DCA for genome-wide epistasis analysis: The statistical genetics perspective

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DCA for genome-wide epistasis analysis : The statistical genetics perspective. / Gao, Chen Yi; Cecconi, Fabio; Vulpiani, Angelo; Zhou, Hai Jun; Aurell, Erik.

julkaisussa: Physical Biology, Vuosikerta 16, Nro 2, 026002, 29.01.2019.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Gao, Chen Yi ; Cecconi, Fabio ; Vulpiani, Angelo ; Zhou, Hai Jun ; Aurell, Erik. / DCA for genome-wide epistasis analysis : The statistical genetics perspective. Julkaisussa: Physical Biology. 2019 ; Vuosikerta 16, Nro 2.

Bibtex - Lataa

@article{d9664bc2404b4cdbb8e9f1590ce2ef95,
title = "DCA for genome-wide epistasis analysis: The statistical genetics perspective",
abstract = "Direct coupling analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the quasi-linkage equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of clonal competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3000 genomes of the human pathogen Streptococcus pneumoniae.",
keywords = "direct coupling analysis, genome-scale, quasi-linkage equilibrium",
author = "Gao, {Chen Yi} and Fabio Cecconi and Angelo Vulpiani and Zhou, {Hai Jun} and Erik Aurell",
year = "2019",
month = "1",
day = "29",
doi = "10.1088/1478-3975/aafbe0",
language = "English",
volume = "16",
journal = "Physical Biology",
issn = "1478-3967",
publisher = "IOP Publishing Ltd.",
number = "2",

}

RIS - Lataa

TY - JOUR

T1 - DCA for genome-wide epistasis analysis

T2 - The statistical genetics perspective

AU - Gao, Chen Yi

AU - Cecconi, Fabio

AU - Vulpiani, Angelo

AU - Zhou, Hai Jun

AU - Aurell, Erik

PY - 2019/1/29

Y1 - 2019/1/29

N2 - Direct coupling analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the quasi-linkage equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of clonal competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3000 genomes of the human pathogen Streptococcus pneumoniae.

AB - Direct coupling analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the quasi-linkage equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of clonal competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3000 genomes of the human pathogen Streptococcus pneumoniae.

KW - direct coupling analysis

KW - genome-scale

KW - quasi-linkage equilibrium

UR - http://www.scopus.com/inward/record.url?scp=85060569494&partnerID=8YFLogxK

U2 - 10.1088/1478-3975/aafbe0

DO - 10.1088/1478-3975/aafbe0

M3 - Article

C2 - 30605896

AN - SCOPUS:85060569494

VL - 16

JO - Physical Biology

JF - Physical Biology

SN - 1478-3967

IS - 2

M1 - 026002

ER -

ID: 39041153