SpaceMix – Understanding Population Structure and Admixture

SpaceMix

:: DESCRIPTION

SpaceMix is a method for inferring and visualizing spatial patterns of population genetic structure and admixture.

::DEVELOPER

the Bradburd Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / Linux / MacOS
  • R package

:: DOWNLOAD

SpaceMix

:: MORE INFORMATION

Citation

Bradburd GS, Ralph PL, Coop GM.
A Spatial Framework for Understanding Population Structure and Admixture.
PLoS Genet. 2016 Jan 15;12(1):e1005703. doi: 10.1371/journal.pgen.1005703. PMID: 26771578; PMCID: PMC4714911.

iMAAPs – Infer multiple-wave Admixture by fitting ALD using a p-spectrum

iMAAPs

:: DESCRIPTION

iMAAPs is a powerful tool to estimate multiple-wave population admixed time, which is currently designed to infer the two-way, multiple-wave admixture based on admixture induced LD. This software can deal with genotype data, haplotype data and the data re-coded according to admixture ancestries.

::DEVELOPER

Population Genomics Group (PGG)

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

iMAAPs

:: MORE INFORMATION

Citation

Zhou Y, Yuan K, Yu Y, Ni X, Xie P, Xing EP, Xu S.
Inference of multiple-wave population admixture by modeling decay of linkage disequilibrium with polynomial functions.
Heredity (Edinb). 2017 May;118(5):503-510. doi: 10.1038/hdy.2017.5. Epub 2017 Feb 15. PMID: 28198814; PMCID: PMC5564381.

Admixture 1.3.0 – Fast Ancestry Estimation

Admixture 1.3.0

:: DESCRIPTION

ADMIXTURE is a software tool for maximum likelihood estimation of individual ancestries from multilocus SNP genotype datasets. It uses the same statistical model as STRUCTURE but calculates estimates much more rapidly using a fast numerical optimization algorithm.

::DEVELOPER

David Alexander

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / MacOsX

:: DOWNLOAD

Admixture

:: MORE INFORMATION

Citation

Fast model-based estimation of ancestry in unrelated individuals.
Alexander DH, Novembre J, Lange K.
Genome Res. 2009 Sep;19(9):1655-64. doi: 10.1101/gr.094052.109.

BMC Bioinformatics. 2011 Jun 18;12:246. doi: 10.1186/1471-2105-12-246.
Enhancements to the ADMIXTURE algorithm for individual ancestry estimation.
Alexander DH, Lange K.

LEADMIX 1.0 – Likelihood Estimation of ADMIXture

LEADMIX 1.0

:: DESCRIPTION

LEADMIX is a Fortran program to estimate the admixture proportions and genetic drift using data on genetic markers

:: DEVELOPER

Dr Jinliang Wang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows / MacOsX / Linux
  • Fortran 90/95 compiler

:: DOWNLOAD

 LEADMIX

:: MORE INFORMATION

Citation

Wang, J. (2003)
Maximum Likelihood Estimation of Admixture Proportions from Genetic Data.
Genetics 164: 747-765.

treemix 1.13 – Inference of Population Trees with Admixture

treemix 1.13

:: DESCRIPTION

TreeMix is a method for inferring the patterns of population splits and mixtures in the history of a set of populations. In the underlying model, the modern-day populations in a species are related to a common ancestor via a graph of ancestral populations. We use the allele frequencies in the modern populations to infer the structure of this graph.

::DEVELOPER

Pritchard Lab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

  treemix

:: MORE INFORMATION

Citation:

Pickrell JK and Pritchard JK.
Inference of population splits and mixtures from genome-wide allele frequency data.
PLoS Genet 8(11): e1002967. doi:10.1371/journal.pgen.1002967

SimRA – Simulates Generic Multiple Population Evolution Model with Admixture

SimRA

:: DESCRIPTION

SimRA (Simulation based on Random-graphs Algorithms) is a framework for simulating generic and complex evolutionary scenarios of multiple populations with subdivision and admixture.

::DEVELOPER

IBM Computational Genomics

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / WIndows
  • JRE

:: DOWNLOAD

 SimRA

:: MORE INFORMATION

Citation

Sampling ARG of multiple populations under complex configurations of subdivision and admixture.
Carrieri AP, Utro F, Parida L.
Bioinformatics. 2015 Dec 7. pii: btv716.

ADMIXMAP 3.8.3103 – Model Admixture using Marker Genotype data

ADMIXMAP 3.8.3103

:: DESCRIPTION

ADMIXMAP is a general-purpose program for modelling admixture, using marker genotypes and trait data on a sample of individuals from an admixed population (such as African-Americans), where the markers have been chosen to have extreme differentials in allele frequencies between two or more of the ancestral populations between which admixture has occurred. The main difference between ADMIXMAP and classical programs for estimation of admixture such as ADMIX is that ADMIXMAP is based on a multilevel model for the distribution of individual admixture in the population and the stochastic variation of ancestry on hybrid chromosomes. This makes it possible to model the associations of ancestry between linked marker loci, and the association of a trait with individual admixture or with ancestry at a linked marker locus.

::DEVELOPER

Paul McKeigue

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

 ADMIXMAP

:: MORE INFORMATION

Citation

Design and analysis of admixture mapping studies.
Hoggart CJ, Shriver MD, Kittles RA, Clayton DG, McKeigue PM.
Am J Hum Genet. 2004 May;74(5):965-78. Epub 2004 Apr 14.

LEA / parLEA – Likelihood-based Estimation of Admixture

LEA / parLEA

:: DESCRIPTION

LEA (Likelihood-based estimation of admixture)is a program to simultaneously estimate admixture and the time since admixture

parLEA is a parallelized version of LEA.

::DEVELOPER

the Population and Conservation Genetics group , parLEA Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/Windows

:: DOWNLOAD

 LEA / parLEA

:: MORE INFORMATION

Citation

Langella, Chikhi, Beaumont (2001),
LEA (Likelihood-based estimation of admixture): a program to simultaneously estimate admixture and the time since admixture“,
Molecular Ecology Notes, 1(4):357-358.

A novel parallel approach to the likelihood-based estimation of admixture in populatin genetics
Ambra Giovannini; Gaetano Zanghirati; Mark A. Beaumont; Lounes Chikhi; Guido Barbujani
Bioinformatics 2009 25: 1440-1441

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