Literature DB >> 9972095

A problem with synthetic maps.

R R Sokal1, N L Oden, B A Thomson.   

Abstract

Synthetic maps of human gene frequencies, which are maps of principal component scores based on correlation of interpolated surfaces, have been popularized widely by L. Cavalli-Sforza, P. Menozzi, and A. Piazza. Such maps are used to make ethnohistorical inferences or to support various demographic or historical hypotheses. We show from first principles and by analyses of real and simulated data that synthetic maps are subject to large errors and that apparent geographic trends may be detected in spatially random data. We conclude that results featured as synthetic maps should be approached with considerable caution.

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Year:  1999        PMID: 9972095

Source DB:  PubMed          Journal:  Hum Biol        ISSN: 0018-7143            Impact factor:   0.553


  6 in total

1.  Geographic patterns of mtDNA diversity in Europe.

Authors:  L Simoni; F Calafell; D Pettener; J Bertranpetit; G Barbujani
Journal:  Am J Hum Genet       Date:  2000-01       Impact factor: 11.025

2.  Two sources of the Russian patrilineal heritage in their Eurasian context.

Authors:  Oleg Balanovsky; Siiri Rootsi; Andrey Pshenichnov; Toomas Kivisild; Michail Churnosov; Irina Evseeva; Elvira Pocheshkhova; Margarita Boldyreva; Nikolay Yankovsky; Elena Balanovska; Richard Villems
Journal:  Am J Hum Genet       Date:  2008-01       Impact factor: 11.025

3.  Interpreting principal component analyses of spatial population genetic variation.

Authors:  John Novembre; Matthew Stephens
Journal:  Nat Genet       Date:  2008-04-20       Impact factor: 38.330

4.  Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated.

Authors:  Eran Elhaik
Journal:  Sci Rep       Date:  2022-08-29       Impact factor: 4.996

5.  Detecting Traces of Prehistoric Human Migrations by Geographic Synthetic Maps of Polyomavirus JC.

Authors:  Angelo Pavesi
Journal:  J Mol Evol       Date:  2004-03       Impact factor: 2.395

6.  Probabilistic models of genetic variation in structured populations applied to global human studies.

Authors:  Wei Hao; Minsun Song; John D Storey
Journal:  Bioinformatics       Date:  2015-11-06       Impact factor: 6.937

  6 in total

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