A deep learning approach to prediction of blood group antigens from genomic data.

BACKGROUND: Deep learning methods are revolutionizing natural science. In this study, we aim to apply such techniques to develop blood type prediction models based on cheap to analyze and easily scalable screening array genotyping platforms. METHODS: Combining existing blood types from blood banks and imputed screening array genotypes for ~111,000 Danish and 1168 Finnish blood donors, we used deep learning techniques to train and validate blood type prediction models for 36 antigens in 15 blood group systems. To account for missing genotypes a denoising autoencoder initial step was utilized, followed by a convolutional neural network blood type classifier. RESULTS: Two thirds of the trained blood type prediction models demonstrated an F1-accuracy above 99%. Models for antigens with low or high frequencies like, for example, Cw, low training cohorts like, for example, Cob, or very complicated genetic underpinning like, for example, RhD, proved to be more challenging for high accuracy (>99%) DL modeling. However, in the Danish cohort only 4 out of 36 models (Cob, Cw, D-weak, Kpa) failed to achieve a prediction F1-accuracy above 97%. This high predictive performance was replicated in the Finnish cohort. DISCUSSION: High accuracy in a variety of blood groups proves viability of deep learning-based blood type prediction using array chip genotypes, even in blood groups with nontrivial genetic underpinnings. These techniques are suitable for aiding in identifying blood donors with rare blood types by greatly narrowing down the potential pool of candidate donors before clinical grade confirmation.

Dyslipidemia, inflammation, calcification, and adiposity in aortic stenosis: a genome-wide study.

AIMS: Although highly heritable, the genetic etiology of calcific aortic stenosis (AS) remains incompletely understood. The aim of this study was to discover novel genetic contributors to AS and to integrate functional, expression, and cross-phenotype data to identify mechanisms of AS. METHODS AND RESULTS: A genome-wide meta-analysis of 11.6 million variants in 10 cohorts involving 653 867 European ancestry participants (13 765 cases) was performed. Seventeen loci were associated with AS at P ≤ 5 × 10-8, of which 15 replicated in an independent cohort of 90 828 participants (7111 cases), including CELSR2-SORT1, NLRP6, and SMC2. A genetic risk score comprised of the index variants was associated with AS [odds ratio (OR) per standard deviation, 1.31; 95% confidence interval (CI), 1.26-1.35; P = 2.7 × 10-51] and aortic valve calcium (OR per standard deviation, 1.22; 95% CI, 1.08-1.37; P = 1.4 × 10-3), after adjustment for known risk factors. A phenome-wide association study indicated multiple associations with coronary artery disease, apolipoprotein B, and triglycerides. Mendelian randomization supported a causal role for apolipoprotein B-containing lipoprotein particles in AS (OR per g/L of apolipoprotein B, 3.85; 95% CI, 2.90-5.12; P = 2.1 × 10-20) and replicated previous findings of causality for lipoprotein(a) (OR per natural logarithm, 1.20; 95% CI, 1.17-1.23; P = 4.8 × 10-73) and body mass index (OR per kg/m2, 1.07; 95% CI, 1.05-1.9; P = 1.9 × 10-12). Colocalization analyses using the GTEx database identified a role for differential expression of the genes LPA, SORT1, ACTR2, NOTCH4, IL6R, and FADS. CONCLUSION: Dyslipidemia, inflammation, calcification, and adiposity play important roles in the etiology of AS, implicating novel treatments and prevention strategies.

Genome-wide association meta-analysis of knee and hip osteoarthritis uncovers genetic differences between patients treated with joint replacement and patients without joint replacement.

OBJECTIVES: Osteoarthritis is a common and severe, multifactorial disease with a well-established genetic component. However, little is known about how genetics affect disease progression, and thereby the need for joint placement. Therefore, we aimed to investigate whether the genetic associations of knee and hip osteoarthritis differ between patients treated with joint replacement and patients without joint replacement. METHODS: We included knee and hip osteoarthritis cases along with healthy controls, altogether counting >700 000 individuals. The cases were divided into two groups based on joint replacement status (surgical vs non-surgical) and included in four genome-wide association meta-analyses: surgical knee osteoarthritis (N = 22 525), non-surgical knee osteoarthritis (N = 38 626), surgical hip osteoarthritis (N = 20 221) and non-surgical hip osteoarthritis (N = 17 847). In addition, we tested for genetic correlation between the osteoarthritis groups and the pain phenotypes intervertebral disc disorder, dorsalgia, fibromyalgia, migraine and joint pain. RESULTS: We identified 52 sequence variants associated with knee osteoarthritis (surgical: 17, non-surgical: 3) or hip osteoarthritis (surgical: 34, non-surgical: 1). For the surgical phenotypes, we identified 10 novel variants, including genes involved in autophagy (rs2447606 in ATG7) and mechanotransduction (rs202127176 in PIEZO1). One variant, rs13107325 in SLC39A8, associated more strongly with non-surgical knee osteoarthritis than surgical knee osteoarthritis. For all other variants, significance and effect sizes were higher for the surgical phenotypes. In contrast, genetic correlations with pain phenotypes tended to be stronger in the non-surgical groups. CONCLUSIONS: Our results indicate differences in genetic associations between knee and hip osteoarthritis depending on joint replacement status.

A large cohort study of the effects of Lewis, ABO, 13 other blood groups, and secretor status on COVID-19 susceptibility, severity, and long COVID-19.

BACKGROUND: Previous studies have reported Blood type O to confer a lower risk of SARS-CoV-2 infection, while secretor status and other blood groups have been suspected to have a similar effect as well. STUDY DESIGN AND METHODS: To determine whether any other blood groups influence testing positive for SARS-CoV-2, COVID-19 severity, or prolonged COVID-19, we used a large cohort of 650,156 Danish blood donors with varying available data for secretor status and blood groups ABO, Rh, Colton, Duffy, Diego, Dombrock, Kell, Kidd, Knops, Lewis, Lutheran, MNS, P1PK, Vel, and Yt. Of these, 36,068 tested positive for SARS-CoV-2 whereas 614,088 tested negative between 2020-02-17 and 2021-08-04. Associations between infection and blood groups were assessed using logistic regression models with sex and age as covariates. RESULTS: The Lewis blood group antigen Lea displayed strongly reduced SARS-CoV-2 susceptibility OR 0.85 CI[0.79-0.93] p < .001. Compared to blood type O, the blood types B, A, and AB were found more susceptible toward infection with ORs 1.1 CI[1.06-1.14] p < .001, 1.17 CI[1.14-1.2] p < .001, and 1.2 CI[1.14-1.26] p < .001, respectively. No susceptibility associations were found for the other 13 blood groups investigated. There was no association between any blood groups and COVID-19 hospitalization or long COVID-19. No secretor status associations were found. DISCUSSION: This study uncovers a new association to reduced SARS-CoV-2 susceptibility for Lewis type Lea and confirms the previous link to blood group O. The new association to Lea could be explained by a link between mucosal microbiome and SARS-CoV-2.

Genetic prediction of 33 blood group phenotypes using an existing genotype dataset.

BACKGROUND: Accurate blood type data are essential for blood bank management, but due to costs, few of 43 blood group systems are routinely determined in Danish blood banks. However, a more comprehensive dataset of blood types is useful in scenarios such as rare blood type allocation. We aimed to investigate the viability and accuracy of predicting blood types by leveraging an existing dataset of imputed genotypes for two cohorts of approximately 90,000 each (Danish Blood Donor Study and Copenhagen Biobank) and present a more comprehensive overview of blood types for our Danish donor cohort. STUDY DESIGN AND METHODS: Blood types were predicted from genome array data using known variant determinants. Prediction accuracy was confirmed by comparing with preexisting serological blood types. The Vel blood group was used to test the viability of using genetic prediction to narrow down the list of candidate donors with rare blood types. RESULTS: Predicted phenotypes showed a high balanced accuracy >99.5% in most cases: A, B, C/c, Coa /Cob , Doa /Dob , E/e, Jka /Jkb , Kna /Knb , Kpa /Kpb , M/N, S/s, Sda , Se, and Yta /Ytb , while some performed slightly worse: Fya /Fyb , K/k, Lua /Lub , and Vel ~99%-98% and CW and P1 ~96%. Genetic prediction identified 70 potential Vel negatives in our cohort, 64 of whom were confirmed correct using polymerase chain reaction (negative predictive value: 91.5%). DISCUSSION: High genetic prediction accuracy in most blood groups demonstrated the viability of generating blood types using preexisting genotype data at no cost and successfully narrowed the pool of potential individuals with the rare Vel-negative phenotype from 180,000 to 70.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection Fatality Rate Among Elderly Danes: A Cross-sectional Study on Retired Blood Donors.

BACKGROUND: Although the vast majority of individuals succumbing to infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are elderly, infection fatality rate (IFR) estimates for the age group ≥70 years are still scarce. To this end, we assessed SARS-CoV-2 seroprevalence among retired blood donors and combined it with national coronavirus disease 2019 (COVID-19) survey data to provide reliable population-based IFR estimates for this age group. METHODS: We identified 60 926 retired blood donors aged ≥70 years in the rosters of 3 regionwide Danish blood banks and invited them to fill in a questionnaire on COVID-19-related symptoms and behaviors. Among 24 861 (40.8%) responders, we invited a random sample of 3200 individuals for blood testing. Overall, 1201 (37.5%) individuals were tested for SARS-CoV-2 antibodies (Wantai) and compared with 1110 active blood donors aged 17-69 years. Seroprevalence 95% confidence intervals (CIs) were adjusted for assay sensitivity and specificity. RESULTS: Among retired (aged ≥70 years) and active (aged 17-69 years) blood donors, adjusted seroprevalences were 1.4% (95% CI, .3-2.5%) and 2.5% (95% CI, 1.3-3.8%), respectively. Using available population data on COVID-19-related fatalities, IFRs for patients aged ≥70 years and for 17-69 years were estimated at 5.4% (95% CI, 2.7-6.4%) and .083% (95% CI, .054-.18%), respectively. Only 52.4% of SARS-CoV-2-seropositive retired blood donors reported having been sick since the start of the pandemic. CONCLUSIONS: COVID-19 IFR in the age group >69 years is estimated to be 65 times the IFR for people aged 18-69 years.

Postoperative neoadjuvant temozolomide before radiotherapy versus standard radiotherapy in patients 60 years or younger with anaplastic astrocytoma or glioblastoma: a randomized trial.

INTRODUCTION: A pilot study of temozolomide (TMZ) given before radiotherapy (RT) for anaplastic astrocytoma (AA) and glioblastoma (GBM) resulted in prolonged survival compared to historical controls receiving RT alone. We therefore investigated neoadjuvant TMZ (NeoTMZ) in a randomized trial. During enrollment, concomitant and adjuvant radio-chemotherapy with TMZ became standard treatment. The trial was amended to include concurrent TMZ. PATIENTS AND METHODS: Patients, after surgery for GBM or AA, age ≤60 years and performance status (PS) 0-2, were randomized to either 2-3 cycles of TMZ, 200 mg/m2 days 1-5 every 28 days, followed by RT 60 Gy in 30 fractions or RT only. Patients without progressive disease after two TMZ cycles, received the third cycle. From March 2005, TMZ 75 mg/m2 was administered daily concomitant with RT. TMZ was recommended first-line treatment at progression. Primary endpoint was overall survival and secondary safety. RESULTS: The study closed prematurely after enrolling 144 patients, 103 with GBM and 41 with AA. Median age was 53 years (range 24-60) and 89 (62%) were male. PS was 0-1 for 133 (92%) patients, 53 (37%) had complete surgical resection and 18 (12%) biopsy. Ninety-two (64%) received TMZ concomitant with RT. Seventy-two (50%) were randomized to neoadjuvant treatment. For the overall study population survival was 20.3 months for RT and 17.7 months for NeoTMZ (p = .76), this not reaching the primary objective. For the preplanned subgroup analysis, we found that NeoTMZ AA patients had a median survival of 95.1 months compared to 35.2 months for RT (p = .022). For patients with GBM, no difference in survival was observed (p = .10). MGMT and IDH status affected outcome. CONCLUSIONS: No advantage of NeoTMZ was noted for the overall study population or subgroup of GBM, while NeoTMZ resulted in 5 years longer median survival for patients diagnosed as AA.

Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction.

Restless legs syndrome (RLS) affects up to 10% of older adults. Their healthcare is impeded by delayed diagnosis and insufficient treatment. To advance disease prediction and find new entry points for therapy, we performed meta-analyses of genome-wide association studies in 116,647 individuals with RLS (cases) and 1,546,466 controls of European ancestry. The pooled analysis increased the number of risk loci eightfold to 164, including three on chromosome X. Sex-specific meta-analyses revealed largely overlapping genetic predispositions of the sexes (rg = 0.96). Locus annotation prioritized druggable genes such as glutamate receptors 1 and 4, and Mendelian randomization indicated RLS as a causal risk factor for diabetes. Machine learning approaches combining genetic and nongenetic information performed best in risk prediction (area under the curve (AUC) = 0.82-0.91). In summary, we identified targets for drug development and repurposing, prioritized potential causal relationships between RLS and relevant comorbidities and risk factors for follow-up and provided evidence that nonlinear interactions are likely relevant to RLS risk prediction.

Lifestyle and demographic associations with 47 inflammatory and vascular stress biomarkers in 9876 blood donors.

BACKGROUND: The emerging use of biomarkers in research and tailored care introduces a need for information about the association between biomarkers and basic demographics and lifestyle factors revealing expectable concentrations in healthy individuals while considering general demographic differences. METHODS: A selection of 47 biomarkers, including markers of inflammation and vascular stress, were measured in plasma samples from 9876 Danish Blood Donor Study participants. Using regression models, we examined the association between biomarkers and sex, age, Body Mass Index (BMI), and smoking. RESULTS: Here we show that concentrations of inflammation and vascular stress biomarkers generally increase with higher age, BMI, and smoking. Sex-specific effects are observed for multiple biomarkers. CONCLUSION: This study provides comprehensive information on concentrations of 47 plasma biomarkers in healthy individuals. The study emphasizes that knowledge about biomarker concentrations in healthy individuals is critical for improved understanding of disease pathology and for tailored care and decision support tools.

Blood-based biomarkers are circulating molecules that can help to indicate health or disease. Biomarker levels may vary depending on demographic and lifestyle factors such as age, sex, smoking status, and body mass index. Here, we examine the effects of these demographic and lifestyle factors on levels of biomarkers related to activation of the immune system and cardiovascular stress. Measurements of 47 different proteins were performed on blood samples from nearly 10,000 healthy Danish blood donors. Measurement data were linked with questionnaire data to assess effects of lifestyle. We found that immune activation and vascular stress generally increased with age, BMI, and smoking. As these measurements are from healthy blood donors they can serve as a reference for expectable effects and inflammation levels in healthy individuals. Knowledge about the healthy state is important for understanding disease progression and optimizing care.

IL-6 Autoantibodies Predict Lower Platelet Counts and Altered Plasma Cytokine Profiles in Healthy Blood Donors: Results From the Danish Blood Donor Study.

Cytokine-specific autoantibodies (c-aAb) represent a novel type of immune dysfunction. Though they have been detected in both patient cohorts and healthy individuals, and have immunomodulatory properties, the full extent of their influence remains unknown. Based on the critical role of several cytokines in thrombopoiesis, we investigated if there is an association between c-aAb and platelet variables in healthy individuals, with a specific focus on c-aAb against a known thrombopoietic cytokine, IL-6. Using platelet count and mean platelet volume in 3,569 healthy participants of the Danish Blood Donor Study as dependent variables, we performed a series of multivariate regression analyses using five cytokine autoantibodies, including IL-6 c-aAb, as independent variables. In men, high titers of IL-6 c-aAb were negatively associated with platelet counts (ß = -24 *109/l (95% confidence interval -43 to -6), p = 0.008) and positively associated with mean platelet volume (ß = 0.4 fL (95% confidence interval 0.0-0.7) p = 0.043). These associations were exacerbated when adjusting for undetectable C-reactive protein levels, which we used as a proxy for c-aAb mediated IL-6 inhibition in vivo. Furthermore, in a smaller subgroup, individuals with high vs. low titer IL-6 c-aAb had different profiles of plasma IL-6, IL-10, TNFα and TPO, further suggesting a functional inhibition of IL-6 by high titers of circulating IL-6 c-aAb. We therefore speculate that in addition to their immunomodulatory potential IL-6 c-aAb may interfere with thrombopoiesis - directly or indirectly - under normal physiological conditions. This study is the first to suggest an influence of c-aAb on platelets in healthy individuals, beyond their apparent effects on immune competence.