scispace - formally typeset
D

Daniel Falush

Researcher at University of Bath

Publications -  92
Citations -  37057

Daniel Falush is an academic researcher from University of Bath. The author has contributed to research in topics: Population & Genome. The author has an hindex of 49, co-authored 87 publications receiving 32481 citations. Previous affiliations of Daniel Falush include Max Planck Society & University of Tokyo.

Papers
More filters
Journal ArticleDOI

Inference of Population Structure Using Multilocus Genotype Data: Linked Loci and Correlated Allele Frequencies

TL;DR: Extensions to the method of Pritchard et al. for inferring population structure from multilocus genotype data are described and methods that allow for linkage between loci are developed, which allows identification of subtle population subdivisions that were not detectable using the existing method.
Journal ArticleDOI

Inference of population structure using multilocus genotype data: dominant markers and null alleles

TL;DR: A simple approach for accounting for genotypic ambiguity in studies of population structure and apply it to AFLP data from whitefish is presented.
Journal ArticleDOI

A Draft Sequence of the Neandertal Genome

TL;DR: The genomic data suggest that Neandertals mixed with modern human ancestors some 120,000 years ago, leaving traces of Ne andertal DNA in contemporary humans, suggesting that gene flow from Neand Bertals into the ancestors of non-Africans occurred before the divergence of Eurasian groups from each other.
Journal ArticleDOI

Roary: Rapid large-scale prokaryote pan genome analysis

TL;DR: Roary, a tool that rapidly builds large-scale pan genomes, identifying the core and accessory genes, is introduced, making construction of the pan genome of thousands of prokaryote samples possible on a standard desktop without compromising on the accuracy of results.
Journal ArticleDOI

Inferring weak population structure with the assistance of sample group information.

TL;DR: It is demonstrated that the new models developed for the structure program allow structure to be detected at lower levels of divergence, or with less data, than the original structure models or principal components methods, and that they are not biased towards detecting structure when it is not present.