Biology subjects

Sillanpaa, M. J.

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

3 recordsLinked to original sources

bioRxiv2022

Genetic fine-mapping from summary data using a non-local prior improves detection of multiple causal variants

Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium (LD) patterns. The use of GWAS summary statistics and an adequate LD reference enable large sample sizes for fine-mapping, without direct access to individual-level data. We present FiniMOM (fine-mapping using a product inverse-moment priors), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a non-local inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the LD reference. We test the performance of our method against a current state-of-the-art fine-mapping method SuSiE (sum-of-single-effects) across a range of simulated scenarios aimed to mimic a typical GWAS on circulating protein levels, and an applied example. The results show improved credible set coverage and power of the proposed method, especially in the case of multiple causal variants within a locus. The superior performance and the flexible parameterization to control for misspecified LD reference make FiniMOM a competitive alternative to other fine-mapping methods for summarized genetic data.

bioinformatics↗

bioRxiv2021

Machine Learning Speeding Up the Development of Portfolio of New Crop Varieties to Adapt to and Mitigate Climate Change

Climate change poses serious challenges to achieving food security in a time of a need to produce more food to keep up with the worlds increasing demand for food. There is an urgent need to speed up the development of new high yielding varieties with traits of adaptation and mitigation to climate change. Mathematical approaches, including ML approaches, have been used to search for such traits, leading to unprecedented results as some of the traits, including heat traits that have been long sought-for, have been found within a short period of time.

plant biology↗

bioRxiv2021

Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing among full-sibs

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components, and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogenous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.

evolutionary biology↗