Polygenic Scores (PSs) describe the genetic component of an individual’s quantitative phenotype or their susceptibility to diseases with a genetic basis. Currently, PSs rely on population-dependent contributions of many associated alleles, with limited applicability to understudied populations and recently admixed individuals. Here we introduce a combination of local ancestry deconvolution and partial PS computation to account for the population-specific nature of the association signals in individuals with admixed ancestry. We demonstrate partial PS to be a proxy for the total PS and that a portion of the genome is enough to improve susceptibility predictions for the traits we test. By combining partial PSs from different populations, we are able to improve trait predictability in admixed individuals with some European ancestry. These results may extend the applicability of PSs to subjects with a complex history of admixture, where current methods cannot be applied.

Aims/hypothesis: Type 2 diabetes mellitus is a major health burden disproportionately affecting those with lower educational attainment (EA). We aimed to obtain causal estimates of the association between EA and type 2 diabetes and to quantify mediating effects of known modifiable risk factors. Methods: We applied two-step, two-sample multivariable Mendelian randomisation (MR) techniques using SNPs as genetic instruments for exposure and mediators, thereby minimising bias due to confounding and reverse causation. We leveraged summary data on genome-wide association studies for EA, proposed mediators (i.e. BMI, blood pressure, smoking, television watching) and type 2 diabetes. The total effect of EA on type 2 diabetes was decomposed into a direct effect and indirect effects through multiple mediators. Additionally, traditional mediation analysis was performed in a subset of the National Health and Nutrition Examination Survey 2013–2014. Results: EA was inversely associated with type 2 diabetes (OR 0.53 for each 4.2 years of schooling; 95% CI 0.49, 0.56). Individually, the largest contributors were BMI (51.18% mediation; 95% CI 46.39%, 55.98%) and television watching (50.79% mediation; 95% CI 19.42%, 82.15%). Combined, the mediators explained 83.93% (95% CI 70.51%, 96.78%) of the EA–type 2 diabetes association. Traditional analysis yielded smaller effects but showed consistent direction and priority ranking of mediators. Conclusions/interpretation: These results support a potentially causal protective effect of EA against type 2 diabetes, with considerable mediation by a number of modifiable risk factors. Interventions on these factors thus have the potential of substantially reducing the burden of type 2 diabetes attributable to low EA. Graphical

The genetic regulation of post-prandial glucose levels is poorly understood. Here, we characterise the genetic architecture of blood glucose variably measured within 0 and 24 h of fasting in 368,000 European ancestry participants of the UK Biobank. We found a near-linear increase in the heritability of non-fasting glucose levels over time, which plateaus to its fasting state value after 5 h post meal (h² = 11%; standard error: 1%). The genetic correlation between different fasting times is > 0.77, suggesting that the genetic control of glucose is largely constant across fasting durations. Accounting for heritability differences between fasting times leads to a ~16% improvement in the discovery of genetic variants associated with glucose. Newly detected variants improve the prediction of fasting glucose and type 2 diabetes in independent samples. Finally, we meta-analysed summary statistics from genome-wide association studies of random and fasting glucose (N = 518,615) and identified 156 independent SNPs explaining 3% of fasting glucose variance. Altogether, our study demonstrates the utility of random glucose measures to improve the discovery of genetic variants associated with glucose homeostasis, even in fasting conditions.

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

As many cases of type 2 diabetes (T2D) are likely to remain undiagnosed, better tools for early detection of high‐risk individuals are needed to prevent or postpone the disease. We investigated the value of the doubly weighted genetic risk score (dwGRS) for the prediction of incident T2D in the Lifelines and Estonian Biobank (EstBB) cohorts. The dwGRS uses an additional weight for each single nucleotide polymorphism in the risk score, to correct for “Winner's curse” bias in the effect size estimates. The traditional (single‐weighted genetic risk score; swGRS) and dwGRS were calculated for participants in Lifelines (n = 12,018) and EstBB (n = 34,129). The dwGRS was found to have stronger association with incident T2D (hazard ratio [HR] = 1.26 [95% confidence interval: 1.10–1.43] and HR = 1.35 [1.28–1.42]) compared to the swGRS (HR = 1.21 [1.07–1.38] and HR = 1.25 [1.19–1.32]) in Lifelines and EstBB, respectively. Comparing the 5‐year predicted risks from the models with and without the dwGRS, the continuous net reclassification index was 0.140 (0.034–0.243; p = .009 Lifelines), and 0.257 (0.194–0.319; p < 2 × 10⁻¹⁶ EstBB). The dwGRS provided incremental value to the T2D prediction model with established phenotypic predictors. It clearly distinguished the risk groups for incident T2D in both biobanks thereby showing its clinical relevance.

Eating disorders (EDs) affect 2%–5% of the population, while subclinical symptoms are more prevalent. Previous diagnosis-based genome-wide association studies (GWASs) identified risk variants, but the genetic architecture of ED symptoms in the general population remains unclear. Using the five-item SCOFF screening tool, we conducted six GWASs in the Estonian Biobank (EstBB; N = 212,000): one for higher ED risk and one for each SCOFF item. We estimated genetic correlations with 59 external phenotypes, evaluated polygenic risk scores (PRSs) for uncontrolled eating in an independent subsample, and performed phenome-wide association studies (PheWAS) linking genetic risk to medical diagnoses. We found 14 independent genome-wide significant SNPs associated with increased ED risk and cognitive-behavioral symptoms like loss of control and food dominates , with SNP heritability ranging from 2% to 8%. Notably, variants in FTO and MC4R were linked to both higher ED risk and cognitive-behavioral symptoms , while ROBO2 and TMEM18 correlated with higher ED risk and loss of control , highlighting appetite regulation pathways. Cognitive-behavioral symptoms exhibited moderate-to-strong genetic correlations with anthropometric, psychiatric, lifestyle, personality, and somatic traits. PheWASs identified associations between PRSs of ED symptoms and medical conditions (28, 27, and 24 significant associations for higher ED risk , loss of control , and food dominates , respectively), mainly cardiometabolic. All PRSs predicted uncontrolled eating in the independent subsample. Our findings suggest genetic liability and health burden related to ED symptoms in the general population are predominantly driven by cognitive-behavioral than weight-compensatory symptoms. Symptom-based approach could complement current diagnosis-based methods and lead towards improved intervention and prevention.