| Overview of LASSO-related penalized regression methods for quantitative trait mapping and genomic selection Z Li, MJ Sillanpää Theoretical and applied genetics 125, 419-435, 2012 | 215 | 2012 |
| Population genomic evidence for adaptive differentiation in the Baltic Sea herring B Guo, Z Li, J Merilä Molecular ecology 25 (12), 2833–2852, 2016 | 108 | 2016 |
| Dynamic quantitative trait locus analysis of plant phenomic data Z Li, MJ Sillanpää Trends in plant science 20 (12), 822-833, 2015 | 69 | 2015 |
| Genome‐Wide Association Study (GWAS) identified novel candidate loci affecting wood formation in Norway spruce J Baison, A Vidalis, L Zhou, ZQ Chen, Z Li, MJ Sillanpää, C Bernhardsson, ... The Plant Journal, 2019 | 62 | 2019 |
| Estimation of quantitative trait locus effects with epistasis by variational Bayes algorithms Z Li, MJ Sillanpää Genetics 190 (1), 231-249, 2012 | 42 | 2012 |
| Functional multi-locus QTL mapping of temporal trends in Scots pine wood traits Z Li, HR Hallingbäck, S Abrahamsson, A Fries, BA Gull, MJ Sillanpää, ... G3: Genes| Genomes| Genetics 4 (12), 2365-2379, 2014 | 38 | 2014 |
| Linkage disequilibrium clustering‐based approach for association mapping with tightly linked genomewide data Z Li, P Kemppainen, P Rastas, J Merilä Molecular ecology resources 18 (4), 809-824, 2018 | 37 | 2018 |
| A Bayesian nonparametric approach for mapping dynamic quantitative traits Z Li, MJ Sillanpää Genetics 194 (4), 997-1016, 2013 | 35 | 2013 |
| Genetic population structure constrains local adaptation in sticklebacks K Petri, Z Li, P Rastas, A Löytynoja, F Bohao, J Yang, B Guo, T Shikano, ... Molecular Ecology 30 (9), 1946-1961, 2021 | 32 | 2021 |
| Deciphering the genomic architecture of the stickleback brain with a novel multi‐locus gene‐mapping approach Z Li, B Guo, J Yang, G Herczeg, A Gonda, G Balázs, T Shikano, ... Molecular Ecology, 2017 | 27 | 2017 |
| The roles of climate, geography and natural selection as drivers of genetic and phenotypic differentiation in a widespread amphibian Hyla annectans (Anura: Hylidae) S Wei, Z Li, P Momigliano, C Fu, H Wu, J Merilä Molecular Ecology, 2020 | 25 | 2020 |
| Population transcriptomics reveals weak parallel genetic basis in repeated marine and freshwater divergence in nine‐spined sticklebacks Y Wang, Y Zhao, Y Wang, Z Li, B Guo, J Merilä Molecular ecology 29 (9), 1642-1656, 2020 | 23 | 2020 |
| Effects of marker type and filtering criteria on QST-FST comparisons Z Li, A Löytynoja, A Fraimout, J Merilä Royal Society Open Science 6 (11), 190666, 2019 | 21 | 2019 |
| A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits Z Li, J Möttönen, MJ Sillanpää Heredity 115 (6), 556-564, 2015 | 20 | 2015 |
| Cotton breeding in Australia: Meeting the challenges of the 21 st Century WC Conaty, KJ Broughton, LM Egan, X Li, Z Li, S Liu, DJ Llewellyn, ... Frontiers in Plant Science, 1324, 2022 | 19 | 2022 |
| An efficient genome-wide multilocus epistasis search HP Kärkkäinen, Z Li, MJ Sillanpää Genetics 201 (3), 865-870, 2015 | 17 | 2015 |
| A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotypic data J Vanhatalo, Z Li, MJ Sillanpää Bioinformatics 35 (19), 3684-3692, 2019 | 14 | 2019 |
| Genomic prediction of cotton fibre quality and yield traits using Bayesian regression methods Z Li, S Liu, W Conaty, QH Zhu, P Moncuquet, W Stiller, I Wilson Heredity 129 (2), 103-112, 2022 | 13 | 2022 |
| Aging three‐spined sticklebacks Gasterosteus aculeatus: comparison of estimates from three structures A Yurtseva, K Noreikiene, D Lajus, Z Li, T Alapassi, T Ivanova, M Ivanov, ... Journal of Fish Biology 95 (3), 802-811, 2019 | 11 | 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 A Calleja-Rodriguez, Z Li, HR Hallingbäck, MJ Sillanpää, HX Wu, ... Journal of theoretical biology 462, 283-292, 2019 | 10 | 2019 |
