Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases.

Rare genetic disorders, which can now be studied systematically with affordable genome sequencing, are often caused by high-penetrance rare variants. Such disorders are often heterogeneous and characterized by abnormalities spanning multiple organ systems ascertained with variable clinical precision. Existing methods for identifying genes with variants responsible for rare diseases summarize phenotypes with unstructured binary or quantitative variables. The Human Phenotype Ontology (HPO) allows composite phenotypes to be represented systematically but association methods accounting for the ontological relationship between HPO terms do not exist. We present a Bayesian method to model the association between an HPO-coded patient phenotype and genotype. Our method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease. We thus formalize a clinical approach to phenotyping that is lacking in standard regression techniques for rare disease research. We demonstrate the power of our method by uncovering a number of true associations in a large collection of genome-sequenced and HPO-coded cases with rare diseases.This work was supported by NIHR award RG65966 (D.G. and E.T.) and the Medical Research Council programme grant MC UP 0801/1 (D.G. and S.R.). The NIHR BioResource – Rare Diseases projects were approved by Research Ethics Committees in the UK and appropriate national ethics authorities in non-UK enrolment centres (see Supplemental Note). We are grateful to Dr William J Astle for advice on the statistical model and for providing comments on the manuscript. We are particularly thankful to the BPD project members for granting access to detailed HPO terms of patientsThis is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.ajhg.2016.01.00

A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases

We present a rapid and powerful inference procedure for identifying loci associated with rare hereditary disorders using Bayesian model comparison. Under a baseline model, disease risk is fixed across all individuals in a study. Under an association model, disease risk depends on a latent bipartition of rare variants into pathogenic and non-pathogenic variants, the number of pathogenic alleles that each individual carries, and the mode of inheritance. A parameter indicating presence of an association and the parameters representing the pathogenicity of each variant and the mode of inheritance can be inferred in a Bayesian framework. Variant-specific prior information derived from allele frequency databases, consequence prediction algorithms, or genomic datasets can be integrated into the inference. Association models can be fitted to different subsets of variants in a locus and compared using a model selection procedure. This procedure can improve inference if only a particular class of variants confers disease risk and can suggest particular disease etiologies related to that class. We show that our method, called BeviMed, is more powerful and informative than existing rare variant association methods in the context of dominant and recessive disorders. The high computational efficiency of our algorithm makes it feasible to test for associations in the large non-coding fraction of the genome. We have applied BeviMed to whole-genome sequencing data from 6,586 individuals with diverse rare diseases. We show that it can identify multiple loci involved in rare diseases, while correctly inferring the modes of inheritance, the likely pathogenic variants, and the variant classes responsible.This work was supported by NIHR award RG65966 (D.G. and E.T.) and the Medical Research Council program grant MC_UP_0801/1 (D.G. and S.R.)

Coagulation factor V is a T-cell inhibitor expressed by leukocytes in COVID-19

This work was supported by the NIHR BioResource, the NIHR Cambridge Biomedical Research Centre and the NIHR Cambridge Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care. We thank NIHR BioResource volunteers for their participation, and gratefully acknowledge NIHR BioResource centres, NHS Trusts and staff for their contribution. FV plasma assays were performed by the NIHR Cambridge Biochemical Assay Laboratory. The Cambridge University Hospitals Research Tissue Bank is supported by the NIHR Cambridge Biomedical Research Centre. FV constructs were prepared and expressed by Peak Proteins. Neutrophil proteins were characterized at the Mass Spectrometry Facility at the University of Dundee and the QMRI flow cytometry and cell sorting facility. Sequencing was supported by Paul Coupland from the CRUK Cambridge Institute Genomics Core. The graphical abstract was produced using Biorender (https://biorender.com/). The work was funded by awards from NIHR to the NIHR BioResource and the NIHR Cambridge Biomedical Research Centre, Evelyn Trust, Addenbrooke's Charitable Trust, UKRI/NIHR funding through the UK Coronavirus Immunology Consortium (UK-CIC) and a CSO award (COV/DUN/20/01). KGCS holds a Wellcome Trust Investigator award. BG holds an award from the Aging Biology Foundation Europe to BG. RKG holds a Wellcome Senior Fellowship (WT108082AIA). PK is supported by the Australian and New Zealand Society of Nephrology and the Royal Australasian College of Physicians. SRW holds a Wellcome Trust Senior Clinical Fellowship (209220). ERW holds a Wellcome Clinical Training Fellowship award (108717/Z/15/Z). NM was supported by a DFG Research Fellowship. PFC is a Wellcome Trust Principal Research Fellow (212219/Z/18/Z), and a NIHR Senior Investigator, and receives support from the Medical Research Council (MRC) Mitochondrial Biology Unit, the MRC International Centre for Genomic Medicine in Neuromuscular Disease, the Leverhulme Trust, an MRC research grant, and an Alzheimer's Society Project Grant. JAN holds a Wellcome Trust Senior Research Fellowship (215477/Z/19/Z).Peer reviewedPublisher PD

Factors associated with family planning status and voluntary childlessness in women of childbearing age with inflammatory bowel diseases

© 2023 The Authors. Published by MDPI. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3390/jcm12134267Background: Women with Inflammatory Bowel Diseases (IBD) have fewer children and stay childless more often. The decision-making process around family planning choices remains incompletely understood. Methods: We examined family status in women who at recruitment to the UK IBD Bioresource had not had children yet via an electronic survey. The primary outcome was the proportion of women with voluntary childlessness. Secondary outcomes were factors associated with family planning status. Results: Of 326 responders, 10.7% had either given birth, were currently pregnant or were currently trying to conceive; 12.6% were planning to conceive within 12 months; 54.4% were contemplating conception in the distant future (vague plans); and 22.3% were voluntarily childless. Factors associated with family planning status fell into three areas: general background (age, household income, perceived support to raise a child), relationship status (sexual orientation, being single, not cohabiting, perception of being ‘in the right relationship to raise a child’, perception of a good sex life) and the expression of having a child as a goal in life. On binary logistics regression analysis with voluntary childlessness versus vague family plans as the outcomes of choice, having a household income of <£30,000 (p = 0.046), not seeing a child as a life goal (p < 0.0001) and identifying as lesbian or bisexual (p = 0.047) were independent predictors of voluntary childlessness. Conclusions: Clinicians should consider sexual orientation, income, younger age, current relationship and lack of expression of having a child as a life goal as important factors for family planning when providing care. Pre-pregnancy advice should be made widely available for women with IBD

Predicting the Occurrence of Variants in RAG1 and RAG2

Abstract: While widespread genome sequencing ushers in a new era of preventive medicine, the tools for predictive genomics are still lacking. Time and resource limitations mean that human diseases remain uncharacterized because of an inability to predict clinically relevant genetic variants. A strategy of targeting highly conserved protein regions is used commonly in functional studies. However, this benefit is lost for rare diseases where the attributable genes are mostly conserved. An immunological disorder exemplifying this challenge occurs through damaging mutations in RAG1 and RAG2 which presents at an early age with a distinct phenotype of life-threatening immunodeficiency or autoimmunity. Many tools exist for variant pathogenicity prediction, but these cannot account for the probability of variant occurrence. Here, we present a method that predicts the likelihood of mutation for every amino acid residue in the RAG1 and RAG2 proteins. Population genetics data from approximately 146,000 individuals was used for rare variant analysis. Forty-four known pathogenic variants reported in patients and recombination activity measurements from 110 RAG1/2 mutants were used to validate calculated scores. Probabilities were compared with 98 currently known human cases of disease. A genome sequence dataset of 558 patients who have primary immunodeficiency but that are negative for RAG deficiency were also used as validation controls. We compared the difference between mutation likelihood and pathogenicity prediction. Our method builds a map of most probable mutations allowing pre-emptive functional analysis. This method may be applied to other diseases with hopes of improving preparedness for clinical diagnosis

Vitamin A deficiency due to bi-allelic mutation of RBP4: There's more to it than meets the eye

Vitamin A deficiency is the leading cause of preventable blindness in children worldwide and results in a well-recognized ocular phenotype. Herein we describe a patient presenting to the eye clinic with a retinal dystrophy and ocular colobomata. This combination of clinical signs and consanguineous pedigree structure suggested a genetic basis for the disease, a hypothesis that was tested using whole genome sequencing. Bi-allelic mutations in RBP4 were identified (c.248+1G>A), consistent with a diagnosis of inherited vitamin A deficiency. We describe a constellation of signs that appear to be characteristic for this disease, increasing clinical awareness of this rare condition

PIGO deficiency: palmoplantar keratoderma and novel mutations.

Background Several genetic defects have been identified in the glycosylphosphatidylinositol (GPI) anchor synthesis, including mutations in PIGO encoding phosphatidylinositol glycan anchor biosynthesis class O protein. These defects constitute a subgroup of the congenital disorders of glycosylation (CDG). Seven patients from five families have been reported carrying variants in PIGO that cause an autosomal recessive syndrome characterised by dysmorphism, psychomotor disability, epilepsy and hyperphosphatasemia. Methods Whole exome sequencing was performed in a boy with dysmorphism, psychomotor disability, epilepsy, palmoplantar keratoderma, hyperphosphatasemia and platelet dysfunction without a clinical bleeding phenotype. Results Two novel variants in PIGO were detected. The missense variant encoding p. His871Pro was inherited from the boy’s father while the frameshift variant encoding p. Arg604ProfsTer40 was maternally inherited. Conclusion A boy with two novel PIGO variants is reported. The skin phenotype and platelet dysfunction in this patient have not been described in previously reported patients with PIGO deficiency but it is of course uncertain whether these are caused by this disorder. The literature on PIGO deficiency is reviewed

Autosomal Dominant STAT6 Gain of Function Causes Severe Atopy Associated with Lymphoma

The transcription factor STAT6 (Signal Transducer and Activator of Transcription 6) is a key regulator of Th2 (T-helper 2) mediated allergic inflammation via the IL-4 (interleukin-4) JAK (Janus kinase)/STAT signalling pathway. We identified a novel heterozygous germline mutation STAT6 c.1255G > C, p.D419H leading to overactivity of IL-4 JAK/STAT signalling pathway, in a kindred affected by early-onset atopic dermatitis, food allergy, eosinophilic asthma, anaphylaxis and follicular lymphoma. STAT6 D419H expression and functional activity were compared with wild type STAT6 in transduced HEK293T cells and to healthy control primary skin fibroblasts and peripheral blood mononuclear cells (PBMC). We observed consistently higher STAT6 levels at baseline and higher STAT6 and phosphorylated STAT6 following IL-4 stimulation in D419H cell lines and primary cells compared to wild type controls. The pSTAT6/STAT6 ratios were unchanged between D419H and control cells suggesting that elevated pSTAT6 levels resulted from higher total basal STAT6 expression. The selective JAK1/JAK2 inhibitor ruxolitinib reduced pSTAT6 levels in D419H HEK293T cells and patient PBMC. Nuclear staining demonstrated increased STAT6 in patient fibroblasts at baseline and both STAT6 and pSTAT6 after IL-4 stimulation. We also observed higher transcriptional upregulation of downstream genes (XBP1 and EPAS1) in patient PBMC. Our study confirms STAT6 gain of function (GOF) as a novel monogenetic cause of early onset atopic disease. The clinical association of lymphoma in our kindred, along with previous data linking somatic STAT6 D419H mutations to follicular lymphoma suggest that patients with STAT6 GOF disease may be at higher risk of lymphomagenesis.245 words