Assessing the effect of sequencing depth and sample size in population genetics inferences.

Next-Generation Sequencing (NGS) technologies have dramatically revolutionised research in many fields of genetics. The ability to sequence many individuals from one or multiple populations at a genomic scale has greatly enhanced population genetics studies and made it a data-driven discipline. Rece...

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Autor principal:	Matteo Fumagalli
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eb25095a1abd467cbff7c7283ac1fefe
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spelling	oai:doaj.org-article:eb25095a1abd467cbff7c7283ac1fefe2021-11-18T08:45:56ZAssessing the effect of sequencing depth and sample size in population genetics inferences.1932-620310.1371/journal.pone.0079667https://doaj.org/article/eb25095a1abd467cbff7c7283ac1fefe2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24260275/?tool=EBIhttps://doaj.org/toc/1932-6203Next-Generation Sequencing (NGS) technologies have dramatically revolutionised research in many fields of genetics. The ability to sequence many individuals from one or multiple populations at a genomic scale has greatly enhanced population genetics studies and made it a data-driven discipline. Recently, researchers have proposed statistical modelling to address genotyping uncertainty associated with NGS data. However, an ongoing debate is whether it is more beneficial to increase the number of sequenced individuals or the per-sample sequencing depth for estimating genetic variation. Through extensive simulations, I assessed the accuracy of estimating nucleotide diversity, detecting polymorphic sites, and predicting population structure under different experimental scenarios. Results show that the greatest accuracy for estimating population genetics parameters is achieved by employing a large sample size, despite single individuals being sequenced at low depth. Under some circumstances, the minimum sequencing depth for obtaining accurate estimates of allele frequencies and to identify polymorphic sites is [Formula: see text], where both alleles are more likely to have been sequenced. On the other hand, inferences of population structure are more accurate at very large sample sizes, even with extremely low sequencing depth. This all points to the conclusion that under various experimental scenarios, in cost-limited population genetics studies, large sample sizes at low sequencing depth are desirable to achieve high accuracy. These findings will help researchers design their experimental set-ups and guide further investigation on the effect of protocol design for genetic research.Matteo FumagalliPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e79667 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matteo Fumagalli Assessing the effect of sequencing depth and sample size in population genetics inferences.
description	Next-Generation Sequencing (NGS) technologies have dramatically revolutionised research in many fields of genetics. The ability to sequence many individuals from one or multiple populations at a genomic scale has greatly enhanced population genetics studies and made it a data-driven discipline. Recently, researchers have proposed statistical modelling to address genotyping uncertainty associated with NGS data. However, an ongoing debate is whether it is more beneficial to increase the number of sequenced individuals or the per-sample sequencing depth for estimating genetic variation. Through extensive simulations, I assessed the accuracy of estimating nucleotide diversity, detecting polymorphic sites, and predicting population structure under different experimental scenarios. Results show that the greatest accuracy for estimating population genetics parameters is achieved by employing a large sample size, despite single individuals being sequenced at low depth. Under some circumstances, the minimum sequencing depth for obtaining accurate estimates of allele frequencies and to identify polymorphic sites is [Formula: see text], where both alleles are more likely to have been sequenced. On the other hand, inferences of population structure are more accurate at very large sample sizes, even with extremely low sequencing depth. This all points to the conclusion that under various experimental scenarios, in cost-limited population genetics studies, large sample sizes at low sequencing depth are desirable to achieve high accuracy. These findings will help researchers design their experimental set-ups and guide further investigation on the effect of protocol design for genetic research.
format	article
author	Matteo Fumagalli
author_facet	Matteo Fumagalli
author_sort	Matteo Fumagalli
title	Assessing the effect of sequencing depth and sample size in population genetics inferences.
title_short	Assessing the effect of sequencing depth and sample size in population genetics inferences.
title_full	Assessing the effect of sequencing depth and sample size in population genetics inferences.
title_fullStr	Assessing the effect of sequencing depth and sample size in population genetics inferences.
title_full_unstemmed	Assessing the effect of sequencing depth and sample size in population genetics inferences.
title_sort	assessing the effect of sequencing depth and sample size in population genetics inferences.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/eb25095a1abd467cbff7c7283ac1fefe
work_keys_str_mv	AT matteofumagalli assessingtheeffectofsequencingdepthandsamplesizeinpopulationgeneticsinferences
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Assessing the effect of sequencing depth and sample size in population genetics inferences.

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