Evaluation of the Minimum Sampling Design for Population Genomic and Microsatellite Studies: An Analysis Based on Wild Maize

Massive parallel sequencing (MPS) is revolutionizing the field of molecular ecology by allowing us to understand better the evolutionary history of populations and species, and to detect genomic regions that could be under selection. However, the economic and computational resources needed generate...
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Gespeichert in:

Autor*in:	Jonás A. Aguirre-Liguori [verfasserIn] Javier A. Luna-Sánchez [verfasserIn] Jaime Gasca-Pineda [verfasserIn] Luis E. Eguiarte [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	genomics of populations landscape genomics local adaptation massive parallel sequencing Mexican wild maize sampling design

Übergeordnetes Werk:	In: Frontiers in Genetics - Frontiers Media S.A., 2011, 11(2020)
Übergeordnetes Werk:	volume:11 ; year:2020

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fgene.2020.00870

Katalog-ID:	DOAJ010499164

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authorswithroles_txt_mv	Jonás A. Aguirre-Liguori @@aut@@ Javier A. Luna-Sánchez @@aut@@ Jaime Gasca-Pineda @@aut@@ Luis E. Eguiarte @@aut@@
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callnumber-first	Q - Science
author	Jonás A. Aguirre-Liguori
spellingShingle	Jonás A. Aguirre-Liguori misc QH426-470 misc genomics of populations misc landscape genomics misc local adaptation misc massive parallel sequencing misc Mexican wild maize misc sampling design misc Genetics Evaluation of the Minimum Sampling Design for Population Genomic and Microsatellite Studies: An Analysis Based on Wild Maize
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topic_title	QH426-470 Evaluation of the Minimum Sampling Design for Population Genomic and Microsatellite Studies: An Analysis Based on Wild Maize genomics of populations landscape genomics local adaptation massive parallel sequencing Mexican wild maize sampling design
topic	misc QH426-470 misc genomics of populations misc landscape genomics misc local adaptation misc massive parallel sequencing misc Mexican wild maize misc sampling design misc Genetics
topic_unstemmed	misc QH426-470 misc genomics of populations misc landscape genomics misc local adaptation misc massive parallel sequencing misc Mexican wild maize misc sampling design misc Genetics
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title	Evaluation of the Minimum Sampling Design for Population Genomic and Microsatellite Studies: An Analysis Based on Wild Maize
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title_full	Evaluation of the Minimum Sampling Design for Population Genomic and Microsatellite Studies: An Analysis Based on Wild Maize
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title_sort	evaluation of the minimum sampling design for population genomic and microsatellite studies: an analysis based on wild maize
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title_auth	Evaluation of the Minimum Sampling Design for Population Genomic and Microsatellite Studies: An Analysis Based on Wild Maize
abstract	Massive parallel sequencing (MPS) is revolutionizing the field of molecular ecology by allowing us to understand better the evolutionary history of populations and species, and to detect genomic regions that could be under selection. However, the economic and computational resources needed generate a tradeoff between the amount of loci that can be obtained and the number of populations or individuals that can be sequenced. In this work, we analyzed and compared two simulated genomic datasets fitting a hierarchical structure, two extensive empirical genomic datasets, and a dataset comprising microsatellite information. For all datasets, we generated different subsampling designs by changing the number of loci, individuals, populations, and individuals per population to test for deviations in classic population genetics parameters (HS, FIS, FST). For the empirical datasets we also analyzed the effect of sampling design on landscape genetic tests (isolation by distance and environment, central abundance hypothesis). We also tested the effect of sampling a different number of populations in the detection of outlier SNPs. We found that the microsatellite dataset is very sensitive to the number of individuals sampled when obtaining summary statistics. FIS was particularly sensitive to a low sampling of individuals in the simulated, genomic, and microsatellite datasets. For the empirical and simulated genomic datasets, we found that as long as many populations are sampled, few individuals and loci are needed. For the empirical datasets, we found that increasing the number of populations sampled was important in obtaining precise landscape genetic estimates. Finally, we corroborated that outlier tests are sensitive to the number of populations sampled. We conclude by proposing different sampling designs depending on the objectives.
abstractGer	Massive parallel sequencing (MPS) is revolutionizing the field of molecular ecology by allowing us to understand better the evolutionary history of populations and species, and to detect genomic regions that could be under selection. However, the economic and computational resources needed generate a tradeoff between the amount of loci that can be obtained and the number of populations or individuals that can be sequenced. In this work, we analyzed and compared two simulated genomic datasets fitting a hierarchical structure, two extensive empirical genomic datasets, and a dataset comprising microsatellite information. For all datasets, we generated different subsampling designs by changing the number of loci, individuals, populations, and individuals per population to test for deviations in classic population genetics parameters (HS, FIS, FST). For the empirical datasets we also analyzed the effect of sampling design on landscape genetic tests (isolation by distance and environment, central abundance hypothesis). We also tested the effect of sampling a different number of populations in the detection of outlier SNPs. We found that the microsatellite dataset is very sensitive to the number of individuals sampled when obtaining summary statistics. FIS was particularly sensitive to a low sampling of individuals in the simulated, genomic, and microsatellite datasets. For the empirical and simulated genomic datasets, we found that as long as many populations are sampled, few individuals and loci are needed. For the empirical datasets, we found that increasing the number of populations sampled was important in obtaining precise landscape genetic estimates. Finally, we corroborated that outlier tests are sensitive to the number of populations sampled. We conclude by proposing different sampling designs depending on the objectives.
abstract_unstemmed	Massive parallel sequencing (MPS) is revolutionizing the field of molecular ecology by allowing us to understand better the evolutionary history of populations and species, and to detect genomic regions that could be under selection. However, the economic and computational resources needed generate a tradeoff between the amount of loci that can be obtained and the number of populations or individuals that can be sequenced. In this work, we analyzed and compared two simulated genomic datasets fitting a hierarchical structure, two extensive empirical genomic datasets, and a dataset comprising microsatellite information. For all datasets, we generated different subsampling designs by changing the number of loci, individuals, populations, and individuals per population to test for deviations in classic population genetics parameters (HS, FIS, FST). For the empirical datasets we also analyzed the effect of sampling design on landscape genetic tests (isolation by distance and environment, central abundance hypothesis). We also tested the effect of sampling a different number of populations in the detection of outlier SNPs. We found that the microsatellite dataset is very sensitive to the number of individuals sampled when obtaining summary statistics. FIS was particularly sensitive to a low sampling of individuals in the simulated, genomic, and microsatellite datasets. For the empirical and simulated genomic datasets, we found that as long as many populations are sampled, few individuals and loci are needed. For the empirical datasets, we found that increasing the number of populations sampled was important in obtaining precise landscape genetic estimates. Finally, we corroborated that outlier tests are sensitive to the number of populations sampled. We conclude by proposing different sampling designs depending on the objectives.
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title_short	Evaluation of the Minimum Sampling Design for Population Genomic and Microsatellite Studies: An Analysis Based on Wild Maize
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