Population Structure and Diversity in European Honey Bees (
Apis mellifera
diversity
pool-sequencing
population structure
whole-genome sequencing
Journal
Genes
ISSN: 2073-4425
Titre abrégé: Genes (Basel)
Pays: Switzerland
ID NLM: 101551097
Informations de publication
Date de publication:
21 01 2022
21 01 2022
Historique:
received:
15
12
2021
revised:
27
12
2021
accepted:
30
12
2021
entrez:
25
2
2022
pubmed:
26
2
2022
medline:
26
4
2022
Statut:
epublish
Résumé
Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees ( We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches. Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers. If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.
Sections du résumé
BACKGROUND
Whole-genome sequencing has become routine for population genetic studies. Sequencing of individuals provides maximal data but is rather expensive and fewer samples can be studied. In contrast, sequencing a pool of samples (pool-seq) can provide sufficient data, while presenting less of an economic challenge. Few studies have compared the two approaches to infer population genetic structure and diversity in real datasets. Here, we apply individual sequencing (ind-seq) and pool-seq to the study of Western honey bees (
METHODS
We collected honey bee workers that belonged to 14 populations, including 13 subspecies, totaling 1347 colonies, who were individually (139 individuals) and pool-sequenced (14 pools). We compared allele frequencies, genetic diversity estimates, and population structure as inferred by the two approaches.
RESULTS
Pool-seq and ind-seq revealed near identical population structure and genetic diversities, albeit at different costs. While pool-seq provides genome-wide polymorphism data at considerably lower costs, ind-seq can provide additional information, including the identification of population substructures, hybridization, or individual outliers.
CONCLUSIONS
If costs are not the limiting factor, we recommend using ind-seq, as population genetic structure can be inferred similarly well, with the advantage gained from individual genetic information. Not least, it also significantly reduces the effort required for the collection of numerous samples and their further processing in the laboratory.
Identifiants
pubmed: 35205227
pii: genes13020182
doi: 10.3390/genes13020182
pmc: PMC8872436
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Références
Anim Genet. 2017 Dec;48(6):704-707
pubmed: 28872253
Mol Ecol. 2014 Feb;23(3):502-12
pubmed: 24304095
Gigascience. 2015 Feb 25;4:7
pubmed: 25722852
Bioinformatics. 2018 Sep 1;34(17):i884-i890
pubmed: 30423086
Genetics. 2010 Sep;186(1):207-18
pubmed: 20457880
Sci Rep. 2018 Jul 24;8(1):11145
pubmed: 30042407
Nat Rev Genet. 2016 Feb;17(2):81-92
pubmed: 26729255
BMC Evol Biol. 2012 Jun 22;12:94
pubmed: 22726891
Mol Ecol Resour. 2015 Sep;15(5):1179-91
pubmed: 25684545
Bioinformatics. 2011 Dec 15;27(24):3435-6
pubmed: 22025480
Bioinformatics. 2009 Jul 15;25(14):1754-60
pubmed: 19451168
Sci Rep. 2016 Jun 03;6:27168
pubmed: 27255426
Proc Natl Acad Sci U S A. 2009 Nov 10;106(45):19096-101
pubmed: 19861545
Nat Commun. 2016 May 31;7:11693
pubmed: 27243207
BMC Genomics. 2020 Oct 8;21(1):704
pubmed: 33032523
Genetics. 2010 Sep;186(1):41-3
pubmed: 20855575
Trends Ecol Evol. 2010 Jan;25(1):44-52
pubmed: 19733933
Ecol Evol. 2019 Sep 26;9(19):11448-11463
pubmed: 31641485
Mol Ecol. 2005 Jul;14(8):2611-20
pubmed: 15969739
BMC Genomics. 2021 Feb 3;22(1):101
pubmed: 33535965
Nat Rev Genet. 2014 Nov;15(11):749-63
pubmed: 25246196
Mol Biol Evol. 2018 Sep 1;35(9):2260-2271
pubmed: 29931308
Bioinformatics. 2014 Aug 1;30(15):2114-20
pubmed: 24695404
Science. 2006 Oct 27;314(5799):642-5
pubmed: 17068261
Mol Biol Evol. 2016 May;33(5):1337-48
pubmed: 26823447
Proc Natl Acad Sci U S A. 2019 Aug 20;116(34):17115-17120
pubmed: 31387977
Bioinformatics. 2012 Oct 1;28(19):2520-2
pubmed: 22908215
Genet Sel Evol. 2018 Mar 22;50(1):9
pubmed: 29566643
BMC Genomics. 2014 Jan 30;15:86
pubmed: 24479613
Genome Biol Evol. 2017 Feb 1;9(2):457-472
pubmed: 28164223
Genetics. 2018 Oct;210(2):719-731
pubmed: 30131346
BMC Genomics. 2018 May 9;19(1):347
pubmed: 29743012
PLoS Genet. 2013 Jun;9(6):e1003534
pubmed: 23754958
Nat Rev Genet. 2011 Jun 17;12(7):499-510
pubmed: 21681211
Mol Ecol. 1992 Oct;1(3):145-54
pubmed: 1364272
Mol Ecol. 2013 Jul;22(14):3766-79
pubmed: 23730833
PLoS One. 2013 Nov 07;8(11):e80422
pubmed: 24244686
Genetics. 2013 Nov;195(3):693-702
pubmed: 24026093
Mol Ecol. 2000 Jul;9(7):907-21
pubmed: 10886654
Genome Biol Evol. 2020 Dec 6;12(12):2535-2551
pubmed: 32877519
Mol Ecol. 2015 Jun;24(12):2973-92
pubmed: 25930679
Nat Methods. 2018 Jul;15(7):475-476
pubmed: 29967506
Ecol Evol. 2019 May 26;9(11):6606-6623
pubmed: 31236247
Mol Ecol. 2012 Sep;21(18):4414-21
pubmed: 22564213
Saudi J Biol Sci. 2020 Dec;27(12):3615-3621
pubmed: 33304172
Genetics. 2014 Mar;196(3):829-40
pubmed: 24381334
PLoS One. 2008;3(10):e3376
pubmed: 18852878
BMC Bioinformatics. 2014 Nov 25;15:356
pubmed: 25420514
BMC Genomics. 2016 Oct 19;17(1):812
pubmed: 27760519
Mol Ecol Resour. 2018 Mar;18(2):194-203
pubmed: 28977733
Nat Methods. 2015 Oct;12(10):966-8
pubmed: 26258291
Evolution. 1984 Nov;38(6):1358-1370
pubmed: 28563791
Infect Genet Evol. 2015 Apr;31:169-76
pubmed: 25660040
Bioinformatics. 2009 Aug 15;25(16):2078-9
pubmed: 19505943
Biochem Genet. 2005 Oct;43(9-10):471-83
pubmed: 16341763
Genetika. 2016 Aug;52(8):931-42
pubmed: 29368906
Nat Genet. 2014 Oct;46(10):1081-8
pubmed: 25151355
Elife. 2019 Oct 24;8:
pubmed: 31647416
Annu Rev Anim Biosci. 2013 Jan;1:261-81
pubmed: 25387020
J Hum Genet. 2021 Jan;66(1):11-23
pubmed: 32948841