Explorative genetic association study of <i>GSTT2B</i> copy number variant in complex disease risks

Iorio, Andrea; Polimanti, Renato; Calandro, Melania; Graziano, Maria Eleonora; Piacentini, Sara; Bucossi, Serena; Squitti, Rosanna; Lazzarin, Natalia; Scano, Giuseppina; Limbruno, Giancarlo Maria; Manfellotto, Dario; Fuciarelli, Maria

doi:10.3109/03014460.2015.1049206

Cited by 2 publications

References 34 publications

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Genetics of Early Miscarriages

Vaiman

2016

Encyclopedia of Life Sciences

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Early miscarriage or early pregnancy loss (EPL) is defined by an embryonic foetal loss occurring before 13 weeks of gestation. In this article, we focus on the genetics of this complex human disease from studies of human patients, sometimes including foetal losses occurring before 20 weeks. Most of the mouse studies on EPL are based on gene invalidation experiments having much wider and pleiotropic effects than a mere defect of implantation or placental development, and therefore they will not be analyzed here. In addition, many good reviews are already available on gene polymorphisms relevant to early miscarriage and are only briefly summarised here. We develop more in‐depth aspects connected to microRNA and copy number variations with EPL, issues that have been much less reviewed in the past. Key Concepts Early pregnancy loss is a complex multifactorial disease, leading to embryo loss in an estimated >70% of the fertilisation events in humans. Besides chromosome anomalies, detected by cytogenetics or CGH arrays (array‐based comparative genomic hybridisation), gene variants are associated with an increased risk of pregnancy loss. These genes may be regulators of immune response including cytokines, modulators of the coagulation cascade, equilibrators of the oxidative stress or, rarely, genes involved in chromosome segregation at meiosis or mitosis. Other genes may be found by genome‐wide association studies. Recent novel studies are developing on the impact of epigenetic regulators (essentially microRNAs) on miscarriages. These can be relevant markers to categorise gene expression deregulation in pregnancy losses.

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Genetics of Early Miscarriages

Vaiman

2016

Encyclopedia of Life Sciences

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Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

Wang

Song

Ming

et al. 2022

Mol Neurodegeneration

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Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

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Explorative genetic association study of GSTT2B copy number variant in complex disease risks

Abstract: The data highlights that GSTT2B CNV is not associated with the investigated complex diseases in Italian patients. However, further investigations are necessary to replicate these findings in larger sample sizes and to explore other health-related phenotypes.

Cited by 2 publications

References 34 publications

Genetics of Early Miscarriages

Genetics of Early Miscarriages

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

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