@article {816,
	title = {Characterization of the Common Genetic Variation in the Spanish Population of Navarre.},
	journal = {Genes (Basel)},
	volume = {15},
	year = {2024},
	month = {2024 May 04},
	abstract = {<p>Large-scale genomic studies have significantly increased our knowledge of genetic variability across populations. Regional genetic profiling is essential for distinguishing common benign variants from disease-causing ones. To this end, we conducted a comprehensive characterization of exonic variants in the population of Navarre (Spain), utilizing whole genome sequencing data from 358 unrelated individuals of Spanish origin. Our analysis revealed 61,410 biallelic single nucleotide variants (SNV) within the Navarrese cohort, with 35\% classified as common (MAF > 1\%). By comparing allele frequency data from 1000 Genome Project (excluding the Iberian cohort of Spain, IBS), Genome Aggregation Database, and a Spanish cohort (including IBS individuals and data from Medical Genome Project), we identified 1069 SNVs common in Navarre but rare (MAF <= 1\%) in all other populations. We further corroborated this observation with a second regional cohort of 239 unrelated exomes, which confirmed 676 of the 1069 SNVs as common in Navarre. In conclusion, this study highlights the importance of population-specific characterization of genetic variation to improve allele frequency filtering in sequencing data analysis to identify disease-causing variants.</p>},
	keywords = {Cohort Studies, Exome, Female, Gene Frequency, Genetic Variation, Genetics, Population, Genome, Human, Humans, Male, Polymorphism, Single Nucleotide, Spain, Whole Genome Sequencing},
	issn = {2073-4425},
	doi = {10.3390/genes15050585},
	author = {Maillo, Alberto and Huergo, Estefania and Apell{\'a}niz-Ruiz, Mar{\'\i}a and Urrutia-Lafuente, Edurne and Miranda, Mar{\'\i}a and Salgado, Josefa and Pasalodos-Sanchez, Sara and Delgado-Mora, Luna and Teijido, {\'O}scar and Goicoechea, Ibai and Carmona, Rosario and Perez-Florido, Javier and Aquino, Virginia and L{\'o}pez-L{\'o}pez, Daniel and Pe{\~n}a-Chilet, Maria and Beltran, Sergi and Dopazo, Joaquin and Lasa, I{\~n}igo and Beloqui, Juan Jos{\'e} and Alonso, {\'A}ngel and Gomez-Cabrero, David}
}
@article {705,
	title = {SMN1 copy-number and sequence variant analysis from next-generation sequencing data.},
	journal = {Hum Mutat},
	volume = {41},
	year = {2020},
	month = {2020 12},
	pages = {2073-2077},
	abstract = {<p>Spinal muscular atrophy (SMA) is a severe neuromuscular autosomal recessive disorder affecting 1/10,000 live births. Most SMA patients present homozygous deletion of SMN1, while the vast majority of SMA carriers present only a single SMN1 copy. The sequence similarity between SMN1 and SMN2, and the complexity of the SMN locus makes the estimation of the SMN1 copy-number by next-generation sequencing (NGS) very difficult. Here, we present SMAca, the first python tool to detect SMA carriers and estimate the absolute SMN1 copy-number using NGS data. Moreover, SMAca takes advantage of the knowledge of certain variants specific to SMN1 duplication to also identify silent carriers. This tool has been validated with a cohort of 326 samples from the Navarra 1000 Genomes Project (NAGEN1000). SMAca was developed with a focus on execution speed and easy installation. This combination makes it especially suitable to be integrated into production NGS pipelines. Source code and documentation are available at https://www.github.com/babelomics/SMAca.</p>},
	keywords = {Base Sequence, DNA Copy Number Variations, High-Throughput Nucleotide Sequencing, Humans, Reproducibility of Results, Software, Survival of Motor Neuron 1 Protein},
	issn = {1098-1004},
	doi = {10.1002/humu.24120},
	author = {L{\'o}pez-L{\'o}pez, Daniel and Loucera, Carlos and Carmona, Rosario and Aquino, Virginia and Salgado, Josefa and Pasalodos, Sara and Miranda, Mar{\'\i}a and Alonso, {\'A}ngel and Dopazo, Joaquin}
}
