Browsing by Author "Bernier, François P."

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    Exposure to Arsenic and Mercury: Associated Pregnancy Outcomes, and Early Infant Developmental Outcomes in Gold Mining Areas in Tanzania
    (2020-04-29) Nyanza, Elias Charles; Dewey, Deborah; Manyama, Mange F.; Bernier, François P.; Hatfield, Jennifer M.; Martin, Jonathan W.
    The problem: Prenatal exposure to mercury and arsenic through artisanal and small-scale gold mining (ASGM) is an under-explored yet salient issue in Tanzania. ASGM operations are known to expose the entire community—including vulnerable pregnant women and children—to arsenic and mercury via the water they drink, the food they eat, the soil in which their food is grown, and the air they breathe. Prenatal exposure to arsenic and mercury is associated with adverse reproductive outcome including spontaneous abortion, stillbirth, low birth weight, and congenital anomalies, and with poorer developmental outcomes in the children. This study used a longitudinal prospective approach to examine the associations between level of exposure prenatally to arsenic and mercury, and reproductive outcomes and early developmental outcomes in ASGM communities in Tanzania. Methods: A total of 1056 (883 in ASGM and 173 in non-ASGM) out of 1078 pregnant women who were recruited during their antenatal care clinics visits consented to participate in this research. We used minimally invasive techniques to collect urine and blood samples for total arsenic (T-As) and total mercury (T-Hg), respectively. For T-As an unprovoked morning urine sample was collected, whereas for T-Hg, a drop of whole blood was collected on filter paper (Whatman #903) following a simple finger prick. All samples were analyzed using inductively coupled plasma mass spectrometry. Measures of association between maternal T-As or T-Hg exposure, and birth outcomes and early infants’ neurodevelopmental outcome were examined by calculating the coefficient of regression/correlation between variables with their respective 95% confidence interval. Conclusion: Findings from this study revealed that pregnant women living in ASGM communities have elevated arsenic and mercury levels compared to those in non-mining communities. Women in gold mining areas of northern Tanzania had higher incidence of adverse birth outcomes associated with arsenic and mercury exposure. Maternal exposure to mercury but not arsenic was associated with an increased prevalence of severe developmental impairment among infants in gold mining areas of northern Tanzania. These findings suggest that prenatal exposure to arsenic and mercury were associated with adverse reproductive and early developmental outcomes in ASGM communities in Tanzania.
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    Predicting the Evolutionary and Medical Significance of Human Genetic Variations with Machine Learning
    (2019-04-30) Saha Mandal, Arnab; De Koning, A. P. Jason; Bernier, François P.; Wasmuth, James D.; Rodrigue, Nicolas
    The advent of inexpensive and high-throughput genome sequencing technologies has facilitated the acquisition of patient exome and genome sequences at a vast scale. One of the primary challenges of such data is its functional interpretation, and specifically, the ability to distinguish functionally important, deleterious, and pathogenic variants from neutral or benign variants (“variant impact prediction” or VIP). Over the last two decades, many approaches have been proposed for VIP, which utilize data from patterns of evolutionary conservation, population genomics, protein structures and other sources to inform machine learning classification algorithms. However, existing approaches are fraught with limitations, especially when they are trained on databases of putatively pathogenic variants that may have been identified with reference to existing prediction methods (a type of ‘circularity’). This dissertation identifies shortcomings of existing variant impact prediction methods and discusses how they can be better understood (Chapter 1). Approaches to overcome these shortcomings are presented (Chapter 2), and a new method, TAIGA (Transformation and Integration of Genomic Annotations), is developed. The utility of this method and its accompanying refinements are evaluated (Chapter 3) and later scrutinized (Chapter 4). As part of this work, I have produced TAIGA scores for all protein coding positions of the human genome, and I show these have substantially superior performance in distinguishing known pathogenic variations from neutral variations in a number of high-quality datasets. Variant prediction scores from TAIGA are later integrated with clinical information from human phenotypes (Chapter 5) and this extension demonstrated the highest sensitivity and smallest candidate gene search space over a large set of rare genetic disorders. It is my hope that TAIGA will aide clinicians and researchers alike in the new era of personalized genomic medicine in which we find ourselves.