Human genetics
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SARS-CoV-2 is responsible for the coronavirus disease 2019 (COVID-19) and the current health crisis. Despite intensive research efforts, the genes and pathways that contribute to COVID-19 remain poorly understood. We, therefore, used an integrative genomics (IG) approach to identify candidate genes responsible for COVID-19 and its severity. ⋯ Plasma ABO protein, which is associated with blood type in humans, demonstrated a significant causal relationship with COVID-19 in the MR analysis; increased plasma levels were associated with an increased risk of COVID-19 and, in particular, severe COVID-19. In summary, our study identified genes associated with COVID-19 that may be prioritized for future investigations. Importantly, this is the first study to demonstrate a causal association between plasma ABO protein and COVID-19.
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Globozoospermia is a rare phenotype of primary male infertility inducing the production of round-headed spermatozoa without acrosome. Anomalies of DPY19L2 account for 50-70% of all cases and the entire deletion of the gene is by far the most frequent defect identified. Here, we present a large cohort of 69 patients with 20-100% of globozoospermia. ⋯ Only one homozygous novel truncating variant was identified in the GGN gene in one patient, confirming the association of GGN with globozoospermia. In view of these results, we propose a novel diagnostic strategy focusing on patients with at least 50% of globozoospermia and based on a classical qualitative PCR to detect DPY19L2 homozygous deletions. In the absence of the latter, we recommend to perform whole-exome sequencing to search for defects in DPY19L2 as well as in the other previously described candidate genes.
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Stillbirth after 20 weeks gestation happens in 1 in 200 pregnancies and occurs more commonly than neonatal loss and sudden infant death syndrome (SIDs) combined. The stillbirth rate is several times greater in low as opposed to high-resource countries. However, among high-resource countries, although a lower overall stillbirth rate exists, there has been little change for several decades. ⋯ The channelopathy disorders are included as initial examples of genetic conditions with variable presentation including an association with sudden infant death syndrome. Highlighted are the challenges when numerous genes and variants are involved, and the task of assigning pathogenicity. The advantages and limitations of genetic evaluations are presented and avenues for further research considered.
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Haploinsufficiency of FOXF1 causes alveolar capillary dysplasia with misalignment of pulmonary veins (ACDMPV), a lethal neonatal lung developmental disorder. We describe two similar heterozygous CNV deletions involving the FOXF1 enhancer and re-analyze FOXF1 missense mutation, all associated with an unexpectedly mitigated disease phenotype. In one case, the deletion of the maternal allele of the FOXF1 enhancer caused pulmonary hypertension and histopathologically diagnosed MPV without the typical ACD features. ⋯ Sequencing of these alleles revealed two rare SNVs, rs150502618-A and rs79301423-T, mapping to the partially overlapping binding sites for TFAP2s and CTCF in the core region of the enhancer. Moreover, in a family with three histopathologically-diagnosed ACDMPV siblings whose missense FOXF1 mutation was inherited from the healthy non-mosaic carrier mother, we have identified a rare SNV rs28571077-A within 2-kb of the above-mentioned non-coding SNVs in the FOXF1 enhancer in the mother, that was absent in the affected newborns and 13 unrelated ACDMPV patients with CNV deletions of this genomic region. Based on the low population frequencies of these three variants, their absence in ACDMPV patients, the results of reporter assay, RNAi and EMSA experiments, and in silico predictions, we propose that the described SNVs might have acted on FOXF1 enhancer as hypermorphs.
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In the field of cancer genomics, the broad availability of genetic information offered by next-generation sequencing technologies and rapid growth in biomedical publication has led to the advent of the big-data era. Integration of artificial intelligence (AI) approaches such as machine learning, deep learning, and natural language processing (NLP) to tackle the challenges of scalability and high dimensionality of data and to transform big data into clinically actionable knowledge is expanding and becoming the foundation of precision medicine. In this paper, we review the current status and future directions of AI application in cancer genomics within the context of workflows to integrate genomic analysis for precision cancer care. ⋯ Publicly available tools or algorithms for key NLP technologies in the literature mining for evidence-based clinical recommendations are reviewed and compared. In addition, the present paper highlights the challenges to AI adoption in digital healthcare with regard to data requirements, algorithmic transparency, reproducibility, and real-world assessment, and discusses the importance of preparing patients and physicians for modern digitized healthcare. We believe that AI will remain the main driver to healthcare transformation toward precision medicine, yet the unprecedented challenges posed should be addressed to ensure safety and beneficial impact to healthcare.