Shall genomic correlation structure be considered in copy number variants detection?

Identification of a family with van der Hoeve's syndrome harboring a novel COL1A1 mutation and generation of patient-derived iPSC lines and CRISPR/Cas9-corrected isogenic iPSCs

Van der Hoeve's syndrome, also known as osteogenesis imperfecta (OI), is a genetic connective tissue disorder characterized by fragile, fracture-prone bone and hearing loss. The disease is caused by a gene mutation in one of the two type I collagen genes COL1A1 or COL1A2. In this study, we identified a novel frameshift mutation of the COL1A1 gene (c.1607delG) in a family with OI using whole-exome sequencing, bioinformatics analysis and Sanger sequencing. This mutation may lead to the deletion of a portion of exon 23 and the generation of a premature stop codon in the COL1A1 gene. To further investigate the impact of this mutation, we established two induced pluripotent stem cell (iPSC) lines from peripheral blood mononuclear cells of OI patients carrying a novel mutation in the COL1A1 gene. Osteoblasts (OB) derived from OI-iPSCs exhibited reduced production of type I collagen and diminished ability to differentiate into osteoblasts. Using a CRISPR-based homology-directed repair strategy, we corrected the OI disease-causing COL1A1 novel mutations in iPSCs generated from an affected individual. Our results demonstrated that the diminished expression of type I collagen and osteogenic potential were enhanced in OB induced from corrected OI-iPSCs compared to those from OI-iPSCs. Overall, our results provide new insights into the genetic basis of Van der Hoeve's syndrome and highlight the potential of iPSC technology for disease modeling and therapeutic development.

OSCAA: A two-dimensional Gaussian mixture model for copy number variation association analysis

Copy number variants (CNVs) are prevalent in the human genome and are found to have a profound effect on genomic organization and human diseases. Discovering disease-associated CNVs is critical for understanding the pathogenesis of diseases and aiding their diagnosis and treatment. However, traditional methods for assessing the association between CNVs and disease risks adopt a two-stage strategy conducting quantitative CNV measurements first and then testing for association, which may lead to biased association estimation and low statistical power, serving as a major barrier in routine genome-wide assessment of such variation. In this article, we developed One-Stage CNV-disease Association Analysis (OSCAA), a flexible algorithm to discover disease-associated CNVs for both quantitative and qualitative traits. OSCAA employs a two-dimensional Gaussian mixture model that is built upon the PCs from copy number intensities, accounting for technical biases in CNV detection while simultaneously testing for their effect on outcome traits. In OSCAA, CNVs are identified and their associations with disease risk are evaluated simultaneously in a single step, taking into account the uncertainty of CNV identification in the statistical model. Our simulations demonstrated that OSCAA outperformed the existing one-stage method and traditional two-stage methods by yielding a more accurate estimate of the CNV-disease association, especially for short CNVs or CNVs with weak signals. In conclusion, OSCAA is a powerful and flexible approach for CNV association testing with high sensitivity and specificity, which can be easily applied to different traits and clinical risk predictions.