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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Fast analysis of biobank-size data and meta-analysis using the BGLR R-package.

Paulino Pérez-Rodríguez¹, Gustavo de Los Campos^2,3,4, Hao Wu²

¹Colegio de Postgraduados, Montecillo, Estado de México 56230, México.

G3 (Bethesda, Md.)

|December 10, 2024

Summary

This summary is machine-generated.

New Bayesian methods in the BGLR R-package efficiently analyze large genomic datasets using sufficient statistics. This enables joint analysis of multiple biobanks without sharing individual data, improving polygenic score prediction for under-represented groups.

Keywords:

Bayesian models genomic prediction meta-analysis polygenic scores sufficient statistics summary statistics

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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Published on: December 7, 2021

Area of Science:

Genomics
Bioinformatics
Statistical Genetics

Background:

Analyzing large-scale genomic datasets (n>p) is computationally intensive.
Existing methods struggle with massive sample sizes common in biobanks and genetic evaluations.
Sharing individual genotype-phenotype data across sources is often infeasible due to privacy concerns.

Purpose of the Study:

To develop and implement efficient computational methods for Bayesian genomic analyses using sufficient statistics.
To enable joint analysis of multi-source genomic data without sharing individual-level information.
To improve the prediction accuracy of polygenic scores, particularly for under-represented populations.

Main Methods:

Incorporated functionality into the BGLR R-package to generate posterior samples from sufficient statistics.
Developed Bayesian shrinkage and variable selection models compatible with sufficient statistics.
Utilized UK-Biobank, All of Us, and Hispanic Community Health Study/Study of Latinos cohort data for real-world application.

Main Results:

Demonstrated computational efficiency of sufficient statistics-based methods compared to individual data.
Successfully implemented joint analysis of multiple cohorts without data sharing.
Showcased improved prediction accuracy for polygenic scores in Hispanic populations.

Conclusions:

The BGLR R-package now supports efficient Bayesian genomic analyses using sufficient statistics.
Joint analysis of distributed genomic data is feasible and beneficial, especially for under-represented groups.
This approach enhances the utility of biobank data for developing more equitable genomic prediction models.