D
David J. Balding
Researcher at University of Melbourne
Publications - 236
Citations - 31667
David J. Balding is an academic researcher from University of Melbourne. The author has contributed to research in topics: Population & Genome-wide association study. The author has an hindex of 74, co-authored 229 publications receiving 29419 citations. Previous affiliations of David J. Balding include University of California, San Francisco & Imperial College London.
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Journal ArticleDOI
A genome-wide association study identifies novel risk loci for type 2 diabetes
Robert Sladek,Ghislain Rocheleau,Johan Rung,Christian Dina,Lishuang Shen,David Serre,Philippe Boutin,Daniel Vincent,Alexandre Belisle,Samy Hadjadj,Beverley Balkau,Barbara Heude,Guillaume Charpentier,Thomas J. Hudson,Thomas J. Hudson,Alexandre Montpetit,Alexey V. Pshezhetsky,Marc Prentki,Barry I. Posner,David J. Balding,David Meyre,Constantin Polychronakos,Philippe Froguel,Philippe Froguel +23 more
TL;DR: Four loci containing variants that confer type 2 diabetes risk are identified and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.
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Approximate Bayesian computation in population genetics.
TL;DR: A key advantage of the method is that the nuisance parameters are automatically integrated out in the simulation step, so that the large numbers of nuisance parameters that arise in population genetics problems can be handled without difficulty.
Journal Article
Approximate Bayesian computation in population genetics.
TL;DR: In this paper, the authors proposed a new method for approximate Bayesian statistical inference on the basis of summary statistics, which is suited to complex problems that arise in population genetics, extending ideas developed in this setting by earlier authors.
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A tutorial on statistical methods for population association studies
TL;DR: An overview of statistical approaches to population association studies, including preliminary analyses (Hardy–Weinberg equilibrium testing, inference of phase and missing data, and SNP tagging), and single-SNP and multipoint tests for association.
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Epigenome-wide association studies for common human diseases
TL;DR: This work discusses EWAS design, cohort and sample selections, statistical significance and power, confounding factors and follow-up studies, and how integration of EWASs with GWASs can help to dissect complex GWAS haplotypes for functional analysis.