Prof David Balding
Honorary (Professorial Fellow)
School of Mathematics and Statistics
246 Scholarly works
6 Projects
HIGHLIGHTS
2026
Journal article
Correction to "Recent Statistical Innovations in Human Genetics".
DOI: 10.1111/ahg.700292026
Journal article
Erratum: Correction: How convincing is a matching Y-chromosome profile? (PLoS genetics (2017) 13 11 DOI: 10.1371/journal.pgen.1007028.)
DOI: 10.1371/journal.pgen.10120992025
Journal article
Recent Statistical Innovations in Human Genetics
DOI: 10.1111/ahg.126062021
Research grants (ARC, NHMRC, MRFF)
Demographic and Evolutionary Inferences From Large, Whole-Genome Datasets
2021
Research Grant
Demographic and Evolutionary Inferences From Large, Whole-Genome Datasets
2020
Journal article
Evaluating and improving heritability models using summary statistics
DOI: 10.1038/s41588-020-0600-y2019
Research Grant
Improved Models to Understand the Genomic Architecture of Complex Traits
RECENT SCHOLARLY WORKS
2025
Journal article
Estimating evolutionary and demographic parameters via ARG-derived IBD
DOI: 10.1371/journal.pgen.10115372024
Journal article
Detecting co-selection through excess linkage disequilibrium in bacterial genomes
DOI: 10.1093/nargab/lqae0612024
Journal article
Four-week inhibition of the renin-angiotensin system in spontaneously hypertensive rats results in persistently lower blood pressure with reduced kidney renin and changes in expression of relevant gene networks
DOI: 10.1093/cvr/cvae0532023
Conference Proceedings
LDAK-GBAT - a powerful and efficient tool for gene-based analysis of GWAS data
2023
Conference Proceedings
Disentangling signatures of selection before and after European colonization in Latin Americans
2023
Journal article
Fast and accurate joint inference of coancestry parameters for populations and/or individuals
DOI: 10.1371/journal.pgen.10100542022
Conference Proceedings
LDAK-GBAT - a Powerful and Efficient Tool for Gene-based Analysis of GWAS Data
RECENT PROJECTS
2019
Research Contracts
Building Polygenic Risk Statistical Framework(s) to Study the Combined Effects of Common and Rare Variants Leveraging Large Sequenced Human Cohorts