In the period 2019 to 2020, allergies affected approximately ** percent of women and ** percent of men in the United Kingdom. Furthermore, just under a fifth of both genders suffered from high blood pressure, while back disorder or defects affected **** percent and **** percent of women and men respectively.

We asked UK consumers about "Prevalence of health conditions" and found that *************************************************************** takes the top spot, while ********************************************************************** is at the other end of the ranking.These results are based on a representative online survey conducted in 2025 among 6,176 consumers in the UK.

In the United Kingdom (UK), deaths caused by cardiovascular disease as a share of total deaths have decreased from almost ** percent in 2001 to ** percent in 2020. The most common CVDs are heart attack, stroke, heart failure, arrhythmia and heart valve complications. Symptoms of CVDs include chest pain, breathlessness, fatigue, swollen limbs and irregular heartbeat.

This data shows premature deaths (Age under 75) from Respiratory Disease, numbers and rates by gender, as 3-year range.

Smoking is the major cause of chronic obstructive pulmonary disease (COPD), one of the major Respiratory diseases. COPD (which includes chronic bronchitis and emphysema) results in many hospital admissions. Respiratory diseases can also be caused by environmental factors (such as pollution, or housing conditions) and influenza. Respiratory disease mortality rates show a socio-economic gradient.

Directly Age-Standardised Rates (DASR) are shown in the data, where numbers are sufficient, so that death rates can be directly compared between areas. The DASR calculation applies Age-specific rates to a Standard (European) population to cancel out possible effects on crude rates due to different age structures among populations, thus enabling direct comparisons of rates.

A limitation on using mortalities as a proxy for prevalence of health conditions is that mortalities may give an incomplete view of health conditions in an area, as ill-health might not lead to premature death.

Data source: Office for Health Improvement and Disparities (OHID) Public Health Outcomes Framework (PHOF) indicator 4.07i. This data is updated annually.

A dataset providing GP recorded coronary heart disease. Coronary heart disease (CHD) is the leading cause of death both in the UK and worldwide. It's responsible for more than 73,000 deaths in the UK each year. About 1 in 6 men and 1 in 10 women die from CHD. In the UK, there are an estimated 2.3 million people living with CHD and around 2 million people affected by angina (the most common symptom of coronary heart disease). CHD generally affects more men than women, although from the age of 50 the chances of developing the condition are similar for both sexes. As well as angina (chest pain), the main symptoms of CHD are heart attacks and heart failure. However, not everyone has the same symptoms and some people may not have any before CHD is diagnosed. CHD is sometimes called ischaemic heart disease.

Disease prevalence and achievement/exception performance against key indicators for GP Practices as part of the Quality and Outcomes Framework (QOF).

Data is presented in CSV files for the 2013/14 year onwards. For previous years, data is available in summary tables from the links provided.

The Quality and Outcomes Framework (QOF) is the annual reward and incentive programme detailing GP practice achievement results.

QOF is a voluntary process for all surgeries in England and was introduced as part of the GP contract in 2004. It is reported in two formats; this publication, and the on-line search function (www.qof.hscic.gov.uk)

QOF awards surgeries achievement points for:

• managing some of the most common chronic diseases, e.g. asthma, diabetes
• implementing preventative measures, e.g. regular blood pressure checks
• the extra services offered such as child health care and maternity services
• the quality and productivity of the service, including the avoidance of emergency admissions to hospital
• compliance with the minimum time a GP should spend with each patient at each appointment

Achievement information is based on practice level achievement againsts primary care indicators. Prevalence figures are based on numbers of patients on GP clinical registers for specific conditions. Exceptions data presents information on numbers of patients with specific clinical conditions who are not included in QOF indicator data used to measure achievement. There are a number of criteria to determine exception reported patients.

All data are presented at GP practice, CCG, Area Team, Region and England

Prevalence registers included: Atrial Fibrillation (AF) Asthma (AST) Cancer (CAN) Coronary Heart Disease (CHD) Chronic Kidney Disease (CKD) Chronic Obstructive Pulmonary Disease (COPD) Cardiovascular Disease - Primary Prevention (CVDPP) Dementia (DEM) Depression (DEP) Diabetes (DM) Epilepsy (EP) Heart Failure (HF) Heart Failure due to LVD (HF) Hypertension (HYP) Learning Disabilities (LD) Mental Health (MH) Obesity (OB) Osteoporosis (OST) Peripheral Atrial Disease (PAD) Palliative Care (PC) Rheumatoid Arthritis (RA) Smoking Indicators (SMOK) Stroke (STIA) Thyroid (THY)

BackgroundMultimorbidity in people with cardiovascular disease (CVD) is common, but large-scale contemporary reports of patterns and trends in patients with incident CVD are limited. We investigated the burden of comorbidities in patients with incident CVD, how it changed between 2000 and 2014, and how it varied by age, sex, and socioeconomic status (SES).Methods and findingsWe used the UK Clinical Practice Research Datalink with linkage to Hospital Episode Statistics, a population-based dataset from 674 UK general practices covering approximately 7% of the current UK population. We estimated crude and age/sex-standardised (to the 2013 European Standard Population) prevalence and 95% confidence intervals for 56 major comorbidities in individuals with incident non-fatal CVD. We further assessed temporal trends and patterns by age, sex, and SES groups, between 2000 and 2014. Among a total of 4,198,039 people aged 16 to 113 years, 229,205 incident cases of non-fatal CVD, defined as first diagnosis of ischaemic heart disease, stroke, or transient ischaemic attack, were identified. Although the age/sex-standardised incidence of CVD decreased by 34% between 2000 to 2014, the proportion of CVD patients with higher numbers of comorbidities increased. The prevalence of having 5 or more comorbidities increased 4-fold, rising from 6.3% (95% CI 5.6%–17.0%) in 2000 to 24.3% (22.1%–34.8%) in 2014 in age/sex-standardised models. The most common comorbidities in age/sex-standardised models were hypertension (28.9% [95% CI 27.7%–31.4%]), depression (23.0% [21.3%–26.0%]), arthritis (20.9% [19.5%–23.5%]), asthma (17.7% [15.8%–20.8%]), and anxiety (15.0% [13.7%–17.6%]). Cardiometabolic conditions and arthritis were highly prevalent among patients aged over 40 years, and mental illnesses were highly prevalent in patients aged 30–59 years. The age-standardised prevalence of having 5 or more comorbidities was 19.1% (95% CI 17.2%–22.7%) in women and 12.5% (12.0%–13.9%) in men, and women had twice the age-standardised prevalence of depression (31.1% [28.3%–35.5%] versus 15.0% [14.3%–16.5%]) and anxiety (19.6% [17.6%–23.3%] versus 10.4% [9.8%–11.8%]). The prevalence of depression was 46% higher in the most deprived fifth of SES compared with the least deprived fifth (age/sex-standardised prevalence of 38.4% [31.2%–62.0%] versus 26.3% [23.1%–34.5%], respectively). This is a descriptive study of routine electronic health records in the UK, which might underestimate the true prevalence of diseases.ConclusionsThe burden of multimorbidity and comorbidity in patients with incident non-fatal CVD increased between 2000 and 2014. On average, older patients, women, and socioeconomically deprived groups had higher numbers of comorbidities, but the type of comorbidities varied by age and sex. Cardiometabolic conditions contributed substantially to the burden, but 4 out of the 10 top comorbidities were non-cardiometabolic. The current single-disease paradigm in CVD management needs to broaden and incorporate the large and increasing burden of comorbidities.

In 2018 there were over 40 thousand deaths caused by ischaemic heart diseases in the United Kingdom, making it the leading cause of death in that year. Since 2001 there has been a noticeable increase in the number of people dying from dementia or alzheimers, which caused 26.5 thousand deaths in 2018, an increase of almost ten thousand when compared with 2012.

Supplementary information files for article: 'Association between physical activity and sub-types of cardiovascular disease death causes in a general population cohort'.Abstract:Physical activity is thought to be cardioprotective, but associations with different subtypes of cardiovascular disease (CVD) are poorly understood. We examined associations between physical activity and seven major CVD death causes. The sample comprised 65,093 adults (aged 58 ± 12 years, 45.4% men) followed up over mean [SD] 9.4 ± 4.5 years, recruited from The Health Survey for England and the Scottish Health Surveys. A CVD diagnosis was reported in 9.2% of the sample at baseline. Physical activity was self-reported. Outcomes were subtypes of CVD death; acute myocardial infarction; chronic ischaemic heart disease; pulmonary heart disease; a composite of cardiac arrest, arrhythmias, and sudden cardiac death; heart failure; cerebrovascular; composite of aortic aneurysm and other peripheral vascular diseases. There were 3050 CVD deaths (30.8% of all deaths). In Cox proportional hazards models adjusted for confounders, physical activity was associated with reduced relative risk of all CVD outcomes; compared with the lowest, the highest physical activity quintile was associated with reduced risk of acute myocardial infarction (Hazard ratio 0.66: 95% CI 0.50, 0.89), chronic ischaemic heart disease (0.49: 0.38, 0.64), pulmonary heart disease (0.48: 0.22, 1.07), arrhythmias (0.18: 0.04, 0.76); heart failure (0.35: 0.20, 0.63), cerebrovascular events (0.53: 0.38, 0.75); aneurysm and peripheral vascular diseases (0.54: 0.34, 0.93). Results were largely consistent across participants with and without existing CVD at baseline. Physical activity was associated with reduced risk of seven major CVD death causes. Protective benefits were apparent even at levels of activity below the current recommendations.

IntroductionThe demographic shift leads to a tremendous increase in age-related diseases, which are often chronic. Therefore, a focus of chronic disease management should be set on the maintenance or even improvement of the patients’ quality of life (QoL). One indicator to objectively measure QoL is the EQ-5D questionnaire, which was validated in a disease- and world region-specific manner. The aim of this study was to conduct a systematic literature review and meta-analysis on the QoL across the most frequent chronic diseases that utilized the EQ-5D and performed a disease-specific meta-analysis for treatment-dependent QoL improvement.Materials and methodsThe most common chronic disease in Germany were identified by their ICD-10 codes, followed by a systematic literature review of these ICD-10 codes and the EQ-5D index values. Finally, out of 10,016 independently -screened studies by two persons, 538 studies were included in the systematic review and 216 studies in the meta-analysis, respectively.ResultsWe found significant medium to large effect sizes of treatment effects, i.e., effect size >0.5, in musculoskeletal conditions with the exception of fractures, for chronic depression and for stroke. The effect size did not differ significantly from zero for breast and lung cancer and were significantly negative for fractures.ConclusionOur analysis showed a large variation between baseline and post-treatment scores on the EQ-5D health index, depending on the health condition. We found large gains in health-related quality of life mainly for interventions for musculoskeletal disease.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020150936, PROSPERO identifier CRD42020150936.

The percentage of patients with coronary heart disease (CHD), as recorded on practice disease register.

Rationale Coronary heart disease (CHD) is the single most common cause of premature death in the UK. The research evidence relating to the management of CHD is well established and if implemented can reduce the risk of death from CHD and improve the quality of life for patients. This indicator set focuses on the management of patients with established CHD consistent with clinical priorities in the four nations.

Definition of numerator Number of patients on the coronary heart disease (CHD) register.

Definition of denominator Total practice list size.

This dataset provides the directly age-standardised mortality rate from respiratory diseases in individuals under the age of 75. Respiratory diseases, including conditions such as chronic obstructive pulmonary disease (COPD), asthma, and pneumonia, are a major cause of premature death. This indicator supports the monitoring of respiratory health and the effectiveness of interventions aimed at reducing early mortality in the Birmingham and Solihull area.

Rationale Reducing premature mortality from respiratory diseases is a key objective in improving population health and reducing health inequalities. This indicator helps to track progress in respiratory disease prevention, early diagnosis, and management, and supports strategic planning and resource allocation.

Numerator The numerator is the number of deaths from respiratory diseases (ICD-10 codes J00–J99) registered in the respective calendar years, for individuals aged under 75.

Denominator The denominator is the population of individuals under 75 years of age, also aggregated into quinary age bands. For single-year rates, the population is based on the 2021 Census. For three-year rolling averages, the denominator is the aggregated population-years over the three years.

Caveats Data may not align with published Office for National Statistics (ONS) figures due to differences in postcode lookup versions and the application of comparability ratios used in Office for Health Improvement and Disparities (OHID) data.

External references Fingertips Public Health Profiles – Respiratory Disease Indicator

Localities ExplainedThis dataset contains data based on either the resident locality or registered locality of the patient, a distinction is made between resident locality and registered locality populations:Resident Locality refers to individuals who live within the defined geographic boundaries of the locality. These boundaries are aligned with official administrative areas such as wards and Lower Layer Super Output Areas (LSOAs).Registered Locality refers to individuals who are registered with GP practices that are assigned to a locality based on the Primary Care Network (PCN) they belong to. These assignments are approximate—PCNs are mapped to a locality based on the location of most of their GP surgeries. As a result, locality-registered patients may live outside the locality, sometimes even in different towns or cities.This distinction is important because some health indicators are only available at GP practice level, without information on where patients actually reside. In such cases, data is attributed to the locality based on GP registration, not residential address.

Click here to explore more from the Birmingham and Solihull Integrated Care Partnerships Outcome Framework.

Legacy unique identifier: P00841

Legacy unique identifier: P00838

In 2014, 29% of Black women had experienced a common mental disorder in the week before being surveyed, a higher rate than for White women.

SUMMARYThis analysis, designed and executed by Ribble Rivers Trust, identifies areas across England with the greatest levels of physical illnesses that are linked with obesity and inactivity. Please read the below information to gain a full understanding of what the data shows and how it should be interpreted.ANALYSIS METHODOLOGYThe analysis was carried out using Quality and Outcomes Framework (QOF) data, derived from NHS Digital, relating to:- Asthma (in persons of all ages)- Cancer (in persons of all ages)- Chronic kidney disease (in adults aged 18+)- Coronary heart disease (in persons of all ages)- Diabetes mellitus (in persons aged 17+)- Hypertension (in persons of all ages)- Stroke and transient ischaemic attack (in persons of all ages)This information was recorded at the GP practice level. However, GP catchment areas are not mutually exclusive: they overlap, with some areas covered by 30+ GP practices. Therefore, to increase the clarity and usability of the data, the GP-level statistics were converted into statistics based on Middle Layer Super Output Area (MSOA) census boundaries.For each of the above illnesses, the percentage of each MSOA’s population with that illness was estimated. This was achieved by calculating a weighted average based on:- The percentage of the MSOA area that was covered by each GP practice’s catchment area- Of the GPs that covered part of that MSOA: the percentage of patients registered with each GP that have that illnessThe estimated percentage of each MSOA’s population with each illness was then combined with Office for National Statistics Mid-Year Population Estimates (2019) data for MSOAs, to estimate the number of people in each MSOA with each illness, within the relevant age range.For each illness, each MSOA was assigned a relative score between 1 and 0 (1 = worst, 0 = best) based on:A) the PERCENTAGE of the population within that MSOA who are estimated to have that illnessB) the NUMBER of people within that MSOA who are estimated to have that illnessAn average of scores A & B was taken, and converted to a relative score between 1 and 0 (1= worst, 0 = best). The closer to 1 the score, the greater both the number and percentage of the population in the MSOA predicted to have that illness, compared to other MSOAs. In other words, those are areas where a large number of people are predicted to suffer from an illness, and where those people make up a large percentage of the population, indicating there is a real issue with that illness within the population and the investment of resources to address that issue could have the greatest benefits.The scores for each of the 7 illnesses were added together then converted to a relative score between 1 – 0 (1 = worst, 0 = best), to give an overall score for each MSOA: a score close to 1 would indicate that an area has high predicted levels of all obesity/inactivity-related illnesses, and these are areas where the local population could benefit the most from interventions to address those illnesses. A score close to 0 would indicate very low predicted levels of obesity/inactivity-related illnesses and therefore interventions might not be required.LIMITATIONS1. GPs do not have catchments that are mutually exclusive from each other: they overlap, with some geographic areas being covered by 30+ practices. This dataset should be viewed in combination with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset to identify where there are areas that are covered by multiple GP practices but at least one of those GP practices did not provide data. Results of the analysis in these areas should be interpreted with caution, particularly if the levels of obesity/inactivity-related illnesses appear to be significantly lower than the immediate surrounding areas.2. GP data for the financial year 1st April 2018 – 31st March 2019 was used in preference to data for the financial year 1st April 2019 – 31st March 2020, as the onset of the COVID19 pandemic during the latter year could have affected the reporting of medical statistics by GPs. However, for 53 GPs (out of 7670) that did not submit data in 2018/19, data from 2019/20 was used instead. Note also that some GPs (997 out of 7670) did not submit data in either year. This dataset should be viewed in conjunction with the ‘Health and wellbeing statistics (GP-level, England): Missing data and potential outliers’ dataset, to determine areas where data from 2019/20 was used, where one or more GPs did not submit data in either year, or where there were large discrepancies between the 2018/19 and 2019/20 data (differences in statistics that were > mean +/- 1 St.Dev.), which suggests erroneous data in one of those years (it was not feasible for this study to investigate this further), and thus where data should be interpreted with caution. Note also that there are some rural areas (with little or no population) that do not officially fall into any GP catchment area (although this will not affect the results of this analysis if there are no people living in those areas).3. Although all of the obesity/inactivity-related illnesses listed can be caused or exacerbated by inactivity and obesity, it was not possible to distinguish from the data the cause of the illnesses in patients: obesity and inactivity are highly unlikely to be the cause of all cases of each illness. By combining the data with data relating to levels of obesity and inactivity in adults and children (see the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset), we can identify where obesity/inactivity could be a contributing factor, and where interventions to reduce obesity and increase activity could be most beneficial for the health of the local population.4. It was not feasible to incorporate ultra-fine-scale geographic distribution of populations that are registered with each GP practice or who live within each MSOA. Populations might be concentrated in certain areas of a GP practice’s catchment area or MSOA and relatively sparse in other areas. Therefore, the dataset should be used to identify general areas where there are high levels of obesity/inactivity-related illnesses, rather than interpreting the boundaries between areas as ‘hard’ boundaries that mark definite divisions between areas with differing levels of these illnesses. TO BE VIEWED IN COMBINATION WITH:This dataset should be viewed alongside the following datasets, which highlight areas of missing data and potential outliers in the data:- Health and wellbeing statistics (GP-level, England): Missing data and potential outliersDOWNLOADING THIS DATATo access this data on your desktop GIS, download the ‘Levels of obesity, inactivity and associated illnesses: Summary (England)’ dataset.DATA SOURCESThis dataset was produced using:Quality and Outcomes Framework data: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.GP Catchment Outlines. Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital. Data was cleaned by Ribble Rivers Trust before use.COPYRIGHT NOTICEThe reproduction of this data must be accompanied by the following statement:© Ribble Rivers Trust 2021. Analysis carried out using data that is: Copyright © 2020, Health and Social Care Information Centre. The Health and Social Care Information Centre is a non-departmental body created by statute, also known as NHS Digital.CaBA HEALTH & WELLBEING EVIDENCE BASEThis dataset forms part of the wider CaBA Health and Wellbeing Evidence Base.

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Infectious Disease Market Size 2025-2029

The infectious disease market size is forecast to increase by USD 160.8 billion at a CAGR of 14.7% between 2024 and 2029.

The market is experiencing significant growth due to the rising prevalence of bacterial diseases such as Clostridium and Staphylococcus, which necessitate advanced diagnostics. Immunodiagnostics and next-generation sequencing (NGS) are emerging as key technologies in infectious disease diagnostics, offering faster and more accurate results than traditional methods. The development of novel drugs for tuberculosis (TB) and sepsis is another growth driver, as is the increasing demand for molecular diagnostics. However, the market faces challenges such as the adverse effects of generic drugs and the high cost of developing new anti-infective drugs. The use of NGS in infectious disease diagnostics is a major trend, enabling the identification of multiple pathogens in a single test and facilitating personalized treatment plans.
In summary, the market is driven by the rising prevalence of infectious diseases, the development of novel drugs, and the adoption of advanced diagnostics, but is challenged by the high cost of drug development and the adverse effects of generic drugs. Immunodiagnostics and NGS are key technologies driving market growth.

What will be the Size of the Infectious Disease Market During the Forecast Period?

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The market encompasses diagnostic tools and technologies designed to promptly identify various pathogens, including bacteria, viruses, and parasites. This market is driven by the urgent need for accurate and rapid results in diverse healthcare settings, such as point-of-care diagnostic testing in urgent care centers, emergency rooms, and ambulances. The importance of infectious disease diagnostics extends beyond healthcare facilities, as personal health and infection control are increasingly prioritized in everyday life. 
Market dynamics are influenced by several factors, including inadequate infrastructure and poor water sanitation in certain regions, which contribute to the spread of infectious diseases. The ongoing demand for improved patient outcomes necessitates the development of advanced diagnostic technologies, such as immunodiagnostics, clinical microbiology, DNA sequencing, next-generation sequencing (NGS), DNA microarray, and various tests for diseases like hepatitis, syphilis, mosquito-borne diseases, gonorrhea, and RNA viruses.
Healthcare professionals are under constant pressure to provide accurate diagnoses and implement effective infection control measures. As a result, there is a growing emphasis on training and education to ensure the proper use and interpretation of diagnostic tools. The market is expected to continue growing as the global population's healthcare needs evolve and advancements in diagnostics technology are made.

How is this Infectious Disease Industry segmented and which is the largest segment?

The industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Drugs
  Vaccines


End-user

  Hospital
  Multispecialty clinic
  Others


Type

  Bacterial infections
  Viral infections
  Fungal infections
  Parasitic infections


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  Asia

    China
    India
    Japan


  Rest of World (ROW)

By Product Insights

The drugs segment is estimated to witness significant growth during the forecast period. The market is driven by several key factors, including increasing government initiatives and non-profit organization efforts, the prevalence of various infectious diseases, and rising research and development funding. Infectious diseases such as influenza, giardiasis, HIV/AIDS, mononucleosis, and the common cold continue to pose a significant health concern. Point-of-care diagnostic testing, which offers rapid results and prompt diagnosis, is increasingly being adopted in urgent care centers, emergency rooms, ambulances, and physician offices. Inadequate infrastructure, poor water sanitation, and lack of training for healthcare professionals remain challenges in controlling the spread of infectious diseases. Pathogens such as bacteria, viruses, fungi, and parasites can cause respiratory diseases, hospital-acquired infections, sexually transmitted infections, and mosquito-borne diseases.

Technologies like chest X-rays, CT scans, physical exams, laboratory tests, PCR testing, and immunodiagnostics are used for diagnosis. Infection control, personal health, hygiene, and preventative healthcare are essential to mitigate the impact of infectious diseases. The market for infectious disease diagnostics includes kits, cons

Genetically informed, deep-phenotyped biobanks are an important research resource and it is imperative that the most powerful, versatile, and efficient analysis approaches are used. Here, we apply our recently developed Bayesian grouped mixture of regressions model (GMRM) in the UK and Estonian Biobanks and obtain the highest genomic prediction accuracy reported to date across 21 heritable traits. When compared to other approaches, GMRM accuracy was greater than annotation prediction models run in the LDAK or LDPred-funct software by 15% (SE 7%) and 14% (SE 2%), respectively, and was 18% (SE 3%) greater than a baseline BayesR model without single-nucleotide polymorphism (SNP) markers grouped into minor allele frequency–linkage disequilibrium (MAF-LD) annotation categories. For height, the prediction accuracy R 2 was 47% in a UK Biobank holdout sample, which was 76% of the estimated h SNP 2 . We then extend our GMRM prediction model to provide mixed-linear model association (MLMA) SNP marker estimates for genome-wide association (GWAS) discovery, which increased the independent loci detected to 16,162 in unrelated UK Biobank individuals, compared to 10,550 from BoltLMM and 10,095 from Regenie, a 62 and 65% increase, respectively. The average χ2 value of the leading markers increased by 15.24 (SE 0.41) for every 1% increase in prediction accuracy gained over a baseline BayesR model across the traits. Thus, we show that modeling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for both genomic prediction and discovery in large-scale individual-level studies. Methods From the measurements, tests, and electronic health record data available in the UK Biobank data, we selected 12 blood based biomarkers, 3 of the most common heritable complex diseases, and 6 quantitative measures. The full list of traits, the UK Biobank coding of the data used, and the covariates adjusted for are given in Table S1. For the quantitative measures and blood-based biomarkers we adjusted the values by the covariates, removed any individuals with a phenotype greater or less than 7 SD from the mean (assuming these are measurement errors), and standardized the values to have zero mean and variance 1. For the common complex diseases, we determined disease status using a combination of information available. For high blood pressure (BP), we used self-report information of whether high blood pressure was diagnosed by a doctor (UK Biobank code 6150-0.0), the age high blood pressure was diagnosed (2966-0.0), and whether the individual reported taking blood pressure medication (6153-0.0, 6177-0.0). For type-2 diabetes (T2D), we used self-report information of whether diabetes was diagnosed by a doctor (2443-0.0), the age diabetes was diagnosed (2976-0.0), and whether the individual reported taking diabetes medication (6153-0.0, 6177-0.0). For cardiovascular disease (CAD), we used self-report information of whether a heart attack was diagnosed by a doctor (3894-0.0), the age angina was diagnosed (3627-0.0), and whether the individual reported heart problem diagnosed by a doctor (6150-0.0) the date of myocardial infarction (42000-0.0). For each disease, we then combined this with primary death ICD10 codes (40001-0.0), causes of operative procedures (41201-0.0), and the main (41202-0.0), secondary (41204-0.0) and inpatient ICD10 codes (41270-0.0). For BP we selected ICD10 codes I10, for T2D we selected ICD10 codes E11 to E14 and excluded from the analysis individuals with E10 (type-1 diabetes), and for CAD we selected ICD10 code I20-I29. Thus, for the purposes of this analysis, we define these diseases broadly simply to maximise the number of cases available for analysis. For each disease, individuals with neither a self-report indication or a relevant ICD10 diagnosis, were then assigned a zero value as a control. We restricted our discovery analysis of the UK Biobank to a sample of European-ancestry individuals. To infer ancestry, we used both self-reported ethnic background (21000-0) selecting coding 1 and genetic ethnicity (22006-0) selecting coding 1. We also took the 488,377 genotyped participants and projected them onto the first two genotypic principal components (PC) calculated from 2,504 individuals of the 1,000 Genomes project with known ancestries. Using the obtained PC loadings, we then assigned each participant to the closest population in the 1000 Genomes data: European, African, East-Asian, South-Asian or Admixed, selecting individuals with PC1 projection < absolute value 4 and PC 2 projection < absolute value 3. Samples were excluded if in the UK Biobank quality control procedures they (i) were identified as extreme heterozygosity or missing genotype outliers; (ii) had a genetically inferred gender that did not match the self-reported gender; (iii) were identified to have putative sex chromosome aneuploidy; (iv) were excluded from kinship inference; (v) had withdrawn their consent for their data to be used. We used the imputed autosomal genotype data of the UK Biobank provided as part of the data release. We used the genotype probabilities to hard-call the genotypes for variants with an imputation quality score above 0.3. The hard-call-threshold was 0.1, setting the genotypes with probability <=0.9 as missing. From the good quality markers (with missingness less than 5% and p-value for Hardy-Weinberg test larger than 10-6, as determined in the set of unrelated Europeans) we selected those with minor allele frequency (MAF) > 0.0002 and rs identifier, in the set of European-ancestry participants, providing a data set 9,144,511 SNPs. From this we took the overlap with the Estonian Genome centre data described below to give a final set of 8,430,446 markers. For computational convenience we then removed markers in very high LD selecting one marker from any set of markers with LD R2 > 0.8 within a 1MB window. These filters resulted in a data set with 458,747 individuals and 2,174,071 markers. We apply our GMRM model to each UK Biobank trait, running two short chains for 5000 iterations and combining the last 2000 posterior samples together. Here, we provide the posterior mean effect size estimates fo each SNP and the mixed-linear model association regression coefficient, SE, t-statistic, and association p-value.