Single Nucleotide Polymorphism Clustering in Systemic Autoimmune Diseases
Metadatos
Mostrar el registro completo del ítemAutor
Charlon, Thomas; Martínez Bueno, Manuel; Bossini Castillo, Lara María; Carmona López, Francisco David; Di Cara, Alessandro; Wojcik, Jérôme; Voloshynovskiy, Sviatoslav; Martín Ibáñez, Javier; Alarcón Riquelme, Marta EugeniaEditorial
Plos One
Fecha
2016-08-04Referencia bibliográfica
Charlon T, Martínez-Bueno M, Bossini- Castillo L, Carmona FD, Di Cara A, Wojcik J, et al. (2016) Single Nucleotide Polymorphism Clustering in Systemic Autoimmune Diseases. PLoS ONE 11(8): e0160270. doi:10.1371/journal.pone.0160270
Patrocinador
Quartz Bio S.A.; EU/EFPIA/ Innovative Medicines Initiative Joint Undertaking PRECISESADS grant no. 115565Resumen
Systemic Autoimmune Diseases, a group of chronic inflammatory conditions, have variable
symptoms and difficult diagnosis. In order to reclassify them based on genetic markers
rather than clinical criteria, we performed clustering of Single Nucleotide Polymorphisms.
However naive approaches tend to group patients primarily by their geographic origin. To
reduce this “ancestry signal”, we developed SNPClust, a method to select large sources of
ancestry-independent genetic variations from all variations detected by Principal Component
Analysis. Applied to a Systemic Lupus Erythematosus case control dataset, SNPClust
successfully reduced the ancestry signal. Results were compared with association studies
between the cases and controls without or with reference population stratification correction
methods. SNPClust amplified the disease discriminating signal and the ratio of significant
associations outside the HLA locus was greater compared to population stratification correction
methods. SNPClust will enable the use of ancestry-independent genetic information
in the reclassification of Systemic Autoimmune Diseases. SNPClust is available as an R
package and demonstrated on the public Human Genome Diversity Project dataset at
https://github.com/ThomasChln/snpclust.