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Deep learning techniques are increasingly being used to classify medical imaging data with high accuracy. Despite this, due to often limited training data, these models can lack sufficient generalizability to predict unseen test data, produced in different domains, with comparable performance. This study focuses on thyroid histopathology image classification and investigates whether a Generative Adversarial Network [GAN], trained with just 156 patient samples, can produce high quality synthetic images to sufficiently augment training data and improve overall model generalizability. Utilizing a StyleGAN2 approach, the generative network produced images with an Fréchet Inception Distance (FID) score of 5.05, matching state-of-the-art GAN results in non-medical domains with comparable dataset sizes. Augmenting the training data with these GAN-generated images increased model generalizability when tested on external data sourced from three separate domains, improving overall precision and AUC by 7.45% and 7.20% respectively compared with a baseline model. Most importantly, this performance improvement was observed on minority class images, tumour subtypes which are known to suffer from high levels of inter-observer variability when classified by trained pathologists.

Hypothyroidism and hyperthyroidism are observationally associated with sex hormone concentrations and sexual dysfunction, but causality is unclear. We investigated whether TSH, fT4, hypo- and hyperthyroidism are causally associated with sex hormones and sexual function. We used publicly available summary statistics from genome-wide association studies on TSH and fT4 and hypo- and hyperthyroidism from the ThyroidOmics Consortium (N ≤ 54,288). Outcomes from UK Biobank (women ≤ 194,174/men ≤ 167,020) and ReproGen (women ≤ 252,514) were sex hormones (sex hormone binding globulin [SHBG], testosterone, estradiol, free androgen index [FAI]) and sexual function (ovulatory function in women: duration of menstrual period, age at menarche and menopause, reproductive lifespan, and erectile dysfunction in men). We performed two-sample Mendelian randomization (MR) analyses on summary level, and unweighted genetic risk score (GRS) analysis on individual level data. One SD increase in TSH was associated with a 1.332 nmol/L lower (95% CI:-0.717,-1.946; p = 2 × 10⁻⁵) SHBG and a 0.103 nmol/l lower (-0.051, V0.154; p = 9 × 10⁻⁵) testosterone in two-sample MR, supported by the GRS approach. Genetic predisposition to hypothyroidism was associated with decreased and genetic predisposition to hyperthyroidism with increased SHBG and testosterone in both approaches. The GRS for fT4 was associated with increased testosterone and estradiol in women only. The GRS for TSH and hypothyroidism were associated with increased and the GRS for hyperthyroidism with decreased FAI in men only. While genetically predicted thyroid function was associated with sex hormones, we found no association with sexual function.

Epidemiological studies suggest that individuals with comorbid conditions including diabetes, chronic lung, inflammatory and vascular disease, are at higher risk of adverse COVID-19 outcomes. Genome-wide association studies have identified several loci associated with increased susceptibility and severity for COVID-19. However, it is not clear whether these associations are genetically determined or not. We used a Phenome-Wide Association (PheWAS) approach to investigate the role of genetically determined COVID-19 susceptibility on disease related outcomes. PheWAS analyses were performed in order to identify traits and diseases related to COVID-19 susceptibility and severity, evaluated through a predictive COVID-19 risk score. We utilised phenotypic data in up to 400,000 individuals from the UK Biobank, including Hospital Episode Statistics and General Practice data. We identified a spectrum of associations between both genetically determined COVID-19 susceptibility and severity with a number of traits. COVID-19 risk was associated with increased risk for phlebitis and thrombophlebitis (OR = 1.11, p = 5.36e -08 ). We also identified significant signals between COVID-19 susceptibility with blood clots in the leg (OR = 1.1, p = 1.66e -16 ) and with increased risk for blood clots in the lung (OR = 1.12, p = 1.45 e -10 ). Our study identifies significant association of genetically determined COVID-19 with increased blood clot events in leg and lungs. The reported associations between both COVID-19 susceptibility and severity and other diseases adds to the identification and stratification of individuals at increased risk, adverse outcomes and long-term effects.

Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.

The epidemiologic link between schizophrenia (SCZ) and type 2 diabetes (T2D) remains poorly understood. Here, we investigate the presence and extent of a shared genetic background between SCZ and T2D using genome-wide approaches. We performed a genome-wide association study (GWAS) and polygenic risk score analysis in a Greek sample collection (GOMAP) comprising three patient groups: SCZ only (n = 924), T2D only (n = 822), comorbid SCZ and T2D (n = 505); samples from two separate Greek cohorts were used as population-based controls (n = 1,125). We used genome-wide summary statistics from two large-scale GWAS of SCZ and T2D from the PGC and DIAGRAM consortia, respectively, to perform genetic overlap analyses, including a regional colocalisation test. We show for the first time that patients with comorbid SCZ and T2D have a higher genetic predisposition to both disorders compared to controls. We identify five genomic regions with evidence of colocalising SCZ and T2D signals, three of which contain known loci for both diseases. We also observe a significant excess of shared association signals between SCZ and T2D at nine out of ten investigated p value thresholds. Finally, we identify 29 genes associated with both T2D and SCZ, several of which have been implicated in biological processes relevant to these disorders. Together our results demonstrate that the observed comorbidity between SCZ and T2D is at least in part due to shared genetic mechanisms.

Height has been associated with many clinical traits but whether such associations are causal versus secondary to confounding remains unclear in many cases. To systematically examine this question, we performed a Mendelian Randomization-Phenome-wide association study (MR-PheWAS) using clinical and genetic data from a national healthcare system biobank. Analyses were performed using data from the US Veterans Affairs (VA) Million Veteran Program in non-Hispanic White (EA, n = 222,300) and non-Hispanic Black (AA, n = 58,151) adults in the US. We estimated height genetic risk based on 3290 height-associated variants from a recent European-ancestry genome-wide meta-analysis. We compared associations of measured and genetically-predicted height with phenome-wide traits derived from the VA electronic health record, adjusting for age, sex, and genetic principal components. We found 345 clinical traits associated with measured height in EA and an additional 17 in AA. Of these, 127 were associated with genetically-predicted height at phenome-wide significance in EA and 2 in AA. These associations were largely independent from body mass index. We confirmed several previously described MR associations between height and cardiovascular disease traits such as hypertension, hyperlipidemia, coronary heart disease (CHD), and atrial fibrillation, and further uncovered MR associations with venous circulatory disorders and peripheral neuropathy in the presence and absence of diabetes. As a number of traits associated with genetically-predicted height frequently co-occur with CHD, we evaluated effect modification by CHD status of genetically-predicted height associations with risk factors for and complications of CHD. We found modification of effects of MR associations by CHD status for atrial fibrillation/flutter but not for hypertension, hyperlipidemia, or venous circulatory disorders. We conclude that height may be an unrecognized but biologically plausible risk factor for several common conditions in adults. However, more studies are needed to reliably exclude horizontal pleiotropy as a driving force behind at least some of the MR associations observed in this study.

Obesity is a major risk factor for a wide range of cardiometabolic diseases, however the impact of specific aspects of body morphology remains poorly understood. We combined the GWAS summary statistics of fourteen anthropometric traits from UK Biobank through principal component analysis to reveal four major independent axes: body size, adiposity, predisposition to abdominal fat deposition, and lean mass. Mendelian randomization analysis showed that although body size and adiposity both contribute to the consequences of BMI, many of their effects are distinct, such as body size increasing the risk of cardiac arrhythmia (b = 0.06, p = 4.2 ∗ 10−17) while adiposity instead increased that of ischemic heart disease (b = 0.079, p = 8.2 ∗ 10−21). The body mass-neutral component predisposing to abdominal fat deposition, likely reflecting a shift from subcutaneous to visceral fat, exhibited health effects that were weaker but specifically linked to lipotoxicity, such as ischemic heart disease (b = 0.067, p = 9.4 ∗ 10−14) and diabetes (b = 0.082, p = 5.9 ∗ 10−19). Combining their independent predicted effects significantly improved the prediction of obesity-related diseases (p < 10−10). The presented decomposition approach sheds light on the biological mechanisms underlying the heterogeneity of body morphology and its consequences on health and lifestyle.Jonathan Sulc et al. use principal component analysis and Mendelian randomization to conduct a comprehensive analysis of 14 body morphology metrics using data from the UK Biobank. Their results suggest that body size and adiposity have distinct impacts on obesity-related outcomes and highlight a BMI-neutral component affecting body fat distribution, providing further insight into the genetic basis of body shape and its consequences on human health and lifestyle.

Background Obesity is thought to increase cardiovascular disease (CVD) risk partly through dyslipidemia. Yet, obesity’s effects on dyslipidemia are not uniform. Understanding the shared genetic basis between obesity and lipid traits can provide insight into this heterogeneity and its implications for CVD risk. Methods We examined local genetic correlations between three lipid measures [high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and triglycerides (TG)] and body mass index (BMI) using genome-wide association study summary statistics from European ancestry UK Biobank participants. We identified genomic loci with opposing genetic effects on obesity and dyslipidemia risk (protective BMI-lipid loci) and those with concordant directions for both obesity and dyslipidemia risk (adverse BMI-lipid loci). Gene-based association analyses were used to prioritize potential causal genes. We then constructed polygenic risk scores for BMI (PRS BMI ) based on protective and adverse loci and assessed their associations with BMI, lipid levels, CVD, and related traits in the diverse Population Architecture using Genomics and Epidemiology (PAGE) study. PheWAS was performed in the All of Us cohort. Mendelian randomization (MR) was conducted to assess the causal impact of protective/adverse loci on cardiometabolic outcomes. Finally, we investigated the associations with fat distribution traits using MRI-based fat measures in the UK Biobank. Results Among 2495 regions, we identified 789 HDL, 26 LDL, and 494 TG loci with significant local genetic correlation with BMI (including overlapping loci). Of these, 3 HDL, 10 LDL, and 8 TG loci showed protective correlations. Gene-based analyses prioritized 18 candidate causal genes. The protective PRS BMI(+)HDL(+) was associated with higher BMI but favorable lipid profiles and reduced CVD risk in PAGE. PheWAS revealed protective associations with hyperlipidemia, atrial fibrillation, and Alzheimer’s disease. MR supported the favorable causal effects of these protective loci on several cardiometabolic outcomes. Notably, protective PRS BMI(+)TG(−) was uniquely associated with decreased visceral-to-abdominal subcutaneous adipose tissue ratio. Conclusions Identifying and validating genomic loci with shared genetic signals between BMI and lipid levels further supports the importance of genetics in defining the heterogeneous impact of obesity on dyslipidemia and CVD.

Background and aims Central obesity is a condition that poses a significant risk to global health and requires the employment of novel scientific methods for exploration. The objective of this study is to use DNA methylation analysis to detect DNA methylation loci linked to obesity phenotypes, i.e . waist circumference and waist-to-hip ratio adjusted for BMI. Methods and results Two-hundred and ten healthy European participants from the STANISLAS Family Study (SFS), comprising 73 nuclear families, were comprehensively assessed for methylation status using Illumina Infinium HumanMethylation450 BeadChip. An epigenome-wide association study was performed, which identified a CpG site cg16170243 located on chromosome 18q21.2 significantly associated with waist circumference, after adjusting for BMI (β = 2.32, SE = 0.41, P adj  = 0.048). Cg16170243 corresponds to a 50 bp-length human methylation oligoprobe located within the AC090241.2 gene that overlaps ST8SIA5 gene. No significant association was observed with waist-to-hip ratio adjusted for BMI (P adj  > 0.05). Conclusions A novel association between DNA methylation and WC was identified, which is demonstrating that epigenetic mechanisms may have a significant impact on waist circumference ratio in healthy individuals. Further studies are warranted to address the causal effects of this association.

There is evidence that lower height is associated with a higher risk of coronary artery disease (CAD) and increased risk of type 2 diabetes (T2D). It is not clear though whether these associations are causal, direct or mediated by other factors. Here we show that one standard deviation higher genetically determined height (~6.5 cm) is causally associated with a 16% decrease in CAD risk (OR = 0.84, 95% CI 0.80–0.87). This causal association remains after performing sensitivity analyses relaxing pleiotropy assumptions. The causal effect of height on CAD risk is reduced by 1–3% after adjustment for potential mediators (lipids, blood pressure, glycaemic traits, body mass index, socio-economic status). In contrast, our data suggest that lung function (measured by forced expiratory volume [FEV1] and forced vital capacity [FVC]) is a mediator of the effect of height on CAD. We observe no direct causal effect of height on the risk of T2D. Eirini Marouli et al. use Mendelian randomisation analyses to investigate the causal relationship between adult height, coronary artery disease (CAD) and type 2 diabetes (T2D) in the UK Biobank. They find that height has a causal effect on CAD, which is mediated by lung function, while there is no direct effect on the risk of T2D.