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Carioscia, S. A.

Publications and source records attributed to Carioscia, S. A..

3 recordsLinked to original sources

A complete diploid human genome benchmark for personalized genomics

Human genome resequencing typically involves mapping reads to a reference genome to call variants; however, this approach suffers from both technical and reference biases, leaving many duplicated and structurally polymorphic regions of the genome unmapped. Consequently, existing variant benchmarks, generated by the same methods, fail to assess these complex regions. To address this limitation, we present a telomere-to-telomere genome benchmark that achieves near-perfect accuracy (i.e. no detectable errors) across 99.4% of the complete, diploid HG002 genome. This benchmark adds 701.4 Mb of autosomal sequence and both sex chromosomes (216.8 Mb), totaling 15.3% of the genome that was absent from prior benchmarks. We also provide a diploid annotation of genes, transposable elements, segmental duplications, and satellite repeats, including 39,144 protein-coding genes across both haplotypes. To facilitate application of the benchmark, we developed tools for measuring the accuracy of sequencing reads, phased variant call sets, and genome assemblies against a diploid reference. Genome-wide analyses show that state-of-the-art de novo assembly methods resolve 2-7% more sequence and outperform variant calling accuracy by an order of magnitude, yielding just one error per 100 kb across 99.9% of the benchmark regions. Adoption of genome-based benchmarking is expected to accelerate the development of cost-effective methods for complete genome sequencing, expanding the reach of genomic medicine to the entire genome and enabling a new era of personalized genomics.

genomics↗

Approximate Bayesian computation supports a high incidence of chromosomal mosaicism in blastocyst-stage human embryos

Chromosome mis-segregation is common in human meiosis and mitosis, and the resulting aneuploidies are the leading cause of pregnancy loss. Preimplantation genetic testing for aneuploidy (PGT-A) prioritizes chromosomally normal embryos for transfer based on analysis of a biopsy of approximately five trophectoderm cells from blastocyst-stage in vitro fertilized (IVF) embryos. While modern PGT-A platforms classify these biopsies as aneuploid, euploid, or mosaic (a mixture of normal and aneuploid cells), the underlying incidences of aneuploid, euploid, and mosaic embryos and the rates of meiotic and mitotic error that produced them remain largely unknown. To address this knowledge gap, we paired a method for embryo simulation with approximate Bayesian computation (ABC) to infer rates of meiotic and mitotic error that explain published PGT-A data. Using simulation, we also evaluated the chromosomal status of entire embryos. For a published clinical sample, we estimated a 40-58% probability of meiotic error per meiosis and a 1.5-6.3% probability of mitotic error per mitosis, depending on assumptions about spatial organization. In addition, our analyses suggest that less than 1% of blastocysts are fully euploid and that many embryos possess low-level mosaic clones that are not captured during biopsy. These conclusions were relatively insensitive to misclassification of mosaic biopsies. Together, our findings imply that low-level mosaicism is a normal feature of embryogenesis and are consistent with clinical data demonstrating the developmental potential of mosaic-testing embryos. More broadly, our work helps overcome the limitations of embryo biopsies to estimate fundamental rates of chromosome mis-segregation in human development. SummaryChromosome errors are the main cause of human pregnancy loss. Researchers used computer simulations and statistics to estimate the rates of these errors based on published clinical data from in vitro fertilized embryos. To explain data, the researchers estimated that 40-58% of cell divisions that produced the egg or sperm were affected by an error, along with 1.5-6.3% of cell divisions after fertilization. Their results also showed that fewer than 1% of embryos are completely normal in all cells and that many embryos contain small groups of abnormal cells that testing often misses. These findings improve understanding of early development.

genetics↗

Strict adherence to Mendel's First Law across a large sample of human sperm genomes

Mendels Law of Segregation states that the offspring of a diploid, heterozygous parent will inherit either allele with equal probability. While the vast majority of loci adhere to this rule, research in model and non-model organisms has uncovered numerous exceptions whereby "selfish" alleles are disproportionately transmitted to the next generation. Evidence of such "transmission distortion" (TD) in humans remains equivocal in part because scans of human pedigrees have been under-powered to detect small effects. Recently published single-cell sequencing data from individual human sperm (n = 41,189; 969-3,377 cells from each of 25 donors) offer an opportunity to revisit this question with unprecedented statistical power, but require new methods tailored to extremely low-coverage data ([~]0.01 x per cell). To this end, we developed a method, named rhapsodi, that leverages sparse gamete genotype data to phase the diploid genomes of the donor individuals, impute missing gamete genotypes, and discover meiotic recombination breakpoints, benchmarking its performance across a wide range of study designs. After applying rhapsodi to the sperm sequencing data, we then scanned the gametes for evidence of TD. Our results exhibited close concordance with binomial expectations under balanced transmission, in contrast to tenuous signals of TD that were previously reported in pedigree-based studies. Together, our work excludes the existence of even weak TD in this sample, while offering a powerful quantitative framework for testing this and related hypotheses in other cohorts and study systems.

genomics↗