Biology subjects

Sillanpää, M. J.

Publications and source records attributed to Sillanpää, M. J..

4 recordsLinked to original sources

bioRxiv2024

Principal component analysis revisited: fast multi-trait genetic evaluations with smooth convergence

A cornerstone in breeding and population genetics is the genetic evaluation procedure, needed to make important decisions on population management. Multivariate mixed model analysis, in which many traits is considered jointly, utilizes genetic and environmental correlations between traits to improve the accuracy. However, the number of parameters in the multi-trait model grows exponentially with the number of traits which reduces its scalability. Here, we suggest using principal component analysis (PCA) to reduce the dimensions of the response variables, and then using the computed principal components (PC) as separate responses in the genetic evaluation analysis. As PCs are orthogonal to each other, multivariate analysis is no longer needed and separate univariate analyses can be performed instead. We compared the approach to traditional multivariate analysis in terms of computational requirement and rank lists according to predicted genetic merit on two forest tree datasets with 22 and 27 measured traits respectively. Obtained rank lists of the top 50 individuals were in good agreement. Interestingly, the required computational time of the approach only took a few seconds without convergence issues, unlike the traditional approach which required considerably more time to run (seven and ten hours respectively). Our approach can easily handle missing data and can be used with all available linear mixed models software as it does not require any specific implementation. The approach can help to mitigate difficulties with multi-trait genetic analysis in both breeding and wild populations.

genetics↗

bioRxiv2020

Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

A wide variety of parametric approaches and co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. However, a little is known about the practical correspondence and synergistic potential of these different schemes. We provide a framework for parallel consideration of parametric interaction models with quantitative traits and co-expression networks based on a previously uncharacterized link between them. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a more parametric sense and 2) exploits the underlying parametric model itself in the estimation process. It is tailored for simultaneous identification and classification of molecular interactions and pathways regulating complex traits by accounting for common characteristics of genetic architectures due to which the mainstream methods often lack efficiency. A remarkable advance over the state-of-art methods is illustrated theoretically and through comprehensive simulated scenarios. In particular, prognostically important novel findings in acute myeloid leukemia analysis demonstrate the methods immediate practical relevance. Author summaryHere we built up a mathematically justified bridge between parametric approaches and co-expression networks that have become prevalent for identifying molecular interactions underlying complex traits. We first shared our concern that methodological improvements around these schemes adjusting only their power and scalability are bounded by more fundamental scheme-specific limitations. Subsequently, our theoretical results were exploited to overcome these limitations to find gene-by-gene interactions neither of which can capture alone. We also aimed to illustrate theoretically and empirically how this framework enables the interpretation of co-expression networks in a more parametric sense to achieve systematic insights into complex biological processes more reliably. The main procedure was fit for various types of biological applications and high-dimensional data to cover the area of systems biology as broadly as possible. In particular, we chose to illustrate the methods applicability for gene-profile based risk-stratification in cancer research using public acute myeloid leukemia datasets.

systems biology↗

bioRxiv2018

Functional Quantitative Trait Loci (QTL) analysis for adaptive traits in a three-generation Scots pine pedigree

In forest tree breeding, QTL identification aims to accelerate the breeding cycle and increase the genetic gain of traits with economical and ecological value. In our study, both phenotypic data and predicted breeding values were used in the identification QTL linked to the adaptive value in a three-generation pedigree population, for the first time in a conifer species (Pinus sylvestris L.). A total of 11 470 open pollinated F2-progeny trees established at three different locations, were measured for growth and adaptive traits. Breeding values were predicted for their 360 mothers, originating from a single cross of two parents. A multilevel LASSO association analysis was conducted to detect QTL using genotypes of the mothers with the corresponding phenotypes and estimated breeding values (EBVs). Different levels of genotype-by-environment (GxE) effects among sites and ages were detected for survival and height. Moderate-to-low narrow sense heritabilities and EBVs accuracies were found for all traits and all sites. We identified 18 AFLPs and 12 SNPs to be associated with QTL for one or more traits. 62 QTL were significant with percentages of variance explained ranging from 1.7 to 18.9%, mostly for traits based on phenotypic data. Two SNP-QTL showed pleiotropic effects for traits related with survival, seed and flower production. Furthermore, we detected several QTL with significant effects across multiple ages, which could be considered as strong candidate loci for early selection. The lack of reproducibility of some QTL detected across sites may be due to environmental heterogeneity and QTL-by-environment effects.

genetics↗

bioRxiv2018

Association mapping identified novel candidate loci affecting wood formation in Norway spruce

[tpltrtarr] Norway spruce (Picea abies) is an important boreal forest tree species of significant ecological and economic importance. Hence there is a strong imperative to dissect the genetics controlling important wood quality traits in the species.\n[tpltrtarr]We performed a functional genome-wide association mapping of 17 wood traits in Norway spruce using 178101 single-nucleotide polymorphisms (SNPs) generated from exome genotyping of 517 mother trees. The wood traits were defined using functional modelling of wood properties across annual growth rings.\n[tpltrtarr]Association mapping was performed using a multilocus LASSO penalized regression method and we detected a total of 51 significant SNPs from 39 candidate genes that are involved in wood formation.\n[tpltrtarr]Our study represents the first functional multi-locus genome-wide association mapping (AM) in Norway spruce. The results advance our understanding of the genetics influencing wood traits, identify novel candidate genes for further functional studies and support current Norway spruce breeding efforts.

genetics↗