Search Results

BG2: Bayesian variable selection in generalized linear mixed models with nonlocal priors for non-Gaussian GWAS data

• Authors : Shuangshuang Xu; Jacob Williams; Marco A. R. Ferreira

Subjects: Bayesian statistics; GLMM; GWAS

• Source: BMC Bioinformatics, Vol 24, Iss 1, Pp 1-17 (2023)

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SumStatsRehab: an efficient algorithm for GWAS summary statistics assessment and restoration

• Authors : Mykyta Matushyn; Madhuchanda Bose; Abdallah Amr Mahmoud

Subjects: Bioinformatics; GWAS; Summary statistics

• Source: BMC Bioinformatics, Vol 23, Iss 1, Pp 1-12 (2022)

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fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS

• Authors : Anna Hutchinson; James Liley; Chris Wallace

Subjects: GWAS; Functional genomics; Power

• Source: BMC Bioinformatics, Vol 23, Iss 1, Pp 1-15 (2022)

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BICOSS: Bayesian iterative conditional stochastic search for GWAS

• Authors : Jacob Williams; Marco A. R. Ferreira; Tieming Ji

Subjects: Bayesian method; GWAS; Model selection

• Source: BMC Bioinformatics, Vol 23, Iss 1, Pp 1-14 (2022)

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Wavelet Screening: a novel approach to analyzing GWAS data

• Authors : William R. P. Denault; Håkon K. Gjessing; Julius Juodakis

Subjects: SNP; GWAS; Multiple testing

• Source: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-20 (2021)

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A gene based combination test using GWAS summary data.

• Authors : Zhang, Jianjun¹ (AUTHOR); Liang, Xiaoyu² (AUTHOR); Gonzales, Samantha¹ (AUTHOR)

• Source: BMC Bioinformatics. 1/3/2023, Vol. 24 Issue 1, p1-19. 19p.

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TS: a powerful truncated test to detect novel disease associated genes using publicly available gWAS summary data

• Authors : Jianjun Zhang; Xuan Guo; Samantha Gonzales

Subjects: Genome-wide association studies (GWAS); Quadratic test methods; Burden tests

• Source: BMC Bioinformatics, Vol 21, Iss 1, Pp 1-15 (2020)

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Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

• Authors : Afrah Shafquat; Ronald G. Crystal; Jason G. Mezey

Subjects: GWAS; Misclassification; Bayesian

• Source: BMC Bioinformatics, Vol 21, Iss 1, Pp 1-25 (2020)

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A new method for exploring gene–gene and gene–environment interactions in GWAS with tree ensemble methods and SHAP values

• Authors : Pål V. Johnsen; Signe Riemer-Sørensen; Andrew Thomas DeWan

Subjects: GWAS; Tree ensemble models; XGBoost

• Source: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-29 (2021)

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BigTop: a three-dimensional virtual reality tool for GWAS visualization

• Authors : Samuel T. Westreich; Maria Nattestad; Christopher Meyer

Subjects: Virtual reality; User Interface; Visualization

• Source: BMC Bioinformatics, Vol 21, Iss 1, Pp 1-8 (2020)

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