Expanded utility of the R package qgg with applications within genomic medicine

Rohde, Palle Duun; Sørensen, Izel Fourie; Sørensen, Peter

doi:10.1101/2022.09.03.506466

Cited by 3 publications

References 67 publications

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A genome-wide genomic score added to standard recommended stratification tools does not improve the identification of patients with very low bone mineral density

Therkildsen¹,

Rohde²,

Nissen³

et al. 2023

Osteoporos Int

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Summary The role of integrating genomic scores (GSs) needs to be assessed. Adding a GS to recommended stratification tools does not improve the prediction of very low bone mineral density. However, we noticed that the GS performed equally or above individual risk factors in discrimination. Purpose We aimed to investigate whether adding a genomic score (GS) to recommended stratification tools improves the discrimination of participants with very low bone mineral density (BMD). Methods BMD was measured in three thoracic vertebrae using CT. All participants provided information on standard osteoporosis risk factors. GSs and FRAX scores were calculated. Participants were grouped according to mean BMD into very low (<80 mg/cm 3 ), low (80–120 mg/cm 3 ), and normal (>120 mg/cm 3 ) and according to the Bone Health and Osteoporosis Foundation recommendations for BMD testing into an “indication for BMD testing” and “no indication for BMD testing” group. Different models were assessed using the area under the receiver operating characteristics curves (AUC) and reclassification analyses. Results In the total cohort ( n =1421), the AUC for the GS was 0.57 (95% CI 0.52–0.61) corresponding to AUCs for osteoporosis risk factors. In participants without indication for BMD testing, the AUC was 0.60 (95% CI 0.52–0.69) above or equal to AUCs for osteoporosis risk factors. Adding the GS to a clinical risk factor (CRF) model resulted in AUCs not statistically significant from the CRF model. Using probability cutoff values of 6, 12, and 24%, we found no improved reclassification or risk discrimination using the CRF-GS model compared to the CRF model. Conclusion Our results suggest adding a GS to a CRF model does not improve prediction. However, we noticed that the GS performed equally or above individual risk factors in discrimination. Clinical risk factors combined showed superior discrimination to individual risk factors and the GS, underlining the value of combined CRFs in routine clinics as a stratification tool. Supplementary Information The online version contains supplementary material available at 10.1007/s00198-023-06857-w.

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A genome-wide genomic score added to standard recommended stratification tools does not improve the identification of patients with very low bone mineral density

Therkildsen¹,

Rohde²,

Nissen³

et al. 2023

Osteoporos Int

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Emerging applications of machine learning in genomic medicine and healthcare

Chafai,

Bonizzi,

Botti

et al. 2023

Critical Reviews in Clinical Laboratory Sciences

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Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex Diseases

Gholipourshahraki,

Bai,

Shrestha

et al. 2024

Preprint

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Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the model’s performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR model’s ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits, potentially leading to tailored therapeutic interventions.

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Expanded utility of the R package qgg with applications within genomic medicine

Cited by 3 publications

References 67 publications

A genome-wide genomic score added to standard recommended stratification tools does not improve the identification of patients with very low bone mineral density

A genome-wide genomic score added to standard recommended stratification tools does not improve the identification of patients with very low bone mineral density

Emerging applications of machine learning in genomic medicine and healthcare

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex Diseases

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