Comparing parametric and non-parametric Bayesian approaches for genetic prediction of complex traits (CROSBI ID 719968)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija
Podaci o odgovornosti
Pleić, Nikolina ; Gunjača, Ivana ; Babić Leko, Mirjana, Polašek, Ozren ; Zemunik, Tatijana
engleski
Comparing parametric and non-parametric Bayesian approaches for genetic prediction of complex traits
Predicting phenotypes from genotypes can greatly aid the development of precision medicine by using genetic markers to personalize the treatment and predict the outcome. However, predictions require the development of statistical methods that can accurately model the polygenic architecture of the studied trait. This is achieved by constructing a polygenic score (PGS). The simplest PGS is essentially a weighted sum of genotypes across single nucleotide polymorphisms (SNPs), where weights are the estimated genetic effect sizes. We decided to utilize two Bayesian approaches for genomic prediction of thyroid-stimulating hormone (TSH) levels measured in 2, 566 individuals belonging to the Korčula cohort of the Croatian biobank “10, 001 Dalmatians”. Firstly, we used the parametric Bayesian sparse linear-mixed model (BSLMM), which is a hybrid of a linear mixed model and a sparse regression model. Secondly, we used a latent Dirichlet process regression (DPR), a Bayesian non-parametric model. BSLMM assumes a mixture of two normal distributions for the prior of the SNP effect sizes, while DPR does not assume any fixed parametric distribution for the effect size distribution. We performed 10 Monte Carlo cross-validation data splits. In each data split, we fitted models in a training set with 80% of individuals and evaluated method performance using R2 or MSE in a test set with the remaining 20% of individuals. We compared the performance of the two methods by taking the R2 difference or MSE difference with respect to DPR. These results will provide additional insights into the modeling of the polygenic architecture of complex traits.
bayesian genomic prediction ; comparison ; polygenic score ; thyroid-stimulating hormone
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
12-12.
2022.
objavljeno
Podaci o matičnoj publikaciji
Book of Abstracts of the ISCCRO – International Statistical Conference in Croatia, Volume 4, No. 1, 2022
Berislav Žmuk, Anita Čeh Časni
Zagreb: Hrvatsko statističko društvo
1849-9864
2584-3850
Podaci o skupu
4th International Statistical Conference in Croatia (ISCCRO 2022)
predavanje
05.05.2022-06.05.2022
Opatija, Hrvatska
Povezanost rada
Biologija, Biotehnologija u biomedicini (prirodno područje, biomedicina i zdravstvo, biotehničko područje), Interdisciplinarne biotehničke znanosti, Matematika