Rohde, Palle Duun

Shrestha, Merina

Sørensen, Peter

English

    Aalborg UniversitetEvaluation of Fine Mapping Approaches using Bayesian Linear Regression ModelsShrestha, Merina; Rohde, Palle Duun; Sørensen, PeterPublication date:2022Document VersionPublisher's PDF, also known as Version of recordLink to publication from Aalborg UniversityCitation for published version (APA):Shrestha, M., Rohde, P. D., & Sørensen, P. (2022). Evaluation of Fine Mapping Approaches using BayesianLinear Regression Models. Poster presented at NSHG-PM 2022, København, Denmark.General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright ownersand it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.            - Users may download and print one copy of any publication from the public portal for the purpose of private study or research.            - You may not further distribute the material or use it for any profit-making activity or commercial gain            - You may freely distribute the URL identifying the publication in the public portal -Take down policyIf you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access tothe work immediately and investigate your claim.Downloaded from vbn.aau.dk on: November 19, 2022Evaluation of Fine Mapping Approaches using Bayesian Linear Regression ModelsM. Shrestha1, P. D. Rohde2, P. Sørensen1 1 Center for Quantitative Genetics and Genomics (QGG), Faculty of Technical Sciences, Aarhus University, Aarhus, Denmark2 Department of Health Science and Technology (HST), Aalborg University, Aalborg, DenmarkCorresponding author: mesh@qgg.au.dkBackground and AimsFine mapping identifies the underlying causal genetic variants and is essential for the future of precision medicine. Wepropose a novel statistical method, Bayesian linear regression (BLR) models, and aim to investigate their fine mappingefficiency (power and precision) based on definition of:1.  Prior distribution for marker effects  2. Marker sets 3. Marker set association statisticsAuthors would like to acknowledge Open Discovery Innovation Network (ODIN) for funding this study (grant no.: NNF20SA0061466).ConclusionsBLR fine mapping is influenced by the choice of the markersets definition, the prior distributions, and type of markerset association statistic. Further plan includes comparison ofBLR models to other fine mapping methods.Figure 2. Average F1 score of marker set association statistics, Sum of geneticvariance (TVAR)and Sum of posterior inclusion probability (TPIP) for π 0.1% and GA1.Figure 1. Scatter plot for marginal effect and adjusted effect obtained from BayesC model for causal SNPs (π) 0.1% and genetic architecture scenario (GA1).Materials and MethodsMarker setsTVARTPIP• UK biobank (UKB) genotypes (ID: 96479)• QC: MAF, HWE, MHC region, multiallelic and indel markers• 335,744 White British Unrelated (WBU) and 533,679 SNPs Data• Quantitative phenotype for 268,595 WBU (10 replicates)• h2 30% and Causal SNPs (π): 0.1%, 1% and 5%• Two genetic architecture (GA) scenarios: GA1 (Bayes C) and GA2 (Bayes R)Simulation• Single SNP linear regression à marginal β estimates• marginal β estimates + linkage disequilibrium (LD) matrix à BLR models à adj. β estimates• Estimate association test statistics for marker sets• qgg package in RFine mappingMarker sets- LD (r2 > 0.7 and r2 > 0.5)- Physical map – 1MB, 3MB- Genetic map – 1CM, 3CMPrior distribution of marker effects - Low – Bayes C- Moderate – Bayes R and Bayes A- High – Bayes NMarker set association statistics- Sum of genetic variance (TVAR)- Sum of posterior inclusion probability (TPIP)𝐅𝟏 𝐜𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬𝐜𝐨𝐫𝐞 =𝟐𝒑𝒓𝐩 + 𝒓,Precision: 𝑝 = 𝑇𝑃/ 𝑇𝑃 + 𝐹𝑃Power:𝑟 = 𝑇𝑃/ 𝑇𝑃 + 𝐹𝑁AU AARHUSUNIVERSITYResults• Larger number of true positive (TP) SNPs along with low number offalse positive (FP) SNPs were identified by Bayes C and Bayes Rprior distribution models.• Highest average F1 score for the marker set, linkage disequilibrium(LD) (r2 > 0.7) across all prior models and marker set associationstatistics suggests inclusion of LD information while designing amarker set.• Higher F1 score of the marker set association statistic, sum ofposterior inclusion probability (TPIP) in the stringent significancethreshold (p<1e-05) suggests use of the PIP information comparedto the genetic variance information.

Evaluation of Fine Mapping Approaches using Bayesian Linear Regression Models

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Evaluation of Fine Mapping Approaches using Bayesian Linear Regression Models

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