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Biology subjects

Harting, J.

Publications and source records attributed to Harting, J..

2 recordsLinked to original sources

Comprehensive SMN1 and SMN2 profiling for spinal muscular atrophy analysis using long-read PacBio HiFi sequencing

Spinal muscular atrophy, a leading cause of early infant death, is caused by biallelic mutations of the SMN1 gene. Sequence analysis of SMN1 is challenging due to high sequence similarity with its paralog SMN2. Both genes have variable copy numbers across populations. Furthermore, without pedigree information, it is impossible to identify silent carriers (2+0) with two copies of SMN1 on one chromosome and zero copies on the other. We developed Paraphase, an informatics method that identifies full-length SMN1 and SMN2 haplotypes, determines the gene copy numbers and calls phased variants using long-read PacBio HiFi data. The SMN1 and SMN2 copy number calls by Paraphase are highly concordant with orthogonal methods (99.2% for SMN1 and 100% for SMN2). We applied Paraphase to 438 samples across five ethnic populations to conduct a population-wide haplotype analysis of these highly homologous genes. We identified major SMN1 and SMN2 haplogroups and characterized their co-segregation through pedigree-based analyses. We identified two SMN1 haplotypes that form a common two-copy SMN1 allele in African populations. Testing positive for these two haplotypes in an individual with two copies of SMN1 gives a silent carrier risk of 88.5%, which is significantly higher than the currently used marker (1.7-3.0%). Extending beyond simple copy number testing, Paraphase can detect pathogenic variants and enable potential haplotype-based screening of silent carriers through statistical phasing of haplotypes into alleles. Future analysis of larger population data will allow identification of more diverse haplotypes and genetic markers for silent carriers.

genetics↗

Aldy 4: An efficient genotyper and star-allele caller for pharmacogenomics

High-throughput sequencing provides sufficient means for determining genotypes of clinically important pharmacogenes that can be used to tailor medical decisions to individual patients. However, pharmacogene genotyping, also known as star-allele calling, is a challenging problem that requires accurate copy number calling, structural variation discovery, variant calling and phasing within each pharmacogene copy present in the sample. Here we introduce Aldy 4, a fast and efficient tool for genotyping pharmacogenes that utilizes combinatorial optimization for accurate star-allele calling across different sequencing technologies. Aldy 4 adds support for long reads and ships with a novel phasing model and improved copy number and variant calling models. We compare Aldy 4 against the current state-of-the-art star-allele callers on a large and diverse set of samples and genes sequenced by various sequencing technologies, such as whole-genome and targeted Illumina sequencing, barcoded 10X Genomics and PacBio HiFi. We show that Aldy 4 is the most accurate star-allele caller with near-perfect accuracy in all evaluated contexts. We hope that Aldy remains an invaluable tool in the clinical toolbox even with the advent of long-read sequencing technologies. AvailabilityAldy 4 is available at https://github.com/0xTCG/aldy.

bioinformatics↗