. Sulkama, E. Salmela, M. K. Hytonen, M. J. Sillanpaa, and H. Lohi (2019) Two novel genomic regions associated with fearfulness in dogs overlap human neuropsychiatric loci. Translational Psychiatry 9: 18.

Calleja-Rodriguez, A., Z. Li, H. R. Hallingback, M. J. Sillanpaa, H. X. Wu, S. Abrahamsson, and R. Garcia-Gil (2019) Analysis of phenotypic- and Estimated Breeding Values (EBV) to dissect the genetic architecture of complex traits in a Scots pine three-generation pedigree design. Journal of Theoretical Biology 462: 283-292.

Rinta-aho M., and M. J. Sillanpaa (2019) Stochastic search variable selection based on two mixture components and continuous-scale weighting. Biometrical Journal 61: 729-746.

Seppa K., H. Rue, T. Hakulinen, E. Laara, M. J. Sillanpaa, and J. Pitkaniemi (2019) Estimating multilevel regional variation in excess mortality of cancer patients using integrated nested Laplace approximation. Statistics in Medicine 38: 778-791.

Mathew B., M. J. Sillanpaa and J. Leon (2019) Advances in statistical methods to handle large data sets for GWAS in crop breeding. A book chapter in "Advances in crop breeding techniques in cereal crops" (Ed. Prof. Frank Ordon and Prof. Wolfgang Friedt), Burleigh Dodds Science Publishing, pp. 437-450. DOI: 10.19103/AS.2019.0051.20

Mathew B., J. Leon, W. Sannemann, and M. J. Sillanpaa (2018) Detection of epistasis for flowering time using Bayesian multilocus estimation in a barley MAGIC population. Genetics 208: 525-536.

Mathew B., J. Leon, and M. J. Sillanpaa (2018) A novel linkage-disequilibrium corrected genomic relationship matrix for SNP-heritability estimation and genomic prediction. Heredity 120: 356-368.

He, L., J. Pitkaniemi, K. Silventoinen, and M. J. Sillanpaa (2017) ACEt: An R-package for estimating dynamic heritability and comparing twin models. Behavior Genetics 47: 620-641.

Kuismin, M. O., and M. J. Sillanpaa (2017) Estimation of covariance and precision matrix, network structure and a view towards systems biology. WIRES Computational Statistics 9: e1415.

Kuismin, M. O., Kemppainen, J. T., and M. J. Sillanpää (2017) Precision matrix estimation with ROPE. Journal of Computational and Graphical Statistics 26: 682-694.

Kujala, S., T. Knurr, K. Karkkainen, D. B. Neale, M. J. Sillanpaa, and O. Savolainen (2017) Genetic heterogeneity underlying variation in a locally adaptive clinal trait in Pinus sylvestris revealed by a Bayesian multipopulation analysis. Heredity 118: 413-423.

Karvanen, J., and M. J. Sillanpää (2017) Prioritizing covariates in the planning of future studies in the meta-analytic framework. Biometrical Journal 59: 110-125.

Kuismin, M., and M. J. Sillanpää (2016) Use of Wishart prior and simple extensions for sparse precision matrix estimation. PLoS ONE 11: e0148171.

Bari, A., H. Khazaei, F. L Stoddard, K. Street, M. J. Sillanpää, Y. P. Chaubey, S. Dayanandan, D. T. F. Endresen, E. De Pauw, and A. B. Damania (2016) In silico evaluation of plant genetic resources to search for traits for adaptation to climate change. Climatic Change 134: 667-680.

Mathew, B., A. M. Holand, P. Koistinen, J. Leon, and M. J. Sillanpää (2016) Reparametrization-based estimation of genetic parameters in multi-trait animal model using Integrated Nested Laplace Approximation. Theoretical and Applied Genetics 129: 215-225.

Bari, A., Chaubey, Y. P., Sillanpää, M. J., Stoddard, F. L., Damania, A. B., Alaoui, S. B. and Mackay, M. (2016) "Applied mathematics in genetic resources: Toward a synergistic approach combining innovations with theoretical aspects." in Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits. Bari, A., Damania, A. B., Mackay, M. and Dayanandan, S. (editors). Oxford: CRC Press, Taylor & Francis Group.

Lausser, L., F. Schmid, M. Platzer, M. J. Sillanpää, and H. A. Kestler (2016) Semantic multi-classifier systems for the analysis of gene expression profiles. Archives of Data Science Series A 1: 157-176.

Li, Z., and M. J. Sillanpää (2015) Dynamic quantitative trait locus analysis of plant phenomic data. Trends in Plant Science 20: 822-833.

Bhattacharjee, M., M. S. Rajeevan, and M. J. Sillanpää (2015) Prediction of complex human diseases from pathway-focused candidate markers by joint estimation of marker effects: case of chronic fatigue syndrome. Human Genomics 9: 8.

Li, Z., J. Möttönen, and M. J. Sillanpää (2015) A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits. Heredity 115: 556-564.

Möttönen, J., and M. J. Sillanpää (2015) Robust variable selection and coefficient estimation in multivariate multiple regression using LAD-Lasso, pp. 235-247 in Modern Nonparametric, Robust, and Multivariate Methods. Festschrift in honour of Hannu Oja, edited by K. Nordhausen and S. Taskinen. Springer International Publishing, Switzerland.

Li, Z., and M. J. Sillanpää (2015) Efficient use of systems mapping without expert knowledge: Comment on "Mapping complex traits as a dynamic system" by L. Sun and R. Wu. Physics of Life Reviews 13: 192-193.

Pasanen, L., L. Holmström, and M. J. Sillanpää (2015) Bayesian LASSO, scale space and decision making in association genetics. PLoS ONE 10: e0120017.

He, L., J. Pitkäniemi, A-P. Sarin, V. Salomaa, M. J. Sillanpää, and S. Ripatti (2015) Hierarchical Bayesian model for rare variant association analysis integrating genotype uncertainty in human sequence data. Genetic Epidemiology 39: 89-100.

Khazaei, H., D. M. O'Sullivan, H. Jones, N. Pitts, M. J. Sillanpää, P. Pärssinen, O. Manninen and F. L. Stoddard (2015) Flanking SNP markers for vicine-convicine concentration in faba bean (Vicia faba L.). Molecular Breeding 35: 38.

Mathew, B., J. Leon, and M. J. Sillanpää (2015) Integrated nested Laplace approximation inference and cross-validation to tune variance components in breeding value estimation. Molecular Breeding 35: 99.

Khazaei, H., D. M. O'Sullivan, M. J. Sillanpää, and F. L. Stoddard (2014) Use of synteny to identify candidate genes underlying QTL controlling stomatal traits in faba bean (Vicia faba L.). Theoretical and Applied Genetics 127: 2371-2385.

Khazaei, H., D. M. O'Sullivan, M. J. Sillanpää, and F. L. Stoddard (2014) Genetic analysis reveals a novel locus in Vicia faba L. decoupling pigmentation in the flower from that in the extra-floral nectaries. Molecular Breeding 34: 1507-1513.

He, L., M. J. Sillanpää, S. Ripatti, and J. Pitkäniemi (2014) Bayesian latent variable collapsing model for detecting rare variant interaction effect in twin study. Genetic Epidemiology 38: 310-324.

Pikkuhookana, P., and M. J. Sillanpää (2014) Combined linkage disequilibrium and linkage mapping: Bayesian multilocus approach. Heredity 112: 351-360.

Makgahlela, M. L., I. Strandén, U. S. Nielsen, M. J. Sillanpää, and E. A. Mäntysaari (2014) Using the unified relationship matrix adjusted by breed-wise allele frequencies in genomic evaluation of a cross-breed population. Journal of Dairy Science 97: 1117-1127.

Kärkkäinen, H. P., and M. J. Sillanpää (2013) Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data. G3: Genes, Genomes, Genetics 3: 1511-1523.

Knürr, T., E. Läärä, and M. J. Sillanpää (2013) Impact of prior specifications in a shrinkage-inducing Bayesian model for quantitative trait mapping and genomic prediction. Genetics Selection Evolution 45: 24.

Li, Z., and M. J. Sillanpää (2013) A Bayesian non-parametric approach for mapping dynamic quantitative traits. Genetics 194: 997-1016.

Häggman, J., J. Juga, M. J. Sillanpää, and R. Thompson (2013) Genetic parameters for claw health and feet and leg conformation traits in Finnish Ayrshire cows. Journal of Animal Breeding and Genetics 130: 89-97.

Makgahlela, M. L., E. A. Mäntysaari, I. Stranden, M. Koivula, U. S. Nielsen, M. J. Sillanpää, and J. Juga (2013) Across breed multi-trait random regression genomic predictions in the Nordic Red dairy cattle. Journal of Animal Breeding and Genetics 130: 10-19.

Makgahlela, M. L., I. Strandén, U. S. Nielsen, M. J. Sillanpää, and E. A. Mäntysaari (2013) The estimation of genomic relationships using breedwise allele frequencies among animals in multibreed populations. Journal of Dairy Science 98: 5364-5375.

Meyer P, Siwo G, Zeevi D, Sharon E, Norel R, DREAM6 Promoter Prediction Consortium (including Sillanpää MJ), Segal E, Stolovitzky G (2013) Inferring gene expression from ribosomal promoter sequences, a crowdsourcing approach. Genome Research 23: 1928-1937.

Sillanpää, M. J., P. Pikkuhookana, S. Abrahamsson, T. Knürr, A. Fries, E. Lerceteau, P. Waldmann, and M. R. Garcia-Gil (2012) Simultaneous estimation of multiple quantitative trait loci and growth curve parameters through hierarchical Bayesian modeling. Heredity 108: 134-146.

Kärkkäinen, H. P., and M. J. Sillanpää (2012) Back to basics for Bayesian model building in genomic selection. Genetics 191: 969-987.

Li, Z., and M. J. Sillanpää (2012) Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection. Theoretical and Applied Genetics 125: 419-435.

Mutshinda, C. M., N. Noykova, and M. J. Sillanpää (2012) A hierarchical Bayesian approach to multi-trait clinical quantitative trait locus modeling. Frontiers in Genetics 3: 97.

Mathew, B., A. M. Bauer, P. Koistinen, T. C. Reetz, J. Leon, and M. J. Sillanpää (2012) Bayesian adaptive Markov Chain Monte Carlo estimation of genetic parameters. Heredity 109: 235-245.

Mutshinda, C. M., and M. J. Sillanpää (2012) Swift block-updating EM and pseudo-EM procedures for Bayesian shrinkage analysis of quantitative trait loci. Theoretical and Applied Genetics 125: 1575-1587.

Kärkkäinen, H. P., and M. J. Sillanpää (2012) Robustness of Bayesian multilocus association models to cryptic relatedness. Annals of Human Genetics 76: 510-523.

Mutshinda, C. M., and M. J. Sillanpää (2012) A decision rule for quantitative trait locus detection under the extended Bayesian LASSO model. Genetics 192: 1483-1491.

Kaakinen, M., F. Ducci, M. J. Sillanpää, E. Läärä, and M.-R. Järvelin (2012) Associations between variation in CHRNA5-CHRNA3-CHRNB4, body mass index and blood pressure in the northern Finland birth cohort 1966. PLoS ONE 7: e46557.

Sillanpää, M. J. (2011) On statistical methods for estimating heritability in wild populations. Molecular Ecology 20: 1324-1332.

Gasbarra, D., M. Pirinen, S. Kulathinal, and M. J. Sillanpää (2011) Estimating haplotype frequencies by combining data from large DNA pools with database information. IEEE/ACM Transactions on Computational Biology and Bioinformatics 8: 36-44.

Mutshinda, C. M., and M. J. Sillanpää (2011) Bayesian shrinkage analysis of QTLs under shape-adaptive shrinkage priors, and accurate re-estimation of genetic effects. Heredity 107: 405-412.

Knürr, T., E. Läärä, and M. J. Sillanpää (2011) Genetic analysis of complex traits via Bayesian variable selection: the utility of a mixture of uniform priors. Genetics Research 93: 303-318.

Bhattacharjee, M., and M. J. Sillanpää (2011) A Bayesian mixed regression based prediction of quantitative traits from molecular marker and gene expression data. PLoS ONE 6: e26959.

Hallander J., P. Waldmann, C. Wang, and M. J. Sillanpää (2010) Bayesian inference of genetic parameters based on conditional decompositions of multivariate normal distributions. Genetics 185: 645-654.

Jolma, A., T. Kivioja, J. Toivonen, L. Cheng, G. Wei, M. Enge, M. Taipale, J. M. Vaquerizas, J. Yan, M. J. Sillanpää, M. Bonke, K. Palin, S. Talukder, T. R. Hughes, N. M. Luscombe, E. Ukkonen, and J. Taipale (2010) Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Research 20: 861-873.

O'Hara, R. B., and M. J. Sillanpää (2009) Review of Bayesian variable selection methods: what, how and which. Bayesian Analysis 4: 85-118.

Bhattacharjee, M., and M. J. Sillanpää (2009) Bayesian joint disease-marker-expression analysis applied to clinical characteristics of chronic fatique syndrome. In: Methods of Microarray Data Analysis VI. Eds. P. McConnell, S. Lim, and A. J. Cuticchia. CreateSpace Publishing, Scotts Valley, California, pp.15-34.

Bauer, A., F. Hoti, M. von Korff, K. Pillen, J. Leon, and M. J. Sillanpää (2009) Advanced backcross-QTL analysis in spring barley (H. vulgare ssp. spontaneum) comparing a REML vs. a Bayesian model in multi-environmental field trials. Theoretical and Applied Genetics 119: 105-123.

Bauer, A., F. Hoti, T. C. Reetz, W.-D. Schuh, J. Leon, and M. J. Sillanpää (2009) Bayesian prediction of breeding values by accounting for genotype-by-environment interaction in self-pollinating crops. Genetics Research 91: 193-207.

Pikkuhookana, P., and M. J. Sillanpää (2009) Correcting for relatedness in Bayesian models for genomic data association analysis. Heredity 103: 223-237.

Gasbarra, D., M. Pirinen, M. J. Sillanpää, and E. Arjas (2009) Bayesian QTL mapping based on reconstruction of recent genetic histories. Genetics 183: 709-721.

Waldmann, P., J. Hallander, F. Hoti, and M. J. Sillanpää (2008) Efficient MCMC implementation of Bayesian analysis of additive and dominance genetic variances in non-inbred pedigrees. Genetics 179: 1101-1112.

Sillanpää, M. J., and N. Noykova (2008) Hierarchical modeling of clinical and expression quantitative trait loci. Heredity 101: 271-284.

Bhattacharjee, M., C. H. Botting, and M. J. Sillanpää (2008) Bayesian biomarker identification based on marker-expression-proteomics data. Genomics 92: 384-392.

Pirinen, M., S. Kulathinal, D. Gasbarra, and M. J. Sillanpää (2008) Estimating population haplotype frequencies from pooled DNA samples using PHASE algorithm. Genetics Research 90: 509-524.

Gasbarra, D., M. Pirinen, M. J. Sillanpää, E. Salmela, and E. Arjas (2007) Estimating genealogies from unlinked marker data: a Bayesian approach. Theoretical Population Biology 72: 305-322.

Gasbarra, D., M. Pirinen, M. J. Sillanpää, and E. Arjas (2007) Estimating genealogies from linked marker data: a Bayesian approach. BMC Bioinformatics 8: 411.

Hoti, F. and M. J. Sillanpää (2006) Bayesian mapping of genotype × expression interactions in quantitative and qualitative traits. Heredity 97: 4-18.

Gasbarra, D. and M. J. Sillanpää (2006) Constructing parental linkage phase and genetic map over distances < 1 cM using pooled haploid DNA. Genetics 172: 1325-1335.

Gasbarra, D., M. J. Sillanpää, and E. Arjas (2005) Backward simulation of ancestors of sampled individuals. Theoretical Population Biology 67: 75-83.

Waldmann, P., M. R. Garcia-Gil, and M. J. Sillanpää (2005) Comparing Bayesian estimates of genetic differentiation of molecular markers and quantitative traits: An application to Pinus sylvestris. Heredity 94: 623-629.

Pekkinen, M., S. Varvio, K. K. M. Kulju, H. Kärkkäinen, S. Smolander, A. Viherä-Aarnio, V. Koski, and M. J. Sillanpää (2005) Linkage map of birch, Betula pendula Roth, based on microsatellites and amplified fragment length polymorphisms. Genome 48: 619-625.

Martinez, V., G. Thorgaard, B. Robison, and M. J. Sillanpää (2005) An application of Bayesian QTL mapping to early development in double haploid lines of rainbow trout including environmental effects. Genetical Research 86: 209-221.

Martinez, V., G. Thorgaard, B. Robinson, and M. J. Sillanpää (2005) Posterior evidence of multiple QTL influencing early development in double haploid lines of rainbow trout, Oncorhynchus mykiss. Aquaculture 247: 25-25. (Meeting Abstract).

Sillanpää, M. J., D. Gasbarra, and E. Arjas (2004) Comment on the "On the Metropolis-Hastings acceptance probability to add or drop a quantitative trait locus in Markov Chain Monte Carlo-based Bayesian analyses". Genetics 167: 1037-1037.

Sillanpää, M. J. and K. Auranen (2004) Replication in genetic studies of complex traits. Annals of Human Genetics 68: 646-657.

Churchill GA, Airey DC, Allayee H, Angel JM, Attie AD, Beatty J, Beavis WD, Belknap JK, Bennett B, Berrettini W, Bleich A, Bogue M, Broman KW, Buck KJ, Buckler E, Burmeister M, Chesler EJ, Cheverud JM, Clapcote S, Cook MN, Cox RD, Crabbe JC, Crusio WE, Darvasi A, Deschepper CF, Doerge RW, Farber CR, Forejt J, Gaile D, Garlow SJ, Geiger H, Gershenfeld H, Gordon T, Gu J, Gu W, de Haan G, Hayes NL, Heller C, Himmelbauer H, Hitzemann R, Hunter K, Hsu HC, Iraqi FA, Ivandic B, Jacob HJ, Jansen RC, Jepsen KJ, Johnson DK, Johnson TE, Kempermann G, Kendziorski C, Kotb M, Kooy RF, Llamas B, Lammert F, Lassalle JM, Lowenstein PR, Lu L, Lusis A, Manly KF, Marcucio R, Matthews D, Medrano JF, Miller DR, Mittleman G, Mock BA, Mogil JS, Montagutelli X, Morahan G, Morris DG, Mott R, Nadeau JH, Nagase H, Nowakowski RS, O'hara BF, Osadchuk AV, Page GP, Paigen B, Paigen K, Palmer AA, Pan HJ, Peltonen-Palotie L, Peirce J, Pomp D, Pravenec M, Prows DR, Qi Z, Reeves RH, Roder J, Rosen GD, Schadt EE, Schalkwyk LC, Seltzer Z, Shimomura K, Shou S, Sillanpaa MJ, Siracusa LD, Snoeck HW, Spearow JL, Svenson K, Tarantino LM, Threadgill D, Toth LA, Valdar W, de Villena FP, Warden C, Whatley S, Williams RW, Wiltshire T, Yi N, Zhang D, Zhang M, Zou F. (2004) The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nature Genetics 36: 1133-1137.

Kilpikari, R. and M. J. Sillanpää (2003) Bayesian analysis of multilocus association in quantitative and qualitative traits. Genetic Epidemiology 25: 122-135.

Bhattacharjee, M., C. Pritchard, M. J. Sillanpää, and E. Arjas (2003) Bayesian characterization of natural variation in gene expression. In: Methods of Microarray Data Analysis III. Eds. K. F. Johnson and S. M. Lin. Kluwer Academic Publishers, Boston, pp. 155-172.

Hoti, F. J., M. J. Sillanpää, and L. Holmström (2002) A note on estimating the posterior density of a quantitative trait locus from a Markov chain Monte Carlo sample.

Genetic Epidemiology 22: 369-376.

Sillanpää, M. J. and J. Corander (2002) Model choice in gene mapping: what and why. Trends in Genetics 18: 301-307.

Sillanpää, M. J. (2002) Mathematics-assisted mapping in analysis of medical disease. Annals of Medicine 34: 291-298.

Corander, J. and M. J. Sillanpää (2002) A unified approach to joint modeling of multiple quantitative and qualitative traits in gene mapping. Journal of Theoretical Biology 218: 435-446.

Balding DJ, Carothers AD, Marchini JL, Cardon LR, Vetta A, Griffiths B, Weir BS, Hill WG, Goldstein D, Strimmer K, Myers S, Beaumont MA, Glasbey CA, Mayer CD, Richardson S, Marshall C, Durrett R, Nielsen R, Visscher PM, Knott SA, Haley CS, Ball RD, Hackett CA, Holmes S, Husmeier D, Jansen RC, ter Braak CJF, Maliepaard CA, Boer MP, Joyce P, Li N, Stephens M, Marcoulides GA, Drezner Z, Mardia K, McVean G, Meng XL, Ochs MF, Pagel M, Sha N, Vannucci M, Sillanpaa MJ, Sisson S, Yandell BS, Jin CF, Satagopan JM, Gaffney PJ, Zeng ZB, Broman KW, Speed TP, Fearnhead P, Donnelly P, Larget B, Simon DL, Kadane JB, Nicholson G, Smith AV, Jonsson F, Gustafsson O, Stefansson K, Donnelly P, Parmigiani G, Garrett ES, Anbazhagan R, Gabrielson E (2002) Discussion on the meeting on 'Statistical modelling and analysis of genetic data'. Journal of Royal Statistical Society, Series B 64: 735-775.

Martinez, V., M. Sillanpää, G. Thorgaard, B. Robinson, J. Woolliams, and S. Knott (2002) Evidence of a pleiotropic QTL influencing components of early development in double haploid lines of rainbow trout. 7th World Congress on Genetics Applied to Livestock Production. CD-ROM. communication N° 06-08.

Ripatti, S., J. Pitkäniemi, and M. J. Sillanpää (2001) Joint modeling of genetic association and population stratification using latent class models. Genetic Epidemiology 21 Suppl 1: S409-S414.

Sillanpää, M. J., R. Kilpikari, S. Ripatti, P. Onkamo, and P. Uimari (2001) Bayesian association mapping for quantitative traits in a mixture of two populations.

Genetic Epidemiology 21 Suppl 1: S692-S699.

Maliepaard, C., M. J. Sillanpää, J. Van Ooijen, R. C. Jansen, and E. Arjas (2001) Bayesian versus frequentist analysis of multiple quantitative trait loci with an application to an outbred apple cross. Theoretical and Applied Genetics 103: 1243-1253.

Sillanpää, M. J. (2000) Bayesian QTL mapping in inbred and outbred experimental designs. Ph.D. thesis. University of Helsinki. Rolf Nevanlinna Institute Reasearch Reports A30. Yliopistopaino, Helsinki. ISBN 952-9528-56-6. ISSN 0787-8338. Reprint (PDF) Version of Ph.D. Thesis

Sillanpää, M. J. and E. Arjas (1999) Bayesian mapping of multiple quantitative trait loci from incomplete outbred offspring data. Genetics 151: 1605-1619.

Sillanpää, M. J. and E. Arjas (1998) Bayesian mapping of multiple quantitative trait loci from incomplete inbred line cross data. Genetics 148: 1373-1388.

Kuittinen, H., M. J. Sillanpää, and O. Savolainen (1997) Genetic basis of adaptation: flowering time in Arabidopsis thaliana. Theoretical and Applied Genetics 95: 573-583.

Other reports :

Mikko J. Sillanpää (2013) Mihin tilastollista mallia tarvitaan biologiassa? Dimensio 1/2013 : 37-39.

Mikko J. Sillanpää (2012) Perinnöllisyyttä ja tilastotiedettä. Solmu 3/2012.

Haastattelu (Kaleva, syksy 2011): Matematiikan suosio huolettaa

Tietoa vanhasta huippuyksiköstä

Sillanpää M. J. and E. Arjas (2001) Statistical methods for the genetic mapping of complex traits in

CSC Report on Scientific Computing 1999-2000, pp. 138-140.

Sirpa Kotila and Juha Haataja (eds.)

Helsingin Yliopiston lehdistötiedote: Väitös 31. 1. 2000

Sillanpää M. ja P. Uimari (1998) Geenikartoitusmenetelmien kehitystyötä.1 Kansanterveys 4/98 s. 9-10.

Mikko Sillanpää <mjs@rolf.helsinki.fi> Last modified: Fri Feb 14 15:24:03 2003 #