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Are you ready to push the boundaries of genomics?
¿Preparados para traspasar los límites de la genética?
We at Health in Code re delighted to have the honor of taking part in the European Society in Human Genetics Conference, held from 10th to 13th June in Glasgow, UK. This congress is the perfect opportunity for experts from all around the globe to come together and discuss the latest breakthroughs in genetics.
Discover at #ESHG2023 our innovations in genomic diagnostics and other multiomics areas.
Hypertrophic cardiomyopathy (HCM) is the most common inherited heart disorder, and the underlying genetic etiology remains unknown in more than 60% of cases. At our center in Galicia, we have identified a recurrent variant in TNNT2 associated with HCM: p.Asn271Ile. Our goal was to determine if carriers of this variant share a common haplotype and to estimate the age of the mutation. This pathogenic variant explains more than 1% of HCM cases in the Galician population. A founder effect has been demonstrated, with the variant arising approximately 650 years ago.
NF1 is an AD genetic disorder with complete penetrance. In this study, we report a case of a young woman who was clinically diagnosed and whose germline blood sample underwent further genomic analysis at our center. She seems to be the first patient who presents two pathogenic variants in trans, one of them a mosaicism and the other one in heterozygosity. Her phenotype, however, is not particularly severe.
Dilated cardiomyopathy (DCM) is one of the most common cardiomyopathies, and, despite significant advances in the field, the diagnostic yield in the different studies is still less than 40%. This study evaluates a cohort of individuals with DCM sequenced by NGS, concluding that systematic CNV analysis in NGS studies improves the yield of genetic testing to diagnose DCM.
Loss-of-function variants in the FLNC gene have been associated with a phenotype of arrhythmogenic/dilated cardiomyopathy. Our objective was to identify the exact breakpoints of 3 different CNVs detected in FLNC in patients with this phenotype and to determine the possible underlying mechanism. It revealed the presence of microhomology at nearby sites promoting DNA breaks, with this intron being a possible hot spot.
The purpose of this study is to evaluate intronic rare variant prediction performance with two machine learning algorithms, a neural-network-based (SpliceAI) and a random-forest-based (SPiP) one, in 762 intronic single-nucleotide variants. SpliceAI showed better performance in group 2 (>100 nucleotides away from the natural splice sites) vs group 1 (splice sites), in which SPiP performed better; therefore, prediction accuracy could be optimized by choosing the employed machine learning algorithm based on the position of the intronic variant.
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