Nalazite se na CroRIS probnoj okolini. Ovdje evidentirani podaci neće biti pohranjeni u Informacijskom sustavu znanosti RH. Ako je ovo greška, CroRIS produkcijskoj okolini moguće je pristupi putem poveznice www.croris.hr
izvor podataka: crosbi !

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

Pleić, Nikolina ; Gunjača, Ivana ; Babić Leko, Mirjana, Polašek, Ozren ; Zemunik, Tatijana Comparing parametric and non-parametric Bayesian approaches for genetic prediction of complex traits // Book of Abstracts of the ISCCRO – International Statistical Conference in Croatia, Volume 4, No. 1, 2022 / Berislav Žmuk, Anita Čeh Časni (ur.). Zagreb: Hrvatsko statističko društvo, 2022. str. 12-12

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

Poveznice