Up to 30% of people who test positive to SARS-CoV-2 will develop severe COVID-19 and require hospitalisation. Age, gender, and comorbidities are known to be risk factors for severe COVID-19 but are generally considered independently without accurate knowledge of the magnitude of their effect on risk, potentially resulting in incorrect risk estimation. There is an urgent need for accurate prediction of the risk of severe COVID-19 for use in workplaces and healthcare settings, and for individual risk management. Clinical risk factors and a panel of 64 single-nucleotide polymorphisms were identified from published data. We used logistic regression to develop a model for severe COVID-19 in 1,582 UK Biobank participants aged 50 years and over who tested positive for the SARS-CoV-2 virus: 1,018 with severe disease and 564 without severe disease. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC). A model incorporating the SNP score and clinical risk factors (AUC = 0.786; 95% confidence interval = 0.763 to 0.808) had 111% better discrimination of disease severity than a model with just age and gender (AUC = 0.635; 95% confidence interval = 0.607 to 0.662). The effects of age and gender are attenuated by the other risk factors, suggesting that it is those risk factors–not age and gender–that confer risk of severe disease. In the whole UK Biobank, most are at low or only slightly elevated risk, but one-third are at two-fold or more increased risk. We have developed a model that enables accurate prediction of severe COVID-19. Continuing to rely on age and gender alone (or only clinical factors) to determine risk of severe COVID-19 will unnecessarily classify healthy older people as being at high risk and will fail to accurately quantify the increased risk for younger people with comorbidities.

Methotrexate (MTX), used as a graft-versus-host disease (GvHD) prophylactic agent in hematopoietic stem cell transplantation (HSCT), exerts its effect via folate cycle inhibition. A critical enzyme involved in folate metabolism is 5,10-methylenetetrahydrofolate reductase (MTHFR). We examined the association of a single nucleotide polymorphism (SNP) at position 677 in the MTHFR gene on GvHD outcomes in allogeneic HSCT patients administered MTX. MTHFR genotyping was performed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) on 193 HSCT patients and donors. A total of 140 patients were transplanted with an HLA-matched related donor and 53 with an unrelated donor. GvHD outcomes were compared between genotypes by univariate and multivariate analysis. The combined donor 677CT and TT genotypes were associated with a decreased incidence of GvHD (acute and chronic combined) in HSCT recipients with an HLA-matched related donor (75% at 1 year in the CT and TT group compared with 91% in the wild type CC group, P ¼ 0.01), increased time to onset of first GvHD (P ¼ 0.001) and time to first GvHD treated with systemic therapy (P ¼ 0.022). Unrelated donor MTHFR genotype was not associated with outcome parameters and no associations of recipient genotype in either related or unrelated donor cohorts were observed.

Clinical and genetic risk factors for severe coronavirus disease 2019 (COVID-19) are often considered independently and without knowledge of the magnitudes of their effects on risk. Using severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk = 1.77, 95% confidence interval (CI) 1.64–1.90) and had acceptable discrimination (area under the receiver operating characteristic curve = 0.732, 95% CI 0.708–0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α = −0.08; 95% CI −0.21−0.05) and no evidence or over-or under-dispersion of risk (β = 0.90, 95% CI 0.80–1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.

Age, sex, and comorbidities are known risk factors for severe COVID-19 but are frequently considered independently and without accurate knowledge of the magnitude of their effects on risk. Single-nucleotide polymorphisms (SNPs) associated with risk of severe COVID-19 have appeared in the literature, but their application in predictive risk testing has not been validated. Reliance on age and sex alone to determine risk of severe COVID-19 will fail to accurately quantify risk. Here, we report the development and validation of a clinical and genetic model to predict risk of severe COVID-19 using confirmed SARS-CoV-2 positive participants from the UK Biobank. Our new model out-performed an age and sex model and had excellent discrimination and was well calibrated in the validation dataset. We also report validation studies of our prototype model and polygenic risk scores based on 8-SNP and 6-SNP panels identified in the literature. Accurate prediction of individual risk will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.

Whole-genome sequencing of preimplantation human embryos to detect and screen for genetic diseases is a technically challenging extension to preconception screening. combining preconception genetic screening with preimplantation testing of human embryos facilitates the detection of de novo mutations and self-validates transmitted variant detection in both the reproductive couple and the embryo's samples. Here we describe a trio testing workflow that involves whole-genome sequencing of amplified DNA from biopsied embryo trophectoderm cells and genomic DNA from both parents. Variant prediction software and annotation databases were used to assess variants of unknown significance and previously not described de novo variants in five single-gene preimplantation genetic testing couples and eleven of their embryos. Pathogenic variation, tandem repeat, copy number and structural variations were examined against variant calls for compound heterozygosity and predicted disease status was ascertained. Multiple trio testing showed complete concordance with known variants ascertained by single-nucleotide polymorphism array and uncovered de novo and transmitted pathogenic variants. this pilot study describes a method of whole-genome sequencing and analysis for embryo selection in high-risk couples to prevent early life fatal genetic conditions that adversely affect the quality of life of the individual and families. Whole-genome sequencing in the iVf clinic. For over two decades, preimplantation genetic testing (PGT) has been available for couples who are aware they carry a genetic condition or have had a child affected by a genetic disease. In vitro fertilisation (IVF) used in conjunction with monogenic PGT is available for couples to prevent transmission of known hereditary monogenic disorders. PGT for aneuploidy screens embryos for large segmental or whole-chromosome copy number changes and is commonly used for older women (>35 years) who have a history of infertility, miscarriages or chromosomally abnormal conceptions 1-3. The most recent developments in clinical PGT are low-coverage next-generation sequencing and Karyomapping, which uses a highly polymorphic single-nucleotide polymorphism (SNP) microarray to identify disease-causing haplotypes. Nextgeneration sequencing PGT for aneuploidy (typically <0.1× depth) is useful for high-throughput screening at a reasonable cost for detecting chromosomal aneuploidies, structural variations and large copy-number variations (CNVs) 4-6. In addition to pedigree analysis for monogenic disorders, Karyomapping has been reported to identify partial chromosomal aneuploidies as small as 1.8 Mb 7. For couples seeking to ascertain their risk of having an affected child, around 6,000 diseases exist that may be genetically screened for 8. A mutation or disease-causing variant in one or both copies of approximately 5,000 human genes can cause a syndromic disease or phenotype 9-14. Between 0.5-5% of infants are born with a genetic condition or disorder 15,16. The preconception genetic screening...