Characterization of the micro-substructure of a rural population from the Pyrenees from a geodesic and technical point of view using NGS data : Quantification of batch effects in whole genome sequence data

Maceda Porto, Iago

Characterization of the micro-substructure of a rural population from the Pyrenees from a geodesic and technical point of view using NGS data : Quantification of batch effects in whole genome sequence data

Document Type

Doctoral thesis
Published Version

Author

Maceda Porto, Iago

Director

Lao Grueso, Oscar

Publication Date

2021-04-28

Pages

217 p.

License

Citation

Maceda Porto, I. Characterization of the micro-substructure of a rural population from the Pyrenees from a geodesic and technical point of view using NGS data : Quantification of batch effects in whole genome sequence data. 2021. handle: http://hdl.handle.net/10803/671600

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Doctoral program

Programa de doctorat en Biomedicina

Abstract

In this work we present a new whole genome sequencing dataset with samples gathered from the Spanish Eastern Pyrenees (SEP) with more than 40x coverage. We apply both classical and new methods to unveil their particular demographic histories and we present the use of a newly in-house developed algorithm to detect genetic barriers taking into account the use of geo-statistics. With these analyses we detect fine population substructure for the first time in this region. We also report the presence of an important batch effect in one of the most important datasets used in genomics: the 1,000 Genomes Project. We find this batch effect when considering very low frequency variants, such as loss of function mutations and the amount of singletons (both ancestral and derived) detected in each sample.
En aquest treball presentem un nou dataset de whole genome sequencing amb mostres recollides del Pirineu Oriental espanyol (SEP) amb un coverage superior a 40x. Apliquem mètodes clàssics i nous per descobrir les seves particulars històries demogràfiques i presentem l’ús d’un algorisme desenvolupat recentment en el nostre laboratori per detectar barreres genètiques tenint en compte l’ús de geoestadística. Amb aquestes anàlisis detectem, per primera vegada, una delicada subestructura de poblacions en aquesta regió. També informem de la presència d’un important batch effect en un dels datasets més importants utilitzats en genòmica: the 1.000 Genomes Project. Trobem aquest batch effect quan considerem variants rares, com per exemple mutacions que comporten pèrdua de funció i la quantitat de singletons (tant ancestrals com derivats) detectats en cada mostra.