Publications by authors named "Mary Reeve"

Sex chromosome trisomies (SCT) are the most common whole chromosome aneuploidy in humans. Yet, our understanding of the prevalence and associated health outcomes is largely driven by observational studies of clinically diagnosed cases, resulting in a disproportionate focus on 47,XXY and associated hypogonadism. We analyzed microarray intensity data of sex chromosomes for 1.

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  • Age-related macular degeneration (AMD) is a common cause of vision loss among older adults, and current treatment options are limited.
  • Researchers identified genetic factors contributing to AMD risk by analyzing data from 12,495 AMD cases and 461,686 controls, discovering four key protective haplotypes.
  • The study suggests that lowering levels of the protein FHR-5 could enhance certain immune pathways and potentially serve as a strategy to prevent or treat AMD.
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The high prevalence of autoimmune hypothyroidism (AIHT) - more than 5% in human populations - provides a unique opportunity to unlock the most complete picture to date of genetic loci that underlie systemic and organ-specific autoimmunity. Using a meta-analysis of 81,718 AIHT cases in FinnGen and the UK Biobank, we dissect associations along axes of thyroid dysfunction and autoimmunity. This largest-to-date scan of hypothyroidism identifies 418 independent associations (p < 5×10), more than half of which have not previously been documented in thyroid disease.

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Lichen planus (LP) is a T-cell-mediated inflammatory disease affecting squamous epithelia in many parts of the body, most often the skin and oral mucosa. Cutaneous LP is usually transient and oral LP (OLP) is most often chronic, so we performed a large-scale genetic and epidemiological study of LP to address whether the oral and non-oral subgroups have shared or distinct underlying pathologies and their overlap with autoimmune disease. Using lifelong records covering diagnoses, procedures, and clinic identity from 473,580 individuals in the FinnGen study, genome-wide association analyses were conducted on carefully constructed subcategories of OLP (n = 3,323) and non-oral LP (n = 4,356) and on the combined group.

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Background: Predictive models show promise in healthcare, but their successful deployment is challenging due to limited generalizability. Current external validation often focuses on model performance with restricted feature use from the original training data, lacking insights into their suitability at external sites. Our study introduces an innovative methodology for evaluating features during both the development phase and the validation, focusing on creating and validating predictive models for post-surgery patient outcomes with improved generalizability.

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The clinical spectrum of thyrotropin receptor-mediated (TSHR-mediated) diseases varies from loss-of-function mutations causing congenital hypothyroidism to constitutively active mutations (CAMs) leading to nonautoimmune hyperthyroidism (NAH). Variation at the TSHR locus has also been associated with altered lipid and bone metabolism and autoimmune thyroid diseases. However, the extrathyroidal roles of TSHR and the mechanisms underlying phenotypic variability among TSHR-mediated diseases remain unclear.

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Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting more than 16 million pregnancies annually worldwide. GDM is related to an increased lifetime risk of type 2 diabetes (T2D), with over a third of women developing T2D within 15 years of their GDM diagnosis. The diseases are hypothesized to share a genetic predisposition, but few studies have sought to uncover the genetic underpinnings of GDM.

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Motivation: Biobank scale genetic associations results over thousands of traits can be difficult to visualize and navigate.

Results: We have created LAVAA, a visualization web-application to generate genetic volcano plots for simultaneously considering the -value, effect size, case counts, trait class and fine-mapping posterior probability at a single-nucleotide polymorphism (SNP) across a range of traits from a large set of genome-wide association study. We find that user interaction with association results in LAVAA can enrich and enhance the biological interpretation of individual loci.

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  • Population isolates like Finland provide a unique advantage for genetic research by having concentrated deleterious alleles in low-frequency variants due to historical bottlenecks.
  • The FinnGen study aims to analyze data from 500,000 Finnish individuals, focusing on their genomes and health records, particularly as many participants are older and have disease-related data.
  • From the analysis of 224,737 participants and additional biobank data, researchers discovered 30 new associations and a total of 2,733 significant genetic links across various diseases, highlighting the importance of low-frequency variants in understanding common diseases.
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  • Central hypothyroidism (CeH) is a rare disorder that can impact normal development, often going undetected in newborn screenings as they focus on more common forms of hypothyroidism.
  • A study identified a novel genetic mutation in two brothers diagnosed with CeH, utilizing targeted next-generation sequencing (NGS) and extensive diagnostic tests.
  • The research highlights a strong genetic association with thyroid disorders in a Finnish population, indicating potential links between the identified mutation and broader thyroid disease risk.
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There is currently limited understanding of the genetic aetiology of obstructive sleep apnoea (OSA). We aimed to identify genetic loci associated with OSA risk, and to test if OSA and its comorbidities share a common genetic background.We conducted the first large-scale genome-wide association study of OSA using the FinnGen study (217 955 individuals) with 16 761 OSA patients identified using nationwide health registries.

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Multivariate methods are known to increase the statistical power to detect associations in the case of shared genetic basis between phenotypes. They have, however, lacked essential analytic tools to follow-up and understand the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits).

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